Extracting Structured Data From PDFs | Full Python AI project for beginners (ft Docker)

Introduction

Every year, in mid-September, the Ig Nobel Prizes are awarded to researchers who receive recognition for their unusual studies. In the spirit of these humorous awards and the current AI hype, I've decided to embark on an absurd project: building an app that utilizes a large language model to extract key information from Ig Nobel Prize research papers. This will include titles, summaries, authors, and years of publication.

You might ask, do we really need AI for that? Not necessarily; one could read the papers and extract the information manually. However, a smart application like this can be beneficial for several purposes, such as automatically organizing information from unstructured data—think PDF documents, books, invoices, customer queries, and images—into a neat table. Imagine the hours of labor saved if it works efficiently.

Project Overview

In this article, we will interact with a large language model, like GPT-4, through its API. We will build a document retrieval system to answer our questions based on given research papers in PDF format. This project will be elevated by employing a new feature from OpenAI’s API called structured outputs, which we’ll discuss further. Additionally, we'll ensure that our app cites the exact text sources utilized for generating answers, enhancing its trustworthiness and reliability.

Finally, we will create a user-friendly Streamlit interface and containerize and deploy our application using Docker. Although this might sound complex, I will guide you through each step, ensuring that even those unfamiliar with Python coding can grasp the concepts involved.

Information Retrieval with AI

As AI matures, businesses are realizing that one of its most valuable use cases is information retrieval—extracting structured data from unstructured formats. This entails sifting through extensive reports or documents, understanding them, and organizing the relevant information methodically.

Most people tend to avoid such tedious tasks, which can be enormously time-consuming. The good news is that this entire process is becoming increasingly automated thanks to advanced language models like GPT-4. Such systems fall under the category of Retrieval-Augmented Generation (RAG). Unlike chat models, RAG systems answer based on specific documents, allowing for more accurate results and reducing the likelihood of 'hallucinations'—instances where a model might fabricate information.

How RAG Works

A RAG system generally involves three steps:

1. Document Processing: Breaking down documents into manageable chunks.
2. Information Retrieval: Using a query to find relevant sections within the documents.
3. Augmented Generation: Crafting a response using a language model.

The first two steps focus on retrieval while the final step leverages the language model's capabilities for generating accurate responses.

Setting Up Your Project

Project Folder and Environment

Start by creating a new project folder called ragrms and set up a virtual environment within that folder. Activate your virtual environment and open your chosen code editor.

Next, install the necessary Python packages. You'll need:

• LangChain: A framework for building applications with large language models.
• LangChain Community: Essential third-party integrations for LangChain.
• LangChain OpenAI: Integration for OpenAI.
• Chroma DB: An open-source vector database.
• PyPDF: For reading and parsing PDF documents.
• Pandas: For data manipulation.
• Streamlit: For building interactive web apps in Python.
• python-dotenv: For managing environment variables.

API Configuration

Sign up for an OpenAI account if you haven’t done so already. Create an API key, then, to secure it, add it to a hidden .env file in your project directory. Use the load_dotenv function from the dotenv package to access this API key within your code.

Loading and Processing PDFs

To pull data from PDFs, use P PDF to load the documents from your data folder. Convert them into manageable page objects, and then further split these pages into smaller chunks using a recursive character text splitter. This step is critical because it ensures that each chunk is relevant and focused, enhancing retrieval accuracy.

Implementing Vectorization

For efficient information retrieval, every chunk needs to be represented numerically—this is where text embeddings come into play. We’ll utilize OpenAI’s embedding model to translate each text chunk into a vector.

Database Creation

To store and manage these embeddings, we will establish a Chroma database. The chunks will be converted into vectors, allowing for efficient querying and information retrieval based on similarity measures.

Querying the Database

Create a retriever that can utilize this database for answering questions. Using OpenAI’s language model, generate responses based on the relevant document chunks retrieved. This includes creating a structured output to further enhance the data's organization.

User Interface with Streamlit

Next, we will implement a user-friendly interface using Streamlit. This framework allows us to craft a simple, easy-to-navigate web application for interacting with the model.

Dockerizing Your Application

Finally, Docker will be employed to wrap the application in a container. This ensures that everyone can run the app on any system without worrying about setting up dependencies.

Running the Application

After building and running your Docker image, you can access your Streamlit application in a browser. You can share it easily through Docker Hub or by exporting the image as a tar file.

Conclusion

Congratulations on reaching the end of this project! By leveraging structured outputs from large language models and Docker, you've built an efficient app for extracting key information from PDFs seamlessly.

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Keywords

• AI
• Information Retrieval
• PDFs
• Structured Outputs
• Python
• Docker
• LangChain
• OpenAI
• Streamlit

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FAQ

Q1: Why is it useful to extract structured data from PDFs?
A1: Extracting structured data from PDFs allows for better data organization and analysis, saving time in research and data processing tasks.

Q2: What are text embeddings, and why are they important?
A2: Text embeddings are numerical representations of text data. They are crucial because they allow for efficient similarity comparisons and retrieval tasks.

Q3: What is Docker, and why should I use it?
A3: Docker is a platform that packages applications in isolated containers, ensuring your app runs consistently across different environments without installation worries.

Q4: How can I deploy my application?
A4: You can deploy your application using Docker Hub or save it as a tar file to share it with others.

Q5: What is LangChain?
A5: LangChain is a flexible framework that simplifies building applications powered by large language models. It allows for integrating various functionalities easily.