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 ## 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