arize-dataset

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

Flags

Get Dataset: ax datasets get

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

Flags

Response fields

Export Dataset: ax datasets export

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

Flags

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:

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

Create Dataset: ax datasets create

Create a new dataset from a data file.

Flags

Passing data via stdin

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

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

Supported file formats

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:

From a file

To a specific version

Flags

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:

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

Flags

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):

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

Download dataset for offline analysis

1. ax datasets list --space SPACE -- find the dataset name
2. ax datasets export DATASET_NAME --space SPACE -- download to file
3. Parse the JSON: jq '.[] | .question' dataset_*/examples.json

Export a specific version

Iterate on a dataset

1. Export current version: ax datasets export DATASET_NAME --space SPACE
2. Modify the examples locally
3. Append new rows: ax datasets append DATASET_NAME --space SPACE --file new_rows.csv
4. Or create a fresh version: ax datasets create --name "eval-set-v2" --space SPACE --file updated_data.json

Pipe export to other tools

Dataset Example Schema

Examples are free-form JSON objects. There is no fixed schema -- columns are whatever fields you provide. System-managed fields are added by the server:

Related Skills

• arize-trace: Export production spans to understand what data to put in datasets → use arize-trace
• arize-experiment: Run evaluations against this dataset → next step is arize-experiment
• arize-prompt-optimization: Use dataset + experiment results to improve prompts → use arize-prompt-optimization

Troubleshooting

Save Credentials for Future Use

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