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
import os
from openai import AsyncOpenAI
from chainlit.playground.providers.openai import ChatOpenAI
import chainlit as cl
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.step(type="llm", root=True, language="sql")
async def text_to_sql(text: str):
generation = cl.ChatGeneration(
provider=ChatOpenAI.id,
messages=[
cl.GenerationMessage(
role="user",
template=template,
formatted=template.format(input=text),
)
],
settings=settings,
inputs={"input": text},
)
stream = await client.chat.completions.create(
messages=[m.to_openai() for m in generation.messages], stream=True, **settings
)
current_step = cl.context.current_step
async for part in stream:
if token := part.choices[0].delta.content or "":
await current_step.stream_token(token)
generation.completion = current_step.output
current_step.generation = generation
@cl.on_message
async def main(message: cl.Message):
await text_to_sql(message.content)
Try it out
You can ask questions like Compute the number of customers who watched more than 50 minutes of video this month.
