https://github.com/andreaschandra/kb-chatbot
knowledge based chatbot using langchain
https://github.com/andreaschandra/kb-chatbot
chatbot langchain-python streamlit
Last synced: 11 months ago
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knowledge based chatbot using langchain
- Host: GitHub
- URL: https://github.com/andreaschandra/kb-chatbot
- Owner: andreaschandra
- License: mit
- Created: 2025-06-25T00:28:05.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2025-07-06T08:03:09.000Z (12 months ago)
- Last Synced: 2025-07-17T17:20:26.684Z (11 months ago)
- Topics: chatbot, langchain-python, streamlit
- Language: Python
- Homepage:
- Size: 1020 KB
- Stars: 5
- Watchers: 0
- Forks: 1
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 🧠 AI-Powered Knowledge Base Chatbot
[](https://python.org)
[](https://streamlit.io)
[](https://langchain.com)
[](https://anthropic.com)
*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**
```bash
git clone https://github.com/yourusername/kb-chatbot.git
cd kb-chatbot
```
2. **Set up Python environment**
```bash
# Using uv (recommended)
uv sync
```
3. **Configure environment variables**
```bash
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**
```bash
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
```env
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
```bash
streamlit run app.py
```
### Docker Deployment
```bash
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](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](https://github.com/andreaschandra/kb-chatbot/issues)
- 📖 Documentation: [Wiki](https://github.com/andreaschandra/kb-chatbot/wiki)
---
**Built with ❤️ for intelligent document interaction**
[⭐ Star this repository](https://github.com/andreaschandra/kb-chatbot) if you find it helpful!