Chatbot development tutorial: Setting Up and Running a Chatbot with LangChain and Chainlit

Screenshot of a Chainlit chatbot parsing journal entries - chatbot development tutorial

In this tutorial, we’ll walk through the process of setting up and running a chatbot using LangChain and Chainlit. LangChain is a framework for developing applications powered by large language models (LLMs), and Chainlit is a tool for rapidly developing and sharing LLM-powered applications.


Before we begin, make sure you have the following:

  • Python 3.6 or later installed on your machine. You can download Python from the official website.
  • An OpenAI API key. You can get one by signing up on the OpenAI website.

Step 1: Set Up a Virtual Environment

First, we’ll set up a virtual environment for our project. A virtual environment is a self-contained Python environment in that we can install packages without affecting other Python projects on our machine.

Open a terminal window, navigate to the directory where you want to create your project and run the following command to create a new virtual environment:

python3 -m venv env

This command creates a new virtual environment in a directory called env.

Next, we’ll activate the virtual environment. The command to do this depends on your operating system:

  • On Unix or MacOS, run: source env/bin/activate
  • On Windows, run: .\env\Scripts\activate

When the virtual environment is activated, you should see (env) or something similar at the start of your command prompt.

Step 2: Install the Necessary Python Packages

With our virtual environment activated, we’ll install the necessary Python packages: LangChain and Chainlit. Run the following command to install these packages:

pip install langchain chainlit

Step 3: Create the Chatbot Script

Next, we’ll create a new Python file for our chatbot script. You can name this file anything you like, but for this tutorial, we’ll call it

Open in a text editor and add the following code:

# Import necessary modules from LangChain
from langchain import PromptTemplate
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationChain
# Import Chainlit

import chainlit as cl# Define the prompt template for the chatbot
prompt = PromptTemplate.from_template(”’
You are a helpful chatbot.
Please answer my questions to the best of your abilities.
Previous Conversation:
Human: {input}
# Create a ChatOpenAI object with your OpenAI API key
llm = ChatOpenAI(openai_api_key=‘YOUR OWN API KEY’)# Create a ConversationChain object with the ChatOpenAI object and the prompt
chain = ConversationChain(llm=llm, prompt=prompt)# Define a factory function for Chainlit
def factory():
return chain

Replace 'YOUR OWN API KEY' with your actual OpenAI API key.

This script sets up a chatbot using LangChain and Chainlit. The PromptTemplate sets the context for the chatbot’s responses, the ChatOpenAI object interfaces with the OpenAI API to generate responses and the ConversationChain object manages the conversation with the user.

The @cl.langchain_factory decorator defines a factory function for Chainlit. This function returns the ConversationChain object when called, which Chainlit uses to create the chatbot when the application starts### Step 4: Run the Chatbot

With our chatbot script ready, we can now run our chatbot. Make sure your virtual environment is activated, then open a terminal and navigate to the directory where you saved Run the following command to start your chatbot:

chainlit run

This command starts the Chainlit application defined in

Step 5: Update and Restart the Chatbot

If you make changes to your file, you’ll need to restart the Chainlit application for the changes to take effect.

First, stop the running application by pressing Ctrl+C in the terminal. Then, start the application again with the chainlit run command.

Wrapping Up

That’s it! You’ve set up and run a chatbot using LangChain and Chainlit. Remember, this is a basic setup. You’ll need to add more code based on how you want to use the chatbot. Specifically, I recommend using a system prompt when you find yourself constantly pasting the same prompt to chatgpt.

For more detailed guidance, check out the LangChain and Chainlit documentation. Happy coding!

Video of Chainlit chatbot code in VSCode - chatbot development tutorial
This video showcases the coding process of a Chainlit chatbot in VSCode, as part of our comprehensive chatbot development tutorial.




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