Putting LLMs in Shiny apps
Three patterns, increasing complexity:
- Chat interface — a conversational UI (
shinychat)
- Tool calling — let the LLM take actions in your app
- Data chat — query your dataframe with natural language (
querychat)
shinychat
shinychat provides a ready-made chat UI component for Shiny:
from shiny.express import ui
from shinychat import chat_ui, chat_server
from chatlas import ChatGithub
ui.page_opts(title="My Chatbot")
chat_ui("chat")
@chat_server("chat")
def _():
return ChatGithub(model="gpt-4.1", system_prompt="You are a helpful assistant.")
Docs: https://posit-dev.github.io/shinychat/py/
Demo: shinychat
code/03-llm/app-shinychat.py
from shiny.express import ui
from shinychat import chat_ui, chat_server
from chatlas import ChatGithub
ui.page_opts(title="Shiny Chatbot", fillable=True)
chat_ui("chat", height="100%")
@chat_server("chat")
def _():
return ChatGithub(model="gpt-4.1")
Chatting with your data
What if users could ask questions about your dataset in plain English?
querychat translates natural language → SQL → runs against a DuckDB table:
import querychat
import seaborn as sns
tips = sns.load_dataset("tips")
qc = querychat.init(tips, "tips")
Docs: https://github.com/posit-dev/querychat
Demo: querychat
import querychat
import seaborn as sns
from shiny.express import ui
tips = sns.load_dataset("tips")
qc = querychat.init(tips, "tips",
greeting="Ask me about the tips dataset!")
ui.page_opts(title="Tips Q&A", fillable=True)
querychat.chat_ui("chat")
querychat.dataframe_ui("df")
querychat.chat_server("chat", qc)
querychat.dataframe_server("df", qc)
Sidebot pattern
Use an LLM to drive filters in a dashboard via natural language:
- User types “show me only lunch orders over $20”
- LLM translates to filter parameters
- Dashboard updates reactively
Template: shiny create --mode core --github jcheng5/py-databot
