https://github.com/ss-369/intellidoc-ai
A production-ready RAG-based Document Q&A system using Streamlit and FastAPI. Combines ChromaDB for semantic retrieval and Gemini 2.5 Flash for accurate, grounded answer generation. Includes TruLens-powered evaluation for measuring answer quality, relevance, and source attribution.
https://github.com/ss-369/intellidoc-ai
chromadb fastapi gemini-api question-answering rag trulens vector-database
Last synced: 12 months ago
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A production-ready RAG-based Document Q&A system using Streamlit and FastAPI. Combines ChromaDB for semantic retrieval and Gemini 2.5 Flash for accurate, grounded answer generation. Includes TruLens-powered evaluation for measuring answer quality, relevance, and source attribution.
- Host: GitHub
- URL: https://github.com/ss-369/intellidoc-ai
- Owner: ss-369
- Created: 2025-07-13T07:30:41.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2025-07-13T14:43:20.000Z (12 months ago)
- Last Synced: 2025-07-13T16:26:58.414Z (12 months ago)
- Topics: chromadb, fastapi, gemini-api, question-answering, rag, trulens, vector-database
- Language: Python
- Homepage:
- Size: 1.37 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🤖 Intelligent Document Q&A System
A production-ready Retrieval-Augmented Generation (RAG) system for intelligent document question-answering with comprehensive evaluation capabilities.
## 🏗️ Architecture
*Complete RAG system architecture showing data flow from document upload to answer generation*
*Detailed sequence diagram showing component interactions and data flow*
- **Frontend**: Streamlit web interface with real-time configuration
- **Backend**: Dual-mode backend with:
- **Streamlit** for interactive user experience
- **FastAPI** for RESTful API access (`/upload`, `/ask`, `/evaluate`)
- **Vector Database**: ChromaDB for persistent document embeddings
- **LLM**: Google Gemini 2.5 Flash for answer generation
- **Evaluation**: TruLens framework for RAG quality assessment
## 🚀 Features
*Main application interface with document upload, Q&A, and evaluation tabs*
### Core Capabilities
- **Multi-format Document Processing**: PDF, TXT with intelligent chunking
- **Advanced Retrieval**: Semantic search with contextual compression
- **AI-Powered Q&A**: Context-aware answer generation with source attribution
- **Quality Evaluation**: Comprehensive metrics for answer relevance and groundedness
- **Interactive Interface**: Real-time parameter adjustment and conversation history
### Technical Highlights
- **Production-Ready**: Environment-based configuration and comprehensive logging
- **Scalable Architecture**: Modular design supporting multiple LLM and embedding providers
- **Persistent Storage**: ChromaDB with automatic document versioning
- **Performance Monitoring**: Real-time evaluation metrics and statistics
- **Source Attribution**: Document-level source tracking and citation
*Document processing workflow showing chunking and vector storage*
*Source attribution and document tracking in Q&A responses*
## 📊 System Capabilities
- **Processing**: Efficient document chunking and vector storage with ChromaDB
- **Retrieval**: Fast semantic search with configurable similarity thresholds
- **Scalability**: Modular architecture designed for high-volume document processing
- **Evaluation**: Comprehensive quality assessment using TruLens framework
- **Reliability**: Production-ready error handling and session management
## 🛠️ Technology Stack
| Component | Technology | Purpose |
|-----------|------------|---------|
| **Frontend** | Streamlit | Interactive web interface |
| **LLM** | Google Gemini 2.5 Flash | Answer generation |
| **Vector DB** | ChromaDB | Document embeddings storage |
| **Processing** | LangChain, PyPDF2 | Document processing pipeline |
| **Evaluation** | TruLens | RAG quality assessment |
| **Configuration** | Environment Variables | Secure settings management |
## 🚀 Quick Start
1. **Install Dependencies**:
```bash
pip install -r requirements.txt
```
2. **Set API Keys**:
```bash
export GEMINI_API_KEY="your_gemini_api_key"
```
3. **Run Application**:
```bash
streamlit run app.py --server.port 5000
```
4. **Access Interface**:
## 📸 Demo Screenshots
### Main Application Interface
### Document Processing Workflow
### Source Attribution & Q&A
### TruLens Evaluation Dashboard
## 📁 Project Structure
```
IntelligentDocumentQA/
├── app.py # Main Streamlit application
├── utils/ # Core utilities
│ ├── config.py # Configuration management
│ ├── document_processor.py # Document loading and chunking
│ ├── vector_store.py # Vector database operations
│ ├── retriever.py # Advanced retrieval logic
│ ├── qa_chain.py # Question answering with LLM
│ └── evaluation.py # RAG evaluation framework
├── .streamlit/config.toml # Streamlit configuration
└── chroma_db/ # Persistent vector database
```
## 🎯 Use Cases
- **Enterprise Knowledge Management**: Internal document Q&A systems
- **Customer Support**: Automated FAQ and documentation assistance
- **Research & Analysis**: Academic paper and report analysis
- **Legal Document Review**: Contract and compliance document processing
- **Educational Tools**: Textbook and course material Q&A systems
## 🔮 Roadmap
- [ ] Multi-LLM support (OpenAI, Claude, local models)
- [ ] REST API endpoints
- [ ] Advanced caching and performance optimization
- [ ] Multi-language support
- [ ] Enterprise authentication integration
## 📈 Performance & Evaluation
*TruLens evaluation dashboard showing comprehensive RAG quality metrics*
The system includes comprehensive evaluation using TruLens framework:
- **Answer Relevance**: Measures how well answers address questions
- **Context Relevance**: Evaluates retrieval quality and relevance
- **Groundedness**: Assesses answer factual accuracy against sources
- **Real-time Metrics**: Live performance monitoring and statistics
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*Built with ❤️ using modern AI/ML best practices and production-ready architecture.*