Adding LangChain Logic to Your Streamlit Application

We've already covered what LangChain and Streamlit are and how they work in previous lessons. Now, it's time to apply that knowledge directly to our app.

Think of our Streamlit app as a car body, shiny and new, but without an engine. Right now, it looks good but doesn't do much, right? LangChain will be our engine, the powerful component that makes everything run smoothly and efficiently. Integrating LangChain into our Streamlit application is akin to how JavaScript interacts with HTML and CSS in web development. Streamlit provides the basic structure of our app's interface, defining elements like sliders, buttons, and text inputs, while LangChain adds dynamic logic and functionality. It helps us link different parts of our app, process data, and generate responses based on user inputs.

How to create the main function for generating responses

We will define a function called generate_response that takes an uploaded file, the OpenAI API key, and the query text. This function processes the uploaded file, splits it into chunks, creates embeddings, and uses a retrieval question-answering (QA) model to generate a response. The first thing that we need to check is if an uploaded_file is provided. If it is not None, we read the file and decode its contents. This step assumes that the uploaded file contains the text document we want to process. Let’s see how we will do it: