From c3021e9322c39799ec8550db8d2462eae8883d8c Mon Sep 17 00:00:00 2001 From: Soham Das <61263629+Sodas0@users.noreply.github.com> Date: Tue, 29 Oct 2024 07:07:22 -0700 Subject: [PATCH] docs: fix typo in query analysis documentation (#27697) **PR Title**: `docs: fix typo in query analysis documentation` **Description**: This PR corrects a typo on line 68 in the query analysis documentation, changing **"pharsings"** to **"phrasings"** for clarity and accuracy. **Issue**: N/A **Dependencies**: None **Additional Notes**: No functional changes were made; this is a documentation fix only. --- docs/docs/concepts/retrieval.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/docs/concepts/retrieval.mdx b/docs/docs/concepts/retrieval.mdx index 1735f356574..8ae57d9c2c6 100644 --- a/docs/docs/concepts/retrieval.mdx +++ b/docs/docs/concepts/retrieval.mdx @@ -65,7 +65,7 @@ Various techniques have been developed to leverage models for query re-writing, | Name | When to use | Description | |-----------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| [Multi-query](/docs/how_to/MultiQueryRetriever/) | When you want to ensure high recall in retrieval by providing multiple pharsings of a question. | Rewrite the user question with multiple pharsings, retrieve documents for each rewritten question, return the unique documents for all queries. | +| [Multi-query](/docs/how_to/MultiQueryRetriever/) | When you want to ensure high recall in retrieval by providing multiple phrasings of a question. | Rewrite the user question with multiple pharsings, retrieve documents for each rewritten question, return the unique documents for all queries. | | [Decomposition](https://github.com/langchain-ai/rag-from-scratch/blob/main/rag_from_scratch_5_to_9.ipynb) | When a question can be broken down into smaller subproblems. | Decompose a question into a set of subproblems / questions, which can either be solved sequentially (use the answer from first + retrieval to answer the second) or in parallel (consolidate each answer into final answer). | | [Step-back](https://github.com/langchain-ai/rag-from-scratch/blob/main/rag_from_scratch_5_to_9.ipynb) | When a higher-level conceptual understanding is required. | First prompt the LLM to ask a generic step-back question about higher-level concepts or principles, and retrieve relevant facts about them. Use this grounding to help answer the user question. [Paper](https://arxiv.org/pdf/2310.06117). | | [HyDE](https://github.com/langchain-ai/rag-from-scratch/blob/main/rag_from_scratch_5_to_9.ipynb) | If you have challenges retrieving relevant documents using the raw user inputs. | Use an LLM to convert questions into hypothetical documents that answer the question. Use the embedded hypothetical documents to retrieve real documents with the premise that doc-doc similarity search can produce more relevant matches. [Paper](https://arxiv.org/abs/2212.10496). |