llama_search_agent

ChatGroq API

This repository contains a FastAPI application that leverages LangChain to initialize various tools and an LLM (Language Learning Model) for handling user inputs. The application is designed to handle multiple types of queries using integrated tools such as SerpAPI, Wikipedia, DuckDuckGo, ArXiv, PubMed, and more.

Table of Contents

• Getting Started
• Environment Variables
• Available Endpoints
• Running the Application
• Testing

Getting Started

Prerequisites

• Python 3.12

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/your-repo-name.git cd your-repo-name

2. Create and activate a virtual environment:

   python -m venv venv source venv/bin/activate # On Windows use venv\Scripts\activate

3. Install the required packages:

   pip install -r requirements.txt

Environment Variables

Create a .env file in the root directory of the project and add the necessary API keys:

GROQ_API_KEY=your_groq_api_key
SERPAPI_API_KEY=your_serpapi_api_key

Available Endpoints

POST /search

Endpoint to handle search queries using the initialized agent.

Request Body:

{
  "input": "your query here"
}

Response:

{
  "result": "search result"
}

Running the Application

1. Ensure you have set up the virtual environment and installed the dependencies as shown above.

2. Run the FastAPI application:

   uvicorn main:app --reload

The application will be available at http://127.0.0.1:8000

Testing

You can test the API using the base URL: https://llamachat-ipea.onrender.com/