081fb6b6d6
- Add basic LangGraph chatbot implementation with OpenAI GPT-4 - Include shell tool integration for system command execution - Set up project with uv package manager - Add comprehensive README with setup instructions - Include proper .gitignore for Python projects
131 lines
3.5 KiB
Markdown
131 lines
3.5 KiB
Markdown
# LangGraph Basic Chatbot
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A basic chatbot built with LangGraph following the official tutorial. This chatbot uses OpenAI's GPT-4 model to provide conversational AI capabilities.
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## Features
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- 🤖 Basic conversational AI using LangGraph
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- 🔄 State management with message history
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- 🚀 Built with uv for fast dependency management
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- 🔐 Secure API key handling
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## Prerequisites
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- Python 3.8 or higher
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- [uv](https://docs.astral.sh/uv/) package manager
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- OpenAI API key
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## Setup
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### 1. Clone and Navigate to Project
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```bash
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cd /Users/ghsioux/tmp/langgraph-pard0x
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```
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### 2. Install Dependencies
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Dependencies are already installed with uv. The virtual environment is automatically managed.
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### 3. Set up OpenAI API Key
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You have several options:
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#### Option A: Use the setup script
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```bash
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uv run setup_env.py
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```
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#### Option B: Set environment variable manually
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```bash
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export OPENAI_API_KEY='your-api-key-here'
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```
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#### Option C: Create a .env file
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Create a `.env` file in the project root:
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```
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OPENAI_API_KEY=your-api-key-here
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```
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### 4. Run the Chatbot
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```bash
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uv run main.py
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```
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## Usage
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1. Start the chatbot with `uv run main.py`
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2. Type your messages and press Enter
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3. The bot will respond using GPT-4
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4. Type `quit`, `exit`, or `q` to end the conversation
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### Example Conversation
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```
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🤖 LangGraph Basic Chatbot
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Type 'quit', 'exit', or 'q' to exit the chat.
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----------------------------------------
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✅ Chatbot initialized successfully!
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User: Hello! What is LangGraph?
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Assistant: LangGraph is a library designed to help build stateful multi-agent applications using language models...
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User: quit
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👋 Goodbye!
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```
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## Project Structure
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```
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langgraph-pard0x/
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├── main.py # Main chatbot implementation
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├── setup_env.py # Environment setup helper
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├── pyproject.toml # Project configuration and dependencies
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├── README.md # This file
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└── .venv/ # Virtual environment (auto-managed by uv)
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```
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## Dependencies
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- **langgraph**: Graph-based workflow library for LLM applications
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- **langchain**: Framework for developing applications with LLMs
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- **langchain-openai**: OpenAI integration for LangChain
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- **langsmith**: Observability and debugging for LangChain applications
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## How It Works
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The chatbot uses LangGraph's `StateGraph` to manage conversation state:
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1. **State Definition**: Uses a `TypedDict` with `messages` field that appends new messages
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2. **Graph Creation**: Creates a state graph with a single "chatbot" node
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3. **LLM Integration**: Uses `init_chat_model` to initialize OpenAI's GPT-4
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4. **Message Processing**: The chatbot node processes incoming messages and returns responses
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5. **Streaming**: Responses are streamed back to the user in real-time
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## Next Steps
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This is the foundation for more advanced LangGraph features:
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- Add web search tools
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- Implement memory persistence
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- Add human-in-the-loop capabilities
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- Create multi-agent workflows
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Check out the [LangGraph tutorials](https://langchain-ai.github.io/langgraph/tutorials/) for more advanced features!
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## Troubleshooting
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### "Please set your OPENAI_API_KEY environment variable"
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- Make sure you've set your OpenAI API key using one of the methods above
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- Verify your API key is valid and active
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### "Error initializing chatbot"
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- Check your internet connection
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- Verify your OpenAI API key has sufficient credits
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- Make sure all dependencies are installed correctly
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### Import Errors
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- Run `uv sync` to ensure all dependencies are properly installed
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- Check that you're using the virtual environment created by uv
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