User Interface¶

Configure the Lumen AI chat interface.

ChatUI vs ExplorerUI¶

Lumen provides two interfaces:

ExplorerUI - Split view with table explorer, multiple explorations, and breadcrumbs. 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¶

Minimal setup

import lumen.ai as lmai

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

Common parameters¶

Load data¶

Multiple sources

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

Configure LLM¶

Change provider

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

Add agents¶

Custom agents

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

Add tools¶

Custom tools

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

Change title¶

Custom title

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

Custom suggestions¶

Quick action buttons

ui = lmai.ExplorerUI(
    data='penguins.csv',
    suggestions=[
        ("search", "What data is available?"),
        ("bar_chart", "Show trends"),
    ]  # (1)!
)

Tuples of (Material icon name, button text)

Advanced parameters¶

Enable chat logging¶

Log conversations

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

Configure coordinator¶

Coordinator options

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

Custom file handlers¶

Handle custom file types

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¶

Pre-populate context

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

Available to all agents

Custom notebook export¶

Add preamble to exports

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

Complete example¶

Full configuration
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
`default_agents`	list	Replace default 8 agents
`tools`	list	Custom tools
`analyses`	list	Custom analyses
`title`	str	App title
`suggestions`	list	Quick action buttons
`coordinator`	type	Planner or DependencyResolver
`coordinator_params`	dict	Coordinator configuration
`log_level`	str	DEBUG/INFO/WARNING/ERROR
`logs_db_path`	str	Chat logging database path
`context`	dict	Initial context
`notebook_preamble`	str	Export header

See parameter docstrings in code for complete details.