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

# Export prompt and response data

> How to export prompt and response data

<CardGroup cols={2}>
  <Card title="Open In Colab" icon="infinity" iconType="solid" href="https://colab.research.google.com/github/Labelbox/labelbox-notebooks/blob/main/exports/export_data.ipynb" horizontal />

  <Card title="GitHub" icon="github" iconType="solid" href="https://github.com/Labelbox/labelbox-notebooks/blob/main/exports/export_data.ipynb" horizontal />
</CardGroup>

## Export JSON annotations

<CodeGroup>
  ```python Export theme={null}
  # Set the export params to include/exclude certain fields.
  export_params= {
    "attachments": True,
    "metadata_fields": True,
    "data_row_details": True,
    "project_details": True,
    "label_details": True,
    "performance_details": True,
    "interpolated_frames": True
  }

  # Note: Filters follow AND logic, so typically using one filter is sufficient.

  filters= {
    "last_activity_at": ["2000-01-01 00:00:00", "2050-01-01 00:00:00"],
    "workflow_status": "<wkf-status>"
  }

  export_task = project.export(params=export_params, filters=filters)
  export_task.wait_till_done()

  # Stream the export using a callback function

  def json_stream_handler(output: labelbox.BufferedJsonConverterOutput):
    print(output.json)

  export_task.get_buffered_stream(stream_type=labelbox.StreamType.RESULT).start(stream_handler=json_stream_handler)

  # Collect all exported data into a list

  export_json = [data_row.json for data_row in export_task.get_buffered_stream()]

  print("file size: ", export_task.get_total_file_size(stream_type=lb.StreamType.RESULT))
  print("line count: ", export_task.get_total_lines(stream_type=lb.StreamType.RESULT))

  ```
</CodeGroup>

## Prompt export format

### Humans generate prompts

<CodeGroup>
  ```json JSON theme={null}
  {
    "feature_id": "cldne96y301wy13yd0wp5z87y",
    "feature_schema_id": "cljg9my6h01000741aemmcln8",
    "name": "sample_name",
    "text_answer": {
      "content": "sample text"
    }
  }
  ```
</CodeGroup>

## Response export formats

### Response - Radio

<CodeGroup>
  ```json JSON theme={null}
  {
    "feature_id": "cldne96y201wq13ydu0qcc2up",
    "feature_schema_id": "cljggfygv0chn070w1v131s3v",
    "name": "sample_radio_name",
    "radio_answer": {
      "feature_id": "cldne96y201wr13yd23kr1pcr",
      "feature_schema_id": "cljgggcs200083b6lqbpml10p",
      "name": "first_radio_answer",
      "classifications": []
    }
  }
  ```
</CodeGroup>

### Response - Checklist

<CodeGroup>
  ```json JSON theme={null}
  {
    "feature_id": "cldne96y201wu13ydohrclpra",
    "feature_schema_id": "cljgegd1p07m4073cfmfy5xkx",
    "name": "checklist_question",
    "checklist_answers": [
      {
        "feature_id": "cldne96y301wv13ydatuxugbt",
        "name": "first_checklist_answer",
        "classifications": []
      },
      {
        "feature_id": "cldne96y301ww13yds4zkk49u",
        "name": "second_checklist_answer",
        "classifications": []
      },
      {
        "feature_id": "cldne96y301wx13ydvb5x2w6o",
        "name": "third_checklist_answer",
        "classifications": []
      }
    ]
  }
  ```
</CodeGroup>

### Response - Text

<CodeGroup>
  ```json JSON theme={null}
  {
    "feature_id": "cldne96y301wy13yd0wp5z87y",
    "feature_schema_id": "cljg9my6h01000741aemmcln8",
    "name": "sample_name",
    "text_answer": {
      "content": "sample text"
    }
  }
  ```
</CodeGroup>

## Sample project export

<CodeGroup>
  ```json JSON expandable theme={null}
  {
    "data_row": {
      "id": "clpvnouh04uyy0723mmru42qn",
      "global_key": "clpvnou2v03js07xsghfo2nzc",
      "row_data": "{\"type\":\"application/llm.prompt-response-creation\",\"version\":1}",
      "details": {
        "dataset_id": "clpvnou0z004c0724pd4cmw8g",
        "dataset_name": "test-humans-generate-prompts-reponses-dataset",
        "created_at": "2023-12-07T20:34:06.540+00:00",
        "updated_at": "2023-12-07T20:34:06.815+00:00",
        "last_activity_at": "2024-04-10T15:21:31.000+00:00",
        "created_by": "[email protected]"
      }
    },
    "media_attributes": {
      "mime_type": "application/llm.prompt-response-creation"
    },
    "attachments": [],
    "metadata_fields": [],
    "projects": {
      "clpvnotzb03jo07xs48r7ewka": {
        "name": "Andrea-test-humans-generate-prompts-responses",
        "labels": [
          {
            "label_kind": "Default",
            "version": "1.0.0",
            "id": "clutyida4041a07h4a8ojbu1g",
            "label_details": {
              "created_at": "2024-04-10T15:21:31.000+00:00",
              "updated_at": "2024-04-10T15:21:31.000+00:00",
              "created_by": "[email protected]",
              "content_last_updated_at": "2024-04-10T15:21:31.137+00:00",
              "reviews": []
            },
            "performance_details": {
              "seconds_to_create": 28,
              "seconds_to_review": 0,
              "skipped": false
            },
            "annotations": {
              "objects": [],
              "classifications": [
                {
                  "feature_id": "clutykado00013b6rw65zj51e",
                  "feature_schema_id": "clutyjef000xk07wfeurhc2qb",
                  "name": "Is this a shirt?",
                  "value": "Is this a shirt?",
                  "text_answer": {
                    "content": "Potentially this is a shirt, but keep in mind this is not a good quality prompt"
                  }
                },
                {
                  "feature_id": "clutykap700033b6rmmlkpo6u",
                  "feature_schema_id": "clutyjef000xm07wf5ym80ud6",
                  "name": "Yes",
                  "value": "yes",
                  "radio_answer": {
                    "feature_id": "clutykap700023b6rhktauyzc",
                    "feature_schema_id": "clutyjef000xn07wf6smvc54b",
                    "name": "Red shirt",
                    "value": "red_shirt",
                    "classifications": []
                  }
                }
              ],
              "relationships": []
            }
          }
        ],
        "project_details": {
          "ontology_id": "clpvnqsqz01kv07zydvbscxzq",
          "task_id": "14b02ec0-71f3-4d1f-b720-c6318e9a9346",
          "task_name": "Initial review task",
          "batch_id": "00870bc0-9540-11ee-a202-8d3b90bd1707",
          "batch_name": "batch_clpvnotzb03jo07xs48r7ewka",
          "workflow_status": "IN_REVIEW",
          "priority": 5,
          "consensus_expected_label_count": 2,
          "workflow_history": [
            {
              "action": "Move",
              "created_at": "2024-04-10T15:21:31.517+00:00",
              "created_by": "[email protected]",
              "previous_task_name": "Initial labeling task",
              "previous_task_id": "1d0062f9-dfd9-0f86-baed-823235868a8c",
              "next_task_name": "Initial review task",
              "next_task_id": "14b02ec0-71f3-4d1f-b720-c6318e9a9346"
            },
            {
              "action": "Move",
              "created_at": "2024-04-10T15:21:31.506+00:00",
              "created_by": "[email protected]",
              "next_task_name": "Initial labeling task",
              "next_task_id": "1d0062f9-dfd9-0f86-baed-823235868a8c"
            }
          ]
        },
        "project_tags": []
      }
    }
  }
  ```
</CodeGroup>