MCP Server with RAG and ChromaDB for Document Retrieval

MCP Server with RAG and ChromaDB for Document Retrieval

This repository provides a foundational MCP server implementation enhanced with Retrieval Augmented Generation (RAG) capabilities, leveraging LangChain and ChromaDB. It's designed for developers building AI applications that require sophisticated document retrieval and processing across multiple formats. The setup facilitates the creation of intelligent agents capable of understanding and responding to queries based on a corpus of documents.

What it Does

The core functionality of this tool is to establish an MCP server that integrates RAG. This means it can ingest documents in various formats, process them, and make them searchable and retrievable. When a query is made, the RAG system retrieves relevant information from the document corpus, which is then used by a language model (via LangChain) to generate a contextually aware response. ChromaDB serves as the vector database, efficiently storing and querying document embeddings.

Key Features

• MCP Server Foundation: Provides a robust base for building MCP-based AI agents and applications.
• RAG Integration: Implements Retrieval Augmented Generation for enhanced question-answering and information extraction.
• Multi-Document Format Handling: Designed to process and retrieve information from diverse document types.
• LangChain Framework: Utilizes LangChain for orchestrating LLM interactions and RAG pipelines.
• ChromaDB Vector Store: Employs ChromaDB for efficient semantic search and document embedding management.
• Developer-Focused: Structured for direct integration into custom AI development workflows.

Who it's For

This tool is specifically for AI developers and ML engineers looking to build advanced AI applications. It is ideal for those who need to:

• Develop agents that can query and synthesize information from large document sets.
• Implement RAG pipelines for custom knowledge bases.
• Integrate document retrieval into existing MCP server architectures.
• Experiment with vector databases like ChromaDB for semantic search.
• Build applications requiring natural language understanding over structured and unstructured data.