RAG y conocimiento
llm-graph-builder
Build Neo4j knowledge graphs from unstructured data using LLMs — then chat with your graph.
★ 5,3k estrellasApache-2.0
Ver la guía →graphrag es un proyecto de RAG y conocimiento de código abierto de microsoft (microsoft/graphrag en GitHub). Tiene 36.264 estrellas en GitHub y 3.835 bifurcaciones, se publica bajo la licencia MIT y se actualizó por última vez en GitHub el 8 de octubre de 2026.
Microsoft's graph-based RAG: index a corpus into a knowledge graph, then query it with an LLM
Decide what you want to achieve with graphrag and check the README on GitHub for requirements before installing.
Clone the repository from GitHub (or take the packaged install route in step 3 if the README offers one).
Follow the project's documented install commands — the Run section below lists the exact ones — and add any required API keys or models.
Work through the README's quickstart example end to end, then adapt it to your own project or data.
An LLM extracts entities, relationships and claims from your documents into a structured graph during indexing.
Query modes cover both corpus-wide sensemaking questions (global search over graph communities) and specific entity questions (local search).
Includes tooling to tune the extraction and summarization prompts to your domain for better index quality.
The indexing pipeline is configurable, so you can adapt chunking, extraction and embedding steps to your data.
A maintained docs site walks through the CLI quickstart, configuration and responsible-AI considerations.
Published by Microsoft Research as a methodology for enhancing LLM outputs with knowledge-graph memory structures.
Get the GraphRAG source and documentation.
git clone https://github.com/microsoft/graphrag.git
cd graphragInstall GraphRAG from PyPI into a fresh Python environment.
pip install graphragCreate a project, drop documents into its input folder, add your LLM key to the generated .env, then run indexing and a first query.
graphrag init --root ./ragtest
# add your documents to ./ragtest/input and your API key to ./ragtest/.env, then:
# graphrag index --root ./ragtest (see the docs quickstart)graphrag is maintained by the microsoft organization/account on GitHub, together with open-source contributors, in the public repository microsoft/graphrag.
Yes — the code is open source under the MIT license. You can use, modify and redistribute it under that license's terms.
Yes — GraphRAG calls an LLM (and usually an embedding model) during indexing and querying, so you need a paid provider API key, and Microsoft warns indexing large corpora can be expensive; start small.
Vector RAG is cheaper and simpler for lookup questions; GraphRAG's graph index handles global, sensemaking questions better but costs more to build and maintain.
Clone the repository from GitHub, follow the install commands in the Run section (taken from the project's README), and complete the documented quickstart example before adapting it to your own use case.
Build Neo4j knowledge graphs from unstructured data using LLMs — then chat with your graph.
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Gratis y abierto: consigue el código fuente, dale una estrella al repositorio y empieza a crear.