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PDF Summarization with Indexify and Mistral

PDFIndexify+1

In this cookbook, we'll explore how to create a PDF summarization pipeline using Indexify and Mistral's large language models. By the end of the document, you should have a pipeline capable of ingesting 1000s of PDF documents, and using Mistral for summarization.

Introduction

The summarization pipeline is going to be composed of two steps -

  • PDF to Text extraction. We are going to use a pre-built extractor for this - tensorlake/pdfextractor.
  • We use Mistral for summarization.

Prerequisites

Before we begin, ensure you have the following:

  • Create a virtual env with Python 3.9 or later 
    python3.9 -m venv ve
source ve/bin/activate
  • pip (Python package manager)
  • A Mistral API key
  • Basic familiarity with Python and command-line interfaces

Setup

Install Indexify

First, let's install Indexify using the official installation script in a terminal:

curl https://getindexify.ai | sh

Start the Indexify server:

./indexify server -d

This starts a long running server that exposes ingestion and retrieval APIs to applications.

Install Required Extractors

Next, we'll install the necessary extractors in a new terminal:

pip install indexify-extractor-sdk
indexify-extractor download tensorlake/pdfextractor
indexify-extractor download tensorlake/mistral

Once the extractors are downloaded, you can start them:

indexify-extractor join-server

Creating the Extraction Graph

The extraction graph defines the flow of data through our summarization pipeline. We'll create a graph that first extracts text from PDFs, then sends that text to Mistral for summarization.

from indexify import IndexifyClient, ExtractionGraph

client = IndexifyClient()

extraction_graph_spec = """
name: 'pdf_summarizer'
extraction_policies:
  - extractor: 'tensorlake/pdfextractor'
    name: 'pdf_to_text'
  - extractor: 'tensorlake/mistral'
    name: 'text_to_summary'
    input_params:
      model_name: 'mistral-large-latest'
      key: 'YOUR_MISTRAL_API_KEY'
      system_prompt: 'Summarize the following text in a concise manner, highlighting the key points:'
    content_source: 'pdf_to_text'
"""

extraction_graph = ExtractionGraph.from_yaml(extraction_graph_spec)
client.create_extraction_graph(extraction_graph)

Replace 'YOUR_MISTRAL_API_KEY' with your actual Mistral API key.

Implementing the Summarization Pipeline

Now that we have our extraction graph set up, we can upload files and make the pipeline generate summaries:

import os
import requests
from indexify import IndexifyClient

def download_pdf(url, save_path):
    response = requests.get(url)
    with open(save_path, 'wb') as f:
        f.write(response.content)
    print(f"PDF downloaded and saved to {save_path}")

def summarize_pdf(pdf_path):
    client = IndexifyClient()
    
    # Upload the PDF file
    content_id = client.upload_file("pdf_summarizer", pdf_path)
    
    # Wait for the extraction to complete
    client.wait_for_extraction(content_id)
    
    # Retrieve the summarized content
    summary = client.get_extracted_content(
        content_id=content_id,
        graph_name="pdf_summarizer",
        policy_name="text_to_summary"
    )
    
    return summary[0]['content'].decode('utf-8')
pdf_url = "https://arxiv.org/pdf/2310.06825.pdf"
pdf_path = "reference_document.pdf"

# Download the PDF
download_pdf(pdf_url, pdf_path)

# Summarize the PDF
summary = summarize_pdf(pdf_path)
print("Summary of the PDF:")
print(summary)

Customization and Advanced Usage

You can customize the summarization process by modifying the system_prompt in the extraction graph. For example:

  • To generate bullet-point summaries:

    system_prompt: 'Summarize the following text as a list of bullet points:'

  • To focus on specific aspects of the document:

    system_prompt: 'Summarize the main arguments and supporting evidence from the following text:'

You can also experiment with different Mistral models by changing the model_name parameter to find the best balance between speed and accuracy for your specific use case.

Conclusion

While the example might look simple, there are some unique advantages of using Indexify for this -

  1. Scalable and Highly Availability: Indexify server can be deployed on a cloud and it can process 1000s of PDFs uploaded into it, and if any step in the pipeline fails it automatically retries on another machine.
  2. Flexibility: You can use any other PDF extraction model we used here doesn't work for the document you are using.

Next Steps