Building a PDF Chatbot with OpenAI and Hugging Face Models
Learn to build a chatbot that chats with multiple PDFs using openai and hugging face models. Extract text, convert to embeddings, and use a language model to answer questions.
00:00:00 Learn how to build a chatbot application that allows you to chat with multiple PDFs. Explore the process of uploading, embedding, and querying information from PDF documents using 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.
00:09:41 This tutorial demonstrates how to use LangChain App to chat with multiple PDFs in Python. It covers creating API keys, loading environment variables, dividing PDFs into text chunks, converting text chunks into embeddings, and using a language model to answer questions based on the context provided.
📃 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.
00:19:23 This tutorial demonstrates how to extract text from multiple PDFs using Python's PiPDF library and split the text into manageable chunks.
✨ 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.
00:29:04 Learn how to create a vector store using OpenAI and Hugging Face embeddings in this tutorial. The process is quick and can be done locally for free.
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.
00:38:48 Learn how to create a conversation chain in the LangChain app using Python. See how to pass embeddings into the Vector store and the benefits of adding memory.
📚 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.
00:48:31 Learn how to display chat messages using custom HTML templates in a Python app. Handle user input to generate responses using a conversation chain.
📚 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.
00:58:13 Tutorial on how to use the LangChain App in Python to chat with multiple PDFs and display the chat 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.
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