From 765201f9efdd08fe9faf1085fa57926536dee323 Mon Sep 17 00:00:00 2001 From: kennethcheng Date: Wed, 15 Apr 2026 20:49:50 +0800 Subject: [PATCH] v0.0.11 mcp --- .env | 6 +- .gitignore | 24 + .python-version | 2 +- README.md | 36 +- mcp/get_weather_server.py | 20 + mcp/math_server.py | 29 + mcp/mcp_client.py | 12 + mcp/mcp_client_with_agent.py | 102 +++ mcp/mcp_client_with_agent_simple.py | 56 ++ pyproject.toml | 11 +- uv.lock | 1305 +++++++++++++++++++++++++-- 11 files changed, 1495 insertions(+), 108 deletions(-) create mode 100644 .gitignore create mode 100644 mcp/get_weather_server.py create mode 100644 mcp/math_server.py create mode 100644 mcp/mcp_client.py create mode 100644 mcp/mcp_client_with_agent.py create mode 100644 mcp/mcp_client_with_agent_simple.py diff --git a/.env b/.env index ff95a3e..c75308f 100644 --- a/.env +++ b/.env @@ -12,4 +12,8 @@ MINIMAX_API_KEY = "sk-cp-wWkzvRP-BiQia-6izxvqgehEsHSz8v4_PtDJAuT3OI0s8QFcEOsxIHc MINIMAX_BASE_URL = "https://api.minimaxi.com/v1" BAILIAN_API_KEY = "sk-8c8bec7a613249dbbed08bc3affeef72" -BAILIAN_BASE_URL = "https://dashscope.aliyuncs.com/compatible-mode/v1" \ No newline at end of file +BAILIAN_BASE_URL = "https://dashscope.aliyuncs.com/compatible-mode/v1" + +# gemma4:e2b +CHERRY_API_KEY = "w6qWTWfnmF5t9OKGDPYpCoLJlga4F7Ezj4OT2XiDtJ3PFCqG" +CHERRY_BASE_URL = "https://open.cherryin.cc/v1" diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..d4283a9 --- /dev/null +++ b/.gitignore @@ -0,0 +1,24 @@ +# Python +.venv/ +__pycache__/ +*.pyc +*.pyo +*.pyd +.Python +*.egg-info/ +dist/ +build/ + +# Env +.env + +# IDE +.idea/ +.vscode/ +*.swp +*.swo + +# OS +.DS_Store +Thumbs.db +.env diff --git a/.python-version b/.python-version index 2c07333..24ee5b1 100644 --- a/.python-version +++ b/.python-version @@ -1 +1 @@ -3.11 +3.13 diff --git a/README.md b/README.md index 0f354cc..fd0ba76 100644 --- a/README.md +++ b/README.md @@ -1,14 +1,16 @@ # LangChain Learning -[![](https://img.shields.io/badge/version-0.0.10-blue.svg)](https://github.com/your-repo/langchain-learning) +[![](https://img.shields.io/badge/version-0.0.11-blue.svg)](https://github.com/your-repo/langchain-learning) [![](https://img.shields.io/badge/python-3.11+-green.svg)](https://www.python.org/) -[![](https://img.shields.io/badge/LangChain-v1.2-orange.svg)](https://www.langchain.com/) +[![](https://img.shields.io/badge/LangChain-v0.3.27-orange.svg)](https://www.langchain.com/) -> LangChain 框架学习项目，集成 SiliconFlow & Ollama API +> 基于 LangChain 0.3.27 的学习项目，集成 SiliconFlow & Ollama API ## 功能特性 - **多 LLM 集成**：支持 OpenAI API、SiliconFlow、Ollama 及 LangChain 抽象层 +- **MCP 协议支持**：通过 MultiServerMCPClient 连接多个 MCP 服务器 +- **Agent 智能体**：基于 LangGraph 的 ReAct Agent 实现自主推理与工具调用 - **RAG 检索增强生成**：基于向量库（FAISS）的文档检索与问答 - **流式响应**：实时流式输出，带来更好的使用体验 - **Prompt 工程**：多种 Prompt 模板构建方式 @@ -18,14 +20,13 @@ - **内存管理**：实现对话历史持久化（ConversationBufferMemory, SummaryMemory） - **Rich 终端界面**：支持 Markdown 渲染、多行输入等高级交互 - **模型测速工具**：测试模型的首字延迟 (TTFT) 和每秒生成速度 (TPS) -- **实战示例**：从基础到进阶的使用模式 ## 快速开始 ### 1. 安装依赖 ```bash -pip install langchain>=1.2.15 langchain-community>=0.4.1 langchain-siliconflow>=1.0.0 requests>=2.33.1 rich openai pydantic +uv sync ``` ### 2. 配置环境变量 @@ -80,6 +81,21 @@ OLLAMA_API_KEY=ollama | 工具定义 | `python tools/tool_definition.py` | 演示 @tool 装饰器和 StructuredTool 定义方式 | | 工具调用 | `python tools/tool_demo.py` | 演示模型如何调用工具并获取结果 | +**MCP 示例** + +| 示例 | 命令 | 说明 | +|------|------|------| +| MCP 客户端 | `python mcp/mcp_client.py` | 连接 MCP 服务器并获取工具列表 | +| Agent + MCP | `python mcp/mcp_client_with_agent.py` | 使用 ReAct Agent 调用 MCP 工具 | +| Agent + MCP (简易版) | `python mcp/mcp_client_with_agent_simple.py` | 简化版的 Agent MCP 调用 | + +**MCP 服务端示例** + +| 示例 | 命令 | 说明 | +|------|------|------| +| 天气服务 | `python mcp/get_weather_server.py` | 提供天气查询的 MCP 服务端 | +| 数学服务 | `python mcp/math_server.py` | 提供数学计算的 MCP 服务端 | + **Token 用量示例** | 示例 | 命令 | 说明 | @@ -124,6 +140,12 @@ langchain-learning/ ├── tools/ │ ├── tool_definition.py # 工具定义方式演示 │ └── tool_demo.py # 工具调用完整流程演示 +├── mcp/ +│ ├── mcp_client.py # MCP 客户端基础用法 +│ ├── mcp_client_with_agent.py # Agent + MCP 工具调用 +│ ├── mcp_client_with_agent_simple.py # Agent + MCP 简易版 +│ ├── get_weather_server.py # 天气查询 MCP 服务端 +│ └── math_server.py # 数学计算 MCP 服务端 ├── token/ │ └── token_demo.py # Token 用量追踪示例 ├── memory/ @@ -156,8 +178,10 @@ langchain-learning/ | 类别 | 技术 | |------|------| -| 框架 | LangChain | +| 框架 | LangChain 0.3.27 | +| Agent | LangGraph | | LLM 提供商 | SiliconFlow, Ollama | +| MCP | langchain-mcp-adapters | | 向量库 | FAISS | | 终端美化 | Rich | | 数据验证 | Pydantic | diff --git a/mcp/get_weather_server.py b/mcp/get_weather_server.py new file mode 100644 index 0000000..7b78ea9 --- /dev/null +++ b/mcp/get_weather_server.py @@ -0,0 +1,20 @@ +# uv add fastmcp +import logging + +from fastmcp import FastMCP + +mcp = FastMCP("mcp demo") + + +@mcp.tool() +async def get_weather(city: str) -> str: + """获取传入的城市的天气信息""" + logging.info(f"调用了查询天气服务，传入的参数为{city}") + return f"{city}的天气很好，阳光明媚，晴空万里" + + + +if __name__ == '__main__': + logging.info("启动一个可以通过MCP调用获取天气的服务") + mcp.run(transport="streamable-http", host="127.0.0.1", port=9000) + # mcp.run(transport="stdio", host="127.0.0.1", port=9000) diff --git a/mcp/math_server.py b/mcp/math_server.py new file mode 100644 index 0000000..162f3a8 --- /dev/null +++ b/mcp/math_server.py @@ -0,0 +1,29 @@ +from fastmcp import FastMCP +import logging + +# 配置日志记录器 +logging.basicConfig( + level=logging.INFO, # 设置日志级别为 INFO + format="%(asctime)s - %(levelname)s - %(message)s" # 日志格式 +) +logger = logging.getLogger(__name__) + +# 创建 FastMCP 实例 +mcp = FastMCP("Math") + +@mcp.tool() +def add(a: int, b: int) -> int: + """Add two numbers""" + logger.info("The add method is called: a=%d, b=%d", a, b) # 记录加法调用日志 + return a + b + +@mcp.tool() +def multiply(a: int, b: int) -> int: + """Multiply two numbers""" + logger.info("The multiply method is called: a=%d, b=%d", a, b) # 记录乘法调用日志 + return a * b + +if __name__ == "__main__": + logger.info("Start math server through MCP") # 记录服务启动日志 + # mcp.run(transport="streamable-http",port=8081,path='/mcp') # 启动服务并使用标准输入输出通信 + mcp.run(transport="stdio") # 启动服务并使用标准输入输出通信（子进程） diff --git a/mcp/mcp_client.py b/mcp/mcp_client.py new file mode 100644 index 0000000..561301c --- /dev/null +++ b/mcp/mcp_client.py @@ -0,0 +1,12 @@ +import asyncio +from fastmcp import Client + +client = Client("http://localhost:9000/mcp") + +async def call_tool(city: str): + async with client: + result = await client.call_tool("get_weather", {"city": city}) + print(result) +## 启动服务端后，再启动客户端进行连接和调用 +## 这种方式是通过fastmcp的客户端直接调用的 +asyncio.run(call_tool("北京")) \ No newline at end of file diff --git a/mcp/mcp_client_with_agent.py b/mcp/mcp_client_with_agent.py new file mode 100644 index 0000000..c6677e3 --- /dev/null +++ b/mcp/mcp_client_with_agent.py @@ -0,0 +1,102 @@ +import asyncio +import logging + +# from langchain.agents import create_react_agent +from langgraph.prebuilt import create_react_agent +from langchain_openai import ChatOpenAI +from langchain_mcp_adapters.client import MultiServerMCPClient +import os +import dotenv + +logging.basicConfig( + level=logging.DEBUG, + format="%(asctime)s - %(name)s - %(levelname)s - %(message)s" +) + +dotenv.load_dotenv() + +## 设置环境变量 +os.environ['OPENAI_API_KEY'] = os.getenv("OLLAMA_API_KEY") +os.environ['OPENAI_BASE_URL'] = os.getenv("OLLAMA_BASE_URL") + +# 默认的 'model_name': 'deepseek-ai/DeepSeek-V3.1', +llm = ChatOpenAI(model="gemma4:26b") + + +def print_optimized_result(agent_response): + """ + 解析代理响应并输出优化后的结果。 + :param agent_response: 代理返回的完整响应 + """ + messages = agent_response.get("messages", []) + steps = [] # 用于记录计算步骤 + final_answer = None # 最终答案 + + for message in messages: + if hasattr(message, "additional_kwargs") and "tool_calls" in message.additional_kwargs: + # 提取工具调用信息 + tool_calls = message.additional_kwargs["tool_calls"] + for tool_call in tool_calls: + tool_name = tool_call["function"]["name"] + tool_args = tool_call["function"]["arguments"] + steps.append(f"调用工具: {tool_name}({tool_args})") + elif message.type == "tool": + # 提取工具执行结果 + tool_name = message.name + tool_result = message.content + steps.append(f"{tool_name} 的结果是: {tool_result}") + elif message.type == "ai": + # 提取最终答案 + final_answer = message.content + + # 打印优化后的结果 + print("\n计算过程:") + for step in steps: + print(f"- {step}") + if final_answer: + print(f"\n最终答案: {final_answer}") + + +async def execute(): + # 1. 创建langchain中的mcp客户端 —— uv add langchain_mcp_adapters + client = MultiServerMCPClient( + # mcp.run(transport="streamable-http", host="127.0.0.1", port=9000) + { + # 这里是定义服务端信息的，可以有多个服务端 + "weather": { + "url": "http://localhost:9000/mcp", + "transport": "streamable_http", + }, + "math": { + "command": "python", # npx uvx + "args": ["./mcp/math_server.py"], + "transport": "stdio" + } + } + ) + + try: + # 2. 通过客户端获取工具列表 + ## 这里是通过服务端获取，所以可能会有异常（比如服务端没有启动，或者网络连接有问题 + tools = await client.get_tools() + + # 3. 创建一个智能代理，能够完成 思考--> 行动 --> 观察 --> 思考 --> 行动 --> ... --> 最终答案 + agent = create_react_agent(model=llm, tools=tools) + while True: + user_input = input("请输入你的问题（输入exit则退出） > ") + if user_input == "exit": + print("感谢使用，再见👋🏻") + break + + agent_response = await agent.ainvoke({"messages": user_input}) + ## agent会自己去调用 tools ，不需要我们去进行调用 + print_optimized_result(agent_response) + + finally: + # 资源回收 + if hasattr(client, 'close'): + client.close() + + +if __name__ == '__main__': + asyncio.run(execute()) diff --git a/mcp/mcp_client_with_agent_simple.py b/mcp/mcp_client_with_agent_simple.py new file mode 100644 index 0000000..e777bea --- /dev/null +++ b/mcp/mcp_client_with_agent_simple.py @@ -0,0 +1,56 @@ +import asyncio +import logging + +# from langchain.agents import create_react_agent +from langgraph.prebuilt import create_react_agent +from langchain_openai import ChatOpenAI +from langchain_mcp_adapters.client import MultiServerMCPClient +import os +import dotenv + +logging.basicConfig( + level=logging.DEBUG, + format="%(asctime)s - %(name)s - %(levelname)s - %(message)s" +) + +dotenv.load_dotenv() + +## 设置环境变量 +os.environ['OPENAI_API_KEY'] = os.getenv("OLLAMA_API_KEY") +os.environ['OPENAI_BASE_URL'] = os.getenv("OLLAMA_BASE_URL") + +# 默认的 'model_name': 'deepseek-ai/DeepSeek-V3.1', +llm = ChatOpenAI(model="gemma4:26b") + + +async def execute(): + # 1. 创建langchain中的mcp客户端 + # uv add langchain_mcp_adapters + client = MultiServerMCPClient( + { + # 这里是定义服务端信息的，可以有多个服务端 + "weather": { + "url": "http://localhost:9000/mcp", + "transport": "streamable_http", + } + } + ) + + try: + # 2. 通过客户端获取工具列表 + ## 这里是通过服务端获取，所以可能会有异常（比如服务端没有启动，或者网络连接有问题 + tools = await client.get_tools() + + # 3. 创建一个智能代理，能够完成 思考--> 行动 --> 观察 --> 思考 --> 行动 --> ... --> 最终答案 + agent = create_react_agent(model=llm, tools=tools) + agent_response = await agent.ainvoke({"messages":"北京的天气怎么样？适合出门吗？"}) + print(agent_response) + + finally: + # 资源回收 + if hasattr(client, 'close'): + client.close() + + +if __name__ == '__main__': + asyncio.run(execute()) diff --git a/pyproject.toml b/pyproject.toml index 12fa2d2..f9f0d56 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -6,9 +6,14 @@ readme = "README.md" requires-python = ">=3.11" dependencies = [ "faiss-cpu>=1.13.2", - "langchain==0.3.27", - "langchain-community==0.3.31", - "langchain-siliconflow==0.1.3", + "fastmcp>=3.2.4", + "langchain>=0.3.27", + "langchain-community>=0.3.31", + "langchain-core>=0.3.40", + "langchain-mcp-adapters>=0.0.1", + "langchain-openai>=0.3.35", + "langchain-siliconflow>=0.1.3", + "langgraph>=1.0.1", "requests>=2.33.1", "rich>=15.0.0", ] diff --git a/uv.lock b/uv.lock index 9a9defc..c8fb440 100644 --- a/uv.lock +++ b/uv.lock @@ -6,6 +6,18 @@ resolution-markers = [ "python_full_version < '3.13'", ] +[[package]] +name = "aiofile" +version = "3.9.0" +source = { registry = "https://pypi.org/simple" } +dependencies = [ + { name = "caio" }, +] +sdist = { url = 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