LangChain

In this tutorial, we’ll walk through the steps to create a Chainlit application integrated with LangChain.

Preview of what you will build

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Prerequisites

Before getting started, make sure you have the following:

• A working installation of Chainlit
• The LangChain package installed
• An OpenAI API key
• Basic understanding of Python programming

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Step 1: Create a Python file

Create a new Python file named app.py in your project directory. This file will contain the main logic for your LLM application.

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Step 2: Write the Application Logic

In app.py, import the necessary packages and define one function to handle a new chat session and another function to handle messages incoming from the UI.

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With Langchain Expression language (LCEL)

Let’s go through a small example.

This code sets up an instance of Runnable with a custom ChatPromptTemplate for each chat session. The Runnable is invoked everytime a user sends a message to generate the response.

The callback handler is responsible for listening to the chain’s intermediate steps and sending them to the UI.

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[Deprecated] With Legacy Chain Interface

This code sets up an instance of LLMChain with a custom ChatPromptTemplate for each chat session. The LLMChain is invoked everytime a user sends a message to generate the response.

The callback handler is responsible for listening to the chain’s intermediate steps and sending them to the UI.

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Step 3: Run the Application

To start your app, open a terminal and navigate to the directory containing app.py. Then run the following command:

chainlit run app.py -w

The -w flag tells Chainlit to enable auto-reloading, so you don’t need to restart the server every time you make changes to your application. Your chatbot UI should now be accessible at http://localhost:8000.