🧠 AI-Powered Knowledge Base Chatbot

Transform your documents into an intelligent, conversational knowledge base

🚀 Overview

This AI-Powered Knowledge Base Chatbot revolutionizes how organizations interact with their document repositories. Built with cutting-edge AI technologies, it enables natural language conversations with your PDF and text documents, providing instant, contextual answers backed by source citations.

Perfect for:

• 📊 Data Teams - Query research papers, documentation, and reports
• 🏢 Enterprise Knowledge Management - Centralized document intelligence
• 🎯 Business Intelligence - Extract insights from company documents
• 📚 Research & Development - Accelerate information discovery

✨ Key Features

🔍 Intelligent Document Processing

• Multi-format Support: PDF and TXT document ingestion
• Smart Chunking: Optimized text segmentation for better retrieval
• Vector Embeddings: State-of-the-art sentence transformers for semantic search

💬 Conversational AI Interface

• Natural Language Queries: Ask questions in plain English
• Context-Aware Responses: Maintains conversation history for coherent interactions
• Source Attribution: Every answer includes relevant document citations
• Real-time Processing: Instant responses with visual loading indicators

🛠 Enterprise-Ready Architecture

• Scalable Vector Database: ChromaDB for efficient similarity search
• Advanced LLM Integration: Powered by Anthropic's Claude 3.5 Haiku
• Memory Management: Conversation buffer for context retention
• Modular Design: Clean separation of concerns for maintainability

🎨 Intuitive User Experience

• Streamlit Interface: Modern, responsive web application
• Drag-and-Drop Upload: Effortless document management
• Chat History: Persistent conversation threads
• Source Exploration: Expandable source document previews

🖥️ Interface

🏗 Technical Architecture

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   Streamlit UI  │───▶│  Document Loader │───▶│   Text Splitter │
└─────────────────┘    └──────────────────┘    └─────────────────┘
                                                         │
┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   Claude 3.5    │◀───│  Retrieval Chain │◀───│  Vector Store   │
│     Haiku       │    │   (LangChain)    │    │   (ChromaDB)    │
└─────────────────┘    └──────────────────┘    └─────────────────┘

🚀 Getting Started

Prerequisites

• Python 3.11 or higher
• Anthropic API key

Installation

1. Clone the repository

   git clone https://github.com/yourusername/kb-chatbot.git
   cd kb-chatbot

2. Set up Python environment

   # Using uv (recommended)
   uv sync

3. Configure environment variables

   cp .env.example .env
   # Edit .env and add your Anthropic API key
   echo "ANTHROPIC_API_KEY=your_api_key_here" >> .env

4. Launch the application

   streamlit run app.py

5. Access the interface Open your browser to http://localhost:8501

Quick Start Guide

1. Upload Documents: Use the sidebar to upload PDF or TXT files
2. Process Documents: Click "Process Documents" to create the knowledge base
3. Start Chatting: Ask questions about your documents in natural language
4. Explore Sources: Click on source citations to view relevant document excerpts

📋 Usage Examples

Business Intelligence Queries

"What are the key performance metrics mentioned in the Q3 report?"
"Summarize the main risks identified in our compliance documentation"
"What budget allocations were discussed for the marketing department?"

Research & Development

"What methodologies were used in the machine learning research papers?"
"Compare the experimental results across different studies"
"What are the limitations mentioned in the technical documentation?"

Policy & Compliance

"What are the requirements for data privacy compliance?"
"Summarize the employee handbook policies on remote work"
"What security protocols are outlined in our IT documentation?"

🔧 Configuration

Environment Variables

ANTHROPIC_API_KEY=your_anthropic_api_key

Customization Options

• Chunk Size: Modify chunk_size in chatbot.py:57
• Retrieval Count: Adjust k parameter in chatbot.py:65
• Model Selection: Change Claude model in chatbot.py:26

🛠 Development

Project Structure

kb-chatbot/
├── app.py              # Streamlit web interface
├── chatbot.py          # Core chatbot logic
├── pyproject.toml      # Project dependencies
└── .env.example        # Environment template

Key Components

• KnowledgeBaseChatbot: Main chatbot class handling document processing and Q&A
• Document Loaders: PDF and text file processing utilities
• Vector Store: ChromaDB integration for semantic search
• Conversation Chain: LangChain orchestration for retrieval-augmented generation

🚀 Deployment

Local Development

streamlit run app.py

Docker Deployment

docker build -t kb-chatbot .
docker run -p 8501:8501 kb-chatbot

Still underconstruction

Cloud Deployment

Compatible with:

• Streamlit Cloud
• AWS EC2/ECS
• Google Cloud Run
• Azure Container Instances

🤝 Contributing

Contributions are welcomed!

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Anthropic for Claude 3.5 Haiku LLM
• LangChain for the RAG framework
• LangGraph for the RAG framework
• Streamlit for the web interface
• ChromaDB for vector storage
• Hugging Face for embedding models

📞 Support

• 📧 Discord: andreaschandra#4851
• 💬 Issues: GitHub Issues
• 📖 Documentation: Wiki

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