What is the workflow of a HyDE RAG pipeline?
A HyDE (Hypothetical Document Embeddings) RAG (Retrieval-Augmented Generation) pipeline enhances traditional RAG by first using a large language model to generate a hypothetical answer document. This generated document then serves as a richer and semantically more robust query for the retrieval phase, aiming to improve the relevance of retrieved documents, especially for complex or nuanced queries.
The HyDE RAG Workflow
The HyDE RAG pipeline introduces a unique initial step to leverage the generative power of LLMs even before traditional retrieval. This pre-retrieval generation helps in creating a better query representation for the vector search.
1. Hypothetical Document Generation
Given a user's original query, a Large Language Model (LLM) generates one or more 'hypothetical documents' or answers. Crucially, this generation occurs *without access to any external knowledge base* and is purely based on the LLM's internal knowledge and ability to understand and predict a likely answer structure. The purpose is not to be factually accurate, but to capture the semantic intent and potential content of a relevant response.
2. Embedding of Hypothetical Document
The hypothetical document(s) generated in the previous step are then converted into dense vector embeddings using an embedding model. These embeddings serve as a semantically rich and expanded representation of the user's original query, often capturing more context and nuance than the raw query itself.
3. Vector Similarity Search (Retrieval)
The embeddings of the hypothetical document(s) are used to perform a vector similarity search against a pre-indexed vector database, which contains embeddings of real documents from the actual knowledge base. The system retrieves the top-k real documents that are most semantically similar to the hypothetical document, effectively finding documents that would likely contain the information needed to answer the hypothetical response.
4. (Optional) Reranking
The initial set of retrieved documents may undergo an optional reranking step. A more sophisticated reranking model (e.g., a cross-encoder model) can further refine the relevance of the documents, prioritizing the most pertinent ones and filtering out less useful ones to be passed to the final generator.
5. Context Augmentation
The top-k most relevant real documents obtained from the retrieval (and optional reranking) phase are then concatenated. This combined text forms the augmented context that will be provided to the final generation LLM.
6. Answer Generation
Finally, the generation LLM receives the original user query along with the augmented context (the retrieved real documents). Using this information, the LLM synthesizes a coherent, factual, and precise answer, directly addressing the user's original question.