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Importance Notes

v1.4.9 introduces key enhancements focused on refining the reference output format and incorporating structured references into query results. All query API endpoints now include a references field, enabling frontend applications to retrieve cited documents and their corresponding identifiers associated with LightRAG query results.

The context format sent to the LLM has been updated. The streaming response from the LLM now includes a references field (ignored by the frontend by default). If your application relies on context data returned by the LightRAG query API, you may need to update your code accordingly.

By leveraging the user_prompt parameter, users can instruct the LLM to generate responses with footnote annotations. The footnote numbers in the LLM output can be seamlessly mapped to the document IDs returned in the references field. This integration enables tighter alignment between LightRAG and your business system, empowering users to access original source materials directly.

user_prompt act as additional output instruction for LLM. Here provide two examples:

  1. user_prompt for gpt-4.1-mini

    For inline citations, employ the footnote reference format [^1], where the ^ following the opening square bracket denotes a superscript link. When multiple citations are required at a single location, enclose each reference ID within separate footnote markers (e.g., [^1][^2][^3]).

  2. user_prompt for Qwen3 or DeepSeek models:

    内嵌引文标注使用Markdown脚注格式[^1], 当某处有多个引文标注时,应将每个引用ID分别置于独立的中括号内(例如:[^1][^2][^3])。仅需对回答的关键事实和依据信息给出标注。出现在引文标中的引文ID都应该列在最后生成的参考文献中段落中。不要在参考文献段落之后生成脚注段落。

This screenshot illustrates the functionality of the user_prompt parameter in the WebUI:

image

What's New

What's Fixed

New Contributors

Full Changelog: https://github.com/HKUDS/LightRAG/compare/v1.4.8.2...v1.4.9

Source: README.md, updated 2025-09-28