RAGReader

Next-Generation Document QA with Multi-Method RAG & Multi-LLM Consensus

RAGReader is an advanced AI-powered application designed to revolutionize how you interact with your documents. While it functions as a high-speed document chat interface using standard Vector Embedding RAG, its true power lies in its "Deep Dive" capability—orchestrating multiple retrieval strategies, rigorous evaluation metrics, and a democratic voting system between the world's leading LLMs to ensure the most accurate answer possible.

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Key Features

1. Standard Mode (Fast & Efficient)

• Dense RAG: Utilizes high-performance vector embeddings to quickly retrieve relevant chunks from your uploaded files.
• LLM Backend: Powered by OpenAI GPT for rapid and coherent response generation.

2. Deep Dive Mode (The "Click" Feature)

When accuracy is paramount, click the answer to trigger a comprehensive analysis pipeline:

• Multi-Method Retrieval: simultaneously employs:
  • Dense RAG: Semantic search.
  • Sparse RAG: Keyword-based search (BM25/SPLADE).
  • Hybrid RAG: Combining semantic and keyword scores.
  • Iterative RAG: Multi-step reasoning to find complex answers.
• Advanced Refinement:
  • Reranking: Re-orders retrieved chunks using a cross-encoder to ensure the highest relevance.

3. Evaluation & Validation

• Metrics: Automatically calculates MRR (Mean Reciprocal Rank) and Context Relevance scores to grade the quality of retrieved data.
• The "Council of AI": A consensus voting mechanism where three distinct LLMs evaluate the generated answers to pick the winner:
  • OpenAI GPT
  • Anthropic Claude
  • Google Gemini

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Architecture Workflow

graph TD
    User[User Input] --> Standard[Standard Dense RAG]
    Standard --> Answer[Initial Answer]
    Answer -- User Clicks Deep Dive --> Complex[Advanced Pipeline]
    
    subgraph "Retrieval Strategies"
        Complex --> Dense[Dense Vector Search]
        Complex --> Sparse[Sparse/Keyword Search]
        Complex --> Iterative[Iterative Search]
    end
    
    Dense & Sparse & Iterative --> Hybrid[Hybrid Fusion]
    Hybrid --> Rerank[Reranker Model]
    
    subgraph "Evaluation & Generation"
        Rerank --> Gen[Generate Candidate Answers]
        Gen --> EvalMetrics[Calc MRR & Context Relevance]
    end
    
    subgraph "Voting Council"
        EvalMetrics --> VoteGPT[GPT Vote]
        EvalMetrics --> VoteClaude[Claude Vote]
        EvalMetrics --> VoteGemini[Gemini Vote]
    end
    
    VoteGPT & VoteClaude & VoteGemini --> Dashboard[Dashboard with Score]

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Tech Stack

• LLM Orchestration: OpenAI GPT, Anthropic Claude, Google Gemini.
• Embedding Models: OpenAI Embeddings and Mini LM.
• Retrieval: Hybrid Search (Dense + Sparse), Rerankers (e.g., Cohere/BGE).
• Framework: LangChain.
• Frontend: (e.g., React, Vite).

Usage Guide

1. Upload: Drag and drop your PDF, TXT, or MD files into the sidebar.
2. Ask: Type your question in the chat input.
3. Read: Get an immediate answer via Standard Dense RAG.
4. Verify: Click the answer to activate the "Deep Dive."
   • Watch as the system reranks chunks and calculates MRR.
   • Wait for the "Council of AI" (GPT, Claude, Gemini) to vote.
   • View the final, consensus-based result.

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Contributing

Contributions make the open-source community an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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License

Distributed under the MIT License. See LICENSE for more information.