Can I manage relationships within the knowledge graph?

Yes, you can easily add new relationships (edges) between entities and traverse the knowledge graph from specific entities to discover deep connections and insights within your indexed data.

How does it enhance data retrieval for AI applications?

It enhances retrieval by merging semantic vector search with explicit graph relationships, allowing for more precise, context-aware, and navigable data exploration than traditional methods, crucial for complex AI tasks.

What types of querying methods does it support?

The tool supports multi-method querying including vector (semantic search), keyword (FTS5), hybrid (combining both), and direct graph traversal to find connections and relationships within your data.

RAG Knowledge Graph

Name: RAG Knowledge Graph
Author: CSOAI-ORG

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Combines vector embeddings with knowledge graph relationships to enable powerful hybrid retrieval and hierarchical navigation for AI applications.

The RAG Knowledge Graph tool from MEOK AI Labs provides a sophisticated system for building intelligent knowledge retrieval and management solutions. It seamlessly integrates Retrieval-Augmented Generation (RAG) capabilities with a robust knowledge graph, facilitating advanced hybrid retrieval that leverages both semantic vector embeddings and explicit graph relationships. Users can efficiently index documents to generate embeddings and extract entities, then query the knowledge base using diverse methods including vector search, keyword matching, hybrid approaches, or relationship traversal. This unified context retrieval mechanism is designed to significantly improve the accuracy, relevance, and explainability of information fetched for various AI-driven applications.

Key Features

01Hybrid retrieval combining vector embeddings and graph relationships

02Hierarchical navigation for complex data exploration

03Document indexing with embedding generation and entity extraction

04Multi-method querying: vector, keyword, hybrid, and graph traversal

05Knowledge graph management to add and traverse relationships

060 GitHub stars

Use Cases

01Developing intelligent Q&A systems with enhanced contextual understanding

02Powering AI agents with precise, unified knowledge retrieval

03Managing and querying complex enterprise documents and data