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:material-application: User Interface

Configure the Lumen AI chat interface.

ChatUI vs ExplorerUI

Lumen provides two interfaces:

  • ExplorerUI - Split view with table explorer, multiple explorations, and a navigation tree. Best for most use cases.
  • ChatUI - Simple chat-only interface. Best for embedded applications.

Use ExplorerUI unless you specifically need the simpler ChatUI.

Basic configuration

import lumen.ai as lmai

ui = lmai.ExplorerUI(data='penguins.csv')
ui.servable()

Common parameters

Load data

ui = lmai.ExplorerUI(data=['customers.csv', 'orders.csv'])

Configure LLM

ui = lmai.ExplorerUI(
    data='penguins.csv',
    llm=lmai.llm.Anthropic()
)

Add agents

ui = lmai.ExplorerUI(
    data='penguins.csv',
    agents=[MyCustomAgent()]  # Adds to 8 default agents
)

Add tools

ui = lmai.ExplorerUI(
    data='penguins.csv',
    tools=[my_function]  # Functions become tools automatically
)

Change title

ui = lmai.ExplorerUI(
    data='penguins.csv',
    title='Sales Analytics'
)

Custom suggestions

ui = lmai.ExplorerUI(
    data='penguins.csv',
    suggestions=[
        ("search", "What data is available?"),
        ("bar_chart", "Show trends"),
    ]  # (1)!
)
  1. Tuples of (Material icon name, button text)

Follow-up suggestions

After each successful query, a lightbulb icon appears in the message footer. Clicking it populates the chat input with an AI-generated follow-up suggestion that references actual column names from the data.

ui = lmai.ExplorerUI(
    data='penguins.csv',
    follow_up_suggestions=False
)

Users can also toggle this at runtime via Settings > Follow-Up Suggestions.

Advanced parameters

Enable chat logging

ui = lmai.ExplorerUI(
    data='penguins.csv',
    logs_db_path='logs.db'  # SQLite database for all messages
)

Configure coordinator

ui = lmai.ExplorerUI(
    data='penguins.csv',
    coordinator_params={
        'verbose': True,
        'validation_enabled': False
    }
)

Custom file handlers

def handle_hdf5(file_bytes, alias, filename):
    # Process file and add to source
    return True

ui = lmai.ExplorerUI(
    data='penguins.csv',
    table_upload_callbacks={'hdf5': handle_hdf5}
)

Provide initial context

ui = lmai.ExplorerUI(
    data='penguins.csv',
    context={'company': 'Acme', 'year': 2024}  # (1)!
)
  1. Available to all agents

Custom notebook export

ui = lmai.ExplorerUI(
    data='penguins.csv',
    notebook_preamble='# Analysis by Data Team\n# Generated: 2024'
)

Source controls

Source controls provide UI interfaces for loading data from external services like APIs and databases.

See the Source Controls guide for details on building and using controls.

Complete example

import lumen.ai as lmai
from lumen.sources.snowflake import SnowflakeSource

source = SnowflakeSource(
    account='acme',
    database='sales',
    authenticator='externalbrowser'
)

llm = lmai.llm.OpenAI(
    model_kwargs={
        'default': {'model': 'gpt-4o-mini'},
        'sql': {'model': 'gpt-4o'},
    }
)

analysis_agent = lmai.agents.AnalysisAgent(analyses=[MyAnalysis])

ui = lmai.ExplorerUI(
    data=source,
    llm=llm,
    agents=[analysis_agent],
    tools=[my_tool],
    title='Sales Analytics',
    suggestions=[
        ("trending_up", "Revenue trends"),
        ("people", "Top customers"),
    ],
    log_level='INFO',
    logs_db_path='logs.db'
)

ui.servable()

All parameters

Quick reference:

Parameter Type Purpose
data str/Path/Source/list Data sources to load
llm Llm LLM provider (default: OpenAI)
agents list Additional agents
analyses list Custom analyses
context dict Initial context
coordinator type Planner or DependencyResolver
coordinator_params dict Coordinator configuration
default_agents list Replace default agents
demo_inputs list Demo prompts for the coordinator
document_vector_store VectorStore Vector store for document tools
export_functions dict Map exporter names to export functions
interface type Chat interface class
llm_choices list LLM model choices shown in Settings
log_level str DEBUG/INFO/WARNING/ERROR
logfire_tags list Log LLM calls to Logfire with tags
logs_db_path str Chat logging database path
notebook_preamble str Export header
provider_choices dict LLM providers shown in Settings
source_controls list Source control components for data
follow_up_suggestions bool AI follow-up suggestion icon after queries (default: True)
suggestions list Quick action buttons
title str App title
tools list Custom tools
upload_handlers dict File extension upload handlers
vector_store VectorStore Vector store for non-doc tools

See parameter docstrings in code for complete details.

See also