AI + LLM Dashboards

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Putting LLMs in Shiny apps

Three patterns, increasing complexity:

  1. Chat interface — a conversational UI (shinychat)
  2. Tool calling — let the LLM take actions in your app
  3. 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

Demo: LLMs + Chat

Generative AI shiny templates: https://shiny.posit.co/py/templates/#generative-ai

Exercise

SQL is a great communication layer

Upcoming tool: ggsql https://ggsql.org/

Trt it: https://ggsql.org/wasm/

See also: Querychat + Vis docs: https://posit-dev.github.io/querychat/py/visualize.html

VISUALISE FROM ggsql:penguins
DRAW point
    MAPPING
      bill_dep AS x,
      body_mass AS y,
      species AS color