arize-annotation

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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 reviewer@example.com \

  --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.<name>.label or annotation.<name>.score

annotations_df = pd.DataFrame([

    {

        "context.span_id": "span_001",

        "annotation.Correctness.label": "correct",

        "annotation.Correctness.updated_by": "reviewer@example.com",

    },

    {

        "context.span_id": "span_002",

        "annotation.Correctness.label": "incorrect",

        "annotation.Correctness.updated_by": "reviewer@example.com",

    },

])

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.

Troubleshooting

Problem

Solution

ax: command not found

See references/ax-setup.md

401 Unauthorized

API key may not have access to this space. Verify at https://app.arize.com/admin > API Keys

Annotation config not found

ax annotation-configs list --space SPACE (or use ax annotation-configs get NAME_OR_ID --space SPACE)

409 Conflict on create

Name already exists in the space. Use a different name or get the existing config ID.

Queue not found

ax annotation-queues list --space SPACE; verify the queue name or ID

Record not appearing in queue

Ensure the annotation config linked to the queue exists; check ax annotation-configs list --space SPACE

Span SDK errors or missing spans

Confirm project_name, space_id, and span IDs; use arize-trace to export spans

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.