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Raphaël MANSUY
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README.md
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README.md
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---
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## 🎉 News
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- [x] [2025.11.05]🎯📢Add **RAGAS-based** Evaluation Framework and **Langfuse** observability for LightRAG (API can return retrieved contexts with query results).
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- [x] [2025.10.22]🎯📢Eliminate bottlenecks in processing **large-scale datasets**.
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- [x] [2025.09.15]🎯📢Significantly enhances KG extraction accuracy for **small LLMs** like Qwen3-30B-A3B.
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- [x] [2025.08.29]🎯📢**Reranker** is supported now , significantly boosting performance for mixed queries(Set as default query mode now).
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- [x] [2025.08.04]🎯📢**Document deletion** with KG regeneration to ensure query performance.
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- [x] [2025.06.16]🎯📢Our team has released [RAG-Anything](https://github.com/HKUDS/RAG-Anything) an All-in-One Multimodal RAG System for seamless text, image, table, and equation processing.
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- [x] [2025.06.05]🎯📢LightRAG now supports comprehensive multimodal data handling through [RAG-Anything](https://github.com/HKUDS/RAG-Anything) integration, enabling seamless document parsing and RAG capabilities across diverse formats including PDFs, images, Office documents, tables, and formulas. Please refer to the new [multimodal section](https://github.com/HKUDS/LightRAG/?tab=readme-ov-file#multimodal-document-processing-rag-anything-integration) for details.
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- [x] [2025.03.18]🎯📢LightRAG now supports citation functionality, enabling proper source attribution.
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- [x] [2025.02.12]🎯📢You can now use MongoDB as all in-one Storage.
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- [x] [2025.02.05]🎯📢Our team has released [VideoRAG](https://github.com/HKUDS/VideoRAG) understanding extremely long-context videos.
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- [x] [2025.01.13]🎯📢Our team has released [MiniRAG](https://github.com/HKUDS/MiniRAG) making RAG simpler with small models.
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- [x] [2025.01.06]🎯📢You can now use PostgreSQL as all in-one Storage.
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- [x] [2024.11.19]🎯📢A comprehensive guide to LightRAG is now available on [LearnOpenCV](https://learnopencv.com/lightrag). Many thanks to the blog author.
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- [x] [2024.11.09]🎯📢Introducing the LightRAG Webui, which allows you to insert, query, visualize LightRAG knowledge.
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- [x] [2024.11.04]🎯📢You can now [use Neo4J for Storage](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#using-neo4j-for-storage).
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- [x] [2024.10.18]🎯📢We've added a link to a [LightRAG Introduction Video](https://youtu.be/oageL-1I0GE). Thanks to the author!
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- [x] [2024.10.17]🎯📢We have created a [Discord channel](https://discord.gg/yF2MmDJyGJ)! Welcome to join for sharing and discussions! 🎉🎉
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- [x] [2024.10.16]🎯📢LightRAG now supports [Ollama models](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#quick-start)!
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- [2025.11.05]🎯[New Feature]: Integrated **RAGAS for Evaluation** and **Langfuse for Tracing**. Updated the API to return retrieved contexts alongside query results to support context precision metrics.
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- [2025.10.22]🎯[Scalability Enhancement]: Eliminated processing bottlenecks to support **Large-Scale Datasets Efficiently**.
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- [2025.09.15]🎯Significantly enhances KG extraction accuracy for **small LLMs** like Qwen3-30B-A3B.
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- [2025.08.29]🎯**Reranker** is supported now , significantly boosting performance for mixed queries(Set as default query mode now).
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- [2025.08.04]🎯**Document deletion** with KG regeneration to ensure query performance.
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- [2025.06.16]🎯Our team has released [RAG-Anything](https://github.com/HKUDS/RAG-Anything) an All-in-One Multimodal RAG System for seamless text, image, table, and equation processing.
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- [2025.06.05]🎯LightRAG now supports comprehensive multimodal data handling through [RAG-Anything](https://github.com/HKUDS/RAG-Anything) integration, enabling seamless document parsing and RAG capabilities across diverse formats including PDFs, images, Office documents, tables, and formulas. Please refer to the new [multimodal section](https://github.com/HKUDS/LightRAG/?tab=readme-ov-file#multimodal-document-processing-rag-anything-integration) for details.
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- [2025.03.18]🎯LightRAG now supports citation functionality, enabling proper source attribution.
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- [2025.02.12]🎯You can now use MongoDB as all in-one Storage.
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- [2025.02.05]🎯Our team has released [VideoRAG](https://github.com/HKUDS/VideoRAG) understanding extremely long-context videos.
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- [2025.01.13]🎯Our team has released [MiniRAG](https://github.com/HKUDS/MiniRAG) making RAG simpler with small models.
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- [2025.01.06]🎯You can now use PostgreSQL as all in-one Storage.
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- [2024.11.19]🎯A comprehensive guide to LightRAG is now available on [LearnOpenCV](https://learnopencv.com/lightrag). Many thanks to the blog author.
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- [2024.11.09]🎯Introducing the LightRAG Webui, which allows you to insert, query, visualize LightRAG knowledge.
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- [2024.11.04]🎯You can now [use Neo4J for Storage](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#using-neo4j-for-storage).
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- [2024.10.18]🎯We've added a link to a [LightRAG Introduction Video](https://youtu.be/oageL-1I0GE). Thanks to the author!
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- [2024.10.17]🎯We have created a [Discord channel](https://discord.gg/yF2MmDJyGJ)! Welcome to join for sharing and discussions! 🎉🎉
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- [2024.10.16]🎯LightRAG now supports [Ollama models](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#quick-start)!
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<details>
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<summary style="font-size: 1.4em; font-weight: bold; cursor: pointer; display: list-item;">
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@ -845,7 +845,7 @@ see test_neo4j.py for a working example.
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For production level scenarios you will most likely want to leverage an enterprise solution. PostgreSQL can provide a one-stop solution for you as KV store, VectorDB (pgvector) and GraphDB (apache AGE). PostgreSQL version 16.6 or higher is supported.
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* PostgreSQL is lightweight,the whole binary distribution including all necessary plugins can be zipped to 40MB: Ref to [Windows Release](https://github.com/ShanGor/apache-age-windows/releases/tag/PG17%2Fv1.5.0-rc0) as it is easy to install for Linux/Mac.
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* If you prefer docker, please start with this image if you are a beginner to avoid hiccups (DO read the overview): https://hub.docker.com/r/shangor/postgres-for-rag
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* If you prefer docker, please start with this image if you are a beginner to avoid hiccups (Default user password:rag/rag): https://hub.docker.com/r/gzdaniel/postgres-for-rag
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* How to start? Ref to: [examples/lightrag_zhipu_postgres_demo.py](https://github.com/HKUDS/LightRAG/blob/main/examples/lightrag_zhipu_postgres_demo.py)
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* For high-performance graph database requirements, Neo4j is recommended as Apache AGE's performance is not as competitive.
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