ASI1-mini Integration with LangChain and Tavily Search

This project demonstrates how to integrate a ASI1-mini API with LangChain and utilize the Tavily Search tool to process search queries. The code defines a custom LLM class that sends prompts to your API and then integrates with LangChain’s agent framework to combine LLM responses with search results.

Features

• Custom LLM Integration:
  Implements a custom LangChain LLM that calls the ASI1-mini API using a defined payload.

• Tavily Search Tool:
  Leverages the Tavily Search API to fetch search results as part of an agent chain.

• Agent Chain Execution:
  Sets up an agent chain that processes a search query, calls the ASI1-mini LLM, and returns a combined result.

• Environment-Based Configuration:
  Manages API keys and sensitive data through environment variables loaded from a .env file.

Prerequisites

• Python: Version 3.8 or higher.

• Dependencies:

  • LangChain
  • Requests
  • Pydantic
  • python-dotenv
  • LangChain community tools for Tavily search[langchain.tools]

• Environment Variables

  • ASI_LLM_KEY=<asi1-api_key>
  • TAVILY_API_KEY=<tavily_api_key>

To Run:

• Run command: python ASI_Langchain.py