Chat with Multiple PDFs | LangChain App Tutorial in Python (Free LLMs and Embeddings)

About this Video

• Video Title: Chat with Multiple PDFs | LangChain App Tutorial in Python (Free LLMs and Embeddings)
• Channel: Alejandro AO - Software & Ai
• Speakers: Alejandro AO
• Duration: 01:07:30

Introduction

This video tutorial demonstrates building a Python chatbot application using LangChain that allows users to interact with multiple PDF documents simultaneously. The application uses a graphical user interface (GUI) built with Streamlit, handles multiple PDF uploads, extracts text, splits it into chunks, creates embeddings (using both OpenAI and Hugging Face models), and employs a ConversationChain for persistent chat history. The tutorial emphasizes using free Hugging Face LLMs as a cost-effective alternative to OpenAI.

Key Takeaways

1. Building a Multi-PDF Chatbot: The video details creating a chatbot application capable of answering questions based on information from several uploaded PDFs.
2. LangChain Integration: The tutorial showcases LangChain's capabilities for managing document interactions, creating embeddings, and building conversational chains.
3. Streamlit GUI Development: The process of building the user interface using Streamlit is explained step-by-step.
4. Embedding Methods: The tutorial explores two embedding methods: paid OpenAI embeddings and the free Hugging Face Instructor embeddings, comparing their performance and cost.
5. Persistent Conversation: The video demonstrates using LangChain's memory features to maintain conversation context across multiple user queries.