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legacy-ai-chart-agent

legacy-ai-chart-agent

Maintain and inspect the deprecated vizro-ai chart agent, response models, and tests without using it for new greenfield workflows.

Independent GitHub snapshot · Automated classification · Original documentation governs

Skill documentation

GitHub

name: legacy-ai-chart-agent description: "Maintain and inspect the deprecated vizro-ai chart agent, response models, and tests without using it for new greenfield workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Legacy AI Chart Agent

Use this sub-skill when the task explicitly mentions vizro-ai, the chart agent, chart-agent tests, response models, safeguards, or legacy AI chart-generation behavior.

For new agent-assisted dashboard work, route to ../mcp-agent-workflows/SKILL.md instead unless the user specifically asks for vizro-ai.

Deprecation facts

  • vizro-ai is deprecated in this checkout. Importing vizro_ai emits a package deprecation warning that points users toward Vizro e2e-flow or Vizro-MCP.
  • There is no top-level VizroAI class exported from vizro_ai.__init__ in this snapshot.
  • The active chart agent evidence is in vizro_ai.agents._chart_agent.chart_agent.

Current chart-agent surface

import pandas as pd
from vizro_ai.agents._chart_agent import add_df, chart_agent
from vizro_ai.agents.response_models import BaseChartPlan

# chart_agent is a pydantic-ai Agent that expects a pandas DataFrame dependency.

add_df(ctx):

  • Requires ctx.deps to be a pandas DataFrame.
  • Samples five rows and appends DataFrame.info() text to the agent instructions.
  • Raises ValueError when dependency is not a DataFrame.

Safe maintenance workflow

  1. Keep deprecation messaging intact unless the task is explicitly to change deprecation policy.
  2. Avoid live model/provider calls for routine tests. Unit-test helpers, response models, and safeguards with local DataFrames.
  3. If provider-backed tests/examples are requested, confirm API keys, model choices, network permission, and cost/budget first.
  4. For chart code generated by the agent, run/validate the generated Plotly code before embedding it in a Vizro dashboard.

Focused tests

From vizro-ai/:

hatch run test-unit tests/unit/vizro-ai/agents/test_chart_agent.py
hatch run test-unit tests/unit/vizro-ai/test_init.py

If using a prepared environment instead of Hatch, run equivalent pytest from vizro-ai/ after local package install.

Common pitfalls

  • Treating vizro_ai as a public high-level SDK: it is deprecated and narrow in this snapshot.
  • Importing a non-existent top-level VizroAI class.
  • Passing non-DataFrame deps into chart-agent instruction helpers.
  • Allowing live provider calls in tests without explicit user authorization.
  • Forgetting that new dashboard generation should usually use Vizro-MCP or e2e-flow guidance instead.

Evidence anchors

  • vizro-ai/src/vizro_ai/__init__.py: deprecation warning and public package initialization.
  • vizro-ai/src/vizro_ai/agents/_chart_agent.py: chart_agent, add_df.
  • vizro-ai/src/vizro_ai/agents/response_models.py: chart response models.
  • vizro-ai/tests/unit/vizro-ai/agents/test_chart_agent.py: behavior expectations.
  • vizro-ai/docs/pages/API-reference/vizro-ai.md and vizro-ai/examples/example.py: public/deprecated documentation context.
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