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README.md 2026-06-02 30.6 kB
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Major Improvements

  • 🎉✨Feacture: LightRAG now supports multimodal document processing and can fully leverage images, tables, and formulas within documents to answer queries. All RagAnything’s multimodal processing capabilities are merged into LightRAG; ( RagAnything will no longer receive core feature updates or maintenance going forward)
  • ✨🎉Feacture: Fully upgraded the document processing pipeline, with support for using MinerU and Docling to parse and extract file content, seamlessly integrating with multimodal content analysis and entity-relationship extraction.
  • 💡✨Feacture: LightRAG now introduces Native Parsing, enabling high-quality content extraction from DOCX documents. It supports accurate reconstruction of Word auto-numbering, as well as extraction of images, tables, and formulas, providing seamless integration with multimodal content analysis and entity-relationship extraction. Expanded format support for the Native Parser is coming soon.
  • 💡🧠Feacture: Introduced four selectable text chunking strategies: Fix, Recursive, Vector, and Paragraph. The parameters for each chunking strategy can be configured through environment variables.
  • 💡🎯Feacture: The file processing pipeline supports selecting the file parsing engine and text chunking strategy either based on file extensions or on a per-file basis. For detailed usage instructions, refer to FileProcessingPipeline.md (github.com).
  • 🚀⚡Performance: Optimized the vector storage persistence logic by deferring vector computations until the end of each file processing batch, enabling centralized bulk computation. This significantly reduced the number of vector model invocations and significantly enhance the upsert speed of all vector DB LightRAG supported.
  • Enable task-aware embedding support for asymmetric models, including voyage-3, text-embedding-004, embed-multilingual-v3.0, and jina-embeddings-v3.
  • Improved entity/relation extraction reliability by introducing LLM JSON-structured output; set ENTITY_EXTRACTION_USE_JSON=true to enable it.
  • Introduce ENTITY_TYPE_PROMPT_FILE to empower users with enhanced guidance for LLM-driven entity type recognition and extraction.
  • Fully support Amazon and Anthropic models on AWS Bedrock API.
  • 🎯✨🎉Feacture: Implemented role-specific LLM configuration support, introducing four distinct roles: EXTRACT, QUERY, KEYWORDS, and VLM, each with independent LLM settings. It is recommended to configure the KEYWORDS role with a small-parameter, non-reasoning, high-speed model to optimize query latency; the EXTRACT role with a medium-parameter, non-reasoning model to balance accuracy and throughput; and the QUERY role with a large-parameter reasoning model to enhance query quality. For detailed usage instructions, refer to RoleSpecificLLMConfiguration.md (github.com).

What's Broken

  • 💥Warning: Upgrades require careful planning for users currently utilizing OpenSearch and MongoDB graph storage, as data migration and system restarts may cause extended service outages.
  • ⚠️The ENTITY_TYPES environment variable has been deprecated; Please replace it with ENTITY_TYPE_PROMPT_FILE before launching this new version.
  • 💔Removed deprecated field: Deleted QueryParam.model_func from lightrag/base.py
  • For OpenSearch versions prior to 3.3.0, upgrade OpenSearch before upgrading LightRAG (see https://github.com/HKUDS/LightRAG/pull/2991).
  • ⚠️Move delete_entity/delete_relation from document API to graph API
Before After
DELETE /documents/delete_entity DELETE /graph/entity/delete
DELETE /documents/delete_relation DELETE /graph/relation/delete

What's Changed

New Contributors

Full Changelog: https://github.com/HKUDS/LightRAG/compare/v1.4.16...v1.5.0

Source: README.md, updated 2026-06-02