Which integrations are available for this server?

Provides graph storage for knowledge graphs, enabling entity and relationship extraction and multiple query modes (local, global, hybrid, etc.). Provides LLM, embedding, and vision models for knowledge graph operations, document processing, and multimodal queries. Provides vector storage with pgvector for embedding-based retrieval, supporting RAG and hybrid search.

How do I use RAG-Anything MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@RAG-Anything MCP Server ingest the document 'climate.pdf' and perform a hybrid query on 'global warming effects'" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

RAG-Anything MCP Server

by serkanyasr

Overview Schema Related Servers Score Discussions

Python

Hybrid

RAG-Anything MCP Server

🚀 Model Context Protocol server for advanced RAG with Knowledge Graphs

License: MIT Python 3.13+ MCP

Knowledge Graph + Document Processing + Multimodal AI

📋 Overview

RAG-Anything MCP Server is a production-ready Model Context Protocol (MCP) server that combines:

🧠 Knowledge Graph Queries - Multiple query modes (naive, local, global, hybrid, mix, bypass)
📄 Document Ingestion - Text and PDF processing with multimodal content extraction
🔍 Entity Extraction - Automatic entity and relationship extraction from documents
💾 Hybrid Storage - Neo4j (graph) + PostgreSQL with pgvector (vectors)
🖼️ Multimodal Support - Process images, tables, and equations from PDFs
📡 MCP Compliant - Standard Model Context Protocol implementation

✨ Features

Core Capabilities

Feature	Description
📄 Document Ingestion	Text and PDF processing with multimodal content extraction
🧠 Knowledge Graph Queries	Multiple query modes (naive, local, global, hybrid, mix, bypass)
🔍 Entity Extraction	Automatic entity and relationship extraction from documents
💾 Hybrid Storage	Neo4j (graph) + PostgreSQL with pgvector (vectors)
📡 MCP Compliant	Standard Model Context Protocol implementation
🖼️ Multimodal Support	Process images, tables, and equations from PDFs

Query Modes

naive - Simple keyword search
local - Local entity-based search
global - Global community-based search
hybrid - Combines local and global (recommended)
mix - Mixes multiple strategies
bypass - Direct LLM query without graph

🚀 Quick Start

Prerequisites

Docker & Docker Compose (for database services)
Python 3.13+
OpenAI API Key

Option 1: All-in-One Docker (Recommended)

Start everything with Docker Compose:

# Clone the repository
git clone https://github.com/serkanyasr/rag-anythink-mcp.git
cd rag-anythink-mcp

# Copy environment template
cp .env.example .env

# Edit .env and set your OpenAI API key
# OPENAI_API_KEY=sk-...

# Start all services (Neo4j + PostgreSQL + MCP Server)
docker-compose up -d

# View logs
docker-compose logs -f rag-mcp

# Stop services
docker-compose down

This will start:

Neo4j 5.23 on bolt://localhost:7687 (HTTP UI on http://localhost:7474)
PostgreSQL 16 + pgvector on localhost:5432
MCP Server on http://localhost:8000

Option 2: Development Mode

Run the MCP server locally while databases run in Docker:

Windows:

# One-click startup - handles everything automatically
start-dev.bat

Linux/Mac:

# Make executable and run
chmod +x start-dev.sh
./start-dev.sh

Option 3: Manual Setup

# 1. Start Docker services (databases only)
docker-compose up -d neo4j postgres

# 2. Create virtual environment
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate

# 3. Install dependencies
pip install -e .

# 4. Run the server
python main.py

⚙️ Configuration

Create a .env file in the project root:

# =====================
# Neo4j Configuration
# =====================
NEO4J_URI=bolt://localhost:7687
NEO4J_USER=neo4j
NEO4J_PASSWORD=your_neo4j_password

# =====================
# PostgreSQL Configuration (for RAG)
# =====================
RAG_DB_HOST=localhost
RAG_DB_PORT=5432
RAG_DB_NAME=rag_anythink
RAG_DB_USER=postgres
RAG_DB_PASSWORD=your_postgres_password

# =====================
# OpenAI Configuration
# =====================
OPENAI_API_KEY=your_openai_api_key

# =====================
# Document Processing
# =====================
# Parser: mineru, docling
KG_PARSER=mineru
# Parse method: auto, ocr, txt
KG_PARSE_METHOD=auto
# Enable image extraction from PDFs
KG_ENABLE_IMAGE=true
# Enable table extraction from PDFs
KG_ENABLE_TABLE=true
# Enable equation extraction from PDFs
KG_ENABLE_EQUATION=true

# =====================
# RAG Configuration
# =====================
# Working directory for RAG output
KG_WORKING_DIR=./rag_output
# Workspace name (production, development, etc.)
KG_WORKSPACE=production
# Context window for LLM (pages before/after for context)
KG_CONTEXT_WINDOW=1
# Maximum concurrent files for processing
KG_MAX_CONCURRENT_FILES=4
# Embedding dimension (depends on model)
KG_EMBEDDING_DIM=3072
# Maximum token size for embeddings
KG_MAX_TOKEN_SIZE=8192
# LLM model for knowledge graph operations
KG_LLM_MODEL=gpt-4o-mini
# Vision model for multimodal processing
KG_VISION_MODEL=gpt-4o
# Embedding model
KG_EMBEDDING_MODEL=text-embedding-3-large
# Default query mode (naive, local, global, hybrid, mix, bypass)
KG_DEFAULT_MODE=hybrid

# =====================
# MCP Configuration
# =====================
# MCP server name
RAG_MCP_NAME=rag-anything-mcp
# MCP server version
RAG_MCP_VERSION=1.0.0
# MCP server host
RAG_MCP_HOST=localhost
# MCP server port
RAG_MCP_PORT=8055
# MCP log level
RAG_MCP_LOG=info
# MCP transport protocol: stdio, http, sse, streamable-http
RAG_MCP_TRANSPORT=streamable-http
# Application log level: DEBUG, INFO, WARNING, ERROR, CRITICAL
LOG_LEVEL=INFO
# Application log format: json, text
LOG_FORMAT=json

🔌 MCP Tools

The server provides the following MCP tools:

Tool	Description
`ingest_document`	Ingest text or PDF documents
`query_knowledge_graph`	Query with multiple modes (naive, local, global, hybrid)
`query_multimodal`	Query with images, tables, equations
`process_document_file`	Process PDF files with multimodal extraction
`insert_content_list`	Insert pre-parsed content
`delete_data`	Delete documents by ID
`get_graph_statistics`	Get graph statistics
`get_config_info`	Get configuration info

📚 Usage Examples

Python Client

from src.services.kg_service import KGService

# Initialize service
service = KGService()
await service.initialize()

# Ingest a document
result = await service.ingest_text(
    text="Your document text here...",
    metadata={"title": "My Document"}
)

# Query the knowledge graph
response = await service.query(
    query_text="What are the main topics?",
    mode="hybrid"
)

MCP Client (Claude Desktop)

Add to your Claude Desktop MCP config:

{
  "mcpServers": {
    "rag-anything": {
      "command": "docker-compose",
      "args": ["up", "rag-mcp"],
      "env": {
        "OPENAI_API_KEY": "your-api-key"
      }
    }
  }
}

🗂️ Project Structure

rag-anythink-mcp/
├── src/
│   ├── config/         # Pydantic configuration
│   ├── core/           # Interfaces and models
│   ├── database/       # Database connections (Neo4j, PostgreSQL)
│   │   └── kg/         # Knowledge Graph layer
│   ├── mcp/            # MCP servers
│   │   └── kg/         # RAG MCP server
│   ├── services/       # Business logic
│   ├── utils/          # Utilities
│   └── llm.py          # LLM clients
├── main.py             # Entry point
├── Dockerfile          # Docker image for MCP server
├── docker-compose.yml  # Multi-container orchestration
├── pyproject.toml      # Dependencies (uv)
├── start-dev.bat       # Windows dev startup
├── start-dev.sh        # Linux/Mac dev startup
└── README.md

🛠️ Development

Setup Development Environment

# Clone repository
git clone https://github.com/serkanyasr/rag-anythink-mcp.git
cd rag-anythink-mcp

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install in development mode with all extras
pip install -e ".[full]"

Running Tests

# Run all tests
pytest

# Run with coverage
pytest --cov=src

# Run specific test file
pytest tests/test_kg_service.py

Code Quality

# Format code
ruff format src/

# Check linting
ruff check src/

# Type checking
pyright src/

🐳 Docker Deployment

Full Stack Deployment

# Deploy all services
docker-compose up -d

# Check service health
docker-compose ps

# View logs
docker-compose logs -f

# Stop all services
docker-compose down

# Stop and remove volumes (clean slate)
docker-compose down -v

Individual Services

# Only databases (for local dev)
docker-compose up -d neo4j postgres

# Only MCP server (databases must be running)
docker-compose up -d rag-mcp

Health Checks

The services include built-in health checks:

Neo4j: Cypher-shell connectivity test
PostgreSQL: pg_isready checks
MCP Server: Depends on healthy databases

📖 Architecture

System Components

┌─────────────────┐
│   MCP Client    │
│  (Claude, etc)  │
└────────┬────────┘
         │ MCP Protocol
         ▼
┌─────────────────┐
│  MCP Server     │
│  (FastMCP)      │
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│  KG Service     │
│  (RAG-Anything) │
└────────┬────────┘
         │
    ┌────┴────┐
    ▼         ▼
┌──────┐  ┌─────────┐
│Neo4j│  │PostgreSQL│
│Graph│  │+ pgvector│
└──────┘  └─────────┘

Data Flow

Ingestion: Documents → Entity Extraction → Neo4j (graph) + PostgreSQL (vectors)
Query: Query Text → Embedding → Vector Search + Graph Traversal → LLM Synthesis

🤝 Contributing

Contributions are welcome! Please follow these steps:

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Make your changes
Run tests (pytest)
Commit your changes (git commit -m 'Add some amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

Development Guidelines

Write tests for new features
Follow PEP 8 style guidelines
Update documentation as needed
Keep commits atomic and well-described

❓ FAQ

Update the passwords in both .env and docker-compose.yml. Make sure they match.

Yes! Set OPENAI_EMBEDDING_MODEL and adjust RAG_EMBEDDING_DIM in your .env file.

# Neo4j backup
docker exec rag-neo4j neo4j-admin database dump neo4j --to-path=/backups

# PostgreSQL backup
docker exec rag-postgres pg_dump -U postgres rag_anythink > backup.sql

Check Docker is running: docker ps
Check service logs: docker-compose logs
Verify environment variables in .env
Ensure databases are healthy: docker-compose ps

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

This server cannot be installed

license - permissive license

quality - not tested

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

–Releases (12mo)

Commit activity

Resources

GitHub Repository

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