Index

graph TD
    A[Query] --> B[Generate Multiple Hypothetical Documents]
    B --> C1[Hypothetical Doc 1]
    B --> C2[Hypothetical Doc 2]
    B --> C3[Hypothetical Doc N]

    C1 --> D1[Embed Doc 1]
    C2 --> D2[Embed Doc 2]
    C3 --> D3[Embed Doc N]

    D1 & D2 & D3 --> E{Similarity Search}
    E --> F[Retrieve Top-K Documents]
    F --> G[Aggregate Results]
    G --> H[Form Context]
    A --> H
    H --> I[Generate Response]

    subgraph "Document Processing"
        J[Corpus Documents] --> K[Split into Chunks]
        K --> L[Generate Embeddings]
        L --> M[(Vector Store)]
    end

    M -.-> E

Introduction¶

This project implements a Retrieval-Augmented Generation (RAG) system enhanced with Hypothetical Document Embeddings (HyDE), a novel approach to dense retrieval that improves the accuracy and relevance of retrieved information.

Motivation¶

Traditional RAG systems often struggle with semantic understanding of complex queries. HyDE addresses this by generating a hypothetical answer, which serves as a more informative representation of the query intent, leading to more accurate document retrieval.

Method Details¶

Document Preprocessing and Vector Store Creation¶

1. Split documents into manageable chunks
2. Generate embeddings for each chunk using a suitable embedding model
3. Store embeddings in a vector database for efficient similarity search

Retrieval-Augmented Generation Workflow¶

1. Query Processing:
2. Generate a hypothetical document/answer to the query using an LLM
3. Document Embedding:
4. Create an embedding of the hypothetical document
5. Similarity Search:
6. Compare the hypothetical document embedding against the corpus embeddings
7. Retrieval:
8. Fetch the top-K most similar real documents
9. Context Formation:
10. Combine the original query with the retrieved documents
11. Generation:
12. Use an LLM to generate the final response based on the formed context

Key Features of RAG with HyDE¶

• Hypothetical document generation for improved query understanding
• Dense retrieval using document-to-document similarity
• Integration with existing RAG pipelines
• Flexible architecture allowing for different embedding and language models

Benefits of this Approach¶

• Enhanced semantic understanding of complex queries
• Improved retrieval accuracy, especially for nuanced or abstract questions
• Reduced sensitivity to specific query phrasing
• Better handling of out-of-distribution queries

Conclusion¶

The HyDE-enhanced RAG system represents a significant advancement in information retrieval and question-answering technologies. By leveraging hypothetical documents, it bridges the gap between user queries and relevant information, resulting in more accurate and contextually appropriate responses.