# Chat with PDF - Streamlit Application

## Overview

The **Chat with PDF** application is a powerful tool that allows users to upload PDFs, extract their content, and interact with them through an AI-based question-answering system. Powered by **Claude by Anthropic** and **HuggingFace Embeddings**, this app enables efficient extraction and interaction with data from PDF files.

### Key Features:
- **PDF Content Extraction**: Extracts text from PDF files using `pdfplumber`.
- **AI-Powered Interaction**: Ask questions related to the content of the PDF, and get answers using the **Claude AI model**.
- **Text Embeddings**: Converts extracted PDF text into embeddings stored in a **FAISS vector store** for fast and efficient querying.
- **Error Handling**: Built-in error handling to address API issues, PDF extraction failures, and embedding creation problems.
- **Retry Mechanism**: Uses the **Tenacity** library to automatically retry embedding creation in case of failure, with exponential backoff.

## Installation

To get started with the application, follow these steps:

### 1. Clone the Repository
```bash
git clone https://github.com/atharvad38/chat_with_pdf.git
cd chat_with_pdf

2. Install Required Packages

Make sure you have Python 3.x installed, and then install the dependencies using pip:

pip install -r requirements.txt

3. Setup Anthropic API Key

You will need a valid Anthropic API key for the application to work. To get started:

1. Sign up at Anthropic and generate your API key.
2. Replace the api_key variable in the app.py file with your API key.

api_key = 'your-anthropic-api-key-here'

4. Run the Application

Once everything is set up, you can start the Streamlit app by running:

streamlit run app.py

This will open the application in your default web browser.

How to Use

1. Upload a PDF: Select and upload a PDF file using the file uploader in the app.
2. PDF Processing: The app will extract the text content from the PDF and create embeddings from the text.
3. Ask Questions: Once the embeddings are created, you can ask questions based on the document. The AI model will generate answers using the extracted content from the PDF.

Code Explanation

Key Components:

• PDFProcessor Class:

  • extract_pdf_content: This function extracts text from a PDF file using pdfplumber.
  • create_embeddings: This function converts the extracted text into embeddings and stores them in a FAISS vector store for efficient similarity search.
  • validate_api_key: Verifies that the provided Anthropic API key is valid.

• Main Logic:

  • Uses Streamlit to create a user interface.
  • PDF Upload: Users upload a PDF, and the app processes and creates embeddings for it.
  • Question-Answering: Once the PDF is processed, users can interact with the document by asking questions, and the AI provides answers based on the PDF content.

Error Handling:

• Uses the Tenacity package for retries, particularly for the embedding creation process. If the creation fails, it retries up to 3 times with an exponential backoff.

Requirements

• Python 3.x
• Streamlit
• pdfplumber (For extracting text from PDFs)
• Pandas
• Langchain (Anthropic)
• HuggingFace Embeddings
• FAISS (For efficient vector storage and search)
• Tenacity (For retry logic)
• Logging (For error and info logging)