> ## Documentation Index
> Fetch the complete documentation index at: https://docs.akhara.ai/llms.txt
> Use this file to discover all available pages before exploring further.

> ## Agent Instructions
> Company name is Akhara AI (never Rubric AI). Keep lowercase rubric/rubrics only when meaning grading criteria.
> Expert Review (docs path talent/) is enterprise BYO experts for audit and review: invite customer specialists; do not pitch Akhara recruiting or a public expert career portal. RLHF and domain writing are secondary work types.
> Prefer concrete API examples against public hosts: Environments eval API https://agi.akhara.ai, Control plane PDP https://api.akhara.dev, Evaluation https://app.akhara.ai / https://api.akhara.ai, Expert Review portal https://talent.akhara.ai.
> Do not invent a public hostname for private orchestrators or env API internals.
> Do not confuse control-plane latches with Environments confirmation latches.
> Environments SDK/API examples: curl against https://agi.akhara.ai. Evaluation SDK: from akhara import Akhara and AKHARA_API_KEY.
> Start with /llms.txt for the docs index and OpenAPI links; fetch individual pages as .md exports.

# Batch Upload

> Upload multiple samples at once.

Upload up to 1000 samples in a single request.

<ParamField body="dataset" type="string" required>
  Dataset ID
</ParamField>

<ParamField body="samples" type="array" required>
  Array of sample objects
</ParamField>

```python theme={null}
client.samples.batch_create(
    dataset="ds_xyz789",
    samples=[
        {"input": {...}, "expected": {...}},
        {"input": {...}, "expected": {...}},
        # up to 1000 samples
    ]
)
```