RAG based Chatbot with Cohere's Contextual Compressor Reranking, FAISS and OpenAI

Executive Summary

This project aims to build an advanced retrieval system using cutting-edge NLP and deep learning technologies. The system leverages LangChain, a comprehensive NLP library, and OpenAI's GPT-3.5 Turbo model for response generation. The retrieval process involves document embedding, compression, and reranking through Cohere API, ultimately delivering precise and contextually relevant answers to user queries.

Project Overview

1. Objectives

The primary objectives include:

1. Implementing document loading from PDFs using LangChain's PyPDFDirectoryLoader.
2. Splitting documents efficiently into smaller chunks.
3. Utilizing Hugging Face's BAAI/bge-large-en-v1.5 for document embeddings.
4. Building a Faiss-based vector store for efficient document retrieval.
5. Enhancing retrieval results with Cohere's contextual compression reranking.
6. Integrating OpenAI's GPT-3.5 Turbo model for response generation.
7. Creating a RetrievalQA pipeline for seamless interaction with end-users.

2. Technologies Used

• LangChain: A comprehensive NLP library for document loading, text splitting, embeddings, and retrieval.
• OpenAI GPT-3.5 Turbo: A powerful language model for generating human-like responses.
• Cohere API: Utilized for contextual compression and reranking.
• Hugging Face Transformers: Employed for document embeddings.
• Faiss: A GPU-accelerated library for similarity searches in large datasets.

3. Workflow

Document Loading and Splitting

• PDF documents are loaded using PyPDFDirectoryLoader.
• Documents are split into smaller chunks using RecursiveCharacterTextSplitter.

Embedding Model

• Hugging Face's BAAI/bge-large-en-v1.5 is employed for document embeddings.
• Embeddings are computed with GPU acceleration for efficiency.

Vector Store (Faiss) Retrieval

• Faiss is used to build an efficient vector store for document retrieval.
• Retrieval is performed based on cosine similarity.

Reranking with Cohere

• Cohere API is utilized for contextual compression and reranking.
• The compression retriever enhances the quality of retrieved documents.

OpenAI Model Integration

• OpenAI's GPT-3.5 Turbo is used for response generation.
• The model is initialized with the provided API key.

RAG Pipeline with Compressor Retriever

• A RetrievalQA pipeline is created using LangChain, integrating OpenAI, Cohere, and Faiss.

User Interaction

• Users can interact with the system by providing queries.
• The system responds with contextually relevant answers.

Results and Insights

The system demonstrates advanced retrieval capabilities, combining document embeddings, Faiss-based retrieval, and Cohere's contextual compression reranking. OpenAI's GPT-3.5 Turbo enhances user interactions by generating informative and coherent responses.

Challenges and Future Work

1. Scalability: Evaluate the system's efficiency with a large dataset.
2. Model Fine-tuning: Enhance performance through fine-tuning of embedding and language models.
3. User Interface: Develop a user-friendly interface for easy interaction and integration.

Conclusion

This project successfully integrates cutting-edge NLP and deep learning technologies, offering a powerful solution for information retrieval and user interactions. Further improvements and optimizations can be explored in future iterations of the system.