Let’s build a simple app that helps users to create SQL queries with natural language.
Preview of the final result
Prerequisites
This example has extra dependencies. You can install them with:
pip install chainlit openai
Imports
from openai import AsyncOpenAI
import chainlit as cl
cl.instrument_openai()
client = AsyncOpenAI(api_key="YOUR_OPENAI_API_KEY")
Define a prompt template and LLM settings
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": ["```"],
}
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.
@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()
Try it out
You can ask questions like Compute the number of customers who watched more than 50 minutes of video this month.
