From 9b16ff60f6d1be4564ae0b63296c8a9a9737bb36 Mon Sep 17 00:00:00 2001 From: 666ghj <670939375@qq.com> Date: Thu, 9 Oct 2025 14:50:44 +0800 Subject: [PATCH] Update README. --- README-EN.md | 82 ++++++++++++++++++++++------------------------------ README.md | 81 ++++++++++++++++++++++----------------------------- 2 files changed, 70 insertions(+), 93 deletions(-) diff --git a/README-EN.md b/README-EN.md index 8b295db..bee9748 100644 --- a/README-EN.md +++ b/README-EN.md @@ -23,7 +23,9 @@ ## ⚡ Project Overview -**"WeiYu"** is an innovative multi-agent public opinion analysis system built from scratch, not limited to Weibo, and features universal simplicity and applicability across all platforms. +**"BettaFish"** is an innovative multi-agent public opinion analysis system built from scratch. It helps break information cocoons, restore the original public sentiment, predict future trends, and assist decision-making. Users only need to raise analysis needs like chatting; the agents automatically analyze 30+ mainstream social platforms at home and abroad and millions of public comments. + +> Betta is a small yet combative and beautiful fish, symbolizing "small but powerful, fearless of challenges". See the system-generated research report on "Wuhan University Public Opinion": [In-depth Analysis Report on Wuhan University's Brand Reputation](./final_reports/final_report__20250827_131630.html) @@ -103,10 +105,7 @@ Weibo_PublicOpinion_AnalysisSystem/ ├── InsightEngine/ # Private database mining Agent │ ├── agent.py # Agent main logic │ ├── llms/ # LLM interface wrapper -│ │ ├── deepseek.py # DeepSeek API -│ │ ├── kimi.py # Kimi API -│ │ ├── openai_llm.py # OpenAI format API -│ │ └── base.py # LLM base class +│ │ └── base.py # Unified OpenAI-compatible client │ ├── nodes/ # Processing nodes │ │ ├── base_node.py # Base node class │ │ ├── formatting_node.py # Formatting node @@ -130,7 +129,6 @@ Weibo_PublicOpinion_AnalysisSystem/ ├── ReportEngine/ # Multi-round report generation Agent │ ├── agent.py # Agent main logic │ ├── llms/ # LLM interfaces -│ │ └── gemini.py # Gemini API dedicated │ ├── nodes/ # Report generation nodes │ │ ├── template_selection.py # Template selection node │ │ └── html_generation.py # HTML generation node @@ -230,7 +228,7 @@ playwright install chromium #### 4.1 Configure API Keys -Edit the `config.py` file and fill in your API keys (you can also choose your own models and search proxies; please see the config file for details): +Edit the `config.py` file and fill in your API keys (you can also choose your own models and search proxies; see the config file for details): ```python # MySQL Database Configuration @@ -238,26 +236,18 @@ DB_HOST = "localhost" DB_PORT = 3306 DB_USER = "your_username" DB_PASSWORD = "your_password" -DB_NAME = "weibo_analysis" +DB_NAME = "your_db_name" DB_CHARSET = "utf8mb4" -# DeepSeek API (Apply at: https://www.deepseek.com/) -DEEPSEEK_API_KEY = "your_deepseek_api_key" +# LLM configuration +# You can switch each Engine's LLM provider as long as it follows the OpenAI-compatible request format -# Tavily Search API (Apply at: https://www.tavily.com/) -TAVILY_API_KEY = "your_tavily_api_key" - -# Kimi API (Apply at: https://www.kimi.com/) -KIMI_API_KEY = "your_kimi_api_key" - -# Gemini API (Apply at: https://api.chataiapi.com/) -GEMINI_API_KEY = "your_gemini_api_key" - -# Bocha Search API (Apply at: https://open.bochaai.com/) -BOCHA_Web_Search_API_KEY = "your_bocha_api_key" - -# Silicon Flow API (Apply at: https://siliconflow.cn/) -GUIJI_QWEN3_API_KEY = "your_guiji_api_key" +# Insight Agent +INSIGHT_ENGINE_API_KEY = "your_api_key" +INSIGHT_ENGINE_BASE_URL = "https://api.moonshot.cn/v1" +INSIGHT_ENGINE_MODEL_NAME = "kimi-k2-0711-preview" +# Media Agent +... ``` #### 4.2 Database Initialization @@ -373,30 +363,28 @@ SENTIMENT_CONFIG = { ### Integrate Different LLM Models -The system supports multiple LLM providers, switchable in each agent's configuration: +The system supports any LLM provider that follows the OpenAI request format. You only need to fill in KEY, BASE_URL, and MODEL_NAME in `config.py`. -```python -# Configure in each Engine's utils/config.py -class Config: - default_llm_provider = "deepseek" # Options: "deepseek", "openai", "kimi", "gemini", "qwen" - - # DeepSeek configuration - deepseek_api_key = "your_api_key" - deepseek_model = "deepseek-chat" - - # OpenAI compatible configuration - openai_api_key = "your_api_key" - openai_model = "gpt-3.5-turbo" - openai_base_url = "https://api.openai.com/v1" - - # Kimi configuration - kimi_api_key = "your_api_key" - kimi_model = "moonshot-v1-8k" - - # Gemini configuration - gemini_api_key = "your_api_key" - gemini_model = "gemini-pro" -``` +> What is the OpenAI request format? Here's a simple example: +>```python +>from openai import OpenAI +> +>client = OpenAI(api_key="your_api_key", +> base_url="https://api.siliconflow.cn/v1") +> +>response = client.chat.completions.create( +> model="Qwen/Qwen2.5-72B-Instruct", +> messages=[ +> { +> 'role': 'user', +> 'content': "What new opportunities will reasoning models bring to the market?" +> } +> ], +>) +> +>complete_response = response.choices[0].message.content +>print(complete_response) +>``` ### Change Sentiment Analysis Models diff --git a/README.md b/README.md index ba3daa5..5054659 100644 --- a/README.md +++ b/README.md @@ -23,7 +23,9 @@ ## ⚡ 项目概述 -“**微舆**” 是一个从0实现的创新型 多智能体 舆情分析系统，不止微博，全平台简洁通用。 +“**微舆**” 是一个从0实现的创新型 多智能体 舆情分析系统，帮助大家破除信息茧房，还原舆情原貌，预测未来走向，辅助决策。用户只需像聊天一样提出分析需求，智能体开始全自动分析 国内外30+主流社媒 与 数百万条大众评论。 + +> “微舆”谐音“微鱼”，BettaFish是一种体型很小但非常好斗、漂亮的鱼，它象征着“小而强大，不畏挑战”。 查看系统以“武汉大学舆情”为例，生成的研究报告：[武汉大学品牌声誉深度分析报告](./final_reports/final_report__20250827_131630.html) @@ -103,10 +105,7 @@ Weibo_PublicOpinion_AnalysisSystem/ ├── InsightEngine/ # 私有数据库挖掘Agent │ ├── agent.py # Agent主逻辑 │ ├── llms/ # LLM接口封装 -│ │ ├── deepseek.py # DeepSeek API -│ │ ├── kimi.py # Kimi API -│ │ ├── openai_llm.py # OpenAI格式API -│ │ └── base.py # LLM基类 +│ │ └── base.py # 统一的 OpenAI 兼容客户端 │ ├── nodes/ # 处理节点 │ │ ├── base_node.py # 基础节点类 │ │ ├── formatting_node.py # 格式化节点 @@ -130,7 +129,6 @@ Weibo_PublicOpinion_AnalysisSystem/ ├── ReportEngine/ # 多轮报告生成Agent │ ├── agent.py # Agent主逻辑 │ ├── llms/ # LLM接口 -│ │ └── gemini.py # Gemini API专用 │ ├── nodes/ # 报告生成节点 │ │ ├── template_selection.py # 模板选择节点 │ │ └── html_generation.py # HTML生成节点 @@ -238,31 +236,26 @@ DB_HOST = "localhost" DB_PORT = 3306 DB_USER = "your_username" DB_PASSWORD = "your_password" -DB_NAME = "weibo_analysis" +DB_NAME = "your_db_name" DB_CHARSET = "utf8mb4" -# DeepSeek API（申请地址：https://www.deepseek.com/） -DEEPSEEK_API_KEY = "your_deepseek_api_key" +# LLM配置 +# 您可以更改每个部分LLM使用的API，只要兼容OpenAI请求格式都可以 -# Tavily搜索API（申请地址：https://www.tavily.com/） -TAVILY_API_KEY = "your_tavily_api_key" - -# Kimi API（申请地址：https://www.kimi.com/） -KIMI_API_KEY = "your_kimi_api_key" - -# Gemini API（申请地址：https://api.chataiapi.com/） -GEMINI_API_KEY = "your_gemini_api_key" - -# 博查搜索API（申请地址：https://open.bochaai.com/） -BOCHA_Web_Search_API_KEY = "your_bocha_api_key" - -# 硅基流动API（申请地址：https://siliconflow.cn/） -GUIJI_QWEN3_API_KEY = "your_guiji_api_key" +# Insight Agent +INSIGHT_ENGINE_API_KEY = "your_api_key" +INSIGHT_ENGINE_BASE_URL = "https://api.moonshot.cn/v1" +INSIGHT_ENGINE_MODEL_NAME = "kimi-k2-0711-preview" +# Media Agent +... ``` #### 4.2 数据库初始化 **选择1：使用本地数据库** + +> MindSpider爬虫系统跟舆情系统是各自独立的，所以需要再去`MindSpider\config.py`配置一下 + ```bash # 本地MySQL数据库初始化 cd MindSpider @@ -373,30 +366,26 @@ SENTIMENT_CONFIG = { ### 接入不同的LLM模型 -系统支持多种LLM提供商，可在各Agent的配置中切换： +支持任意openAI调用格式的LLM提供商，只需要在/config.py中填写对应的KEY、BASE_URL、MODEL_NAME即可。 -```python -# 在各Engine的utils/config.py中配置 -class Config: - default_llm_provider = "deepseek" # 可选: "deepseek", "openai", "kimi", "gemini"，"qwen"等 - - # DeepSeek配置 - deepseek_api_key = "your_api_key" - deepseek_model = "deepseek-chat" - - # OpenAI兼容配置 - openai_api_key = "your_api_key" - openai_model = "gpt-3.5-turbo" - openai_base_url = "https://api.openai.com/v1" - - # Kimi配置 - kimi_api_key = "your_api_key" - kimi_model = "moonshot-v1-8k" - - # Gemini配置 - gemini_api_key = "your_api_key" - gemini_model = "gemini-pro" -``` +> 什么是openAI调用格式？下面提供一个简单的例子： +>```python +>from openai import OpenAI +> +>client = OpenAI(api_key="your_api_key", +> base_url="https://api.siliconflow.cn/v1") +> +>response = client.chat.completions.create( +> model="Qwen/Qwen2.5-72B-Instruct", +> messages=[ +> {'role': 'user', +> 'content': "推理模型会给市场带来哪些新的机会"} +> ], +>) +> +>complete_response = response.choices[0].message.content +>print(complete_response) +>``` ### 更改情感分析模型