MCP RAG with ChromaDB - Multi-Format Document Support

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A powerful MCP (Model Context Protocol) server that provides RAG (Retrieval-Augmented Generation) capabilities with support for multiple document formats. Powered by LangChain, ChromaDB, with OpenAI or Ollama embeddings.

Screenshots

Embedding Options

Choose your embedding provider:

• OpenAI (Recommended for Claude Desktop/MCP) - Reliable, fast, cloud-based. See OPENAI_SETUP.md

• Ollama (Free, Local) - No cost, runs locally, may have MCP connection issues

Quick Start with OpenAI:

1. Get API key from https://platform.openai.com/api-keys

2. Copy .env.example to .env

3. Set OPENAI_API_KEY=sk-your-key-here

4. Run python server.py

See OPENAI_SETUP.md for detailed instructions.

Supported Document Formats

• PDF (.pdf) - via PyPDF2

• Microsoft Word (.docx, .doc) - via python-docx

• PowerPoint (.pptx, .ppt) - via python-pptx

• Excel (.xlsx, .xls) - via openpyxl

• OpenDocument Text (.odt) - via odfpy

• HTML (.html, .htm) - via BeautifulSoup4

• Plain Text (.txt)

• Markdown (.md, .markdown)

• CSV (.csv)

• JSON (.json)

• XML (.xml)

• RTF (.rtf)

Features

• Multi-format document ingestion with automatic format detection

• Vector storage using ChromaDB with persistent storage

• Dual embedding support: OpenAI (recommended for MCP) or Ollama (free, local)

• Concurrent document processing for improved performance

• Batch database writes for efficiency (OpenAI processes 100 docs/batch)

• Support for URLs, single files, and entire directories

• Semantic search and retrieval with similarity scoring

• Graceful fallbacks when optional dependencies are missing

• Environment-based configuration via .env file

Installation

1. Clone or download this repository

2. Install Python dependencies:

OR

3. Choose your embedding provider:

   Option A: OpenAI (Recommended)

   # Copy environment file cp .env.example .env # Edit .env and add your API key # Get key from: https://platform.openai.com/api-keys OPENAI_API_KEY=sk-your-key-here

   Option B: Ollama (Free, Local)

   # Download from https://ollama.ai # Pull the embedding model ollama pull nomic-embed-text # Edit .env EMBEDDING_PROVIDER=ollama

   Note: Ollama may have connection issues in MCP/Claude Desktop context.

Usage

Running the Server

Integration with Claude Desktop

To use this MCP server with Claude Desktop, add the following configuration to your Claude Desktop config file:

For MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json For Windows: %APPDATA%\Claude\claude_desktop_config.json

Replace /path/to/mcp_rag_server with the actual path to your installation directory.

Alternatively, you can also integrate it directly from Github by updating claude desktop as follows:

If this is your first MCP server then use this instead:

Available MCP Tools

1. ingest_document

Ingest documents from various sources and formats.

Parameters:

• source (str): URL, file path, or directory path

Examples:

• Ingest from URL: ingest_document("https://example.com/document.pdf")

• Ingest single file: ingest_document("/path/to/document.docx")

• Ingest entire directory: ingest_document("/path/to/documents/")

Supported sources:

• HTTP/HTTPS URLs (downloads file first)

• Local file paths (any supported format)

• Directory paths (processes all supported files)

2. retrieve

Search for relevant document chunks based on a query.

Parameters:

• query (str): Search query

• n (int, optional): Number of results to return (default: 5, max: 100)

Returns: List of chunks with text, metadata, and similarity scores

3. db_info

Get information about the database and its contents.

Returns: Database statistics, configuration, and unique sources

4. clear_db

Clear all data from the database.

Returns: Status message

5. ingest_pdf (Legacy)

Backwards-compatible PDF ingestion tool. Redirects to ingest_document.

Configuration

Edit the configuration section in server.py and .env:

server.py settings:

.env settings:

Architecture

Document Processing Pipeline

1. Source Detection: Determines if source is URL, file, or directory

2. Download (if URL): Downloads file to local storage

3. Format Detection: Identifies document format by extension

4. Text Extraction: Uses appropriate extractor for the format

5. Text Chunking: Splits text into overlapping chunks

6. Embedding: Generates vector embeddings using OpenAI or Ollama (configurable)

7. Storage: Stores chunks and embeddings in ChromaDB

Extraction Methods

Each format has a dedicated extraction function:

• extract_text_from_pdf() - Extracts text from PDF pages

• extract_text_from_docx() - Extracts paragraphs and tables from Word docs

• extract_text_from_pptx() - Extracts text from PowerPoint slides

• extract_text_from_xlsx() - Extracts data from Excel sheets

• extract_text_from_odt() - Extracts text from OpenDocument files

• extract_text_from_html() - Parses HTML and extracts clean text

• extract_text_from_markdown() - Processes Markdown files

• And more...

The main extract_text() function routes to the appropriate extractor based on file extension.

Dependencies

Required

• langchain-chroma - ChromaDB integration

• langchain-ollama - Ollama embeddings

• langchain-core - Core LangChain functionality

• langchain-text-splitters - Text chunking

• requests - HTTP downloads

• fastmcp - MCP server framework

Document Format Support

• PyPDF2 - PDF extraction

• python-docx - DOCX extraction

• beautifulsoup4 + lxml - HTML/XML parsing

• python-pptx - PowerPoint extraction

• openpyxl - Excel extraction

• odfpy - OpenDocument extraction

• markdown - Markdown processing (optional)

Error Handling

The server includes comprehensive error handling:

• Graceful degradation when optional dependencies are missing

• Logging of extraction failures without crashing the entire process

• Informative error messages for unsupported file types

• Fallback to text extraction for unknown formats

Performance Optimizations

• Concurrent Processing: Multiple documents processed in parallel using ThreadPoolExecutor

• Batch Writes: All chunks written to database in a single operation

• Async/Await: Non-blocking I/O operations

• Read-Only Mode: Excel files opened in read-only mode for better performance

• Streaming Downloads: Large files downloaded in chunks

Limitations

• .doc (old Word format) support limited - requires python-docx which works best with .docx

• .ppt (old PowerPoint format) support limited - works best with .pptx

• .xls (old Excel format) support limited - works best with .xlsx

• RTF files use basic text extraction

• Scanned PDFs without OCR will not extract text

• Some complex document layouts may not preserve formatting

Troubleshooting

Ollama Connection Issues

Ensure Ollama is running and accessible:

Empty Text Extraction

• For PDFs: May be scanned images - consider OCR tools

• For other formats: Check file isn't corrupted or password-protected

License

This project is open source and available under the MIT License.

Acknowledgments

This project is built with and depends on several excellent open-source technologies:

Core Framework & Infrastructure:

• FastMCP - MCP server framework

• LangChain - Core LLM application framework

• ChromaDB - Vector database for embeddings storage

Embedding Providers:

• OpenAI - Cloud-based embedding API (recommended)

• Ollama - Local embedding models

Document Processing:

• PyPDF2 - PDF text extraction

• python-docx - Microsoft Word processing

• python-pptx - PowerPoint processing

• openpyxl - Excel spreadsheet processing

• Beautiful Soup - HTML/XML parsing

• odfpy - OpenDocument format support

Special thanks to the developers and maintainers of these projects for making this RAG server possible.

Contributing

Contributions welcome! To add support for new formats:

1. Add extraction function following the pattern extract_text_from_[format]()

2. Add format extension to SUPPORTED_EXTENSIONS

3. Add mapping in extract_text() function

4. Update documentation