README.md

metadata

license: apache-2.0
title: ChanceRAG
sdk: gradio
emoji: 🚀
colorFrom: blue
colorTo: purple
sdk_version: 4.44.0

Chance RAG: Advanced Retrieval-Augmented Generation System

Table of Contents

1. System Architecture
2. Data Flow
3. Detailed Component Descriptions
   • 3.1 Document Processor
   • 3.2 MistralRAGChatbot
   • 3.3 Retrieval Engine
   • 3.4 Reranking Engine
   • 3.5 Response Generator
4. User Interface
5. Performance Optimization
6. Error Handling and Logging
7. Future Enhancements
8. Conclusion

1. System Architecture

[Diagram 1: System Architecture] +-------------------+ +-------------------+ +-------------------+ | PDF Document | | User Interface | | Mistral AI API | | | | (Gradio UI) | | | +--------+----------+ +--------+----------+ +--------+----------+ | | | v v v +-------------------+ +-------------------+ +-------------------+ | Document Processor| | MistralRAGChatbot | | Response Generator| | | | | | | +--------+----------+ +--------+----------+ +--------+----------+ | ^ ^ v | | +-------------------+ +-------------------+ +-------------------+ | Vector Database | | Retrieval Engine | | Reranking Engine | | (Annoy Index) | | | | | +-------------------+ +-------------------+ +-------------------+

2. Data Flow

[Diagram 2: Data Flow] +-------------+ +-----------------+ +------------------+ | PDF Upload | --> | Text Extraction | --> | Chunk Generation | +-------------+ +-----------------+ +------------------+ | v +-------------+ +-----------------+ +------------------+ | User Query | --> | Query Embedding | --> | Document Retrieval| +-------------+ +-----------------+ +------------------+ | v +-------------+ +-----------------+ +------------------+ | Reranking | --> | Context Creation| --> | Response Generation| +-------------+ +-----------------+ +------------------+ | v +------------------+ | Display Response | +------------------+

3. Detailed Component Descriptions

3.1 Document Processor

The Document Processor handles PDF files and prepares them for use in the RAG system.

Key functions:

• store_embeddings_in_vector_db: Processes PDFs and stores embeddings.
• split_text_into_chunks: Divides text into manageable chunks.

3.2 MistralRAGChatbot

This core class orchestrates the entire RAG process.

Key methods:

• generate_response_with_rag: Coordinates retrieval, reranking, and response generation.
• retrieve_documents: Fetches relevant documents using various methods.
• rerank_documents: Applies reranking algorithms to improve relevance.

3.3 Retrieval Engine

The Retrieval Engine uses multiple methods to find relevant documents.

Methods:

1. Annoy (Approximate Nearest Neighbors)
2. TF-IDF (Term Frequency-Inverse Document Frequency)
3. BM25 (Best Matching 25)
4. Word2Vec
5. Euclidean Distance
6. Jaccard Similarity

[Diagram 3: Retrieval Methods Comparison]

Method	Speed	Accuracy	Memory Usage
Annoy	Fast	Good	Low
TF-IDF	Fast	Moderate	Moderate
BM25	Fast	Good	Low
Word2Vec	Slow	Good	High
Euclidean	Fast	Moderate	Low
Jaccard	Slow	Moderate	Low

3.4 Reranking Engine

The Reranking Engine applies advanced algorithms to improve the relevance of retrieved documents.

Methods:

1. Advanced Fusion
2. Reciprocal Rank Fusion
3. Weighted Score Fusion
4. Semantic Similarity Reranking

[Diagram 4: Reranking Process] +-------------------+ +-------------------+ +-------------------+ | Retrieved Docs | --> | Reranking Methods | --> | Reranked Docs | +-------------------+ +-------------------+ +-------------------+ | +--------+--------+ | | +-------v------+ +-------v------+ | Score Fusion | | Semantic Sim | +--------------+ +--------------+

3.5 Response Generator

Utilizes the Mistral AI API to generate human-like responses based on the retrieved and reranked context.

Key function:

• build_prompt: Constructs the prompt for the Mistral AI model.

4. User Interface

The Gradio-based user interface provides an intuitive way to interact with the Chance RAG system.

Components:

1. PDF Upload
2. User Query Input
3. Response Style Selection
4. Retrieval Methods Selection
5. Reranking Methods Selection
6. Chunk Size and Overlap Adjustment
7. Response Display

5. Performance Optimization

To improve the system's performance, consider the following:

1. Implement caching for embeddings and frequently retrieved documents.
2. Use parallel processing for document retrieval and reranking.
3. Optimize chunk size and overlap based on document characteristics.
4. Implement adaptive retrieval method selection based on query type.

6. Error Handling and Logging

The system includes error handling and logging mechanisms to ensure robustness and facilitate debugging.

Key aspects:

• Exception handling in critical functions
• Logging of important events and errors
• User-friendly error messages in the interface

7. Future Enhancements

1. Multi-document support: Allow processing of multiple PDFs simultaneously.
2. Dynamic model selection: Choose the most appropriate Mistral AI model based on the query complexity.
3. User feedback integration: Incorporate user feedback to improve retrieval and reranking over time.
4. Multilingual support: Extend the system to handle multiple languages.
5. Advanced analytics: Implement usage analytics and performance metrics tracking.

8. Conclusion

The Chance RAG system provides a powerful and flexible solution for context-aware question answering based on PDF documents. By leveraging multiple retrieval and reranking methods, along with the advanced language capabilities of the Mistral AI API, it offers highly relevant and coherent responses to user queries.

This documentation provides a comprehensive overview of the system architecture, components, and processes. For specific implementation details, refer to the inline comments and docstrings in the code.