HyDE (Hypothetical Document Embedding) is a technique that enhances document retrieval by generating a hypothetical, more precise version of a user query using an LLM. This generated text serves as a bridge between vague or informal queries and formal documentation, improving the accuracy of vector search results.
Key Concepts
- HyDE Prompt: A prompt template used to generate a hypothetical document based on the user's query.
- LLM (Large Language Model): Used to create the hypothetical document by inferring context from the original query.
- Vector Store: Stores embeddings of actual documents and hypothetical documents for similarity search.
Example Implementation
Step-by-Step Guide:
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Define HyDE Prompt:
- The prompt should guide the LLM to generate a more precise version of the user's query, including relevant keywords or context from the documentation.
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Generate Hypothetical Document:
- Use an LLM to process the user's query and produce a hypothetical document that captures the intent more accurately.
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Embedding and Search:
- Embed both actual documents and the generated hypothetical document using a base embedding model.
- Perform similarity search on the vector store
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