https://github.com/cskwork/rag-langchain
conversational RAG system, this chatbot turns any document into a command-driven assistant that remembers multi-threaded chat history, retrieves context-aware answers via Chroma vector storage and LangGraph flow, and persists conversations for seamless follow-ups
https://github.com/cskwork/rag-langchain
Last synced: 18 days ago
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conversational RAG system, this chatbot turns any document into a command-driven assistant that remembers multi-threaded chat history, retrieves context-aware answers via Chroma vector storage and LangGraph flow, and persists conversations for seamless follow-ups
- Host: GitHub
- URL: https://github.com/cskwork/rag-langchain
- Owner: cskwork
- Created: 2025-07-16T09:10:32.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-07-21T05:30:22.000Z (11 months ago)
- Last Synced: 2025-07-21T07:19:59.196Z (11 months ago)
- Language: JavaScript
- Size: 133 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# RAG ChatBot with Chat History
A conversational RAG (Retrieval Augmented Generation) system that remembers your conversation history and provides contextual answers from documents.
## 🎯 What It Does
Transform any document into an intelligent chatbot that:
- **Remembers conversations**: Maintains context across multiple questions
- **Answers from documents**: Retrieves relevant information to answer your questions
- **Interactive chat**: Real-time conversation with command support
- **Persistent storage**: Saves chat history using Chroma vector database
## 🚀 Quick Start
1. **Install dependencies**:
```bash
pnpm install
```
2. **Set up environment**:
```bash
cp env.example .env
```
3. **Add your API keys** to `.env`:
```env
OPENROUTER_API_KEY=your_openrouter_key_here
OPENAI_API_KEY=your_openai_key_here
```
4. **Start chatting**:
```bash
# Interactive chat mode
pnpm start --interactive
# Sample questions mode
pnpm start
# Streaming answers
pnpm start --streaming
```
## 💬 Usage Examples
### Interactive Chat
```bash
$ pnpm start --interactive
💬 You: What is task decomposition?
🤖 Assistant: Task decomposition is the process of breaking down complex tasks into smaller, manageable steps...
💬 You: Can you give me an example?
🤖 Assistant: Based on our previous discussion about task decomposition, here's an example...
```
### Available Commands
- `/help` - Show available commands
- `/reset` - Start a new conversation
- `/history` - View conversation history
- `/threads` - Show all conversation threads
- `/switch ` - Switch to different conversation
- `/summary` - Generate conversation summary
- `/exit` - Exit the chat
## 🏗️ Architecture
The system uses a **conversational StateGraph** approach:
```
User Input → Query Reformulation → Document Retrieval →
Contextual Response → Chat History Storage
```
**Key Components:**
- **Chroma Vector Database**: Stores documents and embeddings
- **LangGraph StateGraph**: Manages conversation flow
- **Chat History Manager**: Handles conversation persistence
- **OpenRouter Integration**: LLM and embedding models
## 📁 Project Structure
```
rag-langchain/
├── src/
│ ├── wrappers/
│ │ ├── chroma-wrapper.js # Chroma database wrapper
│ │ └── embeddings-openai.js # OpenAI embeddings wrapper
│ ├── chat-history.js # Conversation management
│ ├── interactive-chat.js # CLI chat interface
│ ├── rag.js # Main RAG system
│ └── config.js # Configuration
├── index.js # Entry point
└── README.md
```
## ⚙️ Configuration
Edit `src/config.js` to customize:
- **Models**: Change LLM and embedding models
- **Chroma Settings**: Database configuration
- **Chat History**: Conversation persistence options
- **Prompts**: System prompts in Korean/English
## 🔧 Use Cases
### 1. Document Q&A Chatbot
- Load company documents, manuals, or knowledge bases
- Create an interactive assistant that answers questions
- Maintain conversation context for follow-up questions
### 2. Customer Support Assistant
- Upload FAQ documents and product manuals
- Provide contextual customer support
- Remember previous conversation for better assistance
### 3. Research Assistant
- Load research papers or articles
- Ask complex questions with follow-ups
- Generate summaries of long conversations
### 4. Personal Knowledge Base
- Store personal documents and notes
- Create a conversational interface to your knowledge
- Search and retrieve information naturally
## 🎨 Customization Examples
### Add New Document Loaders
```javascript
// Add PDF loader
import { PDFLoader } from "@langchain/community/document_loaders/fs/pdf";
// Add to RAGSystem class
async loadPDF(filePath) {
const loader = new PDFLoader(filePath);
return await loader.load();
}
```
### Custom Chat Commands
```javascript
// Add to interactive-chat.js
case '/export':
await this.exportConversation();
break;
```
### Connect to External Chroma Server
```env
CHROMA_USE_LOCAL_DB=false
CHROMA_HOST=your-chroma-server.com
CHROMA_PORT=8000
```
## 🛠️ Models
**Default Models:**
- **LLM**: `moonshotai/kimi-k2:free` (via OpenRouter)
- **Embeddings**: `text-embedding-3-small` (via OpenAI)
**Change Models:**
```env
LLM_MODEL=anthropic/claude-3-haiku
EMBEDDING_MODEL=text-embedding-3-large
```
## 🐛 Common Issues
**"Better SQLite3 bindings not found"**
- Conversation persistence will fallback to memory storage
- Install build tools: `pnpm add --dev node-gyp`
**"Chroma client initialization failed"**
- Using memory-based vector store as fallback
- Works normally with in-memory storage
**"API key issues"**
- Ensure both OpenRouter and OpenAI keys are set
- Check API key validity and credits
## 📚 Advanced Features
### Conversation Management
- **Thread Support**: Multiple conversation threads
- **Persistence**: SQLite-based chat history storage
- **Context Awareness**: Query reformulation based on chat history
- **Summarization**: Auto-generate conversation summaries
### Vector Database
- **Chroma Integration**: Production-ready vector storage
- **Similarity Search**: Efficient document retrieval
- **Embeddings**: High-quality text embeddings
- **Fallback**: Memory storage for development
## 🎉 Getting Started Tips
1. **Start with interactive mode** to experience the conversational flow
2. **Try follow-up questions** to see context awareness in action
3. **Use `/help`** to explore available commands
4. **Experiment with different document URLs** in the config
5. **Check conversation history** with `/history` command
---
**Ready to chat with your documents? 🚀**
```bash
pnpm start --interactive
```