Hybrid Retrieval in RAG (Retrieval-Augmented Generation)

1. Introduction

Hybrid Retrieval is a crucial technique in RAG that combines traditional information retrieval methods with generative models. This approach enhances the quality of generated content by providing contextually relevant information from a variety of sources.

2. Key Concepts

• Retrieval-Augmented Generation (RAG): A framework that combines retrieval of documents and generation of text to produce coherent and relevant outputs.
• Hybrid Retrieval: A method that integrates keyword-based retrieval and semantic retrieval to enhance the search effectiveness.
• Query Expansion: Technique used to improve search results by reformulating the original query.

3. Step-by-Step Process

The implementation of Hybrid Retrieval can be broken down into the following steps:

graph TD;
                    A[User Query] --> B[Document Retrieval];
                    B --> C{Is Document Relevant?};
                    C -- Yes --> D[Generate Response];
                    C -- No --> E[Query Expansion];
                    E --> B;
                

3.1 Document Retrieval

Utilize a combination of keyword-based and semantic search techniques to retrieve documents relevant to the user’s query.

3.2 Query Expansion

Enhance the user query using synonyms or related terms to increase the chances of retrieving relevant documents.

3.3 Generate Response

Once the relevant documents are identified, use a generative model to produce a coherent and contextually relevant response.

4. Best Practices

• Ensure a diverse dataset for training your retrieval models to capture various contexts and nuances.
• Implement feedback loops to refine query expansion techniques based on user interactions.
• Use pre-trained models for document retrieval to save time and improve accuracy.

5. FAQ