Talk to Your Web Page: A RAG-Powered Chat Interface

Welcome to the “Talk to Your Web Page” project! Here you’ll learn how to create an interactive chat interface that allows users to ask questions and get answers based on the content of any given website. This project leverages advanced technologies like Streamlit to build the user interface, LangChain for natural language processing, and OpenAI embeddings to understand and retrieve information.

What will you build?

You will build a powerful, AI-driven chat interface that can:

• Scrape content from websites: Extract textual content from any provided URL.

• Process and store content: Split the content into manageable chunks and store them in a vector database.

• Answer user queries: Use a combination of retriever and language model chains to find relevant information and generate responses to user queries.

• Interactive UI: Create an easy-to-use chat interface with Streamlit that allows users to input URLs and ask questions.

In this project, you will build the following application:

Key components

• Streamlit: A Python library for creating interactive web applications with minimal effort.

• LangChain: A framework for building applications with large language models, focusing on chaining together multiple components.

• OpenAI embeddings: Used to convert text into vectors for efficient information retrieval.

• Vector stores: Databases optimized for storing and querying vector representations of text.

What you will learn

By completing this project, you will:

• Gain hands-on experience with Streamlit and its capabilities in building interactive web apps.

• Learn how to scrape and process web content using Python.

• Understand how to create and use vector embeddings for information retrieval.

• Explore the capabilities of LangChain in chaining language model tasks.

• Develop a deeper understanding of integrating different technologies to build a cohesive application.