Let’s build a simple app that helps users to create SQL queries with natural language.

Preview of the final result

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Prerequisites

This example has extra dependencies. You can install them with:

pip install chainlit openai

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Imports

app.py
from openai import AsyncOpenAI


import chainlit as cl

cl.instrument_openai()

client = AsyncOpenAI(api_key="YOUR_OPENAI_API_KEY")

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Define a prompt template and LLM settings

app.py
template = """SQL tables (and columns):
* Customers(customer_id, signup_date)
* Streaming(customer_id, video_id, watch_date, watch_minutes)

A well-written SQL query that {input}:
```"""


settings = {
    "model": "gpt-3.5-turbo",
    "temperature": 0,
    "max_tokens": 500,
    "top_p": 1,
    "frequency_penalty": 0,
    "presence_penalty": 0,
    "stop": ["```"],
}

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Add the Assistant Logic

Here, we decorate the main function with the @on_message decorator to tell Chainlit to run the main function each time a user sends a message.

Then, we wrap our text to sql logic in a Step.

app.py
@cl.set_starters
async def starters():
    return [
       cl.Starter(
           label=">50 minutes watched",
           message="Compute the number of customers who watched more than 50 minutes of video this month."
       )
    ]

@cl.on_message
async def main(message: cl.Message):
    stream = await client.chat.completions.create(
        messages=[
            {
                "role": "user",
                "content": template.format(input=message.content),
            }
        ], stream=True, **settings
    )

    msg = await cl.Message(content="", language="sql").send()

    async for part in stream:
        if token := part.choices[0].delta.content or "":
            await msg.stream_token(token)

    await msg.update()

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Try it out

chainlit run app.py -w

You can ask questions like Compute the number of customers who watched more than 50 minutes of video this month.

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