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:
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Natural Language Processing (NLP):
- Document parsing and text extraction from PDFs using PyPDF2
- Text Chunking with RecursiveCharacterTextSplitter
- Text Embedding with Sentence Transformers
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Vector Database Integration:
- Utilizing Pinecone for efficient document similarity search.
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Large Language Models (LLMs):
- Integrating and fine-tuning a pre-trained LLM (mistralai/Mixtral-8x7B-Instruct-v0.1) for context-aware response generation.
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Conversational AI:
- Building a Conversational Retrieval Chain with memory capabilities using Langchain libraries.
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Web Development Framework:
- Utilizing Streamlit for rapid development of a user-friendly web application interface.
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Efficient Document Search:
- Quickly access information from uploaded documents through natural language queries.
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Conversational Exploration:
- Refine your understanding by engaging in a back-and-forth dialogue with the application.
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Contextual Awareness:
- Uncover insights from documents tailored to your specific questions.
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Improved Decision-Making:
- Gain the knowledge you need to make informed choices based on in-depth document analysis.