Langchain PDF App: Create ChatGPT for Your PDF

Langchain PDF App: Create ChatGPT for Your PDF

Table of Contents

1. Introduction
2. Setting Up the Environment
3. Parsing the PDF
4. Creating the Graphical User Interface
5. Splitting the Text into Chunks
6. Converting Chunks into Embeddings
7. Building the Knowledge Base
8. User Interaction
9. Answering Questions from Chunks
10. Tracking Spending

Introduction

In this tutorial, we will learn how to build an application that can extract and answer questions from a PDF document using Python. The application will have a graphical user interface and will utilize language models and semantic search to find Relevant information.

Setting Up the Environment

Before we begin, we need to set up our development environment. We will install the necessary dependencies, such as the PDF2 library for reading PDF files, Python-dotenv for managing environment variables, and Streamlit for creating the graphical user interface. We will also Create a .env file to store our API keys securely and a .gitignore file to ensure our secrets are not tracked by Git.

Parsing the PDF

Once our environment is set up, we will write code to parse the PDF file. We will use the PDFReader class from the pdf2 library to extract the text content from the PDF. We will then divide the text into chunks and convert each chunk into embeddings, which are vector representations of the text. These embeddings will be stored in a knowledge base for later use.

Creating the Graphical User Interface

Next, we will build the graphical user interface using Streamlit. We will add a file upload component that allows the user to upload a PDF file. We will also create an input field where the user can ask a question about the PDF. Upon uploading the file and asking a question, the application will perform a similarity search in the knowledge base to find relevant chunks of text.

Splitting the Text into Chunks

To improve the search accuracy and efficiency, we will split the text into smaller, manageable chunks. We will use the CharacterTextSplitter class from the langchain library to split the text into chunks of a specified size, with a defined overlap between chunks. This will ensure that we capture relevant information while maintaining the Context of each chunk.

Converting Chunks into Embeddings

Once we have divided the text into chunks, we will convert each chunk into embeddings using the OpenAI embeddings provided by the langchain library. These embeddings will serve as vector representations of the text chunks, enabling us to perform a semantic search later on.

Building the Knowledge Base

With the chunks and corresponding embeddings, we can now build our knowledge base using the Fais class from the langchain library. This knowledge base will allow us to perform efficient searches Based on the similarity between the user's question and the available chunks. We will store the chunks in the knowledge base along with their respective embeddings.

User Interaction

Once the knowledge base is ready, we will add functionality to handle user interaction. We will retrieve the user's question from the input field and use it to search the knowledge base for relevant chunks. We will then generate an answer to the user's question using the language model integrated with the QABlankChain class from the langchain library. The answer will be displayed to the user.

Answering Questions from Chunks

To provide accurate answers, we will utilize the QABlankChain class from the langchain library. This class allows us to input the user's question and the relevant chunks from the knowledge base, and generate a response based on the language model's understanding of the text. We will integrate this functionality into our application to generate informative answers to the user's questions.

Tracking Spending

Lastly, we will implement a feature to track how much We Are spending per question answered by the language model. We will use the get_openai_callback function from the langchain library, which provides a convenient way to monitor the cost of operations executed via the OpenAI API. This will help us keep track of our expenses and optimize our usage.

Conclusion

In this tutorial, we have learned how to build an application that can extract and answer questions from a PDF using Python. We have covered various concepts such as parsing PDFs, creating a graphical user interface, splitting text into chunks, converting chunks into embeddings, building a knowledge base, and tracking expenses. By following this tutorial, You should have a solid understanding of how to implement similar applications and leverage language models to automate tasks.