This tutorial demonstrates how to use cognee to process and index audio, video and text data. GraphRAG (Graph Retrieval Augmented Generation) enhances semantic search capabilities by structuring hierarchical knowledge graphs from raw text. Using LanceDB as the vector database, this system efficiently manages large and complex datasets, allowing for precise querying and insightful responses. cognee allows ingestion from 30+ data sources and lets users create a set of tasks and pipelines for creating custom graphs, ontologies and retrievals
cognee enables the generation of knowledge graphs from textual, audio data and images. Compared to traditional RAG, it allows a better way to structure the data and enable retrievals
- Ingestion from 30+ data sources
- Indexes the data and structures it into a hierarchical knowledge graph
- Utilizes LanceDB for fast, efficient vector similarity searches
- Supports graph projections, complex search patterns
- Anthropic MCP support, sdk, and an API: see here
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For a detailed, interactive walkthrough of this implementation, you can go over the Google Colab notebook I've included below.
For a detailed explanation of how cognee works with the various data formats to generate graphs, check out cognee blog. ‚