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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, use the footnote marker syntax [^1], where the ^ preceding the identifier indicates a footnote reference. When multiple citations are required at a single location, each ID should be enclosed in separate footnote markers (e.g., [^1][^2][^3]).

  2. user_prompt for Qwen3 or DeepSeek models:

    内嵌引文编号使用格式 [^1][^2,3],多个编号之间用逗号隔开。需要按要求生成参考文献,但不需要生成脚注。

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

image

What's Changed

New Contributors

Full Changelog: https://github.com/HKUDS/LightRAG/compare/v1.4.9rc3...v1.4.9rc4

Source: README.md, updated 2025-09-26