MCP Agent with ChromaDB Integration

A powerful tool for converting and querying documents using ChromaDB embeddings with OpenAI and Anthropic Claude integration.

Features

• Convert Word documents to ChromaDB embeddings
• Convert Excel files to ChromaDB embeddings with schema support
• RAG (Retrieval Augmented Generation) system for document querying
• Tool-augmented RAG for advanced analysis
• FastAPI server with Anthropic Claude integration
• Rich CLI interface for document processing
• Support for custom metadata and document chunking
• Python code execution with safety restrictions
• File saving and manipulation capabilities
• Docker support for easy deployment

Prerequisites

• Python 3.8 or higher
• OpenAI API key
• Anthropic API key (for Claude integration)
• Docker and Docker Compose (optional, for containerized deployment)

Installation

Local Installation

1. Clone the repository:

git clone https://github.com/kc099/mcp_agent.git
cd mcp_agent

2. Install dependencies:

pip install -r requirements.txt

3. Create a .env file in the project root with your API keys:

OPENAI_API_KEY=your_openai_api_key
ANTHROPIC_API_KEY=your_anthropic_api_key

Docker Installation

1. Clone the repository and create .env file as described above.

2. Build and run using Docker Compose:

docker-compose up --build

Or run in background:

docker-compose up -d

To stop the container:

docker-compose down

Usage

Converting Word Documents

from embeddings.vectordbdoc import main

# Convert a Word document to ChromaDB embeddings
collection, ids = main(
    "path/to/your/document.docx",
    document_metadata={
        "author": "Your Name",
        "title": "Document Title",
        "tags": ["tag1", "tag2"]
    }
)

Converting Excel Files

from embeddings.vectordbexcel import EnterpriseExcelEmbedder

# Initialize the embedder
embedder = EnterpriseExcelEmbedder()

# Analyze Excel and generate schema recommendations
schema_recommendations = embedder.analyze_excel("path/to/your/excel.xlsx")

# Create embeddings using the recommended schema
stats = embedder.embed_excel("path/to/your/excel.xlsx", collection_name_prefix="enterprise")

Running the MCP Server

Local Run

python mcpserver.py

Docker Run

docker-compose up

The server will start on http://localhost:8000

Using the MCP Client

Local Run

python mcpclient.py

Docker Run

The client is automatically started in the Docker container in interactive mode.

Project Structure

• embeddings/
  • vectordbdoc.py: Word document to ChromaDB conversion
  • vectordbexcel.py: Excel file to ChromaDB conversion
• mcp/
  • tools/: MCP tool implementations
    • python_execute.py: Safe Python code execution
    • file_saver.py: File saving and manipulation
    • browser_use_tool.py: Browser interaction
    • str_replace_editor.py: String replacement
    • bash.py: Shell command execution
    • terminate.py: Process termination
• mcpclient.py: CLI client for interacting with the MCP server
• mcpserver.py: FastAPI server with Claude integration
• rag.py: RAG system implementation
• toolaugmentedrag.py: Tool-augmented RAG system
• chromadbquery.py: ChromaDB query utilities
• Dockerfile: Docker container definition
• docker-compose.yml: Docker Compose configuration

Docker Volumes

The Docker setup includes persistent volumes for:

• /app/config: Configuration files
• /app/data: Application data
• /app/chroma_db: ChromaDB database files

API Documentation

Once the server is running, visit http://localhost:8000/docs for the interactive API documentation.

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Building a Tool-Augmented RAG System with Claude MCP or OpenManus

Instead of directly connecting an LLM to your ChromaDB, you can build a more sophisticated system that uses tools to iteratively analyze and process data. This approach combines the benefits of RAG with the multi-step reasoning capabilities of agents.