Website-Interacting Chatbot with LangChain and GPT-4

In this comprehensive tutorial, Alejandro AO guides viewers through the process of building a website-interacting chatbot using Python, LangChain, GPT-4, and Streamlit. The chatbot can extract and interact with information from any website using a Retrieval-Augmented Generation (RAG) algorithm. The tutorial covers the following key steps:

1. Creating a virtual environment and installing necessary packages like Streamlit, LangChain, and LangChain OpenAI.
2. Setting up the graphical user interface (GUI) using Streamlit.
3. Implementing the chat component and making it interactive.
4. Creating a mock get_response() function to simulate chatbot responses.
5. Adding and displaying persistent chat history.
6. Explaining RAG algorithms and how they work.
7. Scraping HTML pages with LangChain and splitting text into manageable chunks.
8. Creating a vector store using ChromaDB and OpenAI embeddings.
9. Retrieving relevant information using a retriever chain.
10. Testing and refining the conversational RAG chain.

Alejandro emphasizes practical application, making the tutorial suitable for both beginners and experienced programmers. The video also includes detailed explanations of LangChain’s latest features and how to integrate AI technologies like Pinecone, Hugging Face models, and ChromaDB for advanced data handling.