{"id":24401579,"url":"https://github.com/poorvajasathasivam/llm-agent-chatbot","last_synced_at":"2026-05-09T07:02:12.738Z","repository":{"id":273256386,"uuid":"919118913","full_name":"poorvajasathasivam/llm-agent-chatbot","owner":"poorvajasathasivam","description":"AI-powered assistant built using LangChain, FastAPI, and Streamlit. 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The chatbot interacts with users to answer questions using prompts and powerful AI models.\n\n## Features\n\n- **LangChain AI Agent**: A trading assistant powered by LangChain's AI capabilities.\n- **Model Selection**: Predefined set of AI models such as \"llama3-70b-8192\" and \"mixtral-8x7b-32768\" are available for querying.\n- **Customizable System Prompt**: Set a custom system prompt to guide the AI's responses based on the user's needs.\n- **Simple Frontend**: The frontend is built with Streamlit for easy interaction with the AI agent.\n![Screenshot 2025-01-20 at 01 08 17](https://github.com/user-attachments/assets/f51705c5-9550-4af3-b7fa-96fb9368a13d)\n\n## Installation\n\n### Prerequisites\n\n- Python 3.7+\n- Virtual environment (optional but recommended)\n\n### Clone the repository\n\n```bash\ngit clone https://github.com/poorvajasathasivam/llm-agent-chatbot.git\n```\n###  Create a virtual environment and activate it (optional):\n\n```bash\npython -m venv venv\nsource venv/bin/activate  # On Windows: venv\\Scripts\\activate\n```\n### Install the necessary dependencies:\n\n```bash\npip install -r requirements.txt\n```\n\n### Ensure you have the following environment variables set:\n\n**Set up environment variables:**\nCreate a .env file in the root directory to store the environment variables. Example .env:\n\n- GROQ_API_KEY = your_groq_api_key\n- TAVILY_API_KEY = your_tavily_api_key\n### Run the FastAPI Backend\nTo run the FastAPI app:\n\n```bash\npython app.py\n``` \nThe backend will be available at http://127.0.0.1:8000.\n\n### Run the Streamlit Frontend\nTo run the Streamlit frontend:\n\n```bash\nstreamlit run ui.py\n``` \n\nThe frontend will open at http://localhost:8501 by default.\n\n### Usage\nSet the system prompt: In the Streamlit frontend, define a system prompt to guide the AI on how to respond to queries. Example:\n\n```bash\nYou are a financial trading assistant that can help users with trading strategies, market trends, and other financial concepts.\n```\n\n**Select a model:** Choose one of the available models like \"llama3-70b-8192\" or \"mixtral-8x7b-32768.\"\n\n**Enter a message:** Type a trading-related question or prompt in the message input box.\n\n**Click \"Submit\"**: The FastAPI backend will process the input using LangChain and the selected AI model and return a response.\n\n**Example Queries**\n- \"Can you explain what a moving average is and how it is used in stock trading?\"\n- \"What is the difference between short and long positions in trading?\"\n- \"Tell me about risk management in trading.\"\n\n\n\n\n  \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpoorvajasathasivam%2Fllm-agent-chatbot","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpoorvajasathasivam%2Fllm-agent-chatbot","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpoorvajasathasivam%2Fllm-agent-chatbot/lists"}