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Fundamentals of Retrieval-Augmented Generation with LangChain

Adding LangChain Logic to Your Streamlit Application

Explore how to integrate LangChain logic into your Streamlit application to build a frontend for a retrieval-augmented generation system. Learn to process document uploads, split text into chunks, generate embeddings, and handle user queries dynamically. This lesson guides you through creating a user-friendly interface with Streamlit forms that securely capture inputs and display AI-generated responses, enabling you to build an interactive, scalable RAG application.

We'll cover the following...

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:

Python 3.10.4
def generate_response(uploaded_file, openai_api_key, query_text):
    # Load document if file is uploaded
    if uploaded_file is not None:
        documents = [uploaded_file.read().decode()

Let’s break it down:

  • Line 1: This line defines a function called generate_response that takes three arguments: uploaded_file, openai_api_key ...