Langraph_chatbot :

https://tijo-langchat.streamlit.app/

This project is a chatbot application built using FastAPI, Streamlit, and LangGraph. The chatbot interacts with users through a web interface and processes their messages using a LangGraph-based agent.

Project Structure

Agentic_Langraph_chatbot/
├── app.py                                    # Main file with fast api backend
├── Dockerfile                                # DockerFile
├── README.md                                 # Unit and integration tests
├── requirements.txt                          # Dependencies
└── ui.py                                     # Streamlit file for Frond end 
.env(Please add your api keys here, I didn't) # Environment variable template

Tools and Libraries

• FastAPI: A modern, fast (high-performance), web framework for building APIs with Python 3.6+ based on standard Python type hints.
• Streamlit: An open-source app framework for Machine Learning and Data Science teams.
• LangGraph: A library for building and running language models.
• Uvicorn: A lightning-fast ASGI server implementation, using uvloop and httptools.

Setup Instructions

Follow these steps to set up and run the project:

1. Create a Conda Environment:

   conda create -n langchat python=3.11 -y

2. Activate the Conda Environment:

   conda activate langchat

3. Install the Required Dependencies:

   pip install -r requirements.txt

4. Run the FastAPI and Streamlit Servers:

   uvicorn app:app --reload
   streamlit run ui.py

Alternatively, you can use Docker to run the project:

1. Build the Docker Image:

   docker build -t langraph_chatbot .

2. Run the Docker Container:

   docker run -p 8000:8000 -p 8501:8501 langraph_chatbot

Usage

1. Open your web browser and navigate to http://localhost:8501.
2. Define your AI agent by entering a system prompt.
3. Select a model from the dropdown menu.
4. Enter your message(s) and click the "Submit" button.
5. View the agent's response on the web interface.

Notes

• Ensure that the FastAPI server is running on http://127.0.0.1:8000 as the Streamlit app communicates with this endpoint.
• The Dockerfile is configured to expose ports 8000 and 8501 for FastAPI and Streamlit, respectively.

License

This project is licensed under the MIT License.