NVIDIA-NIM Chatbot with FAISS and LangChain Integration

Overview:

The NVIDIA-NIM Chatbot is a Streamlit-based application designed to enable advanced question-answering using NVIDIA's Llama 3.1 Nemotron-70B model. This project integrates LangChain for seamless document processing, FAISS for efficient vector storage and retrieval, and NVIDIA embeddings for high-performance vectorization.

The chatbot processes PDF documents, builds a searchable vector database, and delivers accurate responses based on the provided context. It also includes a feature for displaying document similarity, making it a valuable tool for research, education, and enterprise-level document analysis. In this project I have added a book on CCNA guide, therefore this helps me in clearing my querries in computer networks

Features:

• NVIDIA Embeddings: Utilizes NVIDIA's advanced embeddings for vectorization.
• FAISS Vector Store: Efficiently indexes and retrieves relevant document chunks.
• Document Loader: Automatically processes and parses PDF documents from a directory.
• Custom Text Splitting: Handles long documents with chunking and overlap strategies for optimal retrieval.
• Interactive Chatbot Interface: Streamlit-powered interface for a user-friendly experience.
• Context-Based Responses: Provides answers strictly within the provided context.
• Document Similarity Search: Displays related document sections for enhanced transparency.

How It Works:

--Document Embedding: Loads PDFs from the ./books directory. Splits documents into smaller chunks using LangChain's RecursiveCharacterTextSplitter. Embeds the chunks using NVIDIA's embeddings and stores them in a FAISS vector database.

--Retrieval Chain: Retrieves relevant document chunks using FAISS. Feeds the chunks into a LangChain stuff document chain. Uses NVIDIA's Llama 3.1 model to generate a response based on the context.

--Interactive Q&A: Users input questions through the Streamlit interface. The application retrieves the most relevant context and generates an answer.

Getting Started:

##Prerequisites

• Python 3.8 or higher
• Dependencies: Install using the provided requirements.txt file.
• NVIDIA API Key: Obtain an API key from NVIDIA's AI endpoints.

Code Structure:

• app.py: Main Streamlit application.
• books/: Directory for storing PDF files to be processed.
• requirements.txt: List of dependencies required for the project.
• .env: Configuration file for environment variables (API key).

Customization:

• Modify the chunk_size and chunk_overlap in the RecursiveCharacterTextSplitter configuration for different document types.
• Adjust the LangChain prompt template in the ChatPromptTemplate for specific use cases.
• Add additional preprocessing steps for documents if required.

Future Enhancements:

• Add support for additional file formats (e.g., DOCX, TXT).
• Enhance scalability for larger datasets using distributed FAISS.
• Implement user authentication and role-based access.
• Add GPU acceleration for faster embeddings and inference.

Contributing: Contributions are welcome! Please open an issue or submit a pull request with detailed information about your changes.

License: This project is licensed under the MIT License.

Acknowledgments: NVIDIA for their Llama 3.1 Nemotron-70B model and embeddings. LangChain for its robust document processing tools. FAISS for efficient vector search capabilities. Streamlit for an intuitive UI framework.