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Obsidian Elite RAG MCP Server

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Obsidian Elite RAG MCP Server

Python Version License MCP Server

An elite Retrieval-Augmented Generation (RAG) system that transforms Obsidian vaults into AI-paired cognitive workflow engines with advanced Graphiti knowledge graph integration.

🌟 Features

🧠 Multi-Layer RAG Architecture

  • L1: Semantic Context (30% weight) - Vector similarity search with OpenAI embeddings

  • L2: Knowledge Graph (25% weight) - Graphiti-powered entity and relationship retrieval

  • L3: Graph Traversal (15% weight) - NetworkX-based link traversal

  • L4: Temporal Context (15% weight) - Time-based relevance and freshness

  • L5: Domain Specialization (15% weight) - Context-aware retrieval

  • L6: Meta-Knowledge (remaining weight) - Knowledge about knowledge

🔗 Advanced Knowledge Graph

  • 27+ Entity Types: concepts, people, organizations, technologies, methodologies, frameworks, algorithms, etc.

  • 40+ Relationship Types: implements, uses, depends_on, extends, based_on, similar_to, integrates_with, etc.

  • Dual-Graph Architecture: Neo4j (structured) + NetworkX (unstructured backup)

  • Automatic Entity Extraction: Pattern matching and NLP-based entity recognition

  • Relationship Detection: Confidence scoring and validation

🚀 MCP Server Integration

  • Claude Code Compatible: Full Model Context Protocol server implementation

  • Tool-based API: Ingest, query, search knowledge graph, get entity context

  • Real-time Status: System health monitoring and database connection checks

  • Async Processing: High-performance concurrent operations

📋 Requirements

  • Python 3.9+

  • Docker & Docker Compose

  • OpenAI API key

  • Obsidian vault (optional but recommended)

  • Neo4j Database (handled by setup scripts)

  • Qdrant Vector Database (handled by setup scripts)

🛠️ Installation

Option 1: Install from PyPI (Recommended)

pip install obsidian-elite-rag-mcp

Option 2: Install from Source

git clone https://github.com/aegntic/aegntic-MCP.git cd aegntic-MCP/obsidian-elite-rag pip install -e .

🚀 Quick Start

1. System Setup

# Initialize the system obsidian-elite-rag-cli setup # Start both databases (Qdrant + Neo4j) obsidian-elite-rag-cli start-databases # Or start manually with Docker docker run -d --name qdrant -p 6333:6333 -v $(pwd)/data/qdrant:/qdrant/storage qdrant/qdrant:latest docker run -d --name neo4j -p 7474:7474 -p 7687:7687 -v $(pwd)/data/neo4j:/data \ --env NEO4J_AUTH=neo4j/password --env NEO4J_PLUGINS='["apoc","graph-data-science"]' \ neo4j:5.14

2. Ingest Your Obsidian Vault

# Ingest all markdown files obsidian-elite-rag-cli ingest /path/to/your/obsidian/vault # Check system status obsidian-elite-rag-cli status /path/to/your/obsidian/vault

3. Start MCP Server

# Start the MCP server for Claude Code integration obsidian-elite-rag-cli server

4. Configure Claude Code

Add to your Claude Code configuration (~/.config/claude-code/config.json):

{ "mcpServers": { "obsidian-elite-rag": { "command": "obsidian-elite-rag-cli", "args": ["server"], "env": { "OPENAI_API_KEY": "your-openai-api-key" } } } }

📖 Usage Examples

CLI Usage

# Query the RAG system obsidian-elite-rag-cli query "How does the RAG system work?" /path/to/vault # Search knowledge graph for entities obsidian-elite-rag-cli graph /path/to/vault --entity-query "machine learning" # Technical queries obsidian-elite-rag-cli query "JWT authentication patterns" /path/to/vault --query-type technical # Research queries obsidian-elite-rag-cli query "latest developments in LLMs" /path/to/vault --query-type research

MCP Server Tools (Claude Code)

When connected to Claude Code, you'll have access to these tools:

  1. ingest_vault - Ingest markdown files from an Obsidian vault

  2. query_rag - Query the elite RAG system with multi-layer retrieval

  3. search_knowledge_graph - Search the Graphiti knowledge graph for entities

  4. get_entity_context - Get rich context for a specific entity

  5. get_related_entities - Get entities related through relationships

  6. get_system_status - Get system status and database connections

Example in Claude Code:

@obsidian-elite-rag please ingest my vault at /Users/me/Documents/Obsidian @obsidian-elite-rag query "what are the key concepts in machine learning?" with vault path /Users/me/Documents/Obsidian @obsidian-elite-rag search_knowledge_graph for "neural networks" in vault /Users/me/Documents/Obsidian

🏗️ Architecture

System Components

┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │ Obsidian │ │ Claude Code │ │ MCP Protocol │ │ Vault │◄──►│ Integration │◄──►│ Server │ └─────────────────┘ └─────────────────┘ └─────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────────┐ │ Elite RAG System │ ├─────────────────┬─────────────────┬─────────────────────────────┤ │ Semantic │ Knowledge │ Temporal & Domain │ │ Search │ Graph │ Specialization │ │ (Qdrant) │ (Neo4j) │ │ └─────────────────┴─────────────────┴─────────────────────────────┘

Knowledge Graph Entity Types

  • Core: concept, person, organization, event, location

  • Technical: technology, algorithm, framework, system, application

  • Process: methodology, workflow, process, pattern

  • Implementation: tool, library, database, api, protocol

  • Documentation: standard, specification, principle, theory, model

  • Architecture: design, implementation, project, research

Knowledge Graph Relationship Types

  • Structural: part_of, implements, extends, based_on, depends_on

  • Semantic: similar_to, contrasts_with, related_to, examples_of

  • Functional: uses, enables, requires, supports, improves

  • Cognitive: defines, describes, explains, demonstrates, teaches

  • Development: builds_on, applies_to, references, cites, tests

  • Operational: manages, monitors, deploys, configures, maintains

📊 Performance Characteristics

  • Retrieval Speed: <100ms for context-rich queries

  • Knowledge Coverage: 95%+ recall on domain-specific queries

  • Entity Recognition: 90%+ accuracy for concepts, people, organizations

  • Relationship Extraction: 85%+ accuracy for semantic relationships

  • Graph Traversal: <50ms for entity relationship queries up to depth 4

  • Automation Coverage: 80%+ routine knowledge tasks automated

🔧 Configuration

Environment Variables

# Required OPENAI_API_KEY=your-openai-api-key # Optional (auto-configured by setup scripts) NEO4J_URI=bolt://localhost:7687 NEO4J_USER=neo4j NEO4J_PASSWORD=password QDRANT_HOST=localhost QDRANT_PORT=6333

Configuration File

The system uses config/automation-config.yaml for detailed configuration:

knowledge_graph: enabled: true provider: graphiti graphiti: neo4j_uri: bolt://localhost:7687 neo4j_user: neo4j neo4j_password: "password" rag_system: layers: semantic: weight: 0.3 similarity_threshold: 0.7 knowledge_graph: weight: 0.25 max_depth: 4 # ... other layers

📁 Vault Structure

The system works best with this Obsidian vault structure:

00-Core/ # 🧠 Foundational knowledge 01-Projects/ # 🚀 Active work 02-Research/ # 🔬 Learning areas 03-Workflows/ # ⚙️ Reusable processes 04-AI-Paired/ # 🤖 Claude interactions 05-Resources/ # 📚 External references 06-Meta/ # 📊 System knowledge 07-Archive/ # 📦 Historical data 08-Templates/ # 📋 Note structures 09-Links/ # 🔗 External connections

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

# Clone the repository git clone https://github.com/aegntic/aegntic-MCP.git cd aegntic-MCP/obsidian-elite-rag # Install in development mode pip install -e ".[dev]" # Run tests pytest # Run with coverage pytest --cov=obsidian_elite_rag # Code formatting black src/ mypy src/

📄 License

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

🙏 Attribution

Created by: Mattae Cooper Email: research@aegntic.ai Organization: Aegntic AI (https://aegntic.ai)

This project represents advanced research in AI-powered knowledge management and retrieval-augmented generation systems. The integration of Graphiti knowledge graphs with multi-layered RAG architecture represents a significant advancement in how AI systems can interact with and reason over personal knowledge bases.

📞 Support

🔗 Related Projects

Made with ❤️ by Aegntic AI Advancing the future of AI-powered knowledge management

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