Chat with my Documents

Overview

An intelligent document chat application that allows users to interact with their documents through natural language queries. By implementing Retrieval-Augmented Generation (RAG), the system can accurately answer questions based on document content, making information retrieval more intuitive and efficient.

Key Features

• Retrieval-Augmented Generation (RAG) — Combines retrieval-based methods with generative AI to provide accurate, context-aware answers grounded in document content.
• LangChain Framework — Utilizes LangChain for building robust LLM applications with document loading, text splitting, embeddings, and vector stores.
• Vector Database Integration — Uses Pinecone for efficient similarity search and semantic retrieval of relevant document chunks.
• Natural Language Queries — Users can ask questions in plain language and receive accurate answers extracted from their documents.
• Multi-Document Support — Handles various document formats (PDF, TXT, DOCX) and enables querying across multiple documents simultaneously.
• Contextual Understanding — Leverages Hugging Face Transformers for embeddings and language understanding, ensuring high-quality retrieval.

Technical Architecture

• Framework: LangChain for LLM application orchestration and RAG pipeline
• Language: Python for backend processing and AI integration
• Vector Store: Pinecone for scalable vector similarity search
• Embeddings: Hugging Face Transformers for document and query embeddings
• Document Processing: LangChain document loaders and text splitters
• LLM: Integration with various language models for answer generation

Development Period

May 2024 - August 2024

Use Cases

• Research paper analysis and summarization
• Personal knowledge base management
• Document Q&A for study and work materials
• Information extraction from large document collections