LangGraph_playground

This repository is a study case in basic concepts at LangGraph framework following the quickstart documantation and first tutorials. Here you can follow all my steps and CREATE your First CHAT BOT with complete integration with examples of visual interface (stream lit web app)

🚀 Technologies Used

• Python 🐍
• LangGraph – for building and managing the state graph
• LangChain Together – to use Together.ai's LLMs
• Together.ai – for hosting and running open-source LLMs (like Meta Llama 3)
• python-dotenv – for managing API keys in .env files
• VSCode or Terminal – to run the chatbot interactively

🔐 API Key Setup

To use Together.ai's models, you must have a Together.ai account and obtain your API key.

1. Sign up at together.ai
2. Go to your API Key page
3. Create a .env file in the project root with the following content:

📁 Project Organization

LangGraph_playground/

• config/

  • Example.env – Example of a .env file containing environment variables.

• src/

  • Basic_chat-bot/

    • chat_bot_EN.py – Basic chatbot example using LangGraph (English version).
    • chat_bot_PT-BR.py – Basic chatbot example using LangGraph (Portuguese version).

  • Chat-bot_Integrate-web/

    • chat_bot_integrate.py – Chatbot integrated with external tools (Portuguese).
    • chat_bot_integration_EN.py – Chatbot integrated with external tools (English).
    • ui_web_app_integration.py – Simple web app UI for interacting with the integrated chatbot.

  • Chat-bot_memory-add/

    • chat_bot_add_memory_PT-BR.py – Chatbot with memory support (Portuguese).
    • chat_bot_add_memory_EN.py – Chatbot with memory support (English).

• requirements.txt: Lists all required Python packages. Run pip install -r requirements.txt to install dependencies.

• README.md: Explains how the project works, how to run it, and the technologies used.