```
├── .github/
   ├── workflows/
      ├── docker-build.yml
      ├── version-tag.yml
├── .gitignore
├── .python-version
├── .vscode/
   ├── launch.json
├── Dockerfile
├── LICENSE
├── README.md
├── config/
   ├── social-networks.yml
├── docker-compose.yml
├── example.env
├── main.py
├── modules/
   ├── bots/
      ├── qqchat.py
      ├── wechat.py
      ├── wecom.py
   ├── langchain/
      ├── llm.py
   ├── socialmedia/
      ├── post.py
      ├── truthsocial.py
      ├── twitter.py
├── pyproject.toml
├── run.sh
├── utils/
   ├── redisClient.py
   ├── yaml.py
├── uv.lock
```


## /.github/workflows/docker-build.yml

```yml path="/.github/workflows/docker-build.yml" 
name: Docker Build and Push

on:
  push:
    tags:
      - '*'

jobs:
  build-and-push:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout code
        uses: actions/checkout@v4

      - name: Set up Docker Buildx
        uses: docker/setup-buildx-action@v3

      - name: Login to DockerHub
        uses: docker/login-action@v3
        with:
          username: zkd8907
          password: ${{ secrets.DOCKER_HUB_TOKEN }}

      - name: Extract tag name
        run: |
          echo "TAG=${GITHUB_REF#refs/tags/}" >> $GITHUB_ENV

      - name: Build and push
        uses: docker/build-push-action@v5
        with:
          context: .
          push: true
          tags: zkd8907/secretary:${{ env.TAG }} 
```

## /.github/workflows/version-tag.yml

```yml path="/.github/workflows/version-tag.yml" 
name: Create Version Tag

on:
  push:
    branches:
      - master

jobs:
  create-tag:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout code
        uses: actions/checkout@v4
        with:
          fetch-depth: 0
          ssh-key: ${{ secrets.SSH_DEPLOY_KEY }}

      - name: Conventional Changelog Action
        uses: TriPSs/conventional-changelog-action@v5
        with:
          github-token: ""
          preset: "angular"
          tag-prefix: "v"
          skip-version-file: "true"
          skip-commit: "true"
          skip-tag: "false"
          release-count: 1

```

## /.gitignore

```gitignore path="/.gitignore" 
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class

# C extensions
*.so

# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST

# PyInstaller
#  Usually these files are written by a python script from a template
#  before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec

# Installer logs
pip-log.txt
pip-delete-this-directory.txt

# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
cover/

# Translations
*.mo
*.pot

# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal

# Flask stuff:
instance/
.webassets-cache

# Scrapy stuff:
.scrapy

# Sphinx documentation
docs/_build/

# PyBuilder
.pybuilder/
target/

# Jupyter Notebook
.ipynb_checkpoints

# IPython
profile_default/
ipython_config.py

# pyenv
#   For a library or package, you might want to ignore these files since the code is
#   intended to run in multiple environments; otherwise, check them in:
# .python-version

# pipenv
#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
#   However, in case of collaboration, if having platform-specific dependencies or dependencies
#   having no cross-platform support, pipenv may install dependencies that don't work, or not
#   install all needed dependencies.
#Pipfile.lock

# UV
#   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
#   This is especially recommended for binary packages to ensure reproducibility, and is more
#   commonly ignored for libraries.
#uv.lock

# poetry
#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
#   This is especially recommended for binary packages to ensure reproducibility, and is more
#   commonly ignored for libraries.
#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
#poetry.lock

# pdm
#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
#pdm.lock
#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
#   in version control.
#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
.pdm.toml
.pdm-python
.pdm-build/

# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
__pypackages__/

# Celery stuff
celerybeat-schedule
celerybeat.pid

# SageMath parsed files
*.sage.py

# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/

# Spyder project settings
.spyderproject
.spyproject

# Rope project settings
.ropeproject

# mkdocs documentation
/site

# mypy
.mypy_cache/
.dmypy.json
dmypy.json

# Pyre type checker
.pyre/

# pytype static type analyzer
.pytype/

# Cython debug symbols
cython_debug/

# PyCharm
#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
#  and can be added to the global gitignore or merged into this file.  For a more nuclear
#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
#.idea/

# Ruff stuff:
.ruff_cache/

# PyPI configuration file
.pypirc


*.tw_session
```

## /.python-version

```python-version path="/.python-version" 
3.11

```

## /.vscode/launch.json

```json path="/.vscode/launch.json" 
{
    "version": "0.2.0",
    "configurations": [
        {
            "name": "Python: Debug Current File",
            "type": "debugpy",
            "request": "launch",
            "program": "${file}",
            "console": "integratedTerminal",
            "justMyCode": false,
            "env": {
                "PYTHONPATH": "${workspaceFolder}",
                "DEBUG": "1"
            }
        }
    ]
}
```

## /Dockerfile

``` path="/Dockerfile" 
FROM ghcr.io/astral-sh/uv:python3.11-alpine

WORKDIR /app

RUN apk add --no-cache \
    git \
    dcron \
    curl \
    bash

COPY . /app/
RUN chmod +x /app/run.sh
WORKDIR /app
RUN uv sync

RUN echo "*/5 * * * * /app/run.sh >> /var/log/cron.log 2>&1" > /etc/cron.d/secretary-cron
RUN touch /var/log/cron.log
RUN chmod 0644 /etc/cron.d/secretary-cron

CMD crond -f -l 8 & tail -f /var/log/cron.log | tee /dev/stdout
```

## /LICENSE

``` path="/LICENSE" 
MIT License

Copyright (c) 2024 Secretary

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

```

## /README.md

# Secretary - 社交媒体内容分析助手

## 项目简介

Secretary 是一个自动化的社交媒体分析工具，专门用于监控和分析社交媒体平台上的内容，并通过 AI 进行智能分析。该工具能够自动抓取指定账号的最新发言，根据配置的分析提示词进行内容分析，并将分析结果通过企业微信机器人、个人微信号推送给指定用户。通过灵活配置分析提示词，可以针对不同主题（如财经、政治、科技等）进行定制化分析。

## 主要功能

- 支持多个社交媒体平台的监控（目前支持 Truth Social 和 Twitter）
- 可配置多个监控账号，每个账号可以设置不同的分析提示词
- 支持自定义分析主题和维度，通过配置提示词实现灵活的分析策略
- 使用 AI 进行内容翻译和分析
- 灵活的消息推送支持：
  - 支持企业微信多机器人配置（可同时推送到财经、政治、科技等不同主题的群组）
  - 支持基于 [Gewechat](https://github.com/Devo919/Gewechat) 的个人微信推送
  - 支持基于 [Lagrange](https://github.com/LagrangeDev/Lagrange.Core) 的 个人 QQ 群消息推送
  - 可通过环境变量灵活开启/关闭不同的推送通道
- 使用 AI 进行内容翻译和分析，支持多种 LLM 模型：
  - 支持配置任意符合 OpenAI 接口标准的模型
  - 可自定义模型参数（API 地址、密钥等）
- 支持多维度分析，例如：
  - 财经分析（市场影响、投资机会等）
  - 政治分析（政策影响、国际关系等）
  - 科技分析（技术趋势、创新影响等）
  - 其他自定义分析维度
- 支持调试模式，方便开发和测试

## 安装说明

1. 克隆项目到本地：
```bash
git clone https://github.com/yourusername/secretary.git
cd secretary
```

2. 安装并配置 uv（推荐）：
```bash
# 安装 uv
curl -LsSf https://astral.sh/uv/install.sh | sh

# 使用 uv 安装依赖
uv sync
```

或者使用传统的 venv 方式（不推荐）：
```bash
python -m venv .venv
source .venv/bin/activate  # Linux/Mac
# 或
.venv\Scripts\activate  # Windows
pip install -e .
```

3. 配置环境变量：
创建 `.env` 文件并添加以下配置：
```
# Redis 配置
REDIS_URL=redis://your_redis_url

# Truth Social 配置
TRUTHSOCIAL_TOKEN=your_truthsocial_token

# Twitter 配置
TWITTER_USERNAME=your_twitter_username
TWITTER_PASSWORD=your_twitter_password

# LLM API 配置
LLM_API_MODEL=your_model_name
LLM_API_KEY=your_api_key
LLM_API_BASE=your_api_base_url

# 企业微信机器人配置（可配置多个）
ENABLE_WECOM_BOT=true
WECOM_TRUMP_ROBOT_ID=your_wecom_robot_id_1
WECOM_FINANCE_ROBOT_ID=your_wecom_robot_id_2

# 个人微信号配置
ENABLE_WECHAT_BOT=true
WECHAT_ROBOT_IP=your_wechat_robot_ip
WECHAT_ROBOT_TOKEN=your_wechat_robot_token
WECHAT_ROBOT_APP_ID=your_wechat_robot_app_id
WECHAT_ROBOT_CHATROOM_ID=your_wechat_robot_chatroom_id

# QQ 群机器人配置
ENABLE_QQ_BOT=true
QQ_BOT_URL=your_qq_bot_url
QQ_BOT_GROUP_ID=your_qq_group_id

# 调试模式（可选）
DEBUG=1

# LLM 处理失败时的最大重试次数，默认为 3
LLM_PROCESS_MAX_RETRIED=3
```

4. 配置监控账号：
创建 `config/social-networks.yml` 文件，配置需要监控的社交媒体账号：
```yaml
social_networks:
  - type: truthsocial
    socialNetworkId: realDonaldTrump
    prompt: >-
      你现在是一名财经专家，请对以下美国总统的发言进行分析，并给按我指定的格式返回分析结果。

      这是你需要分析的内容：{content}

      这是输出格式的说明：
      {
          "is_relevant": "是否与财经相关，且与美股市场或美债市场或科技股或半导体股或中国股票市场或香港股票市场或人民币兑美元汇率或中美关系相关。如果相关就返回1，如果不相关就返回0。只需要返回1或0这两个值之一即可",
          "analytical_briefing": "分析简报"
      }

      其中analytical_briefing的值是一个字符串，它是针对内容所做的分析简报，仅在is_relevant为1时会返回这个值。

      analytical_briefing的内容是markdown格式的，它需要符合下面的规范

      ```markdown
      原始正文，仅当需要分析的内容为英文时，这部分内容才会以markdown中引用的格式返回，否则这部分的内容为原始的正文

      翻译后的内容，仅当需要分析的内容为英文时，才会有这部分的内容。

      ## Brief Analysis

      分析结果。这部分会展示一个列表，列表中分别包含美股市场、美债市场、科技股、半导体股、中国股票市场、香港股票市场、人民币兑美元汇率、中美关系这8个选项。
      每个选项的值为分别为📈利多和📉利空。如果分析内容对于该选项没有影响，就不要针对这个选项返回任何内容。

      ## Summarize

      这部分需要用非常简明扼要的文字对分析结果进行总结，以及解释为什么在上面针对不同选项会得出不同的结论。
      ```
    weComRobotId: $WECOM_TRUMP_ROBOT_ID
    sendToWeChat: true
  - type: twitter
    socialNetworkId: 
      - myfxtrader
      - zaobaosg
      - business
      - HAOHONG_CFA
    prompt: >-
      你现在是一名财经专家，请对以下财经博主的发言进行分析，并给按我指定的格式返回分析结果。
      # ... 其他配置与上面类似 ...
    weComRobotId: $WECOM_FINANCE_ROBOT_ID
```

> ⚠️ **重要提示**：
> 1. 在配置 prompt 时，必须确保大模型返回的结果是一个合法的 JSON 字符串，并且包含以下两个必需属性：
>    - `is_relevant`：表示内容是否相关，值为 1 或 0
>    - `analytical_briefing`：分析简报内容，仅在 `is_relevant` 为 1 时返回
> 2. 在配置企业微信机器人 ID 时，需要使用 `$` 前缀来引用环境变量，例如：`$WECOM_TRUMP_ROBOT_ID`
>
> 如果返回的 JSON 格式不正确或缺少必需属性，程序将无法正常处理分析结果。

环境变量说明：
- `REDIS_URL`: Redis 数据库连接地址，用于数据缓存
- `TRUTHSOCIAL_TOKEN`: Truth Social 平台的访问令牌
- `TWITTER_USERNAME`: Twitter 平台的用户名
- `TWITTER_PASSWORD`: Twitter 平台的密码
- `TWITTER_SESSION`: 之前登录过的 Twitter 平台的登录票据
- `LLM_API_MODEL`: 使用的 LLM 模型名称
- `LLM_API_KEY`: LLM API 密钥
- `LLM_API_BASE`: LLM API 基础地址
- `LLM_PROCESS_MAX_RETRIED`: LLM 处理失败时的最大重试次数，默认为 3
- `ENABLE_WECOM_BOT`: 是否启用企业微信机器人，值为 true/false
- `WECOM_*_ROBOT_ID`: 企业微信机器人的 ID，可以配置多个（如 WECOM_TRUMP_ROBOT_ID, WECOM_FINANCE_ROBOT_ID 等）
- `ENABLE_WECHAT_BOT`: 是否启用个人微信机器人，值为 true/false
- `WECHAT_ROBOT_IP`: 个人微信部署服务器 IP/域名
- `WECHAT_ROBOT_TOKEN`: 个人微信 Gewechat 会话 Token
- `WECHAT_ROBOT_APP_ID`: 个人微信部署设备唯一 ID
- `WECHAT_ROBOT_CHATROOM_ID`: 个人微信群聊 ID，也可以为好友 ID
- `ENABLE_QQ_BOT`: 是否启用QQ机器人，值为 true/false
- `QQ_BOT_URL`: QQ机器人 HTTP 服务器地址
- `QQ_BOT_GROUP_ID`: QQ群号
- `DEBUG`: 调试模式开关，设置为 1 时启用调试模式

## 配置说明

### 环境变量

环境变量支持两种引用格式：
- `${VAR}` 格式：例如 `${WECOM_TRUMP_ROBOT_ID}`
- `$VAR` 格式：例如 `$WECOM_TRUMP_ROBOT_ID`

这两种格式可以在 YAML 配置文件中的任何值中使用，包括但不限于：
- 企业微信机器人 ID
- 社交媒体账号 ID
- API 密钥
- 其他配置项

系统会自动递归处理所有配置项中的环境变量，将其替换为对应的环境变量值。如果环境变量不存在，将替换为空字符串。

例如，以下配置都是合法的：
```yaml
social_networks:
  - type: truthsocial
    socialNetworkId: $TRUTH_SOCIAL_ID
    apiKey: ${API_KEY}
    weComRobotId: $WECOM_ROBOT_ID
    customField: "前缀_${CUSTOM_VAR}_后缀"
```

### 监控账号配置

创建 `config/social-networks.yml` 文件，配置需要监控的社交媒体账号：
```yaml
social_networks:
  - type: truthsocial
    socialNetworkId: realDonaldTrump
    prompt: >-
      你现在是一名财经专家，请对以下美国总统的发言进行分析，并给按我指定的格式返回分析结果。

      这是你需要分析的内容：{content}

      这是输出格式的说明：
      {
          "is_relevant": "是否与财经相关，且与美股市场或美债市场或科技股或半导体股或中国股票市场或香港股票市场或人民币兑美元汇率或中美关系相关。如果相关就返回1，如果不相关就返回0。只需要返回1或0这两个值之一即可",
          "analytical_briefing": "分析简报"
      }

      其中analytical_briefing的值是一个字符串，它是针对内容所做的分析简报，仅在is_relevant为1时会返回这个值。

      analytical_briefing的内容是markdown格式的，它需要符合下面的规范

      ```markdown
      原始正文，仅当需要分析的内容为英文时，这部分内容才会以markdown中引用的格式返回，否则这部分的内容为原始的正文

      翻译后的内容，仅当需要分析的内容为英文时，才会有这部分的内容。

      ## Brief Analysis

      分析结果。这部分会展示一个列表，列表中分别包含美股市场、美债市场、科技股、半导体股、中国股票市场、香港股票市场、人民币兑美元汇率、中美关系这8个选项。
      每个选项的值为分别为📈利多和📉利空。如果分析内容对于该选项没有影响，就不要针对这个选项返回任何内容。

      ## Summarize

      这部分需要用非常简明扼要的文字对分析结果进行总结，以及解释为什么在上面针对不同选项会得出不同的结论。
      ```
    weComRobotId: $WECOM_TRUMP_ROBOT_ID
    sendToWeChat: true
  - type: twitter
    socialNetworkId: 
      - myfxtrader
      - zaobaosg
      - business
      - HAOHONG_CFA
    prompt: >-
      你现在是一名财经专家，请对以下财经博主的发言进行分析，并给按我指定的格式返回分析结果。
      # ... 其他配置与上面类似 ...
    weComRobotId: $WECOM_FINANCE_ROBOT_ID
```

> ⚠️ **重要提示**：
> 1. 在配置 prompt 时，必须确保大模型返回的结果是一个合法的 JSON 字符串，并且包含以下两个必需属性：
>    - `is_relevant`：表示内容是否相关，值为 1 或 0
>    - `analytical_briefing`：分析简报内容，仅在 `is_relevant` 为 1 时返回
> 2. 在配置企业微信机器人 ID 时，需要使用 `$` 前缀来引用环境变量，例如：`$WECOM_TRUMP_ROBOT_ID`
>
> 如果返回的 JSON 格式不正确或缺少必需属性，程序将无法正常处理分析结果。

## 使用方法

1. 确保已正确配置环境变量和监控账号
2. 运行主程序：
```bash
python main.py
```

程序会自动：
- 抓取配置的社交媒体账号的最新发言
- 根据每个账号配置的提示词进行 AI 分析和翻译
- 生成分析报告
- 通过配置的企业微信机器人、个人微信推送分析结果

## 输出格式

分析结果将以 Markdown 格式推送，包含：
- 发言时间
- 原文内容（如果是英文内容会以引用格式显示）
- 中文翻译（仅当原文为英文时显示）
- 分析结果（根据提示词配置的格式）
- 分析总结

## 注意事项

- 确保网络连接正常
- 企业微信机器人、个人微信需要正确配置，未配置则不会发送
- 建议使用 Python 3.11 或更高版本
- 需要有效的企业微信机器人 ID
- 调试模式下，消息将直接打印到控制台而不会发送
- 每个监控账号可以配置不同的分析提示词和推送目标
- 提示词配置决定了分析的主题和维度，可以根据需求灵活调整
- Twitter登录后会生成会话文件（*.tw_session），该文件已被添加到.gitignore中，不会被提交到代码仓库
- 当 LLM 返回的 JSON 格式无法解析时，系统会自动重试，重试次数由环境变量 `LLM_PROCESS_MAX_RETRIED` 控制，默认为 3 次
- 如果重试次数用完仍然无法解析 JSON，系统会跳过当前内容的处理并继续处理下一条内容

## 许可证

本项目采用 MIT 许可证。详见 [LICENSE](LICENSE) 文件。


## /config/social-networks.yml

```yml path="/config/social-networks.yml" 
social_networks:
  - type: truthsocial
    socialNetworkId: realDonaldTrump
    prompt: >-
      你现在是一名财经专家，请对以下美国总统的发言进行分析，并给按我指定的格式返回分析结果。
      
      这是你需要分析的内容：{content}

      这是输出格式的说明：
      {{
          "is_relevant": "是否与财经相关，只需要返回1或0这两个值之一即可",
          "analytical_briefing": "分析简报"
      }}

      其中analytical_briefing的值是一个字符串，它是针对内容所做的分析简报，仅在is_relevant为1时会返回这个值。

      analytical_briefing的内容是markdown格式的，它需要符合下面的规范：

      原始正文

      翻译为中文后的内容，仅当需要分析的内容为英文时，才会有这部分的内容。

      ## Brief Analysis

      分析结果。这部分会展示一个列表，列表中分别包含美股市场、美债市场、科技股、半导体股、中国股票市场、香港股票市场、人民币兑美元汇率、中美关系这8个选项。
      每个选项的值为分别为📈利多和📉利空。如果分析内容对于该选项没有影响，就不要针对这个选项返回任何内容。

      ## Summarize

      这部分需要用非常简明扼要的文字对分析结果进行总结，以及解释为什么在上面针对不同选项会得出不同的结论。
    weComRobotId: $WECOM_TRUMP_ROBOT_ID
    sendToWeChat: true
  - type: twitter
    socialNetworkId: 
      - myfxtrader
      - HAOHONG_CFA
    prompt: >-
      你现在是一名财经专家，请对以下财经博主的发言进行分析，并给按我指定的格式返回分析结果。

      这是你需要分析的内容：{content}

      这是输出格式的说明：
      {{
          "is_relevant": "是否与财经相关，且与美股市场或美债市场或科技股或半导体股或中国股票市场或香港股票市场或人民币兑美元汇率或中美关系相关。如果相关就返回1，如果不相关就返回0。只需要返回1或0这两个值之一即可",
          "analytical_briefing": "分析简报"
      }}

      其中analytical_briefing的值是一个字符串，它是针对内容所做的分析简报，仅在is_relevant为1时会返回这个值。

      analytical_briefing的内容是markdown格式的，它需要符合下面的规范

      原始正文，仅当需要分析的内容不是为中文时，这部分内容才会保留，否则这部分的内容为原始的正文

      翻译后的内容，仅当需要分析的内容为英文时，才会有这部分的内容。

      ## Brief Analysis

      分析结果。这部分会展示一个列表，列表中分别包含美股市场、美债市场、科技股、半导体股、中国股票市场、香港股票市场、人民币兑美元汇率、中美关系这8个选项。
      每个选项的值为分别为📈利多和📉利空。如果分析内容对于该选项没有影响，就不要针对这个选项返回任何内容。

      ## Summarize

      这部分需要用非常简明扼要的文字对分析结果进行总结，以及解释为什么在上面针对不同选项会得出不同的结论。
    weComRobotId: $WECOM_FINANCE_ROBOT_ID
  - type: twitter
    socialNetworkId: 
      - dotey
      - imxiaohu
      - AndrewYNg
      - drfeifei
      - sama
      - karpathy
      - fchollet
      - ClementDelangue
      - fi56622380
    prompt: >-
      你现在是一名大模型领域专家，请对以下社交媒体博主的发言进行分析，并给按我指定的格式返回分析结果。

      这是你需要分析的内容：{content}

      这是输出格式的说明：
      {{
          "is_relevant": "是否与大模型、AIGC、人工智能相关，只需要返回1或0这两个值之一即可",
          "analytical_briefing": "分析简报"
      }}

      其中analytical_briefing的值是一个字符串，它是针对内容所做的分析简报，仅在is_relevant为1时会返回这个值。

      analytical_briefing的内容是markdown格式的，它需要符合下面的规范

      原始正文，仅当需要分析的内容不是为中文时，这部分内容才会保留，否则这部分的内容为原始的正文

      翻译后的内容，仅当需要分析的内容为英文时，才会有这部分的内容。

      ## Summarize

      这部分需要用非常简明扼要的文字对博主发表的内容进行总结。
    weComRobotId: $WECOM_AI_ROBOT_ID
```

## /docker-compose.yml

```yml path="/docker-compose.yml" 
version: "3"
services:
  secretary:
    image: zkd8907/secretary:dev
    env_file:
      - .env
    restart: always

```

## /example.env

```env path="/example.env" 
REDIS_URL=

# openai endpoint settings
LLM_API_MODEL=
LLM_API_KEY=
LLM_API_BASE=
LLM_PROCESS_MAX_RETRIED=3

## wecom bots
ENABLE_WECOM_BOT=
DEBUG_ROBOT_ID=
WECOM_TRUMP_ROBOT_ID=
WECOM_FINANCE_ROBOT_ID=
WECOM_AI_ROBOT_ID=

## wechat bot based on Gewechat
ENABLE_WECHAT_BOT=
WECHAT_ROBOT_IP=
WECHAT_ROBOT_TOKEN=
WECHAT_ROBOT_APP_ID=
WECHAT_ROBOT_CHATROOM_ID=

## qq bot based on lagrange
ENABLE_QQ_BOT=
QQ_BOT_URL=
QQ_BOT_GROUP_ID=

TWITTER_USERNAME=
TWITTER_PASSWORD=
TRUTHSOCIAL_TOKEN=
TWITTER_SESSION=
```

## /main.py

```py path="/main.py" 
import json
from modules.socialmedia.truthsocial import fetch as fetchTruthsocial
from modules.socialmedia.twitter import fetch as fetchTwitter
from modules.langchain.llm import get_llm_response
from modules.bots.wecom import send_markdown_msg
from modules.bots.wechat import send_wechat_msg
from utils.yaml import load_config_with_env
from dotenv import load_dotenv
import os

load_dotenv()

# 从环境变量获取最大重试次数，如果不存在或不是整数则使用默认值3
try:
    LLM_PROCESS_MAX_RETRIED = int(os.getenv("LLM_PROCESS_MAX_RETRIED", "3"))
except (ValueError, TypeError):
    LLM_PROCESS_MAX_RETRIED = 3


def main():
    config = load_config_with_env('config/social-networks.yml')

    # 处理socialNetworkId为数组的情况
    new_social_networks = []
    for account in config['social_networks']:
        if isinstance(account['socialNetworkId'], list):
            # 如果socialNetworkId是数组,为每个ID创建一个新的配置
            for social_id in account['socialNetworkId']:
                if len(social_id) == 0:
                    continue

                new_account = account.copy()
                new_account['socialNetworkId'] = social_id
                new_social_networks.append(new_account)
        else:
            # 如果不是数组直接添加原配置
            new_social_networks.append(account)

    # 用新的配置替换原配置
    config['social_networks'] = new_social_networks

    for account in config['social_networks']:
        posts = []
        if account['type'] == 'truthsocial':
            posts = fetchTruthsocial(account['socialNetworkId'])
        if account['type'] == 'twitter':
            posts = fetchTwitter(account['socialNetworkId'])

        if len(posts) == 0:
            print(
                f"在 {account['type']}: {account['socialNetworkId']} 上未发现有更新的内容")
            continue

        for post in posts:
            content = post.content
            prompt = account['prompt'].format(content=content)
            format_result = None
            rawData = ''

            # 在某些情况下，LLM会返回一些非法的json字符串，所以这里需要循环尝试，直到解析成功为止
            retry_count = 0
            while format_result is None and retry_count < LLM_PROCESS_MAX_RETRIED:
                if len(rawData) > 0:
                    prompt += """
你前次基于上面的内容提供给我的json是{rawData}，然而这个json内容有语法错误，无法在python中被解析。针对这个问题重新检查我的要求，按指定要求和格式回答。
"""
                rawData = get_llm_response(prompt).replace('\n', '\\n')
                try:
                    format_result = json.loads(rawData)
                except Exception as e:
                    print(f"解析 JSON 时出错: {e}")
                    print(f"翻译内容: {rawData}")
                    format_result = None
                    retry_count += 1

            if format_result is None:
                print(
                    f"在 {account['type']}: {account['socialNetworkId']} 上处理内容时，LLM返回的JSON格式始终无法解析，已达到最大重试次数 {LLM_PROCESS_MAX_RETRIED}")
                continue

            post_time = post.get_local_time()

            if format_result['is_relevant'] == '0':
                print(
                    f"在 {account['type']}: {account['socialNetworkId']} 上发现有更新的内容，但内容与需要关注的主题无关: {content}")
                continue

            markdown_msg = f"""# [{post.poster_name}]({post.poster_url}) {post_time.strftime('%Y-%m-%d %H:%M:%S')}


{format_result['analytical_briefing']}


origin: [{post.url}]({post.url})"""

            # Send to WeWork bots if enabled
            if os.getenv("ENABLE_WECOM_BOT", "false").lower() == "true":
                robot_ids = []
                if 'weComRobotId' in account:
                    robot_ids.append(account['weComRobotId'])
                else:
                    # Use all configured robot IDs
                    for key in ["WECOM_TRUMP_ROBOT_ID", "WECOM_FINANCE_ROBOT_ID", "WECOM_AI_ROBOT_ID"]:
                        robot_id = os.getenv(key)
                        if robot_id:
                            robot_ids.append(robot_id)
                
                for robot_id in robot_ids:
                    send_markdown_msg(markdown_msg, robot_id)

            # Send to WeChat bot if enabled
            if os.getenv("ENABLE_WECHAT_BOT", "false").lower() == "true":
                send_wechat_msg(
                    markdown_msg,
                    os.getenv("WECHAT_ROBOT_IP", ""),
                    os.getenv("WECHAT_ROBOT_TOKEN", ""),
                    os.getenv("WECHAT_ROBOT_APP_ID", ""),
                    os.getenv("WECHAT_ROBOT_CHATROOM_ID", "")
                )
            
            # Send to QQ bot if enabled
            if os.getenv("ENABLE_QQ_BOT", "false").lower() == "true":
                from modules.bots.qqchat import send_qqgroup_msg
                send_qqgroup_msg(
                    markdown_msg,
                    os.getenv("QQ_BOT_URL", ""),
                    os.getenv("QQ_BOT_GROUP_ID", "")
                )


if __name__ == "__main__":
    main()

```

## /modules/bots/qqchat.py

```py path="/modules/bots/qqchat.py" 
import os
import json
import http.client
from urllib.parse import urlparse

def send_qqgroup_msg(message: str, robot_ip: str, group_id: str):
    if os.getenv('DEBUG') == '1':
        print("[Debug Mode]", message)
        return

    parsed = urlparse(robot_ip if "://" in robot_ip else f"http://{robot_ip}")
    host = parsed.hostname
    port = parsed.port or (443 if parsed.scheme == "https" else 80)
    use_https = parsed.scheme == "https"

    conn = http.client.HTTPSConnection(host, port) if use_https else http.client.HTTPConnection(host, port)

    payload = json.dumps({
        "group_id": int(group_id),
        "message": message,
        "auto_escape": False
    })

    headers = {
        'Content-Type': 'application/json',
    }

    # 发送请求
    conn.request("POST", "/send_group_msg", payload, headers)
    res = conn.getresponse()
    data = res.read()
    print(data.decode("utf-8"))

```

## /modules/bots/wechat.py

```py path="/modules/bots/wechat.py" 
import http.client
import json
import os


def send_wechat_msg(message: str, robot_ip: str, robot_token: str, robot_app_id: str, robot_chatroom_id: str):
    if os.getenv('DEBUG') == '1' or len(robot_token) == 0:
        print(message)
        return

    conn = http.client.HTTPConnection(robot_ip, 2531)
    payload = json.dumps({
        "appId": robot_app_id,
        "toWxid": robot_chatroom_id,
        "ats": "",
        "content": message
    })
    headers = {
        'X-GEWE-TOKEN': robot_token,
        'Content-Type': 'application/json'
    }
    conn.request("POST", "/v2/api/message/postText", payload, headers)
    res = conn.getresponse()
    data = res.read()
    print(data.decode("utf-8"))

```

## /modules/bots/wecom.py

```py path="/modules/bots/wecom.py" 
import requests
import os


def send_markdown_msg(message: str, robot_id: str):
    if os.getenv('DEBUG') == '1' and len(os.getenv('DEBUG_ROBOT_ID', '')) > 0:
        robot_id = os.getenv('DEBUG_ROBOT_ID')

    if len(robot_id) == 0:
        print(message)
        return

    url = f'https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key={robot_id}'
    data = {
        "msgtype": "markdown",
        "markdown": {
            "content": message
        }
    }

    requests.post(url, json=data)

```

## /modules/langchain/llm.py

```py path="/modules/langchain/llm.py" 
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage, SystemMessage
from dotenv import load_dotenv
import os

# 加载环境变量
load_dotenv()


def get_llm_response(prompt: str) -> str:
    """
    使用 langchain-openai 调用模型获取响应

    Args:
        prompt (str): 输入的提示词

    Returns:
        str: 模型的完整响应文本
    """
    # 初始化 ChatOpenAI 客户端
    chat = ChatOpenAI(
        model=os.getenv("LLM_API_MODEL"),
        openai_api_base=os.getenv("LLM_API_BASE"),
        openai_api_key=os.getenv("LLM_API_KEY")
    )

    # 创建消息
    messages = [
        SystemMessage(
            content="""
你接下来回答的所有内容都只能是符合我要求的json字符串同。
字符串中如果有一些特殊的字符需要做好转义，确保最终这个json字符串可以在python中被正确解析。
在最终的回答中除了json字符串本身，不需要其它额外的信息，也不要在json内容前后额外增加markdown的三个点转义。
"""),
        HumanMessage(content=prompt)
    ]

    # 获取响应
    response = chat.invoke(messages)

    return response.content

```

## /modules/socialmedia/post.py

```py path="/modules/socialmedia/post.py" 
from datetime import datetime
import pytz
import time
import os


class Post:
    def __init__(
        self,
        id: str,
        post_on: datetime,
        content: str,
        url: str,
        poster_name: str,
        poster_url: str
    ):
        self.id = id
        # 确保 post_on 是 UTC 时间
        if post_on.tzinfo is None:
            post_on = pytz.UTC.localize(post_on)
        self.post_on = post_on
        self.content = content
        self.url = url
        self.poster_name = poster_name
        self.poster_url = poster_url

    def get_local_time(self) -> datetime:
        """获取本地时区的时间"""
        # 从操作系统获取当前时区
        local_tz_name = time.tzname[0]  # 获取当前时区名称
        try:
            # 尝试使用获取到的时区名称
            local_tz = pytz.timezone(local_tz_name)
        except pytz.exceptions.UnknownTimeZoneError:
            # 如果无法识别时区名称，则使用系统环境变量TZ
            tz_env = os.environ.get('TZ')
            if tz_env:
                try:
                    local_tz = pytz.timezone(tz_env)
                except pytz.exceptions.UnknownTimeZoneError:
                    # 如果环境变量中的时区也无法识别，则使用UTC
                    print(f"警告：无法识别时区 '{tz_env}'，使用UTC时区")
                    local_tz = pytz.UTC
            else:
                # 如果没有设置TZ环境变量，则使用UTC
                print("警告：未设置TZ环境变量，使用UTC时区")
                local_tz = pytz.UTC

        return self.post_on.astimezone(local_tz)

    def __str__(self) -> str:
        return f"Post(id={self.id}, post_on={self.post_on}, content={self.content}, url={self.url}, poster_name={self.poster_name}, poster_url={self.poster_url})"

    def __repr__(self) -> str:
        return self.__str__()

```

## /modules/socialmedia/truthsocial.py

```py path="/modules/socialmedia/truthsocial.py" 
from datetime import datetime
from bs4 import BeautifulSoup
from truthbrush.api import Api
from modules.socialmedia.post import Post
from utils.redisClient import redis_client

api = Api()


def fetch(user_id: str) -> list[Post]:
    last_post_id = redis_client.get(f"truthsocial:{user_id}:last_post_id")
    if last_post_id is None:
        last_post_id = '114344562778183288'
    else:
        last_post_id = str(last_post_id, encoding='utf-8')

    posts = list(api.pull_statuses(username=user_id, since_id=last_post_id))

    noneEmptyPosts = []
    for post in posts:
        content = BeautifulSoup(post['content'], 'html.parser').get_text()
        if len(content) > 0:
            post['content'] = content
            post_time = datetime.strptime(
                post['created_at'], '%Y-%m-%dT%H:%M:%S.%fZ')

            noneEmptyPosts.append(
                Post(post['id'], post_time, content, post['url'], post['account']['display_name'], post['account']['url']))

        if post['id'] > last_post_id:
            last_post_id = post['id']

    redis_client.set(f"truthsocial:{user_id}:last_post_id", last_post_id)

    return noneEmptyPosts


if __name__ == "__main__":
    user_id = "realDonaldTrump"
    posts = fetch(user_id)
    for post in posts:
        print(post['content'])
        print(post['created_at'])

```

## /modules/socialmedia/twitter.py

```py path="/modules/socialmedia/twitter.py" 
import os
from tweety import Twitter
from utils.redisClient import redis_client
from modules.socialmedia.post import Post
from dotenv import load_dotenv

load_dotenv()

twitter_session = os.getenv('TWITTER_SESSION')
if twitter_session and twitter_session.strip():
    with open('session.tw_session', 'w') as f:
        f.write(twitter_session)

app = Twitter('session')
app.connect()

if (app.me is None):
    app.sign_in(os.getenv('TWITTER_USERNAME'), os.getenv('TWITTER_PASSWORD'))


def fetch(user_id: str) -> list[Post]:
    cursor = redis_client.get(f"twitter:{user_id}:last_post_id")

    if cursor is None:
        cursor = ''
    else:
        cursor = str(cursor, encoding='utf-8')

    try:
        posts = app.get_tweets(user_id, cursor=cursor)
    except Exception as e:
        print(e)
        return []

    noneEmptyPosts = []

    for post in posts:
        if 'tweets' in post:
            latest_id = None
            latest_created_on = None
            combined_text = ""
            latest_url = ""
            poster = None

            for tweet in post.tweets:
                if tweet.text:
                    combined_text += tweet.text + "\n"
                if latest_created_on is None or tweet.created_on > latest_created_on:
                    latest_created_on = tweet.created_on
                    latest_id = tweet.id
                    latest_url = tweet.url
                    poster = tweet.author

            if combined_text and latest_id and latest_created_on and poster:
                noneEmptyPosts.append(
                    Post(latest_id, latest_created_on, combined_text.strip(), latest_url, poster.name, poster.profile_url))
        elif post.text:
            noneEmptyPosts.append(Post(post.id, post.created_on, post.text,
                                  post.url, post.author.name, post.author.profile_url))

    redis_client.set(f"twitter:{user_id}:last_post_id", posts.cursor_top)

    return noneEmptyPosts


if __name__ == "__main__":
    posts = fetch('myfxtrader')
    for post in posts:
        print(post.content)

```

## /pyproject.toml

```toml path="/pyproject.toml" 
[project]
name = "secretary"
version = "0.1.0"
description = "社交媒体内容分析助手"
authors = [
    {name = "Bpbby", email = "zkd8907@live.com"},
]
dependencies = [
    "python-dotenv",
    "redis",
    "requests",
    "beautifulsoup4",
    "tweety-ns",
    "truthbrush",
    "langchain-openai",
    "pytz",
]
requires-python = ">=3.11"

[tool.uv.sources]
truthbrush = { git = "https://github.com/stanfordio/truthbrush.git" }

```

## /run.sh

```sh path="/run.sh" 
#!/bin/bash

# 设置脚本所在目录为工作目录
cd "$(dirname "$0")"

uv run python main.py

```

## /utils/redisClient.py

```py path="/utils/redisClient.py" 
import os
import redis
from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()

# Read Redis URL from environment variables
REDIS_URL = os.getenv("REDIS_URL")

# Initialize Redis client
redis_client = redis.from_url(REDIS_URL)


def test_redis_connection():
    try:
        redis_client.ping()
        print("Connected to Redis successfully!")
    except redis.ConnectionError as e:
        print(f"Failed to connect to Redis: {e}")
    except Exception as e:
        print(f"An unexpected error occurred: {e}")


# Uncomment the following line to test the connection when running this file directly
if __name__ == "__main__":
    test_redis_connection()

```

## /utils/yaml.py

```py path="/utils/yaml.py" 

import os
import re
import yaml


def replace_env_vars(value):
    if isinstance(value, str):
        # 匹配 ${VAR} 或 $VAR 格式
        pattern = r'\$\{([^}]+)\}|\$([A-Za-z0-9_]+)'

        def replace(match):
            # 获取匹配到的环境变量名
            env_var = match.group(1) or match.group(2)
            # 返回环境变量值,如不存在返回空字符串
            return os.getenv(env_var, '')

        return re.sub(pattern, replace, value)
    elif isinstance(value, dict):
        return {k: replace_env_vars(v) for k, v in value.items()}
    elif isinstance(value, list):
        return [replace_env_vars(item) for item in value]
    return value


def load_config_with_env(file_path):
    try:
        with open(file_path, 'r', encoding='utf-8') as f:
            config = yaml.safe_load(f)
            # 递归处理所有值中的环境变量
            return replace_env_vars(config)
    except FileNotFoundError:
        raise FileNotFoundError(f"配置文件 {file_path} 不存在")
    except yaml.YAMLError as e:
        raise yaml.YAMLError(f"配置文件格式不正确: {e}")

```

## /uv.lock

```lock path="/uv.lock" 
version = 1
revision = 1
requires-python = ">=3.11"
resolution-markers = [
    "python_full_version >= '3.12.4'",
    "python_full_version < '3.12.4'",
]

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[package.optional-dependencies]
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]

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```


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