Export prompt and response data

How to export prompt and response data

Export JSON annotations

# 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))

Prompt export format

Humans generate prompts

{ "feature_id": "cldne96y301wy13yd0wp5z87y", "feature_schema_id": "cljg9my6h01000741aemmcln8", "name": "sample_name", "text_answer": { "content": "sample text" } }

Response export formats

Response - Radio

{ "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": [] } }

Response - Checklist

{ "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": [] } ] }

Response - Text

{ "feature_id": "cldne96y301wy13yd0wp5z87y", "feature_schema_id": "cljg9my6h01000741aemmcln8", "name": "sample_name", "text_answer": { "content": "sample text" } }

Sample project export

{ "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": "aovalle@labelbox.com" } }, "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": "aovalle@labelbox.com", "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": "aovalle@labelbox.com", "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": "aovalle@labelbox.com", "next_task_name": "Initial labeling task", "next_task_id": "1d0062f9-dfd9-0f86-baed-823235868a8c" } ] }, "project_tags": [] } } }