Beyond Keywords: Mastering HyDE for Smarter Retrieval 🧠

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

1. HyDE Prompt: A prompt template used to generate a hypothetical document based on the user's query.
2. LLM (Large Language Model): Used to create the hypothetical document by inferring context from the original query.
3. Vector Store: Stores embeddings of actual documents and hypothetical documents for similarity search.

Example Implementation

Step-by-Step Guide:

1. 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.

2. Generate Hypothetical Document:

   • Use an LLM to process the user's query and produce a hypothetical document that captures the intent more accurately.

3. 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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