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docs: fix typo in query analysis documentation (#27697)

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**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.
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Soham Das 2024-10-29 07:07:22 -07:00 committed by GitHub
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docs/docs/concepts/retrieval.mdx
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@ -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). |