arize-annotation

by github

INVOKE THIS SKILL when creating, managing, or using annotation configs or annotation queues on Arize (categorical, continuous, freeform), or applying human… npx skills add https://github.com/github/awesome-copilot --skill arize-annotation Download ZIPGitHub36.2k

Arize Annotation Skill

SPACE — All --space flags and the ARIZE_SPACE env var accept a space name (e.g., my-workspace) or a base64 space ID (e.g., U3BhY2U6...). Find yours with ax spaces list.

This skill covers annotation configs (the label schema) and annotation queues (human review workflows), as well as programmatically annotating project spans via the Python SDK.

Direction: Human labeling in Arize attaches values defined by configs to spans, dataset examples, experiment-related records, and queue items in the product UI. This skill covers: ax annotation-configs, ax annotation-queues, and bulk span updates with ArizeClient.spans.update_annotations.

Concepts

What is an Annotation Config?

An annotation config defines the schema for a single type of human feedback label. Before anyone can annotate a span, dataset record, experiment output, or queue item, a config must exist for that label in the space.

Field Description Name Descriptive identifier (e.g. Correctness, Helpfulness). Must be unique within the space. Type categorical (pick from a list), continuous (numeric range), or freeform (free text). Values For categorical: array of {"label": str, "score": number} pairs. Min/Max Score For continuous: numeric bounds. Optimization Direction Whether higher scores are better (maximize) or worse (minimize). Used to render trends in the UI.

Where labels get applied (surfaces)

Surface Typical path Project spans Python SDK spans.update_annotations (below) and/or the Arize UI Dataset examples Arize UI (human labeling flows); configs must exist in the space Experiment outputs Often reviewed alongside datasets or traces in the UI — see arize-experiment, arize-dataset Annotation queue items ax annotation-queues CLI (below) and/or the Arize UI; configs must exist

Always ensure the relevant annotation config exists in the space before expecting labels to persist.

Basic CRUD: Annotation Configs

List

ax annotation-configs list --space SPACE
ax annotation-configs list --space SPACE -o json
ax annotation-configs list --space SPACE --limit 20

Create — Categorical

Categorical configs present a fixed set of labels for reviewers to choose from.

ax annotation-configs create \
 --name "Correctness" \
 --space SPACE \
 --type categorical \
 --value correct \
 --value incorrect \
 --optimization-direction maximize

Common binary label pairs:

• correct / incorrect

• helpful / unhelpful

• safe / unsafe

• relevant / irrelevant

• pass / fail

Create — Continuous

Continuous configs let reviewers enter a numeric score within a defined range.

ax annotation-configs create \
 --name "Quality Score" \
 --space SPACE \
 --type continuous \
 --min-score 0 \
 --max-score 10 \
 --optimization-direction maximize

Create — Freeform

Freeform configs collect open-ended text feedback. No additional flags needed beyond name, space, and type.

ax annotation-configs create \
 --name "Reviewer Notes" \
 --space SPACE \
 --type freeform

Get

ax annotation-configs get NAME_OR_ID
ax annotation-configs get NAME_OR_ID -o json
ax annotation-configs get NAME_OR_ID --space SPACE # required when using name instead of ID

Delete

ax annotation-configs delete NAME_OR_ID
ax annotation-configs delete NAME_OR_ID --space SPACE # required when using name instead of ID
ax annotation-configs delete NAME_OR_ID --force # skip confirmation

Note: Deletion is irreversible. Any annotation queue associations to this config are also removed in the product (queues may remain; fix associations in the Arize UI if needed).

Annotation Queues: ax annotation-queues

Annotation queues route records (spans, dataset examples, experiment runs) to human reviewers. Each queue is linked to one or more annotation configs that define what labels reviewers can apply.

List / Get

ax annotation-queues list --space SPACE
ax annotation-queues list --space SPACE -o json

ax annotation-queues get NAME_OR_ID --space SPACE
ax annotation-queues get NAME_OR_ID --space SPACE -o json

Create

At least one --annotation-config-id is required.

ax annotation-queues create \
 --name "Correctness Review" \
 --space SPACE \
 --annotation-config-id CONFIG_ID \
 --annotator-email [email protected] \
 --instructions "Label each response as correct or incorrect." \
 --assignment-method all # or: random

Repeat --annotation-config-id and --annotator-email to attach multiple configs or reviewers.

Update

List flags (--annotation-config-id, --annotator-email) fully replace existing values when provided — pass all desired values, not just the new ones.

ax annotation-queues update NAME_OR_ID --space SPACE --name "New Name"
ax annotation-queues update NAME_OR_ID --space SPACE --instructions "Updated instructions"
ax annotation-queues update NAME_OR_ID --space SPACE \
 --annotation-config-id CONFIG_ID_A \
 --annotation-config-id CONFIG_ID_B

Delete

ax annotation-queues delete NAME_OR_ID --space SPACE
ax annotation-queues delete NAME_OR_ID --space SPACE --force # skip confirmation

List Records

ax annotation-queues list-records NAME_OR_ID --space SPACE
ax annotation-queues list-records NAME_OR_ID --space SPACE --limit 50 -o json

Submit an Annotation for a Record

Annotations are upserted by config name — call once per annotation config. Supply at least one of --score, --label, or --text.

ax annotation-queues annotate-record NAME_OR_ID RECORD_ID \
 --annotation-name "Correctness" \
 --label "correct" \
 --space SPACE

ax annotation-queues annotate-record NAME_OR_ID RECORD_ID \
 --annotation-name "Quality Score" \
 --score 8.5 \
 --text "Response was accurate but slightly verbose." \
 --space SPACE

Assign a Record

Assign users to review a specific record:

ax annotation-queues assign-record NAME_OR_ID RECORD_ID --space SPACE

Delete Records

ax annotation-queues delete-records NAME_OR_ID --space SPACE

Applying Annotations to Spans (Python SDK)

Use the Python SDK to bulk-apply annotations to project spans when you already have labels (e.g., from a review export or an external labeling tool).

import pandas as pd
from arize import ArizeClient

import os

client = ArizeClient(api_key=os.environ["ARIZE_API_KEY"])

# Build a DataFrame with annotation columns
# Required: context.span_id + at least one annotation. .label or annotation. .score
annotations_df = pd.DataFrame([
 {
 "context.span_id": "span_001",
 "annotation.Correctness.label": "correct",
 "annotation.Correctness.updated_by": "[email protected]",
 },
 {
 "context.span_id": "span_002",
 "annotation.Correctness.label": "incorrect",
 "annotation.Correctness.updated_by": "[email protected]",
 },
])

response = client.spans.update_annotations(
 space_id=os.environ["ARIZE_SPACE"],
 project_name="your-project",
 dataframe=annotations_df,
 validate=True,
)

DataFrame column schema:

Column Required Description context.span_id yes The span to annotate annotation.<name>.label one of Categorical or freeform label annotation.<name>.score one of Numeric score annotation.<name>.updated_by no Annotator identifier (email or name) annotation.<name>.updated_at no Timestamp in milliseconds since epoch annotation.notes no Freeform notes on the span

Limitation: Annotations apply only to spans within 31 days prior to submission.

Related Skills

• arize-trace: Export spans to find span IDs and time ranges

• arize-dataset: Find dataset IDs and example IDs

• arize-evaluator: Automated LLM-as-judge alongside human annotation

• arize-experiment: Experiments tied to datasets and evaluation workflows

• arize-link: Deep links to annotation configs and queues in the Arize UI

Save Credentials for Future Use

See references/ax-profiles.md § Save Credentials for Future Use.