08_RAG_Fusion

flowchart LR
    subgraph "1. Document Processing"
        A[Documents] --> B[Split Text into Chunks]
        B --> C1[Chunk-1]
        B --> C2[Chunk-2]
        B --> C3[Chunk-n]
    end

    subgraph "2. Document Embedding"
        EM1{{Embedding Model}}
        C1 & C2 & C3 --> EM1
        EM1 --> D1[Embedding-1] & D2[Embedding-2] & D3[Embedding-3]
    end

    subgraph "3. Indexing"
        D1 & D2 & D3 --> E[(VectorDB)]
    end

    subgraph "4. Query Processing"
        F[Original Query] --> LLM1{{LLM for Query Generation}}
        LLM1 --> G1[Generated Query 1]
        LLM1 --> G2[Generated Query 2]
        LLM1 --> G3[Generated Query n]
        F & G1 & G2 & G3 --> EM2{{Embedding Model}}
        EM2 --> H1[Query Embedding 1]
        EM2 --> H2[Query Embedding 2]
        EM2 --> H3[Query Embedding 3]
        EM2 --> H4[Query Embedding n]
    end

    subgraph "5. Multi-Query Retrieval"
        H1 & H2 & H3 & H4 -->|Similarity Search| E
        E -->|Top-K Retrieval| I1[Results Set 1]
        E -->|Top-K Retrieval| I2[Results Set 2]
        E -->|Top-K Retrieval| I3[Results Set 3]
        E -->|Top-K Retrieval| I4[Results Set n]
    end

    subgraph "6. Reciprocal Rank Fusion"
        I1 & I2 & I3 & I4 --> J[RRF Algorithm]
        J --> K[Reranked Results]
    end

    subgraph "7. Context Formation"
        K --> L[Original Query + Generated Queries + Reranked Results]
    end

    subgraph "8. Generation"
        L --> M[LLM]
        M --> N[Final Response]
    end

    F --> L

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RAG-Fusion: Enhanced Retrieval-Augmented Generation

Introduction

RAG-Fusion is an advanced approach to information retrieval and text generation that builds upon the foundation of Retrieval-Augmented Generation (RAG). This project implements RAG-Fusion to provide more accurate, contextually relevant, and comprehensive responses to user queries.

Motivation

Traditional RAG systems, while effective, often face limitations in capturing the full scope of user intent and retrieving the most relevant information. RAG-Fusion addresses these challenges by:

1. Generating multiple queries to capture different aspects of the user's intent
2. Utilizing advanced reranking techniques to improve retrieval accuracy
3. Providing a more nuanced context for the language model to generate responses

Method Details

Document Preprocessing and Vector Store Creation

1. Text Chunking: Documents are split into manageable chunks.
2. Embedding Generation: Each chunk is converted into a vector representation using a pre-trained embedding model.
3. Indexing: The embeddings are stored in a vector database for efficient retrieval.

Retrieval-Augmented Generation Workflow

1. Query Expansion: The original user query is expanded into multiple related queries using a language model.
2. Multi-Query Embedding: All queries (original and generated) are embedded.
3. Vector Search: Each query embedding is used to retrieve relevant document chunks from the vector store.
4. Reciprocal Rank Fusion (RRF): Results from multiple queries are combined and reranked using the RRF algorithm.
5. Context Formation: The original query, generated queries, and reranked results form the context.
6. Response Generation: A large language model generates the final response based on the enriched context.

Key Features of RAG-Fusion

• Multi-query generation for comprehensive intent capture
• Reciprocal Rank Fusion for improved result relevance
• Integration of multiple information retrieval techniques
• Flexible architecture supporting various embedding models and language models

Benefits of this Approach

1. Enhanced Query Understanding: By generating multiple queries, RAG-Fusion captures a broader range of potential user intents.
2. Improved Retrieval Accuracy: The use of RRF helps surface the most relevant information across multiple query results.
3. Reduced Hallucination: By providing more comprehensive and accurate context, the chances of model hallucination are reduced.
4. Versatility: The system can be applied to various domains and types of queries.
5. Scalability: The architecture allows for easy scaling to handle large document collections.

Conclusion

RAG-Fusion represents a significant advancement in the field of information retrieval and text generation. By addressing the limitations of traditional RAG systems, it offers a more robust, accurate, and versatile solution for a wide range of applications, from question-answering systems to document summarization tasks.