Simple AI search and summarising agent

AI Summarizer using LangChain and Google Gemini

Author: Subhasish Saha

This project uses Langchain, Tavily, and Google Gemini APIs to create an intelligent summarizer that answers questions based on the latest news and information available on the web.

Overview

The application retrieves the latest relevant news and content using the Tavily API based on a user query, then summarizes it using the ChatGoogleGenerativeAI model powered by Google Gemini.

Key Components

• Tavily API: Used to search and fetch the latest content based on a given query.
• Google Gemini API: Used to summarize the fetched content and generate answers to the user's query.
• Langchain: A framework that integrates various language models and tools to create sophisticated pipelines.
• Langgraph: Used for managing the flow of the pipeline, from fetching content to generating a summary.

Installation

1. Clone the repository:

   git clone https://github.com/subhasishsaha/ai-summariser
   cd ai-summariser
   
   

2. Dependencies

   langchain
   langgraph
   tavily
   langchain_google_genai
   
   

3. Install dependencies:

   pip install -r requirements.txt
   
   

4. Create a .env file in the root of the project and add the following:

   api_key = <TAVILY_API_KEY>
   gemini_api = <GEMINI_API_KEY>
   

How It Works

• Research Phase: The researcher function queries the Tavily API with a user-provided question to fetch the latest relevant content (e.g., news articles).

• Summarization Phase: The summarization function then uses the ChatGoogleGenerativeAI model to summarize the content and provide an answer to the question.

• Graph Workflow: The entire flow is managed by the StateGraph in Langgraph, which orchestrates the "search" (research) and "summarization" stages.