Rag Knowledge Graph

By MEOK AI Labs — MEOK AI Labs — RAG + Knowledge Graph. Hybrid retrieval: vector embeddings + graph relationships + hierarchical navigation.

RAG Knowledge Graph MCP — MEOK AI Labs. Vector search + knowledge graph + unified context retrieval.

Installation

pip install rag-knowledge-graph-mcp

Usage

# Run standalone
python server.py

# Or via MCP
mcp install rag-knowledge-graph-mcp

Tools

index_document

Index a document for RAG retrieval. Generates embeddings and extracts entities.

Parameters:

• content (str)
• metadata (str)
• doc_id (str)

rag_query

Query the knowledge base. Methods: vector (semantic), keyword (FTS5), hybrid (both), graph (relationship traversal).

Parameters:

• query (str)
• top_k (int)
• method (str)

add_graph_edge

Add a relationship to the knowledge graph.

Parameters:

• source_name (str)
• target_name (str)
• relation (str)
• weight (float)

graph_query

Traverse the knowledge graph from an entity to find connections.

Parameters:

• entity_name (str)
• depth (int)

get_knowledge_stats

Get knowledge base statistics.

Authentication

Free tier: 15 calls/day. Upgrade at meok.ai/pricing for unlimited access.

Links

• Website: meok.ai
• GitHub: CSOAI-ORG/rag-knowledge-graph-mcp
• PyPI: pypi.org/project/rag-knowledge-graph-mcp

License

MIT — MEOK AI Labs