Key Features
- Document Upload: Support for PDF, DOCX, TXT files with automatic text extraction
- Intelligent Chunking: Smart document splitting with overlap for context preservation
- Vector Embeddings: Semantic search using state-of-the-art embedding models
- RAG Pipeline: Retrieval-Augmented Generation for accurate, context-aware answers
- Source Citations: Answers include references to source documents and page numbers
- Conversation Memory: Maintains context across multiple questions
- Gradio Interface: User-friendly web interface for easy interaction
System Architecture
┌─────────────────────────────────────────────────────────────┐
│ Document Processing │
│ ┌──────────┐ ┌──────────┐ ┌─────────────────────┐ │
│ │ PDF/DOCX │───▶│ Loader │───▶│ Text Splitter │ │
│ │ TXT │ │ │ │ (Chunking) │ │
│ └──────────┘ └──────────┘ └─────────────────────┘ │
└─────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────┐
│ Vector Store (FAISS/Chroma) │
│ ┌──────────────────────────────────────────────────────┐ │
│ │ Embeddings (OpenAI / HuggingFace / Sentence-BERT) │ │
│ └──────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────┐
│ RAG Pipeline │
│ ┌──────────┐ ┌──────────┐ ┌─────────────────────┐ │
│ │ Query │───▶│ Retriever│───▶│ LLM (GPT-4/Llama) │ │
│ │ │ │ (Top-K) │ │ + Context │ │
│ └──────────┘ └──────────┘ └─────────────────────┘ │
└─────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────┐
│ Gradio Interface │
│ ┌──────────────────────────────────────────────────────┐ │
│ │ Chat UI │ File Upload │ Settings │ History │ │
│ └──────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────┘
Technology Stack
- LangChain: Framework for building LLM applications with RAG capabilities
- Vector Database: FAISS or ChromaDB for efficient similarity search
- Embeddings: OpenAI Ada-002, HuggingFace, or Sentence-Transformers
- LLM: GPT-4, GPT-3.5-Turbo, or open-source models (Llama 2, Mistral)
- Document Loaders: PyPDF2, python-docx, UnstructuredIO
- Gradio: Interactive web interface with file upload and chat components
- Python: Core implementation language with async support
Implementation Highlights
- Chunking Strategy: RecursiveCharacterTextSplitter with 1000 token chunks and 200 token overlap
- Retrieval: Top-K similarity search (K=4) with MMR (Maximal Marginal Relevance) for diversity
- Prompt Engineering: Custom system prompts for accurate, concise answers with source attribution
- Error Handling: Graceful fallbacks for API failures, document parsing errors, and edge cases
- Performance: Caching mechanisms for embeddings and responses to reduce latency
- Security: Input sanitization, rate limiting, and API key management
Use Cases
- Research: Query academic papers, technical documentation, and research reports
- Legal: Search through contracts, legal documents, and case files
- Customer Support: Answer questions from product manuals and knowledge bases
- Education: Interactive learning from textbooks and course materials
- Business Intelligence: Extract insights from reports, presentations, and memos