piizei/cosmosdb-llm-knowledge-graph

Example how to construct knowledge graph from document with LLM (Langchain), and use it with CosmosDB/Neo4j

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This helps data scientists or machine learning engineers transform unstructured text documents into structured knowledge graphs. You provide text documents as input, and it outputs a highly organized knowledge graph that can be used to improve the accuracy and relevance of retrieval-augmented generation (RAG) systems. It's ideal for those working with large volumes of text who need to extract relationships and entities.

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Use this if you need to extract structured relationships and entities from text documents to build a knowledge graph for advanced information retrieval or semantic search.

Not ideal if you are looking for a plug-and-play solution without any programming or setup of database connections.

knowledge-extraction information-retrieval semantic-search text-analytics data-structuring
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 5 / 25
Maturity 16 / 25
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Language

Jupyter Notebook

License

MIT

Last pushed

Apr 02, 2025

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