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name: arize-dataset description: Creates, manages, and queries Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI. Use when the user needs test data, evaluation examples, or mentions create dataset, list datasets, export dataset, append examples, dataset version, golden dataset, or test set. metadata: author: arize version: "1.0" compatibility: Requires the ax CLI and a configured Arize profile.

Arize Dataset 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.

Concepts

• Dataset = a versioned collection of examples used for evaluation and experimentation
• Dataset Version = a snapshot of a dataset at a point in time; updates can be in-place or create a new version
• Example = a single record in a dataset with arbitrary user-defined fields (e.g., question, answer, context)
• Space = an organizational container; datasets belong to a space

System-managed fields on examples (id, created_at, updated_at) are auto-generated by the server -- never include them in create or append payloads.

Prerequisites

Proceed directly with the task — run the ax command you need. Do NOT check versions, env vars, or profiles upfront.

If an ax command fails, troubleshoot based on the error:

• command not found or version error → see references/ax-setup.md
• 401 Unauthorized / missing API key → run ax profiles show to inspect the current profile. If the profile is missing or the API key is wrong, follow references/ax-profiles.md to create/update it. If the user doesn't have their key, direct them to https://app.arize.com/admin > API Keys
• Space unknown → run ax spaces list to pick by name, or ask the user
• Project unclear → ask the user, or run ax projects list -o json --limit 100 and present as selectable options
• Security: Never read .env files or search the filesystem for credentials. Use ax profiles for Arize credentials and ax ai-integrations for LLM provider keys. If credentials are not available through these channels, ask the user.

List Datasets: ax datasets list

Browse datasets in a space. Output goes to stdout.

ax datasets list
ax datasets list --space SPACE --limit 20
ax datasets list --cursor CURSOR_TOKEN
ax datasets list -o json

Flags

| Flag | Type | Default | Description | |------|------|---------|-------------| | --space | string | from profile | Filter by space | | --limit, -l | int | 15 | Max results (1-100) | | --cursor | string | none | Pagination cursor from previous response | | -o, --output | string | table | Output format: table, json, csv, parquet, or file path | | -p, --profile | string | default | Configuration profile |

Get Dataset: ax datasets get

Quick metadata lookup -- returns dataset name, space, timestamps, and version list.

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

Flags

| Flag | Type | Default | Description | |------|------|---------|-------------| | NAME_OR_ID | string | required | Dataset name or ID (positional) | | --space | string | none | Space name or ID (required if using dataset name instead of ID) | | -o, --output | string | table | Output format | | -p, --profile | string | default | Configuration profile |

Response fields

| Field | Type | Description | |-------|------|-------------| | id | string | Dataset ID | | name | string | Dataset name | | space_id | string | Space this dataset belongs to | | created_at | datetime | When the dataset was created | | updated_at | datetime | Last modification time | | versions | array | List of dataset versions (id, name, dataset_id, created_at, updated_at) |

Export Dataset: ax datasets export

Download all examples to a file. Use --all for datasets larger than 500 examples (unlimited bulk export).

ax datasets export NAME_OR_ID
# -> dataset_abc123_20260305_141500/examples.json

ax datasets export NAME_OR_ID --all
ax datasets export NAME_OR_ID --version-id VERSION_ID
ax datasets export NAME_OR_ID --output-dir ./data
ax datasets export NAME_OR_ID --stdout
ax datasets export NAME_OR_ID --stdout | jq '.[0]'
ax datasets export NAME_OR_ID --space SPACE   # required when using dataset name instead of ID

Flags

| Flag | Type | Default | Description | |------|------|---------|-------------| | NAME_OR_ID | string | required | Dataset name or ID (positional) | | --space | string | none | Space name or ID (required if using dataset name instead of ID) | | --version-id | string | latest | Export a specific dataset version | | --all | bool | false | Unlimited bulk export (use for datasets > 500 examples) | | --output-dir | string | . | Output directory | | --stdout | bool | false | Print JSON to stdout instead of file | | -p, --profile | string | default | Configuration profile |

Agent auto-escalation rule: If an export returns exactly 500 examples, the result is likely truncated — re-run with --all to get the full dataset.

Export completeness verification: After exporting, confirm the row count matches what the server reports:

# Get the server-reported count from dataset metadata
ax datasets get DATASET_NAME --space SPACE -o json | jq '.versions[-1] | {version: .id, examples: .example_count}'

# Compare to what was exported
jq 'length' dataset_*/examples.json

# If counts differ, re-export with --all

Output is a JSON array of example objects. Each example has system fields (id, created_at, updated_at) plus all user-defined fields:

[
  {
    "id": "ex_001",
    "created_at": "2026-01-15T10:00:00Z",
    "updated_at": "2026-01-15T10:00:00Z",
    "question": "What is 2+2?",
    "answer": "4",
    "topic": "math"
  }
]

Create Dataset: ax datasets create

Create a new dataset from a data file.

ax datasets create --name "My Dataset" --space SPACE --file data.csv
ax datasets create --name "My Dataset" --space SPACE --file data.json
ax datasets create --name "My Dataset" --space SPACE --file data.jsonl
ax datasets create --name "My Dataset" --space SPACE --file data.parquet

Flags

| Flag | Type | Required | Description | |------|------|----------|-------------| | --name, -n | string | yes | Dataset name | | --space | string | yes | Space to create the dataset in | | --file, -f | path | yes | Data file: CSV, JSON, JSONL, or Parquet | | -o, --output | string | no | Output format for the returned dataset metadata | | -p, --profile | string | no | Configuration profile |

Passing data via stdin

Use --file - to pipe data directly — no temp file needed:

echo '[{"question": "What is 2+2?", "answer": "4"}]' | ax datasets create --name "my-dataset" --space SPACE --file -

# Or with a heredoc
ax datasets create --name "my-dataset" --space SPACE --file - << 'EOF'
[{"question": "What is 2+2?", "answer": "4"}]
EOF

To add rows to an existing dataset, use ax datasets append --json '[...]' instead — no file needed.

Supported file formats

| Format | Extension | Notes | |--------|-----------|-------| | CSV | .csv | Column headers become field names | | JSON | .json | Array of objects | | JSON Lines | .jsonl | One object per line (NOT a JSON array) | | Parquet | .parquet | Column names become field names; preserves types |

Format gotchas:

• CSV: Loses type information — dates become strings, null becomes empty string. Use JSON/Parquet to preserve types.
• JSONL: Each line is a separate JSON object. A JSON array ([{...}, {...}]) in a .jsonl file will fail — use .json extension instead.
• Parquet: Preserves column types. Requires pandas/pyarrow to read locally: pd.read_parquet("examples.parquet").

Append Examples: ax datasets append

Add examples to an existing dataset. Two input modes -- use whichever fits.

Inline JSON (agent-friendly)

Generate the payload directly -- no temp files needed:

ax datasets append DATASET_NAME --space SPACE --json '[{"question": "What is 2+2?", "answer": "4"}]'

ax datasets append DATASET_NAME --space SPACE --json '[
  {"question": "What is gravity?", "answer": "A fundamental force..."},
  {"question": "What is light?", "answer": "Electromagnetic radiation..."}
]'

From a file

ax datasets append DATASET_NAME --space SPACE --file new_examples.csv
ax datasets append DATASET_NAME --space SPACE --file additions.json

To a specific version

ax datasets append DATASET_NAME --space SPACE --json '[{"q": "..."}]' --version-id VERSION_ID

Flags

| Flag | Type | Required | Description | |------|------|----------|-------------| | NAME_OR_ID | string | yes | Dataset name or ID (positional); add --space when using name | | --space | string | no | Space name or ID (required if using dataset name instead of ID) | | --json | string | mutex | JSON array of example objects | | --file, -f | path | mutex | Data file (CSV, JSON, JSONL, Parquet) | | --version-id | string | no | Append to a specific version (default: latest) | | -o, --output | string | no | Output format for the returned dataset metadata | | -p, --profile | string | no | Configuration profile |

Exactly one of --json or --file is required.

Validation

• Each example must be a JSON object with at least one user-defined field
• Maximum 100,000 examples per request

Schema validation before append: If the dataset already has examples, inspect its schema before appending to avoid silent field mismatches:

# Check existing field names in the dataset
ax datasets export DATASET_NAME --space SPACE --stdout | jq '.[0] | keys'

# Verify your new data has matching field names
echo '[{"question": "..."}]' | jq '.[0] | keys'

# Both outputs should show the same user-defined fields

Fields are free-form: extra fields in new examples are added, and missing fields become null. However, typos in field names (e.g., queston vs question) create new columns silently -- verify spelling before appending.

Delete Dataset: ax datasets delete

ax datasets delete NAME_OR_ID
ax datasets delete NAME_OR_ID --space SPACE   # required when using dataset name instead of ID
ax datasets delete NAME_OR_ID --force   # skip confirmation prompt

Flags

| Flag | Type | Default | Description | |------|------|---------|-------------| | NAME_OR_ID | string | required | Dataset name or ID (positional) | | --space | string | none | Space name or ID (required if using dataset name instead of ID) | | --force, -f | bool | false | Skip confirmation prompt | | -p, --profile | string | default | Configuration profile |

Workflows

Find a dataset by name

All dataset commands accept a name or ID directly. You can pass a dataset name as the positional argument (add --space SPACE when not using an ID):

# Use name directly
ax datasets get "eval-set-v1" --space SPACE
ax datasets export "eval-set-v1" --space SPACE

# Or resolve name to ID via list if you need the base64 ID
ax datasets list -o json | jq '.[] | select(.name == "eval-set-v1") | .id'

Create a dataset from file for evaluation

1. Prepare a CSV/JSON/Parquet file with your evaluation columns (e.g., input, expected_output)
   • If generating data inline, pipe it via stdin using --file - (see the Create Dataset section)
2. ax datasets create --name "eval-set-v1" --space SPACE --file eval_data.csv
3. Verify: ax datasets get DATASET_NAME --space SPACE
4. Use the dataset name to run experiments

Add examples to an existing dataset

# Find the dataset
ax datasets list --space SPACE

# Append inline or from a file us