DummyCode

DummyCode A Python-based intelligent agent project that integrates Large Language Models (LLM), Model Context Protocol (MCP), and Retrieval-Augmented Generation (RAG) technologies to create an AI agent with tool invocation capabilities and knowledge base retrieval. ## Project Overview This project demonstrates how to build a modern AI agent that can: - 🤖 Invoke large language models for intelligent conversations - 🔧 Call external tools via the MCP protocol (e.g., web scraping, file operations) - 📚 Retrieve relevant information from a knowledge base using RAG technology - 💾 Persist memory and manage multiple sessions independently - 🔄 Achieve collaborative work between tool invocation and knowledge retrieval ## Core Components | Component | File | Function Description | | ---------------------------- | ----------------------- | ------------------------------------------ | | **Agent** | `src/agent.py` | Intelligent agent core, coordinating LLM, MCP, and RAG | | **MCPClient** | `src/mcp_client.py` | MCP protocol client, connecting to external tool services | | **ChatOpenAI** | `src/chat_openai.py` | Large language model conversation wrapper, supporting streaming output and tool invocation | | **EmbeddingRetriever** | `src/embedding.py` | Embedded model retriever, realizing document vectorization | | **VectorStore** | `src/vector_store.py` | Vector storage, retrieving based on cosine similarity | | **Session** | `src/session.py` | Session management, supporting multi-session independent memory and persistence | ## Quick Start ### 1. Environment Requirements - Python 3.10+ - Node.js and npm (for running MCP server) - uv tool (for running fetch MCP server) ### 2. Install Dependencies ```bash # Install Python dependencies pip install -r requirements.txt # Install MCP server dependencies npm install -g @modelcontextprotocol/server-filesystem # Install uv (if not installed) # Windows: powershell -c "irm https://astral.sh/uv/install.ps1 | iex" # macOS/Linux: curl -LsSf https://astral.sh/uv/install.sh | sh ``` ### 3. Configure Environment Variables Create a new `.env` file, copy the contents from `.env.example`, and fill in your API keys: ```env # Large language model configuration (example using DeepSeek) OPENAI_API_KEY=your_deepseek_api_key OPENAI_BASE_URL=https://api.deepseek.com/v1 # Vector embedding model configuration (example using SiliconFlow) EMBEDDING_KEY=your_siliconflow_api_key EMBEDDING_BASE_URL=https://api.siliconflow.cn/v1 ``` ### 4. Run the Project ```bash cd src python main.py ``` <!--   --> ## Advanced Features ### Multi-Session Management Mode Based on Memory MCP, implement multi-dialogue management, each session has an independent memory space: **Command List:** | Command | Description | | ------------------------------ | -------------------------------------- | | `/new [name]` | Create a new session (optional name, default auto-naming) | | `/list` | List all sessions (display connection status) | | `/switch <number>` | Switch to the specified session by list number | | `/delete <number>` | Delete the specified session by list number | | `/clear` | Clear the conversation history of the current session | | `/history` | View the conversation history of the current session | | `/rag` | Switch to RAG mode (based on knowledge base retrieval) | | `/help` | Display help information | | `exit` / `quit` / `exit` | Exit the program | **Multi-Session Features:** - Each session has an independent Memory MCP and knowledge graph - Session memory isolation, no interference between sessions - Support session persistence (memory files saved in `memory/sessions/` directory) - Session index saved in `memory/index.json`, automatically restored at startup - Automatic memory management: each conversation prioritizes memory query information after the conversation ends ### RAG Mode Input `/rag` to switch to RAG mode, in this mode: - Based on knowledge base retrieval to answer questions, without using session memory - Knowledge base directory: `knowledge/` - Input `/rag` again to exit RAG mode - Support various file reading, such as md, pdf, xlsx, csv **Applicable Scenarios:** - Query specific domain knowledge - Answer questions based on document content - Scenarios requiring accurate citation of knowledge base information ### Docker Deployment - **Dockerhub Address**: [ght2943330996/agent_1.0](https://hub.docker.com/repository/docker/ght2943330996/agent_1.0) - **Mounting Requirements**: Mount API key file `.env` to container's `/app/.env` directory - **Running Method**: `docker run -it -p 8000:8000 -v [.env file path]:/app/.env agent_1.0` ## Technical References - [Model Context Protocol (MCP) Documentation](https://modelcontextprotocol.io/docs) - [OpenAI API Documentation](https://platform.openai.com/docs) - [DeepSeek API Documentation](https://platform.deepseek.com/) - [SiliconFlow Documentation](https://docs.siliconflow.cn/) - [FAISS Documentation](https://github.com/facebookresearch/faiss) - [MCP Reference Project](https://github.com/KelvinQiu802/llm-mcp-rag) - [Enhanced RAG Reference Documentation](https://my.feishu.cn/wiki/ADkHwYg3Vi495Sk8mKOcBd2knbc?from=from_copylink) - [RAG Paper](https://arxiv.org/abs/2312.10997)