ChatWithPDF

This is a Flask web application that provides AI-powered document search and retrieval functionality. It leverages the LangChain framework to process natural language queries and search through PDF documents for relevant information.

Features

• Process natural language queries with a pre-trained language model.
• Upload and index PDF documents for efficient search and retrieval.
• Retrieve relevant document sections based on query similarity.

Installation

Prerequisites

• Python 3.7 or higher

Setting Up the Environment

1. Clone the repository:

   git clone https://github.com/pharth/ChatWithPDF
   cd ChatWithPDF

2.Create a virtual environment:

 python -m venv venv

3.Activate the virtual environment: On Windows:

venv\Scripts\activate

On macOS and Linux:

source venv/bin/activate

4.Install the required packages from requirements.txt:

pip install -r requirements.txt

Usage

1.Run the Flask application:

python app.py

2.Endpoints:

POST /ai

Processes a natural language query using the language model.

Request:

{
  "query": "Your question here"
}

Response:

{
  "answer": "Response from the language model"
}

POST /ask_pdf

Queries the indexed PDF documents.

Request:

{
  "query": "Your question related to the documents"
}

Response:

{
  "answer": "Relevant answer from the documents",
  "sources": [
    {
      "source": "source file name",
      "page_content": "relevant page content"
    }
  ]
}

POST /pdf

Uploads and indexes a PDF document.

Request:

Form-data with the key "file" and the PDF file as value.

Response:

{
  "status": "Successfully Uploaded",
  "filename": "uploaded_file_name.pdf",
  "doc_len": 10,
  "chunks": 20
}