或叫做 Query Expansion、Query Rewriting、Query Transformation 由於用戶的原始查詢很可能不是最好的查詢字串，因此我們可以改寫再去向量資料庫搜尋 做法是將原先用戶的 Query，產生多種變形 Queries，每個再去撈出結果 (後面還需要接 [[Reranking]] 技巧將多個結果列表進行合併排序) * Florian 的 Advanced RAG 06: Exploring Query Rewriting (2024/3/4) * https://medium.com/@florian_algo/advanced-rag-06-exploring-query-rewriting-23997297f2d1 * RAG - Say what? https://div.beehiiv.com/p/rag-say * [[HyDE Retriever]] * [[Rewrite-Retrieve-Read]] * [[Sub Question Query]] * [[Advanced Retrieval for AI with Chroma]] 課程 * Expansion with generated answers * https://arxiv.org/abs/2305.03653 * 這 paper 中有 prompting template 可以參考 * Expansion with multiple queries * 跟 [[Rephrase and Respond (RaR)]] 類似的思路 https://akash-mathur.medium.com/advanced-rag-query-augmentation-for-next-level-search-using-llamaindex-d362fed7ecc3#46d4 * Query Expansion https://twitter.com/virattt/status/1759711417291190518 有個 prompt 可參考 * https://gist.github.com/virattt/9099bf1a32ff2b99383b2fabba0ae763 ``` You are a helpful assistant that expands a user query into sub-queries. The sub-queries should be mutually exclusive and collectively exhaustive. Your response will be a JSON object with a `queries` field, which is a list of `query` objects. ``` * Query Rewriting (2024/5/17) * https://generativeai.pub/advanced-rag-retrieval-strategy-query-rewriting-a1dd61815ff0 * Sub-Question Querying * HyDE * Step-Back Prompting * paper: A Survey of Query Optimization in Large Language Models (2024/12) * https://arxiv.org/abs/2412.17558 * https://ihey.cc/rag/rag-query-optimization-howto/ * Query Optimization 分成四類: Expansion、Decomposition、Disambiguation、Abstraction * Query Expansion with LLMs: Searching Better by Saying More (2025/2/18) * https://jina.ai/news/query-expansion-with-llms-searching-better-by-saying-more/ * 若根據特定領域的檢索任務的需求調整 prompt，效果可以ㄊ更好 * https://x.com/hxiao/status/1894587527232983066 (2025/2/26) * SLM 用於查詢擴展很難 - 因為完美的查詢擴展器必須揭示用戶在簡單查詢字符串背後最深的未說出口的恐懼 - 它必須向用戶展示他們害怕看到但需要看到的東西 ## Langchain * https://blog.langchain.dev/query-transformations/ * [[Rewrite-Retrieve-Read]] * [[Step-Back Prompting]] * Follow Up Questions * Multi-Query Retrieval https://python.langchain.com/docs/modules/data_connection/retrievers/MultiQueryRetriever * [[Multi-Query Retriever]] * [[Reranking]] * Query analysis * https://python.langchain.com/docs/use_cases/query_analysis/ * Rag From Scratch: Query Transformations * https://github.com/langchain-ai/rag-from-scratch/blob/main/rag_from_scratch_5_to_9.ipynb * 這有 prompt 可以參考 * 官方文件 https://python.langchain.com/v0.2/docs/concepts/#retrieval ## llamaindex * https://docs.llamaindex.ai/en/stable/optimizing/advanced_retrieval/query_transformations.html * 單步驟 * HyDE * Sub Question * 多步驟 * https://towardsdatascience.com/advanced-query-transformations-to-improve-rag-11adca9b19d1 * HyDE * Sub Questions * Multi-Step Query Transformation (MultiStepQueryEngine) * self-ask method * 就是 [[Self-Ask prompting]] 思路吧 * 用 RouterQueryEngine 判斷用哪一種策略ㄔㄧㄛ