Q&A Automation using LLaMA-3 Model via Groq API

Setting Up the Environment

Before diving into the script, ensure you have the following:

• Groq API Key: Obtain a valid API key from Groq.
• Python Environment: Set up a Python environment with the necessary libraries. You’ll need requests for API calls and PyPDF2 for handling PDF files.
• Install Libraries: Run pip install requests PyPDF2 to install the required libraries.

Creating the Python Script

The script will involve the following steps:

1. Read PDF Content: Extract text from the PDF file.
2. Send Question to API: Use the Groq API to send the question and the extracted text to the LLaMA-3 model.
3. Get Answer: Receive the answer from the API and print it.

Step 1: Read PDF Content

First, we’ll write a function to extract text from a PDF file using PyPDF2.

import PyPDF2

  def extract_text_from_pdf(file_path):
    pdf_file_obj = open(file_path, 'rb')
    pdf_reader = PyPDF2.PdfFileReader(pdf_file_obj)
    num_pages = pdf_reader.numPages
    text = ''
    for page in range(num_pages):
      page_obj = pdf_reader.getPage(page)
      text += page_obj.extractText()
    pdf_file_obj.close()
    return text
  

Step 2: Send Question to API

Next, we’ll create a function to send the question and the PDF content to the Groq API.

import requests
  import json

  def send_question_to_api(question, pdf_content, groq_api_key):
    url = 'https://api.groq.com/openai/v1/chat/completions'
    headers = {
      'Content-Type': 'application/json',
      'Authorization': f'Bearer {groq_api_key}'
    }
    data = {
      'model': 'llama-3.3-70b-versatile',
      'messages': [
        {
          'role': 'user',
          'content': f'Answer the following question based on the provided text: {question}\n\nText: {pdf_content}'
        }
      ]
    }
    response = requests.post(url, headers=headers, data=json.dumps(data))
    return response
  

Step 3: Get Answer

Finally, we’ll parse the API response to get the answer.

def 
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