ai-summariser

# 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:

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

2. Dependencies

   ```bash
   langchain
   langgraph
   tavily
   langchain_google_genai

3. Install dependencies:

   ```bash
   pip install -r requirements.txt

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

   ```env
   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.