Building a PDF Chatbot with OpenAI and Hugging Face Models

📚 The video tutorial shows how to build a chatbot application that allows users to chat with multiple PDFs.

🖥️ The application can process and embed PDFs into a database, and can answer questions related to the uploaded PDFs.

💻 The tutorial explains how to create the application using openai and hugging face free models, while maintaining security of API keys.

📃 To use the platform, create an account and generate API keys for PDFs and Hugging Face.

📦 Load environment variables using the 'load.tnv' function to enable access to API keys.

📄 The application works by taking user's PDFs, dividing them into text chunks, converting the chunks into embeddings, and storing them in a vector store.

✨ The video tutorial demonstrates how to extract and process text from multiple PDFs using Python.

📚 The tutorial includes the creation of a function called 'get PDF text' that retrieves the raw text from the PDFs and concatenates it into a single string.

✂️ Another function called 'get text chunks' is shown, which uses the 'character text splitter' class from the LangChain library to divide the text into smaller chunks.

This video tutorial demonstrates how to use LangChain App in Python to create vector representations of text chunks for similarity search.

The tutorial explains two methods for creating embeddings: using open AI embeddings, which is paid, and using Instructor embeddings, which is free.

The video highlights that Instructor embeddings are ranked higher than open AI embeddings in terms of performance and recommends using Instructor if you have the necessary hardware.

📚 Using embeddings from Hugging Face to enhance Vector store performance.

⏱️ Processing time significantly increased when using embeddings.

💭 Creating a conversation chain with memory using LangChain.

📚 Initializing and utilizing session state objects in a Python application allows for persistent variables throughout the application's lifecycle.

💬 Customizing chat message display in a Streamlit application can be done by inserting custom HTML templates into the application.

⚙️ Handling user input and generating responses using language models can be achieved by storing and manipulating conversation history.

🔑 The tutorial demonstrates how to extract key information from multiple PDFs in Python using the LangChain App.

💻 By using a combination of session state and templates, the chat history can be formatted and displayed for a user-friendly interface.

📚 The tutorial also explores the option of using Hugging Face models instead of OpenAI models for language processing.