diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 1dfb143..886494c 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -23,13 +23,16 @@ jobs:
           cache-dependency-path: |
             requirements.txt
             pyproject.toml
+            mcp_server/pyproject.toml
+            uniparser_agent/pyproject.toml
 
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          pip install ruff pytest
           pip install -r requirements.txt
-          pip install -e .
+          pip install -e ".[test]"
+          pip install -e "./mcp_server[dev]"
+          pip install -e "./uniparser_agent[dev]"
 
       - name: Ruff lint
         run: ruff check .
@@ -39,3 +42,9 @@ jobs:
 
       - name: Unit tests
         run: pytest tests/unit/ -v -m "not live"
+
+      - name: MCP tests
+        run: pytest mcp_server/tests/ -v
+
+      - name: Agent tests
+        run: pytest uniparser_agent/tests/ -v
diff --git a/.gitignore b/.gitignore
index c69f283..beb861d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -5,6 +5,7 @@
 *.vscode
 *.txt
 *.__pycache__
+.venv/
 
 # compilation and distribution
 __pycache__
@@ -68,5 +69,8 @@ deploy
 # env
 .env
 
+# uv project lock files (generated locally; CI installs with pip)
+uv.lock
+
 *.bak
 .cursor/
diff --git a/README.md b/README.md
index a1d136c..2e3df77 100644
--- a/README.md
+++ b/README.md
@@ -58,6 +58,22 @@ pip install -e .
 
 > **说明：** 仅执行 `pip install -r requirements.txt` **不会**注册 `uniparser` 命令；SDK 开发用前者即可，CLI 必须执行 `pip install -e .`。
 
+开发和运行测试时，请安装测试依赖：
+
+```bash
+pip install -e ".[test]"
+```
+
+当 `.env` 中配置了 `UNIPARSER_API_KEY` 时，完整测试会连接真实服务，并分别
+通过本地 PDF、公开 HTTPS PDF 和图片 snip 提交 3 次计费解析：
+
+```bash
+python -m pytest -q
+```
+
+若 `.env` 不在当前仓库，可通过 `UNIPARSER_DOTENV_PATH=/path/to/.env`
+显式指定。
+
 安装后验证：
 
 ```bash
@@ -101,12 +117,39 @@ uniparser parse report.pdf
 
 ```python
 import os
+
 parser = UniParserClient(
     host="https://uniparser.dp.tech/",
     api_key=os.getenv("UNIPARSER_API_KEY"),
+    request_timeout=(10, 60),  # 普通请求：连接/读取超时
+    sync_request_timeout=(10, 1860),  # 同步解析请求：连接/读取超时
+    upload_request_timeout=(60, 300),  # TOS 上传：socket/响应超时
 )
 ```
 
+`request_timeout` 用于健康检查、结果获取和异步任务提交；`sync_request_timeout`
+只用于 `sync=True` 的解析请求；`upload_request_timeout` 用于 TOS 文件内容上传。
+它们是客户端 HTTP 超时，不等同于服务端解析预算。
+客户端可作为上下文管理器使用，以及时关闭连接池：
+
+```python
+with UniParserClient(host=host, api_key=api_key) as parser:
+    result = parser.version()
+```
+
+`version()` 会原样返回 `release/v1.3` 的模型路由信息，包括
+`default_version`、`backend_versions`，以及后端声明的 `capabilities`；
+可据此选择 `trigger_*()` 的 `model_version`。`get_constants()` 返回服务端
+当前的 `LayoutType`、解析/格式枚举和 token 规则。`health()`、`version()`
+和 `get_constants()` 也都支持单次 `http_timeout=`。
+
+```python
+service = parser.version()
+default_model = service["default_version"]
+capabilities = service["backend_versions"][default_model].get("capabilities", {})
+constants = parser.get_constants()
+```
+
 ## 解析配置：7 个语义类 + 2 个枚举
 
 提交解析任务时（`trigger_file` / `trigger_snip` / `trigger_url`），可分别设置 7 类语义元素的处理模式：
@@ -141,6 +184,54 @@ parser = UniParserClient(
 | `2` | `OCRHighQuality` | 高质 OCR，支持行内公式 |
 | `3` | `DigitalExported` | 从数字原生 PDF 直接抽取文字 |
 
+### 提交任务的通用参数
+
+三个提交入口已与 `release/v1.3` 对齐。`trigger_file`、`trigger_snip` 和
+`trigger_url` 都支持以下参数：
+
+| 参数 | 默认 | 说明 |
+|------|------|------|
+| `timeout` | `1800` | 服务端解析预算（秒），不是 HTTP 超时 |
+| `http_timeout` | `None` | 仅覆盖本次请求的客户端 HTTP 超时 |
+| `inplace_update` | `False` | 是否允许更新同 token 的已有任务 |
+| `preset_layout` | `None` | 预设版面；可传 JSON 字符串或 Python 列表 |
+| `model_version` | `None` | 指定服务端 `/version` 返回的模型版本 |
+| `server_generated_token` | `False` | token 为空时交给服务端生成；默认保留历史确定性 token |
+| `callback_url` / `callback_secret` | `None` | 异步任务完成回调及其验证密钥 |
+
+`padding_snip` 只适用于文件和图片入口；`proxy` 只适用于 URL 入口。URL
+入口支持服务端接受的 HTTP(S) 以及 S3、OSS、TOS 对象地址。`preset_layout`
+在三个入口中都会按服务端契约编码成 JSON 字符串。
+
+```python
+result = parser.trigger_url(
+    "tos://bucket/document.pdf",
+    sync=False,
+    model_version="v1.3",
+    preset_layout=[[{"type": "textual", "bbox": [0, 0, 100, 30]}]],
+    server_generated_token=True,
+)
+token = result["token"]
+```
+
+### TOS 预签名上传
+
+本地文件可以先上传到 TOS，再把返回的 `source_url` 交给 `trigger_url`。
+上传与解析刻意分成两步，调用上传助手不会自动启动计费解析：
+
+```python
+uploaded = parser.upload_files_to_tos(["./large-document.pdf"])
+source_url = uploaded["files"][0]["source_url"]
+result = parser.trigger_url(source_url, server_generated_token=True)
+```
+
+如需自行执行上传，可调用 `request_tos_upload_links()` 获取预签名 `PUT`
+地址。该地址是短期 bearer credential，不应记录到日志或转发给其他服务。
+客户端向预签名地址上传时不会携带 UniParser API Key；高层
+`upload_files_to_tos()` 完成上传后也不会在返回值中保留 `upload_url`。
+可通过客户端的 `upload_request_timeout=` 或单次调用的 `http_timeout=`
+调整上传超时。
+
 ## 快速开始
 
 > ‼️‼️‼️ 以下仅为代码功能示例，具体运行代码请参考 `playground/*.ipynb` ‼️‼️‼️
@@ -152,13 +243,10 @@ import os
 from uniparser_tools.api.clients import UniParserClient
 
 # 设置 API 密钥
-api_key = os.getenv('UNIPARSER_API_KEY')
+api_key = os.getenv("UNIPARSER_API_KEY")
 
 # 初始化客户端
-parser = UniParserClient(
-    host="https://uniparser.dp.tech/",
-    api_key=api_key
-)
+parser = UniParserClient(host="https://uniparser.dp.tech/", api_key=api_key)
 ```
 
 ### 2. 解析 PDF 文件（科学文献推荐默认）
@@ -168,14 +256,14 @@ from uniparser_tools.common.constant import ParseMode, ParseModeTextual
 
 # 科学文献解析模式（推荐默认值）
 result = parser.trigger_file(
-    pdf_path="./example.pdf",
+    file_path="./example.pdf",
     textual=ParseModeTextual.OCRHighQuality,  # high quality
-    equation=ParseMode.OCRHighQuality,        # high quality
-    table=ParseMode.OCRHighQuality,           # high quality
-    chart=ParseMode.DumpBase64,               # original image base64
-    figure=ParseMode.DumpBase64,              # original image base64
-    expression=ParseMode.DumpBase64,          # original image base64
-    molecule=ParseMode.OCRFast,               # fast
+    equation=ParseMode.OCRHighQuality,  # high quality
+    table=ParseMode.OCRHighQuality,  # high quality
+    chart=ParseMode.DumpBase64,  # original image base64
+    figure=ParseMode.DumpBase64,  # original image base64
+    expression=ParseMode.DumpBase64,  # original image base64
+    molecule=ParseMode.OCRFast,  # fast
 )
 
 if result["status"] == "success":
@@ -189,14 +277,15 @@ if result["status"] == "success":
 
 | 开关 | 默认 | 说明 |
 |------|------|------|
-| `content` | `True` | 返回全文纯/富文本，适合 LLM |
+| `content` | `False` | 返回全文纯/富文本，适合 LLM |
 | `objects` | `False` | JSON 语义块列表，适合语义分析 |
 | `pages_dict` | `False` | 按页组织的原始解析布局 |
 | `pages_tree` | `False` | 带父子关系的嵌套树，适合复杂分析 |
-| `return_half` | `False` | 解析进行中即取已完成部分 |
 | `molecule_source` | `False` | 返回分子原始源（SMILES/mol 等） |
 
 同一 token 可复用，多次获取不同组合不会重复计费。
+两个结果接口都可用 `http_timeout=` 覆盖单次读取超时，适合包含大量对象或
+Base64 源的大文档。
 
 #### 输出格式（`FormatFlag`，仅作用于 `content` / `objects` 中的文本字段）
 
@@ -224,6 +313,46 @@ if result["status"] == "success":
     print(result["content"])
 ```
 
+如需 MinerU 兼容结构，可直接调用第三方格式结果接口：
+
+```python
+from uniparser_tools.common.constant import ThirdPartyFormatter
+
+result = parser.get_third_party_output(
+    token,
+    formatter=ThirdPartyFormatter.MinerU,
+)
+```
+
+`dict2obj()` / `build_item()` 已对齐 `release/v1.3` 的结果模型，包括
+文本块的 `contents + types` 行内公式/分子表示、分子的 `esmi` 字段，以及
+完整 HTML 表格的 span 升级。服务端未来增加未知字段时，转换器会忽略未知
+字段，而不是让已有客户代码因构造参数不匹配而崩溃。
+
+### 账户与用量（只读）
+
+解析客户端提供 `account` 命名空间，使用同一连接池和 API Key：
+
+```python
+profile = parser.account.get_current_user()
+balance = parser.account.get_balance()
+summary = parser.account.get_usage_summary(period="current_month")
+usage = parser.account.list_usage_records(page=1, size=20)
+transactions = parser.account.list_balance_transactions(page=1, size=20)
+```
+
+也可以独立创建只读账户客户端：
+
+```python
+from uniparser_tools.api.account import UniParserAccountClient
+
+with UniParserAccountClient(host=host, api_key=api_key) as account:
+    print(account.get_balance())
+```
+
+该封装不提供注册、资料更新、API Key 管理、充值或管理员写操作。用量明细
+遵循服务端当前契约，只返回最近 14 天并分页；`size` 的服务端上限为 100。
+
 ### 4. 使用异步回调 (Callbacks)
 
 UniParser 支持在异步任务完成后通过 HTTP POST 回调结果到指定地址。这对于长耗时任务非常有用，无需轮询结果。
@@ -231,7 +360,7 @@ UniParser 支持在异步任务完成后通过 HTTP POST 回调结果到指定
 ```python
 # 提交带回调地址的异步解析任务
 result = parser.trigger_file(
-    pdf_path="./example.pdf",
+    file_path="./example.pdf",
     sync=False,  # 必须为 False 才能触发异步回调
     callback_url="https://your-server.com/api/callback",
     callback_secret="your-shared-secret",  # 用于校验回调内容的签名
@@ -249,7 +378,15 @@ if result["status"] == "success":
     print(f"异步任务已提交，完成后将回调到指定地址。Token: {token}")
 ```
 
-回调请求的 Payload 将包含 `checksum` 和 `content`。你可以使用 `callback_secret` 对 `content` 进行 HMAC-SHA256 签名校验，以确保内容未被篡改。
+`release/v1.3` 的回调 body 是原始 JSON 结果，不再包成
+`{"checksum": ..., "content": ...}`。服务端对实际收到的 body bytes 使用
+`callback_secret` 计算 HMAC-SHA256，并在
+`X-UniParser-Signature: sha256=<hex>` 中发送签名；接收方必须在解析 JSON
+之前，对原始 body bytes 验签。`Idempotency-Key` 可用于去重，
+`X-UniParser-Callback-Attempt` 表示当前重试次数。
+
+`callback_url` 仅允许搭配 `sync=False` 使用，且必须与 `callback_secret`
+同时提供；部署方还可能对回调 host 配置 allowlist。
 
 ### 5. 解析图片文件
 
@@ -327,9 +464,9 @@ from uniparser_tools.common.constant import FormatFlag
 result = parser.get_formatted(
     token,
     content=True,
-    textual=FormatFlag.Markdown,    # 文本使用 Markdown
-    table=FormatFlag.Html,          # 表格使用 HTML
-    equation=FormatFlag.Latex,       # 公式使用 LaTeX
+    textual=FormatFlag.Markdown,  # 文本使用 Markdown
+    table=FormatFlag.Html,  # 表格使用 HTML
+    equation=FormatFlag.Latex,  # 公式使用 LaTeX
 )
 
 if result["status"] == "success":
@@ -354,10 +491,11 @@ token = result["token"]
 
 | 字段 | 出现场景 | 说明 |
 |------|------|------|
-| `status` | 始终存在 | `"success"` / `"error"`（见 `StatusFlag`） |
+| `status` | 任务响应或错误响应 | `"success"` / `"error"`（见 `StatusFlag`）；`version` 等信息接口不保证该字段 |
 | `token` | 触发/查询类接口 | 本次任务的 token，出错也会带上以便追溯 |
-| `description` | 错误时 | 业务层错误原因，通常取自 `ErrorFlag`（如 `Token_Invalid`、`File_Size_Exceeded`、`Domain_Not_Allowed`…）或本地 traceback |
-| `message` | 错误时 | 服务端返回的原始报文（非 JSON 时才填充） |
+| `description` | 错误时 | 服务端业务错误，或不含本地 traceback 的网络错误摘要 |
+| `message` | 错误时 | 客户端请求阶段说明；非 JSON 响应会额外保留在 `body` |
+| `http_status` | HTTP 4xx/5xx 时 | 原始 HTTP 状态码，同时保留服务端 JSON 错误体 |
 
 > 直接调用 REST API（curl / 自研客户端）时才需要关注 `401/403/429/…` 等原始 HTTP 状态码，详见各部署实例 `<host>/api` 上的 Authentication 章节。
 
@@ -417,33 +555,37 @@ UniParser 提供了基于 [Model Context Protocol](https://modelcontextprotocol.
 
 ### 可用工具
 
-| 工具 | 说明 |
-|------|------|
-| `uniparser_health` | 检查服务健康状态 |
-| `uniparser_version` | 获取服务版本信息 |
-| `uniparser_parse_file` | 解析本机 PDF（传入绝对路径），返回 `content` 文本 |
-| `uniparser_parse_url` | 解析公网 PDF URL，返回 `content` 文本 |
+
+| 工具                | 说明                                                               |
+| ----------------- | ---------------------------------------------------------------- |
+| `uniparser_parse` | 解析本地 PDF / 图片或公网 PDF URL；落盘 Markdown 与 `pages_tree.json`；返回路径与预览 |
+
+
+健康检查、版本查询、按 token 恢复请使用 CLI（`uniparser health` / `version` / `fetch`）。详见 [mcp_server/README.md](./mcp_server/README.md)。
 
 ### 快速启动
 
 ```bash
 cd mcp_server
-uv sync                          # 安装依赖（与主项目隔离）
-uv run python -m uniparser_mcp   # 启动 MCP 服务（stdio 模式）
+uv sync
+uv run python -m uniparser_mcp
 ```
 
-运行时必须设置以下环境变量：
 
-| 变量 | 说明 |
-|------|------|
-| `UNIPARSER_BASE_URL` | UniParser 用户服务根 URL，例如 `http://127.0.0.1:40001` |
-| `UNIPARSER_API_KEY` | API 密钥，对应请求头 `X-API-Key` |
+| 变量                   | 说明                                |
+| -------------------- | --------------------------------- |
+| `UNIPARSER_API_KEY`  | 必填                                |
+| `UNIPARSER_BASE_URL` | 可选，默认 `https://uniparser.dp.tech` |
+
+
 
-默认解析参数和输出格式见 `mcp_server/config.yaml`。
 
 ### 接入 Cursor / Claude Code
 
-在 MCP 配置文件中添加（将路径替换为本机实际路径）：
+先克隆本仓库并在 `mcp_server/` 下执行 `uv sync`，再在 MCP 配置中增加如下内容。**必须**将两处占位符改成你的本机值，否则 MCP 无法启动：
+
+1. `"--directory"` 后的路径：把 `/path/to/UniParser-Tools/mcp_server` 替换为克隆到本机后的 `mcp_server` **绝对路径**（例如 macOS：`/Users/<you>/UniParser-Tools/mcp_server`）。
+2. `UNIPARSER_API_KEY`：把 `your-api-key` 替换为你在 [https://uniparser.dp.tech/](https://uniparser.dp.tech/) 申请的真实 API Key。
 
 ```json
 {
@@ -459,7 +601,6 @@ uv run python -m uniparser_mcp   # 启动 MCP 服务（stdio 模式）
         "uniparser_mcp"
       ],
       "env": {
-        "UNIPARSER_BASE_URL": "http://127.0.0.1:40001",
         "UNIPARSER_API_KEY": "your-api-key"
       }
     }
@@ -467,9 +608,9 @@ uv run python -m uniparser_mcp   # 启动 MCP 服务（stdio 模式）
 }
 ```
 
-传输模式默认为 `stdio`，可通过 `UNIPARSER_MCP_TRANSPORT` 环境变量切换为 `sse` 或 `streamable-http`。
+传输模式默认为 `stdio`，可通过 `UNIPARSER_MCP_TRANSPORT` 切换为 `sse` 或 `streamable-http`。
 
-详细文档见 [`mcp_server/README.md`](./mcp_server/README.md)。
+详细文档见 [mcp_server/README.md](./mcp_server/README.md)。
 
 ## 项目结构
 
@@ -485,10 +626,9 @@ uniparser_tools/
 ├── utils/            # 工具函数
 └── order/            # 排序算法
 
-mcp_server/           # MCP 服务（独立子项目）
-├── uniparser_mcp/    # MCP server 实现
-├── config.yaml       # 默认解析参数配置
-└── pyproject.toml    # 独立依赖管理
+mcp_server/           # MCP 服务（独立子项目，仅 uniparser_parse tool）
+├── uniparser_mcp/
+└── pyproject.toml
 
 playground/
 ├── 01.quick_start.ipynb          # 快速开始教程
diff --git a/mcp_server/README.md b/mcp_server/README.md
index 65a2069..3ab9d90 100644
--- a/mcp_server/README.md
+++ b/mcp_server/README.md
@@ -1,79 +1,48 @@
 # UniParser MCP Server
 
-基于 [Model Context Protocol](https://modelcontextprotocol.io/) 的 **stdio** 服务，通过 MCP `tools` 调用 UniParser 用户面 HTTP API。不在 MCP 进程内加载解析栈，仅做 HTTP 转发，并与 [`config.yaml`](./config.yaml) 中的默认参数合并。
+基于 [Model Context Protocol](https://modelcontextprotocol.io/) 的 MCP 服务，通过单一工具 `uniparser_parse` 调用 [UniParser](https://uniparser.dp.tech/) API（经 `uniparser-tools` 的 `UniParserClient`）。
 
-## 前置条件
+## Tool
 
-1. **已运行 UniParser 用户服务**（提供 `GET /health`、`POST /trigger-file-async`、`POST /trigger-url-async`、`POST /get-formatted` 等）。若在 UniParser 主仓库中开发，典型启动方式为（路径以主仓库为准）：
-
-   ```bash
-   python services/server_user.py
-   ```
-
-   默认监听 **40001** 端口；本地联调时请将 `UNIPARSER_BASE_URL` 设为 `http://127.0.0.1:40001`。
-
-2. **Python 3.10+**，推荐使用 [uv](https://github.com/astral-sh/uv) 在 **本目录**（`src/mcp_server`）安装依赖，避免与主项目其它依赖版本冲突：
-
-   ```bash
-   cd mcp_server
-   uv sync
-   ```
-
-## 环境变量
-
-MCP 进程通过 [`UniParserClient`](../uniparser_tools/api/clients.py) 访问服务，**以下两项在运行 MCP 时均为必填**（未设置时工具会返回明确错误，而非静默使用默认 URL）：
-
-| 变量 | 说明 |
+| Tool | 说明 |
 |------|------|
-| `UNIPARSER_BASE_URL` | 用户服务根 URL，**无代码内默认值**。本地示例：`http://127.0.0.1:40001` |
-| `UNIPARSER_API_KEY` | 对应请求头 `X-API-Key`；与服务端鉴权配置一致 |
+| `uniparser_parse` | 解析本地 PDF、本地图片或公网 PDF URL；落盘 Markdown + `pages_tree.json`；返回路径与 `content_preview` |
 
-密钥仅通过环境变量注入，勿写入 MCP 工具参数或提交到版本库。
+### `uniparser_parse` 参数
 
-**集成测试**（`pytest` 标记 `integration`）还会读取：
+提供 **三选一** 输入：`file_path`、`image_path`、`pdf_url`。
 
-| 变量 | 说明 |
+| 参数 | 说明 |
 |------|------|
-| `UNIPARSER_BASE_URL` | 未设置时，[`tests/conftest.py`](./tests/conftest.py) 默认使用 `https://uniparser.dp.tech`（与 MCP 手动配置本地 URL 的行为不同，请注意） |
-| `UNIPARSER_API_KEY` | 未设置时跳过全部集成测试 |
-| `UNIPARSER_INTEGRATION` | 设为 `0` / `false` / `no` / `skip` 时，不探测服务并跳过集成测试（适用于 CI 无后端场景） |
-
-## 配置文件 `config.yaml`
+| `output_dir` | 可选的首选目录；默认 `~/Uni-Parser-Skill/<stem>/`；已存在时自动使用同级 `<name>_1`、`<name>_2` 等新目录 |
+| `async_mode` | `sync=false` 提交后轮询直至完成 |
+| `textual` … `molecule` | 7 个语义字段，默认 scientific-paper preset |
 
-与 `uniparser_mcp` 包同级目录下的 [`config.yaml`](./config.yaml) 控制 **trigger** 与 **get-formatted** 的默认字段（与 HTTP API 一致）：
-
-| 段名 | 用途 |
-|------|------|
-| `default_trigger_file` | `uniparser_parse_file` → `POST /trigger-file-async` 的默认参数（如 `lang`、`sync`、`textual`、`table` 及各模态解析档位） |
-| `default_trigger_url` | `uniparser_parse_url` → `POST /trigger-url-async` 的默认参数 |
-| `default_get_result` | 解析完成后 `POST /get-formatted` 的默认参数（如各类型输出为 `markdown`、`content: true` 等） |
+成功返回 JSON（Pydantic）：`markdown_path`、`pages_tree_path`、`content_preview`（默认 2000 字）、`message` 等。
+调用方应以返回的 `output_dir` 为准；服务不会复用或删除已有目录。
 
-修改解析行为时，优先编辑该文件；若需在工具层暴露更多 MCP 参数，需扩展 [`uniparser_mcp/server.py`](./uniparser_mcp/server.py)。
+> 出于安全原因，根目录、HOME、当前工作目录及 Git 元数据目录不能作为首选输出目录。
 
-## Tools
+健康检查、版本查询、按 token 手动恢复请使用 CLI：`uniparser health`、`uniparser version`、`uniparser fetch`。
 
-| Tool | 说明 |
-|------|------|
-| `uniparser_health` | `GET /health`，返回服务健康状态字符串 |
-| `uniparser_version` | `GET /version`，返回版本信息 |
-| `uniparser_parse_file` | 参数：本机 PDF **绝对路径**。流程：`POST /trigger-file-async` → 成功后再 `POST /get-formatted`，返回合并后的 `content` 文本 |
-| `uniparser_parse_url` | 参数：公网可访问的 PDF **URL**。流程：`POST /trigger-url-async` → `POST /get-formatted`，返回 `content` |
+## 环境变量
 
-`uniparser_parse_file` 仅适合**同机**或**体积较小**的 PDF；大文件或带宽受限时，建议将文件放到公网可访问地址后使用 `uniparser_parse_url`。
+| 变量 | 说明 | 默认 |
+|------|------|------|
+| `UNIPARSER_API_KEY` | 必填，`X-API-Key` | — |
+| `UNIPARSER_BASE_URL` | API 根 URL | `https://uniparser.dp.tech` |
+| `OUTPUT_DIR` | 输出根目录 | `~/Uni-Parser-Skill` |
+| `UNIPARSER_PREVIEW_CHARS` | `content_preview` 长度 | `2000` |
+| `UNIPARSER_MCP_TRANSPORT` | `stdio` / `sse` / `streamable-http` | `stdio` |
 
-## 本地运行
+## 安装与运行
 
 ```bash
 cd mcp_server
+uv sync
 uv run python -m uniparser_mcp
 ```
 
-或：
-
-```bash
-uv run uniparser-mcp
-```
-
 ## 测试
 
 ```bash
@@ -82,25 +51,12 @@ uv sync --extra dev
 uv run pytest tests/ -v
 ```
 
-调试时要看 `print` 或中间步骤输出，可关闭输出捕获：
+## Cursor / Claude Code 接入示例
 
-```bash
-uv run pytest tests/ -v -s
-# 或
-uv run pytest tests/ -v --capture=no
-```
+先克隆本仓库并在本目录执行 `uv sync`，再在 MCP 配置中增加如下内容。**必须**将两处占位符改成你的本机值，否则 MCP 无法启动：
 
-需要把 **logging** 打到终端时：
-
-```bash
-uv run pytest tests/ -v -s -o log_cli=true -o log_cli_level=DEBUG
-```
-
-- **集成测试**：[`tests/test_client_integration.py`](./tests/test_client_integration.py)（标记 `integration`）。会话开始时用 `httpx` 请求 `{UNIPARSER_BASE_URL}/health`；不可达或非正常状态则**整会话 skip**。仅跑集成：`uv run pytest tests/test_client_integration.py -v`。CI 无服务：`UNIPARSER_INTEGRATION=0 uv run pytest tests/ -v`。
-
-## Cursor / Claude 等 MCP 接入示例
-
-在 MCP 配置中增加（将 `/path/to/UniParser-Tools/src/mcp_server` 换为你的本机路径）：
+1. `"--directory"` 后的路径：把 `/path/to/UniParser-Tools/mcp_server` 替换为克隆到本机后的 `mcp_server` **绝对路径**（例如 macOS：`/Users/<you>/UniParser-Tools/mcp_server`）。
+2. `UNIPARSER_API_KEY`：把 `your-api-key` 替换为你在 [https://uniparser.dp.tech/](https://uniparser.dp.tech/) 申请的真实 API Key。
 
 ```json
 {
@@ -110,13 +66,12 @@ uv run pytest tests/ -v -s -o log_cli=true -o log_cli_level=DEBUG
       "args": [
         "run",
         "--directory",
-        "/path/to/UniParser-Tools/src/mcp_server",
+        "/path/to/UniParser-Tools/mcp_server",
         "python",
         "-m",
         "uniparser_mcp"
       ],
       "env": {
-        "UNIPARSER_BASE_URL": "http://127.0.0.1:40001",
         "UNIPARSER_API_KEY": "your-api-key"
       }
     }
@@ -124,17 +79,7 @@ uv run pytest tests/ -v -s -o log_cli=true -o log_cli_level=DEBUG
 }
 ```
 
-`UNIPARSER_BASE_URL`、`UNIPARSER_API_KEY` 均需在 `env` 中配置（示例仅作占位）。
-
-## 故障排查
-
-| 现象 | 可能原因 |
-|------|----------|
-| 工具返回「未设置 UNIPARSER_BASE_URL」或「未设置 UNIPARSER_API_KEY」 | MCP 的 `env` 未注入或拼写错误 |
-| `uniparser_health` 失败 | 用户服务未启动、端口/防火墙不一致、或 `UNIPARSER_BASE_URL` 与真实监听地址不符 |
-| `uniparser_parse_file` 失败 | 路径不存在、无读权限，或服务端无法访问该路径（远程部署时常见） |
-| `uniparser_parse_url` 长时间无结果 | PDF 较大或 URL 较慢；`UniParserClient` 对单次请求使用固定超时（见客户端实现），可适当调大服务或网关超时 |
-
-## 与主仓库的关系
+`UNIPARSER_BASE_URL` 可省略（默认云服务）；本地自托管时设置为 `http://127.0.0.1:40001` 等。
 
-本目录为**独立**子项目（自有 [`pyproject.toml`](./pyproject.toml)），通过 `[tool.uv.sources]` 以可编辑方式依赖上一级 [`uniparser_tools`](../)。主仓库根 `pyproject.toml` 中若有可选依赖组 `mcp-server`，仅作文档引用；**推荐**始终在 `src/mcp_server/` 下执行 `uv sync`，以保证 MCP 与主服务依赖隔离。
+同步解析使用 `UniParserClient.sync_request_timeout`，轮询和结果获取使用
+`request_timeout`；二者均为客户端 HTTP 超时，与服务端解析预算相互独立。
diff --git a/mcp_server/config.yaml b/mcp_server/config.yaml
deleted file mode 100644
index 3fe891d..0000000
--- a/mcp_server/config.yaml
+++ /dev/null
@@ -1,39 +0,0 @@
-# UniParser MCP：trigger / get-result 默认参数。
-# 服务根 URL 与 API Key 由进程环境提供（建议在 Cursor ``.cursor/mcp.json`` 的 ``env`` 中设置
-# ``UNIPARSER_BASE_URL``、``UNIPARSER_API_KEY``）。
-
-default_trigger_url:
-  lang: unknown
-  sync: true
-  textual: 2
-  table: 2
-  molecule: 1
-  chart: 1
-  figure: 0
-  expression: 1
-  equation: 2
-
-default_trigger_file:
-  lang: unknown
-  sync: true
-  textual: 2
-  table: 2
-  molecule: 1
-  chart: 1
-  figure: 0
-  expression: 1
-  equation: 2
-
-default_get_result:
-  textual: markdown
-  table: markdown
-  molecule: markdown
-  chart: markdown
-  figure: markdown
-  expression: markdown
-  equation: markdown
-  content: true
-  objects: false
-  pages_dict: false
-  pages_tree: false
-  molecule_source: true
diff --git a/mcp_server/pyproject.toml b/mcp_server/pyproject.toml
index 3afaf2a..348741d 100644
--- a/mcp_server/pyproject.toml
+++ b/mcp_server/pyproject.toml
@@ -1,13 +1,12 @@
 [project]
 name = "uniparser-mcp"
-version = "0.1.0"
-description = "MCP server exposing UniParser HTTP API (stdio)"
+version = "0.2.0"
+description = "MCP server exposing UniParser parse via uniparser-tools"
 readme = "README.md"
-requires-python = ">=3.10"
+requires-python = ">=3.11"
 dependencies = [
-    "mcp>=1.10.0",
-    "httpx>=0.27.0",
-    "pyyaml>=6.0",
+    "mcp>=1.10.0,<2",
+    "pydantic>=2.0",
     "uniparser-tools",
 ]
 
@@ -28,12 +27,6 @@ build-backend = "hatchling.build"
 
 [tool.hatch.build.targets.wheel]
 packages = ["uniparser_mcp"]
-# 将仓库根目录的 config.yaml 打进 wheel 的 uniparser_mcp/，供已安装包加载默认参数
-[tool.hatch.build.targets.wheel.force-include]
-"config.yaml" = "uniparser_mcp/config.yaml"
 
 [tool.pytest.ini_options]
 testpaths = ["tests"]
-markers = [
-    "integration: 需要可访问的 UniParser HTTP 服务（默认探测失败则 skip，可用 UNIPARSER_INTEGRATION=0 关闭）",
-]
diff --git a/mcp_server/tests/conftest.py b/mcp_server/tests/conftest.py
new file mode 100644
index 0000000..e69de29
diff --git a/mcp_server/tests/test_input.py b/mcp_server/tests/test_input.py
new file mode 100644
index 0000000..6a244d1
--- /dev/null
+++ b/mcp_server/tests/test_input.py
@@ -0,0 +1,26 @@
+from pathlib import Path
+
+from uniparser_mcp.input import InputKind, resolve_request
+from uniparser_mcp.schemas import ParseRequest
+
+
+def test_resolve_pdf_url():
+    req = ParseRequest(pdf_url="https://example.com/paper.pdf")
+    resolved = resolve_request(req)
+    assert resolved.kind == InputKind.URL
+    assert resolved.token_seed == "https://example.com/paper.pdf"
+    assert resolved.source_stem == "paper"
+
+
+def test_resolve_missing_file(tmp_path: Path):
+    req = ParseRequest(file_path=str(tmp_path / "missing.pdf"))
+    assert "not found" in resolve_request(req).lower()
+
+
+def test_resolve_image(tmp_path: Path):
+    image = tmp_path / "fig.png"
+    image.write_bytes(b"png")
+    req = ParseRequest(image_path=str(image))
+    resolved = resolve_request(req)
+    assert resolved.kind == InputKind.IMAGE
+    assert resolved.path == image.resolve()
diff --git a/mcp_server/tests/test_output.py b/mcp_server/tests/test_output.py
new file mode 100644
index 0000000..275358c
--- /dev/null
+++ b/mcp_server/tests/test_output.py
@@ -0,0 +1,36 @@
+from pathlib import Path
+
+import pytest
+from uniparser_mcp.pipeline.output import resolve_output_dir
+
+
+def test_default_output_uses_configured_root(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> None:
+    output_root = tmp_path / "managed"
+    monkeypatch.setenv("OUTPUT_DIR", str(output_root))
+
+    actual = resolve_output_dir("paper", None)
+
+    assert actual == output_root / "paper"
+    assert actual.is_dir()
+
+
+def test_existing_output_is_preserved_and_suffixed(tmp_path: Path) -> None:
+    preferred = tmp_path / "paper"
+    preferred.mkdir()
+    (preferred / "old.txt").write_text("old", encoding="utf-8")
+
+    actual = resolve_output_dir("paper", str(preferred))
+
+    assert actual == tmp_path / "paper_1"
+    assert actual.is_dir()
+    assert (preferred / "old.txt").read_text(encoding="utf-8") == "old"
+
+
+def test_existing_file_is_preserved_and_suffixed(tmp_path: Path) -> None:
+    preferred = tmp_path / "paper"
+    preferred.write_text("old", encoding="utf-8")
+
+    actual = resolve_output_dir("paper", str(preferred))
+
+    assert actual == tmp_path / "paper_1"
+    assert preferred.read_text(encoding="utf-8") == "old"
diff --git a/mcp_server/tests/test_parse_options.py b/mcp_server/tests/test_parse_options.py
new file mode 100644
index 0000000..6ac736d
--- /dev/null
+++ b/mcp_server/tests/test_parse_options.py
@@ -0,0 +1,25 @@
+from uniparser_mcp.parse_options import SCIENTIFIC_PAPER_DEFAULTS, resolve_trigger_kwargs
+from uniparser_mcp.schemas import ParseModeChoice, TextualChoice
+
+from uniparser_tools.common.constant import ParseMode, ParseModeTextual
+
+
+def test_scientific_paper_defaults_match_cli():
+    assert SCIENTIFIC_PAPER_DEFAULTS["textual"] == ParseModeTextual.OCRHighQuality
+    assert SCIENTIFIC_PAPER_DEFAULTS["chart"] == ParseMode.DumpBase64
+    assert SCIENTIFIC_PAPER_DEFAULTS["molecule"] == ParseMode.OCRFast
+
+
+def test_resolve_trigger_kwargs_sync():
+    kwargs = resolve_trigger_kwargs(
+        sync=False,
+        textual=TextualChoice.ocr_hq,
+        equation=ParseModeChoice.ocr_hq,
+        table=ParseModeChoice.ocr_hq,
+        chart=ParseModeChoice.base64,
+        figure=ParseModeChoice.base64,
+        expression=ParseModeChoice.base64,
+        molecule=ParseModeChoice.ocr_fast,
+    )
+    assert kwargs["sync"] is False
+    assert kwargs["equation"] == ParseMode.OCRHighQuality
diff --git a/mcp_server/tests/test_pipeline_parse.py b/mcp_server/tests/test_pipeline_parse.py
new file mode 100644
index 0000000..a88fdaf
--- /dev/null
+++ b/mcp_server/tests/test_pipeline_parse.py
@@ -0,0 +1,84 @@
+import asyncio
+from pathlib import Path
+from unittest.mock import MagicMock
+
+from uniparser_mcp.pipeline.parse import run_parse
+from uniparser_mcp.schemas import ParseRequest
+
+
+def test_duplicate_triggers_internal_complete(tmp_path: Path):
+    pdf = tmp_path / "paper.pdf"
+    pdf.write_bytes(b"%PDF-1.4")
+
+    client = MagicMock()
+    client.to_token.return_value = "tok123"
+    client.trigger_file.return_value = {
+        "status": "error",
+        "message": "Token is duplicated",
+        "token": "tok123",
+    }
+    client.get_result.side_effect = [
+        {"status": "success"},
+        {"status": "success", "pages_tree": True},
+    ]
+    client.get_formatted.return_value = {
+        "status": "success",
+        "content": "# Hello",
+    }
+
+    req = ParseRequest(file_path=str(pdf), output_dir=str(tmp_path / "out"))
+    result = asyncio.run(run_parse(client, req))
+
+    assert result.ok is True
+    assert result.token == "tok123"
+    assert result.trigger_meta_path is None
+    assert Path(result.markdown_path).is_file()
+    client.trigger_file.assert_called_once()
+
+
+def test_success_parse_writes_trigger_meta(tmp_path: Path):
+    pdf = tmp_path / "paper.pdf"
+    pdf.write_bytes(b"%PDF-1.4")
+
+    client = MagicMock()
+    client.trigger_file.return_value = {"status": "success", "token": "tok456"}
+    client.get_result.side_effect = [
+        {"status": "success"},
+        {"status": "success"},
+    ]
+    client.get_formatted.return_value = {
+        "status": "success",
+        "content": "# Title",
+    }
+
+    out = tmp_path / "out"
+    req = ParseRequest(file_path=str(pdf), output_dir=str(out))
+    result = asyncio.run(run_parse(client, req))
+
+    assert result.ok is True
+    assert result.trigger_meta_path is not None
+    assert Path(result.trigger_meta_path).is_file()
+
+
+def test_existing_output_is_preserved_and_result_uses_suffixed_sibling(tmp_path: Path):
+    pdf = tmp_path / "paper.pdf"
+    pdf.write_bytes(b"%PDF-1.4")
+    existing = tmp_path / "existing"
+    existing.mkdir()
+    (existing / "keep.txt").write_text("keep", encoding="utf-8")
+
+    client = MagicMock()
+    client.trigger_file.return_value = {"status": "success", "token": "tok789"}
+    client.get_result.side_effect = [
+        {"status": "success"},
+        {"status": "success", "pages_tree": {}},
+    ]
+    client.get_formatted.return_value = {"status": "success", "content": "# New"}
+    req = ParseRequest(file_path=str(pdf), output_dir=str(existing))
+    result = asyncio.run(run_parse(client, req))
+
+    assert result.ok is True
+    assert Path(result.output_dir) == tmp_path / "existing_1"
+    assert (existing / "keep.txt").read_text(encoding="utf-8") == "keep"
+    assert Path(result.markdown_path).read_text(encoding="utf-8") == "# New"
+    client.trigger_file.assert_called_once()
diff --git a/mcp_server/tests/test_schemas.py b/mcp_server/tests/test_schemas.py
new file mode 100644
index 0000000..4a98708
--- /dev/null
+++ b/mcp_server/tests/test_schemas.py
@@ -0,0 +1,19 @@
+import pytest
+from uniparser_mcp.schemas import ParseModeChoice, ParseRequest, TextualChoice
+
+
+def test_parse_request_requires_exactly_one_input():
+    with pytest.raises(ValueError):
+        ParseRequest()
+    with pytest.raises(ValueError):
+        ParseRequest(file_path="/a.pdf", pdf_url="https://x.com/a.pdf")
+    req = ParseRequest(file_path="/tmp/paper.pdf")
+    assert req.file_path == "/tmp/paper.pdf"
+
+
+def test_scientific_paper_defaults():
+    req = ParseRequest(pdf_url="https://example.com/paper.pdf")
+    assert req.textual == TextualChoice.ocr_hq
+    assert req.equation == ParseModeChoice.ocr_hq
+    assert req.chart == ParseModeChoice.base64
+    assert req.molecule == ParseModeChoice.ocr_fast
diff --git a/mcp_server/uniparser_mcp/__init__.py b/mcp_server/uniparser_mcp/__init__.py
index 6b633ed..45a7248 100644
--- a/mcp_server/uniparser_mcp/__init__.py
+++ b/mcp_server/uniparser_mcp/__init__.py
@@ -1,3 +1,3 @@
-"""UniParser MCP Server — stdio tools over the UniParser HTTP API."""
+"""UniParser MCP server — parse-only tool over UniParser API."""
 
-__version__ = "0.1.0"
+__version__ = "0.2.0"
diff --git a/mcp_server/uniparser_mcp/client.py b/mcp_server/uniparser_mcp/client.py
new file mode 100644
index 0000000..372eb1b
--- /dev/null
+++ b/mcp_server/uniparser_mcp/client.py
@@ -0,0 +1,13 @@
+"""UniParserClient factory."""
+
+from __future__ import annotations
+
+from uniparser_mcp.config import get_api_key, get_base_url
+from uniparser_tools.api.clients import UniParserClient
+
+
+def get_client() -> UniParserClient:
+    api_key = get_api_key()
+    if not api_key:
+        raise ValueError("UNIPARSER_API_KEY is not set")
+    return UniParserClient(get_base_url(), api_key)
diff --git a/mcp_server/uniparser_mcp/config.py b/mcp_server/uniparser_mcp/config.py
new file mode 100644
index 0000000..b3a3b76
--- /dev/null
+++ b/mcp_server/uniparser_mcp/config.py
@@ -0,0 +1,36 @@
+"""Environment configuration for the UniParser MCP server."""
+
+from __future__ import annotations
+
+import os
+from pathlib import Path
+
+from uniparser_mcp.defaults import DEFAULT_PREVIEW_CHARS, UNIPARSER_BASE_URL
+
+
+def get_api_key() -> str | None:
+    key = (os.environ.get("UNIPARSER_API_KEY") or "").strip()
+    return key or None
+
+
+def get_base_url() -> str:
+    raw = (os.environ.get("UNIPARSER_BASE_URL") or "").strip().rstrip("/")
+    return raw or UNIPARSER_BASE_URL
+
+
+def get_output_root() -> Path:
+    raw = (os.environ.get("OUTPUT_DIR") or "").strip()
+    if raw:
+        return Path(raw).expanduser().resolve()
+    return (Path.home() / "Uni-Parser-Skill").expanduser().resolve()
+
+
+def get_preview_chars() -> int:
+    raw = (os.environ.get("UNIPARSER_PREVIEW_CHARS") or "").strip()
+    if not raw:
+        return DEFAULT_PREVIEW_CHARS
+    try:
+        value = int(raw)
+    except ValueError:
+        return DEFAULT_PREVIEW_CHARS
+    return max(0, value)
diff --git a/mcp_server/uniparser_mcp/defaults.py b/mcp_server/uniparser_mcp/defaults.py
new file mode 100644
index 0000000..d4f254d
--- /dev/null
+++ b/mcp_server/uniparser_mcp/defaults.py
@@ -0,0 +1,10 @@
+UNIPARSER_BASE_URL = "https://uniparser.dp.tech"
+
+POLL_INTERVAL_SEC = 3
+POLL_TIMEOUT_SEC = 1800
+
+PENDING_STATUSES = frozenset({"undefined", "waiting", "processing"})
+
+IMAGE_SUFFIXES = frozenset({".png", ".jpg", ".jpeg", ".webp", ".gif", ".bmp", ".tif", ".tiff"})
+
+DEFAULT_PREVIEW_CHARS = 2000
diff --git a/mcp_server/uniparser_mcp/errors.py b/mcp_server/uniparser_mcp/errors.py
new file mode 100644
index 0000000..0b0333e
--- /dev/null
+++ b/mcp_server/uniparser_mcp/errors.py
@@ -0,0 +1,24 @@
+"""Structured MCP errors."""
+
+from __future__ import annotations
+
+from uniparser_mcp.schemas import ErrorDetail, ErrorResult
+
+
+def config_error(message: str) -> ErrorResult:
+    return ErrorResult(error=ErrorDetail(code="CONFIG_ERROR", message=message))
+
+
+def input_error(message: str) -> ErrorResult:
+    return ErrorResult(error=ErrorDetail(code="INPUT_ERROR", message=message))
+
+
+def parse_error(stage: str, result: dict) -> ErrorResult:
+    return ErrorResult(
+        error=ErrorDetail(
+            code="PARSE_ERROR",
+            message=result.get("description") or result.get("message") or str(result),
+            stage=stage,
+            token=result.get("token"),
+        )
+    )
diff --git a/mcp_server/uniparser_mcp/input.py b/mcp_server/uniparser_mcp/input.py
new file mode 100644
index 0000000..e3efe1d
--- /dev/null
+++ b/mcp_server/uniparser_mcp/input.py
@@ -0,0 +1,86 @@
+"""Input resolution for uniparser_parse."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from enum import Enum
+from pathlib import Path
+from urllib.parse import urlparse
+
+from uniparser_mcp.defaults import IMAGE_SUFFIXES
+from uniparser_mcp.schemas import ParseRequest
+
+
+class InputKind(str, Enum):
+    FILE = "file"
+    IMAGE = "image"
+    URL = "url"
+
+
+@dataclass(frozen=True)
+class ResolvedInput:
+    kind: InputKind
+    source_stem: str
+    raw: str
+    token_seed: str
+    path: Path | None = None
+
+
+def source_stem_from_path(path: Path) -> str:
+    return path.stem or "document"
+
+
+def source_stem_from_url(url: str) -> str:
+    segment = urlparse(url).path.rstrip("/").rsplit("/", 1)[-1]
+    if not segment:
+        return "url_document"
+    lower = segment.lower()
+    for ext in (".pdf", ".png", ".jpg", ".jpeg", ".webp"):
+        if lower.endswith(ext):
+            segment = segment[: -len(ext)]
+            break
+    return segment or "url_document"
+
+
+def display_label(resolved: ResolvedInput) -> str:
+    if resolved.path is not None:
+        return resolved.path.name
+    segment = urlparse(resolved.raw).path.rstrip("/").rsplit("/", 1)[-1]
+    return segment or "url_document"
+
+
+def resolve_request(req: ParseRequest) -> ResolvedInput | str:
+    if req.pdf_url:
+        url = req.pdf_url.strip()
+        if not url.startswith(("http://", "https://")):
+            return "pdf_url must start with http:// or https://"
+        return ResolvedInput(
+            kind=InputKind.URL,
+            source_stem=source_stem_from_url(url),
+            raw=url,
+            token_seed=url,
+        )
+
+    path_str = (req.file_path or req.image_path or "").strip()
+    path = Path(path_str).expanduser().resolve()
+    if not path.is_file():
+        return f"File not found: {path}"
+
+    if req.image_path:
+        if path.suffix.lower() not in IMAGE_SUFFIXES:
+            return f"Not a supported image type: {path.suffix}"
+        return ResolvedInput(
+            kind=InputKind.IMAGE,
+            source_stem=source_stem_from_path(path),
+            raw=path_str,
+            token_seed=str(path),
+            path=path,
+        )
+
+    return ResolvedInput(
+        kind=InputKind.FILE,
+        source_stem=source_stem_from_path(path),
+        raw=path_str,
+        token_seed=str(path),
+        path=path,
+    )
diff --git a/mcp_server/uniparser_mcp/instructions.py b/mcp_server/uniparser_mcp/instructions.py
new file mode 100644
index 0000000..0e3765a
--- /dev/null
+++ b/mcp_server/uniparser_mcp/instructions.py
@@ -0,0 +1,25 @@
+"""Agent guidance for the UniParser MCP server."""
+
+SERVER_INSTRUCTIONS = """
+You are connected to the UniParser MCP server for document parsing.
+
+## When to call uniparser_parse
+
+Call uniparser_parse immediately, without asking for confirmation, when:
+- The user provides an absolute local path to a PDF or image (for example `/Users/.../paper.pdf`)
+- The user provides a public PDF URL (`http://` or `https://`)
+- The user asks to parse, convert, read, or extract text from a document
+
+## How to call it
+
+- Provide exactly one input: `file_path`, `image_path`, or `pdf_url`
+- Use absolute paths for local files when possible
+- Optional parse fields default to the scientific-paper preset (high-quality OCR for text/tables/equations)
+- Use `output_dir` only when the user requests a specific save location. It is a preferred path: if it is
+  occupied, a new sibling such as `<name>_1` is created automatically. Use the actual `output_dir` returned.
+- Large documents: read `markdown_path` and `pages_tree_path` from the response; `content_preview` is only a short preview
+
+## After calling uniparser_parse
+
+Always include the `message` field from the tool response in your reply to the user.
+"""
diff --git a/mcp_server/uniparser_mcp/parse_options.py b/mcp_server/uniparser_mcp/parse_options.py
new file mode 100644
index 0000000..e7db253
--- /dev/null
+++ b/mcp_server/uniparser_mcp/parse_options.py
@@ -0,0 +1,74 @@
+"""Parse mode aliases aligned with the uniparser CLI."""
+
+from __future__ import annotations
+
+from typing import Any
+
+from uniparser_mcp.schemas import ParseModeChoice, TextualChoice
+from uniparser_tools.common.constant import ParseMode, ParseModeTextual
+
+
+TEXTUAL_ALIASES: dict[str, ParseModeTextual] = {
+    "disable": ParseModeTextual.Disable,
+    "ocr-fast": ParseModeTextual.OCRFast,
+    "ocr-hq": ParseModeTextual.OCRHighQuality,
+    "digital": ParseModeTextual.DigitalExported,
+    "base64": ParseModeTextual.DumpBase64,
+}
+
+PARSE_MODE_ALIASES: dict[str, ParseMode] = {
+    "disable": ParseMode.Disable,
+    "ocr-fast": ParseMode.OCRFast,
+    "ocr-hq": ParseMode.OCRHighQuality,
+    "base64": ParseMode.DumpBase64,
+}
+
+SCIENTIFIC_PAPER_DEFAULTS: dict[str, ParseMode | ParseModeTextual] = {
+    "textual": ParseModeTextual.OCRHighQuality,
+    "equation": ParseMode.OCRHighQuality,
+    "table": ParseMode.OCRHighQuality,
+    "chart": ParseMode.DumpBase64,
+    "figure": ParseMode.DumpBase64,
+    "expression": ParseMode.DumpBase64,
+    "molecule": ParseMode.OCRFast,
+}
+
+
+def resolve_trigger_kwargs(
+    *,
+    sync: bool,
+    textual: TextualChoice,
+    equation: ParseModeChoice,
+    table: ParseModeChoice,
+    chart: ParseModeChoice,
+    figure: ParseModeChoice,
+    expression: ParseModeChoice,
+    molecule: ParseModeChoice,
+) -> dict[str, Any]:
+    return {
+        "textual": TEXTUAL_ALIASES[textual.value],
+        "equation": PARSE_MODE_ALIASES[equation.value],
+        "table": PARSE_MODE_ALIASES[table.value],
+        "chart": PARSE_MODE_ALIASES[chart.value],
+        "figure": PARSE_MODE_ALIASES[figure.value],
+        "expression": PARSE_MODE_ALIASES[expression.value],
+        "molecule": PARSE_MODE_ALIASES[molecule.value],
+        "sync": sync,
+    }
+
+
+def serialize_trigger_kwargs(kwargs: dict[str, Any]) -> dict[str, Any]:
+    textual_by_mode = {mode: alias for alias, mode in TEXTUAL_ALIASES.items()}
+    parse_by_mode = {mode: alias for alias, mode in PARSE_MODE_ALIASES.items()}
+    serialized: dict[str, Any] = {}
+    for field in ("textual", "equation", "table", "chart", "figure", "expression", "molecule"):
+        if field not in kwargs:
+            continue
+        value = kwargs[field]
+        if field == "textual":
+            serialized[field] = textual_by_mode[value]
+        else:
+            serialized[field] = parse_by_mode[value]
+    if "sync" in kwargs:
+        serialized["sync"] = kwargs["sync"]
+    return serialized
diff --git a/mcp_server/uniparser_mcp/pipeline/__init__.py b/mcp_server/uniparser_mcp/pipeline/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/mcp_server/uniparser_mcp/pipeline/output.py b/mcp_server/uniparser_mcp/pipeline/output.py
new file mode 100644
index 0000000..954831b
--- /dev/null
+++ b/mcp_server/uniparser_mcp/pipeline/output.py
@@ -0,0 +1,91 @@
+"""Persist parse artifacts to disk."""
+
+from __future__ import annotations
+
+import json
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+from uniparser_mcp.config import get_output_root
+from uniparser_tools.common.output_dir import create_unique_output_dir
+
+
+def default_output_dir(source_stem: str) -> Path:
+    return get_output_root() / source_stem
+
+
+def _ensure_output_dir(out_dir: Path) -> None:
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+
+def resolve_output_dir(
+    source_stem: str,
+    output_dir: str | None,
+) -> Path:
+    preferred = Path(output_dir).expanduser() if output_dir else default_output_dir(source_stem)
+    return create_unique_output_dir(preferred)
+
+
+def write_trigger_meta(
+    out_dir: Path,
+    *,
+    token: str,
+    input_type: str,
+    input_value: str,
+    trigger_kwargs: dict | None = None,
+) -> Path:
+    _ensure_output_dir(out_dir)
+    meta_path = out_dir / "trigger_meta.json"
+    payload: dict[str, Any] = {
+        "token": token,
+        "input_type": input_type,
+        "input": input_value,
+        "submitted_at": datetime.now(timezone.utc).isoformat(),
+    }
+    if trigger_kwargs is not None:
+        payload["trigger_kwargs"] = trigger_kwargs
+    meta_path.write_text(json.dumps(payload, indent=2, ensure_ascii=False), encoding="utf-8")
+    return meta_path
+
+
+def save_stage_error(out_dir: Path, filename: str, payload: dict) -> None:
+    _ensure_output_dir(out_dir)
+    (out_dir / filename).write_text(json.dumps(payload, indent=2, ensure_ascii=False), encoding="utf-8")
+
+
+def save_parse_results(
+    *,
+    out_dir: Path,
+    source_stem: str,
+    pages_tree: dict,
+    formatted: dict,
+) -> dict[str, Any]:
+    _ensure_output_dir(out_dir)
+    stem = source_stem or "document"
+
+    pages_tree_path = out_dir / "pages_tree.json"
+    pages_tree_path.write_text(
+        json.dumps(pages_tree, indent=2, ensure_ascii=False, default=str),
+        encoding="utf-8",
+    )
+
+    md_path = out_dir / f"{stem}.md"
+    content = formatted.get("content", "")
+    md_path.write_text(content, encoding="utf-8")
+
+    meta = {k: v for k, v in formatted.items() if k != "content"}
+    formatted_meta_path = out_dir / "formatted_meta.json"
+    formatted_meta_path.write_text(
+        json.dumps(meta, indent=2, ensure_ascii=False, default=str),
+        encoding="utf-8",
+    )
+
+    return {
+        "output_dir": str(out_dir),
+        "pages_tree_path": str(pages_tree_path),
+        "markdown_path": str(md_path),
+        "formatted_meta_path": str(formatted_meta_path),
+        "content_chars": len(content),
+        "content": content,
+    }
diff --git a/mcp_server/uniparser_mcp/pipeline/parse.py b/mcp_server/uniparser_mcp/pipeline/parse.py
new file mode 100644
index 0000000..76e617b
--- /dev/null
+++ b/mcp_server/uniparser_mcp/pipeline/parse.py
@@ -0,0 +1,186 @@
+"""Parse pipeline for uniparser_parse."""
+
+from __future__ import annotations
+
+import asyncio
+from typing import Any
+
+from mcp.server.fastmcp import Context
+
+from uniparser_mcp.errors import input_error, parse_error
+from uniparser_mcp.input import InputKind, ResolvedInput, display_label, resolve_request
+from uniparser_mcp.parse_options import resolve_trigger_kwargs, serialize_trigger_kwargs
+from uniparser_mcp.pipeline.output import (
+    resolve_output_dir,
+    save_parse_results,
+    save_stage_error,
+    write_trigger_meta,
+)
+from uniparser_mcp.pipeline.poll import poll_until_success
+from uniparser_mcp.schemas import ErrorResult, ParseRequest, ParseResult
+from uniparser_mcp.tools.response import build_parse_success
+from uniparser_tools.api.clients import UniParserClient
+from uniparser_tools.common.constant import ErrorFlag, FormatFlag
+
+
+def _is_token_duplicated(trigger: dict[str, Any]) -> bool:
+    if trigger.get("status") == "success":
+        return False
+    message = str(trigger.get("message") or trigger.get("description") or "")
+    return message == ErrorFlag.Token_Duplicated or "Token is duplicated" in message
+
+
+async def _ctx_info(ctx: Context | None, message: str) -> None:
+    if ctx is None:
+        return
+    if ctx.request_context:
+        await ctx.info(message)
+
+
+async def _trigger_input(
+    client: UniParserClient,
+    resolved: ResolvedInput,
+    *,
+    trigger_kwargs: dict[str, Any],
+) -> tuple[dict[str, Any], str]:
+    if resolved.kind is InputKind.FILE:
+        trigger = await asyncio.to_thread(
+            client.trigger_file,
+            str(resolved.path),
+            token=None,
+            **trigger_kwargs,
+        )
+        return trigger, "trigger_file"
+    if resolved.kind is InputKind.IMAGE:
+        trigger = await asyncio.to_thread(
+            client.trigger_snip,
+            str(resolved.path),
+            token=None,
+            **trigger_kwargs,
+        )
+        return trigger, "trigger_snip"
+    trigger = await asyncio.to_thread(
+        client.trigger_url,
+        resolved.raw,
+        token=None,
+        **trigger_kwargs,
+    )
+    return trigger, "trigger_url"
+
+
+async def _fetch_pages_tree(client: UniParserClient, token: str) -> dict[str, Any]:
+    return await asyncio.to_thread(
+        client.get_result,
+        token,
+        pages_tree=True,
+        objects=False,
+    )
+
+
+async def _fetch_markdown(client: UniParserClient, token: str) -> dict[str, Any]:
+    return await asyncio.to_thread(
+        client.get_formatted,
+        token,
+        content=True,
+        textual=FormatFlag.Markdown,
+        table=FormatFlag.Markdown,
+        equation=FormatFlag.Latex,
+    )
+
+
+async def _complete_by_token(
+    client: UniParserClient,
+    token: str,
+    *,
+    resolved: ResolvedInput,
+    out_dir,
+    ctx: Context | None,
+    write_trigger_meta_file: bool,
+    trigger_kwargs: dict[str, Any] | None = None,
+) -> ParseResult | ErrorResult:
+    poll_result = await poll_until_success(client, token)
+    if isinstance(poll_result, ErrorResult):
+        return poll_result
+
+    pages_tree = await _fetch_pages_tree(client, token)
+    if pages_tree.get("status") != "success":
+        save_stage_error(out_dir, "pages_tree_error.json", pages_tree)
+        return parse_error("get_result_pages_tree", pages_tree)
+
+    formatted = await _fetch_markdown(client, token)
+    if formatted.get("status") != "success":
+        save_stage_error(out_dir, "formatted_error.json", formatted)
+        return parse_error("get_formatted", formatted)
+
+    summary = save_parse_results(
+        out_dir=out_dir,
+        source_stem=resolved.source_stem,
+        pages_tree=pages_tree,
+        formatted=formatted,
+    )
+    summary["token"] = token
+    summary["input_type"] = resolved.kind.value
+    if write_trigger_meta_file and trigger_kwargs is not None:
+        meta_path = write_trigger_meta(
+            out_dir,
+            token=token,
+            input_type=resolved.kind.value,
+            input_value=resolved.raw,
+            trigger_kwargs=serialize_trigger_kwargs(trigger_kwargs),
+        )
+        summary["trigger_meta_path"] = str(meta_path)
+    return build_parse_success(summary)
+
+
+async def run_parse(client: UniParserClient, req: ParseRequest, ctx: Context | None = None) -> ParseResult:
+    resolved = resolve_request(req)
+    if isinstance(resolved, str):
+        return input_error(resolved)
+
+    try:
+        out_dir = resolve_output_dir(resolved.source_stem, req.output_dir)
+    except (OSError, ValueError) as exc:
+        return input_error(str(exc))
+
+    trigger_kwargs = resolve_trigger_kwargs(
+        sync=not req.async_mode,
+        textual=req.textual,
+        equation=req.equation,
+        table=req.table,
+        chart=req.chart,
+        figure=req.figure,
+        expression=req.expression,
+        molecule=req.molecule,
+    )
+
+    await _ctx_info(ctx, f"Parsing {display_label(resolved)}")
+    trigger, stage = await _trigger_input(client, resolved, trigger_kwargs=trigger_kwargs)
+
+    if _is_token_duplicated(trigger):
+        token = trigger.get("token") or client.to_token(resolved.token_seed)
+        return await _complete_by_token(
+            client,
+            token,
+            resolved=resolved,
+            out_dir=out_dir,
+            ctx=ctx,
+            write_trigger_meta_file=False,
+        )
+
+    if trigger.get("status") != "success":
+        save_stage_error(out_dir, "trigger_error.json", trigger)
+        return parse_error(stage, trigger)
+
+    token = trigger.get("token")
+    if not token:
+        return parse_error(stage, {"status": "error", "message": "trigger response missing token"})
+
+    return await _complete_by_token(
+        client,
+        token,
+        resolved=resolved,
+        out_dir=out_dir,
+        ctx=ctx,
+        write_trigger_meta_file=True,
+        trigger_kwargs=trigger_kwargs,
+    )
diff --git a/mcp_server/uniparser_mcp/pipeline/poll.py b/mcp_server/uniparser_mcp/pipeline/poll.py
new file mode 100644
index 0000000..4bccf81
--- /dev/null
+++ b/mcp_server/uniparser_mcp/pipeline/poll.py
@@ -0,0 +1,46 @@
+"""Poll UniParser jobs until completion."""
+
+from __future__ import annotations
+
+import asyncio
+import time
+from typing import Any
+
+from uniparser_mcp.defaults import PENDING_STATUSES, POLL_INTERVAL_SEC, POLL_TIMEOUT_SEC
+from uniparser_mcp.errors import parse_error
+from uniparser_mcp.schemas import ErrorResult
+from uniparser_tools.api.clients import UniParserClient
+
+
+async def poll_until_success(client: UniParserClient, token: str) -> dict[str, Any] | ErrorResult:
+    deadline = time.time() + POLL_TIMEOUT_SEC
+    last: dict[str, Any] = {}
+
+    while time.time() < deadline:
+        last = await asyncio.to_thread(
+            client.get_result,
+            token,
+            content=False,
+            objects=False,
+            pages_dict=False,
+            pages_tree=False,
+        )
+        status = last.get("status")
+        if status == "success":
+            return last
+        if status == "error":
+            return parse_error("get_result_poll", last)
+        if status in PENDING_STATUSES or status is None:
+            await asyncio.sleep(POLL_INTERVAL_SEC)
+            continue
+        return parse_error("get_result_poll", last)
+
+    return parse_error(
+        "get_result_poll",
+        {
+            "status": "error",
+            "description": f"Timed out after {POLL_TIMEOUT_SEC}s waiting for parsing to finish.",
+            "token": token,
+            "last_status": last.get("status"),
+        },
+    )
diff --git a/mcp_server/uniparser_mcp/schemas.py b/mcp_server/uniparser_mcp/schemas.py
new file mode 100644
index 0000000..24aadde
--- /dev/null
+++ b/mcp_server/uniparser_mcp/schemas.py
@@ -0,0 +1,96 @@
+"""Pydantic models for the uniparser_parse tool."""
+
+from __future__ import annotations
+
+from enum import Enum
+from typing import Literal
+
+from pydantic import BaseModel, Field, model_validator
+
+
+class TextualChoice(str, Enum):
+    disable = "disable"
+    ocr_fast = "ocr-fast"
+    ocr_hq = "ocr-hq"
+    digital = "digital"
+    base64 = "base64"
+
+
+class ParseModeChoice(str, Enum):
+    disable = "disable"
+    ocr_fast = "ocr-fast"
+    ocr_hq = "ocr-hq"
+    base64 = "base64"
+
+
+class ParseRequest(BaseModel):
+    file_path: str | None = Field(default=None, description="Absolute path to a local PDF file.")
+    image_path: str | None = Field(
+        default=None,
+        description="Absolute path to a local image snippet (.png, .jpg, etc.).",
+    )
+    pdf_url: str | None = Field(default=None, description="Publicly accessible PDF URL.")
+    output_dir: str | None = Field(
+        default=None,
+        description=(
+            "Preferred output directory. If occupied, an available suffixed sibling is created. "
+            "Default: ~/Uni-Parser-Skill/<source_stem>/."
+        ),
+    )
+    async_mode: bool = Field(
+        default=False,
+        description="Submit with sync=false, then poll until the job completes.",
+    )
+    textual: TextualChoice = Field(default=TextualChoice.ocr_hq)
+    equation: ParseModeChoice = Field(default=ParseModeChoice.ocr_hq)
+    table: ParseModeChoice = Field(default=ParseModeChoice.ocr_hq)
+    chart: ParseModeChoice = Field(default=ParseModeChoice.base64)
+    figure: ParseModeChoice = Field(default=ParseModeChoice.base64)
+    expression: ParseModeChoice = Field(default=ParseModeChoice.base64)
+    molecule: ParseModeChoice = Field(default=ParseModeChoice.ocr_fast)
+
+    @model_validator(mode="after")
+    def exactly_one_input(self) -> ParseRequest:
+        provided = [
+            name
+            for name, value in (
+                ("file_path", self.file_path),
+                ("image_path", self.image_path),
+                ("pdf_url", self.pdf_url),
+            )
+            if value
+        ]
+        if len(provided) != 1:
+            raise ValueError("Provide exactly one of file_path, image_path, or pdf_url.")
+        return self
+
+
+class ErrorDetail(BaseModel):
+    code: str
+    message: str
+    stage: str | None = None
+    output_dir: str | None = None
+    token: str | None = None
+
+
+class ParseSuccess(BaseModel):
+    ok: Literal[True] = True
+    status: Literal["success"] = "success"
+    output_dir: str
+    markdown_path: str
+    pages_tree_path: str
+    formatted_meta_path: str
+    trigger_meta_path: str | None = None
+    token: str
+    input_type: Literal["file", "image", "url"]
+    content_chars: int
+    content_preview: str
+    message: str
+
+
+class ErrorResult(BaseModel):
+    ok: Literal[False] = False
+    error: ErrorDetail
+
+
+ParseResult = ParseSuccess | ErrorResult
diff --git a/mcp_server/uniparser_mcp/server.py b/mcp_server/uniparser_mcp/server.py
index e8d4d6d..5898602 100644
--- a/mcp_server/uniparser_mcp/server.py
+++ b/mcp_server/uniparser_mcp/server.py
@@ -1,159 +1,23 @@
-"""FastMCP server: UniParser HTTP API tools.
-
-传输层：默认 **stdio**（本地 MCP 子进程）；设为 **streamable-http** 时可作为 HTTP 服务被远程连接。
-
-- ``UNIPARSER_BASE_URL`` / ``UNIPARSER_API_KEY``：从环境读取（建议 ``.cursor/mcp.json`` 的 ``env``）。
-- ``UNIPARSER_MCP_TRANSPORT``：``stdio``（默认）、``sse``、``streamable-http``（别名 ``http`` / ``streamable_http``）。
-- Streamable HTTP 监听地址：``FASTMCP_HOST``、``FASTMCP_PORT``（默认 ``127.0.0.1:8000``）、路径 ``FASTMCP_STREAMABLE_HTTP_PATH``（默认 ``/mcp``）。
-
-trigger / get-result 默认项见 ``mcp_server/config.yaml``。
-"""
+"""UniParser MCP server entrypoint."""
 
 from __future__ import annotations
 
-import asyncio
-import json
 import logging
-import os
-from functools import lru_cache
-from pathlib import Path
-from typing import Any, Literal
+from typing import Literal
 
-import yaml
 from mcp.server.fastmcp import FastMCP
 
-from uniparser_tools.api.clients import UniParserClient
-from uniparser_tools.common.constant import FormatFlag
-
-
-mcp = FastMCP("UniParser")
-
-
-def _config_yaml_path() -> Path:
-    """包内（wheel/sdist）或与源码树中 ``mcp_server/config.yaml`` 同级。"""
-    here = Path(__file__).resolve().parent
-    for candidate in (here / "config.yaml", here.parent / "config.yaml"):
-        if candidate.is_file():
-            return candidate
-    raise ValueError(f"未找到 config.yaml（已尝试: {here / 'config.yaml'}, {here.parent / 'config.yaml'}）")
-
-
-@lru_cache(maxsize=1)
-def _yaml_config() -> dict[str, Any]:
-    path = _config_yaml_path()
-    data = yaml.safe_load(path.read_text(encoding="utf-8"))
-    if not isinstance(data, dict):
-        raise ValueError("config.yaml 根节点必须是映射")
-    return data
-
-
-def _cfg_section(key: str) -> dict[str, Any]:
-    v = _yaml_config().get(key)
-    if not isinstance(v, dict):
-        raise ValueError(f"config.yaml 中键 {key!r} 缺失或不是对象")
-    return dict(v)
-
-
-def _get_formatted_kwargs(merged: dict[str, Any]) -> dict[str, Any]:
-    fmt = ("textual", "table", "molecule", "chart", "figure", "expression", "equation")
-    out: dict[str, Any] = {}
-    for k, v in merged.items():
-        out[k] = FormatFlag(v) if k in fmt and isinstance(v, str) else v
-    return out
+from uniparser_mcp.instructions import SERVER_INSTRUCTIONS
+from uniparser_mcp.tools.register import register_tools
 
 
-def _client() -> UniParserClient:
-    base = (os.environ.get("UNIPARSER_BASE_URL") or "").strip().rstrip("/")
-    key = (os.environ.get("UNIPARSER_API_KEY") or "").strip()
-    if not base:
-        raise ValueError("未设置 UNIPARSER_BASE_URL（可在 .cursor/mcp.json 的 env 中配置）")
-    if not key:
-        raise ValueError("未设置 UNIPARSER_API_KEY（可在 .cursor/mcp.json 的 env 中配置）")
-    return UniParserClient(base, key)
-
-
-def _trigger_error(trig: Any) -> str | None:
-    if not isinstance(trig, dict):
-        return str(trig)
-    if not trig:
-        return "{}"
-    if trig.get("status") != "success":
-        return json.dumps(trig, ensure_ascii=False)
-    return None
-
-
-async def _fetch_content(client: UniParserClient, trig: Any, token_seed: str) -> str:
-    err = _trigger_error(trig)
-    if err is not None:
-        return err
-    token = trig.get("token") if isinstance(trig, dict) else None
-    if not token:
-        token = client.to_token(token_seed)
-    body = dict(_cfg_section("default_get_result"))
-    body["token"] = token
-    res = await asyncio.to_thread(client.get_formatted, **_get_formatted_kwargs(body))
-    if res.get("status") == "error":
-        return res.get("description", "获取结果失败")
-    return res["content"]
-
-
-@mcp.tool()
-async def uniparser_health() -> str:
-    """调用 UniParser 服务 ``GET /health``。"""
-    try:
-        r = await asyncio.to_thread(_client().health)
-    except ValueError as e:
-        return str(e)
-    if r.get("status") == "error":
-        return r.get("description", "健康检查失败")
-    return r["status"]
-
-
-@mcp.tool()
-async def uniparser_version() -> str:
-    """调用 UniParser 服务 ``GET /version``。"""
-    try:
-        r = await asyncio.to_thread(_client().version)
-    except ValueError as e:
-        return str(e)
-    if r.get("status") == "error":
-        return r.get("description", "版本检查失败")
-    return r["version"]
-
-
-@mcp.tool()
-async def uniparser_parse_file(file_path: str) -> str:
-    """本机 PDF：``trigger-file-async`` → ``get-result``，返回 ``content`` 文本。"""
-    try:
-        client = _client()
-    except ValueError as e:
-        return str(e)
-    trig = await asyncio.to_thread(
-        client.trigger_file,
-        file_path,
-        token=None,
-        **_cfg_section("default_trigger_file"),
-    )
-    return await _fetch_content(client, trig, file_path)
-
-
-@mcp.tool()
-async def uniparser_parse_url(url: str) -> str:
-    """公网 PDF：``trigger-url-async`` → ``get-result``，返回 ``content`` 文本。"""
-    try:
-        client = _client()
-    except ValueError as e:
-        return str(e)
-    trig = await asyncio.to_thread(
-        client.trigger_url,
-        url,
-        token=None,
-        **_cfg_section("default_trigger_url"),
-    )
-    return await _fetch_content(client, trig, url)
+mcp = FastMCP("UniParser", instructions=SERVER_INSTRUCTIONS)
+register_tools(mcp)
 
 
 def _resolve_mcp_transport() -> Literal["stdio", "sse", "streamable-http"]:
+    import os
+
     raw = (os.environ.get("UNIPARSER_MCP_TRANSPORT") or "stdio").strip().lower()
     if raw in ("http", "streamable-http", "streamable_http"):
         return "streamable-http"
@@ -161,10 +25,9 @@ def _resolve_mcp_transport() -> Literal["stdio", "sse", "streamable-http"]:
         return "sse"
     if raw == "stdio":
         return "stdio"
-    raise ValueError(f"UNIPARSER_MCP_TRANSPORT 无效: {raw!r}，应为 stdio、sse 或 streamable-http")
+    raise ValueError(f"UNIPARSER_MCP_TRANSPORT invalid: {raw!r}")
 
 
 def main() -> None:
     logging.basicConfig(level=logging.WARNING)
-    # stdio 传输时 stdout 仅用于 MCP JSON-RPC，调试信息必须写到 stderr
     mcp.run(transport=_resolve_mcp_transport())
diff --git a/mcp_server/uniparser_mcp/tools/__init__.py b/mcp_server/uniparser_mcp/tools/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/mcp_server/uniparser_mcp/tools/register.py b/mcp_server/uniparser_mcp/tools/register.py
new file mode 100644
index 0000000..34db668
--- /dev/null
+++ b/mcp_server/uniparser_mcp/tools/register.py
@@ -0,0 +1,97 @@
+"""Register MCP tools."""
+
+from __future__ import annotations
+
+from typing import Annotated
+
+from mcp.server.fastmcp import Context, FastMCP
+from pydantic import Field
+
+from uniparser_mcp.client import get_client
+from uniparser_mcp.errors import config_error, input_error
+from uniparser_mcp.pipeline.parse import run_parse
+from uniparser_mcp.schemas import (
+    ParseModeChoice,
+    ParseRequest,
+    ParseResult,
+    TextualChoice,
+)
+
+
+_PARSE_DESCRIPTION = (
+    "Parse a local PDF, local image snippet, or public PDF URL with UniParser. "
+    "Uploads content to the configured UniParser service. "
+    "Returns saved file paths, a short markdown preview, and metadata. "
+    "Use absolute paths for local files."
+)
+
+
+def register_tools(mcp: FastMCP) -> None:
+    @mcp.tool(
+        title="Parse document",
+        description=_PARSE_DESCRIPTION,
+        annotations={
+            "readOnlyHint": False,
+            "destructiveHint": False,
+            "openWorldHint": True,
+        },
+    )
+    async def uniparser_parse(
+        file_path: Annotated[str | None, Field(description="Absolute path to a local PDF file.")] = None,
+        image_path: Annotated[
+            str | None,
+            Field(description="Absolute path to a local image (.png, .jpg, etc.)."),
+        ] = None,
+        pdf_url: Annotated[str | None, Field(description="Publicly accessible PDF URL.")] = None,
+        output_dir: Annotated[
+            str | None,
+            Field(
+                description=(
+                    "Preferred directory for saved results. If occupied, a suffixed sibling is used. "
+                    "Default: ~/Uni-Parser-Skill/<stem>/."
+                )
+            ),
+        ] = None,
+        async_mode: Annotated[
+            bool,
+            Field(description="Submit with sync=false and poll until completion."),
+        ] = False,
+        textual: Annotated[TextualChoice, Field(description="Textual parse mode.")] = TextualChoice.ocr_hq,
+        equation: Annotated[ParseModeChoice, Field(description="Equation parse mode.")] = ParseModeChoice.ocr_hq,
+        table: Annotated[ParseModeChoice, Field(description="Table parse mode.")] = ParseModeChoice.ocr_hq,
+        chart: Annotated[ParseModeChoice, Field(description="Chart parse mode.")] = ParseModeChoice.base64,
+        figure: Annotated[ParseModeChoice, Field(description="Figure parse mode.")] = ParseModeChoice.base64,
+        expression: Annotated[
+            ParseModeChoice,
+            Field(description="Expression parse mode."),
+        ] = ParseModeChoice.base64,
+        molecule: Annotated[ParseModeChoice, Field(description="Molecule parse mode.")] = ParseModeChoice.ocr_fast,
+        ctx: Context = None,
+    ) -> ParseResult:
+        try:
+            client = get_client()
+        except ValueError as exc:
+            return config_error(str(exc))
+
+        try:
+            try:
+                req = ParseRequest(
+                    file_path=file_path,
+                    image_path=image_path,
+                    pdf_url=pdf_url,
+                    output_dir=output_dir,
+                    async_mode=async_mode,
+                    textual=textual,
+                    equation=equation,
+                    table=table,
+                    chart=chart,
+                    figure=figure,
+                    expression=expression,
+                    molecule=molecule,
+                )
+            except ValueError as exc:
+                return input_error(str(exc))
+
+            return await run_parse(client, req, ctx)
+        finally:
+            client.close()
diff --git a/mcp_server/uniparser_mcp/tools/response.py b/mcp_server/uniparser_mcp/tools/response.py
new file mode 100644
index 0000000..b2779de
--- /dev/null
+++ b/mcp_server/uniparser_mcp/tools/response.py
@@ -0,0 +1,34 @@
+"""Build tool responses from pipeline summaries."""
+
+from __future__ import annotations
+
+from uniparser_mcp.config import get_preview_chars
+from uniparser_mcp.schemas import ParseSuccess
+
+
+def build_message(summary: dict) -> str:
+    lines = [
+        "Parse complete.",
+        f"Markdown: {summary['markdown_path']}",
+        f"Layout: {summary['pages_tree_path']}",
+        f"Output: {summary['output_dir']}",
+    ]
+    return "\n".join(lines)
+
+
+def build_parse_success(summary: dict) -> ParseSuccess:
+    content = summary.get("content", "")
+    preview_limit = get_preview_chars()
+    preview = content[:preview_limit] if preview_limit else ""
+    return ParseSuccess(
+        output_dir=summary["output_dir"],
+        markdown_path=summary["markdown_path"],
+        pages_tree_path=summary["pages_tree_path"],
+        formatted_meta_path=summary["formatted_meta_path"],
+        trigger_meta_path=summary.get("trigger_meta_path"),
+        token=summary["token"],
+        input_type=summary["input_type"],
+        content_chars=summary["content_chars"],
+        content_preview=preview,
+        message=build_message(summary),
+    )
diff --git a/playground/01.quick_start.ipynb b/playground/01.quick_start.ipynb
index a67cc13..e32411c 100644
--- a/playground/01.quick_start.ipynb
+++ b/playground/01.quick_start.ipynb
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "bca4caa0",
    "metadata": {},
    "outputs": [],
@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "3a98b8d3",
    "metadata": {},
    "outputs": [],
@@ -83,18 +83,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "75a2ef72",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "41c7700e6fbf5896884ea2f28002a85f\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# 设置解析文件路径\n",
     "pdf_path = \"./tasks/He_Deep_Residual_Learning_CVPR_2016_paper.pdf\"\n",
@@ -143,7 +135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "03cf9226",
    "metadata": {},
    "outputs": [],
@@ -217,7 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "d545817b",
    "metadata": {},
    "outputs": [],
@@ -246,60 +238,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "f0d53f1d",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'class': 'documenttitle',\n",
-       "  'confidence': 0.888671875,\n",
-       "  'float_xyxy': [0.24928193933823528,\n",
-       "   0.1336669921875,\n",
-       "   0.7216222426470589,\n",
-       "   0.1522216796875],\n",
-       "  'page': 0,\n",
-       "  'str': '\\\\begin{documenttitle}\\nDeep Residual Learning for Image Recognition\\n\\\\end{documenttitle}\\n'},\n",
-       " {'class': 'paragraph',\n",
-       "  'confidence': 0.533203125,\n",
-       "  'float_xyxy': [0.22179457720588236,\n",
-       "   0.1917724609375,\n",
-       "   0.7538488051470589,\n",
-       "   0.2474365234375],\n",
-       "  'page': 0,\n",
-       "  'str': 'Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun Microsoft Research {kahe, v-xiangz, v-shren, jiansun}@microsoft.com\\n'},\n",
-       " {'class': 'title',\n",
-       "  'confidence': 0.85595703125,\n",
-       "  'float_xyxy': [0.23743393841911764,\n",
-       "   0.284912109375,\n",
-       "   0.3119973575367647,\n",
-       "   0.297119140625],\n",
-       "  'page': 0,\n",
-       "  'str': '\\\\begin{title}\\nAbstract\\n\\\\end{title}\\n'},\n",
-       " {'class': 'paragraph',\n",
-       "  'confidence': 0.97998046875,\n",
-       "  'float_xyxy': [0.0800924862132353,\n",
-       "   0.310546875,\n",
-       "   0.4694967830882353,\n",
-       "   0.5341796875],\n",
-       "  'page': 0,\n",
-       "  'str': 'Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learn- ing residual functions with reference to the layer inputs, in- stead of learning unreferenced functions. We provide com- prehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8× deeper than VGG nets [40] but still having lower complex- ity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers.\\n'},\n",
-       " {'class': 'paragraph',\n",
-       "  'confidence': 0.97705078125,\n",
-       "  'float_xyxy': [0.07882869944852941,\n",
-       "   0.537109375,\n",
-       "   0.4694967830882353,\n",
-       "   0.6552734375],\n",
-       "  'page': 0,\n",
-       "  'str': 'The depth of representations is of central importance for many visual recognition tasks. Solely due to our ex- tremely deep representations, we obtain a 28% relative im- provement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet local- ization, COCO detection, and COCO segmentation.\\n'}]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "result[\"objects\"][:5]  # 全文的前5个语义块（所有页面合并）"
    ]
@@ -317,7 +259,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "9d5e44a6",
    "metadata": {},
    "outputs": [],
@@ -346,1237 +288,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "f837f24e",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'token': '41c7700e6fbf5896884ea2f28002a85f',\n",
-       "  'page': 0,\n",
-       "  'block': 14,\n",
-       "  'bbox': {'x1': 0.24928193933823528,\n",
-       "   'y1': 0.1336669921875,\n",
-       "   'x2': 0.7216222426470589,\n",
-       "   'y2': 0.1522216796875},\n",
-       "  'conf': 0.888671875,\n",
-       "  'page_size': [1224, 1584],\n",
-       "  'type': 'documenttitle',\n",
-       "  'hidden': False,\n",
-       "  'order': 0,\n",
-       "  'lang': 'en',\n",
-       "  'direction': -1,\n",
-       "  'source': '',\n",
-       "  'bboxes': [{'x1': 0.25073202295241015,\n",
-       "    'y1': 0.13367483350965711,\n",
-       "    'x2': 0.30150634167241114,\n",
-       "    'y2': 0.15178872118092546},\n",
-       "   {'x1': 0.30736691344018074,\n",
-       "    'y1': 0.13367483350965711,\n",
-       "    'x2': 0.39463965721379696,\n",
-       "    'y2': 0.15178872118092546},\n",
-       "   {'x1': 0.40049970539566737,\n",
-       "    'y1': 0.13367483350965711,\n",
-       "    'x2': 0.4926251679464103,\n",
-       "    'y2': 0.15178872118092546},\n",
-       "   {'x1': 0.49848539065691383,\n",
-       "    'y1': 0.13367483350965711,\n",
-       "    'x2': 0.5278343250548917,\n",
-       "    'y2': 0.15178872118092546},\n",
-       "   {'x1': 0.5336946973613664,\n",
-       "    'y1': 0.13367483350965711,\n",
-       "    'x2': 0.5961897108289931,\n",
-       "    'y2': 0.15178872118092546},\n",
-       "   {'x1': 0.6020501828661152,\n",
-       "    'y1': 0.13367483350965711,\n",
-       "    'x2': 0.721859801049326,\n",
-       "    'y2': 0.15178872118092546}],\n",
-       "  'contents': ['Deep', 'Residual', 'Learning', 'for', 'Image', 'Recognition'],\n",
-       "  'text': 'Deep Residual Learning for Image Recognition'},\n",
-       " {'token': '41c7700e6fbf5896884ea2f28002a85f',\n",
-       "  'page': 0,\n",
-       "  'block': 19,\n",
-       "  'bbox': {'x1': 0.22179457720588236,\n",
-       "   'y1': 0.1917724609375,\n",
-       "   'x2': 0.7538488051470589,\n",
-       "   'y2': 0.2474365234375},\n",
-       "  'conf': 0.533203125,\n",
-       "  'page_size': [1224, 1584],\n",
-       "  'type': 'paragraph',\n",
-       "  'hidden': False,\n",
-       "  'order': 1,\n",
-       "  'lang': 'en',\n",
-       "  'direction': -1,\n",
-       "  'source': '',\n",
-       "  'bboxes': [{'x1': 0.22286109674989787,\n",
-       "    'y1': 0.19239574008517796,\n",
-       "    'x2': 0.2912323396969465,\n",
-       "    'y2': 0.20749068982673413},\n",
-       "   {'x1': 0.29611796958773745,\n",
-       "    'y1': 0.19239574008517796,\n",
-       "    'x2': 0.3188953524321512,\n",
-       "    'y2': 0.20749068982673413},\n",
-       "   {'x1': 0.36284849378797746,\n",
-       "    'y1': 0.19239574008517796,\n",
-       "    'x2': 0.43012581619561885,\n",
-       "    'y2': 0.20749068982673413},\n",
-       "   {'x1': 0.43500947640612236,\n",
-       "    'y1': 0.19239574008517796,\n",
-       "    'x2': 0.4849204767763225,\n",
-       "    'y2': 0.20749068982673413},\n",
-       "   {'x1': 0.5288738175934437,\n",
-       "    'y1': 0.19239574008517796,\n",
-       "    'x2': 0.6026756934870302,\n",
-       "    'y2': 0.20749068982673413},\n",
-       "   {'x1': 0.6075592041015625,\n",
-       "    'y1': 0.19239574008517796,\n",
-       "    'x2': 0.6390295091018178,\n",
-       "    'y2': 0.20749068982673413},\n",
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-       "  'contents': ['Kaiming',\n",
-       "   'He',\n",
-       "   'Xiangyu',\n",
-       "   'Zhang',\n",
-       "   'Shaoqing',\n",
-       "   'Ren',\n",
-       "   'Jian',\n",
-       "   'Sun',\n",
-       "   'Microsoft',\n",
-       "   'Research',\n",
-       "   '{kahe,',\n",
-       "   'v-xiangz,',\n",
-       "   'v-shren,',\n",
-       "   'jiansun}@microsoft.com'],\n",
-       "  'text': 'Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun Microsoft Research {kahe, v-xiangz, v-shren, jiansun}@microsoft.com'},\n",
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-       "  'text': 'Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learn- ing residual functions with reference to the layer inputs, in- stead of learning unreferenced functions. We provide com- prehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8× deeper than VGG nets [40] but still having lower complex- ity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers.'},\n",
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-       "   {'x1': 0.1485765369889004,\n",
-       "    'y1': 0.6283086526273477,\n",
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-       "   {'x1': 0.17288080851236978,\n",
-       "    'y1': 0.6283086526273477,\n",
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-       "    'y2': 0.6408876746591895},\n",
-       "   {'x1': 0.2098497627607358,\n",
-       "    'y1': 0.6283086526273477,\n",
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-       "    'y2': 0.6408876746591895},\n",
-       "   {'x1': 0.22692619273865144,\n",
-       "    'y1': 0.6283086526273477,\n",
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-       "   {'x1': 0.29430389404296875,\n",
-       "    'y1': 0.6283086526273477,\n",
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-       "    'y2': 0.6408876746591895},\n",
-       "   {'x1': 0.36256052154341556,\n",
-       "    'y1': 0.6283086526273477,\n",
-       "    'x2': 0.4255266376570159,\n",
-       "    'y2': 0.6408876746591895},\n",
-       "   {'x1': 0.4299381729824091,\n",
-       "    'y1': 0.6283086526273477,\n",
-       "    'x2': 0.46791645124846815,\n",
-       "    'y2': 0.6408876746591895},\n",
-       "   {'x1': 0.08188241447498595,\n",
-       "    'y1': 0.6434040262241556,\n",
-       "    'x2': 0.13027915455936606,\n",
-       "    'y2': 0.6559830482559975},\n",
-       "   {'x1': 0.13434870102826288,\n",
-       "    'y1': 0.6434040262241556,\n",
-       "    'x2': 0.17957111433440565,\n",
-       "    'y2': 0.6559830482559975},\n",
-       "   {'x1': 0.1836389653822955,\n",
-       "    'y1': 0.6434040262241556,\n",
-       "    'x2': 0.24738658331578073,\n",
-       "    'y2': 0.6559830482559975},\n",
-       "   {'x1': 0.2514564414429509,\n",
-       "    'y1': 0.6434040262241556,\n",
-       "    'x2': 0.2758745430341733,\n",
-       "    'y2': 0.6559830482559975},\n",
-       "   {'x1': 0.27994268080767465,\n",
-       "    'y1': 0.6434040262241556,\n",
-       "    'x2': 0.3251650442484937,\n",
-       "    'y2': 0.6559830482559975},\n",
-       "   {'x1': 0.32923480264501637,\n",
-       "    'y1': 0.6434040262241556,\n",
-       "    'x2': 0.4194680008233762,\n",
-       "    'y2': 0.6559830482559975}],\n",
-       "  'contents': ['The',\n",
-       "   'depth',\n",
-       "   'of',\n",
-       "   'representations',\n",
-       "   'is',\n",
-       "   'of',\n",
-       "   'central',\n",
-       "   'importance',\n",
-       "   'for',\n",
-       "   'many',\n",
-       "   'visual',\n",
-       "   'recognition',\n",
-       "   'tasks.',\n",
-       "   'Solely',\n",
-       "   'due',\n",
-       "   'to',\n",
-       "   'our',\n",
-       "   'ex-',\n",
-       "   'tremely',\n",
-       "   'deep',\n",
-       "   'representations,',\n",
-       "   'we',\n",
-       "   'obtain',\n",
-       "   'a',\n",
-       "   '28%',\n",
-       "   'relative',\n",
-       "   'im-',\n",
-       "   'provement',\n",
-       "   'on',\n",
-       "   'the',\n",
-       "   'COCO',\n",
-       "   'object',\n",
-       "   'detection',\n",
-       "   'dataset.',\n",
-       "   'Deep',\n",
-       "   'residual',\n",
-       "   'nets',\n",
-       "   'are',\n",
-       "   'foundations',\n",
-       "   'of',\n",
-       "   'our',\n",
-       "   'submissions',\n",
-       "   'to',\n",
-       "   'ILSVRC',\n",
-       "   '&',\n",
-       "   'COCO',\n",
-       "   '2015',\n",
-       "   'competitions1,',\n",
-       "   'where',\n",
-       "   'we',\n",
-       "   'also',\n",
-       "   'won',\n",
-       "   'the',\n",
-       "   '1st',\n",
-       "   'places',\n",
-       "   'on',\n",
-       "   'the',\n",
-       "   'tasks',\n",
-       "   'of',\n",
-       "   'ImageNet',\n",
-       "   'detection,',\n",
-       "   'ImageNet',\n",
-       "   'local-',\n",
-       "   'ization,',\n",
-       "   'COCO',\n",
-       "   'detection,',\n",
-       "   'and',\n",
-       "   'COCO',\n",
-       "   'segmentation.'],\n",
-       "  'text': 'The depth of representations is of central importance for many visual recognition tasks. Solely due to our ex- tremely deep representations, we obtain a 28% relative im- provement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet local- ization, COCO detection, and COCO segmentation.'}]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "result[\"pages_dict\"][0][:5]  # 第一个页面的前5个语义块 (按照页面进行了拆分)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "877ba504",
    "metadata": {},
    "outputs": [],
@@ -1586,25 +308,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "074956b3",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "[TextualResult(token='41c7700e6fbf5896884ea2f28002a85f', page=0, block=14, bbox=BBox(x1=0.24928193933823528, y1=0.1336669921875, x2=0.7216222426470589, y2=0.1522216796875), conf=0.888671875, page_size=[1224, 1584], type='documenttitle', hidden=False, order=0, lang='en', direction=<Direction.Normal: -1>, source='', bboxes=[BBox(x1=0.25073202295241015, y1=0.13367483350965711, x2=0.30150634167241114, y2=0.15178872118092546), BBox(x1=0.30736691344018074, y1=0.13367483350965711, x2=0.39463965721379696, y2=0.15178872118092546), BBox(x1=0.40049970539566737, y1=0.13367483350965711, x2=0.4926251679464103, y2=0.15178872118092546), BBox(x1=0.49848539065691383, y1=0.13367483350965711, x2=0.5278343250548917, y2=0.15178872118092546), BBox(x1=0.5336946973613664, y1=0.13367483350965711, x2=0.5961897108289931, y2=0.15178872118092546), BBox(x1=0.6020501828661152, y1=0.13367483350965711, x2=0.721859801049326, y2=0.15178872118092546)], contents=['Deep', 'Residual', 'Learning', 'for', 'Image', 'Recognition'], text='Deep Residual Learning for Image Recognition'),\n",
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Solely due to our ex- tremely deep representations, we obtain a 28% relative im- provement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet local- ization, COCO detection, and COCO segmentation.')]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "pages_dict[0][:5]"
    ]
@@ -1634,18 +341,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "8d3c4c1e",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "632927fcb88c5b329c164b607d97d5af\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# 设置解析文件路径\n",
     "snip_path = \"./tasks/0711.2032v1_page20.png\"\n",
@@ -1669,7 +368,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "64c8d1fe",
    "metadata": {},
    "outputs": [],
@@ -1740,7 +439,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "0115c016",
    "metadata": {},
    "outputs": [],
@@ -1769,7 +468,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "631d81c5",
    "metadata": {},
    "outputs": [],
diff --git a/playground/02.advance.ipynb b/playground/02.advance.ipynb
index e428834..cbd2a93 100644
--- a/playground/02.advance.ipynb
+++ b/playground/02.advance.ipynb
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "dabd73e6",
    "metadata": {},
    "outputs": [],
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "0dc28c62",
    "metadata": {},
    "outputs": [],
@@ -84,18 +84,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "4b12dbcd",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "trigger file success, token is: 41c7700e6fbf5896884ea2f28002a85f\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# 设置解析文件路径\n",
     "pdf_path = \"./tasks/He_Deep_Residual_Learning_CVPR_2016_paper.pdf\"\n",
@@ -128,14 +120,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "f32cd485",
    "metadata": {},
    "outputs": [],
    "source": [
     "assert trigger_file_result[\"status\"] == \"success\"\n",
     "token = trigger_file_result[\"token\"]\n",
-    "# token = \"e4d85a146b4554c8b23a9753232253cf\"\n",
+    "# token = \"<TASK_TOKEN>\"\n",
     "formatted = FormatFlag.Plain  # formatted 只对objects和content产生作用，pages_dict和pages_tree不受影响\n",
     "result = parser.get_formatted(\n",
     "    token,\n",
@@ -166,7 +158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "63460a89",
    "metadata": {},
    "outputs": [],
@@ -185,37 +177,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "9a88e09a",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[TextualResult(token='41c7700e6fbf5896884ea2f28002a85f', page=0, block=14, bbox=BBox(x1=0.24928193933823528, y1=0.1336669921875, x2=0.7216222426470589, y2=0.1522216796875), conf=0.888671875, page_size=[1224, 1584], type='documenttitle', hidden=False, order=0, lang='en', direction=<Direction.Normal: -1>, source='', bboxes=[BBox(x1=0.25073202295241015, y1=0.13367483350965711, x2=0.30150634167241114, y2=0.15178872118092546), BBox(x1=0.30736691344018074, y1=0.13367483350965711, x2=0.39463965721379696, y2=0.15178872118092546), BBox(x1=0.40049970539566737, y1=0.13367483350965711, x2=0.4926251679464103, y2=0.15178872118092546), BBox(x1=0.49848539065691383, y1=0.13367483350965711, x2=0.5278343250548917, y2=0.15178872118092546), BBox(x1=0.5336946973613664, y1=0.13367483350965711, x2=0.5961897108289931, y2=0.15178872118092546), BBox(x1=0.6020501828661152, y1=0.13367483350965711, x2=0.721859801049326, y2=0.15178872118092546)], contents=['Deep', 'Residual', 'Learning', 'for', 'Image', 'Recognition'], text='Deep Residual Learning for Image Recognition'),\n",
-       " TextualResult(token='41c7700e6fbf5896884ea2f28002a85f', page=0, block=19, bbox=BBox(x1=0.22179457720588236, y1=0.1917724609375, x2=0.7538488051470589, y2=0.2474365234375), conf=0.533203125, page_size=[1224, 1584], type='paragraph', hidden=False, order=1, lang='en', direction=<Direction.Normal: -1>, source='', bboxes=[BBox(x1=0.22286109674989787, y1=0.19239574008517796, x2=0.2912323396969465, y2=0.20749068982673413), BBox(x1=0.29611796958773745, y1=0.19239574008517796, x2=0.3188953524321512, y2=0.20749068982673413), BBox(x1=0.36284849378797746, y1=0.19239574008517796, x2=0.43012581619561885, y2=0.20749068982673413), BBox(x1=0.43500947640612236, y1=0.19239574008517796, x2=0.4849204767763225, y2=0.20749068982673413), BBox(x1=0.5288738175934437, y1=0.19239574008517796, x2=0.6026756934870302, y2=0.20749068982673413), BBox(x1=0.6075592041015625, y1=0.19239574008517796, x2=0.6390295091018178, y2=0.20749068982673413), BBox(x1=0.682982450996349, y1=0.19239574008517796, x2=0.7144527559966044, y2=0.20749068982673413), BBox(x1=0.7193361170151654, y1=0.19239574008517796, x2=0.7497320237502553, y2=0.20749068982673413), BBox(x1=0.4070931228936887, y1=0.21352893174296678, x2=0.48413774078967525, y2=0.22862388148452295), BBox(x1=0.48902160046147364, y1=0.21352893174296678, x2=0.5606160382040186, y2=0.22862388148452295), BBox(x1=0.3194303824231515, y1=0.23355274007777976, x2=0.36237365124272364, y2=0.24624978653108232), BBox(x1=0.3664432351106133, y1=0.23355274007777976, x2=0.4274722828584559, y2=0.2461317813757694), BBox(x1=0.43154039569929536, y1=0.23355274007777976, x2=0.48443005131740197, y2=0.2461317813757694), BBox(x1=0.48849821402356514, y1=0.23355274007777976, x2=0.6531606935987285, y2=0.24624978653108232)], contents=['Kaiming', 'He', 'Xiangyu', 'Zhang', 'Shaoqing', 'Ren', 'Jian', 'Sun', 'Microsoft', 'Research', '{kahe,', 'v-xiangz,', 'v-shren,', 'jiansun}@microsoft.com'], text='Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun Microsoft Research {kahe, v-xiangz, v-shren, jiansun}@microsoft.com'),\n",
-       " TextualResult(token='41c7700e6fbf5896884ea2f28002a85f', page=0, block=17, bbox=BBox(x1=0.23743393841911764, y1=0.284912109375, x2=0.3119973575367647, y2=0.297119140625), conf=0.85595703125, page_size=[1224, 1584], type='title', hidden=False, order=2, lang='en', direction=<Direction.Normal: -1>, source='', bboxes=[BBox(x1=0.2385537390615426, y1=0.28434749564739187, x2=0.31124212227615655, y2=0.2994424453889481)], contents=['Abstract'], text='Abstract'),\n",
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Solely due to our ex- tremely deep representations, we obtain a 28% relative im- provement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet local- ization, COCO detection, and COCO segmentation.'),\n",
-       " TextualResult(token='41c7700e6fbf5896884ea2f28002a85f', page=0, block=16, bbox=BBox(x1=0.08167221966911764, y1=0.6767578125, x2=0.20836684283088236, y2=0.6884765625), conf=0.86865234375, page_size=[1224, 1584], type='title', hidden=False, order=5, lang='en', direction=<Direction.Normal: -1>, source='', bboxes=[BBox(x1=0.08188241447498595, y1=0.6757390956686, x2=0.09653339510649638, y2=0.6908340454101562), BBox(x1=0.10141686208887038, y1=0.6757390956686, x2=0.20743109509835836, y2=0.6908340454101562)], contents=['1.', 'Introduction'], text='1. Introduction'),\n",
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An obstacle to answering this question was the notorious problem of vanishing/exploding gradients [14, 1, 8], which hamper convergence from the beginning. This problem, however, has been largely addressed by normalized initial- ization [23, 8, 36, 12] and intermediate normalization layers [16], which enable networks with tens of layers to start con- verging for stochastic gradient descent (SGD) with back- propagation [22].'),\n",
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accuracy gets saturated (which might be unsurprising) and then degrades rapidly. 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-      "text/markdown": [
-       "# Deep Residual Learning for Image Recognition\n",
-       "\n",
-       "\n",
-       "Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun Microsoft Research {kahe, v-xiangz, v-shren, jiansun}@microsoft.com\n",
-       "\n",
-       "\n",
-       "# Abstract\n",
-       "\n",
-       "\n",
-       "Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learn- ing residual functions with reference to the layer inputs, in- stead of learning unreferenced functions. We provide com- prehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8× deeper than VGG nets [40] but still having lower complex- ity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers.\n",
-       "\n",
-       "\n",
-       "The depth of representations is of central importance for many visual recognition tasks. Solely due to our ex- tremely deep representations, we obtain a 28% relative im- provement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet local- ization, COCO detection, and COCO segmentation.\n",
-       "\n",
-       "\n",
-       "# 1. Introduction\n",
-       "\n",
-       "\n",
-       "Deep convolutional neural networks [22, 21] have led to a series of breakthroughs for image classification [21, 49, 39]. Deep networks naturally integrate low/mid/high- level features [49] and classifiers in an end-to-end multi- layer fashion, and the “levels” of features can be enriched by the number of stacked layers (depth). Recent evidence [40, 43] reveals that network depth is of crucial importance, and the leading results [40, 43, 12, 16] on the challenging ImageNet dataset [35] all exploit “very deep” [40] models, with a depth of sixteen [40] to thirty [16]. Many other non- trivial visual recognition tasks [7, 11, 6, 32, 27] have also\n",
-       "\n",
-       "\n",
-       "\n",
-       "\n",
-       "![chart](data:image/png;base64,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)\n",
-       "\n",
-       "![chart](data:image/png;base64,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)\n",
-       "\n",
-       "Figure 1. Training error (left) and test error (right) on CIFAR-10 with 20-layer and 56-layer “plain” networks. The deeper network has higher training error, and thus test error. Similar phenomena on ImageNet is presented in Fig. 4.\n",
-       "\n",
-       "\n",
-       "greatly benefited from very deep models.\n",
-       "\n",
-       "\n",
-       "Driven by the significance of depth, a question arises: Is learning better networks as easy as stacking more layers? An obstacle to answering this question was the notorious problem of vanishing/exploding gradients [14, 1, 8], which hamper convergence from the beginning. This problem, however, has been largely addressed by normalized initial- ization [23, 8, 36, 12] and intermediate normalization layers [16], which enable networks with tens of layers to start con- verging for stochastic gradient descent (SGD) with back- propagation [22].\n",
-       "\n",
-       "\n",
-       "When deeper networks are able to start converging, a degradation problem has been exposed: with the network depth increasing, accuracy gets saturated (which might be unsurprising) and then degrades rapidly. Unexpectedly, such degradation is not caused by overfitting, and adding more layers to a suitably deep model leads to higher train- ing error, as reported in [10, 41] and thoroughly verified by our experiments. Fig. 1 shows a typical example.\n",
-       "\n",
-       "\n",
-       "The degradation (of training accuracy) indicates that not all systems are similarly easy to optimize. Let us consider a shallower architecture and its deeper counterpart that adds more layers onto it. There exists a solution by construction to the deeper model: the added layers are identity mapping, and the other layers are copied from the learned shallower model. The existence of this constructed solution indicates that a deeper model should produce no higher training error than its shallower counterpart. But experiments show that our current solvers on hand are unable to find solutions that\n",
-       "\n",
-       "\n",
-       "CyF\n",
-       "\n",
-       "\n",
-       "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the version available on IEEE Xplore.\n",
-       "\n",
-       "\n",
-       "1http://image-net.org/challenges/LSVRC/2015/ and http://mscoco.org/dataset/#detections-challenge2015.\n",
-       "\n",
-       "\n",
-       "1770\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "display(Markdown(\"\\n\\n\".join([item.format_as(FormatFlag.Markdown) for item in pages_tree[0]])))"
    ]
@@ -312,7 +213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "8f601804",
    "metadata": {},
    "outputs": [],
@@ -322,46 +223,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "73ff6cf9",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "group\n",
-      "├─ image\n",
-      "│  ├─ chart\n",
-      "│  └─ chart\n",
-      "└─ imagecaption\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(tree_repr(img_cap_group))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "ae292e50",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<img 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\" />"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "one_fig = img_cap_group.items[0].items[0]\n",
     "\n",
@@ -370,102 +245,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "477eb3e0",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<p></p><img 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' alt='chart'/><br>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "display(HTML(one_fig.format_as(FormatFlag.Html)))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "621e27c9",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<p></p><img 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' alt='chart'/><br>\n",
-       "<p></p><img src='data:image/png;base64,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' alt='chart'/><br>\n",
-       "<caption>Figure 1. Training error (left) and test error (right) on CIFAR-10 with 20-layer and 56-layer “plain” networks. The deeper network has higher training error, and thus test error. Similar phenomena on ImageNet is presented in Fig. 4.</caption><br>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "display(HTML(img_cap_group.format_as(FormatFlag.Html)))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "c57dda06",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       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)\n",
-       "\n",
-       "![chart](data:image/png;base64,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)\n",
-       "\n",
-       "Figure 1. Training error (left) and test error (right) on CIFAR-10 with 20-layer and 56-layer “plain” networks. The deeper network has higher training error, and thus test error. Similar phenomena on ImageNet is presented in Fig. 4.\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "display(Markdown(img_cap_group.format_as(FormatFlag.Markdown)))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "84c33584",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "\\includegraphics[width=0.5\\textwidth]{}\n",
-       "\n",
-       "\\includegraphics[width=0.5\\textwidth]{}\n",
-       "\n",
-       "\\textbf{Figure 1. Training error (left) and test error (right) on CIFAR-10 with 20-layer and 56-layer {\\textquotedblleft}plain{\\textquotedblright} networks. The deeper network has higher training error, and thus test error. Similar phenomena on ImageNet is presented in Fig. 4.}\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Latex object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# LaTeX 不支持 base64 图片，理论上支持图片文件，但目前未测试\n",
     "display(Latex(img_cap_group.format_as(FormatFlag.Latex)))"
@@ -481,7 +294,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "43024e4f",
    "metadata": {},
    "outputs": [],
@@ -491,48 +304,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "45287e87",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "group\n",
-      "├─ equation\n",
-      "└─ equationid\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(tree_repr(math_group))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "712e2911",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "$$\n",
-       "\\mathbf{y}=\\mathcal{F}(\\mathbf{x},\\{W_{i}\\})+W_{s}\\mathbf{x}.\n",
-       "$$\n",
-       "\n",
-       "(2)\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "display(Markdown(math_group.format_as(FormatFlag.Markdown)))"
    ]
@@ -547,7 +332,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "id": "986b4a3a",
    "metadata": {},
    "outputs": [],
@@ -557,57 +342,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "e8cf8c7c",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "group\n",
-      "├─ table\n",
-      "└─ tablecaption\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(tree_repr(table_group))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "id": "f5eaef29",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "| model          | top-1 err.   | top-5 err.   |\n",
-       "|:---------------|:-------------|:-------------|\n",
-       "| VGG-16 [40]    | 28.07        | 9.33         |\n",
-       "| GoogLeNet [43] | -            | 9.15         |\n",
-       "| PReLU-net [12] | 24.27        | 7.38         |\n",
-       "| plain-34       | 28.54        | 10.02        |\n",
-       "| ResNet-34 A    | 25.03        | 7.76         |\n",
-       "| ResNet-34 B    | 24.52        | 7.46         |\n",
-       "| ResNet-34 C    | 24.19        | 7.40         |\n",
-       "| ResNet-50      | 22.85        | 6.71         |\n",
-       "| ResNet-101     | 21.75        | 6.05         |\n",
-       "| ResNet-152     | 21.43        | 5.71         |\n",
-       "\n",
-       "Table 3. Error rates (%, 10-crop testing) on ImageNet validation. VGG-16 is based on our test. ResNet-50/101/152 are of option B that only uses projections for increasing dimensions.\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "display(Markdown(table_group.format_as(FormatFlag.Markdown)))"
    ]
@@ -622,7 +370,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "id": "dc68c987",
    "metadata": {},
    "outputs": [],
diff --git a/playground/03.vis.ipynb b/playground/03.vis.ipynb
index aecd4b6..783c76a 100644
--- a/playground/03.vis.ipynb
+++ b/playground/03.vis.ipynb
@@ -22,8 +22,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "json_path = \"./results/aeb129da-4a36-4627-9cb2-c721def237f0.json\"\n",
-    "pdf_path = \"./tasks/aeb129da-4a36-4627-9cb2-c721def237f0.pdf\"\n",
+    "json_path = \"./results/<TASK_TOKEN>.json\"\n",
+    "pdf_path = \"./tasks/<TASK_TOKEN>.pdf\"\n",
     "\n",
     "with open(json_path, \"r\") as f:\n",
     "    pages_dict = json.load(f)\n",
diff --git a/playground/04.use_callbacks.py b/playground/04.use_callbacks.py
index 676d145..850a580 100644
--- a/playground/04.use_callbacks.py
+++ b/playground/04.use_callbacks.py
@@ -54,7 +54,7 @@ def main():
         print(f"Token: {token}")
         print("\nUniParser will now process the file in the background.")
         print("Once finished, it will POST the result to your callback URL.")
-        print("The payload will include a 'checksum' for you to verify using your 'callback_secret'.")
+        print("Verify X-UniParser-Signature over the exact raw callback body using your callback_secret.")
     else:
         print(f"Failed to submit task: {result.get('message')}")
         if "description" in result:
diff --git a/playground/app.caption_extraction.ipynb b/playground/app.caption_extraction.ipynb
index 837fb34..ab50fb9 100644
--- a/playground/app.caption_extraction.ipynb
+++ b/playground/app.caption_extraction.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "9d663a75",
    "metadata": {},
    "outputs": [],
@@ -27,8 +27,8 @@
     "# 初始化客户端\n",
     "parser = UniParserClient(host=host, api_key=api_key)\n",
     "\n",
-    "token = \"aeb129da-4a36-4627-9cb2-c721def237f0\"\n",
-    "input_file = \"./tasks/aeb129da-4a36-4627-9cb2-c721def237f0.pdf\"\n",
+    "token = \"<TASK_TOKEN>\"\n",
+    "input_file = \"./tasks/<TASK_TOKEN>.pdf\"\n",
     "save_dir = \"./outputs/caption_extraction\"\n",
     "os.makedirs(save_dir, exist_ok=True)"
    ]
@@ -43,18 +43,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "69eae6f5",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "trigger success, token: aeb129da-4a36-4627-9cb2-c721def237f0\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "trigger_result = parser.trigger_file(\n",
     "    file_path=input_file,\n",
@@ -76,7 +68,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "88dd0a33",
    "metadata": {},
    "outputs": [],
@@ -106,7 +98,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "97c93f9c",
    "metadata": {},
    "outputs": [],
diff --git a/playground/app.crop_images.ipynb b/playground/app.crop_images.ipynb
index de290ea..afa895a 100644
--- a/playground/app.crop_images.ipynb
+++ b/playground/app.crop_images.ipynb
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -49,20 +49,14 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "✅ 准备就绪\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "host = \"https://uniparser.dp.tech/\"  # 官网\n",
     "\n",
-    "# 替换为你的认证 api key\n",
-    "api_key = \"xxxxxxx\"\n",
+    "# 从环境变量读取认证 API key，避免把凭据写入 notebook\n",
+    "api_key = os.getenv(\"UNIPARSER_API_KEY\")\n",
+    "if not api_key:\n",
+    "    raise RuntimeError(\"请先设置 UNIPARSER_API_KEY 环境变量\")\n",
     "# 初始化客户端\n",
     "parser = UniParserClient(host=host, api_key=api_key)\n",
     "\n",
@@ -82,17 +76,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "任务提交成功, Token: df84e1c227415776be33b7e0c97c9baa\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# 设置解析文件路径\n",
     "pdf_path = \"./tasks/He_Deep_Residual_Learning_CVPR_2016_paper.pdf\"\n",
@@ -119,18 +105,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "正在获取 pages_tree 数据...\n",
-      "✅ 获取成功，共 9 页数据\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# 等待并获取 pages_tree 数据\n",
     "json_data = None\n",
@@ -170,17 +147,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "全文共找到 222 个具有边界框的结构化元素\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "def traverse_tree(node, result_list):\n",
     "    \"\"\"递归遍历 JSON 节点树，提取所有包含 bbox 和 type 的节点\"\"\"\n",
@@ -205,22 +174,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "📊 PDF 图片类型元素统计:\n",
-      "  - chart          :   9 个\n",
-      "  - table          :   9 个\n",
-      "  - image          :   7 个\n",
-      "  - figure         :   4 个\n",
-      "  - equation       :   2 个\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# 看看这篇文章里都有哪些图片元素\n",
     "IMAGE_TYPES = ['figure', 'image', 'figuregroup', 'chart', 'table', 'formula', 'equation']\n",
@@ -247,7 +203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -334,25 +290,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " 开始切图 (DPI: 150) | 目标类型: ['figure', 'table'] ...\n",
-      "✅ 第 1 页: 裁剪了 1 张图片\n",
-      "✅ 第 3 页: 裁剪了 1 张图片\n",
-      "✅ 第 4 页: 裁剪了 2 张图片\n",
-      "✅ 第 5 页: 裁剪了 5 张图片\n",
-      "✅ 第 6 页: 裁剪了 2 张图片\n",
-      "✅ 第 7 页: 裁剪了 2 张图片\n",
-      "🎉 切图完成！共保存在 ./outputs/crop_images/figs_and_tables 下的 13 个文件中。\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "if os.path.exists(pdf_path):\n",
     "    out_path1 = os.path.join(save_dir, \"figs_and_tables\")\n",
@@ -378,20 +318,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " 开始切图 (DPI: 300) | 目标类型: ['equation', 'formula'] ...\n",
-      "✅ 第 2 页: 裁剪了 2 张图片\n",
-      "🎉 切图完成！共保存在 ./outputs/crop_images/equations_highres 下的 2 个文件中。\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "if os.path.exists(pdf_path):\n",
     "    out_path2 = os.path.join(save_dir, \"equations_highres\")\n",
diff --git a/playground/app.molecule_extracrtion.ipynb b/playground/app.molecule_extracrtion.ipynb
index 4151be5..23db75f 100644
--- a/playground/app.molecule_extracrtion.ipynb
+++ b/playground/app.molecule_extracrtion.ipynb
@@ -1,1378 +1,356 @@
 {
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "3a68e1a2",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import json\n",
-        "import os\n",
-        "from typing import List\n",
-        "\n",
-        "import fitz\n",
-        "from fitz.utils import get_pixmap\n",
-        "from IPython.display import HTML, Markdown, display\n",
-        "from PIL import Image\n",
-        "\n",
-        "from uniparser_tools.api.clients import UniParserClient\n",
-        "from uniparser_tools.common.constant import FormatFlag, LayoutType, ParseMode, ParseModeTextual\n",
-        "from uniparser_tools.common.dataclass import BBox, Direction, GroupedResult, LayoutItem, SemanticItem\n",
-        "from uniparser_tools.utils.convert import dict2obj\n",
-        "from uniparser_tools.utils.log import get_root_logger\n",
-        "from uniparser_tools.utils.processor import tree_repr\n",
-        "\n",
-        "\n",
-        "###### 以下为示例代码，请自行修改\n",
-        "\n",
-        "# ==============================================================================================\n",
-        "host = \"https://uniparser.dp.tech/\"  # 官网\n",
-        "\n",
-        "# 替换为你的认证api key\n",
-        "api_key = os.getenv('UNIPARSER_API_KEY')\n",
-        "\n",
-        "# 初始化客户端\n",
-        "parser = UniParserClient(host=host, api_key=api_key)\n",
-        "\n",
-        "token = \"2ff6db102b6d527bb4deef6e5cec442a\"\n",
-        "input_file = \"./tasks/CN1275981A.pdf\"\n",
-        "save_dir = \"./outputs/molecule_extracrtion\"\n",
-        "os.makedirs(save_dir, exist_ok=True)\n",
-        "os.makedirs(f\"{save_dir}/{token}\", exist_ok=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "id": "330fb53d",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "trigger success, token: 2ff6db102b6d527bb4deef6e5cec442a\n"
-          ]
-        }
-      ],
-      "source": [
-        "trigger_result = parser.trigger_file(\n",
-        "    file_path=input_file,\n",
-        "    token=token,\n",
-        "    textual=ParseModeTextual.DigitalExported,\n",
-        "    table=ParseMode.OCRFast,\n",
-        "    molecule=ParseMode.OCRFast,\n",
-        "    chart=ParseMode.DumpBase64,\n",
-        "    figure=ParseMode.DumpBase64,\n",
-        "    expression=ParseMode.DumpBase64,\n",
-        "    equation=ParseMode.OCRFast,\n",
-        ")\n",
-        "if trigger_result[\"status\"] != \"success\":\n",
-        "    print(json.dumps(trigger_result, indent=4))\n",
-        "    raise Exception(\"trigger file failed\")\n",
-        "print(f\"trigger success, token: {trigger_result['token']}\")"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 5,
-      "id": "d5bec1f0",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "result = parser.get_result(token, pages_tree=True)\n",
-        "if result[\"status\"] != \"success\":\n",
-        "    print(json.dumps(result, indent=4))\n",
-        "    raise Exception(\"get result failed\")\n",
-        "json.dump(result[\"pages_tree\"], open(f\"{save_dir}/{token}.json\", \"w\"), indent=4)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 6,
-      "id": "319b7daa",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "pages_tree = dict2obj(result[\"pages_tree\"]) "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 7,
-      "id": "2fbbbe31",
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "[GroupedResult(token='2ff6db102b6d527bb4deef6e5cec442a', page=2, block=2087102168, bbox=BBox(x1=0.2971211665818671, y1=0.10839842843896136, x2=0.7110178715040704, y2=0.17773435687792272), conf=0.96435546875, page_size=[1190, 1684], type='image', hidden=False, order=0, lang='ko', direction=<Direction.Unknown: -2>, source='', level=1, method='default', items=[GroupedResult(token='2ff6db102b6d527bb4deef6e5cec442a', page=2, block=1317312281, bbox=BBox(x1=0.2971211665818671, y1=0.10839842843896136, x2=0.7110178715040704, y2=0.17773435687792272), conf=0.9677734375, page_size=[1190, 1684], type='moleculegroup', hidden=False, order=1, lang='ko', direction=<Direction.Unknown: -2>, source='', level=1, method='default', items=[MoleculeResult(token='2ff6db102b6d527bb4deef6e5cec442a', page=2, block=1379245026, bbox=BBox(x1=0.2971211665818671, y1=0.10839842843896136, x2=0.4470636960839023, y2=0.17773435687792272), conf=1.0, page_size=[1190, 1684], type='molecule', hidden=False, order=2, lang='ko', direction=<Direction.Unknown: -2>, 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style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' /> <path class='bond-6 atom-6 atom-7' d='M 108.4,48.9 L 123.9,39.9' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' /> <path class='bond-7 atom-7 atom-8' d='M 134.3,39.9 L 149.8,48.9' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' /> <path class='bond-8 atom-8 atom-9' d='M 149.8,48.9 L 149.8,66.5' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' /> <path class='bond-9 atom-8 atom-10' d='M 149.8,48.9 L 170.5,36.9' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' /> <path class='bond-10 atom-10 atom-11' d='M 169.0,36.9 L 169.0,19.5' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' /> <path class='bond-10 atom-10 atom-11' d='M 171.9,36.9 L 171.9,19.5' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' /> <path class='bond-11 atom-10 atom-12' d='M 170.5,36.9 L 185.3,45.5' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' /> <path class='bond-12 atom-12 atom-13' d='M 197.0,45.5 L 207.1,39.6' style='fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1' /> <path class='atom-0' d='M 27.3 53.1 L 25.3 49.6 L 25.3 49.6 L 23.9 49.6 L 23.9 53.1 L 22.7 53.1 L 22.7 44.6 L 25.3 44.6 Q 26.7 44.6, 27.4 45.3 Q 28.2 45.9, 28.2 47.1 Q 28.2 48.0, 27.7 48.6 Q 27.3 49.2, 26.4 49.5 L 28.6 53.1 L 27.3 53.1 M 23.9 48.7 L 25.3 48.7 Q 26.1 48.7, 26.5 48.3 Q 27.0 47.9, 27.0 47.1 Q 27.0 46.4, 26.5 46.0 Q 26.1 45.6, 25.3 45.6 L 23.9 45.6 L 23.9 48.7 ' fill='#000000'/> <path class='atom-0' d='M 31.6 54.4 Q 32.1 54.6, 32.4 54.9 Q 32.7 55.3, 32.7 55.8 Q 32.7 56.3, 32.4 56.7 Q 32.2 57.0, 31.7 57.3 Q 31.3 57.4, 30.7 57.4 Q 30.1 57.4, 29.7 57.2 Q 29.2 57.0, 28.9 56.6 L 29.3 56.1 Q 29.7 56.5, 30.0 56.7 Q 30.3 56.8, 30.7 56.8 Q 31.2 56.8, 31.5 56.5 Q 31.9 56.3, 31.9 55.8 Q 31.9 55.3, 31.5 55.0 Q 31.2 54.7, 30.5 54.7 L 30.1 54.7 L 30.1 54.2 L 30.5 54.2 Q 31.1 54.2, 31.4 53.9 Q 31.7 53.6, 31.7 53.1 Q 31.7 52.8, 31.5 52.6 Q 31.2 52.3, 30.7 52.3 Q 30.3 52.3, 30.0 52.5 Q 29.7 52.7, 29.5 53.1 L 28.9 52.8 Q 29.1 52.3, 29.6 52.0 Q 30.1 51.7, 30.7 51.7 Q 31.6 51.7, 32.0 52.1 Q 32.5 52.5, 32.5 53.1 Q 32.5 53.6, 32.3 53.9 Q 32.0 54.2, 31.6 54.4 ' fill='#000000'/> <path class='atom-2' d='M 42.5 13.1 Q 42.5 11.0, 43.5 9.9 Q 44.5 8.7, 46.4 8.7 Q 48.2 8.7, 49.2 9.9 Q 50.3 11.0, 50.3 13.1 Q 50.3 15.1, 49.2 16.3 Q 48.2 17.5, 46.4 17.5 Q 44.5 17.5, 43.5 16.3 Q 42.5 15.1, 42.5 13.1 M 46.4 16.5 Q 47.7 16.5, 48.3 15.6 Q 49.1 14.8, 49.1 13.1 Q 49.1 11.4, 48.3 10.6 Q 47.7 9.7, 46.4 9.7 Q 45.1 9.7, 44.4 10.5 Q 43.7 11.4, 43.7 13.1 Q 43.7 14.8, 44.4 15.6 Q 45.1 16.5, 46.4 16.5 ' fill='#000000'/> <path class='atom-3' d='M 65.2 44.6 L 68.0 49.1 Q 68.2 49.6, 68.7 50.4 Q 69.1 51.2, 69.1 51.2 L 69.1 44.6 L 70.3 44.6 L 70.3 53.1 L 69.1 53.1 L 66.1 48.2 Q 65.8 47.6, 65.4 47.0 Q 65.0 46.3, 64.9 46.1 L 64.9 53.1 L 63.8 53.1 L 63.8 44.6 L 65.2 44.6 ' fill='#000000'/> <path class='atom-3' d='M 63.7 54.0 L 64.9 54.0 L 64.9 57.6 L 69.2 57.6 L 69.2 54.0 L 70.4 54.0 L 70.4 62.5 L 69.2 62.5 L 69.2 58.5 L 64.9 58.5 L 64.9 62.5 L 63.7 62.5 L 63.7 54.0 ' fill='#000000'/> <path class='atom-5' d='M 89.4 17.3 L 87.3 13.8 L 87.3 13.8 L 86.0 13.8 L 86.0 17.3 L 84.8 17.3 L 84.8 8.8 L 87.3 8.8 Q 88.8 8.8, 89.5 9.5 Q 90.2 10.1, 90.2 11.3 Q 90.2 12.2, 89.8 12.8 Q 89.4 13.4, 88.5 13.6 L 90.7 17.3 L 89.4 17.3 M 86.0 12.8 L 87.3 12.8 Q 88.1 12.8, 88.6 12.4 Q 89.0 12.0, 89.0 11.3 Q 89.0 10.5, 88.6 10.1 Q 88.2 9.8, 87.3 9.8 L 86.0 9.8 L 86.0 12.8 ' fill='#000000'/> <path class='atom-5' d='M 91.0 17.0 Q 91.2 16.5, 91.7 16.2 Q 92.1 15.9, 92.8 15.9 Q 93.7 15.9, 94.1 16.3 Q 94.6 16.8, 94.6 17.6 Q 94.6 18.4, 94.0 19.2 Q 93.4 20.0, 92.1 20.9 L 94.7 20.9 L 94.7 21.5 L 90.9 21.5 L 90.9 21.0 Q 92.0 20.3, 92.6 19.7 Q 93.2 19.2, 93.5 18.7 Q 93.8 18.2, 93.8 17.6 Q 93.8 17.1, 93.6 16.8 Q 93.3 16.5, 92.8 16.5 Q 92.4 16.5, 92.1 16.7 Q 91.8 16.9, 91.6 17.3 L 91.0 17.0 ' fill='#000000'/> <path class='atom-7' d='M 127.2 32.7 L 130.0 37.2 Q 130.3 37.6, 130.7 38.4 Q 131.2 39.2, 131.2 39.3 L 131.2 32.7 L 132.3 32.7 L 132.3 41.2 L 131.2 41.2 L 128.2 36.3 Q 127.8 35.7, 127.5 35.0 Q 127.1 34.4, 127.0 34.2 L 127.0 41.2 L 125.9 41.2 L 125.9 32.7 L 127.2 32.7 ' fill='#000000'/> <path class='atom-7' d='M 125.8 23.3 L 126.9 23.3 L 126.9 26.9 L 131.3 26.9 L 131.3 23.3 L 132.4 23.3 L 132.4 31.8 L 131.3 31.8 L 131.3 27.9 L 126.9 27.9 L 126.9 31.8 L 125.8 31.8 L 125.8 23.3 ' fill='#000000'/> <path class='atom-9' d='M 151.5 77.0 L 149.4 73.5 L 149.4 73.5 L 148.0 73.5 L 148.0 77.0 L 146.9 77.0 L 146.9 68.5 L 149.4 68.5 Q 150.8 68.5, 151.6 69.2 Q 152.3 69.8, 152.3 71.0 Q 152.3 71.9, 151.9 72.5 Q 151.4 73.1, 150.6 73.4 L 152.7 77.0 L 151.5 77.0 M 148.0 72.6 L 149.4 72.6 Q 150.2 72.6, 150.7 72.2 Q 151.1 71.8, 151.1 71.0 Q 151.1 70.3, 150.7 69.9 Q 150.2 69.5, 149.4 69.5 L 148.0 69.5 L 148.0 72.6 ' fill='#000000'/> <path class='atom-9' d='M 153.3 80.6 L 154.5 80.6 L 154.5 76.4 L 153.2 76.9 L 153.0 76.4 L 154.7 75.6 L 155.3 75.7 L 155.3 80.6 L 156.4 80.6 L 156.4 81.3 L 153.3 81.3 L 153.3 80.6 ' fill='#000000'/> <path class='atom-11' d='M 166.6 13.1 Q 166.6 11.0, 167.6 9.9 Q 168.6 8.7, 170.5 8.7 Q 172.4 8.7, 173.4 9.9 Q 174.4 11.0, 174.4 13.1 Q 174.4 15.1, 173.4 16.3 Q 172.3 17.5, 170.5 17.5 Q 168.6 17.5, 167.6 16.3 Q 166.6 15.1, 166.6 13.1 M 170.5 16.5 Q 171.8 16.5, 172.5 15.6 Q 173.2 14.8, 173.2 13.1 Q 173.2 11.4, 172.5 10.6 Q 171.8 9.7, 170.5 9.7 Q 169.2 9.7, 168.5 10.5 Q 167.8 11.4, 167.8 13.1 Q 167.8 14.8, 168.5 15.6 Q 169.2 16.5, 170.5 16.5 ' fill='#000000'/> <path class='atom-12' d='M 187.3 48.9 Q 187.3 46.9, 188.3 45.7 Q 189.3 44.6, 191.2 44.6 Q 193.1 44.6, 194.1 45.7 Q 195.1 46.9, 195.1 48.9 Q 195.1 51.0, 194.0 52.1 Q 193.0 53.3, 191.2 53.3 Q 189.3 53.3, 188.3 52.1 Q 187.3 51.0, 187.3 48.9 M 191.2 52.3 Q 192.5 52.3, 193.2 51.5 Q 193.9 50.6, 193.9 48.9 Q 193.9 47.2, 193.2 46.4 Q 192.5 45.5, 191.2 45.5 Q 189.9 45.5, 189.2 46.4 Q 188.5 47.2, 188.5 48.9 Q 188.5 50.6, 189.2 51.5 Q 189.9 52.3, 191.2 52.3 ' fill='#000000'/> <path class='atom-13' d='M 209.5 39.9 Q 209.5 39.9, 209.9 40.1 Q 210.3 40.3, 210.8 40.4 Q 211.2 40.5, 211.6 40.5 Q 212.5 40.5, 212.9 40.1 Q 213.4 39.7, 213.4 39.0 Q 213.4 38.6, 213.1 38.3 Q 212.9 38.0, 212.6 37.8 Q 212.2 37.7, 211.6 37.5 Q 210.8 37.3, 210.4 37.0 Q 209.9 36.8, 209.6 36.4 Q 209.3 35.9, 209.3 35.1 Q 209.3 34.1, 210.0 33.4 Q 210.8 32.8, 212.2 32.8 Q 213.2 32.8, 214.3 33.2 L 214.0 34.1 Q 213.0 33.7, 212.2 33.7 Q 211.4 33.7, 211.0 34.1 Q 210.5 34.4, 210.5 35.0 Q 210.5 35.5, 210.7 35.7 Q 211.0 36.0, 211.3 36.2 Q 211.7 36.3, 212.2 36.5 Q 213.0 36.7, 213.5 37.0 Q 213.9 37.2, 214.2 37.7 Q 214.6 38.2, 214.6 39.0 Q 214.6 40.2, 213.8 40.8 Q 213.0 41.5, 211.7 41.5 Q 210.9 41.5, 210.4 41.3 Q 209.8 41.1, 209.1 40.9 L 209.5 39.9 ' fill='#000000'/> <path class='atom-13' d='M 222.0 37.1 L 222.0 40.7 Q 220.8 41.3, 219.0 41.3 Q 217.1 41.3, 216.1 40.2 Q 215.1 39.1, 215.1 37.0 Q 215.1 34.9, 216.1 33.8 Q 217.1 32.6, 219.0 32.6 Q 220.0 32.6, 220.7 32.9 Q 221.4 33.2, 221.9 33.9 L 221.2 34.5 Q 220.4 33.6, 219.0 33.6 Q 217.7 33.6, 217.0 34.5 Q 216.4 35.3, 216.4 37.0 Q 216.4 38.6, 217.1 39.5 Q 217.8 40.4, 219.1 40.4 Q 220.0 40.4, 220.8 40.1 L 220.8 37.1 L 222.0 37.1 ' fill='#000000'/> <path class='atom-13' d='M 223.2 44.9 L 224.4 44.9 L 224.4 40.8 L 223.1 41.2 L 222.9 40.7 L 224.6 39.9 L 225.2 40.0 L 225.2 44.9 L 226.3 44.9 L 226.3 45.6 L 223.2 45.6 L 223.2 44.9 ' fill='#000000'/> </svg>\"), TextualResult(token='2ff6db102b6d527bb4deef6e5cec442a', page=2, block=1499092466, bbox=BBox(x1=0.6968527914095326, y1=0.45214840125584543, x2=0.7276013799074317, y2=0.46777340125584543), conf=0.84716796875, page_size=[1190, 1684], type='moleculeid', hidden=False, order=13, lang='ko', direction=<Direction.Unknown: -2>, source='', bboxes=[], contents=[], text='(IV)')])]),\n",
-              " TextualResult(token='2ff6db102b6d527bb4deef6e5cec442a', page=2, block=2042119221, bbox=BBox(x1=0.12195787189387473, y1=0.5332030887558454, x2=0.9120928596047794, y2=0.6064452762558454), conf=0.9501953125, page_size=[1190, 1684], type='paragraph', hidden=False, order=14, lang='ko', direction=<Direction.Normal: -1>, source='', bboxes=[], contents=[], text='并与一种酰化剂  $ R^{4}COCl $ （V）反应并去除保护基 SG1，从而得到如权利要求 1 中所述的通式 I 的化合物，其中取代基  $ R^{1} $ 、 $ R^{2} $ 、 $ R^{3} $  和  $ R^{4} $  具有上述含义且  $ SG_{1} $  是一种保护基。'),\n",
-              " TextualResult(token='2ff6db102b6d527bb4deef6e5cec442a', page=2, block=214294481, bbox=BBox(x1=0.12264885141068146, y1=0.6137694587616909, x2=0.9120928596047794, y2=0.6596678962616909), conf=0.94189453125, page_size=[1190, 1684], type='paragraph', hidden=False, order=15, lang='ko', direction=<Direction.Normal: -1>, source='', bboxes=[], contents=[], text='4. 如权利要求 1 所述的通式 I 的化合物、其具有生理活性的盐或其组合物用于制备治疗疾病的药物制剂的用途。'),\n",
-              " TextualResult(token='2ff6db102b6d527bb4deef6e5cec442a', page=2, block=2101772102, bbox=BBox(x1=0.12161238213547138, y1=0.6679686775116909, x2=0.9114018800879727, y2=0.7402343025116909), conf=0.9580078125, page_size=[1190, 1684], type='paragraph', hidden=False, order=16, lang='ko', direction=<Direction.Normal: -1>, source='', bboxes=[], contents=[], text='5. 如权利要求 1 所述的通式 I 的化合物、其具有生理活性的盐或其组合物用于制备治疗与血管收缩或其它内皮缩血管肽的生物效能相关的疾病的药物制剂的用途。'),\n",
-              " TextualResult(token='2ff6db102b6d527bb4deef6e5cec442a', page=2, block=629761521, bbox=BBox(x1=0.12195787189387473, y1=0.7475585212616909, x2=0.9120928596047794, y2=0.9282225837616909), conf=0.97705078125, page_size=[1190, 1684], type='paragraph', hidden=False, order=17, lang='ko', direction=<Direction.Normal: -1>, source='', bboxes=[], contents=[], text='6. 如权利要求 1 所述的通式 I 的化合物、其具有生理活性的盐或其组合物用于制备治疗疾病的药物制剂的用途，所述的疾病选自高血压、肺动脉高血压、心肌梗塞、慢性心衰、心绞痛、急性/慢性肾衰竭、肾功能不全、脑血管痉挛、大脑局部缺血、蛛网膜下出血、偏头痛、哮喘、动脉粥样硬化、内毒素休克、内毒素诱发的器官衰竭、血管内凝血、血管成形术后再狭窄、良性前列腺增生、局部缺血和中毒诱发的肾衰竭或高血压、环孢素诱发的肾衰竭、间充质瘤转移和生'),\n",
-              " LayoutItem(token='2ff6db102b6d527bb4deef6e5cec442a', page=2, block=144603682, bbox=BBox(x1=0.5707490295923057, y1=0.01959228289099034, x2=0.841613000180541, y2=0.05792235875073068), conf=0.86962890625, page_size=[1190, 1684], type='watermark', hidden=False, order=18, lang='ko', direction=<Direction.Unknown: -2>),\n",
-              " TextualResult(token='2ff6db102b6d527bb4deef6e5cec442a', page=2, block=199587202, bbox=BBox(x1=0.068752455511013, y1=0.36718746375584543, x2=0.09483693227046679, y2=0.8359374275116909), conf=0.76611328125, page_size=[1190, 1684], type='pagebar', hidden=False, order=19, lang='ko', direction=<Direction.Normal: -1>, source='', bboxes=[], contents=[], text='10\\n15'),\n",
-              " TextualResult(token='2ff6db102b6d527bb4deef6e5cec442a', page=2, block=2067803715, bbox=BBox(x1=0.5275628097918855, y1=0.9467772712616909, x2=0.5379275025439864, y2=0.9565428962616909), conf=0.732421875, page_size=[1190, 1684], type='pagenumber', hidden=False, order=20, lang='ko', direction=<Direction.Normal: -1>, source='', bboxes=[], contents=[], text='2')]"
-            ]
-          },
-          "execution_count": 7,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "pages_tree[2]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 8,
-      "id": "9c965c29",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "page\n",
-            "├─ image\n",
-            "│  └─ moleculegroup\n",
-            "│     ├─ molecule\n",
-            "│     └─ moleculeid\n",
-            "├─ paragraph\n",
-            "├─ image\n",
-            "│  └─ moleculegroup\n",
-            "│     ├─ molecule\n",
-            "│     └─ moleculeid\n",
-            "├─ paragraph\n",
-            "├─ image\n",
-            "│  └─ moleculegroup\n",
-            "│     ├─ molecule\n",
-            "│     └─ moleculeid\n",
-            "├─ paragraph\n",
-            "├─ paragraph\n",
-            "├─ paragraph\n",
-            "├─ paragraph\n",
-            "├─ watermark\n",
-            "├─ pagebar\n",
-            "└─ pagenumber\n",
-            "\n"
-          ]
-        }
-      ],
-      "source": [
-        "print(tree_repr(GroupedResult.clone(pages_tree[2][0], type=LayoutType.Page, items=pages_tree[2])))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 9,
-      "id": "b8d0d3e2",
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "GroupedResult(token='2ff6db102b6d527bb4deef6e5cec442a', page=2, block=1317312281, bbox=BBox(x1=0.2971211665818671, y1=0.10839842843896136, x2=0.7110178715040704, y2=0.17773435687792272), conf=0.9677734375, page_size=[1190, 1684], type='moleculegroup', hidden=False, order=1, lang='ko', direction=<Direction.Unknown: -2>, source='', level=1, method='default', items=[MoleculeResult(token='2ff6db102b6d527bb4deef6e5cec442a', page=2, block=1379245026, bbox=BBox(x1=0.2971211665818671, y1=0.10839842843896136, x2=0.4470636960839023, y2=0.17773435687792272), conf=1.0, page_size=[1190, 1684], type='molecule', hidden=False, order=2, lang='ko', direction=<Direction.Unknown: -2>, 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source='', bboxes=[], contents=[], text='(11)')])"
-            ]
-          },
-          "execution_count": 9,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "pages_tree[2][0].items[0]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 10,
-      "id": "3e73fca9",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "mol_group = pages_tree[2][0].items[0]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 11,
-      "id": "e85172c7",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "moleculegroup\n",
-            "├─ molecule\n",
-            "└─ moleculeid\n",
-            "\n"
-          ]
-        }
-      ],
-      "source": [
-        "print(tree_repr(mol_group))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 12,
-      "id": "ed96d932",
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
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-            ]
-          },
-          "execution_count": 12,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "mol_group.items[0]"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 14,
-      "id": "72195ba1",
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/markdown": [
-              "****C(N)C(=O)O*<sep><a>0:R[1]</a><a>6:SG[1]</a>***\n",
-              "\n",
-              "\n",
-              "(11)\n"
-            ],
-            "text/plain": [
-              "<IPython.core.display.Markdown object>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "display(Markdown(mol_group.format_as(FormatFlag.Markdown)))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 15,
-      "id": "3b6f4bb3",
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/html": [
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\" />"
-            ],
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "display(HTML(f'<img src=\"data:image/png;base64,{mol_group.items[0].source}\" />'))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 16,
-      "id": "8d8d7558",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "doc = fitz.Document(input_file)\n",
-        "# dpi = 300\n",
-        "\n",
-        "# group \n",
-        "# page = doc[group.page]\n",
-        "# max_dpi = min(dpi, max(1, int(4096 * 72 / max(page.rect.width, page.rect.height))))  # max 4096 pixels\n",
-        "# group_clip: BBox = group.bbox * [page.rect.width, page.rect.height] + tuple(page.rect.top_left)\n",
-        "# pix = get_pixmap(page, clip=fitz.Rect(*group_clip.xyxy), dpi=max_dpi)\n",
-        "# group_image = Image.frombytes(\"RGB\", [pix.width, pix.height], pix.samples)\n",
-        "# if save_dir:\n",
-        "#     pix.save(save_dir / f\"{group_name}.group.png\")\n",
-        "# group_size = [pix.width, pix.height]\n",
-        "\n",
-        "\n",
-        "def crop_item(\n",
-        "    page: fitz.Page, item: LayoutItem, dpi: int = 144, max_size: int = None, rotated: bool = True\n",
-        ") -> Image.Image:\n",
-        "    bbox: BBox = item.bbox * [page.rect.width, page.rect.height] + tuple(page.rect.top_left)\n",
-        "    clip = fitz.Rect(*bbox.xyxy)\n",
-        "    if max_size is not None:\n",
-        "        max_dpi = min(dpi, max(1, int(max_size * 72 / max(clip.width, clip.height))))  # max pixels\n",
-        "    else:\n",
-        "        max_dpi = dpi\n",
-        "    pix = get_pixmap(page=page, alpha=False, dpi=max_dpi, clip=clip)\n",
-        "    cropped = Image.frombytes(\"RGB\", (pix.width, pix.height), pix.samples)\n",
-        "    if min(cropped.size) == 0:\n",
-        "        get_root_logger().warning(\n",
-        "            f\"{item.token} Crop config: {bbox=}, {clip=}, {page.rect=}, pix.shape={pix.width, pix.height}, {max_dpi=}\"\n",
-        "        )\n",
-        "        \n",
-        "    if min(cropped.size) == 0:\n",
-        "        get_root_logger().warning(f\"{item.token} Crop error: size={cropped.size} {item=}, {dpi=}, {max_size=}\")\n",
-        "        cropped = Image.new(\"RGB\", [10, 10], (255, 255, 255))\n",
-        "    if rotated and item.direction in [Direction.Rotate_90, Direction.Rotate_180, Direction.Rotate_270]:\n",
-        "        get_root_logger().debug(f\"{item.token} Crop rotate: {item.direction=}\")\n",
-        "        if item.direction == Direction.Rotate_90:\n",
-        "            cropped = cropped.transpose(Image.Transpose.ROTATE_90)\n",
-        "        elif item.direction == Direction.Rotate_180:\n",
-        "            cropped = cropped.transpose(Image.Transpose.ROTATE_180)\n",
-        "        elif item.direction == Direction.Rotate_270:\n",
-        "            cropped = cropped.transpose(Image.Transpose.ROTATE_270)\n",
-        "    return cropped\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 17,
-      "id": "b3820422",
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "def recursive_find_groups(\n",
-        "    item: SemanticItem,\n",
-        "    required_types: List[LayoutType] = [\n",
-        "        LayoutType.MoleculeGroup,\n",
-        "    ],\n",
-        ") -> List[SemanticItem]:\n",
-        "    for t in required_types:\n",
-        "        assert 'group' in t.value, t\n",
-        "    if item.type in required_types:\n",
-        "        return [item]\n",
-        "    elif isinstance(item, GroupedResult):\n",
-        "        items = [itt for it in item.items for itt in recursive_find_groups(it, required_types)]\n",
-        "        return items\n",
-        "    else:\n",
-        "        return []"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 18,
-      "id": "fc5b925f",
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "33\n"
-          ]
-        }
-      ],
-      "source": [
-        "all_mol_groups: List[GroupedResult] = []\n",
-        "for page_idx, page in enumerate(pages_tree):\n",
-        "    # print(f\"page {page_idx}\")\n",
-        "    for item in page:\n",
-        "        # print(item)\n",
-        "        all_mol_groups.extend(recursive_find_groups(item, required_types=[LayoutType.MoleculeGroup]))\n",
-        "print(len(all_mol_groups))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 20,
-      "id": "be2994f7",
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
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HKIOkfSY2h08mkRIlWUZgFExNgSabqVS8cizwgTH74KLEgdNhsINixVboIDYyCtcPf/hDHZ0C0UuAxIU5IkcsvGb8Bvz617+mB4910jOSVATWJCDX9aOo7b777myPMiGFwHXyySdvxXvbn1zEWBYDoTtet9Rwlfa78yYl+tttbO6tt95SsYX5UPdgaIdGQGYIFWYe9BI2Jz3gpUXkLE4qEaMKD2JcdwcKPGR9oFIhcdkcT1HGyyN+CeUj8dgc8oUbwI/1ht4i09/DlRjO+4QaNGiA4ICNSJGqaAHNNz1wufHLqF8ZC5ZR0d5yaQ+vYS8my8P9ixmBEMNcesilDVs2j9Mx1sKFC7Gh5/J6Hm3YNEA0GJaLFBsIaJvdhJLHWPZKdgpsY3NTpkxRgwMTo2yO9zF+t23bVmYLUSh7j8Gn2IZEwafzPWLECHIwzJw5M9i4GDXwOC9RF1kc8KVEJ+UOh6pOGH2PHj0AVdkcbXBKoD3KEbc9ZTZTrk6EtyAUqHHx8+A1LtQtPxL0j3Vb6UmBDQsQEllcqFEwAiDNuUOQiQ9dT6H6l37Y7xNM7Q2EF0gotSs5NDHLdHvqqae6SEE3iPnnP/+5kp17r+OXE8wjgjIuv/2Q17ndVpQC29gcb95555041xD4hus8Zju3L370qOQiFYxbn2MZN+NgYD97K/LT59hD3s2uvPJKnP7clU05VC3CEI899hg4suNDsvv973+fy6BESvAK8aHeEKiB89ZmZhkX2WfgwIHeWAg+5CnL8lYej9hIIrx73youk3l0lekVfucIOPNw4ZZtZqZXKl+Pz3C7du28QfEuJjS78p1rD/TmDQE/jVghqMCkoPDUU0/939briiuuEFUDMorUsNUQBLUm6Dm0HZujNTpLj8EVikaoDBDivLlH8YcLe6GGCPaDEBf0gGd3WfD4vmeffZaP1sMOGZbsBkGoKlODEOfJPg888ACstjJ9ZnmXnZc42QhqaO5wwcMsm+WVHB8BNjs4j2LcIjIXaXesgKGt9/aSjIvmHtQqqYWEQdMJhm/XsZTOUca5xi6FxAq5UICvVUMS1AsCGUIWDzZSOnnyySdVSa32Q+3cZ3P6oBgFdKt4D3GxpAREWQE4tfOrjqK0UIMiiSBh0S3bHxkIZrfrrrsiPDI6cQKFGsjth0HpHDGQgZS3UgYMdEyV0XbzLq4A9MMlCiY4HT0jejMi47pgFLzM7ptR3O8WJ1UgwRU577HYXGO00TXAImYIuYI/xXmPkuVFNiUyHDYoXSTAw+/uVjLvS7gLLC/HWcM/Sd4SA44kApEld/vttzNQkZZcFgTT9Ig9liwV9MWqS/XYHLwFJ1D57pBjvFUUKZtT0vNlkuVJ8j7rWmfn9de//pWEaNosjwI90wnpBlyRh18Atsw8ynHV5jGuvsIQDIQ3Nb8tCgMFtFoAlofbLfI1cqJ2JeRCAxgNOooXw6E7x3lN5yvoLYgONxOFsYq46g6EX9nmI9ChD83uKqEwFKmAAQS7pzpOCYIQnG8GG0UmjBQYPjDPuMGLdIgqMOI5UpDSVEC0Z8Mnk8KyUdTQgyEjUy/SHPXMlDpgs1a1JYXSsDmBANsuniuueReg0RCz7Lg8u7gLdJayptIXuvCXXwB8RLO8UqRHLPHvf//7nm0X00eFdAJkxPbS0/P5YRtRfUSRgM/ULYIJUyaX55cnwf9BhSYyEbNJEgH8jd3fMCrph6z0mcaKuB7RmIgREQd08UDtRx55JAskRMuz+/Z+hPjYsrxij3KnAJwLcVimgwyYru8k5ZNOOolHyubo9rrrrhPex88Mbq06UCnZnACBchGdHZe7dwD6wYMH5/4jz878F1svz6R7ySWXgLliG30BVk76M5kn+YtnMpC6cxAKlWBUs2ZN913KsGwYaOgrpa0Up1b80cDuoYceEmDY+qEnVhQQcivj7BIBgngFeDEY7NDByLOQIphTyeWqF/nqti7kEXk4XUWAWhKHQJQTSTmYL8/btCp2ZP8VXyj08lpZejanoKDY1u+BAr+r3gZbW3oFvJOCYQD8opIvNw4fFYobIPGEVvJ/kTUs1OkEzzseBTMV4zpLP/kJuR7FinHr+u7rCiPfmfvrRehl9C6TFUXWs7rImuTHiUkRxRD+Sepf5a5YuF5Fx7L22SmgbI4fS69lJjZHMzkVRBchNTFic8huRGW7SUFgDfz+I7hyeUjKLfWISxLj4i44fPfd/VHouxFXwryCTieYYr7EzcEOO4zrcqxI4ZmM/S5imCs0HOk3mD45dQXnfbzh5HW2pVh+BBH24BXqs1SNUSPiYedtRUGBEDqyQqnVT2cHr2xwL0kYTKlIFM24CDoizaWHzQnhYHYcLokZUdYQMh3WKy7kAm+zxlrEPivflS44WAlOzuw7opmGCo2CaIkNzrUwAjaoIXjyhcgVZIXU4OeVo2BbIXiK0ViP13LzBaE5lglKCpuDMhAcwRN9kC6tTAVWYPxF1GLMdQR96k9+JdncV/4WmaYw+nqYN+IoWzP22GLRx5IFGOjd8ZjHbI8NhVsSl6MwRiPuQkhAAh5knsnMbVDaMgyOCC1MEERZYHIVYMAOQypXKGwktibQQpl+aBurLAYF0O9woUKFi7F1yj6E91ubvbE9zZ0CaDlzb5yl5c5ZnpXwEf73uIB4Zi/4mhqMMSywTXAhROTBKBZbHqegkqGXbGjsfbQmS4G8FAnlcQTGs093UcPoHFQvuA2sbBQoEgViJ80pniNHjkR2IzQHa51WslElvJRbZBytpMAZH0RcumYv92ncyiQ4wusHBwv3MCcPSNSLIKVZs72n8b/lJwcgMStpoDQHJ2pq3PjDnwlC/E7Qq5AwKlMDq48hBeLL5lAA9+/fH5U8VFNOR7QpKbA9OpJRFvnIczb22sTtlpAgtI1YWtm9kgFFwUMaJYMAtzjiJgsjRUELcDo4tfeDpE+TVcCOjIcTMJNuB50UXiZuws5k4VKG0MaXzclkoM/SUDWpYSeLcxzqEn5UpQbnsiRyBFFvezYHduJupEESVyTmYzJ0IvUgX5P5AwusnDKVRFwUZjYWrqd30N1HW1ohhhSIqW4uE6XQCrPXw0DB30xtEl0f9AdOHDo4yrFRxfcCkQfBB390TMyJw8IAThMFksTm2MbiKUIYPAXsrZ4clNBZQVzFwzuhwGcBm9M/cAYWTpelmT0yCkRAgSSxOXxN0NwLUciDFvTSjIBeBR8C17/UaHkmTJiAMmHt2rWzZ8+GUKQGw9lb3DsLTjfr0CiQOwWSxOZwD/ayPOaOp7UsNgV+85vfkMFU2BzJOQhoZ0RkuvSxORy5iV0tNj2tfyjA1s3zKlOycLCMhO5rjRQIY8ff1qtMEptD6eOdR+khY7clpACRxdjBBQAyMuEJJGWWKRfW5AsuuKCE4BVwaDy68fEuYIfWVSYKtG/fXkLu4Hf4kLrNcE7CWCerS+pRBMPjSHmPPESNvCiPksTmiHwgeFDgtr9xowDpAiVKHzMRdkm0ckDIUsP+QGgKpnCSHcYNZoMn/hSQHKWc+sahRRxHSQC4wkyOJn5yiPWmhniJn//856SeJkoP3kewwKRJk7Rl3B1KFFAKfD/urZVjRQFWFS4+yHEk82C1KWxEcRD4waU1VjAK5EgBfjiff/55cbFidXGkNzm+OPFZXpefVSmjDiZDvZQx62nC4a9ayj/7axSoPAU4HK7ynVgPRgGXAqTJxDdb0+VyFgoSnLshdRtnKidp05oJB6uPIQVIDoz3D9r6GMJmICWIAug6CIIiEZnAzBkvO+Rx3oHLvGhsLkEzniRQOY0JtbEaJZIEehisHOVBTtOwJ1ZXdAoQq04aWhlGzlAODokyDiOYXMHYgSTp5po1a4a6Ef1iEEmriRUFpk+fLr+oLVq0QKBLaJIVl6Qc60HqAbfGylFSgIQdkrwevVvouIQPZYkgShLLIIzfS74UinCyKvHx8RIWJQv+UGixdonvUmqkOTh1Cph16GQlopIwdlJSc+1wxxqKTpLYHO4wmFpC0UhuJUne3UNhkouIQW4UiC0FksTmYktEA6ysKIBSPDWuzmUyccbmymSio0OTw/1I/hzdeJGPhCfq8uXLIx/WBsyfAsbm8qedvRlKAZw5g6au0Jbxr5SwIQ9O2Bx59LxKu40zBZLK5jhMMxigG2dC5wIb2Ury07Dm0nmUbYgujHK44o3FEZrB825wvpcIpOKNaz0XlgJJZXNDhw5dtmxZYWlR8t5Ijx7/I3t2SKVXXnklNUaVUGmOX6O77rprh3SwBvGhQFLZXHwoWEBICAvFFaOAHVpXRgGjABQwNhejZUDSUGNzMZoPAyUtFIg7m0PXO3r06LRQuyzw4AhakgaXBaqGZEIoEHc2d+SRR4bmEK9WrZqbhiUh1N4xmCk4HWa33XZL97HTpDzj5Mkdz6W1iA0F4s7mSLEdGmRD0oKWLVvGhowFA4S43YL1ZR0VhwIfffQRGRyL07f1WhQKxJ3NFQXpGHd63nnnxRg6A+1LCtStW3fs2LFGiwRRwNhcvCbruuuuixdABo1RIPkUMDaX/DmMGQZvvPGGMeuYzUm5g2NsrtxXQMHxRz1PWGvBuy1Jh6IXJsWhZ+8K1pQEPBs0RwoYm8uRUNYsVwqQStM9VCnX12LZbuXKlU2aNDnhhBO8ZASnnnoqZ2+T/gyoDzzwwFjCbkBto4CxuW20KEmJEz1atWpVkqGLN2g6InOhD4Z+TpZq06ZNECOS2XKUFCmFaRB8WjzaWs95UCABSdKPPfZYjr/jAGB2CkOGDFEk+/Xr9/LLL+OlxVHHWpm4Annup06d2r9/f6LBDz/88FDvmcQhlSaAyS6XyduZWeNKE7JpxWWnRCSTIEofdQ9sjpT87i/nww8/zOGtHTt2TPr0vPrqqyS558zmHSIyYMAAYsKQMnbYsoQNYNySi4nJIm2R8e4SzoUNDQWSweZsqvg14hAZzsoiqXqDBg046IgkGS7HjxWJ0srmRCaILdljtQZiBUwCNq2xoldJgOFYNtRDnDrOBQB4bKxevRrpD704e/aSgFRWg5JybsOGDaDcrVs3DmaE/mwsCDesXr16WdEhuciaCSIBc0cQJVs/4XECLjUTJ05EuEsA9MkHEb3wPluvp556CneZfffdl7u+ffsuXLgwNUeXJX+WsmFgbC4bdWLyjOiiU045RYDhsJWLLrooJoCFgkHI5+OPPx76KImVmzdvfvDBB4OQ33vvvW3btv3FL37BL1DwqdXEigLJ27TiyoQOHiJefvnlaKliRc2KAkOWXb4i7y1sr15keP369UEZUYKWPXv2RKCIrRw3aNCgNWvWhPIFD81E3KKM4yDkKVOmKLSkBUNdoIvwxhtv/PTTT2+55RZT2CmJ4lhgIhN0ffjhh6TnFzoedthhGzduTBDwHqh9+vTxfOsFL5Q++NDAyLz2ert27VppOW7cOK2MSQHg3VXOx8+RHTGBLQ8wzj33XJd/4RJM3lP6YeENHDhQMK1atSo6hDw6t1cio0BiNq18LTA4rlWrVsny4swBHNCPPvpo97tKUBlNduhRA8h3ixcvRoiYNWsW6yCI0ZYtW4KVpa1BSd+rV69atWrFELbKUGbp0qUyBfwgXX/99bgE48BEh8Q/NGzYUH+lbN9aGSJH8W5kDLUyA7EPOuqoo4QcmLeQ47jEyMiGDifhdevWVab/Ur2L1CZI4RssSPG3UaNGOvGvv/66BxvbJXGag6fcc8893tPob/nCof8ll1yiMLuFpEtzrC5Bh1+dIG3hevK0cePGwadWEx8KfCM+oGSC5LXXXuvQoYOsJw6+IsJGWg4fPrxKlSpSjyY40+txrlfF3DXXXKNwuieWeWxuwYIFcDdB+dJLL9VXSlVAiMuejAQ2d99995UKvMqPK2yOHyE8AYO9GZsL0iSeNQnYtHKsHxffNs5K11577ZVXXinfOaxhxIgRrupE6tP697HHHsPGygG1giAbW6IpS4gsHi380lxxxRUeDPhbEKwilSx6wiFiazDxIM90i6qkd+/emZ5affwpkAA2p0S88847MeTpLYXBgwe7t8kts/3ECYut3yeffIKYFkQERk+Y15tvvqmPXth66W3EhTFjxvB7EzyNCHEb8e3ss89WeJBY77jjDr21glGgBBSIp5CpUCGw1KxZU+iChVHrpYDCW6S55G5aNTq1Tp06xH5qWCui6+TJk8WuhyJc96ruEqlduzb08WhS7FsYLn48XgAAs4BwRyDaO++8AwCbNm2CKSuo6BZ4Ra7gJBYb4Mr0L5tWQlBCO7FNayhZYlgZd785NNwIOPrBpKyAFQVNHIlJwOvjjz/m73vvvSc4Dhs2DE9gmB23rBtcFqTe/Yt7XZSGZsBYtGjR6aefrgZiuBtefoCEHEpUgEDL7UMPPYQrmYKKlfztt9+W2/fff59+2NjqUysYBYpNgSRtWotNi5L0r04J3uiwDOUaJKoMavoPOOAA/GlEmPXeLcYtW+S5c+eeeOKJyuMY5bTTToMvc6GhE2iJSEOHiO01lC/zCny5efPmHL9bDCCL1CcBrerGVKQhrNuiUiDu0lxRkY9P5+SnPf7444EHwYezGSk8++yzONbhqSBAHnHEEZ6yv3Xr1q4KTJoV/C9SGLo2usUz9t1335X+GRp+R3nkyJHuiLfffjvCGnYht5JID92Yz5gxg13t+vXru3fvriFr+Gp4TsXu66Utk9MQsRqufeGFFwq14ebIrSKrLl++HPD4odLda2mhtdEzUiCGG2kXJMIeNGtmUK2TdN0cmOIHx9yoQ4l7HDVxrKDvUiPiMlbd9u3be0uHA8JfeuklD5I5c+ZgUSUDpdcYYdONgpg3b16NGjW8NpzZGNsoAgDzziqEzXXu3NlFwaIgvMUQw9sE+M0R0iSrKn1sDs3jIYccAnahbI56z28uygXE9pPdpfs9SxmhxgMDo0QmXRt5Tz0WhgSkm3HpEFZ45plnen3G55ZVFySCW0OscXygNUhCKZAkNoewgDmCcEK5yA9BEDULjvgb9RkORTKeleTwUbsqtkjcZeAsMAW2q/oVcVIECT+ihB/hS8jrBmNgKiG8CVswj4hUF3iAn1t0dm7OO3gWLdmQIpZyocgLAo+/N20QghRNGB9TiUVFhvY4Y7CHKGsABkTQJABz8ILHxQraKCmToLESwOZQ6OCGrp9EsHDOOeckiOIKKps1VVqBFPYEEsHzFC6A74WiiSuJvlLsApzL0wACBrnVZs6cGRya3asCqYVMvhfB1+++++6WLVt6jinSz9NPPx1sbzVGgbwpkAA2B27Tp08n55p+S24BKSCGiTpynA/3wBTyl+lb+G1gXRU0cUDT+qIW8OPVCBOlcL169UKjteBEmHq1GYXvfe97eMC4mrhcoP3Vr34VDDDAfzCUsebSobUxCgQpkAxLa48ePdgNYeFyk7Xiq8WmD+dhtdm5X10iyjBoFdywpSrMnGRGgjl2r2B92WWXaX1RC7I7c4dAS0A+ta5du7qVK1asmDZt2qOPPqqGV55iLRk/fnzTpk3dlrmUr7rqKja2qPbcgAqcoplrPJ8xgGg+0Vx6szZGgVAKJOnIm2eeeQZhQdHAAwtdid5aofIUQDV29dVXS9ZShDhEOTxd3G5JFYN7sDhSSD1smmgNdtwkJnJbVqiMxRyejszuhYVxciD+HBXqKntjXPzIkUnMTPZm9jRlFEgSm0sZ6eOJDjFb2HnwDkEl56oOMY/wGwMvI+eVQo7GAOe+7JpTbbzDAlmREeJQxbLpkMZYYLjIy5KHnBgcjq0AdhUYNzvi4FOrSTEFkrFpTfEExA21iy++GJDYLLuA4dBLLizi0tjYaj1hXmxgxditlZUpYI4ghQHMiISpEhxGmQsOW5lu9V1ETs3vopVWKAcKGJsrh1muAI4ug2MvOX/+fF5G74ZLsPSClMcpsZTxsSggj5POGR1XO8TGLl26UEOIFdKiPKrkX9V44AeHN2KTJk0q2aG9niAK2KY1QZMVKagIbhwqxFGw7qgYfGB5hGq5lcUrP/nkk+SnwtaMy15wFALLCC/TeoTBIUOG6K1bIEiDTj744AOpPOmkk9A/kqjZbWPlFFPA2FyKJ3c71CTAXqtIh4vfht4GCzi73HrrraomowG3GCWIWAg2jrgGqHBDIRLOPSkRL2VOqAESzhJyY9TgcVir3VR9tMHcgfk+YrBtuJJRIOhjYjXpowChCwcddJC7yFq1apUdTS9+C76Ajiz7K5E9RdLU/PguUlLGxXLJkiUKDBEywTagow2skHoK7BxcAVaTMgrA0UjASWoQAirkwoRKkBaZfrm8banivnr1avRiyEewSK5u3bq5hldtVpICKjzV2Y0aNUogJKmn6AqR8jCnEiQHN+TEWFxkSgKkDRofChibi89cFAUS2AEOIhgTmjVrhsFULhT8eB0S9sDFsTWhA7MHxP2CBJlkSeLy4u1DX4myUgNpiWgWCPHgmz17tsAA5ORHwoEZTDMhGCW0NlZpKWBsrrT0L/roZH8jVBZegDimgxFYRk4kueXoHDeeQdskpUBgHGpHoIVlC5sjFx7cGWsJPndnnHFGUhAxOItHAXMoKR5tS98zvEzy9KLJIv9HKECwBpJHegGqoS3jWQlHQ7UE/GxdMbySt4YQOkmvwg4duzByKE4q8QTeoIqGAsbmoqFzaUZho8r3T8oTeIEHAZLOpEmTiDqg/mc/+xmZAjIlR/BejOHtrFmzFCryprhp8jizBk6Hko4gXG1jhXKjgLG5NM84PE48zrBCeHiiutJMSujv3GBhr2XSb8nvwCadDKYcLUTmONDBv6RTp05Jx8vgz50CxuZyp1WqWpIXRE/bQtaTk/oSiiGMDJMx/i5oITmehstLxf5lZOzuu2seYDfSI6EoG9gVooCZICpErvQ05ihVzbCU9M8elRxHahAvwfSQhBmfuNB5Ak25Qp9aZYopYGwuxZO7DTU5LWzbfapLGF4SbTtO9eSUBjljc6Whe8SjcqTO4MGDIx404uHYqJKfjkEfeeQREtCTOy9iAGy4+FIg9XEeZY4gIQEHH3ww648IVjgdKjmudu3akZVIFiXnqOKKkSwqYTmVE9EEhf322498n6CA69zw4cPZmVLPEbdgmiy8DNoiUcCkOflSUvuXvEnE2/Plk3kchzKCB7hIr0RoFD7DOJFgjSWKIFn4g84TTzwhMGNbIA2BeMOAGpmB8Y/hlsCPZCFl0BaPApahpHi0jVHPHTt25DRVFyAcSjhDg1zBbmWCyriGcEwExtMxY8a4AR4JQsFAjYwCxuYiI3UpB8ItzouCQPARTVYpwarc2CQjePHFFzt37ly5buzt9FPA2Fz659gwNAqUOQVMN1fmC8DQNwqknwLG5tI/x4ahUaDMKfD/FE3bYaqb2u4AAAAASUVORK5CYII=",
-            "text/plain": [
-              "<PIL.Image.Image image mode=RGB size=418x158>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "====================================================================================================\n"
-          ]
-        },
-        {
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",
-            "text/plain": [
-              "<PIL.Image.Image image mode=RGB size=509x120>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "====================================================================================================\n"
-          ]
-        },
-        {
-          "data": {
-            "image/jpeg": 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iRYvyqdevX1/VYM3y9NNPk54+ffqCBQtUZvHixZs1a6bSDz/8sFr+qFvn3+eff37z5s1m9Gl20KHNRcC59NJLTdOPs21CcwingMEbVjJy5Eg9ELThuVusWLEWLVroTEkIAiERSDfel1h6sftqpNCSug6GUaNixYq6mk7UqlWLuI2uTXQmCpTRo0frJjpBGGqaf/XVV7pm0hLjx49HmaopUYn+/fu///77/pGYkoaGDBQzArLAOQIdX869996rPyoiAB0pOzqFaQP5wgrtwc84dh+c046ua99R4aeffrrrrrucnvWsmH744QekA7tNYu5XrVrF+sWSpBCR4KfpZWlKDDzSa3QICCs5gpfpKY9IElbEwOhbpkwZK1YjbhffHL68v0Y6v+WWW2hesmRJzbxIwI/Q0dLBtm3bjlAW79TGjRuRgBjLHBrFEPT06tUr3qNJf4FAQFjJ/x7zzp07e/TooT8tttJE+PxRneAzohvqxKBBg8JKKAyBHQf/N+eKqXHjxrC23377LUIyIqz29ddf062TYEJhe/O+CPuXaoFFQFjJP4+ebx57iuYCRDmNhAvol2batGkoU3Pnzq17UAkUsbqOd2LEiBFWW3W7aNEi74ZRlaLlcQ0B3bRp059//jmqrqSyIGAhIKzkH0CQQcwvmZ9uC6ZIbmEo1pIBB1k4VCRtv/vuO2IUEKLZJIP0ZZddhpNYJD2ErQOzqFatmtU/jnaM++OPP4ZtLhUEAW8EhJX8g8/555+vvjF4AQJCbIpP1B/ffvvt2LFjTYbCgaFoQ7gwM3s/CUpR5aIlgQHpHqZMmZJFMwpU3XDDDRCAWVfzETS+aFuhFgVwWKqkgiAQCQLCSv5BSTuPsSTJ4qdL8yeeeALVqY5RoD5gGMRHH33EgaFhnwqMbO7cufTAha962PoeFXbv3o1yVzMmRUnevHnZWxgbu/QYS4oCjoCwkv8uXLhQG0T79esXrxcChQvsQwsCKsEGlviqPzyoxWLdpUsXi4CzzjrrnXfe8WglRYJAbAjY77r15mX87ezZs2+99ValKylbtqxTmxAzArii4frFjz/hprHyqH4QE4j/ygYcbjHcOC0pMQ+nG2Kg2bBhA7fr16/HEV7l4y1y9913I57gCBeow300LJJIOAKxcaDMaMXWmFKlSimI2XeDf2ci5sUn7YxgwqBXXnkloUAOHjwYr0EJUMQOGhxnrZeGDUQvvvhiWDeZeJEh/QQTgUAvcFjOqK+OLf/IDol7A1CRsI24RIkS1kfO7RVXXJH1cXFOIXbBmWee6ewf/11EoawPIT0IAt4IBHQ7Hz/Rb7311o033sgChM/vjDPO4Gt3fofxzWEZtX379goVKtAt/mBcJFh0oEDh4ItHHnmEWxTAlpbUgwbVCQ8YR1Wc2XRNjuzERsNibeLEiXBJSwGsq0lCEIgnAt6cJlNL8eMwQUx+6BD24FauXNkZBiUSmzEPBa6E+hbrjDkL0ggmdCsm3kx9b/08ryAucLDX6qUNnx++nkQ5S8lDYvVhbepDP4q+1jsMCp64ZqQCzU2I2ESYpZRMRAYVBILISljU6EVEy5YtU+gzzjpr2LBhyqCjOQIJwqCwdfizzz5zfUHNvUKqFcbsIUOG4I3iWl8yBYEkIBBEVtKoUSP93b7wwgtJQNl7CJZXGF8wEmuqVIIQzTjOs89QNSdyErdcSC5mTaKccTiG9xBSKggkGoHAsZKGDRvqNQUB04iNlmiII+yfvTbIR04rTP78+QmDgpJY+9FpPoIL3IQJE+JoTo6QVKkmCDgRCJyLGjYUlhXqa+TjxMChv8zUJrDjcKEPZlMfW3g1McRt5iJcIw9PZ6KvxW+VA9I5r0tnSkIQSCECwWIlbGnhswRumEiDBg3Mk6JS+AzMoTHcfvrpp/AIMtkpg3ubKtV8BF9VDMbszWOnj9lQ0oJAihFwCiqZmoMW85JLLlFw47Hu/2kStcByXcXYJIZe/z+4YFJ4TIo5WRKHh5V8/vnnDIiKYcyYMUkcOcahOGnUYiWoZonGGmN30kwQSCQCQfF2hY9guFEuocQQwU9U24MTCW9W+yaCCaswTDx0xDEXuMOgT8lqp9JeEEgAAoGQSvBJ+/7777VruT4xKwF4xrlLpBJtbyIevfCROOMr3cUPgUCwEoys+lwo/Mo5PiotRJL4PWXpSRBIOAKZz0rQgalT9RSWeIXqmGkJR1cGEAQCg0DmG4PxQB8+fLh6oG3atGETbWAerkxUEEgeApkvlbz55psIJgpRDtxzHmqZPLBlJEEgcxHIcKkEeypGEB4fBmDEE+eu/Mx9sjIzQSCpCGQyK8Fqoxy6QLRmzZocl5dUaGUwQSBICGTsAodjsZBBlE8abvLmbuAgPV+ZqyCQJAQylpVw6AzxOxSKmGzYBJwkRGUYQSCQCGQsKwnk05RJCwIpQyADdSXYa3CTv+eeexSoX331VdGiRVMGsAwsCAQDgQxkJTjI/9///Z8yALMZn/1vp512WjCepsxSEEgZAhm4wOEoPO1IMn78eA6mSBm6MrAgEBgEMo2V4NjaqVMn9fjq1q2biJM0A/NuyEQFgSgQyDRWMmLECGKdKgA4SVNYSRTvglQVBLKAQEaxEg4S37Rpk0KDk/c6duyYBWSkqSAgCESBQOawEg552Lhx44EDB5g9wY1QuMoO4CheBKkqCGQNgQxhJVu2bCG0svZJu+iiiwYMGJA1ZKS1ICAIRIFAhrCS995775VXXlHzzp07d9u2baPAIE2q4r+7atWqNCFWyAwcApnAStCP6LDPhEcbN25c586dM+9Jzp8/n/hvmTcvmVFmIJD2Lmp79uzBUqPPoJoxY8bVV1+dGc/GmkWXLl0ydWrWTOU2HRFIe6kE31bNR4iifO6552Zq3FbcdvPmzZuOL5nQnAQE6tevz/tfrly5hI7F7xmjaDupOVbasxKOxVTz4bTd2bNnc/ylOT1JCwJBQADb5bZt25DQ9+7dm7j5EkXshx9+YJSyZcuytc0aKI1ZCfYa4j8r6y+zIjpJjRo1rOnJrSAQBASeeuqppUuXJnqmK1eu5ExrRuEYKYSgOXPmmCOmMSt57rnnMAD/9ddfzIcTPJmbOTFJCwIBQWDBggUffvghk82RI8dDDz0U1awfffTRrl27Dh48OJJWnFddrVo1VZMDYZ599lmzVdqrXdVkypQpwwE35sQkLQgEBIHly5cr0x6nr0XiBoFkcfPNNytwvvjii3379p100klvvfUWOfAU7++oT58+n3766Zo1a1Rz82+6spLHH3986tSpzAQl6wknnIB7qzkrSQsCAUGA0Dz33ntvhJNlYYKaA79wdXSsbrVr166PP/6YW2QNtC14ZukiK8HZL/nz51+7di2b77GW8hkSGAhpiGppyUr++OMPdMhqaQNDtXCxJi+3gkCmIsDOVcyX6EHVBM855xzvmbIxDb3p33//bVajFaxB5TRu3Bi+gJzCCQ1mHTONq2S+fPn46PgAEVJKlCjRvHlzKoRsYDb2VRo+8uCDD44ePVpRde2112aq9ddXsAsxPkQATwhzRTNv3jx9wrQrtVOmTOGnd/fu3Xz/1atXV3XYTN+yZct3332XW2QNLte2OtP83MzK6cdKtm7dOnToUD2xkSNH6rQkBAFBwBuBvn377t+/v0KFCvwG65pYMG677TZihumcGBJpxkpAQUc2YrajRo3KlStXDNOWJoJAhiEwcODAU089NeykUKxs2LDhzjvvJMCgWZlDo8zbGNJpxkpYnim7F1NFMCMiiceiLgY4pIkgkKYIcIjt8ccf7038oUOHkEdQcyDae9eMoTSd/Eo4as90ZkWTnDNnzhjmLE0EgQAigGLlggsuWL16ddb5SJEiRTSAv/76q3ITTSephAOAgUPNAaNUoUKF9HwkIQgIAh4IYOghxqAZpIIviEs3wbKzYsUKfeudWLx4MSdeqjp33HHH5Zdfzv6VtGElM2fOJL6Roh7bOC7zl112mfeEpVQQEAQUAkTz+eCDDzQa5cuXJ6B6rVq1dA6hOeA1+tY7MWzYMGeFtGElOKSxm0hNgO2/JgrOWUmOICAIhEKAWKVjx47VxuBQ1TzyBw0apEuRStSRdemkK1HUYxJn85KeiSQEAUEgKgQIVWHxEVzvo928o0fk+DoVRDkNpBKcZ7CBYwaGenzk0RuJAVg/SEkIAtEiwAZfrelQbfF/xadepbHyKNbAXllT+gg7it9ZCdZfZm5GYRA+EvahSgVBwAMBGIf5QTlrqlL14+0sDZXj9wUOBuC7775bU9+uXTudloQgIAhEiAD7fVFqJPTwbF9LJcha5hKuZ8+ehFeIEDuplu4IsIDHrGDO4uKLL1ZbTtj60aNHD30Mo6rD7pLHHnvMrJ/xaabcvXt3tY8Eq0rFihVPPvlk11kTFZgLFYl2p3CtZmbihGLe6jSrHvb7qduGDRtyUMz/iv7ZvuPXCxHL3J6ENduvlCaQLnZhqkcFV03gMD7rGj9uTP769VUJnBIJtwOliOjOdS7nzPtsEskgRzt/AxEOr/z6JnrUSpUqqceBFRVvFD2crxc4559/Pi+NolsxXZWWvxmPADoytopY08TjmwWvykQBb5X+8ssvN9xwg5WZ8bf81upgPcQxSvR8CbmmT1BhuaTMwGpQ/7IS3iReDkXl6aefThRo2W6T6BfFP/2XKlWKoN8FChQwSYJTsLGVHPa5f/vtt2YRaaySkyZNsjIz/pZZv/DCC0oJgoCgI48kYuJ8j4ghal3Jfh8+yaNG0fKJ3xI1a9ZUhBYuXPj111/3G3lJo0ctcHhsb775ZtIG9clApqaMl4HIPZowc82PpN26dWtecV0atATCgvpYcD9L3Nz79++vRuFHneBp1kA+lUpwkycev6Ib9Y8K06Rug/kXjWPTpk2DNnd4hKvyj5fYdHno3bv3Sy+9pMICBg0iNd8GDRrgupm0ud9///1PPPGEPZzFWnxyi+FKEYqIG0xtq34QSirBSU/nBCrBeSt6Ozjy6csvv8z0OahE6+PxD+AImEBh4jrZhQsXzpo1i8OzXUvjksmikpMw+TC3b9/u7PB/IdicBanKIVwtQhpWLsVKihcvnipKUj4u2vhGjRqpbwYtI9HAUUKnnKrkE8CmeO2diSMmr4fSmqExadWqFUGPk09SYEckpABhlV3tRD5iJT/++OPnn39uialERiAKdgCfHF8IqgEzjiY8hd2crPsCyFCc51eCBnzW9Z0O4Nvihyn7hZUsWrQIM429+jp8j5oA1QmOBn7AKzk0ILHrk0qcmHBkSXLI8M8o+ERpNbwChHWff8gTSkAgO/+cL2syczigkAM1UJs5zXsmGU2aNMGZj7BpxMs38zMsPWTIEAKCAwWAhJoadrj77rvPivQbqnLG5BOYBz8jPR1YiWnE0fmSSBkCKWSoHOSDDO88DYzzBInU4nR2BCNUBikkOKFDv/jii6DhdOIM9WagsQ+OeRipBL9vEwr8sv7zn/8k9IlI51EhkJoFDqt9Vrnmj4x+Szj9FzUbc8BFDe29pTphhYzdK/OUBbBOZanROKjEa6+9xsZodXG8gKk6oQLmLY4+yTw0nG8wi1wLGW75EQrC3J1o+DMnqayEcLI4zHFh2rQOE2QbEjss0K4RN8FECu898rn0zzXchLZYtuknA7ySmAWaIO37rD4YbtWscYXWaLAjiXPY8G4093ey2MG6gcaafpDydOVMSmB6zJMnj5OV8Ca0b9+eHfGuk2X5AyZi33EFJxGZyWMlfPbI8M4XomDBgnweqF29pzdgwADn+zRhwgTOTPVu6NtSZA3OoLd2AyB3XHrppWx79SBbMRTnqY5YNOApHg3TtMiUXrEE87aYG3A6d+78+++/m1PjeG11GCUvGzvcEG/NUkknCIEksZJXX331ySeftPgIggZH+1AU4dxwsOvSpYuzE0ykRFqLsBM/VCPwNzQTodaaC7ec7BOh0I7h3Klm4sxXek4v8YSlLrtIQj2XOXPm4JmmgIJ7sjGHmhYbZenH8a+6B+3fqFrhVQXgulQSCUIgGazk+eefN2Vy9YD5+R0zZky0s2KJxIHBqgfzL+8Wrn7R9pb8+ggUvPf4g5vEq/T111/PYYNUiJwq/IBd/coJ6pEuDheE6UWswCORA9KcE+eZaq4BQ5k7d66qg2rJctxu06aNXu0i67Eb0EQYwPHHcfbvzIEL84C49FjOOpLjikACWQmeINUOXyz79XOtW7fup/9esb3urI1VB3Xq1NHdkmDDG6MR3AWdgutUU5uJDRvyUCqbNLPa571X01HK5miJ/Oabb2j+9ttvm93CphHsD2Nf7Y033oi2z0TXZz2iaOOvDtXDi2GOq14ec+8pCjJ+RVQdBDeWgeaUSTNlWICqoBaPwKvrYDtnODZzmaNYabrV8ZNxs6Z+MN0jLVgivI0/K0FvimdEyZIlVbBZ/Sx5FQiaEkfZm65QrTkd2zg5FS4DDT7hKUhS/fr1sxSrwML6jkBhET4n72p8A0DBSezO4wqBHdsQzs5RyTvew8Vcik2XlYgVOgAo8Gk2Vyi8QkRF12+OToAhJ+MyOhzZMmapOiieOBNXkwe86KRN5DEI4mvPu8FD0dV0AhitszKRpl1r6iaS0AjEmZXgpnndddfpZ68TiKk8Pz1qHBN0i1rO/PnSg6K8jCPnio1mPp7HH39ck6QSfNvQjGowtj49WuHhRs9OhsK4LBDWr1/v0TbRRYhdNWrUsKDgFqaAOsMcHdBcOQX8haPUqKlXPVZvMA7Fa8zeOGfbaWjnoTCKWY20k5Ug1+AaZ1WTW1cE4uPtStcsX/mLVozYIvoB84VzCCC3RDpw/dpVTRxekUhJo5lv1qyZyozq7yeffMIpQTTBqRz2oduy/ZwjCPlldmVwuloiEqzzkeQxqeCZqvvnFxIZGx9w9rbqzLgniPfJ0AQcY8Fvdo5iRcXcxt3Lqb0ya8Y3jcTEi8FSa/r06WbPeItg/udzfe6558zFCIJAhw4dqIlYgRpF7wxmxaH07r169dLh1MwOcWs00dZFhHFkITlx4kQEVZ1JIFh6btGihc7hBeYNJICbziGBkEtDtSPWzJe0jYArg4k2E07h/BnhZY1koY4lgldEkZX18Jz8alnmVXrGiqxigkY7r9jqz58/nxcaI7eF9V133YXUEFufMbRiUYM7n0WDumVjvvM3OYYhImmCgsN5TgCaCN6ZsOEj+P75jYlklEjqAD6PwASE5Yyp7nVKJaoy1KaFUj8SEBJXJw4LHH4irBUmD4DQeLwHYelGa8jxpfrpZp2VMCLmQ+fxffA1xJaw9GS9An4ulvlAzQ6fugQt8TxoxmyBRha5TCOsE7Vr10Yy8mgblyKGsPyVIYB1CsE14tJ/tJ3wCHgQGgQS6H3r16+vwqDA8kzhyKwGD4p2rKDVj52VICtysQ/C5CM8CRbqXMQ18IASux1tUaxYP91EnfVoFXkRMiqLYcgwZSVEZczSh6mOv4kHmZzXFAcq01zFu3gYjJxobUI5ZUY+qZhrYoaAR0OJJa+Rw4nTABKbKS0UPfy2K5DpnCH0BwlP4Za3JbU+Yxw+DRlc5ruBiYAFuIWPppyEenmYWqhZS36MrGTJkiW8GVzm82CdidQKm+DyQBY/6Kuuuko1N58WYqTlNe/RSdginjpkoK5n25smEk7HuJhO4huyAAdtjFP0bE6HNIqAw2AcjO+3GnbuzgqKDDbsoB3gq9B0ggxkI+SjkY3Ld0InSIX0yaVhZzgGZRRFhpO8ZOaoFwNK2DUaSgbR+JgJKk+ePJmGyaQ2jcaKmpXg4T5t2jSLf/PScLjOAw884DFzfrfRunGx/9V8QqT55HD6RkjxaB5zEZ8xEqw1Ir/SUMLxwzF3qxsSEMDZP8OhW/WD/VXTqRMcA2LZ6RU4/FzrOjEn6MSCmluGY9CY+0xcQ/aCOan1zknQW5q4OSat5+hYCQrF0qVLO7Hm3Lywv2lW9HDdCad7EYQxoRPGZ0Gd6qYHVQm+djYfxzw0W8WYFL5VVreXXXYZ+bG5nMVMTFQNURupUyBMyhHWYvA/1uPCRJg1nZh9kmag5GipNCWRJ5CC8bW1CPa+Nf1WIh8oCDUjZSUIwJjNnNtG+ETZCO+tCECypa1TkGEzHm3DqvHj8hiwHTIWV5kyZcx3pUqVKtAWrfjAeaO0uuaaa8yuWH6rIdLCEwFBHWoxG5tTwPDJvLhYwEYIO8AebtHCinjOD75Cw+crAhwRTATCplnmcFJEhOAEqlp4VoK4wc+vtu1rrFnUsFTBZcADL/waWLw4xWm2nH399ddhBRmPnmMuwqjhdKnA4QJiIqGHOuxR1lGLFRpAwZWOe5SR15w7JJkUOkhsyd4gAwW/6k5zFVDgt5IWCwGm73yx9RseKnH22WdH8qp4o5d5pWFYCUwEY61TasUXCEkERX0oRLDgoFUxfZZ5MAgm+Lkr1WyohknIZ1JObqK84zzEE3Qu+FlRzRKv8H/jlGzQSNPXi4dI1A+eCyzA/HiY6ZYtW0I9DvwArc3+qi08CCiwoIVq6J98vPicr4GJQKg0QOGwk3zTvn+gc6XEnZXwReEVwqV8VTWmCLH4PqNi8GAiBCuiod6mpdui0mc15EpE8jM3b97MRLgsazR7/A/Pe4Fpc1F7w1DT6rmQ4H3iW6IHtvMmn/5EjIhDJ9MxfdJxAFFo6DAo8AiVQzQDEw2s4P+gWaNGWnxgSKbMAuHCnEK0afYis7hLxINI0z5dWMno0aPRLVnIsgWrW7du3mFBEImHDh2qXVd1D1jdaItq1ocY4ZsEbaZrjCJbWRxYhTEjp4aIOjhNmOzGh1OLjSRUV84wKMToBgeuhx9+WD9WnWCNkEbOoOvWreOF1MRnJYGrgWwd1q+ZzUpYfVirErBGhg/rAo+4GMpNW7kS6iF9mMByYb1SgIBu3zWwCLuE4LZp8fMbG9RwE/YEWoC43rLWAwocamMbKCWtiK3tOpfYMs2ImSmZjn8GtVkJB0RYmLKXKaw+Hy09PlpWQ5bN+Edz4cHlnwm7UoJ8Ye5CtCZi3nJwr5+tvK6ziyFzzZo1bPw3J+5MoyFKjvUtBvo9msSXlaBJTPnuc4/JJrPoCCthGWzumMCYh6DB5WHMQ2PCTlMsiFzmq4bGjviaaDeTOZMsjsU01XzZuWs5QaJ1xlkTUZYKHnrZLBLgt+Y7duxQgGBuYz8kqi4eMRoiVgcqP00DefDSsg2HGXFZmmbzHY48XahQoeC8FR5v6RFWgq+Eho+Ph016Hs0oQsJ3nm6FIQDDarwi+ngTkLhSVAMmNyEifOLGSpee0Q3xZK3AIulCfCg60esxKS5L+64/hAgTaIsiDPgYipIMyHdnJewl9Z4bW9Sd7pIE12GZ4N0wXUpRD+nXSFhJujy1mOmcMmUK5kUuZFL93KNKENht2bJlMROQAQ2PjQovXRnfHhS0+hbxhCUA1rWYn4TuShKCQPIRYDnPxbj8RiKkaAL4wtn7jseQzgmVIOwO59VGqK4O1Ula58fISvA4IJAMe+fV5DmDghVjWgMRiniOAXcaR0NVlvx0RwA1n7lsh5UgbvBXzYsXnnMw0n2OCaI/RlaCrZTo3py3oMhyBv5NELnJ7xbHcGck6uSTISOmBAH0smYoxvfffx/rBBEqTNdN1M8poc1vg8bISpgGbl1EKvLbfIQeQSBxCKgfFV571kF6FHWOAreI6jozgInYWUkAwZIpCwIKAWUaV2niVAgsIHCMoCAICAKCQNYREFaSdQylB0FAEAiqVPLDDz8QSEVdopOX70AQyDoCAdWVoHXn3AwFHxEhidPjjM+UdXClB0EgOAjIAicbO7KsaEbm48chmi0nZo6kBQFBwImAsJJs7HC1AiyaMHGsH5EWzRxJCwKCgBMBYSVOTCRHEBAEokZAWEnUkEkDQUAQcCIgrMSJyVE5xLZwnnRzVA25EQQEAXFRC/sOELqFA27CVpMKgkDAERCpJOAvgExfEIgPAsJK4oOj9CIIBBwBYSUBfwFk+oJAfBAQVpKtfv36BA0PBefgwYPfe++9UKWSLwgIAgoBYSXZOFvDI+IegbNcT8ORF0gQEARMBISVmGi4pImjxeVSIFmCgCBgICCsJFuXLl2c56sbEElSEBAEwiMgrCQbB4k5zwwOj5zUEAQEAQMBYSUGGJIUBASBWBEQVpKNGAL6dAInjBxCSJBxZ77kCAKCgImAsJJsnE7ACWEmKGZ60qRJS5cuNXMkLQgIAk4EhJU4MTkqp3379lWrVj0qS24EAUHAgYCwEgckR2e89dZbK1asODpP7gQBQcBGQFiJjYh1zzmPxYoVszLlVhAQBCwEhJVYgNi3RYoUkQjSNihyLwg4EBBW4oBEMgQBQSB6BISVRI+ZtBAEBAEHAsJKHJBIhiAgCESPgLCSbKNHj/Y4vCJ6SKWFIBBEBISVZKtcubLHkVpBfClkzoJA9AgIK3HB7Pvvv/dwpXdpIFmCQOAREFbi8goQOW3//v0uBZIlCAgCIRAIOiu57rrrChcubIEzbNgwCTtgYSK3goA3AkFnJY0aNTrjjDO8MZJSQUAQCItAoFmJBFsM+35IBUEgQgSOjbBehlXLkSNHoUKF7rjjjlatWmXY1GQ6gkBKEAgoKylevPiWLVtSgrgMKghkJAKBXuBk5BOVSQkCKUFAWElKYJdBBYFMQyBGVkI81IsPX6+99lqmQSLzEQQEgegRiJGVHDx48IvDF/EKCeexbt266IeWFoKAIJA5CMTISjQAf/75JzGWy5Ur9/nnn2ceQznttNMw9OjJSkIQEARCIeBuwdm+fTtnbl9xxRWhmp1wwgmcREXpZ599tmrVqkOHDrEp7rzzzuvevTuZjRs3Pv3000O19X/+G2+8gdgFnQ0bNkTs8j/BQqEgkHoE2LemrkcffdSkpkyZMm+//fa/hSH/h+M88cQTJ598stl24cKFIRukQwFClppO27Zt04FeoVEQSD0C2TQJGzdurFu3rskRcL6oVavWpk2bdJ1QiQULFhxzzJG1UqVKlerVq/f333+Hqu/b/PHjxzPl3LlzCyvx7TMSwvyJwBFWAn07duy46KKLTG5COn/+/Kx3OKHOewJffvklW+B04A980jk+Bk0KF8sf77YpL1V0vvnmm3ny5NHTZwodOnRIOW1CgCCQFggcxUqgmM8emQJ5RH9RJJA4KlSosGbNmt9++81jVrR95plnzPjsNOR68skn+VY9Gqa2CLELmqET3qFnXapUqSZNmqSjYJVaMGX0wCJgsxIFBLF/mjVrhhpVf1oq0bJly61bt3qDNWbMmHz58lkN4SZTp071bpiSUgxPZ599tkUtCz0/876UACWDCgLeCLizEtXm/fff1wpI/bHddNNN/fr1w8Dh0S/c5N5779VNVOK4444bO3asR6vkF3300UcVK1a06GzatOm2bduST4yMKAikNQLZod76lszbxYsXb968ec+ePXAQnc9C4JprrkFm6d+/v860Eix2ZsyYsWjRooEDB+oiDD21a9fmtnfv3lWqVNH5SU7AB2+44QYGZcn29ddf69GrV69+zz33YNXmGC2dKQlBQBCICIFIGCF8gaNzLW1Crly5HnzwQW9twu7du/Fbw83EIoVoQ999910kQ8e3DtTCREqWLGnRg8kGOsOu3eJLjPQmCGQSAl4LHHOefIQYcRBD4CD6OyTqB4sdxBbv9c6BAwfwdjOVmvTAegfzszlEotPwNfzNLDLy5s1LQMZff/010aNL/4JAZiMQZoGjuYZO9O3bl2ULahSYi86cNGkSalrrK9WlKsGa6JdffsF7TeefdNJJU6ZMUbfoLPBS10VxTPD88KPjL7ZelDiqZ7x1L730UtI9e/a0vGniOLR0JQgECIEYOCVMpFu3biZGrH3QicBQvHtjBdGrVy/1DZvNSePBsXPnTu/msZWOGjUK8szhEKYGDBgQW2/SShAQBFwRiHSBYzXet2/fc88917p1a/MT5efdquZ6u3TpUqcjHP2gxfBeKLn25p2JU7/z5L2RI0d6t5JSQUAQiBaBGFmJGoZv0mQlLFieeuqpSChYv349IQpQl5jNSWPfiaR5hHUgBpKsIeCA6G4i7EGqCQKCQIQIRK0rMb9MvklcucghCtLatWtJwB3UCTLLli1D92Ft8zPbkt61axd/CZ7UtWtXdLqkzznnHAy0VrWobol4gA71wgsvpBV9qpOxWODUr1//9ddfJxNjDQucqPqUyoKAIBAWgSyxEt071uJLLrmElYvOIXHmmWei78QD3cx0TSPdTJgwAa3tJ598onfxuNb0yMSQRORnjsj64YcfzGp0Cx+ZPXu2mSlpQUAQiDMCEUovYasRovHqq6+2iEM6wMMVl42wzbNYAZmI2CLW6OoWhQ6cLov9S3NBQBDwRiA+Uon6aNkXh8GV9PDhw0nrD/uqq67CAZ+1D3YTnRmvRJ8+fVjFrF69mugquk+2I7JoUre33nqrud9X15GEICAIxBGBeLISTRYBB4j6gdpC55BAQ0EkIQKCmJlZSROriQUU+2gQOnQ/LGfmzJmDmsbVSKSrSUIQEATii0BCWAkk4o2GmaZGjRqoZjXF6EH4yAnaiGNYzLpPpCz0qVhnHnnkEaX0Vf3TOdeHH36I1sbbWU7TIwlBQBCIFwKJYiWKvnnz5nGYJml85JWNRuWjZ61Tpw7cJBKlrJ4qiyZ4x4YNG1DKwFBUPtaZ0qVLk77zzjtvu+02YSIaLkkIAslEILGsRM+EHbcIKYQs0TkkUGHg5UG4EIItmfmu6SVLlrRo0YJNgFbpjTfe+Morr1iZcisICALJRsBbKxvHUqSSTp06OacHH/n444+9B6KCU/dBYJEHHniAbr3bSqkgIAgkAYEkSSWKg6CIZR/gxIkTrTP9zj33XBYp48aNcz3yAiUu0VLMwCL0Rjw3nOIlsIiTNUuOIJASBJLKStQMCTrNBZskXix7efS0ixUrRhwTDtZB38HFpsFq1arhurp3796ffvpJV0O9go3m1MOXzpSEICAIpBaBFLASPWGsPIRi46/OIZEzZ86bb76ZoErNmzfH+dUsQmbBE/+bb77BUmPmS1oQEARSjkAqWQmTZ8cNKg9r8eIKCkwH/zcO+nItlUxBQBBILQIpZiVMnpjvKgDS008/bXm1UcqxEl26dCHB5ho2DaYWLBldEBAEQiGQelaiKSNyEpZd03UVxxMUtNdff72uIwlBQBDwJwI+YiUARGgC/EfUwebczp8/3+MIdH8CKlQJAsFEwF/6ywsuuKB8+fKYbFC7Tp48mY08wXwqMmtBIO0Q8JdUknbwCcGCgCCgEDgqfrKAIggIAoJAbAgIK4kNN2klCAgCRyEgrOQoOORGEBAEYkNAWElsuEkrQUAQOAoBYSVHwSE3goAgEBsC/jIGxzaHdG/FLiQiSzILnGhc90brCXJMqgqay1HH9erV0/lJS7D9cvr06a7DValSpUSJEq5FWc/EH9oMx6c6JCIfAbScnbOVnMjkxMQKe/gsfdIzZEO8sx/JiQ6BJAQykCG8ESCIpHpm7GMkyiRbokPVxxtY1Tz//PND1Ulc/uDBg3v06BHq9br22msfeughwtzFnYDRo0erw5WsoQkxwYjOYDcqcB+u0kOGDPEmBvcl+gTMDz74wLumlIZFIEun84XtXSpEgoBmJbzWbIwOddTGq6++quOzJJ+V3H///bly5bI+ZuctDCWSKUdYh20TnFrvfTAbhxkghpgdKlYCbRwyD68xi6y0Porg4YcftorkNloEhJVEi1j867Nq4I1XnyWBWqpXr+7kJu+88w6LGlWHvxwwFH86QvSIlERwfz5LPTpE8puvL/N0d4o4OtpJf4i+vbI5jYSgNKEGZXRdRJib77//XvelWQkVODhF51sJVoiacmElFjgx3IraVb+QKUvwa8/BPeoXkkdI8Cd+ik1qOJWdIwd37NihMjkIme3UZoWEpolux0JAB6lirUEcTG71NWvWrKJFi3KCKmRA/9y5c1FSODd5R04knbBs4Yw0QmSpVoULF0aRpEcksXXr1gIFCqhSImMREAtuEvkQ1OQ4R7hkVE2ksgcCwko8wEleEVqS9u3bq/H4kPg21BnMKocI2x07dlRpZBYOaU9a8Cf418qVK/UnBx/h7BFkKBZi+uIcVTgdP+xqJQL9xAMnXGbM8BGvt2bNmnqPOFP+9ttvOb1Aj0gC/fSMGTP0EByr5lSdIkk5QwLrJpKILwLCSuKLZ+y9XXnllYSkVO2/+OILYvGrNCIJh6XqfseMGWMF7tdFiUgQlWrEiBGqZxYviCcDBw50HYhDBfr37++qH3WtH3kmaldzeaUbIqo4z5bVpSQQW/r162fmSDpxCIgxOHHYRtcz8jxnoSJ1d+jQgR92Vg3I8HTBj7M+naNz584sJaLrN361hw4dymFDHv2hpEA7ax545FHZowjBBwRUBabMOfaulYECkgjc+eSTT65YscK1jmQmDwGemVz+QQBBXesCrZeAaHIqvHbSqOVI+ZIlS2oyfvzxx7BDayUOEsG7774btr5rBRxG1KDwkUimzAIQCYVgN6o3rXaFNXPKmusQZBLOQk9N1K6hUIo8X6QS/Tr5IoEWYNWqVRg4LWoaNWrELzDfhpWf0FvWVs4zzLxH1JRv27Zt586d3pXDlhYsWNC0W4Wqf9ZZZ+GTpvS+Zh34MmvDCRMmmJk6TVgcnZZE1hEQVpJ1DOPcAxbQsmXLElhf94uJh6OCIucj/JIsXryYv7oHjwTLB+2u4lENHhE5AR79JKjIyUfUQCwPd+/eHdugq1ev1tywUKFCWNli6ycgrYSV+O5B81OMAIJZRFOGOjaUslPXMRMvvfRSu3bttNnFLHKm8RlBweHMt3JQvno79Vv10/0Wkzw2Nc3QsZoNGzaMYw/SfV6Jo18sOInDNm49s1gYP3585N05t6tE3lZqbty4sVu3bihcNB8BE8IMk4NSRvAJhYBIJaGQSVn+zz//jGHVHB7nDgw6WHbMTI80XiooLCORSi6//PI2bdp4dJXuRVWrVh00aJDrLEDJyRpY0TRp0gQNrrMJ+3Q4s4m/kahvnM0zPkdYib8e8a5du7AscLypRdbs2bMHDBjQq1cv02HcqqNvqbNlyxZ965HADcR7h4tuyzlE6A5QGegc1wTb+Vzzo8rkS0aDg64HvS/KDu8ps0BTni/YjPCmswZC8VSjRg0rU91ytgGj7NmzxyxFoDP5yIknnsgRkTi/KQs3RTwgYSUmYjotrERD4YsExw+afATbBP7gfE68yn379sXJtWvXrpG4gaHXwEKBrB5qVpicUe6GKlX5fMNswFed8HMdiZiDA7tqmz9//giZlJMGbQzG5w11b6tWrZx1dA68QJ0V681xdH2dgMvgdKdvnQn0rER1YIMPkgiiCtIidUqXLo3bvndDZ1dByBFdiV+eMttb2QjLa6oI4hNq2bIl+1natm2rcviVRioxHepDkU7NiRMnoqlFTRjqYkMgw3Fhew7VT758+bR3XKg6Vr42G7HCYl+fVRrDLepPvfnI2RxbNbKSMz/rOeAzc+ZM+AhdsUrq3bu36hO2Tn7W+8+8HkQq8cUzxXaLuLFo0SJFDb/tCO1YDbjFM4KdfsOHD1dFHIeKWH7fffd50I340Lp1a1YHHnUQc4h+go0Zt3SPaoyFG676eIhXgqCBoKS5m9kQd37kf7XAYY0Wr7PQRo0axdeLScuSxeC5jz/+OPzX3Ilj0pOVNEfKYq8xfdh0b0Rs2bBhg76VxBEE+BmRK+UIqNW+eiqsTZCrTZLwjDB5BzuDzVJnGlbCx//YY48decyOFF8mHyH6AmdzK8fiNaxcYC7oblQ1lDLccpke/TizW50gXlEHX1gr3/UW3vHMM8+YJGMav/3223XlW2+91RJ5WHEQlEBV0N6u9OARZIDKznglcFhgWb58uR6LBCwSnqvowZpmFklaIyDxSjQUKUt8+umnLCXUm4o2hJCLTlL4zdfrc9QcfFrOOlYOv9v8foa60IBY9UPdEi6Afcl6dEUn6gzUKFwmB1FFqGCs740vEwZEKS4z7CEONZCZrxxVVYfqLw5yakT+4hNsFgEIylEYqOrBZCUNGjQwu7XSTlZiVYAMtg6oZQ4jMpA1Nat+kG+FlaT46S9dulTrC7FBsHzgN9lJE98Jm9a0T2eSo6hBEoKGptP8jK00WkkWVhb9yD66GqzNKg11S7Qnlld6yroHM4GFhTo4fZidKFYC70PH4STGrOnBStj7g8JbM0okQQaypEWzK0kLK0nlOwCD0II6elZLrnZShnaAd5pvKcmsRFGC/oX1QuXKlc2PWachiVLWYk6yY2Mlqh+8QtSU9UA6ccopp6BDcQ6HOgkpD8nFlSmb9dnZpHqztvPBRDB+64EQwdBYmQ0l7UQgO1kaMkkkHwG+PeUUjyherVq1sATwxa5ZswYJxXu/f9h+Yq6A0eT555+n+YsvvsheYUyq7L7llm+vYsWKrt2iT+nUqZPS3SKVsEJxrRYqE0WSMsTiNjJt2jSqYR7i80ZkIOHa6tlnn4UdwCCsdZlVGc0IbilkouHWZ90T5wG9jBpI1UeFjOBjtZVbCwFhJRYgfr/F7RXuw3Ij5YTOmTMHhw5EhsaNG4clBu6jwsTFwEp05+h3CNTIbdjY0bpJtAk80G644QbWVqohyhEoJ4KB6ykZ0Xae2fWFlWT28/XF7AjFRkxmRBgsu7fccotvdxgjoSNYYa/RqKEewrydtPCXetx0TIiLWjo+tTSjGVsSfAQFKkpZ3/IRham5hY8ctC24yaOdVReCGOqtNEM/WeQKK0kW0gEeh++QUM/4pxC/Nu1gwDFfX9jg4SZpN4XkECwLnOTgLKOkAQIscNDjWt5xJt3sDEbXi9nIW5trNglOWlhJcJ61zDQ8AqzFsP6Y9djHSCgp5avC+T5h90abbQOVFlYSqMctk40aARxe2ULpuh8n6r4yuoGwkox+vDI5QSBZCPw/l0PMxkVk5RQAAAAASUVORK5CYII=",
-            "text/plain": [
-              "<PIL.Image.Image image mode=RGB size=366x304>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "====================================================================================================\n"
-          ]
-        },
-        {
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",
-            "text/plain": [
-              "<PIL.Image.Image image mode=RGB size=266x173>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "====================================================================================================\n"
-          ]
-        },
-        {
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",
-            "text/plain": [
-              "<PIL.Image.Image image mode=RGB size=321x192>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "====================================================================================================\n"
-          ]
-        },
-        {
-          "data": {
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",
-            "text/plain": [
-              "<PIL.Image.Image image mode=RGB size=511x122>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "====================================================================================================\n"
-          ]
-        },
-        {
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",
-            "text/plain": [
-              "<PIL.Image.Image image mode=RGB size=510x114>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "====================================================================================================\n"
-          ]
-        },
-        {
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",
-            "text/plain": [
-              "<PIL.Image.Image image mode=RGB size=512x104>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "====================================================================================================\n"
-          ]
-        },
-        {
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",
-            "text/plain": [
-              "<PIL.Image.Image image mode=RGB size=512x121>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "====================================================================================================\n"
-          ]
-        },
-        {
-          "data": {
-            "image/jpeg": 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",
-            "text/plain": [
-              "<PIL.Image.Image image mode=RGB size=358x300>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "====================================================================================================\n"
-          ]
-        },
-        {
-          "data": {
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",
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-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "====================================================================================================\n"
-          ]
-        },
-        {
-          "data": {
-            "image/jpeg": 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",
-            "text/plain": [
-              "<PIL.Image.Image image mode=RGB size=319x190>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "====================================================================================================\n"
-          ]
-        },
-        {
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",
-            "text/plain": [
-              "<PIL.Image.Image image mode=RGB size=511x123>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "====================================================================================================\n"
-          ]
-        },
-        {
-          "data": {
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",
-            "text/plain": [
-              "<PIL.Image.Image image mode=RGB size=513x115>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "====================================================================================================\n"
-          ]
-        },
-        {
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",
-            "text/plain": [
-              "<PIL.Image.Image image mode=RGB size=509x102>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "====================================================================================================\n"
-          ]
-        },
-        {
-          "data": {
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",
-            "text/plain": [
-              "<PIL.Image.Image image mode=RGB size=356x302>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "====================================================================================================\n"
-          ]
-        },
-        {
-          "data": {
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",
-            "text/plain": [
-              "<PIL.Image.Image image mode=RGB size=265x172>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "====================================================================================================\n"
-          ]
-        },
-        {
-          "data": {
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",
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",
-            "text/plain": [
-              "<PIL.Image.Image image mode=RGB size=238x189>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "====================================================================================================\n"
-          ]
-        },
-        {
-          "data": {
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",
-            "text/plain": [
-              "<PIL.Image.Image image mode=RGB size=246x207>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "====================================================================================================\n"
-          ]
-        },
-        {
-          "data": {
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",
-            "text/plain": [
-              "<PIL.Image.Image image mode=RGB size=266x214>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "====================================================================================================\n"
-          ]
-        },
-        {
-          "data": {
-            "image/jpeg": 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",
-            "text/plain": [
-              "<PIL.Image.Image image mode=RGB size=453x218>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "====================================================================================================\n"
-          ]
-        },
-        {
-          "data": {
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5x7+Ghy30UTUQH4Bzzz33sHECDjY+vK3o67bA878/jz32mMyQeKjQbt988814PVFdHE/t6uFOo2LK+JvFPODDDz80gKOcJX5WEeZY9ifOns+KnwLrWRmGMWPGoHS3qzhpFEb4ODmZemkQCM5nyccvkA+gIGwSEZzP8jLgVMMX2oGCgPEYgWMAZz9FGDCSySrPLoy1DS2wo2iXDEcakx5Ye+qpp2x+SfO9AQd+Tz75pMMp2kPyg9NeVH/orJzC4bu057MAlXhtXVy9we67745G1AGtW7duZnRizmfj6g1EzmLE0LlzZxlgZLHTgV4miUBMOUvdO+64Q+A1iajJWZ4xom84IHDZrl07Xph48DI7s43DTXXmtkileFU8zT/jjDOC4BCNCPvQxBxh0CLaPGnBmAwlruj7XWZ7eH8Jywnk7BtvvIHcNCWT1M/ioRDEJ105S4s4lqFNN6RkVs7yicZEgR9mOkHSQ5Zjy9mDDz6YS8MgosSRFwnk7DnnnBMyWBAiQQNGFmLM0YqcnLJhcPjhh8vrZBLss/NEYfQSDqDQ4KNIcXjkEvutIhk0e60o9Jzq7LiOGzeuyOpeF8BEXbhGCMbkBSNU284nF3IWSWp/+rCix7iXcTL0iYMaZQjHgIYoJt3FzRQXY5S8NIvVrfRY3KbyWx6y+eGZhwceiSAx6MXEn8QMf7169dq0aWMKY4UjzwSJmHKWTCBiqchfTJ2oaOoG+/Iix9DP2tbe12Kzix9bW8xVk+QCE3Gcc0xFG0OsR00XSbZTUMUM5bgbMNb2lqBhE6chvkDGuT4ZsvFc4IV1IGJjgMaJsUJfyTTiXZnff/9d4sKgPDWQCrPmEqtBeWZw6MI5KwGbAG6eVeaz0o6Ut+ezwbt/6w2gae+995YuTQLTGUInmIZQkHEImAhiEsRtlD5STmD9d9ZZZ8m2Bs2iUUKLz45zkNaUe8lqRaCDWoCCcvYf2YUkEZyI4Y3nwMsljz4+ypAHDrbZc0w5e+655zpA0RFRvtgyyiqDmW2cYQUuhhji+YmQ5WFDoOCwSDAjtheK1SkvALWY2yKgbZBNF4gYenQ0vMVqP8eFcXgjyjPEO1afBM+ETX4p8EIVU9d5DoEd0GRplWNO0+wOoBhZfvHaGTZsGAyaR8I8DLhTmSpIVXJ4YeWBwc01Xjsmnx1+U5jXkLqoaI2M4vHDTllkI480KgtnOfW3nMVSJOZ+GQoyu2+2euXFKFI3ZFdMnMZ9Rag0nCCA3nnnncS1CuEue9zOkh/6GbDgh5GVLIFBZVBNgteJT65hBHWbGXU0M+w3xuSO/R8HKNqpWLEis5KY5Qswk5WaA4JhgQVT+tTy1eFzxYZYsAviM3E3/S6y3QL7gczxHfoJLw1fzqubGiVYd6HCdqQti1ecHVJrMF+1sDo1On2k3owZM+KRQfRRB8yYl82bN2euE68R8jEH4MV06uJry9frhhtucPK5RBFqf73+lrM0hOzHS9r5BdcmrFhNGWLLJiCruLeIzYoQt8klTAPa2+K2k7Py+F+MGjVKdiqFcvbH4+3D8J7Y8KIrQJlgE8xXkeFBWWlnOukLL7xQ+pIEM53gBqhTMe+XbDiAmB19w9CPxpnPfAbJQ/MIzjKREZTIkd3dDHaXqaaYnYEPwUyFYJNABEyfPj1TvZh2iF7EN97uCKUWvZOf2Y6y0RrvEaTam/P4/cfriO9rPFcXYZ+gtEUuCu3lplREzrKKkksngWJdqCpByrmdr0u2CBGsmAFiOmZoYDGO1SRp5nq2ujpfFJp+UZiwMcgO5uOPP25TQoQezNqYtDZr1szOL1aaZ4gZn3Pkmt0CC0BxZn/ggQckhjEeZSw+TEkMGETDYNfNS5phNVr4t956i9dYaOATRTQMLjkqDWWL5GcqwVnLJoyI/UShlGRhYbpgd4gHLFPdpdMO7td4rjP0duQQRtAYonDQCTt76bQfsy5eUkzwuYWSykSXJ80jxIPEusoOSB+zel4y77zzTjaNoBbpKQRgGoylP2tiyXESeF3KFjQ7KLynjo6URxFJ7dRyLtlVY6mBZhxB1LdvX3MXnSc6Lj7eTmFzyUqX7dy/b4nELZAEK3F2hxy6Udjz+U1BLZVxpghKYB+cbujEJhGXpNwv3nnm6NfR4kESA5xxxlNrkLVYzPOQMLdgoFNrs7i16ChokwBK++yzT3C5VtzG0yzP4afscTtqZWi79957P/jggzQbT7I69vbi/22eZ3SDUMWkIckWclMMISvOF4ZO/qJhS7zed2jjFER7Oe/cTXyJNpYvU3DrJXEtc/dfvUEypXNThp0lpvFBTPnS8inLi4clENP17bff7gg1JmK8Evn9ADC5dqhCu41kgeBiPYKZHVyGCXEvQTnlxUDJxYPOEGe2u8StsSTHJoG9CyHDJHAzy9cTRb9M8NGt2yShfEevwjQ2x08USlus6JxVBbNapop599Y1r959991nD58Bivcxj0944kfOuVuIctaQiAIUbT0/R72C/QSWg/xy8yyyo0Vf6Bbt9wFBZmjjG+sAmpdLNjGgxzYAMtQSQhDic/Ys8kpACT3ys63EIYZBNIglsBjPNnQszA0NjrqA7RQIRhZnmwDaxyODvnACdBQ7zCGgjaB3OaAhXheYJEKD881GlkFw7vW2eGDTLz8CNDuvHit378ztC1fOytNAVFwnOLnBPZ6lqlRMP8Hxvags7GE2aYQIMiX99jPbAqs/PkJBuxFCkeaAWiJaYK0VNIcAMdRnDGJmmU2nNTYoglE7UFMiiNNptsi6nG4t5wDaDxVaUQ5NKbJ6bgoQccaZ2EIqs2x2I3JjsIFCn76C+4EGseOPPz7vU+wUBsIDOQtX4I7dj/MOo0XKiA1vPNTY5A3uPxAxZ8iQIRhzxKuV93y2Yp25ErNvFoBZJYwgx86hIOatYMgYOIYvq72n0Dg7rhLBR0QeFiDo31NoLZkq7KLEFLIcqYfmJ5kWclYGEMTCX8AhgesHm7RZJYNtJfZF7U4ljR0Or14e10PpMO6HnIVDZmS8rgK6SaCyYWueneV0IIhZl5UdayinOxaYuOjELF9Qmfa5GIYFTJoACiVXNujEPuTffVULMl5X9vpzMJVOjSnUo0GgUCmk1lriWuijg4ZBPGCEekre8y1xF5m9ixYFrydrMP9Oot/gQSIAWGa7M63hiRP0wDYdo+vnKJpsdJqbNr2Rs8DBegFtKT/MKZijyUPAdi0O4PwwZ0sTNQxa2WmlKQm5bXphmUm/6befJnlJVoeLLTh9j2mqDRQBRLbgtAtawiSbSlCM4SAABw06E0MsQ0zved/NT0C8uWWAYlXEvNsAlSU5S0fEaDY+PvxlYY4hFx7DRVKYxwJoURhHpKqzjuSN4HVg3Fna8wyk/x2lBbb+aNDWV4AS/eKsZJ6llI0E8gig3bVPclboZnSxqw0OPz4bKT+7PFXYfKApswUTjxSbS9jW5cXIQfhNOQFQqJKDukjWAbjTpNwsazew2m233eRTZxKo0bEdzs3+ZMrEBysiBNkqxHEIfUvTpk2DBYqVwzcsJrZIExwoGAs8SnK2M1ksymMW5hGCWj6oQS873hQkI6cHFddP2u6Ixtlocd5l1MFEGuNW+kLc7iuPaS/lrMELg3NHJvK286rHc8dKjLIY/9uyAw9aZHfiioV/Fzly6KGH2nyR5rUhDmmCqIMJ+CJigNMal0jYIp1qErRZCLe6d+8e/EggRABKfo7fHVaucsskmJexbigEdjJLAxNwYnHEVBDhzpeaqGXvFF2W8yyBXlb3XTILS5KtFZA/mAN3kZd864wpNd7N6NqkPMLRxOskGkDwIyzFJMG5fny0Z86cSRATycSTlQkOGxeoESTT3wTqNl4GzCccAzX8BdB8AVdMiw6HXyb1xk0Il2sBHNd444qDMocIZE4V3y95xpivAZ0wwsyLs7ng1Hgq4oXBgedy1ySQFEz5ncxwXPKZmTp1KsKFt4bvtzCFMtd4WwAIB5dJfswEzjWcFMkt9kh5qKQMZm3Y/2Gdgtu0ZIYkkaQ8LuRiGNnhkRV8yZGSRRoGYM3jrFkYV54Y/PMKmeXUaMO4FaAISuI8u/gdJmPShNGiUxHXOHa6UiPGi1rI2ZgeqCBm6I/pYx3K+awzXrgIM8txngcusYN0pvxORZabjtsrtTDj4ckM8ZmtHusNnPFjCRx0ISMGh1NMLvHYY7yD0UaQsAhuKRa+BAtAZ+eKB50wIjzr8Zglyg9YmW0cebVQ2qSmoonXS2HmoyXE5U+4NgmRs4QRQXXg3I2CnGWwcGmLKWpZ4jDHD44mU1d8nYP2f9gYZMmAIUhDvnLCI2dBkNUx9lhIW/FpQRYw50KTix24/cMUzA49yZSWWvyws2E1lK/ByFm/AIWHD/zavoyIUbDiR/BjGysWd7ZBLrhtgapquL9G9ligVnJi3wHIddddZ8ow50UhY4valDdj7U69SPMRwmSNLzSPhI0AG1nMe8TWFYnME4Uuzi7Dk0YtTNSLXHR6AUViIkMlZ4VVAlkFp2z2GNtp3hnv3PiE0zQTnNaHQs3xsrfBsdN8jSiM2E2zUx+ro460oSDds2dPMTay45BhFYtY8ZHHdGhmdkKkOqxrbZQwjmTbg1/QuYtZLUuBdHr0q2445SxjYLbC7FGPl0bp7teYZZxaJmvsGcbDR/IJB5fxrn1pEFuCoAc2n2djYmHLWVZUvjCVcTpjBnGX50cSxPTJWTSyjPOYWoMlhfmQJWLajQZ5RLFAgPNgfqRyiC+OeQ0RQ4I7XQYHPlpM6Ew41EghI8yix7cVTSaf1QD+gVLGJADK3kN37uolCOD7QDj8SEERWjmLuZITlilS41pcZlGecFIGnxxMGm27nLZt25KDPSNbYbaWtrjt+14eDSyW1IYLbODE0gtXUUw1bO6InhxloGwoNC0IhFbOCoe4srCb6fyIZygFNCEI4PyGR4MYHWOJjE0xOcGIi1Ilagk8oDiJAOsLYw6I8pFdMhsENnaCloJ2geikWQGYH85dvIDB87WiA0Xp0LPK5CJohO9sj4YehGIxiJxlmczil510XQI70KFo4qvDaon9QFwYuBuMs+5UieYlHyT2A40tIKDxc+wCIwVL+OezkRrOjDCLopaZLE1hmhPz2MeM9OJ1I0gNIDIHTaNqdLzsvGYtg8QjWM0PuDLYrI9NqZz1cdRyRDMT2y5duuSoM9+6we/WRI1gym/7iRJVHatS37hRerOLgMrZ7OLrdesskAm56zULWSUeQ7dgXBWcx1TOZhV2HxsPrZzl+A1OpvRxSAqKZqwFC4qegiIGr+7gQZO4FOINVVB0KjF5RyC0cha3SKNkzDvE/hJA3Jnk3T38ZbNIypmfivEWH55ft/zY5KEioaecPVXiIqKULLJNLRApBEL7QLDdqY97mo8yMoV4rGk24nt11K+jR4/GusAwAiC4k6K5xnKWM1mxW8Lf1Hces0G/wmKjGlo5azOpaUUgZQTwrMWDI1idw30J7Ua+HbY4WCyyORMnTtSJjox+aOXs2LFjFy5cKHxqQhFIDQGs62Mat3FObYsWLWiTKa3MdlPrQmuFHoHQ+ikQ8J9wSqEfP2Uw2wgQzAwxSjwq8SEkvuqkSZP4awL94QhDtHikLWEi2BUIWiBkm0Jtv/ARCK2cRac2Z86cTz75pPDHQCkscAQ4X4PIDzJpZTns7H0Rxpf4D5wuXKlSJQRugbOj5OUegdDKWaYhGs4jteeJs2w5Ejy1umGtReR4CR4fk0fEa9DGK2bJiGR++eWXbBhGhNki2QytfrZIzrVAPAQwPSYGM3eZtbVu3TpeMc1XBBIgwGnhanwt+IRWzrILTIgg4VMTySOAr62Ziey88879+vVLvqKWVAQEAaIVa1gDQSO0cpbjb1GWCZ+aSB6BTp06SWjE5GtpSUVAEYiHQGjlLAe4ctBbPLY1PwEChEPUFV8CfLgFPqNGjcL1ix8HESYuHM27GL3xIEWT9yDXod0H6927N/GYgwxrTpEIcFYYh1xRDN+nZcuWJX+iZZEth6YAHgoXXXSRkSMqTWIOq36qbVhCO581x4PbrGo6SQQEup9++un+++9PslakiiFEVLwmHvHp06erP5hAFFo5y1lvalYiw5xagkXxoEGDUqurtRQBRUAQCK2cJQxzzZo1hU9NKAKKgCKQLwRCK2c5u+mLL77IF6zaryKgCCgCgkBo5SxOTevWrRM+NaEIKAKKQL4QCK2czRegYeqXD9X8+fPDxJHyogjkBQGVs3mB3Y9OV6xYQdgzP2hVKhWBAkZA5WwBD06+SWMjsVevXvmmQvtXBLxHQOWs90OoDCgCikCBI6BytsAHSMlTBBQB7xFQOev9ECoDioAiUOAIqJwt8AFS8hQBRcB7BFTOej+EyoAioAgUOAIqZwt8gJQ8RUAR8B4BlbPeD6EyoAgoAgWOgMrZAh8gJU8RUAS8R0DlrPdDqAwoAopAgSOgcrbAB0jJUwQUAe8RCK2c7dGjR+3atb0fH2VAEVAE/EcgtHL2kEMO2X777f0foDxwMGTIkN133z0PHWuXikBIEQitnE08XqVLl95qq60Sl4ns3d12261ChQqRZV8ZzyoC0Xz1QnveLc8Kk7KNGzfWrVs3+Nyccsopy5cvv/fee0ts+QULRDwH6DhqsF69ehHHQdkvFgIVK1Zs0qTJ4sWLq1Spgjx16u65557Tpk075phjBg8e7NwK/WUJPf439GOsDGYcgSlTpnDQp2mWKL1Vq1bNeBf+Njhu3LjmzZs3aNDAYYFj6gmzue+++/bv39+5FfpLlbOhH2JlMPMIqJzNPKahbjGi+tlQj6kypwgoAoWFgMrZwhoPH6nZvHnzddddhxWd/K655hoyfeRFaVYEsoGAq6vORh/aZrgRuP/++6+99lpb0T906FDMOQYMGFC2bNlw867cKQLJIKDz2WRQ0jJxEWAXaN68ebaQpSiXV1555Z133rlhw4a4NfWGIhAZBCI0n3322Wc/++yzgQMHlilTJjLjm3VGV69e/d1339HNqaeeakzofvjhB3acybn00ktXrlzJ3BbbuazToR0oAgWMQCTsDT788EPmVu+9996CBQuwnN11111vuOGGAh6UPJDWp0+fP/74o1mzZnXq1HnmmWdsCo477rjOnTvbOU76m2++mTFjxmGHHVajRg1u/fLLL2gMjKhFe7Bu3bqSJcO2bFJ7A+cZiHm5bNmyBx988LLLLot5N1KZIZezf/755/77779q1aqFCxfKuJYrVw47/L59+yJcJDPKiW7duk2YMGHTpk2VK1cuX748gtJGY4cddqhevTo5W2+99X/+8x/7Vrz0yJEjL7roIu6qnI0HUbjzzzzzzFmzZmEwu2jRIrPKefzxx/faa69wc52IO1RpofytWbPmgAMOwEFFmK9UqVKpUqXkEhHAXZ4GdsZDiUCSTLG6JxBEMkt7yoDYOeeck7hldLKyXABkxHfi8t7dhaPx48fLg4SG2jsWskQwgpU57HnnnWe/aAYovt/bbrvt0qVLUTRlqfdCbvZ/Wxah/J1xxhnyJjDABx544Ouvv961a1c8VSSfBLKDhW0oESgWU6z6bVhIAxSgmR8Ayl38JpmkxGt8/fr1d911lxSmesjkLF/lp59+WhgkoXJWHoa3337bRiZmuk2bNj/++KNUiUginHKWWSpvuBnmbbbZhjgGMpxIVdQFhx56qLmLnL3vvvvkbmQTTEL5CMmL0aVLF/vzA4CAJh7r119/fUygHnvssZtvvlka6dSp0++//x6zpKeZbKXyFXHm/ipnzWii37/44otl9FHKs/ThseFHZAPJJ3H11Vd7+gCkTHYI5eynn37aokULM66sWx999NEgOkgEGXjUtcOHDw+WiVrO119/LZh8/PHHQfYliBfwYrkRLLDzzjtLCyTYeAyW8TRn6tSpeOXvuOOONoMmrXLWjCnhYwSc888/n51n0ci98cYbqOxFiYdCb+bMmZ4+CamRHUI5++KLL8p4Ixpi4sJWj61YYOBjFotUZvJylmggqOFscEaPHs160PZKYB8Moy67jKfpX3/9FdawUZGHykmonDUjK3KWD1LMoUerIEsBJjqePg+pkR1y+1n7zbdfD/bQMWCyczSdGAEUr1Jgl112sTW2Y8eOZTNt7dq1FOBFIsL6k08+iXECWx9SxccEuhQ2dho3boyotennobLRsG9FNs3UdZ999jHsE7+YuIhBKJjNSGa/fv323ntvsJWccCdKhps9Y0Ifk0c22bFAMLcw//r5559jFotmJr4GX/3fX6NGjYwkdQBBjUthucXK+s033+Qz5ruQhc2jjjqKr4UjZFE1AsxOO+0kOMyfP1/SUU6wxEmefRT3xIZOvrzvJUM+n5V1SnCcUCEhEZ5//nluzZ0796qrrsJPP1gsmjn4JsRjnMmsbGswrUO1TXADKYzYnTRpklyyGXLCCSckGAUpWTgJbI/eeust6EG5xCLREFa/fv2mTZuSfuCBB7AyZmNQ4s+2atWKVTC6adkVKBxeckbJSy+9JFjlrFOPOgqznEWNGE9v4NEIFRSpTFcRrMcff7yhilkJcQxsClFWnnTSSZJj/BR8kbNIiptuuun7779HmAoLJLC0v+OOO4488kjJxM8F9YgxY+JjA8sNGzbE1ZCPChN/KRadBF4/GPBFh99ic5qaWrdgay1ZskSmFVh3Jabzgw8+kB1khHLiwqG/a++DxXyM2C9m293GAX0LgilmYZPpl5/C2WefHXQR3m677ThtxebapDFuCzKO0TG+3cHCoc+pVauWoDFq1KiY/LLWsb+4c+bMiVkslJlh089ixDd9+nQz5Cx+cT6R4Q8m2MBhkUg+S0JxYQoWi2AO4kZ+8m4Qf+fggw+20WC5wF68lDQJU56/5tIuX5hp9nD4nXXWWQ8//DAJQ6ShH5YJjoFmIEg5Mzi+TM7sFXOlgw46iB0h2kFeBGuFNYexDitrGeErbOhgfyNhUHA7SbC7hQhmhciGMjhiQsvWTUYADUcjxIVh9mF+w4YNAx/4wliHYBH8tXlk9fBPwb//o9xklYCym2siSxTyG8jj8eqrr7Jlx++RRx4xDwNzcOgfM2YM9OO9Hc+iCxGMYvrzzz+30SBNJA2sj2kQNQKrK6b8ToFQXrI3KG4syTDIiWrRCpsXvll6MvazcI1TijwQaj8LILbewPFTsON19ejRIxzPDFqjoPoeIXvBBRcUySDLoJ49e8rzkyDBSROssYps0PcCzN9Z2RgciG8Q06aY75asjaJmPxu2+azzxGM7wjzFyeTyiy++4DUL5mtOTAQ6dOggBrN4gjHbjVnMo8wXXnihY8eOjhksOvrLL7+cLa8EjLDbw04gUYwfeughp1jLli332GMPJ5PPOZrc0NswIUAxmja845r8ySefODhwiYN7MDMiOSGMi8jCjSmJMdhiFFmh4K2Prz0BULhkHcddQqaazWKeD3yZatasSYGIDHlMNnHfwp+H6IjmLvvm7P8Ai9hsTZw48bTTTjPnI+y3336oINFoV6tWLWZrhZyJvhVzAoIKYfYrdMIv5gSs9DGYlUwnQWS/d955BznrSFjkMloCCgML8zjMac0zZlcnTiDL6tatW3fv3t3OD1MaJQkPjOEIozcM3dC9mAksvu/sJfLXfNi4O2LEiHr16oWJ/SJ48X3BEpN+RIaYNxr+MSznNeAnXismn+eA5UzMRqKTiQtyTOUa7jo2CBLfwEDXpEkTxK5doMDTrN95ABx9K/ZYbF4hIxITzzo35keFfOaqTl1ynnjiieCLh2sMqyuncGgueRgwKxauebMIG7/lndsPxiWfhMaRCc2g/8VLxRaN+ZzaY+yki7T9Cg8icTjBXyMYLdSg5MhZLJZs9MCW6UmcVgsrm08ppr4y2xIucAjEgiIxrWgeX375ZTNjlYok0OSyEA4KWdMa+cxtESjOB4x2UFmELFakAMhGIkoVkLGBctJHH310BGc2ofVTYPL1/vvvY2TDlrEZadaJ7JUjU+y47jEnKc6TEe5L9AM2IDazzsqOCJP4pEsB5Cw/uSzMBAYPWJWgdX3llVeEQs7XMUfsYAIY3AqTYt9++y0PD15hOOBKJt8eIzpR5vbu3VvynQSPGQqrIUOGYNaCAQZilzBylEHEoBfGe8oYbjPRc4KcOe34dQkyKKCY2KKSlvdOWDDQ4VlnzFckPwqJEOpn4w3b3XffjW6OMSYRr4zmhwYB5ows6lmv2HHH4Q69AXOuItXxPCpYVrBfagPCXMyEyLEzk0mzumIXnl0B5+Cfdu3aYdWP7iKZRjwqg/cK5iuIWnab+aTxMUO8EilRIop4xEtGSI2QnM0IXtqIFwggW1m+3H777Ta1vO1s6+FZgPmEne+kqYiekdgX6G3tW7i90KwdQca+m0yaXXj2YNlMswtzkgXdOVpju4C/aXQ1LCMIjMfkxkSH8JeXdCkX3YomFIFwIHDbbbcR6iX4YkyePLlIBlGqOicb0Q57p7gzZOS0FWZ5NGXOtRQK2SxCNRFWpW2RmEehgM5n5WnXREgQQFYS5IXJI+pjNIYXXnghs0h4YyoaU6HMThcyDtMrY7aF7YEBgrr8vvzyS3T9mdXj44fGD+c607Xpzihq0fkSmqeQnehC8pTkmI0ofEyUx6ghgGaA+I2cAscGFLIsAfvYut56661sW9kiGCtaqmPyGc+cIEGDyd+icZSzzvsOGTfeeCNUJd9O+iWxE2C3kF80D6NNH8AiW9D5rPOc62WEEMBgi+mqfXogzOP5xnb5ueeemwMgpkyZcs8997BZ5ATJRvSzOca2Ww5ooAt8K4xtCZGVQuxJkRswY/YSWruumNxqpiIgCOAHiBGSiR1jMplLXnHFFUSbJXaXFMtqAh9FfsQJ4+AP/OuYFpnuOAcIlQV2EVg3Z5UAGked/dNPP2W7l4i3r/PZiD8AUWSfiG5PP/008YZYJgv/l112GdbBJ598cl50o0hYvFSx/RJ6SHDQFja8dk420tjwymxa57PZQJg2dT6bJWC12QJFgKU6OgHn1C/2yji4KI/WnUyliTKO5VkC34cCBVTJSgIBlbNJgKRF8oQAxzviQBWzc2ad+KfFvBXMxGAeF2GcBTjZjN0nEw2HYqhiEXDMYYmxm/dwqBCAzUO3bt3wXiNmDeThURbkRXJwbsRMQi7jJRIABSBEz1m+fLnUBSjwQWUhOZrIDAJF7pRpAUUgLwjg8JpgTwZDKzxZkySsa9euwbcFq9gcb+snSW0yxYirjR45yFQwB7/qefPmBdtctGhRTKe42bNnBwtrTpoIhDz+bPCx0xwvEGBdP2DAACLpxaP2t99+I/q448Yar3Awn3kcrkoJghsEqxRUDjZnhG5IhiR0r5ybSXmnMBHFTOxQbNpOPfVUcxc/YCe2llNLL1NDQOVsarhprewiQBBhohM4fRC/tW3btpLJnI7tLLlMMoHzFVarGBt4fVIRpznIEaJFMs5mmkQWDhYmFgHxbkw+Cwj7RMVgYc1JDQG1N0gNN62VRQQwA+AQFM7OMX0gI4xM4S/aRoyQCMvNfJa7hDpEYhJ5wJSM9xeJbMpTAIVDgwYN4pX0KJ8FPsfntG/fXmgmxo0dfuGSSy4hGo65C1DEIz/xxBPNJflopYnkgPUuRmxEvDT2s0R7wQyDv9KmJjKCgCq8MwKjNpJJBNiKESHL8hapytpWOkAvydk5yFx2gQjozswXj6/ExlgI1nDIVgGBxL777ovSkJPecXsz+cTVtkNZEkWBXT7KcBeg+Jli5KD7RshyOWjQIHzn0FObW4RfYFuMAlQ0Ofo3Iwio3iAjMGoj2UKgSpUqtpA13chhZVxikhXZo94QiM7xw/YwoH6NqQSwd8CoTlwFOwAxp5wRZ8tuR9PpI6ByNn0MtYVcI8BsS84lMvO1XFNQGP1hrMbCPx4t7777LoZxwbuExZGzabkLgDaGdjpYV3NSQ0D1BqnhprWyiAD+polbR0uAWlaO2sRvFVeuihUrJq4VvrucgnPLLbe89tprhjUCadsz/XHjxqHADXKNpdeDDz7Yp0+fN954I3jXuEsE8zUnHQR0Hywd9LRuVhBgwiXuCbhvxQyngkoRx3ycuAwFKBy9th9IGUcU2c7RojGbIooCVhZ2NHGMEFBzU/j000+3/Y/ZXkumwZi9aGY8BHQ+Gw8ZzS9oBAjRjyaxoEksGOKY/hMqwRaykMaOmdk0c45NZFuM03oSnK9eMGz5RIjKWZ9GKyK0slFudsMT8IuxgUQ/SVAsUreY0SNSiSBucw2YmGQE9xLtMpLG01f1s4JGBhMqZzMIpjaVGQRYutp6xpiNIk1OOukkcwvXA2dSFrNK6DPxjkPUcuCYfbIZe4aJweFkdTEwIGjZ4YcfHnqgcs+gytncY649ZhiBkSNHJg65kuH+Crg5DkabNGkS50VyBo9xUiBYDP4Lp5xySjyqU/Cpi9eU5sdDQO264iGj+QWBAF5MwU1zHBOYeRUEfQVDBLFrzU5g7dq1CT+GqG3cuDHUscdFOPNevXpxgm/BEBs5QlTORm7IC59hJmUS3ACjJfyd7FMPoJ/d8/HjxxtG8GiK7JnVzFWJo2hw4AweO7IMp/aKDy46Fgy5jHVB4Y9+KClUORvKYfWbKTZt9thjD+GBsH4EkSKUFGpEfph5cTS32a5B88imeR7jcwuRuU8QKxYrAmwDTNec8cWRYhwfifA1ORwbYc7QNZcDBw7ko6XbXLkfKXpUOZsX2LXTIhDAhBb//Ro1aphyiFcc+fHB5cfEzQgLbI+INZWzs7yKoDjntw888ECshu1ukbwE2enbt6/JBMMmTZpIpAIsjgk641gj2NU1nT0EVM5mD1ttOXUEmJrNmjULpy97RmY3hxDBScE5qtYuEM003yHO+BLeJ0+eLLFlJVMTuUdA7Q1yj7n2mCwCROpi6sqKmGNo7TNasBJlq6dfv37JNhSNckxdBw8ebPNKDgEQ0Kvwt2PHjq1atVIHBBufnKXV7zZnUGtHqSOA7ZHETkXC4hiKulZWxKm363NNopShKLA5ABD2DPkI2ZmkV6xY8dFHHxEZElME55Ze5gYBlbO5wVl7SQsBDLmWLFlimmBPzN/zZtJCQSt7i4DKWW+HTglXBBQBTxBwlxiekK1kKgKKgCLgDQIqZ70ZKiVUEVAEPEVA5aynA6dkKwKKgDcIqJz1ZqiUUEVAEfAUAZWzng6ckq0IKALeIKBy1puhUkIVAUXAUwRUzno6cEq2IqAIeIOAyllvhkoJVQQUAU8RUDnr6cAp2YqAIuANAipnvRkqJVQRUAQ8RUDlrKcDp2QrAoqANwionPVmqJRQRUAR8BQBlbOeDpySrQgoAt4goHLWm6FSQhUBRcBTBFTOejpwSrYioAh4g4DKWW+GSglVBBQBTxFQOevpwCnZioAi4A0CKme9GSolVBFQBDxFQOWspwOnZCsCioA3CKic9WaolFBFQBHwFAGVs54OnJKtCCgC3iCgctaboVJCFQFFwFMEVM56OnBKtiKgCHiDgMpZb4ZKCVUEFAFPEVA56+nAKdmKgCLgDQIqZ70ZKiVUEVAEPEVA5aynA6dkKwKKgDcIqJz1ZqiUUEVAEfAUAZWzng6ckq0IKALeIKBy1puhUkIVAUXAUwRUzno6cEq2IqAIeIOAyllvhkoJVQQUAU8RUDnr6cAp2YqAIuANAipnvRkqJVQRUAQ8RUDlrKcDp2QrAoqANwionPVmqJRQRUAR8BSB0p7SrWQrAkEENm3a9Oeff5YtW7Z06bgP9saNG9evXx+sKzmlSpUqV66cXDqJxo0bf/jhhxUqVHDy9VIRSICAzmcTgKO3fEJgw4YN9913X6VKlW6++eZ169bFJB0pPHz4cMok+LVq1erTTz9dtmxZsIXvvvtu8eLFDRo0WLBgQfCu5igC8RBQORsPGc33CYHNmzePGTOmb9++ED1lypQlS5bEpP7rr78eOHBgzFuSOXv27M6dO8+cOVNyJPHYY4+tXbt20aJFxx133Ny5cyVfE4pAYgRUzibGR+/6gQDagIsvvtjQevbZZ++6664x6a5Ro8aJJ54Y85Zk1qlTZ+TIkUceeaTkSOKaa66pXLkylx9//PHEiRMlXxOKQGIE4qqxElfTu4pAQSHQo0ePv/76q0iSdthhhzvuuIPCpuQJJ5yAtsGkhwwZ0qxZM9LbbrttixYtTKb+VQQygoDK2YzAqI3kEwGUBm+++SYUlChRol+/fl26dElATc0tP1OALS8p2bx586OOOkou4yWkyl133dWkSRP6otN4hTVfETAIqN5AnwTvEWjbtu2vv/4KGyz2ma6yx5U9lubPn28E66pVq7p27Tpnzpzs9aUthwYBlbOhGcroMoI5l2EeCViyZHYfadtijHl0MsqK6A6Mcv4PAtl9KP/pRf8rAtlC4NVXX8UAIM3WDznkkN122y3NRrS6IhAPAZWz8ZDRfA8QQMj279/fWLNWq1ZNTA6KS3r79u3r16+fTC3ms8OGDZOS1157LZZecqkJRSAmAipnY8KimX4g8Pnnn3/11VeGVtSy7dq1S43uUaNGTZs2LZm66CWwUpCSkydPFosFydSEIuAgoHLWAUQvo4gAfg2rV6+OIufKc04QUDmbE5i1kxAhQPyE3XffPUQMKStZR0DlbNYh1g5ChsBOO+00YsSIkDGl7GQVAZWzWYVXGw8hAoSYeeCBB0LImLKUNQRUzmYNWm3YHwR69+69//77J0kvHgrPPfdckoW1mCIAAipn9TGIFgI4F4hfg3Beu3ZtzBUSx6WVwppQBIqLgMrZ4iKm5QsIgapVq1apUiV5gpCkOOZiZvvNlp94c1199dWE7kbUYiiWvtdD8vRoyYggoHI2IgMdTjbPPPPMbt26JckbsROJUYuQxVoWrwR+hP02dRG4zHMp0KhRI6IWJNkgxQitUKZMmeTLa8loIqByNprjHh6uO3TowKofflasWEEc7gSMIUk5ECFBgWRuoXOwjQ1uvfXWihUrJlNRy0QZAZWzUR79MPBOMMNatWrBycqVK5988skELG211VbXX389froJyhR5Czk7evToIotpAUXARkDjz9poaNp7BNAAJAgIizJ36NCh5ngbWN17771FdcDZYm3atCGzfPnySaJw0003oWdIsrAWizICKmejPPoh4Z0TEAg7gFrglVdeufTSS5GkCWTl1lt+hvPly5cLBMx2k4mpuHTpUlMFdUHdunUTnIwrLWtCESghW66KhSLgLwLVq1c3ob5h4aGHHmJ/LEu8cPLNb7/9RuODBw9GC5GlXrTZkCGg+tmQDWhE2TnrrLNywPmECRPMieXMZFu2bJmDHrWLcCCgcjYc4xh1Lphayuzy0UcfNRFpMw4Ktrdr1qyh2YYNGx599NEZb18bDCsCqjcI68hGji98EDju27DduHHjbJhb4cVgwifiH9GgQYPIQawMp4qAytlUkdN6ioAioAgkh8D/B7uRKSWCLbSWAAAAAElFTkSuQmCC",
-            "text/plain": [
-              "<PIL.Image.Image image mode=RGB size=461x265>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "====================================================================================================\n"
-          ]
-        },
-        {
-          "data": {
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",
-            "text/plain": [
-              "<PIL.Image.Image image mode=RGB size=391x231>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "====================================================================================================\n"
-          ]
-        },
-        {
-          "data": {
-            "image/jpeg": 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",
-            "text/plain": [
-              "<PIL.Image.Image image mode=RGB size=462x251>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "====================================================================================================\n"
-          ]
-        },
-        {
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",
-            "text/plain": [
-              "<PIL.Image.Image image mode=RGB size=236x263>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "====================================================================================================\n"
-          ]
-        },
-        {
-          "data": {
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",
-            "text/plain": [
-              "<PIL.Image.Image image mode=RGB size=265x219>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "====================================================================================================\n"
-          ]
-        },
-        {
-          "data": {
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CmNkiabL0YmWNSysvQISpDiQNKNha7bTTThYKEjvssMNdd93FrcAqEch0pXDYjaZCkceFsWbNmqnUykGZgK23O5app7feemvCrN18883otjy1KlWqBJesX7++Jz9Kl6ybGC2XIr4ZX3zxReXKlU3mP/7xDxYRfDB4PdxiUU0jyMeBFZtqQLA0ssNgJrCoxNOZH5oueyvCCfxMUGW0a9fOEz0Ltefll1+OFnTmzJmeyRNhNEJKWsDWG6V2oD8J/ssetzzefNZE1k4KLkB0NRZN//vf/1w4brnlFhyY3JzopS+++GL/a4+YkigALVu2hF4i0SGsRJOD9Vz0yLcUYT+LLJvL++67zzMNbJnPP/9855135hLPJdgHk40pZ+9GJmHfF76RHv0nU4L5gJkdcPEjNAYKT1YYvEr77rtvZBBwCUEEd9RRR7k5xaZZarRo0aLYYjkqYFat7tY7kcE5bpjoc3fbbTe3Z4kMzk2z8fk7duxYu5Q2W29D+5dffklod4MYHxLgcrVb0dt6+zWBLKaYNvbniiMMLOxFEOBGZqqwlcbSAwk1loDum2LSRLAGCryeWWbykfAUwJaQr2lkoHC33kyDVOhyt95YUPEVSaVWDsqU2DOH8B7u6IpRmkFq2LChhcVllNx1pdFMFzRgtmSUGCVkInr37EX4NhD63p3H+Gv59f5ElurVqxex/tySIU3jlgrVdojdBJyRsFuWLlbWsNSmTZu6ZVhsogi1ZUKdEKP8c2TTYZSIbC644IJQTwXb+SSMkrXD6aef/idkTioyjPLBBx8MDKw5ceJEv7KCfSjxBBwYtiRR9bDctpCGMUEsXhbIftJwW8ZEjGhbfqKIzkeoLbcKcipENOzN/YXDlRNrRon2ygolGd1SM0oW1QiqMCFCFWh+eCwV/jxAAsvWiQ4jgnBZQBJGCVGETvGsxM2LYRklzfbs2ZNmFy9eXPgg2B4SxtSMHRoJ+6qznurWrZtR1yRScGMjYQoQ7di1lEDTRYN77bUXAl/7lAJPMA1gggYH64Fn0OCSZTKUEp41CRXcReVlRTemLv5dxx57bKjZJaOM0sKQwygjiSYnCQ4A5XorFZSdTIm33tBpTamBIB1G6TGp41MMNChGk0CZx1tMWQLu2vjM+NikzijpNrzD8xaBnmWUWAuY+YQIj6egJcsjpSk+mnfbbyTLmP7zn/90kUneGiXhlYZ29y82Eji/Jq9bIHfRWbu8HioQrrEq5IfkOvVOsuOmCgzXxYG9CF6AqTeS35J0ldmLBNZ2A8tZl5zTTjvN3vInXn/9dbcw2jB/mXzllIZRnnPOOXZmlJpRQjBAuLiYNCIbwnzmC45Ez2VlhBrX7a2HUXKQBq+HKeCRUdo2MQpzWyDtZ5SmAIIqbP5txQJMwB385DArSrESpMqpp57qV3xjg8lbV4C02y6xkHziiSc83z9AwMDelilpgjCm7kKE+UAgW57icp+StpmD8nSPTnbp0oUOw+/sEz2MklFOJIFln27fIPMWYKOe+hfXPjFLiQDzINPLJH9hGUZCTxkOoWWD4FGFJ6nr3sJC+7rrriOH7QnKPnMLPxYElzj8wI49M8atm8v0o48+il1Uv3793Id6Qr/ss88+iUT4thaeS4ROYextjk0gk2rcuDEenyYHCxsKH3bYYVyyefG8irZWXhLI1AjMzDlIbnAXmBo/+uNR5qTSQ7acvGN8GGiW8swuY29orCwAAdaDhqQULafy9NKV4W1H/ApTAw23hTPOOAMDezOr3fzU06zH+Sp/9tlnGNWRoCIeCvz4EiOXQCjBI1JvLTclcehm+T9o0CDzuFGjRjVp0sRIYzjmAIc0lj7mFt9X3u7AKDk0gjjO7TB22cisPC+aWyCnacOAUzEPsqwagZrLFHhDMJ21d0uRgEEw7VzxBBAg1AOmUrSWwSoc39GhQwfPboi+YQTjkbYkl1GaLjEPPN9Mu6KkwOzZswEBtbhn+Nu3b4+jWwaJKnVTkAwaBx54oNtDFBF0G35R6mY9FY1FofsI0kQY8BTL4yVfzebNm3t6yCVxvIkUlZGO8TXl8+x/BGL9hx9+OCOPyFQjfBU8Jl/4QLsbCyY2/N1PSyo5tMyUKwSSt2y9+aSb3TQcMJEdpUUWRmlfeGrhwZoRcye+ojRrumFABF/iIfNdzf0KnIfOmjWrWrVq7nDSNwLHYdWB3taiYRKsB01J/LuTCJXeeOMNl0CXUZp2WFgxBG4ZmmXN5XqLeh6dm0sAMRFnXUDwucqGTJldhQcElpNsz/MyE1x46QDrI7/MAWSYFcY7yy2fTpp3ijYxpfJMBny9EH3Sk3QaT78uHRgzZgwbKVePx9ygt+7W2zyISeJZBrmzqNg0JLN4zy/JWxglncBio3r16ilKhbCjpjA/NgUZ5GKsWXBl2dxwdbPfBHeUPOy8MLLhl8FnJZorqCDZRzD87oaXLzm9QhSd6JOAqoc9F2WwJUzUMvn0n+M0DIH8Rc3nLwwIEyZMMGXsBwk2YRBI1AF/O+nn8CzzUP5alQvCWdv/zLIGt8OAwHtoHmQ23WYmcOqn6ZJbONtpvovmoe6+gdcb8096SBTF7OmmeTHxXzI4WIbCrOCl4FWlV9kbgkSosmrmc04H7OSkY4yR6SSGYoEvKdYypoD569Y1dLEocQuYdIGQDBRbGGUiUPKYj0TcjybSzHfeeSdLvWLaEUHPE3WcbyZCgCSLxCx1xjSLIN8Tv5LVhP+LnY0+MN3ZZ9iZahJ8P4ihl43HJWkTsZ0r6qEnvJZEeEVik6RWKrdgx4w4C7dEheFTFPCIp5GtMyX45WYgTN/4aCHasxsXOy6HHHIIPcyNVRnLI57lP8oGFc3JJ5+cCMPAfNQ+BkP716/jNiSzhLfEmgRvRM5Itp0vXEZJFwkZ4MGISxRHrDotARlJsGQg5hUvpOdxyKQJ65CRR5S6EfrmsVfnRUWmXOoGU6yIUsWz6cPSEw1DitUzWAyWjSqD5bxndDgnmlELtJHgeEucXortA59G2kThQDt+kyy+SZ6z4SjMSoq9V7EtZ6kAsmB668GBSzQ8gbuTTHWDVSQQIQfzPBpVG8qWrK5qk5BMl/jUpU4j7gxUSfRD7Z6kqYJmlAhDOXXHRjO0g8Qhn6hZklBV0lto2G3jNsFaEvuPkjaVjfK8AygKbMdIsDFBWkrUiWw8Di5M4x7ZE8p3V0KfjecmbxOmFniKBu8q2hVbFzMMOo+1NpITEuwE7S1/wjBKAyzCZc7ItGWQKXkk1BRDcYELpi2TlwQxYXkp9txzT3c+kGZNh6VENrrEp5qzbjyP4xLk01/Up9LhRCTTh65duyLGTd4Iu35mAr/kAcyA1BQLNOAtaEZp6Gfbi7kMP3dycDYTe2QThSU5TMnvEoqcdjw7O9S4PK6gLBnhlXTJsw2vXbs2nc/UUgKjHFrj53peMRfROPPovFsgMI5sMOkJP48dAi+ADXfocSSHtyaZAC6jhFIubWH3aHu2h+a5hWPMiGsGXeI0Abptf0gkzAimY91sESDBiSY06Anhwz6D0BU83f0+ubWylA4kGdr5otPJJCQH2mtb0PwJNvuYJ3qoCAGjtD3mg+95QyASFQpfPFsmxQSyJ958/GHYSblIcYkSn7sptpPjYphh4bzhWeuhT0SfUAoQ3M6Dhh9bbJX+/ve/Fw53sB2GLfLaoFYyY4cRiRUiexglA3rbbbfZip5EEkaJnAHy+fHaFMJHwtNzc8lEZflPJz3SfJDB0LXU3TZvB34T/reDZ2GTENiZ3GTSN9StCMo9JDMH2EwE9qGkjJJJxccAq0e3tTAxSvoNpyBENstJl7shScQAOPWFFVhzXIXbgkmjxEy+U3OBy2OaieI3TGNx7S6IUu8eyzTQ80voCz8UObZWdJKfuwH0MEpGFpbn7qldZJIwSrdY4adxZLCfDTuxedvZtJa087wdHDdvG7EJbHSGDRtW0tayV95DMnYp6PcCH+dhlMDCFsTMHPvXHwYUF0G3tdJ45ljscp9Aj8FbzcofMQ2eG0gT6AOzAbuNf/3rX8cddxybaKT+YBHYNxwD0NwhAHY3VpTE+IYPFN8QPLIDKxZUJgwd7T8cgciv1jOErVD37t2N2geJkifCgr//WLSw6CAf1bb7YiD/NVstDvbwSCT8jeQ3x/jDFdsHXm/QAC6sPostHNICrJrpOb5SrLWfe+45QwXvObIaE+WPxQTbjmKp48ASPpwegbh5O2gKV5liW8hZAUuyeSJzPtDhx98fhLnWbNzehZka1TGWDAR5svl/JlyuGa40NqiwjD8p2Zzi3cZtJpAQDBj9pgZwVV4htEaBVQo8k2lN5/1nSxhHyeSdxzTVAx0fIVqDBSevWOB3IcFDl7nEvtVv7RiZFaUdFFwYYAR+BBC6JbGCMtURzfs3Fngc+nGzjwtFwrOiTG5KZY0rPCvKkG29PQPDYhuzZM+0YA+C258nXixuNliTeEpiDwSInjZDd4mxlGfpxyV6/ESEIJkFH2PFbQFhDR52FmnoTcQoodQvbI0eowQEtlkeezIzymg8EnlYYpvMlPB7HLHIIpJeookUlnysNTBrs1M9joySocIqlS0kr71nu42KAy05sTYogHrOo8klGhiZCPvCMthJ+omZIdI6EHBBwDiGnTi08zOeEvwlTegB5PF20gAatfClBQ1TLMmDQnErCaP0RySIJKNkmDBsZECxb2Nw7ViTwEwCrsE0QBBJMf6SZqfpeTuohRSLFkqtDiq0qYKBusUhCaMEEOQMpmSkVpRmPHjD2R1waJdfMIfkEZbhUZChKA/pXjvR/DMIAAJLAKupZF0J7fymTp2KNTXWtqTdN4eSWAtTKxos0oCThFFiF+lZUkWVURoomOTQi4OGPQoUFrB5RpRnw8GU4FAzLt23A4h4O5gSMNBEky10+dDi2lEFKrjACkCIPmM3ZxFklHbk8N/w80r7JbGJRDpQ206oEwRgZ/ZbYhMlKMMxNaGmNLDzLqNE6opa00UAj3W3VrQZpaUUG0MXhERptD0EprG1IpPAdMyN9sSHwbrbYSuN1wY//Ag8sESZUTK0qUTGjDajBASszDyj7rlkV84CPDJvgkuIyyjhkhj94Q1tyUcM54pixSgtMiQ8Yn0X1bCn3a03lKIExjmVn8eW3kXDwyhDZh7kUuJPEzoU6YzJP+aYY0455RRbBlO7IUOG2MtoJ2CChIlkcuO++t///tclljOmOQYSxWg6Ya/cBgs2bUyCMIth3PkwYEpFVzGgQaLtPye2YKnISMdwDye6Nl+IjLQWgUbgEliGJCGEQAfeqOFh/1a4/beGVASX9RxV5poKRn5FaTFxl1dMC4IaBPqx2vIRSFiS2XdbcjjeBz2GeTFwQLKBYGOyogQHyxfwgkfHjZDKoIHRpZXKxWdFmYRFcotI+0bZZecPia2S1wnXXdy5iBZBnzEt9njvhIuQTPWW14NAbeZN4C9Wcn5DuUw9q9DaIf60PYcHRyYYhOkhLu0YThNHvdA6nJv+IJtGYIeJGKbp/LCvzM1z8/gU1NwYPyXvAG8H9sj8iDvVsWNH88q4VSK19SbEKYJblzx/GmtKV/PrLxClHLafrJ4QO6DpQ1TPYSZRoi45LawC4InYvpi1JAcS4COPRzy1MJGZN28eGl783pI3Er27xj8Hgwd+UIe2N3o0eihCe8M+ev78+SYfybWr6DeZ5kQTT0X3MlKMkmPb2EoYv0aXSDdtggi4OUpHFQHeDaLDEVbSEIjM2h5Eg8YfG9I77rgjqrQH0sVCCfeEwFsRzkTYgt6/bdu2hkY2Ex670VRoj9TWOxWC41YGCzKPJC5uCFh6iQ+AI6O9ZMmJ3Zy9VCKqCOBoQLCbNKmLHaNkKYErW5qohag6Wiz8f0PU4ex1lUhcnnAP2XuWWi4cBPAv8js6l7R70WSURCbnFKpALLAx9AekCiypzOghwJkzHOTgF9VHj9IUKcLIFL+sFAvHuVg0GSXSejR6geOKWlzvSSAy0ctEw2uV3YY6NBhst/v376+PpQGEWIVxex2S6zASvQXRZJSJqCWf+BH4fiYpoFuRQQALB7+XAXyBEHOc7hAZMtMhxK//Tae1UNQlxHUpFtHRZJQo+xMZiOGhgTVpKEZUncweAoQd9PgmcZChG1cpe4/OY8vorzicMo8dKIRHE2HTE2kslV5Fk1ESYAqfrUD6gSmVwBmBdZUZGQTQ6qAJdS1qCaiDvXFkCAwkBEZJWOvAWxHOJHgSZwhaAvHCEKO0aCixBQGUWv6IxRFGh8ONORANAjEvd4Nr+Ul2HTB4eZo1a+YvE7EcxA4xtKPE2gFXFHPSA1GCPGLrFIc4UgbnKdIcq2L4pTBR4kMyyjrCXuD1j08O4ZmTEA7XMCch46TQr18/46mSpHw0bqVyck40KHWpwPGfFQMWL3w+3W2EWyZ5WowyOT6hv/vVV1/Fym6UAUOjjcoulZFjr33U5l8qhVUm1Aj84bNZ9GzqEpETTRllEgg4qDYO/q0WAb6i+DXbSyWEgBAoBQKxY5SE4TKREUoBVhircIBafCIGhXGA8ttnTt1Cw5PfPoTi6dFklNgAmeOM/WOAwy9yCn9+VHM4Bh1f16hSJ7rSROCwww5Ls4WYVI8mozSn0AUOIYd2xsrIllNBxCgDZ4IyQaB169alsJWJIXTRZJQoPW+99dYYDqdIFgKlRoDwnRs2bCh19WhXjCaj5CMZNw/WRNOUQ2OI957orvJjjgBvij1AhcCdixYtijkgiciPJqNMRG0M8y+77DIOQogh4SI5FQRYTzRq1MiUvPDCC/fdd99UasWwjBhlDAddJAuBLQgQ9+Duu+8WHMUiIEZZLEQqIASEQNwREKOM+wwQ/UJACBSLgBhlsRCpgBAQAnFHQIwy7jNA9AsBIVAsAmKUxUKkAkJACMQdATHKuM8A0R8fBHDrJrR7fOjNIKVilBkEU00JgYJGAEY5c+bMgu5ioXYudozy66+/LsXRQoU6fOqXECgZAhwnVbIKKr0ZgdgxyuOPP54D0TX6QiCGCOCHM2PGjBgSnj7JsWOU6UOmFoSAEIgbArFjlAMGDIjVGTJxm9CiVwhkA4HYMUoC9yqWVDZmktoMKQKKR5nKwEWQUe60006es+0tEBwmRRRbxb63gCgRcwTKly//8ccfxxyEVMiPGqNs2bLlkiVLOLDUQ/yOO+54yCGHjBs3bunSpZE/596lnZPF1q5d6+YoLQRcBDi00r1UOhCBqB1X62eRhmy45GuvvRYIQYQz33777QsuuODzzz9n4cApYxGmVKQlR+Dggw/u1atXYEBr1g3conrdunWTNxLnu2W0D43w8Pfr1+/666+HQA6A18k5ER5okZZtBCK1orzooos4fzEQMlaUt912W+AtZQqBOCBArPu33nrLUHryySfzssSB6ozRyIoy1D/cbDine8SIEdWqVWODmQiXrbfemgJjxoyhcKjpLVHnOcTcALJgwYISVVThaCDAbOc3cuRIJj+vgH07sJAjZ/r06aYAdiDRoDd7VIR+RclHEvkLMwCM7DzwJzZu3Iga54wzzuDW7NmzmzZt6i8T4ZyqVatGmDqRFogAX8f69esbYzjP22H4Y5s2bUxFTvceP348rDOwHWWCQLi13gz/4Ycfzl9+7nDuvPPObC74eRiEKQljnTp1qlteaSEQMQQIfnHooYeuX7/ezPlA6swt/rK07NGjx1dffRVYTJkgEPoVpWcU8Wa96qqrOEzurLPO4hZbcqJgrFy5cujQobYk39hLL71U5mMWECWih8DAgQO/++47D129e/fGxHjhwoWjR4/23Jo4cSI5vCY1atTw3NIlCISYUd5yyy3vvPPOpk2b7EDecMMNjRs3PuGEE6yzwbnnnstdDAk5vJjNxeOPP24Kwz3vvPNO2KWtq4QQiAwC2AvPmjXLknPkkUf27NmTy3bt2lWsWHHZsmUdOnQwd7t167Zq1SqThleSfuaZZ7CRMDn6axEIMaNkczF58mRLSd++fS+//HLmgc2xie22265jx45IZNiJPP300+SvXr0aG0M2HZal2sJKCIGwIzB37lx3Obnnnnsy/y1RuK7ZywYNGtSrV49Da83dV1555aCDDpozZ44trIRBINwySjuKaPFq1aoVyCVtGZxzdt9993Lltnwb+Or279/f3lVCCEQDAVYD1hcLSdQxxxxz3333JSJtv/32++STT9y7X375pXuptEEgIowSieR5551X7KAOGTKET6gphl3RL7/8UmyVaBRwN2LRoEhUJEKAHTQCSnO3cuXKzz//vF0cBFapVKmSfSkCCygTBCLCKDWWyRGwW63kxXQ3hghUr1799ttvjyHhJSI5rIxy0qRJH330UYlIVWEhIASEQOkQCCujxM4cKwdDc8OGDVHjlI5+1RICQsBFAOO5Tp06uTlKg0BYGSXeimXLljVDSPgT9HopDue0adNsmDVMi6y+L8XqIS2GdB9d59lnn+1aU4WUFnU7qwggu3///fez+ogwNh5WRnndddcde+yxBvF169Z9//33KaKPwa01CSJMhrEWSrFueIthCLV8+fJRo0YRCkFhhMI7jup5vhAIK6N08eIDmLqhzwsvvGBXVXjC1qxZ020qYmkIJAxllSpVLF3Dhg3DLF+80gKihBBIBYEQG5ynQp6/DA6ORAQw+eiCDzzwQH+ZyOSctPk3fPjwRYsW4chk6MIliRAh7MS5JDgI5/dGhl4RIgSyhEDsGGWWcCzkZol3gMUoHhevvvrqoEGD6Oq9995rOoyVPutr2Gkh9199EwJ5RyAKW29AJNDkgw8+iCQuOaB9+vSxfghIKq2wMnmtCNzF5JiVI9HOv/jiCxx+LUWsNHGHR8GFCN9mKhFqBDwTu6QjyxE6iq3lnwAhZpSI3oh0Ypgdvtt45kyYMCGRFhu/LhQ+WBSx6wQFGMc555yDRsiPSIRzCHaw1157YYJKvHegM7vvFStWHHXUUbjDEygEiEAywgjEgbRTTz31mmuuMZQyuBy3x7CuWbMmEe28MhSzd3mhoi24t5SWLMEqLLw/vpYVKlRwCX7iiSdeeuml9957zxI1Y8YMcm666Sa3GFoOWyC2iU8//ZSgSrVr13aROfHEE+GYscUkGoQ/8sgjnii8LAswe/BTxxs0duxYdwIQashfTDl/BAYP78/PKM2QE48Sd1fzY63kzgPS2GByK7xUZ7bnzz77rMcKlRhc4MMaJLMPUmu5RIAPnmfawyuRULN+RN7C0sF0hugHHgEUq8tc9jMszwo3owTlu+++2zMhir2EdYZleHLTzylTpnhCwYNhly5d+A7lpgN6SsYR8DNKxhSeSADK9u3bs6OCb/Ij7Jb7vlx99dU2KHrGuxTqBkN/XC36XDbaTz311IABA9whT5R+7LHH6tSp07x580QF4pn/4YcfEsHQ2vADAi8VKPEXjsnh4PGEJbxUz58/n7Uhp0EYoXwqhCDZxHjOvwNLpW70y4SazdvO8xm85JJLUFZ49hF2/NDecBfNOIzV1lLCRYD1I2dmYGXpOcwSKfADDzzAls0trHQoEOAYHKa9R45vXwqbwBv4yiuv1FmMScY09FtvD22IouvWrWtnAAkCODdq1IjTQjwldZkIgVtvvdW/rEDkT7imRFWUX8gIsN/y6HbcF4RjbM8///xC7n8h9C30W293yE2aHcfFF19s8zkvyUbBsJlKJEcANx4MTmGOzFFbkjeKJTk26px8aTOVCAUCWIOgtQvsKlZibCMCbynTIhBBRmlpUyIdBNiJs7ScN2/eo48+6raDhJdzgE855RTW6W6+m+Y8IvZxrVu3djOVFgLhRUCMMrxjl4ueL126FOkVYYc8D2Ox6Y9ayPITZSs+kRigIAvGuN1Ty1zusssuI0eO9N+CI7PwIR/HgWLFav7qyhEC2UNAjDJ72EakZXx1UPLAAdGMW8cn9mtY8nM0FdozTrAypMIcUR2wlkxOOYq13XbbrVevXhwXTF2jf4PJonU1kTuInpn8nLjk7euuEMg4AmKUGYc0mg3CIom6hCX/4sWLDYWoSuFxbMBnz55NDo4fxN3AlzxF+mGR/IhUTwQjqjz55JOnnXYa+33SYpQpYqhiOUNgy1ogZ8/Tg0KKAGyR1SLCx8aNGxsSYJ0sIVetWmUOL8KSOXUuSQvwRKpzODt/uWRFabhkSPFRt6ONgBhltMc3w9RxMDoW+5glY3Rlml6wYMGZZ575+uuvBz6JJecVV1xB+SOOOCKwABvwJPEaAqsoUwjkHgFtvXOPeRSeOH36dFgkQkZjWcL2ma336aef/sMPPxjyiGFDPDc21507d+Yvxkb2bPFly5bBPS0KXbt2RbeDaTS2rpMnTybf3XrjoopsdMSIEUg2bRUlhECuEWDLo58QKB0CHhP0ffbZx07fJGFH0PYMHjzYlkRvw9OJgcgB0yYTu6K5c+fCfPlVqlSJzCZNmpDGXKl0/VQtIZAmAvpK2xdWiRIj0KBBgxLX2RwM1PJEWx1Zp1UTsZFv0aIF60p715wLiO85KngYq1aXFhklcoOAZJS5wVlP2YIAH3aCFbExt4j4uS1lXC5pS+IXxCGaHGhhc5QQArlBQCvK3OAcu6egH/ebqYMCunIOn3Dh4FxMLvfYYw/06R988IF7q1mzZmTCN7FFtzrx559/HjskGVq6QCmddQSYhfoJgYwg4MooU5y4KHBsAESCIdpaWKQjkWQPbjrGaT/2Folvv/02Ix1WI0IgRQS09XZfQKVzjcBZZ53l91bEYBP7c4yKWrVqZTpEeJtc90zPEwIOAmKUDhhKpoEAe2o+zik2gH0lEZ44nKdhw4b+KhwEyJFY/nzlCIF8ISAZZb6Qj9pzWfQRC6NYqjBZx8mnbdu2d9xxB/aVgeWx0PTkUwVFuVGLE1ODS08BXQqBrCIgRplVeGPU+PDhw19++WVOIDA0E40tMHoQJ7BXrlw5OS5+PsiRPmzGTdw2lqLovpO3oLtCILMIiFFmFk+1tgUBXL/79OmTDTj69u3LMT677rprNhpXm0IgEIHgvU9gUWUKASEgBOKJgBhlPMddVAsBIVACBMQoSwCWiuYGAfwUc/MgPUUIpIiAGGWKQKlY7hDwnKOZuwfrSUIgAQJilAmAUbYQEAJC4P8REKP8fyT0P3MIYC6uI20zB6dayj8CYpT5H4No9ODaa6/FBdvQwkE6BxxwQInoIhIwJpamyrhx40pUV4WFQLYREKPMNsJxaZ9o5zVr1iw1tQQE6tix49abf4ccckiSdsaPH49zTpICuiUEMo6AGGXGIY1pg1WqVOG8B+OQw2G2JUUB/+5BgwZx0CO/wBBqFMDVh59xgixp+yovBNJBQGfmpIOe6goBIRALBLSijMUwi0ghIATSQUCMMh30VFcICIFYICBGGYthFpFCQAikg4AYZTroqa4QEAKxQECMMhbDLCKFgBBIBwExynTQU10hIARigYAYZSyGWUQKASGQDgJilOmgp7pCQAjEAgExylgMs4gUAkIgHQTEKNNBT3WFgBCIBQJilLEYZhEpBIRAOgiIUaaDnuoKASEQCwTEKGMxzCJSCAiBdBAQo0wHPdUVAkIgFgiIUcZimEWkEBAC6SAgRpkOeqorBIRALBAQo4zFMItIISAE0kFAjDId9FQ3rAgMHTp0xYoVYe29+p1zBMQocw65HphXBCZPnty6dWvOjGzXrt0vv/yS177o4aFBQGfmhGao1NE0Efj9999nzZp11FFHrVmzxjRVr169OXPmpNmsqscBgXJxIFI0CgEQePvtt1u1agW7tGho922hUCI5Atp6J8dHd6ODQJs2bVwumQphv/7665QpU1IpqTLRRkArymiPr6jbgsB9991XUonkQw89tHDhwmHDhvXu3ZtWTjrppPr16wvQeCIgGWU8xz12VNepU+ezzz7zkF29evXvv//ek8nlhAkTRo8ePX369JUrV9q7LVq02H333bfddtuxY8faTCVigoBWlDEZ6PiSyXb7ggsuWLBgQYoQwB/PPffc5cuXe8q/s/m31VZboQt65plnPHd1GW0ExCijPb5xp279+vU33HDD/fff/9tvv4FF+fLlK1as+OOPPybCZdGiRUceeWSSTTrtfP3114mqKz+qCIhRRnVkRdcfCEycOHHAgAEGC7hkt27d2EF37949EB3Wnm+++abLJbG4LFfuj3fkq80/U4sV5dy5c+vWrRvYiDIjiYC03pEcVhH1BwJsn12d9aWXXopmJgk0sMhOnTrZAqSnTp36yuYfFS+55JKqVatyd/78+bfccostpkQcENCKMg6jHEcaN2zY0LNnz/Hjx1vir7/+epsuNtGlS5dBgwahujElceP55ptvSmpdVOxTVCAsCGhFGZaRUj9LhsCmTZtcLjlu3LgKFSokb4JdNvpuyhguWa1aNVN+2rRpLVu2vOqqq2SgnhzACN/VijLCgxtf0n7++WfcEw39iCYffvjhjh07orD2IMIKEW3PNttsY/LLlCnTvn17DIPOOeecSZMm2cIbN25cu3atvVQihgiIUcZw0KNP8sEHH2x106huGjVqhPrFkP3tt99a+pcsWXLooYeOGjWqRo0ayB/hmy+//HLnzp0pAAO1xZQQAjI41xyIIAJNmjT54IMPUids8ODBuN+wW2d1aQyJPHUbNmy4evVqHHVM/hlnnMHC01NGlxFGQCvKCA9uTEl76qmnFi9eXCLin3vuueOPP37XXXe1tTp06NC0aVN7iZ/4smXL0A798MMPNlOJ+CAgRhmfsY4LpXvvvTdW5SWitnbt2jvssIOtcvLJJ9955501a9a0OSaB3lyM0oNJTC7FKGMy0DEik333q6++umrVqsaNG/vJRhDpbq6RZo4ZMwYBZaVKldh6m/KwWty63bpUueiii+bNm+dmKh0fBMQo4zPWMaJ0t80/dN9+mlHd9OjRw+QTFGPGjBnG98YtOXDgwF122eXUU0+1mXfddRfxhyyHXbdu3U8//eQuQm1JJSKJgBhlJIdVRP2BAIZBfiDKli3rZvq5JHdhiLjx8HNLumnMLeGS8FNra+neVTp6CHgty6JHoSgSAtlA4JFHHpk5c2Y2WlabBYiAGGUBDoq6lB8EWGzedttt+Xm2nlrYCMiOsrDHR73LNAJY+WAOeeGFF/bt27dWrVrYortPwAnno48+cnP8aVQ6jz32WL9+/dD5VK5c2V9AOdFDQIwyemMqiopHAJdEAl74nRqLr/mXv3CQDhE3SmqBlErLKlOwCPwf90pKwrpqGH0AAAAASUVORK5CYII=",
-            "text/plain": [
-              "<PIL.Image.Image image mode=RGB size=440x252>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "====================================================================================================\n"
-          ]
-        },
-        {
-          "data": {
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",
-            "text/plain": [
-              "<PIL.Image.Image image mode=RGB size=480x339>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "====================================================================================================\n"
-          ]
-        },
-        {
-          "data": {
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",
-            "text/plain": [
-              "<PIL.Image.Image image mode=RGB size=266x152>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "====================================================================================================\n"
-          ]
-        },
-        {
-          "data": {
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",
-            "text/plain": [
-              "<PIL.Image.Image image mode=RGB size=511x350>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "====================================================================================================\n"
-          ]
-        },
-        {
-          "data": {
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",
-            "text/plain": [
-              "<PIL.Image.Image image mode=RGB size=512x337>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "====================================================================================================\n"
-          ]
-        },
-        {
-          "data": {
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",
-            "text/plain": [
-              "<PIL.Image.Image image mode=RGB size=512x323>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "====================================================================================================\n"
-          ]
-        },
-        {
-          "data": {
-            "image/jpeg": 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jLMbubFEjXNO01X3S9Nqzn3LKKcVe0PfyBkgosPfHBop3+5YWnyuFnGNcaCeY2yWYSkIqtsA5MxIVQSCsgCoyEDUlQ9SZVvOvWZtpuIvQYry9c/ZDsVpGn1aWF110UTZnZ7xgYkllyLpZvjg1WoGmDdU+GJyjUMV2Tr3l7VUK3U4lkCS59DBiPzSFoD+ZzutIp3N88YtfzC2KKZbc01rFbc8lI9F/BIIB9B/zYWrR2VNW5WLKmnxOOOEE6bwDYIs44Z6WGABRx3bbbUemlLmCHUA+vBZXSKIFUnFbjRwFObdebxEQhzoHuDupUSf/53/+x0GMaUGte5dccgltsD0R9ig67gUXXDDhw78yspNJ7LbbbvZrdEHFSsz+vELM/uuttx5rgSy2KpaJ9EAQEEGWdG4gTUejw4GAOdflhAbdSUIepxA2ds0EJVK9ycothj3ZbahY0l1llMyx690VvHKZZZaRIO3hUprLX3311UQO6Wd6h0mYU2zwxhmmxgzALM/ORzwf4LL4zKIePWeBK9qG9bW0iEvUv/hwBmiwCQyciiJ5ApNcMQ1Ky//BUhWtlxDgJOh7K2XGz0BgvAgU5fusP0UfuPfee1mIfvKTn0xVec1IaRIDsFf4yEc+Yl3IwyvJBlIZfmQHHHCAkp7NBCy++OIsX5xBW/IGSHbwqRgOYWLcaKONeB3nB4uJujIAkzvVroh6OoOvpiO9UscwPeCmuAvsZE2vs802W7HPg0rz+bILMYSYVmIAmRJDK5OGIOdEYrAIOC2L2Qbt/RZbbDFYSqL1WiMg5pj1qNXquuuua2pKwRu4BLka+2USEKoAz1CM1D4X+Pa3v52WI3zRc2ZKYBhJriDmzeWXXy6TlRGpeGrIhEMNUHqk+LOWDMDsb+2P++nJ/PPPb2LNChBCLrM/0ZBbmK0yFZn90SM6R1oOkFllaZV9XFoUbL311vlwguIIRXogCDgz3bukaR8wEeJAaIhGa4SAAO/M+RoJFgiSaaZ8K3FyGB4ATzzxBFvEpZZaqrFwzmGvmNPk2Gk2l8OLmLg43aLsPfnkk/kYE3IQHDlgygzj1rTTTkse3qlxedYG1CiRT1OxZBYRKVNOA0zaldCxxMZj860qJJDnyHjk2Y6JDJFJ+vznPy9zxhlnpM/ImZEYLALpRDDjErGABjsQdWndtG4Wzs6DEi5SCmEbXBdeeGH65NMENS4lsNOH0pxGksOoPSuBrSY14f0sGTcWYwExfXQmhKtkHplRreUOIMHhLzsNy+f8k/PXFVdc4Sddh2hrVYu1QEHNFVAEKCsCr0tWSmMA1v5MhjFzG7rcnUgEAoFAXRAQOISe1rGygs+jmdUJjS63JH6p3eqCfUMWdajTZEK7SVCZLF9oEdK5h8nDIDX62CsXGlrtYmvMADDAFNohdVXIPeJ1nI30VpDntBvqFvTdqkcECDZbjgHwNwUEVfNUU03VqJ3vVotRTyAQCPQHAd5YjE2Kzl+t2iXVefTRR4luWhVozJ999tnNbMV803o+31C+QKHMTImXi2XwjFVWWUXcIQGii/k5XWMGkPuQEjqJGVpWU6nncNClMtX5SaVDNZRYN6oSA6D/oSky0tWhMygJBAKBbiHgk0+63FtuucVpJR0yALMEM3fSf8Ee2lBit8H1DPuhMMgmkXiGKBFtnhqeWECUvYyruNRWf/Y3HjwAOW3ngaG+xwOIgBh15cxIDBwBdtx0MwMnIwgYDgQI4tnzjLcv1vXCSLSf/VOd/L84CuRgM500VGMGQNqTPZ5TVynZiVM66fYAyxgeyglrAZbmmYxspatH+pXzIzFYBJhRF8/fGCwx0XrdEfDJswQZby+IevJhMjTJtgLjrUF5QieSZwrI0rO1ZAB030m10qmpU6nTA/0pMniK4u1twAYSLfQZKZwRk9ZgAAMdn2g8EKguAhgAmyL05RmjSCvuwv9JdJycSTO89957Ozpmww03ZCfKJlWapCRPMrXUAZCSk598+tOfzv2sY+Lggw/mv8adD/Hkd86GZCJmwDh2F02b6ti1oDkQCATaIMBkqKi/bVOy1S36TiZAzH6KRp/OHjjiiCPy5O5ZClF/k+JBgqJY0AimhmJFcBeQU0sGgPslrSmfN3afa665pp7U6OKbJy40dk1pwy4oqYJ5tIkLxKkPo06ObDXq0bCS+tBDDznO06JpWDsY/eobAiYrjj6pOc5cncj029NmDnSyoSg4qZhFpMPiRYxo/5S75s8sc64lA9AHAhNSIPH2GFPWjgHQ0RP+YAN2c8IEJgbAJMBuoBQtbsyxjAI9RcDh78yLgwH0FOThrtwKPQWqIb1h5dHdzjLuzKFKxA6aQMSzWuoAgMjnK8HqE3366ae7C2uva6PDSLaeNgHFMH6pXbszneo1DVF/IBAI9AEB87LvnVw3z/5sOpnnd6VpoRA4w6ZrArM/GurKAOxirKDtepyvQpzSFTT7WQl/BfE6iORoAnK766yzjhG98847S94cuUAkAoFAoF4IMCMmiLfR5yaWKGcEmO30x9sXho5OPVGb80XMgeN9vLF8XUVAerLSSiuBQ2A4QbtoObDZxu5VNsdEv+iii5aMgsWHopmxoRE1kDa4GA+2sh0JwmqKgDWpmMM1Jb5eZDuJxWWFzsrDRz0xO87UZXJvUSO72P06TZqtur3NNtuIkdTqbsXzTfeNvuP77rtvDnhXcfqDvJoiQCRNhVZT4utINhMPEZ5FA8UMqkN/jRmAHVCOk1cdQDunhCUAxa+D4ugzxIZLD9Ljp62MuKFFc67Oq42SgUAgUE0EhAutmoV3jRmAMc7HpxWNYas59o1UUQQ5wwEbswl4/vnnUwFcQUQgaefYND4SOYFAIBAIdBGBejOABAQFwISVKl2EcgJVCfzAmHfnnXfOGnzLf/FnJlBVPBIIBAKBwHgRqDcDcJzWpz71KX12bNNpp5023s4PvDwlNp+v9ddfv5ESZwYceuihjfmRM0AE8Ga+PK6mp3sPkLCmTfM1cWgUa2mXk0malonMEUegxlZARk6MpBVWWIFqhRKY02btxtLy3+mdRbJ5BggULse2hi1Q8VakB44AG4zVV1994GR0QgC/U2ej3njjjSm+bLcMzztpOsrUCIF67wAALVij419qhHh7Uhn5psMILTaLLgLtn4q7gUARAYbRW265pZ1Kmv2dCJg2ysUykQ4EIFB7BqAPjCZ5hNkHOBuzvoNqw37cccexa2L8s8MOOzghoCuOHvUFZLCUsy3u8LyOwdJZbN2b41RC6yE+hg4ZdcvZGDYBVhXbb799sWSkA4GEQL1FQKkP7Gq5VziN08KnpuNK8uNwZ4cDSzjazJkB73//+2val+Eg22naKQi7sBy1iAfnhKn77ruPSTEXQkPA09Ds72TapmejYhUKD8dIRS8mhYBXoe6XtXMKbWrPW9O+EPen+KbGkrtgTXsxZGQLsZI+LaETzzvvvCr3zkF4RSm/NYQQKW0IZjExxRRTpN4tvfTSbUrGreFG4FXD0b1LLrnE22zJI5Ja7XokIhBDoCTw4SZuyVm7LgwlwcwKjjzyyDRLUskI3fHggw9Wraf77bcfwsSXRycb4o033lj40lZ0Mi1zHpHyRKbKC6nL6URM+ap1KujpGwJDwgCYgYqn750WXr9v2HWloV//+td59ifLYrfXlWqjkq4gIM5gURNgo+lova7UPPlKrBsoirIvpJd/k002sRtuVbPgIun0ISVdWJoziBibtSof+aOAwJAwAEPlKByvNd/aeg2b2CCvfI+vMvvbyNeL+FGg9pZbbjnooIOyQt4uk0qgCh0XCz69Of46XURkNzY/rQjjbV481cARFOnw0VblI39EEBg2BsBS22mRtRg8cUwdZcOXzQdsC28TUwuyR5BImnlyku222y7paaaffnrRvJ1ISt7oVp8BEVZ+7rnnRkAKFiu4vEOEnnnmmaZk/OpXv3JilMKzzTZb4haOudcXlbTZKzStKjKHEoHhYQBCK6fJ1MHHtRiqL37xi+mb9JfcthY0jzKRpCW77bZbcR1tW3DKKaf0ExNnRcw000z5tXGskGNgWxGAMZD1572LcCMipjgxvFX5yB9BBIaHARg8hx37NmrBACgYs5HJuuuu++yzz47gy1fHLj/22GOMdPMUzFR08803d6xbH/pC/TDPPPPkpkUKMcW3atdRqXnV75F3vetdmEerwpE/sggMIQPg9FjlZQ5nhdVWW+3tb3+7z9L0YUJhmzGy718dOy6MvuizLCnnmGOONMlOM800Zthtt92WorW7QiGCGlZhwgWqnwraa6NRbzgC2PuX0HvxxRcRwBVAYTpeL5gtgvWQwq1kRKUa4ueoITBUDIDHIx2A9769EfQAx5jUuBhMhtFed+eLAXZtNJvGzouRSMhbBPCgN+4WGuLF5qPu+Ka1EfhoMdlBeP/Ttcoqq7RRC3eLwqin1ggMFQMwmaaFD2NnEfZLA0PqQtiSrgsvvNDaKl/9MYZzBpNjLNPH6avecMMNTR8lIuNn7RBguXvxxRfzvTKmSeDOcpRQyNs1sffKU56lFvKu0vF6YVQrIdJJU3DSa8wKSHBZJZHBDRhJTz31VNPykRkIZASGigHoFamoD4aV2z333JM7mRLWZWny9Zdvpw11vsROufeVi0Cm9FS3ftqDWyqmCYLLgp+NLKpbbUU9/UeAXIgih6wmGec4AdzbRVYj4kLnmzxTufLsizybfLW8yXRFJ598svqbdop4h3ZXeXtfzr1OlEOG4LhNC0dmIFBCYNgYgGk8zfKLLbZYqatWanvssceuu+7qo8qcoJRgjG8LbzFVenaSP1lhZ4cdnvcOgJxkhfF4NRGweHeWZ97nebuw/GOPPbZDatkUlV5OQdxwhaaPMyP2rnrP8ztMRtS0ZGQGAq0QGDYG4KsQCNcnwd+9UUsGBcsxpy1ec801CyywgEVTvoofnpWU+dpn7PI9E93k64UXXmgFZat8K33G1+kr1dz+++/fqmTkDwcCVuWOqTDWyW+Aqv/EE0/0CrXv3bnnnpsEmJ7yrGiA3lLvc+kpPCa9jYI8p5fK6+pUCYVvvfXWUuH4GQi0R2DYGIDeHnLIIT4MklBhUtp03rz8l8J19NFHM/Eumtmlr8vXOFXhssjymaWrk6A95oJsOY4n2chPgIW06UXcqiYC5mgv19lnn00440UiFyLSufrqq9ts/tjwbLXVVt4W8Xk8K9JDY9ccFnTllVfiKF5Jb7ia3/ve95JbjsldGquKnEAAAkPIAMzOTOV8G1TB45Xpi6m7zz77UOil2b/9X7tvRxEI4t/mTbKByJW0N+FoU0ncqi8Cwog6WiC/A/aCJvGJdYcVwxJLLJGrombwrjbquiZWeTw1mgi8WrfzKzU0CYb2Vlu6Y0Wf7CjEhyjGy3WLHrgo9in2/f7777f+kmPBvsEGGxRvSZMsWXDlTKYXZvlddtkl5+SEj1NsZwcVaGjrrbcmsU2avVwgEqOAgNic6XQH63qfGxMAXlpeS3KeTrpPaOlt5CxCLCmEg0fsSr11AseWzhPtpLYoEwj8BwLDx/essAhb/qOTr3wztuHFi6ZX9Igxl2M25qXrW9/6FgbjojFOrTDbaITxjjvuSNO9rboCrVR5jQ9GzpAhQGrvFTKJM+sUS8474z0UubMT928PkmSm9za9bPa1gtPJV+2QARXd6T8CQygCSjqAEgNo9dOWXIAXXp0TgJ7F3pdfuSzoGh/HIVKjHER9ro0FImcEEbAsSDzAu7HRRhuN+WIUA5F6xIYyfEdG8LXpXZeHjQGQzzhW16dim+wwvDPPPPOBBx5wzLp06bKqShO0v8SpNuYughoB+ovXBOI0/Pa3vxWCUZ1oYBge3pi9e33rWLNoDYK/0uKyEGXJ42X7zW9+U1rOm+XlH3DAAc5s8X7S+np711tvvU42DXXEJGgeFAJDxQB8RY5w8l25rOuLH5V06XIA78deuZLtXWYG6fH8l1WGg1U7P2ySxFZkmPR4iYZBjXG0WzUEvIrMB9Ir51WhDGAvlI7xEsDZ+8bYTH4qwK3XCe/p7a1aR4KeuiMwVEpgcnYGPOl8XWuoDjWul156qYWVQyVZUmc2UEpYr4nbkzO32GILR3Dkn8UEoyDzPlvPnXba6bDDDssnrxbLRDoQYCBAHGSDSAqU0KC4confQDWV8dl77715Dorqk3MiEQh0EYHhYQBcZkjbrZ7IXr761a/aLydD6Q7BYvBD2uOwp2KYfs8KLEqkU6pEUEa7cpm+z2IssIsuusjKziaAUZB6mGqUHoyfgUBGwOKRQUGyIOAv4me6ZUvKRojFJ22W5X+sITJikeg6AsPDAAh/hHkAkPi349IDt8dUFHWOObkM2zvGfPlnY8LOnXCpaP7fWCZyAoEiAhRXlAEO+JVpxrd8EZyqWCDSgUCPEBgSBmAB5XhFZjnis/uQ8lkrXUeNH6Y9wQknnGCvoPK8assNMfm3YyjpFfLdSAQCTRGgB3bkJDmk6CNtRJFNn43MQGDCCLxuwk9W50H6sRVXXFFgBqtvh8L3bvbX5XToGBPP5AvGToN/r3zeXmRQEs7iaOVf5m5cgUBTBBj5MELDAJrejcxAoEcIDAMDYCMhujKAWEmLqNUjpIrVEtG65FD5pnwHdwjcyNmHAWiHyudihZEOBAKBQKD/CLwcT6ruF2N/Yhm9cDrroPSuzuFjKVR3JIP+QCAQGCkEas8A+Msw4DFmM8wwQ0jeR+rdjc4GAoHAJBGoNwNgv2/Vn6TwHOtz4OVJghKPBwKBQCAwCgjUngE4/529PzUAEXwVBsyZfGI/VIGSoCEQCAQCgfYI1FsJfPfdd+ues73EdKuI6lXQdo5g7UGPu4FAIBAIVAGBGu8AHLUhdhsQ+dxWZPavwogGDYFAIBAIdIhAXRmAYDviLjD+ES6xCr7yxFAcODsEPYoFAoFAIFAFBGrJAARj+MxnPnPFFVcI+8PuviLnIjmdtQojGjQEAoFAINAhArXUAQiZe9JJJ+mhwCmf/OQnO+xqFAsEAoFAIBAoIlDLHUAKzynwwznnnFPsTKQDgUAgEAgEOkegfgzg1ltvTbESHY4aUpfORzpKBgKBQCBQQqB+DODkk0925ItuiJ5YQeMfkem6GIy6NFrxMxAIBAKBLiJQMwbA9NMBXvq/ySabpMCcXcRiklU52M9BMQJERzjfSSIZjwcCgUB/EKgTAxBvmceviJsicbL8GVTct1YDQx61xx57tLob+YFAIBAIVA2BOjEAkX8cyAVBC23HuVcNyqAnEAgEAoF6IVAnBpBlPnPOOWe9UA5qA4FAIBCoIAK1YQCEP08//TQEHf2Y9gEVRDNICgQCgUCgRgjUhgE4Le+Pf/wjA1BWQBU0/klDvv766y+11FIOp4ywEDX6BoLUQGBkEagHA3DqC+9fgzTTTDNNP/30lR0tBwJPN910IlU8+uijlSUyCAsEAoFAICFQAwbA+EfUz6uvvnrqqacm/4mRCwQCgUAgEOgKAjWIBbTBBhs49UVvL7zwwpVWWqkr3Y5KAoFAIBAIBKq+A/jhD394zz33pHGae+65Y8ACgUAgEAgEuoVA5XYABOgOeMndu+666ygA/Nxwww2JgHJ+JAKBQCAQCAQmicCAGYDACWl+33LLLR9//HGdcZ7iT3/608ZerbzyyqL/N+ZXM8dJNY6sERyimuQFVYFAIBAIQGBgDOCZZ5658cYbn3/++W222UYANZyg/XjccccdH/3oR9/4xje2L1aRu8cff7xgFauttlpF6AkyAoFAIBBoRKBPDOAnP/nJYYcdVmz+qaee+s53vlPMyenFFlsMV8g/n3vuud133535/+te97rjjjsu51czsf32219//fUYWzXJC6oCgUAgEMgI9IoBiNj8+9//XjNnnnnmGWecQR6SRD254ZywqJ9hhhnSz09/+tPrrLOOHNb0uQAPAMzDITB4gL2C9Jve9KZ8t2qJD33oQyQ/wQCqNi5BTyAQCDQi0GUGcN9995HVaIbh5je+8Y3G9nIOn6l0lu8CCywgsn/Ob0xgD6rabLPNfvzjH5944olLL730xhtv3FgscgKBQCAQCATGhcBEGAB5fRLZm+Ut8IvtPfLIIw888EDOec1r/mlmOs888+yzzz45X8K0vswyyxRz2qSXXHLJs88+G8OwD3AKPB7jryMh2zwStyaMAM87Jy2nIS5VAva8XSvdqtTPX/ziF7vuuqvX75RTTmlqO7Dvvvs+9NBDaN5///3DvLhvY2cHf+ihh957773FFvfaa6/555/f51z6or2Be+6552OPPWbB19QD9LbbbjvmmGOOPvpoAQKKFUZ6HAi8PJeP53r44YcPPPBAAZldrUQxIvVb1y+88MJPPvnkb165yPHH00jzsj/60Y+cAq9vr33ta3fbbbfmhQady4rpbW97GyK//e1vD5qWcbdv6ud4scQSS7R6geaaay5z67jr7fsDefepL8wNGttffvnlUx+dMNp4dyA5aYXE1bHD1l988UWDlS8S1w4fHFSxK6+80qQxxRRTlN4u5t3y6fmYgBdpO+igg6j9FMYzivkpbV5xLoi78847b+PdyOkQgVd1WE6x+++/n/y9fSA235uBxJY7r7ZVSWs3EUBLd2Wmt8eewG6jdLcKP2vNAL71rW+VPk6RtzHyYuaiiy5qk1cFqFvRYJlZ1CGZ64VmKhWuGgOwVEpe7h0yAKfOWTgXx8UJGT5PhhWlnlbkp4P8xjThu+WWW4rUfuELX0gdbMoAqAbTjsGM5L0tPhjpzhHoVAREhWsj9uCDDxoSO+vXv/71hD+JAxffwne/+93dCtbvbbYiWGihhUz6M888c2pl1VVX9YXwDiMFEiLCZ2DtUCQg0hNGQLDV/fbbLz1OOvf5z39emsDHks30JH3CCSdgyXfffTenPJZO73jHOybcVj8fvOmmm7y33sx+NjretkyO0047bSdP2UxvtNFGdsO2YsXyX3/lolqbb775fJvVGR0vj1hexD4sI+zdCd+Y+RUp33vvve+66y451o7LLrusD794d8y0bav48OIEj1kyCjRBoENekby0PD/llFPamv35z39OxvsdPj6BYmuvvXYi11GLxccdCZnWSu5anBZvVSFd3x0A7XoCnAiLI1sjmH//+99nm222VObnP/95Y4GK5JR2AAgmq8TeiuRVbQdg8ZtkI2PuAKx+irLyOeaY4/2vXMWVkEVY46an2P1+pvGq9M74u9NOO7300kul1rG0vDlYbrnl8t0OdwCq/fjHP56fisS4EPinkjaP0JgJi3GaGV9U8S0c86kJFOBLlZ6y+bjssstyDTjQBRdckE4HwwxyfiQmiUAG08gCubE25q1Zq994t8o51p6nn356lSn83//9XzL9MSkk6yDq8YUrSfRhx3bRRRfRhbpslD/zmc+kGrjdHHnkkWPW1ucCArmvvvrqSaxfbJqW/hOf+EQxJ9L9Q6BDdpGdWvsmZOQ6QNtM1gQL+1mrHJwgU0uzZ9Nn4SBfpOheb0dyu2MmaroDgPZ///d/p9fuvPPOa9XNFJZVMRZZrcoMPL+4A5hqqqnSytqS5aSTTsq0VW0HQIVrGQvY9juAHXbYIY3RZz/72R/84Ae5OymBhVs1pxmW7O6GG24oFRjIz7QDsHQoifiLxBDjbL755rqGq5122mnpVt4BHHDAAUQOpYvDUIKC6IwmvFhbpDtHoNMdQJL+s+2hA0y49/qv79ZW45BDDmFTROP/ve99j8FGbtT7ffnll/uq5a+xxhrJ6SzfHWCCrNMmFwESmMEAKRlX074xSv70CCFyq2eZ66Vb6X1oVawi+STOJCFJsUGfRBnwu9/9riK0lcigVx/XQadUGiVJugrt21i1rrvuutJ0pBXpLEaVOtvmvbIJoL1QDCco6TZkUhuY5UtXlgr49iu43Uldrv7fjhiAQPw8e3Xmc5/7nGHoW6+sGnbZZZesmWT6WWzaSieJoUy4nISLtwaY9gWaaxBAtcVCeYCUjKtpIgirhnE9UovClhErrLBCsvQnOeRq3omkpf9ds2sxBJNv1yfjmnw93arBS0W1O8naknzMwBWv/LpKDHClZfNhVqwIr50AzmO/K4QD1tr2X/ZcyXc3NWNUnHzrVh6JCTTfySMkm1/+8petbn75y18yCsqmimZ/1g72+AiwgHXr5ptv7qTC7paBw7PPPmtvjoAiDT5pca3l2DOl8yy72+5ga6u4SL0IDtNk0q20VnCyNF/C4t0apal/0y6BLdx6663XivKDDz64aAXbqljt8vFy/ZpmmmnooiRs7wbYBazIvsTXTRnJbIkBxcsf/0IL2Rmn9d8AaRtX02MzANZllk4qBX0xIj+lk5BtXsRei1+oAXh1Hn744Zb8+C3Tz6yuXGWVVexOeIcxUHGLMiCJX8YFwWQKs0YVn452i4kkAlw4Ja9Fp8MTUqFTjgNtSJwp6+o79TRC1LhPbyxTnRxu5MlqwHdrQ1DTsWDIlFaaPsM2G/FZZpmlUdFanbGYACUkzz4oe2taQI7BX/rSlyQYB06gqq484i3ihIEeX3eykBalRtqFB+y4445daaU/lYzNAKyvXSVq5HBc9Nc6tw8COMs3UTbTSy8aQdEFhiqY1sg+AIUcRuiNS6T26KfZfNttt6W4s0EBQoJIJDiaRiwTW0IVHUZq3fshlrXyfeZPPeq7ak1Gvau86zWbEPMpQyxkjEXRNrHrzY23Qm/FscceO96nRqe8r8wHRdJCusX3KAX5IBxOu7r+40AobT2aPvlS66YCWklfOtvW0q2K/tSNNhfh2hFHHIF01p80sbmkdJqO2ef4lnJ+TxNsz7kgIMaewLRbtFV/4oknkuOSfSLjAUvvHlHCDYr+zZV0VpkYykYXWVCxXfsSmWZ/dtleXO/r7LPP7llRIhLPKBYebNrS0tI4vaMCNxUNrjJhynDATmVq4QdASmB7mug3FpZsjeatgw0FwQHK++CtSHOZv5y8MuClBNlCMpdk+9DGUsvSJPkE2OiUauj/T+95NhzwgjUNCaOMLyIFUHnf+96XhyxbAbX3BPZC9scPwEFVvC6Ml0lGowi23v/Zz372yqf/8h/zfo5KZB+m5Ne+9jVTaP9h77zFMUJBpA2O3vIAKlZqDZUmAh7exfxep0n53/Oe96Smwc2+O7fIPjVPylYHtmn5VlcSht+ivrT1JtvBeDqp/4orrth0002T4NILJFhNJ0/1sww9fxYd8LDT2eLn6v3mpJOQ/8AHPlBidf2kc8y2TChJCF5kAOkp4tpkWJw64u8AGYCpgaVApkSCQNVuoE0Hsxlo0aS1VJ6fdqqzCgwAbcXNVtMQXnhzxR3BCDx9Dub0PFizzjorlUwJeT8tkkoxLglI7AkaS1YkZwwGYKGnz2npWqR4UAwADbh9Xi7xBigupdmD5ls2jMVbReI7THs8XbQdwmAstdRSafg14SL6P+ecc+w8OqxNMR88xZFn1UNtoE5MRROd1zD5kvgiy1qN+uowJErRXCcGUJocue0wZKLvRWr2E0a/dU1+in7bt/FPpPrbFzSkdk2LKHTpQspMS+BGBsCioRQcNDMAu0aAqASr6+mgJJohL5ZJ5rhQZb5p8Z6BbZpgxZReQpptyDeWEaIjeWursJEB9LRfiZjUu/Q35WAA6Z1Huc0llVixjBVkxoF2txhCsfMdwCabbNIIxeRzEp0kDUS7xW/fF9TGp8H7Y2awUsy9JgCgLCxiMnnaulXDGAwgWf2bBYryHwe8pK30Vltt1TthS6se+shtnKlYDQkBlH0A2+FU2ObR95yioJjLlDn33HNZ6bSqqlW+mVqsYAFM1ODiiJCGf8YZZ/zgBz+I67gmUK3mTECepdFKqwlvifqpNIiJvB+t6OliPrKZzGoUB/KCEiYQaqX6EYA2qh3fYeqvvwY6r878JAJSprjxsgVW4GWYZpiBUQDc+rbn/e53v5vaTfsqUso8gfo+UdvIAPTxxhtvNA2lTuETufu092lrnwYFDr14t1HIlA7ZkC+CbCWBgDEHmuqlKKmDdt6lPf3002b/FKuR8MEwFenH7y0+mEuwyi9+y2O2OK4CaKCwTYPCQzM9a0+DGHNFGiaSk1Qg/c04GAv2HcWvoEMGoJ7E+MdF6piFhT32Rqncl5JGStd0UF/a79JSzUBW0tIkSSzo7VN/WU7iEGO23rcCYzAA0kmdtwbMBHnhsgkafXfO73PCyTD5+ykuY5GBP+db3rmzzjprXLRxF6JJztw7VeU1pXX4/ve/P66q2hSmiiTuzHSaZ/sgTLv99tsZ1BaX+Sb0xk0MkWuymcnkSQgEDYFGyY8ZbcUVV8wl4WZo2nS8W7dMFklqnJr2cVpa5sqTnxpzrKKiKN+VELDW6ptWIGeaIjHm3BEJjLk4H+WSE0tgvUzFiobUuS1y/07mlNSuhX/x5bR8PuqVa/HFF88VWnKWiDRzZdGE9WzjoJfKj/enRRgqijSURKOQ/NSnPpUpLCWIzpnwlRpNDMCGBmMr3fIz4+DZxruTzPGlpI0UOi0LTHRefqfYlqr1dlkc63hmw6UCfuIEni3aLFlHttlANNbQ05x2DIBlgoWSt425fSaC1CINHinYAMMCe4OzSNprZ+2fl+Qo5J2Qd/qmCTuy9l+yu75Ao0iIlHiePjI5MEe47LuvvfbajEC3EsJZsxfKAXYsNC6++GJk9GIFrU47cZN4GjtdYzeF5dh5NO2OxQstRfHKK+XG8saCLgRQyQUJ8vgHuae+5EFpfGpiOSqkoFZ/0vekMbIl5cZcrNB8hPimQpJUTD0cSkrd9616CkqpLzglew+9cBUrH286vV3MkIqsNw2EhogvCI47rxOjEgfCg0U2kGpLL61WSkuiVDlrDk+lkgR6NhxdGZ2EDx6WakaVVlCIzlKnbF+23HJLd0sXs06buVJhP63B6aJaCdDVT8yrqvGu8BobSjlpmMzpHJ7Sl5L6YhoszR6+Jr220Fl55ZVTr805rapN+TZedpaoTaNmW4DH9OJLb09G4912DMCuTffolAgu8pOJAegGg9ycOZAEo4i8qLHzLa04II4xpB2x9XXTNyyRbWY0xqx0UmFdNk6e9WLZWrqK3e9uT71Y6qcmShIJdCKDYWubmWsCBHCgI7nKe20TtPWvr3ECVbV6xGejIxYK4hNAD4Z6pC8WffJbPTWufNqLO++800STWbulGSWq+rsykWVizIzqtFXSBZOpXpDXjfmF58dLCQ9yo1FJgiXNF+kv8xjMrHGiLNXQ+BMUKMzhuXKdjFLIedwqTVipBg3hNHmR7m1nWp3Fp42tjJmD8YsNpWuuxPstniyhENDKBAMN7pauNvzVN96GjIRDV0YfYr53HcmTADlhentL0zS7GDgrmQSGvKNAKrMNnemWL07HxUPNH4jpa8ynel2gJQOwuk8KAJqxIhHJbgEDaGopWCzZh7Q1Qn77yRxLCzoEZOmEsWz6ntldZsutXBVD46afUO96ZBeZPqFEAx5gvTb55nwb5DlZhaVyL19PRTRaLLmhen8mMMc19l1H0uopD5NXsbFYF3PYeucWHTtFXtQoAWvfHNVfq/MmBb6f5BLKhAKB4tWUqaOBn3xW25AFeYSSIMHo7M9Wk3X7rlk2ZV6SqjItin/V/qlq3iWQofIp7s9IF5rO6WYY80x+A22jcfdxdcrEQiKUaiAL8lLRoo2rhu4Wbs4AUJl9/UsMIE2XBBe9UyV13kMLz5133jkpl2BKhFKaa6iL8Wq3TK/UxeldN0n5HqyGsIds2mX4KXtp2CgJVds5DV0piTnRPTBwsglILwd6yKDROTFWlPqIk+WFp97Z0rGjdasrNLeqxGaLzFRzSZlsDmU2iuvoS2l0WtVQzPeIB83FaZOUhokk3TAVlZzFR7qVNigpFmEeFJ7n3o2mK4lSo3aNRHzMxtNoFv+CRWgKB2yUHunFT4t0NBgCrxYY82KWQM/SFZi+C+vQU089tfPR8RHZU+bIbr4+Y+07quYJfe1RNUyYVpL6gkJHTBfUS6XRgRt8qLswOUMJTyXNPKJwt6+/6V3bGm8vHpOWF4sssoi5SP29/jCbEtOcARABezl01cvqG8hPYvskWfLHlKrnR3qdMDYmykStTRnzuFKLxQHeYostLPmJXHzS+avWHZ1iL2RCmdhqqNTihH+aWbDV9JKhyqeFyPEuMbSOZyRLrbyrIJmh4ehk5pow8cUHNQRMNirZaQMf0hcT37iWvVTl9JkeTDyemkQMyH4Ok29Sc7R/6Vv1FzGM2X2uxf6W0szG7boS/zOOpcupar0TKpYowQCS+jFRzg0ivwP6xX43KdLT6FjRtx8dX4cQ01TluWsStheqmgBrL5Ha/5/AsZxPkhxjxCBCR1wlSiCWYpHlXhtcL0DmpqXyHf7UEM1Zeje8VC7zkgVrh493q1hzBoDDJ8qo2ootsZJO+bSXxfzBps131EGJMHNEozkBwWi62/QvSWilulPaZpLdU28Qf3cIsgW4EBTep9xZ71n7D7vDmidQjAbPDJ4pkbDe7JAY/AM7z8/CoQ+GUk37aNWflZyJnu22267Ryt6zXkWx2GwUMtmlhHVfh91vSskEMpmuEWcnMiwISqJFC4W0eEoFCKaaij5Su7qWGGEqTNzfldO/J9CpST6Cr5O9UIzl0cECufs0Vms3gN/nhZTyVC/dEn5YZ2c1cqKE4WlTk6dGwrqV05wBmIMSQUSfuSVMPtnJ4YQWYjm/CgmThbVMepXZ2/IKLkpO8LO84Ur9soNT3uEbWG7/BT5jIuYj9B7YpCMyLX5tsW2/2ujEjA5TKKaQ3iF99JRniePJN4l9x2yxdwVIBpifI8aVpg+WNkhtXGolGiwz3eWBmJeu6dnBHvyNJK/KggsumKJOQdiuUTwG+en9od+TJnMrzqfpZct/GYNbL/cO6lY120h5f7zzKCFJo9tj1ZY+EH9FyoMw2tLo2KWZ4PLoWOrqF/A9lWpQCU2pE1u7NQ+2IrsX+ZbtukManD4rfdFxZn4+nJIEhnrcN0hMn4ZP362GrcO6a6CBB+DQ5OoZW2k+p+PVNk0Yq+YMINsLFhUUBIVeER8Ac+bi9Drhtrv4IHoMLc/VNFo+QvYJxfp9pQliA8+IUFhd5UtDXixfhTQKXemkJP1COaUFC9dG+QPDFW5rCqSlimHyiqfHq9MRLCqtdxCJVLvJK6+8sjQEFCHMJd11paFca6217MH1pSIdQSH3gsQGUkd0yqDwY0JzmkMT5aW/LC/NKaX+9q1TAGQbmn2ayD2IFtlQICB9O16qLJHQEcapJn3TE/9kP4tLYC4XFiiD6shkEDPVCiVZHCbaqabmsFYt1r6513xLbfh69xKq2YxUtJ4AspdqXPbBE0OmOQNIiy8+JtmuIKlMvdN0ShNrqQ9PmVAYICPSyNnbFlu0okkMwAqoXvJKrIsevjibYAnFWGDU9T7XYgEmT9XsowmF0CA7xdh386PJw2T4khd36otvQOE+fAOZgM4T3i5q1SLm7dM4d0mv2HlbXSwpIFXxeBYSNpuDXD9Vh5MGckcwibyiSpnYnvEquVzkxyuewLmz7VPqTlOHOBtQkcT676QJPRLObCCUKLRI6h3XSePVhAHYzCa1eFEo5vVNNFGVVHmkTXwsumzWSgbOXm5icSbkUK7a9mVMPLFhSxJ7cNvDZAzDVsEuLV3JLt7H6S6llpIWNWPWOcACFpV5saM7/+rHXMm+wGtGwiCTKnWARI7ZtEHh1pTX1OnraPqXTHIgkp+mXRCz3ntCspE2K+ZEOglCwqSXJnlgCFfax1hOeYR7/ISdIZpS0p9MUlO943WVI8ToCzEpO6iiCMucQFCspJczdd9fKDEwwSP7NmPYh1HgoTB9IJC3CkcV2npEQ5kB0IxbraT3uMgA8pmLqOnPyHW3FToAnSJe6G61/a9NLBHi19JEY8Ysemv3n6rxtnhr4XjnUl8oDAYr7h9XX5gG2n65EmMu9cVPmsY2mptxtdXFwtaVZGtFai2b0mKTes/OLN9ipEi6WEeBD7isAou7Z3M6JU0jjJii+IbZHEjfWeKxmc4WU42P9DrHMi5bA6LH4pVesxeNlhkAe5g09lb6xe1h8rx3q44MgDTNKgzxQ8AAvAQW+OxZ0zD5S9w/KPOYCb+RbRgAJ7gJVzvAB0WHzSOSE9QDlT04wfqX8DAbCJGMC8kAQB9+op/IlJ3oAMO9TH40s/5Mj1h8ENw1ikaFZhNwIg8ZNmCNVQWPNjRYdmdm3KOF0X8wAEsAUldYaJWbQx4ATr9JG27DWwUH4ExYh4lsBjocDECvLSpJSNJVQSumMcclMwAyK70QXZyMMcU6rikDMI/kSUTCgsNOuk2MsDEh6k8B6zkuXezN0My6j2wkL4SLkZn7Q0zXW0mzlr/YWGksjBeJls10Eg3ZHJCGUXBW6rAmYh8mc6hKLghFl6xuYfUfDMDknuRfICs2sNlmm6WXm/9XMb8u6cQAdIoPTl1oHm46MwMgIck95e7gNRsCBlAKTZo7WNmE7yJZkaXPPP0VTauyBHdIWGIAjpMslTerZrPL1Fmi/75555WI6eRnirrRCwbwmuKQN00n+tyiNOcn0rRMxTN1AYUYadNgvBUnPsirBQI2x45DSKRSOBWlCtWn33dx/PHH841K6z8Ec3zLXtzVp3+8FBKtMAtMT1G02oAKVZK3PuOtrQ/lLWHJ6HrR0L+1PcXazZX5J60Ak2Ho8OHMmoB8t/oJCxme3IZ5iF/o6o/C0FPI9SRZbnADtlKrXX/NLyxErYuFiiNUZAXPO6x2veiQYNIe/m4Kk3XTnzE/a6XD77DCLhbjaObi916chM29ZrAutpKr+o9KWZenG5Q/uYSdkYtlEgF6zqxLgvUCsRXjKgyM/1pdyA46a4cAO+N0jhDFb3ITq10X8DCaP/Kr2lE+XoK5g1GxuqwOLWqrM/vriNOebR9ZGI+3UxMr/28GQANsZ5SkJROrq4JPYQCJmbVx0K8g2UFSTRHw+WSD6Zp2IcgeKQT+zQA4ILD71Hlbj6whsfZPmTUFRfwcPADxPLlzp2ralyA7EAgERhOBZIzbi77/mwHwgWZ1pA2e0CmgmLT4sQcddJBE0aK2F3T0qE5iTZa/Ki9Gx+xRW1Ft5wiITZZc8zp/pBYlKVFr+qXUAt7RIZL7iKM7cn+FiCChyT+7mPgnA1A7BqBexsuOxMwNpGNhrJ37JpPKTXcxwYq2Rzr0LhI5UlUxyhZncJi6zOLQaX96VDpZfpj6GH3pGwI08P0xWvknA+B8z/+NppStZym8CQ803uGUwH3rfLcaMsUIJaY2pwWIxditaqOeySPAmZkL0uTrqU4NLDc4A9MBiJJfHaqCkpoiYEWeDVV72oV/MgDCH5fQ2PlkldQq3xxRoFm2ZQPhnlLTxcr5l/Jfu++++9TJgqp29HcRiqiqDwiY+pO2qUdb9T50IZqoDgIPPvhgtsnsKVUvMwAhk5KvrKMSHH9TbE9EpGKE2OKtiqd5NTuxE5FiKjm/qeLUBnl1R4AIiPKs7r0I+quMgAAwXV9evMwAxJIU+NPRfWp3ht8QCGctxxy7rGvW/kILOKyjyuMatA0BAo46OOqoo4agI9GFKiBgypp//vkzJSKmmMpE67MzyJldSfxTBORoiLTSv+OOO0hORArsSu2DqsRmnFeO1nn6feELXxgUGdHu6CDgi01HxNh69mfzPjrYjmBPRWkVnih33LEEzFisa3NOtxL/ZACqExDcAWkSJCc1Ffs0gkKhHdL/Rlgip+sIOJYnfT4iaw7N59N1lKLCiSGQjgab2LPtn/o3A/DiCoWaToNxSBChUD4avn0VFbzr8IRk1cqMtVJ+3hXEKkjqCgLsmr773e+qis95iq7clWqjktFEgFGZc+pz3/1My/90VlLOn3zi3wxAXVYxTsZxcoLj7pwr65yjOsqCxHr95Cc/mfy/Jg9Q1BAIdIKAo8HOPvvsTkpGmUBgTATEX3BEdi6WREB+HnHEEcw1c/7kE//BAFTHRfOSSy5JpyeLlLT88svXzl774YcfppHTF+5zouhNHqOoIRAYEwE+NHX0lWnVLyu//tihtyJgxPPJLYqmK0kJDJMjjzyyu2FBywxAG/YBooGnATCZOjtURJ26jIcXNx02QvovLqPPsi6UB521RsBX43SzWnehSLx4mTX66ouUD0d6scUWa2q8Llxrb3cACT47Dh5hidXcddddTIN6oYDuxVD98pe/JCZTsyivq622Wi+aiDoDgUYE+JxffPHFjfk1zWFzEtYTNR27cZHdZAfgecFzDjjgAAcApLMoHdZ67bXXjqveKBwIBAL1RUAIgHRkSn27EJR3gkBzBuBJxgwMQ5MRPWngxz72saJSopOq+1/GNuXMM8/Urr3Lpptu2n8CosWRRUD0ro985CND0/2InzjYoSxFA+0dMS0ZQGrys5/9bDqq+w9/+AO7oHSoeu+omWTNpGNnnHGGSjCAT3ziE5OsLR4PBDpHwCKJwqzz8lEyEGiDAAZw0003NRY45phjeq4ELrZqIcAQKJk3sEVde+21/S0WqFT6/vvvT7Ey7F75AFeKtiBmuBFgdjwEMVSGe4yGoHcLLrhgd3UzY+wAQMYtQKRoWwFp06t4FJdffnk1oRTSTrwktFFghP9XNccoqAoEAoExEZh99tmTT24qKSRzijU75oPjLTA2A1Djhz/84e23395B9dLHH388N6tLL720u9ZIbejWEBOLdPG1cWJyuko0WIIl719HMkX0/zZ4VuGWESRRrAIl3aJBNFAmet2qLeoZcQQYMabIIgmH888/3zRoUdt1wUanchIOYiZc54KJs//ss8869oiz1Tve8Y6ejpO2OKMJUr311lunhqh5XSmNBkaf+fiaE0888ZprrnELVYyye0pYVD5JBKaaaqo8cJOsqiKP+xyOPvroihATZAwlAiTw+bDebnWwUwagPbKg0047zbQrUAR2tMcee4i0vN5663WLFPWwpC6qPvhDtzI/5eK7wQYbFCf6xBsAtOaaa3aRpKiqRwisvvrqeEDxhWZFQ9s066yz9qjFnlY73XTTeSGt1HraSlQ+ygice+65zmvprnnuOBgA6PEAblZedAIZcdbsCWgFPvrRj05AMU1iYyehzttvvz0ZGkkLqIK7lMaYT4r9dcokjEpH1rz1rW8tukq/9NJL6UHf4VJLLVWqIX5WEAERyF2JMONu4JZ85aogqZ2Q5FAN5hLBADrBKspUB4HxMQB0E6+TzHALuPDCC825gkbQTohW2mGX7JSF6lTYyQYCG7V5ykn0WlGAhbUIGG1KWvuzjtp///3blIlbVUPg3nvvZbWVqPryl7/sOC2cXuiODTfcsLt2Dv3puGigljL9aStaCQS6hcC4GUBqeIcddlh00UWFizD5Svtic/igEmUOlHflTFakTaU6Bx10UFEaoDwGMKZ8CTtJNFx00UUesRHJm4ncYiSqhoDt2jbbbEO788ADD2TaHD4qzf1QTHLvEulivlWLhE1M8kGpBbVBZCCQEJggAyCCJ2mhld5zzz35iFHS+qqFjy7Jgu6++24euRZHJbjJeaecckqZDEznmGMOCfIcH3+pWONPew4sRIBSbblLAFUMWC2WdfEYncbHI6cKCFjjEx4mZb5XiMEDqv76178+9dRT3iKCTj7nOLpYflWgdrw0pO6M96koHwgMBIEJMgC0Mkiy9r/hhhss8Fnfb7HFFmRB/ha74WSMeeed9/vf/37KNMUz/rPTt98fr67vuuuue+ihh+yyL7jggmITOc2y0KakxIHy3UhUBAEvww9/+MM0+3uF7Px22mkntDl96IQTTjC4OPqf/vQnTMKaukajSSm1wgorOIn6nHPOqQjUQUYgMCYCE2cAqWq2QGJVi7zPLiiJccVgyDJc7gyC8zzyyCOpsA9ehM72XzUu4mD6RvUAkfGTTz6Z6sFCJHbddddiAN73vOc9xWOUU8n4WzUErr766p/+9KeoIucxy2dRz1xzzUWRs84661x//fX8+CwpDjnkkBoJ9OxdWEZgbHvvvbfT9KoGe9BTRMBkZaRMU+3nouIjw5qeLANgyGHdTZ6z++67k8+Q7RLlF33YfNiuMeEjzEkLQ64GXOoJBBofoSFcYIEF5FsnTjvttGzJI9x/I0p1yZl77rlZgpaoXW655awSCAYdrX7nnXeW7lb55/PPP0+nbU5JPvNVJjVos/jgM8Sa0SKD+ValAGEJTZjRN840WQYAO+txvsEOYhQqjnEnGS4NQYdTs/JHHXUUhiyWFmlAkgw0jocIGIzEp5566mQD2lggcmqBAOVNOq6n5OhYJJ6ZmU0ku6BiZo3SRd+UGpE9UqTaXz733HNcR007yy67rNgB1ek+ITl6zKL9IakLDCARattOdMNrly3Eo48+utlmmxVhNcWL02C6x3VtlnPfLPzNCMV5X/i5JEGyj85n7PEDmGeeefJTkagpAl4MEh7E0wCRHDbtBesgh5K65Z0hD0zivqYlK5XpFSUCZa9MHCpgSScWDZWif/iIMc8UJ5ZiB601uYs6cstkxR6B2sZ4sTuoCPNGjPmzSHAP0zDq1sW0Px0mjFxWOpiBmh9//HGasWOPPZZZp6tVTxZffHF+NC7KQLtpV5LTdYu2qGfgCLzwwgsEd+kFEOekFT3WBCmojqnfa9OqWAXzTz75ZL2zfOEtT5VdQQrHJMnXympDLw488MAxC1e8gDk0LSXFiCROLFFrbWH2Z6WSZ6RFFlnETMWmsVSyCj9Nj4lOqtDu0tO1HQD65ptvPmsfuNPySSCUEviWW265+eabM8qlBOUBQ1LjxJC015GFSk3Hzz4jYEKn9T3uuOPat6sYNYAyPlHMoH3hCt712ltLWr7QZzhKr4IUtiLJ7O/jZWuXCpx00knUbMneutUj1cwn3iFYdpatsUAhBuCtY42SLQ5kErKzULfWNkElzn3PPffYldJB6nh6AyvSO5tmstNeEdNdfqI2Z0k3hmIGKDs5l68CN84X0GOl3/UhqGyFPEJSENA2OwDEMyjwuntV+HxUti+NhFn1M3IVNjF9q/a7Fm782ir+huOy7K+QSi2fZ5l3vvOdZn8frHmzsaeVzbHLZDlGZZg7khMCT7FYEcWA3L9IP32A6cjLlqIdYwysWmw9mbQUi/U/TWCORRmXLAlnds86pruUvKq71anN+yQ6ULL8Sefk0d/y1yWSc7nb9RajwhohYDnmmyRnIOtrSjb7H8agypDSNi1Q8UxnOSVbtTT1WPqkc/SqSbZtFpPros0JgkXceu9735voP/TQQ6tJeSNV5MaEh0nsk4jHw1KEwfTTX73jK9q4sDAv2QrkYgDBD6gHxKNsbKgPOeKkia9T7AsBiViZXW+6+wwgkWgpZBfGXrPrFEeFtUYgMQBfmthNTTsi2mD6DmvKAHTqvvvu8/KnFaW+iHNHM2xn3LS/g8209jcn5olv1VVXdeCH2TAdBi4/MQBBO3RBZmV3M8LwFb1QxSkwBKT84PVGcVfKfZRYa621SvsAxcSW90jWYqbyA9GFsJchLC0SzDSmF7O/XveKAQz2tY7WK4sA/94UBFTwD4Fti2sxAiKTfgqlQPhgKV3ZXnRCmMUjebr1Zlpf6xdLNhMKMUUnj/euDKEHSQidJ3pIa1lsmx+R6jI6qV3irJRjKSrniiuuMB9Rz9Bq9I6wCdRMUPOhD31IR9iIo5DYbYYZZqB0NJsXayNO0R08mFYyTaxzzjmnpxivpw7mwk888YSSJBbJjstbqhhRjLBjBBi5WC8SHAnFO9Ac8Ula+/vLcxY9fGl7xHqDAfRiKKPOdgjst99+ppL0HTIcYISXLrN/yvSXFLFHb3w7ynpwj4xFX3K/JAipez2VtOoHSAXxFWQ3yxbMnvZkrcqnfByL1z3KrUOtkXNhu/wU0ynn9DNhCc9YHv0ZWwIf+tv2r41VBdEKDUd6Cg5ikfHga6R8n332KTq0Ksk3pX3ljZV0nmOP5VvI44I8mgx7GruBziuZQMlgABMALR6ZFAIkDHvttVf+bhsTzChTWIVJNVOZh/XFSUpZM2lDYHIZCHXcFIoemmussUZppdyUKsvnHXfc0TAVZ0CzP6WxDQS37aZP9TqT10XxzWG/9PWvf73DRsUjKZqk24myTWjcmbG9KTqrg04rotl32EqHxXwOPBJsyIrdsUFhINNhDZMpFgxgMujFsxNEwJxi4Unemo/68UH6SX362GOPDdwAY4K9avsYoYrlJxckPTWVMBbi79a7FWWmhWG72CpWl1pMsx5piaYR07nNO/2q8nk1as76yle+YsJiWpNFKEL4EeLldnuR0JZpkRehviQRDf0K/1O0jbdpQjBP2Qyl3YClNxmdagm7ioMCIsXY9Xo5E+9k2KKYbcQk31IyJXxIVdpNQkLfApW15lxF0WgvkMx1BgPIUERiAAgQboof5aKNHEDzg2jSsXpprUf72vmidWKU0oLaTuWlpYmGVEH43onVlp8yPSVhBQvFlGn+FcDDApmCIRfrboJ0vijwwcw4DxH3T7IV3g/ZzQpQ+EordesXv/jFoogG85iYjawdoSgURVMx7bI99S1Msi8TeDwYwARAi0cCgYkjYM6yD0hTiQWsCZpykqqguPCceO3/ejK50dFhFmd/Zjx5Ff+vghP5v+222+IlLpEf0/Om0dSQg30mUmPrZ8CiL4KACRKVmkhNE/e3fmh8d+BPQKfaNChAw5gbRwR0tgIW6RlSfqyMaJXssD19YVVVPOhCi1jOWWedJYxCh5V0t1gwgO7iGbUFAmMjQAlMHG8NaNIxmzB5wgkoWhvdfAhbRMnNxjmNVRNlNAqvyalV6Er1zzbbbAwfVdJYsrHCTnJEeVGbK6tqFlpooTQtFicyEpvOpUxN29X9U089VUeyrarVOmGapt1q+sjEMtGpTkoOmmQdodvQqJmafpj4q1inLQ7dST7OhGJcSXLLYpnGtO2Lqog9izoYm5iDDz5Yu93l/Y2tt8kJBtAGnLgVCPQQAWyAzCTNm+kvP6bSvOaQHJM4btFIB6mL5aQJ0eSS73JBkFkMarbYYosVJ+VcsouJq666imQ8dQFDSjXzJ8B1LJkn3BDPLM4iiYelyoVqIDqfcIWdPEi3kZlNatTmo3GNz2inqLYV/KZ95UVBnGqNmrDhRx99dPun+nA3GEAfQI4mAoHmCIhaI9hOXhWyjqUh4NOUS6eVNcPwxinGCZpphnJ0di6fA9Kp0+KU0efDDz+c7/YoQRZPlKFF5vPJwcrCVuvIKzIA+48Se2tKjzL0q7yRs08WWFQufAhBU9NHuphprndUAMN/LSZO4C8/CcS7ig0BlhY3XY1bNyVV5RHxj1dZZRXeCdBIHeFlPRBxf5H4nA4GkKGIRCAwAATMEQQp4uA63s7KnYlOUbWYRStmkNJUnhkAS5JMd2IAlt72B67+yBbYdOEBmkv+DVTESdGNZkfGJtrMhsxdKCEyqU0Tpn4KhiSHSezN3oIRrcobfXeb1tCVTIOiRVsQSo40cfPr1h2x69mGdtIETNg6eyrpFVRi1X/YYYeptim36KTOXpQJBtALVKPOQGAiCLCpL87+qsgMwAzCqbhYaVMG4DQFxipmmWLJPqcTE0Lw9ttvn5sWFYbImyK3TXQd/MPRUh7Ml21ECZBcYX8SXHNzLLZElWgZwlW1b/3yyy9PIc1zR+zSemcf1Z6Y9neDAbTHJ+4GAoNEoMgAnKZQlKhYY6bjJ4s7gEHS+q+2EwMgu+erJc/aH0NKHlUsXshM/lXw3/9NjnQGSy65ZJL4WzVbOwt+eccdd/RnE/NvUhpSlLdslNGTtREOEeP00NSeyuqej0Uxsr3djMA+2VuiofoBZwQDGPAARPOBQBsEigzAcpLLaF5IkkUkIbXphk6yTSV9viUCtmUyh4Mk8RfKhjw9zZ7WxfgBepwNR3WcLgF5yL5ydBCxGQQFMZN2ojDoT9eSNN/uJMtz9Mj+Jgu4EhlGgcgo65Cl4UAgVp2ONMIVDKARk8gJBKqCQIkB4AE77bRTWhRbVqeYkdQGZtKqUNxAByv7LAlhUZPu84TImcUExe9tt93WUEclMmy5Dj/8cMv/TLDVvR5xRaa6cIhFjvahgLPGuh40ohcoBAPoBapRZyDQHQQSAzC5UCG+5S1vMbNwU0rG9ZaljETlcEeiXexOez2oJTMAUWCTEac9QbKKSTOpZTVTev0S3M3OoAckdLNK0ZN0BLUproYuUBLks3RsdNzCJ6pj59O+88EA2uMTdwOBQSKQGACrRAajFABpxrTwRxMlcJJI0AEMXFDeBqPEAMyMSQ6OVG4NqSPpr6CbnK2SaKhNPdW5hVQX26Siv4W+COcgqIlbVR6OEoz/PguiOCSRDgQCgeogwBLGJiALoJn6sEckLfExJyLzrerQnCkR40zoGxTSo8o0P4oHnu86p5ftU15N5/wqJ5I+g5KDCIiZEF9lvtyHHHKIcyVF868y5Y20vTq/Q433IicQCAQGi4BIxaxQ2MKzhxG3kh09sQ9zSTITulOuA8izAxA/crB0dt66U9eFqMMMdEHoTZ3KXsSdV1KdkpTVrEIpfo1CdajqnJLYAXSOVZQMBAaDwN577y1AvLYFD2AtQ4RCnMLYfDDUTK5V0yUJCROaVnrgyVXf76dtArLHcr/b7kZ7L8eiiisQCAQqiADnqSeffBJhTkzLbl9s/5PAh+K3gjSPSRLincsolvKYJaNAHxCIHUAfQI4mAoGJICAeDgsT6l8m/2wQUxWkDcQmmR9MpN6BPiP0sR0Ap7aBUhGN/xOB2AHEqxAIVBQBRibJq0iQNe5RiUpTJ+l/6TiRinagGVkk/jH7NwNmMHnBAAaDe7QaCEwYgYUXXpjJfD5Nc8L1xIOBQIiA4h0IBKqLAA8jagBH75ZItAMQOYeBECu++eefv3Q3fgYCHSIQZqAdAhXFAoEBICAGMltJMeWbto0BMKsvnkzStFhkBgKtEAgG0AqZyA8EAoFAYMgRCB3AkA9wdC8QCAQCgVYIBANohUzkBwKVQ+Cb3/ym0xbDe79yA1NbgoIB1HbogvDRQ0CE4aOOOkr4gdHrevS4JwgEA+gJrFFpINAjBH772986gLdHlUe1o4ZAMIBRG/Hob+0R4Elb+z5EB6qBQDCAaoxDUBEIBAKBQN8RCAbQd8ijwUBgQgjwCHPO1IQejYcCgeYIBANojkvkBgJVQ8A5MI4fqRpVQU+tEQgGUOvhC+JHCAGHZ2222WYj1OHoau8RCAbQe4yjhUCgGwg4jvzxxx/vRk1RRyDwTwSCAcSrEAjUA4H/+q//qtfZufWAdbSpDAYw2uMfva8PApb/Dz74YH3oDUprgEAwgBoMUpAYCEBgmWWWWWuttQKKQKCLCAQD6CKYUVUg0EME+H8J/tzDBqLq0UMgGMDojXn0uJ4IXHnllaeeemo9aQ+qK4pAMICKDkyQFQiUEPjHP/7x0ksvlTLjZyAwGQSCAUwGvXg2EAgEAoEaIxAMoMaDF6SPFAIzzDDDbLPNNlJdjs72GoFgAL1GOOoPBLqDwJvf/Oapp566O3VFLYHAKwgEA4gXYQgREDZn/vnnv+yyy1pFTt58882dtF6vk1V++tOf3nfffUM4WtGlwSEQDGBw2EfLvUHAiYkf/vCHf/SjH62//vp4QNNGnnjiia9//etbbLGF81WaFojMQGAUEAgGMAqjPHJ9TKfmspp3eNbFF1/cqv8XXnjhj3/841Z3Iz8QGHoEggEM/RCPdAd/97vfbbXVVjfeeONIoxCdDwRaIBAMoAUwkT0sCPzxj3+8/PLLX3zxxWHpUPQjEOgaAsEAugZlVFRZBI499tjPf/7zlSWvQ8Le9773rbLKKgp/73vfu/baa5OYq8Nno1gg0BSBYABNYYnMYUDg1a9+9eyzz64n5sqrrrrq17/+da17NfPMM7/3ve/VBWdDrrvuusycnnrqqVr3KIgfOALBAAY+BEFArxB4zWteI37Ocsstp4GHHnroK1/5Sq9a6nu9f/7zn/VozTXXjADRfcd+qBoMBjBUw9ndzgg+c/PNNzuJcKONNpLubuX9qW2mmWY6/fTTF198cc2x+1xjjTX+9re/9afprrfCpyG5NWBsr3/96+1v7rnnHkOz6aabRpTQrqM9IhW+bkT6Gd0cLwI///nPV1999UcffdSMaa7hM7XnnnsuvPDCzqUab1WDLU8K9Pa3vx0N+vKLX/yivnOl3cwpp5yiI+uss87Xvva1DTfc8Fvf+tYDDzzA4+H5558/4YQTUjcHi3a0Xi8EYgdQr/HqE7WPPPIIR1nihbReJkP/xje+sdRSSx144IF9oqCrzZgrp5xySlWa/c8888yu1t2nyo4//vhzzz1XNNA3velNhkZYiPPPPx8n0LxtAV+HT33qU8ccc0yfqIlmhgYB33ZcgQAEzCN/+ctfjj766A984APzzDOPN9zCf6qppvrsZz9r9k8TqOW/uy6etJUFTUemm2469L/2ta/9/e9/n7p21llnTTHFFDJNneh/61vfmj7hW2+9tbIdSZRfd911CH7jG9+YCL7ooosywdTa6L/00kvTtswYfelLXzKIEMhlIhEItEHgZQOJuAIBCFx//fVmGZNmmmjmmmsupiZ2AKT/JpTvfOc7K620Uj6UnEUKyUM1cWtkAOiUud9++6WuFf9WmQH85Cc/yZN7pvmcc84pwa5r3/zmN2eccUZlDB824GepTPwMBJoiEAygKSwjl3n11VdPP/30eZaZd955xVNrROHQQw/NZRZccEFKyMYyA89pygBQxXw+mVHmLkhUlgGQwjH8L5Ka0vZk5513XiPIJv28rZlmmmn22GOPH/7wh43FIicQKCIQDKCIxmilOcceccQR//3KlWZ/tiXTTjvtJZdc8thjjzXFwiP33nvvxhtv/La3vc18NOuss3p6r732evbZZ4nXmz7S60wGkVo/44wzyEYSDa0YAEoefvjhLEtJ82lmAJ7605/+1Gtq29ePBrErDjvsMKi++93vRqE53YhA+KabbiLXSoI44CtAQ1OqTY7RwZXTsw4PUOyAAw6Aj5pLheNnIACBYAAj+hoIj0MkQsqf5kF/V1hhBTreTuZxs8mvfvUrAqJZZpnFgyoheRBYrf9QPv7448svvzyzSDT4+8wzz6ABeSUdQCaMEnW11VbLXZbIDAAjWWKJJWhTr7jiikFNl9/+9rcpKvKgcPXCiY1IokeCFRAdRqJfH1ELgdy7nOAgZupPxdLoYJCxIcj4RCIjEAwgQzFCCeaDjpdKE0T6y6XoN7/5zXgh+PjHP54rcVbJdttt10+hkMXyBz/4wUyA2M5sVXWhDQNwF+tiUTPnnHN6kJ3rL3/5y9RrDCBpif0VOmK8UEyyPHE/9IpSuPnmm68pmOQ/epp7DQE4NLbOPJTNbi4mgSU0bhoaH4yckUIgGMAIDbewaD/72c/MLGTEZgSmI3vvvbdjRlzJWqYpFvvuu+8c/7ruuuuuYhmTqWePPPLId73rXUl7jK8oSwDNMr1YsotpSunTTjtNK+l8RH0hK0eGGTy3YuEsR2dbbWiskRUoTp1K8hTTEQyAmEj9JlBmNrnOXiT+/ve/i+uw9tprv/Od7zQiVutrrbUWwlxPP/10qxb1VAG9TuMIB9SeeuqpmZmlB+2HFDvkkEN0yvZI/RTFNkBapNtvVXnkjxQCwQBGZbgt8E0uZoF0ve51rzvooIM66fz222//r4deRfp8yy23ND5l0b3TTjstueSSueSOO+4oBmdjyUnm3HbbbfhNboVSV84k6yw9/oUvfCFNl1qxau6dsRNO9sUvfjH3RYJUjZCqRE+bnyW1NkewpmgYHQ7DxYZ233136pw2NcetEUEgGMCQD7SP33XwwQevuuqqaQqwzLTk5EZkAuqk83feeedxxx2X5OxqoGCkkEzVlh5nuEKMrn6XkriFVpqWLD3YyU/1FOc7TZjvbr/99k6ebVNGtaW7ZkYuV84US71YdNFFt9lmm8Zipac6/5kAYd/JPQ0bBtTLeL361by6HE/WeT2pJAS4bng8DS6OSC733HPPlQi2w8MDXmnn5T+2a/hcY1uJtuLfxjKRM0wIBAMYptEs94W71nve8x4b/+QoxJ5kgQUWoDYcr7kL8QjxgotHkomGaYo6SaLJlEpNmmgU45LKYVVJf5UUjm0CU1uqGZfCV1zi+aQj0U2afBRIgboin/nYxz5GXM5OptQREDksjPBELzA/xdDwwgsvlIqN96eGPvKRj8CkqMvlYwG0oghrXNWCyOOf/vSnk/EPgukStt1229IocxBTLF+lERH544ILLkBY8bJlLErJxkVVFK4FAsEAajFMEyGSvJ45v+kgXaTzjEwmUlHhGUtL7mD/qvJVm2yySatp66tf/aoVbi658sorm2IKNXWaPOmkk/LyVm1MlegtOn24g3KJpZGMn3jiiY1bIryBFCj3okOhWdNmMVF9oXbOtVmGm6Z5YDQtP4FMwY5y5RI49Nlnn91JPXfccYeFQvHZnBY+r8QqOqkwytQFgWAAdRmpTum0fzeR0X/m2d8EagdgmdlpFW3LUSHiAUnla5owszOqaZw61fGHP/xhs802UzKJ1IUSInpWsiSdaNWakkWrR/XYSXQ9BMWyyy6bJjsbC+b2jeThAbSsqQtgNCHio4q1IrsxP40IXTo3C22pSl9c2Mm46mmsuZTDCIreBUoqT52yz4ChVlopw+UzDbKjSuXTX48XmS6mNS7NRImq+FllBIIBVHl0xk0b2xsxO4lKkoSBcQgRNq1po1B43FUXHjDRkIfwAkvzBbMZ4v5sUF8o+P9YuWj6+9//vrDMCptk0UZ64/FisVKaiQ43BXUmyZUHiWJMxMnKs1R4kj8tb5lFpY4k8jg0lFgUWYpoSJBMfljJRqipAX5TYoj79TrN/vZhYvvAxNWjWRVKsEItAPULhlpfZJFFREJtJI88KgnrlPTOeMqWCCbeGVufxAb8tZlrfDZyhgCBYABDMIj/7IJp63Of+1yay9Lfyct82qBDxESjkJvjl8TplKtt00fojdPhXKm8WaZpsZRpo5CrNdtSV5JRtCk/yVtsRvM+QLtmeUrgpnUWg6GaH++7776mxXImzrf//vvnAEoqdzJlvtvrBI+BotszzFmFlhrNjhSY38knn1y8i3ln26G55567p0NQbDfS/UQgGEA/0e5VW8y62bDzibXM9CWbcch/brjhhkbdZncpcCiVVszXWkyrRSyBc4CNSKNwA8OwJk0LYWpkU48cs0wmifmNteeHPvSh5GBMEKFaa//SejyX72LCch6ztATWIhENbTnpuV6UtL5+kuTkzpoWKYcVcxWJtLSXY5djB5Y4mUGx0Bauh5i+i2SPWRUegNpMMNcBquD8FDrJi1BoULgUNDoHEBuiPHVBXzrf9OQmIlFxBIIBVHyAxiCPuxBpCcuNNLGKFMYCx+xTnFjHqGLSt80jxA4sRlKoNStoMw7D08ZwcggjkrbcziJ1UxL600VhkGQsZpzFFlsseZM1zkqTprdlBchzJQdaeOoFLlWSnGBsyhCJ5IjZL8+vb3iDqAx6wTCJJf6uu+4qJ8mv8MWll16aa7GnikyiJRFdvaFF7Rodoh7rA+e7FWkQOzoNBOWHYo0t6+wuu+ySGIC/4UjcCFHdc4IB1HgEmeg5rzF/n2YlpiwD7A+PAfGFstiBMWJTzTOGwbI+k11KCNJAbNIVE8+JQZGcHjJVtNyYWXHeTNUKrUOwnoulBAeLLFJPOQLnNZ1bJ0Zbd5/K8bElWtXsNIjcx2AArVCqb34wgLqOHWvIJIK36qflczBsm3AO/ewkQ5SiztMa3zalpPNkPLrDDjsoVpwuLf9FRGilRehbFyx7yaZsX8zvsDX9EYPsvPPOulDyeyASIdXRC9db3vKWPFEmvatMhpie6hvl422oEwZgA5RjaAcDGC/C1S8fDKD6Y9ScwhSIjSkO/ao1dSs7v+YP9zLXYhk9VvFJvpwmUHMoFTFbGt5VqXEEK3bttdemeZNbL0ckOb0kbXx14wR8j5N3FUmOLlB04w2lWtDsEs0tdQTPcDxvyqzOoJRoTj87YQBKLrTQQqlrwQCawljrzJdDRMVVXwR4MJlnzU1JmFuFjqS50gaFWJzVPJLMpC45flJLJiIRjOxksW7JTHhFoSqnCl1INKCN78Lpp59OVp56QanOMAZXKBKJZlfqiL4Lvsa9K2VWZ1CKBEc6EMgIBAPIUNQvwVpmn332KdJtnmW0XswZYNp62exJpCPCPjV1UoryGqMqKFFlvSxCdSmzIj+prBnXcuLVBVoNK30h25ZZZhlubk0pXG+99Zrm1zTz8MMPZ85UU+KD7DERCAYwJkTVLcAqkV9ukT4ClryvL+YPKs24kK2khT81gMiX1sWm0eSkViSJ1IjzVzGnUmmr+0996lO6YCpkTc/OVaR+AqtKETkZYii0BYttWgPDXK9Z01uROQQIVGjHPQRo9rkLTGUssQXgLLbL8EaInmIEm+LdQaXJRph1Mup3ylUyoBwUJRNuVxfodc8880xKF4J+AqsJV1W1Bx0CSl3BBToZE2fyeBRfddVV6ecGG2yQDi3IdyMxBAgEAxiCQfx3F8R6FL+ejMWJ7VUTQKMHZ/o3rfVM0QkntXA9yf8Pqj/zmc9Y+4tUQY1JTCcetcjSuQTrVSawtjtyRH8S3a9x65YLR6KmCIQIqKYD14RsgpTku+9wQQaLTUpEViBQQIDpKkHW+uuvLw8PYNLK3Tdf1hBp9qe84eUXs38BueFJBgOo91gmE7TUBzKKSikA6o3saFBPK8NoNfGApj32Un35y19m19T0bmTWHYFgAPUeQas2/v2pD77VbHpf714F9X1EQOAjES8Y+5eOBOD/JVMQC7O/V6uPFEVT/UMgdAD9w7oXLaUYL72oOeocHQRM8S5RUQUEzb12xNjMM8+cf0ZiKBEIBlDvYSX3d1VN31tvTEeVesrtQw89dFR7P6L9DhFQvQeeGehll11W7z4E9YFAIDAgBIIBDAj4LjXLh5b3b5cqi2oCgUBgtBAIBlDv8ea5w5Cj3n0YIuoZZbHBHaIORVeGHIFgAPUe4BVXXHHJJZcs9cGRJiWXzlKB+Nk7BDji9a7yqDkQ6C4CwQC6i2e/axOSTGz9UqtiRKfglKX8+BkIBAKBQBGBYABFNOqXFvNHtLUS3Y5kcSRkKTN+BgKBQCBQQiAYQAmQYfj5/ve/f4opphiGnkQfAoFAoJcIBAPoJboDqts5i+G6ORDswe5E+IE0HY0GAhNAIBjABECr+iPXXHNNxHAf1CClQ9AG1Xq0GwiMC4FgAOOCKwr3BAE2S6usskpPqu57pZU92qzvSESDNUAgGEANBqlDEhmhO3i9w8KVKibg8I477lgpkiZGjCE47LDDJvZsPBUI9B+BYAD9x7wLLTpa3ZHrpYrMPtdff71MB2+FGWgJnPgZCAQCjQgEA2jEpAY5f/vb3xh6vvWtb+UI1kjuMccc46yPxvzI6R0CzlNMlbeJrd+71qPmQGBiCAQDmBhulXhquummW2+99TIphxxyyJ/+9CeRQWtkAuQo2kx/rRPHHXcc+iG/ww471LojQfxIIRAMYHiG++67737xxRe32WabVVddtRa9so9JR6vzZasR06oFtkFkINAJAsEAOkGpTmUoVOsSCIjS4q9//StwLZ8jpF2dXrKgdVgQCAZQv5F0VHeYmlRz2Dhgx1ammkMTVDVFIBhAU1gqnUn9+5Of/KTSJI4qcaeccso73vGOUe199Lt+CAQDqN+YBcWVRYDwLXYAlR2dIKwRgWAAjZhETiAQCAQCI4FAMIAaD/MyyyxTY+qD9EAgEBg0AsEABj0Ck2j/S1/60iSejkcDgUBg1BEIBjDqb0D0PxAIBEYWgThPfGSHPjo+bgR4rgnBxH1h7bXXfvOb3zzu5+OBQKBiCAQDqNiATIKcT3/604LRzzvvvJOooyePPvHEE/vuu2+qeq1Xrp4008tK77rrLt5qGECK+bP00ksHA+gl3lF3nxAIBtAnoPvQzPLLL9+HVsbVxHPPPbfVVlt997vf/c1vfsM+8t3vfveiiy46rhoGXvi3v/3tz3/+c9E1fve73yHGvM8Lb9ZZZx04YUFAIDB5BIIBTB7DqKEJAuQkp5122je/+c0rrrgi3WYjf9999zmusknpqmY9/vjjW2655Y033pgIfOMb3yjS6uabb16i97rrrksxLUr58TMQqDgCwQAqPkDtyDM3rbnmmltvvXV1nI/+7//+78knn9xll10wAGdjvfTSS+b9M8444w1veIMwpVNOOWXuz/nnn3/BBRcoIOdzn/uc7cs+++xTkY4g/swzz/zGN77xzDPP3HHHHSgk83HYL/pXW2213IWUuPbaaw3EH/7wh1J+/AwEaoCAdz2ueiHw9NNPzzLLLOndEvrtyCOPfOihh6rQBXIeDMkpBfm9n3766a+66ipcoUTeX/7yl09+8pO5mAQ+sdtuu1lHl0r2/+c//vGPk046KfOqd77znZiTONuNlBAN6d1UU02VOiI699VXX91YLHICgcoi8KrKUhaEtUHgBz/4wXzzzZcn0Le//e0Ogm9Tvg+3iMhL56GLimN+bGz63HPPtWvJxBcTVtlOCGh8pJ85J554os1Kpuqoo45q2voDDzyw2GKL5WJTTz31pZde2rRkZAYClUXg1SjLL3EkaoTAj370o3vvvdc6mhTFpOkYSFH1za2YQT974f0h5T/++OMt3m+//XZN25Sw9KH7pS993/veVyTm/vvvt8x3bgHlsHwFvv71r1v7C2/30Y9+lI2NoNDLLbccQdCxxx6rO8Vne53W+jrrrGP5f+edd1rvAxZJGp1zzjnzfstPUAPcXeAbAjlO3yTC+p//+R9iol4TGfUHAl1GoLKsKQgbEwGiFdPuXnvtJQpxei1Mmo8++uiYD3axwGWXXZZbR4N9Cbn5Cy+80NjEPffcY5mcX1+GNPYxuRhLoeI8i41ZYue7vU4wVCW9wYoSedNMM41Tlxsb1a/999+fKjjrKkiKDjzwQCyhsXDkBALVRyBEQNUfo7Ep3GOPPfLEuuSSSzK6f/7558d+bHIlTOjUttkcngRcu021EZQWSrIBzUSa62+++eZS+060n2mmmXKZueaay1P0CqViXf/J1Me2I7drTj/77LObtnLQQQflYhIUHkcffXTTkpEZCNQCgWAAtRimMYgkvmCsQgqRjCytT5nbE78wT2xUwI5RVwe3cZePfOQjeUK3A6D4vemmm0qPEqf88Y9/dEbu/PPPn+ZN0hKn1Z988smEJ6XC6adVv44wqsl8ZcEFFyQ1Uk8vdAMogdI888yDPKBR9mrdvqQEGgaGrSqZNMP+6ruSfWBOTVGKzECgWwgEA+gWkoOvx4T7s5/9rOgJbGrurnIYp2Hazysqy0BWXHHFr3zlK84ibuw/iT+hSi65xBJL7LzzzkqWptfGB3Xk97///eKLL57YBpWser761a/aczQWnnAOrfUGG2yQmiDoX3fddfWusTa8KrM66g1c9sorrwyZTyNQkVNHBIIB1HHU2tFMxXr44Yezu09TG0NMVvk//vGP2z3T8b2irEn9G2200Z///OemT/MC22STTRINM8888xFHHEHO3rRkq0zMzFPoT5X4Syj0ve99r1X58eY/9dRTK620kmrnmGOOVtU++OCDiyyySCbggAMOGG8rUT4QqDICwQCqPDoTp+2RRx6xdCWoSQtwGlc7g0MPPbSperZ9M0xiVl55ZY+7bCkIQMzLKnfxfsrPWhQ7rHibbbZJJclwmPSYvk8//fTHHnssFxtvAhvQELNR4iMT8Ywzzqj+nXbaqStKDjIclZc4kw0Ku6bUC7ZVGp122mnXX399JSeA3nj7G+UDgX4iEAygn2j3uy2yFBaZeQErQU8rs3M6uPWa/XMNOMoJJ5zQ9HGWoFnao7w0gc+Y0p6mVTVmqkdt73//+zMlWIJpurHk5HOY8ydmoy1eYBtuuCEQutWRyZMXNQQCXUQgGEAXwaxiVWKZOak86TnNaKa2zTbb7PLLL++EVmt/Iv485xLpNHWIpf6lts1CJ+WJVoiAWPd30krnZVi4LrvsspkeGm/eBq2kN62qVYmn7FQal/PW+G4xZ0pNMPcMz95WMEb+cCAQDGA4xnGMXhDOEHTst99+s802m7U5ox1qT4oBDlltniTiF+eApaNnXSVxP2ZguiRcYjVvxqSnnX322c866ywlqXDbVDuZW88++6z6KYS1RcSkXfM1Gvbcc89UrbhDfqarqQKc9Y6ngMDtK1PC3dcjSd9A50w5rBUL/1wgEoHAUCIQDGAoh7VlpxjhMHdJK1x/iXR+/etftyptimco2fTuOeec84lPfCLXI7H77rs3Ldm7TOcfZAJIhFJDXIhz5vbbb9+4C0kMQJkPfOADmTYOB/mpjTfeuBcmp7mtSAQC1UEgGEB1xqJPlNxyyy0Wua405X34wx/GFTqf8pQk7iceSY+rh5LAMSlNbSh72iVe0CJJ8OFCA5FOaqvIAMi72HoWaaCpxvNQbr8iWGm+xdrHlmihhRZSYe+2L7m5SAQCFUEgGEBFBqJ/ZJjuWb/84he/WHjhhcVrIwwRd8FFMTCmqlPABiWTP5RplH0n19+SaKh/PXmlJdsU3ck0FBkACnlvCU2RScK9RPqUr9c4X86XIA0rGjUVb0U6EBhWBIIBDOvIdtQvSk6GlSZEl2mdnahQaK2eJFJnEJkK+8u/l89Bq8KDyi8xAHQWZVMUv/lEmqIIaFDURruBwGAR+KccIH/VkRgpBFZZZRVuruQhLpOjqJYk+z/96U+JSuwGEhRWzX4K1MPUh3gkFXZeinBpRSepquH2spDrFTGXY9yz3J/emDNX1UgNegKBQSEQDGBQyFelXet6gnIXg3eCER5kRENW+px+hXd2bbrppn6uvvrqpEbsO1Phj3/84wsssEBV+tBABxXFIYccwl/MjC/WG7ITD+ADcfDBBzcUj4xAYFQRGOwGJFqvDgIMZsQ6Ljpblb4J6tPqK0iTCIiiwlk0pvu3ve1tqRei44E6REDVed+CkiogEDuA0iw3uj9FOtt7773ZdxLuM/93JRGK8A/SVKas7/N8WnGYuL995zvfQf9xxx2HftRyemC8RJyVKCfIKsV2rniPgrxAoBcIxKHwvUC1xnVysEqqXX1w7hUBkdgPZn+TqUmzXh0j0SLXcnLv5z//eacQkwilsNJ6QavB9v+GG26oV4+C2kCguwgEA+gunsNQW1oy6wmLyWuvvdaxJ/XtFR4gmoWD0ug2LP+/8IUv5L7gATkdiUBgNBEIEdBojntHvaZKrfXsnzrpHAIeXqJfdNTnKBQIjBICwQBGabRHpq/OHXOUWO7u+eefbyuTf0YiEAgEEgLBAOJNGEIEWH/mQyV1jyBIFLw11lgjd1UBZ1Xmn5EIBEYTgWAAoznuQ95r5qq33XZbsZMMQ3kD5CAWwgSloEDFMpEOBEYNgWAAozbiI9Hfqaeemui/1FVLfgGxa2fLVOpF/AwEuohAWAF1EcyoqhIIEPiIUscAtJGa3XbbjXfYXnvtpUzj3cgJBEYNgVfzRhu1Pkd/hxgBMaLFByXiJ/Np2k3Wn06VwQAc/xJsoClEkTk6CPx/+2BpNIpx1iUAAAAASUVORK5CYII=",
-            "text/plain": [
-              "<PIL.Image.Image image mode=RGB size=512x339>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "====================================================================================================\n",
-            "\n",
-            "提取到的所有 id-smiles 分子对信息：\n",
-            "[\n",
-            "    {\n",
-            "        \"index\": 0,\n",
-            "        \"molecular_info\": [\n",
-            "            \"(1)\"\n",
-            "        ],\n",
-            "        \"SMILES\": \"*C(=O)NC(*)CN(C(*)=O)C(*)C(=O)O\",\n",
-            "        \"E-SMILES\": \"*C(=O)NC(*)CN(C(*)=O)C(*)C(=O)O<sep><a>0:R[3]</a><a>5:R[2]</a><a>9:R[4]</a><a>12:R[1]</a>\",\n",
-            "        \"is_markush\": true\n",
-            "    },\n",
-            "    {\n",
-            "        \"index\": 1,\n",
-            "        \"molecular_info\": [\n",
-            "            \"(Ⅰ)\"\n",
-            "        ],\n",
-            "        \"SMILES\": \"*C(=O)NC(*)CN(C(*)=O)C(*)C(=O)O\",\n",
-            "        \"E-SMILES\": \"*C(=O)NC(*)CN(C(*)=O)C(*)C(=O)O<sep><a>0:R[3]</a><a>5:R[2]</a><a>9:R[4]</a><a>12:R[1]</a>\",\n",
-            "        \"is_markush\": true\n",
-            "    },\n",
-            "    {\n",
-            "        \"index\": 2,\n",
-            "        \"molecular_info\": [\n",
-            "            \"(a)\"\n",
-            "        ],\n",
-            "        \"SMILES\": \"**CC1c2ccccc2-c2ccccc21\",\n",
-            "        \"E-SMILES\": \"**CC1C2C(=CC=CC=2)C2C1=CC=CC=2<sep><a>0:<dum></a><a>1:X</a>\",\n",
-            "        \"is_markush\": true\n",
-            "    },\n",
-            "    {\n",
-            "        \"index\": 3,\n",
-            "        \"molecular_info\": [\n",
-            "            \"(b)\"\n",
-            "        ],\n",
-            "        \"SMILES\": \"*CC(*)*\",\n",
-            "        \"E-SMILES\": \"*CC(*)*<sep><a>0:R[5]</a><a>3:Y</a><a>4:SY</a>\",\n",
-            "        \"is_markush\": true\n",
-            "    },\n",
-            "    {\n",
-            "        \"index\": 4,\n",
-            "        \"molecular_info\": [\n",
-            "            \"(c)\"\n",
-            "        ],\n",
-            "        \"SMILES\": \"c1c*c2ccccc2c1\",\n",
-            "        \"E-SMILES\": \"C1C=*C2C=CC=CC=2C=1<sep><r>0:R</r><a>2:Z</a>\",\n",
-            "        \"is_markush\": true\n",
-            "    },\n",
-            "    {\n",
-            "        \"index\": 5,\n",
-            "        \"molecular_info\": [\n",
-            "            \"(11)\"\n",
-            "        ],\n",
-            "        \"SMILES\": \"*C(N)C(=O)O*\",\n",
-            "        \"E-SMILES\": \"*C(N)C(=O)O*<sep><a>0:R[1]</a><a>6:SG[1]</a>\",\n",
-            "        \"is_markush\": true\n",
-            "    },\n",
-            "    {\n",
-            "        \"index\": 6,\n",
-            "        \"molecular_info\": [\n",
-            "            \"(III)\"\n",
-            "        ],\n",
-            "        \"SMILES\": \"*C(=O)NC(*)C=O\",\n",
-            "        \"E-SMILES\": \"*C(=O)NC(*)C=O<sep><a>0:R[3]</a><a>5:R[2]</a>\",\n",
-            "        \"is_markush\": true\n",
-            "    },\n",
-            "    {\n",
-            "        \"index\": 7,\n",
-            "        \"molecular_info\": [\n",
-            "            \"(IV)\"\n",
-            "        ],\n",
-            "        \"SMILES\": \"*C(=O)NC(*)CNC(*)C(=O)O*\",\n",
-            "        \"E-SMILES\": \"*C(=O)NC(*)CNC(*)C(=O)O*<sep><a>0:R[3]</a><a>5:R[2]</a><a>9:R[1]</a><a>13:SG[1]</a>\",\n",
-            "        \"is_markush\": true\n",
-            "    },\n",
-            "    {\n",
-            "        \"index\": 8,\n",
-            "        \"molecular_info\": [\n",
-            "            \"(Ⅰ)\"\n",
-            "        ],\n",
-            "        \"SMILES\": \"*C(=O)NC(*)CN(C(*)=O)C(*)C(=O)O\",\n",
-            "        \"E-SMILES\": \"*C(=O)NC(*)CN(C(*)=O)C(*)C(=O)O<sep><a>0:R[3]</a><a>5:R[2]</a><a>9:R[4]</a><a>12:R[1]</a>\",\n",
-            "        \"is_markush\": true\n",
-            "    },\n",
-            "    {\n",
-            "        \"index\": 9,\n",
-            "        \"molecular_info\": [\n",
-            "            \"(a)\"\n",
-            "        ],\n",
-            "        \"SMILES\": \"**CC1c2ccccc2-c2ccccc21\",\n",
-            "        \"E-SMILES\": \"**CC1C2C(=CC=CC=2)C2C1=CC=CC=2<sep><a>0:<dum></a><a>1:X</a>\",\n",
-            "        \"is_markush\": true\n",
-            "    },\n",
-            "    {\n",
-            "        \"index\": 10,\n",
-            "        \"molecular_info\": [\n",
-            "            \"(b)\"\n",
-            "        ],\n",
-            "        \"SMILES\": \"*CC(*)*\",\n",
-            "        \"E-SMILES\": \"*CC(*)*<sep><a>0:R5</a><a>3:<dum></a><a>4:SY</a>\",\n",
-            "        \"is_markush\": true\n",
-            "    },\n",
-            "    {\n",
-            "        \"index\": 11,\n",
-            "        \"molecular_info\": [\n",
-            "            \"(c)\"\n",
-            "        ],\n",
-            "        \"SMILES\": \"c1c*c2ccccc2c1\",\n",
-            "        \"E-SMILES\": \"C1C=*C2C=CC=CC=2C=1<sep><r>0:R</r><a>2:Z</a>\",\n",
-            "        \"is_markush\": true\n",
-            "    },\n",
-            "    {\n",
-            "        \"index\": 12,\n",
-            "        \"molecular_info\": [\n",
-            "            \"(II)\"\n",
-            "        ],\n",
-            "        \"SMILES\": \"*C(N)C(=O)O*\",\n",
-            "        \"E-SMILES\": \"*C(N)C(=O)O*<sep><a>0:R[1]</a><a>6:SG[1]</a>\",\n",
-            "        \"is_markush\": true\n",
-            "    },\n",
-            "    {\n",
-            "        \"index\": 13,\n",
-            "        \"molecular_info\": [\n",
-            "            \"(II)\"\n",
-            "        ],\n",
-            "        \"SMILES\": \"*C(=O)NC(*)C=O\",\n",
-            "        \"E-SMILES\": \"*C(=O)NC(*)C=O<sep><a>0:R[3]</a><a>5:R[2]</a>\",\n",
-            "        \"is_markush\": true\n",
-            "    },\n",
-            "    {\n",
-            "        \"index\": 14,\n",
-            "        \"molecular_info\": [\n",
-            "            \"(IV)\"\n",
-            "        ],\n",
-            "        \"SMILES\": \"*C(=O)NC(*)CNC(*)C(=O)O*\",\n",
-            "        \"E-SMILES\": \"*C(NC(CNC(C(O*)=O)*)*)=O<sep><a>0:R[3]</a><a>9:SG[1]</a><a>11:R[1]</a><a>12:R[2]</a>\",\n",
-            "        \"is_markush\": true\n",
-            "    },\n",
-            "    {\n",
-            "        \"index\": 15,\n",
-            "        \"molecular_info\": [\n",
-            "            \"(a)\"\n",
-            "        ],\n",
-            "        \"SMILES\": \"**CC1c2ccccc2-c2ccccc21\",\n",
-            "        \"E-SMILES\": \"**CC1C2C(=CC=CC=2)C2C1=CC=CC=2<sep><a>0:<dum></a><a>1:X</a>\",\n",
-            "        \"is_markush\": true\n",
-            "    },\n",
-            "    {\n",
-            "        \"index\": 16,\n",
-            "        \"molecular_info\": [\n",
-            "            \"(b)\"\n",
-            "        ],\n",
-            "        \"SMILES\": \"*CC(*)*\",\n",
-            "        \"E-SMILES\": \"*CC(*)*<sep><a>0:R[5]</a><a>3:<dum></a><a>4:SY</a>\",\n",
-            "        \"is_markush\": true\n",
-            "    },\n",
-            "    {\n",
-            "        \"index\": 17,\n",
-            "        \"molecular_info\": [\n",
-            "            \"$ Z = CH, N $\"\n",
-            "        ],\n",
-            "        \"SMILES\": \"c1c*c2ccccc2c1\",\n",
-            "        \"E-SMILES\": \"C1C=*C2C=CC=CC=2C=1<sep><r>0:R</r><a>2:Z</a>\",\n",
-            "        \"is_markush\": true\n",
-            "    },\n",
-            "    {\n",
-            "        \"index\": 18,\n",
-            "        \"molecular_info\": [\n",
-            "            \"(II)\"\n",
-            "        ],\n",
-            "        \"SMILES\": \"*C(N)C(=O)O*\",\n",
-            "        \"E-SMILES\": \"*C(C(O*)=O)N<sep><a>0:R[1]</a><a>4:SG[1]</a>\",\n",
-            "        \"is_markush\": true\n",
-            "    },\n",
-            "    {\n",
-            "        \"index\": 19,\n",
-            "        \"molecular_info\": [\n",
-            "            \"(II)\"\n",
-            "        ],\n",
-            "        \"SMILES\": \"*C(=O)NC(*)C=O\",\n",
-            "        \"E-SMILES\": \"*C(=O)NC(*)C=O<sep><a>0:R[3]</a><a>5:R[2]</a>\",\n",
-            "        \"is_markush\": true\n",
-            "    },\n",
-            "    {\n",
-            "        \"index\": 20,\n",
-            "        \"molecular_info\": [\n",
-            "            \"(IV)\"\n",
-            "        ],\n",
-            "        \"SMILES\": \"*C(=O)NC(*)CNC(*)C(=O)O*\",\n",
-            "        \"E-SMILES\": \"*C(NC(CNC(C(O*)=O)*)*)=O<sep><a>0:R[3]</a><a>9:SG[2]</a><a>11:R[1]</a><a>12:R[2]</a>\",\n",
-            "        \"is_markush\": true\n",
-            "    },\n",
-            "    {\n",
-            "        \"index\": 21,\n",
-            "        \"molecular_info\": [\n",
-            "            \"(Ⅰ)\"\n",
-            "        ],\n",
-            "        \"SMILES\": \"*C(=O)NC(*)CN(C(*)=O)C(*)C(=O)O\",\n",
-            "        \"E-SMILES\": \"*C(=O)NC(*)CN(C(*)=O)C(*)C(=O)O<sep><a>0:R[3]</a><a>5:R[2]</a><a>9:R[4]</a><a>12:R[1]</a>\",\n",
-            "        \"is_markush\": true\n",
-            "    },\n",
-            "    {\n",
-            "        \"index\": 22,\n",
-            "        \"molecular_info\": [\n",
-            "            \"(IV)\"\n",
-            "        ],\n",
-            "        \"SMILES\": \"*C(=O)NC(*)CNC(*)C(=O)O\",\n",
-            "        \"E-SMILES\": \"*C(=O)NC(*)CNC(*)C(=O)O<sep><a>0:R[3]</a><a>5:R[2]</a><a>9:R[1]</a>\",\n",
-            "        \"is_markush\": true\n",
-            "    },\n",
-            "    {\n",
-            "        \"index\": 23,\n",
-            "        \"molecular_info\": [\n",
-            "            \"(Ⅰ)\"\n",
-            "        ],\n",
-            "        \"SMILES\": \"*C(=O)NC(*)CN(C(*)=O)C(*)C(=O)O\",\n",
-            "        \"E-SMILES\": \"*C(=O)NC(*)CN(C(*)=O)C(*)C(=O)O<sep><a>0:R[3]</a><a>5:R[2]</a><a>9:R[4]</a><a>12:R[1]</a>\",\n",
-            "        \"is_markush\": true\n",
-            "    },\n",
-            "    {\n",
-            "        \"index\": 24,\n",
-            "        \"molecular_info\": [],\n",
-            "        \"SMILES\": \"C(c1ccccc1)C(N)C(=O)O*\",\n",
-            "        \"E-SMILES\": \"*CC(N)C(=O)O*<sep><a>0:Ph</a><a>7:P</a>\",\n",
-            "        \"is_markush\": true\n",
-            "    },\n",
-            "    {\n",
-            "        \"index\": 25,\n",
-            "        \"molecular_info\": [\n",
-            "            \"3a\"\n",
-            "        ],\n",
-            "        \"SMILES\": \"*C(=O)NC(C=O)Cc1ccccc1\",\n",
-            "        \"E-SMILES\": \"*C(NC(C([H])=O)C*)=O<sep><a>0:R3</a><a>7:Ph</a>\",\n",
-            "        \"is_markush\": true\n",
-            "    },\n",
-            "    {\n",
-            "        \"index\": 26,\n",
-            "        \"molecular_info\": [\n",
-            "            \"4a\"\n",
-            "        ],\n",
-            "        \"SMILES\": \"**C(=O)C(Cc1ccccc1)NCC(Cc1ccccc1)NC(*)=O\",\n",
-            "        \"E-SMILES\": \"**C(C(NCC(NC(=O)*)C*)C*)=O<sep><a>0:P</a><a>1:Q</a><a>10:R[3]</a><a>12:Ph</a><a>14:Ph</a>\",\n",
-            "        \"is_markush\": true\n",
-            "    },\n",
-            "    {\n",
-            "        \"index\": 27,\n",
-            "        \"molecular_info\": [\n",
-            "            \"1a\"\n",
-            "        ],\n",
-            "        \"SMILES\": \"C(c1ccccc1)C(CN(C(=O)c1ccc(Cl)s1)C(Cc1ccccc1)C(=O)O)NC(*)=O\",\n",
-            "        \"E-SMILES\": \"*CC(NC(=O)*)CN(C(=O)C1=CC=C(Cl)S1)C(C*)C(=O)O<sep><a>0:Ph</a><a>6:R3</a><a>19:Ph</a>\",\n",
-            "        \"is_markush\": true\n",
-            "    },\n",
-            "    {\n",
-            "        \"index\": 28,\n",
-            "        \"molecular_info\": [\n",
-            "            \"5a\"\n",
-            "        ],\n",
-            "        \"SMILES\": \"O=C(Cl)c1ccc(Cl)s1\",\n",
-            "        \"E-SMILES\": \"O=C(Cl)c1ccc(Cl)s1<sep>\",\n",
-            "        \"is_markush\": false\n",
-            "    },\n",
-            "    {\n",
-            "        \"index\": 29,\n",
-            "        \"molecular_info\": [\n",
-            "            \"手性\"\n",
-            "        ],\n",
-            "        \"SMILES\": \"O=C(N[C@@H](Cc1ccccc1)CN(C(=O)c1ccc(Cl)s1)[C@@H](Cc1ccccc1)C(=O)O)OCC1c2ccccc2-c2ccccc21\",\n",
-            "        \"E-SMILES\": \"O=C(N[C@H](CN([C@H](C(O)=O)Cc1ccccc1)C(c1sc(Cl)cc1)=O)Cc1ccccc1)OCC1c2c(cccc2)-c2c1cccc2<sep>\",\n",
-            "        \"is_markush\": false\n",
-            "    },\n",
-            "    {\n",
-            "        \"index\": 30,\n",
-            "        \"molecular_info\": [],\n",
-            "        \"SMILES\": \"CC(C)(C)SSC[C@H](CN(C(=O)c1ccc(Cl)s1)[C@@H](Cc1ccccc1)C(=O)O)NC(=O)OCC1c2ccccc2-c2ccccc21\",\n",
-            "        \"E-SMILES\": \"CC(SSC[C@@H](NC(OCC1c2c(cccc2)-c2c1cccc2)=O)CN([C@H](C(O)=O)Cc1ccccc1)C(c1sc(Cl)cc1)=O)(C)C<sep>\",\n",
-            "        \"is_markush\": false\n",
-            "    },\n",
-            "    {\n",
-            "        \"index\": 31,\n",
-            "        \"molecular_info\": [\n",
-            "            \"手性\"\n",
-            "        ],\n",
-            "        \"SMILES\": \"O=C(N[C@@H](CS)CN(C(=O)c1ccc(Cl)s1)[C@@H](Cc1ccccc1)C(=O)O)OCC1c2ccccc2-c2ccccc21\",\n",
-            "        \"E-SMILES\": \"O=C(N[C@H](CN([C@H](C(O)=O)Cc1ccccc1)C(c1sc(Cl)cc1)=O)CS)OCC1c2c(cccc2)-c2c1cccc2<sep>\",\n",
-            "        \"is_markush\": false\n",
-            "    },\n",
-            "    {\n",
-            "        \"index\": 32,\n",
-            "        \"molecular_info\": [\n",
-            "            \"手性\"\n",
-            "        ],\n",
-            "        \"SMILES\": \"O=C(N[C@@H](Cc1ccccc1)CN(C(=O)c1cnc2ccccc2n1)[C@@H](Cc1ccccc1)C(=O)O)OCC1c2ccccc2-c2ccccc21\",\n",
-            "        \"E-SMILES\": \"O=C(N[C@H](CN([C@H](C(O)=O)Cc1ccccc1)C(c1nc2c(cccc2)nc1)=O)Cc1ccccc1)OCC1c2c(cccc2)-c2c1cccc2<sep>\",\n",
-            "        \"is_markush\": false\n",
-            "    }\n",
-            "]\n"
-          ]
-        }
-      ],
-      "source": [
-        "all_pairs = []\n",
-        "for mol_group_ in all_mol_groups:\n",
-        "    # print(tree_repr(mol_group_))\n",
-        "    # display(HTML(f'<span>分子</span> <img src=\"data:image/png;base64,{mol_group_.items[0].source}\" />'))\n",
-        "    mol_group_image = crop_item(doc[mol_group_.page], mol_group_, dpi=200, max_size=512)\n",
-        "    mol_group_image.save(f\"{save_dir}/{token}/{mol_group_.page}_{mol_group_.order}.png\")\n",
-        "    mol_group_desc = mol_group_.format_as(FormatFlag.Markdown)\n",
-        "    open(f\"{save_dir}/{token}/{mol_group_.page}_{mol_group_.order}.txt\", \"w\").write(mol_group_desc)\n",
-        "    \n",
-        "    for mol in mol_group_.items:\n",
-        "        if getattr(mol, 'type', '') in ('molecule', LayoutType.Molecule):\n",
-        "            smiles = getattr(mol, 'smi', getattr(mol, 'plain', ''))\n",
-        "            caption = getattr(mol, 'caption', getattr(mol, 'plain', ''))\n",
-        "            markush = getattr(mol, 'markush', False)\n",
-        "            # 有时候 plain 属性包含 <sep> 后面的占位符内容，因此如果有明确的 smi 优先用 smi\n",
-        "            # 如果没有 smi 只能用 plain 时，尝试将 <sep> 后面的去掉以得到纯净的 smiles\n",
-        "            if smiles and '<sep>' in smiles:\n",
-        "                smiles = smiles.split('<sep>')[0]\n",
-        "                \n",
-        "            mol_id = ''\n",
-        "            if hasattr(mol, 'items'):\n",
-        "                for sub in mol.items:\n",
-        "                    t = getattr(sub, 'type', '')\n",
-        "                    if t == 'SMILES':\n",
-        "                        smiles = getattr(sub, 'smi', getattr(sub, 'plain', smiles))\n",
-        "                    elif t in ('moleculeid', LayoutType.MoleculeID):\n",
-        "                        mol_id = getattr(sub, 'text', getattr(sub, 'plain', mol_id))\n",
-        "            \n",
-        "            if not mol_id:\n",
-        "                for peer in mol_group_.items:\n",
-        "                    if peer != mol and getattr(peer, 'type', '') in ('moleculeid', LayoutType.MoleculeID):\n",
-        "                        mol_id = getattr(peer, 'text', getattr(peer, 'plain', mol_id))\n",
-        "                        break\n",
-        "                        \n",
-        "            # 针对没有明显 moleculeid 但却提取出了其他杂乱 caption 的情况，清除它\n",
-        "            if mol_id and '<sep>' in mol_id:\n",
-        "                mol_id = ''\n",
-        "                \n",
-        "            if smiles:\n",
-        "                all_pairs.append({\n",
-        "                    'index': len(all_pairs),\n",
-        "                    'molecular_info': [mol_id.strip()] if mol_id else [],\n",
-        "                    'SMILES': smiles.strip() if smiles else smiles,\n",
-        "                    'E-SMILES': caption.strip() if caption else caption,\n",
-        "                    'is_markush': bool(markush),\n",
-        "                })\n",
-        "    \n",
-        "    display(mol_group_image)\n",
-        "    # display(Markdown(mol_group_desc))\n",
-        "    print(\"==\" * 50)\n",
-        "\n",
-        "print(\"\\n提取到的所有 id-smiles 分子对信息：\")\n",
-        "print(json.dumps(all_pairs, indent=4, ensure_ascii=False))"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "2c45be25",
-      "metadata": {},
-      "outputs": [],
-      "source": []
-    }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "base",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.10.15"
-    }
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a68e1a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import os\n",
+    "from typing import List\n",
+    "\n",
+    "import fitz\n",
+    "from fitz.utils import get_pixmap\n",
+    "from IPython.display import HTML, Markdown, display\n",
+    "from PIL import Image\n",
+    "\n",
+    "from uniparser_tools.api.clients import UniParserClient\n",
+    "from uniparser_tools.common.constant import FormatFlag, LayoutType, ParseMode, ParseModeTextual\n",
+    "from uniparser_tools.common.dataclass import BBox, Direction, GroupedResult, LayoutItem, SemanticItem\n",
+    "from uniparser_tools.utils.convert import dict2obj\n",
+    "from uniparser_tools.utils.log import get_root_logger\n",
+    "from uniparser_tools.utils.processor import tree_repr\n",
+    "\n",
+    "\n",
+    "###### 以下为示例代码，请自行修改\n",
+    "\n",
+    "# ==============================================================================================\n",
+    "host = \"https://uniparser.dp.tech/\"  # 官网\n",
+    "\n",
+    "# 替换为你的认证api key\n",
+    "api_key = os.getenv('UNIPARSER_API_KEY')\n",
+    "\n",
+    "# 初始化客户端\n",
+    "parser = UniParserClient(host=host, api_key=api_key)\n",
+    "\n",
+    "token = \"<TASK_TOKEN>\"\n",
+    "input_file = \"./tasks/CN1275981A.pdf\"\n",
+    "save_dir = \"./outputs/molecule_extracrtion\"\n",
+    "os.makedirs(save_dir, exist_ok=True)\n",
+    "os.makedirs(f\"{save_dir}/{token}\", exist_ok=True)"
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 5
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "330fb53d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trigger_result = parser.trigger_file(\n",
+    "    file_path=input_file,\n",
+    "    token=token,\n",
+    "    textual=ParseModeTextual.DigitalExported,\n",
+    "    table=ParseMode.OCRFast,\n",
+    "    molecule=ParseMode.OCRFast,\n",
+    "    chart=ParseMode.DumpBase64,\n",
+    "    figure=ParseMode.DumpBase64,\n",
+    "    expression=ParseMode.DumpBase64,\n",
+    "    equation=ParseMode.OCRFast,\n",
+    ")\n",
+    "if trigger_result[\"status\"] != \"success\":\n",
+    "    print(json.dumps(trigger_result, indent=4))\n",
+    "    raise Exception(\"trigger file failed\")\n",
+    "print(f\"trigger success, token: {trigger_result['token']}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d5bec1f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result = parser.get_result(token, pages_tree=True)\n",
+    "if result[\"status\"] != \"success\":\n",
+    "    print(json.dumps(result, indent=4))\n",
+    "    raise Exception(\"get result failed\")\n",
+    "json.dump(result[\"pages_tree\"], open(f\"{save_dir}/{token}.json\", \"w\"), indent=4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "319b7daa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pages_tree = dict2obj(result[\"pages_tree\"]) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2fbbbe31",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pages_tree[2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9c965c29",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(tree_repr(GroupedResult.clone(pages_tree[2][0], type=LayoutType.Page, items=pages_tree[2])))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b8d0d3e2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pages_tree[2][0].items[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3e73fca9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mol_group = pages_tree[2][0].items[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e85172c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(tree_repr(mol_group))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ed96d932",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mol_group.items[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "72195ba1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "display(Markdown(mol_group.format_as(FormatFlag.Markdown)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3b6f4bb3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "display(HTML(f'<img src=\"data:image/png;base64,{mol_group.items[0].source}\" />'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8d8d7558",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "doc = fitz.Document(input_file)\n",
+    "# dpi = 300\n",
+    "\n",
+    "# group \n",
+    "# page = doc[group.page]\n",
+    "# max_dpi = min(dpi, max(1, int(4096 * 72 / max(page.rect.width, page.rect.height))))  # max 4096 pixels\n",
+    "# group_clip: BBox = group.bbox * [page.rect.width, page.rect.height] + tuple(page.rect.top_left)\n",
+    "# pix = get_pixmap(page, clip=fitz.Rect(*group_clip.xyxy), dpi=max_dpi)\n",
+    "# group_image = Image.frombytes(\"RGB\", [pix.width, pix.height], pix.samples)\n",
+    "# if save_dir:\n",
+    "#     pix.save(save_dir / f\"{group_name}.group.png\")\n",
+    "# group_size = [pix.width, pix.height]\n",
+    "\n",
+    "\n",
+    "def crop_item(\n",
+    "    page: fitz.Page, item: LayoutItem, dpi: int = 144, max_size: int = None, rotated: bool = True\n",
+    ") -> Image.Image:\n",
+    "    bbox: BBox = item.bbox * [page.rect.width, page.rect.height] + tuple(page.rect.top_left)\n",
+    "    clip = fitz.Rect(*bbox.xyxy)\n",
+    "    if max_size is not None:\n",
+    "        max_dpi = min(dpi, max(1, int(max_size * 72 / max(clip.width, clip.height))))  # max pixels\n",
+    "    else:\n",
+    "        max_dpi = dpi\n",
+    "    pix = get_pixmap(page=page, alpha=False, dpi=max_dpi, clip=clip)\n",
+    "    cropped = Image.frombytes(\"RGB\", (pix.width, pix.height), pix.samples)\n",
+    "    if min(cropped.size) == 0:\n",
+    "        get_root_logger().warning(\n",
+    "            f\"{item.token} Crop config: {bbox=}, {clip=}, {page.rect=}, pix.shape={pix.width, pix.height}, {max_dpi=}\"\n",
+    "        )\n",
+    "        \n",
+    "    if min(cropped.size) == 0:\n",
+    "        get_root_logger().warning(f\"{item.token} Crop error: size={cropped.size} {item=}, {dpi=}, {max_size=}\")\n",
+    "        cropped = Image.new(\"RGB\", [10, 10], (255, 255, 255))\n",
+    "    if rotated and item.direction in [Direction.Rotate_90, Direction.Rotate_180, Direction.Rotate_270]:\n",
+    "        get_root_logger().debug(f\"{item.token} Crop rotate: {item.direction=}\")\n",
+    "        if item.direction == Direction.Rotate_90:\n",
+    "            cropped = cropped.transpose(Image.Transpose.ROTATE_90)\n",
+    "        elif item.direction == Direction.Rotate_180:\n",
+    "            cropped = cropped.transpose(Image.Transpose.ROTATE_180)\n",
+    "        elif item.direction == Direction.Rotate_270:\n",
+    "            cropped = cropped.transpose(Image.Transpose.ROTATE_270)\n",
+    "    return cropped\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b3820422",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def recursive_find_groups(\n",
+    "    item: SemanticItem,\n",
+    "    required_types: List[LayoutType] = [\n",
+    "        LayoutType.MoleculeGroup,\n",
+    "    ],\n",
+    ") -> List[SemanticItem]:\n",
+    "    for t in required_types:\n",
+    "        assert 'group' in t.value, t\n",
+    "    if item.type in required_types:\n",
+    "        return [item]\n",
+    "    elif isinstance(item, GroupedResult):\n",
+    "        items = [itt for it in item.items for itt in recursive_find_groups(it, required_types)]\n",
+    "        return items\n",
+    "    else:\n",
+    "        return []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fc5b925f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_mol_groups: List[GroupedResult] = []\n",
+    "for page_idx, page in enumerate(pages_tree):\n",
+    "    # print(f\"page {page_idx}\")\n",
+    "    for item in page:\n",
+    "        # print(item)\n",
+    "        all_mol_groups.extend(recursive_find_groups(item, required_types=[LayoutType.MoleculeGroup]))\n",
+    "print(len(all_mol_groups))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "be2994f7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_pairs = []\n",
+    "for mol_group_ in all_mol_groups:\n",
+    "    # print(tree_repr(mol_group_))\n",
+    "    # display(HTML(f'<span>分子</span> <img src=\"data:image/png;base64,{mol_group_.items[0].source}\" />'))\n",
+    "    mol_group_image = crop_item(doc[mol_group_.page], mol_group_, dpi=200, max_size=512)\n",
+    "    mol_group_image.save(f\"{save_dir}/{token}/{mol_group_.page}_{mol_group_.order}.png\")\n",
+    "    mol_group_desc = mol_group_.format_as(FormatFlag.Markdown)\n",
+    "    open(f\"{save_dir}/{token}/{mol_group_.page}_{mol_group_.order}.txt\", \"w\").write(mol_group_desc)\n",
+    "    \n",
+    "    for mol in mol_group_.items:\n",
+    "        if getattr(mol, 'type', '') in ('molecule', LayoutType.Molecule):\n",
+    "            smiles = getattr(mol, 'smi', getattr(mol, 'plain', ''))\n",
+    "            caption = getattr(mol, 'caption', getattr(mol, 'plain', ''))\n",
+    "            markush = getattr(mol, 'markush', False)\n",
+    "            # 有时候 plain 属性包含 <sep> 后面的占位符内容，因此如果有明确的 smi 优先用 smi\n",
+    "            # 如果没有 smi 只能用 plain 时，尝试将 <sep> 后面的去掉以得到纯净的 smiles\n",
+    "            if smiles and '<sep>' in smiles:\n",
+    "                smiles = smiles.split('<sep>')[0]\n",
+    "                \n",
+    "            mol_id = ''\n",
+    "            if hasattr(mol, 'items'):\n",
+    "                for sub in mol.items:\n",
+    "                    t = getattr(sub, 'type', '')\n",
+    "                    if t == 'SMILES':\n",
+    "                        smiles = getattr(sub, 'smi', getattr(sub, 'plain', smiles))\n",
+    "                    elif t in ('moleculeid', LayoutType.MoleculeID):\n",
+    "                        mol_id = getattr(sub, 'text', getattr(sub, 'plain', mol_id))\n",
+    "            \n",
+    "            if not mol_id:\n",
+    "                for peer in mol_group_.items:\n",
+    "                    if peer != mol and getattr(peer, 'type', '') in ('moleculeid', LayoutType.MoleculeID):\n",
+    "                        mol_id = getattr(peer, 'text', getattr(peer, 'plain', mol_id))\n",
+    "                        break\n",
+    "                        \n",
+    "            # 针对没有明显 moleculeid 但却提取出了其他杂乱 caption 的情况，清除它\n",
+    "            if mol_id and '<sep>' in mol_id:\n",
+    "                mol_id = ''\n",
+    "                \n",
+    "            if smiles:\n",
+    "                all_pairs.append({\n",
+    "                    'index': len(all_pairs),\n",
+    "                    'molecular_info': [mol_id.strip()] if mol_id else [],\n",
+    "                    'SMILES': smiles.strip() if smiles else smiles,\n",
+    "                    'E-SMILES': caption.strip() if caption else caption,\n",
+    "                    'is_markush': bool(markush),\n",
+    "                })\n",
+    "    \n",
+    "    display(mol_group_image)\n",
+    "    # display(Markdown(mol_group_desc))\n",
+    "    print(\"==\" * 50)\n",
+    "\n",
+    "print(\"\\n提取到的所有 id-smiles 分子对信息：\")\n",
+    "print(json.dumps(all_pairs, indent=4, ensure_ascii=False))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2c45be25",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
 }
diff --git a/playground/private.semantic_recognition_pipeline.ipynb b/playground/private.semantic_recognition_pipeline.ipynb
index 49b5797..b5236c1 100644
--- a/playground/private.semantic_recognition_pipeline.ipynb
+++ b/playground/private.semantic_recognition_pipeline.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "fa4f8ca9",
    "metadata": {},
    "outputs": [],
@@ -48,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "35e69a50",
    "metadata": {},
    "outputs": [],
@@ -67,18 +67,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "4f7ac01e",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'code': 0, 'data': [{'bboxes': [{'x1': 5.0, 'x2': 1045.0, 'y1': 6.0, 'y2': 93.0}, {'x1': 289.0, 'x2': 669.0, 'y1': 4.0, 'y2': 107.0}, {'x1': 486.0, 'x2': 754.0, 'y1': 10.0, 'y2': 96.0}, {'x1': 644.0, 'x2': 988.0, 'y1': 15.0, 'y2': 93.0}, {'x1': 833.0, 'x2': 1313.0, 'y1': 8.0, 'y2': 118.0}, {'x1': 1245.0, 'x2': 1562.0, 'y1': 12.0, 'y2': 100.0}, {'x1': 1498.0, 'x2': 1805.0, 'y1': 13.0, 'y2': 99.0}, {'x1': 1761.0, 'x2': 2019.0, 'y1': 2.0, 'y2': 125.0}, {'x1': 2.0, 'x2': 274.0, 'y1': 90.0, 'y2': 171.0}, {'x1': 268.0, 'x2': 470.0, 'y1': 92.0, 'y2': 168.0}, {'x1': 450.0, 'x2': 616.0, 'y1': 92.0, 'y2': 167.0}, {'x1': 626.0, 'x2': 860.0, 'y1': 93.0, 'y2': 168.0}, {'x1': 835.0, 'x2': 1223.0, 'y1': 93.0, 'y2': 169.0}, {'x1': 1206.0, 'x2': 1496.0, 'y1': 93.0, 'y2': 170.0}, {'x1': 1496.0, 'x2': 1774.0, 'y1': 93.0, 'y2': 172.0}, {'x1': 1780.0, 'x2': 2019.0, 'y1': 90.0, 'y2': 173.0}, {'x1': 1.0, 'x2': 262.0, 'y1': 166.0, 'y2': 247.0}, {'x1': 271.0, 'x2': 470.0, 'y1': 167.0, 'y2': 241.0}, {'x1': 451.0, 'x2': 624.0, 'y1': 167.0, 'y2': 241.0}, {'x1': 629.0, 'x2': 869.0, 'y1': 168.0, 'y2': 241.0}, {'x1': 845.0, 'x2': 1230.0, 'y1': 169.0, 'y2': 242.0}, {'x1': 1214.0, 'x2': 1508.0, 'y1': 170.0, 'y2': 242.0}, {'x1': 1502.0, 'x2': 1786.0, 'y1': 170.0, 'y2': 243.0}, {'x1': 1794.0, 'x2': 2019.0, 'y1': 167.0, 'y2': 245.0}, {'x1': 1.0, 'x2': 280.0, 'y1': 237.0, 'y2': 314.0}, {'x1': 276.0, 'x2': 472.0, 'y1': 236.0, 'y2': 311.0}, {'x1': 452.0, 'x2': 625.0, 'y1': 237.0, 'y2': 310.0}, {'x1': 629.0, 'x2': 869.0, 'y1': 238.0, 'y2': 311.0}, {'x1': 840.0, 'x2': 1228.0, 'y1': 238.0, 'y2': 314.0}, {'x1': 1211.0, 'x2': 1506.0, 'y1': 240.0, 'y2': 314.0}, {'x1': 1500.0, 'x2': 1785.0, 'y1': 239.0, 'y2': 314.0}, {'x1': 1794.0, 'x2': 2020.0, 'y1': 236.0, 'y2': 316.0}, {'x1': 2.0, 'x2': 282.0, 'y1': 310.0, 'y2': 385.0}, {'x1': 275.0, 'x2': 477.0, 'y1': 308.0, 'y2': 383.0}, {'x1': 454.0, 'x2': 631.0, 'y1': 309.0, 'y2': 384.0}, {'x1': 631.0, 'x2': 874.0, 'y1': 310.0, 'y2': 386.0}, {'x1': 842.0, 'x2': 1232.0, 'y1': 310.0, 'y2': 389.0}, {'x1': 1215.0, 'x2': 1510.0, 'y1': 311.0, 'y2': 389.0}, {'x1': 1502.0, 'x2': 1788.0, 'y1': 310.0, 'y2': 388.0}, {'x1': 1793.0, 'x2': 2020.0, 'y1': 306.0, 'y2': 390.0}, {'x1': 2.0, 'x2': 287.0, 'y1': 382.0, 'y2': 457.0}, {'x1': 275.0, 'x2': 478.0, 'y1': 380.0, 'y2': 457.0}, {'x1': 458.0, 'x2': 635.0, 'y1': 381.0, 'y2': 457.0}, {'x1': 631.0, 'x2': 875.0, 'y1': 381.0, 'y2': 459.0}, {'x1': 842.0, 'x2': 1232.0, 'y1': 381.0, 'y2': 462.0}, {'x1': 1217.0, 'x2': 1511.0, 'y1': 382.0, 'y2': 461.0}, {'x1': 1506.0, 'x2': 1789.0, 'y1': 381.0, 'y2': 460.0}, {'x1': 1794.0, 'x2': 2020.0, 'y1': 377.0, 'y2': 462.0}, {'x1': 2.0, 'x2': 287.0, 'y1': 455.0, 'y2': 530.0}, {'x1': 274.0, 'x2': 480.0, 'y1': 453.0, 'y2': 530.0}, {'x1': 456.0, 'x2': 639.0, 'y1': 455.0, 'y2': 531.0}, {'x1': 631.0, 'x2': 879.0, 'y1': 454.0, 'y2': 533.0}, {'x1': 844.0, 'x2': 1237.0, 'y1': 454.0, 'y2': 536.0}, {'x1': 1219.0, 'x2': 1515.0, 'y1': 456.0, 'y2': 534.0}, {'x1': 1508.0, 'x2': 1793.0, 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-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "image_path = \"./snip/60c75518bdbb8914d5a3a787_008_5.png\"\n",
     "image_path = Path(image_path)\n",
@@ -90,70 +82,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "db23ce20",
    "metadata": {},
-   "outputs": [
-    {
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OnPjzzz9VysTa2rpx48bMMXZER8+yQM3xq6usra3v3r27YMGCoKCg+/fvlzu3BHP8IuZMBip5/vw5vcw8fpWgJ+wmhDx58oS/3LSaKHuTYIeCZlBvS+YGAGpDk1IyNwD+6MARh8MN+KYDhwnnuYGu0oHaLlZV1/Rh6rJYW1sPHDhwz549hJCYmJgbN274+PgoXyUiIqJKlSqdOnVStaw3b978/vvvxf6THtrl6tWr48ePLyoq6tGjx4QJEypVqnT9+vU//vgjOjp606ZNq1atCgoKUrVEaXr37t2HDx8IIe7u7vQMyyoZO3ZsXFxcdnb2zJkzNY+nTp06a9euHTt2LP0/06ZN69ChA3MqCa1Tu3ZtAwODoqKi27dvFxYWav46gmQ5ODjMnz+fTcqvX78+fPiQ/tilSxf1SkxLS6OXWX6xzHr+6dMn/nLTdsLvTYIdChpDvUW9BeAQmhQ0KSAkbT/icLiBALT9MOE8N9Bh2l7bxarqPD6/PGHCBHqZzSS0mzdvHjdunBoFtW3bNikp6eXLl3/88YeJiQnzTydPnuzatWufPn2io6PXrl07dOjQvn37rl69+ujRo3p6egqFYvr06fRgMtouOjqaWqhfv756ORgbGy9btmzTpk1qTF1bqjFjxvTu3Zv+mJOTM3jw4MLCQk4yF4WBgUGtWrUIIbm5uZGRkWKHIwlHjhyh96ment7o0aPVyyczM5NeZtkIGhgY0MvFxjjjNreKg6u9SbBDQUCotwDAITQpAEKS5hGHww0kRZqHCee5ARCp1naxqjqPzwh7e3vXr1+fen3gyJEjGzZsUDLiz8uXL+/evXvkyBE1CjI0NLSzs7Ozs3N3d4+Kitq0aRP1/5GRkQEBAfv37+/Xr1+xVbp37962bdubN28SQn766adevXqpWujFixfz8vJYJhZmiC66y57N/MWC2b59u6enZ2JiIvXx4cOH8+fPX7JkibhRaaJu3bqxsbGEkNjY2ObNm4sdjsjkcvnixYvpj0OGDFH7jpEajSAzWXJyMn+5VRAc7k2CHQpCQb0FAA6hSQEQkmSPOBxuIB2SPUw4zw1AsrVdrKrO77Ae48ePp4adkclku3fvnjZtWlkpw8LCevXqZW9vr2GJ1BPQhBCFQjFu3LjvvvuuZH89pWfPnlSXfWxs7OvXr1Udvf38+fPnz5/XMFpuRUVFUQtVq1YVNxKmKlWq7Nq169tvv6X/Z+nSpd9//72Xlxd/hVLzKJiZmfGROT2wz5s3b/jIX7scOHCAuoFBCKlRo8b69evVziojI4NeZt6QVIKZLD8/n7/cKggO9ybBDgWhoN4CAIfQpAAISbJHHA43kA7JHiac5wYg2douVlXnd2LPwMBAeqQaJWPjFBQU7N69W42JZ0ui57wtKCh4+fLlxo0by0rp6OhIL9N1gr3Fixens/bs2TM1tkVV9BQNmkypzIeuXbtOnjyZ/mhlZWVubs5riR07dpwxY4avry8fmdMvi7x+/ZqP/LVIbm7ur7/+Si2bmpoeOXKE5dzZpcrKyqKXWTaCzPuWBQUF/OVWEXC7Nwl2KAgC9RYAOIQmBUBIUj7icLiBREj5MOE8N6jgpFzbxarq/D5lb2Nj069fv/379xNCXrx4cevWrVKnBj527FilSpX8/f25Lb1Hjx62trZl/ZX5p9TUVFUzNzU1Zc7/q1x2draq+auBflODfWCCWbly5eXLl58/f25iYnLq1ClV32lQVZUqVZYvX85T5hYWFtTCx48feSpCW0ycOPHVq1eEEAMDg4iIiNatW6udlUKhKCoqUmMtepnZbnKbWwXB4d4k2KEgFNRbAOAQmhQAIUn2iMPhBtIh2cOE89wAJFvbRazq/D5lT9hNQhsWFjZ27Fg9PT1ui/bx8VHyV+ZXJpPJuC1aFHSXvdSesieEGBoaOjk56evrHzp0qF27dmKHoxFLS0tq4evXr+JGIq69e/fu2rWLEGJkZBQREdGjRw9Ncit2+DNbNyWYyZg3YLnNrSLgdm8S7FAQBOotAHAITQqAkKR8xOFwA4mQ8mHCeW5QwUm5totY1Xnvsm/fvj09G+rhw4dLTsT69u3bq1evjho1ivOiHRwcWKaUy+Wcly48+k0NCT5lP2rUqIsXL27ZsqV3795ix6Ip+in7itxl//Dhw3HjxhFCTExMjh079t1332mep7GxMb1MTxGuHPNWZ7Fqz21uuo2PvUmwQ4FnqLcAwCE0KQBCkv4Rh8MNRCf9w4Tz3KDCkn5tF6uq895lTwihvnpCiEwm27NnT7G/bt269dtvv61Rowbn5dJPQ5dLN7rsc3JyqAW+R4pXVUhIyO7du0NDQ0ePHi12LByoVKkStaAbL2eo4e3bt926dcvJyalcufLly5e7d+/OSbbMG48sh/piJivWCHKbmw7jaW8S7FDgE+otAHAITQqAkLTiiMPhZq3FUQAA8xxJREFUBuLSisOE89ygYtKK2i5WVReiy37EiBFGRkbUcrGxcYqKinbs2MEcPEcULN9rkDj6to+knv5etWrVihUrJk+ePGfOHFXX/euvv5o0aSK1OUnomQnovvsKJTk52c/PLzk52dnZ+c6dO97e3lzlXK1aNXqZ5e0Q5rzbxRpBbnPTVfztTYIdCrxBvQUADqFJARCSthxxONxARNpymHCeG1RA2lLbxarqQnTZV6lSpW/fvtTys2fP7ty5Q//p9OnT+vr6HN5FqcjoHuSSow+JJTw8/KeffgoICFi/fr0aq1+/fj0+Pp6+3yMRUp4zgG9ZWVl+fn6vX79u0qTJvXv36tWrx2HmzJGsmPNxK4+HXi7WCHKbm07idW8S7FDgB+otAHAITQqAkLToiMPhBmLRosOE89ygotGi2i5WVReiy54QMn78eHqZ+aB9WFjYyJEj9fUFCkO30WOsS6TL/uzZsyNGjOjcuXN4eLh6cwtHR0e7uLhwHpiG6C579iMv6QaZTNajR4/Hjx9/++23N2/erF69Orf5N2zYkF7OyMhgswq9LwghdevW5S833cP33iTYocAD1FsA4BCaFAAhadcRh8MNRKFdhwnnuUGFol21XayqLlBfua+vb+3atanlQ4cOUVsYHx9/4cKFsWPHChODznNycqIWpNBlf+/evf79+zdu3PjYsWPqPSafn59/584dV1dXzmPTEP31Vqin7IuKigYMGHDjxo0RI0acPn26rPkS9u3bp/arTK1bt6aXk5KS2Kzy5csXasHR0dHe3p6/3HSMAHuTYIcC11BvAYBDaFIAhKR1RxwONxCe1h0mnOcGFYfW1Xaxqrpwj7fTXfN5eXnh4eGEkO3bt/v6+kqwT1ZL1axZk1pgec+HP8+fP+/WrVuNGjXOnTtHP/uvqgsXLmRnZ0v5KXv6C68IRowYcerUqR9//HHHjh1KXou5evVqbm6uekW0atWKXo6Liys3fWpqKl0WswHlIzcdI8DeJNihwDXUWwDgEJoUACFp3RGHww2Ep3WHCee5QcWhdbVdrKouXJf9yJEjDQ0NqeXNmzcrFIo///yTOWAOaMjNzY1aoG/miCI+Pt7Pz8/Y2Pjvv/+2s7NTO5+NGzcSxkZJR0pKCrXAfDVGt02bNi08PHz27Nnlzknw4MEDtQcgc3Fxady4MbX85s2bctO/ffuWXh40aBCvuekSYfYmwQ4FTqHeAgCH0KQACEkbjzgcbiAwbTxMOM8NKghtrO1iVXXhuuzt7e179+5NLT99+vTXX3/Ny8uj/wc016BBA2ohPj5erBi+fPni5+eXlZV14cIFTR5CP3PmzLlz5wghEnzKPiYmhlqgv3DdFhoaum7duqVLly5ZskR5ytTU1MjISE9Pz1L/KpfLr169eu7cOSW3SSdOnEgtREZGlns39ebNm9SChYVFr169+M5NN3C1Nwl2KAgI9RYAOIQmBUBIQl5KEE6PERxuIBicmKDiwElBNQqO1K1bt2HDhsrTnD9/nll0SEiI8vR//vknnTggIIBNGBs2bKBX+fvvv5WkpHqEKdRT/8rl5eUxg1+xYgWbeCjF+tB37tzJfl32Pn/+TOXv5eXFR/7lysnJadWqlbGx8bVr1zTJ5+7du/Qw8Q8ePOAqPK5UqlSJEKKvr5+fny92LKwwb4y9evVKpXX/+OMPQsjGjRuVpCkqKsrIyHjx4sWYMWMIIYcPHy6ZJjc3l34bqHbt2p8+fSo1q+zsbHokpZMnTyqPrUuXLlTKoKAgAXKTCCnsTQV2KKhO7aqLegsAJaFJQZMCgpHCj0+Wh5uC02MEhxuoBCcmHCkVBE4KQlZ1brrsX7x4QQjR09N7/fq18pTMJ6/L3bvU0CiUPn36sIlk+fLl9CrKv8S//vqLTsmmy77YAPGhoaFs4qG8fv2auW5YWBj7dVVSq1YtQoixsXFRURFPRZSlsLDw22+/1dPTO3r0qNqZpKenz5o1y8zMjP6ukpKSOAxSc+/evaMC8/b2FjsWtujGghASFRXFfsUDBw7o6ekRQvTLRiVgKrWIdevWMdNMmjSprELpe62dO3dWEtvTp0+pZDY2NqmpqcLkJgVS2JsK7FBQnXpVF/UWAEqFJgVNCghGCj8+2R9uCk6PERxuwB5OTDhSKgicFISs6hx02cfExDRr1owKqGXLlrGxsUoSh4aGUik7duxYbs4BAQH0PnB2ds7JySl3FeYgQQsXLlSS8pdffqFTjh49utycT506xawTXbt2LXcV2s6dO5nrDhkyhP26KgkMDKSKePLkCU9FlGXo0KGEkBkzZrxi5+XLl1FRUQ8ePLh06dKOHTvmzZvXvn17IyMj5hdlamoq8FaUa8+ePVRsv/32m9ixsBIfH1+5cmWWBwXT+fPn6ckn2NPX15fJZCVzmz59OjNZ9+7dyyq3sLCQnhP84MGDZaVp164dlebUqVNKtoLb3EQnkb2pwA4FFalXdVFvAaBUaFLQpIBgJPLjk/3hpuD0GMHhBizhxIQjpYLASUHgqq5+l/3ChQuHDh3q5eVV7DaInp5e48aNhw4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",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {
-      "image/png": {
-       "width": 800
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "IPythonImage(filename=image_path, width=800)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "eea93c2a",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'<table><tr><td></td><td>rec-0 rec-1 rec-8</td><td>rec-2</td><td>rec-7</td><td>rec-3 rec-5</td><td>rec-9</td><td>rec-6 rec-10</td><td>rec-4</td></tr><tr><td>rec-12 rec-11</td><td>rec-13</td><td>rec-14</td><td>rec-15</td><td>rec-16</td><td>rec-17</td><td>rec-18</td><td>rec-19</td></tr><tr><td>rec-20 rec-21</td><td>rec-22</td><td>rec-23</td><td>rec-24</td><td>rec-25</td><td>rec-26</td><td>rec-27</td><td>rec-28</td></tr><tr><td>rec-30 rec-29</td><td>rec-31</td><td>rec-32</td><td>rec-33</td><td>rec-34</td><td>rec-35</td><td>rec-36</td><td>rec-37</td></tr><tr><td>rec-39 rec-38</td><td>rec-40</td><td>rec-41</td><td>rec-42</td><td>rec-43</td><td>rec-44</td><td>rec-45</td><td>rec-46</td></tr><tr><td>rec-48 rec-47</td><td>rec-49</td><td>rec-50</td><td>rec-51</td><td>rec-52</td><td>rec-53</td><td>rec-54</td><td>rec-55</td></tr><tr><td>rec-57 rec-56</td><td>rec-58</td><td>rec-59</td><td>rec-60</td><td>rec-61</td><td>rec-62</td><td>rec-63</td><td>rec-64</td></tr><tr><td>rec-66 rec-65</td><td>rec-67</td><td>rec-68</td><td>rec-69</td><td>rec-70</td><td>rec-71</td><td>rec-72</td><td>rec-73</td></tr><tr><td>rec-75 rec-74</td><td>rec-76</td><td>rec-77</td><td>rec-78</td><td>rec-79</td><td>rec-80</td><td>rec-81</td><td>rec-82</td></tr><tr><td>rec-84 rec-83</td><td>rec-85</td><td>rec-86</td><td>rec-87</td><td>rec-88</td><td>rec-89</td><td>rec-90</td><td>rec-91</td></tr><tr><td>rec-93 rec-92</td><td>rec-94</td><td>rec-95</td><td>rec-96</td><td>rec-97</td><td>rec-98</td><td>rec-99</td><td>rec-100</td></tr></table>'"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "r.json()[\"data\"][0]['html']"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "1525999f",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table><tr><td></td><td>rec-0 rec-1 rec-8</td><td>rec-2</td><td>rec-7</td><td>rec-3 rec-5</td><td>rec-9</td><td>rec-6 rec-10</td><td>rec-4</td></tr><tr><td>rec-12 rec-11</td><td>rec-13</td><td>rec-14</td><td>rec-15</td><td>rec-16</td><td>rec-17</td><td>rec-18</td><td>rec-19</td></tr><tr><td>rec-20 rec-21</td><td>rec-22</td><td>rec-23</td><td>rec-24</td><td>rec-25</td><td>rec-26</td><td>rec-27</td><td>rec-28</td></tr><tr><td>rec-30 rec-29</td><td>rec-31</td><td>rec-32</td><td>rec-33</td><td>rec-34</td><td>rec-35</td><td>rec-36</td><td>rec-37</td></tr><tr><td>rec-39 rec-38</td><td>rec-40</td><td>rec-41</td><td>rec-42</td><td>rec-43</td><td>rec-44</td><td>rec-45</td><td>rec-46</td></tr><tr><td>rec-48 rec-47</td><td>rec-49</td><td>rec-50</td><td>rec-51</td><td>rec-52</td><td>rec-53</td><td>rec-54</td><td>rec-55</td></tr><tr><td>rec-57 rec-56</td><td>rec-58</td><td>rec-59</td><td>rec-60</td><td>rec-61</td><td>rec-62</td><td>rec-63</td><td>rec-64</td></tr><tr><td>rec-66 rec-65</td><td>rec-67</td><td>rec-68</td><td>rec-69</td><td>rec-70</td><td>rec-71</td><td>rec-72</td><td>rec-73</td></tr><tr><td>rec-75 rec-74</td><td>rec-76</td><td>rec-77</td><td>rec-78</td><td>rec-79</td><td>rec-80</td><td>rec-81</td><td>rec-82</td></tr><tr><td>rec-84 rec-83</td><td>rec-85</td><td>rec-86</td><td>rec-87</td><td>rec-88</td><td>rec-89</td><td>rec-90</td><td>rec-91</td></tr><tr><td>rec-93 rec-92</td><td>rec-94</td><td>rec-95</td><td>rec-96</td><td>rec-97</td><td>rec-98</td><td>rec-99</td><td>rec-100</td></tr></table>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "display(HTML(r.json()[\"data\"][0]['html']))"
    ]
@@ -168,18 +120,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "3e937f0c",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'code': 0, 'data': [{'bboxes': [[48, 0, 1367, 69], [-3, 69, 1364, 123], [-3, 133, 1366, 187], [-3, 193, 1365, 256], [-3, 258, 1367, 318], [-3, 326, 1366, 382], [-3, 387, 1367, 442], [-3, 451, 1367, 507], [-3, 515, 1369, 572], [-3, 573, 1371, 642], [-3, 638, 1362, 704], [-3, 707, 1366, 762], [-3, 767, 1360, 829], [-3, 829, 1363, 893], [-3, 891, 1371, 960], [-3, 964, 1366, 1016], [-3, 1022, 1367, 1080], [-3, 1083, 1369, 1153], [-3, 1151, 1360, 1211], [-3, 1212, 1367, 1272], [-3, 1278, 1366, 1340], [-3, 1342, 1366, 1400], [-3, 1405, 1367, 1465], [-1, 1474, 1367, 1525], [-3, 1525, 1363, 1598], [-3, 1596, 1369, 1656], [-3, 1656, 1369, 1718], [-3, 1723, 1364, 1781], [-3, 1787, 1367, 1847], [-3, 1852, 1366, 1909]], 'confs': [0.9700400233268738, 0.9820594787597656, 0.9775648713111877, 0.9920864701271057, 0.9714311957359314, 0.9859472513198853, 0.9837521910667419, 0.9660004377365112, 0.987454891204834, 0.9648963809013367, 0.992371678352356, 0.9742422103881836, 0.987845242023468, 0.9768880605697632, 0.9302970170974731, 0.9746555685997009, 0.9703190922737122, 0.9231763482093811, 0.9889820218086243, 0.9877480864524841, 0.9906859993934631, 0.9882194995880127, 0.9819818139076233, 0.9832364320755005, 0.9949278831481934, 0.996972382068634, 0.9901785254478455, 0.9873738884925842, 0.9902420043945312, 0.9683494567871094], 'contents': ['Consider Fe(H2O)2+, for which SSB persists not only', 'at the UHF level but at all DFT and κ-UOOMP2 theo-', 'ries. A CASSCF calculation with 6 electrons in 5 metal d', 'orbitals yields NOONs of 1.960, 1.954, 1.954, 0.066, and', '0.066. The small LUNO (O.066), in the context of the', 'above study of the metal hydride diatomics, suggests SR', 'character (though we note cautiously that CASSCF with', '5 active orbitals is not the same as FCI in the full orbital', 'space), and thus it seems that SSB simply relects the', 'HS-below-LS relative energetics. For FeX6, with X =', 'F, Cl, Br proceeding toward weaker-field/π-donating lig-', 'ands, the expected 1oDq decrease is a small effect, with', 'LUNO populations resulting from minimal (e.g. 6e5o)', 'CASSCF calculations of 0.076, 0.100, and 0.105, respec-', 'tively. For FeF6– we verified that using a larger active', 'space of 6e15o to include a second d shell yielded a sim-', 'ilar LUNO of 0.070 (vs 0.076 from 6e5o). The LS state', 'of Fe(I)Br6– is less MR than NiH, CoH, and FeH (with', 'LUNO occupations of 0.14, 0.22, and 0.35, respectively),', 'and its LUNO value is strikingly similar to that of the', 'lowest singlet state of CH2 (O.09), which is also predom-', 'inately closed shell. This analysis suggests that neither', 'the CO nor H2O LS Fe(II) complexes are MR, in agree-', 'ment with previously reported D1 diagnostic values of', '0.14 and 0.06, which are below the 0.15 threshold sug-', 'gested by Wilson and coworkers suggested for transition', 'metals.31 Rather, the SSB observed from theories that', 'include dynamic correlation is a manifestation of varia-', 'tional collapse. We thus reiterate that for excited-states,', 'SSB should be used together with NOONs from a MR'], 'full': ' Consider Fe(H2O)2+, for which SSB persists not only at the UHF level but at all DFT and κ-UOOMP2 theo- ries. A CASSCF calculation with 6 electrons in 5 metal d orbitals yields NOONs of 1.960, 1.954, 1.954, 0.066, and 0.066. The small LUNO (O.066), in the context of the above study of the metal hydride diatomics, suggests SR character (though we note cautiously that CASSCF with 5 active orbitals is not the same as FCI in the full orbital space), and thus it seems that SSB simply relects the HS-below-LS relative energetics. For FeX6, with X = F, Cl, Br proceeding toward weaker-field/π-donating lig- ands, the expected 1oDq decrease is a small effect, with LUNO populations resulting from minimal (e.g. 6e5o) CASSCF calculations of 0.076, 0.100, and 0.105, respec- tively. For FeF6– we verified that using a larger active space of 6e15o to include a second d shell yielded a sim- ilar LUNO of 0.070 (vs 0.076 from 6e5o). The LS state of Fe(I)Br6– is less MR than NiH, CoH, and FeH (with LUNO occupations of 0.14, 0.22, and 0.35, respectively), and its LUNO value is strikingly similar to that of the lowest singlet state of CH2 (O.09), which is also predom- inately closed shell. This analysis suggests that neither the CO nor H2O LS Fe(II) complexes are MR, in agree- ment with previously reported D1 diagnostic values of 0.14 and 0.06, which are below the 0.15 threshold sug- gested by Wilson and coworkers suggested for transition metals.31 Rather, the SSB observed from theories that include dynamic correlation is a manifestation of varia- tional collapse. We thus reiterate that for excited-states, SSB should be used together with NOONs from a MR'}], 'format': 'text', 'type': 'text', 'usage': {}}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "image_path = \"./snip/60c75518bdbb8914d5a3a787_010_8.png\"\n",
     "image_path = Path(image_path)\n",
@@ -191,49 +135,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "ab31bc1c",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {
-      "image/png": {
-       "width": 500
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "IPythonImage(filename=image_path, width=500)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "e80117dc",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       " Consider Fe(H2O)2+, for which SSB persists not only at the UHF level but at all DFT and κ-UOOMP2 theo- ries. A CASSCF calculation with 6 electrons in 5 metal d orbitals yields NOONs of 1.960, 1.954, 1.954, 0.066, and 0.066. The small LUNO (O.066), in the context of the above study of the metal hydride diatomics, suggests SR character (though we note cautiously that CASSCF with 5 active orbitals is not the same as FCI in the full orbital space), and thus it seems that SSB simply relects the HS-below-LS relative energetics. For FeX6, with X = F, Cl, Br proceeding toward weaker-field/π-donating lig- ands, the expected 1oDq decrease is a small effect, with LUNO populations resulting from minimal (e.g. 6e5o) CASSCF calculations of 0.076, 0.100, and 0.105, respec- tively. For FeF6– we verified that using a larger active space of 6e15o to include a second d shell yielded a sim- ilar LUNO of 0.070 (vs 0.076 from 6e5o). The LS state of Fe(I)Br6– is less MR than NiH, CoH, and FeH (with LUNO occupations of 0.14, 0.22, and 0.35, respectively), and its LUNO value is strikingly similar to that of the lowest singlet state of CH2 (O.09), which is also predom- inately closed shell. This analysis suggests that neither the CO nor H2O LS Fe(II) complexes are MR, in agree- ment with previously reported D1 diagnostic values of 0.14 and 0.06, which are below the 0.15 threshold sug- gested by Wilson and coworkers suggested for transition metals.31 Rather, the SSB observed from theories that include dynamic correlation is a manifestation of varia- tional collapse. We thus reiterate that for excited-states, SSB should be used together with NOONs from a MR"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "display(Markdown(r.json()[\"data\"][0]['full']))"
    ]
@@ -248,18 +163,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "da8895df",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'code': 0, 'data': [{'full': 'P_{g}(t)=\\\\frac{R_{L}a_{0}^{2}|G(j\\\\omega)|^{2}}{(R+R_{L})^{2}}\\\\sin^{2}(\\\\omega t-\\\\angle G(j\\\\omega))'}], 'format': 'latex', 'time_dict': {'model_time': 0.519058283418417}, 'type': 'equation', 'usage': {}}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "image_path = \"./snip/802.1778_018_25.png\"\n",
     "image_path = Path(image_path)\n",
@@ -271,49 +178,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "1185f613",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {
-      "image/png": {
-       "width": 500
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "IPythonImage(filename=image_path, width=500)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "2fc76ec9",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "$$P_{g}(t)=\\frac{R_{L}a_{0}^{2}|G(j\\omega)|^{2}}{(R+R_{L})^{2}}\\sin^{2}(\\omega t-\\angle G(j\\omega))$$"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "display(Markdown(\"$$\" + r.json()[\"data\"][0]['full'] + \"$$\"))"
    ]
@@ -328,18 +206,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "dcbb0787",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'code': 0, 'data': [{'full': 'TITLE |  \\n Pt loading (µg⋅ cm⋅) | ECSA (cm⋅, mg⋅) | Specific Activity (µA cm⋅) | Mass Activity (A 9pt) \\n 3.2 | 368 | 308 | 114 \\n 4.5 | 515 | 450 | 235 \\n 5.8 | 575 | 600 | 345 \\n 8.4 | 455 | 541 | 245 \\n 10.9 | 354 | 522 | 184 \\n 13.5 | 293 | 511 | 150'}], 'format': 'text', 'time_dict': {}, 'type': 'chart', 'usage': {}}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "image_path = \"./snip/63e096721f23f0032d42225a_010_2.png\"\n",
     "image_path = Path(image_path)\n",
@@ -351,51 +221,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "c933a02a",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {
-      "image/png": {
-       "width": 800
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "IPythonImage(filename=image_path, width=800)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "72e6f99a",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "TITLE |  \n",
-      " Pt loading (µg⋅ cm⋅) | ECSA (cm⋅, mg⋅) | Specific Activity (µA cm⋅) | Mass Activity (A 9pt) \n",
-      " 3.2 | 368 | 308 | 114 \n",
-      " 4.5 | 515 | 450 | 235 \n",
-      " 5.8 | 575 | 600 | 345 \n",
-      " 8.4 | 455 | 541 | 245 \n",
-      " 10.9 | 354 | 522 | 184 \n",
-      " 13.5 | 293 | 511 | 150\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(r.json()[\"data\"][0]['full'])"
    ]
diff --git a/playground/private.semantic_recognition_vl.ipynb b/playground/private.semantic_recognition_vl.ipynb
index d71dda6..5a060a0 100644
--- a/playground/private.semantic_recognition_vl.ipynb
+++ b/playground/private.semantic_recognition_vl.ipynb
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "6b599ea2",
    "metadata": {},
    "outputs": [],
@@ -75,18 +75,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "c351a74a",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'data': [{'full': '<table><tr><td></td><td>$ S^{2}_{exact} $</td><td>$ S^{2}_{UHF} $</td><td>$ S^{2}_{UB3LYP} $</td><td>$ S^{2}_{UCAM-B3LYP} $</td><td>$ S^{2}_{UB5050LYP} $</td><td>$ S^{2}_{\\\\kappa UOOMP2} $</td><td>$ S^{2}_{UOCCD} $</td></tr><tr><td>ScH ( $ ^{1}\\\\Sigma^{+} $ )</td><td>0.00</td><td>1.00</td><td>0.60</td><td>0.58</td><td>0.54</td><td>1.00</td><td>1.00</td></tr><tr><td>TiH ( $ ^{4}\\\\Phi $ )</td><td>3.75</td><td>3.75</td><td>3.76</td><td>3.76</td><td>3.75</td><td>3.75</td><td>3.75</td></tr><tr><td>VH ( $ ^{5}\\\\Delta $ )</td><td>6.00</td><td>6.01</td><td>6.01</td><td>6.01</td><td>6.01</td><td>6.01</td><td>6.01</td></tr><tr><td>CrH ( $ ^{6}\\\\Sigma^{+} $ )</td><td>8.75</td><td>8.87</td><td>8.79</td><td>8.79</td><td>8.80</td><td>8.82</td><td>8.79</td></tr><tr><td>MnH ( $ ^{7}\\\\Sigma^{+} $ )</td><td>12.00</td><td>12.00</td><td>12.00</td><td>12.00</td><td>12.00</td><td>12.00</td><td>12.00</td></tr><tr><td>FeH ( $ ^{4}\\\\Delta $ )</td><td>3.75</td><td>4.72</td><td>4.05</td><td>4.10</td><td>4.39</td><td>4.44</td><td>4.48</td></tr><tr><td>CoH ( $ ^{3}\\\\Phi $ )</td><td>2.00</td><td>2.95</td><td>2.16</td><td>2.19</td><td>2.48</td><td>2.23</td><td>2.43</td></tr><tr><td>NiH ( $ ^{2}\\\\Delta $ )</td><td>0.75</td><td>1.68</td><td>0.81</td><td>0.82</td><td>0.98</td><td>0.79</td><td>0.92</td></tr><tr><td>CuH ( $ ^{1}\\\\Sigma^{+} $ )</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td></tr><tr><td>ZnH ( $ ^{2}\\\\Sigma^{+} $ )</td><td>0.75</td><td>0.76</td><td>0.75</td><td>0.76</td><td>0.76</td><td>0.76</td><td>0.76</td></tr></table>'}], 'code': 0, 'usage': {'prompt_tokens': 2173, 'completion_tokens': 570, 'total_tokens': 2743}, 'type': 'table', 'format': 'html'}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "image_path = \"./snip/60c75518bdbb8914d5a3a787_008_5.png\"\n",
     "image_path = Path(image_path)\n",
@@ -98,70 +90,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "fbd9efca",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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LzbqGhobbtm3TMH6t1r9/f/rb4OoEyccOnThxIiGkX79+JetbSkpK37596dW7dOnCyWkSpIyPektJTk6m+tmdnZ2fPn1a7K9yuXzevHlUuWvXri03NzREAFqBvyalqKjoy5cv9+7d27VrV58+fYq9iPzq1SuVcuO2gQIQC09HHLeXMFFRUa6uroSQypUrX7lypdhf09LSOnXqRAixtLS8efMmV5sAQOPvxCRkuWJtBWgXrajtCQkJtWrVqlGjxvXr10tN8PDhw+bNmxNCvLy8Pn/+rGFs3HTZ5+XlMX93rlixQqXV4+PjLS0tCSHbt2/nJB6uYouPj2euu3PnTs7DK4mqLu3bt1eebPjw4W3atCksLCz5J5667H18fCpXrpyRkdGxY0fm13Lo0CFO8pfL5fXr1yeEBAYGcpIhaGjBggUGBganT59WkiYiIoKuCbVq1crMzNSkxKioKKKujh070hNOMLHpKfP09Lx7964mkWu7gwcPMr8Qrk6QnO/QBQsWEEJCQkKUFBoYGEhn0qFDh1Iv1UA38FRvFQpFUVFR+/btCSEmJiZRUVFlJRs5ciRVdLkvnKEhApA+/poU6qkUmr29/bRp01q0aEH/j0pd9pw3UACi4O+I4/AS5s2bN7a2tlSykydPlpomIyODmo7C3Nw8Li6Ogw0A+P/4O0yELFesrQDtohW1PTc3t0GDBnZ2djExMUqSZWZm1q5dmxDi4+NTVFSkSXiS6LJXKBS//fYbIWTu3LmcxMNVbMJ32V+8eJEqq+Q9fKY///yzSpUq8fHxpf6Vjy57KjDqHYXbt28zv5Z69eqV2lWqhv379xNC9PT0lFyBgDDS09NNTU0rVark6+urZLSigoIC5iiQCxcu1KTQw4cP01mZmZnZ2NjY2dnZK0WVXrVq1Y8fP5aap5eXl7GxcVnPerdo0SI8PLzUW18VR1JSUpUqVZhfEVcnSG536KNHjwghI0aMUF6oTCZjTr43e/ZsTrYFpIa/eqtQKJYvX07lOX36dCXJUlNTqbfibGxs0tLSlKREQwQgcbw2Kffv3w8JCVm6dOnevXufP39O/Wzu3bs3XZZKXfacN1AAwuPviOPwEkYmkzVo0IBKMGzYMCWF7t27l0rm7+/PwTYAKBQKnk9MgpUr1laAdtGW2k69xXj48OFyc46MjKReqdRwyFOpdNl//PhRX19f+blQ+NiE77KnXqxr1qyZ8mSWlpbOzs6+ZfDw8KAC9vT0pP9Tw0FLWrVqVbVq1ezsbOrjt99+y/xmwsPDNcmcJpPJqAcZ+vbty0mGoDZmZytROmiVr68vnaxu3bqaFLpo0SJCyO7du/Pz81mu8ssvvxBCzpw5U1YCLy8vf3//7OzsK1euhIWFhYaGzp49e82aNUeOHMEQTJTvvvvOw8OD2XfA1QmS2x3avXt3U1PThISEcjNZuHAhvS1GRkZ44kkn8Vdv09LS6Fl2lD9AoVAoRowYQaUMDg5WkgwNEYDE8deklEW9Lns+GigA4fF3xHF4CbNy5Uo6we3bt5UUKpPJzMzMqJR4rwW4IvyJiY9yxdoK0C5aUdvz8/OpqVBYxtanTx9CSMeOHTWJUP1RhrlVvXr1Ro0avXv3TuxAxPTs2bMrV64QQn744QflKSdNmjRw4MAmZaDvEdWsWZP+T+pBG/WcOXPm3r17s2fPpjMJDQ1lJvj111+LiorUzp9mbGw8dOhQQsiJEycSExM1zxDU9unTJ+ZHqmaWqnr16vTymzdvNCk0Ojq6evXqw4cPLzbGa1muXr26ZMmSoKCgbt26KU9pbm7esWPHcePGzZkzZ8mSJdOmTevXr5+dnZ0m0eqG/fv3Hz9+fNeuXaamppxnzuEOTUtLO3funKur665du168eKE8n4kTJ9IlFhQU7Nu3T43gQcp4rbfh4eHULDiurq70LfCy9OjRg1rYvn17QUGB8sRoiACkidcmhVv8NVAAguH1iOPqEiY/P5++4DU1NW3VqpWSQo2NjX18fKjlP/74Q6WAAUol1omJ23K16PQKItKW2n7jxo309HQTExMTExM26du2bUsI0WSsYEKIoSYrc6tNmzanTp0SOwoxrVmzhhBiYGAwePBg5SmXLl2q5K+fP3+murSGDx8+aNAgzQObO3dutWrVJk2aRP9Ps2bN+vTpc/z4cerj69evd+3aNWrUKM3LGjVq1Pr164uKinbv3h0SEqJ5hqAeal4Bmru7e1kpCwsL6WXmb181vHjxgnrRhI3U1NQhQ4Y0bdp02bJlmhRakSUmJk6ePDk4OLh169Z85M/hDr1586ZcLo+JiZkzZ86vv/5669Yt5ijAxVStWtXd3f3Zs2fUx+PHj8+aNUuN+EGa+K63u3fvphY8PT3LTdyoUSNqIT09/ezZs8xnNABAK/DdpHALDRRoO76POK4uYS5duvTlyxdq2cPDo6yh7WgNGza8cOECIeT8+fMJCQnU6PYA6hHrxMRtudp1egWxaFFtf/XqFSFEJpO9fPmy3McmCCHGxsaEEOpJC7VJ5Sl7QoiXl9fHjx85eVhbG+Xl5YWHhxNC2rdvL6ln7v76669Hjx7NnTu32K2n0NBQPT09+uOCBQs4eX6nSZMm9vb2hJCtW7dqnhuorUuXLvRNmoEDB1JvP5Tq/fv39HKxX8mqiomJoSbXZiMwMDArK+vQoUMsn+CGksaNG2dnZ1fspRkOcbhDExIS6OWCgoJyn2Byc3Ojl9++fcsyBtAKvNbb9+/f379/n1pm81OsVq1a9KmQmo4FALQL36dCDqGBAh3A9xHH1SUM81FCS0vLcsul7w0oFIojR46wDxigJLFOTNyWq0WnVxCRFtV2+jfVggUL2KSnfrO5uLioER5NhC77P//8c8uWLSX/v06dOnK5/MOHD8KHJAWXL1/Ozc0lhHTu3FnDrOiuc0760OfNm+fk5DRu3Lhi/+/p6RkQEEB/fP/+/bZt2zQvjhBCDSwYGxt77949TjIE9WzcuPHr16/p6emHDh0yMDAoNY1cLo+OjqY/+vv7q11cWlpamzZtqLeHyrVmzZozZ86EhYVRM3GDGvbu3Xvy5Mndu3ezfLFLVdzu0GJPPykUCuUZOjk50ctJSUlsYgCtwHe9ZZ53HBwcyk1vZGRETcFCCLlz5w4fIQEAf/huUriFBgq0nTBHHCeXMNTTlJRKlSqVWyg9yQQh5J9//lEtYgAGsU5M3JarXadXEIt21XYvLy9qYd++fRMmTFDeI6FQKG7fvk0I6d69uyahijAwzu7du83MzEp2Abdq1SoiIoIazr+koqKid+/excfH5+fnV6tWzc3Njc3tbi1y8uRJaqFjx45qrB4fHx8ZGfn169c3b96cOXOG+s+lS5dmZ2fb2dmZmpr6+flR72Wo5ODBg9HR0Vu2bCl13YULFx4+fFgul1MfFy1aNHLkSM0PNn9//wMHDhBC/v77b+XjBgLfyv2FevPmzczMTGrZ3t5+4sSJapdlY2Nz6dIlNin//fffn3/+OTAwcMiQIWoXV8F9/vz5xx9//Omnn/g7xLjdoR07dnR1dY2LiyOEGBgYjBkzRnme6enp9LKVlRWriEHyBKi39BOshBAbGxs2q9jY2KSmphJC4uLi0tPTy/oZAwBSI0CTwi00UKDVhDziNL+E+fz5M71MTy2rBLNr4uHDh2wDBfhfYp2YuC1X606vIAqtq+2NGjXS09OjeurDwsI+f/68ZcsWaoyQkjZt2vT27Vtra2sNR+gV4Sn7sobysbKy6t+/f8mfklFRUWPGjLG3t69Tp46vr2/37t0bN25sY2PTpUuXo0eP8h6uUE6fPk0IMTQ0bNmypRqrX7hwYcCAAePHj1+9enXlypVdXFycnZ3T0tLmzp07cuTI7777jtmBxZJcLp8/f37NmjVHjhxZagIPD49hw4bRHz9+/Lh582Y1gi+GfixXyXxBIBEbNmyglxcsWMDmF62GcnNzBw0aVLNmTUzupIkxY8ZUq1Zt8eLFYgfCdodaW1vfvXt3wYIFQUFB9+/fL/fhfeZk5jVr1uQiUhCfAPX2+fPn9DLLJwOYD9Y9efKE+5gAgB/SORWyhAYKtJqkjjiVLmGYg8GWhfmASExMTE5OjibhQYUl1mHCbbmSOthBsrSutleqVInZNXr8+HEPD4+wsLCSKWNjY6me+s2bN2s4tYnQT9l//fr19evXbN7lJITk5ub+9NNPYWFhcrl8wIABM2bMaNq0qZ6e3uPHj/ft27du3bpLly75+/sfOnSI+XtUG717944aEcjd3V29gblHjRrFyeyvTOHh4S9fvty5c6ehYZn1ZMGCBfv27aNn71myZMm4ceM07LetXbu2gYFBUVHR7du3CwsLlZQO4rp79+7hw4ep5ZkzZ06YMEGAQmfPnv3q1atbt26xeUe1mGfPnh08ePDatWvx8fG5ubmOjo6NGjX6/vvvu3Tpwua3uM7YvXv32bNn79y5I4UXFdnvUAcHh/nz57PJ8+vXr8znm7p06aJRiCANwtTbtLQ0epnl2Yd51v706VO56dEQAUiBpE6FLAnQQAHwRFJHHJtLmGrVqtHL1Oi1yslkMnpZoVC8f/++Xr16GkcKFYtYhwm35UrqYAfJ0tLavnTp0sOHD2dlZVEfMzIyJkyYsHPnzq1btzZs2JD6z8ePH/v7+2dnZ4eGhn7//fcaBiz0U/b79+//+vUrm5SpqakdO3b8448/5HJ5WFjY4cOHW7VqZWxsbGRk1Lx581WrVh07dszAwODChQudOnWivzItRQ+lp+HsnRwqKir6z3/+4+7uPnz4cCXJij2Dn5SUtH79eg2LNjAwqFWrFiEkNzc3MjJSw9yAJ0+fPu3Rowe1PHPmzKVLlwpQ6J07d9atW/fdd995e3urtGJKSsrAgQM9PT0PHz7cunXrn3/+edGiRa1atfrrr7/8/f2bNWvGfNlct338+HHq1Kk//fSTei/0cEvtHarckSNH6PuIenp6o0eP5jBzEIVg9ZZ+TZ6w7hFjjpOrfOIENEQAEiGpUyF7vDZQAPyR1BHH8hKmTp069HJeXl652RabkK+scQUAyiLWYcJtuZI62EGytLe229nZbdu2rdhvsLt37zZp0iQkJCQnJ2fr1q3t2rXLyck5evTonDlzNI9ZuOeXCwoKDh48GBwczCZxfn5+165dqcckQ0JCSg58Twjp2bPnzJkzlyxZ8ujRo+nTp//5559scr548SKbky5FjcFk1EN32Xt4eAhTYrl27Njx9u3b/fv36+uXc1/n119/3bVrV35+PvXx999/nzRpkoWFhSal161bNzY2lhASGxvbvHlzTbICDuXl5SUnJ8fGxv71119hYWEFBQU+Pj4rVqwQpqnNz88fMWKEvr7+smXLVFoxLi6uWbNmZmZm169f9/HxYf7pl19+6du37927d9u0abNr166hQ4dyGrIUjR49unr16lJ4UVHtHaqcXC5nbt2QIUOkcysU1CZYvVWjR4yZLDk5uaxkaIgApEM6p0KV8NdAAfBK9CNOjUuYb7/9lh6zMTs7u9wiqPmWaOiyB1WJdZhwW67oBztoBa2u7QMHDqxcufLQoUOZP6uKiopWrFixadOmnJycESNGLF26lPmqliZ46bLfvHkzPQMqIUQmk6Wnp8fGxtK9uuUKCgqi+uurVav2n//8p6xks2bNWrNmTU5OzrZt24KCgug3EZQ4f/78+fPnWYYhmKioKGqhatWq4kZCKSgoWLhwYYMGDQYNGlRuYkdHx/Hjx9MP13/58mXNmjVz587VJAB3d3dq4c2bN5rkA1w5duxYQEBAQUEB8z979uy5Z88ewaYy27x588uXLwMCAujqwYaenl5MTMy333577Nixkm8/OTg4HDlypFGjRl++fBk2bJiJicmAAQM4jVpaduzYceHChbt376oxGTXn1Nuh5Tpw4AB1w48QUqNGDc3f+wHRCVlvmdfYzKdTlWAmK+t3DhoiAOmQ1KlQJTw1UAC8EveIU/sSpnPnzlZWVtR9MvqHpRKXLl1ifkSXPahErMOE23K19/QKQtKB2u7n5/f8+fOpU6fu27eP+f/ULCZfv36lpqjlBC8D43z9+jWFITU19cuXL+x/Jj5//pyexXTYsGFKBji2tLTs1asXtbxx40Y2mS9evDidtWfPnrGMWUP0TInMiWtEtGXLlg8fPixatIjluLpz585ljl+/fPlyDX+m2NjYUAuvX7/WJB/gSufOnU+cOHHs2LFjx47t27fvt99+a9GixalTp6pVqxYQEHDr1i2+A/j69euiRYsIIX379lVpxcqVK7do0eLo0aNljVZWo0aNyZMnU8ujR48u9oyMLklISAgKCgoJCWnRooXYsai/Q5XLzc399ddfqWVTU9MjR47QjQloKYHrLXOcPZY9YsyHWIt1CtDQEAFIhKROhariqYEC4I/oR5zalzDm5ubTpk2jlrOyspR3C7x586ZYVto+bC8ISazDhNtyRT/YQSvoRm0nhFSpUmXmzJk1a9Ys+afDhw/Xq1dv165dnBREFFwoNtTMihUrSqb58uXL2rVrqYtVf39/JbkNHjyYzur27dvKi6afoHR1dVU7trLEx8cz1925cyf7dVXi5eVFFXHw4EGeimAvNzfXwcGhSZMmKq01Y8YM5nc1Z84cTWJYtWoVlU/Xrl01yQd4dfjwYeqOmp6e3qJFi3gta+HChYQQAwODtLQ0lVZMTk7OzMxUnob5Msf333+vfpTS5u/vX79+fZlMVupfAwIC6C8hLy+P72DU3qHK/fDDD9QmGBgYnDp1isOcQSxC1lu5XM48kZ07d47NWn5+fvQqISEhpaZBQwQgEdI5Ffbu3Zsu69WrV+Wm56+BAuCPdI44JpaXMBkZGS4uLlR4M2bMUJLhnDlzXF1dmUfooUOHeAgcdJNYhwm35UrzYAep0Y3anpqaOn78eH19/erVqx8+fPjnn38u9UGK/v375+bmahi5cNPP2tjYTJ06dfny5cqT5efnHz9+nP7YtGlT5ek9PT2phbi4OO2dVYkem1IKT9lv2rTp8+fPqg7wNHv2bOb49WvXrk1NTVU7BktLS2qB5WTFIIoBAwZQrwIpFIp58+b17t2behWIczKZbM2aNYSQdu3aqToOT9WqVenqVBY3Nzc620OHDr19+1aNICVu27Ztf//99+7du6XwoqImO1SJvXv3UnezjYyMIiIi6LnFQHsJXG+LvVimYPdKIzNZWW91oCECkAJJnQpVxV8DBcATyR5xLC9hrKysjhw5QgW/cePGss7LL168WLVqFfUwCq1KlSo8BA46SKzDhNtyJXuwg6ToRm2Pjo5u2LBhWFjYkCFDXrx4MWDAgN9///3x48dt2rQplvLIkSPdu3fXsItMuC57SmBgoKmpqZIEN27coDfJwcFBeWJCiK2tLb388eNHzSMUBf3qnLW1tbiR5OTk/Pbbby1btlS1t8vW1jYoKIj++PXr16VLl6odBt37jy57ievTpw89P/DJkydHjx7NRykHDhz48uULIaRdu3Z85E8Iod90kcvlR48e5akUsXz48GH69Ok///yzRCZz5mOHPnz4kJqo3MTE5NixY9999x1XOYNYRKm3zF9yhYWFbFYpKiqilzU8iet2QwQgLqmdCtUgbgMFoBKJH3EsL2GaN29+/vx5Kyur3Nzc7t27l+y1f/78ub+/f3BwMH0Gp6DLHtgQ6zDhtlyJH+wgEbpR2+/cudOuXbtPnz6NGzduz5499CPXnp6et2/f3rp1a7HnEa9cuUL1UahN6C57a2vrOnXqKEnw4sULejk/P390eUJDQ+n02jtmHH2XwtzcXNxI1q1bl5KSwvxW2QsJCWFW0I0bNyYmJqoXBj2BgUwmUy8HEAzzVs2BAwdOnjzJeREbNmygFurWrct55pRGjRrRyxKcoVpDo0aNcnZ2Lvb4j4g436Fv377t1q1bTk5O5cqVL1++3L17d06yBXGJUm+ZT6GyHPeZmUzDHjHdbogAxCW1U6EaxG2gAFQi/SOO5SVMx44d79+/7+vr++LFC09Pzzlz5ly/fv3JkycnT56cMGHCN998M2DAgNDQ0GITuTk4OPAaPOgGsQ4TbsuV/sEOUqADtT0+Pt7Pzy89PX3QoEFhYWElE4wZM+bly5dDhw5l/ufevXtv3LihdqFCd9kTQpQPg/Dp0yd62cTExLA8NjY248aNGz9+fFBQULGb21qEfmpG3IfKs7Kyli1b1q5duy5duqixupWVVUhICP0xNzdXva5/Qkh2dja1oGTyYZCIgQMHMl/W5rwVjo2NffDgAbXs7u7ObeY05rMwMTExPJUiii1btly5cmX37t1GRkZix0IIDzs0OTnZz88vOTnZ2dn5zp073t7emucJohOr3larVo1eZnnPOD8/n17WsEdMhxsiAHFJ7VSoHnEbKAD2tOKIY38J4+HhcenSpUuXLg0ePHjPnj2dOnVq3Ljx5MmTP3/+fP36dWoOtvT0dDq9i4sL82gFKJVYhwm35WrFwQ6i043aHhQU9PXrVwsLC/oBxJLs7OzCw8PPnDlTtWpV+j+nT5+udqGGaq+ptr59+xabQImJOeSio6NjqfcudE+lSpWoO/PMk73wVqxYkZaWpnY/OyFk2rRpq1evTklJoT6GhYXNnDnT0dFR1XwkNbg/KGdsbFytWrXPnz9THx88eBAfH+/k5MRV/szRITw8PNiv+OrVq61btxoYGIwdO9bNzU15YjMzM3pZ7bdDJOj9+/c//fTTzz///M0334gdy/9Re4eWKisry8/P7/Xr102aNDlz5kz16tU1zBCkQMR66+Dg8OTJE2qZ5at7zGQle8TQEAGIToKnQvVw3kAB8EFbjjhVL2F8fX19fX0JIXK5PDc3t9jL8cxZ3Hx8fPgJGXSHWIcJt+Vqy8EO4tKN2n7jxo0jR44QQn7++Wdmd3ypunXrdvv2bV9f3/j4eELIw4cPP3z44OzsrEa5InTZBwcHK/krs3tX3P5rIdFDt4u4yWlpaatXr/b19W3fvr3amZibm8+ePfunn36iPubn5y9atGjz5s2q5kN32Zc7Xx/w5M6dOxEREZUrVx49enSNGjWUJ3ZycqJ/7xJCIiMj+eiyt7KyYs5dodznz59btGhBHVCbN2+OiYmxs7NTkp55H7HYDG9abd26ddnZ2RcvXrx27ZrylMxHejt16sT8Eo4fP17uaYk99XZoqWQyWY8ePR4/fvztt99GRESIPrAYcEXEetuwYcMLFy5Qy8Veci8LfcIiJcZ6QkMEIAUSPBWqh9sGCoAn4h5xAlzC6Ovrl/zN+e+//9LLmlxNQwUh1mHCbbk6c3oFXulGbacmLSeE9O/fn03p7u7u+/bto08HUVFRWtNlr1zt2rXp5YrTZe/k5BQbG0tE3eRly5ZlZWUtWbJEw3wmTZq0fPly+tfPtm3bZs2aVbNmTZUyob8HPGUviuPHj9Ozd65ZsyYqKkr5T95iwzXSr1lo7uvXr/QgKsrH1Crm77//pmtRWlra33//PXjwYCXp6bGYCCHl/r7XIs7Ozi1btiQs5qmj5oOlFBYW8tRdqPYOLamoqGjAgAE3btwYMWLEtm3b9PVLH+dt3759GzZsuH37tiZlgcBErLetW7eml5OSktisQsfg6Ohob2/P/BMaIgApkNqpUG3cNlAAPBHxiBPxEob+fWtkZNS3b1+184EKQqzDhNtydeb0CrzSjdoeGRlJCDE0NGQ/ToCPj0/v3r1PnDhBCElOTma5VjGS67Lv2LFjpUqVqOlYv3z58vXr14rw4CTdo83ykRnOpaSkrFu3rlu3bq1atdIwK1NT07lz5/7444/Ux8LCwgULFuzYsUOlTOhnglTt6wdObNy4kV7+8uXL3r17mbMUlMTsZiKEcHhNeOfOHfqxU5UmNqCndKaUe0uW2YbWr1+ffUESN23atGnTprFJaW5uTn9pN27cMDEx4SMetXdoSSNGjDh16tSPP/64fv16JcmuXr2am5urSUEgPBHrLfMkGBcXV2761NRUuoIxe9M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izsOQ/aZNm+jcODs7azLfkEwmc3V1paLCkL2C2NhYKhU2M26/evWK+pluZWX17t27SsNkZWXROR83bhz7nJSUlJw/f/7bb7/18fGp6llXiUTSuHHjoKCgCxcuaPLE6/Tp0wkhxsbGmBlQlzIzMyUSScOGDZcvX65y+q20tDTmlKy//PIL53Q3b95MReLp6akyMN1i2tnZVToExm9s+kuos0nfciOESKVSla8J9+rViwqsfKouasiec65AXwhVbpXgPGSfmJhIXfMTQg4dOlRVsHv37lHT3BNCOnbsyEeWAeD/GEaToqWOFYB3IqxxBw8eNDIyio6OphaSpSgfnbl06RIhxNzcnGUSq1atIoQ4OjrykV8wfEJVE97TZY4HEqUPg44aNYoOZmpqyvkQQO8YRqcgSFGvfLyVvby8PGpCIkpwcLC1tTXn2CQSyfjx4zXMkqHasGEDIcTY2HjEiBEqA9+4caOwsJAQ8vHjR+oNlIri4uLo7U8++YRNHnJyckJCQlxdXXv16rVv37527dr98ccfL1++7NChAx1m48aNDx482LJlS/PmzUNDQ3v27Onl5bVmzZq8vDw2SSigyoNMJtu3bx+H3YGb69evl5eXx8XFBQcH+/n53blzR0lgJyenBg0a0B9PnDjBOV36LPv6+qoM3LRpU2ojOzv7zJkz2o5Nfwl1Ni9dupSZmUltK7m9R2vSpAm1ce7cuaSkJM7pgmEQqtxqw9KlS7OzswkhLi4uQ4cOrSpYixYtrl69OnXq1CVLlpw6dUp3+QOoBgyjSUHHCvpCbDUuJSVl2rRps2fPZs4NoNKLFy8IIcXFxc+fP2cT3szMjBCSk5PDLZNQ3QhVTXhPl1qAkMYMr6CsrIzerl27thqZBj1nGJ2CIEVd0yH7FStWZGRk0B+Zi+1yQ09PAUxFRUVhYWGEkK5duzo7O6sMTy05RQixt7enptOp6N69e9SGiYnJl19+qTLOffv2eXh4LFq0KCsr6/vvv//w4cO+ffuGDh1ar149+sFAQoixsXGzZs2mTJly4MCBxMTE77777t27d3PmzPHx8YmIiFCZioLmzZvXrFmTELJjxw519wXOmNd1paWlv/32m/LwXl5e9DZzwiW1vH37lm67fXx8VIZnFjxq2QPtxabXBDmbhBDmmKONjY3K8HSfJ5fLjx07xjldMAxClVvepaen0ytPDhkyxMTEREngLl26bNmyZfHixcylfQBAc4bRpKBjBX0htho3efJkZ2fnkJAQtfaiLw2YTygqQV16eHh4qJs9qJ6Eqia8p9ujR4+pU6dS28OGDWM+X6zg7du39LbC6CcYNsPoFAQp6hoN2cvlcuZAqq+vr7u7uyYREkK8vb2Zqw0A5fLly9RT8wEBAWzCf/rppz4+Pm5ubvfu3av0vYe8vDx6iqgff/xR+W+LkpKSwMDAr7/+Ojc319LS8u+//16zZg2bSwUHB4fNmzeHh4dLJJLk5OTBgwdPmTJFLpezOQQaNQ9UfHz87du31doROFO4E6jylNGrTxNCUlNTuSXKPL8uLi4qw5uamtKvh0dHR2s1Nr0myNkk/300iWJpaakyPL3wACHkn3/+4ZwuGAahyi3vNm/eTM+/x7IHBwDeGUaTgo4V9IWoatyBAwdOnjy5b98+c3NztXb08/OjNg4ePKjyGlYul9+8eZMQ0q9fP85ZhWpFqGqijXS3bNny8ePH7OzsI0eOGBsbVxqmvLycnuqZEEJP3QbVgWF0CkSIoq7RkH10dHRaWhr9ka/+iZplhfngthK5ubkxMTHnz5+/du3ay5cvy8vLecmD2Jw8eZLa6NatG5vwEonk0qVLlpaWgwYNOnv2rMLXkpiYOHjw4OTkZELId999t3jxYiVRlZSUDBw4kJ7pe8eOHb1791Yr80OGDFm3bh21HRoaOnz4cLVG7ekifvHiRbXSBc66devm6elJbRsbG0+cOFF5eGrOB4qtrS23RJmvR7F8wpQOlpCQwMwD77HpNUHOJiGEamEo9Ap4SjDvAt69e5dzumAYhCq3vAsPD6e3mZPIAYAuGUaTgo4V9IV4alxycvJ33333/ffft2vXTt19mzZtSo9IhIaGDh48WMnI0datW1+/fl2jRo358+dzzy5UJ0JVEy2la2lpybxPXNH169dzc3Op7Zo1a3777bcsMwwGwDA6BYqOi7qyF7RVOn78OPNjVROwqMvf3//06dPKf4mWlJTs3Lnz0KFD169fl8vllpaWBQUFhBBHR8eBAwdOnTq1devWvGRGJP7++29CiImJSdu2bVnuUqdOnRs3bvznP/8ZPny4VCpt06ZNgwYNTExMnjx5cvXq1Y8fP3bo0GHDhg0qI5wzZ865c+eobX9//5EjR3LI//Tp07dv3/7kyRNCyNGjR1u3bj137lyW+3bq1InaiIyMXLhwIYfUQV01atS4devW9u3bs7KyxowZ06JFC+Xh37x5Q2/XrVuXW6JPnz6lt9m8w0H+9/GxR48ede3aVUux6TVBzqYCNrdgmZ1xXFxcQUEBm0cIwVCJodxqLi0tjer4CCHm5uYKb/wkJiZmZWXVrl3byclJiNwBVCOG0aQwoWMFMRNPjZs4cWKtWrWWLVvGYV9LS8tx48bt2rWL+njixIkrV66sXLnym2++UQgZHx9PjdRv27aNnp8WQDmhqolQ6f7666/09pIlS9jceAaDYRidAks8F3VN1q5t2bIlM6q4uDhNYmMqKChQ8teIiAjqFk27du0OHjyYnZ0tl8uLiooeP348Z84catztyy+/TE1NZZMW/cY6Zc2aNezzmZiYyNx3z5497Pdlj568qVGjRhx2LygoOHr06PLly7/55pvBgwePGzcuJCTk5s2bbPZVmIsmIiKi0mAdO3ZkFtBKwzALromJycuXL1nmv6ysjHrlxMLCorS0lOVeoDP5+fnM2Zm///57bvHQ92YIIUeOHGGzC3O1kMOHD2svtuqDr7Mpl8t79uxJx9OrVy+V4c+fP89sbapaSt7Pz4+OLTY2dvHixZ9++qm3t7erq2ubNm3Gjx9//vz58vJyztkGfcRjuVVi4MCBdBIvXrxgs8uRI0foXXx8fKj//PDhw7fffsscOHN2dp40adKDBw+0kW0AUJdomxQtdawAwtJejdu7d69EIrl9+zbzPwMDA+m0ioqKlMeQmppa8dGf9u3bx8TE0GEePHhALb0WEhLCV84BFOimY9JeusyJZ+fNm8dvJsHAiLlTUIn3oq7RxDjv3r1jfqTfdNCckhsRixcv/vzzzxMSEn755Zfo6OgRI0ZQY/Tm5ua+vr6rVq2KjY1t1arV4cOHW7duzZxCSH/RR8Ft4QILC4uhQ4cuWLBg27Ztx48f37VrV3BwMMvX8zds2EBvGxkZafIiRd++fentsrIy5gi+csbGxvXq1SOEFBYWPnjwgHMGQEuOHTtGr4htZGQ0YcIEbvHQbw8RQpSv0EhjTh+m8KYqv7FVH3ydTUJI/fr16W2FO6OVUuhQcnJylAROT08fNmyYr6/v0aNH27dvP3fu3KVLl7Zr1+7PP//s1atXy5YtlS9DDwaGx3LLL+aPNmrp+Hv37n3yyScvXrwIDw9/9+5dXl7erVu3Bg4cuGPHjhYtWkycOJF6ZRAABCTaJkWrHSuAULRU496/fz9jxozvv/+e/UvqFTk7O+/cuVPhUuLWrVvNmzefM2dOQUHBjh07OnfuXFBQcPz48eDgYI1zDVA5oTomXtJ9/Phx//79qe158+atWLGCt/yBIRJzp6CcNoo694lxZDJZeno6/dHY2JjD5P3qmjdv3qpVqwghs2fPrmqeODc3t8jIyObNm7969apTp07//vsv8weuShcuXGDzI5iimwmv6SF7Hx8fHSTHdOLECXrb09NTk2mk6tWrZ2xsLJPJqI8RERH0BPcqNWzYMD4+nhASHx9vYFMe6bvy8nLmW0UjR47kvCI2h0F2ZjDmuhq8x1ZN8Hg2CSF9+vSh14LPz89XGT4hIYH5UcnIQkJCQsuWLS0sLKKiorp06cL808KFCwcPHnzr1q0OHTrs3btXyTLuYDD4Lbf8SklJobelUml8fHxAQMDixYtnz55N/3+7du3atWs3ZMiQQYMG7dy58/79+2fOnKGe1wMA3RNzk6K9jhVAKNqrcRMmTKhdu7bmsx8MGzbMzs5u1KhRzKsDmUy2Zs2arVu3FhQUjB07dsWKFbVq1dIwIYCqCNUxaZJuUVFRWlpafHz8n3/+GRoaWlpa2qVLlzVr1mhvtBQMg/g7BQXaLurch+yTk5PljEVErays+MiPMkePHqXG6xs2bLh69WolIW1sbLZv396jR4+cnJyBAwfeuXOHffbOnTtHT90uEjExMdSGjqe7TUlJYT7u5+DgoGGEzs7O9MJZCQkJZWVlLMdSGzRoQG28evVKwzwAvw4fPkzdTSGEuLq6bt68mXNUzCvJqlbfVsAMVlJSor3YqgkezyYhJCAgwNbWlrp3QkerxKVLl5gfqxpZMDIyiouL69OnT0RERMX7xC4uLseOHWvatGlmZubo0aPNzc2HDh3K9QhAP/BbbvnFvLyXSqVTp04dMWIEc7ye1rdv31WrVs2aNevevXt9+/aNjo42MzPTYU4B4P+IuUnRUscKICAt1bjdu3efP3/+1q1bvHSmPXv2fPr06YwZMw4ePMj8f+pK+ePHj8xREQDeCdUxcUs3IiIiMDCwtLSU+Z8DBgzYv3+/nZ0d75kEA6MXnQJFN0Wd+8Q4Cqseabuj+vjx49SpU6ntmTNnSiQqch4QEEA9k/706VO13kdYtmxZNmv0snJaRa+9wO9aySqp/JLVxbmQ2NvbUxsvX77kLzugqcLCwp9++onalkqlx44do88UB3l5efQ2y0F25i0fhbaS39iqA37PJiHEyspq5syZ1HZeXp7y1vLVq1c3btxg/g/zDDLZ2dm1adPm+PHjVb3X5erqOm3aNGp7woQJCs8YgoHhvdzyi/kyYnR09I0bN6gnDyo1ffp06uH6e/fu/fDDD7rIHwD8L5E3KVrqWAGEoqUal5SUFBQUNGfOnDZt2mgeG8XR0XHevHmVroJ49OjRTz75ZO/evXylBcAkVMfEOd2AgIC//vorIiIiIiLi4MGDv/zyS5s2bU6dOlWrVq3AwECFjgmASY86BaKros59TNbFxYU5aq/t2Vc3bNhAX/rS0wMp17t3b2pj/fr1zMtm5aRSaQ3WdDOGTk/xQc3arzPOzs7W1tb0x4yMDA0jZMbg6enJ8hF7Qgidjffv32uYB+DRt99+++LFC0KIsbFxeHg4c/lWdcnlcnrSJLX2oreZ4/L8xlZN8Hg2aT/88IOHhwe1vXv3biUhd+7cqbAaSsXFvijh4eGXLl1SvvD6uHHjqI3c3NyqplADw6CNcssj5k+jzMzML774gtmrKjA2NqZfCtm6dSt9tx4AdEbkTQrRTscKIBQt1bjx48e7ubn9/PPPvMRGCMnMzJwyZUqLFi2Ki4uPHj06d+5chSuFnJycsWPHDh06lP38ugAsCdUxcU7XxsamT58+gwYNGjRo0IgRI+bPn//PP/8cPXrUxMTk6NGjXbp04X1mEjAY+tIpUHRT1LkP2UskEmotNYpMJtNqF/X7779TGw4ODvRPVeXoWygfP34MDw/XVs60jx6y1/FT9oSQwYMH09vv3r1jzg+urri4OHoRCULIF198wX5f+hrj48ePnDMA/Dpw4AD1OImpqWl4eDjLG2lV4fbWDjMY8wYsv7FVB/yeTZqtre2xY8eot8+2bNny+vXrSoM9e/Zs3bp1Cp2oo6NjpYGdnJxUDjp4eXnRL6MdOXKkqnRB32mp3PJIodembyZVhZ70UCaTKZ8AEAB4J/4mhWinYwUQhJZq3M6dOy9evLhv3z6+Zj+IjY1t0qRJaGjoyJEjnz17NnTo0JUrVz58+LBDhw4KIY8dO9avXz8sIw88Eqpj4j3doUOHUvNKyeXyH3/8ceDAgagpoEBfOgXleC/qGs184u7uzvyovSfCnjx5Qkfu5ubGci/mD9OIiAi+M6U79HusOn7KnhASFBREj36Wl5dfvnyZc1Tnz5+nt01NTemZK9igH0vEkL1I3L17d/LkyYQQc3PziIiIzz//XPM4mW0o8+6OEsxH6RVqB7+xGTZtnE1a69atz507Z2trW1hY2K9fv4qDC0+fPu3Vq9fs2bP9/PyY/6/hyAIdW3l5+fHjxzWJCsRJq+WWLwotSadOnZSHr1evHr39119/aSVPAFAZvWhSKEJ1rAA80lKNe/fu3axZs+bOndu6dWteIoyOju7cufOHDx8mT568f/9++k68r6/vzZs3d+zYoTBhcWRkJHVcAJoTqmPSUrqDBg2iK+bJkycnTJjAS7RgGPSlU2CD36Ku0ZC9v78/8yNf84xnZ2fv2bOH+T///PMPvc3+6Vfm6+f//vsvH1kTBn1bRgdr/Cpo2bLl999/T39ct24dt3jKy8s3btxIf1y5cqWXlxf73S0tLamN4uJibhkAHr1+/bpv374FBQV2dnaXL1/u168fL9EyqzbLqeSZwRSGxviNzYBp6WwydevW7c6dO/7+/s+ePfP19Q0ODo6Kinr06NHJkyenTJnSqlWroUOHhoSEKCyL5+LiokmiTZs2pbfFtqI4aE4H5ZYXzJbE0dFRKpUqD093doSQxMTE58+faytnAMCgL00KTZCOFYAv2qtx48ePd3d352v2g8TExJ49e2ZnZw8fPjw0NLRigIkTJz5//nzUqFHM/zxw4MC1a9d4yQBUZ0J1TFpNNygoiN4+fPjwyZMneYwc9Je+dArs8VjUNRqyV5jbRJNHsJlOnz49btw45vPUHz58oLfZT4DOnDwhIyNDf9eTpJ8XFuQZ8+XLlw8YMIDavnbt2v79+zlEsnLlSvqOzpgxY2bNmqXW7vn5+dQGczgDBJGWltazZ8+0tDR3d/fo6OiOHTvyFXOtWrXobZb3ZkpKSuhthUF2fmMzVNo7mwp8fHwuXbp06dKlESNG7N+/v3v37s2aNZs2bVpycnJUVBR1LzA7O5sO7+HhwTyDHDCfJYyLi9MkKhAbnZVbzTFbkjp16qgMrzCmj+nsAXRAj5oUJt13rAC80F6N2759e2Rk5L59+0xNTXmJMCgo6OPHj9bW1r/++mtVYZydncPCwk6fPu3k5ET/p7qXugAKhOqYtJ3usGHDmBPY6n4gFURIjzoF9ngs6hoN2bdv35754+/06dOaxEZ78uSJhYUF84nyrKwseps5rKacwuC+JvOwC4sep2b+8tYZU1PTP//8k358YPLkyfv27VMrhq1bty5cuJDanjlzJjVBlVoEnM0fmPLy8nr27Pny5cvmzZvfvn37k08+4TFy5sNf9GRQKvNDbysMsvMbm0HS6tmslL+//86dO9+9e1daWpqfn//27duIiAj6rTHm8tRdunSpuPuLFy/mzZsXHBzMZm565vq0KSkpGucdxEL35VYTzOyx+bGosK4yii6AtulXk1KRhh0rgI5pr8a9ffv2+++/nzt3bqtWrXiJ8Nq1a8eOHSOEzJ07lzkcX6m+ffvevHmTvjd/9+7dd+/e8ZINqIaE6ph0kK6ZmRlz/PDff/9NTEzkPRXQI3rUKaiFx6LO9on1qkycODEkJITafvbsWUJCgqenp4ZxPn/+XGGBWeboG/uRd+Zj9cbGxg4ODhpmTCj0DD+CDNkTQkxMTMLCwgYOHPjtt99mZmZ+/fXXR48e/f3331U+rRMXFzdhwoQbN24QQurUqfP777/37t2bQwbok65y2UnQnuLi4v79+z98+LBPnz7h4eG8T9PUpEkTesEDhVe5q8JsDRo2bKi92AyPts+mchKJpGKK9+7do7e7du2q8Nfk5OQ2bdpQbeC2bdvi4uKY659XVF5eTm8rLEcM+kvYcssB8zci/bqYEswVNcj/rskBALzTuyZFCQ4dK4COabXGbdq0KT8//8KFC1evXlUekvnyZffu3Zm/Ek+cOEGPzlPrBxJChgwZwiYDDRo0OHjwIF3RYmJiFJb9A2BDqI5Jk3Sjo6PDw8Pt7OwmTJjg6uqqPHCdOnWSk5Ppjw8ePGDzHioYJP3qFIhARV2jp+wJIfPnz2ceQ3h4uIYRymSyK1eu9OzZk/mfzBF8lg/MEkIyMzPpbTc3N/0dtaFPrVBD9pTAwMA3b96sXr3axcXl1KlTXl5evXv3Xr169d27d5mjYyUlJdHR0StXruzXr5+fn9+NGze8vLx+/fXX58+fcxuvJ4wDx1P2QpHJZEOHDr127drYsWP//vvvqtrTgwcPcn6VqX379vR2amoqm13oOu7m5lazZk3txWZgdHA2OaBXHDE1NR08eLDCXy9evEi3A1lZWRcvXlQeG3N4VGWHCnpBnOVWOeaQPfN9waoUFRUxP2LiaQDt0ccmRV3KO1YAXdJ2jXN3d2/btq1EIilThTlEoPAnZoQPHjwghJiYmPj4+LDMQ5cuXQYOHEhtp6WlcTgKqOaE6pg0SffEiRMdO3Zct27d4sWLmzZt+v79e+VpKfy4TU9P1yTnoL/0rlMQqqhr+pS9tbX1Tz/9NH36dOrjL7/8MmnSJE3GVc+ePZuWlqZwNzsgIMDY2Jh6+oz5hSrHfPWgV69enLMkuLp161IbLB8W1h4bG5sffvhh1qxZ165di4iIuHjxYnBwsEJRnj17NiHEzMyscePGs2bN6t+/f+fOnTW8X0I//kx/FaBjY8eOPXXq1Hfffbd582Ylwa5cuVJYWMgtiXbt2tHbCQkJKsNnZGTQaTEH6LURm4HRwdlUV1lZGXVpRAgJDAys+AQ9vQo3ReUbyswrpUaNGvGQRRCaCMutSjVq1GjYsCH17EZqamppaany6XEUcs5+mAAA1KWPTYpaVHasALqk7Ro3c+bMmTNnsglpZWVF/6q8du2aubl5pcGo624PDw/2C+kRQrp06fLXX38RQvT3/X4QkFAdkybpbtmyhd7OzMw8cODAnDlzlESi8NapYT8nB0roXacgVFHX9Cl7QsiUKVM6dOhAbWdkZCxfvlyT2NauXduiRYtu3box/9Pe3p7+n5ycnFevXrGJ6s6dO/T2sGHDNMmVsLy8vKgN9rcrtMrY2Lhbt24bNmx4/PhxUVHR06dPfX196b/OnDnzzZs3RUVF9+/fX7FiRZcuXTR/v4G+JdWkSRMNowIOZs6cGRYWtmDBAuXtKSHk33//5TwBmYeHR7NmzahtNnWcOaH58OHDtRqbIdHN2aQdOXJk2LBhM2bMUD4r94kTJ+hVgivtXJnvWn322WcBAQHK033y5Am9jUcLDYCOyy2PvvrqK2pDLperXE6WOT1Xs2bNateurb2MAVRn+tukEP46VgCd0ccaR3XB6j4X6e3trbABwJJQ1UTDdJlTfxAWC08yF1kh1WA2WqiUPnYKghV1OR9SU1PpydrMzc3v3bvHLZ79+/cTQs6fP1/xT9euXaPzvHXrVjax0T1l27ZtlQRTeA99zZo17DOssIbAnj172O/LHj3d0IgRI7QRv+aY76r8+uuvvMffvHlzKvKYmBjeIwflli1bRghZsWKFypDp6elGRkZLly6t9K8ymSwyMvLMmTMFBQVVxbBt2zbqRJuYmCgJRlm/fj0V2NraurCwUNuxGQa+zqac3Qm9dOkS3TJ06NBBSXL0ZGiBgYGVBsjLyzM1Nf36668fP36sMvOlpaVSqZSK0MrKKiMjQ+UuIGY6LrdVod92J4S8ePGC5V5JSUkSyf89HrF//37lgTdu3EgnsXz5cnVzCABs6HWTwmPHCqAburyUYMPS0pKuREVFRVUFCwoKosLExsayj5zqx729vTXJIVRDQnVMmqf72Wef0RVKKpU+e/ZMSSRFRUXM9039/PxUpguGR087BaGKOj9D9nK5/OHDh/T0Q87Ozux/etJiYmLs7OzGjRtXVYChQ4dS8devX7+8vFx5bOfOnaMCSySSmzdvKgkp/iF7+n6OaBs1bQ/ZU7VIIpGUlJTwHjko8dt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",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {
-      "image/png": {
-       "width": 800
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "IPythonImage(filename=image_path, width=800)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "0dedcdab",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'<table><tr><td></td><td>$ S^{2}_{exact} $</td><td>$ S^{2}_{UHF} $</td><td>$ S^{2}_{UB3LYP} $</td><td>$ S^{2}_{UCAM-B3LYP} $</td><td>$ S^{2}_{UB5050LYP} $</td><td>$ S^{2}_{\\\\kappa UOOMP2} $</td><td>$ S^{2}_{UOCCD} $</td></tr><tr><td>ScH ( $ ^{1}\\\\Sigma^{+} $ )</td><td>0.00</td><td>1.00</td><td>0.60</td><td>0.58</td><td>0.54</td><td>1.00</td><td>1.00</td></tr><tr><td>TiH ( $ ^{4}\\\\Phi $ )</td><td>3.75</td><td>3.75</td><td>3.76</td><td>3.76</td><td>3.75</td><td>3.75</td><td>3.75</td></tr><tr><td>VH ( $ ^{5}\\\\Delta $ )</td><td>6.00</td><td>6.01</td><td>6.01</td><td>6.01</td><td>6.01</td><td>6.01</td><td>6.01</td></tr><tr><td>CrH ( $ ^{6}\\\\Sigma^{+} $ )</td><td>8.75</td><td>8.87</td><td>8.79</td><td>8.79</td><td>8.80</td><td>8.82</td><td>8.79</td></tr><tr><td>MnH ( $ ^{7}\\\\Sigma^{+} $ )</td><td>12.00</td><td>12.00</td><td>12.00</td><td>12.00</td><td>12.00</td><td>12.00</td><td>12.00</td></tr><tr><td>FeH ( $ ^{4}\\\\Delta $ )</td><td>3.75</td><td>4.72</td><td>4.05</td><td>4.10</td><td>4.39</td><td>4.44</td><td>4.48</td></tr><tr><td>CoH ( $ ^{3}\\\\Phi $ )</td><td>2.00</td><td>2.95</td><td>2.16</td><td>2.19</td><td>2.48</td><td>2.23</td><td>2.43</td></tr><tr><td>NiH ( $ ^{2}\\\\Delta $ )</td><td>0.75</td><td>1.68</td><td>0.81</td><td>0.82</td><td>0.98</td><td>0.79</td><td>0.92</td></tr><tr><td>CuH ( $ ^{1}\\\\Sigma^{+} $ )</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td></tr><tr><td>ZnH ( $ ^{2}\\\\Sigma^{+} $ )</td><td>0.75</td><td>0.76</td><td>0.75</td><td>0.76</td><td>0.76</td><td>0.76</td><td>0.76</td></tr></table>'"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "r.json()[\"data\"][0]['full']"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "32868d17",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table><tr><td></td><td>$ S^{2}_{exact} $</td><td>$ S^{2}_{UHF} $</td><td>$ S^{2}_{UB3LYP} $</td><td>$ S^{2}_{UCAM-B3LYP} $</td><td>$ S^{2}_{UB5050LYP} $</td><td>$ S^{2}_{\\kappa UOOMP2} $</td><td>$ S^{2}_{UOCCD} $</td></tr><tr><td>ScH ( $ ^{1}\\Sigma^{+} $ )</td><td>0.00</td><td>1.00</td><td>0.60</td><td>0.58</td><td>0.54</td><td>1.00</td><td>1.00</td></tr><tr><td>TiH ( $ ^{4}\\Phi $ )</td><td>3.75</td><td>3.75</td><td>3.76</td><td>3.76</td><td>3.75</td><td>3.75</td><td>3.75</td></tr><tr><td>VH ( $ ^{5}\\Delta $ )</td><td>6.00</td><td>6.01</td><td>6.01</td><td>6.01</td><td>6.01</td><td>6.01</td><td>6.01</td></tr><tr><td>CrH ( $ ^{6}\\Sigma^{+} $ )</td><td>8.75</td><td>8.87</td><td>8.79</td><td>8.79</td><td>8.80</td><td>8.82</td><td>8.79</td></tr><tr><td>MnH ( $ ^{7}\\Sigma^{+} $ )</td><td>12.00</td><td>12.00</td><td>12.00</td><td>12.00</td><td>12.00</td><td>12.00</td><td>12.00</td></tr><tr><td>FeH ( $ ^{4}\\Delta $ )</td><td>3.75</td><td>4.72</td><td>4.05</td><td>4.10</td><td>4.39</td><td>4.44</td><td>4.48</td></tr><tr><td>CoH ( $ ^{3}\\Phi $ )</td><td>2.00</td><td>2.95</td><td>2.16</td><td>2.19</td><td>2.48</td><td>2.23</td><td>2.43</td></tr><tr><td>NiH ( $ ^{2}\\Delta $ )</td><td>0.75</td><td>1.68</td><td>0.81</td><td>0.82</td><td>0.98</td><td>0.79</td><td>0.92</td></tr><tr><td>CuH ( $ ^{1}\\Sigma^{+} $ )</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td></tr><tr><td>ZnH ( $ ^{2}\\Sigma^{+} $ )</td><td>0.75</td><td>0.76</td><td>0.75</td><td>0.76</td><td>0.76</td><td>0.76</td><td>0.76</td></tr></table>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "display(HTML(r.json()[\"data\"][0]['full']))"
    ]
@@ -176,18 +128,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "c88ae362",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'data': [{'full': 'Consider  $ \\\\mathrm{Fe}(\\\\mathrm{H}_{2}\\\\mathrm{O})_{6}^{2+} $ , for which SSB persists not only at the UHF level but at all DFT and  $ \\\\kappa $ -UOOMP2 theories. A CASSCF calculation with 6 electrons in 5 metal d orbitals yields NOONs of 1.960, 1.954, 1.954, 0.066, and 0.066. The small LUNO (0.066), in the context of the above study of the metal hydride diatomics, suggests SR character (though we note cautiously that CASSCF with 5 active orbitals is not the same as FCI in the full orbital space), and thus it seems that SSB simply reflects the HS-below-LS relative energetics. For  $ FeX_{6}^{4-} $ , with X = F, Cl, Br proceeding toward weaker-field/ $ \\\\pi $ -donating ligands, the expected 10Dq decrease is a small effect, with LUNO populations resulting from minimal (e.g. 6e5o) CASSCF calculations of 0.076, 0.100, and 0.105, respectively. For  $ FeF_{6}^{4-} $  we verified that using a larger active space of 6e15o to include a second d shell yielded a similar LUNO of 0.070 (vs 0.076 from 6e5o). The LS state of  $ \\\\mathrm{Fe}(\\\\mathrm{II})\\\\mathrm{Br}_{6}^{4-} $  is less MR than NiH, CoH, and FeH (with LUNO occupations of 0.14, 0.22, and 0.35, respectively), and its LUNO value is strikingly similar to that of the lowest singlet state of  $ CH_{2} $  (0.09), which is also predominantly closed shell. This analysis suggests that neither the CO nor  $ H_{2}O $  LS  $ \\\\mathrm{Fe}(\\\\mathrm{II}) $  complexes are MR, in agreement with previously reported D1 diagnostic values of 0.14 and 0.06, which are below the 0.15 threshold suggested by Wilson and coworkers suggested for transition metals. $ ^{31} $  Rather, the SSB observed from theories that include dynamic correlation is a manifestation of variational collapse. We thus reiterate that for excited-states, SSB should be used together with NOONs from a MR'}], 'code': 0, 'usage': {'prompt_tokens': 3345, 'completion_tokens': 573, 'total_tokens': 3918}, 'type': 'text', 'format': 'text'}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "image_path = \"./snip/60c75518bdbb8914d5a3a787_010_8.png\"\n",
     "image_path = Path(image_path)\n",
@@ -199,49 +143,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "c88ae362",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {
-      "image/png": {
-       "width": 500
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "IPythonImage(filename=image_path, width=500)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "6e4d8e80",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "Consider  $ \\mathrm{Fe}(\\mathrm{H}_{2}\\mathrm{O})_{6}^{2+} $ , for which SSB persists not only at the UHF level but at all DFT and  $ \\kappa $ -UOOMP2 theories. A CASSCF calculation with 6 electrons in 5 metal d orbitals yields NOONs of 1.960, 1.954, 1.954, 0.066, and 0.066. The small LUNO (0.066), in the context of the above study of the metal hydride diatomics, suggests SR character (though we note cautiously that CASSCF with 5 active orbitals is not the same as FCI in the full orbital space), and thus it seems that SSB simply reflects the HS-below-LS relative energetics. For  $ FeX_{6}^{4-} $ , with X = F, Cl, Br proceeding toward weaker-field/ $ \\pi $ -donating ligands, the expected 10Dq decrease is a small effect, with LUNO populations resulting from minimal (e.g. 6e5o) CASSCF calculations of 0.076, 0.100, and 0.105, respectively. For  $ FeF_{6}^{4-} $  we verified that using a larger active space of 6e15o to include a second d shell yielded a similar LUNO of 0.070 (vs 0.076 from 6e5o). The LS state of  $ \\mathrm{Fe}(\\mathrm{II})\\mathrm{Br}_{6}^{4-} $  is less MR than NiH, CoH, and FeH (with LUNO occupations of 0.14, 0.22, and 0.35, respectively), and its LUNO value is strikingly similar to that of the lowest singlet state of  $ CH_{2} $  (0.09), which is also predominantly closed shell. This analysis suggests that neither the CO nor  $ H_{2}O $  LS  $ \\mathrm{Fe}(\\mathrm{II}) $  complexes are MR, in agreement with previously reported D1 diagnostic values of 0.14 and 0.06, which are below the 0.15 threshold suggested by Wilson and coworkers suggested for transition metals. $ ^{31} $  Rather, the SSB observed from theories that include dynamic correlation is a manifestation of variational collapse. We thus reiterate that for excited-states, SSB should be used together with NOONs from a MR"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "display(Markdown(r.json()[\"data\"][0]['full']))"
    ]
@@ -256,17 +171,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'data': [{'full': 'P_{g}(t)=\\\\frac{R_{L}a_{0}^{2}|G(j\\\\omega)|^{2}}{(R+R_{L})^{2}}\\\\sin^{2}(\\\\omega t-\\\\angle G(j\\\\omega))'}], 'code': 0, 'usage': {'prompt_tokens': 259, 'completion_tokens': 53, 'total_tokens': 312}, 'type': 'formula', 'format': 'latex'}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "image_path = \"./snip/802.1778_018_25.png\"\n",
     "image_path = Path(image_path)\n",
@@ -278,47 +185,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {
-      "image/png": {
-       "width": 500
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "IPythonImage(filename=image_path, width=500)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "$$P_{g}(t)=\\frac{R_{L}a_{0}^{2}|G(j\\omega)|^{2}}{(R+R_{L})^{2}}\\sin^{2}(\\omega t-\\angle G(j\\omega))$$"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "display(Markdown(\"$$\" + r.json()[\"data\"][0]['full'] + \"$$\"))"
    ]
@@ -332,17 +210,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'data': [{'full': 'Pt loading (µgPt cm⁻²) | ECSA (cm²Pt mgPt⁻¹) | Specific Activity (µA cm⁻²) | Mass Activity (A gPt⁻¹)\\n3.2 | 370 | 310 | 115\\n4.5 | 515 | 450 | 230\\n5.8 | 580 | 600 | 345\\n8.4 | 450 | 540 | 245\\n10.9 | 350 | 520 | 185\\n13.5 | 290 | 510 | 150'}], 'code': 0, 'usage': {'prompt_tokens': 1613, 'completion_tokens': 168, 'total_tokens': 1781}, 'type': 'chart', 'format': 'text'}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "image_path = \"./snip/63e096721f23f0032d42225a_010_2.png\"\n",
     "image_path = Path(image_path)\n",
@@ -354,48 +224,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {
-      "image/png": {
-       "width": 800
-      }
-     },
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "IPythonImage(filename=image_path, width=800)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Pt loading (µgPt cm⁻²) | ECSA (cm²Pt mgPt⁻¹) | Specific Activity (µA cm⁻²) | Mass Activity (A gPt⁻¹)\n",
-      "3.2 | 370 | 310 | 115\n",
-      "4.5 | 515 | 450 | 230\n",
-      "5.8 | 580 | 600 | 345\n",
-      "8.4 | 450 | 540 | 245\n",
-      "10.9 | 350 | 520 | 185\n",
-      "13.5 | 290 | 510 | 150\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(r.json()[\"data\"][0]['full'])"
    ]
diff --git a/pyproject.toml b/pyproject.toml
index 471157d..a09cdfb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -3,9 +3,16 @@ name = "uniparser-tools"
 dynamic = ["version", "dependencies"]
 description = "Document parser tools based on UniParser"
 readme = "README.md"
-requires-python = ">=3.8"
+requires-python = ">=3.11"
 keywords = ["ocr", "document-parser", "uniparser"]
 
+[project.optional-dependencies]
+test = [
+    "pytest>=7,<10",
+    "python-dotenv>=1,<2",
+    "ruff>=0.8",
+]
+
 [project.scripts]
 uniparser = "uniparser_tools.cli.main:cli"
 
@@ -15,6 +22,7 @@ build-backend = "setuptools.build_meta"
 
 [tool.setuptools.packages.find]
 where = ['.']
+include = ['uniparser_tools*']
 exclude = ['build', 'workspace', 'temp', 'temp.*', 'models', 'models.*', 'tests', 'tests.*']
 
 [tool.setuptools.dynamic]
@@ -51,5 +59,5 @@ minversion = "7.0"
 testpaths = ["tests"]
 addopts = "-ra --strict-markers"
 markers = [
-    "live: tests that hit a real UniParser backend (skipped unless UNIPARSER_TEST_API_KEY and UNIPARSER_TEST_HOST are set)",
+    "live: tests that hit a real UniParser backend (credentials may be loaded from a dotenv file)",
 ]
diff --git a/requirements.txt b/requirements.txt
index b7b53b2..f691768 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,14 +1,14 @@
 click>=8.1.0,<9
-PyMuPDF==1.25.4
+pypdfium2==5.11.0
 pytz==2024.2
 html5lib==1.1
 markdown==3.6
-latex2mathml==3.77.0
 pylatexenc==2.10
 tabulate==0.9.0
 pandas==2.3.3
 pillow>=10.4.0
 requests==2.32.4
+beautifulsoup4==4.12.3
 scipy==1.17.1
 numpy==1.26.4
 lxml==5.3.0
@@ -17,4 +17,4 @@ PyYAML==6.0.2
 opencv-python==4.10.0.84
 opencv-python-headless==4.10.0.84
 opencv-contrib-python==4.10.0.84
-opencv-contrib-python-headless==4.10.0.84
\ No newline at end of file
+opencv-contrib-python-headless==4.10.0.84
diff --git a/skills/UniParser-Tools/SKILL.md b/skills/UniParser-Tools/SKILL.md
index 5acd85c..3b435b6 100644
--- a/skills/UniParser-Tools/SKILL.md
+++ b/skills/UniParser-Tools/SKILL.md
@@ -96,7 +96,6 @@ Optional flags:
 ```bash
 uniparser parse "./paper.pdf" -o "./results"
 uniparser parse "./paper.pdf" --async
-uniparser parse "./paper.pdf" --overwrite
 ```
 
 Recovery (existing server job—see **Common issues**):
@@ -107,7 +106,7 @@ uniparser fetch --token "TASK_TOKEN_FROM_PRIOR_RUN"
 
 Token sources: stdout JSON from a prior `uniparser --json parse …`, `trigger_meta.json` under the output directory, or the `token` field in a failed parse stderr JSON.
 
-**Default output for** `fetch` (when `-o` / `--output-dir` is omitted): `~/Uni-Parser-Skill/token_<prefix>/`, where `<prefix>` is the first 8 characters of the token (e.g. `~/Uni-Parser-Skill/token_a1b2c3d4/token_a1b2c3d4.md`). To write into the same directory as a prior `parse`, pass `-o` explicitly (e.g. `-o ~/Uni-Parser-Skill/paper/`).
+**Default output for** `fetch` (when `-o` / `--output-dir` is omitted): `~/Uni-Parser-Skill/token_<prefix>/`, where `<prefix>` is the first 8 characters of the token (e.g. `~/Uni-Parser-Skill/token_a1b2c3d4/token_a1b2c3d4.md`). Passing a prior `parse` directory with `-o` treats it only as the preferred path; because that directory already exists, `fetch` writes to an available sibling such as `paper_1`. Always use the returned `output_dir`.
 
 **Default output for** `parse` (when `-o` / `--output-dir` is omitted): `~/Uni-Parser-Skill/<source_stem>/`
 
@@ -182,7 +181,10 @@ uniparser parse paper.pdf --json    # wrong
 | `trigger_meta_path` | Path to `trigger_meta.json`              |
 
 
-**Common error codes** (stderr JSON): `CONFIG_ERROR`, `INPUT_ERROR`, `DIR_EXISTS`, `PARSE_ERROR`.
+If the preferred output directory already exists, the CLI creates an available sibling such as
+`results_1` or `results_2`. Existing paths are never reused or deleted; use the returned `output_dir`.
+
+**Common error codes** (stderr JSON): `CONFIG_ERROR`, `INPUT_ERROR`, `PARSE_ERROR`.
 
 ## Common issues
 
@@ -192,7 +194,6 @@ On failure, show stderr JSON `error.message`. Do not substitute vision-only read
 | Problem                                                                  | Cause                                                                           | Solution                                                                                                                                                      |
 | ------------------------------------------------------------------------ | ------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `CONFIG_ERROR`                                                           | No API key or `uniparser` not installed                                         | **Configuration** + `pip install "git+https://github.com/dptech-corp/UniParser-Tools.git"`; `uniparser auth --verify`                                                                                  |
-| `DIR_EXISTS`                                                             | Output directory already exists                                                 | Ask user; re-run with `--overwrite` if they agree                                                                                                             |
 | `Token is duplicated`                                                    | Job for this API key + exact input already exists                               | Do **not** re-run `uniparser parse`. Read `token` from stderr JSON or `trigger_meta.json`; run `uniparser fetch --token TOKEN`                                |
 | Job not done / long wait / CLI interrupted / `processing` / poll timeout | Sync or poll still running; or local process stopped while server job continues | Wait; do **not** start a second `uniparser parse` for the same input. Use saved `token` with `uniparser fetch --token TOKEN`; files appear only after exit 0  |
 | `502 Bad Gateway` on URL input                                           | Server failed fetching or processing remote PDF                                 | Retry `uniparser parse "same url"` once; or download and `uniparser parse local.pdf`; or `uniparser fetch --token TOKEN` if a prior job exists                |
@@ -220,5 +221,3 @@ Optional MCP server setup is in the [UniParser-Tools GitHub repo](https://github
 | Layout types    | [layout-types.md](./references/layout-types.md)   |
 | Utilities       | [utilities.md](./references/utilities.md)         |
 | Important notes | [notes.md](./references/notes.md)                 |
-
-
diff --git a/skills/UniParser-Tools/references/api-reference.md b/skills/UniParser-Tools/references/api-reference.md
index 7036256..8423634 100644
--- a/skills/UniParser-Tools/references/api-reference.md
+++ b/skills/UniParser-Tools/references/api-reference.md
@@ -4,20 +4,47 @@
 
 | Method | Description |
 |--------|-------------|
+| `health(...)` | Check service/backend health |
+| `version(...)` | Get frontend and model-backend version metadata |
+| `get_constants(...)` | Get live enums, layout types, and token rules |
 | `trigger_file(file_path, ...)` | Submit PDF file for parsing |
 | `trigger_snip(snip_path, ...)` | Submit image for parsing |
-| `trigger_url(pdf_url, ...)` | Submit PDF URL for parsing |
+| `trigger_url(pdf_url, ...)` | Submit HTTP(S), S3, OSS, or TOS source for parsing |
+| `request_tos_upload_links(files, ...)` | Request presigned TOS upload targets |
+| `upload_files_to_tos(file_paths, ...)` | Upload local files without starting a parse |
 | `get_result(token, ...)` | Get raw parsing results |
 | `get_formatted(token, ...)` | Get formatted output |
+| `get_third_party_output(token, ...)` | Get a third-party-compatible result payload |
+
+`version()` returns `default_version`, `backend_versions`, and backend
+`capabilities` when the deployment exposes multiple parser models. Use a key
+from `backend_versions` as the trigger `model_version`. Service-discovery
+methods accept `http_timeout=`.
+
+## Read-only account APIs
+
+The parsing client exposes the same authenticated transport through
+`parser.account`:
+
+| Method | Description |
+|--------|-------------|
+| `get_current_user()` | Get the authenticated user/profile |
+| `get_balance()` | Get balance, currency, account status, and permissions |
+| `get_usage_summary(period)` | Get `current_month`, `last_month`, or rolling 30-day summary |
+| `list_usage_records(page, size)` | Get paginated usage records from the last 14 days |
+| `list_balance_transactions(page, size)` | Get paginated balance ledger |
+
+Use `UniParserAccountClient` directly when parsing methods are not needed.
+No account, API-key, balance, or administrative write operations are exposed.
 
 ### trigger_file() - Async Callback Parameters
 
 ```python
 result = parser.trigger_file(
     file_path="./document.pdf",
-    sync=False,                    # Required for async mode
-    callback_url="https://...",    # Your callback endpoint
-    callback_secret="your-secret", # For signature verification
+    sync=False,  # Required for async mode
+    callback_url="https://...",  # Your callback endpoint
+    callback_secret="your-secret",  # For signature verification
     # ... other parse mode parameters
 )
 ```
@@ -28,6 +55,13 @@ result = parser.trigger_file(
 | `callback_url` | str | `None` | HTTP POST endpoint for completion notification |
 | `callback_secret` | str | `None` | Shared secret for HMAC-SHA256 payload verification |
 
+The release/v1.3 callback body is the raw JSON result. Verify
+`X-UniParser-Signature: sha256=<hex>` by computing HMAC-SHA256 over the exact
+raw body bytes before JSON parsing. Use `Idempotency-Key` for deduplication and
+inspect `X-UniParser-Callback-Attempt` for retries. The URL and secret must be
+provided together, callbacks require `sync=False`, and deployments may enforce
+a callback-host allowlist.
+
 ### trigger_snip() - Async Callback Parameters
 
 Same parameters as `trigger_file()` for image parsing.
@@ -36,6 +70,38 @@ Same parameters as `trigger_file()` for image parsing.
 
 Same parameters as `trigger_file()` for URL-based parsing.
 
+`padding_snip` is not accepted by the URL endpoint; `proxy` is URL-only.
+
+### Common release/v1.3 trigger parameters
+
+| Parameter | Default | Description |
+|-----------|---------|-------------|
+| `timeout` | `1800` | Server-side parse budget in seconds |
+| `http_timeout` | `None` | Per-call client HTTP timeout override |
+| `inplace_update` | `False` | Update an existing task with the same token |
+| `preset_layout` | `None` | JSON string or Python list describing known layout |
+| `model_version` | `None` | Model version advertised by `/version` |
+| `server_generated_token` | `False` | Ask the server to generate a token when none is supplied |
+
+The default remains a deterministic client-generated token for backward
+compatibility. `preset_layout` is serialized to a JSON string for all three
+trigger endpoints, including `trigger_url`.
+
+### TOS presigned upload
+
+```python
+uploaded = parser.upload_files_to_tos(["./document.pdf"])
+source_url = uploaded["files"][0]["source_url"]
+result = parser.trigger_url(source_url, server_generated_token=True)
+```
+
+Uploading does not start parsing. Presigned `PUT` requests do not include the
+UniParser API key. Treat URLs returned by `request_tos_upload_links()` as
+short-lived bearer credentials: do not log or forward them. The high-level
+`upload_files_to_tos()` helper removes each `upload_url` from its result after
+the upload completes. TOS content uploads use the client's longer
+`upload_request_timeout` default; pass `http_timeout=` to override one call.
+
 ## Parse Modes
 
 **For textual:**
@@ -45,10 +111,10 @@ Same parameters as `trigger_file()` for URL-based parsing.
 - `ParseModeTextual.Disable` - Skip textual extraction
 
 **For table, molecule, chart, figure, expression, equation:**
-- `ParseMode.OCRFast` - Fast OCR (default)
+- `ParseMode.OCRFast` - Fast OCR
 - `ParseMode.OCRHighQuality` - High quality OCR
 - `ParseMode.DumpBase64` - Return raw image as base64
-- `ParseMode.Disable` - Skip extraction
+- `ParseMode.Disable` - Skip extraction (client default)
 
 ## Format Flags
 
@@ -71,6 +137,12 @@ Same parameters as `trigger_file()` for URL-based parsing.
 | `molecule_source=True` | Include molecule source images | Chemical structure analysis |
 
 **Note:** `get_formatted()` also supports `marginalia=True` to include page headers/footers/numbers.
+Both result methods accept `http_timeout=` for large response payloads.
+
+`get_third_party_output()` currently accepts
+`ThirdPartyFormatter.MinerU`. The local `build_item()` / `dict2obj()`
+conversion path understands release/v1.3 inline `contents + types`, molecule
+`esmi`, and full-HTML table spans, and ignores unknown response fields.
 
 ## Ordering Methods
 
diff --git a/skills/UniParser-Tools/references/data-classes.md b/skills/UniParser-Tools/references/data-classes.md
index d8c6e4c..56dfbd3 100644
--- a/skills/UniParser-Tools/references/data-classes.md
+++ b/skills/UniParser-Tools/references/data-classes.md
@@ -54,6 +54,6 @@ for page in pages_tree:
         print(f"LaTeX: {item.latex}")
 
         # For tables, access DataFrame
-        if hasattr(item, 'df'):
+        if hasattr(item, "df"):
             print(f"DataFrame:\n{item.df}")
 ```
diff --git a/skills/UniParser-Tools/references/notes.md b/skills/UniParser-Tools/references/notes.md
index 4513e39..7f9a46a 100644
--- a/skills/UniParser-Tools/references/notes.md
+++ b/skills/UniParser-Tools/references/notes.md
@@ -14,10 +14,16 @@
    import hmac
    import hashlib
 
-   def verify_callback(content, checksum, secret):
-       expected = hmac.new(secret.encode(), content.encode(), hashlib.sha256).hexdigest()
-       return hmac.compare_digest(expected, checksum)
+
+   def verify_callback(raw_body: bytes, signature: str, secret: str) -> bool:
+       if not signature.startswith("sha256="):
+           return False
+       expected = hmac.new(secret.encode("utf-8"), raw_body, hashlib.sha256).hexdigest()
+       return hmac.compare_digest(expected, signature[len("sha256=") :])
    ```
+   Read `raw_body` before JSON parsing and take `signature` from the
+   `X-UniParser-Signature` header. The body is not wrapped in
+   `checksum` / `content` fields.
 
 5. **Ordering Methods**: Default is `GapTree`; alternatives: `Naive`, `XYCut`, `XYCutExp`
 
diff --git a/skills/UniParser-Tools/references/patterns.md b/skills/UniParser-Tools/references/patterns.md
index ed60131..f893feb 100644
--- a/skills/UniParser-Tools/references/patterns.md
+++ b/skills/UniParser-Tools/references/patterns.md
@@ -56,47 +56,50 @@ result = parser.trigger_file(
 
 ### Callback Payload
 
-When the parsing task completes, the service sends a POST request to `callback_url`:
+When parsing completes, the service sends the raw JSON result to
+`callback_url`. It signs the exact body bytes with HMAC-SHA256 and puts the
+signature in the `X-UniParser-Signature: sha256=<hex>` header. The body is not
+wrapped in `content` / `checksum` fields.
 
 ```json
 {
     "token": "abc123...",
-    "status": "success",
-    "content": { ... },
-    "checksum": "hmac-sha256-signature"
+    "status": "success"
 }
 ```
 
+Use `Idempotency-Key` to deduplicate callback retries. The
+`X-UniParser-Callback-Attempt` header reports the current attempt number.
+
 ### Verify Callback Signature
 
 ```python
-import hmac
 import hashlib
+import hmac
+
 from flask import Flask, request
 
 app = Flask(__name__)
 CALLBACK_SECRET = "your-secret-key"
 
-@app.route('/callback', methods=['POST'])
+
+@app.route("/callback", methods=["POST"])
 def handle_callback():
-    data = request.json
-    content = data['content']
-    received_checksum = data['checksum']
-    
-    # Verify signature
-    expected = hmac.new(
-        CALLBACK_SECRET.encode(),
-        json.dumps(content).encode(),
-        hashlib.sha256
-    ).hexdigest()
-    
-    if not hmac.compare_digest(received_checksum, expected):
-        return {'error': 'Invalid signature'}, 401
-    
-    # Process the result
-    token = data['token']
+    raw_body = request.get_data(cache=True)
+    received_signature = request.headers.get("X-UniParser-Signature", "")
+    prefix = "sha256="
+    if not received_signature.startswith(prefix):
+        return {"error": "Missing or invalid signature"}, 401
+
+    expected = hmac.new(CALLBACK_SECRET.encode("utf-8"), raw_body, hashlib.sha256).hexdigest()
+
+    if not hmac.compare_digest(received_signature[len(prefix) :], expected):
+        return {"error": "Invalid signature"}, 401
+
+    data = request.get_json()
+    token = data["token"]
     print(f"Task {token} completed!")
-    return {'status': 'ok'}
+    return {"status": "ok"}
 ```
 
 ## Pattern 4: Mixed Format Output
@@ -105,10 +108,10 @@ def handle_callback():
 result = parser.get_formatted(
     token,
     content=True,
-    textual=FormatFlag.Markdown,   # Text as Markdown
-    table=FormatFlag.Html,         # Tables as HTML
-    equation=FormatFlag.Latex,     # Equations as LaTeX
-    figure=FormatFlag.Markdown,    # Figures as Markdown img
+    textual=FormatFlag.Markdown,  # Text as Markdown
+    table=FormatFlag.Html,  # Tables as HTML
+    equation=FormatFlag.Latex,  # Equations as LaTeX
+    figure=FormatFlag.Markdown,  # Figures as Markdown img
 )
 ```
 
diff --git a/skills/UniParser-Tools/references/utilities.md b/skills/UniParser-Tools/references/utilities.md
index 2b90562..530d0e8 100644
--- a/skills/UniParser-Tools/references/utilities.md
+++ b/skills/UniParser-Tools/references/utilities.md
@@ -25,22 +25,22 @@ clean_text = clean_scientific_text(text, strict=False)
 from uniparser_tools.utils.bbox import BBox, Point
 
 # BBox properties
-bbox.area        # Area
-bbox.width       # Width
-bbox.height      # Height
-bbox.tl          # Top-left point
-bbox.br          # Bottom-right point
-bbox.ctr         # Center point
-bbox.xyxy        # (x1, y1, x2, y2) tuple
-bbox.xywh        # (x, y, w, h) tuple
+bbox.area  # Area
+bbox.width  # Width
+bbox.height  # Height
+bbox.tl  # Top-left point
+bbox.br  # Bottom-right point
+bbox.ctr  # Center point
+bbox.xyxy  # (x1, y1, x2, y2) tuple
+bbox.xywh  # (x, y, w, h) tuple
 
 # BBox operations
-bbox.iou(other)           # Intersection over Union
-bbox.iof(other)           # Intersection over Foreground
+bbox.iou(other)  # Intersection over Union
+bbox.iof(other)  # Intersection over Foreground
 bbox.intersection(other)  # Intersection box
-bbox.union(other)         # Union box
-bbox.expand(pix, wh)      # Expand by pixels
-bbox.shrink(pix, wh)      # Shrink by pixels
+bbox.union(other)  # Union box
+bbox.expand(pix, wh)  # Expand by pixels
+bbox.shrink(pix, wh)  # Shrink by pixels
 ```
 
 ## Text Processing
diff --git a/tests/conftest.py b/tests/conftest.py
index bf04b3b..d0d501f 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -6,12 +6,23 @@
 from pathlib import Path
 
 import pytest
+from dotenv import load_dotenv
 
 
 REPO_ROOT = Path(__file__).resolve().parent.parent
 TESTS_DIR = Path(__file__).resolve().parent
 
 
+def _load_test_dotenv() -> None:
+    configured_path = os.environ.get("UNIPARSER_DOTENV_PATH")
+    dotenv_path = Path(configured_path).expanduser() if configured_path else REPO_ROOT / ".env"
+    if dotenv_path.is_file():
+        load_dotenv(dotenv_path=dotenv_path, override=False)
+
+
+_load_test_dotenv()
+
+
 @pytest.fixture(scope="session")
 def repo_root() -> Path:
     return REPO_ROOT
@@ -20,7 +31,8 @@ def repo_root() -> Path:
 @pytest.fixture(scope="session")
 def demo_pdf_path(repo_root: Path) -> Path:
     path = repo_root / "demo_file.pdf"
-    assert path.is_file(), f"demo_file.pdf missing at {path}"
+    if not path.is_file():
+        pytest.skip(f"demo_file.pdf missing at {path}")
     return path
 
 
@@ -33,19 +45,20 @@ def demo_img_path() -> Path:
 
 @pytest.fixture(scope="session")
 def api_key() -> str | None:
-    return os.environ.get("UNIPARSER_TEST_API_KEY")
+    return os.environ.get("UNIPARSER_TEST_API_KEY") or os.environ.get("UNIPARSER_API_KEY")
 
 
 @pytest.fixture(scope="session")
 def api_host() -> str | None:
-    return os.environ.get("UNIPARSER_TEST_HOST")
+    return os.environ.get("UNIPARSER_TEST_HOST") or os.environ.get("UNIPARSER_BASE_URL") or "https://uniparser.dp.tech"
 
 
 @pytest.fixture(scope="session")
 def live_client(api_key: str | None, api_host: str | None):
-    """Real UniParserClient, only when API creds are in env. Skip otherwise."""
+    """Real UniParserClient, skipped when no API credential is configured."""
     if not api_key or not api_host:
-        pytest.skip("Live API tests require UNIPARSER_TEST_API_KEY and UNIPARSER_TEST_HOST env vars")
+        pytest.skip("Live API tests require UNIPARSER_TEST_API_KEY or UNIPARSER_API_KEY")
     from uniparser_tools.api.clients import UniParserClient
 
-    return UniParserClient(host=api_host, api_key=api_key)
+    with UniParserClient(host=api_host, api_key=api_key) as client:
+        yield client
diff --git a/tests/integration/test_client_live.py b/tests/integration/test_client_live.py
index e81458f..bc1a895 100644
--- a/tests/integration/test_client_live.py
+++ b/tests/integration/test_client_live.py
@@ -1,16 +1,45 @@
-"""Live integration tests for ``UniParserClient``.
-
-Skipped automatically unless ``UNIPARSER_TEST_API_KEY`` and
-``UNIPARSER_TEST_HOST`` are set.
-"""
+"""Live integration tests for release/v1.3 client contracts."""
 
 from __future__ import annotations
 
+import re
+import uuid
 from pathlib import Path
 
 import pytest
+from PIL import Image, ImageDraw, ImageFont
 
 from uniparser_tools.common.constant import FormatFlag, ParseMode, ParseModeTextual
+from uniparser_tools.utils.convert import dict2obj
+
+
+TEST_TEXT = "UNIPARSER LIVE OCR TEST 2026"
+TEST_URL = "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf"
+
+
+def _make_text_image(path: Path) -> Image.Image:
+    image = Image.new("RGB", (1200, 360), "white")
+    font = ImageFont.load_default(size=64)
+    draw = ImageDraw.Draw(image)
+    draw.text((60, 90), TEST_TEXT, fill="black", font=font)
+    image.save(path)
+    return image
+
+
+def _assert_formatted_text(live_client, token: str, expected_text: str) -> dict:
+    formatted = live_client.get_formatted(
+        token,
+        content=True,
+        textual=FormatFlag.Markdown,
+        table=FormatFlag.Markdown,
+    )
+    assert formatted.get("status") == "success", formatted
+    content = formatted.get("content")
+    assert isinstance(content, str) and content, formatted
+    normalized_content = re.sub(r"[^a-z0-9]", "", content.lower())
+    normalized_expected = re.sub(r"[^a-z0-9]", "", expected_text.lower())
+    assert normalized_expected in normalized_content, formatted
+    return formatted
 
 
 @pytest.mark.live
@@ -30,30 +59,88 @@ def test_version(self, live_client) -> None:
         assert isinstance(result, dict)
         assert "http_status" not in result, result
         assert "description" not in result, result
+        assert "version" in result, result
+
+    def test_constants(self, live_client) -> None:
+        result = live_client.get_constants()
+        assert isinstance(result, dict)
+        assert "LayoutType" in result, result
+        assert "TokenRegEx" in result, result
+
+    def test_read_only_account_endpoints(self, live_client) -> None:
+        profile = live_client.account.get_current_user()
+        balance = live_client.account.get_balance()
+        usage = live_client.account.get_usage_summary(period="current_month")
+        usage_records = live_client.account.list_usage_records(page=1, size=1, http_timeout=(10, 120))
+        transactions = live_client.account.list_balance_transactions(page=1, size=1, http_timeout=(10, 120))
 
-    def test_trigger_file_and_fetch_markdown(self, live_client, demo_pdf_path: Path) -> None:
+        assert profile.get("id"), profile
+        assert "balance" in balance, balance
+        assert "total_requests" in usage, usage
+        assert isinstance(usage_records.get("items"), list), usage_records
+        assert isinstance(transactions.get("items"), list), transactions
+
+    def test_trigger_file_and_fetch_release_results(self, live_client, tmp_path: Path) -> None:
+        image_path = tmp_path / "release-v1.3-file-live-test.png"
+        page = _make_text_image(image_path)
+        document_path = tmp_path / "release-v1.3-live-test.pdf"
+        page.save(document_path, "PDF", resolution=150.0)
+        page.close()
+
+        version = live_client.version()
         trigger = live_client.trigger_file(
-            file_path=str(demo_pdf_path),
-            textual=ParseModeTextual.DigitalExported,
-            table=ParseMode.OCRFast,
+            file_path=str(document_path),
+            textual=ParseModeTextual.OCRFast,
+            table=ParseMode.Disable,
+            model_version=version.get("default_version"),
+            server_generated_token=True,
         )
+        if trigger.get("http_status") == 400 and trigger.get("description") == "Token is required":
+            # Backward-compatible fallback for deployments that have not yet
+            # rolled out release/v1.3's optional-token contract.
+            trigger = live_client.trigger_file(
+                file_path=str(document_path),
+                textual=ParseModeTextual.OCRFast,
+                table=ParseMode.Disable,
+                model_version=version.get("default_version"),
+                token=uuid.uuid4().hex,
+            )
         assert trigger.get("status") == "success", trigger
         token = trigger["token"]
 
-        formatted = live_client.get_formatted(
-            token,
-            content=True,
-            textual=FormatFlag.Markdown,
-            table=FormatFlag.Markdown,
+        raw = live_client.get_result(token, pages_dict=True)
+        assert raw.get("status") == "success", raw
+        assert isinstance(raw.get("pages_dict"), list), raw
+        converted_pages = dict2obj(raw["pages_dict"])
+        assert isinstance(converted_pages, list)
+
+        _assert_formatted_text(live_client, token, "uniparser")
+
+        third_party = live_client.get_third_party_output(token)
+        assert third_party.get("status") == "success", third_party
+
+    def test_trigger_url_and_fetch_text(self, live_client) -> None:
+        version = live_client.version()
+        trigger = live_client.trigger_url(
+            TEST_URL,
+            token=uuid.uuid4().hex,
+            textual=ParseModeTextual.DigitalExported,
+            table=ParseMode.Disable,
+            model_version=version.get("default_version"),
         )
-        assert formatted.get("status") == "success", formatted
-        assert "content" in formatted
-        assert isinstance(formatted["content"], str) and len(formatted["content"]) > 0
+        assert trigger.get("status") == "success", trigger
+        _assert_formatted_text(live_client, trigger["token"], "dummy pdf file")
+
+    def test_trigger_snip_and_fetch_text(self, live_client, tmp_path: Path) -> None:
+        image_path = tmp_path / "release-v1.3-snip-live-test.png"
+        image = _make_text_image(image_path)
+        image.close()
 
-    def test_trigger_snip(self, live_client, demo_img_path: Path) -> None:
         trigger = live_client.trigger_snip(
-            snip_path=str(demo_img_path),
+            snip_path=str(image_path),
+            token=uuid.uuid4().hex,
             textual=ParseModeTextual.OCRFast,
+            table=ParseMode.Disable,
         )
         assert trigger.get("status") == "success", trigger
-        assert "token" in trigger
+        _assert_formatted_text(live_client, trigger["token"], "uniparser")
diff --git a/tests/unit/test_account_client.py b/tests/unit/test_account_client.py
new file mode 100644
index 0000000..6d7ee25
--- /dev/null
+++ b/tests/unit/test_account_client.py
@@ -0,0 +1,101 @@
+from __future__ import annotations
+
+import json
+
+from uniparser_tools.api.account import UniParserAccountClient
+from uniparser_tools.api.clients import UniParserClient
+
+
+class FakeResponse:
+    def __init__(self, payload):
+        self.status_code = 200
+        self._payload = payload
+        self.text = ""
+        self.reason = "OK"
+
+    def json(self):
+        if self._payload is None:
+            raise json.JSONDecodeError("invalid", "", 0)
+        return self._payload
+
+
+class FakeSession:
+    def __init__(self, responses=None):
+        self.responses = list(responses or [])
+        self.calls = []
+        self.closed = False
+
+    def request(self, method, url, **kwargs):
+        self.calls.append((method, url, kwargs))
+        return self.responses.pop(0) if self.responses else FakeResponse({"status": "success"})
+
+    def close(self):
+        self.closed = True
+
+
+class TestAccountClient:
+    def test_endpoints_compose_correctly(self) -> None:
+        client = UniParserAccountClient(host="https://example.com/", api_key="k")
+
+        assert client.current_user_endpoint == "https://example.com/users/me"
+        assert client.balance_endpoint == "https://example.com/balance"
+        assert client.usage_summary_endpoint == "https://example.com/billing/usage"
+        assert client.usage_records_endpoint == "https://example.com/billing/usage-records"
+        assert client.balance_transactions_endpoint == "https://example.com/balance/transactions"
+
+    def test_profile_and_balance_are_read_only_gets(self) -> None:
+        session = FakeSession(
+            responses=[
+                FakeResponse({"username": "customer"}),
+                FakeResponse({"balance": "10.00", "currency": "CNY"}),
+            ]
+        )
+        client = UniParserAccountClient(host="https://example.com", api_key="k", session=session)
+
+        assert client.get_current_user()["username"] == "customer"
+        assert client.get_balance(http_timeout=(1, 2))["balance"] == "10.00"
+        assert [call[0] for call in session.calls] == ["GET", "GET"]
+        assert session.calls[1][2]["timeout"] == (1, 2)
+
+    def test_usage_summary_sends_period(self) -> None:
+        session = FakeSession(responses=[FakeResponse({"total_requests": 3})])
+        client = UniParserAccountClient(host="https://example.com", api_key="k", session=session)
+
+        result = client.get_usage_summary("last_month")
+
+        assert result["total_requests"] == 3
+        assert session.calls[0][2]["params"] == {"period": "last_month"}
+
+    def test_paginated_read_methods_send_page_and_size(self) -> None:
+        session = FakeSession(
+            responses=[
+                FakeResponse({"items": [], "page": 2, "size": 5}),
+                FakeResponse({"items": [], "page": 3, "size": 10}),
+            ]
+        )
+        client = UniParserAccountClient(host="https://example.com", api_key="k", session=session)
+
+        client.list_usage_records(page=2, size=5)
+        client.list_balance_transactions(page=3, size=10)
+
+        assert session.calls[0][2]["params"] == {"page": 2, "size": 5}
+        assert session.calls[1][2]["params"] == {"page": 3, "size": 10}
+
+    def test_main_client_account_namespace_shares_transport(self) -> None:
+        session = FakeSession(responses=[FakeResponse({"balance": "8.50"})])
+        client = UniParserClient(host="https://example.com", api_key="k", session=session)
+
+        result = client.account.get_balance()
+
+        assert result["balance"] == "8.50"
+        assert session.calls[0][1] == "https://example.com/balance"
+        assert session.calls[0][2]["headers"]["X-API-Key"] == "k"
+
+    def test_standalone_context_closes_owned_session(self, monkeypatch) -> None:
+        session = FakeSession()
+        monkeypatch.setattr("uniparser_tools.api.transport.requests.Session", lambda: session)
+
+        with UniParserAccountClient(host="https://example.com", api_key="k"):
+            pass
+
+        assert session.closed is True
diff --git a/tests/unit/test_cli.py b/tests/unit/test_cli.py
index e8406b4..2482963 100644
--- a/tests/unit/test_cli.py
+++ b/tests/unit/test_cli.py
@@ -150,6 +150,9 @@ def test_parse_success_writes_trigger_meta_and_json_token(
         monkeypatch.setattr("uniparser_tools.cli.commands.parse.make_client", lambda ctx: (mock_client, None))
 
         out = tmp_path / "out"
+        out.mkdir()
+        (out / "keep.txt").write_text("keep", encoding="utf-8")
+        actual_out = tmp_path / "out_1"
         result = runner.invoke(
             cli,
             ["--json", "parse", str(pdf), "-o", str(out)],
@@ -159,13 +162,15 @@ def test_parse_success_writes_trigger_meta_and_json_token(
         assert "Parsing... paper.pdf" in result.stderr
         payload = json.loads(result.stdout)
         assert payload["token"] == "tok-parse-1"
-        assert (out / "trigger_meta.json").is_file()
-        meta = json.loads((out / "trigger_meta.json").read_text(encoding="utf-8"))
+        assert Path(payload["output_dir"]) == actual_out
+        assert (out / "keep.txt").read_text(encoding="utf-8") == "keep"
+        assert (actual_out / "trigger_meta.json").is_file()
+        meta = json.loads((actual_out / "trigger_meta.json").read_text(encoding="utf-8"))
         assert meta["token"] == "tok-parse-1"
         assert meta["trigger_kwargs"]["textual"] == "ocr-hq"
         assert meta["trigger_kwargs"]["sync"] is True
         assert "preset" not in meta
-        assert (out / "paper.md").is_file()
+        assert (actual_out / "paper.md").is_file()
 
     def test_parse_default_trigger_kwargs(
         self,
@@ -261,6 +266,9 @@ def test_fetch_success(
         monkeypatch.setattr("uniparser_tools.cli.commands.fetch.make_client", lambda ctx: (mock_client, None))
 
         out = tmp_path / "fetch-out"
+        out.mkdir()
+        (out / "keep.txt").write_text("keep", encoding="utf-8")
+        actual_out = tmp_path / "fetch-out_1"
         result = runner.invoke(
             cli,
             ["--json", "fetch", "--token", "abcdef123456", "-o", str(out)],
@@ -271,7 +279,9 @@ def test_fetch_success(
         payload = json.loads(result.stdout)
         assert payload["token"] == "abcdef123456"
         assert payload["fetched_by_token"] is True
-        assert (out / "token_abcdef12.md").is_file()
+        assert Path(payload["output_dir"]) == actual_out
+        assert (out / "keep.txt").read_text(encoding="utf-8") == "keep"
+        assert (actual_out / "token_abcdef12.md").is_file()
 
 
 class TestHealthVersion:
@@ -332,12 +342,14 @@ def test_parse_help(self, runner: CliRunner) -> None:
         assert "--async" in result.stdout
         assert "--textual" in result.stdout
         assert "--molecule" in result.stdout
+        assert "--overwrite" not in result.stdout
         assert "--verbose" not in result.stdout
 
     def test_fetch_help(self, runner: CliRunner) -> None:
         result = runner.invoke(cli, ["fetch", "--help"])
         assert result.exit_code == 0
         assert "--token" in result.stdout
+        assert "--overwrite" not in result.stdout
 
 
 class TestAuthCommand:
diff --git a/tests/unit/test_client.py b/tests/unit/test_client.py
index 43a9edc..50e8c44 100644
--- a/tests/unit/test_client.py
+++ b/tests/unit/test_client.py
@@ -6,10 +6,47 @@
 
 from __future__ import annotations
 
+import json
+
 import pytest
+import requests
+from PIL import Image
+
+from uniparser_tools.api.clients import TOSUploadFile, UniParserClient
+from uniparser_tools.common.constant import ThirdPartyFormatter
+
+
+class FakeResponse:
+    def __init__(self, status_code: int = 200, payload=None, text: str = "", reason: str = "OK"):
+        self.status_code = status_code
+        self._payload = payload
+        self.text = text
+        self.reason = reason
+
+    def json(self):
+        if self._payload is None:
+            raise json.JSONDecodeError("invalid", self.text, 0)
+        return self._payload
+
 
-from uniparser_tools.api import clients as clients_mod
-from uniparser_tools.api.clients import UniParserClient
+class FakeSession:
+    def __init__(self, response=None, responses=None, error=None):
+        self.response = response or FakeResponse(payload={"status": "success"})
+        self.responses = list(responses or [])
+        self.error = error
+        self.calls = []
+        self.closed = False
+
+    def request(self, method, url, **kwargs):
+        self.calls.append((method, url, kwargs))
+        if self.error:
+            raise self.error
+        if self.responses:
+            return self.responses.pop(0)
+        return self.response
+
+    def close(self):
+        self.closed = True
 
 
 class TestClientConstruction:
@@ -22,12 +59,18 @@ def test_rejects_non_http_host(self) -> None:
             UniParserClient(host="example.com", api_key="k")
 
     def test_endpoints_compose_correctly(self) -> None:
-        c = UniParserClient(host="https://example.com", api_key="k")
+        c = UniParserClient(host="https://example.com/", api_key="k")
+        assert c.host == "https://example.com"
         assert c.trigger_file_endpoint.endswith("/trigger-file-async")
         assert c.trigger_url_endpoint.endswith("/trigger-url-async")
         assert c.trigger_snip_endpoint.endswith("/trigger-snip-async")
+        assert c.request_tos_upload_links_endpoint.endswith("/request-tos-upload-links")
+        assert c.health_endpoint.endswith("/health")
+        assert c.version_endpoint.endswith("/version")
+        assert c.get_constants_endpoint.endswith("/get-constants")
         assert c.get_result_endpoint.endswith("/get-result")
         assert c.get_formatted_endpoint.endswith("/get-formatted")
+        assert c.get_third_party_output_endpoint.endswith("/get-third-party-output")
 
 
 class TestTokenHelpers:
@@ -60,33 +103,372 @@ def test_validate_token_rejects_empty(self) -> None:
 class TestClientErrorShapes:
     """When the underlying request raises, we expect structured error dicts."""
 
-    def _raise_conn_err(self, *args, **kwargs):
-        import requests as _requests
-
-        raise _requests.ConnectionError("simulated")
-
-    def test_health_returns_error_dict_on_request_failure(self, monkeypatch) -> None:
-        monkeypatch.setattr(clients_mod.requests, "get", self._raise_conn_err)
-        c = UniParserClient(host="https://example.com", api_key="k")
+    def test_health_returns_error_dict_on_request_failure(self) -> None:
+        session = FakeSession(error=requests.ConnectionError("simulated"))
+        c = UniParserClient(host="https://example.com", api_key="k", session=session)
         result = c.health()
         assert isinstance(result, dict)
         assert result.get("status") == "error"
         assert "description" in result
 
-    def test_version_returns_error_dict_on_request_failure(self, monkeypatch) -> None:
-        monkeypatch.setattr(clients_mod.requests, "get", self._raise_conn_err)
-        c = UniParserClient(host="https://example.com", api_key="k")
+    def test_version_returns_error_dict_on_request_failure(self) -> None:
+        session = FakeSession(error=requests.ConnectionError("simulated"))
+        c = UniParserClient(host="https://example.com", api_key="k", session=session)
         result = c.version()
         assert isinstance(result, dict)
         assert result.get("status") == "error"
         assert "description" in result
 
-    def test_trigger_file_returns_error_dict_on_request_failure(self, monkeypatch, tmp_path) -> None:
+    def test_trigger_file_returns_error_dict_on_request_failure(self, tmp_path) -> None:
         p = tmp_path / "dummy.pdf"
         p.write_bytes(b"%PDF-1.4 tiny")
-        monkeypatch.setattr(clients_mod.requests, "post", self._raise_conn_err)
-        c = UniParserClient(host="https://example.com", api_key="k")
+        session = FakeSession(error=requests.ConnectionError("simulated"))
+        c = UniParserClient(host="https://example.com", api_key="k", session=session)
         result = c.trigger_file(file_path=str(p))
         assert isinstance(result, dict)
         assert result.get("status") == "error"
         assert "token" in result
+
+
+class TestHTTPTransport:
+    def test_uses_short_timeout_for_async_trigger(self, tmp_path) -> None:
+        p = tmp_path / "dummy.pdf"
+        p.write_bytes(b"%PDF-1.4 tiny")
+        session = FakeSession()
+        c = UniParserClient(
+            host="https://example.com",
+            api_key="k",
+            request_timeout=(1, 2),
+            sync_request_timeout=(3, 4),
+            session=session,
+        )
+
+        c.trigger_file(file_path=str(p), sync=False)
+
+        assert session.calls[0][2]["timeout"] == (1, 2)
+
+    def test_uses_sync_timeout_for_sync_trigger(self, tmp_path) -> None:
+        p = tmp_path / "dummy.pdf"
+        p.write_bytes(b"%PDF-1.4 tiny")
+        session = FakeSession()
+        c = UniParserClient(
+            host="https://example.com",
+            api_key="k",
+            request_timeout=(1, 2),
+            sync_request_timeout=(3, 4),
+            session=session,
+        )
+
+        c.trigger_file(file_path=str(p), sync=True)
+
+        assert session.calls[0][2]["timeout"] == (3, 4)
+
+    def test_http_error_preserves_json_body(self) -> None:
+        session = FakeSession(
+            response=FakeResponse(
+                status_code=429,
+                payload={"status": "error", "description": "rate limited"},
+                reason="Too Many Requests",
+            )
+        )
+        c = UniParserClient(host="https://example.com", api_key="k", session=session)
+
+        result = c.health()
+
+        assert result["description"] == "rate limited"
+        assert result["http_status"] == 429
+
+    def test_client_context_closes_owned_session(self, monkeypatch) -> None:
+        session = FakeSession()
+        monkeypatch.setattr("uniparser_tools.api.transport.requests.Session", lambda: session)
+        c = UniParserClient(host="https://example.com", api_key="k")
+
+        with c:
+            pass
+
+        assert session.closed is True
+
+
+class TestResultAPIs:
+    def test_get_result_accepts_per_call_http_timeout(self) -> None:
+        session = FakeSession()
+        client = UniParserClient(host="https://example.com", api_key="k", session=session)
+
+        client.get_result("task-token", objects=True, http_timeout=(4, 5))
+
+        assert session.calls[0][2]["timeout"] == (4, 5)
+        assert session.calls[0][2]["json"]["objects"] is True
+
+    def test_get_formatted_accepts_per_call_http_timeout(self) -> None:
+        session = FakeSession()
+        client = UniParserClient(host="https://example.com", api_key="k", session=session)
+
+        client.get_formatted("task-token", content=True, http_timeout=(6, 7))
+
+        assert session.calls[0][2]["timeout"] == (6, 7)
+        assert session.calls[0][2]["json"]["content"] is True
+
+    def test_get_third_party_output_sends_formatter(self) -> None:
+        session = FakeSession(response=FakeResponse(payload={"status": "success", "content": {}}))
+        client = UniParserClient(host="https://example.com", api_key="k", session=session)
+
+        result = client.get_third_party_output(
+            "task-token",
+            formatter=ThirdPartyFormatter.MinerU,
+            http_timeout=(8, 9),
+        )
+
+        assert result["status"] == "success"
+        assert session.calls[0][2]["json"] == {
+            "token": "task-token",
+            "formatter": "mineru",
+        }
+        assert session.calls[0][2]["timeout"] == (8, 9)
+
+
+class TestServiceDiscovery:
+    def test_health_accepts_per_call_timeout(self) -> None:
+        session = FakeSession(response=FakeResponse(payload={"status": "Healthy"}))
+        client = UniParserClient(host="https://example.com", api_key="k", session=session)
+
+        result = client.health(http_timeout=(1, 2))
+
+        assert result["status"] == "Healthy"
+        assert session.calls[0][2]["timeout"] == (1, 2)
+
+    def test_version_preserves_model_backend_metadata(self) -> None:
+        payload = {
+            "version": "frontend-1.3",
+            "default_version": "v1.3",
+            "backend_versions": {
+                "v1.3": {
+                    "available": True,
+                    "capabilities": {"preset_layout_reparse": True},
+                }
+            },
+        }
+        session = FakeSession(response=FakeResponse(payload=payload))
+        client = UniParserClient(host="https://example.com", api_key="k", session=session)
+
+        result = client.version()
+
+        assert result == payload
+        assert result["default_version"] == "v1.3"
+        assert result["backend_versions"]["v1.3"]["available"] is True
+
+    def test_get_constants_returns_service_contract(self) -> None:
+        payload = {
+            "LayoutType": {"paragraph": "paragraph"},
+            "TokenRegEx": r"^[-\\._?=&a-zA-Z0-9]{1,128}$",
+        }
+        session = FakeSession(response=FakeResponse(payload=payload))
+        client = UniParserClient(host="https://example.com", api_key="k", session=session)
+
+        result = client.get_constants(http_timeout=(3, 4))
+
+        assert result == payload
+        assert session.calls[0][1].endswith("/get-constants")
+        assert session.calls[0][2]["timeout"] == (3, 4)
+
+
+class TestSubmissionPayloads:
+    @staticmethod
+    def _preset_layout():
+        return [[{"type": "textual", "bbox": [0, 0, 20, 20]}]]
+
+    def test_trigger_file_sends_latest_form_fields(self, tmp_path) -> None:
+        path = tmp_path / "document.pdf"
+        path.write_bytes(b"%PDF-1.4 tiny")
+        session = FakeSession()
+        client = UniParserClient(host="https://example.com", api_key="k", session=session)
+
+        client.trigger_file(
+            str(path),
+            timeout=321,
+            padding_snip=False,
+            inplace_update=True,
+            preset_layout=self._preset_layout(),
+            model_version="v1.3",
+            http_timeout=(2, 3),
+        )
+
+        payload = session.calls[0][2]["data"]
+        assert payload["timeout"] == 321
+        assert payload["padding_snip"] is False
+        assert payload["inplace_update"] is True
+        assert json.loads(payload["preset_layout"]) == self._preset_layout()
+        assert payload["model_version"] == "v1.3"
+        assert session.calls[0][2]["timeout"] == (2, 3)
+
+    def test_trigger_snip_sends_latest_form_fields(self, tmp_path) -> None:
+        path = tmp_path / "snip.png"
+        Image.new("RGB", (2, 2), "white").save(path)
+        session = FakeSession()
+        client = UniParserClient(host="https://example.com", api_key="k", session=session)
+
+        client.trigger_snip(
+            str(path),
+            timeout=123,
+            padding_snip=False,
+            inplace_update=True,
+            preset_layout=self._preset_layout(),
+            model_version="v1.3",
+        )
+
+        payload = session.calls[0][2]["data"]
+        assert payload["timeout"] == 123
+        assert payload["padding_snip"] is False
+        assert payload["inplace_update"] is True
+        assert json.loads(payload["preset_layout"]) == self._preset_layout()
+        assert payload["model_version"] == "v1.3"
+        assert payload["img"]
+
+    def test_trigger_url_serializes_preset_layout_inside_json_body(self) -> None:
+        session = FakeSession()
+        client = UniParserClient(host="https://example.com", api_key="k", session=session)
+
+        client.trigger_url(
+            "tos://bucket/document.pdf",
+            timeout=456,
+            inplace_update=True,
+            preset_layout=self._preset_layout(),
+            model_version="v1.3",
+        )
+
+        payload = session.calls[0][2]["json"]
+        assert payload["timeout"] == 456
+        assert payload["inplace_update"] is True
+        assert isinstance(payload["preset_layout"], str)
+        assert json.loads(payload["preset_layout"]) == self._preset_layout()
+        assert payload["model_version"] == "v1.3"
+        assert "padding_snip" not in payload
+
+    def test_server_generated_token_omits_deterministic_token(self, tmp_path) -> None:
+        path = tmp_path / "document.pdf"
+        path.write_bytes(b"%PDF-1.4 tiny")
+        session = FakeSession(response=FakeResponse(payload={"status": "waiting", "token": "server-token"}))
+        client = UniParserClient(host="https://example.com", api_key="k", session=session)
+
+        result = client.trigger_file(str(path), sync=False, server_generated_token=True)
+
+        assert session.calls[0][2]["data"]["token"] is None
+        assert result["token"] == "server-token"
+
+
+class TestTOSUpload:
+    def test_requests_upload_links_for_names_and_explicit_tokens(self) -> None:
+        session = FakeSession(response=FakeResponse(payload={"files": []}))
+        client = UniParserClient(host="https://example.com", api_key="k", session=session)
+
+        client.request_tos_upload_links(
+            [
+                "first.pdf",
+                TOSUploadFile(filename="second.png", token="explicit-token"),
+            ]
+        )
+
+        assert session.calls[0][2]["json"] == {
+            "files": [
+                {"filename": "first.pdf", "token": None},
+                {"filename": "second.png", "token": "explicit-token"},
+            ]
+        }
+
+    def test_upload_helper_puts_without_api_key(self, tmp_path) -> None:
+        path = tmp_path / "document.pdf"
+        path.write_bytes(b"%PDF-1.4 tiny")
+        link = {
+            "filename": "document.pdf",
+            "token": "server-token",
+            "upload_url": "https://tos.example.com/upload?signature=secret",
+            "source_url": "tos://bucket/document.pdf",
+        }
+        session = FakeSession(
+            responses=[
+                FakeResponse(payload={"files": [link]}),
+                FakeResponse(status_code=200, payload=None),
+            ]
+        )
+        client = UniParserClient(host="https://example.com", api_key="k", session=session)
+
+        result = client.upload_files_to_tos([str(path)])
+
+        assert result["status"] == "success"
+        assert result["files"][0]["source_url"] == "tos://bucket/document.pdf"
+        assert result["files"][0]["uploaded"] is True
+        assert "upload_url" not in result["files"][0]
+        assert session.calls[1][0] == "PUT"
+        assert session.calls[1][1] == link["upload_url"]
+        assert "X-API-Key" not in session.calls[1][2]["headers"]
+        assert session.calls[1][2]["timeout"] == (60.0, 300.0)
+
+    def test_transport_redacts_presigned_url_from_request_errors(self) -> None:
+        session = FakeSession(
+            error=requests.ConnectionError("failed for https://tos.example.com/upload?X-Tos-Signature=FAKE_BEARER")
+        )
+        client = UniParserClient(host="https://example.com", api_key="k", session=session)
+
+        result = client._transport.request(
+            "PUT",
+            "https://tos.example.com/upload?X-Tos-Signature=FAKE_BEARER",
+            authenticated=False,
+            expect_json=False,
+        )
+
+        assert "FAKE_BEARER" not in result["description"]
+        assert "?<redacted>" in result["description"]
+
+    def test_transport_redacts_presigned_url_from_json_errors(self) -> None:
+        session = FakeSession(
+            response=FakeResponse(
+                status_code=403,
+                payload={
+                    "status": "error",
+                    "description": ("upload denied for https://tos.example.com/upload?X-Tos-Signature=FAKE_BEARER"),
+                },
+                reason="Forbidden",
+            )
+        )
+        client = UniParserClient(host="https://example.com", api_key="k", session=session)
+
+        result = client.health()
+
+        assert "FAKE_BEARER" not in result["description"]
+        assert "?<redacted>" in result["description"]
+
+    def test_upload_count_mismatch_does_not_return_presigned_urls(self, tmp_path) -> None:
+        path = tmp_path / "document.pdf"
+        path.write_bytes(b"%PDF-1.4 tiny")
+        session = FakeSession(
+            response=FakeResponse(
+                payload={
+                    "files": [
+                        {
+                            "filename": "document.pdf",
+                            "upload_url": "https://tos.example.com/upload?X-Tos-Signature=FAKE_BEARER",
+                            "source_url": "tos://bucket/document.pdf",
+                        },
+                        {
+                            "filename": "unexpected.pdf",
+                            "upload_url": "https://tos.example.com/upload?X-Tos-Signature=OTHER_FAKE_BEARER",
+                            "source_url": "tos://bucket/unexpected.pdf",
+                        },
+                    ]
+                }
+            )
+        )
+        client = UniParserClient(host="https://example.com", api_key="k", session=session)
+
+        result = client.upload_files_to_tos([str(path)])
+
+        assert result["status"] == "error"
+        assert all("upload_url" not in item for item in result["files"])
+
+    def test_upload_helper_rejects_token_count_mismatch(self, tmp_path) -> None:
+        path = tmp_path / "document.pdf"
+        path.write_bytes(b"%PDF-1.4 tiny")
+        client = UniParserClient(host="https://example.com", api_key="k", session=FakeSession())
+
+        result = client.upload_files_to_tos([str(path)], tokens=[])
+
+        assert result["status"] == "error"
diff --git a/tests/unit/test_convert.py b/tests/unit/test_convert.py
index 99d46b6..421df7f 100644
--- a/tests/unit/test_convert.py
+++ b/tests/unit/test_convert.py
@@ -10,6 +10,7 @@
 import pytest
 
 from tests.utils import make_reaction_dict
+from uniparser_tools.common.constant import LayoutType
 from uniparser_tools.common.dataclass import (
     ChartResult,
     EquationResult,
@@ -87,6 +88,36 @@ def test_build_item_strips_pages_key() -> None:
     assert not hasattr(item, "pages")
 
 
+def test_build_item_tolerates_new_and_unknown_result_fields() -> None:
+    block = {
+        **BASE_BLOCK,
+        "text": "Yield \\(x^2\\) from `CCO`",
+        "bboxes": [],
+        "contents": ["Yield ", "x^2", " from ", "CCO"],
+        "types": ["text", "equationinline", "text", "molecule"],
+        "release_v1_3_extra": "future-compatible",
+    }
+    item = build_item(block)
+    assert isinstance(item, TextualResult)
+    assert item.markdown == "Yield $x^2$ from `CCO`"
+    assert not hasattr(item, "release_v1_3_extra")
+
+
+def test_build_item_upgrades_full_html_table_spans() -> None:
+    block = {
+        **BASE_BLOCK,
+        "type": "table",
+        "placeholders": [],
+        "contents": [],
+        "structure": "<table><tr><td>value \\(x^2\\)</td></tr></table>",
+    }
+    item = build_item(block)
+    assert isinstance(item, TabularResult)
+    assert item.placeholders
+    assert LayoutType.EquationInline in item.types
+    assert "$x^2$" in item.markdown
+
+
 def test_dict2obj_returns_nested_list() -> None:
     pages = [
         [
diff --git a/tests/unit/test_dataclass_results.py b/tests/unit/test_dataclass_results.py
index 67da43d..c26398a 100644
--- a/tests/unit/test_dataclass_results.py
+++ b/tests/unit/test_dataclass_results.py
@@ -11,7 +11,7 @@
 import pytest
 
 from tests.utils import make_chart_data, make_reaction_dict, make_tabular_payload
-from uniparser_tools.common.constant import LayoutType
+from uniparser_tools.common.constant import Direction, Language, LayoutType
 from uniparser_tools.common.dataclass import (
     BBox,
     ChartResult,
@@ -38,6 +38,29 @@
 
 
 class TestTextualResult:
+    def test_legacy_positional_arguments_keep_their_meaning(self) -> None:
+        r = TextualResult(
+            "tok",
+            0,
+            0,
+            BBox(0, 0, 1, 1),
+            1.0,
+            (100, 100),
+            LayoutType.Text,
+            False,
+            -1,
+            Language.Unknown,
+            Direction.Unknown,
+            "legacy-source",
+            [],
+            ["legacy text"],
+            "legacy text",
+        )
+
+        assert r.source == "legacy-source"
+        assert r.text == "legacy text"
+        assert r.types == [LayoutType.Text]
+
     def test_plain_is_text_field(self) -> None:
         r = TextualResult(
             **ITEM_KWARGS,
@@ -59,18 +82,28 @@ def test_bboxes_from_dict_are_coerced(self) -> None:
     def test_title_markdown_gets_heading(self) -> None:
         kw = {**ITEM_KWARGS, "type": LayoutType.Title}
         r = TextualResult(**kw, bboxes=[], contents=[], text="Chapter 1")
-        assert r.markdown == "# Chapter 1"
+        assert r.markdown == "## Chapter 1"
 
     def test_title_html_uses_h2(self) -> None:
         kw = {**ITEM_KWARGS, "type": LayoutType.Title}
         r = TextualResult(**kw, bboxes=[], contents=[], text="Chapter 1")
-        assert r.html == "<h2>Chapter 1</h2>"
+        assert r.html == '<h2 class="title">Chapter 1</h2>'
 
     def test_document_title_latex_uses_title_macro(self) -> None:
         kw = {**ITEM_KWARGS, "type": LayoutType.DocumentTitle}
         r = TextualResult(**kw, bboxes=[], contents=[], text="Doc")
         assert r.latex == "\\title{Doc}"
 
+    def test_inline_types_format_equation_and_molecule(self) -> None:
+        r = TextualResult(
+            **ITEM_KWARGS,
+            bboxes=[],
+            contents=["Yield ", "x^2", " for ", "CCO"],
+            types=[LayoutType.Text, LayoutType.EquationInline, LayoutType.Text, LayoutType.Molecule],
+        )
+        assert r.plain == r"Yield \(x^2\) for CCO"
+        assert r.markdown == "Yield $x^2$ for `CCO`"
+
 
 class TestTabularResult:
     @pytest.fixture()
@@ -186,6 +219,32 @@ def test_empty_data_falls_back_to_empty_df(self) -> None:
 
 
 class TestMoleculeResult:
+    def test_legacy_positional_arguments_keep_their_meaning(self) -> None:
+        r = MoleculeResult(
+            "tok",
+            0,
+            0,
+            BBox(0, 0, 1, 1),
+            1.0,
+            (100, 100),
+            LayoutType.Molecule,
+            False,
+            -1,
+            Language.Unknown,
+            Direction.Unknown,
+            "legacy-source",
+            "legacy caption",
+            False,
+            "CCO",
+            True,
+            "legacy drawing",
+        )
+
+        assert r.source == "legacy-source"
+        assert r.sru is True
+        assert r.drawing == "legacy drawing"
+        assert r.esmi == ""
+
     def test_plain_prefers_smi(self) -> None:
         r = MoleculeResult(
             **{**ITEM_KWARGS, "type": LayoutType.Molecule},
@@ -203,27 +262,35 @@ def test_plain_falls_back_to_caption_for_markush(self) -> None:
         )
         assert r.plain == "*NC(=O)*"
 
-    def test_markdown_wraps_in_bold_italic(self) -> None:
+    def test_markdown_wraps_in_code(self) -> None:
+        r = MoleculeResult(
+            **{**ITEM_KWARGS, "type": LayoutType.Molecule},
+            smi="CCO",
+        )
+        assert r.markdown == "`CCO`"
+
+    def test_esmi_is_preserved(self) -> None:
         r = MoleculeResult(
             **{**ITEM_KWARGS, "type": LayoutType.Molecule},
             smi="CCO",
+            esmi="[CH3][CH2][OH]",
         )
-        assert r.markdown == "***CCO***"
+        assert r.esmi == "[CH3][CH2][OH]"
 
 
 class TestEquationResult:
-    def test_latex_identity(self) -> None:
+    def test_latex_uses_display_delimiters(self) -> None:
         r = EquationResult(**{**ITEM_KWARGS, "type": LayoutType.Equation}, latex_repr="a+b")
-        assert r.latex == "a+b"
+        assert r.latex == "\\[\na+b\n\\]"
         assert r.markdown == "$$\na+b\n$$"
 
-    def test_html_returns_mathml_like(self) -> None:
+    def test_html_uses_mathjax_compatible_delimiters(self) -> None:
         r = EquationResult(**{**ITEM_KWARGS, "type": LayoutType.Equation}, latex_repr="a+b")
-        assert "<math" in r.html
+        assert r.html == "\\[\na+b\n\\]"
 
-    def test_html_fallback_on_unconvertible(self) -> None:
+    def test_html_preserves_unknown_latex_commands(self) -> None:
         r = EquationResult(**{**ITEM_KWARGS, "type": LayoutType.Equation}, latex_repr="\\unknown_cmd{xx}")
-        assert "<math" in r.html
+        assert "\\unknown_cmd{xx}" in r.html
 
 
 class TestFigureResult:
diff --git a/tests/unit/test_notebook_hygiene.py b/tests/unit/test_notebook_hygiene.py
new file mode 100644
index 0000000..0064ea1
--- /dev/null
+++ b/tests/unit/test_notebook_hygiene.py
@@ -0,0 +1,45 @@
+"""Regression checks for customer-facing examples and documentation."""
+
+from __future__ import annotations
+
+import json
+import re
+from pathlib import Path
+
+
+REPO_ROOT = Path(__file__).resolve().parents[2]
+TASK_TOKEN_PATTERN = re.compile(
+    r"(?i)(?<![0-9a-f])(?:[0-9a-f]{32}|[0-9a-f]{8}(?:-[0-9a-f]{4}){3}-[0-9a-f]{12})(?![0-9a-f])"
+)
+HARDCODED_API_KEY_PATTERN = re.compile(r"""(?i)\b(?:api_key|UNIPARSER_API_KEY)\s*=\s*["'][^"']+["']""")
+
+
+def test_playground_notebooks_have_no_saved_outputs_or_real_task_tokens() -> None:
+    notebooks = sorted((REPO_ROOT / "playground").glob("*.ipynb"))
+    assert notebooks
+
+    for notebook_path in notebooks:
+        notebook = json.loads(notebook_path.read_text(encoding="utf-8"))
+        for cell_index, cell in enumerate(notebook.get("cells", [])):
+            if cell.get("cell_type") == "code":
+                assert cell.get("outputs", []) == [], f"{notebook_path}: cell {cell_index} has saved output"
+                assert cell.get("execution_count") is None, f"{notebook_path}: cell {cell_index} has an execution count"
+                source = "".join(cell.get("source", []))
+                assert HARDCODED_API_KEY_PATTERN.search(source) is None, (
+                    f"{notebook_path}: cell {cell_index} hardcodes an API key"
+                )
+
+            serialized_cell = json.dumps(cell, ensure_ascii=False)
+            assert TASK_TOKEN_PATTERN.search(serialized_cell) is None, (
+                f"{notebook_path}: cell {cell_index} contains a task-token-shaped value"
+            )
+
+
+def test_callback_pattern_uses_release_signature_contract() -> None:
+    pattern_path = REPO_ROOT / "skills" / "UniParser-Tools" / "references" / "patterns.md"
+    content = pattern_path.read_text(encoding="utf-8")
+
+    assert 'data["checksum"]' not in content
+    assert '"checksum":' not in content
+    assert "X-UniParser-Signature" in content
+    assert "request.get_data(cache=True)" in content
diff --git a/tests/unit/test_output_dir.py b/tests/unit/test_output_dir.py
new file mode 100644
index 0000000..6151113
--- /dev/null
+++ b/tests/unit/test_output_dir.py
@@ -0,0 +1,85 @@
+"""Tests for collision-safe output directory allocation."""
+
+from __future__ import annotations
+
+from concurrent.futures import ThreadPoolExecutor
+from pathlib import Path
+
+import pytest
+
+from uniparser_tools.common.output_dir import create_unique_output_dir
+
+
+def test_creates_preferred_directory(tmp_path: Path) -> None:
+    preferred = tmp_path / "results"
+
+    actual = create_unique_output_dir(preferred)
+
+    assert actual == preferred
+    assert actual.is_dir()
+
+
+def test_uses_first_available_suffixed_sibling(tmp_path: Path) -> None:
+    preferred = tmp_path / "results"
+    preferred.mkdir()
+    (preferred / "keep.txt").write_text("original", encoding="utf-8")
+    preferred.with_name("results_1").mkdir()
+
+    actual = create_unique_output_dir(preferred)
+
+    assert actual == tmp_path / "results_2"
+    assert (preferred / "keep.txt").read_text(encoding="utf-8") == "original"
+
+
+def test_existing_file_is_preserved_and_suffixed(tmp_path: Path) -> None:
+    preferred = tmp_path / "results"
+    preferred.write_text("original", encoding="utf-8")
+
+    actual = create_unique_output_dir(preferred)
+
+    assert actual == tmp_path / "results_1"
+    assert preferred.read_text(encoding="utf-8") == "original"
+
+
+@pytest.mark.parametrize("broken", [False, True])
+def test_final_symlink_is_not_followed(tmp_path: Path, broken: bool) -> None:
+    target = tmp_path / "target"
+    if not broken:
+        target.mkdir()
+        (target / "keep.txt").write_text("keep", encoding="utf-8")
+    preferred = tmp_path / "results"
+    preferred.symlink_to(target, target_is_directory=True)
+
+    actual = create_unique_output_dir(preferred)
+
+    assert actual == tmp_path / "results_1"
+    assert preferred.is_symlink()
+    if not broken:
+        assert (target / "keep.txt").read_text(encoding="utf-8") == "keep"
+
+
+@pytest.mark.parametrize("protected", [Path.home(), Path.cwd(), Path(Path.cwd().anchor)])
+def test_rejects_protected_output_targets(protected: Path) -> None:
+    with pytest.raises(ValueError, match="protected output directory"):
+        create_unique_output_dir(protected)
+
+
+def test_rejects_git_metadata_target(tmp_path: Path) -> None:
+    with pytest.raises(ValueError, match="Git metadata"):
+        create_unique_output_dir(tmp_path / ".GIT" / "results")
+
+
+def test_rejects_parent_directory_name(tmp_path: Path) -> None:
+    with pytest.raises(ValueError, match="Invalid output directory name"):
+        create_unique_output_dir(tmp_path / "nested" / "..")
+
+
+def test_concurrent_allocations_are_unique(tmp_path: Path) -> None:
+    preferred = tmp_path / "results"
+
+    with ThreadPoolExecutor(max_workers=8) as executor:
+        actual = list(executor.map(create_unique_output_dir, [preferred] * 8))
+
+    assert len(set(actual)) == 8
+    assert {path.name for path in actual} == {"results", *(f"results_{index}" for index in range(1, 8))}
+    assert all(path.is_dir() for path in actual)
diff --git a/tests/unit/test_pdf_render.py b/tests/unit/test_pdf_render.py
new file mode 100644
index 0000000..9d53360
--- /dev/null
+++ b/tests/unit/test_pdf_render.py
@@ -0,0 +1,299 @@
+"""Unit tests for ``uniparser_tools.utils.pdf_render``.
+
+``pdf_render`` is the permissive-licensed (pypdfium2 / PDFium, BSD-3/Apache-2.0)
+replacement for the AGPL PyMuPDF (fitz) that the toolkit previously used only to
+rasterise PDF pages / clipped regions. These tests pin the small fitz-compatible
+contract the call sites rely on:
+
+  * ``Document`` open / ``len`` / indexing, ``page.rect`` geometry
+  * full-page render dims = ``round(size_pt * dpi / 72)``, RGB, packed samples
+  * clipped ``get_pixmap`` == cropping the full render at the same scale
+    (pixel-identical -- the property that makes the swap behaviour-preserving)
+  * ``Rect`` helpers, tuple clips, degenerate-clip guard, render caching, save
+"""
+
+from __future__ import annotations
+
+import numpy as np
+import pypdfium2 as pdfium
+import pytest
+from PIL import Image
+
+from uniparser_tools.utils import pdf_render
+
+
+def _build_synthetic_pdf_bytes() -> bytes:
+    """Two-page PDF: page0 200x300 with colored rects, page1 blank 400x200."""
+    objects = [
+        b"1 0 obj<< /Type /Catalog /Pages 2 0 R >>endobj\n",
+        b"2 0 obj<< /Type /Pages /Kids [3 0 R 5 0 R] /Count 2 >>endobj\n",
+        (
+            b"3 0 obj<< /Type /Page /Parent 2 0 R /MediaBox [0 0 200 300] "
+            b"/Contents 4 0 R /Resources<< /ProcSet [/PDF] >> >>endobj\n"
+        ),
+    ]
+    stream = b"1 0 0 rg\n20 40 80 120 re f\n0 0 1 rg\n100 150 60 80 re f\n"
+    objects.append(f"4 0 obj<< /Length {len(stream)} >>stream\n".encode() + stream + b"endstream\nendobj\n")
+    objects.append(
+        b"5 0 obj<< /Type /Page /Parent 2 0 R /MediaBox [0 0 400 200] /Resources<< /ProcSet [/PDF] >> >>endobj\n"
+    )
+
+    header = b"%PDF-1.4\n"
+    body = b""
+    offsets = [0]
+    pos = len(header)
+    for obj in objects:
+        offsets.append(pos)
+        body += obj
+        pos += len(obj)
+
+    xref = [b"xref\n", f"0 {len(offsets)}\n".encode(), b"0000000000 65535 f \n"]
+    for off in offsets[1:]:
+        xref.append(f"{off:010d} 00000 n \n".encode())
+    trailer = f"trailer<< /Size {len(offsets)} /Root 1 0 R >>\nstartxref\n{pos}\n%%EOF\n".encode()
+    return header + body + b"".join(xref) + trailer
+
+
+@pytest.fixture(scope="module")
+def synthetic_pdf(tmp_path_factory) -> str:
+    """Deterministic 2-page PDF with distinct sizes + page-0 content.
+
+    Built in-process so CI needs no checked-in ``*.pdf`` (gitignored).
+    """
+    path = tmp_path_factory.mktemp("pdf_render") / "synthetic.pdf"
+    path.write_bytes(_build_synthetic_pdf_bytes())
+    return str(path)
+
+
+# --- open / len / indexing / rect geometry ---------------------------------
+
+
+def test_open_len_and_indexing(synthetic_pdf):
+    doc = pdf_render.Document(synthetic_pdf)
+    assert len(doc) == 2
+    assert isinstance(doc[0], pdf_render.Page)
+    doc.close()
+
+
+def test_page_rect_geometry(synthetic_pdf):
+    doc = pdf_render.Document(synthetic_pdf)
+    r0, r1 = doc[0].rect, doc[1].rect
+    assert (r0.width, r0.height) == (200.0, 300.0)
+    assert (r1.width, r1.height) == (400.0, 200.0)
+    # fitz reports a top-left origin at (0, 0); call sites add it explicitly.
+    assert r0.top_left == (0.0, 0.0)
+    doc.close()
+
+
+# --- full-page render ------------------------------------------------------
+
+
+@pytest.mark.parametrize("dpi", [72, 144, 100])
+def test_render_page_dims_and_samples(synthetic_pdf, dpi):
+    doc = pdf_render.Document(synthetic_pdf)
+    pix = doc[0].get_pixmap(dpi=dpi)
+    scale = dpi / 72.0
+    assert pix.width == round(200 * scale)
+    assert pix.height == round(300 * scale)
+    # packed RGB bytes -> Image.frombytes round-trips (the exact call-site pattern)
+    assert len(pix.samples) == pix.width * pix.height * 3
+    img = Image.frombytes("RGB", (pix.width, pix.height), pix.samples)
+    assert img.size == (pix.width, pix.height)
+    assert img.mode == "RGB"
+    doc.close()
+
+
+# --- Rect helpers ----------------------------------------------------------
+
+
+def test_rect_helpers():
+    rect = pdf_render.Rect(10, 20, 110, 220)
+    assert rect.width == 100 and rect.height == 200
+    assert rect.top_left == (10, 20)
+    assert tuple(rect) == (10.0, 20.0, 110.0, 220.0)
+
+
+# --- clipped render: the behaviour-preserving property ---------------------
+
+
+def test_clip_matches_full_crop_pixel_identical(synthetic_pdf):
+    """A clipped pixmap must equal cropping the full-page render at the same
+    scale -- proves the coordinate math (offset *and* size) is correct on a
+    content-bearing page (no checked-in PDF; ``*.pdf`` is gitignored)."""
+    doc = pdf_render.Document(synthetic_pdf)
+    rect = doc[0].rect
+    dpi = 120
+    full_pix = doc[0].get_pixmap(dpi=dpi)
+    full = Image.frombytes("RGB", (full_pix.width, full_pix.height), full_pix.samples)
+    # an off-origin sub-rectangle (points, top-left origin)
+    clip = pdf_render.Rect(0.15 * rect.width, 0.10 * rect.height, 0.65 * rect.width, 0.55 * rect.height)
+    pix = pdf_render.get_pixmap(doc[0], clip=clip, dpi=dpi)
+    got = Image.frombytes("RGB", (pix.width, pix.height), pix.samples)
+
+    s = dpi / 72.0
+    box = (round(clip.x0 * s), round(clip.y0 * s), round(clip.x1 * s), round(clip.y1 * s))
+    ref = full.crop(box)
+    assert got.size == ref.size
+    assert int(np.abs(np.asarray(got).astype(int) - np.asarray(ref).astype(int)).max()) == 0
+    doc.close()
+
+
+def test_clip_accepts_plain_tuple(synthetic_pdf):
+    doc = pdf_render.Document(synthetic_pdf)
+    pix = doc[0].get_pixmap(clip=(0, 0, 100, 150), dpi=72)
+    assert (pix.width, pix.height) == (100, 150)
+    doc.close()
+
+
+def test_degenerate_clip_returns_white_1x1(synthetic_pdf):
+    doc = pdf_render.Document(synthetic_pdf)
+    # zero-area / inverted clip -> guarded 1x1 white tile, never a crash
+    pix = doc[0].get_pixmap(clip=(50, 50, 50, 50), dpi=72)
+    assert (pix.width, pix.height) == (1, 1)
+    assert Image.frombytes("RGB", (1, 1), pix.samples).getpixel((0, 0)) == (255, 255, 255)
+    doc.close()
+
+
+def test_clip_clamped_to_page_bounds(synthetic_pdf):
+    doc = pdf_render.Document(synthetic_pdf)
+    # clip extends past the page; result is clamped to the page extent
+    pix = doc[0].get_pixmap(clip=(-20, -20, 9999, 9999), dpi=72)
+    assert (pix.width, pix.height) == (200, 300)
+    doc.close()
+
+
+# --- module function delegates to the page method --------------------------
+
+
+def test_module_get_pixmap_delegates(synthetic_pdf):
+    doc = pdf_render.Document(synthetic_pdf)
+    page = doc[0]
+    a = pdf_render.get_pixmap(page, dpi=72)
+    b = page.get_pixmap(dpi=72)
+    assert (a.width, a.height) == (b.width, b.height) == (200, 300)
+    doc.close()
+
+
+# --- render caching --------------------------------------------------------
+
+
+def test_full_render_is_cached(synthetic_pdf):
+    doc = pdf_render.Document(synthetic_pdf)
+    first = doc._render_full(0, 144)
+    second = doc._render_full(0, 144)
+    assert first is second  # same (index, dpi) -> reused PIL image
+    assert doc._render_full(1, 144) is not first  # different page -> distinct
+    doc.close()
+
+
+def test_render_cache_is_lru_bounded(synthetic_pdf):
+    """The full-page cache must not grow without bound: a full raster can be
+    tens of MB, so a long PDF would blow up memory. Bound it to ``cache_pages``
+    with LRU eviction, keeping the recently-touched pages."""
+    doc = pdf_render.Document(synthetic_pdf, cache_pages=1)
+    a1 = doc._render_full(0, 72)
+    assert list(doc._full_cache) == [(0, 72)]
+    # touching page 1 evicts page 0 (capacity 1)
+    doc._render_full(1, 72)
+    assert list(doc._full_cache) == [(1, 72)]
+    # page 0 must be re-rendered (fresh object) and still be correct
+    a2 = doc._render_full(0, 72)
+    assert a2 is not a1
+    assert a2.size == a1.size
+    doc.close()
+
+
+def test_render_cache_lru_keeps_recent(synthetic_pdf):
+    """With room for 2 pages, re-touching the older page keeps it hot so the
+    next insert evicts the *other* one (true LRU, not FIFO)."""
+    doc = pdf_render.Document(synthetic_pdf, cache_pages=2)
+    doc._render_full(0, 72)
+    doc._render_full(1, 72)
+    doc._render_full(0, 72)  # page 0 now most-recently-used
+    doc._render_full(0, 144)  # third distinct key -> evicts LRU == (1, 72)
+    assert (1, 72) not in doc._full_cache
+    assert (0, 72) in doc._full_cache
+    doc.close()
+
+
+# --- rotation consistency --------------------------------------------------
+
+
+def test_rotated_page_size_and_render_agree(tmp_path):
+    """The clip math assumes ``rect`` (from ``get_size``) and the rendered
+    pixmap share one orientation. Pin that for an intrinsically /Rotate-90 page:
+    the reported rect and the pixmap dims must both be the rotated extent."""
+    src = pdfium.PdfDocument.new()
+    page = src.new_page(200, 300)  # portrait before rotation
+    page.set_rotation(90)
+    path = tmp_path / "rotated.pdf"
+    src.save(str(path))
+    src.close()
+
+    doc = pdf_render.Document(str(path))
+    rect = doc[0].rect
+    assert (rect.width, rect.height) == (300.0, 200.0)  # rotated extent
+    pix = doc[0].get_pixmap(dpi=72)
+    assert (pix.width, pix.height) == (300, 200)  # render matches rect -> clip math stays valid
+    doc.close()
+
+
+def test_rotated_page_clip_matches_full_crop(tmp_path):
+    """Clip == full-crop must still hold once rotation is in play."""
+    src = pdfium.PdfDocument.new()
+    page = src.new_page(200, 300)
+    page.set_rotation(90)
+    path = tmp_path / "rotated2.pdf"
+    src.save(str(path))
+    src.close()
+
+    doc = pdf_render.Document(str(path))
+    dpi = 100
+    full_pix = doc[0].get_pixmap(dpi=dpi)
+    full = Image.frombytes("RGB", (full_pix.width, full_pix.height), full_pix.samples)
+    clip = pdf_render.Rect(40, 30, 180, 150)
+    pix = doc[0].get_pixmap(clip=clip, dpi=dpi)
+    got = Image.frombytes("RGB", (pix.width, pix.height), pix.samples)
+    s = dpi / 72.0
+    ref = full.crop((round(clip.x0 * s), round(clip.y0 * s), round(clip.x1 * s), round(clip.y1 * s)))
+    assert got.size == ref.size
+    assert np.array_equal(np.asarray(got), np.asarray(ref))
+    doc.close()
+
+
+# --- Pixmap normalizes to packed RGB ---------------------------------------
+
+
+def test_pixmap_converts_non_rgb_to_rgb():
+    """Call sites do ``Image.frombytes("RGB", (w, h), pix.samples)``; a render
+    that comes back RGBA/LA/L must be normalized so ``samples`` stays 3-byte."""
+    rgba = Image.new("RGBA", (5, 4), (10, 20, 30, 128))
+    pix = pdf_render.Pixmap(rgba)
+    assert pix.pil.mode == "RGB"
+    assert (pix.width, pix.height) == (5, 4)
+    assert len(pix.samples) == 5 * 4 * 3
+    assert Image.frombytes("RGB", (5, 4), pix.samples).getpixel((0, 0)) == (10, 20, 30)
+
+
+def test_missing_file_raises(tmp_path):
+    with pytest.raises(Exception):
+        pdf_render.Document(str(tmp_path / "does_not_exist.pdf"))
+
+
+# --- save + context manager ------------------------------------------------
+
+
+def test_pixmap_save(synthetic_pdf, tmp_path):
+    doc = pdf_render.Document(synthetic_pdf)
+    out = tmp_path / "page0.png"
+    doc[0].get_pixmap(dpi=72).save(out)
+    assert out.is_file() and out.stat().st_size > 0
+    assert Image.open(out).size == (200, 300)
+    doc.close()
+
+
+def test_context_manager_closes(synthetic_pdf):
+    with pdf_render.Document(synthetic_pdf) as doc:
+        assert len(doc) == 2
+    # cache cleared on close
+    assert doc._full_cache == {}
diff --git a/tests/unit/test_wheel_packaging.py b/tests/unit/test_wheel_packaging.py
new file mode 100644
index 0000000..13270a0
--- /dev/null
+++ b/tests/unit/test_wheel_packaging.py
@@ -0,0 +1,48 @@
+"""Regression test for setuptools namespace-package discovery."""
+
+from __future__ import annotations
+
+import subprocess
+import sys
+import zipfile
+from pathlib import Path
+
+
+REPO_ROOT = Path(__file__).resolve().parents[2]
+
+
+def test_repeated_wheel_builds_only_package_uniparser_tools(tmp_path: Path) -> None:
+    wheels = []
+    for build_number in range(2):
+        wheel_dir = tmp_path / f"wheel-{build_number}"
+        wheel_dir.mkdir()
+        subprocess.run(
+            [
+                sys.executable,
+                "-m",
+                "pip",
+                "wheel",
+                str(REPO_ROOT),
+                "--no-cache-dir",
+                "--no-deps",
+                "--no-build-isolation",
+                "--wheel-dir",
+                str(wheel_dir),
+            ],
+            check=True,
+            capture_output=True,
+            text=True,
+        )
+        built_wheels = list(wheel_dir.glob("*.whl"))
+        assert len(built_wheels) == 1
+        wheels.append(built_wheels[0])
+
+    with zipfile.ZipFile(wheels[-1]) as wheel:
+        unexpected = [
+            name for name in wheel.namelist() if not name.startswith(("uniparser_tools/", "uniparser_tools-"))
+        ]
+        metadata_path = next(name for name in wheel.namelist() if name.endswith(".dist-info/METADATA"))
+        metadata = wheel.read(metadata_path).decode("utf-8")
+
+    assert unexpected == []
+    assert "Requires-Python: >=3.11" in metadata
diff --git a/uniparser_agent/README.md b/uniparser_agent/README.md
new file mode 100644
index 0000000..a7479d5
--- /dev/null
+++ b/uniparser_agent/README.md
@@ -0,0 +1,11 @@
+# UniParser Agent
+
+UniParser Agent 是基于 UniParser 的文档处理包，提供统一的命令行入口：
+
+- `parse`：将 PDF、图片或公开 PDF URL 解析为结构化文档。
+- `vqa`：从习题、试卷、题册或答案册中提取题目、答案、解析和相关图片，并生成结构化 VQA 数据。
+
+本包包含文档解析、LLM 调用和 pdf2vqa 流程所需的公共模块。
+
+安装方式、配置说明、命令参数、处理流程和输出格式，请参阅
+[pdf2vqa 完整文档](pdf2vqa/README.md)。
diff --git a/uniparser_agent/__init__.py b/uniparser_agent/__init__.py
new file mode 100644
index 0000000..fc87af0
--- /dev/null
+++ b/uniparser_agent/__init__.py
@@ -0,0 +1,3 @@
+"""UniParser document parsing and exam VQA extraction."""
+
+__version__ = "0.1.0"
diff --git a/uniparser_agent/cli.py b/uniparser_agent/cli.py
new file mode 100644
index 0000000..f7881ac
--- /dev/null
+++ b/uniparser_agent/cli.py
@@ -0,0 +1,151 @@
+from __future__ import annotations
+
+import json
+from typing import Optional
+
+import typer
+
+from uniparser_agent.llm import LLMConfig, resolve_llm_config
+from uniparser_agent.parse.service import parse_document
+from uniparser_agent.pdf2vqa.pipeline import run_vqa_pipeline
+
+
+app = typer.Typer(
+    name="uniparser-agent",
+    help="UniParser document parsing and exam VQA extraction.",
+    no_args_is_help=True,
+)
+
+
+@app.command("parse")
+def parse_cmd(
+    input_path: str = typer.Argument(..., help="Local PDF/image path or public PDF URL."),
+    output_dir: Optional[str] = typer.Option(
+        None,
+        "-o",
+        "--output-dir",
+        help="Preferred output directory; a suffixed sibling is used if occupied.",
+    ),
+    json_output: bool = typer.Option(False, "--json", help="Print machine-readable JSON."),
+) -> None:
+    """Parse a document with UniParser scientific-paper defaults."""
+    result = parse_document(input_path, output_dir=output_dir)
+    if json_output:
+        typer.echo(json.dumps(result, ensure_ascii=False, indent=2))
+        return
+    typer.echo(f"Token: {result.get('token', '')}")
+    typer.echo(f"Pages tree: {result['pages_tree_path']}")
+    typer.echo(f"Markdown: {result['markdown_path']}")
+    typer.echo(f"Output directory: {result['output_dir']}")
+
+
+@app.command("vqa")
+def vqa_cmd(
+    input_path: Optional[str] = typer.Argument(
+        None,
+        help="Local PDF/image path or public PDF URL. Omit when using --pages-tree.",
+    ),
+    output_dir: Optional[str] = typer.Option(
+        None,
+        "-o",
+        "--output-dir",
+        help="Preferred VQA output directory; a suffixed sibling is used if occupied.",
+    ),
+    answer_pdf: Optional[str] = typer.Option(
+        None,
+        "--answer-pdf",
+        help="Answer booklet PDF. Merged after the question booklet (local PDFs only).",
+    ),
+    pages_tree: Optional[str] = typer.Option(
+        None,
+        "--pages-tree",
+        help="Skip UniParser parse and use an existing pages_tree.json.",
+    ),
+    api_key: Optional[str] = typer.Option(
+        None,
+        "--api-key",
+        help="LLM API key (overrides OPENAI_API_KEY).",
+        envvar=[],
+    ),
+    base_url: Optional[str] = typer.Option(
+        None,
+        "--base-url",
+        help="LLM base URL (overrides OPENAI_BASE_URL).",
+    ),
+    model: Optional[str] = typer.Option(
+        None,
+        "--model",
+        help="LLM model name (overrides OPENAI_MODEL).",
+    ),
+    enable_thinking: bool = typer.Option(
+        False,
+        "--enable-thinking/--no-enable-thinking",
+        help="Pass chat_template_kwargs.enable_thinking for Qwen-compatible servers.",
+    ),
+    json_output: bool = typer.Option(False, "--json", help="Print machine-readable JSON."),
+) -> None:
+    """Parse with UniParser (unless --pages-tree), then extract VQA pairs via LLM."""
+    if answer_pdf and pages_tree:
+        raise typer.BadParameter("Use either --answer-pdf or --pages-tree, not both.")
+    if answer_pdf and not input_path:
+        raise typer.BadParameter("--answer-pdf requires the question booklet as INPUT.")
+    if not input_path and not pages_tree:
+        raise typer.BadParameter("Provide INPUT (pdf/url/image) or --pages-tree.")
+    if input_path and pages_tree:
+        raise typer.BadParameter("Use either INPUT or --pages-tree, not both.")
+
+    llm_config = _build_llm_config(
+        api_key=api_key,
+        base_url=base_url,
+        model=model,
+        enable_thinking=enable_thinking,
+    )
+    result = run_vqa_pipeline(
+        input_path=input_path,
+        answer_pdf=answer_pdf,
+        pages_tree_path=pages_tree,
+        output_dir=output_dir,
+        llm_config=llm_config,
+    )
+    if json_output:
+        typer.echo(json.dumps(result, ensure_ascii=False, indent=2))
+        return
+
+    paths = result["paths"]
+    if paths.get("merged_pdf"):
+        typer.echo(f"Merged PDF: {paths['merged_pdf']}")
+    typer.echo(f"Pages tree: {paths['pages_tree']}")
+    typer.echo(f"Content list items: {result['n_content_items']}")
+    typer.echo(f"VQA images: {result.get('n_vqa_images', 0)} -> {paths.get('vqa_images', '')}")
+    typer.echo(f"Merged VQA pairs: {result['n_merged_vqa']}")
+    typer.echo(f"JSONL: {paths['merged_vqa_pairs_jsonl']}")
+    typer.echo(f"Markdown: {paths['merged_vqa_pairs_md']}")
+    if paths.get("vqa_sharegpt"):
+        typer.echo(f"ShareGPT: {paths['vqa_sharegpt']}")
+    typer.echo(f"Output directory: {paths['output_dir']}")
+
+
+def _build_llm_config(
+    *,
+    api_key: Optional[str],
+    base_url: Optional[str],
+    model: Optional[str],
+    enable_thinking: bool,
+) -> LLMConfig:
+    try:
+        return resolve_llm_config(
+            api_key=api_key,
+            base_url=base_url,
+            model=model,
+            enable_thinking=enable_thinking,
+        )
+    except ValueError as exc:
+        raise typer.BadParameter(str(exc)) from exc
+
+
+def main() -> None:
+    app()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/uniparser_agent/llm/__init__.py b/uniparser_agent/llm/__init__.py
new file mode 100644
index 0000000..603ef80
--- /dev/null
+++ b/uniparser_agent/llm/__init__.py
@@ -0,0 +1,11 @@
+"""Shared OpenAI-compatible LLM helpers for uniparser_agent."""
+
+from uniparser_agent.llm.client import OpenAICompatLLM
+from uniparser_agent.llm.config import LLMConfig, resolve_llm_config
+
+
+__all__ = [
+    "LLMConfig",
+    "OpenAICompatLLM",
+    "resolve_llm_config",
+]
diff --git a/uniparser_agent/llm/client.py b/uniparser_agent/llm/client.py
new file mode 100644
index 0000000..83aa2a9
--- /dev/null
+++ b/uniparser_agent/llm/client.py
@@ -0,0 +1,86 @@
+"""OpenAI-compatible chat client."""
+
+from __future__ import annotations
+
+from typing import Any
+
+from openai import OpenAI
+
+from uniparser_agent.llm.config import LLMConfig, resolve_llm_config
+
+
+class OpenAICompatLLM:
+    """Thin wrapper around ``openai.OpenAI`` chat completions."""
+
+    def __init__(
+        self,
+        config: LLMConfig | None = None,
+        *,
+        api_key: str | None = None,
+        base_url: str | None = None,
+        model: str | None = None,
+        timeout: float | None = None,
+        max_tokens: int | None = None,
+        enable_thinking: bool | None = None,
+        extra_body: dict[str, Any] | None = None,
+    ) -> None:
+        self.config = resolve_llm_config(
+            config=config,
+            api_key=api_key,
+            base_url=base_url,
+            model=model,
+            timeout=timeout,
+            max_tokens=max_tokens,
+            enable_thinking=enable_thinking,
+            extra_body=extra_body,
+        )
+        self._client = OpenAI(
+            api_key=self.config.api_key,
+            base_url=self.config.base_url,
+            timeout=self.config.timeout,
+        )
+
+    @property
+    def api_key(self) -> str:
+        return self.config.api_key
+
+    @property
+    def base_url(self) -> str:
+        return self.config.base_url
+
+    @property
+    def model(self) -> str:
+        return self.config.model
+
+    @property
+    def timeout(self) -> float:
+        return self.config.timeout
+
+    @property
+    def max_tokens(self) -> int:
+        return self.config.max_tokens
+
+    @property
+    def enable_thinking(self) -> bool:
+        return self.config.enable_thinking
+
+    def chat(self, *, system_prompt: str, user_content: str) -> str:
+        kwargs: dict[str, Any] = {
+            "model": self.config.model,
+            "messages": [
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": user_content},
+            ],
+            "max_tokens": self.config.max_tokens,
+        }
+        extra = self.config.resolved_extra_body()
+        if extra is not None:
+            kwargs["extra_body"] = extra
+        response = self._client.chat.completions.create(**kwargs)
+        content = response.choices[0].message.content
+        if content is None:
+            raise RuntimeError("LLM returned empty content")
+        return content
+
+    def meta(self) -> dict[str, Any]:
+        return self.config.meta()
diff --git a/uniparser_agent/llm/config.py b/uniparser_agent/llm/config.py
new file mode 100644
index 0000000..a25c6c5
--- /dev/null
+++ b/uniparser_agent/llm/config.py
@@ -0,0 +1,109 @@
+"""Shared LLM configuration (OpenAI-compatible, no hardcoded defaults)."""
+
+from __future__ import annotations
+
+import os
+from dataclasses import dataclass, replace
+from typing import Any
+
+
+@dataclass(frozen=True)
+class LLMConfig:
+    """OpenAI-compatible LLM settings.
+
+    Required fields have no library defaults: set via constructor, CLI, or
+    ``OPENAI_API_KEY`` / ``OPENAI_BASE_URL`` / ``OPENAI_MODEL``.
+    """
+
+    api_key: str
+    base_url: str
+    model: str
+    timeout: float = 3600.0
+    max_tokens: int = 81920
+    enable_thinking: bool = False
+    extra_body: dict[str, Any] | None = None
+
+    def resolved_extra_body(self) -> dict[str, Any] | None:
+        """Return request ``extra_body``, applying Qwen thinking kwargs when needed."""
+        if self.extra_body is not None:
+            return self.extra_body
+        if self.enable_thinking or "qwen" in self.model.lower():
+            return {
+                "chat_template_kwargs": {"enable_thinking": self.enable_thinking},
+            }
+        return None
+
+    def meta(self) -> dict[str, Any]:
+        return {
+            "base_url": self.base_url,
+            "model": self.model,
+            "timeout": self.timeout,
+            "max_tokens": self.max_tokens,
+            "enable_thinking": self.enable_thinking,
+            "extra_body": self.resolved_extra_body(),
+        }
+
+
+def _env(name: str) -> str:
+    return (os.environ.get(name) or "").strip()
+
+
+def resolve_llm_config(
+    *,
+    api_key: str | None = None,
+    base_url: str | None = None,
+    model: str | None = None,
+    timeout: float | None = None,
+    max_tokens: int | None = None,
+    enable_thinking: bool | None = None,
+    extra_body: dict[str, Any] | None = None,
+    config: LLMConfig | None = None,
+) -> LLMConfig:
+    """Resolve LLM settings: explicit args / ``config`` override ``OPENAI_*`` env.
+
+    Raises:
+        ValueError: if api_key, base_url, or model is missing after resolution.
+    """
+    if config is not None:
+        overrides: dict[str, Any] = {}
+        if api_key is not None:
+            overrides["api_key"] = api_key
+        if base_url is not None:
+            overrides["base_url"] = base_url
+        if model is not None:
+            overrides["model"] = model
+        if timeout is not None:
+            overrides["timeout"] = timeout
+        if max_tokens is not None:
+            overrides["max_tokens"] = max_tokens
+        if enable_thinking is not None:
+            overrides["enable_thinking"] = enable_thinking
+        if extra_body is not None:
+            overrides["extra_body"] = extra_body
+        resolved = replace(config, **overrides) if overrides else config
+    else:
+        resolved = LLMConfig(
+            api_key=(api_key if api_key is not None else _env("OPENAI_API_KEY")),
+            base_url=(base_url if base_url is not None else _env("OPENAI_BASE_URL")).rstrip("/"),
+            model=(model if model is not None else _env("OPENAI_MODEL")),
+            timeout=3600.0 if timeout is None else timeout,
+            max_tokens=81920 if max_tokens is None else max_tokens,
+            enable_thinking=False if enable_thinking is None else enable_thinking,
+            extra_body=extra_body,
+        )
+
+    missing: list[str] = []
+    if not resolved.api_key:
+        missing.append("OPENAI_API_KEY (or --api-key / LLMConfig.api_key)")
+    if not resolved.base_url:
+        missing.append("OPENAI_BASE_URL (or --base-url / LLMConfig.base_url)")
+    if not resolved.model:
+        missing.append("OPENAI_MODEL (or --model / LLMConfig.model)")
+    if missing:
+        raise ValueError(
+            "Missing required LLM config: "
+            + "; ".join(missing)
+            + ". Set environment variables or pass them explicitly."
+        )
+
+    return replace(resolved, base_url=resolved.base_url.rstrip("/"))
diff --git a/uniparser_agent/output_dir.py b/uniparser_agent/output_dir.py
new file mode 100644
index 0000000..a8e7c12
--- /dev/null
+++ b/uniparser_agent/output_dir.py
@@ -0,0 +1,33 @@
+"""Safe output directory resolution."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+from uniparser_tools.common.output_dir import create_unique_output_dir
+
+
+def default_parse_output_dir(source_stem: str) -> Path:
+    """Return a contained parse output directory for ``source_stem``."""
+    if (
+        not source_stem
+        or source_stem in {".", ".."}
+        or "/" in source_stem
+        or "\\" in source_stem
+        or Path(source_stem).name != source_stem
+    ):
+        raise ValueError(f"Unsafe source name for output directory: {source_stem!r}")
+
+    base = (Path.home() / "Uni-Parser-Skill").resolve()
+    candidate = base / source_stem
+    if candidate == base or not candidate.is_relative_to(base):
+        raise ValueError(f"Output directory escapes the managed root: {candidate}")
+    return candidate
+
+
+def resolve_output_dir(output_dir: str | Path | None, *, default: Path) -> Path:
+    """Resolve an explicit output path or use the supplied safe default."""
+    return Path(output_dir).expanduser() if output_dir else default
+
+
+__all__ = ["create_unique_output_dir", "default_parse_output_dir", "resolve_output_dir"]
diff --git a/uniparser_agent/parse/__init__.py b/uniparser_agent/parse/__init__.py
new file mode 100644
index 0000000..b473e24
--- /dev/null
+++ b/uniparser_agent/parse/__init__.py
@@ -0,0 +1,6 @@
+"""Shared UniParser parse helpers."""
+
+from uniparser_agent.parse.service import load_pages_tree, make_client, parse_document
+
+
+__all__ = ["load_pages_tree", "make_client", "parse_document"]
diff --git a/uniparser_agent/parse/api_client.py b/uniparser_agent/parse/api_client.py
new file mode 100644
index 0000000..926cd53
--- /dev/null
+++ b/uniparser_agent/parse/api_client.py
@@ -0,0 +1,245 @@
+"""Lightweight UniParser HTTP client (no uniparser-tools / OpenCV)."""
+
+from __future__ import annotations
+
+import base64
+import json
+import uuid
+from pathlib import Path
+from typing import Any
+from urllib.parse import urlparse
+
+import requests
+
+from uniparser_agent.parse.options import SCIENTIFIC_PAPER_TRIGGER
+from uniparser_agent.parse.transport import (
+    DEFAULT_REQUEST_TIMEOUT,
+    DEFAULT_SYNC_REQUEST_TIMEOUT,
+    RequestTimeout,
+    UniParserHTTPTransport,
+)
+
+
+PENDING_STATUSES = frozenset({"undefined", "waiting", "processing"})
+IMAGE_SUFFIXES = frozenset({".png", ".jpg", ".jpeg", ".webp", ".gif", ".bmp", ".tif", ".tiff"})
+
+
+class UniParserApiClient:
+    def __init__(
+        self,
+        host: str,
+        api_key: str,
+        *,
+        request_timeout: RequestTimeout = DEFAULT_REQUEST_TIMEOUT,
+        sync_request_timeout: RequestTimeout = DEFAULT_SYNC_REQUEST_TIMEOUT,
+        session: requests.Session | None = None,
+    ) -> None:
+        self._transport = UniParserHTTPTransport(
+            host,
+            api_key,
+            request_timeout=request_timeout,
+            session=session,
+        )
+        self.api_key = api_key
+        self.host = self._transport.host
+        self.request_timeout = request_timeout
+        self.sync_request_timeout = sync_request_timeout
+        self._user = uuid.uuid5(uuid.NAMESPACE_DNS, api_key)
+
+    def close(self) -> None:
+        self._transport.close()
+
+    def __enter__(self) -> UniParserApiClient:
+        return self
+
+    def __exit__(self, exc_type, exc_value, traceback) -> None:
+        self.close()
+
+    def to_token(self, task_id: str) -> str:
+        return uuid.uuid5(self._user, task_id).hex
+
+    def _trigger_data(
+        self,
+        *,
+        trigger_kwargs: dict[str, Any] | None,
+        allow_padding_snip: bool,
+    ) -> dict[str, Any]:
+        data = dict(SCIENTIFIC_PAPER_TRIGGER)
+        if trigger_kwargs:
+            data.update(trigger_kwargs)
+        if not allow_padding_snip:
+            data.pop("padding_snip", None)
+        preset_layout = data.get("preset_layout")
+        if isinstance(preset_layout, (list, dict)):
+            data["preset_layout"] = json.dumps(preset_layout, ensure_ascii=False)
+        return data
+
+    def _trigger_http_timeout(
+        self,
+        sync: bool,
+        http_timeout: RequestTimeout | None,
+    ) -> RequestTimeout:
+        if http_timeout is not None:
+            return http_timeout
+        return self.sync_request_timeout if sync else self.request_timeout
+
+    def trigger_file(
+        self,
+        file_path: str,
+        *,
+        trigger_kwargs: dict[str, Any] | None = None,
+        server_generated_token: bool = False,
+        http_timeout: RequestTimeout | None = None,
+    ) -> dict[str, Any]:
+        token = None if server_generated_token else self.to_token(file_path)
+        data = self._trigger_data(trigger_kwargs=trigger_kwargs, allow_padding_snip=True)
+        data["token"] = token
+        try:
+            with open(file_path, "rb") as fh:
+                return self._transport.request(
+                    "POST",
+                    "/trigger-file-async",
+                    files={"file": fh},
+                    data=data,
+                    timeout=self._trigger_http_timeout(bool(data.get("sync", True)), http_timeout),
+                    error_message="trigger file failed",
+                    token=token,
+                )
+        except OSError as exc:
+            return {
+                "status": "error",
+                "token": token,
+                "message": "trigger file failed",
+                "description": str(exc),
+                "error_type": type(exc).__name__,
+            }
+
+    def trigger_url(
+        self,
+        pdf_url: str,
+        *,
+        trigger_kwargs: dict[str, Any] | None = None,
+        server_generated_token: bool = False,
+        http_timeout: RequestTimeout | None = None,
+    ) -> dict[str, Any]:
+        token = None if server_generated_token else self.to_token(pdf_url)
+        data = self._trigger_data(trigger_kwargs=trigger_kwargs, allow_padding_snip=False)
+        data["url"] = pdf_url
+        data["token"] = token
+        return self._transport.request(
+            "POST",
+            "/trigger-url-async",
+            json=data,
+            timeout=self._trigger_http_timeout(bool(data.get("sync", True)), http_timeout),
+            error_message="trigger url failed",
+            token=token,
+        )
+
+    def trigger_snip(
+        self,
+        snip_path: str,
+        *,
+        trigger_kwargs: dict[str, Any] | None = None,
+        server_generated_token: bool = False,
+        http_timeout: RequestTimeout | None = None,
+    ) -> dict[str, Any]:
+        token = None if server_generated_token else self.to_token(snip_path)
+        data = self._trigger_data(trigger_kwargs=trigger_kwargs, allow_padding_snip=True)
+        data["token"] = token
+        try:
+            raw = Path(snip_path).read_bytes()
+            img_b64 = base64.b64encode(raw).decode("ascii")
+            return self._transport.request(
+                "POST",
+                "/trigger-snip-async",
+                data={"img": img_b64, **data},
+                timeout=self._trigger_http_timeout(bool(data.get("sync", True)), http_timeout),
+                error_message="trigger snip failed",
+                token=token,
+            )
+        except OSError as exc:
+            return {
+                "status": "error",
+                "token": token,
+                "message": "trigger snip failed",
+                "description": str(exc),
+                "error_type": type(exc).__name__,
+            }
+
+    def get_result(
+        self,
+        token: str,
+        *,
+        pages_tree: bool = False,
+        http_timeout: RequestTimeout | None = None,
+    ) -> dict[str, Any]:
+        payload = {
+            "token": token,
+            "content": False,
+            "objects": False,
+            "pages_dict": False,
+            "pages_tree": pages_tree,
+            "molecule_source": False,
+        }
+        return self._transport.request(
+            "POST",
+            "/get-result",
+            json=payload,
+            timeout=http_timeout,
+            error_message="get result failed",
+            token=token,
+        )
+
+    def get_formatted(
+        self,
+        token: str,
+        *,
+        http_timeout: RequestTimeout | None = None,
+    ) -> dict[str, Any]:
+        payload = {
+            "token": token,
+            "content": True,
+            "objects": False,
+            "pages_dict": False,
+            "pages_tree": False,
+            "molecule_source": False,
+            "textual": "markdown",
+            "table": "markdown",
+            "molecule": "markdown",
+            "chart": "markdown",
+            "figure": "markdown",
+            "expression": "markdown",
+            "equation": "latex",
+            "marginalia": False,
+        }
+        return self._transport.request(
+            "POST",
+            "/get-formatted",
+            json=payload,
+            timeout=http_timeout,
+            error_message="get formatted failed",
+            token=token,
+        )
+
+
+def resolve_input(raw: str) -> tuple[str, str, Path | None]:
+    """Return (kind, source_stem, path) where kind is file|image|url."""
+    text = raw.strip()
+    if not text:
+        raise ValueError("INPUT must not be empty.")
+    if text.startswith("http://") or text.startswith("https://"):
+        segment = urlparse(text).path.rstrip("/").rsplit("/", 1)[-1]
+        stem = segment or "url_document"
+        for ext in (".pdf", ".png", ".jpg", ".jpeg", ".webp"):
+            if stem.lower().endswith(ext):
+                stem = stem[: -len(ext)]
+                break
+        return "url", stem or "url_document", None
+
+    path = Path(text).expanduser().resolve()
+    if not path.is_file():
+        raise ValueError(f"File not found: {path}")
+    stem = path.stem or "document"
+    if path.suffix.lower() in IMAGE_SUFFIXES:
+        return "image", stem, path
+    return "file", stem, path
diff --git a/uniparser_agent/parse/config.py b/uniparser_agent/parse/config.py
new file mode 100644
index 0000000..1268a21
--- /dev/null
+++ b/uniparser_agent/parse/config.py
@@ -0,0 +1,19 @@
+"""UniParser API configuration."""
+
+from __future__ import annotations
+
+import os
+
+
+DEFAULT_BASE_URL = "https://uniparser.dp.tech"
+
+
+def get_api_key() -> str:
+    key = (os.environ.get("UNIPARSER_API_KEY") or "").strip()
+    if not key:
+        raise ValueError("UNIPARSER_API_KEY is not set.")
+    return key
+
+
+def get_base_url() -> str:
+    return (os.environ.get("UNIPARSER_BASE_URL") or DEFAULT_BASE_URL).strip().rstrip("/")
diff --git a/uniparser_agent/parse/options.py b/uniparser_agent/parse/options.py
new file mode 100644
index 0000000..727804b
--- /dev/null
+++ b/uniparser_agent/parse/options.py
@@ -0,0 +1,21 @@
+"""Scientific-paper parse preset (matches uniparser_tools.cli.core.parse_options)."""
+
+from __future__ import annotations
+
+
+SCIENTIFIC_PAPER_TRIGGER: dict[str, object] = {
+    "lang": "unknown",
+    "sync": True,
+    "timeout": 1800,
+    "padding_snip": True,
+    "inplace_update": False,
+    "preset_layout": "",
+    "textual": 2,
+    "equation": 2,
+    "table": 2,
+    "chart": -1,
+    "figure": -1,
+    "expression": -1,
+    "molecule": 1,
+    "ordering_method": "xy_cut_exp",
+}
diff --git a/uniparser_agent/parse/service.py b/uniparser_agent/parse/service.py
new file mode 100644
index 0000000..7105718
--- /dev/null
+++ b/uniparser_agent/parse/service.py
@@ -0,0 +1,85 @@
+from __future__ import annotations
+
+import json
+from pathlib import Path
+from typing import Any
+
+from uniparser_agent.output_dir import (
+    create_unique_output_dir,
+    default_parse_output_dir,
+    resolve_output_dir,
+)
+from uniparser_agent.parse.api_client import UniParserApiClient, resolve_input
+from uniparser_agent.parse.config import get_api_key, get_base_url
+from uniparser_agent.parse.storage import (
+    complete_parse_job,
+    save_stage_error,
+    write_trigger_meta,
+)
+
+
+def make_client() -> UniParserApiClient:
+    return UniParserApiClient(get_base_url(), get_api_key())
+
+
+def parse_document(
+    input_path: str,
+    *,
+    output_dir: str | None = None,
+) -> dict[str, Any]:
+    """Parse with scientific-paper defaults via HTTP API (no OpenCV)."""
+    kind, source_stem, path = resolve_input(input_path)
+    default_out = default_parse_output_dir(source_stem)
+    preferred = resolve_output_dir(output_dir, default=default_out)
+    client = make_client()
+
+    try:
+        out = create_unique_output_dir(preferred)
+        if kind == "file":
+            trigger = client.trigger_file(str(path))
+            input_type = "file"
+        elif kind == "image":
+            trigger = client.trigger_snip(str(path))
+            input_type = "image"
+        else:
+            trigger = client.trigger_url(input_path)
+            input_type = "url"
+
+        if trigger.get("status") != "success":
+            save_stage_error(out, "trigger_error.json", trigger)
+            raise RuntimeError(trigger.get("message") or trigger.get("description") or "trigger failed")
+
+        token = trigger.get("token")
+        if not token:
+            raise RuntimeError("trigger response missing token")
+
+        meta_path = write_trigger_meta(
+            out,
+            token=token,
+            input_type=input_type,
+            input_value=input_path,
+        )
+
+        summary = complete_parse_job(client, token, out_dir=out, source_stem=source_stem)
+        return {
+            "output_dir": summary["output_dir"],
+            "pages_tree_path": summary["pages_tree_path"],
+            "markdown_path": summary["markdown_path"],
+            "token": summary.get("token", ""),
+            "input_type": input_type,
+            "source_stem": source_stem,
+            "source": input_path,
+            "trigger_meta_path": str(meta_path),
+        }
+    finally:
+        close = getattr(client, "close", None)
+        if callable(close):
+            close()
+
+
+def load_pages_tree(pages_tree_path: str | Path) -> dict[str, Any]:
+    path = Path(pages_tree_path).expanduser().resolve()
+    data = json.loads(path.read_text(encoding="utf-8"))
+    if "pages_tree" not in data:
+        raise ValueError(f"Invalid pages_tree file (missing pages_tree key): {path}")
+    return data
diff --git a/uniparser_agent/parse/storage.py b/uniparser_agent/parse/storage.py
new file mode 100644
index 0000000..ce29303
--- /dev/null
+++ b/uniparser_agent/parse/storage.py
@@ -0,0 +1,128 @@
+"""Parse artifact persistence and polling."""
+
+from __future__ import annotations
+
+import json
+import time
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+from uniparser_agent.parse.api_client import PENDING_STATUSES, UniParserApiClient
+
+
+POLL_INTERVAL_SEC = 3
+POLL_TIMEOUT_SEC = 1800
+
+
+def write_trigger_meta(
+    out_dir: Path,
+    *,
+    token: str,
+    input_type: str,
+    input_value: str,
+) -> Path:
+    out_dir.mkdir(parents=True, exist_ok=True)
+    meta_path = out_dir / "trigger_meta.json"
+    payload = {
+        "token": token,
+        "input_type": input_type,
+        "input": input_value,
+        "submitted_at": datetime.now(timezone.utc).isoformat(),
+        "parse_preset": "scientific-paper",
+    }
+    meta_path.write_text(json.dumps(payload, indent=2, ensure_ascii=False), encoding="utf-8")
+    return meta_path
+
+
+def save_parse_results(
+    *,
+    out_dir: Path,
+    source_stem: str,
+    pages_tree: dict[str, Any],
+    formatted: dict[str, Any],
+) -> dict[str, Any]:
+    out_dir.mkdir(parents=True, exist_ok=True)
+    stem = source_stem or "document"
+
+    pages_tree_path = out_dir / "pages_tree.json"
+    pages_tree_path.write_text(
+        json.dumps(pages_tree, indent=2, ensure_ascii=False, default=str),
+        encoding="utf-8",
+    )
+
+    md_path = out_dir / f"{stem}.md"
+    content = formatted.get("content", "")
+    md_path.write_text(content, encoding="utf-8")
+
+    meta = {k: v for k, v in formatted.items() if k != "content"}
+    (out_dir / "formatted_meta.json").write_text(
+        json.dumps(meta, indent=2, ensure_ascii=False, default=str),
+        encoding="utf-8",
+    )
+
+    return {
+        "output_dir": str(out_dir),
+        "pages_tree_path": str(pages_tree_path),
+        "markdown_path": str(md_path),
+        "content_chars": len(content),
+    }
+
+
+def poll_until_success(client: UniParserApiClient, token: str) -> dict[str, Any]:
+    deadline = time.time() + POLL_TIMEOUT_SEC
+    last: dict[str, Any] = {}
+    while time.time() < deadline:
+        last = client.get_result(token, pages_tree=False)
+        status = last.get("status")
+        if status == "success":
+            return last
+        if status == "error":
+            return last
+        if status in PENDING_STATUSES or status is None:
+            time.sleep(POLL_INTERVAL_SEC)
+            continue
+        return last
+    return {
+        "status": "error",
+        "description": f"Timed out after {POLL_TIMEOUT_SEC}s waiting for parsing to finish.",
+        "token": token,
+        "last_status": last.get("status"),
+    }
+
+
+def complete_parse_job(
+    client: UniParserApiClient,
+    token: str,
+    *,
+    out_dir: Path,
+    source_stem: str,
+) -> dict[str, Any]:
+    poll_result = poll_until_success(client, token)
+    if poll_result.get("status") != "success":
+        save_stage_error(out_dir, "poll_error.json", poll_result)
+        raise RuntimeError(poll_result.get("description") or poll_result.get("message") or "poll failed")
+
+    pages_tree = client.get_result(token, pages_tree=True)
+    if pages_tree.get("status") != "success":
+        save_stage_error(out_dir, "pages_tree_error.json", pages_tree)
+        raise RuntimeError(pages_tree.get("description") or "get_result pages_tree failed")
+
+    formatted = client.get_formatted(token)
+    if formatted.get("status") != "success":
+        save_stage_error(out_dir, "formatted_error.json", formatted)
+        raise RuntimeError(formatted.get("description") or "get_formatted failed")
+
+    summary = save_parse_results(
+        out_dir=out_dir,
+        source_stem=source_stem,
+        pages_tree=pages_tree,
+        formatted=formatted,
+    )
+    summary["token"] = token
+    return summary
+
+
+def save_stage_error(out_dir: Path, filename: str, payload: dict[str, Any]) -> None:
+    out_dir.mkdir(parents=True, exist_ok=True)
+    (out_dir / filename).write_text(json.dumps(payload, indent=2, ensure_ascii=False), encoding="utf-8")
diff --git a/uniparser_agent/parse/transport.py b/uniparser_agent/parse/transport.py
new file mode 100644
index 0000000..42c5d7f
--- /dev/null
+++ b/uniparser_agent/parse/transport.py
@@ -0,0 +1,122 @@
+"""Lightweight release/v1.3 HTTP transport for the standalone agent package."""
+
+from __future__ import annotations
+
+import re
+from typing import Any
+from urllib.parse import urlparse
+
+import requests
+
+
+RequestTimeout = float | tuple[float, float | None]
+
+DEFAULT_REQUEST_TIMEOUT: RequestTimeout = (10.0, 60.0)
+DEFAULT_SYNC_REQUEST_TIMEOUT: RequestTimeout = (10.0, 1860.0)
+
+
+def _redact_url_queries(value: str) -> str:
+    """Remove bearer-style query strings from URLs included in diagnostics."""
+    return re.sub(r"(?P<url>(?:https?://|/)[^\s?]+)\?[^\s]+", r"\g<url>?<redacted>", value)
+
+
+def _redact_diagnostic_value(value: Any) -> Any:
+    if isinstance(value, dict):
+        return {key: _redact_diagnostic_value(item) for key, item in value.items()}
+    if isinstance(value, list):
+        return [_redact_diagnostic_value(item) for item in value]
+    if isinstance(value, str):
+        return _redact_url_queries(value)
+    return value
+
+
+class UniParserHTTPTransport:
+    """Authenticated, reusable HTTP transport aligned with the main v1.3 client."""
+
+    def __init__(
+        self,
+        host: str,
+        api_key: str,
+        *,
+        request_timeout: RequestTimeout = DEFAULT_REQUEST_TIMEOUT,
+        session: requests.Session | None = None,
+    ) -> None:
+        parsed = urlparse(host)
+        if parsed.scheme not in {"http", "https"} or not parsed.netloc:
+            raise ValueError("host must be a valid http or https URL")
+        if not api_key:
+            raise ValueError("api_key can not be empty")
+
+        self.host = host.rstrip("/")
+        self.api_key = api_key
+        self.request_timeout = request_timeout
+        self.session = session or requests.Session()
+        self._owns_session = session is None
+
+    def request(
+        self,
+        method: str,
+        path: str,
+        *,
+        timeout: RequestTimeout | None = None,
+        error_message: str = "request failed",
+        token: str | None = None,
+        **kwargs: Any,
+    ) -> dict[str, Any]:
+        headers = dict(kwargs.pop("headers", {}) or {})
+        headers.setdefault("X-API-Key", self.api_key)
+        url = path if path.startswith(("http://", "https://")) else f"{self.host}/{path.lstrip('/')}"
+
+        try:
+            response = self.session.request(
+                method,
+                url,
+                headers=headers,
+                timeout=self.request_timeout if timeout is None else timeout,
+                **kwargs,
+            )
+        except requests.RequestException as exc:
+            result: dict[str, Any] = {
+                "status": "error",
+                "message": error_message,
+                "description": _redact_url_queries(str(exc)),
+                "error_type": type(exc).__name__,
+            }
+            if token is not None:
+                result["token"] = token
+            return result
+
+        try:
+            payload = response.json()
+        except ValueError:
+            payload = None
+
+        if response.status_code >= 400:
+            if isinstance(payload, dict):
+                result = _redact_diagnostic_value(payload)
+                result.setdefault("status", "error")
+                result.setdefault("description", response.reason or error_message)
+            else:
+                result = {
+                    "status": "error",
+                    "description": response.reason or error_message,
+                    "body": _redact_url_queries(response.text),
+                }
+            result["http_status"] = response.status_code
+            if token is not None:
+                result.setdefault("token", token)
+            return result
+
+        if isinstance(payload, dict):
+            return payload
+        return {
+            "status": "error",
+            "message": error_message,
+            "description": "response body is not valid JSON",
+            "body": _redact_url_queries(response.text),
+            **({"token": token} if token is not None else {}),
+        }
+
+    def close(self) -> None:
+        if self._owns_session:
+            self.session.close()
diff --git a/uniparser_agent/pdf2vqa/README.md b/uniparser_agent/pdf2vqa/README.md
new file mode 100644
index 0000000..d504c6c
--- /dev/null
+++ b/uniparser_agent/pdf2vqa/README.md
@@ -0,0 +1,331 @@
+# 习题 VQA 抽取（`uniparser-agent vqa`）
+
+从习题、试卷、题册或答案册中提取结构化问答数据，包括题号、题干、短答案和详细解析，并生成适合题库整理、人工检查或模型训练的 JSONL、Markdown 和 ShareGPT 数据。
+
+支持本地 PDF、图片和公开 PDF URL；题册与答案册分开时，也可以同时输入两个本地 PDF 自动配对。
+
+## 核心能力
+
+- 从习题或试卷中提取题号、题干、答案和解析
+- 支持题目与答案位于同一文档
+- 支持“题册 + 答案册”两个独立 PDF，并自动配对相同题号
+- 保留题干和解析中的公式
+- 导出题目相关图片，生成图文结合的 VQA 样本
+- 输出 JSONL 和 Markdown，方便数据处理与人工检查
+- 输出 ShareGPT 格式，可用于多模态或纯文本模型训练
+- 支持复用已有 `pages_tree.json`，方便更换模型后重新抽取
+
+## 安装
+
+运行要求：
+
+- Python 3.11+
+- 已安装 `uniparser-agent`
+- 输入原始文档时，需要 UniParser API Key
+- OpenAI 兼容的 LLM 服务
+
+安装项目依赖：
+
+```bash
+cd uniparser_agent
+uv sync
+```
+
+或在已有虚拟环境中安装：
+
+```bash
+cd uniparser_agent
+uv pip install -e ".[dev]"
+```
+
+
+
+## 配置
+
+设置 UniParser 和 LLM 环境变量：
+
+```bash
+export UNIPARSER_API_KEY="your-uniparser-key"
+export OPENAI_API_KEY="your-llm-key"
+export OPENAI_BASE_URL="https://example.com/v1"
+export OPENAI_MODEL="your-model"
+```
+
+
+| 变量                   | 是否必填               | 用途                 |
+| -------------------- | ------------------ | ------------------ |
+| `UNIPARSER_API_KEY`  | 输入 PDF、图片或 URL 时必填 | 解析原始文档             |
+| `OPENAI_API_KEY`     | 必填                 | 调用问答抽取模型           |
+| `OPENAI_BASE_URL`    | 必填                 | OpenAI 兼容服务地址      |
+| `OPENAI_MODEL`       | 必填                 | 问答抽取模型名称           |
+| `UNIPARSER_BASE_URL` | 可选                 | 自定义 UniParser 服务地址 |
+
+
+使用已有 `pages_tree.json` 时不需要 `UNIPARSER_API_KEY`。
+
+也可以通过 `--api-key`、`--base-url` 和 `--model` 在命令行中覆盖 LLM 配置。
+
+不要把真实 API Key 写入源码或提交到仓库。
+
+## 快速开始
+
+
+
+### 从一个 PDF 中抽取
+
+适用于题目、答案和解析位于同一文档：
+
+```bash
+uv run uniparser-agent vqa /path/to/exam.pdf \
+  -o ./vqa_out
+```
+
+完成后主要查看：
+
+```text
+vqa_out/merged_vqa_pairs.jsonl
+vqa_out/merged_vqa_pairs.md
+```
+
+
+
+## 使用指南
+
+
+
+### 题册与答案册分别存放
+
+题册和答案册都必须是本地 PDF：
+
+```bash
+uv run uniparser-agent vqa /path/to/questions.pdf \
+  --answer-pdf /path/to/answers.pdf \
+  -o ./vqa_out
+```
+
+程序会按“题册在前、答案册在后”的顺序处理，并根据章节和题号配对题干、答案与解析。
+
+`--answer-pdf` 不能与图片、URL 或 `--pages-tree` 同时使用。
+
+### 输入图片
+
+```bash
+uv run uniparser-agent vqa /path/to/page.png \
+  -o ./vqa_out
+```
+
+适合单页试题、截图或扫描图片。
+
+### 输入公开 PDF URL
+
+```bash
+uv run uniparser-agent vqa "https://example.com/exam.pdf" \
+  -o ./vqa_out
+```
+
+URL 必须能够公开访问，并直接返回 PDF。
+
+### 使用已有解析结果
+
+如果文档已完成 UniParser 解析：
+
+```bash
+uv run uniparser-agent vqa \
+  --pages-tree /path/to/pages_tree.json \
+  -o ./vqa_out
+```
+
+该方式适合更换模型或重新抽取，不会再次消耗 UniParser 解析额度。
+
+## 常用参数
+
+```text
+uniparser-agent vqa [OPTIONS] [INPUT_PATH]
+```
+
+
+| 参数                    | 用途                     |
+| --------------------- | ---------------------- |
+| `INPUT_PATH`          | 本地 PDF、图片或公开 PDF URL   |
+| `-o` / `--output-dir` | 首选输出目录；默认 `./vqa_out`；已存在时自动使用同级后缀目录 |
+| `--answer-pdf`        | 输入独立答案册 PDF            |
+| `--pages-tree`        | 复用已有 `pages_tree.json` |
+| `--json`              | 在终端输出机器可读的运行摘要         |
+
+
+输入规则：
+
+- `INPUT_PATH` 与 `--pages-tree` 二选一
+- `--answer-pdf` 必须与题册 PDF 一起使用
+- `--answer-pdf` 与 `--pages-tree` 不能同时使用
+
+查看全部参数：
+
+```bash
+uv run uniparser-agent vqa --help
+```
+
+
+
+## 输出结果
+
+默认输出目录如下：
+
+```text
+vqa_out/
+├── merged_vqa_pairs.jsonl
+├── merged_vqa_pairs.md
+├── vqa_sharegpt.json
+├── vqa_images/
+├── run_meta.json
+├── parse/
+│   └── pages_tree.json
+├── merge/
+│   └── merged.pdf
+├── extracted_vqa.jsonl
+├── llm_content_list.json
+└── llm_raw_response.txt
+```
+
+`merge/merged.pdf` 只在使用“题册 + 答案册”模式时生成。`vqa_images/` 中是否有图片取决于原始解析结果。
+
+### 主要结果
+
+
+| 文件                       | 用途                     |
+| ------------------------ | ---------------------- |
+| `merged_vqa_pairs.jsonl` | **结构化主结果**，每行一条完整问答对   |
+| `merged_vqa_pairs.md`    | 便于人工阅读和检查的 Markdown 版本 |
+| `vqa_sharegpt.json`      | ShareGPT 格式的模型训练数据     |
+| `vqa_images/`            | 与题目或解析相关的图片            |
+| `run_meta.json`          | 本次运行的模型、题量、图片数、耗时和文件路径 |
+
+
+
+
+### 辅助结果
+
+
+| 文件                      | 用途                     |
+| ----------------------- | ---------------------- |
+| `parse/pages_tree.json` | UniParser 解析结果，可用于重新抽取 |
+| `merge/merged.pdf`      | 合并后的题册与答案册，仅双 PDF 模式生成 |
+| `extracted_vqa.jsonl`   | 合并前的题目、答案和解析片段         |
+| `llm_content_list.json` | 送入问答抽取阶段的文档内容          |
+| `llm_raw_response.txt`  | 模型原始返回，主要用于问题排查        |
+
+
+
+
+### JSONL 数据格式
+
+`merged_vqa_pairs.jsonl` 每行是一道题：
+
+```json
+{
+  "question_chapter_title": "第一章",
+  "answer_chapter_title": "第一章答案",
+  "label": 1,
+  "question": "题干内容",
+  "answer": "A",
+  "solution": "详细解析"
+}
+```
+
+字段说明：
+
+
+| 字段                       | 含义                |
+| ------------------------ | ----------------- |
+| `question_chapter_title` | 题目所在章节或栏目         |
+| `answer_chapter_title`   | 答案所在章节或栏目         |
+| `label`                  | 题号                |
+| `question`               | 题干，可包含公式和图片引用     |
+| `answer`                 | 短答案，如选项字母、数值或填空结果 |
+| `solution`               | 解题过程或详细解析         |
+
+
+题目没有章节标题时，对应字段可能为空。
+
+### ShareGPT 数据格式
+
+`vqa_sharegpt.json` 是 JSON 数组，每条数据包含：
+
+```json
+{
+  "messages": [
+    {
+      "role": "user",
+      "content": "<image>题干内容"
+    },
+    {
+      "role": "assistant",
+      "content": "答案\n\n详细解析"
+    }
+  ],
+  "images": [
+    "/absolute/path/to/vqa_images/question_1.png"
+  ]
+}
+```
+
+- 有图片时，user 内容包含对应数量的 `<image>` 标记
+- `images` 保存相关图片路径
+- 无图片时，`images` 为空数组，可作为纯文本问答使用
+- 没有题干或没有答案/解析的数据不会进入 ShareGPT 主结果
+
+
+
+## 结果检查
+
+1. 打开 `merged_vqa_pairs.md`，快速检查题目、答案和解析是否配对。
+2. 使用 `merged_vqa_pairs.jsonl` 进行数据处理或导入题库。
+3. 训练模型前检查 `vqa_sharegpt.json` 中的图片路径是否在目标环境可访问。
+4. 如果题目数量明显偏少，查看 `extracted_vqa.jsonl` 判断是抽取不足还是配对失败。
+5. 如果公式、图片或章节识别不正确，结合 `parse/pages_tree.json` 检查原始解析质量。
+
+
+
+## 常见问题
+
+
+
+### 为什么最终题目数量比原文少？
+
+主结果只保留能够识别题号并完成题目与答案配对的数据。题号不清晰、章节不一致、答案缺失或模型未正确识别都可能导致数量减少。
+
+### 为什么题目图片没有导出？
+
+只有原始解析结果中实际包含图片数据的内容才能写入 `vqa_images/`。PDF 中看得到图片不代表解析结果一定包含可导出的图片。
+
+### 题册和答案册可以使用 URL 吗？
+
+不可以。双文档模式要求题册和答案册都是本地 PDF。单文档模式支持公开 PDF URL。
+
+### 输出目录已存在时会怎样？
+
+程序不会报错、复用或删除旧目录，而是自动创建第一个可用的同级目录。例如 `vqa_out`
+已存在时使用 `vqa_out_1`，两者都存在时使用 `vqa_out_2`。请以运行结果中的
+`output_dir` 为准；失败任务的部分产物也保留在本次新目录中，便于排查。
+
+> 出于安全原因，根目录、HOME、当前工作目录及 Git 元数据目录不能作为首选输出目录。
+
+
+
+### 如何更换模型后重新抽取？
+
+保留首次运行生成的 `parse/pages_tree.json`，然后使用 `--pages-tree` 重新运行。
+
+### ShareGPT 中为什么使用绝对图片路径？
+
+结果会记录当前运行环境中的图片绝对路径。将数据迁移到其他机器或训练环境后，需要同步图片并按需要更新路径。
+
+## 当前限制
+
+- 双 PDF 模式只支持本地 PDF
+- 题号需要能够识别为正整数
+- 题目和答案主要依靠章节与题号配对
+- 跨页题目、复杂多栏版式或严重粘连内容可能配对不准确
+- 图片导出取决于 UniParser 是否提供可用图片数据
+- 公式和题目结构的准确性依赖原始文档解析质量
+- 抽取结果适合批量整理，但正式训练或入库前仍建议抽样检查
diff --git a/uniparser_agent/pdf2vqa/__init__.py b/uniparser_agent/pdf2vqa/__init__.py
new file mode 100644
index 0000000..68a9957
--- /dev/null
+++ b/uniparser_agent/pdf2vqa/__init__.py
@@ -0,0 +1,6 @@
+"""pdf2vqa: UniParser pages_tree → LLM VQA extraction."""
+
+from uniparser_agent.pdf2vqa.pipeline import run_vqa_pipeline
+
+
+__all__ = ["run_vqa_pipeline"]
diff --git a/uniparser_agent/pdf2vqa/image_export.py b/uniparser_agent/pdf2vqa/image_export.py
new file mode 100644
index 0000000..0f3ab2a
--- /dev/null
+++ b/uniparser_agent/pdf2vqa/image_export.py
@@ -0,0 +1,189 @@
+"""Export UniParser block ``source`` fields into a local ``vqa_images`` directory."""
+
+from __future__ import annotations
+
+import base64
+import hashlib
+import re
+import shutil
+from pathlib import Path
+from typing import Any
+
+
+_DATA_URL_RE = re.compile(
+    r"^data:image/(?P<fmt>[a-zA-Z0-9.+-]+);base64,(?P<data>.+)$",
+    re.DOTALL,
+)
+
+# Types that may carry visual payloads under SCIENTIFIC_PAPER_TRIGGER.
+IMAGE_SOURCE_TYPES = frozenset(
+    {
+        "figure",
+        "image",
+        "chart",
+        "table",
+        "figuregroup",
+        "imagegroup",
+        "molecule",
+    }
+)
+
+_SKIP_EXPORT_TYPES = frozenset(
+    {
+        "figurecaption",
+        "imagecaption",
+        "paragraph",
+        "title",
+        "documenttitle",
+        "equation",
+        "expression",
+        "hline",
+        "pageheader",
+        "pagefooter",
+        "pagenumber",
+    }
+)
+
+
+def _block_key(block: dict[str, Any]) -> tuple[Any, Any]:
+    return (block.get("page"), block.get("block"))
+
+
+def _ordered_dict_blocks(blocks: list[Any]) -> list[dict[str, Any]]:
+    return sorted(
+        [b for b in blocks if isinstance(b, dict)],
+        key=lambda b: b.get("order") if b.get("order") is not None else 10**9,
+    )
+
+
+def iter_all_blocks(pages_tree: list[Any]) -> list[dict[str, Any]]:
+    """Flatten pages including nested ``items`` at any depth (DFS, reading order)."""
+    flat: list[dict[str, Any]] = []
+
+    def _walk(blocks: list[Any]) -> None:
+        for block in _ordered_dict_blocks(blocks):
+            flat.append(block)
+            items = block.get("items")
+            if isinstance(items, list) and items:
+                _walk(items)
+
+    for page in pages_tree:
+        if not isinstance(page, list):
+            continue
+        _walk(page)
+    return flat
+
+
+def _guess_ext(fmt: str | None, raw: bytes) -> str:
+    if fmt:
+        fmt = fmt.lower().replace("jpeg", "jpg")
+        if fmt in {"jpg", "jpeg", "png", "gif", "webp", "bmp"}:
+            return "jpg" if fmt == "jpeg" else fmt
+    if raw.startswith(b"\x89PNG"):
+        return "png"
+    if raw.startswith(b"GIF8"):
+        return "gif"
+    if raw.startswith(b"RIFF") and b"WEBP" in raw[:16]:
+        return "webp"
+    return "jpg"
+
+
+def _looks_like_filesystem_path(source: str) -> bool:
+    """Heuristic: avoid treating long base64 blobs as paths (OSError: name too long)."""
+    if len(source) >= 4096:
+        return False
+    if source.startswith(("http://", "https://", "data:")):
+        return False
+    return ("/" in source) or ("\\" in source) or bool(Path(source).suffix)
+
+
+def decode_source_to_bytes(source: str) -> tuple[bytes, str] | None:
+    """Return (bytes, extension) for a block source string, or None if unsupported."""
+    source = source.strip()
+    if not source:
+        return None
+
+    match = _DATA_URL_RE.match(source)
+    if match:
+        fmt = match.group("fmt")
+        raw = base64.b64decode(match.group("data"), validate=False)
+        return raw, _guess_ext(fmt, raw)
+
+    if _looks_like_filesystem_path(source):
+        path = Path(source)
+        try:
+            if path.is_file():
+                raw = path.read_bytes()
+                return raw, path.suffix.lstrip(".").lower() or "jpg"
+        except OSError:
+            pass
+
+    try:
+        raw = base64.b64decode(source, validate=False)
+    except Exception:
+        return None
+    if len(raw) < 32:
+        return None
+    return raw, _guess_ext(None, raw)
+
+
+def export_images_from_pages_tree(
+    pages_tree_data: dict[str, Any] | list[Any],
+    images_dir: str | Path,
+) -> dict[tuple[Any, Any], Path]:
+    """Decode / copy block ``source`` images into ``images_dir``.
+
+    Returns a map from ``(page, block)`` to the written absolute path.
+    Duplicate content hashes share one file on disk.
+    """
+    if isinstance(pages_tree_data, dict):
+        pages = pages_tree_data.get("pages_tree")
+        if pages is None:
+            raise ValueError("Invalid pages_tree data: missing 'pages_tree' key")
+    else:
+        pages = pages_tree_data
+    if not isinstance(pages, list):
+        raise ValueError(f"Expected pages_tree list, got {type(pages)}")
+
+    out_dir = Path(images_dir).expanduser().resolve()
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    hash_to_path: dict[str, Path] = {}
+    key_to_path: dict[tuple[Any, Any], Path] = {}
+
+    for block in iter_all_blocks(pages):
+        btype = (block.get("type") or "").strip().lower()
+        if btype in _SKIP_EXPORT_TYPES:
+            continue
+
+        source = block.get("source")
+        if not isinstance(source, str) or not source.strip():
+            continue
+
+        decoded = decode_source_to_bytes(source)
+        if decoded is None:
+            continue
+        raw, ext = decoded
+        digest = hashlib.sha256(raw).hexdigest()
+        if digest in hash_to_path:
+            key_to_path[_block_key(block)] = hash_to_path[digest]
+            continue
+
+        filename = f"{digest}.{ext or 'jpg'}"
+        dest = out_dir / filename
+        if not dest.exists():
+            copied = False
+            if _looks_like_filesystem_path(source):
+                src_path = Path(source.strip())
+                try:
+                    if src_path.is_file():
+                        shutil.copy2(src_path, dest)
+                        copied = True
+                except OSError:
+                    copied = False
+            if not copied:
+                dest.write_bytes(raw)
+        hash_to_path[digest] = dest
+        key_to_path[_block_key(block)] = dest
+
+    return key_to_path
diff --git a/uniparser_agent/pdf2vqa/layout_adapter.py b/uniparser_agent/pdf2vqa/layout_adapter.py
new file mode 100644
index 0000000..1301bf0
--- /dev/null
+++ b/uniparser_agent/pdf2vqa/layout_adapter.py
@@ -0,0 +1,236 @@
+"""Convert UniParser pages_tree into a flat LLM content list with ids."""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+from typing import Any
+
+from uniparser_agent.pdf2vqa.image_export import (
+    IMAGE_SOURCE_TYPES,
+    export_images_from_pages_tree,
+    iter_all_blocks,
+)
+
+
+SKIP_TYPES = frozenset({"hline", "pagebar", "pageheader", "pagefooter", "pagenote", "pagenumber", "watermark"})
+TEXT_TYPES = frozenset({"paragraph", "title", "documenttitle"})
+CAPTION_TYPES = frozenset(
+    {
+        "algorithmcaption",
+        "expressioncaption",
+        "figurecaption",
+        "imagecaption",
+        "tablecaption",
+    }
+)
+IMAGE_TYPES = frozenset({"figure", "image", "chart", "table", "molecule", "figuregroup", "imagegroup"})
+
+
+def _format_inline(content: str, content_type: str) -> str:
+    normalized_type = content_type.strip().lower()
+    if normalized_type in {"equation", "equationinline"}:
+        if content.startswith(("$", r"\(", r"\[")):
+            return content
+        return f"${content}$"
+    if normalized_type == "molecule":
+        return content if content.startswith("`") else f"`{content}`"
+    return content
+
+
+def _inline_contents(block: dict[str, Any]) -> str:
+    contents = block.get("contents")
+    if not isinstance(contents, list) or not contents:
+        return ""
+    types = block.get("types")
+    if not isinstance(types, list) or len(types) != len(contents):
+        types = ["text"] * len(contents)
+    return "".join(_format_inline(str(content), str(content_type)) for content, content_type in zip(contents, types))
+
+
+def _table_text(block: dict[str, Any]) -> str:
+    structure = block.get("structure") or block.get("html") or ""
+    placeholders = block.get("placeholders")
+    contents = block.get("contents")
+    types = block.get("types")
+    if (
+        isinstance(structure, str)
+        and isinstance(placeholders, list)
+        and isinstance(contents, list)
+        and len(placeholders) == len(contents)
+    ):
+        if not isinstance(types, list) or len(types) != len(contents):
+            types = ["text"] * len(contents)
+        for placeholder, content, content_type in zip(
+            reversed(placeholders),
+            reversed(contents),
+            reversed(types),
+        ):
+            structure = structure.replace(
+                str(placeholder),
+                _format_inline(str(content), str(content_type)),
+            )
+        return structure.strip()
+    return _inline_contents(block)
+
+
+def _block_text(block: dict[str, Any]) -> str:
+    btype = (block.get("type") or "").strip().lower()
+    if btype == "equation":
+        latex = (block.get("latex_repr") or "").strip()
+        if latex:
+            if latex.startswith("$$") or latex.startswith("$"):
+                return latex
+            return f"$$\n{latex}\n$$"
+    if btype == "table":
+        table_text = _table_text(block)
+        if table_text:
+            return table_text
+    inline_text = _inline_contents(block)
+    if inline_text:
+        return inline_text.strip()
+    if btype == "molecule":
+        molecule_text = block.get("esmi") or block.get("smi") or block.get("caption")
+        if isinstance(molecule_text, str) and molecule_text.strip():
+            return f"`{molecule_text.strip()}`"
+    return (block.get("text") or "").strip()
+
+
+def _iter_blocks(pages_tree: list[Any]) -> list[dict[str, Any]]:
+    """Flatten pages into a reading-order list including nested ``items``."""
+    return iter_all_blocks(pages_tree)
+
+
+def _caption_from_group(block: dict[str, Any]) -> list[str]:
+    captions: list[str] = []
+    items = block.get("items")
+    if not isinstance(items, list):
+        return captions
+    for child in items:
+        if not isinstance(child, dict):
+            continue
+        ctype = (child.get("type") or "").strip().lower()
+        if ctype in CAPTION_TYPES:
+            text = _block_text(child)
+            if text:
+                captions.append(text)
+    return captions
+
+
+def _resolve_image_source_block(block: dict[str, Any]) -> dict[str, Any] | None:
+    """Return the block that actually holds image ``source`` (self or first child)."""
+    source = block.get("source")
+    if isinstance(source, str) and source.strip():
+        return block
+    items = block.get("items")
+    if isinstance(items, list):
+        for child in items:
+            if not isinstance(child, dict):
+                continue
+            child_source = child.get("source")
+            if isinstance(child_source, str) and child_source.strip():
+                return child
+    return None
+
+
+def pages_tree_to_content_list(
+    pages_tree_data: dict[str, Any] | list[Any],
+    *,
+    image_path_map: dict[tuple[Any, Any], Path] | None = None,
+    image_prefix: str = "vqa_images",
+) -> list[dict[str, Any]]:
+    """Adapt UniParser pages_tree envelope (or raw list) to LLM content list."""
+    if isinstance(pages_tree_data, dict):
+        pages = pages_tree_data.get("pages_tree")
+        if pages is None:
+            raise ValueError("Invalid pages_tree data: missing 'pages_tree' key")
+    else:
+        pages = pages_tree_data
+
+    if not isinstance(pages, list):
+        raise ValueError(f"Expected pages_tree list, got {type(pages)}")
+
+    image_path_map = image_path_map or {}
+    content: list[dict[str, Any]] = []
+    next_id = 0
+    emitted_image_files: set[str] = set()
+
+    for block in _iter_blocks(pages):
+        btype = (block.get("type") or "").strip().lower()
+        if btype in SKIP_TYPES or btype in CAPTION_TYPES:
+            continue
+
+        text = _block_text(block)
+
+        if btype == "equation":
+            source = block.get("source") or ""
+            has_source = isinstance(source, str) and bool(source.strip())
+            if not text and not has_source:
+                continue
+            content.append({"id": next_id, "type": "equation", "text": text})
+            next_id += 1
+            continue
+
+        if btype in IMAGE_TYPES or btype in IMAGE_SOURCE_TYPES:
+            source_block = _resolve_image_source_block(block)
+            if source_block is not None:
+                key = (source_block.get("page"), source_block.get("block"))
+                img_file = image_path_map.get(key)
+                if img_file is not None and img_file.is_file():
+                    name = img_file.name
+                    if name not in emitted_image_files:
+                        captions = _caption_from_group(block)
+                        if not captions:
+                            desc = (block.get("desc") or source_block.get("desc") or "").strip()
+                            if desc:
+                                captions = [desc]
+                        content.append(
+                            {
+                                "id": next_id,
+                                "type": "image",
+                                "img_path": f"{image_prefix}/{name}",
+                                "image_caption": captions,
+                            }
+                        )
+                        emitted_image_files.add(name)
+                        next_id += 1
+                    continue
+            if btype in {"figuregroup", "imagegroup"}:
+                continue
+
+        if btype == "table" and text:
+            content.append({"id": next_id, "type": "table", "table_body": text})
+            next_id += 1
+            continue
+
+        if btype in TEXT_TYPES or text:
+            if not text:
+                continue
+            content.append({"id": next_id, "type": "text", "text": text})
+            next_id += 1
+            continue
+
+    return content
+
+
+def adapt_pages_tree_file(
+    pages_tree_path: str | Path,
+    output_path: str | Path,
+    *,
+    images_dir: str | Path | None = None,
+) -> list[dict[str, Any]]:
+    path = Path(pages_tree_path).expanduser().resolve()
+    data = json.loads(path.read_text(encoding="utf-8"))
+
+    if images_dir is None:
+        images_dir = Path(output_path).expanduser().resolve().parent / "vqa_images"
+    else:
+        images_dir = Path(images_dir).expanduser().resolve()
+
+    image_path_map = export_images_from_pages_tree(data, images_dir)
+    content = pages_tree_to_content_list(data, image_path_map=image_path_map)
+
+    out = Path(output_path).expanduser().resolve()
+    out.parent.mkdir(parents=True, exist_ok=True)
+    out.write_text(json.dumps(content, ensure_ascii=False, indent=2), encoding="utf-8")
+    return content
diff --git a/uniparser_agent/pdf2vqa/llm_client.py b/uniparser_agent/pdf2vqa/llm_client.py
new file mode 100644
index 0000000..12be7b7
--- /dev/null
+++ b/uniparser_agent/pdf2vqa/llm_client.py
@@ -0,0 +1,65 @@
+"""VQA LLM client (OpenAI-compatible via shared llm module)."""
+
+from __future__ import annotations
+
+from typing import Any
+
+from uniparser_agent.llm import LLMConfig, OpenAICompatLLM, resolve_llm_config
+
+
+class VQALLMClient:
+    def __init__(
+        self,
+        *,
+        api_key: str | None = None,
+        base_url: str | None = None,
+        model: str | None = None,
+        timeout: float | None = None,
+        max_tokens: int | None = None,
+        enable_thinking: bool = False,
+        extra_body: dict[str, Any] | None = None,
+        config: LLMConfig | None = None,
+    ) -> None:
+        self._llm = OpenAICompatLLM(
+            config=config,
+            api_key=api_key,
+            base_url=base_url,
+            model=model,
+            timeout=timeout,
+            max_tokens=max_tokens,
+            enable_thinking=enable_thinking,
+            extra_body=extra_body,
+        )
+
+    @property
+    def api_key(self) -> str:
+        return self._llm.api_key
+
+    @property
+    def base_url(self) -> str:
+        return self._llm.base_url
+
+    @property
+    def model(self) -> str:
+        return self._llm.model
+
+    @property
+    def timeout(self) -> float:
+        return self._llm.timeout
+
+    @property
+    def max_tokens(self) -> int:
+        return self._llm.max_tokens
+
+    @property
+    def enable_thinking(self) -> bool:
+        return self._llm.enable_thinking
+
+    def chat(self, *, system_prompt: str, user_content: str) -> str:
+        return self._llm.chat(system_prompt=system_prompt, user_content=user_content)
+
+    def meta(self) -> dict[str, Any]:
+        return self._llm.meta()
+
+
+__all__ = ["VQALLMClient", "resolve_llm_config", "LLMConfig"]
diff --git a/uniparser_agent/pdf2vqa/output_parser.py b/uniparser_agent/pdf2vqa/output_parser.py
new file mode 100644
index 0000000..862fd6b
--- /dev/null
+++ b/uniparser_agent/pdf2vqa/output_parser.py
@@ -0,0 +1,78 @@
+"""Parse LLM id-based VQA responses back into text VQA items."""
+
+from __future__ import annotations
+
+import json
+import re
+from pathlib import Path
+from typing import Any
+
+
+def _id_to_text(input_ids: str, content_list: list[dict[str, Any]], image_prefix: str = "vqa_images") -> str:
+    texts: list[str] = []
+    for raw_id in input_ids.replace(" ", "").split(","):
+        if not raw_id:
+            continue
+        try:
+            idx = int(raw_id)
+        except ValueError:
+            continue
+        if idx < 0 or idx >= len(content_list):
+            continue
+        item = content_list[idx]
+        if "text" in item and item["text"]:
+            texts.append(str(item["text"]))
+        elif "table_body" in item and item["table_body"]:
+            texts.append(str(item["table_body"]))
+        elif "img_path" in item:
+            img_name = Path(str(item.get("img_path", ""))).name
+            caption = item.get("image_caption") or ["image"]
+            if isinstance(caption, list):
+                alt = " ".join(str(c) for c in caption)
+            else:
+                alt = str(caption)
+            texts.append(f"![{alt}]({image_prefix}/{img_name})")
+    return "\n".join(texts)
+
+
+def parse_llm_response(
+    response: str,
+    content_list: list[dict[str, Any]],
+    *,
+    image_prefix: str = "vqa_images",
+) -> list[dict[str, Any]]:
+    if "<empty>" in response and "</empty>" in response and "<vqa_pair>" not in response:
+        return []
+
+    qa_list: list[dict[str, Any]] = []
+    for chapter_block in re.findall(r"<chapter>(.*?)</chapter>", response, flags=re.DOTALL):
+        title_match = re.search(r"<title>(.*?)</title>", chapter_block, flags=re.DOTALL)
+        chapter_title = _id_to_text(title_match.group(1).strip(), content_list, image_prefix) if title_match else ""
+        for pair in re.findall(r"<vqa_pair>(.*?)</vqa_pair>", chapter_block, flags=re.DOTALL):
+            q_match = re.search(r"<question>(.*?)</question>", pair, flags=re.DOTALL)
+            a_match = re.search(r"<answer>(.*?)</answer>", pair, flags=re.DOTALL)
+            s_match = re.search(r"<solution>(.*?)</solution>", pair, flags=re.DOTALL)
+            label_match = re.search(r"<label>(.*?)</label>", pair, flags=re.DOTALL)
+            if not label_match:
+                continue
+            if not ((q_match and label_match) or (a_match and label_match) or (s_match and label_match)):
+                continue
+            qa_list.append(
+                {
+                    "question": (_id_to_text(q_match.group(1).strip(), content_list, image_prefix) if q_match else ""),
+                    "answer": a_match.group(1).strip() if a_match else "",
+                    "solution": (_id_to_text(s_match.group(1).strip(), content_list, image_prefix) if s_match else ""),
+                    "label": label_match.group(1).strip(),
+                    "chapter_title": chapter_title,
+                }
+            )
+    return qa_list
+
+
+def write_vqa_jsonl(qa_list: list[dict[str, Any]], output_path: str | Path) -> Path:
+    out = Path(output_path).expanduser().resolve()
+    out.parent.mkdir(parents=True, exist_ok=True)
+    with out.open("w", encoding="utf-8") as fh:
+        for qa in qa_list:
+            fh.write(json.dumps(qa, ensure_ascii=False) + "\n")
+    return out
diff --git a/uniparser_agent/pdf2vqa/pdf_merger.py b/uniparser_agent/pdf2vqa/pdf_merger.py
new file mode 100644
index 0000000..bd221b4
--- /dev/null
+++ b/uniparser_agent/pdf2vqa/pdf_merger.py
@@ -0,0 +1,38 @@
+"""Merge local PDFs in order (question booklet then answer booklet)."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+
+def merge_pdfs(paths: list[str | Path], output_path: str | Path) -> Path:
+    """Append PDFs in order into ``output_path`` and return the resolved path."""
+    from pypdf import PdfWriter
+
+    if not paths:
+        raise ValueError("At least one PDF path is required.")
+
+    resolved: list[Path] = []
+    for raw in paths:
+        path = Path(raw).expanduser().resolve()
+        if not path.is_file():
+            raise FileNotFoundError(f"PDF not found: {path}")
+        if path.suffix.lower() != ".pdf":
+            raise ValueError(f"Not a PDF file: {path}")
+        resolved.append(path)
+
+    out = Path(output_path).expanduser().resolve()
+    out.parent.mkdir(parents=True, exist_ok=True)
+
+    writer = PdfWriter()
+    try:
+        for path in resolved:
+            writer.append(str(path))
+        if len(writer.pages) == 0:
+            raise ValueError("Merged PDF has no pages.")
+        with out.open("wb") as fh:
+            writer.write(fh)
+    finally:
+        writer.close()
+
+    return out
diff --git a/uniparser_agent/pdf2vqa/pipeline.py b/uniparser_agent/pdf2vqa/pipeline.py
new file mode 100644
index 0000000..a26e26b
--- /dev/null
+++ b/uniparser_agent/pdf2vqa/pipeline.py
@@ -0,0 +1,210 @@
+"""End-to-end VQA pipeline: UniParser parse → adapt → LLM extract → merge."""
+
+from __future__ import annotations
+
+import json
+import time
+from pathlib import Path
+from typing import Any
+
+from uniparser_agent.llm import LLMConfig
+from uniparser_agent.output_dir import create_unique_output_dir, resolve_output_dir
+from uniparser_agent.parse.api_client import resolve_input
+from uniparser_agent.parse.service import load_pages_tree, parse_document
+from uniparser_agent.pdf2vqa.layout_adapter import adapt_pages_tree_file
+from uniparser_agent.pdf2vqa.llm_client import VQALLMClient
+from uniparser_agent.pdf2vqa.output_parser import parse_llm_response, write_vqa_jsonl
+from uniparser_agent.pdf2vqa.pdf_merger import merge_pdfs
+from uniparser_agent.pdf2vqa.prompts import build_vqa_extract_prompt
+from uniparser_agent.pdf2vqa.vqa_formatter import write_sharegpt
+from uniparser_agent.pdf2vqa.vqa_merger import jsonl_to_md, merge_vqa_pairs, write_merged_jsonl
+
+
+def _resolve_output_dir(output_dir: str | Path | None) -> Path:
+    return resolve_output_dir(output_dir, default=Path.cwd() / "vqa_out")
+
+
+def _require_local_pdf(path: str | Path, *, label: str) -> Path:
+    resolved = Path(path).expanduser().resolve()
+    if not resolved.is_file():
+        raise FileNotFoundError(f"{label} not found: {resolved}")
+    if resolved.suffix.lower() != ".pdf":
+        raise ValueError(f"{label} must be a local PDF file: {resolved}")
+    return resolved
+
+
+def run_vqa_pipeline(
+    input_path: str | None = None,
+    *,
+    answer_pdf: str | None = None,
+    pages_tree_path: str | None = None,
+    output_dir: str | None = None,
+    strict_title_match: bool = False,
+    llm_config: LLMConfig | None = None,
+    llm_client: VQALLMClient | None = None,
+) -> dict[str, Any]:
+    """Run pdf2vqa extraction.
+
+    Primary path: ``input_path`` (pdf/url/image) → UniParser parse → extract.
+    Dual PDF: ``input_path`` (question) + ``answer_pdf`` → merge → parse → extract.
+    Bypass: ``pages_tree_path`` skips UniParser parse.
+    """
+    if answer_pdf and pages_tree_path:
+        raise ValueError("Use either answer_pdf or pages_tree_path, not both.")
+    if answer_pdf and not input_path:
+        raise ValueError("answer_pdf requires input_path (question booklet PDF).")
+    if not input_path and not pages_tree_path:
+        raise ValueError("Provide input_path (pdf/url/image) or pages_tree_path.")
+
+    started = time.time()
+    pages_tree_bytes: bytes | None = None
+    question_pdf: Path | None = None
+    answer_path: Path | None = None
+    if pages_tree_path:
+        src_tree = Path(pages_tree_path).expanduser().resolve()
+        if not src_tree.is_file():
+            raise FileNotFoundError(f"pages_tree not found: {src_tree}")
+        load_pages_tree(src_tree)
+        pages_tree_bytes = src_tree.read_bytes()
+    elif answer_pdf:
+        assert input_path is not None
+        question_pdf = _require_local_pdf(input_path, label="question PDF")
+        answer_path = _require_local_pdf(answer_pdf, label="answer PDF")
+    else:
+        assert input_path is not None
+        resolve_input(input_path)
+
+    out = create_unique_output_dir(_resolve_output_dir(output_dir))
+    return _run_vqa_pipeline_in_dir(
+        out=out,
+        started=started,
+        input_path=input_path,
+        pages_tree_bytes=pages_tree_bytes,
+        question_pdf=question_pdf,
+        answer_path=answer_path,
+        strict_title_match=strict_title_match,
+        llm_config=llm_config,
+        llm_client=llm_client,
+    )
+
+
+def _run_vqa_pipeline_in_dir(
+    *,
+    out: Path,
+    started: float,
+    input_path: str | None,
+    pages_tree_bytes: bytes | None,
+    question_pdf: Path | None,
+    answer_path: Path | None,
+    strict_title_match: bool,
+    llm_config: LLMConfig | None,
+    llm_client: VQALLMClient | None,
+) -> dict[str, Any]:
+    parse_dir = out / "parse"
+    parse_meta: dict[str, Any] = {}
+    merged_pdf_path: Path | None = None
+
+    if pages_tree_bytes is not None:
+        parse_dir.mkdir(parents=True, exist_ok=True)
+        dest_tree = parse_dir / "pages_tree.json"
+        dest_tree.write_bytes(pages_tree_bytes)
+        tree_path = dest_tree
+        parse_meta = {"mode": "pages_tree", "pages_tree_path": str(tree_path)}
+    else:
+        assert input_path is not None
+        parse_source = input_path
+        if answer_path is not None:
+            assert question_pdf is not None
+            merge_dir = out / "merge"
+            merge_dir.mkdir(parents=True, exist_ok=True)
+            merged_pdf_path = merge_pdfs(
+                [question_pdf, answer_path],
+                merge_dir / "merged.pdf",
+            )
+            parse_source = str(merged_pdf_path)
+
+        parse_result = parse_document(parse_source, output_dir=str(parse_dir))
+        tree_path = Path(parse_result["pages_tree_path"])
+        if answer_path is not None:
+            assert question_pdf is not None and merged_pdf_path is not None
+            parse_meta = {
+                "mode": "dual_pdf",
+                "question_pdf": str(question_pdf),
+                "answer_pdf": str(answer_path),
+                "merged_pdf": str(merged_pdf_path),
+                "token": parse_result.get("token", ""),
+                "pages_tree_path": parse_result["pages_tree_path"],
+                "markdown_path": parse_result.get("markdown_path", ""),
+            }
+        else:
+            parse_meta = {
+                "mode": "parse",
+                "source": input_path,
+                "token": parse_result.get("token", ""),
+                "pages_tree_path": parse_result["pages_tree_path"],
+                "markdown_path": parse_result.get("markdown_path", ""),
+            }
+
+    load_pages_tree(tree_path)
+
+    images_dir = out / "vqa_images"
+    content_list_path = out / "llm_content_list.json"
+    content_list = adapt_pages_tree_file(
+        tree_path,
+        content_list_path,
+        images_dir=images_dir,
+    )
+    n_images = len(list(images_dir.glob("*"))) if images_dir.is_dir() else 0
+
+    llm = llm_client or VQALLMClient(config=llm_config)
+    system_prompt = build_vqa_extract_prompt()
+    user_content = json.dumps(content_list, ensure_ascii=False)
+    llm_started = time.time()
+    raw_response = llm.chat(system_prompt=system_prompt, user_content=user_content)
+    llm_elapsed = time.time() - llm_started
+
+    raw_path = out / "llm_raw_response.txt"
+    raw_path.write_text(raw_response, encoding="utf-8")
+
+    extracted = parse_llm_response(raw_response, content_list, image_prefix="vqa_images")
+    extracted_path = out / "extracted_vqa.jsonl"
+    write_vqa_jsonl(extracted, extracted_path)
+
+    merged = merge_vqa_pairs(extracted, strict_title_match=strict_title_match)
+    merged_jsonl = out / "merged_vqa_pairs.jsonl"
+    merged_md = out / "merged_vqa_pairs.md"
+    write_merged_jsonl(merged, merged_jsonl)
+    jsonl_to_md(merged_jsonl, merged_md)
+
+    sharegpt_path = out / "vqa_sharegpt.json"
+    write_sharegpt(merged, images_dir, sharegpt_path, base_dir=out)
+
+    paths: dict[str, str] = {
+        "output_dir": str(out),
+        "pages_tree": str(tree_path),
+        "llm_content_list": str(content_list_path),
+        "llm_raw_response": str(raw_path),
+        "extracted_vqa": str(extracted_path),
+        "merged_vqa_pairs_jsonl": str(merged_jsonl),
+        "merged_vqa_pairs_md": str(merged_md),
+        "vqa_images": str(images_dir),
+        "vqa_sharegpt": str(sharegpt_path),
+    }
+    if merged_pdf_path is not None:
+        paths["merged_pdf"] = str(merged_pdf_path)
+
+    meta = {
+        "parse": parse_meta,
+        "llm": llm.meta(),
+        "n_content_items": len(content_list),
+        "n_vqa_images": n_images,
+        "n_extracted": len(extracted),
+        "n_merged_vqa": len(merged),
+        "llm_elapsed_sec": round(llm_elapsed, 2),
+        "total_elapsed_sec": round(time.time() - started, 2),
+        "paths": paths,
+    }
+    meta_path = out / "run_meta.json"
+    meta_path.write_text(json.dumps(meta, ensure_ascii=False, indent=2), encoding="utf-8")
+    meta["paths"]["run_meta"] = str(meta_path)
+    return meta
diff --git a/uniparser_agent/pdf2vqa/prompts.py b/uniparser_agent/pdf2vqa/prompts.py
new file mode 100644
index 0000000..c39056d
--- /dev/null
+++ b/uniparser_agent/pdf2vqa/prompts.py
@@ -0,0 +1,69 @@
+from __future__ import annotations
+
+
+def build_vqa_extract_prompt() -> str:
+    return """
+        You are an expert in answer college-level questions. You are given a json file. Your task is to segment the content, insert images tags, and extract labels:
+1. Every json item has an "id" field. Your main task is to output this field.
+2. You need to segment the content into multiple `<vqa_pair>`…`</vqa_pair>` blocks, each containing a question and its corresponding answer with solution.
+3. If the problem or answer/solution is not complete, omit them. An answer/solution should be considered complete as long as either the answer or solution exists.
+4. You need to put the images id into proper positions. You could look at the caption or context to decide where to put the image tags.
+5. You will also need to extract the chapter title and each problem's label/number from the text.
+6. You only need to output "id" field for **chapter titles, questions and solutions**. DO NOT OUTPUT ORIGINAL TEXT. Use ',' to separate different ids.
+7. However, use original labels/numbers for labels, and use original text for answers. DO NOT output "id" field for labels and answers. You will need to extract them from the text.
+
+Strict extraction rules:
+** About questions and answers/solutions **
+- Preserve each problem’s original label/number, such as "例1", "Example 3", "习题1", "11". Do not include the period after the number. Use Arabic numerals only. For example, if the label is "例一", convert it to "例1". If the label is "IV", convert it to "4".
+- If the full label is "三、16", keep only "16". If the full label is "5.4", keep only "4".
+- If there are multiple sub-questions (such as "(1)", "(a)") under one main question, always put them together in the same `<vqa_pair>`…`</vqa_pair>` block.
+- If a question and its answer/solution are contiguous, wrap them together as a single `<vqa_pair>`…`</vqa_pair>` block, e.g.:
+  `<vqa_pair><label>1</label><question>…</question><answer>…</answer><solution>…</solution></vqa_pair>`
+- If a question and its answer/solution are NOT contiguous (e.g. only question; only answer and/or solution; all questions at the front and all answers/solutions at the back), wrap each question or answer/solution in a `<vqa_pair>`…`</vqa_pair>` block with the missing part left empty. For example, if only questions appear:
+  `<vqa_pair><label>1</label><question>…</question><answer></answer><solution></solution></vqa_pair>`
+- In total, there are 7 possibilities: only question, only answer, only solution, question with answer, question with solution, answer with solution, full question and answer and solution.
+- If multiple vqa pairs appear, wrap each vqa pair in its own `<vqa_pair>`…`</vqa_pair>` block.
+- If you do not see the full solution, only extract the short answer and leave the solution empty. YOU MUST KEEP SHORT ANSWERS !!!
+- For answer text, output exactly what appears (no translation). Render all mathematical expressions in LaTeX.
+** About chapter/section titles **
+- Always enclose vqa pairs in a `<chapter>`…`</chapter>` block, where <title>MAIN_TITLE_ID</title> is the id of the chapter title or section title.
+- Normally, chapter/section titles appear before the questions/answers in an independent json item.
+- There could be multiple `<chapter>`…`</chapter>` blocks if multiple chapters/sections exist.
+- **Any title followed by a question/answer whose label/number is not 1, or title with a score such as "一、选择题（每题1分，共10分）", should NOT be extracted.**
+- Do not use nested titles.
+- Leave the title blank if there is no chapter title.
+** About figures/diagrams **
+- Whenever the question or answer/solution refers to a figure or diagram, record its "id" in question/answer/solution just like other text content.
+- You MUST include all images referenced in the question/answer/solution.
+
+
+If no qualifying content is found, output:
+<empty></empty>
+
+Output format (all tags run together, no extra whitespace or newlines except between entries):
+<chapter><title>MAIN_TITLE_ID</title>
+<vqa_pair><label>LABEL(EXTRACTED FROM TEXT)</label><question>QUESTION_IDS</question>
+<answer>ANSWER(EXTRACTED FROM SOLUTION)</answer><solution>SOLUTION_IDS</solution></vqa_pair>
+<vqa_pair><label>LABEL(EXTRACTED FROM TEXT)</label><question>QUESTION_IDS</question>
+<answer>ANSWER(EXTRACTED FROM SOLUTION)</answer><solution></solution></vqa_pair>
+</chapter>
+<chapter><title>MAIN_TITLE_ID</title>
+<vqa_pair><label>LABEL(EXTRACTED FROM TEXT)</label><question>QUESTION_IDS</question>
+<answer>ANSWER(EXTRACTED FROM SOLUTION)</answer><solution>SOLUTION_IDS</solution></vqa_pair>
+</chapter>
+
+
+Example:
+<chapter><title>7</title>
+<vqa_pair><label>1</label><question>2,3</question>
+<answer>Yes</answer><solution>5,6,7</solution></vqa_pair>
+<vqa_pair><label>2</label><question>8,9,10</question>
+<answer>3.14</answer><solution></solution></vqa_pair>
+</chapter>
+<chapter><title>12</title>
+<vqa_pair><label>1</label><question></question>
+<answer>\\(2^6\\)</answer><solution>16</solution></vqa_pair>
+</chapter>
+
+Please now process the provided json and output your result.
+""".strip()
diff --git a/uniparser_agent/pdf2vqa/vqa_formatter.py b/uniparser_agent/pdf2vqa/vqa_formatter.py
new file mode 100644
index 0000000..59ac30a
--- /dev/null
+++ b/uniparser_agent/pdf2vqa/vqa_formatter.py
@@ -0,0 +1,106 @@
+"""Convert merged VQA pairs with Markdown image refs into ShareGPT VQA format."""
+
+from __future__ import annotations
+
+import json
+import re
+from pathlib import Path
+from typing import Any
+
+
+_IMAGE_PATTERN = re.compile(r"!\[.*?\]\((.*?)\)")
+
+
+def extract_image_paths(text: str) -> list[str]:
+    return _IMAGE_PATTERN.findall(text or "")
+
+
+def strip_image_tags(text: str) -> str:
+    cleaned = _IMAGE_PATTERN.sub("", text or "")
+    cleaned = re.sub(r"\n{3,}", "\n\n", cleaned).strip()
+    return cleaned
+
+
+def _build_user_content(question: str, images: list[str], placeholder: str = "<image>") -> str:
+    prefix = "".join(placeholder for _ in images)
+    question_clean = strip_image_tags(question)
+    return f"{prefix}{question_clean}" if prefix else question_clean
+
+
+def _build_assistant_content(answer: str, solution: str) -> str:
+    ans_text = (answer or "").strip()
+    sol_text = strip_image_tags(solution)
+    if ans_text and sol_text:
+        return f"{ans_text}\n\n{sol_text}"
+    if ans_text:
+        return ans_text
+    return sol_text
+
+
+def _index_images(images_dir: Path) -> dict[str, Path]:
+    index: dict[str, Path] = {}
+    if not images_dir.is_dir():
+        return index
+    for path in images_dir.iterdir():
+        if path.is_file():
+            index[path.name] = path.resolve()
+    return index
+
+
+def convert_vqa_pair_to_sharegpt(
+    qa: dict[str, Any],
+    *,
+    image_index: dict[str, Path],
+    base_dir: Path,
+    placeholder: str = "<image>",
+) -> dict[str, Any] | None:
+    question = str(qa.get("question") or "").strip()
+    answer = str(qa.get("answer") or "").strip()
+    solution = str(qa.get("solution") or "").strip()
+    if not question:
+        return None
+
+    abs_images: list[str] = []
+    for rel in extract_image_paths(question) + extract_image_paths(solution):
+        name = Path(rel).name
+        if name in image_index:
+            abs_images.append(str(image_index[name]))
+        else:
+            candidate = (base_dir / rel).resolve()
+            abs_images.append(str(candidate))
+
+    assistant = _build_assistant_content(answer, solution)
+    if not assistant:
+        return None
+
+    return {
+        "messages": [
+            {"role": "user", "content": _build_user_content(question, abs_images, placeholder)},
+            {"role": "assistant", "content": assistant},
+        ],
+        "images": abs_images,
+    }
+
+
+def write_sharegpt(
+    merged_pairs: list[dict[str, Any]],
+    images_dir: str | Path,
+    output_json: str | Path,
+    *,
+    base_dir: str | Path | None = None,
+) -> Path:
+    """Write ShareGPT JSON list; returns output path."""
+    images_path = Path(images_dir).expanduser().resolve()
+    out = Path(output_json).expanduser().resolve()
+    out.parent.mkdir(parents=True, exist_ok=True)
+    root = Path(base_dir).expanduser().resolve() if base_dir else out.parent
+    index = _index_images(images_path)
+
+    records: list[dict[str, Any]] = []
+    for qa in merged_pairs:
+        item = convert_vqa_pair_to_sharegpt(qa, image_index=index, base_dir=root)
+        if item is not None:
+            records.append(item)
+
+    out.write_text(json.dumps(records, ensure_ascii=False, indent=2), encoding="utf-8")
+    return out
diff --git a/uniparser_agent/pdf2vqa/vqa_merger.py b/uniparser_agent/pdf2vqa/vqa_merger.py
new file mode 100644
index 0000000..055eae8
--- /dev/null
+++ b/uniparser_agent/pdf2vqa/vqa_merger.py
@@ -0,0 +1,153 @@
+"""Merge extracted question/answer fragments into final VQA pairs."""
+
+from __future__ import annotations
+
+import json
+import re
+from pathlib import Path
+from typing import Any
+
+
+def refine_title(title: str, strict_title_match: bool = False) -> str:
+    title = re.sub(r"\s+", "", title)
+    if strict_title_match:
+        return title
+    try:
+        return re.search(r"\d+\.\d+|\d+", title).group()  # type: ignore[union-attr]
+    except Exception:
+        try:
+            return re.search(r"[一二三四五六七八九零十百]+", title).group()  # type: ignore[union-attr]
+        except Exception:
+            return title
+
+
+def merge_vqa_pairs(
+    extracted: list[dict[str, Any]],
+    *,
+    strict_title_match: bool = False,
+) -> list[dict[str, Any]]:
+    question_list: list[dict[str, Any]] = []
+    answer_list: list[dict[str, Any]] = []
+    for data in extracted:
+        if data.get("question"):
+            question_list.append(dict(data))
+        else:
+            answer_list.append(dict(data))
+
+    merged: list[dict[str, Any]] = []
+    chapter_title = ""
+    label = float("inf")
+    questions: dict[tuple[str, int], dict[str, Any]] = {}
+    answers: dict[tuple[str, int], dict[str, Any]] = {}
+    already_complete = 0
+
+    for data in question_list:
+        label_match = re.search(r"\d+", str(data.get("label", "")))
+        if label_match:
+            data["label"] = label_match.group()
+        if not data.get("chapter_title"):
+            data["chapter_title"] = chapter_title
+        try:
+            data["label"] = int(data["label"])
+        except Exception:
+            continue
+
+        if data["chapter_title"] and data["chapter_title"] != chapter_title:
+            if data["label"] < label:
+                chapter_title = data["chapter_title"]
+            else:
+                data["chapter_title"] = chapter_title
+        label = data["label"]
+        data["chapter_title"] = refine_title(data["chapter_title"], strict_title_match)
+
+        if data["label"] <= 0:
+            continue
+        if data.get("answer") or data.get("solution"):
+            already_complete += 1
+            merged.append(
+                {
+                    "question_chapter_title": data["chapter_title"],
+                    "answer_chapter_title": data["chapter_title"],
+                    "label": data["label"],
+                    "question": data["question"],
+                    "answer": data.get("answer", ""),
+                    "solution": data.get("solution", ""),
+                }
+            )
+        else:
+            questions[(data["chapter_title"], data["label"])] = data
+
+    chapter_title = ""
+    label = float("inf")
+    for data in answer_list:
+        label_match = re.search(r"\d+", str(data.get("label", "")))
+        if label_match:
+            data["label"] = label_match.group()
+        if not data.get("chapter_title"):
+            data["chapter_title"] = chapter_title
+        try:
+            data["label"] = int(data["label"])
+        except Exception:
+            continue
+
+        if data["chapter_title"] and data["chapter_title"] != chapter_title:
+            if data["label"] < label:
+                chapter_title = data["chapter_title"]
+            else:
+                data["chapter_title"] = chapter_title
+        label = data["label"]
+        data["chapter_title"] = refine_title(data["chapter_title"], strict_title_match)
+
+        if data["label"] <= 0:
+            continue
+        key = (data["chapter_title"], data["label"])
+        existing = answers.get(key)
+        if not existing:
+            answers[key] = data
+        else:
+            if not existing.get("solution") and data.get("solution"):
+                existing["solution"] = data["solution"]
+            if not existing.get("answer") and data.get("answer"):
+                existing["answer"] = data["answer"]
+
+    for key, qdata in questions.items():
+        if key not in answers:
+            continue
+        adata = answers[key]
+        merged.append(
+            {
+                "question_chapter_title": qdata["chapter_title"],
+                "answer_chapter_title": adata["chapter_title"],
+                "label": key[1],
+                "question": qdata["question"],
+                "answer": adata.get("answer", ""),
+                "solution": adata.get("solution", ""),
+            }
+        )
+
+    return merged
+
+
+def write_merged_jsonl(merged: list[dict[str, Any]], output_path: str | Path) -> Path:
+    out = Path(output_path).expanduser().resolve()
+    out.parent.mkdir(parents=True, exist_ok=True)
+    with out.open("w", encoding="utf-8") as fh:
+        for item in merged:
+            fh.write(json.dumps(item, ensure_ascii=False) + "\n")
+    return out
+
+
+def jsonl_to_md(jsonl_path: str | Path, md_path: str | Path) -> Path:
+    src = Path(jsonl_path).expanduser().resolve()
+    out = Path(md_path).expanduser().resolve()
+    out.parent.mkdir(parents=True, exist_ok=True)
+    with src.open("r", encoding="utf-8") as infile, out.open("w", encoding="utf-8") as outfile:
+        for line in infile:
+            data = json.loads(line)
+            outfile.write(f"### Question {data['label']}\n\n")
+            outfile.write(f"{data['question']}\n\n")
+            outfile.write(f"**Answer:** {data['answer']}\n\n")
+            if data.get("solution"):
+                outfile.write(f"**Solution:**\n\n{data['solution']}\n\n")
+            outfile.write("---\n\n")
+    return out
diff --git a/uniparser_agent/pyproject.toml b/uniparser_agent/pyproject.toml
new file mode 100644
index 0000000..e3d1a90
--- /dev/null
+++ b/uniparser_agent/pyproject.toml
@@ -0,0 +1,43 @@
+[project]
+name = "uniparser-agent"
+version = "0.1.0"
+description = "UniParser document parsing and exam VQA extraction"
+readme = "README.md"
+requires-python = ">=3.11"
+dependencies = [
+    "openai>=1.0.0",
+    "pypdf>=4.0.0",
+    "requests>=2.32.0",
+    "typer>=0.12.0",
+    "uniparser-tools",
+]
+
+[tool.uv.sources]
+uniparser-tools = { path = "../", editable = true }
+
+[project.scripts]
+uniparser-agent = "uniparser_agent.cli:app"
+
+[project.optional-dependencies]
+dev = [
+    "pytest>=8.0",
+]
+
+[build-system]
+requires = ["setuptools>=61.0", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[tool.setuptools.package-dir]
+"uniparser_agent" = "."
+
+[tool.setuptools]
+packages = [
+    "uniparser_agent",
+    "uniparser_agent.parse",
+    "uniparser_agent.pdf2vqa",
+    "uniparser_agent.llm",
+]
+
+[tool.pytest.ini_options]
+testpaths = ["tests"]
+pythonpath = [".."]
diff --git a/uniparser_agent/tests/test_api_client_v13.py b/uniparser_agent/tests/test_api_client_v13.py
new file mode 100644
index 0000000..76bae76
--- /dev/null
+++ b/uniparser_agent/tests/test_api_client_v13.py
@@ -0,0 +1,142 @@
+"""Release/v1.3 compatibility tests for the standalone agent HTTP client."""
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+import requests
+
+from uniparser_agent.parse.api_client import UniParserApiClient
+
+
+class _Response:
+    def __init__(
+        self,
+        payload: dict[str, Any] | None,
+        *,
+        status_code: int = 200,
+        reason: str = "OK",
+        text: str = "",
+    ) -> None:
+        self.payload = payload
+        self.status_code = status_code
+        self.reason = reason
+        self.text = text
+
+    def json(self) -> dict[str, Any]:
+        if self.payload is None:
+            raise ValueError("not json")
+        return self.payload
+
+
+class _Session:
+    def __init__(
+        self,
+        responses: list[_Response] | None = None,
+        *,
+        error: requests.RequestException | None = None,
+    ) -> None:
+        self.responses = list(responses or [])
+        self.error = error
+        self.calls: list[tuple[str, str, dict[str, Any]]] = []
+        self.closed = False
+
+    def request(self, method: str, url: str, **kwargs: Any) -> _Response:
+        self.calls.append((method, url, kwargs))
+        if self.error is not None:
+            raise self.error
+        return self.responses.pop(0)
+
+    def close(self) -> None:
+        self.closed = True
+
+
+def test_trigger_url_uses_v13_payload_and_sync_timeout() -> None:
+    session = _Session([_Response({"status": "success", "token": "server-token"})])
+    client = UniParserApiClient(
+        "https://example.com/",
+        "key",
+        request_timeout=(1, 2),
+        sync_request_timeout=(3, 4),
+        session=session,  # type: ignore[arg-type]
+    )
+
+    client.trigger_url(
+        "tos://bucket/document.pdf",
+        trigger_kwargs={
+            "preset_layout": [[{"type": "textual"}]],
+            "model_version": "v1.3",
+            "padding_snip": True,
+        },
+        server_generated_token=True,
+    )
+
+    payload = session.calls[0][2]["json"]
+    assert payload["timeout"] == 1800
+    assert payload["inplace_update"] is False
+    assert payload["model_version"] == "v1.3"
+    assert payload["preset_layout"] == '[[{"type": "textual"}]]'
+    assert payload["token"] is None
+    assert "padding_snip" not in payload
+    assert session.calls[0][2]["timeout"] == (3, 4)
+
+
+def test_trigger_file_uses_short_timeout_for_async_request(tmp_path: Path) -> None:
+    source = tmp_path / "document.pdf"
+    source.write_bytes(b"%PDF")
+    session = _Session([_Response({"status": "success", "token": "task-token"})])
+    client = UniParserApiClient(
+        "https://example.com",
+        "key",
+        request_timeout=(1, 2),
+        sync_request_timeout=(3, 4),
+        session=session,  # type: ignore[arg-type]
+    )
+
+    client.trigger_file(str(source), trigger_kwargs={"sync": False})
+
+    assert session.calls[0][2]["timeout"] == (1, 2)
+    assert session.calls[0][2]["data"]["padding_snip"] is True
+
+
+def test_transport_normalizes_http_error_and_redacts_query_credentials() -> None:
+    session = _Session(
+        [
+            _Response(
+                {"description": ("upload denied for https://tos.example.com/upload?X-Tos-Signature=SECRET")},
+                status_code=403,
+                reason="Forbidden",
+            )
+        ]
+    )
+    client = UniParserApiClient(
+        "https://example.com",
+        "key",
+        session=session,  # type: ignore[arg-type]
+    )
+
+    result = client.get_result("task-token")
+
+    assert result["status"] == "error"
+    assert result["http_status"] == 403
+    assert result["token"] == "task-token"
+    assert "SECRET" not in result["description"]
+    assert "?<redacted>" in result["description"]
+
+
+def test_transport_redacts_request_exception() -> None:
+    session = _Session(
+        error=requests.ConnectionError("failed for https://tos.example.com/upload?X-Tos-Signature=SECRET")
+    )
+    client = UniParserApiClient(
+        "https://example.com",
+        "key",
+        session=session,  # type: ignore[arg-type]
+    )
+
+    result = client.get_result("task-token")
+
+    assert result["status"] == "error"
+    assert "SECRET" not in result["description"]
+    assert result["error_type"] == "ConnectionError"
diff --git a/uniparser_agent/tests/test_llm_config.py b/uniparser_agent/tests/test_llm_config.py
new file mode 100644
index 0000000..009536e
--- /dev/null
+++ b/uniparser_agent/tests/test_llm_config.py
@@ -0,0 +1,117 @@
+"""Unit tests for shared LLM config resolution."""
+
+from __future__ import annotations
+
+import pytest
+
+from uniparser_agent.llm.config import LLMConfig, resolve_llm_config
+
+
+def test_resolve_from_openai_env(monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setenv("OPENAI_API_KEY", "sk-env")
+    monkeypatch.setenv("OPENAI_BASE_URL", "http://example.com/v1/")
+    monkeypatch.setenv("OPENAI_MODEL", "gpt-test")
+    cfg = resolve_llm_config()
+    assert cfg.api_key == "sk-env"
+    assert cfg.base_url == "http://example.com/v1"
+    assert cfg.model == "gpt-test"
+    assert cfg.enable_thinking is False
+
+
+def test_explicit_overrides_env(monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setenv("OPENAI_API_KEY", "sk-env")
+    monkeypatch.setenv("OPENAI_BASE_URL", "http://env/v1")
+    monkeypatch.setenv("OPENAI_MODEL", "env-model")
+    cfg = resolve_llm_config(
+        api_key="sk-cli",
+        base_url="http://cli/v1",
+        model="cli-model",
+        enable_thinking=True,
+    )
+    assert cfg.api_key == "sk-cli"
+    assert cfg.base_url == "http://cli/v1"
+    assert cfg.model == "cli-model"
+    assert cfg.enable_thinking is True
+
+
+def test_config_object_with_partial_override(monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.delenv("OPENAI_API_KEY", raising=False)
+    monkeypatch.delenv("OPENAI_BASE_URL", raising=False)
+    monkeypatch.delenv("OPENAI_MODEL", raising=False)
+    base = LLMConfig(
+        api_key="sk-base",
+        base_url="http://base/v1",
+        model="base-model",
+    )
+    cfg = resolve_llm_config(config=base, model="override-model")
+    assert cfg.api_key == "sk-base"
+    assert cfg.model == "override-model"
+
+
+def test_missing_required_raises(monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.delenv("OPENAI_API_KEY", raising=False)
+    monkeypatch.delenv("OPENAI_BASE_URL", raising=False)
+    monkeypatch.delenv("OPENAI_MODEL", raising=False)
+    with pytest.raises(ValueError, match="OPENAI_API_KEY"):
+        resolve_llm_config()
+    monkeypatch.setenv("OPENAI_API_KEY", "sk")
+    with pytest.raises(ValueError, match="OPENAI_BASE_URL"):
+        resolve_llm_config()
+    monkeypatch.setenv("OPENAI_BASE_URL", "http://x/v1")
+    with pytest.raises(ValueError, match="OPENAI_MODEL"):
+        resolve_llm_config()
+
+
+def test_legacy_env_names_ignored(monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.delenv("OPENAI_API_KEY", raising=False)
+    monkeypatch.delenv("OPENAI_BASE_URL", raising=False)
+    monkeypatch.delenv("OPENAI_MODEL", raising=False)
+    monkeypatch.setenv("API_KEY", "legacy-key")
+    monkeypatch.setenv("ARK_API_KEY", "ark-key")
+    monkeypatch.setenv("BASE_URL", "http://legacy/v1")
+    monkeypatch.setenv("MODEL_NAME", "legacy-model")
+    with pytest.raises(ValueError, match="OPENAI_API_KEY"):
+        resolve_llm_config()
+
+
+def test_qwen_extra_body_injected() -> None:
+    cfg = LLMConfig(
+        api_key="sk",
+        base_url="https://api.openai.com/v1",
+        model="qwen-test-model",
+        enable_thinking=False,
+    )
+    assert cfg.resolved_extra_body() == {
+        "chat_template_kwargs": {"enable_thinking": False},
+    }
+
+
+def test_non_qwen_no_extra_body() -> None:
+    cfg = LLMConfig(
+        api_key="sk",
+        base_url="https://api.openai.com/v1",
+        model="gpt-4o-mini",
+    )
+    assert cfg.resolved_extra_body() is None
+
+
+def test_enable_thinking_forces_extra_body() -> None:
+    cfg = LLMConfig(
+        api_key="sk",
+        base_url="https://api.openai.com/v1",
+        model="gpt-4o-mini",
+        enable_thinking=True,
+    )
+    assert cfg.resolved_extra_body() == {
+        "chat_template_kwargs": {"enable_thinking": True},
+    }
+
+
+def test_explicit_extra_body_wins() -> None:
+    cfg = LLMConfig(
+        api_key="sk",
+        base_url="https://api.openai.com/v1",
+        model="Qwen3",
+        extra_body={"foo": 1},
+    )
+    assert cfg.resolved_extra_body() == {"foo": 1}
diff --git a/uniparser_agent/tests/test_output_safety.py b/uniparser_agent/tests/test_output_safety.py
new file mode 100644
index 0000000..c34b89b
--- /dev/null
+++ b/uniparser_agent/tests/test_output_safety.py
@@ -0,0 +1,125 @@
+"""Regression tests for collision-safe output allocation."""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+import pytest
+
+from uniparser_agent.output_dir import default_parse_output_dir
+from uniparser_agent.parse import service as parse_service
+from uniparser_agent.pdf2vqa.pipeline import run_vqa_pipeline
+
+
+class _TriggerFailureClient:
+    def trigger_url(self, _url: str) -> dict[str, str]:
+        return {"status": "error", "message": "trigger failed"}
+
+
+class _LLMFailureClient:
+    def chat(self, *, system_prompt: str, user_content: str) -> str:
+        raise RuntimeError("LLM failed")
+
+
+def _write_pages_tree(path: Path) -> None:
+    path.write_text(
+        json.dumps(
+            {
+                "pages_tree": [
+                    [
+                        {
+                            "type": "paragraph",
+                            "page": 1,
+                            "block": 1,
+                            "text": "Question 1",
+                        }
+                    ]
+                ]
+            }
+        ),
+        encoding="utf-8",
+    )
+
+
+@pytest.mark.parametrize("source_stem", ["", ".", "..", "../escape", r"..\escape"])
+def test_default_parse_output_rejects_unsafe_source_stem(source_stem: str) -> None:
+    with pytest.raises(ValueError, match="Unsafe source name"):
+        default_parse_output_dir(source_stem)
+
+
+def test_default_parse_output_is_contained_in_managed_root(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    monkeypatch.setenv("HOME", str(tmp_path))
+
+    output = default_parse_output_dir("exam")
+
+    assert output == (tmp_path / "Uni-Parser-Skill" / "exam").resolve()
+
+
+def test_parse_url_traversal_is_rejected_before_client_creation(monkeypatch: pytest.MonkeyPatch) -> None:
+    def unexpected_client() -> None:
+        raise AssertionError("client must not be created")
+
+    monkeypatch.setattr(parse_service, "make_client", unexpected_client)
+    with pytest.raises(ValueError, match="Unsafe source name"):
+        parse_service.parse_document("https://example.com/..")
+
+
+def test_parse_failure_preserves_existing_output_and_keeps_partial_sibling(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    output = tmp_path / "parse"
+    output.mkdir()
+    (output / "previous.txt").write_text("previous", encoding="utf-8")
+    monkeypatch.setattr(parse_service, "make_client", _TriggerFailureClient)
+
+    with pytest.raises(RuntimeError, match="trigger failed"):
+        parse_service.parse_document(
+            "https://example.com/exam.pdf",
+            output_dir=str(output),
+        )
+
+    assert (output / "previous.txt").read_text(encoding="utf-8") == "previous"
+    assert (tmp_path / "parse_1" / "trigger_error.json").is_file()
+
+
+def test_vqa_validates_pages_tree_before_allocating_output(tmp_path: Path) -> None:
+    output = tmp_path / "vqa"
+    output.mkdir()
+    (output / "previous.txt").write_text("previous", encoding="utf-8")
+    invalid_tree = tmp_path / "invalid.json"
+    invalid_tree.write_text("{}", encoding="utf-8")
+
+    with pytest.raises(ValueError, match="missing pages_tree key"):
+        run_vqa_pipeline(
+            pages_tree_path=str(invalid_tree),
+            output_dir=str(output),
+            llm_client=_LLMFailureClient(),  # type: ignore[arg-type]
+        )
+
+    assert (output / "previous.txt").read_text(encoding="utf-8") == "previous"
+    assert not (tmp_path / "vqa_1").exists()
+
+
+def test_vqa_failure_preserves_existing_output_and_keeps_partial_sibling(tmp_path: Path) -> None:
+    output = tmp_path / "vqa"
+    output.mkdir()
+    (output / "previous.txt").write_text("previous", encoding="utf-8")
+    pages_tree = tmp_path / "pages_tree.json"
+    _write_pages_tree(pages_tree)
+
+    with pytest.raises(RuntimeError, match="LLM failed"):
+        run_vqa_pipeline(
+            pages_tree_path=str(pages_tree),
+            output_dir=str(output),
+            llm_client=_LLMFailureClient(),  # type: ignore[arg-type]
+        )
+
+    sibling = tmp_path / "vqa_1"
+    assert (output / "previous.txt").read_text(encoding="utf-8") == "previous"
+    assert (sibling / "parse" / "pages_tree.json").is_file()
+    assert (sibling / "llm_content_list.json").is_file()
diff --git a/uniparser_agent/tests/test_pdf_merger.py b/uniparser_agent/tests/test_pdf_merger.py
new file mode 100644
index 0000000..18da1d7
--- /dev/null
+++ b/uniparser_agent/tests/test_pdf_merger.py
@@ -0,0 +1,42 @@
+"""Tests for PDF merge helper."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import pytest
+from pypdf import PdfReader, PdfWriter
+
+from uniparser_agent.pdf2vqa.pdf_merger import merge_pdfs
+
+
+def _write_blank_pdf(path: Path, n_pages: int) -> Path:
+    writer = PdfWriter()
+    for _ in range(n_pages):
+        writer.add_blank_page(width=200, height=200)
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with path.open("wb") as fh:
+        writer.write(fh)
+    writer.close()
+    return path
+
+
+def test_merge_pdfs_sums_pages(tmp_path: Path):
+    a = _write_blank_pdf(tmp_path / "a.pdf", 2)
+    b = _write_blank_pdf(tmp_path / "b.pdf", 3)
+    out = merge_pdfs([a, b], tmp_path / "merged.pdf")
+    assert out.is_file()
+    assert len(PdfReader(str(out)).pages) == 5
+
+
+def test_merge_pdfs_rejects_missing(tmp_path: Path):
+    a = _write_blank_pdf(tmp_path / "a.pdf", 1)
+    with pytest.raises(FileNotFoundError):
+        merge_pdfs([a, tmp_path / "missing.pdf"], tmp_path / "out.pdf")
+
+
+def test_merge_pdfs_rejects_non_pdf(tmp_path: Path):
+    txt = tmp_path / "notes.txt"
+    txt.write_text("x", encoding="utf-8")
+    with pytest.raises(ValueError, match="Not a PDF"):
+        merge_pdfs([txt], tmp_path / "out.pdf")
diff --git a/uniparser_agent/tests/test_vqa_adapter.py b/uniparser_agent/tests/test_vqa_adapter.py
new file mode 100644
index 0000000..ed2fc34
--- /dev/null
+++ b/uniparser_agent/tests/test_vqa_adapter.py
@@ -0,0 +1,117 @@
+"""Unit tests for UniParser → LLM content-list adapter."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+from uniparser_agent.pdf2vqa.layout_adapter import pages_tree_to_content_list
+
+
+def test_adapter_skips_noise_and_numbers_ids():
+    data = {
+        "pages_tree": [
+            [
+                {"type": "hline", "order": 0, "text": ""},
+                {"type": "pageheader", "order": 1, "text": "header"},
+                {"type": "paragraph", "order": 2, "text": "Q1 text"},
+                {
+                    "type": "equation",
+                    "order": 3,
+                    "latex_repr": "x^2=1",
+                    "text": "",
+                },
+                {"type": "pagenumber", "order": 4, "text": "1"},
+            ]
+        ]
+    }
+    content = pages_tree_to_content_list(data)
+    assert [c["id"] for c in content] == [0, 1]
+    assert content[0]["type"] == "text"
+    assert content[0]["text"] == "Q1 text"
+    assert content[1]["type"] == "equation"
+    assert "x^2=1" in content[1]["text"]
+
+
+def test_adapter_supports_v13_inline_spans_and_table_structure():
+    data = {
+        "pages_tree": [
+            [
+                {
+                    "type": "paragraph",
+                    "order": 0,
+                    "contents": ["Energy ", "E=mc^2", " in ", "CCO"],
+                    "types": ["text", "equationinline", "text", "molecule"],
+                },
+                {
+                    "type": "table",
+                    "order": 1,
+                    "structure": "<table><tr><td>[[VL_TABLE_0_0]]</td></tr></table>",
+                    "placeholders": ["[[VL_TABLE_0_0]]"],
+                    "contents": ["x^2"],
+                    "types": ["equationinline"],
+                },
+                {
+                    "type": "molecule",
+                    "order": 2,
+                    "esmi": "C1=CC=CC=C1",
+                },
+            ]
+        ]
+    }
+
+    content = pages_tree_to_content_list(data)
+
+    assert content[0] == {
+        "id": 0,
+        "type": "text",
+        "text": "Energy $E=mc^2$ in `CCO`",
+    }
+    assert content[1]["type"] == "table"
+    assert "<td>$x^2$</td>" in content[1]["table_body"]
+    assert content[2] == {
+        "id": 2,
+        "type": "text",
+        "text": "`C1=CC=CC=C1`",
+    }
+
+
+def test_adapter_uses_v13_caption_contents(tmp_path: Path):
+    image_path = tmp_path / "figure.png"
+    image_path.write_bytes(b"image")
+    data = {
+        "pages_tree": [
+            [
+                {
+                    "type": "figuregroup",
+                    "order": 0,
+                    "items": [
+                        {
+                            "type": "figure",
+                            "page": 1,
+                            "block": 1,
+                            "source": "image-data",
+                        },
+                        {
+                            "type": "figurecaption",
+                            "contents": ["Figure ", "x=1"],
+                            "types": ["text", "equationinline"],
+                        },
+                    ],
+                }
+            ]
+        ]
+    }
+
+    content = pages_tree_to_content_list(
+        data,
+        image_path_map={(1, 1): image_path},
+    )
+
+    assert content == [
+        {
+            "id": 0,
+            "type": "image",
+            "img_path": "vqa_images/figure.png",
+            "image_caption": ["Figure $x=1$"],
+        }
+    ]
diff --git a/uniparser_agent/tests/test_vqa_images.py b/uniparser_agent/tests/test_vqa_images.py
new file mode 100644
index 0000000..8984b35
--- /dev/null
+++ b/uniparser_agent/tests/test_vqa_images.py
@@ -0,0 +1,198 @@
+"""Tests for VQA image export, adapter image items, and ShareGPT formatting."""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+from uniparser_agent.pdf2vqa.image_export import export_images_from_pages_tree
+from uniparser_agent.pdf2vqa.layout_adapter import adapt_pages_tree_file, pages_tree_to_content_list
+from uniparser_agent.pdf2vqa.output_parser import parse_llm_response
+from uniparser_agent.pdf2vqa.vqa_formatter import convert_vqa_pair_to_sharegpt, write_sharegpt
+
+
+# Minimal valid 1x1 PNG (preferred — magic bytes detect format)
+_PNG_1X1_B64 = "iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mP8z8BQDwAEhQGAhKmMIQAAAABJRU5ErkJggg=="
+
+
+def _tiny_image_b64() -> str:
+    return _PNG_1X1_B64
+
+
+def test_export_and_adapt_figuregroup(tmp_path: Path):
+    b64 = _tiny_image_b64()
+    tree = {
+        "pages_tree": [
+            [
+                {
+                    "type": "figuregroup",
+                    "page": 0,
+                    "block": 10,
+                    "order": 0,
+                    "source": "",
+                    "items": [
+                        {
+                            "type": "figure",
+                            "page": 0,
+                            "block": 11,
+                            "order": 0,
+                            "source": b64,
+                            "desc": "",
+                        },
+                        {
+                            "type": "figurecaption",
+                            "page": 0,
+                            "block": 12,
+                            "order": 1,
+                            "text": "Fig. 1 Energy diagram",
+                        },
+                    ],
+                },
+                {"type": "paragraph", "page": 0, "block": 13, "order": 1, "text": "Q1 text"},
+            ]
+        ]
+    }
+    images_dir = tmp_path / "vqa_images"
+    path_map = export_images_from_pages_tree(tree, images_dir)
+    assert path_map
+    assert list(images_dir.iterdir())
+
+    content = pages_tree_to_content_list(tree, image_path_map=path_map)
+    image_items = [c for c in content if c.get("type") == "image"]
+    assert len(image_items) == 1
+    assert image_items[0]["img_path"].startswith("vqa_images/")
+    assert "Fig. 1" in " ".join(image_items[0].get("image_caption") or [])
+    assert any(c.get("text") == "Q1 text" for c in content)
+
+
+def test_export_deeply_nested_figure(tmp_path: Path):
+    """UniParser often nests figure under group → image → figuregroup."""
+    b64 = _tiny_image_b64()
+    tree = {
+        "pages_tree": [
+            [
+                {
+                    "type": "group",
+                    "page": 0,
+                    "block": 1,
+                    "order": 0,
+                    "source": "",
+                    "items": [
+                        {
+                            "type": "image",
+                            "page": 0,
+                            "block": 2,
+                            "order": 0,
+                            "source": "",
+                            "items": [
+                                {
+                                    "type": "figuregroup",
+                                    "page": 0,
+                                    "block": 3,
+                                    "order": 0,
+                                    "source": "",
+                                    "items": [
+                                        {
+                                            "type": "figure",
+                                            "page": 0,
+                                            "block": 4,
+                                            "order": 0,
+                                            "source": b64,
+                                        }
+                                    ],
+                                }
+                            ],
+                        }
+                    ],
+                }
+            ]
+        ]
+    }
+    images_dir = tmp_path / "vqa_images"
+    path_map = export_images_from_pages_tree(tree, images_dir)
+    assert len(path_map) == 1
+    assert list(images_dir.iterdir())
+    content = pages_tree_to_content_list(tree, image_path_map=path_map)
+    assert sum(1 for c in content if c.get("type") == "image") == 1
+
+
+def test_adapt_pages_tree_file_writes_images(tmp_path: Path):
+    b64 = _tiny_image_b64()
+    tree = {
+        "pages_tree": [
+            [
+                {
+                    "type": "molecule",
+                    "page": 1,
+                    "block": 2,
+                    "order": 0,
+                    "source": b64,
+                    "text": "",
+                }
+            ]
+        ]
+    }
+    tree_path = tmp_path / "pages_tree.json"
+    tree_path.write_text(json.dumps(tree), encoding="utf-8")
+    content_path = tmp_path / "llm_content_list.json"
+    content = adapt_pages_tree_file(tree_path, content_path, images_dir=tmp_path / "vqa_images")
+    assert (tmp_path / "vqa_images").is_dir()
+    assert any(c.get("type") == "image" for c in content)
+
+
+def test_parser_and_sharegpt_image_placeholders(tmp_path: Path):
+    images_dir = tmp_path / "vqa_images"
+    images_dir.mkdir()
+    img = images_dir / "abc123.jpg"
+    img.write_bytes(b"\xff\xd8\xff" + b"\x00" * 64)
+
+    content = [
+        {"id": 0, "type": "text", "text": "What is shown?"},
+        {
+            "id": 1,
+            "type": "image",
+            "img_path": "vqa_images/abc123.jpg",
+            "image_caption": ["diagram"],
+        },
+        {"id": 2, "type": "text", "text": "It is A."},
+    ]
+    response = (
+        "<chapter><title></title>"
+        "<vqa_pair><label>1</label><question>0,1</question>"
+        "<answer>A</answer><solution>2</solution></vqa_pair>"
+        "</chapter>"
+    )
+    extracted = parse_llm_response(response, content)
+    assert len(extracted) == 1
+    assert "![diagram](vqa_images/abc123.jpg)" in extracted[0]["question"]
+
+    merged = [
+        {
+            "label": 1,
+            "question": extracted[0]["question"],
+            "answer": "A",
+            "solution": extracted[0]["solution"],
+            "question_chapter_title": "",
+            "answer_chapter_title": "",
+        }
+    ]
+    out = write_sharegpt(merged, images_dir, tmp_path / "vqa_sharegpt.json", base_dir=tmp_path)
+    records = json.loads(out.read_text(encoding="utf-8"))
+    assert len(records) == 1
+    user = records[0]["messages"][0]["content"]
+    assert user.startswith("<image>")
+    assert len(records[0]["images"]) == 1
+    assert user.count("<image>") == len(records[0]["images"])
+    assert "What is shown?" in user
+    assert "![" not in user
+
+
+def test_sharegpt_no_images_ok():
+    item = convert_vqa_pair_to_sharegpt(
+        {"question": "2+2?", "answer": "4", "solution": ""},
+        image_index={},
+        base_dir=Path("."),
+    )
+    assert item is not None
+    assert item["images"] == []
+    assert "<image>" not in item["messages"][0]["content"]
diff --git a/uniparser_agent/tests/test_vqa_parser.py b/uniparser_agent/tests/test_vqa_parser.py
new file mode 100644
index 0000000..d62af04
--- /dev/null
+++ b/uniparser_agent/tests/test_vqa_parser.py
@@ -0,0 +1,71 @@
+"""Tests for LLM response parsing and VQA merge."""
+
+from __future__ import annotations
+
+from uniparser_agent.pdf2vqa.output_parser import parse_llm_response
+from uniparser_agent.pdf2vqa.vqa_merger import merge_vqa_pairs
+
+
+def test_parse_and_merge_contiguous_qa():
+    content = [
+        {"id": 0, "type": "text", "text": "Chapter Title"},
+        {"id": 1, "type": "text", "text": "1. What is 1+1?"},
+        {"id": 2, "type": "text", "text": "A. 1 B. 2"},
+        {"id": 3, "type": "text", "text": "【答案】B"},
+        {"id": 4, "type": "text", "text": "【解析】1+1=2"},
+    ]
+    response = (
+        "<chapter><title>0</title>"
+        "<vqa_pair><label>1</label><question>1,2</question>"
+        "<answer>B</answer><solution>3,4</solution></vqa_pair>"
+        "</chapter>"
+    )
+    extracted = parse_llm_response(response, content)
+    assert len(extracted) == 1
+    assert "What is 1+1?" in extracted[0]["question"]
+    assert extracted[0]["answer"] == "B"
+    merged = merge_vqa_pairs(extracted)
+    assert len(merged) == 1
+    assert merged[0]["label"] == 1
+    assert "1+1=2" in merged[0]["solution"]
+
+
+def test_merge_question_only_and_answer_only_rows():
+    extracted = [
+        {
+            "label": "1",
+            "chapter_title": "1.1",
+            "question": "What is 2+2?",
+            "answer": "",
+            "solution": "",
+        },
+        {
+            "label": "2",
+            "chapter_title": "1.1",
+            "question": "What is 3+3?",
+            "answer": "",
+            "solution": "",
+        },
+        {
+            "label": "1",
+            "chapter_title": "1.1",
+            "question": "",
+            "answer": "4",
+            "solution": "2+2=4",
+        },
+        {
+            "label": "2",
+            "chapter_title": "1.1",
+            "question": "",
+            "answer": "6",
+            "solution": "3+3=6",
+        },
+    ]
+    merged = merge_vqa_pairs(extracted)
+    assert len(merged) == 2
+    by_label = {item["label"]: item for item in merged}
+    assert by_label[1]["question"] == "What is 2+2?"
+    assert by_label[1]["answer"] == "4"
+    assert "2+2=4" in by_label[1]["solution"]
+    assert by_label[2]["answer"] == "6"
+    assert "3+3=6" in by_label[2]["solution"]
diff --git a/uniparser_tools/api/account.py b/uniparser_tools/api/account.py
new file mode 100644
index 0000000..5ddf0bd
--- /dev/null
+++ b/uniparser_tools/api/account.py
@@ -0,0 +1,145 @@
+from __future__ import annotations
+
+from typing import Optional
+
+import requests
+
+from uniparser_tools.api.transport import (
+    DEFAULT_REQUEST_TIMEOUT,
+    RequestTimeout,
+    UniParserHTTPTransport,
+)
+
+
+class UniParserAccountClient:
+    """Read-only account and billing API client."""
+
+    def __init__(
+        self,
+        host: str,
+        api_key: str,
+        *,
+        request_timeout: RequestTimeout = DEFAULT_REQUEST_TIMEOUT,
+        session: Optional[requests.Session] = None,
+    ):
+        self._transport = UniParserHTTPTransport(
+            host,
+            api_key,
+            request_timeout=request_timeout,
+            session=session,
+        )
+        self._owns_transport = True
+        self.host = self._transport.host
+        self.api_key = api_key
+        self.request_timeout = request_timeout
+
+    @classmethod
+    def from_transport(cls, transport: UniParserHTTPTransport) -> "UniParserAccountClient":
+        """Create an account namespace sharing another client's transport."""
+        client = cls.__new__(cls)
+        client._transport = transport
+        client._owns_transport = False
+        client.host = transport.host
+        client.api_key = transport.api_key
+        client.request_timeout = transport.request_timeout
+        return client
+
+    def close(self) -> None:
+        if self._owns_transport:
+            self._transport.close()
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        self.close()
+
+    @property
+    def current_user_endpoint(self):
+        return f"{self.host}/users/me"
+
+    @property
+    def balance_endpoint(self):
+        return f"{self.host}/balance"
+
+    @property
+    def usage_summary_endpoint(self):
+        return f"{self.host}/billing/usage"
+
+    @property
+    def usage_records_endpoint(self):
+        return f"{self.host}/billing/usage-records"
+
+    @property
+    def balance_transactions_endpoint(self):
+        return f"{self.host}/balance/transactions"
+
+    def get_current_user(self, *, http_timeout: Optional[RequestTimeout] = None):
+        return self._transport.request(
+            "GET",
+            "/users/me",
+            timeout=http_timeout,
+            error_message="current user request failed",
+        )
+
+    def get_balance(self, *, http_timeout: Optional[RequestTimeout] = None):
+        return self._transport.request(
+            "GET",
+            "/balance",
+            timeout=http_timeout,
+            error_message="balance request failed",
+        )
+
+    def get_usage_summary(
+        self,
+        period: str = "current_month",
+        *,
+        http_timeout: Optional[RequestTimeout] = None,
+    ):
+        return self._transport.request(
+            "GET",
+            "/billing/usage",
+            params={"period": period},
+            timeout=http_timeout,
+            error_message="usage summary request failed",
+        )
+
+    def list_usage_records(
+        self,
+        page: int = 1,
+        size: int = 20,
+        *,
+        http_timeout: Optional[RequestTimeout] = None,
+    ):
+        return self._transport.request(
+            "GET",
+            "/billing/usage-records",
+            params={"page": page, "size": size},
+            timeout=http_timeout,
+            error_message="usage records request failed",
+        )
+
+    def list_balance_transactions(
+        self,
+        page: int = 1,
+        size: int = 20,
+        *,
+        http_timeout: Optional[RequestTimeout] = None,
+    ):
+        return self._transport.request(
+            "GET",
+            "/balance/transactions",
+            params={"page": page, "size": size},
+            timeout=http_timeout,
+            error_message="balance transactions request failed",
+        )
+
+    def get_balance_transactions(
+        self,
+        page: int = 1,
+        size: int = 20,
+        *,
+        http_timeout: Optional[RequestTimeout] = None,
+    ):
+        """Alias for ``list_balance_transactions``."""
+        return self.list_balance_transactions(page=page, size=size, http_timeout=http_timeout)
diff --git a/uniparser_tools/api/clients.py b/uniparser_tools/api/clients.py
index c222069..d3afdf7 100644
--- a/uniparser_tools/api/clients.py
+++ b/uniparser_tools/api/clients.py
@@ -1,13 +1,21 @@
 import json
+import os
 import re
-import traceback
 import uuid
 from dataclasses import asdict, dataclass
-from typing import List, Union
+from typing import Any, Dict, List, Optional, Sequence, Union
 
 import requests
 from PIL import Image
 
+from uniparser_tools.api.account import UniParserAccountClient
+from uniparser_tools.api.transport import (
+    DEFAULT_REQUEST_TIMEOUT,
+    DEFAULT_SYNC_REQUEST_TIMEOUT,
+    DEFAULT_UPLOAD_REQUEST_TIMEOUT,
+    RequestTimeout,
+    UniParserHTTPTransport,
+)
 from uniparser_tools.common.constant import (
     FormatFlag,
     IntEnum,
@@ -16,6 +24,7 @@
     ParseMode,
     ParseModeTextual,
     StatusFlag,
+    ThirdPartyFormatter,
 )
 from uniparser_tools.utils.image import dump_image_base64_str
 
@@ -24,9 +33,21 @@ def int_enum_factory(items):
     return {k: int(v) if isinstance(v, IntEnum) else v for k, v in items}
 
 
+PresetLayout = Union[str, List[Any]]
+
+
+def serialize_preset_layout(preset_layout: Optional[PresetLayout]) -> str:
+    """Serialize preset layout consistently for form and JSON endpoints."""
+    if preset_layout is None:
+        return ""
+    if isinstance(preset_layout, str):
+        return preset_layout
+    return json.dumps(preset_layout, ensure_ascii=False)
+
+
 @dataclass
 class TriggerFileData:
-    token: str
+    token: Optional[str]
     lang: Language
     sync: bool
     textual: Union[ParseModeTextual, bool]
@@ -40,12 +61,17 @@ class TriggerFileData:
     ordering_method: OrderingMethod = OrderingMethod.XYCutExp
     callback_url: str = None
     callback_secret: str = None
+    timeout: int = 1800
+    padding_snip: bool = True
+    inplace_update: bool = False
+    preset_layout: str = ""
+    model_version: Optional[str] = None
 
 
 @dataclass
 class TriggerURLData:
     url: str
-    token: str
+    token: Optional[str]
     lang: Language
     sync: bool
     textual: Union[ParseModeTextual, bool]
@@ -60,6 +86,16 @@ class TriggerURLData:
     proxy: str = None
     callback_url: str = None
     callback_secret: str = None
+    timeout: int = 1800
+    inplace_update: bool = False
+    preset_layout: str = ""
+    model_version: Optional[str] = None
+
+
+@dataclass
+class TOSUploadFile:
+    filename: str
+    token: Optional[str] = None
 
 
 @dataclass
@@ -90,13 +126,57 @@ class GetFormattedData:
     marginalia: bool
 
 
+@dataclass
+class GetThirdPartyData:
+    token: str
+    formatter: ThirdPartyFormatter
+
+
 class UniParserClient:
-    def __init__(self, host: str, api_key: str):
-        assert api_key, "api_key can not be empty"
-        assert host.startswith("http"), "host must start with http or https"
+    def __init__(
+        self,
+        host: str,
+        api_key: str,
+        *,
+        request_timeout: RequestTimeout = DEFAULT_REQUEST_TIMEOUT,
+        sync_request_timeout: RequestTimeout = DEFAULT_SYNC_REQUEST_TIMEOUT,
+        upload_request_timeout: RequestTimeout = DEFAULT_UPLOAD_REQUEST_TIMEOUT,
+        session: Optional[requests.Session] = None,
+    ):
+        self._transport = UniParserHTTPTransport(
+            host,
+            api_key,
+            request_timeout=request_timeout,
+            session=session,
+        )
         self.api_key = api_key
         self.user = uuid.uuid5(uuid.NAMESPACE_DNS, self.api_key)
-        self.host = host
+        self.host = self._transport.host
+        self.request_timeout = request_timeout
+        self.sync_request_timeout = sync_request_timeout
+        self.upload_request_timeout = upload_request_timeout
+        self.account = UniParserAccountClient.from_transport(self._transport)
+
+    def close(self):
+        self._transport.close()
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        self.close()
+
+    def _trigger_http_timeout(self, sync: bool, http_timeout: Optional[RequestTimeout] = None):
+        if http_timeout is not None:
+            return http_timeout
+        return self.sync_request_timeout if sync else self.request_timeout
+
+    def _trigger_token(self, seed: str, token: Optional[str], server_generated_token: bool) -> Optional[str]:
+        if token is None and not server_generated_token:
+            token = self.to_token(seed)
+        if token is not None:
+            self.validate_token(token)
+        return token
 
     @property
     def trigger_file_endpoint(self):
@@ -110,6 +190,22 @@ def trigger_url_endpoint(self):
     def trigger_snip_endpoint(self):
         return f"{self.host}/trigger-snip-async"
 
+    @property
+    def request_tos_upload_links_endpoint(self):
+        return f"{self.host}/request-tos-upload-links"
+
+    @property
+    def health_endpoint(self):
+        return f"{self.host}/health"
+
+    @property
+    def version_endpoint(self):
+        return f"{self.host}/version"
+
+    @property
+    def get_constants_endpoint(self):
+        return f"{self.host}/get-constants"
+
     @property
     def get_result_endpoint(self):
         return f"{self.host}/get-result"
@@ -118,6 +214,10 @@ def get_result_endpoint(self):
     def get_formatted_endpoint(self):
         return f"{self.host}/get-formatted"
 
+    @property
+    def get_third_party_output_endpoint(self):
+        return f"{self.host}/get-third-party-output"
+
     def to_token(self, task_id: str):
         token = uuid.uuid5(self.user, task_id).hex
         return token
@@ -125,52 +225,24 @@ def to_token(self, task_id: str):
     def validate_token(self, token: str):
         assert re.match(r"^[-\._?=&a-zA-Z0-9]{1,128}$", token), f"token: {token} contains illegal characters"
 
-    def health(self):
-        try:
-            headers = {"X-API-Key": self.api_key}
-            response = requests.get(f"{self.host}/health", headers=headers, timeout=30)
-        except Exception:
-            return {
-                "status": StatusFlag.Error,
-                "description": traceback.format_exc(),
-            }
-        if response.status_code >= 400:
-            return {
-                "status": "error",
-                "http_status": response.status_code,
-                "description": response.reason_phrase,
-                "body": response.text,
-            }
-        try:
-            return response.json()
-        except json.decoder.JSONDecodeError:
-            return {"status": StatusFlag.Error, "message": response.text}
+    def health(self, *, http_timeout: Optional[RequestTimeout] = None):
+        return self._transport.request("GET", "/health", timeout=http_timeout, error_message="health check failed")
 
-    def version(self):
-        try:
-            headers = {"X-API-Key": self.api_key}
-            response = requests.get(f"{self.host}/version", headers=headers, timeout=30)
-        except Exception:
-            return {
-                "status": StatusFlag.Error,
-                "description": traceback.format_exc(),
-            }
-        if response.status_code >= 400:
-            return {
-                "status": "error",
-                "http_status": response.status_code,
-                "description": response.reason_phrase,
-                "body": response.text,
-            }
-        try:
-            return response.json()
-        except json.decoder.JSONDecodeError:
-            return {"status": StatusFlag.Error, "message": response.text}
+    def version(self, *, http_timeout: Optional[RequestTimeout] = None):
+        return self._transport.request("GET", "/version", timeout=http_timeout, error_message="version request failed")
+
+    def get_constants(self, *, http_timeout: Optional[RequestTimeout] = None):
+        return self._transport.request(
+            "GET",
+            "/get-constants",
+            timeout=http_timeout,
+            error_message="constants request failed",
+        )
 
     def trigger_file(
         self,
         file_path: str,
-        token: str = None,
+        token: Optional[str] = None,
         lang: Language = Language.Unknown,
         sync: bool = True,
         textual: Union[ParseModeTextual, bool] = ParseModeTextual.DigitalExported,
@@ -184,16 +256,23 @@ def trigger_file(
         ordering_method: OrderingMethod = OrderingMethod.GapTree,
         callback_url: str = None,
         callback_secret: str = None,
+        timeout: int = 1800,
+        padding_snip: bool = True,
+        inplace_update: bool = False,
+        preset_layout: Optional[PresetLayout] = None,
+        model_version: Optional[str] = None,
+        server_generated_token: bool = False,
+        http_timeout: Optional[RequestTimeout] = None,
         **kwargs,
     ):
         """
         sync: True=同步解析，该请求会在解析完成后才返回; False=异步解析，该请求会立即返回，解析结果需要通过GetResult接口获取
+        timeout: 服务端解析预算（秒），与客户端 HTTP 超时 http_timeout 相互独立
         callback_url: 异步解析完成后的回调地址
         callback_secret: 回调验证密钥
+        server_generated_token: token 为空时由服务端生成；默认 False 以保持历史确定性 token 行为
         """
-        if not token:
-            token = self.to_token(file_path)
-        self.validate_token(token)
+        token = self._trigger_token(file_path, token, server_generated_token)
         trigger_data = TriggerFileData(
             token=token,
             lang=lang,
@@ -206,33 +285,41 @@ def trigger_file(
             expression=expression,
             equation=equation,
             pages=pages,
+            timeout=timeout,
             ordering_method=ordering_method,
+            padding_snip=padding_snip,
+            inplace_update=inplace_update,
+            preset_layout=serialize_preset_layout(preset_layout),
+            model_version=model_version,
             callback_url=callback_url,
             callback_secret=callback_secret,
         )
 
         try:
-            headers = {"X-API-Key": self.api_key}
-            files = {"file": open(file_path, "rb")}
             data = asdict(trigger_data, dict_factory=int_enum_factory)
-            response = requests.post(self.trigger_file_endpoint, files=files, data=data, headers=headers)
-        except Exception:
+            with open(file_path, "rb") as file_obj:
+                return self._transport.request(
+                    "POST",
+                    "/trigger-file-async",
+                    files={"file": file_obj},
+                    data=data,
+                    timeout=self._trigger_http_timeout(sync, http_timeout),
+                    error_message="trigger file failed",
+                    token=token,
+                )
+        except OSError as exc:
             return {
                 "status": StatusFlag.Error,
                 "token": token,
                 "message": "trigger file failed",
-                "description": traceback.format_exc(),
+                "description": str(exc),
+                "error_type": type(exc).__name__,
             }
 
-        try:
-            return response.json()
-        except json.decoder.JSONDecodeError:
-            return {"status": StatusFlag.Error, "token": token, "message": response.text}
-
     def trigger_snip(
         self,
         snip_path: str,
-        token: str = None,
+        token: Optional[str] = None,
         lang: Language = Language.Unknown,
         sync: bool = True,
         textual: Union[ParseModeTextual, bool] = ParseModeTextual.DigitalExported,
@@ -246,11 +333,16 @@ def trigger_snip(
         ordering_method: OrderingMethod = OrderingMethod.GapTree,
         callback_url: str = None,
         callback_secret: str = None,
+        timeout: int = 1800,
+        padding_snip: bool = True,
+        inplace_update: bool = False,
+        preset_layout: Optional[PresetLayout] = None,
+        model_version: Optional[str] = None,
+        server_generated_token: bool = False,
+        http_timeout: Optional[RequestTimeout] = None,
         **kwargs,
     ):
-        if not token:
-            token = self.to_token(snip_path)
-        self.validate_token(token)
+        token = self._trigger_token(snip_path, token, server_generated_token)
         trigger_data = TriggerFileData(
             token=token,
             lang=lang,
@@ -263,32 +355,41 @@ def trigger_snip(
             expression=expression,
             equation=equation,
             pages=pages,
+            timeout=timeout,
             ordering_method=ordering_method,
+            padding_snip=padding_snip,
+            inplace_update=inplace_update,
+            preset_layout=serialize_preset_layout(preset_layout),
+            model_version=model_version,
             callback_url=callback_url,
             callback_secret=callback_secret,
         )
 
         try:
-            headers = {"X-API-Key": self.api_key}
-            img = dump_image_base64_str(Image.open(snip_path).convert("RGB"))
+            with Image.open(snip_path) as source_image:
+                img = dump_image_base64_str(source_image.convert("RGB"))
             data = {"img": img, **asdict(trigger_data, dict_factory=int_enum_factory)}
-            result = requests.post(self.trigger_snip_endpoint, data=data, headers=headers)
-        except Exception:
+            return self._transport.request(
+                "POST",
+                "/trigger-snip-async",
+                data=data,
+                timeout=self._trigger_http_timeout(sync, http_timeout),
+                error_message="trigger snip failed",
+                token=token,
+            )
+        except (OSError, ValueError) as exc:
             return {
                 "status": StatusFlag.Error,
                 "token": token,
                 "message": "trigger snip failed",
-                "description": traceback.format_exc(),
+                "description": str(exc),
+                "error_type": type(exc).__name__,
             }
-        try:
-            return result.json()
-        except json.decoder.JSONDecodeError:
-            return {"status": StatusFlag.Error, "token": token, "message": result.text}
 
     def trigger_url(
         self,
         pdf_url: str,
-        token: str = None,
+        token: Optional[str] = None,
         lang: Language = Language.Unknown,
         sync: bool = True,
         textual: Union[ParseModeTextual, bool] = ParseModeTextual.DigitalExported,
@@ -303,11 +404,15 @@ def trigger_url(
         proxy: str = None,
         callback_url: str = None,
         callback_secret: str = None,
+        timeout: int = 1800,
+        inplace_update: bool = False,
+        preset_layout: Optional[PresetLayout] = None,
+        model_version: Optional[str] = None,
+        server_generated_token: bool = False,
+        http_timeout: Optional[RequestTimeout] = None,
         **kwargs,
     ):
-        if not token:
-            token = self.to_token(pdf_url)
-        self.validate_token(token)
+        token = self._trigger_token(pdf_url, token, server_generated_token)
         trigger_data = TriggerURLData(
             url=pdf_url,
             token=token,
@@ -321,26 +426,150 @@ def trigger_url(
             expression=expression,
             equation=equation,
             pages=pages,
+            timeout=timeout,
             ordering_method=ordering_method,
             proxy=proxy,
+            inplace_update=inplace_update,
+            preset_layout=serialize_preset_layout(preset_layout),
+            model_version=model_version,
             callback_url=callback_url,
             callback_secret=callback_secret,
         )
-        try:
-            headers = {"X-API-Key": self.api_key}
-            data = asdict(trigger_data, dict_factory=int_enum_factory)
-            result = requests.post(self.trigger_url_endpoint, json=data, headers=headers)
-        except Exception:
+        data = asdict(trigger_data, dict_factory=int_enum_factory)
+        return self._transport.request(
+            "POST",
+            "/trigger-url-async",
+            json=data,
+            timeout=self._trigger_http_timeout(sync, http_timeout),
+            error_message="trigger url failed",
+            token=token,
+        )
+
+    def request_tos_upload_links(
+        self,
+        files: Sequence[Union[str, TOSUploadFile, Dict[str, Optional[str]]]],
+        *,
+        http_timeout: Optional[RequestTimeout] = None,
+    ):
+        """Request authenticated, presigned TOS upload targets.
+
+        ``files`` may contain filenames, ``TOSUploadFile`` instances, or
+        dictionaries with ``filename`` and optional ``token`` fields.
+        """
+        normalized_files = []
+        for item in files:
+            if isinstance(item, str):
+                upload_file = TOSUploadFile(filename=item)
+            elif isinstance(item, TOSUploadFile):
+                upload_file = item
+            elif isinstance(item, dict):
+                upload_file = TOSUploadFile(filename=item.get("filename", ""), token=item.get("token"))
+            else:
+                return {
+                    "status": StatusFlag.Error,
+                    "message": "request TOS upload links failed",
+                    "description": f"unsupported file descriptor: {type(item).__name__}",
+                }
+            normalized_files.append(asdict(upload_file))
+
+        return self._transport.request(
+            "POST",
+            "/request-tos-upload-links",
+            json={"files": normalized_files},
+            timeout=http_timeout,
+            error_message="request TOS upload links failed",
+        )
+
+    def upload_files_to_tos(
+        self,
+        file_paths: Sequence[str],
+        *,
+        tokens: Optional[Sequence[Optional[str]]] = None,
+        http_timeout: Optional[RequestTimeout] = None,
+    ):
+        """Upload local PDF/image files to TOS and return their ``source_url`` values.
+
+        This helper requests presigned URLs and performs unauthenticated ``PUT``
+        uploads. It intentionally leaves parsing as a separate ``trigger_url``
+        call so uploading alone never starts a billable parse.
+        """
+        if tokens is not None and len(tokens) != len(file_paths):
             return {
                 "status": StatusFlag.Error,
-                "token": token,
-                "message": "trigger url failed",
-                "description": traceback.format_exc(),
+                "message": "upload files to TOS failed",
+                "description": "tokens and file_paths must have the same length",
+            }
+
+        upload_files = [
+            TOSUploadFile(
+                filename=os.path.basename(file_path),
+                token=tokens[index] if tokens is not None else None,
+            )
+            for index, file_path in enumerate(file_paths)
+        ]
+        links_response = self.request_tos_upload_links(upload_files, http_timeout=http_timeout)
+        if not isinstance(links_response, dict) or not isinstance(links_response.get("files"), list):
+            return links_response
+
+        links = links_response["files"]
+        if len(links) != len(file_paths):
+            return {
+                "status": StatusFlag.Error,
+                "message": "upload files to TOS failed",
+                "description": "the service returned an unexpected number of upload links",
+                "files": [
+                    {key: value for key, value in link.items() if key != "upload_url"}
+                    for link in links
+                    if isinstance(link, dict)
+                ],
             }
-        try:
-            return result.json()
-        except json.decoder.JSONDecodeError:
-            return {"status": StatusFlag.Error, "token": token, "message": result.text}
+
+        uploaded_files = []
+        for file_path, link in zip(file_paths, links):
+            upload_url = link.get("upload_url") if isinstance(link, dict) else None
+            if not upload_url:
+                return {
+                    "status": StatusFlag.Error,
+                    "message": "upload files to TOS failed",
+                    "description": "the service returned an upload target without upload_url",
+                    "files": uploaded_files,
+                }
+            try:
+                with open(file_path, "rb") as file_obj:
+                    upload_result = self._transport.request(
+                        "PUT",
+                        upload_url,
+                        authenticated=False,
+                        expect_json=False,
+                        data=file_obj,
+                        timeout=self.upload_request_timeout if http_timeout is None else http_timeout,
+                        error_message="TOS upload failed",
+                    )
+            except OSError as exc:
+                return {
+                    "status": StatusFlag.Error,
+                    "message": "upload files to TOS failed",
+                    "description": str(exc),
+                    "error_type": type(exc).__name__,
+                    "files": uploaded_files,
+                }
+
+            if upload_result.get("status") == StatusFlag.Error:
+                return {
+                    "status": StatusFlag.Error,
+                    "message": "upload files to TOS failed",
+                    "description": f"TOS upload failed for {file_path}",
+                    "upload": upload_result,
+                    "files": uploaded_files,
+                }
+            uploaded_files.append(
+                {
+                    **{key: value for key, value in link.items() if key != "upload_url"},
+                    "uploaded": True,
+                }
+            )
+
+        return {"status": StatusFlag.Success, "files": uploaded_files}
 
     def get_result(
         self,
@@ -350,6 +579,7 @@ def get_result(
         pages_dict: bool = False,
         pages_tree: bool = False,
         molecule_source: bool = False,
+        http_timeout: Optional[RequestTimeout] = None,
     ):
         data = GetResultData(
             token=token,
@@ -359,21 +589,15 @@ def get_result(
             pages_tree=pages_tree,
             molecule_source=molecule_source,
         )
-        try:
-            headers = {"X-API-Key": self.api_key}
-            data = asdict(data, dict_factory=int_enum_factory)
-            result = requests.post(self.get_result_endpoint, json=data, headers=headers)
-        except Exception:
-            return {
-                "status": StatusFlag.Error,
-                "token": token,
-                "message": "get result failed",
-                "description": traceback.format_exc(),
-            }
-        try:
-            return result.json()
-        except json.decoder.JSONDecodeError:
-            return {"status": StatusFlag.Error, "token": token, "message": result.text}
+        payload = asdict(data, dict_factory=int_enum_factory)
+        return self._transport.request(
+            "POST",
+            "/get-result",
+            json=payload,
+            timeout=http_timeout,
+            error_message="get result failed",
+            token=token,
+        )
 
     def get_formatted(
         self,
@@ -391,6 +615,7 @@ def get_formatted(
         expression: FormatFlag = FormatFlag.Markdown,
         equation: FormatFlag = FormatFlag.Markdown,
         marginalia: bool = False,
+        http_timeout: Optional[RequestTimeout] = None,
     ):
         data = GetFormattedData(
             token=token,
@@ -408,18 +633,30 @@ def get_formatted(
             equation=equation,
             marginalia=marginalia,
         )
-        try:
-            headers = {"X-API-Key": self.api_key}
-            data = asdict(data, dict_factory=int_enum_factory)
-            result = requests.post(self.get_formatted_endpoint, json=data, headers=headers)
-        except Exception:
-            return {
-                "status": StatusFlag.Error,
-                "token": token,
-                "message": "get formatted failed",
-                "description": traceback.format_exc(),
-            }
-        try:
-            return result.json()
-        except json.decoder.JSONDecodeError:
-            return {"status": StatusFlag.Error, "token": token, "message": result.text}
+        payload = asdict(data, dict_factory=int_enum_factory)
+        return self._transport.request(
+            "POST",
+            "/get-formatted",
+            json=payload,
+            timeout=http_timeout,
+            error_message="get formatted failed",
+            token=token,
+        )
+
+    def get_third_party_output(
+        self,
+        token: str,
+        formatter: ThirdPartyFormatter = ThirdPartyFormatter.MinerU,
+        *,
+        http_timeout: Optional[RequestTimeout] = None,
+    ):
+        data = GetThirdPartyData(token=token, formatter=formatter)
+        payload = asdict(data, dict_factory=int_enum_factory)
+        return self._transport.request(
+            "POST",
+            "/get-third-party-output",
+            json=payload,
+            timeout=http_timeout,
+            error_message="get third-party output failed",
+            token=token,
+        )
diff --git a/uniparser_tools/api/transport.py b/uniparser_tools/api/transport.py
new file mode 100644
index 0000000..9fa71ce
--- /dev/null
+++ b/uniparser_tools/api/transport.py
@@ -0,0 +1,136 @@
+from __future__ import annotations
+
+import re
+from typing import Any, Dict, Optional, Tuple, Union
+from urllib.parse import urlparse
+
+import requests
+
+from uniparser_tools.common.constant import StatusFlag
+
+
+RequestTimeout = Union[float, Tuple[float, Optional[float]]]
+
+DEFAULT_REQUEST_TIMEOUT: RequestTimeout = (10.0, 60.0)
+DEFAULT_SYNC_REQUEST_TIMEOUT: RequestTimeout = (10.0, 1860.0)
+DEFAULT_UPLOAD_REQUEST_TIMEOUT: RequestTimeout = (60.0, 300.0)
+
+
+def _redact_url_queries(value: str) -> str:
+    """Remove bearer-style query strings from URLs included in diagnostics."""
+    return re.sub(r"(?P<url>(?:https?://|/)[^\s?]+)\?[^\s]+", r"\g<url>?<redacted>", value)
+
+
+def _redact_diagnostic_value(value: Any) -> Any:
+    if isinstance(value, dict):
+        return {key: _redact_diagnostic_value(item) for key, item in value.items()}
+    if isinstance(value, list):
+        return [_redact_diagnostic_value(item) for item in value]
+    if isinstance(value, str):
+        return _redact_url_queries(value)
+    return value
+
+
+class UniParserHTTPTransport:
+    """Shared HTTP transport for UniParser API clients."""
+
+    def __init__(
+        self,
+        host: str,
+        api_key: str,
+        *,
+        request_timeout: RequestTimeout = DEFAULT_REQUEST_TIMEOUT,
+        session: Optional[requests.Session] = None,
+    ):
+        parsed = urlparse(host)
+        assert parsed.scheme in {"http", "https"} and parsed.netloc, "host must be a valid http or https URL"
+        assert api_key, "api_key can not be empty"
+
+        self.host = host.rstrip("/")
+        self.api_key = api_key
+        self.request_timeout = request_timeout
+        self.session = session or requests.Session()
+        self._owns_session = session is None
+
+    def endpoint(self, path: str) -> str:
+        return f"{self.host}/{path.lstrip('/')}"
+
+    def request(
+        self,
+        method: str,
+        path: str,
+        *,
+        timeout: Optional[RequestTimeout] = None,
+        authenticated: bool = True,
+        expect_json: bool = True,
+        error_message: str = "request failed",
+        token: Optional[str] = None,
+        **kwargs,
+    ) -> Any:
+        headers = dict(kwargs.pop("headers", {}) or {})
+        if authenticated:
+            headers.setdefault("X-API-Key", self.api_key)
+
+        url = path if path.startswith(("http://", "https://")) else self.endpoint(path)
+        try:
+            response = self.session.request(
+                method,
+                url,
+                headers=headers,
+                timeout=self.request_timeout if timeout is None else timeout,
+                **kwargs,
+            )
+        except requests.RequestException as exc:
+            payload: Dict[str, Any] = {
+                "status": StatusFlag.Error,
+                "message": error_message,
+                "description": _redact_url_queries(str(exc)),
+                "error_type": type(exc).__name__,
+            }
+            if token is not None:
+                payload["token"] = token
+            return payload
+
+        try:
+            payload = response.json()
+        except ValueError:
+            payload = None
+
+        if response.status_code >= 400:
+            if isinstance(payload, dict):
+                result = _redact_diagnostic_value(payload)
+                result.setdefault("status", StatusFlag.Error)
+                result.setdefault("description", response.reason or error_message)
+            else:
+                result = {
+                    "status": StatusFlag.Error,
+                    "description": response.reason or error_message,
+                    "body": _redact_url_queries(response.text),
+                }
+            result["http_status"] = response.status_code
+            if token is not None:
+                result.setdefault("token", token)
+            return result
+
+        if not expect_json:
+            return {
+                "status": "success",
+                "http_status": response.status_code,
+            }
+
+        if payload is not None:
+            return payload
+
+        result = {
+            "status": StatusFlag.Error,
+            "message": error_message,
+            "description": "response body is not valid JSON",
+            "body": _redact_url_queries(response.text),
+        }
+        if token is not None:
+            result["token"] = token
+        return result
+
+    def close(self) -> None:
+        if self._owns_session:
+            self.session.close()
diff --git a/uniparser_tools/cli/README.md b/uniparser_tools/cli/README.md
index 51b0201..0c2df24 100644
--- a/uniparser_tools/cli/README.md
+++ b/uniparser_tools/cli/README.md
@@ -182,8 +182,7 @@ uniparser parse https://example.com/paper.pdf
 
 | 选项 | 说明 |
 |------|------|
-| `-o` / `--output-dir DIR` | 指定输出目录（默认 `~/Uni-Parser-Skill/<文件名>/`） |
-| `--overwrite` | 输出目录已存在时，先清空再写入 |
+| `-o` / `--output-dir DIR` | 首选输出目录（默认 `~/Uni-Parser-Skill/<文件名>/`）；已存在时自动使用同级后缀目录 |
 | `--async` | 异步提交任务（适合较大文档） |
 
 ### 解析配置（7 类语义）
@@ -216,7 +215,7 @@ uniparser parse paper.pdf --molecule disable
 uniparser parse paper.pdf --textual digital --table ocr-fast --molecule disable
 
 # 与输出目录、异步组合
-uniparser parse paper.pdf -o ./results/ --overwrite --async
+uniparser parse paper.pdf -o ./results/ --async
 ```
 
 `trigger_meta.json` 会记录本次实际提交的 `trigger_kwargs`，便于复现配置。
@@ -264,7 +263,7 @@ Trigger meta: /Users/you/Uni-Parser-Skill/paper/trigger_meta.json
 #### 指定输出目录与解析参数
 
 ```bash
-uniparser parse paper.pdf -o ./out/paper --overwrite --textual digital --molecule disable
+uniparser parse paper.pdf -o ./out/paper --textual digital --molecule disable
 ```
 
 `trigger_meta.json` 中 `trigger_kwargs` 会反映覆盖项，例如：
@@ -302,16 +301,21 @@ uniparser parse /no/such/file.pdf
 {"ok": false, "error": {"code": "INPUT_ERROR", "message": "File not found: /no/such/file.pdf"}}
 ```
 
-输出目录已存在且未加 `--overwrite` 时：
+输出目录已存在时，旧目录保持不变，并自动创建第一个可用的同级后缀目录：
 
 ```bash
 uniparser parse paper.pdf -o ./out/paper
 ```
 
-```json
-{"ok": false, "error": {"code": "DIR_EXISTS", "message": "Output directory already exists: ...", "output_dir": "..."}}
+```text
+./out/paper    # 已有结果，不修改
+./out/paper_1  # 本次实际输出；若也存在则继续使用 paper_2
 ```
 
+请以成功结果中的 `output_dir` 为准。程序不会复用或删除任何已有目录。
+
+> 出于安全原因，根目录、HOME、当前工作目录及 Git 元数据目录不能作为首选输出目录。
+
 > 说明：成功信息在 stdout；进度 `Parsing...` 与部分路径提示在 stderr；错误统一为 stderr 单行 JSON。
 
 ### 输出说明
@@ -341,8 +345,7 @@ token 可在上次 `parse` 输出目录的 `trigger_meta.json` 中找到。
 
 | 选项 | 说明 |
 |------|------|
-| `-o` / `--output-dir DIR` | 指定输出目录（默认 `~/Uni-Parser-Skill/token_<前8位>/`） |
-| `--overwrite` | 输出目录已存在时，先清空再写入 |
+| `-o` / `--output-dir DIR` | 首选输出目录（默认 `~/Uni-Parser-Skill/token_<前8位>/`）；已存在时自动使用同级后缀目录 |
 
 ```bash
 uniparser fetch --token abcdef1234567890 -o ./out/
@@ -444,9 +447,9 @@ uniparser --json parse paper.pdf 2>/dev/null | python3 -c "import sys,json; prin
 
 检查 `parse` 后的文件路径是否正确（建议使用绝对路径）。
 
-**提示输出目录已存在**
+**指定的输出目录已存在**
 
-加上 `--overwrite`，或换用 `-o` 指定新目录。
+无需额外参数。程序会自动创建同级后缀目录（例如 `paper_1`），并在结果中返回实际路径。
 
 **命令 `uniparser` 找不到**
 
diff --git a/uniparser_tools/cli/commands/fetch.py b/uniparser_tools/cli/commands/fetch.py
index bc50402..3d86d9b 100644
--- a/uniparser_tools/cli/commands/fetch.py
+++ b/uniparser_tools/cli/commands/fetch.py
@@ -5,7 +5,7 @@
 import click
 
 from uniparser_tools.cli.core.config import ctx_flag, make_client
-from uniparser_tools.cli.core.errors import missing_token_error
+from uniparser_tools.cli.core.errors import input_error, missing_token_error
 from uniparser_tools.cli.core.output import (
     emit_success,
     fetch_source_stem,
@@ -17,14 +17,16 @@
 
 @click.command("fetch")
 @click.option("--token", required=True, help="Task token from a prior successful parse trigger response")
-@click.option("--output-dir", "-o", help="Output directory (default: ~/Uni-Parser-Skill/token_<prefix>/)")
-@click.option("--overwrite", is_flag=True, help="Overwrite output directory if it already exists")
+@click.option(
+    "--output-dir",
+    "-o",
+    help="Preferred output directory; an available suffixed sibling is used if it exists",
+)
 @click.pass_context
 def fetch_cmd(
     ctx: click.Context,
     token: str,
     output_dir: str | None,
-    overwrite: bool,
 ) -> None:
     """Poll and download results for an existing job using its token."""
     resolved_token = (token or "").strip()
@@ -36,13 +38,10 @@ def fetch_cmd(
         raise SystemExit(err)
 
     source_stem = fetch_source_stem(resolved_token)
-    out_dir, dir_code = resolve_fetch_output_dir(
-        resolved_token,
-        output_dir,
-        overwrite=overwrite,
-    )
-    if dir_code is not None:
-        raise SystemExit(dir_code)
+    try:
+        out_dir = resolve_fetch_output_dir(resolved_token, output_dir)
+    except (OSError, ValueError) as exc:
+        raise SystemExit(input_error(str(exc))) from exc
 
     print_parsing_status(source_stem)
     summary = complete_fetch(
diff --git a/uniparser_tools/cli/commands/parse.py b/uniparser_tools/cli/commands/parse.py
index cf1b4a2..3f58b8c 100644
--- a/uniparser_tools/cli/commands/parse.py
+++ b/uniparser_tools/cli/commands/parse.py
@@ -21,8 +21,11 @@ def _parse_mode_option(name: str, help_text: str):
 
 @click.command("parse")
 @click.argument("source", metavar="INPUT")
-@click.option("--output-dir", "-o", help="Output directory (default: ~/Uni-Parser-Skill/<stem>/)")
-@click.option("--overwrite", is_flag=True, help="Overwrite output directory if it already exists")
+@click.option(
+    "--output-dir",
+    "-o",
+    help="Preferred output directory; an available suffixed sibling is used if it exists",
+)
 @click.option("--async", "async_mode", is_flag=True, help="Submit with sync=false and poll until success")
 @click.option(
     "--textual",
@@ -41,7 +44,6 @@ def parse_cmd(
     ctx: click.Context,
     source: str,
     output_dir: str | None,
-    overwrite: bool,
     async_mode: bool,
     textual: str | None,
     equation: str | None,
@@ -60,9 +62,10 @@ def parse_cmd(
     if err is not None:
         raise SystemExit(err)
 
-    out_dir, dir_code = resolve_output_dir(resolved.source_stem, output_dir, overwrite=overwrite)
-    if dir_code is not None:
-        raise SystemExit(dir_code)
+    try:
+        out_dir = resolve_output_dir(resolved.source_stem, output_dir)
+    except (OSError, ValueError) as exc:
+        raise SystemExit(input_error(str(exc))) from exc
 
     trigger_kwargs = resolve_trigger_kwargs(
         sync=not async_mode,
diff --git a/uniparser_tools/cli/core/errors.py b/uniparser_tools/cli/core/errors.py
index d15cd1a..571fc0a 100644
--- a/uniparser_tools/cli/core/errors.py
+++ b/uniparser_tools/cli/core/errors.py
@@ -2,7 +2,6 @@
 
 import json
 import sys
-from pathlib import Path
 
 
 def emit_json_stderr(payload: dict) -> None:
@@ -32,22 +31,6 @@ def missing_token_error() -> int:
     return 1
 
 
-def dir_exists_error(output_dir: Path) -> int:
-    emit_json_stderr(
-        {
-            "ok": False,
-            "error": {
-                "code": "DIR_EXISTS",
-                "message": (
-                    f"Output directory already exists: {output_dir}. Re-run with --overwrite if you want to replace it."
-                ),
-                "output_dir": str(output_dir),
-            },
-        }
-    )
-    return 1
-
-
 def parse_error(stage: str, result: dict) -> int:
     emit_json_stderr(
         {
diff --git a/uniparser_tools/cli/core/output.py b/uniparser_tools/cli/core/output.py
index 35d2f51..bb5703f 100644
--- a/uniparser_tools/cli/core/output.py
+++ b/uniparser_tools/cli/core/output.py
@@ -1,17 +1,17 @@
 from __future__ import annotations
 
 import json
-import shutil
 import sys
 from datetime import datetime, timezone
 from pathlib import Path
 from typing import Any
 
-from uniparser_tools.cli.core.errors import dir_exists_error
+from uniparser_tools.common.output_dir import create_unique_output_dir
 
 
 def default_output_dir(source_stem: str) -> Path:
-    return (Path.home() / "Uni-Parser-Skill" / source_stem).expanduser().resolve()
+    root = (Path.home() / "Uni-Parser-Skill").expanduser().resolve()
+    return root / source_stem
 
 
 def fetch_source_stem(token: str) -> str:
@@ -19,36 +19,24 @@ def fetch_source_stem(token: str) -> str:
 
 
 def default_fetch_output_dir(token: str) -> Path:
-    return (Path.home() / "Uni-Parser-Skill" / fetch_source_stem(token)).expanduser().resolve()
-
-
-def _ensure_output_dir(out: Path, *, overwrite: bool) -> tuple[Path | None, int | None]:
-    if out.exists() and not overwrite:
-        return None, dir_exists_error(out)
-    if out.exists() and overwrite:
-        shutil.rmtree(out)
-    return out, None
+    root = (Path.home() / "Uni-Parser-Skill").expanduser().resolve()
+    return root / fetch_source_stem(token)
 
 
 def resolve_output_dir(
     source_stem: str,
     output_dir: str | None,
-    *,
-    overwrite: bool,
-) -> tuple[Path | None, int | None]:
-    out = Path(output_dir).expanduser().resolve() if output_dir else default_output_dir(source_stem)
-    return _ensure_output_dir(out, overwrite=overwrite)
+) -> Path:
+    preferred = Path(output_dir).expanduser() if output_dir else default_output_dir(source_stem)
+    return create_unique_output_dir(preferred)
 
 
 def resolve_fetch_output_dir(
     token: str,
     output_dir: str | None,
-    *,
-    overwrite: bool,
-) -> tuple[Path | None, int | None]:
-    if output_dir:
-        return resolve_output_dir(fetch_source_stem(token), output_dir, overwrite=overwrite)
-    return _ensure_output_dir(default_fetch_output_dir(token), overwrite=overwrite)
+) -> Path:
+    preferred = Path(output_dir).expanduser() if output_dir else default_fetch_output_dir(token)
+    return create_unique_output_dir(preferred)
 
 
 def write_trigger_meta(
diff --git a/uniparser_tools/common/constant.py b/uniparser_tools/common/constant.py
index 68c2656..0330592 100644
--- a/uniparser_tools/common/constant.py
+++ b/uniparser_tools/common/constant.py
@@ -300,6 +300,7 @@ class ErrorFlag(StrEnum):
     File_Required = "File is required"             # 需要给定文件流
     File_Not_Found = "File not found"              # 文件不存在
     File_Invalid = "File is invalid"               # PDF文件无效/页数为0
+    PDF_Page_Tree_Broken = "PDF page tree is broken or unsupported"  # PDF页树损坏: len(doc)中包含的页索引无法被mupdf解析
     URL_Required = "URL is required"               # 需要给定URL
     Snip_Required = "Snippet is required"          # 需要给定文本片段
     Snip_Not_Found = "Snippet not found"           # 文本片段不存在
@@ -317,6 +318,7 @@ class ErrorFlag(StrEnum):
     PDF_Pages_Exceeded = "PDF page count exceeds limit" # PDF页数超过限制
     File_Type_Not_Allowed = "File type not allowed" # 文件类型不允许
     Domain_Not_Allowed = "Domain not allowed"      # 域名不允许
+    Unsupported_Formatter = "Unsupported formatter" # 不支持的格式化器
 
 
 class LayoutType(StrEnum):
@@ -449,6 +451,10 @@ class SemanticType(StrEnum):
     Ignore = "ignore"           # 忽略
 
 
+class ThirdPartyFormatter(StrEnum):
+    MinerU = "mineru"
+
+
 TextualTypes = [
     LayoutType.Title,
     LayoutType.DocumentTitle,
@@ -480,6 +486,8 @@ class SemanticType(StrEnum):
     LayoutType.ExpressionCaption,
     LayoutType.TextLine,
     LayoutType.Abstract,
+    LayoutType.PageHeader,
+    LayoutType.PageFooter,
 ]
 
 EntityTypes = [
@@ -499,8 +507,6 @@ class SemanticType(StrEnum):
 IgnoreTypes = [
     LayoutType.Abandon,
     LayoutType.Watermark,
-    LayoutType.PageHeader,
-    LayoutType.PageFooter,
 ]
 
 FunctionalTypes = [
@@ -517,12 +523,17 @@ class SemanticType(StrEnum):
 # fmt: on
 
 # convert to ppocr abbr
+# Source: PaddleOCR PP-OCRv5 multilingual docs:
+# https://www.paddleocr.ai/latest/en/version3.x/algorithm/PP-OCRv5/PP-OCRv5_multi_languages.html
 PPOCR_LANG = {
     Language.Chinese_Simplified: "ch",
     Language.Chinese_Traditional: "chinese_cht",
     Language.Japanese: "japan",
     Language.Korean: "korean",
     Language.English: "en",
+    Language.Arabic: "ar",
+    Language.Greek: "el",
+    Language.Hindi: "hi",
     Language.Russian: "ru",
     Language.French: "fr",
     Language.German: "german",
@@ -534,7 +545,11 @@ class SemanticType(StrEnum):
     Language.Swedish: "sv",
     Language.Spanish: "es",
     Language.Serbian: "rs_latin",
-    Language.Latin: "rs_latin",
+    Language.Serbian_Latin: "rs_latin",
+    Language.Latin: "la",
+    Language.Tamil: "ta",
+    Language.Telugu: "te",
+    Language.Thai: "th",
 }
 
 SUPPORTED_LANG = list(PPOCR_LANG.keys())
diff --git a/uniparser_tools/common/dataclass.py b/uniparser_tools/common/dataclass.py
index 1585090..cac5f82 100644
--- a/uniparser_tools/common/dataclass.py
+++ b/uniparser_tools/common/dataclass.py
@@ -10,7 +10,6 @@
 from typing import Any, Dict, List, Tuple, Union
 from urllib.parse import quote
 
-import latex2mathml.converter
 import pandas as pd
 from pylatexenc.latexencode import unicode_to_latex
 
@@ -50,7 +49,7 @@ def item2format_(item: SemanticItem, item_format: FormatFlag):
         if not isinstance(item, GroupedResult):
             s = getattr(item, item_format)
             if not item.plain and getattr(item, "source", ""):
-                if B64_RE.match(item.source):
+                if B64_RE.fullmatch(item.source):
                     if item_format == FormatFlag.Markdown:
                         s += f"![{item.type}](data:image/png;base64,{item.source})"
                     elif item_format == FormatFlag.Html:
@@ -554,6 +553,14 @@ def __post_init__(self):
         if isinstance(self.direction, int):
             self.direction = Direction(self.direction)
 
+    @staticmethod
+    def _coerce_bbox(bbox):
+        if isinstance(bbox, list):
+            return BBox(*bbox)
+        if isinstance(bbox, dict):
+            return BBox(**bbox)
+        return bbox
+
     @property
     def r_bbox(self):
         # return relative bbox, 0-1
@@ -662,17 +669,54 @@ class TextualResult(SemanticItem):
     type: LayoutType = LayoutType.Text
     bboxes: List[BBox] = field(default_factory=list)
     contents: List[str] = field(default_factory=list)
-    text: str = ""
+    text: str = ""  # Deprecated: legacy plain text field; use contents/types/bboxes instead.
+    types: List[LayoutType] = field(default_factory=list)
 
     def __post_init__(self):
         super().__post_init__()
-        assert len(self.bboxes) == len(self.contents)
-        if len(self.bboxes) and isinstance(self.bboxes[0], dict):
-            self.bboxes = [BBox(**b) for b in self.bboxes]
+        if not self.contents and self.text:
+            from uniparser_tools.utils.format_utils import parse_inline_text
+
+            self.contents, self.types = parse_inline_text(self.text)
+            self.bboxes = []
+        elif self.contents and not self.types:
+            self.types = [LayoutType.Text] * len(self.contents)
+        if not self.text:
+            self.text = "".join(self.contents)
+        self.bboxes = [self._coerce_bbox(bbox) for bbox in self.bboxes]
+        self.types = [LayoutType(t) if isinstance(t, str) else t for t in self.types]
+        if len(self.contents) != len(self.types):
+            raise ValueError("TextualResult contents/types length mismatch")
+        if self.bboxes and len(self.bboxes) != len(self.contents):
+            raise ValueError("TextualResult bboxes must be empty or match contents length")
+        if any(bbox is None for bbox in self.bboxes):
+            raise ValueError("TextualResult bboxes must not contain None")
+
+    @staticmethod
+    def _content_format(content: str, content_type: LayoutType, item_format: FormatFlag) -> str:
+        if content_type in [LayoutType.Equation, LayoutType.EquationInline]:
+            return EquationResult.format_latex_repr(content, item_format, inline=True)
+        if content_type == LayoutType.Molecule:
+            return MoleculeResult.format_molecule_text(content, item_format, inline=True)
+        if item_format == FormatFlag.Html:
+            return escape(content)
+        if item_format == FormatFlag.Latex:
+            try:
+                return unicode_to_latex(content, unknown_char_policy="keep", unknown_char_warning=False)
+            except Exception:
+                get_root_logger().exception(f"unicode_to_latex failed: {content}")
+                return content
+        return content
+
+    def _inline_text(self, item_format: FormatFlag) -> str:
+        return "".join(
+            self._content_format(content, content_type, item_format)
+            for content, content_type in zip(self.contents, self.types)
+        )
 
     @property
     def plain(self):
-        return self.text
+        return self._inline_text(FormatFlag.Plain)
 
     @property
     def markup(self):
@@ -683,35 +727,51 @@ def markup(self):
 
     @property
     def markdown(self):
+        plain = self._inline_text(FormatFlag.Markdown)
         if self.type in [LayoutType.Paragraph, LayoutType.Text, LayoutType.Description]:
-            return self.plain
+            return plain
         elif self.type == LayoutType.Legend:
-            return f"{self.plain}"
+            return f"*{plain}*"
         elif self.type in [LayoutType.Token, LayoutType.EquationID, LayoutType.MoleculeID]:
-            return f"{self.plain}"
+            return f"{plain}"
         elif self.type in [
             LayoutType.Caption,
             LayoutType.TableCaption,
             LayoutType.FigureCaption,
             LayoutType.ImageCaption,
+            LayoutType.AlgorithmCaption,
+            LayoutType.ExpressionCaption,
         ]:
-            return f"{self.plain}"
+            return f"**{plain}**"
+        elif self.type in [
+            LayoutType.AlgorithmFootnote,
+            LayoutType.ImageFootnote,
+            LayoutType.TableFootnote,
+            LayoutType.PageNote,
+        ]:
+            return f"*{plain}*"
+        elif self.type == LayoutType.Algorithm:
+            body = plain.rstrip("\n")
+            return f"```\n{body}\n```"
         elif self.type == LayoutType.Title:
-            return f"# {self.plain}"
+            return f"## {plain}"
         elif self.type == LayoutType.DocumentTitle:
-            return f"# {self.plain}"
+            return f"# {plain}"
         else:
-            return self.plain
+            return plain
 
     @property
     def latex(self):
-        try:
-            # 对于无法映射到 LaTeX 的字符（例如中文“物”），使用 unknown_char_policy=\"keep\"
-            # 以保留原始字符并避免 pylatexenc 打印告警。
-            plain = unicode_to_latex(self.plain, unknown_char_policy="keep", unknown_char_warning=False)
-        except Exception:
-            get_root_logger().exception(f"unicode_to_latex failed: {self.plain}")
-            plain = self.plain
+        if self.contents:
+            plain = self._inline_text(FormatFlag.Latex)
+        else:
+            try:
+                # 对于无法映射到 LaTeX 的字符（例如中文“物”），使用 unknown_char_policy=\"keep\"
+                # 以保留原始字符并避免 pylatexenc 打印告警。
+                plain = unicode_to_latex(self.plain, unknown_char_policy="keep", unknown_char_warning=False)
+            except Exception:
+                get_root_logger().exception(f"unicode_to_latex failed: {self.plain}")
+                plain = self.plain
         if self.type in [LayoutType.Paragraph, LayoutType.Text, LayoutType.Description]:
             return plain
         elif self.type == LayoutType.Legend:
@@ -723,8 +783,21 @@ def latex(self):
             LayoutType.TableCaption,
             LayoutType.FigureCaption,
             LayoutType.ImageCaption,
+            LayoutType.AlgorithmCaption,
+            LayoutType.ExpressionCaption,
         ]:
             return f"\\textbf{{{plain}}}"
+        elif self.type in [
+            LayoutType.AlgorithmFootnote,
+            LayoutType.ImageFootnote,
+            LayoutType.TableFootnote,
+            LayoutType.PageNote,
+        ]:
+            return f"\\textit{{{plain}}}"
+        elif self.type == LayoutType.Algorithm:
+            # verbatim needs raw source text, not latex-escaped body.
+            body = "".join(self.contents) if self.contents else self.plain
+            return f"\\begin{{verbatim}}\n{body.rstrip()}\n\\end{{verbatim}}"
         elif self.type == LayoutType.Title:
             return f"\\section{{{plain}}}"
         elif self.type == LayoutType.DocumentTitle:
@@ -734,25 +807,38 @@ def latex(self):
 
     @property
     def html(self):
+        plain = self._inline_text(FormatFlag.Html)
+        type_cls = getattr(self.type, "value", "text")
         if self.type in [LayoutType.Paragraph, LayoutType.Text, LayoutType.Description]:
-            return f"<p>{self.plain}</p>"
+            return f"<p>{plain}</p>"
         elif self.type == LayoutType.Legend:
-            return f"<legend>{self.plain}</legend>"
+            return f"<legend>{plain}</legend>"
         elif self.type in [LayoutType.Token, LayoutType.EquationID, LayoutType.MoleculeID]:
-            return f"<em>{self.plain}</em>"
+            return f"<em>{plain}</em>"
         elif self.type in [
             LayoutType.Caption,
             LayoutType.TableCaption,
             LayoutType.FigureCaption,
             LayoutType.ImageCaption,
+            LayoutType.AlgorithmCaption,
+            LayoutType.ExpressionCaption,
+        ]:
+            return f'<caption class="caption-{type_cls}">{plain}</caption>'
+        elif self.type in [
+            LayoutType.AlgorithmFootnote,
+            LayoutType.ImageFootnote,
+            LayoutType.TableFootnote,
+            LayoutType.PageNote,
         ]:
-            return f"<caption>{self.plain}</caption>"
+            return f'<aside class="footnote footnote-{type_cls}"><em>{plain}</em></aside>'
+        elif self.type == LayoutType.Algorithm:
+            return f'<pre class="algorithm"><code>{plain}</code></pre>'
         elif self.type == LayoutType.Title:
-            return f"<h2>{self.plain}</h2>"
+            return f'<h2 class="title">{plain}</h2>'
         elif self.type == LayoutType.DocumentTitle:
-            return f"<h1>{self.plain}</h1>"
+            return f'<h1 class="document-title">{plain}</h1>'
         else:
-            return f"<p>{self.plain}</p>"
+            return f"<p>{plain}</p>"
 
 
 @dataclass
@@ -773,6 +859,7 @@ class MoleculeResult(SemanticItem):
     smi: str = ""
     sru: bool = False
     drawing: str = ""
+    esmi: str = ""
 
     @property
     def plain(self):
@@ -785,21 +872,31 @@ def plain(self):
     def markup(self):
         return f"\\begin{{{self.type.value}}}\n{self.plain}\n\\end{{{self.type.value}}}"
 
+    @staticmethod
+    def format_molecule_text(text: str, item_format: FormatFlag, inline: bool = False):
+        if item_format == FormatFlag.Markdown:
+            return f"`{text}`"
+        if item_format == FormatFlag.Latex:
+            return f"\\texttt{{{unicode_to_latex(text, unknown_char_policy='keep', unknown_char_warning=False)}}}"
+        if item_format == FormatFlag.Html:
+            return f"<code>{escape(text)}</code>"
+        return text
+
     @property
     def markdown(self):
         # if self.drawing.strip():
         #     return f"<div><div>{self.drawing}</div><code>{self.plain}</code></div>"
-        return f"***{self.plain}***"
+        return self.format_molecule_text(self.plain, FormatFlag.Markdown)
 
     @property
     def latex(self):
-        return f"\\textit{{\\textbf{{{self.plain}}}}}"
+        return self.format_molecule_text(self.plain, FormatFlag.Latex)
 
     @property
     def html(self):
         # if self.drawing.strip():
         #     return f"<div><div>{self.drawing}</div><code>{self.plain}</code></div>"
-        return f"<code>{self.plain}</code>"
+        return self.format_molecule_text(self.plain, FormatFlag.Html)
 
 
 @dataclass
@@ -815,6 +912,28 @@ def __post_init__(self):
         elif isinstance(self.bbox, dict):
             self.bbox = BBox(**self.bbox)
 
+    @property
+    def is_molecule(self):
+        return self.category_id == 1 or self.category == "[Mol]"
+
+    def format_text(self, item_format: FormatFlag) -> str:
+        text = str(self.text or "").strip()
+        if not text:
+            return ""
+        if self.is_molecule:
+            return TextualResult._content_format(text, LayoutType.Molecule, item_format)
+
+        from uniparser_tools.utils.format_utils import parse_inline_text
+
+        contents, types = parse_inline_text(text)
+        if not contents:
+            contents, types = [text], [LayoutType.Text]
+        content_types = types or [LayoutType.Text] * len(contents)
+        return "".join(
+            TextualResult._content_format(str(content), content_type, item_format)
+            for content, content_type in zip(contents, content_types)
+        )
+
 
 @dataclass
 class Reaction(DataClassGeneric):
@@ -866,16 +985,26 @@ def __post_init__(self):
         if len(self.reactions) and isinstance(self.reactions[0], dict):
             self.reactions = [Reaction(**r) for r in self.reactions]
 
-    @functools.cached_property
-    def df(self) -> pd.DataFrame:
+    @staticmethod
+    def _format_components(components, item_format: FormatFlag, sep: str = ",") -> str:
+        texts = []
+        for component in components or []:
+            if isinstance(component, dict):
+                component = ReactionComponent(**component)
+            text = component.format_text(item_format)
+            if text:
+                texts.append(text)
+        return sep.join(texts)
+
+    def _df_for_format(self, item_format: FormatFlag) -> pd.DataFrame:
         try:
             reactions = []
             for r in self.reactions:
                 reactions.append(
                     dict(
-                        reactants=",".join([repr(r.text) for r in r.reactants]),
-                        products=",".join([repr(p.text) for p in r.products]),
-                        conditions=",".join([repr(c.text) for c in r.conditions]),
+                        reactants=self._format_components(r.reactants, item_format),
+                        products=self._format_components(r.products, item_format),
+                        conditions=self._format_components(r.conditions, item_format),
                     )
                 )
             if not len(reactions):
@@ -890,6 +1019,10 @@ def df(self) -> pd.DataFrame:
             )
             return pd.DataFrame()
 
+    @functools.cached_property
+    def df(self) -> pd.DataFrame:
+        return self._df_for_format(FormatFlag.Plain)
+
     @property
     def plain(self):
         if self.df.shape[-1] == 0:
@@ -902,15 +1035,39 @@ def markup(self):
 
     @property
     def markdown(self):
-        return self.df.to_markdown(index=False, disable_numparse=True)
+        df = self._df_for_format(FormatFlag.Markdown)
+        if df.shape[-1] == 0:
+            return ""
+        return df.to_markdown(index=False, disable_numparse=True)
 
     @property
     def latex(self):
-        return self.df.style.hide(axis="index").format(escape="latex").to_latex()
+        return self._df_for_format(FormatFlag.Latex).style.hide(axis="index").to_latex()
 
     @property
     def html(self):
-        return self.df.to_html(index=False)
+        def component_html(components):
+            return self._format_components(components, FormatFlag.Html, sep="<br>")
+
+        rows = []
+        for reaction in self.reactions or []:
+            reactants = (
+                reaction.get("reactants", []) if isinstance(reaction, dict) else getattr(reaction, "reactants", [])
+            )
+            products = reaction.get("products", []) if isinstance(reaction, dict) else getattr(reaction, "products", [])
+            conditions = (
+                reaction.get("conditions", []) if isinstance(reaction, dict) else getattr(reaction, "conditions", [])
+            )
+            rows.append(
+                "<tr>"
+                f"<td>{component_html(reactants)}</td>"
+                f"<td>{component_html(conditions)}</td>"
+                f"<td>{component_html(products)}</td>"
+                "</tr>"
+            )
+        if not rows:
+            return ""
+        return "<table><tr><th>reactants</th><th>conditions</th><th>products</th></tr>" + "".join(rows) + "</table>"
 
 
 @dataclass
@@ -925,31 +1082,53 @@ class TabularResult(SemanticItem):
 
     def __post_init__(self):
         super().__post_init__()
-        assert len(self.bboxes) == len(self.labels) == len(self.placeholders) == len(self.contents)
+        assert len(self.placeholders) == len(self.contents)
+        if self.bboxes:
+            assert len(self.bboxes) == len(self.placeholders)
+        if self.labels:
+            assert len(self.labels) == len(self.placeholders)
         if self.types:  # 兼容旧版本
-            assert len(self.bboxes) == len(self.types)
+            assert len(self.types) == len(self.placeholders)
         if len(self.bboxes) and isinstance(self.bboxes[0], dict):
             self.bboxes = [BBox(**b) for b in self.bboxes]
         if len(self.labels) and isinstance(self.labels[0], int):
             self.labels = [TableBBoxType(i) for i in self.labels]
-        if len(self.types) and isinstance(self.types[0], int):
-            self.types = [LayoutType(i) for i in self.types]
+        if len(self.types):
+            self.types = [LayoutType(i) if isinstance(i, (int, str)) else i for i in self.types]
 
-    @functools.cached_property
-    def full_html(self) -> str:
+    def _table_html_for_format(self, item_format: FormatFlag) -> str:
         html = self.structure
-        for placeholder, content in zip(self.placeholders[::-1], self.contents[::-1]):
-            # escape tag > < / in content to avoid html parse error
-            html = html.replace(placeholder, escape(content))
+        content_types = self.types or [LayoutType.Text] * len(self.contents)
+        for placeholder, content, content_type in zip(
+            self.placeholders[::-1], self.contents[::-1], content_types[::-1]
+        ):
+            formatted = TextualResult._content_format(str(content), content_type, item_format)
+            if item_format != FormatFlag.Html:
+                # Preserve table structure while letting the cell text carry
+                # markdown/latex/plain inline formatting literally.
+                formatted = escape(formatted)
+            html = html.replace(placeholder, formatted)
+        return html
+
+    def _table_html(self) -> str:
+        html = self._table_html_for_format(FormatFlag.Html)
+        html = "\n" + html + "\n"
         return html
 
+    @functools.cached_property
+    def full_html(self) -> str:
+        return self._table_html_for_format(FormatFlag.Html)
+
+    def _df_for_format(self, item_format: FormatFlag) -> pd.DataFrame:
+        df = read_html(StringIO(self._table_html_for_format(item_format)))[0]
+        df = df.dropna(how="all")  # drop empty 'NaN' rows
+        df = df[df.astype(bool).sum(axis=1) > 0]  # remove empty 'blacksapce' rows
+        return df
+
     @functools.cached_property
     def df(self) -> pd.DataFrame:
         try:
-            df = read_html(StringIO(self.full_html))[0]
-            df = df.dropna(how="all")  # drop empty 'NaN' rows
-            df = df[df.astype(bool).sum(axis=1) > 0]  # remove empty 'blacksapce' rows
-            return df
+            return self._df_for_format(FormatFlag.Plain)
         except Exception:
             get_root_logger().warning(
                 f"{self.token} {self.page} {self.block} {self.type} convert html to dataframe error: {repr(self.structure)}"
@@ -968,11 +1147,11 @@ def markup(self):
 
     @property
     def markdown(self):
-        return self.df.to_markdown(index=False, disable_numparse=True)
+        return self._df_for_format(FormatFlag.Markdown).to_markdown(index=False, disable_numparse=True)
 
     @property
     def latex(self):
-        return self.df.style.hide(axis="index").format(escape="latex").to_latex()
+        return self._df_for_format(FormatFlag.Latex).style.hide(axis="index").to_latex()
 
     @property
     def html(self):
@@ -1007,9 +1186,18 @@ class ChartResult(SemanticItem):
     def df(self):
         try:
             # chart is not standard markdown table, so we convert it to markdown table
-            underlying_df = pd.DataFrame([[col.strip() for col in row.split("|")] for row in self.data.split("\n")])
+            underlying_df = pd.DataFrame(
+                [[col.strip() for col in row.strip("|").split("|")] for row in self.data.split("\n")]
+            )
+            underlying_df = underlying_df.replace(r"^\s*$", None, regex=True).dropna(how="all")
+            if underlying_df.empty:
+                return pd.DataFrame()
+
             underlying_df.columns = underlying_df.iloc[0]
             underlying_df = underlying_df[1:]
+            if underlying_df.empty:
+                return pd.DataFrame()
+
             return underlying_df
         except Exception:
             get_root_logger().warning(
@@ -1037,7 +1225,7 @@ def latex(self):
 
     @property
     def html(self):
-        return self.df.style.hide(axis="index").to_html(exclude_styles=True)
+        return self.df.to_html(index=False)
 
 
 @dataclass
@@ -1079,21 +1267,30 @@ def plain(self):
     def markup(self):
         return f"\\begin{{equation}}\n{self.latex_repr}\n\\end{{equation}}"
 
+    @staticmethod
+    def format_latex_repr(latex_repr: str, item_format: FormatFlag, inline: bool = False):
+        if item_format == FormatFlag.Plain:
+            # only for textual result, not for equation result
+            return f"\\({latex_repr}\\)" if inline else f"\\[\n{latex_repr}\n\\]"
+        if item_format == FormatFlag.Markdown:
+            return f"${latex_repr}$" if inline else f"$$\n{latex_repr}\n$$"
+        if item_format == FormatFlag.Latex:
+            return f"\\({latex_repr}\\)" if inline else f"\\[\n{latex_repr}\n\\]"
+        if item_format == FormatFlag.Html:
+            return f"\\({latex_repr}\\)" if inline else f"\\[\n{latex_repr}\n\\]"
+        return latex_repr
+
     @property
     def markdown(self):
-        return f"$$\n{self.latex_repr}\n$$"
+        return self.format_latex_repr(self.latex_repr, FormatFlag.Markdown)
 
     @property
     def latex(self):
-        return self.latex_repr
+        return self.format_latex_repr(self.latex_repr, FormatFlag.Latex)
 
     @property
     def html(self):
-        try:
-            return latex2mathml.converter.convert(self.latex_repr)
-        except Exception:
-            get_root_logger().exception(f"Failed to convert latex to mathml: {self.latex_repr}")
-            return f"<math>{self.latex_repr}</math>"
+        return self.format_latex_repr(self.latex_repr, FormatFlag.Html)
 
 
 @dataclass
@@ -1133,6 +1330,11 @@ def latex(self):
     def html(self):
         return f"{self.prefix}" + "\n\n".join([item.html for item in self.items]) + f"{self.suffix}"
 
+    @classmethod
+    def clone(cls, item, **extra):
+        extra.setdefault("method", "default-clone")
+        return super().clone(item, **extra)
+
 
 if __name__ == "__main__":
     a = LayoutItem(
diff --git a/uniparser_tools/common/output_dir.py b/uniparser_tools/common/output_dir.py
new file mode 100644
index 0000000..e883cd6
--- /dev/null
+++ b/uniparser_tools/common/output_dir.py
@@ -0,0 +1,47 @@
+"""Collision-safe output directory allocation."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+
+def _absolute_without_following_final_symlink(path: str | Path) -> Path:
+    raw = Path(path).expanduser()
+    if not raw.is_absolute():
+        raw = Path.cwd() / raw
+    return raw.parent.resolve() / raw.name
+
+
+def _validate_output_target(target: Path) -> None:
+    protected = {
+        Path(target.anchor).resolve(),
+        Path.home().resolve(),
+        Path.cwd().resolve(),
+    }
+    if any(target == path or path.is_relative_to(target) for path in protected):
+        raise ValueError(f"Refusing to use protected output directory: {target}")
+    if target.name in {"", ".", ".."}:
+        raise ValueError(f"Invalid output directory name: {target}")
+    if any(part.casefold() == ".git" for part in target.parts):
+        raise ValueError(f"Refusing to use Git metadata as an output directory: {target}")
+
+
+def create_unique_output_dir(preferred: str | Path) -> Path:
+    """Atomically create ``preferred`` or the first available suffixed sibling.
+
+    Existing paths are never reused, followed, modified, or removed. For example,
+    if ``results`` and ``results_1`` already exist, ``results_2`` is created.
+    """
+    target = _absolute_without_following_final_symlink(preferred)
+    _validate_output_target(target)
+    target.parent.mkdir(parents=True, exist_ok=True)
+
+    index = 0
+    while True:
+        candidate = target if index == 0 else target.with_name(f"{target.name}_{index}")
+        try:
+            candidate.mkdir(exist_ok=False)
+        except FileExistsError:
+            index += 1
+            continue
+        return candidate
diff --git a/uniparser_tools/tools/caption_extraction/main.py b/uniparser_tools/tools/caption_extraction/main.py
index 8ed1150..db83197 100644
--- a/uniparser_tools/tools/caption_extraction/main.py
+++ b/uniparser_tools/tools/caption_extraction/main.py
@@ -8,8 +8,6 @@
 from pathlib import Path
 from typing import Dict, List, Union
 
-import fitz  # PyMuPDF
-from fitz.utils import get_pixmap
 from PIL import Image
 
 from uniparser_tools.common.constant import LayoutType, LayoutTypeBot, LayoutTypeTop, OrderingMethod
@@ -24,8 +22,10 @@
     TextualResult,
 )
 from uniparser_tools.order.structure_order import StructureOrder, count_items, set_item_order
+from uniparser_tools.utils import pdf_render as fitz  # PDFium-backed, permissive license (was PyMuPDF)
 from uniparser_tools.utils.convert import dict2obj
 from uniparser_tools.utils.log import get_root_logger
+from uniparser_tools.utils.pdf_render import get_pixmap
 from uniparser_tools.utils.processor import (
     clean_scientific_text,
     find_figure_caption_kws,
diff --git a/uniparser_tools/utils/convert.py b/uniparser_tools/utils/convert.py
index 78424a2..9d482e2 100644
--- a/uniparser_tools/utils/convert.py
+++ b/uniparser_tools/utils/convert.py
@@ -1,3 +1,5 @@
+from functools import lru_cache
+from inspect import Parameter, signature
 from typing import Dict, List
 
 from uniparser_tools.common.constant import FormatFlag, LayoutType, ParseMode, to_semantic
@@ -13,34 +15,68 @@
     TabularResult,
     TextualResult,
 )
+from uniparser_tools.utils.format_utils import parse_inline_text, parse_table_full_html
+from uniparser_tools.utils.log import get_root_logger
+
+
+@lru_cache(maxsize=None)
+def _init_param_names(cls):
+    return {
+        name
+        for name, param in signature(cls).parameters.items()
+        if param.kind in (Parameter.POSITIONAL_OR_KEYWORD, Parameter.KEYWORD_ONLY)
+    }
+
+
+def _filter_init_kwargs(cls, block: Dict):
+    valid_keys = _init_param_names(cls)
+    return {key: value for key, value in block.items() if key in valid_keys}
+
+
+def _upgrade_table_structure_spans(block: Dict) -> None:
+    if "html" in block:
+        block["structure"] = block.pop("html")
+    if "text" in block:
+        block.pop("text")
+
+    if block.get("placeholders"):
+        if block.get("contents") and not block.get("types"):
+            block["types"] = [LayoutType.Text] * len(block["contents"])
+        return
+    if block.get("structure"):
+        block.update(parse_table_full_html(str(block["structure"])))
 
 
 def build_item(block: Dict):
     if "pages" in block:
         block.pop("pages")
     if "reactions" in block:
-        item = ExpressionResult(**block)
+        item = ExpressionResult(**_filter_init_kwargs(ExpressionResult, block))
     elif "placeholders" in block:
-        if "html" in block:
-            block["structure"] = block.pop("html")
-        if "text" in block:
-            block.pop("text")
-        item = TabularResult(**block)
+        _upgrade_table_structure_spans(block)
+        item = TabularResult(**_filter_init_kwargs(TabularResult, block))
     elif "markush" in block:
-        item = MoleculeResult(**block)
+        item = MoleculeResult(**_filter_init_kwargs(MoleculeResult, block))
     elif "data" in block:
-        item = ChartResult(**block)
+        item = ChartResult(**_filter_init_kwargs(ChartResult, block))
     elif "desc" in block:
-        item = FigureResult(**block)
+        item = FigureResult(**_filter_init_kwargs(FigureResult, block))
     elif "latex_repr" in block:
-        item = EquationResult(**block)
+        item = EquationResult(**_filter_init_kwargs(EquationResult, block))
     elif "text" in block:
-        item = TextualResult(**block)
+        kwargs = _filter_init_kwargs(TextualResult, block)
+        if not kwargs.get("contents"):
+            kwargs["contents"], kwargs["types"] = parse_inline_text(kwargs["text"])
+            kwargs["bboxes"] = []
+        if not kwargs.get("types", []):
+            kwargs["types"] = [LayoutType.Text] * len(kwargs["contents"])
+        kwargs["text"] = "".join(kwargs["contents"])
+        item = TextualResult(**kwargs)
     elif "items" in block:
         items = [build_item(child) for child in block["items"]]
-        item = GroupedResult.clone(GroupedResult(**block), items=items)
+        item = GroupedResult.clone(GroupedResult(**_filter_init_kwargs(GroupedResult, block)), items=items)
     else:
-        item = LayoutItem(**block)
+        item = LayoutItem(**_filter_init_kwargs(LayoutItem, block))
     return item
 
 
@@ -75,14 +111,38 @@ def item2format(item: SemanticItem, data: Dict, status: Dict):
         s = ""
     else:
         if not isinstance(item, GroupedResult):
-            item_format = data.__dict__[to_semantic(item.type)]
-            s = getattr(item, item_format)
+            try:
+                item_format = data.__dict__[to_semantic(item.type)]
+            except KeyError:
+                if item.type in [
+                    LayoutType.PageHeader,
+                    LayoutType.PageFooter,
+                ]:
+                    item_format = data.__dict__[to_semantic(LayoutType.Paragraph)]
+                else:
+                    get_root_logger().exception(
+                        f"Failed to get item format for {item.type} -> {to_semantic(item.type)}"
+                    )
+                    return ""
+            # temporarily disabled for compatibility
+            if False and item.type in [
+                LayoutType.Molecule,
+            ]:
+                s = item.source
+            else:
+                s = getattr(item, item_format)
             if not item.plain and getattr(item, "source", ""):
                 if status["dict_cfg"][to_semantic(item.type)] == ParseMode.DumpBase64:
+                    # Use SVG for molecules, PNG for others
+                    # temporarily disabled for compatibility
+                    if False and item.type == LayoutType.Molecule:
+                        mime_type = "image/svg+xml"
+                    else:
+                        mime_type = "image/png"
                     if item_format == FormatFlag.Markdown:
-                        s += f"![{item.type}](data:image/png;base64,{item.source})"
+                        s += f"![{item.type}](data:{mime_type};base64,{item.source})"
                     elif item_format == FormatFlag.Html:
-                        s += f"<img src='data:image/png;base64,{item.source}' alt='{item.type}'/>"
+                        s += f"<img src='data:{mime_type};base64,{item.source}' alt='{item.type}'/>"
                 elif status["dict_cfg"][to_semantic(item.type)] == ParseMode.DumpLocal:
                     if item_format == FormatFlag.Markdown:
                         s += f"![{item.type}]({item.source})"
diff --git a/uniparser_tools/utils/format_utils.py b/uniparser_tools/utils/format_utils.py
new file mode 100644
index 0000000..b194861
--- /dev/null
+++ b/uniparser_tools/utils/format_utils.py
@@ -0,0 +1,115 @@
+"""Formatting helpers shared by release/v1.3 result-model conversion."""
+
+import html
+import re
+from typing import Any, Dict, List, Tuple
+
+from bs4 import BeautifulSoup
+
+from uniparser_tools.common.constant import LayoutType, TableBBoxType
+
+
+def parse_inline_text(text: str) -> Tuple[List[str], List[LayoutType]]:
+    """Split rich text into plain-text, molecule, and inline-equation spans."""
+    if not text:
+        return [], []
+
+    latex_marker = re.compile(r"\\[A-Za-z]+|[\^_{}=]|[A-Za-z]\s*[+\-*/=<>]\s*[A-Za-z0-9\\]")
+    pattern = re.compile(
+        r"(?P<molecule>\*\*\*(?P<molecule_text>.+?)\*\*\*)|"
+        r"(?P<code_molecule><code>(?P<code_molecule_text>.+?)</code>)|"
+        r"(?P<tick_molecule>`(?P<tick_molecule_text>.+?)`)|"
+        r"(?P<equation>"
+        r"\\\[(?P<eq_display_text>.+?)\\\]|"
+        r"\\\((?P<eq_inline_text>.+?)\\\)|"
+        r"\$\$(?P<eq_dollar_display_text>.+?)\$\$|"
+        r"\$(?P<eq_dollar_inline_text>(?:\\.|[^$\\\n])+?)\$"
+        r")",
+        re.DOTALL,
+    )
+    contents: List[str] = []
+    types: List[LayoutType] = []
+    pos = 0
+    for match in pattern.finditer(text):
+        if match.start() > pos:
+            contents.append(text[pos : match.start()])
+            types.append(LayoutType.Text)
+
+        if (
+            match.group("molecule") is not None
+            or match.group("code_molecule") is not None
+            or match.group("tick_molecule") is not None
+        ):
+            molecule_text = (
+                match.group("molecule_text") or match.group("code_molecule_text") or match.group("tick_molecule_text")
+            )
+            contents.append(molecule_text.strip())
+            types.append(LayoutType.Molecule)
+        else:
+            equation_text = (
+                match.group("eq_inline_text")
+                or match.group("eq_display_text")
+                or match.group("eq_dollar_inline_text")
+                or match.group("eq_dollar_display_text")
+                or ""
+            )
+            if (
+                match.group("eq_dollar_inline_text") or match.group("eq_dollar_display_text")
+            ) and not latex_marker.search(equation_text):
+                contents.append(match.group(0))
+                types.append(LayoutType.Text)
+                pos = match.end()
+                continue
+            contents.append(equation_text.strip())
+            types.append(LayoutType.EquationInline)
+        pos = match.end()
+
+    if pos < len(text):
+        contents.append(text[pos:])
+        types.append(LayoutType.Text)
+
+    merged_contents: List[str] = []
+    merged_types: List[LayoutType] = []
+    for content, content_type in zip(contents, types):
+        if merged_types and merged_types[-1] == content_type == LayoutType.Text:
+            merged_contents[-1] += content
+        else:
+            merged_contents.append(content)
+            merged_types.append(content_type)
+    return merged_contents, merged_types
+
+
+def parse_table_full_html(html_text: str) -> Dict[str, Any]:
+    """Convert a full HTML table into release/v1.3 placeholder spans."""
+    soup = BeautifulSoup(html_text, "html.parser")
+    placeholders: List[str] = []
+    contents: List[str] = []
+    types: List[LayoutType] = []
+
+    for cell_idx, cell in enumerate(soup.find_all(["td", "th"])):
+        span_contents, span_types = parse_inline_text(cell.decode_contents())
+        if not span_contents:
+            continue
+
+        cell_placeholders = []
+        for span_idx, (span_content, span_type) in enumerate(zip(span_contents, span_types)):
+            placeholder = f"[[VL_TABLE_{cell_idx}_{span_idx}]]"
+            placeholders.append(placeholder)
+            if span_type == LayoutType.Text:
+                span_content = BeautifulSoup(span_content, "html.parser").get_text("", strip=False)
+            else:
+                span_content = html.unescape(span_content)
+            contents.append(span_content)
+            types.append(span_type)
+            cell_placeholders.append(placeholder)
+        cell.clear()
+        cell.append("".join(cell_placeholders))
+
+    return {
+        "bboxes": [],
+        "labels": [TableBBoxType.Content] * len(placeholders),
+        "types": types,
+        "placeholders": placeholders,
+        "contents": contents,
+        "structure": str(soup),
+    }
diff --git a/uniparser_tools/utils/pdf_render.py b/uniparser_tools/utils/pdf_render.py
new file mode 100644
index 0000000..577b287
--- /dev/null
+++ b/uniparser_tools/utils/pdf_render.py
@@ -0,0 +1,216 @@
+"""Permissive-licensed PDF page rasterizer (PDFium via ``pypdfium2``).
+
+This module replaces the only thing UniParser-Tools used **PyMuPDF / fitz** for:
+rendering a PDF page -- or a clipped sub-rectangle of one -- to a PIL image at a
+target DPI.
+
+Why: PyMuPDF is licensed **AGPL-3.0 (or a paid commercial license)**, whose
+copyleft terms are a real risk to redistribute inside a product. ``pypdfium2``
+wraps Google's PDFium and is licensed **Apache-2.0 / BSD-3-Clause** with no
+copyleft obligation, so it is safe to ship.
+
+To keep the swap low-risk, this module deliberately mirrors the *small* slice of
+the fitz API the toolkit actually called, so the call sites only change their
+import line::
+
+    # before
+    import fitz  # PyMuPDF
+    from fitz.utils import get_pixmap
+
+    # after
+    from uniparser_tools.utils import pdf_render as fitz
+    from uniparser_tools.utils.pdf_render import get_pixmap
+
+Supported surface (everything the codebase used):
+
+  * ``Document(path)``                          -> open, ``len()``, ``doc[i]``
+  * ``page.rect`` -> ``.width`` / ``.height`` / ``.top_left``
+  * ``page.get_pixmap(dpi=...)``                -> full-page ``Pixmap``
+  * ``get_pixmap(page, clip=Rect, dpi=...)``    -> clipped ``Pixmap``
+  * ``Rect(x0, y0, x1, y1)``
+  * ``pix.width`` / ``pix.height`` / ``pix.samples`` (raw RGB bytes) / ``pix.save(path)``
+
+Coordinate convention matches fitz: clip coordinates are PDF userspace points
+(1/72 inch) with a **top-left origin, y growing downward** -- identical to
+``fitz.Page.rect`` and ``fitz.Rect``. A clip is rendered as the corresponding
+sub-region of the full page at the same DPI (full page is rendered once per
+``(page, dpi)`` and cached, then cropped), reproducing fitz's clipped pixmap.
+"""
+
+from __future__ import annotations
+
+from collections import OrderedDict
+from typing import Sequence, Tuple, Union
+
+import pypdfium2 as pdfium
+from PIL import Image
+
+
+class Rect:
+    """Minimal stand-in for ``fitz.Rect`` (the attributes the toolkit used)."""
+
+    __slots__ = ("x0", "y0", "x1", "y1")
+
+    def __init__(self, x0: float, y0: float, x1: float, y1: float):
+        self.x0, self.y0, self.x1, self.y1 = float(x0), float(y0), float(x1), float(y1)
+
+    @property
+    def width(self) -> float:
+        return self.x1 - self.x0
+
+    @property
+    def height(self) -> float:
+        return self.y1 - self.y0
+
+    @property
+    def top_left(self) -> Tuple[float, float]:
+        return (self.x0, self.y0)
+
+    def __iter__(self):
+        return iter((self.x0, self.y0, self.x1, self.y1))
+
+    def __repr__(self) -> str:
+        return f"Rect({self.x0}, {self.y0}, {self.x1}, {self.y1})"
+
+
+class Pixmap:
+    """Wraps a rendered PIL image with the fitz ``Pixmap`` attributes used."""
+
+    __slots__ = ("_img",)
+
+    def __init__(self, img: Image.Image):
+        self._img = img if img.mode == "RGB" else img.convert("RGB")
+
+    @property
+    def width(self) -> int:
+        return self._img.width
+
+    @property
+    def height(self) -> int:
+        return self._img.height
+
+    @property
+    def samples(self) -> bytes:
+        # Raw, tightly-packed RGB bytes so that
+        # ``Image.frombytes("RGB", (pix.width, pix.height), pix.samples)`` round-trips.
+        return self._img.tobytes()
+
+    @property
+    def pil(self) -> Image.Image:
+        return self._img
+
+    def save(self, path) -> None:
+        self._img.save(str(path))
+
+
+def _to_xyxy(clip: Union[Rect, Sequence[float], None]) -> Union[Tuple[float, float, float, float], None]:
+    if clip is None:
+        return None
+    if isinstance(clip, Rect):
+        return (clip.x0, clip.y0, clip.x1, clip.y1)
+    x0, y0, x1, y1 = clip
+    return (float(x0), float(y0), float(x1), float(y1))
+
+
+class Page:
+    """A single page bound to its owning :class:`Document`."""
+
+    __slots__ = ("_doc", "_index")
+
+    def __init__(self, doc: "Document", index: int):
+        self._doc = doc
+        self._index = index
+
+    @property
+    def rect(self) -> Rect:
+        w, h = self._doc._page_size(self._index)
+        # fitz reports the page rect with a top-left origin at (0, 0); the
+        # call sites add ``page.rect.top_left`` explicitly, so keep it (0, 0).
+        return Rect(0.0, 0.0, w, h)
+
+    def get_pixmap(self, clip: Union[Rect, Sequence[float], None] = None, dpi: int = 72) -> Pixmap:
+        full = self._doc._render_full(self._index, dpi)
+        box = _to_xyxy(clip)
+        if box is None:
+            return Pixmap(full)
+        scale = dpi / 72.0
+        x0, y0, x1, y1 = box
+        # clip is in points relative to the page's top-left origin (rect.x0/y0 == 0)
+        px0, py0 = min(x0, x1) * scale, min(y0, y1) * scale
+        px1, py1 = max(x0, x1) * scale, max(y0, y1) * scale
+        crop = (
+            max(0, int(round(px0))),
+            max(0, int(round(py0))),
+            min(full.width, int(round(px1))),
+            min(full.height, int(round(py1))),
+        )
+        if crop[2] <= crop[0] or crop[3] <= crop[1]:
+            return Pixmap(Image.new("RGB", (1, 1), "white"))
+        return Pixmap(full.crop(crop))
+
+
+class Document:
+    """Open a PDF and render its pages. Drop-in for the fitz usage in this repo.
+
+    A full-page render is cached per ``(page_index, dpi)`` so that several clips
+    on the same page (the caption-extraction access pattern) reuse one raster
+    instead of re-rendering. The cache is a small **LRU** bounded by
+    ``cache_pages`` -- unlike the original per-call fitz ``get_pixmap(clip=...)``,
+    a full-page raster can be large (up to ~4096 px per side, tens of MB), so an
+    unbounded cache would accumulate every page of a long PDF. The default keeps
+    the last few pages, which covers same-page and adjacent cross-page groups
+    while bounding peak memory.
+    """
+
+    def __init__(self, path, cache_pages: int = 8):
+        self._doc = pdfium.PdfDocument(str(path))
+        self._cache_pages = max(1, int(cache_pages))
+        self._full_cache: "OrderedDict" = OrderedDict()  # (index, dpi) -> PIL.Image (RGB), LRU
+        self._size_cache: dict = {}  # index -> (w_pt, h_pt)
+
+    def __len__(self) -> int:
+        return len(self._doc)
+
+    def __getitem__(self, index: int) -> Page:
+        return Page(self, index)
+
+    def _page_size(self, index: int) -> Tuple[float, float]:
+        size = self._size_cache.get(index)
+        if size is None:
+            w, h = self._doc[index].get_size()
+            size = (float(w), float(h))
+            self._size_cache[index] = size
+        return size
+
+    def _render_full(self, index: int, dpi: int) -> Image.Image:
+        key = (index, int(dpi))
+        img = self._full_cache.get(key)
+        if img is not None:
+            self._full_cache.move_to_end(key)  # mark most-recently-used
+            return img
+        img = self._doc[index].render(scale=dpi / 72.0).to_pil()
+        if img.mode != "RGB":
+            img = img.convert("RGB")
+        self._full_cache[key] = img
+        while len(self._full_cache) > self._cache_pages:
+            self._full_cache.popitem(last=False)  # evict least-recently-used
+        return img
+
+    def close(self) -> None:
+        try:
+            self._doc.close()
+        except Exception:
+            pass
+        self._full_cache.clear()
+        self._size_cache.clear()
+
+    def __enter__(self) -> "Document":
+        return self
+
+    def __exit__(self, *exc) -> None:
+        self.close()
+
+
+def get_pixmap(page: Page, clip: Union[Rect, Sequence[float], None] = None, dpi: int = 72) -> Pixmap:
+    """Function form mirroring ``fitz.utils.get_pixmap(page, clip=..., dpi=...)``."""
+    return page.get_pixmap(clip=clip, dpi=dpi)
diff --git a/uniparser_tools/utils/visualize_together.py b/uniparser_tools/utils/visualize_together.py
index 8e0781d..d831719 100644
--- a/uniparser_tools/utils/visualize_together.py
+++ b/uniparser_tools/utils/visualize_together.py
@@ -7,7 +7,6 @@
 from pathlib import Path
 from typing import Dict, List, Union
 
-import fitz  # PyMuPDF
 from jinja2 import Environment, FileSystemLoader
 from PIL import Image
 
@@ -25,6 +24,7 @@
     TabularResult,
 )
 from uniparser_tools.order.structure_order import flatten_page
+from uniparser_tools.utils import pdf_render as fitz  # PDFium-backed, permissive license (was PyMuPDF)
 from uniparser_tools.utils.convert import dict2obj
 from uniparser_tools.utils.image import dump_image_base64_str
 from uniparser_tools.utils.log import get_root_logger