Docs-to-AI -- Document Query System with MCP

A A production-ready RAG that runs as a Model Context Protocol (MCP) server, which enables LLMs (like Claude Desktop or any other LLM that supports MCP) to query your documents using semantic search. Organizes documents by topics based on folder structure. Supports: PDF, Word, Excel, Markdown, PowerPoint, HTML, TXT, CSV. Supported extensions: .pdf, .docx, .doc, .xlsx, .xls, .xlsam, .xlsb, .md, .pptx, .html, .htm, .txt, .csv

The model used for document retrieval is all-MiniLM-L6-v2, with 384 dimensions for the embeddings.

Features

• Extract text from PDF, Word, Excel, Markdown, PowerPoint, HTML, TXT, and CSV documents

• Organize documents by topics (using folder structure)

• Generate embeddings for semantic search

• Store documents in a vector database (chromadb)

• Expose MCP tools for Claude to search and retrieve documents

• Filter searches by topic/category

• Handle multiple documents with the same filename across different topics

• Advanced Search: Phrase matching, date-range filtering, regex pattern matching

• Smart Chunking: Fixed-size, by-paragraph, semantic heading-based, or token-based chunking

• Preserve heading/section structure for smarter retrieval

• Optional re-ranker model for improved search quality

Related MCP server: Chalee MCP RAG

Architecture

Documents (PDF, Word, Excel, Markdown, PowerPoint, HTML, TXT, CSV)
 → Text Extraction
 → Chunking (fixed/paragraph/heading/token)
 → Embeddings
 → chromadb (with topic tags)
 ↓
 MCP Server Tools
 ↓
 Claude

Document Organization

This system is designed to work with PDFs organized in a folder structure where:

• Each folder represents a topic or category

• PDFs in that folder are automatically tagged with the topic name

• Documents with the same filename in different folders are handled correctly

Example structure:

pdfs/
├── Machine_Learning/
│ ├── neural_networks.pdf
│ ├── deep_learning.pdf
│ └── introduction.pdf
├── Python_Programming/
│ ├── basics.pdf
│ ├── advanced.pdf
│ └── introduction.pdf # Different from ML's introduction.pdf
└── Data_Science/
 ├── statistics.pdf
 └── visualization.pdf

1. Installation, with local python

This project uses uv for fast, reliable Python package management.

Install uv (if not already installed)

# On macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# On Windows
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

Install and run

uv sync # Install dependencies from pyproject.toml
python -m app.scan_all_my_documents # Ingest documents
python mcp_server.py # Start the MCP Server

Dual Transport Mode

The MCP server now runs BOTH transports concurrently (thanks to FastMCP):

• stdio mode - For local connections via Claude Desktop

• HTTP/SSE mode - For remote connections over HTTP

Both are active simultaneously by default. The server automatically exposes:

• SSE endpoint: http://localhost:38777/sse (for establishing connections)

• Messages endpoint: http://localhost:38777/messages/ (for sending requests)

You can customize the HTTP transport using command line arguments or environment variables:

# Using command line arguments
python mcp_server.py --host 0.0.0.0 --port 38777

# Or using environment variables
export MCP_HOST=0.0.0.0
export MCP_PORT=38777
python mcp_server.py

The script will:

• Recursively scan the directory

• Detect topics from folder names

• Extract and chunk text from each PDF/Words/Markdown/Excel

• Tag chunks with their topic

• Store everything in chromadb

Example output:

Found 15 PDF files
Base directory: /path/to/pdfs

Detected topics: Data_Science, Machine_Learning, Python_Programming

[1/15] Processing: neural_networks.pdf
 Topic: Machine_Learning
 ✓ Added 45 chunks
...

INGESTION SUMMARY
==================
Total PDFs processed: 15
Successful: 15
Failed: 0
Total chunks added: 523

Documents per topic:
 Data_Science: 4 documents, 156 chunks
 Machine_Learning: 6 documents, 234 chunks
 Python_Programming: 5 documents, 133 chunks

Add to your Claude Desktop config (%APPDATA%/Claude/claude_desktop_config.json):

For stdio mode (local):

{
 "mcpServers": {
 "docs-to-ai": {
 "command": "python",
 "args": ["C:/[UPDATE_PATH_TO_DOCS-TO-AI]/docs-to-ai/mcp_server.py"]
 }
 }
}

For HTTP/SSE mode (remote):

{
 "mcpServers": {
 "docs-to-ai": {
 "url": "http://localhost:38777/sse",
 "transport": "sse"
 }
 }
}

2. Installation, with Docker

You need Docker Desktop, or Docker Engine, running. Then just write the following into a file called "docker-compose.yaml" and it will pull and run the image from Docker Hub (Image: https://hub.docker.com/r/dmeric/docs-to-ai ):

services:
 docs-to-ai:
 image: dmeric/docs-to-ai
 container_name: docs-to-ai
 
 volumes:
 - ./cache/chromadb:/app/chromadb # chromadb database (persists the vector store)
 - ./cache/doc_cache:/app/doc_cache # Document cache (persists extracted text)
 - ./my-docs:/app/my-docs:ro # Documents directory (your PDFs and Word docs). Read-only to prevent accidental modifications
 
 # Stdin/stdout - required for MCP protocol in stdio mode
 stdin_open: true
 tty: true

 ports:
 - "${MCP_PORT:-38777}:38777" # for http/sse transport, on http://localhost:38777/sse

 # Restart policy
 restart: unless-stopped
 
 # # Resource limits (optional - adjust based on your needs)
 # deploy:
 # resources:
 # limits:
 # cpus: '2'
 # memory: 4G
 # reservations:
 # cpus: '1'
 # memory: 2G
 

then run, in bash or in Powershell:

docker compose up -d

Docker Configuration

The docker-compose.yml configures:

Volumes:

• ./cache/chromadb:/app/cache/chromadb - Vector store database (persistent)

• ./cache/doc_cache:/app/cache/doc_cache - Document cache (persistent)

• ./my-docs:/app/my-docs:ro - Your documents directory (read-only)

Ports:

• 38777 - HTTP/SSE endpoint for remote MCP connections

Environment Variables:

• FULL_SCAN_ON_BOOT - Set to True to scan documents on startup (default: False)

• FOLDER_WATCHER_ACTIVE_ON_BOOT - Set to True to start folder watcher on startup (default: True)

• MCP_PORT - Customize HTTP port (default: 38777)

Example with custom environment variables:

# Create a .env file
echo "FULL_SCAN_ON_BOOT=True" > .env
echo "FOLDER_WATCHER_ACTIVE_ON_BOOT=True" >> .env
echo "MCP_PORT=38777" >> .env

# Start with environment variables
docker compose up -d

Add to your Claude Desktop config (%APPDATA%/Claude/claude_desktop_config.json):

{
 "mcpServers": {

 "docs-to-ai": {
 "command": "docker",
 "args": [
 "exec",
 "-i",
 "docs-to-ai",
 "python",
 "mcp_server.py"
 ]
 }

 }
}

Finally, put your documents in the folder /my-docs, and ask the server to scan the documents, and optionally to start the folder watcher. You should now be able to ask your LLM questions about the documents.

Project Structure

• mcp_server.py - Main MCP server implementation (FastMCP-based)

• app/document_processor.py - Document text extraction and chunking (PDF, Word, Excel, Markdown)

• app/vector_store.py - Vector database operations (ChromaDB)

• app/scan_all_my_documents.py - Batch document ingestion script

• app/incremental_updater.py - Incremental document update logic

• app/folder_watcher.py - Automatic folder monitoring and change detection

• app/config.py - Configuration settings

• pyproject.toml - Python dependencies and project metadata (using uv)

• Dockerfile - Docker container configuration

• docker-compose.yml - Docker Compose configuration

MCP Tools

The server exposes the following tools for LLMs:

Search & Discovery:

• search_documents - Semantic search across all documents with optional filters:

  • topic - Filter by topic/category

  • phrase_search - Exact phrase matching

  • date_from / date_to - Filter by last_modified timestamp (Unix)

  • regex_pattern - Filter by regex pattern in text

• list_documents - List all available documents (with optional topic filter)

• list_topics - List all topics/categories

• get_collection_stats - Get statistics about the collection (file types, sizes, counts)

Document Management:

• scan_all_my_documents - Manually trigger a full document scan and re-index

• start_watching_folder - Start automatic folder monitoring with incremental updates

• stop_watching_folder - Stop the folder watcher

• get_time_of_last_folder_scan - Check when the last scan occurred and status

Example Queries for Claude

Once configured, you can ask Claude:

General queries:

• "What documents do you have access to?"

• "What topics are available?"

• "Search for information about neural networks"

• "Show me the collection statistics"

Topic-specific queries:

• "Search for Python programming concepts in the Python_Programming topic"

• "Show me all documents about Machine Learning"

• "Find information about data visualization in the Data_Science topic"

Complex queries:

• "Compare what different documents say about deep learning"

• "Find all mentions of pandas across all documents"

• "What are the key concepts in the Machine_Learning documents?"

Advanced search (with filters):

• "Search for 'neural networks' as exact phrase" - Phrase search

• "Find documents about Python modified after 2024-01-01" - Date filtering

• "Search for items matching pattern \d{3}-\d{4}" - Regex pattern search

Document management:

• "Scan all my documents" - Triggers a full re-index

• "Start watching my documents folder for changes" - Enables automatic updates

• "When was the last scan?" - Check folder monitoring status

• "Stop watching the folder" - Disable automatic updates

Configuration

Edit app/config.py to customize:

Document Processing:

• SUPPORTED_EXTENSIONS - File types to process (default: .pdf, .docx, .doc, .md, .xlsx, .xls, .xlsam, .xlsb, .pptx, .html, .htm, .txt, .csv)

• CHUNKING_STRATEGY - Chunking strategy: fixed_size, by_paragraph, semantic_heading, or by_token (default: by_paragraph)

• CHUNK_SIZE - Characters per chunk (or tokens if CHUNK_BY_TOKEN=true) (default: 1000)

• CHUNK_OVERLAP - Overlap between chunks (default: 200)

• CHUNK_BY_TOKEN - Use token-based chunking instead of character-based (env var, default: False)

• TOKENIZER_MODEL - TikToken tokenizer for token chunking (default: cl100k_base)

• PRESERVE_HEADINGS - Preserve heading structure in chunks (env var, default: True)

• MAX_HEADING_CHUNK_SIZE - Max chars per heading-based chunk (default: 2000)

Search Configuration:

• DEFAULT_SEARCH_RESULTS - Default number of results (default: 10)

• MAX_SEARCH_RESULTS - Maximum allowed results (default: 20)

• USE_RERANKER - Enable re-ranker model for improved results (env var, default: True)

• RERANKER_MODEL - Re-ranker model name (default: cross-encoder/ms-marco-MiniLM-L-6-v2)

• RERANKER_TOP_N - Number of results to re-rank (default: 50)

• USE_BM25 - Enable hybrid search with BM25 (env var, default: False)

Embedding Model:

• EMBEDDING_MODEL - Sentence transformer model (default: all-MiniLM-L6-v2)

• EMBEDDING_DIMENSION - Vector dimension (default: 384)

Topic Configuration:

• USE_FOLDER_AS_TOPIC - Use folder hierarchy as topics (default: True)

• DEFAULT_TOPIC - Default topic for uncategorized docs (default: uncategorized)

• TOPIC_SEPARATOR - Display separator for hierarchical topics (default: >)

Storage:

• CHROMADB_DIR - Vector database location (default: cache/chromadb)

• DOC_CACHE_DIR - Document cache location (default: cache/doc_cache)

• DOCS_DIR - Documents directory (default: my-docs, configurable via DOCS_DIR env var)

• CHROMA_COLLECTION_NAME - Collection name (default: my-documents)

Startup Behavior:

• FULL_SCAN_ON_BOOT - Scan documents on server startup (env var, default: False)

• FOLDER_WATCHER_ACTIVE_ON_BOOT - Start folder watcher on startup (env var, default: True)

Server Transport:

• MCP_HOST - HTTP server host (env var, default: 0.0.0.0)

• MCP_PORT - HTTP server port (env var, default: 38777)

License

MIT

Maintenance

Resources

• GitHub Repository
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