This project showcases a cutting-edge Retrieval-Augmented Generation (RAG) application designed to empower users with intuitive, context-driven information retrieval from uploaded documents. By leveraging the power of Generative AI, this application seamlessly blends document understanding with natural language interaction.

• Effortlessly upload PDF documents. The application parses them using the PyPDF2 library, extracting the text content.

• To optimize processing and storage efficiency, the extracted text is chunked into smaller segments using RecursiveCharacterTextSplitter from Langchain.

• Each text chunk is converted into a high-dimensional vector representation using Sentence Transformers' pre-trained all-MiniLM-L6-v2 model. This allows for efficient document similarity search.

• Pinecone, a high-performance vector database, is used to store the generated document embeddings. This enables fast retrieval of relevant documents based on user queries.

• Users interact with the application through a Streamlit-built interface, asking questions in natural language.
• The application leverages a Conversational Retrieval Chain (CRC) to process user queries.
• The CRC utilizes a retriever component that searches Pinecone for documents similar to the user's question based on the text embeddings.
• A Langchain ConversationBufferMemory component maintains conversation history, allowing the application to consider previous interactions when responding to follow-up questions.

• The retrieved documents and conversation history are used to contextually tailor the response. The application employs the mistralai/Mixtral-8x7B-Instruct-v0.1 LLM from Hugging Face Open Source LLMs to generate informative answers to user questions.

• Streamlit provides a clean and intuitive interface for document upload, question input, and response display.

• Users can review the conversation history to stay on track and avoid repeating questions.

• Users can revisit excerpts from uploaded documents that directly relate to their current question, providing deeper context.

This project demonstrates proficiency in various technical aspects:

• Natural Language Processing (NLP):

  • Document parsing and text extraction from PDFs using PyPDF2
  • Text Chunking with RecursiveCharacterTextSplitter
  • Text Embedding with Sentence Transformers

• Vector Database Integration:

  • Utilizing Pinecone for efficient document similarity search.

• Large Language Models (LLMs):

  • Integrating and fine-tuning a pre-trained LLM (mistralai/Mixtral-8x7B-Instruct-v0.1) for context-aware response generation.

• Conversational AI:

  • Building a Conversational Retrieval Chain with memory capabilities using Langchain libraries.

• Web Development Framework:

  • Utilizing Streamlit for rapid development of a user-friendly web application interface.

• Efficient Document Search:

  • Quickly access information from uploaded documents through natural language queries.

• Conversational Exploration:

  • Refine your understanding by engaging in a back-and-forth dialogue with the application.

• Contextual Awareness:

  • Uncover insights from documents tailored to your specific questions.

• Improved Decision-Making:

  • Gain the knowledge you need to make informed choices based on in-depth document analysis.