Langchain: PDF Chat App (GUI) | ChatGPT for Your PDF FILES | Step-by-Step Tutorial

The paragraph introduces a concept of an application that allows users to upload PDF files and interact with them through a chat interface. The video aims to guide viewers through the process of creating such an app with a custom user interface, using Python for the backend and Streamlit for the frontend. The app will utilize a large language model for processing and generating responses. The end goal is to create a system similar to existing PDF chat applications, with the ability to ask questions and receive answers based on the content of the uploaded PDFs.

This section focuses on setting up the development environment for the PDF chat application. It emphasizes the need for Python knowledge and outlines the process of installing required packages using pip and a requirements.txt file. The video also suggests creating a virtual environment for each project to avoid conflicts and discusses the structure of the project files, including the app.py and requirements.txt files.

The paragraph explains the role of Streamlit in creating the graphical user interface for the PDF chat application. Streamlit is introduced as a package that allows the creation of beautiful GUIs within Python code. The video demonstrates how to create a basic structure for the GUI, including a vertical sidebar with interactive links. It also covers how to add a title, markdown text, and a header to the app, as well as how to handle user interactions such as uploading PDF files.

This part of the script discusses the process of reading and processing PDF files using the PDF reader from the PyPDF2 package. The video explains how to handle the uploaded PDF object, read its content page by page, and combine them into a single text object. It also addresses a potential error that may occur if no file is uploaded and provides a solution to check for the presence of a file before attempting to read it.

The paragraph delves into the technicalities of splitting the PDF content into smaller chunks and computing embeddings for each. It explains the concept of embeddings as numerical representations of text and the need for chunking due to the limited context window of large language models. The video introduces the recursive character text splitter from the Link chain library to divide documents into chunks with an overlap to maintain context. It also discusses the use of OpenAI's text embeddings for computing the embeddings of the chunks.

This section focuses on the process of storing computed embeddings to disk and retrieving them for future use to avoid redundant computations and costs. The video demonstrates how to use the file name and the pickle library to save and load embeddings. It also highlights the importance of checking if the embeddings file exists before recomputing embeddings and the use of environment variables to load the OpenAI API key for accessing the embeddings service.

The paragraph explains how to accept user queries and perform a semantic search on the knowledge base to find the most relevant documents. It describes the process of using the vector store's similarity function to compute embeddings for the user's query and find the top three most similar documents. The video also discusses the importance of the context window in large language models and the need to balance the number of documents returned with the model's capacity to process them without error.

This section introduces the integration of the large language model (LLM) from OpenAI for answering user queries based on the retrieved documents. The video explains how to load the LLM and use the question-answering chain from the Lang chain library. It also covers the process of feeding the top three documents and the user's question to the LLM to generate a response. The video further discusses the different types of chains available and the importance of selecting the appropriate model for the task.

The paragraph discusses the cost associated with each query and the use of different OpenAI models to optimize these costs. The video demonstrates how to create a callback to track the cost of each query and compares the costs of using different models. It also highlights the difference in response from the GPT 3.5 turbo model compared to the default model and how it can affect the accuracy and cost of the answers provided by the system.

In the concluding paragraph, the video wraps up the tutorial on creating a PDF chat application. It summarizes the steps covered in the video and emphasizes the learning outcomes. The video also promotes the creator's Discord server for further learning and discussion on AI topics, offers support through Patreon, encourages viewers to ask questions in the comments, and invites subscriptions to the channel for continued content.