LangChain Flask Application with ChatGoogleGenerativeAI

This Flask application demonstrates how to integrate LangChain with the ChatGoogleGenerativeAI model to answer user queries based on content fetched from a URL. The app uses UnstructuredURLLoader to load content, processes the text using RecursiveCharacterTextSplitter, and generates responses with LLMChain.

Features

• Fetch Content from URL: Retrieves and processes content from a given URL.
• Text Chunking: Divides content into manageable chunks using customizable separators.
• Generate Responses: Uses ChatGoogleGenerativeAI to provide answers based on the processed content.

Prerequisites

• Python 3.7 or later
• Flask
• LangChain
• Transformers library
• langchain_google_genai library

1. Clone the Repository

   git clone https://github.com/sarthakm402/URL-readerQA.git
   
   
   

2. Create a requirements.txt file with the following content:

• Flask
• LangChain
• Transformers
• API

Usage

• Start the Flask Application

  python app.py

• Access the Application

  Open your web browser and navigate to http://127.0.0.1:5000(local host).

• Submit Your Query

  URL: Enter the URL of the content you want to analyze. Text: Enter the query related to the content. Click "Submit" to get the generated response based on the content from the URL.

Error Handling

• Initialization Errors: If there’s an issue initializing the ChatGoogleGenerativeAI model, an error message is returned.\

• Response Generation Errors: If there’s an error during response generation, an error message is returned.

Contributing

We welcome contributions to improve the project. To contribute:

• Fork the repository.

• Create a new branch for your changes.

• Submit a pull request with a detailed description of your changes.