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¶

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'
)

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¶

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
`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
`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.