treezy254/hf-codegen_v1 · Datasets at Hugging Face

promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/recordings/test_run_operations_TestFlowRun_test_pf_run_with_env_var.yaml

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0

promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/recordings/test_run_operations_TestFlowRun_test_auto_resolve_requirements.yaml

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"properties": {"description": null, "tags": null, "properties": null, "isDefault": true, "credentials": {"credentialsType": "AccountKey"}, "intellectualProperty": null, "subscriptionId": "00000000-0000-0000-0000-000000000000", "resourceGroup": "00000", "datastoreType": "AzureBlob", "accountName": "fake_account_name", "containerName": "fake-container-name", "endpoint": "core.windows.net", "protocol": "https", "serviceDataAccessAuthIdentity": "WorkspaceSystemAssignedIdentity"}, "systemData": {"createdAt": "2023-04-08T02:53:06.5886442+00:00", "createdBy": "779301c0-18b2-4cdc-801b-a0a3368fee0a", "createdByType": "Application", "lastModifiedAt": "2023-04-08T02:53:07.521127+00:00", "lastModifiedBy": "779301c0-18b2-4cdc-801b-a0a3368fee0a", "lastModifiedByType": "Application"}}' headers: cache-control: - no-cache content-length: - '1227' content-type: - application/json; charset=utf-8 expires: - '-1' pragma: - no-cache strict-transport-security: - max-age=31536000; includeSubDomains transfer-encoding: - chunked vary: - Accept-Encoding,Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.077' status: code: 200 message: OK - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '0' User-Agent: - promptflow-sdk/0.0.1 azure-ai-ml/1.12.0 azsdk-python-mgmt-machinelearningservices/0.1.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) method: POST uri: https://management.azure.com/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/datastores/workspaceblobstore/listSecrets response: body: string: '{"secretsType": "AccountKey", "key": "dGhpcyBpcyBmYWtlIGtleQ=="}' headers: cache-control: - no-cache content-length: - '134' content-type: - application/json; charset=utf-8 expires: - '-1' pragma: - no-cache strict-transport-security: - max-age=31536000; includeSubDomains transfer-encoding: - chunked vary: - Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.124' status: code: 200 message: OK - request: body: null headers: Accept: - application/xml Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - azsdk-python-storage-blob/12.19.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) x-ms-date: - Wed, 17 Jan 2024 10:12:02 GMT x-ms-version: - '2023-11-03' method: HEAD uri: https://fake_account_name.blob.core.windows.net/fake-container-name/LocalUpload/000000000000000000000000000000000000/env_var_names.jsonl response: body: string: '' headers: accept-ranges: - bytes content-length: - '49' content-md5: - quXiEreYvPinSj0HsaNa/g== content-type: - application/octet-stream last-modified: - Wed, 08 Nov 2023 04:26:09 GMT server: - Windows-Azure-Blob/1.0 Microsoft-HTTPAPI/2.0 vary: - Origin x-ms-blob-type: - BlockBlob x-ms-creation-time: - Wed, 08 Nov 2023 04:26:09 GMT x-ms-meta-name: - c4092674-5e53-4c17-b78d-75353ae0edb6 x-ms-meta-upload_status: - completed x-ms-meta-version: - 579021dc-8ac8-4c73-8110-4642bd00c69b x-ms-version: - '2023-11-03' status: code: 200 message: OK - request: body: null headers: Accept: - application/xml Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - azsdk-python-storage-blob/12.19.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) x-ms-date: - Wed, 17 Jan 2024 10:12:08 GMT x-ms-version: - '2023-11-03' method: HEAD uri: https://fake_account_name.blob.core.windows.net/fake-container-name/az-ml-artifacts/000000000000000000000000000000000000/env_var_names.jsonl response: body: string: '' headers: server: - Windows-Azure-Blob/1.0 Microsoft-HTTPAPI/2.0 transfer-encoding: - chunked vary: - Origin x-ms-error-code: - BlobNotFound x-ms-version: - '2023-11-03' status: code: 404 message: The specified blob does not exist. - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - promptflow-sdk/0.0.1 azure-ai-ml/1.12.0 azsdk-python-mgmt-machinelearningservices/0.1.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) method: GET uri: https://management.azure.com/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/datastores/workspaceblobstore response: body: string: '{"id": "/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/datastores/workspaceblobstore", "name": "workspaceblobstore", "type": "Microsoft.MachineLearningServices/workspaces/datastores", "properties": {"description": null, "tags": null, "properties": null, "isDefault": true, "credentials": {"credentialsType": "AccountKey"}, "intellectualProperty": null, "subscriptionId": "00000000-0000-0000-0000-000000000000", "resourceGroup": "00000", "datastoreType": "AzureBlob", "accountName": "fake_account_name", "containerName": "fake-container-name", "endpoint": "core.windows.net", "protocol": "https", "serviceDataAccessAuthIdentity": "WorkspaceSystemAssignedIdentity"}, "systemData": {"createdAt": "2023-04-08T02:53:06.5886442+00:00", "createdBy": "779301c0-18b2-4cdc-801b-a0a3368fee0a", "createdByType": "Application", "lastModifiedAt": "2023-04-08T02:53:07.521127+00:00", "lastModifiedBy": "779301c0-18b2-4cdc-801b-a0a3368fee0a", "lastModifiedByType": "Application"}}' headers: cache-control: - no-cache content-length: - '1227' content-type: - application/json; charset=utf-8 expires: - '-1' pragma: - no-cache strict-transport-security: - max-age=31536000; includeSubDomains transfer-encoding: - chunked vary: - Accept-Encoding,Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.072' status: code: 200 message: OK - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '0' User-Agent: - promptflow-sdk/0.0.1 azure-ai-ml/1.12.0 azsdk-python-mgmt-machinelearningservices/0.1.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) method: POST uri: https://management.azure.com/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/datastores/workspaceblobstore/listSecrets response: body: string: '{"secretsType": "AccountKey", "key": "dGhpcyBpcyBmYWtlIGtleQ=="}' headers: cache-control: - no-cache content-length: - '134' content-type: - application/json; charset=utf-8 expires: - '-1' pragma: - no-cache strict-transport-security: - max-age=31536000; includeSubDomains transfer-encoding: - chunked vary: - Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.120' status: code: 200 message: OK - request: body: null headers: Accept: - application/xml Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - azsdk-python-storage-blob/12.19.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) x-ms-date: - Wed, 17 Jan 2024 10:12:11 GMT x-ms-version: - '2023-11-03' method: HEAD uri: https://fake_account_name.blob.core.windows.net/fake-container-name/LocalUpload/000000000000000000000000000000000000/flow_with_requirements_txt/flow.dag.yaml response: body: string: '' headers: accept-ranges: - bytes content-length: - '304' content-md5: - FfuwUh4d2qJABbXwj4ACMA== content-type: - application/octet-stream last-modified: - Wed, 17 Jan 2024 09:33:34 GMT server: - Windows-Azure-Blob/1.0 Microsoft-HTTPAPI/2.0 vary: - Origin x-ms-blob-type: - BlockBlob x-ms-creation-time: - Wed, 17 Jan 2024 09:33:33 GMT x-ms-meta-name: - 170072d0-60d9-4d21-8c3d-5ac5f8434bb9 x-ms-meta-upload_status: - completed x-ms-meta-version: - '1' x-ms-version: - '2023-11-03' status: code: 200 message: OK - request: body: null headers: Accept: - application/xml Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - azsdk-python-storage-blob/12.19.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) x-ms-date: - Wed, 17 Jan 2024 10:12:16 GMT x-ms-version: - '2023-11-03' method: HEAD uri: https://fake_account_name.blob.core.windows.net/fake-container-name/az-ml-artifacts/000000000000000000000000000000000000/flow_with_requirements_txt/flow.dag.yaml response: body: string: '' headers: server: - Windows-Azure-Blob/1.0 Microsoft-HTTPAPI/2.0 transfer-encoding: - chunked vary: - Origin x-ms-error-code: - BlobNotFound x-ms-version: - '2023-11-03' status: code: 404 message: The specified blob does not exist. - request: body: '{"flowDefinitionDataStoreName": "workspaceblobstore", "flowDefinitionBlobPath": "LocalUpload/000000000000000000000000000000000000/flow_with_requirements_txt/flow.dag.yaml", "runId": "name", "runDisplayName": "name", "runExperimentName": "", "batchDataInput": {"dataUri": "azureml://datastores/workspaceblobstore/paths/LocalUpload/000000000000000000000000000000000000/env_var_names.jsonl"}, "inputsMapping": {}, "connections": {}, "environmentVariables": {}, "runtimeName": "fake-runtime-name", "sessionId": "000000000000000000000000000000000000000000000000", "sessionSetupMode": "SystemWait", "flowLineageId": "0000000000000000000000000000000000000000000000000000000000000000", "runDisplayNameGenerationType": "UserProvidedMacro"}' headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '804' Content-Type: - application/json User-Agent: - promptflow-sdk/0.0.1 azsdk-python-azuremachinelearningdesignerserviceclient/unknown Python/3.11.5 (Windows-10-10.0.22621-SP0) method: POST uri: https://eastus.api.azureml.ms/flow/api/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/BulkRuns/submit response: body: string: '"name"' headers: connection: - keep-alive content-length: - '38' content-type: - application/json; charset=utf-8 strict-transport-security: - max-age=15724800; includeSubDomains; preload x-content-type-options: - nosniff x-request-time: - '18.617' status: code: 200 message: OK - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - promptflow-sdk/0.0.1 azsdk-python-azuremachinelearningdesignerserviceclient/unknown Python/3.11.5 (Windows-10-10.0.22621-SP0) method: GET uri: https://eastus.api.azureml.ms/flow/api/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/BulkRuns/name response: body: string: '{"flowGraph": {"nodes": [{"name": "print_env", "type": "python", "source": {"type": "code", "path": "print_env.py"}, "inputs": {"key": "${inputs.key}"}, "tool": "print_env.py", "reduce": false}], "tools": [{"name": "Azure OpenAI GPT-4 Turbo with Vision", "type": "custom_llm", "inputs": {"connection": {"type": ["AzureOpenAIConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "deployment_name": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "frequency_penalty": {"type": ["double"], "default": 0, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "max_tokens": {"type": ["int"], "default": 512, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "presence_penalty": {"type": ["double"], "default": 0, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "stop": {"type": ["list"], "default": "", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "temperature": {"type": ["double"], "default": 1, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_p": {"type": ["double"], "default": 1, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Use Azure OpenAI GPT-4 Turbo with Vision to leverage AOAI vision ability.", "module": "promptflow.tools.aoai_gpt4v", "class_name": "AzureOpenAI", "function": "chat", "icon": {"dark": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAA2ElEQVR4nJXSzW3CQBAF4DUSTjk+Al1AD0ikESslpBIEheRALhEpgAYSWV8OGUublf/yLuP3PPNmdndS+gdwXZrYDmh7fGE/W+wXbaYd8IYm4rxJPnZ0boI3wZcdJxs/n+AwV7DFK7aFyfQdYIMLPvES8YJNf5yp4jMeeEYdWh38gXOR35YGHe5xabvQdsHv6PLi8qV6gycc8YH3iMfQu6Lh4ASr+F5Hh3XwVWnQYzUkVlX1nccplAb1SN6Y/sfgmlK64VS8wimldIv/0yj2QLkHizG0iWP4AVAfQ34DVQONAAAAAElFTkSuQmCC", "light": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAx0lEQVR4nJWSwQ2CQBBFX0jAcjgqXUgPJNiIsQQrIVCIFy8GC6ABDcGDX7Mus9n1Xz7zZ+fPsLPwH4bUg0dD2wMPcbR48Uxq4AKU4iSTDwZ1LhWXipN/B3V0J6hjBTvgLHZNonewBXrgDpzEvXSIjN0BE3AACmmF4kl5F6tNzcCoLpW0SvGovFvsb4oZ2AANcAOu4ka6axCcINN3rg654sww+CYsPD0OwjcozFNh/Qcd78tqVbCIW+n+Fky472Bh/Q6SYb1EEy8tDzd+9IsVPAAAAABJRU5ErkJggg=="}, "is_builtin": true, "package": "promptflow-tools", "package_version": "1.0.3", "default_prompt": "# system:\nAs an AI assistant, your task involves interpreting images and responding to questions about the image.\nRemember to provide accurate answers based on the information present in the image.\n\n# user:\nCan you tell me what the image depicts?\n![image]({{image_input}})\n", "enable_kwargs": false, "tool_state": "preview"}, {"name": "Content Safety (Text Analyze)", "type": "python", "inputs": {"connection": {"type": ["AzureContentSafetyConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "hate_category": {"type": ["string"], "default": "medium_sensitivity", "enum": ["disable", "low_sensitivity", "medium_sensitivity", "high_sensitivity"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "self_harm_category": {"type": ["string"], "default": "medium_sensitivity", "enum": ["disable", "low_sensitivity", "medium_sensitivity", "high_sensitivity"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "sexual_category": {"type": ["string"], "default": "medium_sensitivity", "enum": ["disable", "low_sensitivity", "medium_sensitivity", "high_sensitivity"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "text": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "violence_category": {"type": ["string"], "default": "medium_sensitivity", "enum": ["disable", "low_sensitivity", "medium_sensitivity", "high_sensitivity"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Use Azure Content Safety to detect harmful content.", "module": "promptflow.tools.azure_content_safety", "function": "analyze_text", "is_builtin": true, "package": "promptflow-tools", "package_version": "1.0.3", "enable_kwargs": false, "deprecated_tools": ["content_safety_text.tools.content_safety_text_tool.analyze_text"], "tool_state": "stable"}, {"name": "Embedding", "type": "python", "inputs": {"connection": {"type": ["AzureOpenAIConnection", "OpenAIConnection"], "allow_manual_entry": false, 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status: code: 200 message: OK - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - promptflow-sdk/0.0.1 azsdk-python-azuremachinelearningdesignerserviceclient/unknown Python/3.11.5 (Windows-10-10.0.22621-SP0) method: GET uri: https://eastus.api.azureml.ms/flow/api/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/BulkRuns/name response: body: string: '{"flowGraph": {"nodes": [{"name": "print_env", "type": "python", "source": {"type": "code", "path": "print_env.py"}, "inputs": {"key": "${inputs.key}"}, "tool": "print_env.py", "reduce": false}], "tools": [{"name": "Azure OpenAI GPT-4 Turbo with Vision", "type": "custom_llm", "inputs": {"connection": {"type": ["AzureOpenAIConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "deployment_name": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "frequency_penalty": {"type": ["double"], "default": 0, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "max_tokens": {"type": ["int"], "default": 512, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "presence_penalty": {"type": ["double"], "default": 0, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "stop": {"type": ["list"], "default": "", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "temperature": {"type": ["double"], "default": 1, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_p": {"type": ["double"], "default": 1, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Use Azure OpenAI GPT-4 Turbo with Vision to leverage AOAI vision ability.", "module": "promptflow.tools.aoai_gpt4v", "class_name": 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"OpenAIConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "deployment_name": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["AzureOpenAIConnection"], "model_list": ["text-embedding-ada-002", "text-search-ada-doc-001", "text-search-ada-query-001"], "capabilities": {"completion": false, "chat_completion": false, "embeddings": true}, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "input": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "model": {"type": ["string"], "enum": ["text-embedding-ada-002", "text-search-ada-doc-001", "text-search-ada-query-001"], "enabled_by": "connection", "enabled_by_type": ["OpenAIConnection"], "allow_manual_entry": true, "is_multi_select": false, "input_type": "default"}}, "description": "Use Open AI''s embedding model to create an embedding vector representing the input text.", "module": 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"promptflow-tools", "package_version": "1.0.3", "enable_kwargs": false, "tool_state": "stable"}, {"name": "OpenAI GPT-4V", "type": "custom_llm", "inputs": {"connection": {"type": ["OpenAIConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "frequency_penalty": {"type": ["double"], "default": 0, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "max_tokens": {"type": ["int"], "default": 512, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "model": {"type": ["string"], "enum": ["gpt-4-vision-preview"], "allow_manual_entry": true, "is_multi_select": false, "input_type": "default"}, "presence_penalty": {"type": ["double"], "default": 0, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "stop": {"type": ["list"], "default": "", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "temperature": {"type": ["double"], "default": 1, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_p": {"type": ["double"], "default": 1, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Use OpenAI GPT-4V to leverage vision ability.", "module": "promptflow.tools.openai_gpt4v", "class_name": "OpenAI", "function": "chat", "icon": {"dark": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAA2ElEQVR4nJXSzW3CQBAF4DUSTjk+Al1AD0ikESslpBIEheRALhEpgAYSWV8OGUublf/yLuP3PPNmdndS+gdwXZrYDmh7fGE/W+wXbaYd8IYm4rxJPnZ0boI3wZcdJxs/n+AwV7DFK7aFyfQdYIMLPvES8YJNf5yp4jMeeEYdWh38gXOR35YGHe5xabvQdsHv6PLi8qV6gycc8YH3iMfQu6Lh4ASr+F5Hh3XwVWnQYzUkVlX1nccplAb1SN6Y/sfgmlK64VS8wimldIv/0yj2QLkHizG0iWP4AVAfQ34DVQONAAAAAElFTkSuQmCC", "light": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAx0lEQVR4nJWSwQ2CQBBFX0jAcjgqXUgPJNiIsQQrIVCIFy8GC6ABDcGDX7Mus9n1Xz7zZ+fPsLPwH4bUg0dD2wMPcbR48Uxq4AKU4iSTDwZ1LhWXipN/B3V0J6hjBTvgLHZNonewBXrgDpzEvXSIjN0BE3AACmmF4kl5F6tNzcCoLpW0SvGovFvsb4oZ2AANcAOu4ka6axCcINN3rg654sww+CYsPD0OwjcozFNh/Qcd78tqVbCIW+n+Fky472Bh/Q6SYb1EEy8tDzd+9IsVPAAAAABJRU5ErkJggg=="}, "is_builtin": true, "package": "promptflow-tools", "package_version": "1.0.3", "default_prompt": "# system:\nAs an AI assistant, your task involves interpreting images and responding to questions about the image.\nRemember to provide accurate answers based on the information present in the image.\n\n# user:\nCan you tell me what the image depicts?\n![image]({{image_input}})\n", "enable_kwargs": false, "tool_state": "preview"}, {"name": "Serp API", "type": "python", "inputs": {"connection": {"type": ["SerpConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "engine": {"type": ["string"], "default": "google", "enum": ["google", "bing"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "location": {"type": ["string"], "default": "", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "num": {"type": ["int"], "default": "10", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "query": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "safe": {"type": ["string"], "default": "off", "enum": ["active", "off"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Use Serp API to obtain search results from a specific search engine.", "module": "promptflow.tools.serpapi", "class_name": "SerpAPI", "function": "search", "is_builtin": true, "package": "promptflow-tools", "package_version": "1.0.3", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Index Lookup", "type": "python", "inputs": {"acs_content_field": {"type": ["string"], "enabled_by": "index_type", "enabled_by_value": ["Azure AI Search"], "dynamic_list": {"func_path": "promptflow_vectordb.tool.common_index_lookup_utils.list_acs_index_fields", "func_kwargs": [{"name": "acs_connection", "optional": false, "reference": "${inputs.acs_index_connection}", "type": ["CognitiveSearchConnection"]}, {"name": "acs_index_name", "optional": false, "reference": "${inputs.acs_index_name}", "type": ["string"]}, {"default": "Edm.String", "name": "field_data_type", "optional": false, "type": ["string"]}]}, "allow_manual_entry": false, "is_multi_select": false, "input_type": "uionly_hidden"}, "acs_embedding_field": {"type": ["string"], "enabled_by": "index_type", "enabled_by_value": ["Azure AI Search"], "dynamic_list": {"func_path": "promptflow_vectordb.tool.common_index_lookup_utils.list_acs_index_fields", "func_kwargs": [{"name": "acs_connection", "optional": false, "reference": 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"is_multi_select": false, "input_type": "uionly_hidden"}, "acs_metadata_field": {"type": ["string"], "enabled_by": "index_type", "enabled_by_value": ["Azure AI Search"], "dynamic_list": {"func_path": "promptflow_vectordb.tool.common_index_lookup_utils.list_acs_index_fields", "func_kwargs": [{"name": "acs_connection", "optional": false, "reference": "${inputs.acs_index_connection}", "type": ["CognitiveSearchConnection"]}, {"name": "acs_index_name", "optional": false, "reference": "${inputs.acs_index_name}", "type": ["string"]}, {"default": "Edm.String", "name": "field_data_type", "optional": false, "type": ["string"]}]}, "allow_manual_entry": false, "is_multi_select": false, "input_type": "uionly_hidden"}, "aoai_embedding_connection": {"type": ["AzureOpenAIConnection"], "enabled_by": "embedding_type", "enabled_by_value": ["Azure OpenAI"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "uionly_hidden"}, "embedding_deployment": {"type": ["string"], "enabled_by": "embedding_type", "enabled_by_value": ["Azure OpenAI"], "dynamic_list": {"func_path": "promptflow_vectordb.tool.common_index_lookup_utils.list_aoai_embedding_deployments", "func_kwargs": [{"name": "aoai_connection", "optional": false, "reference": "${inputs.aoai_embedding_connection}", "type": ["AzurOpenAIConnection"]}]}, "allow_manual_entry": false, "is_multi_select": false, "input_type": "uionly_hidden"}, "embedding_model": {"type": ["string"], "enabled_by": "embedding_type", "enabled_by_value": ["OpenAI", "Hugging Face"], "dynamic_list": {"func_path": "promptflow_vectordb.tool.common_index_lookup_utils.list_embedding_models", "func_kwargs": [{"name": "embedding_type", "optional": false, "reference": "${inputs.embedding_type}", "type": ["string"]}]}, "allow_manual_entry": false, "is_multi_select": false, "input_type": "uionly_hidden"}, "embedding_type": {"type": ["string"], "enabled_by": "index_type", "enabled_by_value": ["Azure AI Search", "FAISS", "Pinecone"], "dynamic_list": 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status: code: 200 message: OK - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - promptflow-sdk/0.0.1 azsdk-python-azuremachinelearningdesignerserviceclient/unknown Python/3.11.5 (Windows-10-10.0.22621-SP0) method: GET uri: https://eastus.api.azureml.ms/flow/api/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/BulkRuns/name response: body: string: '{"flowGraph": {"nodes": [{"name": "print_env", "type": "python", "source": {"type": "code", "path": "print_env.py"}, "inputs": {"key": "${inputs.key}"}, "tool": "print_env.py", "reduce": false}], "tools": [{"name": "Azure OpenAI GPT-4 Turbo with Vision", "type": "custom_llm", "inputs": {"connection": {"type": ["AzureOpenAIConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "deployment_name": {"type": ["string"], "allow_manual_entry": 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"OpenAIConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "deployment_name": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["AzureOpenAIConnection"], "model_list": ["text-embedding-ada-002", "text-search-ada-doc-001", "text-search-ada-query-001"], "capabilities": {"completion": false, "chat_completion": false, "embeddings": true}, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "input": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "model": {"type": ["string"], "enum": ["text-embedding-ada-002", "text-search-ada-doc-001", "text-search-ada-query-001"], "enabled_by": "connection", "enabled_by_type": ["OpenAIConnection"], "allow_manual_entry": true, "is_multi_select": false, "input_type": "default"}}, "description": "Use Open AI''s embedding model to create an embedding vector representing the input text.", "module": 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"promptflow-tools", "package_version": "1.0.3", "enable_kwargs": false, "tool_state": "stable"}, {"name": "OpenAI GPT-4V", "type": "custom_llm", "inputs": {"connection": {"type": ["OpenAIConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "frequency_penalty": {"type": ["double"], "default": 0, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "max_tokens": {"type": ["int"], "default": 512, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "model": {"type": ["string"], "enum": ["gpt-4-vision-preview"], "allow_manual_entry": true, "is_multi_select": false, "input_type": "default"}, "presence_penalty": {"type": ["double"], "default": 0, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "stop": {"type": ["list"], "default": "", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "temperature": {"type": ["double"], "default": 1, 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"pinecone_metadata_field": {"type": ["string"], "enabled_by": "index_type", "enabled_by_value": ["Pinecone"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "uionly_hidden"}, "queries": {"type": ["object"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "query_type": {"type": ["string"], "dynamic_list": {"func_path": "promptflow_vectordb.tool.common_index_lookup_utils.list_available_query_types", "func_kwargs": [{"name": "mlindex_content", "optional": false, "reference": "${inputs.mlindex_content}", "type": ["string"]}]}, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "semantic_configuration": {"type": ["string"], "enabled_by": "index_type", "enabled_by_value": ["Azure AI Search"], "dynamic_list": {"func_path": "promptflow_vectordb.tool.common_index_lookup_utils.list_acs_index_semantic_configurations", "func_kwargs": [{"name": "acs_connection", "optional": false, "reference": 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"is_multi_select": false, "input_type": "default"}, "index_name": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "search_filters": {"type": ["object"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection", "QdrantConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "search_params": {"type": ["object"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection", "QdrantConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "text_field": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection", "QdrantConnection", "WeaviateConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_k": {"type": ["int"], "default": "3", "allow_manual_entry": false, 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status: code: 200 message: OK - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - promptflow-sdk/0.0.1 azsdk-python-azuremachinelearningdesignerserviceclient/unknown Python/3.11.5 (Windows-10-10.0.22621-SP0) method: GET uri: https://eastus.api.azureml.ms/flow/api/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/BulkRuns/name response: body: string: '{"flowGraph": {"nodes": [{"name": "print_env", "type": "python", "source": {"type": "code", "path": "print_env.py"}, "inputs": {"key": "${inputs.key}"}, "tool": "print_env.py", "reduce": false}], "tools": [{"name": "Azure OpenAI GPT-4 Turbo with Vision", "type": "custom_llm", "inputs": {"connection": {"type": ["AzureOpenAIConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "deployment_name": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "frequency_penalty": {"type": ["double"], "default": 0, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "max_tokens": {"type": ["int"], "default": 512, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "presence_penalty": {"type": ["double"], "default": 0, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "stop": {"type": ["list"], "default": "", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "temperature": {"type": ["double"], "default": 1, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_p": {"type": ["double"], "default": 1, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Use Azure OpenAI GPT-4 Turbo with Vision to leverage AOAI vision ability.", "module": "promptflow.tools.aoai_gpt4v", "class_name": 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["string"], "default": "google", "enum": ["google", "bing"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "location": {"type": ["string"], "default": "", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "num": {"type": ["int"], "default": "10", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "query": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "safe": {"type": ["string"], "default": "off", "enum": ["active", "off"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Use Serp API to obtain search results from a specific search engine.", "module": "promptflow.tools.serpapi", "class_name": "SerpAPI", "function": "search", "is_builtin": true, "package": "promptflow-tools", "package_version": "1.0.3", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Index Lookup", "type": "python", "inputs": {"acs_content_field": {"type": ["string"], "enabled_by": "index_type", "enabled_by_value": ["Azure AI Search"], "dynamic_list": {"func_path": "promptflow_vectordb.tool.common_index_lookup_utils.list_acs_index_fields", "func_kwargs": [{"name": "acs_connection", "optional": false, "reference": "${inputs.acs_index_connection}", "type": ["CognitiveSearchConnection"]}, {"name": "acs_index_name", "optional": false, "reference": "${inputs.acs_index_name}", "type": ["string"]}, {"default": "Edm.String", "name": "field_data_type", "optional": false, "type": ["string"]}]}, "allow_manual_entry": false, "is_multi_select": false, "input_type": "uionly_hidden"}, "acs_embedding_field": {"type": ["string"], "enabled_by": "index_type", "enabled_by_value": ["Azure AI Search"], "dynamic_list": {"func_path": "promptflow_vectordb.tool.common_index_lookup_utils.list_acs_index_fields", "func_kwargs": [{"name": "acs_connection", "optional": false, "reference": 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"is_multi_select": false, "input_type": "uionly_hidden"}, "acs_metadata_field": {"type": ["string"], "enabled_by": "index_type", "enabled_by_value": ["Azure AI Search"], "dynamic_list": {"func_path": "promptflow_vectordb.tool.common_index_lookup_utils.list_acs_index_fields", "func_kwargs": [{"name": "acs_connection", "optional": false, "reference": "${inputs.acs_index_connection}", "type": ["CognitiveSearchConnection"]}, {"name": "acs_index_name", "optional": false, "reference": "${inputs.acs_index_name}", "type": ["string"]}, {"default": "Edm.String", "name": "field_data_type", "optional": false, "type": ["string"]}]}, "allow_manual_entry": false, "is_multi_select": false, "input_type": "uionly_hidden"}, "aoai_embedding_connection": {"type": ["AzureOpenAIConnection"], "enabled_by": "embedding_type", "enabled_by_value": ["Azure OpenAI"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "uionly_hidden"}, "embedding_deployment": {"type": ["string"], "enabled_by": 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promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/recordings/test_run_operations_TestFlowRun_test_download_run.yaml

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PromptFlow version: 1.2.0rc1\n2024-01-12 08:10:51 +0000 3638 promptflow-runtime INFO Setting mlflow tracking uri...\n2024-01-12 08:10:51 +0000 3638 promptflow-runtime INFO Validating ''AzureML Data Scientist'' user authentication...\n2024-01-12 08:10:52 +0000 3638 promptflow-runtime INFO Successfully validated ''AzureML Data Scientist'' user authentication.\n2024-01-12 08:10:52 +0000 3638 promptflow-runtime INFO Using AzureMLRunStorageV2\n2024-01-12 08:10:52 +0000 3638 promptflow-runtime INFO Setting mlflow tracking uri to ''azureml://eastus.api.azureml.ms/mlflow/v1.0/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/promptflow-eastus''\n2024-01-12 08:10:52 +0000 3638 promptflow-runtime INFO Initialized blob service client for AzureMLRunTracker.\n2024-01-12 08:10:52 +0000 3638 promptflow-runtime INFO Setting mlflow tracking uri to ''azureml://eastus.api.azureml.ms/mlflow/v1.0/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/promptflow-eastus''\n2024-01-12 08:10:52 +0000 3638 promptflow-runtime INFO Resolve data from url finished in 0.49738570116460323 seconds\n2024-01-12 08:10:53 +0000 3638 promptflow-runtime INFO Starting the aml run ''batch_run_name''...\n2024-01-12 08:10:53 +0000 3638 execution.bulk INFO Using fork, process count: 3\n2024-01-12 08:10:53 +0000 3680 execution.bulk INFO Process 3680 started.\n2024-01-12 08:10:53 +0000 3690 execution.bulk INFO Process 3690 started.\n2024-01-12 08:10:53 +0000 3638 execution.bulk INFO Process name: ForkProcess-46:2, Process id: 3680, Line number: 0 start execution.\n2024-01-12 08:10:53 +0000 3638 execution.bulk INFO Process name: ForkProcess-46:3, Process id: 3690, Line number: 1 start execution.\n2024-01-12 08:10:53 +0000 3638 execution.bulk INFO Process name: ForkProcess-46:2, Process id: 3680, Line number: 0 completed.\n2024-01-12 08:10:53 +0000 3685 execution.bulk INFO Process 3685 started.\n2024-01-12 08:10:53 +0000 3638 execution.bulk INFO Finished 1 / 3 lines.\n2024-01-12 08:10:53 +0000 3638 execution.bulk INFO Process name: ForkProcess-46:4, Process id: 3685, Line number: 2 start execution.\n2024-01-12 08:10:53 +0000 3638 execution.bulk INFO Average execution time for completed lines: 0.22 seconds. Estimated time for incomplete lines: 0.44 seconds.\n2024-01-12 08:10:53 +0000 3638 execution.bulk INFO Process name: ForkProcess-46:3, Process id: 3690, Line number: 1 completed.\n2024-01-12 08:10:53 +0000 3638 execution.bulk INFO Finished 2 / 3 lines.\n2024-01-12 08:10:53 +0000 3638 execution.bulk INFO Average execution time for completed lines: 0.14 seconds. Estimated time for incomplete lines: 0.14 seconds.\n2024-01-12 08:10:53 +0000 3638 execution.bulk INFO Process name: ForkProcess-46:4, Process id: 3685, Line number: 2 completed.\n2024-01-12 08:10:53 +0000 3638 execution.bulk INFO Finished 3 / 3 lines.\n2024-01-12 08:10:53 +0000 3638 execution.bulk INFO Average execution time for completed lines: 0.11 seconds. Estimated time for incomplete lines: 0.0 seconds.\n2024-01-12 08:10:56 +0000 3638 execution.bulk INFO Upload status summary metrics for run batch_run_name finished in 1.1332635823637247 seconds\n2024-01-12 08:10:56 +0000 3638 promptflow-runtime INFO Successfully write run properties {\"azureml.promptflow.total_tokens\": 0, \"_azureml.evaluate_artifacts\": \"[{\\\"path\\\": \\\"instance_results.jsonl\\\", \\\"type\\\": \\\"table\\\"}]\"} with run id ''batch_run_name''\n2024-01-12 08:10:56 +0000 3638 execution.bulk INFO Upload RH properties for run batch_run_name finished in 0.0716887628659606 seconds\n2024-01-12 08:10:56 +0000 3638 promptflow-runtime INFO Creating unregistered output Asset for Run batch_run_name...\n2024-01-12 08:10:56 +0000 3638 promptflow-runtime INFO Created debug_info Asset: azureml://locations/eastus/workspaces/00000/data/azureml_batch_run_name_output_data_debug_info/versions/1\n2024-01-12 08:10:56 +0000 3638 promptflow-runtime INFO Creating unregistered output Asset for Run batch_run_name...\n2024-01-12 08:10:56 +0000 3638 promptflow-runtime INFO Created flow_outputs output Asset: azureml://locations/eastus/workspaces/00000/data/azureml_batch_run_name_output_data_flow_outputs/versions/1\n2024-01-12 08:10:56 +0000 3638 promptflow-runtime INFO Creating Artifact for Run batch_run_name...\n2024-01-12 08:10:56 +0000 3638 promptflow-runtime INFO Created instance_results.jsonl Artifact.\n2024-01-12 08:10:56 +0000 3638 promptflow-runtime INFO Patching batch_run_name...\n2024-01-12 08:10:56 +0000 3638 promptflow-runtime INFO Ending the aml run ''batch_run_name'' with status ''Completed''...\n2024-01-12 08:10:58 +0000 78 promptflow-runtime INFO Process 3638 finished\n2024-01-12 08:10:58 +0000 78 promptflow-runtime INFO [78] Child process finished!\n2024-01-12 08:10:58 +0000 78 promptflow-runtime INFO [batch_run_name] End processing bulk run\n2024-01-12 08:10:58 +0000 78 promptflow-runtime INFO Cleanup working dir /mnt/host/service/app/39415/requests/batch_run_name for bulk run\n"' headers: connection: - keep-alive content-length: - '9817' content-type: - application/json; charset=utf-8 strict-transport-security: - max-age=15724800; includeSubDomains; preload transfer-encoding: - chunked vary: - Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.576' status: code: 200 message: OK version: 1

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/recordings/test_run_operations_TestFlowRun_test_automatic_runtime_with_resources.yaml

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promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/recordings/test_run_operations_TestFlowRun_test_stream_failed_run_logs.yaml

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"default": "google", "enum": ["google", "bing"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "location": {"type": ["string"], "default": "", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "num": {"type": ["int"], "default": "10", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "query": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "safe": {"type": ["string"], "default": "off", "enum": ["active", "off"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Use Serp API to obtain search results from a specific search engine.", "module": "promptflow.tools.serpapi", "class_name": "SerpAPI", "function": "search", "is_builtin": true, "package": "promptflow-tools", "package_version": "0.0.216", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Faiss Index Lookup", "type": "python", "inputs": {"path": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_k": {"type": ["int"], "default": "3", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "vector": {"type": ["list"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Search vector based query from the FAISS index file.", "module": "promptflow_vectordb.tool.faiss_index_lookup", "class_name": "FaissIndexLookup", "function": "search", "is_builtin": true, "package": "promptflow-vectordb", "package_version": "0.0.1", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Vector DB Lookup", "type": "python", "inputs": {"class_name": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["WeaviateConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "collection_name": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["QdrantConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "connection": {"type": ["CognitiveSearchConnection", "QdrantConnection", "WeaviateConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "index_name": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "search_filters": {"type": ["object"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection", "QdrantConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "search_params": {"type": ["object"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection", "QdrantConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "text_field": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection", "QdrantConnection", "WeaviateConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_k": {"type": ["int"], "default": "3", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "vector": {"type": ["list"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "vector_field": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Search vector based query from existing Vector Database.", "module": "promptflow_vectordb.tool.vector_db_lookup", "class_name": "VectorDBLookup", "function": "search", "is_builtin": true, "package": "promptflow-vectordb", "package_version": "0.0.1", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Vector Index Lookup", "type": "python", "inputs": {"path": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "query": {"type": ["object"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_k": {"type": ["int"], "default": "3", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Search text or vector based query from AzureML Vector Index.", "module": "promptflow_vectordb.tool.vector_index_lookup", "class_name": "VectorIndexLookup", "function": "search", "is_builtin": true, "package": "promptflow-vectordb", "package_version": "0.0.1", "enable_kwargs": false, "tool_state": "stable"}, {"name": "print_env.py", "type": "python", "inputs": {"key": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "source": "print_env.py", "function": "get_env_var", "is_builtin": false, "enable_kwargs": false, "tool_state": "stable"}], "inputs": {"key": {"type": "string", "is_chat_input": false}}, "outputs": {"output": {"type": "string", "reference": "${print_env.output.value}", "evaluation_only": false, "is_chat_output": false}}}, "flowRunResourceId": "azureml://locations/eastus/workspaces/00000/flows/failed_run_name/flowRuns/failed_run_name", "flowRunId": "failed_run_name", "flowRunDisplayName": "sdk-cli-test-fixture-failed-run", "batchDataInput": {"dataUri": "azureml://datastores/workspaceblobstore/paths/LocalUpload/74c11bba717480b2d6b04b8e746d09d7/webClassification3.jsonl"}, "flowRunType": "FlowRun", "flowType": "Default", "runtimeName": "test-runtime-ci", "inputsMapping": {}, "outputDatastoreName": "workspaceblobstore", "childRunBasePath": "promptflow/PromptFlowArtifacts/failed_run_name/flow_artifacts", "flowDagFileRelativePath": "flow.dag.yaml", "flowSnapshotId": "27175d15-f6d8-4792-9072-e2b684753205", "studioPortalEndpoint": "https://ml.azure.com/runs/failed_run_name?wsid=/subscriptions/00000000-0000-0000-0000-000000000000/resourcegroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000"}' headers: connection: - keep-alive content-length: - '12855' content-type: - application/json; charset=utf-8 strict-transport-security: - max-age=15724800; includeSubDomains; preload transfer-encoding: - chunked vary: - Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.461' status: code: 200 message: OK - request: body: '{"runId": "failed_run_name", "selectRunMetadata": true, "selectRunDefinition": true, "selectJobSpecification": true}' headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '137' Content-Type: - application/json User-Agent: - python-requests/2.31.0 method: POST uri: https://eastus.api.azureml.ms/history/v1.0/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/rundata response: body: string: '{"runMetadata": {"runNumber": 1705046481, "rootRunId": "failed_run_name", "createdUtc": "2024-01-12T08:01:21.0459935+00:00", "createdBy": {"userObjectId": "00000000-0000-0000-0000-000000000000", "userPuId": null, "userIdp": "https://sts.windows.net/00000000-0000-0000-0000-000000000000/", "userAltSecId": null, "userIss": "https://sts.windows.net/00000000-0000-0000-0000-000000000000/", "userTenantId": "00000000-0000-0000-0000-000000000000", "userName": "4cbd0e2e-aae4-4099-b4ba-94d3a4910587", "upn": null}, "userId": "00000000-0000-0000-0000-000000000000", "token": null, "tokenExpiryTimeUtc": null, "error": {"error": {"code": "UserError", "severity": null, "message": "The input for batch run is incorrect. Couldn''t find these mapping relations: ${data.key}. Please make sure your input mapping keys and values match your YAML input section and input data. For more information, refer to the following documentation: https://aka.ms/pf/column-mapping", "messageFormat": "The input for batch run is incorrect. Couldn''t find these mapping relations: {invalid_relations}. Please make sure your input mapping keys and values match your YAML input section and input data. For more information, refer to the following documentation: https://aka.ms/pf/column-mapping", "messageParameters": {"invalid_relations": "${data.key}"}, "referenceCode": "Executor", "detailsUri": null, "target": null, "details": [], "innerError": {"code": "ValidationError", "innerError": {"code": "InputMappingError", "innerError": null}}, "debugInfo": {"type": "InputMappingError", "message": "The input for batch run is incorrect. Couldn''t find these mapping relations: ${data.key}. Please make sure your input mapping keys and values match your YAML input section and input data. For more information, refer to the following documentation: https://aka.ms/pf/column-mapping", "stackTrace": "Traceback (most recent call last):\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/runtime/runtime.py\", line 671, in execute_bulk_run_request\n batch_engine.run(\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/batch/_batch_engine.py\", line 147, in run\n raise e\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/batch/_batch_engine.py\", line 132, in run\n batch_inputs = batch_input_processor.process_batch_inputs(input_dirs, inputs_mapping)\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/batch/_batch_inputs_processor.py\", line 41, in process_batch_inputs\n return self._validate_and_apply_inputs_mapping(input_dicts, inputs_mapping)\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/batch/_batch_inputs_processor.py\", line 91, in _validate_and_apply_inputs_mapping\n resolved_inputs = self._apply_inputs_mapping_for_all_lines(inputs, inputs_mapping)\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/batch/_batch_inputs_processor.py\", line 163, in _apply_inputs_mapping_for_all_lines\n result = [apply_inputs_mapping(item, inputs_mapping) for item in merged_list]\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/batch/_batch_inputs_processor.py\", line 163, in <listcomp>\n result = [apply_inputs_mapping(item, inputs_mapping) for item in merged_list]\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/batch/_batch_inputs_processor.py\", line 292, in apply_inputs_mapping\n raise InputMappingError(\n", "innerException": null, "data": null, "errorResponse": null}, "additionalInfo": null}, "correlation": null, "environment": null, "location": null, "time": "2024-01-12T08:01:41.930978+00:00", "componentName": "promptflow-runtime/20231204.v4 Designer/1.0 promptflow-sdk/0.0.1 azsdk-python-azuremachinelearningdesignerserviceclient/unknown Python/3.10.13 (Windows-10-10.0.22631-SP0) promptflow/1.2.0rc1"}, "warnings": null, "revision": 7, "statusRevision": 3, "runUuid": "ebad9732-07a7-434c-b7fb-637162729eb8", "parentRunUuid": null, "rootRunUuid": "ebad9732-07a7-434c-b7fb-637162729eb8", "lastStartTimeUtc": null, "currentComputeTime": null, "computeDuration": "00:00:01.7835699", "effectiveStartTimeUtc": null, "lastModifiedBy": {"userObjectId": "00000000-0000-0000-0000-000000000000", "userPuId": null, "userIdp": "https://sts.windows.net/00000000-0000-0000-0000-000000000000/", "userAltSecId": null, "userIss": "https://sts.windows.net/00000000-0000-0000-0000-000000000000/", "userTenantId": "00000000-0000-0000-0000-000000000000", "userName": "18a66f5f-dbdf-4c17-9dd7-1634712a9cbe", "upn": null}, "lastModifiedUtc": "2024-01-12T08:01:41.5560578+00:00", "duration": "00:00:01.7835699", "cancelationReason": null, "currentAttemptId": 1, "runId": "failed_run_name", "parentRunId": null, "experimentId": "1848033e-509f-4c52-92ee-f0a0121fe99e", "status": "Failed", "startTimeUtc": "2024-01-12T08:01:40.3861186+00:00", "endTimeUtc": "2024-01-12T08:01:42.1696885+00:00", "scheduleId": null, "displayName": "sdk-cli-test-fixture-failed-run", "name": null, "dataContainerId": "dcid.failed_run_name", "description": null, "hidden": false, "runType": "azureml.promptflow.FlowRun", "runTypeV2": {"orchestrator": null, "traits": [], "attribution": "PromptFlow", "computeType": "AmlcDsi"}, "properties": {"azureml.promptflow.runtime_name": "test-runtime-ci", "azureml.promptflow.runtime_version": "20231204.v4", "azureml.promptflow.definition_file_name": "flow.dag.yaml", "azureml.promptflow.session_id": "31858a8dfc61a642bb0ab6df4fc3ac7b3807de4ffead00d1", "azureml.promptflow.flow_lineage_id": "de293df4f50622090c0225852d59cd663b6b629e38728f7444fa0f12255a0647", "azureml.promptflow.flow_definition_datastore_name": "workspaceblobstore", "azureml.promptflow.flow_definition_blob_path": "LocalUpload/bc20fa079592a8072922533f187e3184/partial_fail/flow.dag.yaml", "azureml.promptflow.input_data": "azureml://datastores/workspaceblobstore/paths/LocalUpload/74c11bba717480b2d6b04b8e746d09d7/webClassification3.jsonl", "_azureml.evaluation_run": "promptflow.BatchRun", "azureml.promptflow.snapshot_id": "27175d15-f6d8-4792-9072-e2b684753205", "azureml.promptflow.total_tokens": "0", "_azureml.evaluate_artifacts": "[{\"path\": \"instance_results.jsonl\", \"type\": \"table\"}]"}, "parameters": {}, "actionUris": {}, "scriptName": null, "target": null, "uniqueChildRunComputeTargets": [], "tags": {}, "settings": {}, "services": {}, "inputDatasets": [], "outputDatasets": [], "runDefinition": null, "jobSpecification": null, "primaryMetricName": null, "createdFrom": null, "cancelUri": null, "completeUri": null, "diagnosticsUri": null, "computeRequest": null, "compute": null, "retainForLifetimeOfWorkspace": false, "queueingInfo": null, "inputs": null, "outputs": {"debug_info": {"assetId": "azureml://locations/eastus/workspaces/00000/data/azureml_failed_run_name_output_data_debug_info/versions/1", "type": "UriFolder"}}}, "runDefinition": null, "jobSpecification": null, "systemSettings": null}' headers: connection: - keep-alive content-length: - '7988' content-type: - application/json; charset=utf-8 strict-transport-security: - max-age=15724800; includeSubDomains; preload transfer-encoding: - chunked vary: - Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.048' status: code: 200 message: OK - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Type: - application/json User-Agent: - promptflow-sdk/0.0.1 azsdk-python-azuremachinelearningdesignerserviceclient/unknown Python/3.10.13 (Windows-10-10.0.22631-SP0) method: GET uri: https://eastus.api.azureml.ms/flow/api/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/BulkRuns/failed_run_name/logContent response: body: string: '"2024-01-12 08:01:25 +0000 49 promptflow-runtime INFO [failed_run_name] Receiving v2 bulk run request 74219027-a510-47c5-b30f-f9a2e05d3f12: {\"flow_id\": \"failed_run_name\", \"flow_run_id\": \"failed_run_name\", \"flow_source\": {\"flow_source_type\": 1, \"flow_source_info\": {\"snapshot_id\": \"27175d15-f6d8-4792-9072-e2b684753205\"}, \"flow_dag_file\": \"flow.dag.yaml\"}, \"log_path\": \"https://promptfloweast4063704120.blob.core.windows.net/azureml/ExperimentRun/dcid.failed_run_name/logs/azureml/executionlogs.txt?sv=2019-07-07&sr=b&sig=**data_scrubbed**&skoid=55b92eba-d7c7-4afd-ab76-7bb1cd345283&sktid=00000000-0000-0000-0000-000000000000&skt=2024-01-12T07%3A42%3A25Z&ske=2024-01-13T15%3A52%3A25Z&sks=b&skv=2019-07-07&st=2024-01-12T07%3A51%3A24Z&se=2024-01-12T16%3A01%3A24Z&sp=rcw\", \"app_insights_instrumentation_key\": \"InstrumentationKey=**data_scrubbed**;IngestionEndpoint=https://eastus-6.in.applicationinsights.azure.com/;LiveEndpoint=https://eastus.livediagnostics.monitor.azure.com/\", \"data_inputs\": {\"data\": \"azureml://datastores/workspaceblobstore/paths/LocalUpload/74c11bba717480b2d6b04b8e746d09d7/webClassification3.jsonl\"}, \"azure_storage_setting\": {\"azure_storage_mode\": 1, \"storage_account_name\": \"promptfloweast4063704120\", \"blob_container_name\": \"azureml-blobstore-3e123da1-f9a5-4c91-9234-8d9ffbb39ff5\", \"flow_artifacts_root_path\": \"promptflow/PromptFlowArtifacts/failed_run_name\", \"blob_container_sas_token\": \"?sv=2019-07-07&sr=c&sig=**data_scrubbed**&skoid=55b92eba-d7c7-4afd-ab76-7bb1cd345283&sktid=00000000-0000-0000-0000-000000000000&skt=2024-01-12T08%3A01%3A25Z&ske=2024-01-19T08%3A01%3A25Z&sks=b&skv=2019-07-07&se=2024-01-19T08%3A01%3A25Z&sp=racwl\", \"output_datastore_name\": \"workspaceblobstore\"}}\n2024-01-12 08:01:25 +0000 49 promptflow-runtime INFO Runtime version: 20231204.v4. PromptFlow version: 1.2.0rc1\n2024-01-12 08:01:25 +0000 49 promptflow-runtime INFO Updating failed_run_name to Status.Preparing...\n2024-01-12 08:01:25 +0000 49 promptflow-runtime INFO Downloading snapshot to /mnt/host/service/app/39649/requests/failed_run_name\n2024-01-12 08:01:25 +0000 49 promptflow-runtime INFO Get snapshot sas url for 27175d15-f6d8-4792-9072-e2b684753205...\n2024-01-12 08:01:32 +0000 49 promptflow-runtime INFO Downloading snapshot 27175d15-f6d8-4792-9072-e2b684753205 from uri https://promptfloweast4063704120.blob.core.windows.net/snapshotzips/promptflow-eastus:3e123da1-f9a5-4c91-9234-8d9ffbb39ff5:snapshotzip/27175d15-f6d8-4792-9072-e2b684753205.zip...\n2024-01-12 08:01:32 +0000 49 promptflow-runtime INFO Downloaded file /mnt/host/service/app/39649/requests/failed_run_name/27175d15-f6d8-4792-9072-e2b684753205.zip with size 701 for snapshot 27175d15-f6d8-4792-9072-e2b684753205.\n2024-01-12 08:01:32 +0000 49 promptflow-runtime INFO Download snapshot 27175d15-f6d8-4792-9072-e2b684753205 completed.\n2024-01-12 08:01:32 +0000 49 promptflow-runtime INFO Successfully download snapshot to /mnt/host/service/app/39649/requests/failed_run_name\n2024-01-12 08:01:32 +0000 49 promptflow-runtime INFO About to execute a python flow.\n2024-01-12 08:01:32 +0000 49 promptflow-runtime INFO Use spawn method to start child process.\n2024-01-12 08:01:32 +0000 49 promptflow-runtime INFO Starting to check process 3429 status for run failed_run_name\n2024-01-12 08:01:32 +0000 49 promptflow-runtime INFO Start checking run status for run failed_run_name\n2024-01-12 08:01:36 +0000 3429 promptflow-runtime INFO [49--3429] Start processing flowV2......\n2024-01-12 08:01:36 +0000 3429 promptflow-runtime INFO Runtime version: 20231204.v4. PromptFlow version: 1.2.0rc1\n2024-01-12 08:01:36 +0000 3429 promptflow-runtime INFO Setting mlflow tracking uri...\n2024-01-12 08:01:36 +0000 3429 promptflow-runtime INFO Validating ''AzureML Data Scientist'' user authentication...\n2024-01-12 08:01:36 +0000 3429 promptflow-runtime INFO Successfully validated ''AzureML Data Scientist'' user authentication.\n2024-01-12 08:01:36 +0000 3429 promptflow-runtime INFO Using AzureMLRunStorageV2\n2024-01-12 08:01:36 +0000 3429 promptflow-runtime INFO Setting mlflow tracking uri to ''azureml://eastus.api.azureml.ms/mlflow/v1.0/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/promptflow-eastus''\n2024-01-12 08:01:37 +0000 3429 promptflow-runtime INFO Initialized blob service client for AzureMLRunTracker.\n2024-01-12 08:01:37 +0000 3429 promptflow-runtime INFO Setting mlflow tracking uri to ''azureml://eastus.api.azureml.ms/mlflow/v1.0/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/promptflow-eastus''\n2024-01-12 08:01:40 +0000 3429 promptflow-runtime INFO Resolve data from url finished in 2.9352363562211394 seconds\n2024-01-12 08:01:40 +0000 3429 promptflow-runtime INFO Starting the aml run ''failed_run_name''...\n2024-01-12 08:01:40 +0000 3429 execution WARNING Starting run without column mapping may lead to unexpected results. Please consult the following documentation for more information: https://aka.ms/pf/column-mapping\n2024-01-12 08:01:40 +0000 3429 execution.bulk ERROR Error occurred while executing batch run. Exception: The input for batch run is incorrect. Couldn''t find these mapping relations: ${data.key}. Please make sure your input mapping keys and values match your YAML input section and input data. For more information, refer to the following documentation: https://aka.ms/pf/column-mapping\n2024-01-12 08:01:40 +0000 3429 promptflow-runtime ERROR Run failed_run_name failed. Exception: {\n \"message\": \"The input for batch run is incorrect. Couldn''t find these mapping relations: ${data.key}. Please make sure your input mapping keys and values match your YAML input section and input data. For more information, refer to the following documentation: https://aka.ms/pf/column-mapping\",\n \"messageFormat\": \"The input for batch run is incorrect. Couldn''t find these mapping relations: {invalid_relations}. Please make sure your input mapping keys and values match your YAML input section and input data. For more information, refer to the following documentation: https://aka.ms/pf/column-mapping\",\n \"messageParameters\": {\n \"invalid_relations\": \"${data.key}\"\n },\n \"referenceCode\": \"Executor\",\n \"code\": \"UserError\",\n \"innerError\": {\n \"code\": \"ValidationError\",\n \"innerError\": {\n \"code\": \"InputMappingError\",\n \"innerError\": null\n }\n },\n \"debugInfo\": {\n \"type\": \"InputMappingError\",\n \"message\": \"The input for batch run is incorrect. Couldn''t find these mapping relations: ${data.key}. Please make sure your input mapping keys and values match your YAML input section and input data. For more information, refer to the following documentation: https://aka.ms/pf/column-mapping\",\n \"stackTrace\": \"Traceback (most recent call last):\\n File \\\"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/runtime/runtime.py\\\", line 671, in execute_bulk_run_request\\n batch_engine.run(\\n File \\\"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/batch/_batch_engine.py\\\", line 147, in run\\n raise e\\n File \\\"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/batch/_batch_engine.py\\\", line 132, in run\\n batch_inputs = batch_input_processor.process_batch_inputs(input_dirs, inputs_mapping)\\n File \\\"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/batch/_batch_inputs_processor.py\\\", line 41, in process_batch_inputs\\n return self._validate_and_apply_inputs_mapping(input_dicts, inputs_mapping)\\n File \\\"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/batch/_batch_inputs_processor.py\\\", line 91, in _validate_and_apply_inputs_mapping\\n resolved_inputs = self._apply_inputs_mapping_for_all_lines(inputs, inputs_mapping)\\n File \\\"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/batch/_batch_inputs_processor.py\\\", line 163, in _apply_inputs_mapping_for_all_lines\\n result = [apply_inputs_mapping(item, inputs_mapping) for item in merged_list]\\n File \\\"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/batch/_batch_inputs_processor.py\\\", line 163, in <listcomp>\\n result = [apply_inputs_mapping(item, inputs_mapping) for item in merged_list]\\n File \\\"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/batch/_batch_inputs_processor.py\\\", line 292, in apply_inputs_mapping\\n raise InputMappingError(\\n\",\n \"innerException\": null\n }\n}\nTraceback (most recent call last):\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/runtime/runtime.py\", line 671, in execute_bulk_run_request\n batch_engine.run(\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/batch/_batch_engine.py\", line 147, in run\n raise e\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/batch/_batch_engine.py\", line 132, in run\n batch_inputs = batch_input_processor.process_batch_inputs(input_dirs, inputs_mapping)\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/batch/_batch_inputs_processor.py\", line 41, in process_batch_inputs\n return self._validate_and_apply_inputs_mapping(input_dicts, inputs_mapping)\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/batch/_batch_inputs_processor.py\", line 91, in _validate_and_apply_inputs_mapping\n resolved_inputs = self._apply_inputs_mapping_for_all_lines(inputs, inputs_mapping)\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/batch/_batch_inputs_processor.py\", line 163, in _apply_inputs_mapping_for_all_lines\n result = [apply_inputs_mapping(item, inputs_mapping) for item in merged_list]\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/batch/_batch_inputs_processor.py\", line 163, in <listcomp>\n result = [apply_inputs_mapping(item, inputs_mapping) for item in merged_list]\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/batch/_batch_inputs_processor.py\", line 292, in apply_inputs_mapping\n raise InputMappingError(\npromptflow.batch._errors.InputMappingError: The input for batch run is incorrect. Couldn''t find these mapping relations: ${data.key}. Please make sure your input mapping keys and values match your YAML input section and input data. For more information, refer to the following documentation: https://aka.ms/pf/column-mapping\n2024-01-12 08:01:41 +0000 3429 execution.bulk INFO Upload status summary metrics for run failed_run_name finished in 0.7801888957619667 seconds\n2024-01-12 08:01:41 +0000 3429 promptflow-runtime INFO Successfully write run properties {\"azureml.promptflow.total_tokens\": 0, \"_azureml.evaluate_artifacts\": \"[{\\\"path\\\": \\\"instance_results.jsonl\\\", \\\"type\\\": \\\"table\\\"}]\"} with run id ''failed_run_name''\n2024-01-12 08:01:41 +0000 3429 execution.bulk INFO Upload RH properties for run failed_run_name finished in 0.08418271783739328 seconds\n2024-01-12 08:01:41 +0000 3429 promptflow-runtime INFO Creating unregistered output Asset for Run failed_run_name...\n2024-01-12 08:01:41 +0000 3429 promptflow-runtime INFO Created debug_info Asset: azureml://locations/eastus/workspaces/00000/data/azureml_failed_run_name_output_data_debug_info/versions/1\n2024-01-12 08:01:41 +0000 3429 promptflow-runtime INFO Patching failed_run_name...\n2024-01-12 08:01:41 +0000 3429 promptflow-runtime WARNING [failed_run_name] Run failed. Execution stackTrace: Traceback (most recent call last):\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/runtime/runtime.py\", line 671, in execute_bulk_run_request\n batch_engine.run(\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/batch/_batch_engine.py\", line 147, in run\n raise e\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/batch/_batch_engine.py\", line 132, in run\n batch_inputs = batch_input_processor.process_batch_inputs(input_dirs, inputs_mapping)\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/batch/_batch_inputs_processor.py\", line 41, in process_batch_inputs\n return self._validate_and_apply_inputs_mapping(input_dicts, inputs_mapping)\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/batch/_batch_inputs_processor.py\", line 91, in _validate_and_apply_inputs_mapping\n resolved_inputs = self._apply_inputs_mapping_for_all_lines(inputs, inputs_mapping)\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/batch/_batch_inputs_processor.py\", line 163, in _apply_inputs_mapping_for_all_lines\n result = [apply_inputs_mapping(item, inputs_mapping) for item in merged_list]\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/batch/_batch_inputs_processor.py\", line 163, in <listcomp>\n result = [apply_inputs_mapping(item, inputs_mapping) for item in merged_list]\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/batch/_batch_inputs_processor.py\", line 292, in apply_inputs_mapping\n raise InputMappingError(\n\n2024-01-12 08:01:42 +0000 3429 promptflow-runtime INFO Ending the aml run ''failed_run_name'' with status ''Failed''...\n2024-01-12 08:01:43 +0000 49 promptflow-runtime INFO Process 3429 finished\n2024-01-12 08:01:43 +0000 49 promptflow-runtime INFO [49] Child process finished!\n2024-01-12 08:01:43 +0000 49 promptflow-runtime INFO [failed_run_name] End processing bulk run\n2024-01-12 08:01:43 +0000 49 promptflow-runtime ERROR Submit flow request failed Code: 400 InnerException type: InputMappingError Exception type hierarchy: UserError/ValidationError/InputMappingError\n2024-01-12 08:01:43 +0000 49 promptflow-runtime INFO Cleanup working dir /mnt/host/service/app/39649/requests/failed_run_name for bulk run\n"' headers: connection: - keep-alive content-length: - '15117' content-type: - application/json; charset=utf-8 strict-transport-security: - max-age=15724800; includeSubDomains; preload transfer-encoding: - chunked vary: - Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.766' status: code: 200 message: OK version: 1

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/recordings/test_run_operations_TestFlowRun_test_automatic_runtime.yaml

interactions: - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - promptflow-sdk/0.0.1 azure-ai-ml/1.12.0 azsdk-python-mgmt-machinelearningservices/0.1.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) method: GET uri: https://management.azure.com/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000 response: body: string: '{"id": "/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000", "name": "00000", "type": "Microsoft.MachineLearningServices/workspaces", "location": "eastus2euap", "tags": {}, "etag": null, "kind": "Default", "sku": {"name": "Basic", "tier": "Basic"}, "properties": {"discoveryUrl": "https://eastus.api.azureml.ms/discovery"}}' headers: cache-control: - no-cache content-length: - '3601' content-type: - application/json; charset=utf-8 expires: - '-1' pragma: - no-cache strict-transport-security: - max-age=31536000; includeSubDomains transfer-encoding: - chunked vary: - Accept-Encoding,Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.019' status: code: 200 message: OK - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - promptflow-sdk/0.0.1 azure-ai-ml/1.12.0 azsdk-python-mgmt-machinelearningservices/0.1.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) method: GET uri: https://management.azure.com/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/datastores?count=30&isDefault=true&orderByAsc=false response: body: string: '{"value": [{"id": "/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/datastores/workspaceblobstore", "name": "workspaceblobstore", "type": "Microsoft.MachineLearningServices/workspaces/datastores", "properties": {"description": null, "tags": null, "properties": null, "isDefault": true, "credentials": {"credentialsType": "AccountKey"}, "intellectualProperty": null, "subscriptionId": "00000000-0000-0000-0000-000000000000", "resourceGroup": "00000", "datastoreType": "AzureBlob", "accountName": "fake_account_name", "containerName": "fake-container-name", "endpoint": "core.windows.net", "protocol": "https", "serviceDataAccessAuthIdentity": "WorkspaceSystemAssignedIdentity"}, "systemData": {"createdAt": "2023-09-22T05:26:30.7527337+00:00", "createdBy": "779301c0-18b2-4cdc-801b-a0a3368fee0a", "createdByType": "Application", "lastModifiedAt": "2023-09-22T05:26:31.3199607+00:00", "lastModifiedBy": "779301c0-18b2-4cdc-801b-a0a3368fee0a", "lastModifiedByType": "Application"}}]}' headers: cache-control: - no-cache content-length: - '1378' content-type: - application/json; charset=utf-8 expires: - '-1' pragma: - no-cache strict-transport-security: - max-age=31536000; includeSubDomains transfer-encoding: - chunked vary: - Accept-Encoding,Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.628' status: code: 200 message: OK - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - promptflow-sdk/0.0.1 azure-ai-ml/1.12.0 azsdk-python-mgmt-machinelearningservices/0.1.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) method: GET uri: https://management.azure.com/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/datastores/workspaceblobstore response: body: string: '{"id": "/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/datastores/workspaceblobstore", "name": "workspaceblobstore", "type": "Microsoft.MachineLearningServices/workspaces/datastores", "properties": {"description": null, "tags": null, "properties": null, "isDefault": true, "credentials": {"credentialsType": "AccountKey"}, "intellectualProperty": null, "subscriptionId": "00000000-0000-0000-0000-000000000000", "resourceGroup": "00000", "datastoreType": "AzureBlob", "accountName": "fake_account_name", "containerName": "fake-container-name", "endpoint": "core.windows.net", "protocol": "https", "serviceDataAccessAuthIdentity": "WorkspaceSystemAssignedIdentity"}, "systemData": {"createdAt": "2023-09-22T05:26:30.7527337+00:00", "createdBy": "779301c0-18b2-4cdc-801b-a0a3368fee0a", "createdByType": "Application", "lastModifiedAt": "2023-09-22T05:26:31.3199607+00:00", "lastModifiedBy": "779301c0-18b2-4cdc-801b-a0a3368fee0a", "lastModifiedByType": "Application"}}' headers: cache-control: - no-cache content-length: - '1233' content-type: - application/json; charset=utf-8 expires: - '-1' pragma: - no-cache strict-transport-security: - max-age=31536000; includeSubDomains transfer-encoding: - chunked vary: - Accept-Encoding,Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.069' status: code: 200 message: OK - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '0' User-Agent: - promptflow-sdk/0.0.1 azure-ai-ml/1.12.0 azsdk-python-mgmt-machinelearningservices/0.1.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) method: POST uri: https://management.azure.com/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/datastores/workspaceblobstore/listSecrets response: body: string: '{"secretsType": "AccountKey", "key": "dGhpcyBpcyBmYWtlIGtleQ=="}' headers: cache-control: - no-cache content-length: - '134' content-type: - application/json; charset=utf-8 expires: - '-1' pragma: - no-cache strict-transport-security: - max-age=31536000; includeSubDomains transfer-encoding: - chunked vary: - Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.114' status: code: 200 message: OK - request: body: null headers: Accept: - application/xml Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - azsdk-python-storage-blob/12.19.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) x-ms-date: - Fri, 05 Jan 2024 08:28:55 GMT x-ms-version: - '2023-11-03' method: HEAD uri: https://fake_account_name.blob.core.windows.net/fake-container-name/LocalUpload/000000000000000000000000000000000000/env_var_names.jsonl response: body: string: '' headers: accept-ranges: - bytes content-length: - '49' content-md5: - quXiEreYvPinSj0HsaNa/g== content-type: - application/octet-stream last-modified: - Tue, 26 Dec 2023 02:27:07 GMT server: - Windows-Azure-Blob/1.0 Microsoft-HTTPAPI/2.0 vary: - Origin x-ms-blob-type: - BlockBlob x-ms-creation-time: - Tue, 26 Dec 2023 02:27:07 GMT x-ms-meta-name: - bcc45cd4-c343-4bd0-8bdd-cecfafea742d x-ms-meta-upload_status: - completed x-ms-meta-version: - be87bd84-d39b-442b-af5a-e8c209d6d10c x-ms-version: - '2023-11-03' status: code: 200 message: OK - request: body: null headers: Accept: - application/xml Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - azsdk-python-storage-blob/12.19.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) x-ms-date: - Fri, 05 Jan 2024 08:28:56 GMT x-ms-version: - '2023-11-03' method: HEAD uri: https://fake_account_name.blob.core.windows.net/fake-container-name/az-ml-artifacts/000000000000000000000000000000000000/env_var_names.jsonl response: body: string: '' headers: server: - Windows-Azure-Blob/1.0 Microsoft-HTTPAPI/2.0 transfer-encoding: - chunked vary: - Origin x-ms-error-code: - BlobNotFound x-ms-version: - '2023-11-03' status: code: 404 message: The specified blob does not exist. - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - promptflow-sdk/0.0.1 azure-ai-ml/1.12.0 azsdk-python-mgmt-machinelearningservices/0.1.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) method: GET uri: https://management.azure.com/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/datastores/workspaceblobstore response: body: string: '{"id": "/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/datastores/workspaceblobstore", "name": "workspaceblobstore", "type": "Microsoft.MachineLearningServices/workspaces/datastores", "properties": {"description": null, "tags": null, "properties": null, "isDefault": true, "credentials": {"credentialsType": "AccountKey"}, "intellectualProperty": null, "subscriptionId": "00000000-0000-0000-0000-000000000000", "resourceGroup": "00000", "datastoreType": "AzureBlob", "accountName": "fake_account_name", "containerName": "fake-container-name", "endpoint": "core.windows.net", "protocol": "https", "serviceDataAccessAuthIdentity": "WorkspaceSystemAssignedIdentity"}, "systemData": {"createdAt": "2023-09-22T05:26:30.7527337+00:00", "createdBy": "779301c0-18b2-4cdc-801b-a0a3368fee0a", "createdByType": "Application", "lastModifiedAt": "2023-09-22T05:26:31.3199607+00:00", "lastModifiedBy": "779301c0-18b2-4cdc-801b-a0a3368fee0a", "lastModifiedByType": "Application"}}' headers: cache-control: - no-cache content-length: - '1233' content-type: - application/json; charset=utf-8 expires: - '-1' pragma: - no-cache strict-transport-security: - max-age=31536000; includeSubDomains transfer-encoding: - chunked vary: - Accept-Encoding,Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.068' status: code: 200 message: OK - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '0' User-Agent: - promptflow-sdk/0.0.1 azure-ai-ml/1.12.0 azsdk-python-mgmt-machinelearningservices/0.1.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) method: POST uri: https://management.azure.com/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/datastores/workspaceblobstore/listSecrets response: body: string: '{"secretsType": "AccountKey", "key": "dGhpcyBpcyBmYWtlIGtleQ=="}' headers: cache-control: - no-cache content-length: - '134' content-type: - application/json; charset=utf-8 expires: - '-1' pragma: - no-cache strict-transport-security: - max-age=31536000; includeSubDomains transfer-encoding: - chunked vary: - Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.106' status: code: 200 message: OK - request: body: null headers: Accept: - application/xml Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - azsdk-python-storage-blob/12.19.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) x-ms-date: - Fri, 05 Jan 2024 08:29:00 GMT x-ms-version: - '2023-11-03' method: HEAD uri: https://fake_account_name.blob.core.windows.net/fake-container-name/LocalUpload/000000000000000000000000000000000000/print_env_var/flow.dag.yaml response: body: string: '' headers: accept-ranges: - bytes content-length: - '245' content-md5: - F+JA0a3CxcLYZ0ANRdlZbA== content-type: - application/octet-stream last-modified: - Tue, 26 Dec 2023 02:27:07 GMT server: - Windows-Azure-Blob/1.0 Microsoft-HTTPAPI/2.0 vary: - Origin x-ms-blob-type: - BlockBlob x-ms-creation-time: - Tue, 26 Dec 2023 02:27:07 GMT x-ms-meta-name: - 5541d425-c3dc-4f2e-b818-956634d8a470 x-ms-meta-upload_status: - completed x-ms-meta-version: - '1' x-ms-version: - '2023-11-03' status: code: 200 message: OK - request: body: null headers: Accept: - application/xml Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - azsdk-python-storage-blob/12.19.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) x-ms-date: - Fri, 05 Jan 2024 08:29:01 GMT x-ms-version: - '2023-11-03' method: HEAD uri: https://fake_account_name.blob.core.windows.net/fake-container-name/az-ml-artifacts/000000000000000000000000000000000000/print_env_var/flow.dag.yaml response: body: string: '' headers: server: - Windows-Azure-Blob/1.0 Microsoft-HTTPAPI/2.0 transfer-encoding: - chunked vary: - Origin x-ms-error-code: - BlobNotFound x-ms-version: - '2023-11-03' status: code: 404 message: The specified blob does not exist. - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - promptflow-sdk/0.0.1 azure-ai-ml/1.12.0 azsdk-python-mgmt-machinelearningservices/0.1.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) method: GET uri: https://management.azure.com/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/datastores/workspaceblobstore response: body: string: '{"id": "/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/datastores/workspaceblobstore", "name": "workspaceblobstore", "type": "Microsoft.MachineLearningServices/workspaces/datastores", "properties": {"description": null, "tags": null, "properties": null, "isDefault": true, "credentials": {"credentialsType": "AccountKey"}, "intellectualProperty": null, "subscriptionId": "00000000-0000-0000-0000-000000000000", "resourceGroup": "00000", "datastoreType": "AzureBlob", "accountName": "fake_account_name", "containerName": "fake-container-name", "endpoint": "core.windows.net", "protocol": "https", "serviceDataAccessAuthIdentity": "WorkspaceSystemAssignedIdentity"}, "systemData": {"createdAt": "2023-09-22T05:26:30.7527337+00:00", "createdBy": "779301c0-18b2-4cdc-801b-a0a3368fee0a", "createdByType": "Application", "lastModifiedAt": "2023-09-22T05:26:31.3199607+00:00", "lastModifiedBy": "779301c0-18b2-4cdc-801b-a0a3368fee0a", "lastModifiedByType": "Application"}}' headers: cache-control: - no-cache content-length: - '1233' content-type: - application/json; charset=utf-8 expires: - '-1' pragma: - no-cache strict-transport-security: - max-age=31536000; includeSubDomains transfer-encoding: - chunked vary: - Accept-Encoding,Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.058' status: code: 200 message: OK - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '0' User-Agent: - promptflow-sdk/0.0.1 azure-ai-ml/1.12.0 azsdk-python-mgmt-machinelearningservices/0.1.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) method: POST uri: https://management.azure.com/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/datastores/workspaceblobstore/listSecrets response: body: string: '{"secretsType": "AccountKey", "key": "dGhpcyBpcyBmYWtlIGtleQ=="}' headers: cache-control: - no-cache content-length: - '134' content-type: - application/json; charset=utf-8 expires: - '-1' pragma: - no-cache strict-transport-security: - max-age=31536000; includeSubDomains transfer-encoding: - chunked vary: - Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.085' status: code: 200 message: OK - request: body: null headers: Accept: - application/xml Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - azsdk-python-storage-blob/12.19.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) x-ms-date: - Fri, 05 Jan 2024 08:29:09 GMT x-ms-version: - '2023-11-03' method: HEAD uri: https://fake_account_name.blob.core.windows.net/fake-container-name/LocalUpload/000000000000000000000000000000000000/env_var_names.jsonl response: body: string: '' headers: accept-ranges: - bytes content-length: - '49' content-md5: - quXiEreYvPinSj0HsaNa/g== content-type: - application/octet-stream last-modified: - Tue, 26 Dec 2023 02:27:07 GMT server: - Windows-Azure-Blob/1.0 Microsoft-HTTPAPI/2.0 vary: - Origin x-ms-blob-type: - BlockBlob x-ms-creation-time: - Tue, 26 Dec 2023 02:27:07 GMT x-ms-meta-name: - bcc45cd4-c343-4bd0-8bdd-cecfafea742d x-ms-meta-upload_status: - completed x-ms-meta-version: - be87bd84-d39b-442b-af5a-e8c209d6d10c x-ms-version: - '2023-11-03' status: code: 200 message: OK - request: body: null headers: Accept: - application/xml Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - azsdk-python-storage-blob/12.19.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) x-ms-date: - Fri, 05 Jan 2024 08:29:10 GMT x-ms-version: - '2023-11-03' method: HEAD uri: https://fake_account_name.blob.core.windows.net/fake-container-name/az-ml-artifacts/000000000000000000000000000000000000/env_var_names.jsonl response: body: string: '' headers: server: - Windows-Azure-Blob/1.0 Microsoft-HTTPAPI/2.0 transfer-encoding: - chunked vary: - Origin x-ms-error-code: - BlobNotFound x-ms-version: - '2023-11-03' status: code: 404 message: The specified blob does not exist. - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - promptflow-sdk/0.0.1 azure-ai-ml/1.12.0 azsdk-python-mgmt-machinelearningservices/0.1.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) method: GET uri: https://management.azure.com/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/datastores/workspaceblobstore response: body: string: '{"id": "/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/datastores/workspaceblobstore", "name": "workspaceblobstore", "type": "Microsoft.MachineLearningServices/workspaces/datastores", "properties": {"description": null, "tags": null, "properties": null, "isDefault": true, "credentials": {"credentialsType": "AccountKey"}, "intellectualProperty": null, "subscriptionId": "00000000-0000-0000-0000-000000000000", "resourceGroup": "00000", "datastoreType": "AzureBlob", "accountName": "fake_account_name", "containerName": "fake-container-name", "endpoint": "core.windows.net", "protocol": "https", "serviceDataAccessAuthIdentity": "WorkspaceSystemAssignedIdentity"}, "systemData": {"createdAt": "2023-09-22T05:26:30.7527337+00:00", "createdBy": "779301c0-18b2-4cdc-801b-a0a3368fee0a", "createdByType": "Application", "lastModifiedAt": "2023-09-22T05:26:31.3199607+00:00", "lastModifiedBy": "779301c0-18b2-4cdc-801b-a0a3368fee0a", "lastModifiedByType": "Application"}}' headers: cache-control: - no-cache content-length: - '1233' content-type: - application/json; charset=utf-8 expires: - '-1' pragma: - no-cache strict-transport-security: - max-age=31536000; includeSubDomains transfer-encoding: - chunked vary: - Accept-Encoding,Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.097' status: code: 200 message: OK - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '0' User-Agent: - promptflow-sdk/0.0.1 azure-ai-ml/1.12.0 azsdk-python-mgmt-machinelearningservices/0.1.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) method: POST uri: https://management.azure.com/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/datastores/workspaceblobstore/listSecrets response: body: string: '{"secretsType": "AccountKey", "key": "dGhpcyBpcyBmYWtlIGtleQ=="}' headers: cache-control: - no-cache content-length: - '134' content-type: - application/json; charset=utf-8 expires: - '-1' pragma: - no-cache strict-transport-security: - max-age=31536000; includeSubDomains transfer-encoding: - chunked vary: - Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.083' status: code: 200 message: OK - request: body: null headers: Accept: - application/xml Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - azsdk-python-storage-blob/12.19.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) x-ms-date: - Fri, 05 Jan 2024 08:29:13 GMT x-ms-version: - '2023-11-03' method: HEAD uri: https://fake_account_name.blob.core.windows.net/fake-container-name/LocalUpload/000000000000000000000000000000000000/print_env_var/flow.dag.yaml response: body: string: '' headers: accept-ranges: - bytes content-length: - '245' content-md5: - F+JA0a3CxcLYZ0ANRdlZbA== content-type: - application/octet-stream last-modified: - Tue, 26 Dec 2023 02:27:07 GMT server: - Windows-Azure-Blob/1.0 Microsoft-HTTPAPI/2.0 vary: - Origin x-ms-blob-type: - BlockBlob x-ms-creation-time: - Tue, 26 Dec 2023 02:27:07 GMT x-ms-meta-name: - 5541d425-c3dc-4f2e-b818-956634d8a470 x-ms-meta-upload_status: - completed x-ms-meta-version: - '1' x-ms-version: - '2023-11-03' status: code: 200 message: OK - request: body: null headers: Accept: - application/xml Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - azsdk-python-storage-blob/12.19.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) x-ms-date: - Fri, 05 Jan 2024 08:29:14 GMT x-ms-version: - '2023-11-03' method: HEAD uri: https://fake_account_name.blob.core.windows.net/fake-container-name/az-ml-artifacts/000000000000000000000000000000000000/print_env_var/flow.dag.yaml response: body: string: '' headers: server: - Windows-Azure-Blob/1.0 Microsoft-HTTPAPI/2.0 transfer-encoding: - chunked vary: - Origin x-ms-error-code: - BlobNotFound x-ms-version: - '2023-11-03' status: code: 404 message: The specified blob does not exist. version: 1

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/recordings/test_run_operations_TestFlowRun_test_request_id_when_making_http_requests.yaml

interactions: - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - promptflow-sdk/0.0.1 promptflow/0.0.1 azure-ai-ml/1.12.0 azsdk-python-mgmt-machinelearningservices/0.1.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) method: GET uri: https://management.azure.com/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000 response: body: string: '{"id": "/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000", "name": "00000", "type": "Microsoft.MachineLearningServices/workspaces", "location": "eastus", "tags": {}, "etag": null, "kind": "Default", "sku": {"name": "Basic", "tier": "Basic"}, "properties": {"discoveryUrl": "https://eastus.api.azureml.ms/discovery"}}' headers: cache-control: - no-cache content-length: - '3630' content-type: - application/json; charset=utf-8 expires: - '-1' pragma: - no-cache strict-transport-security: - max-age=31536000; includeSubDomains vary: - Accept-Encoding x-cache: - CONFIG_NOCACHE x-content-type-options: - nosniff x-request-time: - '0.017' status: code: 200 message: OK - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - promptflow-sdk/0.0.1 promptflow/0.0.1 azure-ai-ml/1.12.0 azsdk-python-mgmt-machinelearningservices/0.1.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) method: GET uri: https://management.azure.com/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/datastores?count=30&isDefault=true&orderByAsc=false response: body: string: '{"value": [{"id": "/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/datastores/workspaceblobstore", "name": "workspaceblobstore", "type": "Microsoft.MachineLearningServices/workspaces/datastores", "properties": {"description": null, "tags": null, "properties": null, "isDefault": true, "credentials": {"credentialsType": "AccountKey"}, "intellectualProperty": null, "subscriptionId": "00000000-0000-0000-0000-000000000000", "resourceGroup": "00000", "datastoreType": "AzureBlob", "accountName": "fake_account_name", "containerName": "fake-container-name", "endpoint": "core.windows.net", "protocol": "https", "serviceDataAccessAuthIdentity": "WorkspaceSystemAssignedIdentity"}, "systemData": {"createdAt": "2023-04-08T02:53:06.5886442+00:00", "createdBy": "779301c0-18b2-4cdc-801b-a0a3368fee0a", "createdByType": "Application", "lastModifiedAt": "2023-04-08T02:53:07.521127+00:00", "lastModifiedBy": "779301c0-18b2-4cdc-801b-a0a3368fee0a", "lastModifiedByType": "Application"}}]}' headers: cache-control: - no-cache content-length: - '1372' content-type: - application/json; charset=utf-8 expires: - '-1' pragma: - no-cache strict-transport-security: - max-age=31536000; includeSubDomains vary: - Accept-Encoding x-cache: - CONFIG_NOCACHE x-content-type-options: - nosniff x-request-time: - '0.716' status: code: 200 message: OK - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - promptflow-sdk/0.0.1 promptflow/0.0.1 azure-ai-ml/1.12.0 azsdk-python-mgmt-machinelearningservices/0.1.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) method: GET uri: https://management.azure.com/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/datastores/workspaceblobstore response: body: string: '{"id": "/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/datastores/workspaceblobstore", "name": "workspaceblobstore", "type": "Microsoft.MachineLearningServices/workspaces/datastores", "properties": {"description": null, "tags": null, "properties": null, "isDefault": true, "credentials": {"credentialsType": "AccountKey"}, "intellectualProperty": null, "subscriptionId": "00000000-0000-0000-0000-000000000000", "resourceGroup": "00000", "datastoreType": "AzureBlob", "accountName": "fake_account_name", "containerName": "fake-container-name", "endpoint": "core.windows.net", "protocol": "https", "serviceDataAccessAuthIdentity": "WorkspaceSystemAssignedIdentity"}, "systemData": {"createdAt": "2023-04-08T02:53:06.5886442+00:00", "createdBy": "779301c0-18b2-4cdc-801b-a0a3368fee0a", "createdByType": "Application", "lastModifiedAt": "2023-04-08T02:53:07.521127+00:00", "lastModifiedBy": "779301c0-18b2-4cdc-801b-a0a3368fee0a", "lastModifiedByType": "Application"}}' headers: cache-control: - no-cache content-length: - '1227' content-type: - application/json; charset=utf-8 expires: - '-1' pragma: - no-cache strict-transport-security: - max-age=31536000; includeSubDomains vary: - Accept-Encoding x-cache: - CONFIG_NOCACHE x-content-type-options: - nosniff x-request-time: - '0.105' status: code: 200 message: OK - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '0' User-Agent: - promptflow-sdk/0.0.1 promptflow/0.0.1 azure-ai-ml/1.12.0 azsdk-python-mgmt-machinelearningservices/0.1.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) method: POST uri: https://management.azure.com/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/datastores/workspaceblobstore/listSecrets response: body: string: '{"secretsType": "AccountKey", "key": "dGhpcyBpcyBmYWtlIGtleQ=="}' headers: cache-control: - no-cache content-length: - '134' content-type: - application/json; charset=utf-8 expires: - '-1' pragma: - no-cache strict-transport-security: - max-age=31536000; includeSubDomains x-cache: - CONFIG_NOCACHE x-content-type-options: - nosniff x-request-time: - '0.140' status: code: 200 message: OK - request: body: null headers: Accept: - application/xml Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - azsdk-python-storage-blob/12.19.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) x-ms-date: - Thu, 30 Nov 2023 08:11:19 GMT x-ms-version: - '2023-11-03' method: HEAD uri: https://fake_account_name.blob.core.windows.net/fake-container-name/LocalUpload/000000000000000000000000000000000000/env_var_names.jsonl response: body: string: '' headers: accept-ranges: - bytes content-length: - '49' content-md5: - quXiEreYvPinSj0HsaNa/g== content-type: - application/octet-stream last-modified: - Wed, 08 Nov 2023 04:26:09 GMT server: - Windows-Azure-Blob/1.0 Microsoft-HTTPAPI/2.0 vary: - Origin x-ms-blob-type: - BlockBlob x-ms-creation-time: - Wed, 08 Nov 2023 04:26:09 GMT x-ms-meta-name: - c4092674-5e53-4c17-b78d-75353ae0edb6 x-ms-meta-upload_status: - completed x-ms-meta-version: - 579021dc-8ac8-4c73-8110-4642bd00c69b x-ms-version: - '2023-11-03' status: code: 200 message: OK - request: body: null headers: Accept: - application/xml Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - azsdk-python-storage-blob/12.19.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) x-ms-date: - Thu, 30 Nov 2023 08:11:21 GMT x-ms-version: - '2023-11-03' method: HEAD uri: https://fake_account_name.blob.core.windows.net/fake-container-name/az-ml-artifacts/000000000000000000000000000000000000/env_var_names.jsonl response: body: string: '' headers: server: - Windows-Azure-Blob/1.0 Microsoft-HTTPAPI/2.0 transfer-encoding: - chunked vary: - Origin x-ms-error-code: - BlobNotFound x-ms-version: - '2023-11-03' status: code: 404 message: The specified blob does not exist. - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - promptflow-sdk/0.0.1 promptflow/0.0.1 azure-ai-ml/1.12.0 azsdk-python-mgmt-machinelearningservices/0.1.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) method: GET uri: https://management.azure.com/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/datastores/workspaceblobstore response: body: string: '{"id": "/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/datastores/workspaceblobstore", "name": "workspaceblobstore", "type": "Microsoft.MachineLearningServices/workspaces/datastores", "properties": {"description": null, "tags": null, "properties": null, "isDefault": true, "credentials": {"credentialsType": "AccountKey"}, "intellectualProperty": null, "subscriptionId": "00000000-0000-0000-0000-000000000000", "resourceGroup": "00000", "datastoreType": "AzureBlob", "accountName": "fake_account_name", "containerName": "fake-container-name", "endpoint": "core.windows.net", "protocol": "https", "serviceDataAccessAuthIdentity": "WorkspaceSystemAssignedIdentity"}, "systemData": {"createdAt": "2023-04-08T02:53:06.5886442+00:00", "createdBy": "779301c0-18b2-4cdc-801b-a0a3368fee0a", "createdByType": "Application", "lastModifiedAt": "2023-04-08T02:53:07.521127+00:00", "lastModifiedBy": "779301c0-18b2-4cdc-801b-a0a3368fee0a", "lastModifiedByType": "Application"}}' headers: cache-control: - no-cache content-length: - '1227' content-type: - application/json; charset=utf-8 expires: - '-1' pragma: - no-cache strict-transport-security: - max-age=31536000; includeSubDomains vary: - Accept-Encoding x-cache: - CONFIG_NOCACHE x-content-type-options: - nosniff x-request-time: - '0.103' status: code: 200 message: OK - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '0' User-Agent: - promptflow-sdk/0.0.1 promptflow/0.0.1 azure-ai-ml/1.12.0 azsdk-python-mgmt-machinelearningservices/0.1.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) method: POST uri: https://management.azure.com/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/datastores/workspaceblobstore/listSecrets response: body: string: '{"secretsType": "AccountKey", "key": "dGhpcyBpcyBmYWtlIGtleQ=="}' headers: cache-control: - no-cache content-length: - '134' content-type: - application/json; charset=utf-8 expires: - '-1' pragma: - no-cache strict-transport-security: - max-age=31536000; includeSubDomains x-cache: - CONFIG_NOCACHE x-content-type-options: - nosniff x-request-time: - '0.107' status: code: 200 message: OK - request: body: null headers: Accept: - application/xml Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - azsdk-python-storage-blob/12.19.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) x-ms-date: - Thu, 30 Nov 2023 08:11:25 GMT x-ms-version: - '2023-11-03' method: HEAD uri: https://fake_account_name.blob.core.windows.net/fake-container-name/LocalUpload/000000000000000000000000000000000000/print_env_var/flow.dag.yaml response: body: string: '' headers: accept-ranges: - bytes content-length: - '245' content-md5: - F+JA0a3CxcLYZ0ANRdlZbA== content-type: - application/octet-stream last-modified: - Wed, 29 Nov 2023 02:51:35 GMT server: - Windows-Azure-Blob/1.0 Microsoft-HTTPAPI/2.0 vary: - Origin x-ms-blob-type: - BlockBlob x-ms-creation-time: - Thu, 17 Aug 2023 10:30:09 GMT x-ms-meta-name: - 56efdd28-6297-4baa-aad3-be46f4b768a2 x-ms-meta-upload_status: - completed x-ms-meta-version: - '1' x-ms-version: - '2023-11-03' status: code: 200 message: OK - request: body: null headers: Accept: - application/xml Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - azsdk-python-storage-blob/12.19.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) x-ms-date: - Thu, 30 Nov 2023 08:11:26 GMT x-ms-version: - '2023-11-03' method: HEAD uri: https://fake_account_name.blob.core.windows.net/fake-container-name/az-ml-artifacts/000000000000000000000000000000000000/print_env_var/flow.dag.yaml response: body: string: '' headers: server: - Windows-Azure-Blob/1.0 Microsoft-HTTPAPI/2.0 transfer-encoding: - chunked vary: - Origin x-ms-error-code: - BlobNotFound x-ms-version: - '2023-11-03' status: code: 404 message: The specified blob does not exist. - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - promptflow-sdk/0.0.1 promptflow/0.0.1 azure-ai-ml/1.12.0 azsdk-python-mgmt-machinelearningservices/0.1.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) method: GET uri: https://management.azure.com/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/datastores/workspaceblobstore response: body: string: '{"id": "/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/datastores/workspaceblobstore", "name": "workspaceblobstore", "type": "Microsoft.MachineLearningServices/workspaces/datastores", "properties": {"description": null, "tags": null, "properties": null, "isDefault": true, "credentials": {"credentialsType": "AccountKey"}, "intellectualProperty": null, "subscriptionId": "00000000-0000-0000-0000-000000000000", "resourceGroup": "00000", "datastoreType": "AzureBlob", "accountName": "fake_account_name", "containerName": "fake-container-name", "endpoint": "core.windows.net", "protocol": "https", "serviceDataAccessAuthIdentity": "WorkspaceSystemAssignedIdentity"}, "systemData": {"createdAt": "2023-04-08T02:53:06.5886442+00:00", "createdBy": "779301c0-18b2-4cdc-801b-a0a3368fee0a", "createdByType": "Application", "lastModifiedAt": "2023-04-08T02:53:07.521127+00:00", "lastModifiedBy": "779301c0-18b2-4cdc-801b-a0a3368fee0a", "lastModifiedByType": "Application"}}' headers: cache-control: - no-cache content-length: - '1227' content-type: - application/json; charset=utf-8 expires: - '-1' pragma: - no-cache strict-transport-security: - max-age=31536000; includeSubDomains vary: - Accept-Encoding x-cache: - CONFIG_NOCACHE x-content-type-options: - nosniff x-request-time: - '0.070' status: code: 200 message: OK - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '0' User-Agent: - promptflow-sdk/0.0.1 promptflow/0.0.1 azure-ai-ml/1.12.0 azsdk-python-mgmt-machinelearningservices/0.1.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) method: POST uri: https://management.azure.com/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/datastores/workspaceblobstore/listSecrets response: body: string: '{"secretsType": "AccountKey", "key": "dGhpcyBpcyBmYWtlIGtleQ=="}' headers: cache-control: - no-cache content-length: - '134' content-type: - application/json; charset=utf-8 expires: - '-1' pragma: - no-cache strict-transport-security: - max-age=31536000; includeSubDomains x-cache: - CONFIG_NOCACHE x-content-type-options: - nosniff x-request-time: - '0.104' status: code: 200 message: OK - request: body: null headers: Accept: - application/xml Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - azsdk-python-storage-blob/12.19.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) x-ms-date: - Thu, 30 Nov 2023 08:11:35 GMT x-ms-version: - '2023-11-03' method: HEAD uri: https://fake_account_name.blob.core.windows.net/fake-container-name/LocalUpload/000000000000000000000000000000000000/env_var_names.jsonl response: body: string: '' headers: accept-ranges: - bytes content-length: - '49' content-md5: - quXiEreYvPinSj0HsaNa/g== content-type: - application/octet-stream last-modified: - Wed, 08 Nov 2023 04:26:09 GMT server: - Windows-Azure-Blob/1.0 Microsoft-HTTPAPI/2.0 vary: - Origin x-ms-blob-type: - BlockBlob x-ms-creation-time: - Wed, 08 Nov 2023 04:26:09 GMT x-ms-meta-name: - c4092674-5e53-4c17-b78d-75353ae0edb6 x-ms-meta-upload_status: - completed x-ms-meta-version: - 579021dc-8ac8-4c73-8110-4642bd00c69b x-ms-version: - '2023-11-03' status: code: 200 message: OK - request: body: null headers: Accept: - application/xml Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - azsdk-python-storage-blob/12.19.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) x-ms-date: - Thu, 30 Nov 2023 08:11:36 GMT x-ms-version: - '2023-11-03' method: HEAD uri: https://fake_account_name.blob.core.windows.net/fake-container-name/az-ml-artifacts/000000000000000000000000000000000000/env_var_names.jsonl response: body: string: '' headers: server: - Windows-Azure-Blob/1.0 Microsoft-HTTPAPI/2.0 transfer-encoding: - chunked vary: - Origin x-ms-error-code: - BlobNotFound x-ms-version: - '2023-11-03' status: code: 404 message: The specified blob does not exist. - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - promptflow-sdk/0.0.1 promptflow/0.0.1 azure-ai-ml/1.12.0 azsdk-python-mgmt-machinelearningservices/0.1.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) method: GET uri: https://management.azure.com/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/datastores/workspaceblobstore response: body: string: '{"id": "/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/datastores/workspaceblobstore", "name": "workspaceblobstore", "type": "Microsoft.MachineLearningServices/workspaces/datastores", "properties": {"description": null, "tags": null, "properties": null, "isDefault": true, "credentials": {"credentialsType": "AccountKey"}, "intellectualProperty": null, "subscriptionId": "00000000-0000-0000-0000-000000000000", "resourceGroup": "00000", "datastoreType": "AzureBlob", "accountName": "fake_account_name", "containerName": "fake-container-name", "endpoint": "core.windows.net", "protocol": "https", "serviceDataAccessAuthIdentity": "WorkspaceSystemAssignedIdentity"}, "systemData": {"createdAt": "2023-04-08T02:53:06.5886442+00:00", "createdBy": "779301c0-18b2-4cdc-801b-a0a3368fee0a", "createdByType": "Application", "lastModifiedAt": "2023-04-08T02:53:07.521127+00:00", "lastModifiedBy": "779301c0-18b2-4cdc-801b-a0a3368fee0a", "lastModifiedByType": "Application"}}' headers: cache-control: - no-cache content-length: - '1227' content-type: - application/json; charset=utf-8 expires: - '-1' pragma: - no-cache strict-transport-security: - max-age=31536000; includeSubDomains vary: - Accept-Encoding x-cache: - CONFIG_NOCACHE x-content-type-options: - nosniff x-request-time: - '0.073' status: code: 200 message: OK - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '0' User-Agent: - promptflow-sdk/0.0.1 promptflow/0.0.1 azure-ai-ml/1.12.0 azsdk-python-mgmt-machinelearningservices/0.1.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) method: POST uri: https://management.azure.com/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/datastores/workspaceblobstore/listSecrets response: body: string: '{"secretsType": "AccountKey", "key": "dGhpcyBpcyBmYWtlIGtleQ=="}' headers: cache-control: - no-cache content-length: - '134' content-type: - application/json; charset=utf-8 expires: - '-1' pragma: - no-cache strict-transport-security: - max-age=31536000; includeSubDomains x-cache: - CONFIG_NOCACHE x-content-type-options: - nosniff x-request-time: - '0.092' status: code: 200 message: OK - request: body: null headers: Accept: - application/xml Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - azsdk-python-storage-blob/12.19.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) x-ms-date: - Thu, 30 Nov 2023 08:11:40 GMT x-ms-version: - '2023-11-03' method: HEAD uri: https://fake_account_name.blob.core.windows.net/fake-container-name/LocalUpload/000000000000000000000000000000000000/print_env_var/flow.dag.yaml response: body: string: '' headers: accept-ranges: - bytes content-length: - '245' content-md5: - F+JA0a3CxcLYZ0ANRdlZbA== content-type: - application/octet-stream last-modified: - Wed, 29 Nov 2023 02:51:35 GMT server: - Windows-Azure-Blob/1.0 Microsoft-HTTPAPI/2.0 vary: - Origin x-ms-blob-type: - BlockBlob x-ms-creation-time: - Thu, 17 Aug 2023 10:30:09 GMT x-ms-meta-name: - 56efdd28-6297-4baa-aad3-be46f4b768a2 x-ms-meta-upload_status: - completed x-ms-meta-version: - '1' x-ms-version: - '2023-11-03' status: code: 200 message: OK - request: body: null headers: Accept: - application/xml Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - azsdk-python-storage-blob/12.19.0 Python/3.11.5 (Windows-10-10.0.22621-SP0) x-ms-date: - Thu, 30 Nov 2023 08:11:42 GMT x-ms-version: - '2023-11-03' method: HEAD uri: https://fake_account_name.blob.core.windows.net/fake-container-name/az-ml-artifacts/000000000000000000000000000000000000/print_env_var/flow.dag.yaml response: body: string: '' headers: server: - Windows-Azure-Blob/1.0 Microsoft-HTTPAPI/2.0 transfer-encoding: - chunked vary: - Origin x-ms-error-code: - BlobNotFound x-ms-version: - '2023-11-03' status: code: 404 message: The specified blob does not exist. version: 1

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/recordings/test_run_operations_TestFlowRun_test_get_invalid_run_cases.yaml

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0

promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/recordings/test_azure_cli_perf_TestAzureCliPerf_test_pfazure_run_create.yaml

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0

promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/recordings/test_run_operations_TestFlowRun_test_tools_json_ignored.yaml

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0

promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/recordings/test_flow_operations_TestFlow_test_list_flows.yaml

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promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/recordings/test_cli_with_azure_TestCliWithAzure_test_basic_flow_run_bulk_without_env.yaml

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promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/recordings/test_run_operations_TestFlowRun_test_show_metrics.yaml

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"inputs": {"path": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_k": {"type": ["int"], "default": "3", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "vector": {"type": ["list"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Search vector based query from the FAISS index file.", "module": "promptflow_vectordb.tool.faiss_index_lookup", "class_name": "FaissIndexLookup", "function": "search", "is_builtin": true, "package": "promptflow-vectordb", "package_version": "0.0.1", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Vector DB Lookup", "type": "python", "inputs": {"class_name": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["WeaviateConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "collection_name": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["QdrantConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "connection": {"type": ["CognitiveSearchConnection", "QdrantConnection", "WeaviateConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "index_name": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "search_filters": {"type": ["object"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection", "QdrantConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "search_params": {"type": ["object"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection", "QdrantConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "text_field": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection", "QdrantConnection", "WeaviateConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_k": {"type": ["int"], "default": "3", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "vector": {"type": ["list"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "vector_field": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Search vector based query from existing Vector Database.", "module": "promptflow_vectordb.tool.vector_db_lookup", "class_name": "VectorDBLookup", "function": "search", "is_builtin": true, "package": "promptflow-vectordb", "package_version": "0.0.1", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Vector Index Lookup", "type": "python", "inputs": {"path": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "query": {"type": ["object"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_k": {"type": ["int"], "default": "3", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Search text or vector based query from AzureML Vector Index.", "module": "promptflow_vectordb.tool.vector_index_lookup", "class_name": "VectorIndexLookup", "function": "search", "is_builtin": true, "package": "promptflow-vectordb", "package_version": "0.0.1", "enable_kwargs": false, "tool_state": "stable"}, {"name": "calculate_accuracy.py", "type": "python", "inputs": {"grades": {"type": ["object"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "source": "calculate_accuracy.py", "function": "calculate_accuracy", "is_builtin": false, "enable_kwargs": false, "tool_state": "stable"}, {"name": "grade.py", "type": "python", "inputs": {"groundtruth": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "prediction": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "source": "grade.py", "function": "grade", "is_builtin": false, "enable_kwargs": false, "tool_state": "stable"}], "inputs": {"groundtruth": {"type": "string", "default": "APP", "description": "Please specify the groundtruth column, which contains the true label to the outputs that your flow produces.", "is_chat_input": false}, "prediction": {"type": "string", "default": "APP", "description": "Please specify the prediction column, which contains the predicted outputs that your flow produces.", "is_chat_input": false}}, "outputs": {"grade": {"type": "string", "reference": "${grade.output}", "evaluation_only": false, "is_chat_output": false}}}, "flowRunResourceId": 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'13872' content-type: - application/json; charset=utf-8 strict-transport-security: - max-age=15724800; includeSubDomains; preload transfer-encoding: - chunked vary: - Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.438' status: code: 200 message: OK - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - promptflow-sdk/0.0.1 azsdk-python-azuremachinelearningdesignerserviceclient/unknown Python/3.10.13 (Windows-10-10.0.22631-SP0) method: GET uri: https://eastus.api.azureml.ms/flow/api/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/BulkRuns/eval_run_name response: body: string: '{"flowGraph": {"nodes": [{"name": "grade", "type": "python", "source": {"type": "code", "path": "grade.py"}, "inputs": {"groundtruth": "${inputs.groundtruth}", "prediction": "${inputs.prediction}"}, "tool": "grade.py", "reduce": false}, {"name": "calculate_accuracy", "type": "python", "source": {"type": "code", "path": "calculate_accuracy.py"}, "inputs": {"grades": "${grade.output}"}, "tool": "calculate_accuracy.py", "reduce": true}], "tools": [{"name": "Content Safety (Text Analyze)", "type": "python", "inputs": {"connection": {"type": ["AzureContentSafetyConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "hate_category": {"type": ["string"], "default": "medium_sensitivity", "enum": ["disable", "low_sensitivity", "medium_sensitivity", "high_sensitivity"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "self_harm_category": {"type": ["string"], "default": "medium_sensitivity", "enum": ["disable", "low_sensitivity", "medium_sensitivity", "high_sensitivity"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "sexual_category": {"type": ["string"], "default": "medium_sensitivity", "enum": ["disable", "low_sensitivity", "medium_sensitivity", "high_sensitivity"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "text": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "violence_category": {"type": ["string"], "default": "medium_sensitivity", "enum": ["disable", "low_sensitivity", "medium_sensitivity", "high_sensitivity"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Use Azure Content Safety to detect harmful content.", "module": "promptflow.tools.azure_content_safety", "function": "analyze_text", "is_builtin": true, "package": "promptflow-tools", "package_version": "0.0.216", "enable_kwargs": false, "deprecated_tools": ["content_safety_text.tools.content_safety_text_tool.analyze_text"], "tool_state": "stable"}, {"name": "Embedding", "type": "python", "inputs": {"connection": {"type": ["AzureOpenAIConnection", "OpenAIConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "deployment_name": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["AzureOpenAIConnection"], "model_list": ["text-embedding-ada-002", "text-search-ada-doc-001", "text-search-ada-query-001"], "capabilities": {"completion": false, "chat_completion": false, "embeddings": true}, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "input": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "model": {"type": ["string"], "enum": ["text-embedding-ada-002", "text-search-ada-doc-001", "text-search-ada-query-001"], "enabled_by": "connection", "enabled_by_type": ["OpenAIConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Use Open AI''s embedding model to create an embedding vector representing the input text.", "module": "promptflow.tools.embedding", "function": "embedding", "is_builtin": true, "package": "promptflow-tools", "package_version": "0.0.216", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Open Source LLM", "type": "custom_llm", "inputs": {"api": {"type": ["string"], "enum": ["chat", "completion"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "connection": {"type": ["CustomConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "deployment_name": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "endpoint_name": {"type": ["string"], "default": "-- please enter an endpoint name --", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "max_new_tokens": {"type": ["int"], "default": 500, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "model_kwargs": {"type": ["object"], "default": "{}", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default", "advanced": true}, "temperature": {"type": ["double"], "default": 1.0, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_p": {"type": ["double"], "default": 1.0, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default", "advanced": true}}, "description": "Use an Open Source model from the Azure Model catalog, deployed to an AzureML Online Endpoint for LLM Chat or Completion API calls.", "module": "promptflow.tools.open_source_llm", "class_name": "OpenSourceLLM", "function": "call", "icon": "data:image/png;base64,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", "is_builtin": true, "package": "promptflow-tools", "package_version": "0.0.216", "enable_kwargs": false, "tool_state": "stable"}, {"name": "OpenAI GPT-4V", "type": "custom_llm", "inputs": {"connection": {"type": ["OpenAIConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "frequency_penalty": {"type": ["double"], "default": 0, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "max_tokens": {"type": ["int"], "default": "", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "model": {"type": ["string"], "enum": ["gpt-4-vision-preview"], "allow_manual_entry": true, "is_multi_select": false, "input_type": "default"}, "presence_penalty": {"type": ["double"], "default": 0, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "stop": {"type": ["list"], "default": "", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "temperature": {"type": ["double"], "default": 1, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_p": {"type": ["double"], "default": 1, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Use OpenAI GPT-4V to leverage vision ability.", "module": "promptflow.tools.openai_gpt4v", "class_name": "OpenAI", "function": "chat", "is_builtin": true, "package": "promptflow-tools", "package_version": "0.0.216", "default_prompt": "# system:\nAs an AI assistant, your task involves interpreting images and responding to questions about the image.\nRemember to provide accurate answers based on the information present in the image.\n\n# user:\nCan you tell me what the image depicts?\n![image]({{image_input}})\n", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Serp API", "type": "python", "inputs": {"connection": {"type": ["SerpConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "engine": {"type": ["string"], "default": "google", "enum": ["google", "bing"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "location": {"type": ["string"], "default": "", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "num": {"type": ["int"], "default": "10", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "query": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "safe": {"type": ["string"], "default": "off", "enum": ["active", "off"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Use Serp API to obtain search results from a specific search engine.", "module": "promptflow.tools.serpapi", "class_name": "SerpAPI", "function": "search", "is_builtin": true, "package": "promptflow-tools", "package_version": "0.0.216", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Faiss Index Lookup", "type": "python", "inputs": {"path": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_k": {"type": ["int"], "default": "3", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "vector": {"type": ["list"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Search vector based query from the FAISS index file.", "module": "promptflow_vectordb.tool.faiss_index_lookup", "class_name": "FaissIndexLookup", "function": "search", "is_builtin": true, "package": "promptflow-vectordb", "package_version": "0.0.1", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Vector DB Lookup", "type": "python", "inputs": {"class_name": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["WeaviateConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "collection_name": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["QdrantConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "connection": {"type": ["CognitiveSearchConnection", "QdrantConnection", "WeaviateConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "index_name": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "search_filters": {"type": ["object"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection", "QdrantConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "search_params": {"type": ["object"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection", "QdrantConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "text_field": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection", "QdrantConnection", "WeaviateConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_k": {"type": ["int"], "default": "3", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "vector": {"type": ["list"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "vector_field": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Search vector based query from existing Vector Database.", "module": "promptflow_vectordb.tool.vector_db_lookup", "class_name": "VectorDBLookup", "function": "search", "is_builtin": true, "package": "promptflow-vectordb", "package_version": "0.0.1", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Vector Index Lookup", "type": "python", "inputs": {"path": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "query": {"type": ["object"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_k": {"type": ["int"], "default": "3", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Search text or vector based query from AzureML Vector Index.", "module": "promptflow_vectordb.tool.vector_index_lookup", "class_name": "VectorIndexLookup", "function": "search", "is_builtin": true, "package": "promptflow-vectordb", "package_version": "0.0.1", "enable_kwargs": false, "tool_state": "stable"}, {"name": "calculate_accuracy.py", "type": "python", "inputs": {"grades": {"type": ["object"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "source": "calculate_accuracy.py", "function": "calculate_accuracy", "is_builtin": false, "enable_kwargs": false, "tool_state": "stable"}, {"name": "grade.py", "type": "python", "inputs": {"groundtruth": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "prediction": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "source": "grade.py", "function": "grade", "is_builtin": false, "enable_kwargs": false, "tool_state": "stable"}], "inputs": {"groundtruth": {"type": "string", "default": "APP", "description": "Please specify the groundtruth column, which contains the true label to the outputs that your flow produces.", "is_chat_input": false}, "prediction": {"type": "string", "default": "APP", "description": "Please specify the prediction column, which contains the predicted outputs that your flow produces.", "is_chat_input": false}}, "outputs": {"grade": {"type": "string", "reference": "${grade.output}", "evaluation_only": false, "is_chat_output": false}}}, "flowRunResourceId": "azureml://locations/eastus/workspaces/00000/flows/eval_run_name/flowRuns/eval_run_name", "flowRunId": "eval_run_name", "flowRunDisplayName": "sdk-cli-test-fixture-eval-run-without-llm", "batchDataInput": {"dataUri": "azureml://datastores/workspaceblobstore/paths/LocalUpload/74c11bba717480b2d6b04b8e746d09d7/webClassification3.jsonl"}, "flowRunType": "FlowRun", "flowType": "Default", "runtimeName": "test-runtime-ci", "inputsMapping": {"groundtruth": "${data.answer}", "prediction": "${run.outputs.result}"}, "outputDatastoreName": "workspaceblobstore", "childRunBasePath": "promptflow/PromptFlowArtifacts/eval_run_name/flow_artifacts", "flowDagFileRelativePath": "flow.dag.yaml", "flowSnapshotId": "b37a1c40-7c6c-4246-a735-5a9a3881116b", "studioPortalEndpoint": "https://ml.azure.com/runs/eval_run_name?wsid=/subscriptions/00000000-0000-0000-0000-000000000000/resourcegroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000"}' headers: connection: - keep-alive content-length: - '13872' content-type: - application/json; charset=utf-8 strict-transport-security: - max-age=15724800; includeSubDomains; preload transfer-encoding: - chunked vary: - Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.227' status: code: 200 message: OK - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - promptflow-sdk/0.0.1 azsdk-python-azuremachinelearningdesignerserviceclient/unknown Python/3.10.13 (Windows-10-10.0.22631-SP0) method: GET uri: https://eastus.api.azureml.ms/flow/api/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/BulkRuns/eval_run_name response: body: string: '{"flowGraph": {"nodes": [{"name": "grade", "type": "python", "source": {"type": "code", "path": "grade.py"}, "inputs": {"groundtruth": "${inputs.groundtruth}", "prediction": "${inputs.prediction}"}, "tool": "grade.py", "reduce": false}, {"name": "calculate_accuracy", "type": "python", "source": {"type": "code", "path": "calculate_accuracy.py"}, "inputs": {"grades": "${grade.output}"}, "tool": "calculate_accuracy.py", "reduce": true}], "tools": [{"name": "Content Safety (Text Analyze)", "type": "python", "inputs": {"connection": {"type": ["AzureContentSafetyConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "hate_category": {"type": ["string"], "default": "medium_sensitivity", "enum": ["disable", "low_sensitivity", "medium_sensitivity", "high_sensitivity"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "self_harm_category": {"type": ["string"], "default": "medium_sensitivity", "enum": ["disable", "low_sensitivity", "medium_sensitivity", "high_sensitivity"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "sexual_category": {"type": ["string"], "default": "medium_sensitivity", "enum": ["disable", "low_sensitivity", "medium_sensitivity", "high_sensitivity"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "text": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "violence_category": {"type": ["string"], "default": "medium_sensitivity", "enum": ["disable", "low_sensitivity", "medium_sensitivity", "high_sensitivity"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Use Azure Content Safety to detect harmful content.", "module": "promptflow.tools.azure_content_safety", "function": "analyze_text", "is_builtin": true, "package": "promptflow-tools", "package_version": "0.0.216", "enable_kwargs": false, "deprecated_tools": ["content_safety_text.tools.content_safety_text_tool.analyze_text"], "tool_state": "stable"}, {"name": "Embedding", "type": "python", "inputs": {"connection": {"type": ["AzureOpenAIConnection", "OpenAIConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "deployment_name": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["AzureOpenAIConnection"], "model_list": ["text-embedding-ada-002", "text-search-ada-doc-001", "text-search-ada-query-001"], "capabilities": {"completion": false, "chat_completion": false, "embeddings": true}, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "input": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "model": {"type": ["string"], "enum": ["text-embedding-ada-002", "text-search-ada-doc-001", "text-search-ada-query-001"], "enabled_by": "connection", "enabled_by_type": ["OpenAIConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Use Open AI''s embedding model to create an embedding vector representing the input text.", "module": "promptflow.tools.embedding", "function": "embedding", "is_builtin": true, "package": "promptflow-tools", "package_version": "0.0.216", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Open Source LLM", "type": "custom_llm", "inputs": {"api": {"type": ["string"], "enum": ["chat", "completion"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "connection": {"type": ["CustomConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "deployment_name": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "endpoint_name": {"type": ["string"], "default": "-- please enter an endpoint name --", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "max_new_tokens": {"type": ["int"], "default": 500, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "model_kwargs": {"type": ["object"], "default": "{}", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default", "advanced": true}, "temperature": {"type": ["double"], "default": 1.0, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_p": {"type": ["double"], "default": 1.0, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default", "advanced": true}}, "description": "Use an Open Source model from the Azure Model catalog, deployed to an AzureML Online Endpoint for LLM Chat or Completion API calls.", "module": "promptflow.tools.open_source_llm", "class_name": "OpenSourceLLM", "function": "call", "icon": "data:image/png;base64,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", "is_builtin": true, "package": "promptflow-tools", "package_version": "0.0.216", "enable_kwargs": false, "tool_state": "stable"}, {"name": "OpenAI GPT-4V", "type": "custom_llm", "inputs": {"connection": {"type": ["OpenAIConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "frequency_penalty": {"type": ["double"], "default": 0, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "max_tokens": {"type": ["int"], "default": "", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "model": {"type": ["string"], "enum": ["gpt-4-vision-preview"], "allow_manual_entry": true, "is_multi_select": false, "input_type": "default"}, "presence_penalty": {"type": ["double"], "default": 0, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "stop": {"type": ["list"], "default": "", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "temperature": {"type": ["double"], "default": 1, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_p": {"type": ["double"], "default": 1, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Use OpenAI GPT-4V to leverage vision ability.", "module": "promptflow.tools.openai_gpt4v", "class_name": "OpenAI", "function": "chat", "is_builtin": true, "package": "promptflow-tools", "package_version": "0.0.216", "default_prompt": "# system:\nAs an AI assistant, your task involves interpreting images and responding to questions about the image.\nRemember to provide accurate answers based on the information present in the image.\n\n# user:\nCan you tell me what the image depicts?\n![image]({{image_input}})\n", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Serp API", "type": "python", "inputs": {"connection": {"type": ["SerpConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "engine": {"type": ["string"], "default": "google", "enum": ["google", "bing"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "location": {"type": ["string"], "default": "", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "num": {"type": ["int"], "default": "10", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "query": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "safe": {"type": ["string"], "default": "off", "enum": ["active", "off"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Use Serp API to obtain search results from a specific search engine.", "module": "promptflow.tools.serpapi", "class_name": "SerpAPI", "function": "search", "is_builtin": true, "package": "promptflow-tools", "package_version": "0.0.216", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Faiss Index Lookup", "type": "python", "inputs": {"path": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_k": {"type": ["int"], "default": "3", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "vector": {"type": ["list"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Search vector based query from the FAISS index file.", "module": "promptflow_vectordb.tool.faiss_index_lookup", "class_name": "FaissIndexLookup", "function": "search", "is_builtin": true, "package": "promptflow-vectordb", "package_version": "0.0.1", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Vector DB Lookup", "type": "python", "inputs": {"class_name": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["WeaviateConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "collection_name": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["QdrantConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "connection": {"type": ["CognitiveSearchConnection", "QdrantConnection", "WeaviateConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "index_name": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "search_filters": {"type": ["object"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection", "QdrantConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "search_params": {"type": ["object"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection", "QdrantConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "text_field": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection", "QdrantConnection", "WeaviateConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_k": {"type": ["int"], "default": "3", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "vector": {"type": ["list"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "vector_field": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Search vector based query from existing Vector Database.", "module": "promptflow_vectordb.tool.vector_db_lookup", "class_name": "VectorDBLookup", "function": "search", "is_builtin": true, "package": "promptflow-vectordb", "package_version": "0.0.1", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Vector Index Lookup", "type": "python", "inputs": {"path": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "query": {"type": ["object"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_k": {"type": ["int"], "default": "3", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Search text or vector based query from AzureML Vector Index.", "module": "promptflow_vectordb.tool.vector_index_lookup", "class_name": "VectorIndexLookup", "function": "search", "is_builtin": true, "package": "promptflow-vectordb", "package_version": "0.0.1", "enable_kwargs": false, "tool_state": "stable"}, {"name": "calculate_accuracy.py", "type": "python", "inputs": {"grades": {"type": ["object"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "source": "calculate_accuracy.py", "function": "calculate_accuracy", "is_builtin": false, "enable_kwargs": false, "tool_state": "stable"}, {"name": "grade.py", "type": "python", "inputs": {"groundtruth": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "prediction": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "source": "grade.py", "function": "grade", "is_builtin": false, "enable_kwargs": false, "tool_state": "stable"}], "inputs": {"groundtruth": {"type": "string", "default": "APP", "description": "Please specify the groundtruth column, which contains the true label to the outputs that your flow produces.", "is_chat_input": false}, "prediction": {"type": "string", "default": "APP", "description": "Please specify the prediction column, which contains the predicted outputs that your flow produces.", "is_chat_input": false}}, "outputs": {"grade": {"type": "string", "reference": "${grade.output}", "evaluation_only": false, "is_chat_output": false}}}, "flowRunResourceId": "azureml://locations/eastus/workspaces/00000/flows/eval_run_name/flowRuns/eval_run_name", "flowRunId": "eval_run_name", "flowRunDisplayName": "sdk-cli-test-fixture-eval-run-without-llm", "batchDataInput": {"dataUri": "azureml://datastores/workspaceblobstore/paths/LocalUpload/74c11bba717480b2d6b04b8e746d09d7/webClassification3.jsonl"}, "flowRunType": "FlowRun", "flowType": "Default", "runtimeName": "test-runtime-ci", "inputsMapping": {"groundtruth": "${data.answer}", "prediction": "${run.outputs.result}"}, "outputDatastoreName": "workspaceblobstore", "childRunBasePath": "promptflow/PromptFlowArtifacts/eval_run_name/flow_artifacts", "flowDagFileRelativePath": "flow.dag.yaml", "flowSnapshotId": "b37a1c40-7c6c-4246-a735-5a9a3881116b", "studioPortalEndpoint": "https://ml.azure.com/runs/eval_run_name?wsid=/subscriptions/00000000-0000-0000-0000-000000000000/resourcegroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000"}' headers: connection: - keep-alive content-length: - '13872' content-type: - application/json; charset=utf-8 strict-transport-security: - max-age=15724800; includeSubDomains; preload transfer-encoding: - chunked vary: - Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.497' status: code: 200 message: OK - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - promptflow-sdk/0.0.1 azsdk-python-azuremachinelearningdesignerserviceclient/unknown Python/3.10.13 (Windows-10-10.0.22631-SP0) method: GET uri: https://eastus.api.azureml.ms/flow/api/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/BulkRuns/eval_run_name response: body: string: '{"flowGraph": {"nodes": [{"name": "grade", "type": "python", "source": {"type": "code", "path": "grade.py"}, "inputs": {"groundtruth": "${inputs.groundtruth}", "prediction": "${inputs.prediction}"}, "tool": "grade.py", "reduce": false}, {"name": "calculate_accuracy", "type": "python", "source": {"type": "code", "path": "calculate_accuracy.py"}, "inputs": {"grades": "${grade.output}"}, "tool": "calculate_accuracy.py", "reduce": true}], "tools": [{"name": "Content Safety (Text Analyze)", "type": "python", "inputs": {"connection": {"type": ["AzureContentSafetyConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "hate_category": {"type": ["string"], "default": "medium_sensitivity", "enum": ["disable", "low_sensitivity", "medium_sensitivity", "high_sensitivity"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "self_harm_category": {"type": ["string"], "default": "medium_sensitivity", "enum": ["disable", "low_sensitivity", "medium_sensitivity", "high_sensitivity"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "sexual_category": {"type": ["string"], "default": "medium_sensitivity", "enum": ["disable", "low_sensitivity", "medium_sensitivity", "high_sensitivity"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "text": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "violence_category": {"type": ["string"], "default": "medium_sensitivity", "enum": ["disable", "low_sensitivity", "medium_sensitivity", "high_sensitivity"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Use Azure Content Safety to detect harmful content.", "module": "promptflow.tools.azure_content_safety", "function": "analyze_text", "is_builtin": true, "package": "promptflow-tools", "package_version": "0.0.216", "enable_kwargs": false, "deprecated_tools": ["content_safety_text.tools.content_safety_text_tool.analyze_text"], "tool_state": "stable"}, {"name": "Embedding", "type": "python", "inputs": {"connection": {"type": ["AzureOpenAIConnection", "OpenAIConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "deployment_name": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["AzureOpenAIConnection"], "model_list": ["text-embedding-ada-002", "text-search-ada-doc-001", "text-search-ada-query-001"], "capabilities": {"completion": false, "chat_completion": false, "embeddings": true}, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "input": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "model": {"type": ["string"], "enum": ["text-embedding-ada-002", "text-search-ada-doc-001", "text-search-ada-query-001"], "enabled_by": "connection", "enabled_by_type": ["OpenAIConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Use Open AI''s embedding model to create an embedding vector representing the input text.", "module": "promptflow.tools.embedding", "function": "embedding", "is_builtin": true, "package": "promptflow-tools", "package_version": "0.0.216", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Open Source LLM", "type": "custom_llm", "inputs": {"api": {"type": ["string"], "enum": ["chat", "completion"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "connection": {"type": ["CustomConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "deployment_name": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "endpoint_name": {"type": ["string"], "default": "-- please enter an endpoint name --", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "max_new_tokens": {"type": ["int"], "default": 500, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "model_kwargs": {"type": ["object"], "default": "{}", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default", "advanced": true}, "temperature": {"type": ["double"], "default": 1.0, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_p": {"type": ["double"], "default": 1.0, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default", "advanced": true}}, "description": "Use an Open Source model from the Azure Model catalog, deployed to an AzureML Online Endpoint for LLM Chat or Completion API calls.", "module": "promptflow.tools.open_source_llm", "class_name": "OpenSourceLLM", "function": "call", "icon": "data:image/png;base64,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", "is_builtin": true, "package": "promptflow-tools", "package_version": "0.0.216", "enable_kwargs": false, "tool_state": "stable"}, {"name": "OpenAI GPT-4V", "type": "custom_llm", "inputs": {"connection": {"type": ["OpenAIConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "frequency_penalty": {"type": ["double"], "default": 0, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "max_tokens": {"type": ["int"], "default": "", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "model": {"type": ["string"], "enum": ["gpt-4-vision-preview"], "allow_manual_entry": true, "is_multi_select": false, "input_type": "default"}, "presence_penalty": {"type": ["double"], "default": 0, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "stop": {"type": ["list"], "default": "", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "temperature": {"type": ["double"], "default": 1, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_p": {"type": ["double"], "default": 1, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Use OpenAI GPT-4V to leverage vision ability.", "module": "promptflow.tools.openai_gpt4v", "class_name": "OpenAI", "function": "chat", "is_builtin": true, "package": "promptflow-tools", "package_version": "0.0.216", "default_prompt": "# system:\nAs an AI assistant, your task involves interpreting images and responding to questions about the image.\nRemember to provide accurate answers based on the information present in the image.\n\n# user:\nCan you tell me what the image depicts?\n![image]({{image_input}})\n", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Serp API", "type": "python", "inputs": {"connection": {"type": ["SerpConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "engine": {"type": ["string"], "default": "google", "enum": ["google", "bing"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "location": {"type": ["string"], "default": "", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "num": {"type": ["int"], "default": "10", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "query": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "safe": {"type": ["string"], "default": "off", "enum": ["active", "off"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Use Serp API to obtain search results from a specific search engine.", "module": "promptflow.tools.serpapi", "class_name": "SerpAPI", "function": "search", "is_builtin": true, "package": "promptflow-tools", "package_version": "0.0.216", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Faiss Index Lookup", "type": "python", "inputs": {"path": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_k": {"type": ["int"], "default": "3", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "vector": {"type": ["list"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Search vector based query from the FAISS index file.", "module": "promptflow_vectordb.tool.faiss_index_lookup", "class_name": "FaissIndexLookup", "function": "search", "is_builtin": true, "package": "promptflow-vectordb", "package_version": "0.0.1", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Vector DB Lookup", "type": "python", "inputs": {"class_name": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["WeaviateConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "collection_name": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["QdrantConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "connection": {"type": ["CognitiveSearchConnection", "QdrantConnection", "WeaviateConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "index_name": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "search_filters": {"type": ["object"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection", "QdrantConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "search_params": {"type": ["object"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection", "QdrantConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "text_field": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection", "QdrantConnection", "WeaviateConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_k": {"type": ["int"], "default": "3", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "vector": {"type": ["list"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "vector_field": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Search vector based query from existing Vector Database.", "module": "promptflow_vectordb.tool.vector_db_lookup", "class_name": "VectorDBLookup", "function": "search", "is_builtin": true, "package": "promptflow-vectordb", "package_version": "0.0.1", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Vector Index Lookup", "type": "python", "inputs": {"path": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "query": {"type": ["object"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_k": {"type": ["int"], "default": "3", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Search text or vector based query from AzureML Vector Index.", "module": "promptflow_vectordb.tool.vector_index_lookup", "class_name": "VectorIndexLookup", "function": "search", "is_builtin": true, "package": "promptflow-vectordb", "package_version": "0.0.1", "enable_kwargs": false, "tool_state": "stable"}, {"name": "calculate_accuracy.py", "type": "python", "inputs": {"grades": {"type": ["object"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "source": "calculate_accuracy.py", "function": "calculate_accuracy", "is_builtin": false, "enable_kwargs": false, "tool_state": "stable"}, {"name": "grade.py", "type": "python", "inputs": {"groundtruth": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "prediction": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "source": "grade.py", "function": "grade", "is_builtin": false, "enable_kwargs": false, "tool_state": "stable"}], "inputs": {"groundtruth": {"type": "string", "default": "APP", "description": "Please specify the groundtruth column, which contains the true label to the outputs that your flow produces.", "is_chat_input": false}, "prediction": {"type": "string", "default": "APP", "description": "Please specify the prediction column, which contains the predicted outputs that your flow produces.", "is_chat_input": false}}, "outputs": {"grade": {"type": "string", "reference": "${grade.output}", "evaluation_only": false, "is_chat_output": false}}}, "flowRunResourceId": "azureml://locations/eastus/workspaces/00000/flows/eval_run_name/flowRuns/eval_run_name", "flowRunId": "eval_run_name", "flowRunDisplayName": "sdk-cli-test-fixture-eval-run-without-llm", "batchDataInput": {"dataUri": "azureml://datastores/workspaceblobstore/paths/LocalUpload/74c11bba717480b2d6b04b8e746d09d7/webClassification3.jsonl"}, "flowRunType": "FlowRun", "flowType": "Default", "runtimeName": "test-runtime-ci", "inputsMapping": {"groundtruth": "${data.answer}", "prediction": "${run.outputs.result}"}, "outputDatastoreName": "workspaceblobstore", "childRunBasePath": "promptflow/PromptFlowArtifacts/eval_run_name/flow_artifacts", "flowDagFileRelativePath": "flow.dag.yaml", "flowSnapshotId": "b37a1c40-7c6c-4246-a735-5a9a3881116b", "studioPortalEndpoint": "https://ml.azure.com/runs/eval_run_name?wsid=/subscriptions/00000000-0000-0000-0000-000000000000/resourcegroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000"}' headers: connection: - keep-alive content-length: - '13872' content-type: - application/json; charset=utf-8 strict-transport-security: - max-age=15724800; includeSubDomains; preload transfer-encoding: - chunked vary: - Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.348' status: code: 200 message: OK - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - promptflow-sdk/0.0.1 azsdk-python-azuremachinelearningdesignerserviceclient/unknown Python/3.10.13 (Windows-10-10.0.22631-SP0) method: GET uri: https://eastus.api.azureml.ms/flow/api/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/BulkRuns/eval_run_name response: body: string: '{"flowGraph": {"nodes": [{"name": "grade", "type": "python", "source": {"type": "code", "path": "grade.py"}, "inputs": {"groundtruth": "${inputs.groundtruth}", "prediction": "${inputs.prediction}"}, "tool": "grade.py", "reduce": false}, {"name": "calculate_accuracy", "type": "python", "source": {"type": "code", "path": "calculate_accuracy.py"}, "inputs": {"grades": "${grade.output}"}, "tool": "calculate_accuracy.py", "reduce": true}], "tools": [{"name": "Content Safety (Text Analyze)", "type": "python", "inputs": {"connection": {"type": ["AzureContentSafetyConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "hate_category": {"type": ["string"], "default": "medium_sensitivity", "enum": ["disable", "low_sensitivity", "medium_sensitivity", "high_sensitivity"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "self_harm_category": {"type": ["string"], "default": "medium_sensitivity", "enum": ["disable", "low_sensitivity", "medium_sensitivity", "high_sensitivity"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "sexual_category": {"type": ["string"], "default": "medium_sensitivity", "enum": ["disable", "low_sensitivity", "medium_sensitivity", "high_sensitivity"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "text": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "violence_category": {"type": ["string"], "default": "medium_sensitivity", "enum": ["disable", "low_sensitivity", "medium_sensitivity", "high_sensitivity"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Use Azure Content Safety to detect harmful content.", "module": "promptflow.tools.azure_content_safety", "function": "analyze_text", "is_builtin": true, "package": "promptflow-tools", "package_version": "0.0.216", "enable_kwargs": false, "deprecated_tools": ["content_safety_text.tools.content_safety_text_tool.analyze_text"], "tool_state": "stable"}, {"name": "Embedding", "type": "python", "inputs": {"connection": {"type": ["AzureOpenAIConnection", "OpenAIConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "deployment_name": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["AzureOpenAIConnection"], "model_list": ["text-embedding-ada-002", "text-search-ada-doc-001", "text-search-ada-query-001"], "capabilities": {"completion": false, "chat_completion": false, "embeddings": true}, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "input": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "model": {"type": ["string"], "enum": ["text-embedding-ada-002", "text-search-ada-doc-001", "text-search-ada-query-001"], "enabled_by": "connection", "enabled_by_type": ["OpenAIConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Use Open AI''s embedding model to create an embedding vector representing the input text.", "module": "promptflow.tools.embedding", "function": "embedding", "is_builtin": true, "package": "promptflow-tools", "package_version": "0.0.216", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Open Source LLM", "type": "custom_llm", "inputs": {"api": {"type": ["string"], "enum": ["chat", "completion"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "connection": {"type": ["CustomConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "deployment_name": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "endpoint_name": {"type": ["string"], "default": "-- please enter an endpoint name --", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "max_new_tokens": {"type": ["int"], "default": 500, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "model_kwargs": {"type": ["object"], "default": "{}", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default", "advanced": true}, "temperature": {"type": ["double"], "default": 1.0, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_p": {"type": ["double"], "default": 1.0, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default", "advanced": true}}, "description": "Use an Open Source model from the Azure Model catalog, deployed to an AzureML Online Endpoint for LLM Chat or Completion API calls.", "module": "promptflow.tools.open_source_llm", "class_name": "OpenSourceLLM", "function": "call", "icon": "data:image/png;base64,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", "is_builtin": true, "package": "promptflow-tools", "package_version": "0.0.216", "enable_kwargs": false, "tool_state": "stable"}, {"name": "OpenAI GPT-4V", "type": "custom_llm", "inputs": {"connection": {"type": ["OpenAIConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "frequency_penalty": {"type": ["double"], "default": 0, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "max_tokens": {"type": ["int"], "default": "", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "model": {"type": ["string"], "enum": ["gpt-4-vision-preview"], "allow_manual_entry": true, "is_multi_select": false, "input_type": "default"}, "presence_penalty": {"type": ["double"], "default": 0, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "stop": {"type": ["list"], "default": "", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "temperature": {"type": ["double"], "default": 1, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_p": {"type": ["double"], "default": 1, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Use OpenAI GPT-4V to leverage vision ability.", "module": "promptflow.tools.openai_gpt4v", "class_name": "OpenAI", "function": "chat", "is_builtin": true, "package": "promptflow-tools", "package_version": "0.0.216", "default_prompt": "# system:\nAs an AI assistant, your task involves interpreting images and responding to questions about the image.\nRemember to provide accurate answers based on the information present in the image.\n\n# user:\nCan you tell me what the image depicts?\n![image]({{image_input}})\n", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Serp API", "type": "python", "inputs": {"connection": {"type": ["SerpConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "engine": {"type": ["string"], "default": "google", "enum": ["google", "bing"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "location": {"type": ["string"], "default": "", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "num": {"type": ["int"], "default": "10", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "query": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "safe": {"type": ["string"], "default": "off", "enum": ["active", "off"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Use Serp API to obtain search results from a specific search engine.", "module": "promptflow.tools.serpapi", "class_name": "SerpAPI", "function": "search", "is_builtin": true, "package": "promptflow-tools", "package_version": "0.0.216", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Faiss Index Lookup", "type": "python", "inputs": {"path": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_k": {"type": ["int"], "default": "3", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "vector": {"type": ["list"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Search vector based query from the FAISS index file.", "module": "promptflow_vectordb.tool.faiss_index_lookup", "class_name": "FaissIndexLookup", "function": "search", "is_builtin": true, "package": "promptflow-vectordb", "package_version": "0.0.1", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Vector DB Lookup", "type": "python", "inputs": {"class_name": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["WeaviateConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "collection_name": {"type": ["string"], "enabled_by": "connection", 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PromptFlow version: 1.2.0rc1\n2024-01-12 07:56:39 +0000 3118 promptflow-runtime INFO Setting mlflow tracking uri...\n2024-01-12 07:56:39 +0000 3118 promptflow-runtime INFO Validating ''AzureML Data Scientist'' user authentication...\n2024-01-12 07:56:40 +0000 3118 promptflow-runtime INFO Successfully validated ''AzureML Data Scientist'' user authentication.\n2024-01-12 07:56:40 +0000 3118 promptflow-runtime INFO Using AzureMLRunStorageV2\n2024-01-12 07:56:40 +0000 3118 promptflow-runtime INFO Setting mlflow tracking uri to ''azureml://eastus.api.azureml.ms/mlflow/v1.0/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/promptflow-eastus''\n2024-01-12 07:56:40 +0000 3118 promptflow-runtime INFO Initialized blob service client for AzureMLRunTracker.\n2024-01-12 07:56:40 +0000 3118 promptflow-runtime INFO Setting mlflow tracking uri to ''azureml://eastus.api.azureml.ms/mlflow/v1.0/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/promptflow-eastus''\n2024-01-12 07:56:40 +0000 3118 promptflow-runtime INFO Resolve data from url finished in 0.4822304155677557 seconds\n2024-01-12 07:56:40 +0000 3118 promptflow-runtime INFO Starting the aml run ''batch_run_name''...\n2024-01-12 07:56:41 +0000 3118 execution.bulk INFO Using fork, process count: 3\n2024-01-12 07:56:41 +0000 3161 execution.bulk INFO Process 3161 started.\n2024-01-12 07:56:41 +0000 3170 execution.bulk INFO Process 3170 started.\n2024-01-12 07:56:41 +0000 3165 execution.bulk INFO Process 3165 started.\n2024-01-12 07:56:41 +0000 3118 execution.bulk INFO Process name: ForkProcess-38:2, Process id: 3161, Line number: 0 start execution.\n2024-01-12 07:56:41 +0000 3118 execution.bulk INFO Process name: ForkProcess-38:3, Process id: 3170, Line number: 1 start execution.\n2024-01-12 07:56:41 +0000 3118 execution.bulk INFO Process name: ForkProcess-38:4, Process id: 3165, Line number: 2 start execution.\n2024-01-12 07:56:41 +0000 3118 execution.bulk INFO Process name: ForkProcess-38:2, Process id: 3161, Line number: 0 completed.\n2024-01-12 07:56:41 +0000 3118 execution.bulk INFO Finished 1 / 3 lines.\n2024-01-12 07:56:41 +0000 3118 execution.bulk INFO Process name: ForkProcess-38:4, Process id: 3165, Line number: 2 completed.\n2024-01-12 07:56:41 +0000 3118 execution.bulk INFO Process name: ForkProcess-38:3, Process id: 3170, Line number: 1 completed.\n2024-01-12 07:56:41 +0000 3118 execution.bulk INFO Average execution time for completed lines: 0.26 seconds. Estimated time for incomplete lines: 0.52 seconds.\n2024-01-12 07:56:41 +0000 3118 execution.bulk INFO Finished 3 / 3 lines.\n2024-01-12 07:56:41 +0000 3118 execution.bulk INFO Finished 3 / 3 lines.\n2024-01-12 07:56:41 +0000 3118 execution.bulk INFO Average execution time for completed lines: 0.1 seconds. Estimated time for incomplete lines: 0.0 seconds.\n2024-01-12 07:56:41 +0000 3118 execution.bulk INFO Average execution time for completed lines: 0.1 seconds. Estimated time for incomplete lines: 0.0 seconds.\n2024-01-12 07:56:43 +0000 3118 execution.bulk INFO Upload status summary metrics for run batch_run_name finished in 1.1319385208189487 seconds\n2024-01-12 07:56:44 +0000 3118 promptflow-runtime INFO Successfully write run properties {\"azureml.promptflow.total_tokens\": 0, \"_azureml.evaluate_artifacts\": \"[{\\\"path\\\": \\\"instance_results.jsonl\\\", \\\"type\\\": \\\"table\\\"}]\"} with run id ''batch_run_name''\n2024-01-12 07:56:44 +0000 3118 execution.bulk INFO Upload RH properties for run batch_run_name finished in 0.09029437880963087 seconds\n2024-01-12 07:56:44 +0000 3118 promptflow-runtime INFO Creating unregistered output Asset for Run batch_run_name...\n2024-01-12 07:56:44 +0000 3118 promptflow-runtime INFO Created debug_info Asset: azureml://locations/eastus/workspaces/00000/data/azureml_batch_run_name_output_data_debug_info/versions/1\n2024-01-12 07:56:44 +0000 3118 promptflow-runtime INFO Creating unregistered output Asset for Run batch_run_name...\n2024-01-12 07:56:44 +0000 3118 promptflow-runtime INFO Created flow_outputs output Asset: azureml://locations/eastus/workspaces/00000/data/azureml_batch_run_name_output_data_flow_outputs/versions/1\n2024-01-12 07:56:44 +0000 3118 promptflow-runtime INFO Creating Artifact for Run batch_run_name...\n2024-01-12 07:56:44 +0000 3118 promptflow-runtime INFO Created instance_results.jsonl Artifact.\n2024-01-12 07:56:44 +0000 3118 promptflow-runtime INFO Patching batch_run_name...\n2024-01-12 07:56:44 +0000 3118 promptflow-runtime INFO Ending the aml run ''batch_run_name'' with status ''Completed''...\n2024-01-12 07:56:46 +0000 49 promptflow-runtime INFO Process 3118 finished\n2024-01-12 07:56:46 +0000 49 promptflow-runtime INFO [49] Child process finished!\n2024-01-12 07:56:46 +0000 49 promptflow-runtime INFO [batch_run_name] End processing bulk run\n2024-01-12 07:56:46 +0000 49 promptflow-runtime INFO Cleanup working dir /mnt/host/service/app/39649/requests/batch_run_name for bulk run\n"' headers: connection: - keep-alive content-length: - '9814' content-type: - application/json; charset=utf-8 strict-transport-security: - max-age=15724800; includeSubDomains; preload transfer-encoding: - chunked vary: - Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.748' status: code: 200 message: OK - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Type: - application/json User-Agent: - promptflow-sdk/0.0.1 azsdk-python-azuremachinelearningdesignerserviceclient/unknown Python/3.10.13 (Windows-10-10.0.22631-SP0) method: GET uri: https://eastus.api.azureml.ms/flow/api/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/BulkRuns/eval_run_name/logContent response: body: string: '"2024-01-12 07:57:22 +0000 49 promptflow-runtime INFO [eval_run_name] Receiving v2 bulk run request c27f8266-9fb7-41e8-befd-56ba05167594: {\"flow_id\": \"eval_run_name\", \"flow_run_id\": \"eval_run_name\", \"flow_source\": {\"flow_source_type\": 1, \"flow_source_info\": {\"snapshot_id\": \"b37a1c40-7c6c-4246-a735-5a9a3881116b\"}, \"flow_dag_file\": \"flow.dag.yaml\"}, \"log_path\": \"https://promptfloweast4063704120.blob.core.windows.net/azureml/ExperimentRun/dcid.eval_run_name/logs/azureml/executionlogs.txt?sv=2019-07-07&sr=b&sig=**data_scrubbed**&skoid=55b92eba-d7c7-4afd-ab76-7bb1cd345283&sktid=00000000-0000-0000-0000-000000000000&skt=2024-01-12T07%3A46%3A24Z&ske=2024-01-13T15%3A56%3A24Z&sks=b&skv=2019-07-07&st=2024-01-12T07%3A47%3A21Z&se=2024-01-12T15%3A57%3A21Z&sp=rcw\", \"app_insights_instrumentation_key\": \"InstrumentationKey=**data_scrubbed**;IngestionEndpoint=https://eastus-6.in.applicationinsights.azure.com/;LiveEndpoint=https://eastus.livediagnostics.monitor.azure.com/\", \"data_inputs\": {\"data\": \"azureml://datastores/workspaceblobstore/paths/LocalUpload/74c11bba717480b2d6b04b8e746d09d7/webClassification3.jsonl\", \"run.outputs\": \"azureml:/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/data/azureml_batch_run_name_output_data_flow_outputs/versions/1\"}, \"inputs_mapping\": {\"groundtruth\": \"${data.answer}\", \"prediction\": \"${run.outputs.result}\"}, \"azure_storage_setting\": {\"azure_storage_mode\": 1, \"storage_account_name\": \"promptfloweast4063704120\", \"blob_container_name\": \"azureml-blobstore-3e123da1-f9a5-4c91-9234-8d9ffbb39ff5\", \"flow_artifacts_root_path\": \"promptflow/PromptFlowArtifacts/eval_run_name\", \"blob_container_sas_token\": \"?sv=2019-07-07&sr=c&sig=**data_scrubbed**&skoid=55b92eba-d7c7-4afd-ab76-7bb1cd345283&sktid=00000000-0000-0000-0000-000000000000&skt=2024-01-12T07%3A57%3A22Z&ske=2024-01-19T07%3A57%3A21Z&sks=b&skv=2019-07-07&se=2024-01-19T07%3A57%3A21Z&sp=racwl\", \"output_datastore_name\": \"workspaceblobstore\"}}\n2024-01-12 07:57:22 +0000 49 promptflow-runtime INFO Runtime version: 20231204.v4. PromptFlow version: 1.2.0rc1\n2024-01-12 07:57:22 +0000 49 promptflow-runtime INFO Updating eval_run_name to Status.Preparing...\n2024-01-12 07:57:22 +0000 49 promptflow-runtime INFO Downloading snapshot to /mnt/host/service/app/39649/requests/eval_run_name\n2024-01-12 07:57:22 +0000 49 promptflow-runtime INFO Get snapshot sas url for b37a1c40-7c6c-4246-a735-5a9a3881116b...\n2024-01-12 07:57:29 +0000 49 promptflow-runtime INFO Downloading snapshot b37a1c40-7c6c-4246-a735-5a9a3881116b from uri https://promptfloweast4063704120.blob.core.windows.net/snapshotzips/promptflow-eastus:3e123da1-f9a5-4c91-9234-8d9ffbb39ff5:snapshotzip/b37a1c40-7c6c-4246-a735-5a9a3881116b.zip...\n2024-01-12 07:57:29 +0000 49 promptflow-runtime INFO Downloaded file /mnt/host/service/app/39649/requests/eval_run_name/b37a1c40-7c6c-4246-a735-5a9a3881116b.zip with size 1243 for snapshot b37a1c40-7c6c-4246-a735-5a9a3881116b.\n2024-01-12 07:57:29 +0000 49 promptflow-runtime INFO Download snapshot b37a1c40-7c6c-4246-a735-5a9a3881116b completed.\n2024-01-12 07:57:29 +0000 49 promptflow-runtime INFO Successfully download snapshot to /mnt/host/service/app/39649/requests/eval_run_name\n2024-01-12 07:57:29 +0000 49 promptflow-runtime INFO About to execute a python flow.\n2024-01-12 07:57:29 +0000 49 promptflow-runtime INFO Use spawn method to start child process.\n2024-01-12 07:57:29 +0000 49 promptflow-runtime INFO Starting to check process 3221 status for run eval_run_name\n2024-01-12 07:57:29 +0000 49 promptflow-runtime INFO Start checking run status for run eval_run_name\n2024-01-12 07:57:33 +0000 3221 promptflow-runtime INFO [49--3221] Start processing flowV2......\n2024-01-12 07:57:33 +0000 3221 promptflow-runtime INFO Runtime version: 20231204.v4. PromptFlow version: 1.2.0rc1\n2024-01-12 07:57:33 +0000 3221 promptflow-runtime INFO Setting mlflow tracking uri...\n2024-01-12 07:57:33 +0000 3221 promptflow-runtime INFO Validating ''AzureML Data Scientist'' user authentication...\n2024-01-12 07:57:33 +0000 3221 promptflow-runtime INFO Successfully validated ''AzureML Data Scientist'' user authentication.\n2024-01-12 07:57:33 +0000 3221 promptflow-runtime INFO Using AzureMLRunStorageV2\n2024-01-12 07:57:33 +0000 3221 promptflow-runtime INFO Setting mlflow tracking uri to ''azureml://eastus.api.azureml.ms/mlflow/v1.0/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/promptflow-eastus''\n2024-01-12 07:57:33 +0000 3221 promptflow-runtime INFO Initialized blob service client for AzureMLRunTracker.\n2024-01-12 07:57:33 +0000 3221 promptflow-runtime INFO Setting mlflow tracking uri to ''azureml://eastus.api.azureml.ms/mlflow/v1.0/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/promptflow-eastus''\n2024-01-12 07:57:34 +0000 3221 promptflow-runtime INFO Resolve data from url finished in 0.6532626627013087 seconds\n2024-01-12 07:57:35 +0000 3221 promptflow-runtime INFO Resolve data from url finished in 0.6382788131013513 seconds\n2024-01-12 07:57:35 +0000 3221 promptflow-runtime INFO Starting the aml run ''eval_run_name''...\n2024-01-12 07:57:35 +0000 3221 execution.bulk INFO Using fork, process count: 3\n2024-01-12 07:57:35 +0000 3264 execution.bulk INFO Process 3264 started.\n2024-01-12 07:57:35 +0000 3268 execution.bulk INFO Process 3268 started.\n2024-01-12 07:57:35 +0000 3221 execution.bulk INFO Process name: ForkProcess-40:2, Process id: 3264, Line number: 0 start execution.\n2024-01-12 07:57:35 +0000 3273 execution.bulk INFO Process 3273 started.\n2024-01-12 07:57:35 +0000 3221 execution.bulk INFO Process name: ForkProcess-40:3, Process id: 3268, Line number: 1 start execution.\n2024-01-12 07:57:35 +0000 3221 execution.bulk INFO Process name: ForkProcess-40:4, Process id: 3273, Line number: 2 start execution.\n2024-01-12 07:57:35 +0000 3221 execution.bulk INFO Process name: ForkProcess-40:2, Process id: 3264, Line number: 0 completed.\n2024-01-12 07:57:35 +0000 3221 execution.bulk INFO Finished 1 / 3 lines.\n2024-01-12 07:57:35 +0000 3221 execution.bulk INFO Average execution time for completed lines: 0.21 seconds. Estimated time for incomplete lines: 0.42 seconds.\n2024-01-12 07:57:35 +0000 3221 execution.bulk INFO Process name: ForkProcess-40:3, Process id: 3268, Line number: 1 completed.\n2024-01-12 07:57:35 +0000 3221 execution.bulk INFO Process name: ForkProcess-40:4, Process id: 3273, Line number: 2 completed.\n2024-01-12 07:57:35 +0000 3221 execution.bulk INFO Finished 3 / 3 lines.\n2024-01-12 07:57:35 +0000 3221 execution.bulk INFO Finished 3 / 3 lines.\n2024-01-12 07:57:36 +0000 3221 execution.bulk INFO Average execution time for completed lines: 0.09 seconds. Estimated time for incomplete lines: 0.0 seconds.\n2024-01-12 07:57:36 +0000 3221 execution.bulk INFO Average execution time for completed lines: 0.09 seconds. Estimated time for incomplete lines: 0.0 seconds.\n2024-01-12 07:57:37 +0000 3221 execution.bulk INFO Executing aggregation nodes...\n2024-01-12 07:57:37 +0000 3221 execution.bulk INFO Finish executing aggregation nodes.\n2024-01-12 07:57:38 +0000 3221 execution.bulk INFO Upload status summary metrics for run eval_run_name finished in 1.5616551944985986 seconds\n2024-01-12 07:57:39 +0000 3221 execution.bulk INFO Upload metrics for run eval_run_name finished in 0.36921436339616776 seconds\n2024-01-12 07:57:39 +0000 3221 promptflow-runtime INFO Successfully write run properties {\"azureml.promptflow.total_tokens\": 0, \"_azureml.evaluate_artifacts\": \"[{\\\"path\\\": \\\"instance_results.jsonl\\\", \\\"type\\\": \\\"table\\\"}]\"} with run id ''eval_run_name''\n2024-01-12 07:57:39 +0000 3221 execution.bulk INFO Upload RH properties for run eval_run_name finished in 0.07229613605886698 seconds\n2024-01-12 07:57:39 +0000 3221 promptflow-runtime INFO Creating unregistered output Asset for Run eval_run_name...\n2024-01-12 07:57:39 +0000 3221 promptflow-runtime INFO Created debug_info Asset: azureml://locations/eastus/workspaces/00000/data/azureml_eval_run_name_output_data_debug_info/versions/1\n2024-01-12 07:57:39 +0000 3221 promptflow-runtime INFO Creating unregistered output Asset for Run eval_run_name...\n2024-01-12 07:57:39 +0000 3221 promptflow-runtime INFO Created flow_outputs output Asset: azureml://locations/eastus/workspaces/00000/data/azureml_eval_run_name_output_data_flow_outputs/versions/1\n2024-01-12 07:57:39 +0000 3221 promptflow-runtime INFO Creating Artifact for Run eval_run_name...\n2024-01-12 07:57:39 +0000 3221 promptflow-runtime INFO Created instance_results.jsonl Artifact.\n2024-01-12 07:57:39 +0000 3221 promptflow-runtime INFO Patching eval_run_name...\n2024-01-12 07:57:40 +0000 3221 promptflow-runtime INFO Ending the aml run ''eval_run_name'' with status ''Completed''...\n2024-01-12 07:57:41 +0000 49 promptflow-runtime INFO Process 3221 finished\n2024-01-12 07:57:41 +0000 49 promptflow-runtime INFO [49] Child process finished!\n2024-01-12 07:57:41 +0000 49 promptflow-runtime INFO [eval_run_name] End processing bulk run\n2024-01-12 07:57:41 +0000 49 promptflow-runtime INFO Cleanup working dir /mnt/host/service/app/39649/requests/eval_run_name for bulk run\n"' headers: connection: - keep-alive content-length: - '10617' content-type: - application/json; charset=utf-8 strict-transport-security: - max-age=15724800; includeSubDomains; preload transfer-encoding: - chunked vary: - Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.452' status: code: 200 message: OK version: 1

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/recordings/test_run_operations_TestFlowRun_test_update_run.yaml

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"flow_outputs": {"assetId": "azureml://locations/eastus/workspaces/00000/data/azureml_batch_run_name_output_data_flow_outputs/versions/1", "type": "UriFolder"}}}, "runDefinition": null, "jobSpecification": null, "systemSettings": null}' headers: connection: - keep-alive content-length: - '4773' content-type: - application/json; charset=utf-8 strict-transport-security: - max-age=15724800; includeSubDomains; preload transfer-encoding: - chunked vary: - Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.045' status: code: 200 message: OK - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Type: - application/json User-Agent: - promptflow-sdk/0.0.1 azsdk-python-azuremachinelearningdesignerserviceclient/unknown Python/3.10.13 (Windows-10-10.0.22631-SP0) method: GET uri: https://eastus.api.azureml.ms/flow/api/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/BulkRuns/batch_run_name/logContent response: body: string: '"2024-01-12 08:08:37 +0000 78 promptflow-runtime INFO [batch_run_name] Receiving v2 bulk run request e934a20c-24c4-4d15-a844-2f2cb1cba4db: {\"flow_id\": \"batch_run_name\", \"flow_run_id\": \"batch_run_name\", \"flow_source\": {\"flow_source_type\": 1, \"flow_source_info\": {\"snapshot_id\": \"477088e3-9e08-439c-a5ec-266ca0d49abc\"}, \"flow_dag_file\": \"flow.dag.yaml\"}, \"log_path\": 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PromptFlow version: 1.2.0rc1\n2024-01-12 08:08:38 +0000 78 promptflow-runtime INFO Updating batch_run_name to Status.Preparing...\n2024-01-12 08:08:38 +0000 78 promptflow-runtime INFO Downloading snapshot to /mnt/host/service/app/39415/requests/batch_run_name\n2024-01-12 08:08:38 +0000 78 promptflow-runtime INFO Get snapshot sas url for 477088e3-9e08-439c-a5ec-266ca0d49abc...\n2024-01-12 08:08:45 +0000 78 promptflow-runtime INFO Downloading snapshot 477088e3-9e08-439c-a5ec-266ca0d49abc from uri https://promptfloweast4063704120.blob.core.windows.net/snapshotzips/promptflow-eastus:3e123da1-f9a5-4c91-9234-8d9ffbb39ff5:snapshotzip/477088e3-9e08-439c-a5ec-266ca0d49abc.zip...\n2024-01-12 08:08:45 +0000 78 promptflow-runtime INFO Downloaded file /mnt/host/service/app/39415/requests/batch_run_name/477088e3-9e08-439c-a5ec-266ca0d49abc.zip with size 495 for snapshot 477088e3-9e08-439c-a5ec-266ca0d49abc.\n2024-01-12 08:08:45 +0000 78 promptflow-runtime INFO Download snapshot 477088e3-9e08-439c-a5ec-266ca0d49abc completed.\n2024-01-12 08:08:45 +0000 78 promptflow-runtime INFO Successfully download snapshot to /mnt/host/service/app/39415/requests/batch_run_name\n2024-01-12 08:08:45 +0000 78 promptflow-runtime INFO About to execute a python flow.\n2024-01-12 08:08:45 +0000 78 promptflow-runtime INFO Use spawn method to start child process.\n2024-01-12 08:08:45 +0000 78 promptflow-runtime INFO Starting to check process 3535 status for run batch_run_name\n2024-01-12 08:08:45 +0000 78 promptflow-runtime INFO Start checking run status for run batch_run_name\n2024-01-12 08:08:49 +0000 3535 promptflow-runtime INFO [78--3535] Start processing flowV2......\n2024-01-12 08:08:49 +0000 3535 promptflow-runtime INFO Runtime version: 20231204.v4. PromptFlow version: 1.2.0rc1\n2024-01-12 08:08:49 +0000 3535 promptflow-runtime INFO Setting mlflow tracking uri...\n2024-01-12 08:08:49 +0000 3535 promptflow-runtime INFO Validating ''AzureML Data Scientist'' user authentication...\n2024-01-12 08:08:49 +0000 3535 promptflow-runtime INFO Successfully validated ''AzureML Data Scientist'' user authentication.\n2024-01-12 08:08:49 +0000 3535 promptflow-runtime INFO Using AzureMLRunStorageV2\n2024-01-12 08:08:49 +0000 3535 promptflow-runtime INFO Setting mlflow tracking uri to ''azureml://eastus.api.azureml.ms/mlflow/v1.0/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/promptflow-eastus''\n2024-01-12 08:08:49 +0000 3535 promptflow-runtime INFO Initialized blob service client for AzureMLRunTracker.\n2024-01-12 08:08:50 +0000 3535 promptflow-runtime INFO Setting mlflow tracking uri to ''azureml://eastus.api.azureml.ms/mlflow/v1.0/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/promptflow-eastus''\n2024-01-12 08:08:50 +0000 3535 promptflow-runtime INFO Resolve data from url finished in 0.4670193735510111 seconds\n2024-01-12 08:08:50 +0000 3535 promptflow-runtime INFO Starting the aml run ''batch_run_name''...\n2024-01-12 08:08:51 +0000 3535 execution.bulk INFO Using fork, process count: 3\n2024-01-12 08:08:51 +0000 3582 execution.bulk INFO Process 3582 started.\n2024-01-12 08:08:51 +0000 3587 execution.bulk INFO Process 3587 started.\n2024-01-12 08:08:51 +0000 3535 execution.bulk INFO Process name: ForkProcess-44:3, Process id: 3582, Line number: 0 start execution.\n2024-01-12 08:08:51 +0000 3535 execution.bulk INFO Process name: ForkProcess-44:4, Process id: 3587, Line number: 1 start execution.\n2024-01-12 08:08:51 +0000 3578 execution.bulk INFO Process 3578 started.\n2024-01-12 08:08:51 +0000 3535 execution.bulk INFO Process name: ForkProcess-44:3, Process id: 3582, Line number: 0 completed.\n2024-01-12 08:08:51 +0000 3535 execution.bulk INFO Finished 1 / 3 lines.\n2024-01-12 08:08:51 +0000 3535 execution.bulk INFO Process name: ForkProcess-44:2, Process id: 3578, Line number: 2 start execution.\n2024-01-12 08:08:51 +0000 3535 execution.bulk INFO Average execution time for completed lines: 0.21 seconds. Estimated time for incomplete lines: 0.42 seconds.\n2024-01-12 08:08:51 +0000 3535 execution.bulk INFO Process name: ForkProcess-44:4, Process id: 3587, Line number: 1 completed.\n2024-01-12 08:08:51 +0000 3535 execution.bulk INFO Finished 2 / 3 lines.\n2024-01-12 08:08:51 +0000 3535 execution.bulk INFO Average execution time for completed lines: 0.14 seconds. Estimated time for incomplete lines: 0.14 seconds.\n2024-01-12 08:08:51 +0000 3535 execution.bulk INFO Process name: ForkProcess-44:2, Process id: 3578, Line number: 2 completed.\n2024-01-12 08:08:51 +0000 3535 execution.bulk INFO Finished 3 / 3 lines.\n2024-01-12 08:08:51 +0000 3535 execution.bulk INFO Average execution time for completed lines: 0.11 seconds. 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0

promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/recordings/test_run_operations_TestFlowRun_test_show_run_details.yaml

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PromptFlow version: 1.2.0rc1\n2024-01-12 07:54:42 +0000 3015 promptflow-runtime INFO Setting mlflow tracking uri...\n2024-01-12 07:54:42 +0000 3015 promptflow-runtime INFO Validating ''AzureML Data Scientist'' user authentication...\n2024-01-12 07:54:43 +0000 3015 promptflow-runtime INFO Successfully validated ''AzureML Data Scientist'' user authentication.\n2024-01-12 07:54:43 +0000 3015 promptflow-runtime INFO Using AzureMLRunStorageV2\n2024-01-12 07:54:43 +0000 3015 promptflow-runtime INFO Setting mlflow tracking uri to ''azureml://eastus.api.azureml.ms/mlflow/v1.0/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/promptflow-eastus''\n2024-01-12 07:54:43 +0000 3015 promptflow-runtime INFO Initialized blob service client for AzureMLRunTracker.\n2024-01-12 07:54:43 +0000 3015 promptflow-runtime INFO Setting mlflow tracking uri to ''azureml://eastus.api.azureml.ms/mlflow/v1.0/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/promptflow-eastus''\n2024-01-12 07:54:43 +0000 3015 promptflow-runtime INFO Resolve data from url finished in 0.5407421309500933 seconds\n2024-01-12 07:54:43 +0000 3015 promptflow-runtime INFO Starting the aml run ''batch_run_name''...\n2024-01-12 07:54:44 +0000 3015 execution.bulk INFO Using fork, process count: 3\n2024-01-12 07:54:44 +0000 3062 execution.bulk INFO Process 3062 started.\n2024-01-12 07:54:44 +0000 3056 execution.bulk INFO Process 3056 started.\n2024-01-12 07:54:44 +0000 3015 execution.bulk INFO Process name: ForkProcess-36:3, Process id: 3062, Line number: 0 start execution.\n2024-01-12 07:54:44 +0000 3015 execution.bulk INFO Process name: ForkProcess-36:2, Process id: 3056, Line number: 1 start execution.\n2024-01-12 07:54:44 +0000 3015 execution.bulk INFO Process name: ForkProcess-36:3, Process id: 3062, Line number: 0 completed.\n2024-01-12 07:54:44 +0000 3067 execution.bulk INFO Process 3067 started.\n2024-01-12 07:54:44 +0000 3015 execution.bulk INFO Finished 1 / 3 lines.\n2024-01-12 07:54:44 +0000 3015 execution.bulk INFO Process name: ForkProcess-36:4, Process id: 3067, Line number: 2 start execution.\n2024-01-12 07:54:44 +0000 3015 execution.bulk INFO Average execution time for completed lines: 0.22 seconds. Estimated time for incomplete lines: 0.44 seconds.\n2024-01-12 07:54:44 +0000 3015 execution.bulk INFO Process name: ForkProcess-36:2, Process id: 3056, Line number: 1 completed.\n2024-01-12 07:54:44 +0000 3015 execution.bulk INFO Finished 2 / 3 lines.\n2024-01-12 07:54:44 +0000 3015 execution.bulk INFO Average execution time for completed lines: 0.14 seconds. Estimated time for incomplete lines: 0.14 seconds.\n2024-01-12 07:54:44 +0000 3015 execution.bulk INFO Process name: ForkProcess-36:4, Process id: 3067, Line number: 2 completed.\n2024-01-12 07:54:44 +0000 3015 execution.bulk INFO Finished 3 / 3 lines.\n2024-01-12 07:54:44 +0000 3015 execution.bulk INFO Average execution time for completed lines: 0.12 seconds. 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0

promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/recordings/test_run_operations_TestFlowRun_test_basic_evaluation_without_data.yaml

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(Windows-10-10.0.22631-SP0) method: POST uri: https://management.azure.com/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/datastores/workspaceblobstore/listSecrets response: body: string: '{"secretsType": "AccountKey", "key": "dGhpcyBpcyBmYWtlIGtleQ=="}' headers: cache-control: - no-cache content-length: - '134' content-type: - application/json; charset=utf-8 expires: - '-1' pragma: - no-cache strict-transport-security: - max-age=31536000; includeSubDomains transfer-encoding: - chunked vary: - Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.110' status: code: 200 message: OK - request: body: null headers: Accept: - application/xml Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - azsdk-python-storage-blob/12.19.0 Python/3.10.13 (Windows-10-10.0.22631-SP0) x-ms-date: - Fri, 12 Jan 2024 08:18:44 GMT x-ms-version: - '2023-11-03' method: HEAD uri: 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https://fake_account_name.blob.core.windows.net/fake-container-name/az-ml-artifacts/000000000000000000000000000000000000/web_classification/classify_with_llm.jinja2 response: body: string: '' headers: server: - Windows-Azure-Blob/1.0 Microsoft-HTTPAPI/2.0 transfer-encoding: - chunked vary: - Origin x-ms-error-code: - BlobNotFound x-ms-version: - '2023-11-03' status: code: 404 message: The specified blob does not exist. - request: body: '{"flowDefinitionDataStoreName": "workspaceblobstore", "flowDefinitionBlobPath": "LocalUpload/000000000000000000000000000000000000/web_classification/flow.dag.yaml", "runId": "batch_run_name", "runDisplayName": "batch_run_name", "runExperimentName": "", "nodeVariant": "${summarize_text_content.variant_0}", "batchDataInput": {"dataUri": "azureml://datastores/workspaceblobstore/paths/LocalUpload/000000000000000000000000000000000000/webClassification3.jsonl"}, "inputsMapping": {"url": "${data.url}"}, "connections": {}, "environmentVariables": {}, 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string: '{"flowGraph": {"nodes": [{"name": "fetch_text_content_from_url", "type": "python", "source": {"type": "code", "path": "fetch_text_content_from_url.py"}, "inputs": {"fetch_url": "${inputs.url}"}, "tool": "fetch_text_content_from_url.py", "reduce": false}, {"name": "prepare_examples", "type": "python", "source": {"type": "code", "path": "prepare_examples.py"}, "inputs": {}, "tool": "prepare_examples.py", "reduce": false}, {"name": "classify_with_llm", "type": "llm", "source": {"type": "code", "path": "classify_with_llm.jinja2"}, "inputs": {"deployment_name": "gpt-35-turbo", "suffix": "", "max_tokens": "128", "temperature": "0.1", "top_p": "1.0", "logprobs": "", "echo": "False", "stop": "", "presence_penalty": "0", "frequency_penalty": "0", "best_of": "1", "logit_bias": "", "url": "${inputs.url}", "examples": "${prepare_examples.output}", "text_content": "${summarize_text_content.output}"}, "tool": "classify_with_llm.jinja2", "reduce": false, "api": "chat", "provider": 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"inputs": {"path": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_k": {"type": ["int"], "default": "3", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "vector": {"type": ["list"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Search vector based query from the FAISS index file.", "module": "promptflow_vectordb.tool.faiss_index_lookup", "class_name": "FaissIndexLookup", "function": "search", "is_builtin": true, "package": "promptflow-vectordb", "package_version": "0.0.1", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Vector DB Lookup", "type": "python", "inputs": {"class_name": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["WeaviateConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "collection_name": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["QdrantConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "connection": {"type": ["CognitiveSearchConnection", "QdrantConnection", "WeaviateConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "index_name": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "search_filters": {"type": ["object"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection", "QdrantConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "search_params": {"type": ["object"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection", "QdrantConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "text_field": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection", "QdrantConnection", "WeaviateConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_k": {"type": ["int"], "default": "3", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "vector": {"type": ["list"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "vector_field": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Search vector based query from existing Vector Database.", "module": "promptflow_vectordb.tool.vector_db_lookup", "class_name": "VectorDBLookup", "function": "search", "is_builtin": true, "package": "promptflow-vectordb", "package_version": "0.0.1", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Vector Index Lookup", "type": "python", "inputs": {"path": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "query": {"type": ["object"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_k": {"type": ["int"], "default": "3", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Search text or vector based query from AzureML Vector Index.", "module": "promptflow_vectordb.tool.vector_index_lookup", "class_name": "VectorIndexLookup", "function": "search", "is_builtin": true, "package": "promptflow-vectordb", "package_version": "0.0.1", "enable_kwargs": false, "tool_state": "stable"}, {"name": "calculate_accuracy.py", "type": "python", "inputs": {"grades": {"type": ["object"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "source": "calculate_accuracy.py", "function": "calculate_accuracy", "is_builtin": false, "enable_kwargs": false, "tool_state": "stable"}, {"name": "grade.py", "type": "python", "inputs": {"groundtruth": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "prediction": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "source": "grade.py", "function": "grade", "is_builtin": false, "enable_kwargs": false, "tool_state": "stable"}], "inputs": {"groundtruth": {"type": "string", "default": "APP", "description": "Please specify the groundtruth column, which contains the true label to the outputs that your flow produces.", "is_chat_input": false}, "prediction": {"type": "string", "default": "APP", "description": "Please specify the prediction column, which contains the predicted outputs that your flow produces.", "is_chat_input": false}}, "outputs": {"grade": {"type": "string", "reference": "${grade.output}", "evaluation_only": false, "is_chat_output": false}}}, "flowRunResourceId": "azureml://locations/eastus/workspaces/00000/flows/eval_run_name/flowRuns/eval_run_name", "flowRunId": "eval_run_name", "flowRunDisplayName": "eval_run_name", "batchDataInput": {}, "flowRunType": "FlowRun", "flowType": "Default", "runtimeName": "test-runtime-ci", "inputsMapping": {"groundtruth": "${run.inputs.url}", "prediction": "${run.outputs.category}"}, "outputDatastoreName": "workspaceblobstore", "childRunBasePath": "promptflow/PromptFlowArtifacts/eval_run_name/flow_artifacts", "flowDagFileRelativePath": "flow.dag.yaml", "flowSnapshotId": "07c78456-f714-4df6-9398-0dc36e95ed2c", "studioPortalEndpoint": "https://ml.azure.com/runs/eval_run_name?wsid=/subscriptions/00000000-0000-0000-0000-000000000000/resourcegroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000"}' headers: connection: - keep-alive content-length: - '13745' content-type: - application/json; charset=utf-8 strict-transport-security: - max-age=15724800; includeSubDomains; preload transfer-encoding: - chunked vary: - Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.237' status: code: 200 message: OK - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - promptflow-sdk/0.0.1 azsdk-python-azuremachinelearningdesignerserviceclient/unknown Python/3.10.13 (Windows-10-10.0.22631-SP0) method: GET uri: https://eastus.api.azureml.ms/flow/api/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/BulkRuns/eval_run_name response: body: string: '{"flowGraph": {"nodes": [{"name": "grade", "type": "python", "source": {"type": "code", "path": "grade.py"}, "inputs": {"groundtruth": "${inputs.groundtruth}", "prediction": "${inputs.prediction}"}, "tool": "grade.py", "reduce": false}, {"name": "calculate_accuracy", "type": "python", "source": {"type": "code", "path": "calculate_accuracy.py"}, "inputs": {"grades": "${grade.output}"}, "tool": "calculate_accuracy.py", "reduce": true}], "tools": [{"name": "Content Safety (Text Analyze)", "type": "python", "inputs": {"connection": {"type": ["AzureContentSafetyConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "hate_category": {"type": ["string"], "default": "medium_sensitivity", "enum": ["disable", "low_sensitivity", "medium_sensitivity", "high_sensitivity"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "self_harm_category": {"type": ["string"], "default": "medium_sensitivity", "enum": ["disable", "low_sensitivity", "medium_sensitivity", "high_sensitivity"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "sexual_category": {"type": ["string"], "default": "medium_sensitivity", "enum": ["disable", "low_sensitivity", "medium_sensitivity", "high_sensitivity"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "text": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "violence_category": {"type": ["string"], "default": "medium_sensitivity", "enum": ["disable", "low_sensitivity", "medium_sensitivity", "high_sensitivity"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Use Azure Content Safety to detect harmful content.", "module": "promptflow.tools.azure_content_safety", "function": "analyze_text", "is_builtin": true, "package": "promptflow-tools", "package_version": "0.0.216", "enable_kwargs": false, "deprecated_tools": ["content_safety_text.tools.content_safety_text_tool.analyze_text"], "tool_state": "stable"}, {"name": "Embedding", "type": "python", "inputs": {"connection": {"type": ["AzureOpenAIConnection", "OpenAIConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "deployment_name": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["AzureOpenAIConnection"], "model_list": ["text-embedding-ada-002", "text-search-ada-doc-001", "text-search-ada-query-001"], "capabilities": {"completion": false, "chat_completion": false, "embeddings": true}, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "input": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "model": {"type": ["string"], "enum": ["text-embedding-ada-002", "text-search-ada-doc-001", "text-search-ada-query-001"], "enabled_by": "connection", "enabled_by_type": ["OpenAIConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Use Open AI''s embedding model to create an embedding vector representing the input text.", "module": "promptflow.tools.embedding", "function": "embedding", "is_builtin": true, "package": "promptflow-tools", "package_version": "0.0.216", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Open Source LLM", "type": "custom_llm", "inputs": {"api": {"type": ["string"], "enum": ["chat", "completion"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "connection": {"type": ["CustomConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "deployment_name": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "endpoint_name": {"type": ["string"], "default": "-- please enter an endpoint name --", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "max_new_tokens": {"type": ["int"], "default": 500, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "model_kwargs": {"type": ["object"], "default": "{}", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default", "advanced": true}, "temperature": {"type": ["double"], "default": 1.0, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_p": {"type": ["double"], "default": 1.0, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default", "advanced": true}}, "description": "Use an Open Source model from the Azure Model catalog, deployed to an AzureML Online Endpoint for LLM Chat or Completion API calls.", "module": "promptflow.tools.open_source_llm", "class_name": "OpenSourceLLM", "function": "call", "icon": "data:image/png;base64,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", "is_builtin": true, "package": "promptflow-tools", "package_version": "0.0.216", "enable_kwargs": false, "tool_state": "stable"}, {"name": "OpenAI GPT-4V", "type": "custom_llm", "inputs": {"connection": {"type": ["OpenAIConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "frequency_penalty": {"type": ["double"], "default": 0, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "max_tokens": {"type": ["int"], "default": "", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "model": {"type": ["string"], "enum": ["gpt-4-vision-preview"], "allow_manual_entry": true, "is_multi_select": false, "input_type": "default"}, "presence_penalty": {"type": ["double"], "default": 0, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "stop": {"type": ["list"], "default": "", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "temperature": {"type": ["double"], "default": 1, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_p": {"type": ["double"], "default": 1, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Use OpenAI GPT-4V to leverage vision ability.", "module": "promptflow.tools.openai_gpt4v", "class_name": "OpenAI", "function": "chat", "is_builtin": true, "package": "promptflow-tools", "package_version": "0.0.216", "default_prompt": "# system:\nAs an AI assistant, your task involves interpreting images and responding to questions about the image.\nRemember to provide accurate answers based on the information present in the image.\n\n# user:\nCan you tell me what the image depicts?\n![image]({{image_input}})\n", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Serp API", "type": "python", "inputs": {"connection": {"type": ["SerpConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "engine": {"type": ["string"], "default": "google", "enum": ["google", "bing"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "location": {"type": ["string"], "default": "", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "num": {"type": ["int"], "default": "10", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "query": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "safe": {"type": ["string"], "default": "off", "enum": ["active", "off"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Use Serp API to obtain search results from a specific search engine.", "module": "promptflow.tools.serpapi", "class_name": "SerpAPI", "function": "search", "is_builtin": true, "package": "promptflow-tools", "package_version": "0.0.216", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Faiss Index Lookup", "type": "python", "inputs": {"path": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_k": {"type": ["int"], "default": "3", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "vector": {"type": ["list"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Search vector based query from the FAISS index file.", "module": "promptflow_vectordb.tool.faiss_index_lookup", "class_name": "FaissIndexLookup", "function": "search", "is_builtin": true, "package": "promptflow-vectordb", "package_version": "0.0.1", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Vector DB Lookup", "type": "python", "inputs": {"class_name": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["WeaviateConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "collection_name": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["QdrantConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "connection": {"type": ["CognitiveSearchConnection", "QdrantConnection", "WeaviateConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "index_name": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "search_filters": {"type": ["object"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection", "QdrantConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "search_params": {"type": ["object"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection", "QdrantConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "text_field": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection", "QdrantConnection", "WeaviateConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_k": {"type": ["int"], "default": "3", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "vector": {"type": ["list"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "vector_field": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Search vector based query from existing Vector Database.", "module": "promptflow_vectordb.tool.vector_db_lookup", "class_name": "VectorDBLookup", "function": "search", "is_builtin": true, "package": "promptflow-vectordb", "package_version": "0.0.1", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Vector Index Lookup", "type": "python", "inputs": {"path": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "query": {"type": ["object"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_k": {"type": ["int"], "default": "3", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Search text or vector based query from AzureML Vector Index.", "module": "promptflow_vectordb.tool.vector_index_lookup", "class_name": "VectorIndexLookup", "function": "search", "is_builtin": true, "package": "promptflow-vectordb", "package_version": "0.0.1", "enable_kwargs": false, "tool_state": "stable"}, {"name": "calculate_accuracy.py", "type": "python", "inputs": {"grades": {"type": ["object"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "source": "calculate_accuracy.py", "function": "calculate_accuracy", "is_builtin": false, "enable_kwargs": false, "tool_state": "stable"}, {"name": "grade.py", "type": "python", "inputs": {"groundtruth": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "prediction": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "source": "grade.py", "function": "grade", "is_builtin": false, "enable_kwargs": false, "tool_state": "stable"}], "inputs": {"groundtruth": {"type": "string", "default": "APP", "description": "Please specify the groundtruth column, which contains the true label to the outputs that your flow produces.", "is_chat_input": false}, "prediction": {"type": "string", "default": "APP", "description": "Please specify the prediction column, which contains the predicted outputs that your flow produces.", "is_chat_input": false}}, "outputs": {"grade": {"type": "string", "reference": "${grade.output}", "evaluation_only": false, "is_chat_output": false}}}, "flowRunResourceId": "azureml://locations/eastus/workspaces/00000/flows/eval_run_name/flowRuns/eval_run_name", "flowRunId": "eval_run_name", "flowRunDisplayName": "eval_run_name", "batchDataInput": {}, "flowRunType": "FlowRun", "flowType": "Default", "runtimeName": "test-runtime-ci", "inputsMapping": {"groundtruth": "${run.inputs.url}", "prediction": "${run.outputs.category}"}, "outputDatastoreName": "workspaceblobstore", "childRunBasePath": "promptflow/PromptFlowArtifacts/eval_run_name/flow_artifacts", "flowDagFileRelativePath": "flow.dag.yaml", "flowSnapshotId": "07c78456-f714-4df6-9398-0dc36e95ed2c", "studioPortalEndpoint": "https://ml.azure.com/runs/eval_run_name?wsid=/subscriptions/00000000-0000-0000-0000-000000000000/resourcegroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000"}' headers: connection: - keep-alive content-length: - '13745' content-type: - application/json; charset=utf-8 strict-transport-security: - max-age=15724800; includeSubDomains; preload transfer-encoding: - chunked vary: - Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.206' status: code: 200 message: OK - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - promptflow-sdk/0.0.1 azsdk-python-azuremachinelearningdesignerserviceclient/unknown Python/3.10.13 (Windows-10-10.0.22631-SP0) method: GET uri: https://eastus.api.azureml.ms/flow/api/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/BulkRuns/eval_run_name response: body: string: '{"flowGraph": {"nodes": [{"name": "grade", "type": "python", "source": {"type": "code", "path": "grade.py"}, "inputs": {"groundtruth": "${inputs.groundtruth}", "prediction": "${inputs.prediction}"}, "tool": "grade.py", "reduce": false}, {"name": "calculate_accuracy", "type": "python", "source": {"type": "code", "path": "calculate_accuracy.py"}, "inputs": {"grades": "${grade.output}"}, "tool": "calculate_accuracy.py", "reduce": true}], "tools": [{"name": "Content Safety (Text Analyze)", "type": "python", "inputs": {"connection": {"type": ["AzureContentSafetyConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "hate_category": {"type": ["string"], "default": "medium_sensitivity", "enum": ["disable", "low_sensitivity", "medium_sensitivity", "high_sensitivity"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "self_harm_category": {"type": ["string"], "default": "medium_sensitivity", "enum": ["disable", "low_sensitivity", "medium_sensitivity", "high_sensitivity"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "sexual_category": {"type": ["string"], "default": "medium_sensitivity", "enum": ["disable", "low_sensitivity", "medium_sensitivity", "high_sensitivity"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "text": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "violence_category": {"type": ["string"], "default": "medium_sensitivity", "enum": ["disable", "low_sensitivity", "medium_sensitivity", "high_sensitivity"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Use Azure Content Safety to detect harmful content.", "module": "promptflow.tools.azure_content_safety", "function": "analyze_text", "is_builtin": true, "package": "promptflow-tools", "package_version": "0.0.216", "enable_kwargs": false, "deprecated_tools": ["content_safety_text.tools.content_safety_text_tool.analyze_text"], "tool_state": "stable"}, {"name": "Embedding", "type": "python", "inputs": {"connection": {"type": ["AzureOpenAIConnection", "OpenAIConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "deployment_name": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["AzureOpenAIConnection"], "model_list": ["text-embedding-ada-002", "text-search-ada-doc-001", "text-search-ada-query-001"], "capabilities": {"completion": false, "chat_completion": false, "embeddings": true}, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "input": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "model": {"type": ["string"], "enum": ["text-embedding-ada-002", "text-search-ada-doc-001", "text-search-ada-query-001"], "enabled_by": "connection", "enabled_by_type": ["OpenAIConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Use Open AI''s embedding model to create an embedding vector representing the input text.", "module": "promptflow.tools.embedding", "function": "embedding", "is_builtin": true, "package": "promptflow-tools", "package_version": "0.0.216", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Open Source LLM", "type": "custom_llm", "inputs": {"api": {"type": ["string"], "enum": ["chat", "completion"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "connection": {"type": ["CustomConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "deployment_name": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "endpoint_name": {"type": ["string"], "default": "-- please enter an endpoint name --", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "max_new_tokens": {"type": ["int"], "default": 500, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "model_kwargs": {"type": ["object"], "default": "{}", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default", "advanced": true}, "temperature": {"type": ["double"], "default": 1.0, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_p": {"type": ["double"], "default": 1.0, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default", "advanced": true}}, "description": "Use an Open Source model from the Azure Model catalog, deployed to an AzureML Online Endpoint for LLM Chat or Completion API calls.", "module": "promptflow.tools.open_source_llm", "class_name": "OpenSourceLLM", "function": "call", "icon": "data:image/png;base64,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", "is_builtin": true, "package": "promptflow-tools", "package_version": "0.0.216", "enable_kwargs": false, "tool_state": "stable"}, {"name": "OpenAI GPT-4V", "type": "custom_llm", "inputs": {"connection": {"type": ["OpenAIConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "frequency_penalty": {"type": ["double"], "default": 0, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "max_tokens": {"type": ["int"], "default": "", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "model": {"type": ["string"], "enum": ["gpt-4-vision-preview"], "allow_manual_entry": true, "is_multi_select": false, "input_type": "default"}, "presence_penalty": {"type": ["double"], "default": 0, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "stop": {"type": ["list"], "default": "", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "temperature": {"type": ["double"], "default": 1, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_p": {"type": ["double"], "default": 1, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Use OpenAI GPT-4V to leverage vision ability.", "module": "promptflow.tools.openai_gpt4v", "class_name": "OpenAI", "function": "chat", "is_builtin": true, "package": "promptflow-tools", "package_version": "0.0.216", "default_prompt": "# system:\nAs an AI assistant, your task involves interpreting images and responding to questions about the image.\nRemember to provide accurate answers based on the information present in the image.\n\n# user:\nCan you tell me what the image depicts?\n![image]({{image_input}})\n", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Serp API", "type": "python", "inputs": {"connection": {"type": ["SerpConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "engine": {"type": ["string"], "default": "google", "enum": ["google", "bing"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "location": {"type": ["string"], "default": "", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "num": {"type": ["int"], "default": "10", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "query": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "safe": {"type": ["string"], "default": "off", "enum": ["active", "off"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Use Serp API to obtain search results from a specific search engine.", "module": "promptflow.tools.serpapi", "class_name": "SerpAPI", "function": "search", "is_builtin": true, "package": "promptflow-tools", "package_version": "0.0.216", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Faiss Index Lookup", "type": "python", "inputs": {"path": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_k": {"type": ["int"], "default": "3", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "vector": {"type": ["list"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Search vector based query from the FAISS index file.", "module": "promptflow_vectordb.tool.faiss_index_lookup", "class_name": "FaissIndexLookup", "function": "search", "is_builtin": true, "package": "promptflow-vectordb", "package_version": "0.0.1", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Vector DB Lookup", "type": "python", "inputs": {"class_name": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["WeaviateConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "collection_name": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["QdrantConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "connection": {"type": ["CognitiveSearchConnection", "QdrantConnection", "WeaviateConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "index_name": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "search_filters": {"type": ["object"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection", "QdrantConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "search_params": {"type": ["object"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection", "QdrantConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "text_field": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection", "QdrantConnection", "WeaviateConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_k": {"type": ["int"], "default": "3", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "vector": {"type": ["list"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "vector_field": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Search vector based query from existing Vector Database.", "module": "promptflow_vectordb.tool.vector_db_lookup", "class_name": "VectorDBLookup", "function": "search", "is_builtin": true, "package": "promptflow-vectordb", "package_version": "0.0.1", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Vector Index Lookup", "type": "python", "inputs": {"path": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "query": {"type": ["object"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_k": {"type": ["int"], "default": "3", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Search text or vector based query from AzureML Vector Index.", "module": "promptflow_vectordb.tool.vector_index_lookup", "class_name": "VectorIndexLookup", "function": "search", "is_builtin": true, "package": "promptflow-vectordb", "package_version": "0.0.1", "enable_kwargs": false, "tool_state": "stable"}, {"name": "calculate_accuracy.py", "type": "python", "inputs": {"grades": {"type": ["object"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "source": "calculate_accuracy.py", "function": "calculate_accuracy", "is_builtin": false, "enable_kwargs": false, "tool_state": "stable"}, {"name": "grade.py", "type": "python", "inputs": {"groundtruth": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "prediction": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "source": "grade.py", "function": "grade", "is_builtin": false, "enable_kwargs": false, "tool_state": "stable"}], "inputs": {"groundtruth": {"type": "string", "default": "APP", "description": "Please specify the groundtruth column, which contains the true label to the outputs that your flow produces.", "is_chat_input": false}, "prediction": {"type": "string", "default": "APP", "description": "Please specify the prediction column, which contains the predicted outputs that your flow produces.", "is_chat_input": false}}, "outputs": {"grade": {"type": "string", "reference": "${grade.output}", "evaluation_only": false, "is_chat_output": false}}}, "flowRunResourceId": "azureml://locations/eastus/workspaces/00000/flows/eval_run_name/flowRuns/eval_run_name", "flowRunId": "eval_run_name", "flowRunDisplayName": "eval_run_name", "batchDataInput": {}, "flowRunType": "FlowRun", "flowType": "Default", "runtimeName": "test-runtime-ci", "inputsMapping": {"groundtruth": "${run.inputs.url}", "prediction": "${run.outputs.category}"}, "outputDatastoreName": "workspaceblobstore", "childRunBasePath": "promptflow/PromptFlowArtifacts/eval_run_name/flow_artifacts", "flowDagFileRelativePath": "flow.dag.yaml", "flowSnapshotId": "07c78456-f714-4df6-9398-0dc36e95ed2c", "studioPortalEndpoint": "https://ml.azure.com/runs/eval_run_name?wsid=/subscriptions/00000000-0000-0000-0000-000000000000/resourcegroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000"}' headers: connection: - keep-alive content-length: - '13745' content-type: - application/json; charset=utf-8 strict-transport-security: - max-age=15724800; includeSubDomains; preload transfer-encoding: - chunked vary: - Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.333' status: code: 200 message: OK - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - promptflow-sdk/0.0.1 azsdk-python-azuremachinelearningdesignerserviceclient/unknown Python/3.10.13 (Windows-10-10.0.22631-SP0) method: GET uri: https://eastus.api.azureml.ms/flow/api/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/BulkRuns/eval_run_name response: body: string: '{"flowGraph": {"nodes": [{"name": "grade", "type": "python", "source": {"type": "code", "path": "grade.py"}, "inputs": {"groundtruth": "${inputs.groundtruth}", "prediction": "${inputs.prediction}"}, "tool": "grade.py", "reduce": false}, {"name": "calculate_accuracy", "type": "python", "source": {"type": "code", "path": "calculate_accuracy.py"}, "inputs": {"grades": "${grade.output}"}, "tool": "calculate_accuracy.py", "reduce": true}], "tools": [{"name": "Content Safety (Text Analyze)", "type": "python", "inputs": {"connection": {"type": ["AzureContentSafetyConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "hate_category": {"type": ["string"], "default": "medium_sensitivity", "enum": ["disable", "low_sensitivity", "medium_sensitivity", "high_sensitivity"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "self_harm_category": {"type": ["string"], "default": "medium_sensitivity", "enum": ["disable", "low_sensitivity", "medium_sensitivity", "high_sensitivity"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "sexual_category": {"type": ["string"], "default": "medium_sensitivity", "enum": ["disable", "low_sensitivity", "medium_sensitivity", "high_sensitivity"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "text": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "violence_category": {"type": ["string"], "default": "medium_sensitivity", "enum": ["disable", "low_sensitivity", "medium_sensitivity", "high_sensitivity"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Use Azure Content Safety to detect harmful content.", "module": "promptflow.tools.azure_content_safety", "function": "analyze_text", "is_builtin": true, "package": "promptflow-tools", "package_version": "0.0.216", "enable_kwargs": false, "deprecated_tools": ["content_safety_text.tools.content_safety_text_tool.analyze_text"], "tool_state": "stable"}, {"name": "Embedding", "type": "python", "inputs": {"connection": {"type": ["AzureOpenAIConnection", "OpenAIConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "deployment_name": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["AzureOpenAIConnection"], "model_list": ["text-embedding-ada-002", "text-search-ada-doc-001", "text-search-ada-query-001"], "capabilities": {"completion": false, "chat_completion": false, "embeddings": true}, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "input": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "model": {"type": ["string"], "enum": ["text-embedding-ada-002", "text-search-ada-doc-001", "text-search-ada-query-001"], "enabled_by": "connection", "enabled_by_type": ["OpenAIConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Use Open AI''s embedding model to create an embedding vector representing the input text.", "module": "promptflow.tools.embedding", "function": "embedding", "is_builtin": true, "package": "promptflow-tools", "package_version": "0.0.216", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Open Source LLM", "type": "custom_llm", "inputs": {"api": {"type": ["string"], "enum": ["chat", "completion"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "connection": {"type": ["CustomConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "deployment_name": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "endpoint_name": {"type": ["string"], "default": "-- please enter an endpoint name --", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "max_new_tokens": {"type": ["int"], "default": 500, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "model_kwargs": {"type": ["object"], "default": "{}", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default", "advanced": true}, "temperature": {"type": ["double"], "default": 1.0, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_p": {"type": ["double"], "default": 1.0, "allow_manual_entry": false, "is_multi_select": false, "input_type": "default", "advanced": true}}, "description": "Use an Open Source model from the Azure Model catalog, deployed to an AzureML Online Endpoint for LLM Chat or Completion API calls.", "module": "promptflow.tools.open_source_llm", "class_name": "OpenSourceLLM", "function": "call", "icon": 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PromptFlow version: 1.2.0rc1\n2024-01-12 08:18:56 +0000 134 promptflow-runtime INFO Updating batch_run_name to Status.Preparing...\n2024-01-12 08:18:56 +0000 134 promptflow-runtime INFO Downloading snapshot to /mnt/host/service/app/38343/requests/batch_run_name\n2024-01-12 08:18:56 +0000 134 promptflow-runtime INFO Get snapshot sas url for c956855e-d291-4714-a3df-91c99c974de9...\n2024-01-12 08:19:02 +0000 134 promptflow-runtime INFO Downloading snapshot c956855e-d291-4714-a3df-91c99c974de9 from uri https://promptfloweast4063704120.blob.core.windows.net/snapshotzips/promptflow-eastus:3e123da1-f9a5-4c91-9234-8d9ffbb39ff5:snapshotzip/c956855e-d291-4714-a3df-91c99c974de9.zip...\n2024-01-12 08:19:02 +0000 134 promptflow-runtime INFO Downloaded file /mnt/host/service/app/38343/requests/batch_run_name/c956855e-d291-4714-a3df-91c99c974de9.zip with size 5027 for snapshot c956855e-d291-4714-a3df-91c99c974de9.\n2024-01-12 08:19:02 +0000 134 promptflow-runtime INFO Download snapshot c956855e-d291-4714-a3df-91c99c974de9 completed.\n2024-01-12 08:19:02 +0000 134 promptflow-runtime INFO Successfully download snapshot to /mnt/host/service/app/38343/requests/batch_run_name\n2024-01-12 08:19:02 +0000 134 promptflow-runtime INFO About to execute a python flow.\n2024-01-12 08:19:02 +0000 134 promptflow-runtime INFO Use spawn method to start child process.\n2024-01-12 08:19:03 +0000 134 promptflow-runtime INFO Starting to check process 4227 status for run batch_run_name\n2024-01-12 08:19:03 +0000 134 promptflow-runtime INFO Start checking run status for run batch_run_name\n2024-01-12 08:19:06 +0000 4227 promptflow-runtime INFO [134--4227] Start processing flowV2......\n2024-01-12 08:19:07 +0000 4227 promptflow-runtime INFO Runtime version: 20231204.v4. PromptFlow version: 1.2.0rc1\n2024-01-12 08:19:07 +0000 4227 promptflow-runtime INFO Setting mlflow tracking uri...\n2024-01-12 08:19:07 +0000 4227 promptflow-runtime INFO Validating ''AzureML Data Scientist'' user authentication...\n2024-01-12 08:19:07 +0000 4227 promptflow-runtime INFO Successfully validated ''AzureML Data Scientist'' user authentication.\n2024-01-12 08:19:07 +0000 4227 promptflow-runtime INFO Using AzureMLRunStorageV2\n2024-01-12 08:19:07 +0000 4227 promptflow-runtime INFO Setting mlflow tracking uri to ''azureml://eastus.api.azureml.ms/mlflow/v1.0/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/promptflow-eastus''\n2024-01-12 08:19:07 +0000 4227 promptflow-runtime INFO Initialized blob service client for AzureMLRunTracker.\n2024-01-12 08:19:07 +0000 4227 promptflow-runtime INFO Setting mlflow tracking uri to ''azureml://eastus.api.azureml.ms/mlflow/v1.0/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/promptflow-eastus''\n2024-01-12 08:19:08 +0000 4227 promptflow-runtime INFO Resolve data from url finished in 0.49149388913065195 seconds\n2024-01-12 08:19:08 +0000 4227 promptflow-runtime INFO Starting the aml run ''batch_run_name''...\n2024-01-12 08:19:08 +0000 4227 execution.bulk INFO Using fork, process count: 3\n2024-01-12 08:19:08 +0000 4270 execution.bulk INFO Process 4270 started.\n2024-01-12 08:19:08 +0000 4274 execution.bulk INFO Process 4274 started.\n2024-01-12 08:19:08 +0000 4279 execution.bulk INFO Process 4279 started.\n2024-01-12 08:19:08 +0000 4227 execution.bulk INFO Process name: ForkProcess-42:3, Process id: 4270, Line number: 0 start execution.\n2024-01-12 08:19:08 +0000 4227 execution.bulk INFO Process name: ForkProcess-42:2, Process id: 4274, Line number: 1 start execution.\n2024-01-12 08:19:08 +0000 4227 execution.bulk INFO Process name: ForkProcess-42:4, Process id: 4279, Line number: 2 start execution.\n2024-01-12 08:19:10 +0000 4227 execution.bulk INFO Process name: ForkProcess-42:2, Process id: 4274, Line number: 1 completed.\n2024-01-12 08:19:10 +0000 4227 execution.bulk INFO Finished 1 / 3 lines.\n2024-01-12 08:19:10 +0000 4227 execution.bulk INFO Average execution time for completed lines: 1.61 seconds. Estimated time for incomplete lines: 3.22 seconds.\n2024-01-12 08:19:10 +0000 4227 execution.bulk INFO Process name: ForkProcess-42:4, Process id: 4279, Line number: 2 completed.\n2024-01-12 08:19:10 +0000 4227 execution.bulk INFO Finished 2 / 3 lines.\n2024-01-12 08:19:10 +0000 4227 execution.bulk INFO Average execution time for completed lines: 0.82 seconds. Estimated time for incomplete lines: 0.82 seconds.\n2024-01-12 08:19:10 +0000 4227 execution.bulk INFO Process name: ForkProcess-42:3, Process id: 4270, Line number: 0 completed.\n2024-01-12 08:19:10 +0000 4227 execution.bulk INFO Finished 3 / 3 lines.\n2024-01-12 08:19:10 +0000 4227 execution.bulk INFO Average execution time for completed lines: 0.62 seconds. Estimated time for incomplete lines: 0.0 seconds.\n2024-01-12 08:19:14 +0000 4227 execution.bulk INFO Upload status summary metrics for run batch_run_name finished in 2.5736593334004283 seconds\n2024-01-12 08:19:14 +0000 4227 promptflow-runtime INFO Successfully write run properties {\"azureml.promptflow.total_tokens\": 2403, \"_azureml.evaluate_artifacts\": \"[{\\\"path\\\": \\\"instance_results.jsonl\\\", \\\"type\\\": \\\"table\\\"}]\"} with run id ''batch_run_name''\n2024-01-12 08:19:14 +0000 4227 execution.bulk INFO Upload RH properties for run batch_run_name finished in 0.06843763962388039 seconds\n2024-01-12 08:19:14 +0000 4227 promptflow-runtime INFO Creating unregistered output Asset for Run batch_run_name...\n2024-01-12 08:19:14 +0000 4227 promptflow-runtime INFO Created debug_info Asset: azureml://locations/eastus/workspaces/00000/data/azureml_batch_run_name_output_data_debug_info/versions/1\n2024-01-12 08:19:14 +0000 4227 promptflow-runtime INFO Creating unregistered output Asset for Run batch_run_name...\n2024-01-12 08:19:14 +0000 4227 promptflow-runtime INFO Created flow_outputs output Asset: azureml://locations/eastus/workspaces/00000/data/azureml_batch_run_name_output_data_flow_outputs/versions/1\n2024-01-12 08:19:14 +0000 4227 promptflow-runtime INFO Creating Artifact for Run batch_run_name...\n2024-01-12 08:19:14 +0000 4227 promptflow-runtime INFO Created instance_results.jsonl Artifact.\n2024-01-12 08:19:14 +0000 4227 promptflow-runtime INFO Patching batch_run_name...\n2024-01-12 08:19:15 +0000 4227 promptflow-runtime INFO Ending the aml run ''batch_run_name'' with status ''Completed''...\n2024-01-12 08:19:16 +0000 134 promptflow-runtime INFO Process 4227 finished\n2024-01-12 08:19:16 +0000 134 promptflow-runtime INFO [134] Child process finished!\n2024-01-12 08:19:16 +0000 134 promptflow-runtime INFO [batch_run_name] End processing bulk run\n2024-01-12 08:19:16 +0000 134 promptflow-runtime INFO Cleanup working dir /mnt/host/service/app/38343/requests/batch_run_name for bulk run\n"' headers: connection: - keep-alive content-length: - '9863' content-type: - application/json; charset=utf-8 strict-transport-security: - max-age=15724800; includeSubDomains; preload transfer-encoding: - chunked vary: - Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.695' status: code: 200 message: OK - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Type: - application/json User-Agent: - promptflow-sdk/0.0.1 azsdk-python-azuremachinelearningdesignerserviceclient/unknown Python/3.10.13 (Windows-10-10.0.22631-SP0) method: GET uri: https://eastus.api.azureml.ms/flow/api/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/BulkRuns/eval_run_name/logContent response: body: string: '"2024-01-12 08:19:43 +0000 134 promptflow-runtime INFO [eval_run_name] Receiving v2 bulk run request 32c97496-d3ae-4e08-a6f2-758a0c6e418f: {\"flow_id\": \"eval_run_name\", \"flow_run_id\": \"eval_run_name\", \"flow_source\": {\"flow_source_type\": 1, \"flow_source_info\": {\"snapshot_id\": \"07c78456-f714-4df6-9398-0dc36e95ed2c\"}, \"flow_dag_file\": \"flow.dag.yaml\"}, \"log_path\": \"https://promptfloweast4063704120.blob.core.windows.net/azureml/ExperimentRun/dcid.eval_run_name/logs/azureml/executionlogs.txt?sv=2019-07-07&sr=b&sig=**data_scrubbed**&skoid=55b92eba-d7c7-4afd-ab76-7bb1cd345283&sktid=00000000-0000-0000-0000-000000000000&skt=2024-01-12T08%3A01%3A10Z&ske=2024-01-13T16%3A11%3A10Z&sks=b&skv=2019-07-07&st=2024-01-12T08%3A09%3A42Z&se=2024-01-12T16%3A19%3A42Z&sp=rcw\", \"app_insights_instrumentation_key\": \"InstrumentationKey=**data_scrubbed**;IngestionEndpoint=https://eastus-6.in.applicationinsights.azure.com/;LiveEndpoint=https://eastus.livediagnostics.monitor.azure.com/\", \"data_inputs\": {\"run.outputs\": \"azureml:/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/data/azureml_batch_run_name_output_data_flow_outputs/versions/1\", \"run.inputs\": \"azureml://datastores/workspaceblobstore/paths/LocalUpload/74c11bba717480b2d6b04b8e746d09d7/webClassification3.jsonl\"}, \"inputs_mapping\": {\"groundtruth\": \"${run.inputs.url}\", \"prediction\": \"${run.outputs.category}\"}, \"azure_storage_setting\": {\"azure_storage_mode\": 1, \"storage_account_name\": \"promptfloweast4063704120\", \"blob_container_name\": \"azureml-blobstore-3e123da1-f9a5-4c91-9234-8d9ffbb39ff5\", \"flow_artifacts_root_path\": \"promptflow/PromptFlowArtifacts/eval_run_name\", \"blob_container_sas_token\": \"?sv=2019-07-07&sr=c&sig=**data_scrubbed**&skoid=55b92eba-d7c7-4afd-ab76-7bb1cd345283&sktid=00000000-0000-0000-0000-000000000000&skt=2024-01-12T08%3A19%3A43Z&ske=2024-01-19T08%3A19%3A43Z&sks=b&skv=2019-07-07&se=2024-01-19T08%3A19%3A43Z&sp=racwl\", \"output_datastore_name\": \"workspaceblobstore\"}}\n2024-01-12 08:19:43 +0000 134 promptflow-runtime INFO Runtime version: 20231204.v4. PromptFlow version: 1.2.0rc1\n2024-01-12 08:19:43 +0000 134 promptflow-runtime INFO Updating eval_run_name to Status.Preparing...\n2024-01-12 08:19:43 +0000 134 promptflow-runtime INFO Downloading snapshot to /mnt/host/service/app/38343/requests/eval_run_name\n2024-01-12 08:19:43 +0000 134 promptflow-runtime INFO Get snapshot sas url for 07c78456-f714-4df6-9398-0dc36e95ed2c...\n2024-01-12 08:19:50 +0000 134 promptflow-runtime INFO Downloading snapshot 07c78456-f714-4df6-9398-0dc36e95ed2c from uri https://promptfloweast4063704120.blob.core.windows.net/snapshotzips/promptflow-eastus:3e123da1-f9a5-4c91-9234-8d9ffbb39ff5:snapshotzip/07c78456-f714-4df6-9398-0dc36e95ed2c.zip...\n2024-01-12 08:19:50 +0000 134 promptflow-runtime INFO Downloaded file /mnt/host/service/app/38343/requests/eval_run_name/07c78456-f714-4df6-9398-0dc36e95ed2c.zip with size 1243 for snapshot 07c78456-f714-4df6-9398-0dc36e95ed2c.\n2024-01-12 08:19:50 +0000 134 promptflow-runtime INFO Download snapshot 07c78456-f714-4df6-9398-0dc36e95ed2c completed.\n2024-01-12 08:19:50 +0000 134 promptflow-runtime INFO Successfully download snapshot to /mnt/host/service/app/38343/requests/eval_run_name\n2024-01-12 08:19:50 +0000 134 promptflow-runtime INFO About to execute a python flow.\n2024-01-12 08:19:50 +0000 134 promptflow-runtime INFO Use spawn method to start child process.\n2024-01-12 08:19:50 +0000 134 promptflow-runtime INFO Starting to check process 4351 status for run eval_run_name\n2024-01-12 08:19:50 +0000 134 promptflow-runtime INFO Start checking run status for run eval_run_name\n2024-01-12 08:19:54 +0000 4351 promptflow-runtime INFO [134--4351] Start processing flowV2......\n2024-01-12 08:19:54 +0000 4351 promptflow-runtime INFO Runtime version: 20231204.v4. PromptFlow version: 1.2.0rc1\n2024-01-12 08:19:54 +0000 4351 promptflow-runtime INFO Setting mlflow tracking uri...\n2024-01-12 08:19:54 +0000 4351 promptflow-runtime INFO Validating ''AzureML Data Scientist'' user authentication...\n2024-01-12 08:19:55 +0000 4351 promptflow-runtime INFO Successfully validated ''AzureML Data Scientist'' user authentication.\n2024-01-12 08:19:55 +0000 4351 promptflow-runtime INFO Using AzureMLRunStorageV2\n2024-01-12 08:19:55 +0000 4351 promptflow-runtime INFO Setting mlflow tracking uri to ''azureml://eastus.api.azureml.ms/mlflow/v1.0/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/promptflow-eastus''\n2024-01-12 08:19:55 +0000 4351 promptflow-runtime INFO Initialized blob service client for AzureMLRunTracker.\n2024-01-12 08:19:55 +0000 4351 promptflow-runtime INFO Setting mlflow tracking uri to ''azureml://eastus.api.azureml.ms/mlflow/v1.0/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/promptflow-eastus''\n2024-01-12 08:19:56 +0000 4351 promptflow-runtime INFO Resolve data from url finished in 0.6869602408260107 seconds\n2024-01-12 08:19:56 +0000 4351 promptflow-runtime INFO Resolve data from url finished in 0.5102318925783038 seconds\n2024-01-12 08:19:56 +0000 4351 promptflow-runtime INFO Starting the aml run ''eval_run_name''...\n2024-01-12 08:19:57 +0000 4351 execution.bulk INFO Using fork, process count: 3\n2024-01-12 08:19:57 +0000 4398 execution.bulk INFO Process 4398 started.\n2024-01-12 08:19:57 +0000 4394 execution.bulk INFO Process 4394 started.\n2024-01-12 08:19:57 +0000 4403 execution.bulk INFO Process 4403 started.\n2024-01-12 08:19:57 +0000 4351 execution.bulk INFO Process name: ForkProcess-44:4, Process id: 4398, Line number: 0 start execution.\n2024-01-12 08:19:57 +0000 4351 execution.bulk INFO Process name: ForkProcess-44:2, Process id: 4394, Line number: 1 start execution.\n2024-01-12 08:19:57 +0000 4351 execution.bulk INFO Process name: ForkProcess-44:3, Process id: 4403, Line number: 2 start execution.\n2024-01-12 08:19:57 +0000 4351 execution.bulk INFO Process name: ForkProcess-44:4, Process id: 4398, Line number: 0 completed.\n2024-01-12 08:19:57 +0000 4351 execution.bulk INFO Finished 1 / 3 lines.\n2024-01-12 08:19:57 +0000 4351 execution.bulk INFO Average execution time for completed lines: 0.24 seconds. Estimated time for incomplete lines: 0.48 seconds.\n2024-01-12 08:19:57 +0000 4351 execution.bulk INFO Process name: ForkProcess-44:3, Process id: 4403, Line number: 2 completed.\n2024-01-12 08:19:57 +0000 4351 execution.bulk INFO Process name: ForkProcess-44:2, Process id: 4394, Line number: 1 completed.\n2024-01-12 08:19:57 +0000 4351 execution.bulk INFO Finished 3 / 3 lines.\n2024-01-12 08:19:57 +0000 4351 execution.bulk INFO Finished 3 / 3 lines.\n2024-01-12 08:19:57 +0000 4351 execution.bulk INFO Average execution time for completed lines: 0.1 seconds. Estimated time for incomplete lines: 0.0 seconds.\n2024-01-12 08:19:57 +0000 4351 execution.bulk INFO Average execution time for completed lines: 0.11 seconds. Estimated time for incomplete lines: 0.0 seconds.\n2024-01-12 08:19:58 +0000 4351 execution.bulk INFO Executing aggregation nodes...\n2024-01-12 08:19:58 +0000 4351 execution.bulk INFO Finish executing aggregation nodes.\n2024-01-12 08:20:00 +0000 4351 execution.bulk INFO Upload status summary metrics for run eval_run_name finished in 1.5121951373293996 seconds\n2024-01-12 08:20:00 +0000 4351 execution.bulk INFO Upload metrics for run eval_run_name finished in 0.3903973773121834 seconds\n2024-01-12 08:20:00 +0000 4351 promptflow-runtime INFO Successfully write run properties {\"azureml.promptflow.total_tokens\": 0, \"_azureml.evaluate_artifacts\": \"[{\\\"path\\\": \\\"instance_results.jsonl\\\", \\\"type\\\": \\\"table\\\"}]\"} with run id ''eval_run_name''\n2024-01-12 08:20:00 +0000 4351 execution.bulk INFO Upload RH properties for run eval_run_name finished in 0.08290723245590925 seconds\n2024-01-12 08:20:00 +0000 4351 promptflow-runtime INFO Creating unregistered output Asset for Run eval_run_name...\n2024-01-12 08:20:01 +0000 4351 promptflow-runtime INFO Created debug_info Asset: azureml://locations/eastus/workspaces/00000/data/azureml_eval_run_name_output_data_debug_info/versions/1\n2024-01-12 08:20:01 +0000 4351 promptflow-runtime INFO Creating unregistered output Asset for Run eval_run_name...\n2024-01-12 08:20:01 +0000 4351 promptflow-runtime INFO Created flow_outputs output Asset: azureml://locations/eastus/workspaces/00000/data/azureml_eval_run_name_output_data_flow_outputs/versions/1\n2024-01-12 08:20:01 +0000 4351 promptflow-runtime INFO Creating Artifact for Run eval_run_name...\n2024-01-12 08:20:01 +0000 4351 promptflow-runtime INFO Created instance_results.jsonl Artifact.\n2024-01-12 08:20:01 +0000 4351 promptflow-runtime INFO Patching eval_run_name...\n2024-01-12 08:20:01 +0000 4351 promptflow-runtime INFO Ending the aml run ''eval_run_name'' with status ''Completed''...\n2024-01-12 08:20:03 +0000 134 promptflow-runtime INFO Process 4351 finished\n2024-01-12 08:20:03 +0000 134 promptflow-runtime INFO [134] Child process finished!\n2024-01-12 08:20:03 +0000 134 promptflow-runtime INFO [eval_run_name] End processing bulk run\n2024-01-12 08:20:03 +0000 134 promptflow-runtime INFO Cleanup working dir /mnt/host/service/app/38343/requests/eval_run_name for bulk run\n"' headers: connection: - keep-alive content-length: - '10628' content-type: - application/json; charset=utf-8 strict-transport-security: - max-age=15724800; includeSubDomains; preload transfer-encoding: - chunked vary: - Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.856' status: code: 200 message: OK version: 1

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/recordings/test_run_operations_TestFlowRun_test_run_display_name_with_macro.yaml

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0

promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/recordings/test_run_operations_TestFlowRun_test_run_bulk_with_remote_flow.yaml

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08:21:08 GMT x-ms-version: - '2023-11-03' method: HEAD uri: https://fake_account_name.blob.core.windows.net/fake-container-name/az-ml-artifacts/000000000000000000000000000000000000/simple_hello_world.jsonl response: body: string: '' headers: server: - Windows-Azure-Blob/1.0 Microsoft-HTTPAPI/2.0 transfer-encoding: - chunked vary: - Origin x-ms-error-code: - BlobNotFound x-ms-version: - '2023-11-03' status: code: 404 message: The specified blob does not exist. - request: body: '{"flowDefinitionResourceId": "azureml://locations/fake-region/workspaces/00000/flows/00000000-0000-0000-0000-000000000000/", "runId": "name", "runDisplayName": "name", "batchDataInput": {"dataUri": "azureml://datastores/workspaceblobstore/paths/LocalUpload/000000000000000000000000000000000000/simple_hello_world.jsonl"}, "inputsMapping": {"name": "${data.name}"}, "connections": {}, "environmentVariables": {}, "runtimeName": "fake-runtime-name", "sessionId": "000000000000000000000000000000000000000000000000", 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headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - promptflow-sdk/0.0.1 azsdk-python-azuremachinelearningdesignerserviceclient/unknown Python/3.10.13 (Windows-10-10.0.22631-SP0) method: GET uri: https://eastus.api.azureml.ms/flow/api/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/BulkRuns/name response: body: string: '{"flowGraph": {"nodes": [{"name": "hello_world", "type": "python", "source": {"type": "code", "path": "hello_world.py"}, "inputs": {"name": "${inputs.name}"}, "tool": "hello_world.py", "reduce": false}], "tools": [{"name": "Content Safety (Text Analyze)", "type": "python", "inputs": {"connection": {"type": ["AzureContentSafetyConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "hate_category": {"type": ["string"], "default": "medium_sensitivity", "enum": ["disable", "low_sensitivity", 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promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/recordings/test_run_operations_TestFlowRun_test_cancel_run.yaml

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"tool": "fetch_text_content_from_url.py", "reduce": false}, {"name": "prepare_examples", "type": "python", "source": {"type": "code", "path": "prepare_examples.py"}, "inputs": {}, "tool": "prepare_examples.py", "reduce": false}, {"name": "classify_with_llm", "type": "llm", "source": {"type": "code", "path": "classify_with_llm.jinja2"}, "inputs": {"deployment_name": "gpt-35-turbo", "suffix": "", "max_tokens": "128", "temperature": "0.1", "top_p": "1.0", "logprobs": "", "echo": "False", "stop": "", "presence_penalty": "0", "frequency_penalty": "0", "best_of": "1", "logit_bias": "", "url": "${inputs.url}", "examples": "${prepare_examples.output}", "text_content": "${summarize_text_content.output}"}, "tool": "classify_with_llm.jinja2", "reduce": false, "api": "chat", "provider": "AzureOpenAI", "connection": "azure_open_ai_connection", "module": "promptflow.tools.aoai"}, {"name": "convert_to_dict", "type": "python", "source": {"type": "code", "path": "convert_to_dict.py"}, "inputs": 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promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/recordings/test_azure_cli_perf_TestAzureCliPerf_test_pfazure_run_update.yaml

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"computeRequest": null, "compute": null, "retainForLifetimeOfWorkspace": false, "queueingInfo": null, "inputs": null, "outputs": {"debug_info": {"assetId": "azureml://locations/eastus2euap/workspaces/00000/data/azureml_test_run_output_data_debug_info/versions/1", "type": "UriFolder"}, "flow_outputs": {"assetId": "azureml://locations/eastus2euap/workspaces/00000/data/azureml_test_run_output_data_flow_outputs/versions/1", "type": "UriFolder"}}}, "runDefinition": null, "jobSpecification": null, "systemSettings": null}' headers: connection: - keep-alive content-length: - '4332' content-type: - application/json; charset=utf-8 strict-transport-security: - max-age=15724800; includeSubDomains; preload transfer-encoding: - chunked vary: - Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.033' status: code: 200 message: OK version: 1

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/recordings/test_run_operations_TestFlowRun_test_run_bulk_without_retry.yaml

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0

promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/recordings/test_run_operations_TestFlowRun_test_run_without_dump.yaml

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"enabled_by_type": ["QdrantConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "connection": {"type": ["CognitiveSearchConnection", "QdrantConnection", "WeaviateConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "index_name": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "search_filters": {"type": ["object"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection", "QdrantConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "search_params": {"type": ["object"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection", "QdrantConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "text_field": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection", "QdrantConnection", "WeaviateConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_k": {"type": ["int"], "default": "3", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "vector": {"type": ["list"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "vector_field": {"type": ["string"], "enabled_by": "connection", "enabled_by_type": ["CognitiveSearchConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Search vector based query from existing Vector Database.", "module": "promptflow_vectordb.tool.vector_db_lookup", "class_name": "VectorDBLookup", "function": "search", "is_builtin": true, "package": "promptflow-vectordb", "package_version": "0.0.1", "enable_kwargs": false, "tool_state": "stable"}, {"name": "Vector Index Lookup", "type": "python", "inputs": {"path": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "query": {"type": ["object"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "top_k": {"type": ["int"], "default": "3", "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "description": "Search text or vector based query from AzureML Vector Index.", "module": "promptflow_vectordb.tool.vector_index_lookup", "class_name": "VectorIndexLookup", "function": "search", "is_builtin": true, "package": "promptflow-vectordb", "package_version": "0.0.1", "enable_kwargs": false, "tool_state": "stable"}, {"name": "classify_with_llm.jinja2", "type": "prompt", "inputs": {"examples": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "text_content": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "url": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "source": "classify_with_llm.jinja2", "is_builtin": false, "enable_kwargs": false, "tool_state": "stable"}, {"name": "convert_to_dict.py", "type": "python", "inputs": {"input_str": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "source": "convert_to_dict.py", "function": "convert_to_dict", "is_builtin": false, "enable_kwargs": false, "tool_state": "stable"}, {"name": "fetch_text_content_from_url.py", "type": "python", "inputs": {"fetch_url": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "source": "fetch_text_content_from_url.py", "function": "fetch_text_content_from_url", "is_builtin": false, "enable_kwargs": false, "tool_state": "stable"}, {"name": "prepare_examples.py", "type": "python", "source": "prepare_examples.py", "function": "prepare_examples", "is_builtin": false, "enable_kwargs": false, "tool_state": "stable"}, {"name": "summarize_text_content.jinja2", "type": "prompt", "inputs": {"text": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "source": "summarize_text_content.jinja2", "is_builtin": false, "enable_kwargs": false, "tool_state": "stable"}, {"name": "summarize_text_content__variant_1.jinja2", "type": "prompt", "inputs": {"text": {"type": ["string"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}}, "source": "summarize_text_content__variant_1.jinja2", "is_builtin": false, "enable_kwargs": false, "tool_state": "stable"}], "inputs": {"url": {"type": "string", "default": "https://www.microsoft.com/en-us/d/xbox-wireless-controller-stellar-shift-special-edition/94fbjc7h0h6h", "is_chat_input": false}}, "outputs": {"category": {"type": "string", "reference": "${convert_to_dict.output.category}", "evaluation_only": false, "is_chat_output": false}, "evidence": {"type": "string", "reference": "${convert_to_dict.output.evidence}", "evaluation_only": false, "is_chat_output": false}}}, "flowRunResourceId": "azureml://locations/eastus/workspaces/00000/flows/name/flowRuns/name", "flowRunId": "name", "flowRunDisplayName": "name", "batchDataInput": {"dataUri": "azureml://datastores/workspaceblobstore/paths/LocalUpload/107bd3498e44deb2dccc53d2208d32b2/webClassification1.jsonl"}, "flowRunType": "FlowRun", "flowType": "Default", "runtimeName": "test-runtime-ci", "inputsMapping": {"url": "${data.url}"}, "outputDatastoreName": "workspaceblobstore", "childRunBasePath": "promptflow/PromptFlowArtifacts/name/flow_artifacts", "flowDagFileRelativePath": "flow.dag.yaml", "flowSnapshotId": "b235c474-d764-454f-a8da-63b47303ce80", "studioPortalEndpoint": "https://ml.azure.com/runs/name?wsid=/subscriptions/00000000-0000-0000-0000-000000000000/resourcegroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000"}' headers: connection: - keep-alive content-length: - '16239' content-type: - application/json; charset=utf-8 strict-transport-security: - max-age=15724800; includeSubDomains; preload transfer-encoding: - chunked vary: - Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.498' status: code: 200 message: OK - request: body: '{"runId": "name", "selectRunMetadata": true, "selectRunDefinition": true, "selectJobSpecification": true}' headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '137' Content-Type: - application/json User-Agent: - python-requests/2.31.0 method: POST uri: https://eastus.api.azureml.ms/history/v1.0/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/rundata response: body: string: '{"runMetadata": {"runNumber": 1705049270, "rootRunId": "name", "createdUtc": "2024-01-12T08:47:50.4752183+00:00", "createdBy": {"userObjectId": "00000000-0000-0000-0000-000000000000", "userPuId": null, "userIdp": "https://sts.windows.net/00000000-0000-0000-0000-000000000000/", "userAltSecId": null, "userIss": "https://sts.windows.net/00000000-0000-0000-0000-000000000000/", "userTenantId": "00000000-0000-0000-0000-000000000000", "userName": "4cbd0e2e-aae4-4099-b4ba-94d3a4910587", "upn": null}, "userId": "00000000-0000-0000-0000-000000000000", "token": null, "tokenExpiryTimeUtc": null, "error": null, "warnings": null, "revision": 3, "statusRevision": 1, "runUuid": "54649bc5-afa2-485d-8a4b-1a2296754d78", "parentRunUuid": null, "rootRunUuid": "54649bc5-afa2-485d-8a4b-1a2296754d78", "lastStartTimeUtc": null, "currentComputeTime": "00:00:00", "computeDuration": null, "effectiveStartTimeUtc": null, "lastModifiedBy": {"userObjectId": "00000000-0000-0000-0000-000000000000", "userPuId": null, "userIdp": "https://sts.windows.net/00000000-0000-0000-0000-000000000000/", "userAltSecId": null, "userIss": "https://sts.windows.net/00000000-0000-0000-0000-000000000000/", "userTenantId": "00000000-0000-0000-0000-000000000000", "userName": "4cbd0e2e-aae4-4099-b4ba-94d3a4910587", "upn": null}, "lastModifiedUtc": "2024-01-12T08:47:55.0106055+00:00", "duration": null, "cancelationReason": null, "currentAttemptId": 1, "runId": "name", "parentRunId": null, "experimentId": "d30efbeb-f81d-4cfa-b5cc-a0570a049009", "status": "Preparing", "startTimeUtc": null, "endTimeUtc": null, "scheduleId": null, "displayName": "name", "name": null, "dataContainerId": "dcid.name", "description": null, "hidden": false, "runType": "azureml.promptflow.FlowRun", "runTypeV2": {"orchestrator": null, "traits": [], "attribution": "PromptFlow", "computeType": "AmlcDsi"}, "properties": {"azureml.promptflow.runtime_name": "test-runtime-ci", "azureml.promptflow.runtime_version": "20231204.v4", "azureml.promptflow.definition_file_name": "flow.dag.yaml", "azureml.promptflow.session_id": "4dd8f4d5f44dfeb817d3438cf84bd739215d87afd9458597", "azureml.promptflow.flow_lineage_id": "af1a6951de9be2ce13d3b58b23dbd8b6a0cd8fd4918ad9cb22b28fb8395fbcb0", "azureml.promptflow.node_variant": "${summarize_text_content.variant_0}", "azureml.promptflow.flow_definition_datastore_name": "workspaceblobstore", "azureml.promptflow.flow_definition_blob_path": "LocalUpload/a1fa6ef1ead7ff3ce76b36250f6f5461/web_classification/flow.dag.yaml", "azureml.promptflow.input_data": "azureml://datastores/workspaceblobstore/paths/LocalUpload/107bd3498e44deb2dccc53d2208d32b2/webClassification1.jsonl", "azureml.promptflow.inputs_mapping": "{\"url\":\"${data.url}\"}", "_azureml.evaluation_run": "promptflow.BatchRun", "azureml.promptflow.snapshot_id": "b235c474-d764-454f-a8da-63b47303ce80"}, "parameters": {}, "actionUris": {}, "scriptName": null, "target": null, "uniqueChildRunComputeTargets": [], "tags": {}, "settings": {}, "services": {}, "inputDatasets": [], "outputDatasets": [], "runDefinition": null, "jobSpecification": null, "primaryMetricName": null, "createdFrom": null, "cancelUri": null, "completeUri": null, "diagnosticsUri": null, "computeRequest": null, "compute": null, "retainForLifetimeOfWorkspace": false, "queueingInfo": null, "inputs": null, "outputs": null}, "runDefinition": null, "jobSpecification": null, "systemSettings": null}' headers: connection: - keep-alive content-length: - '4011' content-type: - application/json; charset=utf-8 strict-transport-security: - max-age=15724800; includeSubDomains; preload transfer-encoding: - chunked vary: - Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.051' status: code: 200 message: OK version: 1

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/recordings/test_run_operations_TestFlowRun_test_stream_invalid_run_logs.yaml

interactions: - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - promptflow-sdk/0.0.1 azure-ai-ml/1.12.1 azsdk-python-mgmt-machinelearningservices/0.1.0 Python/3.10.13 (Windows-10-10.0.22631-SP0) method: GET uri: https://management.azure.com/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000 response: body: string: '{"id": "/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000", "name": "00000", "type": "Microsoft.MachineLearningServices/workspaces", "location": "eastus", "tags": {}, "etag": null, "kind": "Default", "sku": {"name": "Basic", "tier": "Basic"}, "properties": {"discoveryUrl": "https://eastus.api.azureml.ms/discovery"}}' headers: cache-control: - no-cache content-length: - '3630' content-type: - application/json; charset=utf-8 expires: - '-1' pragma: - no-cache strict-transport-security: - max-age=31536000; includeSubDomains transfer-encoding: - chunked vary: - Accept-Encoding,Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.027' status: code: 200 message: OK - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - promptflow-sdk/0.0.1 azure-ai-ml/1.12.1 azsdk-python-mgmt-machinelearningservices/0.1.0 Python/3.10.13 (Windows-10-10.0.22631-SP0) method: GET uri: https://management.azure.com/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/datastores?count=30&isDefault=true&orderByAsc=false response: body: string: '{"value": [{"id": "/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/datastores/workspaceblobstore", "name": "workspaceblobstore", "type": "Microsoft.MachineLearningServices/workspaces/datastores", "properties": {"description": null, "tags": null, "properties": null, "isDefault": true, "credentials": {"credentialsType": "AccountKey"}, "intellectualProperty": null, "subscriptionId": "00000000-0000-0000-0000-000000000000", "resourceGroup": "00000", "datastoreType": "AzureBlob", "accountName": "fake_account_name", "containerName": "fake-container-name", "endpoint": "core.windows.net", "protocol": "https", "serviceDataAccessAuthIdentity": "WorkspaceSystemAssignedIdentity"}, "systemData": {"createdAt": "2023-04-08T02:53:06.5886442+00:00", "createdBy": "779301c0-18b2-4cdc-801b-a0a3368fee0a", "createdByType": "Application", "lastModifiedAt": "2023-04-08T02:53:07.521127+00:00", "lastModifiedBy": "779301c0-18b2-4cdc-801b-a0a3368fee0a", "lastModifiedByType": "Application"}}]}' headers: cache-control: - no-cache content-length: - '1372' content-type: - application/json; charset=utf-8 expires: - '-1' pragma: - no-cache strict-transport-security: - max-age=31536000; includeSubDomains transfer-encoding: - chunked vary: - Accept-Encoding,Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.088' status: code: 200 message: OK - request: body: '{"runId": "non_exist_run", "selectRunMetadata": true, "selectRunDefinition": true, "selectJobSpecification": true}' headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '137' Content-Type: - application/json User-Agent: - python-requests/2.31.0 method: POST uri: https://eastus.api.azureml.ms/history/v1.0/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/rundata response: body: string: '{"error": {"code": "UserError", "severity": null, "message": "Run runId=non_exist_run was not found", "messageFormat": "Run {runId} was not found", "messageParameters": {"runId": "runId=non_exist_run"}, "referenceCode": null, "detailsUri": null, "target": null, "details": [], "innerError": {"code": "NotFoundError", "innerError": null}, "debugInfo": null, "additionalInfo": null}, "correlation": {"operation": "8ae7e1bb4942865bf920115c263c0e78", "request": "7f0871b4a154f85d"}, "environment": "eastus", "location": "eastus", "time": "2024-01-12T07:58:30.4482197+00:00", "componentName": "run-history", "statusCode": 404}' headers: connection: - keep-alive content-length: - '777' content-type: - application/json; charset=utf-8 strict-transport-security: - max-age=15724800; includeSubDomains; preload transfer-encoding: - chunked vary: - Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.046' status: code: 404 message: Run runId=f1356823-7551-4ab3-93ba-6d60270b9dc6 was not found version: 1

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/recordings/test_arm_connection_operations_TestArmConnectionOperations_test_get_connection.yaml

interactions: - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - promptflow-sdk/0.0.1 promptflow/0.0.1 azure-ai-ml/1.12.1 azsdk-python-mgmt-machinelearningservices/0.1.0 Python/3.10.13 (Windows-10-10.0.22631-SP0) method: GET uri: https://management.azure.com/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/api-version=2023-08-01-preview response: body: string: '{"id": "/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000", "name": "00000", "type": "Microsoft.MachineLearningServices/workspaces", "location": "eastus", "tags": {}, "etag": null, "kind": "Default", "sku": {"name": "Basic", "tier": "Basic"}, "properties": {"discoveryUrl": "https://eastus.api.azureml.ms/discovery"}}' headers: cache-control: - no-cache content-length: - '3630' content-type: - application/json; 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charset=utf-8 expires: - '-1' pragma: - no-cache strict-transport-security: - max-age=31536000; includeSubDomains vary: - Accept-Encoding x-cache: - CONFIG_NOCACHE x-content-type-options: - nosniff x-request-time: - '0.072' status: code: 200 message: OK - request: body: null headers: Accept: - '*/*' Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '0' User-Agent: - python-requests/2.31.0 method: POST uri: https://management.azure.com/subscriptions/00000000-0000-0000-0000-000000000000/resourcegroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/connections/custom_connection/listsecrets?api-version=2023-04-01-preview response: body: string: '{"tags": null, "location": null, "id": "/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/connections/custom_connection", "name": "custom_connection", "type": "Microsoft.MachineLearningServices/workspaces/connections", "properties": {"authType": "CustomKeys", "credentials": {"keys": {}}, "group": "AzureAI", "category": "CustomKeys", "expiryTime": null, "target": "_", "createdByWorkspaceArmId": null, "isSharedToAll": false, "sharedUserList": [], "metadata": {"azureml.flow.connection_type": "Custom", "azureml.flow.module": "promptflow.connections"}}, "systemData": {"createdAt": "2023-06-19T20:56:12.0353964Z", "createdBy": "[email protected]", "createdByType": "User", "lastModifiedAt": "2023-06-19T20:56:12.0353964Z", "lastModifiedBy": "[email protected]", "lastModifiedByType": "User"}}' headers: cache-control: - no-cache content-length: - '1275' content-type: - application/json; charset=utf-8 expires: - '-1' pragma: - no-cache strict-transport-security: - max-age=31536000; includeSubDomains vary: - Accept-Encoding x-cache: - CONFIG_NOCACHE x-content-type-options: - nosniff x-request-time: - '0.075' status: code: 200 message: OK version: 1

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/recordings/test_run_operations_TestFlowRun_test_list_runs.yaml

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promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/recordings/test_flow_operations_TestFlow_test_create_flow.yaml

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no-cache content-length: - '1161' content-type: - application/json; charset=utf-8 expires: - '-1' pragma: - no-cache strict-transport-security: - max-age=31536000; includeSubDomains transfer-encoding: - chunked vary: - Accept-Encoding,Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.129' status: code: 200 message: OK - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '0' User-Agent: - promptflow-sdk/0.0.1 azure-ai-ml/1.12.1 azsdk-python-mgmt-machinelearningservices/0.1.0 Python/3.10.13 (Windows-10-10.0.22631-SP0) method: POST uri: https://management.azure.com/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/datastores/workspaceworkingdirectory/listSecrets response: body: string: '{"secretsType": "AccountKey", "key": "dGhpcyBpcyBmYWtlIGtleQ=="}' headers: cache-control: - no-cache content-length: - '134' content-type: - application/json; charset=utf-8 expires: - '-1' pragma: - no-cache strict-transport-security: - max-age=31536000; includeSubDomains transfer-encoding: - chunked vary: - Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.141' status: code: 200 message: OK - request: body: null headers: Accept: - application/xml Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '0' User-Agent: - azsdk-python-storage-file-share/12.14.0 Python/3.10.13 (Windows-10-10.0.22631-SP0) x-ms-date: - Fri, 12 Jan 2024 03:09:22 GMT x-ms-file-attributes: - none x-ms-file-creation-time: - now x-ms-file-last-write-time: - now x-ms-file-permission: - inherit x-ms-version: - '2023-08-03' method: PUT uri: https://fake_account_name.file.core.windows.net/fake-file-share-name/LocalUpload?restype=directory response: body: string: "\uFEFF<?xml version=\"1.0\" encoding=\"utf-8\"?><Error><Code>ResourceAlreadyExists</Code><Message>The specified resource already exists.\nRequestId:7fae0d6d-401a-0116-3904-450556000000\nTime:2024-01-12T03:09:23.7942671Z</Message></Error>" headers: content-length: - '228' content-type: - application/xml server: - Windows-Azure-File/1.0 Microsoft-HTTPAPI/2.0 x-ms-error-code: - ResourceAlreadyExists x-ms-version: - '2023-08-03' status: code: 409 message: The specified resource already exists. - request: body: null headers: Accept: - application/xml Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '0' User-Agent: - azsdk-python-storage-file-share/12.14.0 Python/3.10.13 (Windows-10-10.0.22631-SP0) x-ms-date: - Fri, 12 Jan 2024 03:09:25 GMT x-ms-file-attributes: - none x-ms-file-creation-time: - now x-ms-file-last-write-time: - now x-ms-file-permission: - inherit x-ms-version: - '2023-08-03' method: PUT uri: https://fake_account_name.file.core.windows.net/fake-file-share-name/Users?restype=directory response: body: string: "\uFEFF<?xml version=\"1.0\" encoding=\"utf-8\"?><Error><Code>ResourceAlreadyExists</Code><Message>The specified resource already exists.\nRequestId:6c56b75e-d01a-00ef-5804-45f879000000\nTime:2024-01-12T03:09:26.0483003Z</Message></Error>" headers: content-length: - '228' content-type: - application/xml server: - Windows-Azure-File/1.0 Microsoft-HTTPAPI/2.0 x-ms-error-code: - ResourceAlreadyExists x-ms-version: - '2023-08-03' status: code: 409 message: The specified resource already exists. - request: body: null headers: Accept: - application/xml Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '0' User-Agent: - azsdk-python-storage-file-share/12.14.0 Python/3.10.13 (Windows-10-10.0.22631-SP0) x-ms-date: - Fri, 12 Jan 2024 03:09:26 GMT x-ms-file-attributes: - none x-ms-file-creation-time: - now x-ms-file-last-write-time: - now x-ms-file-permission: - inherit x-ms-version: - '2023-08-03' method: PUT uri: https://fake_account_name.file.core.windows.net/fake-file-share-name/Users%2Funknown_user?restype=directory response: body: string: "\uFEFF<?xml version=\"1.0\" encoding=\"utf-8\"?><Error><Code>ResourceAlreadyExists</Code><Message>The specified resource already exists.\nRequestId:c96f52da-801a-00e2-4404-4530ad000000\nTime:2024-01-12T03:09:27.0734853Z</Message></Error>" headers: content-length: - '228' content-type: - application/xml server: - Windows-Azure-File/1.0 Microsoft-HTTPAPI/2.0 x-ms-error-code: - ResourceAlreadyExists x-ms-version: - '2023-08-03' status: code: 409 message: The specified resource already exists. - request: body: null headers: Accept: - application/xml Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '0' User-Agent: - azsdk-python-storage-file-share/12.14.0 Python/3.10.13 (Windows-10-10.0.22631-SP0) x-ms-date: - Fri, 12 Jan 2024 03:09:27 GMT x-ms-file-attributes: - none x-ms-file-creation-time: - now x-ms-file-last-write-time: - now x-ms-file-permission: - inherit x-ms-version: - '2023-08-03' method: PUT uri: https://fake_account_name.file.core.windows.net/fake-file-share-name/Users%2Funknown_user%2Fpromptflow?restype=directory response: body: string: "\uFEFF<?xml version=\"1.0\" encoding=\"utf-8\"?><Error><Code>ResourceAlreadyExists</Code><Message>The specified resource already exists.\nRequestId:7d1e602f-401a-00d2-8004-458e62000000\nTime:2024-01-12T03:09:28.1090476Z</Message></Error>" headers: content-length: - '228' content-type: - application/xml server: - Windows-Azure-File/1.0 Microsoft-HTTPAPI/2.0 x-ms-error-code: - ResourceAlreadyExists x-ms-version: - '2023-08-03' status: code: 409 message: The specified resource already exists. - request: body: null headers: Accept: - application/xml Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - azsdk-python-storage-file-share/12.14.0 Python/3.10.13 (Windows-10-10.0.22631-SP0) x-ms-date: - Fri, 12 Jan 2024 03:09:28 GMT x-ms-version: - '2023-08-03' method: GET uri: https://fake_account_name.file.core.windows.net/fake-file-share-name/Users%2Funknown_user%2Fpromptflow%2Fflow_name?restype=directory response: body: string: "\uFEFF<?xml version=\"1.0\" encoding=\"utf-8\"?><Error><Code>ResourceNotFound</Code><Message>The specified resource does not exist.\nRequestId:a23d675d-e01a-00e4-2b04-450312000000\nTime:2024-01-12T03:09:29.1076941Z</Message></Error>" headers: content-length: - '223' content-type: - application/xml server: - Windows-Azure-File/1.0 Microsoft-HTTPAPI/2.0 x-ms-error-code: - ResourceNotFound x-ms-version: - '2023-08-03' status: code: 404 message: The specified resource does not exist. - request: body: null headers: Accept: - application/xml Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '0' User-Agent: - azsdk-python-storage-file-share/12.14.0 Python/3.10.13 (Windows-10-10.0.22631-SP0) x-ms-date: - Fri, 12 Jan 2024 03:09:29 GMT x-ms-file-attributes: - none x-ms-file-creation-time: - now x-ms-file-last-write-time: - now x-ms-file-permission: - inherit x-ms-version: - '2023-08-03' method: PUT uri: https://fake_account_name.file.core.windows.net/fake-file-share-name/Users%2Funknown_user%2Fpromptflow%2Fflow_name?restype=directory response: body: string: '' headers: content-length: - '0' last-modified: - Fri, 12 Jan 2024 03:09:30 GMT server: - Windows-Azure-File/1.0 Microsoft-HTTPAPI/2.0 x-ms-file-attributes: - Directory x-ms-file-change-time: - '2024-01-12T03:09:30.2021517Z' x-ms-file-creation-time: - '2024-01-12T03:09:30.2021517Z' x-ms-file-id: - '13835074620971024384' x-ms-file-last-write-time: - '2024-01-12T03:09:30.2021517Z' x-ms-file-parent-id: - '10088082484072808448' x-ms-request-server-encrypted: - 'true' x-ms-version: - '2023-08-03' status: code: 201 message: Created - request: body: null headers: Accept: - application/xml Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '0' User-Agent: - azsdk-python-storage-file-share/12.14.0 Python/3.10.13 (Windows-10-10.0.22631-SP0) x-ms-date: - Fri, 12 Jan 2024 03:09:30 GMT x-ms-file-attributes: - none x-ms-file-creation-time: - now x-ms-file-last-write-time: - now x-ms-file-permission: - inherit x-ms-version: - '2023-08-03' method: PUT uri: https://fake_account_name.file.core.windows.net/fake-file-share-name/Users%2Funknown_user%2Fpromptflow%2Fflow_name%2F__pycache__?restype=directory response: body: string: '' headers: content-length: - '0' last-modified: - Fri, 12 Jan 2024 03:09:31 GMT server: - Windows-Azure-File/1.0 Microsoft-HTTPAPI/2.0 x-ms-file-attributes: - Directory x-ms-file-change-time: - '2024-01-12T03:09:31.2276463Z' x-ms-file-creation-time: - '2024-01-12T03:09:31.2276463Z' x-ms-file-id: - '13835144989715202048' x-ms-file-last-write-time: - '2024-01-12T03:09:31.2276463Z' x-ms-file-parent-id: - '13835074620971024384' x-ms-request-server-encrypted: - 'true' x-ms-version: - '2023-08-03' status: code: 201 message: Created - request: body: null headers: Accept: - application/xml Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '0' User-Agent: - azsdk-python-storage-file-share/12.14.0 Python/3.10.13 (Windows-10-10.0.22631-SP0) x-ms-content-length: - '14' x-ms-date: - Fri, 12 Jan 2024 03:09:31 GMT x-ms-file-attributes: - none x-ms-file-creation-time: - now x-ms-file-last-write-time: - now x-ms-file-permission: - Inherit x-ms-type: - file x-ms-version: - '2023-08-03' method: PUT uri: https://fake_account_name.file.core.windows.net/fake-file-share-name/Users/unknown_user/promptflow/flow_name/.gitattributes response: body: string: '' headers: content-length: - '0' last-modified: - Fri, 12 Jan 2024 03:09:32 GMT server: - Windows-Azure-File/1.0 Microsoft-HTTPAPI/2.0 x-ms-file-attributes: - Archive x-ms-file-change-time: - '2024-01-12T03:09:32.2511509Z' x-ms-file-creation-time: - '2024-01-12T03:09:32.2511509Z' x-ms-file-id: - '13835109805343113216' x-ms-file-last-write-time: - '2024-01-12T03:09:32.2511509Z' x-ms-file-parent-id: - '13835074620971024384' x-ms-request-server-encrypted: - 'true' x-ms-version: - '2023-08-03' status: code: 201 message: Created - request: body: '* text eol=lf ' headers: Accept: - application/xml Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '14' Content-MD5: - nYmkCopuDuFj82431amzZw== Content-Type: - application/octet-stream User-Agent: - azsdk-python-storage-file-share/12.14.0 Python/3.10.13 (Windows-10-10.0.22631-SP0) x-ms-date: - Fri, 12 Jan 2024 03:09:32 GMT x-ms-range: - bytes=0-13 x-ms-version: - '2023-08-03' x-ms-write: - update method: PUT uri: https://fake_account_name.file.core.windows.net/fake-file-share-name/Users/unknown_user/promptflow/flow_name/.gitattributes?comp=range response: body: string: '' headers: content-length: - '0' content-md5: - nYmkCopuDuFj82431amzZw== last-modified: - Fri, 12 Jan 2024 03:09:33 GMT server: - Windows-Azure-File/1.0 Microsoft-HTTPAPI/2.0 x-ms-file-last-write-time: - '2024-01-12T03:09:33.2915784Z' x-ms-request-server-encrypted: - 'true' x-ms-version: - '2023-08-03' status: code: 201 message: Created - request: body: null headers: Accept: - application/xml Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '0' User-Agent: - azsdk-python-storage-file-share/12.14.0 Python/3.10.13 (Windows-10-10.0.22631-SP0) x-ms-content-length: - '250' x-ms-date: - Fri, 12 Jan 2024 03:09:33 GMT x-ms-file-attributes: - none x-ms-file-creation-time: - now x-ms-file-last-write-time: - now x-ms-file-permission: - Inherit x-ms-type: - file x-ms-version: - '2023-08-03' method: PUT uri: https://fake_account_name.file.core.windows.net/fake-file-share-name/Users/unknown_user/promptflow/flow_name/flow.dag.yaml response: body: string: '' headers: content-length: - '0' last-modified: - Fri, 12 Jan 2024 03:09:34 GMT server: - Windows-Azure-File/1.0 Microsoft-HTTPAPI/2.0 x-ms-file-attributes: - Archive x-ms-file-change-time: - '2024-01-12T03:09:34.2991527Z' x-ms-file-creation-time: - '2024-01-12T03:09:34.2991527Z' x-ms-file-id: - '13835180174087290880' x-ms-file-last-write-time: - '2024-01-12T03:09:34.2991527Z' x-ms-file-parent-id: - '13835074620971024384' x-ms-request-server-encrypted: - 'true' x-ms-version: - '2023-08-03' status: code: 201 message: Created - request: body: "inputs:\n name:\n type: string\n default: hod\noutputs:\n result:\n \ type: string\n reference: ${hello_world.output}\nnodes:\n- name: hello_world\n \ type: python\n source:\n type: code\n path: hello_world.py\n inputs:\n \ name: ${inputs.name}\n" headers: Accept: - application/xml Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '250' Content-MD5: - CT1FTZp5JScB8fq+HjnINw== Content-Type: - application/octet-stream User-Agent: - azsdk-python-storage-file-share/12.14.0 Python/3.10.13 (Windows-10-10.0.22631-SP0) x-ms-date: - Fri, 12 Jan 2024 03:09:34 GMT x-ms-range: - bytes=0-249 x-ms-version: - '2023-08-03' x-ms-write: - update method: PUT uri: https://fake_account_name.file.core.windows.net/fake-file-share-name/Users/unknown_user/promptflow/flow_name/flow.dag.yaml?comp=range response: body: string: '' headers: content-length: - '0' content-md5: - CT1FTZp5JScB8fq+HjnINw== last-modified: - Fri, 12 Jan 2024 03:09:35 GMT server: - Windows-Azure-File/1.0 Microsoft-HTTPAPI/2.0 x-ms-file-last-write-time: - '2024-01-12T03:09:35.3614846Z' x-ms-request-server-encrypted: - 'true' x-ms-version: - '2023-08-03' status: code: 201 message: Created - request: body: null headers: Accept: - application/xml Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '0' User-Agent: - azsdk-python-storage-file-share/12.14.0 Python/3.10.13 (Windows-10-10.0.22631-SP0) x-ms-content-length: - '105' x-ms-date: - Fri, 12 Jan 2024 03:09:35 GMT x-ms-file-attributes: - none x-ms-file-creation-time: - now x-ms-file-last-write-time: - now x-ms-file-permission: - Inherit x-ms-type: - file x-ms-version: - '2023-08-03' method: PUT uri: https://fake_account_name.file.core.windows.net/fake-file-share-name/Users/unknown_user/promptflow/flow_name/hello_world.py response: body: string: '' headers: content-length: - '0' last-modified: - Fri, 12 Jan 2024 03:09:36 GMT server: - Windows-Azure-File/1.0 Microsoft-HTTPAPI/2.0 x-ms-file-attributes: - Archive x-ms-file-change-time: - '2024-01-12T03:09:36.3969355Z' x-ms-file-creation-time: - '2024-01-12T03:09:36.3969355Z' x-ms-file-id: - '13835092213157068800' x-ms-file-last-write-time: - '2024-01-12T03:09:36.3969355Z' x-ms-file-parent-id: - '13835074620971024384' x-ms-request-server-encrypted: - 'true' x-ms-version: - '2023-08-03' status: code: 201 message: Created - request: body: "from promptflow import tool\n\n\n@tool\ndef hello_world(name: str) -> str:\n \ return f\"Hello World {name}!\"\n" headers: Accept: - application/xml Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '105' Content-MD5: - fGMkkiZAjGs8PW/AMiYppA== Content-Type: - application/octet-stream User-Agent: - azsdk-python-storage-file-share/12.14.0 Python/3.10.13 (Windows-10-10.0.22631-SP0) x-ms-date: - Fri, 12 Jan 2024 03:09:36 GMT x-ms-range: - bytes=0-104 x-ms-version: - '2023-08-03' x-ms-write: - update method: PUT uri: https://fake_account_name.file.core.windows.net/fake-file-share-name/Users/unknown_user/promptflow/flow_name/hello_world.py?comp=range response: body: string: '' headers: content-length: - '0' content-md5: - fGMkkiZAjGs8PW/AMiYppA== last-modified: - Fri, 12 Jan 2024 03:09:37 GMT server: - Windows-Azure-File/1.0 Microsoft-HTTPAPI/2.0 x-ms-file-last-write-time: - '2024-01-12T03:09:37.4333819Z' x-ms-request-server-encrypted: - 'true' x-ms-version: - '2023-08-03' status: code: 201 message: Created - request: body: '{"flowName": "flow_display_name", "description": "test flow description", "tags": {"owner": "sdk-test"}, "flowDefinitionFilePath": "Users/unknown_user/promptflow/flow_name/flow.dag.yaml", "flowType": "default"}' headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive Content-Length: - '282' Content-Type: - application/json User-Agent: - promptflow-sdk/0.0.1 azsdk-python-azuremachinelearningdesignerserviceclient/unknown Python/3.10.13 (Windows-10-10.0.22631-SP0) method: POST uri: https://eastus.api.azureml.ms/flow/api/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/Flows response: body: string: '{"eTag": {}, "studioPortalEndpoint": "https://ml.azure.com/prompts/flow/3e123da1-f9a5-4c91-9234-8d9ffbb39ff5/cff27a23-7f84-4db2-924e-b1431ef4d735/details?wsid=/subscriptions/00000000-0000-0000-0000-000000000000/resourcegroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000", "flowId": "cff27a23-7f84-4db2-924e-b1431ef4d735", "flowName": "flow_display_name", "description": "test flow description", "tags": {"owner": "sdk-test"}, "flowType": "Default", "experimentId": "00000000-0000-0000-0000-000000000000", "createdDate": "2024-01-12T03:09:40.5100793Z", "lastModifiedDate": "2024-01-12T03:09:40.5100793Z", "owner": {"userObjectId": "00000000-0000-0000-0000-000000000000", "userTenantId": "00000000-0000-0000-0000-000000000000", "userName": "4cbd0e2e-aae4-4099-b4ba-94d3a4910587"}, "flowResourceId": "azureml://locations/eastus/workspaces/00000/flows/cff27a23-7f84-4db2-924e-b1431ef4d735", "isArchived": false, "flowDefinitionFilePath": "Users/unknown_user/promptflow/flow_name/flow.dag.yaml"}' headers: connection: - keep-alive content-length: - '1100' content-type: - application/json; charset=utf-8 strict-transport-security: - max-age=15724800; includeSubDomains; preload transfer-encoding: - chunked vary: - Accept-Encoding x-content-type-options: - nosniff x-request-time: - '1.060' status: code: 200 message: OK - request: body: null headers: Accept: - application/xml Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - azsdk-python-storage-file-share/12.14.0 Python/3.10.13 (Windows-10-10.0.22631-SP0) x-ms-date: - Fri, 12 Jan 2024 03:09:40 GMT x-ms-version: - '2023-08-03' method: HEAD uri: https://fake_account_name.file.core.windows.net/fake-file-share-name/Users/unknown_user/promptflow/flow_name/flow.dag.yaml response: body: string: '' headers: content-length: - '250' content-type: - application/octet-stream last-modified: - Fri, 12 Jan 2024 03:09:35 GMT server: - Windows-Azure-File/1.0 Microsoft-HTTPAPI/2.0 x-ms-file-attributes: - Archive x-ms-file-change-time: - '2024-01-12T03:09:35.3614846Z' x-ms-file-creation-time: - '2024-01-12T03:09:34.2991527Z' x-ms-file-id: - '13835180174087290880' x-ms-file-last-write-time: - '2024-01-12T03:09:35.3614846Z' x-ms-file-parent-id: - '13835074620971024384' x-ms-type: - File x-ms-version: - '2023-08-03' status: code: 200 message: OK version: 1

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/env_file

FOO=BAR

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/run_with_connections.yaml

flow: ../flows/web_classification data: ../datas/webClassification1.jsonl column_mapping: url: "${data.url}" variant: ${summarize_text_content.variant_0} # run config: env related environment_variables: env_file connections: classify_with_llm: connection: new_ai_connection

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/bulk_run_invalid_flow_path.yaml

name: flow_run_20230629_101205 description: sample bulk run # flow relative to current working directory should not be supported. flow: tests/test_configs/flows/web_classification data: ../datas/webClassification1.jsonl column_mapping: url: "${data.url}" variant: ${summarize_text_content.variant_0} # run config: env related environment_variables: env_file

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/run_with_connections_model.yaml

flow: ../flows/web_classification data: ../datas/webClassification1.jsonl column_mapping: url: "${data.url}" variant: ${summarize_text_content.variant_0} # run config: env related environment_variables: env_file connections: classify_with_llm: connection: new_ai_connection # model is also supported for openai connection model: test_model

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/sample_bulk_run.yaml

name: flow_run_20230629_101205 description: sample bulk run flow: ../flows/web_classification data: ../datas/webClassification1.jsonl column_mapping: url: "${data.url}" variant: ${summarize_text_content.variant_0} # run config: env related environment_variables: env_file

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/bulk_run_invalid_remote_flow_str.yaml

name: flow_run_20230629_101205 description: sample bulk run # invalid remote flow format should not be supported. flow: invalid_remote_flow data: ../datas/webClassification1.jsonl column_mapping: url: "${data.url}" variant: ${summarize_text_content.variant_0} # run config: env related environment_variables: env_file

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/sample_bulk_run_with_resources.yaml

name: flow_run_20230629_101205 flow: ../flows/web_classification data: ../datas/webClassification1.jsonl column_mapping: url: "${data.url}" variant: ${summarize_text_content.variant_0} resources: instance_type: Standard_D2 # optional, server default value idle_time_before_shutdown_minutes: 60 #optional, server default value

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/extra_field.yaml

name: flow_run_20230629_101205 description: sample bulk run flow: ../flows/web_classification data: ../datas/webClassification1.jsonl column_mapping: url: "${data.url}" variant: ${summarize_text_content.variant_0} extra_key: extra_value # run config: env related environment_variables: env_file

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/sample_eval_run.yaml

flow: ../flows/classification_accuracy_evaluation data: ../datas/webClassification1.jsonl column_mapping: groundtruth: "${data.answer}" prediction: "${run.outputs.category}" variant_id: "${data.variant_id}" run: flow_run_20230629_101205 # ./sample_bulk_run.yaml # run config: env related environment_variables: env_file

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/sample_bulk_run_cloud.yaml

name: flow_run_20230629_101205 flow: ../flows/web_classification data: ../datas/webClassification1.jsonl column_mapping: url: "${data.url}" variant: ${summarize_text_content.variant_0} # run config: env related environment_variables: env_file

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/run_with_env.yaml

flow: ../flows/print_env_var data: ../datas/env_var_names.jsonl # run config: env related environment_variables: API_BASE: ${azure_open_ai_connection.api_base}

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/input_with_dict_val.yaml

flow: ../flows/flow_with_dict_input data: ../datas/webClassification1.jsonl column_mapping: key: val1: 1 val2: 2 url: ${data.url}

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/illegal/non_exist_data.yaml

flow: ../flows/classification_accuracy_evaluation data: not_exist column_mapping: groundtruth: "${data.answer}" prediction: "${run.outputs.category}" run: flow_run_20230629_101205 # ./sample_bulk_run.yaml # run config: env related environment_variables: .env # optional connections: node_1: connection: test_llm_connection deployment_name: gpt-35-turbo

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/illegal/missing_data.yaml

flow: ../flows/classification_accuracy_evaluation column_mapping: groundtruth: "${data.answer}" prediction: "${run.outputs.category}" run: flow_run_20230629_101205 # ./sample_bulk_run.yaml # run config: env related environment_variables: .env # optional connections: node_1: connection: test_llm_connection deployment_name: gpt-35-turbo

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100/logs.txt

2023-12-05 04:03:11 +0000 119 promptflow-runtime INFO [web_classification_variant_0_20231205_120253_104100] Receiving v2 bulk run request 679b46cd-3931-4ade-985f-4d74de5e9536: {"flow_id": "web_classification_variant_0_20231205_120253_104100", "flow_run_id": "web_classification_variant_0_20231205_120253_104100", "flow_source": {"flow_source_type": 1, "flow_source_info": {"snapshot_id": "20444e8d-41a6-42c9-a3df-4970aff771df"}, "flow_dag_file": "flow.dag.yaml"}, "connections": "**data_scrubbed**", "log_path": "https://promptfloweast4063704120.blob.core.windows.net/azureml/ExperimentRun/dcid.web_classification_variant_0_20231205_120253_104100/logs/azureml/executionlogs.txt?sv=2019-07-07&sr=b&sig=**data_scrubbed**&skoid=55b92eba-d7c7-4afd-ab76-7bb1cd345283&sktid=72f988bf-86f1-41af-91ab-2d7cd011db47&skt=2023-12-04T20%3A28%3A14Z&ske=2023-12-06T04%3A38%3A14Z&sks=b&skv=2019-07-07&st=2023-12-05T03%3A53%3A10Z&se=2023-12-05T12%3A03%3A10Z&sp=rcw", "app_insights_instrumentation_key": "InstrumentationKey=**data_scrubbed**;IngestionEndpoint=https://eastus-6.in.applicationinsights.azure.com/;LiveEndpoint=https://eastus.livediagnostics.monitor.azure.com/", "data_inputs": {"data": "azureml://datastores/workspaceblobstore/paths/LocalUpload/70c38e61b28e21cb9ddcd67dbe209f05/data.jsonl"}, "inputs_mapping": {"url": "${data.url}"}, "azure_storage_setting": {"azure_storage_mode": 1, "storage_account_name": "promptfloweast4063704120", "blob_container_name": "azureml-blobstore-3e123da1-f9a5-4c91-9234-8d9ffbb39ff5", "flow_artifacts_root_path": "promptflow/PromptFlowArtifacts/web_classification_variant_0_20231205_120253_104100", "blob_container_sas_token": "?sv=2019-07-07&sr=c&sig=**data_scrubbed**&skoid=55b92eba-d7c7-4afd-ab76-7bb1cd345283&sktid=72f988bf-86f1-41af-91ab-2d7cd011db47&skt=2023-12-05T04%3A03%3A10Z&ske=2023-12-12T04%3A03%3A10Z&sks=b&skv=2019-07-07&se=2023-12-12T04%3A03%3A10Z&sp=racwl", "output_datastore_name": "workspaceblobstore"}} 2023-12-05 04:03:11 +0000 119 promptflow-runtime INFO Runtime version: 20231114.v2. PromptFlow version: 1.0.002.dev3 2023-12-05 04:03:11 +0000 119 promptflow-runtime INFO Updating web_classification_variant_0_20231205_120253_104100 to Status.Preparing... 2023-12-05 04:03:11 +0000 119 promptflow-runtime INFO Use spawn method to start child process. 2023-12-05 04:03:11 +0000 119 promptflow-runtime INFO Starting to check process 2795 status for run web_classification_variant_0_20231205_120253_104100 2023-12-05 04:03:11 +0000 119 promptflow-runtime INFO Start checking run status for run web_classification_variant_0_20231205_120253_104100 2023-12-05 04:03:16 +0000 2795 promptflow-runtime INFO [119--2795] Start processing flowV2...... 2023-12-05 04:03:16 +0000 2795 promptflow-runtime INFO Runtime version: 20231114.v2. PromptFlow version: 1.0.002.dev3 2023-12-05 04:03:16 +0000 2795 promptflow-runtime INFO Setting mlflow tracking uri... 2023-12-05 04:03:16 +0000 2795 promptflow-runtime INFO Validating 'AzureML Data Scientist' user authentication... 2023-12-05 04:03:17 +0000 2795 promptflow-runtime INFO Successfully validated 'AzureML Data Scientist' user authentication. 2023-12-05 04:03:17 +0000 2795 promptflow-runtime INFO Using AzureMLRunStorageV2 2023-12-05 04:03:17 +0000 2795 promptflow-runtime INFO Setting mlflow tracking uri to 'azureml://eastus.api.azureml.ms/mlflow/v1.0/subscriptions/96aede12-2f73-41cb-b983-6d11a904839b/resourceGroups/promptflow/providers/Microsoft.MachineLearningServices/workspaces/promptflow-eastus' 2023-12-05 04:03:17 +0000 2795 promptflow-runtime INFO Initialized blob service client for AzureMLRunTracker. 2023-12-05 04:03:17 +0000 2795 promptflow-runtime INFO Setting mlflow tracking uri to 'azureml://eastus.api.azureml.ms/mlflow/v1.0/subscriptions/96aede12-2f73-41cb-b983-6d11a904839b/resourceGroups/promptflow/providers/Microsoft.MachineLearningServices/workspaces/promptflow-eastus' 2023-12-05 04:03:17 +0000 2795 promptflow-runtime INFO Get snapshot sas url for 20444e8d-41a6-42c9-a3df-4970aff771df... 2023-12-05 04:03:23 +0000 2795 promptflow-runtime INFO Downloading snapshot 20444e8d-41a6-42c9-a3df-4970aff771df from uri https://promptfloweast4063704120.blob.core.windows.net/snapshotzips/promptflow-eastus:3e123da1-f9a5-4c91-9234-8d9ffbb39ff5:snapshotzip/20444e8d-41a6-42c9-a3df-4970aff771df.zip... 2023-12-05 04:03:24 +0000 2795 promptflow-runtime INFO Downloaded file /service/app/44783/requests/web_classification_variant_0_20231205_120253_104100/20444e8d-41a6-42c9-a3df-4970aff771df.zip with size 15337 for snapshot 20444e8d-41a6-42c9-a3df-4970aff771df. 2023-12-05 04:03:24 +0000 2795 promptflow-runtime INFO Download snapshot 20444e8d-41a6-42c9-a3df-4970aff771df completed. 2023-12-05 04:03:24 +0000 2795 promptflow-runtime INFO Resolve data from url finished in 0.6008251919411123 seconds 2023-12-05 04:03:24 +0000 2795 promptflow-runtime INFO Starting the aml run 'web_classification_variant_0_20231205_120253_104100'... 2023-12-05 04:03:25 +0000 2795 execution.bulk INFO Using fork, process count: 3 2023-12-05 04:03:25 +0000 2839 execution INFO Process 2839 started. 2023-12-05 04:03:25 +0000 2848 execution INFO Process 2848 started. 2023-12-05 04:03:25 +0000 2795 execution INFO Process name: ForkProcess-20:3, Process id: 2839, Line number: 0 start execution. 2023-12-05 04:03:25 +0000 2795 execution INFO Process name: ForkProcess-20:2, Process id: 2848, Line number: 1 start execution. 2023-12-05 04:03:25 +0000 2843 execution INFO Process 2843 started. 2023-12-05 04:03:25 +0000 2795 execution INFO Process name: ForkProcess-20:4, Process id: 2843, Line number: 2 start execution. 2023-12-05 04:03:27 +0000 2795 execution INFO Process name: ForkProcess-20:3, Process id: 2839, Line number: 0 completed. 2023-12-05 04:03:27 +0000 2795 execution.bulk INFO Finished 1 / 3 lines. 2023-12-05 04:03:27 +0000 2795 execution.bulk INFO Average execution time for completed lines: 2.03 seconds. Estimated time for incomplete lines: 4.06 seconds. 2023-12-05 04:03:28 +0000 2795 execution INFO Process name: ForkProcess-20:2, Process id: 2848, Line number: 1 completed. 2023-12-05 04:03:28 +0000 2795 execution.bulk INFO Finished 2 / 3 lines. 2023-12-05 04:03:28 +0000 2795 execution.bulk INFO Average execution time for completed lines: 1.33 seconds. Estimated time for incomplete lines: 1.33 seconds. 2023-12-05 04:03:28 +0000 2795 execution INFO Process name: ForkProcess-20:4, Process id: 2843, Line number: 2 completed. 2023-12-05 04:03:28 +0000 2795 execution.bulk INFO Finished 3 / 3 lines. 2023-12-05 04:03:28 +0000 2795 execution.bulk INFO Average execution time for completed lines: 1.05 seconds. Estimated time for incomplete lines: 0.0 seconds. 2023-12-05 04:03:32 +0000 2795 execution.bulk INFO Upload status summary metrics for run web_classification_variant_0_20231205_120253_104100 finished in 2.705599319888279 seconds 2023-12-05 04:03:32 +0000 2795 promptflow-runtime INFO Successfully write run properties {"azureml.promptflow.total_tokens": 3316, "_azureml.evaluate_artifacts": "[{\"path\": \"instance_results.jsonl\", \"type\": \"table\"}]"} with run id 'web_classification_variant_0_20231205_120253_104100' 2023-12-05 04:03:32 +0000 2795 execution.bulk INFO Upload RH properties for run web_classification_variant_0_20231205_120253_104100 finished in 0.07165036699734628 seconds 2023-12-05 04:03:32 +0000 2795 promptflow-runtime INFO Creating unregistered output Asset for Run web_classification_variant_0_20231205_120253_104100... 2023-12-05 04:03:32 +0000 2795 promptflow-runtime INFO Created debug_info Asset: azureml://locations/eastus/workspaces/3e123da1-f9a5-4c91-9234-8d9ffbb39ff5/data/azureml_web_classification_variant_0_20231205_120253_104100_output_data_debug_info/versions/1 2023-12-05 04:03:32 +0000 2795 promptflow-runtime INFO Creating unregistered output Asset for Run web_classification_variant_0_20231205_120253_104100... 2023-12-05 04:03:32 +0000 2795 promptflow-runtime INFO Created flow_outputs output Asset: azureml://locations/eastus/workspaces/3e123da1-f9a5-4c91-9234-8d9ffbb39ff5/data/azureml_web_classification_variant_0_20231205_120253_104100_output_data_flow_outputs/versions/1 2023-12-05 04:03:32 +0000 2795 promptflow-runtime INFO Creating Artifact for Run web_classification_variant_0_20231205_120253_104100... 2023-12-05 04:03:33 +0000 2795 promptflow-runtime INFO Created instance_results.jsonl Artifact. 2023-12-05 04:03:33 +0000 2795 promptflow-runtime INFO Patching web_classification_variant_0_20231205_120253_104100... 2023-12-05 04:03:33 +0000 2795 promptflow-runtime INFO Ending the aml run 'web_classification_variant_0_20231205_120253_104100' with status 'Completed'... 2023-12-05 04:03:34 +0000 119 promptflow-runtime INFO Process 2795 finished 2023-12-05 04:03:34 +0000 119 promptflow-runtime INFO [119] Child process finished! 2023-12-05 04:03:34 +0000 119 promptflow-runtime INFO [web_classification_variant_0_20231205_120253_104100] End processing bulk run

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100/instance_results.jsonl

{"line_number": 0, "status": "Completed", "inputs.url": "https://www.youtube.com/watch?v=kYqRtjDBci8", "inputs.line_number": 0, "category": "None", "evidence": "None"} {"line_number": 1, "status": "Completed", "inputs.url": "https://arxiv.org/abs/2307.04767", "inputs.line_number": 1, "category": "Academic", "evidence": "Both"} {"line_number": 2, "status": "Completed", "inputs.url": "https://play.google.com/store/apps/details?id=com.twitter.android", "inputs.line_number": 2, "category": "App", "evidence": "Both"}

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100/meta.json

{"batch_size": 1}

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100/metrics.json

{}

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100/run_metadata.json

{"name": "web_classification_variant_0_20231205_120253_104100", "created_on": "2023-12-05T04:03:05.951095+00:00", "status": "Completed", "display_name": "web_classification_variant_0_20231205_120253_104100", "description": null, "tags": {}, "properties": {"azureml.promptflow.runtime_name": "hod-ci", "azureml.promptflow.runtime_version": "20231114.v2", "azureml.promptflow.definition_file_name": "flow.dag.yaml", "azureml.promptflow.session_id": "e993078a71de767aeffa1e7f34403abafb105076b5822acf", "azureml.promptflow.flow_lineage_id": "a2759552d623c603e771307d04bf932caf85ea6202ad811e3500d6002aaf9d01", "azureml.promptflow.flow_definition_datastore_name": "workspaceblobstore", "azureml.promptflow.flow_definition_blob_path": "LocalUpload/20c734740ecfef7c7dd12c2024a95c3e/web-classification/flow.dag.yaml", "azureml.promptflow.inputs_mapping": "{\"url\":\"${data.url}\"}", "_azureml.evaluation_run": "promptflow.BatchRun", "azureml.promptflow.snapshot_id": "20444e8d-41a6-42c9-a3df-4970aff771df", "azureml.promptflow.total_tokens": "3316", "_azureml.evaluate_artifacts": "[{\"path\": \"instance_results.jsonl\", \"type\": \"table\"}]"}, "creation_context": {"userObjectId": "c05e0746-e125-4cb3-9213-a8b535eacd79", "userPuId": "10032000324F7449", "userIdp": null, "userAltSecId": null, "userIss": "https://sts.windows.net/72f988bf-86f1-41af-91ab-2d7cd011db47/", "userTenantId": "72f988bf-86f1-41af-91ab-2d7cd011db47", "userName": "Honglin Du", "upn": null}, "start_time": "2023-12-05T04:03:25.057138+00:00", "end_time": "2023-12-05T04:03:33.260979+00:00", "duration": "00:00:08.2038409", "portal_url": "https://ml.azure.com/prompts/flow/bulkrun/run/web_classification_variant_0_20231205_120253_104100/details?wsid=/subscriptions/96aede12-2f73-41cb-b983-6d11a904839b/resourceGroups/promptflow/providers/Microsoft.MachineLearningServices/workspaces/promptflow-eastus", "data": "azureml://datastores/workspaceblobstore/paths/LocalUpload/70c38e61b28e21cb9ddcd67dbe209f05/data.jsonl", "data_portal_url": "https://ml.azure.com/data/datastore/workspaceblobstore/edit?wsid=/subscriptions/96aede12-2f73-41cb-b983-6d11a904839b/resourceGroups/promptflow/providers/Microsoft.MachineLearningServices/workspaces/promptflow-eastus&activeFilePath=LocalUpload/70c38e61b28e21cb9ddcd67dbe209f05/data.jsonl#browseTab", "output": "azureml://locations/eastus/workspaces/3e123da1-f9a5-4c91-9234-8d9ffbb39ff5/data/azureml_web_classification_variant_0_20231205_120253_104100_output_data_flow_outputs/versions/1", "output_portal_url": "https://ml.azure.com/data/azureml_web_classification_variant_0_20231205_120253_104100_output_data_flow_outputs/1/details?wsid=/subscriptions/96aede12-2f73-41cb-b983-6d11a904839b/resourceGroups/promptflow/providers/Microsoft.MachineLearningServices/workspaces/promptflow-eastus"}

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100/node_artifacts

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100/node_artifacts/classify_with_llm/000000001.jsonl

{"node_name": "classify_with_llm", "line_number": 1, "run_info": {"node": "classify_with_llm", "flow_run_id": "web_classification_variant_0_20231205_120253_104100", "run_id": "web_classification_variant_0_20231205_120253_104100_classify_with_llm_1", "status": "Completed", "inputs": {"prompt": "system:\nYour task is to classify a given url into one of the following categories:\nMovie, App, Academic, Channel, Profile, PDF or None based on the text content information.\nThe classification will be based on the url, the webpage text content summary, or both.\n\nuser:\nThe selection range of the value of \"category\" must be within \"Movie\", \"App\", \"Academic\", \"Channel\", \"Profile\", \"PDF\" and \"None\".\nThe selection range of the value of \"evidence\" must be within \"Url\", \"Text content\", and \"Both\".\nHere are a few examples:\n{% for ex in examples %}\nURL: {{ex.url}}\nText content: {{ex.text_content}}\nOUTPUT:\n{\"category\": \"{{ex.category}}\", \"evidence\": \"{{ex.evidence}}\"}\n\n{% endfor %}\n\nFor a given URL and text content, classify the url to complete the category and indicate evidence:\nURL: {{url}}\nText content: {{text_content}}.\nOUTPUT:", "deployment_name": "gpt-35-turbo", "model": "gpt-3.5-turbo", "max_tokens": 128, "temperature": 0.2, "url": "https://arxiv.org/abs/2307.04767", "text_content": "The paper introduces Semantic-SAM, a universal image segmentation model that can segment and recognize objects at any desired granularity. The model has two key advantages: semantic-awareness and granularity-abundance. To achieve semantic-awareness, the model consolidates multiple datasets and introduces decoupled classification for objects and parts. This allows the model to capture rich semantic information. For multi-granularity capability, a multi-choice learning scheme is proposed during training, generating masks at multiple levels. The model is trained on SA-1B, generic, and part segmentation datasets. Experimental results and visualizations demonstrate the success of the model in achieving semantic-awareness and", "examples": [{"url": "https://play.google.com/store/apps/details?id=com.spotify.music", "text_content": "Spotify is a free music and podcast streaming app with millions of songs, albums, and original podcasts. It also offers audiobooks, so users can enjoy thousands of stories. It has a variety of features such as creating and sharing music playlists, discovering new music, and listening to popular and exclusive podcasts. It also has a Premium subscription option which allows users to download and listen offline, and access ad-free music. It is available on all devices and has a variety of genres and artists to choose from.", "category": "App", "evidence": "Both"}, {"url": "https://www.youtube.com/channel/UC_x5XG1OV2P6uZZ5FSM9Ttw", "text_content": "NFL Sunday Ticket is a service offered by Google LLC that allows users to watch NFL games on YouTube. It is available in 2023 and is subject to the terms and privacy policy of Google LLC. It is also subject to YouTube's terms of use and any applicable laws.", "category": "Channel", "evidence": "URL"}, {"url": "https://arxiv.org/abs/2303.04671", "text_content": "Visual ChatGPT is a system that enables users to interact with ChatGPT by sending and receiving not only languages but also images, providing complex visual questions or visual editing instructions, and providing feedback and asking for corrected results. It incorporates different Visual Foundation Models and is publicly available. Experiments show that Visual ChatGPT opens the door to investigating the visual roles of ChatGPT with the help of Visual Foundation Models.", "category": "Academic", "evidence": "Text content"}, {"url": "https://ab.politiaromana.ro/", "text_content": "There is no content available for this text.", "category": "None", "evidence": "None"}]}, "output": "{\"category\": \"Academic\", \"evidence\": \"Both\"}", "metrics": null, "error": null, "parent_run_id": "web_classification_variant_0_20231205_120253_104100_1", "start_time": "2023-12-05T04:03:27.670281Z", "end_time": "2023-12-05T04:03:27.957477Z", "index": 1, "api_calls": [{"name": "AzureOpenAI.chat", "type": "Tool", "inputs": {"prompt": "system:\nYour task is to classify a given url into one of the following categories:\nMovie, App, Academic, Channel, Profile, PDF or None based on the text content information.\nThe classification will be based on the url, the webpage text content summary, or both.\n\nuser:\nThe selection range of the value of \"category\" must be within \"Movie\", \"App\", \"Academic\", \"Channel\", \"Profile\", \"PDF\" and \"None\".\nThe selection range of the value of \"evidence\" must be within \"Url\", \"Text content\", and \"Both\".\nHere are a few examples:\n{% for ex in examples %}\nURL: {{ex.url}}\nText content: {{ex.text_content}}\nOUTPUT:\n{\"category\": \"{{ex.category}}\", \"evidence\": \"{{ex.evidence}}\"}\n\n{% endfor %}\n\nFor a given URL and text content, classify the url to complete the category and indicate evidence:\nURL: {{url}}\nText content: {{text_content}}.\nOUTPUT:", "deployment_name": "gpt-35-turbo", "model": "gpt-3.5-turbo", "max_tokens": 128, "temperature": 0.2, "url": "https://arxiv.org/abs/2307.04767", "text_content": "The paper introduces Semantic-SAM, a universal image segmentation model that can segment and recognize objects at any desired granularity. The model has two key advantages: semantic-awareness and granularity-abundance. To achieve semantic-awareness, the model consolidates multiple datasets and introduces decoupled classification for objects and parts. This allows the model to capture rich semantic information. For multi-granularity capability, a multi-choice learning scheme is proposed during training, generating masks at multiple levels. The model is trained on SA-1B, generic, and part segmentation datasets. Experimental results and visualizations demonstrate the success of the model in achieving semantic-awareness and", "examples": [{"url": "https://play.google.com/store/apps/details?id=com.spotify.music", "text_content": "Spotify is a free music and podcast streaming app with millions of songs, albums, and original podcasts. It also offers audiobooks, so users can enjoy thousands of stories. It has a variety of features such as creating and sharing music playlists, discovering new music, and listening to popular and exclusive podcasts. It also has a Premium subscription option which allows users to download and listen offline, and access ad-free music. It is available on all devices and has a variety of genres and artists to choose from.", "category": "App", "evidence": "Both"}, {"url": "https://www.youtube.com/channel/UC_x5XG1OV2P6uZZ5FSM9Ttw", "text_content": "NFL Sunday Ticket is a service offered by Google LLC that allows users to watch NFL games on YouTube. It is available in 2023 and is subject to the terms and privacy policy of Google LLC. It is also subject to YouTube's terms of use and any applicable laws.", "category": "Channel", "evidence": "URL"}, {"url": "https://arxiv.org/abs/2303.04671", "text_content": "Visual ChatGPT is a system that enables users to interact with ChatGPT by sending and receiving not only languages but also images, providing complex visual questions or visual editing instructions, and providing feedback and asking for corrected results. It incorporates different Visual Foundation Models and is publicly available. Experiments show that Visual ChatGPT opens the door to investigating the visual roles of ChatGPT with the help of Visual Foundation Models.", "category": "Academic", "evidence": "Text content"}, {"url": "https://ab.politiaromana.ro/", "text_content": "There is no content available for this text.", "category": "None", "evidence": "None"}]}, "output": "{\"category\": \"Academic\", \"evidence\": \"Both\"}", "start_time": 1701749007.670509, "end_time": 1701749007.956738, "error": null, "children": [{"name": "openai.api_resources.chat_completion.ChatCompletion.create", "type": "LLM", "inputs": {"api_base": "https://gpt-test-eus.openai.azure.com/", "api_type": "azure", "api_version": "2023-07-01-preview", "engine": "gpt-35-turbo", "messages": [{"role": "system", "content": "Your task is to classify a given url into one of the following categories:\nMovie, App, Academic, Channel, Profile, PDF or None based on the text content information.\nThe classification will be based on the url, the webpage text content summary, or both."}, {"role": "user", "content": "The selection range of the value of \"category\" must be within \"Movie\", \"App\", \"Academic\", \"Channel\", \"Profile\", \"PDF\" and \"None\".\nThe selection range of the value of \"evidence\" must be within \"Url\", \"Text content\", and \"Both\".\nHere are a few examples:\nURL: https://play.google.com/store/apps/details?id=com.spotify.music\nText content: Spotify is a free music and podcast streaming app with millions of songs, albums, and original podcasts. It also offers audiobooks, so users can enjoy thousands of stories. It has a variety of features such as creating and sharing music playlists, discovering new music, and listening to popular and exclusive podcasts. It also has a Premium subscription option which allows users to download and listen offline, and access ad-free music. It is available on all devices and has a variety of genres and artists to choose from.\nOUTPUT:\n{\"category\": \"App\", \"evidence\": \"Both\"}\n\nURL: https://www.youtube.com/channel/UC_x5XG1OV2P6uZZ5FSM9Ttw\nText content: NFL Sunday Ticket is a service offered by Google LLC that allows users to watch NFL games on YouTube. It is available in 2023 and is subject to the terms and privacy policy of Google LLC. It is also subject to YouTube's terms of use and any applicable laws.\nOUTPUT:\n{\"category\": \"Channel\", \"evidence\": \"URL\"}\n\nURL: https://arxiv.org/abs/2303.04671\nText content: Visual ChatGPT is a system that enables users to interact with ChatGPT by sending and receiving not only languages but also images, providing complex visual questions or visual editing instructions, and providing feedback and asking for corrected results. It incorporates different Visual Foundation Models and is publicly available. Experiments show that Visual ChatGPT opens the door to investigating the visual roles of ChatGPT with the help of Visual Foundation Models.\nOUTPUT:\n{\"category\": \"Academic\", \"evidence\": \"Text content\"}\n\nURL: https://ab.politiaromana.ro/\nText content: There is no content available for this text.\nOUTPUT:\n{\"category\": \"None\", \"evidence\": \"None\"}\n\n\nFor a given URL and text content, classify the url to complete the category and indicate evidence:\nURL: https://arxiv.org/abs/2307.04767\nText content: The paper introduces Semantic-SAM, a universal image segmentation model that can segment and recognize objects at any desired granularity. The model has two key advantages: semantic-awareness and granularity-abundance. To achieve semantic-awareness, the model consolidates multiple datasets and introduces decoupled classification for objects and parts. This allows the model to capture rich semantic information. For multi-granularity capability, a multi-choice learning scheme is proposed during training, generating masks at multiple levels. The model is trained on SA-1B, generic, and part segmentation datasets. Experimental results and visualizations demonstrate the success of the model in achieving semantic-awareness and.\nOUTPUT:"}], "temperature": 0.2, "top_p": 1.0, "n": 1, "stream": false, "stop": null, "max_tokens": 128, "presence_penalty": 0.0, "frequency_penalty": 0.0, "logit_bias": {}, "user": ""}, "output": {"id": "chatcmpl-8SGwZ8CajHzJc3UkbXeyX6MzNdFcD", "object": "chat.completion", "created": 1701749007, "model": "gpt-35-turbo", "prompt_filter_results": [{"prompt_index": 0, "content_filter_results": {"hate": {"filtered": false, "severity": "safe"}, "self_harm": {"filtered": false, "severity": "safe"}, "sexual": {"filtered": false, "severity": "safe"}, "violence": {"filtered": false, "severity": "safe"}}}], "choices": [{"index": 0, "finish_reason": "stop", "message": {"role": "assistant", "content": "{\"category\": \"Academic\", \"evidence\": \"Both\"}"}, "content_filter_results": {"hate": {"filtered": false, "severity": "safe"}, "self_harm": {"filtered": false, "severity": "safe"}, "sexual": {"filtered": false, "severity": "safe"}, "violence": {"filtered": false, "severity": "safe"}}}], "usage": {"prompt_tokens": 695, "completion_tokens": 14, "total_tokens": 709}}, "start_time": 1701749007.675928, "end_time": 1701749007.956625, "error": null, "children": null, "node_name": null}], "node_name": "classify_with_llm"}], "variant_id": "", "cached_run_id": null, "cached_flow_run_id": null, "logs": {"stdout": "", "stderr": ""}, "system_metrics": {"prompt_tokens": 695, "completion_tokens": 14, "total_tokens": 709, "duration": 0.287196}, "result": "{\"category\": \"Academic\", \"evidence\": \"Both\"}"}, "start_time": "2023-12-05T04:03:27.670281", "end_time": "2023-12-05T04:03:27.957477", "status": "Completed"}

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100/node_artifacts

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100/node_artifacts/classify_with_llm/000000002.jsonl

{"node_name": "classify_with_llm", "line_number": 2, "run_info": {"node": "classify_with_llm", "flow_run_id": "web_classification_variant_0_20231205_120253_104100", "run_id": "web_classification_variant_0_20231205_120253_104100_classify_with_llm_2", "status": "Completed", "inputs": {"prompt": "system:\nYour task is to classify a given url into one of the following categories:\nMovie, App, Academic, Channel, Profile, PDF or None based on the text content information.\nThe classification will be based on the url, the webpage text content summary, or both.\n\nuser:\nThe selection range of the value of \"category\" must be within \"Movie\", \"App\", \"Academic\", \"Channel\", \"Profile\", \"PDF\" and \"None\".\nThe selection range of the value of \"evidence\" must be within \"Url\", \"Text content\", and \"Both\".\nHere are a few examples:\n{% for ex in examples %}\nURL: {{ex.url}}\nText content: {{ex.text_content}}\nOUTPUT:\n{\"category\": \"{{ex.category}}\", \"evidence\": \"{{ex.evidence}}\"}\n\n{% endfor %}\n\nFor a given URL and text content, classify the url to complete the category and indicate evidence:\nURL: {{url}}\nText content: {{text_content}}.\nOUTPUT:", "deployment_name": "gpt-35-turbo", "model": "gpt-3.5-turbo", "max_tokens": 128, "temperature": 0.2, "url": "https://play.google.com/store/apps/details?id=com.twitter.android", "text_content": "The X app is a global digital platform where users can post content, join public conversations, stay updated on breaking news, and follow their interests. It offers features such as Community Notes for extra context, Spaces for live audio or video streaming, Direct Messages for private communication, and the option to subscribe to X Premium for expanded reach and exclusive content creation. Users can also create and join Communities, upload and watch videos, and write and read long-form posts. The app prioritizes data safety and allows users to request data deletion. It has a rating of 3.8 stars based on 21.5 million reviews and has been downloaded over", "examples": [{"url": "https://play.google.com/store/apps/details?id=com.spotify.music", "text_content": "Spotify is a free music and podcast streaming app with millions of songs, albums, and original podcasts. It also offers audiobooks, so users can enjoy thousands of stories. It has a variety of features such as creating and sharing music playlists, discovering new music, and listening to popular and exclusive podcasts. It also has a Premium subscription option which allows users to download and listen offline, and access ad-free music. It is available on all devices and has a variety of genres and artists to choose from.", "category": "App", "evidence": "Both"}, {"url": "https://www.youtube.com/channel/UC_x5XG1OV2P6uZZ5FSM9Ttw", "text_content": "NFL Sunday Ticket is a service offered by Google LLC that allows users to watch NFL games on YouTube. It is available in 2023 and is subject to the terms and privacy policy of Google LLC. It is also subject to YouTube's terms of use and any applicable laws.", "category": "Channel", "evidence": "URL"}, {"url": "https://arxiv.org/abs/2303.04671", "text_content": "Visual ChatGPT is a system that enables users to interact with ChatGPT by sending and receiving not only languages but also images, providing complex visual questions or visual editing instructions, and providing feedback and asking for corrected results. It incorporates different Visual Foundation Models and is publicly available. Experiments show that Visual ChatGPT opens the door to investigating the visual roles of ChatGPT with the help of Visual Foundation Models.", "category": "Academic", "evidence": "Text content"}, {"url": "https://ab.politiaromana.ro/", "text_content": "There is no content available for this text.", "category": "None", "evidence": "None"}]}, "output": "{\"category\": \"App\", \"evidence\": \"Both\"}", "metrics": null, "error": null, "parent_run_id": "web_classification_variant_0_20231205_120253_104100_2", "start_time": "2023-12-05T04:03:28.126270Z", "end_time": "2023-12-05T04:03:28.470558Z", "index": 2, "api_calls": [{"name": "AzureOpenAI.chat", "type": "Tool", "inputs": {"prompt": "system:\nYour task is to classify a given url into one of the following categories:\nMovie, App, Academic, Channel, Profile, PDF or None based on the text content information.\nThe classification will be based on the url, the webpage text content summary, or both.\n\nuser:\nThe selection range of the value of \"category\" must be within \"Movie\", \"App\", \"Academic\", \"Channel\", \"Profile\", \"PDF\" and \"None\".\nThe selection range of the value of \"evidence\" must be within \"Url\", \"Text content\", and \"Both\".\nHere are a few examples:\n{% for ex in examples %}\nURL: {{ex.url}}\nText content: {{ex.text_content}}\nOUTPUT:\n{\"category\": \"{{ex.category}}\", \"evidence\": \"{{ex.evidence}}\"}\n\n{% endfor %}\n\nFor a given URL and text content, classify the url to complete the category and indicate evidence:\nURL: {{url}}\nText content: {{text_content}}.\nOUTPUT:", "deployment_name": "gpt-35-turbo", "model": "gpt-3.5-turbo", "max_tokens": 128, "temperature": 0.2, "url": "https://play.google.com/store/apps/details?id=com.twitter.android", "text_content": "The X app is a global digital platform where users can post content, join public conversations, stay updated on breaking news, and follow their interests. It offers features such as Community Notes for extra context, Spaces for live audio or video streaming, Direct Messages for private communication, and the option to subscribe to X Premium for expanded reach and exclusive content creation. Users can also create and join Communities, upload and watch videos, and write and read long-form posts. The app prioritizes data safety and allows users to request data deletion. It has a rating of 3.8 stars based on 21.5 million reviews and has been downloaded over", "examples": [{"url": "https://play.google.com/store/apps/details?id=com.spotify.music", "text_content": "Spotify is a free music and podcast streaming app with millions of songs, albums, and original podcasts. It also offers audiobooks, so users can enjoy thousands of stories. It has a variety of features such as creating and sharing music playlists, discovering new music, and listening to popular and exclusive podcasts. It also has a Premium subscription option which allows users to download and listen offline, and access ad-free music. It is available on all devices and has a variety of genres and artists to choose from.", "category": "App", "evidence": "Both"}, {"url": "https://www.youtube.com/channel/UC_x5XG1OV2P6uZZ5FSM9Ttw", "text_content": "NFL Sunday Ticket is a service offered by Google LLC that allows users to watch NFL games on YouTube. It is available in 2023 and is subject to the terms and privacy policy of Google LLC. It is also subject to YouTube's terms of use and any applicable laws.", "category": "Channel", "evidence": "URL"}, {"url": "https://arxiv.org/abs/2303.04671", "text_content": "Visual ChatGPT is a system that enables users to interact with ChatGPT by sending and receiving not only languages but also images, providing complex visual questions or visual editing instructions, and providing feedback and asking for corrected results. It incorporates different Visual Foundation Models and is publicly available. Experiments show that Visual ChatGPT opens the door to investigating the visual roles of ChatGPT with the help of Visual Foundation Models.", "category": "Academic", "evidence": "Text content"}, {"url": "https://ab.politiaromana.ro/", "text_content": "There is no content available for this text.", "category": "None", "evidence": "None"}]}, "output": "{\"category\": \"App\", \"evidence\": \"Both\"}", "start_time": 1701749008.126513, "end_time": 1701749008.469702, "error": null, "children": [{"name": "openai.api_resources.chat_completion.ChatCompletion.create", "type": "LLM", "inputs": {"api_base": "https://gpt-test-eus.openai.azure.com/", "api_type": "azure", "api_version": "2023-07-01-preview", "engine": "gpt-35-turbo", "messages": [{"role": "system", "content": "Your task is to classify a given url into one of the following categories:\nMovie, App, Academic, Channel, Profile, PDF or None based on the text content information.\nThe classification will be based on the url, the webpage text content summary, or both."}, {"role": "user", "content": "The selection range of the value of \"category\" must be within \"Movie\", \"App\", \"Academic\", \"Channel\", \"Profile\", \"PDF\" and \"None\".\nThe selection range of the value of \"evidence\" must be within \"Url\", \"Text content\", and \"Both\".\nHere are a few examples:\nURL: https://play.google.com/store/apps/details?id=com.spotify.music\nText content: Spotify is a free music and podcast streaming app with millions of songs, albums, and original podcasts. It also offers audiobooks, so users can enjoy thousands of stories. It has a variety of features such as creating and sharing music playlists, discovering new music, and listening to popular and exclusive podcasts. It also has a Premium subscription option which allows users to download and listen offline, and access ad-free music. It is available on all devices and has a variety of genres and artists to choose from.\nOUTPUT:\n{\"category\": \"App\", \"evidence\": \"Both\"}\n\nURL: https://www.youtube.com/channel/UC_x5XG1OV2P6uZZ5FSM9Ttw\nText content: NFL Sunday Ticket is a service offered by Google LLC that allows users to watch NFL games on YouTube. It is available in 2023 and is subject to the terms and privacy policy of Google LLC. It is also subject to YouTube's terms of use and any applicable laws.\nOUTPUT:\n{\"category\": \"Channel\", \"evidence\": \"URL\"}\n\nURL: https://arxiv.org/abs/2303.04671\nText content: Visual ChatGPT is a system that enables users to interact with ChatGPT by sending and receiving not only languages but also images, providing complex visual questions or visual editing instructions, and providing feedback and asking for corrected results. It incorporates different Visual Foundation Models and is publicly available. Experiments show that Visual ChatGPT opens the door to investigating the visual roles of ChatGPT with the help of Visual Foundation Models.\nOUTPUT:\n{\"category\": \"Academic\", \"evidence\": \"Text content\"}\n\nURL: https://ab.politiaromana.ro/\nText content: There is no content available for this text.\nOUTPUT:\n{\"category\": \"None\", \"evidence\": \"None\"}\n\n\nFor a given URL and text content, classify the url to complete the category and indicate evidence:\nURL: https://play.google.com/store/apps/details?id=com.twitter.android\nText content: The X app is a global digital platform where users can post content, join public conversations, stay updated on breaking news, and follow their interests. It offers features such as Community Notes for extra context, Spaces for live audio or video streaming, Direct Messages for private communication, and the option to subscribe to X Premium for expanded reach and exclusive content creation. Users can also create and join Communities, upload and watch videos, and write and read long-form posts. The app prioritizes data safety and allows users to request data deletion. It has a rating of 3.8 stars based on 21.5 million reviews and has been downloaded over.\nOUTPUT:"}], "temperature": 0.2, "top_p": 1.0, "n": 1, "stream": false, "stop": null, "max_tokens": 128, "presence_penalty": 0.0, "frequency_penalty": 0.0, "logit_bias": {}, "user": ""}, "output": {"id": "chatcmpl-8SGwaHVVKGYxKtoLEQKFdvAmblBBc", "object": "chat.completion", "created": 1701749008, "model": "gpt-35-turbo", "prompt_filter_results": [{"prompt_index": 0, "content_filter_results": {"hate": {"filtered": false, "severity": "safe"}, "self_harm": {"filtered": false, "severity": "safe"}, "sexual": {"filtered": false, "severity": "safe"}, "violence": {"filtered": false, "severity": "safe"}}}], "choices": [{"index": 0, "finish_reason": "stop", "message": {"role": "assistant", "content": "{\"category\": \"App\", \"evidence\": \"Both\"}"}, "content_filter_results": {"hate": {"filtered": false, "severity": "safe"}, "self_harm": {"filtered": false, "severity": "safe"}, "sexual": {"filtered": false, "severity": "safe"}, "violence": {"filtered": false, "severity": "safe"}}}], "usage": {"prompt_tokens": 694, "completion_tokens": 13, "total_tokens": 707}}, "start_time": 1701749008.130586, "end_time": 1701749008.469585, "error": null, "children": null, "node_name": null}], "node_name": "classify_with_llm"}], "variant_id": "", "cached_run_id": null, "cached_flow_run_id": null, "logs": {"stdout": "", "stderr": ""}, "system_metrics": {"prompt_tokens": 694, "completion_tokens": 13, "total_tokens": 707, "duration": 0.344288}, "result": "{\"category\": \"App\", \"evidence\": \"Both\"}"}, "start_time": "2023-12-05T04:03:28.126270", "end_time": "2023-12-05T04:03:28.470558", "status": "Completed"}

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100/node_artifacts

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100/node_artifacts/classify_with_llm/000000000.jsonl

{"node_name": "classify_with_llm", "line_number": 0, "run_info": {"node": "classify_with_llm", "flow_run_id": "web_classification_variant_0_20231205_120253_104100", "run_id": "web_classification_variant_0_20231205_120253_104100_classify_with_llm_0", "status": "Completed", "inputs": {"prompt": "system:\nYour task is to classify a given url into one of the following categories:\nMovie, App, Academic, Channel, Profile, PDF or None based on the text content information.\nThe classification will be based on the url, the webpage text content summary, or both.\n\nuser:\nThe selection range of the value of \"category\" must be within \"Movie\", \"App\", \"Academic\", \"Channel\", \"Profile\", \"PDF\" and \"None\".\nThe selection range of the value of \"evidence\" must be within \"Url\", \"Text content\", and \"Both\".\nHere are a few examples:\n{% for ex in examples %}\nURL: {{ex.url}}\nText content: {{ex.text_content}}\nOUTPUT:\n{\"category\": \"{{ex.category}}\", \"evidence\": \"{{ex.evidence}}\"}\n\n{% endfor %}\n\nFor a given URL and text content, classify the url to complete the category and indicate evidence:\nURL: {{url}}\nText content: {{text_content}}.\nOUTPUT:", "deployment_name": "gpt-35-turbo", "model": "gpt-3.5-turbo", "max_tokens": 128, "temperature": 0.2, "url": "https://www.youtube.com/watch?v=kYqRtjDBci8", "text_content": "The author expresses their excitement about the prompt flow feature in Azure AI, which has helped them accelerate the development of their LLM application.", "examples": [{"url": "https://play.google.com/store/apps/details?id=com.spotify.music", "text_content": "Spotify is a free music and podcast streaming app with millions of songs, albums, and original podcasts. It also offers audiobooks, so users can enjoy thousands of stories. It has a variety of features such as creating and sharing music playlists, discovering new music, and listening to popular and exclusive podcasts. It also has a Premium subscription option which allows users to download and listen offline, and access ad-free music. It is available on all devices and has a variety of genres and artists to choose from.", "category": "App", "evidence": "Both"}, {"url": "https://www.youtube.com/channel/UC_x5XG1OV2P6uZZ5FSM9Ttw", "text_content": "NFL Sunday Ticket is a service offered by Google LLC that allows users to watch NFL games on YouTube. It is available in 2023 and is subject to the terms and privacy policy of Google LLC. It is also subject to YouTube's terms of use and any applicable laws.", "category": "Channel", "evidence": "URL"}, {"url": "https://arxiv.org/abs/2303.04671", "text_content": "Visual ChatGPT is a system that enables users to interact with ChatGPT by sending and receiving not only languages but also images, providing complex visual questions or visual editing instructions, and providing feedback and asking for corrected results. It incorporates different Visual Foundation Models and is publicly available. Experiments show that Visual ChatGPT opens the door to investigating the visual roles of ChatGPT with the help of Visual Foundation Models.", "category": "Academic", "evidence": "Text content"}, {"url": "https://ab.politiaromana.ro/", "text_content": "There is no content available for this text.", "category": "None", "evidence": "None"}]}, "output": "{\"category\": \"None\", \"evidence\": \"None\"}", "metrics": null, "error": null, "parent_run_id": "web_classification_variant_0_20231205_120253_104100_0", "start_time": "2023-12-05T04:03:26.996107Z", "end_time": "2023-12-05T04:03:27.335701Z", "index": 0, "api_calls": [{"name": "AzureOpenAI.chat", "type": "Tool", "inputs": {"prompt": "system:\nYour task is to classify a given url into one of the following categories:\nMovie, App, Academic, Channel, Profile, PDF or None based on the text content information.\nThe classification will be based on the url, the webpage text content summary, or both.\n\nuser:\nThe selection range of the value of \"category\" must be within \"Movie\", \"App\", \"Academic\", \"Channel\", \"Profile\", \"PDF\" and \"None\".\nThe selection range of the value of \"evidence\" must be within \"Url\", \"Text content\", and \"Both\".\nHere are a few examples:\n{% for ex in examples %}\nURL: {{ex.url}}\nText content: {{ex.text_content}}\nOUTPUT:\n{\"category\": \"{{ex.category}}\", \"evidence\": \"{{ex.evidence}}\"}\n\n{% endfor %}\n\nFor a given URL and text content, classify the url to complete the category and indicate evidence:\nURL: {{url}}\nText content: {{text_content}}.\nOUTPUT:", "deployment_name": "gpt-35-turbo", "model": "gpt-3.5-turbo", "max_tokens": 128, "temperature": 0.2, "url": "https://www.youtube.com/watch?v=kYqRtjDBci8", "text_content": "The author expresses their excitement about the prompt flow feature in Azure AI, which has helped them accelerate the development of their LLM application.", "examples": [{"url": "https://play.google.com/store/apps/details?id=com.spotify.music", "text_content": "Spotify is a free music and podcast streaming app with millions of songs, albums, and original podcasts. It also offers audiobooks, so users can enjoy thousands of stories. It has a variety of features such as creating and sharing music playlists, discovering new music, and listening to popular and exclusive podcasts. It also has a Premium subscription option which allows users to download and listen offline, and access ad-free music. It is available on all devices and has a variety of genres and artists to choose from.", "category": "App", "evidence": "Both"}, {"url": "https://www.youtube.com/channel/UC_x5XG1OV2P6uZZ5FSM9Ttw", "text_content": "NFL Sunday Ticket is a service offered by Google LLC that allows users to watch NFL games on YouTube. It is available in 2023 and is subject to the terms and privacy policy of Google LLC. It is also subject to YouTube's terms of use and any applicable laws.", "category": "Channel", "evidence": "URL"}, {"url": "https://arxiv.org/abs/2303.04671", "text_content": "Visual ChatGPT is a system that enables users to interact with ChatGPT by sending and receiving not only languages but also images, providing complex visual questions or visual editing instructions, and providing feedback and asking for corrected results. It incorporates different Visual Foundation Models and is publicly available. Experiments show that Visual ChatGPT opens the door to investigating the visual roles of ChatGPT with the help of Visual Foundation Models.", "category": "Academic", "evidence": "Text content"}, {"url": "https://ab.politiaromana.ro/", "text_content": "There is no content available for this text.", "category": "None", "evidence": "None"}]}, "output": "{\"category\": \"None\", \"evidence\": \"None\"}", "start_time": 1701749006.996314, "end_time": 1701749007.334935, "error": null, "children": [{"name": "openai.api_resources.chat_completion.ChatCompletion.create", "type": "LLM", "inputs": {"api_base": "https://gpt-test-eus.openai.azure.com/", "api_type": "azure", "api_version": "2023-07-01-preview", "engine": "gpt-35-turbo", "messages": [{"role": "system", "content": "Your task is to classify a given url into one of the following categories:\nMovie, App, Academic, Channel, Profile, PDF or None based on the text content information.\nThe classification will be based on the url, the webpage text content summary, or both."}, {"role": "user", "content": "The selection range of the value of \"category\" must be within \"Movie\", \"App\", \"Academic\", \"Channel\", \"Profile\", \"PDF\" and \"None\".\nThe selection range of the value of \"evidence\" must be within \"Url\", \"Text content\", and \"Both\".\nHere are a few examples:\nURL: https://play.google.com/store/apps/details?id=com.spotify.music\nText content: Spotify is a free music and podcast streaming app with millions of songs, albums, and original podcasts. It also offers audiobooks, so users can enjoy thousands of stories. It has a variety of features such as creating and sharing music playlists, discovering new music, and listening to popular and exclusive podcasts. It also has a Premium subscription option which allows users to download and listen offline, and access ad-free music. It is available on all devices and has a variety of genres and artists to choose from.\nOUTPUT:\n{\"category\": \"App\", \"evidence\": \"Both\"}\n\nURL: https://www.youtube.com/channel/UC_x5XG1OV2P6uZZ5FSM9Ttw\nText content: NFL Sunday Ticket is a service offered by Google LLC that allows users to watch NFL games on YouTube. It is available in 2023 and is subject to the terms and privacy policy of Google LLC. It is also subject to YouTube's terms of use and any applicable laws.\nOUTPUT:\n{\"category\": \"Channel\", \"evidence\": \"URL\"}\n\nURL: https://arxiv.org/abs/2303.04671\nText content: Visual ChatGPT is a system that enables users to interact with ChatGPT by sending and receiving not only languages but also images, providing complex visual questions or visual editing instructions, and providing feedback and asking for corrected results. It incorporates different Visual Foundation Models and is publicly available. Experiments show that Visual ChatGPT opens the door to investigating the visual roles of ChatGPT with the help of Visual Foundation Models.\nOUTPUT:\n{\"category\": \"Academic\", \"evidence\": \"Text content\"}\n\nURL: https://ab.politiaromana.ro/\nText content: There is no content available for this text.\nOUTPUT:\n{\"category\": \"None\", \"evidence\": \"None\"}\n\n\nFor a given URL and text content, classify the url to complete the category and indicate evidence:\nURL: https://www.youtube.com/watch?v=kYqRtjDBci8\nText content: The author expresses their excitement about the prompt flow feature in Azure AI, which has helped them accelerate the development of their LLM application..\nOUTPUT:"}], "temperature": 0.2, "top_p": 1.0, "n": 1, "stream": false, "stop": null, "max_tokens": 128, "presence_penalty": 0.0, "frequency_penalty": 0.0, "logit_bias": {}, "user": ""}, "output": {"id": "chatcmpl-8SGwZ2wUSr8u0pse8KUjbPgz08Cvt", "object": "chat.completion", "created": 1701749007, "model": "gpt-35-turbo", "prompt_filter_results": [{"prompt_index": 0, "content_filter_results": {"hate": {"filtered": false, "severity": "safe"}, "self_harm": {"filtered": false, "severity": "safe"}, "sexual": {"filtered": false, "severity": "safe"}, "violence": {"filtered": false, "severity": "safe"}}}], "choices": [{"index": 0, "finish_reason": "stop", "message": {"role": "assistant", "content": "{\"category\": \"None\", \"evidence\": \"None\"}"}, "content_filter_results": {"hate": {"filtered": false, "severity": "safe"}, "self_harm": {"filtered": false, "severity": "safe"}, "sexual": {"filtered": false, "severity": "safe"}, "violence": {"filtered": false, "severity": "safe"}}}], "usage": {"prompt_tokens": 596, "completion_tokens": 13, "total_tokens": 609}}, "start_time": 1701749006.99988, "end_time": 1701749007.33482, "error": null, "children": null, "node_name": null}], "node_name": "classify_with_llm"}], "variant_id": "", "cached_run_id": null, "cached_flow_run_id": null, "logs": {"stdout": "", "stderr": ""}, "system_metrics": {"prompt_tokens": 596, "completion_tokens": 13, "total_tokens": 609, "duration": 0.339594}, "result": "{\"category\": \"None\", \"evidence\": \"None\"}"}, "start_time": "2023-12-05T04:03:26.996107", "end_time": "2023-12-05T04:03:27.335701", "status": "Completed"}

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100/node_artifacts

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100/node_artifacts/summarize_text_content/000000001.jsonl

{"node_name": "summarize_text_content", "line_number": 1, "run_info": {"node": "summarize_text_content", "flow_run_id": "web_classification_variant_0_20231205_120253_104100", "run_id": "web_classification_variant_0_20231205_120253_104100_summarize_text_content_1", "status": "Completed", "inputs": {"prompt": "system:\nPlease summarize the following text in one paragraph. 100 words.\nDo not add any information that is not in the text.\n\nuser:\nText: {{text}}\nSummary: ", "deployment_name": "gpt-35-turbo", "model": "gpt-3.5-turbo", "max_tokens": 128, "temperature": 0.2, "text": "\n\n\n [2307.04767] Semantic-SAM: Segment and Recognize Anything at Any Granularity\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nSkip to main content\n\n\n\n\n\nWe gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate\n\n\n\n\n\n > cs > arXiv:2307.04767\n \n\n\n\n\n\nHelp | Advanced Search\n\n\n\n\nAll fields\nTitle\nAuthor\nAbstract\nComments\nJournal reference\nACM classification\nMSC classification\nReport number\narXiv identifier\nDOI\nORCID\narXiv author ID\nHelp pages\nFull text\n\n\n\n\nSearch\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nopen search\n\n\n\n\n\n\nGO\n\n\n\nopen navigation menu\n\n\nquick links\n\nLogin\nHelp Pages\nAbout\n\n\n\n\n\n\n\n\n\n\n\n\nComputer Science > Computer Vision and Pattern Recognition\n\n\narXiv:2307.04767 (cs)\n \n\n\n\n\n [Submitted on 10 Jul 2023]\nTitle:Semantic-SAM: Segment and Recognize Anything at Any Granularity\nAuthors:Feng Li, Hao Zhang, Peize Sun, Xueyan Zou, Shilong Liu, Jianwei Yang, Chunyuan Li, Lei Zhang, Jianfeng Gao Download a PDF of the paper titled Semantic-SAM: Segment and Recognize Anything at Any Granularity, by Feng Li and 8 other authors\nDownload PDF\n\nAbstract:In this paper, we introduce Semantic-SAM, a universal image segmentation model to enable segment and recognize anything at any desired granularity. Our model offers two key advantages: semantic-awareness and granularity-abundance. To achieve semantic-awareness, we consolidate multiple datasets across three granularities and introduce decoupled classification for objects and parts. This allows our model to capture rich semantic information. For the multi-granularity capability, we propose a multi-choice learning scheme during training, enabling each click to generate masks at multiple levels that correspond to multiple ground-truth masks. Notably, this work represents the first attempt to jointly train a model on SA-1B, generic, and part segmentation datasets. Experimental results and visualizations demonstrate that our model successfully achieves semantic-awareness and granularity-abundance. Furthermore, co"}, "output": "The paper introduces Semantic-SAM, a universal image segmentation model that can segment and recognize objects at any desired granularity. The model has two key advantages: semantic-awareness and granularity-abundance. To achieve semantic-awareness, the model consolidates multiple datasets and introduces decoupled classification for objects and parts. This allows the model to capture rich semantic information. For multi-granularity capability, a multi-choice learning scheme is proposed during training, generating masks at multiple levels. The model is trained on SA-1B, generic, and part segmentation datasets. Experimental results and visualizations demonstrate the success of the model in achieving semantic-awareness and", "metrics": null, "error": null, "parent_run_id": "web_classification_variant_0_20231205_120253_104100_1", "start_time": "2023-12-05T04:03:26.035429Z", "end_time": "2023-12-05T04:03:27.668794Z", "index": 1, "api_calls": [{"name": "AzureOpenAI.chat", "type": "Tool", "inputs": {"prompt": "system:\nPlease summarize the following text in one paragraph. 100 words.\nDo not add any information that is not in the text.\n\nuser:\nText: {{text}}\nSummary: ", "deployment_name": "gpt-35-turbo", "model": "gpt-3.5-turbo", "max_tokens": 128, "temperature": 0.2, "text": "\n\n\n [2307.04767] Semantic-SAM: Segment and Recognize Anything at Any Granularity\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nSkip to main content\n\n\n\n\n\nWe gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate\n\n\n\n\n\n > cs > arXiv:2307.04767\n \n\n\n\n\n\nHelp | Advanced Search\n\n\n\n\nAll fields\nTitle\nAuthor\nAbstract\nComments\nJournal reference\nACM classification\nMSC classification\nReport number\narXiv identifier\nDOI\nORCID\narXiv author ID\nHelp pages\nFull text\n\n\n\n\nSearch\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nopen search\n\n\n\n\n\n\nGO\n\n\n\nopen navigation menu\n\n\nquick links\n\nLogin\nHelp Pages\nAbout\n\n\n\n\n\n\n\n\n\n\n\n\nComputer Science > Computer Vision and Pattern Recognition\n\n\narXiv:2307.04767 (cs)\n \n\n\n\n\n [Submitted on 10 Jul 2023]\nTitle:Semantic-SAM: Segment and Recognize Anything at Any Granularity\nAuthors:Feng Li, Hao Zhang, Peize Sun, Xueyan Zou, Shilong Liu, Jianwei Yang, Chunyuan Li, Lei Zhang, Jianfeng Gao Download a PDF of the paper titled Semantic-SAM: Segment and Recognize Anything at Any Granularity, by Feng Li and 8 other authors\nDownload PDF\n\nAbstract:In this paper, we introduce Semantic-SAM, a universal image segmentation model to enable segment and recognize anything at any desired granularity. Our model offers two key advantages: semantic-awareness and granularity-abundance. To achieve semantic-awareness, we consolidate multiple datasets across three granularities and introduce decoupled classification for objects and parts. This allows our model to capture rich semantic information. For the multi-granularity capability, we propose a multi-choice learning scheme during training, enabling each click to generate masks at multiple levels that correspond to multiple ground-truth masks. Notably, this work represents the first attempt to jointly train a model on SA-1B, generic, and part segmentation datasets. Experimental results and visualizations demonstrate that our model successfully achieves semantic-awareness and granularity-abundance. Furthermore, co"}, "output": "The paper introduces Semantic-SAM, a universal image segmentation model that can segment and recognize objects at any desired granularity. The model has two key advantages: semantic-awareness and granularity-abundance. To achieve semantic-awareness, the model consolidates multiple datasets and introduces decoupled classification for objects and parts. This allows the model to capture rich semantic information. For multi-granularity capability, a multi-choice learning scheme is proposed during training, generating masks at multiple levels. The model is trained on SA-1B, generic, and part segmentation datasets. Experimental results and visualizations demonstrate the success of the model in achieving semantic-awareness and", "start_time": 1701749006.035773, "end_time": 1701749007.668153, "error": null, "children": [{"name": "openai.api_resources.chat_completion.ChatCompletion.create", "type": "LLM", "inputs": {"api_base": "https://gpt-test-eus.openai.azure.com/", "api_type": "azure", "api_version": "2023-07-01-preview", "engine": "gpt-35-turbo", "messages": [{"role": "system", "content": "Please summarize the following text in one paragraph. 100 words.\nDo not add any information that is not in the text."}, {"role": "user", "content": "Text: \n\n\n [2307.04767] Semantic-SAM: Segment and Recognize Anything at Any Granularity\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nSkip to main content\n\n\n\n\n\nWe gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate\n\n\n\n\n\n > cs > arXiv:2307.04767\n \n\n\n\n\n\nHelp | Advanced Search\n\n\n\n\nAll fields\nTitle\nAuthor\nAbstract\nComments\nJournal reference\nACM classification\nMSC classification\nReport number\narXiv identifier\nDOI\nORCID\narXiv author ID\nHelp pages\nFull text\n\n\n\n\nSearch\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nopen search\n\n\n\n\n\n\nGO\n\n\n\nopen navigation menu\n\n\nquick links\n\nLogin\nHelp Pages\nAbout\n\n\n\n\n\n\n\n\n\n\n\n\nComputer Science > Computer Vision and Pattern Recognition\n\n\narXiv:2307.04767 (cs)\n \n\n\n\n\n [Submitted on 10 Jul 2023]\nTitle:Semantic-SAM: Segment and Recognize Anything at Any Granularity\nAuthors:Feng Li, Hao Zhang, Peize Sun, Xueyan Zou, Shilong Liu, Jianwei Yang, Chunyuan Li, Lei Zhang, Jianfeng Gao Download a PDF of the paper titled Semantic-SAM: Segment and Recognize Anything at Any Granularity, by Feng Li and 8 other authors\nDownload PDF\n\nAbstract:In this paper, we introduce Semantic-SAM, a universal image segmentation model to enable segment and recognize anything at any desired granularity. Our model offers two key advantages: semantic-awareness and granularity-abundance. To achieve semantic-awareness, we consolidate multiple datasets across three granularities and introduce decoupled classification for objects and parts. This allows our model to capture rich semantic information. For the multi-granularity capability, we propose a multi-choice learning scheme during training, enabling each click to generate masks at multiple levels that correspond to multiple ground-truth masks. Notably, this work represents the first attempt to jointly train a model on SA-1B, generic, and part segmentation datasets. Experimental results and visualizations demonstrate that our model successfully achieves semantic-awareness and granularity-abundance. Furthermore, co\nSummary: "}], "temperature": 0.2, "top_p": 1.0, "n": 1, "stream": false, "stop": null, "max_tokens": 128, "presence_penalty": 0.0, "frequency_penalty": 0.0, "logit_bias": {}, "user": ""}, "output": {"id": "chatcmpl-8SGwY6eyhMgFtmIhNkFmO6e2awkvq", "object": "chat.completion", "created": 1701749006, "model": "gpt-35-turbo", "prompt_filter_results": [{"prompt_index": 0, "content_filter_results": {"hate": {"filtered": false, "severity": "safe"}, "self_harm": {"filtered": false, "severity": "safe"}, "sexual": {"filtered": false, "severity": "safe"}, "violence": {"filtered": false, "severity": "safe"}}}], "choices": [{"index": 0, "finish_reason": "length", "message": {"role": "assistant", "content": "The paper introduces Semantic-SAM, a universal image segmentation model that can segment and recognize objects at any desired granularity. The model has two key advantages: semantic-awareness and granularity-abundance. To achieve semantic-awareness, the model consolidates multiple datasets and introduces decoupled classification for objects and parts. This allows the model to capture rich semantic information. For multi-granularity capability, a multi-choice learning scheme is proposed during training, generating masks at multiple levels. The model is trained on SA-1B, generic, and part segmentation datasets. Experimental results and visualizations demonstrate the success of the model in achieving semantic-awareness and"}, "content_filter_results": {"hate": {"filtered": false, "severity": "safe"}, "self_harm": {"filtered": false, "severity": "safe"}, "sexual": {"filtered": false, "severity": "safe"}, "violence": {"filtered": false, "severity": "safe"}}}], "usage": {"prompt_tokens": 468, "completion_tokens": 128, "total_tokens": 596}}, "start_time": 1701749006.052093, "end_time": 1701749007.668043, "error": null, "children": null, "node_name": null}], "node_name": "summarize_text_content"}], "variant_id": "", "cached_run_id": null, "cached_flow_run_id": null, "logs": {"stdout": "", "stderr": ""}, "system_metrics": {"prompt_tokens": 468, "completion_tokens": 128, "total_tokens": 596, "duration": 1.633365}, "result": "The paper introduces Semantic-SAM, a universal image segmentation model that can segment and recognize objects at any desired granularity. The model has two key advantages: semantic-awareness and granularity-abundance. To achieve semantic-awareness, the model consolidates multiple datasets and introduces decoupled classification for objects and parts. This allows the model to capture rich semantic information. For multi-granularity capability, a multi-choice learning scheme is proposed during training, generating masks at multiple levels. The model is trained on SA-1B, generic, and part segmentation datasets. Experimental results and visualizations demonstrate the success of the model in achieving semantic-awareness and"}, "start_time": "2023-12-05T04:03:26.035429", "end_time": "2023-12-05T04:03:27.668794", "status": "Completed"}

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100/node_artifacts

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100/node_artifacts/summarize_text_content/000000002.jsonl

{"node_name": "summarize_text_content", "line_number": 2, "run_info": {"node": "summarize_text_content", "flow_run_id": "web_classification_variant_0_20231205_120253_104100", "run_id": "web_classification_variant_0_20231205_120253_104100_summarize_text_content_2", "status": "Completed", "inputs": {"prompt": "system:\nPlease summarize the following text in one paragraph. 100 words.\nDo not add any information that is not in the text.\n\nuser:\nText: {{text}}\nSummary: ", "deployment_name": "gpt-35-turbo", "model": "gpt-3.5-turbo", "max_tokens": 128, "temperature": 0.2, "text": "X - Apps on Google Playgoogle_logo PlayGamesAppsMovies & TVBooksKidsnonesearchhelp_outline Sign in with Googleplay_appsLibrary & devicespaymentPayments & subscriptionsreviewsMy Play activityredeemOffersPlay PasssettingsSettingsPrivacy Policy \u2022 Terms of ServiceGamesAppsMovies & TVBooksKidsXX Corp.Contains adsIn-app purchases3.8star21.5M reviews1B+DownloadsMature 17+infoInstallShareAdd to wishlistAbout this apparrow_forwardThe X app is the trusted global digital town square for everyone.With X, you can:- Post content for the world to see and join public conversations- Stay up to date on breaking news and follow your interests- Stay better informed with extra context from Community Notes- Go live with Spaces for audio or stream live video- Communicate privately with Direct Messages- Subscribe to X Premium to expand your reach, get a blue checkmark, and more- Earn a living creating exclusive content for your paid subscribers and share in the ad revenue generated in replies to your posts- Create and join Communities around topics and interests, from sports to music to technology- Upload and watch videos up to 3 hours in length- Write and read long form posts like essays and blogs- Connect directly with your customers to help your business growUpdated onDec 4, 2023#6 top grossing socialSocialData safetyarrow_forwardSafety starts with understanding how developers collect and share your data. Data privacy and security practices may vary based on your use, region, and age. The developer provided this information and may update it over time.No data shared with third partiesLearn more about how developers declare sharingThis app may collect these data typesLocation, Personal info and 9 othersData is encrypted in transitYou can request that data be deletedSee detailsRatings and reviewsRatings and reviews are verifiedinfo_outlinearrow_forwardRatings and reviews are verifiedinfo_outlinephone_androidPhonetablet_androidTabletwatchWatchlaptopChromebooktvTV3.820.8M reviews54321Alex S"}, "output": "The X app is a global digital platform where users can post content, join public conversations, stay updated on breaking news, and follow their interests. It offers features such as Community Notes for extra context, Spaces for live audio or video streaming, Direct Messages for private communication, and the option to subscribe to X Premium for expanded reach and exclusive content creation. Users can also create and join Communities, upload and watch videos, and write and read long-form posts. The app prioritizes data safety and allows users to request data deletion. It has a rating of 3.8 stars based on 21.5 million reviews and has been downloaded over", "metrics": null, "error": null, "parent_run_id": "web_classification_variant_0_20231205_120253_104100_2", "start_time": "2023-12-05T04:03:26.386617Z", "end_time": "2023-12-05T04:03:28.124504Z", "index": 2, "api_calls": [{"name": "AzureOpenAI.chat", "type": "Tool", "inputs": {"prompt": "system:\nPlease summarize the following text in one paragraph. 100 words.\nDo not add any information that is not in the text.\n\nuser:\nText: {{text}}\nSummary: ", "deployment_name": "gpt-35-turbo", "model": "gpt-3.5-turbo", "max_tokens": 128, "temperature": 0.2, "text": "X - Apps on Google Playgoogle_logo PlayGamesAppsMovies & TVBooksKidsnonesearchhelp_outline Sign in with Googleplay_appsLibrary & devicespaymentPayments & subscriptionsreviewsMy Play activityredeemOffersPlay PasssettingsSettingsPrivacy Policy \u2022 Terms of ServiceGamesAppsMovies & TVBooksKidsXX Corp.Contains adsIn-app purchases3.8star21.5M reviews1B+DownloadsMature 17+infoInstallShareAdd to wishlistAbout this apparrow_forwardThe X app is the trusted global digital town square for everyone.With X, you can:- Post content for the world to see and join public conversations- Stay up to date on breaking news and follow your interests- Stay better informed with extra context from Community Notes- Go live with Spaces for audio or stream live video- Communicate privately with Direct Messages- Subscribe to X Premium to expand your reach, get a blue checkmark, and more- Earn a living creating exclusive content for your paid subscribers and share in the ad revenue generated in replies to your posts- Create and join Communities around topics and interests, from sports to music to technology- Upload and watch videos up to 3 hours in length- Write and read long form posts like essays and blogs- Connect directly with your customers to help your business growUpdated onDec 4, 2023#6 top grossing socialSocialData safetyarrow_forwardSafety starts with understanding how developers collect and share your data. 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promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100/node_artifacts

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100/node_artifacts/prepare_examples/000000000.jsonl

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promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100/node_artifacts

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100/node_artifacts/fetch_text_content_from_url/000000001.jsonl

{"node_name": "fetch_text_content_from_url", "line_number": 1, "run_info": {"node": "fetch_text_content_from_url", "flow_run_id": "web_classification_variant_0_20231205_120253_104100", "run_id": "web_classification_variant_0_20231205_120253_104100_fetch_text_content_from_url_1", "status": "Completed", "inputs": {"url": "https://arxiv.org/abs/2307.04767"}, "output": "\n\n\n [2307.04767] Semantic-SAM: Segment and Recognize Anything at Any Granularity\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nSkip to main content\n\n\n\n\n\nWe gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate\n\n\n\n\n\n > cs > arXiv:2307.04767\n \n\n\n\n\n\nHelp | Advanced Search\n\n\n\n\nAll fields\nTitle\nAuthor\nAbstract\nComments\nJournal reference\nACM classification\nMSC classification\nReport number\narXiv identifier\nDOI\nORCID\narXiv author ID\nHelp pages\nFull text\n\n\n\n\nSearch\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nopen search\n\n\n\n\n\n\nGO\n\n\n\nopen navigation menu\n\n\nquick links\n\nLogin\nHelp Pages\nAbout\n\n\n\n\n\n\n\n\n\n\n\n\nComputer Science > Computer Vision and Pattern Recognition\n\n\narXiv:2307.04767 (cs)\n \n\n\n\n\n [Submitted on 10 Jul 2023]\nTitle:Semantic-SAM: Segment and Recognize Anything at Any Granularity\nAuthors:Feng Li, Hao Zhang, Peize Sun, Xueyan Zou, Shilong Liu, Jianwei Yang, Chunyuan Li, Lei Zhang, Jianfeng Gao Download a PDF of the paper titled Semantic-SAM: Segment and Recognize Anything at Any Granularity, by Feng Li and 8 other authors\nDownload PDF\n\nAbstract:In this paper, we introduce Semantic-SAM, a universal image segmentation model to enable segment and recognize anything at any desired granularity. Our model offers two key advantages: semantic-awareness and granularity-abundance. To achieve semantic-awareness, we consolidate multiple datasets across three granularities and introduce decoupled classification for objects and parts. This allows our model to capture rich semantic information. For the multi-granularity capability, we propose a multi-choice learning scheme during training, enabling each click to generate masks at multiple levels that correspond to multiple ground-truth masks. Notably, this work represents the first attempt to jointly train a model on SA-1B, generic, and part segmentation datasets. Experimental results and visualizations demonstrate that our model successfully achieves semantic-awareness and granularity-abundance. 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Donate\n\n\n\n\n\n > cs > arXiv:2307.04767\n \n\n\n\n\n\nHelp | Advanced Search\n\n\n\n\nAll fields\nTitle\nAuthor\nAbstract\nComments\nJournal reference\nACM classification\nMSC classification\nReport number\narXiv identifier\nDOI\nORCID\narXiv author ID\nHelp pages\nFull text\n\n\n\n\nSearch\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nopen search\n\n\n\n\n\n\nGO\n\n\n\nopen navigation menu\n\n\nquick links\n\nLogin\nHelp Pages\nAbout\n\n\n\n\n\n\n\n\n\n\n\n\nComputer Science > Computer Vision and Pattern Recognition\n\n\narXiv:2307.04767 (cs)\n \n\n\n\n\n [Submitted on 10 Jul 2023]\nTitle:Semantic-SAM: Segment and Recognize Anything at Any Granularity\nAuthors:Feng Li, Hao Zhang, Peize Sun, Xueyan Zou, Shilong Liu, Jianwei Yang, Chunyuan Li, Lei Zhang, Jianfeng Gao Download a PDF of the paper titled Semantic-SAM: Segment and Recognize Anything at Any Granularity, by Feng Li and 8 other authors\nDownload PDF\n\nAbstract:In this paper, we introduce Semantic-SAM, a universal image segmentation model to enable segment and recognize anything at any desired granularity. 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promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100/node_artifacts

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100/node_artifacts/fetch_text_content_from_url/000000002.jsonl

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promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100/node_artifacts

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100/node_artifacts/fetch_text_content_from_url/000000000.jsonl

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promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100/flow_artifacts/000000000_000000024.jsonl

{"line_number": 0, "run_info": {"run_id": "web_classification_variant_0_20231205_120253_104100_0", "status": "Completed", "error": null, "inputs": {"url": "https://www.youtube.com/watch?v=kYqRtjDBci8", "line_number": 0}, "output": {"category": "None", "evidence": "None"}, "metrics": null, "request": null, "parent_run_id": "web_classification_variant_0_20231205_120253_104100", "root_run_id": "web_classification_variant_0_20231205_120253_104100", "source_run_id": null, "flow_id": "default_flow_id", "start_time": "2023-12-05T04:03:25.562559Z", "end_time": "2023-12-05T04:03:27.343463Z", "index": 0, "api_calls": [{"name": "fetch_text_content_from_url", "type": "Tool", "inputs": {"url": "https://www.youtube.com/watch?v=kYqRtjDBci8"}, "output": "Amazing! The prompt flow in Azure AI speeds up the building of my LLM application. - YouTubeAboutPressCopyrightContact usCreatorsAdvertiseDevelopersTermsPrivacyPolicy & SafetyHow YouTube worksTest new featuresNFL Sunday Ticket\u00a9 2023 Google LLC", "start_time": 1701749005.566919, "end_time": 1701749006.619194, "error": null, "children": null, "node_name": "fetch_text_content_from_url"}, {"name": "prepare_examples", "type": "Tool", "inputs": {}, "output": [{"url": "https://play.google.com/store/apps/details?id=com.spotify.music", "text_content": "Spotify is a free music and podcast streaming app with millions of songs, albums, and original podcasts. It also offers audiobooks, so users can enjoy thousands of stories. It has a variety of features such as creating and sharing music playlists, discovering new music, and listening to popular and exclusive podcasts. It also has a Premium subscription option which allows users to download and listen offline, and access ad-free music. It is available on all devices and has a variety of genres and artists to choose from.", "category": "App", "evidence": "Both"}, {"url": "https://www.youtube.com/channel/UC_x5XG1OV2P6uZZ5FSM9Ttw", "text_content": "NFL Sunday Ticket is a service offered by Google LLC that allows users to watch NFL games on YouTube. It is available in 2023 and is subject to the terms and privacy policy of Google LLC. It is also subject to YouTube's terms of use and any applicable laws.", "category": "Channel", "evidence": "URL"}, {"url": "https://arxiv.org/abs/2303.04671", "text_content": "Visual ChatGPT is a system that enables users to interact with ChatGPT by sending and receiving not only languages but also images, providing complex visual questions or visual editing instructions, and providing feedback and asking for corrected results. It incorporates different Visual Foundation Models and is publicly available. Experiments show that Visual ChatGPT opens the door to investigating the visual roles of ChatGPT with the help of Visual Foundation Models.", "category": "Academic", "evidence": "Text content"}, {"url": "https://ab.politiaromana.ro/", "text_content": "There is no content available for this text.", "category": "None", "evidence": "None"}], "start_time": 1701749005.568033, "end_time": 1701749005.572487, "error": null, "children": null, "node_name": "prepare_examples"}, {"name": "AzureOpenAI.chat", "type": "Tool", "inputs": {"prompt": "system:\nPlease summarize the following text in one paragraph. 100 words.\nDo not add any information that is not in the text.\n\nuser:\nText: {{text}}\nSummary: ", "deployment_name": "gpt-35-turbo", "model": "gpt-3.5-turbo", "max_tokens": 128, "temperature": 0.2, "text": "Amazing! The prompt flow in Azure AI speeds up the building of my LLM application. - YouTubeAboutPressCopyrightContact usCreatorsAdvertiseDevelopersTermsPrivacyPolicy & SafetyHow YouTube worksTest new featuresNFL Sunday Ticket\u00a9 2023 Google LLC"}, "output": "The author expresses their excitement about the prompt flow feature in Azure AI, which has helped them accelerate the development of their LLM application.", "start_time": 1701749006.622094, "end_time": 1701749006.993613, "error": null, "children": [{"name": "openai.api_resources.chat_completion.ChatCompletion.create", "type": "LLM", "inputs": {"api_base": "https://gpt-test-eus.openai.azure.com/", "api_type": "azure", "api_version": "2023-07-01-preview", "engine": "gpt-35-turbo", "messages": [{"role": "system", "content": "Please summarize the following text in one paragraph. 100 words.\nDo not add any information that is not in the text."}, {"role": "user", "content": "Text: Amazing! The prompt flow in Azure AI speeds up the building of my LLM application. - YouTubeAboutPressCopyrightContact usCreatorsAdvertiseDevelopersTermsPrivacyPolicy & SafetyHow YouTube worksTest new featuresNFL Sunday Ticket\u00a9 2023 Google LLC\nSummary: "}], "temperature": 0.2, "top_p": 1.0, "n": 1, "stream": false, "stop": null, "max_tokens": 128, "presence_penalty": 0.0, "frequency_penalty": 0.0, "logit_bias": {}, "user": ""}, "output": {"id": "chatcmpl-8SGwYh2QixabybRzI9vKPfKwl9deg", "object": "chat.completion", "created": 1701749006, "model": "gpt-35-turbo", "prompt_filter_results": [{"prompt_index": 0, "content_filter_results": {"hate": {"filtered": false, "severity": "safe"}, "self_harm": {"filtered": false, "severity": "safe"}, "sexual": {"filtered": false, "severity": "safe"}, "violence": {"filtered": false, "severity": "safe"}}}], "choices": [{"index": 0, "finish_reason": "stop", "message": {"role": "assistant", "content": "The author expresses their excitement about the prompt flow feature in Azure AI, which has helped them accelerate the development of their LLM application."}, "content_filter_results": {"hate": {"filtered": false, "severity": "safe"}, "self_harm": {"filtered": false, "severity": "safe"}, "sexual": {"filtered": false, "severity": "safe"}, "violence": {"filtered": false, "severity": "safe"}}}], "usage": {"prompt_tokens": 92, "completion_tokens": 27, "total_tokens": 119}}, "start_time": 1701749006.627222, "end_time": 1701749006.993502, "error": null, "children": null, "node_name": null}], "node_name": "summarize_text_content"}, {"name": "AzureOpenAI.chat", "type": "Tool", "inputs": {"prompt": "system:\nYour task is to classify a given url into one of the following categories:\nMovie, App, Academic, Channel, Profile, PDF or None based on the text content information.\nThe classification will be based on the url, the webpage text content summary, or both.\n\nuser:\nThe selection range of the value of \"category\" must be within \"Movie\", \"App\", \"Academic\", \"Channel\", \"Profile\", \"PDF\" and \"None\".\nThe selection range of the value of \"evidence\" must be within \"Url\", \"Text content\", and \"Both\".\nHere are a few examples:\n{% for ex in examples %}\nURL: {{ex.url}}\nText content: {{ex.text_content}}\nOUTPUT:\n{\"category\": \"{{ex.category}}\", \"evidence\": \"{{ex.evidence}}\"}\n\n{% endfor %}\n\nFor a given URL and text content, classify the url to complete the category and indicate evidence:\nURL: {{url}}\nText content: {{text_content}}.\nOUTPUT:", "deployment_name": "gpt-35-turbo", "model": "gpt-3.5-turbo", "max_tokens": 128, "temperature": 0.2, "url": "https://www.youtube.com/watch?v=kYqRtjDBci8", "text_content": "The author expresses their excitement about the prompt flow feature in Azure AI, which has helped them accelerate the development of their LLM application.", "examples": [{"url": "https://play.google.com/store/apps/details?id=com.spotify.music", "text_content": "Spotify is a free music and podcast streaming app with millions of songs, albums, and original podcasts. It also offers audiobooks, so users can enjoy thousands of stories. It has a variety of features such as creating and sharing music playlists, discovering new music, and listening to popular and exclusive podcasts. It also has a Premium subscription option which allows users to download and listen offline, and access ad-free music. It is available on all devices and has a variety of genres and artists to choose from.", "category": "App", "evidence": "Both"}, {"url": "https://www.youtube.com/channel/UC_x5XG1OV2P6uZZ5FSM9Ttw", "text_content": "NFL Sunday Ticket is a service offered by Google LLC that allows users to watch NFL games on YouTube. It is available in 2023 and is subject to the terms and privacy policy of Google LLC. It is also subject to YouTube's terms of use and any applicable laws.", "category": "Channel", "evidence": "URL"}, {"url": "https://arxiv.org/abs/2303.04671", "text_content": "Visual ChatGPT is a system that enables users to interact with ChatGPT by sending and receiving not only languages but also images, providing complex visual questions or visual editing instructions, and providing feedback and asking for corrected results. It incorporates different Visual Foundation Models and is publicly available. Experiments show that Visual ChatGPT opens the door to investigating the visual roles of ChatGPT with the help of Visual Foundation Models.", "category": "Academic", "evidence": "Text content"}, {"url": "https://ab.politiaromana.ro/", "text_content": "There is no content available for this text.", "category": "None", "evidence": "None"}]}, "output": "{\"category\": \"None\", \"evidence\": \"None\"}", "start_time": 1701749006.996314, "end_time": 1701749007.334935, "error": null, "children": [{"name": "openai.api_resources.chat_completion.ChatCompletion.create", "type": "LLM", "inputs": {"api_base": "https://gpt-test-eus.openai.azure.com/", "api_type": "azure", "api_version": "2023-07-01-preview", "engine": "gpt-35-turbo", "messages": [{"role": "system", "content": "Your task is to classify a given url into one of the following categories:\nMovie, App, Academic, Channel, Profile, PDF or None based on the text content information.\nThe classification will be based on the url, the webpage text content summary, or both."}, {"role": "user", "content": "The selection range of the value of \"category\" must be within \"Movie\", \"App\", \"Academic\", \"Channel\", \"Profile\", \"PDF\" and \"None\".\nThe selection range of the value of \"evidence\" must be within \"Url\", \"Text content\", and \"Both\".\nHere are a few examples:\nURL: https://play.google.com/store/apps/details?id=com.spotify.music\nText content: Spotify is a free music and podcast streaming app with millions of songs, albums, and original podcasts. It also offers audiobooks, so users can enjoy thousands of stories. It has a variety of features such as creating and sharing music playlists, discovering new music, and listening to popular and exclusive podcasts. It also has a Premium subscription option which allows users to download and listen offline, and access ad-free music. It is available on all devices and has a variety of genres and artists to choose from.\nOUTPUT:\n{\"category\": \"App\", \"evidence\": \"Both\"}\n\nURL: https://www.youtube.com/channel/UC_x5XG1OV2P6uZZ5FSM9Ttw\nText content: NFL Sunday Ticket is a service offered by Google LLC that allows users to watch NFL games on YouTube. It is available in 2023 and is subject to the terms and privacy policy of Google LLC. It is also subject to YouTube's terms of use and any applicable laws.\nOUTPUT:\n{\"category\": \"Channel\", \"evidence\": \"URL\"}\n\nURL: https://arxiv.org/abs/2303.04671\nText content: Visual ChatGPT is a system that enables users to interact with ChatGPT by sending and receiving not only languages but also images, providing complex visual questions or visual editing instructions, and providing feedback and asking for corrected results. It incorporates different Visual Foundation Models and is publicly available. Experiments show that Visual ChatGPT opens the door to investigating the visual roles of ChatGPT with the help of Visual Foundation Models.\nOUTPUT:\n{\"category\": \"Academic\", \"evidence\": \"Text content\"}\n\nURL: https://ab.politiaromana.ro/\nText content: There is no content available for this text.\nOUTPUT:\n{\"category\": \"None\", \"evidence\": \"None\"}\n\n\nFor a given URL and text content, classify the url to complete the category and indicate evidence:\nURL: https://www.youtube.com/watch?v=kYqRtjDBci8\nText content: The author expresses their excitement about the prompt flow feature in Azure AI, which has helped them accelerate the development of their LLM application..\nOUTPUT:"}], "temperature": 0.2, "top_p": 1.0, "n": 1, "stream": false, "stop": null, "max_tokens": 128, "presence_penalty": 0.0, "frequency_penalty": 0.0, "logit_bias": {}, "user": ""}, "output": {"id": "chatcmpl-8SGwZ2wUSr8u0pse8KUjbPgz08Cvt", "object": "chat.completion", "created": 1701749007, "model": "gpt-35-turbo", "prompt_filter_results": [{"prompt_index": 0, "content_filter_results": {"hate": {"filtered": false, "severity": "safe"}, "self_harm": {"filtered": false, "severity": "safe"}, "sexual": {"filtered": false, "severity": "safe"}, "violence": {"filtered": false, "severity": "safe"}}}], "choices": [{"index": 0, "finish_reason": "stop", "message": {"role": "assistant", "content": "{\"category\": \"None\", \"evidence\": \"None\"}"}, "content_filter_results": {"hate": {"filtered": false, "severity": "safe"}, "self_harm": {"filtered": false, "severity": "safe"}, "sexual": {"filtered": false, "severity": "safe"}, "violence": {"filtered": false, "severity": "safe"}}}], "usage": {"prompt_tokens": 596, "completion_tokens": 13, "total_tokens": 609}}, "start_time": 1701749006.99988, "end_time": 1701749007.33482, "error": null, "children": null, "node_name": null}], "node_name": "classify_with_llm"}, {"name": "convert_to_dict", "type": "Tool", "inputs": {"input_str": "{\"category\": \"None\", \"evidence\": \"None\"}"}, "output": {"category": "None", "evidence": "None"}, "start_time": 1701749007.337123, "end_time": 1701749007.3376, "error": null, "children": null, "node_name": "convert_to_dict"}], "variant_id": "", "name": "", "description": "", "tags": null, "system_metrics": {"duration": 1.780904, "total_tokens": 728}, "result": {"category": "None", "evidence": "None"}, "upload_metrics": false}, "start_time": "2023-12-05T04:03:25.562559", "end_time": "2023-12-05T04:03:27.343463", "name": "", "description": "", "status": "Completed", "tags": null} {"line_number": 1, "run_info": {"run_id": "web_classification_variant_0_20231205_120253_104100_1", "status": "Completed", "error": null, "inputs": {"url": "https://arxiv.org/abs/2307.04767", "line_number": 1}, "output": {"category": "Academic", "evidence": "Both"}, "metrics": null, "request": null, "parent_run_id": "web_classification_variant_0_20231205_120253_104100", "root_run_id": "web_classification_variant_0_20231205_120253_104100", "source_run_id": null, "flow_id": "default_flow_id", "start_time": "2023-12-05T04:03:25.604746Z", "end_time": "2023-12-05T04:03:27.963985Z", "index": 1, "api_calls": [{"name": "fetch_text_content_from_url", "type": "Tool", "inputs": {"url": "https://arxiv.org/abs/2307.04767"}, "output": "\n\n\n [2307.04767] Semantic-SAM: Segment and Recognize Anything at Any Granularity\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nSkip to main content\n\n\n\n\n\nWe gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate\n\n\n\n\n\n > cs > arXiv:2307.04767\n \n\n\n\n\n\nHelp | Advanced Search\n\n\n\n\nAll fields\nTitle\nAuthor\nAbstract\nComments\nJournal reference\nACM classification\nMSC classification\nReport number\narXiv identifier\nDOI\nORCID\narXiv author ID\nHelp pages\nFull text\n\n\n\n\nSearch\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nopen search\n\n\n\n\n\n\nGO\n\n\n\nopen navigation menu\n\n\nquick links\n\nLogin\nHelp Pages\nAbout\n\n\n\n\n\n\n\n\n\n\n\n\nComputer Science > Computer Vision and Pattern Recognition\n\n\narXiv:2307.04767 (cs)\n \n\n\n\n\n [Submitted on 10 Jul 2023]\nTitle:Semantic-SAM: Segment and Recognize Anything at Any Granularity\nAuthors:Feng Li, Hao Zhang, Peize Sun, Xueyan Zou, Shilong Liu, Jianwei Yang, Chunyuan Li, Lei Zhang, Jianfeng Gao Download a PDF of the paper titled Semantic-SAM: Segment and Recognize Anything at Any Granularity, by Feng Li and 8 other authors\nDownload PDF\n\nAbstract:In this paper, we introduce Semantic-SAM, a universal image segmentation model to enable segment and recognize anything at any desired granularity. Our model offers two key advantages: semantic-awareness and granularity-abundance. To achieve semantic-awareness, we consolidate multiple datasets across three granularities and introduce decoupled classification for objects and parts. This allows our model to capture rich semantic information. For the multi-granularity capability, we propose a multi-choice learning scheme during training, enabling each click to generate masks at multiple levels that correspond to multiple ground-truth masks. Notably, this work represents the first attempt to jointly train a model on SA-1B, generic, and part segmentation datasets. Experimental results and visualizations demonstrate that our model successfully achieves semantic-awareness and granularity-abundance. Furthermore, co", "start_time": 1701749005.608924, "end_time": 1701749006.032803, "error": null, "children": null, "node_name": "fetch_text_content_from_url"}, {"name": "prepare_examples", "type": "Tool", "inputs": {}, "output": [{"url": "https://play.google.com/store/apps/details?id=com.spotify.music", "text_content": "Spotify is a free music and podcast streaming app with millions of songs, albums, and original podcasts. It also offers audiobooks, so users can enjoy thousands of stories. It has a variety of features such as creating and sharing music playlists, discovering new music, and listening to popular and exclusive podcasts. It also has a Premium subscription option which allows users to download and listen offline, and access ad-free music. It is available on all devices and has a variety of genres and artists to choose from.", "category": "App", "evidence": "Both"}, {"url": "https://www.youtube.com/channel/UC_x5XG1OV2P6uZZ5FSM9Ttw", "text_content": "NFL Sunday Ticket is a service offered by Google LLC that allows users to watch NFL games on YouTube. It is available in 2023 and is subject to the terms and privacy policy of Google LLC. It is also subject to YouTube's terms of use and any applicable laws.", "category": "Channel", "evidence": "URL"}, {"url": "https://arxiv.org/abs/2303.04671", "text_content": "Visual ChatGPT is a system that enables users to interact with ChatGPT by sending and receiving not only languages but also images, providing complex visual questions or visual editing instructions, and providing feedback and asking for corrected results. It incorporates different Visual Foundation Models and is publicly available. Experiments show that Visual ChatGPT opens the door to investigating the visual roles of ChatGPT with the help of Visual Foundation Models.", "category": "Academic", "evidence": "Text content"}, {"url": "https://ab.politiaromana.ro/", "text_content": "There is no content available for this text.", "category": "None", "evidence": "None"}], "start_time": 1701749005.610052, "end_time": 1701749005.617312, "error": null, "children": null, "node_name": "prepare_examples"}, {"name": "AzureOpenAI.chat", "type": "Tool", "inputs": {"prompt": "system:\nPlease summarize the following text in one paragraph. 100 words.\nDo not add any information that is not in the text.\n\nuser:\nText: {{text}}\nSummary: ", "deployment_name": "gpt-35-turbo", "model": "gpt-3.5-turbo", "max_tokens": 128, "temperature": 0.2, "text": "\n\n\n [2307.04767] Semantic-SAM: Segment and Recognize Anything at Any Granularity\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nSkip to main content\n\n\n\n\n\nWe gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate\n\n\n\n\n\n > cs > arXiv:2307.04767\n \n\n\n\n\n\nHelp | Advanced Search\n\n\n\n\nAll fields\nTitle\nAuthor\nAbstract\nComments\nJournal reference\nACM classification\nMSC classification\nReport number\narXiv identifier\nDOI\nORCID\narXiv author ID\nHelp pages\nFull text\n\n\n\n\nSearch\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nopen search\n\n\n\n\n\n\nGO\n\n\n\nopen navigation menu\n\n\nquick links\n\nLogin\nHelp Pages\nAbout\n\n\n\n\n\n\n\n\n\n\n\n\nComputer Science > Computer Vision and Pattern Recognition\n\n\narXiv:2307.04767 (cs)\n \n\n\n\n\n [Submitted on 10 Jul 2023]\nTitle:Semantic-SAM: Segment and Recognize Anything at Any Granularity\nAuthors:Feng Li, Hao Zhang, Peize Sun, Xueyan Zou, Shilong Liu, Jianwei Yang, Chunyuan Li, Lei Zhang, Jianfeng Gao Download a PDF of the paper titled Semantic-SAM: Segment and Recognize Anything at Any Granularity, by Feng Li and 8 other authors\nDownload PDF\n\nAbstract:In this paper, we introduce Semantic-SAM, a universal image segmentation model to enable segment and recognize anything at any desired granularity. Our model offers two key advantages: semantic-awareness and granularity-abundance. To achieve semantic-awareness, we consolidate multiple datasets across three granularities and introduce decoupled classification for objects and parts. This allows our model to capture rich semantic information. For the multi-granularity capability, we propose a multi-choice learning scheme during training, enabling each click to generate masks at multiple levels that correspond to multiple ground-truth masks. Notably, this work represents the first attempt to jointly train a model on SA-1B, generic, and part segmentation datasets. Experimental results and visualizations demonstrate that our model successfully achieves semantic-awareness and granularity-abundance. Furthermore, co"}, "output": "The paper introduces Semantic-SAM, a universal image segmentation model that can segment and recognize objects at any desired granularity. The model has two key advantages: semantic-awareness and granularity-abundance. To achieve semantic-awareness, the model consolidates multiple datasets and introduces decoupled classification for objects and parts. This allows the model to capture rich semantic information. For multi-granularity capability, a multi-choice learning scheme is proposed during training, generating masks at multiple levels. The model is trained on SA-1B, generic, and part segmentation datasets. Experimental results and visualizations demonstrate the success of the model in achieving semantic-awareness and", "start_time": 1701749006.035773, "end_time": 1701749007.668153, "error": null, "children": [{"name": "openai.api_resources.chat_completion.ChatCompletion.create", "type": "LLM", "inputs": {"api_base": "https://gpt-test-eus.openai.azure.com/", "api_type": "azure", "api_version": "2023-07-01-preview", "engine": "gpt-35-turbo", "messages": [{"role": "system", "content": "Please summarize the following text in one paragraph. 100 words.\nDo not add any information that is not in the text."}, {"role": "user", "content": "Text: \n\n\n [2307.04767] Semantic-SAM: Segment and Recognize Anything at Any Granularity\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\nSkip to main content\n\n\n\n\n\nWe gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate\n\n\n\n\n\n > cs > arXiv:2307.04767\n \n\n\n\n\n\nHelp | Advanced Search\n\n\n\n\nAll fields\nTitle\nAuthor\nAbstract\nComments\nJournal reference\nACM classification\nMSC classification\nReport number\narXiv identifier\nDOI\nORCID\narXiv author ID\nHelp pages\nFull text\n\n\n\n\nSearch\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nopen search\n\n\n\n\n\n\nGO\n\n\n\nopen navigation menu\n\n\nquick links\n\nLogin\nHelp Pages\nAbout\n\n\n\n\n\n\n\n\n\n\n\n\nComputer Science > Computer Vision and Pattern Recognition\n\n\narXiv:2307.04767 (cs)\n \n\n\n\n\n [Submitted on 10 Jul 2023]\nTitle:Semantic-SAM: Segment and Recognize Anything at Any Granularity\nAuthors:Feng Li, Hao Zhang, Peize Sun, Xueyan Zou, Shilong Liu, Jianwei Yang, Chunyuan Li, Lei Zhang, Jianfeng Gao Download a PDF of the paper titled Semantic-SAM: Segment and Recognize Anything at Any Granularity, by Feng Li and 8 other authors\nDownload PDF\n\nAbstract:In this paper, we introduce Semantic-SAM, a universal image segmentation model to enable segment and recognize anything at any desired granularity. Our model offers two key advantages: semantic-awareness and granularity-abundance. To achieve semantic-awareness, we consolidate multiple datasets across three granularities and introduce decoupled classification for objects and parts. This allows our model to capture rich semantic information. For the multi-granularity capability, we propose a multi-choice learning scheme during training, enabling each click to generate masks at multiple levels that correspond to multiple ground-truth masks. Notably, this work represents the first attempt to jointly train a model on SA-1B, generic, and part segmentation datasets. Experimental results and visualizations demonstrate that our model successfully achieves semantic-awareness and granularity-abundance. Furthermore, co\nSummary: "}], "temperature": 0.2, "top_p": 1.0, "n": 1, "stream": false, "stop": null, "max_tokens": 128, "presence_penalty": 0.0, "frequency_penalty": 0.0, "logit_bias": {}, "user": ""}, "output": {"id": "chatcmpl-8SGwY6eyhMgFtmIhNkFmO6e2awkvq", "object": "chat.completion", "created": 1701749006, "model": "gpt-35-turbo", "prompt_filter_results": [{"prompt_index": 0, "content_filter_results": {"hate": {"filtered": false, "severity": "safe"}, "self_harm": {"filtered": false, "severity": "safe"}, "sexual": {"filtered": false, "severity": "safe"}, "violence": {"filtered": false, "severity": "safe"}}}], "choices": [{"index": 0, "finish_reason": "length", "message": {"role": "assistant", "content": "The paper introduces Semantic-SAM, a universal image segmentation model that can segment and recognize objects at any desired granularity. The model has two key advantages: semantic-awareness and granularity-abundance. To achieve semantic-awareness, the model consolidates multiple datasets and introduces decoupled classification for objects and parts. This allows the model to capture rich semantic information. For multi-granularity capability, a multi-choice learning scheme is proposed during training, generating masks at multiple levels. The model is trained on SA-1B, generic, and part segmentation datasets. Experimental results and visualizations demonstrate the success of the model in achieving semantic-awareness and"}, "content_filter_results": {"hate": {"filtered": false, "severity": "safe"}, "self_harm": {"filtered": false, "severity": "safe"}, "sexual": {"filtered": false, "severity": "safe"}, "violence": {"filtered": false, "severity": "safe"}}}], "usage": {"prompt_tokens": 468, "completion_tokens": 128, "total_tokens": 596}}, "start_time": 1701749006.052093, "end_time": 1701749007.668043, "error": null, "children": null, "node_name": null}], "node_name": "summarize_text_content"}, {"name": "AzureOpenAI.chat", "type": "Tool", "inputs": {"prompt": "system:\nYour task is to classify a given url into one of the following categories:\nMovie, App, Academic, Channel, Profile, PDF or None based on the text content information.\nThe classification will be based on the url, the webpage text content summary, or both.\n\nuser:\nThe selection range of the value of \"category\" must be within \"Movie\", \"App\", \"Academic\", \"Channel\", \"Profile\", \"PDF\" and \"None\".\nThe selection range of the value of \"evidence\" must be within \"Url\", \"Text content\", and \"Both\".\nHere are a few examples:\n{% for ex in examples %}\nURL: {{ex.url}}\nText content: {{ex.text_content}}\nOUTPUT:\n{\"category\": \"{{ex.category}}\", \"evidence\": \"{{ex.evidence}}\"}\n\n{% endfor %}\n\nFor a given URL and text content, classify the url to complete the category and indicate evidence:\nURL: {{url}}\nText content: {{text_content}}.\nOUTPUT:", "deployment_name": "gpt-35-turbo", "model": "gpt-3.5-turbo", "max_tokens": 128, "temperature": 0.2, "url": "https://arxiv.org/abs/2307.04767", "text_content": "The paper introduces Semantic-SAM, a universal image segmentation model that can segment and recognize objects at any desired granularity. The model has two key advantages: semantic-awareness and granularity-abundance. To achieve semantic-awareness, the model consolidates multiple datasets and introduces decoupled classification for objects and parts. This allows the model to capture rich semantic information. For multi-granularity capability, a multi-choice learning scheme is proposed during training, generating masks at multiple levels. The model is trained on SA-1B, generic, and part segmentation datasets. Experimental results and visualizations demonstrate the success of the model in achieving semantic-awareness and", "examples": [{"url": "https://play.google.com/store/apps/details?id=com.spotify.music", "text_content": "Spotify is a free music and podcast streaming app with millions of songs, albums, and original podcasts. It also offers audiobooks, so users can enjoy thousands of stories. It has a variety of features such as creating and sharing music playlists, discovering new music, and listening to popular and exclusive podcasts. It also has a Premium subscription option which allows users to download and listen offline, and access ad-free music. It is available on all devices and has a variety of genres and artists to choose from.", "category": "App", "evidence": "Both"}, {"url": "https://www.youtube.com/channel/UC_x5XG1OV2P6uZZ5FSM9Ttw", "text_content": "NFL Sunday Ticket is a service offered by Google LLC that allows users to watch NFL games on YouTube. It is available in 2023 and is subject to the terms and privacy policy of Google LLC. It is also subject to YouTube's terms of use and any applicable laws.", "category": "Channel", "evidence": "URL"}, {"url": "https://arxiv.org/abs/2303.04671", "text_content": "Visual ChatGPT is a system that enables users to interact with ChatGPT by sending and receiving not only languages but also images, providing complex visual questions or visual editing instructions, and providing feedback and asking for corrected results. It incorporates different Visual Foundation Models and is publicly available. Experiments show that Visual ChatGPT opens the door to investigating the visual roles of ChatGPT with the help of Visual Foundation Models.", "category": "Academic", "evidence": "Text content"}, {"url": "https://ab.politiaromana.ro/", "text_content": "There is no content available for this text.", "category": "None", "evidence": "None"}]}, "output": "{\"category\": \"Academic\", \"evidence\": \"Both\"}", "start_time": 1701749007.670509, "end_time": 1701749007.956738, "error": null, "children": [{"name": "openai.api_resources.chat_completion.ChatCompletion.create", "type": "LLM", "inputs": {"api_base": "https://gpt-test-eus.openai.azure.com/", "api_type": "azure", "api_version": "2023-07-01-preview", "engine": "gpt-35-turbo", "messages": [{"role": "system", "content": "Your task is to classify a given url into one of the following categories:\nMovie, App, Academic, Channel, Profile, PDF or None based on the text content information.\nThe classification will be based on the url, the webpage text content summary, or both."}, {"role": "user", "content": "The selection range of the value of \"category\" must be within \"Movie\", \"App\", \"Academic\", \"Channel\", \"Profile\", \"PDF\" and \"None\".\nThe selection range of the value of \"evidence\" must be within \"Url\", \"Text content\", and \"Both\".\nHere are a few examples:\nURL: https://play.google.com/store/apps/details?id=com.spotify.music\nText content: Spotify is a free music and podcast streaming app with millions of songs, albums, and original podcasts. It also offers audiobooks, so users can enjoy thousands of stories. It has a variety of features such as creating and sharing music playlists, discovering new music, and listening to popular and exclusive podcasts. It also has a Premium subscription option which allows users to download and listen offline, and access ad-free music. It is available on all devices and has a variety of genres and artists to choose from.\nOUTPUT:\n{\"category\": \"App\", \"evidence\": \"Both\"}\n\nURL: https://www.youtube.com/channel/UC_x5XG1OV2P6uZZ5FSM9Ttw\nText content: NFL Sunday Ticket is a service offered by Google LLC that allows users to watch NFL games on YouTube. It is available in 2023 and is subject to the terms and privacy policy of Google LLC. It is also subject to YouTube's terms of use and any applicable laws.\nOUTPUT:\n{\"category\": \"Channel\", \"evidence\": \"URL\"}\n\nURL: https://arxiv.org/abs/2303.04671\nText content: Visual ChatGPT is a system that enables users to interact with ChatGPT by sending and receiving not only languages but also images, providing complex visual questions or visual editing instructions, and providing feedback and asking for corrected results. It incorporates different Visual Foundation Models and is publicly available. Experiments show that Visual ChatGPT opens the door to investigating the visual roles of ChatGPT with the help of Visual Foundation Models.\nOUTPUT:\n{\"category\": \"Academic\", \"evidence\": \"Text content\"}\n\nURL: https://ab.politiaromana.ro/\nText content: There is no content available for this text.\nOUTPUT:\n{\"category\": \"None\", \"evidence\": \"None\"}\n\n\nFor a given URL and text content, classify the url to complete the category and indicate evidence:\nURL: https://arxiv.org/abs/2307.04767\nText content: The paper introduces Semantic-SAM, a universal image segmentation model that can segment and recognize objects at any desired granularity. The model has two key advantages: semantic-awareness and granularity-abundance. To achieve semantic-awareness, the model consolidates multiple datasets and introduces decoupled classification for objects and parts. This allows the model to capture rich semantic information. For multi-granularity capability, a multi-choice learning scheme is proposed during training, generating masks at multiple levels. The model is trained on SA-1B, generic, and part segmentation datasets. Experimental results and visualizations demonstrate the success of the model in achieving semantic-awareness and.\nOUTPUT:"}], "temperature": 0.2, "top_p": 1.0, "n": 1, "stream": false, "stop": null, "max_tokens": 128, "presence_penalty": 0.0, "frequency_penalty": 0.0, "logit_bias": {}, "user": ""}, "output": {"id": "chatcmpl-8SGwZ8CajHzJc3UkbXeyX6MzNdFcD", "object": "chat.completion", "created": 1701749007, "model": "gpt-35-turbo", "prompt_filter_results": [{"prompt_index": 0, "content_filter_results": {"hate": {"filtered": false, "severity": "safe"}, "self_harm": {"filtered": false, "severity": "safe"}, "sexual": {"filtered": false, "severity": "safe"}, "violence": {"filtered": false, "severity": "safe"}}}], "choices": [{"index": 0, "finish_reason": "stop", "message": {"role": "assistant", "content": "{\"category\": \"Academic\", \"evidence\": \"Both\"}"}, "content_filter_results": {"hate": {"filtered": false, "severity": "safe"}, "self_harm": {"filtered": false, "severity": "safe"}, "sexual": {"filtered": false, "severity": "safe"}, "violence": {"filtered": false, "severity": "safe"}}}], "usage": {"prompt_tokens": 695, "completion_tokens": 14, "total_tokens": 709}}, "start_time": 1701749007.675928, "end_time": 1701749007.956625, "error": null, "children": null, "node_name": null}], "node_name": "classify_with_llm"}, {"name": "convert_to_dict", "type": "Tool", "inputs": {"input_str": "{\"category\": \"Academic\", \"evidence\": \"Both\"}"}, "output": {"category": "Academic", "evidence": "Both"}, "start_time": 1701749007.959116, "end_time": 1701749007.959648, "error": null, "children": null, "node_name": "convert_to_dict"}], "variant_id": "", "name": "", "description": "", "tags": null, "system_metrics": {"duration": 2.359239, "total_tokens": 1305}, "result": {"category": "Academic", "evidence": "Both"}, "upload_metrics": false}, "start_time": "2023-12-05T04:03:25.604746", "end_time": "2023-12-05T04:03:27.963985", "name": "", "description": "", "status": "Completed", "tags": null} {"line_number": 2, "run_info": {"run_id": "web_classification_variant_0_20231205_120253_104100_2", "status": "Completed", "error": null, "inputs": {"url": "https://play.google.com/store/apps/details?id=com.twitter.android", "line_number": 2}, "output": {"category": "App", "evidence": "Both"}, "metrics": null, "request": null, "parent_run_id": "web_classification_variant_0_20231205_120253_104100", "root_run_id": "web_classification_variant_0_20231205_120253_104100", "source_run_id": null, "flow_id": "default_flow_id", "start_time": "2023-12-05T04:03:25.734275Z", "end_time": "2023-12-05T04:03:28.477166Z", "index": 2, "api_calls": [{"name": "fetch_text_content_from_url", "type": "Tool", "inputs": {"url": "https://play.google.com/store/apps/details?id=com.twitter.android"}, "output": "X - Apps on Google Playgoogle_logo PlayGamesAppsMovies & TVBooksKidsnonesearchhelp_outline Sign in with Googleplay_appsLibrary & devicespaymentPayments & subscriptionsreviewsMy Play activityredeemOffersPlay PasssettingsSettingsPrivacy Policy \u2022 Terms of ServiceGamesAppsMovies & TVBooksKidsXX Corp.Contains adsIn-app purchases3.8star21.5M reviews1B+DownloadsMature 17+infoInstallShareAdd to wishlistAbout this apparrow_forwardThe X app is the trusted global digital town square for everyone.With X, you can:- Post content for the world to see and join public conversations- Stay up to date on breaking news and follow your interests- Stay better informed with extra context from Community Notes- Go live with Spaces for audio or stream live video- Communicate privately with Direct Messages- Subscribe to X Premium to expand your reach, get a blue checkmark, and more- Earn a living creating exclusive content for your paid subscribers and share in the ad revenue generated in replies to your posts- Create and join Communities around topics and interests, from sports to music to technology- Upload and watch videos up to 3 hours in length- Write and read long form posts like essays and blogs- Connect directly with your customers to help your business growUpdated onDec 4, 2023#6 top grossing socialSocialData safetyarrow_forwardSafety starts with understanding how developers collect and share your data. Data privacy and security practices may vary based on your use, region, and age. The developer provided this information and may update it over time.No data shared with third partiesLearn more about how developers declare sharingThis app may collect these data typesLocation, Personal info and 9 othersData is encrypted in transitYou can request that data be deletedSee detailsRatings and reviewsRatings and reviews are verifiedinfo_outlinearrow_forwardRatings and reviews are verifiedinfo_outlinephone_androidPhonetablet_androidTabletwatchWatchlaptopChromebooktvTV3.820.8M reviews54321Alex S", "start_time": 1701749005.738373, "end_time": 1701749006.384366, "error": null, "children": null, "node_name": "fetch_text_content_from_url"}, {"name": "prepare_examples", "type": "Tool", "inputs": {}, "output": [{"url": "https://play.google.com/store/apps/details?id=com.spotify.music", "text_content": "Spotify is a free music and podcast streaming app with millions of songs, albums, and original podcasts. It also offers audiobooks, so users can enjoy thousands of stories. It has a variety of features such as creating and sharing music playlists, discovering new music, and listening to popular and exclusive podcasts. It also has a Premium subscription option which allows users to download and listen offline, and access ad-free music. It is available on all devices and has a variety of genres and artists to choose from.", "category": "App", "evidence": "Both"}, {"url": "https://www.youtube.com/channel/UC_x5XG1OV2P6uZZ5FSM9Ttw", "text_content": "NFL Sunday Ticket is a service offered by Google LLC that allows users to watch NFL games on YouTube. It is available in 2023 and is subject to the terms and privacy policy of Google LLC. It is also subject to YouTube's terms of use and any applicable laws.", "category": "Channel", "evidence": "URL"}, {"url": "https://arxiv.org/abs/2303.04671", "text_content": "Visual ChatGPT is a system that enables users to interact with ChatGPT by sending and receiving not only languages but also images, providing complex visual questions or visual editing instructions, and providing feedback and asking for corrected results. It incorporates different Visual Foundation Models and is publicly available. Experiments show that Visual ChatGPT opens the door to investigating the visual roles of ChatGPT with the help of Visual Foundation Models.", "category": "Academic", "evidence": "Text content"}, {"url": "https://ab.politiaromana.ro/", "text_content": "There is no content available for this text.", "category": "None", "evidence": "None"}], "start_time": 1701749005.739604, "end_time": 1701749005.74123, "error": null, "children": null, "node_name": "prepare_examples"}, {"name": "AzureOpenAI.chat", "type": "Tool", "inputs": {"prompt": "system:\nPlease summarize the following text in one paragraph. 100 words.\nDo not add any information that is not in the text.\n\nuser:\nText: {{text}}\nSummary: ", "deployment_name": "gpt-35-turbo", "model": "gpt-3.5-turbo", "max_tokens": 128, "temperature": 0.2, "text": "X - Apps on Google Playgoogle_logo PlayGamesAppsMovies & TVBooksKidsnonesearchhelp_outline Sign in with Googleplay_appsLibrary & devicespaymentPayments & subscriptionsreviewsMy Play activityredeemOffersPlay PasssettingsSettingsPrivacy Policy \u2022 Terms of ServiceGamesAppsMovies & TVBooksKidsXX Corp.Contains adsIn-app purchases3.8star21.5M reviews1B+DownloadsMature 17+infoInstallShareAdd to wishlistAbout this apparrow_forwardThe X app is the trusted global digital town square for everyone.With X, you can:- Post content for the world to see and join public conversations- Stay up to date on breaking news and follow your interests- Stay better informed with extra context from Community Notes- Go live with Spaces for audio or stream live video- Communicate privately with Direct Messages- Subscribe to X Premium to expand your reach, get a blue checkmark, and more- Earn a living creating exclusive content for your paid subscribers and share in the ad revenue generated in replies to your posts- Create and join Communities around topics and interests, from sports to music to technology- Upload and watch videos up to 3 hours in length- Write and read long form posts like essays and blogs- Connect directly with your customers to help your business growUpdated onDec 4, 2023#6 top grossing socialSocialData safetyarrow_forwardSafety starts with understanding how developers collect and share your data. Data privacy and security practices may vary based on your use, region, and age. The developer provided this information and may update it over time.No data shared with third partiesLearn more about how developers declare sharingThis app may collect these data typesLocation, Personal info and 9 othersData is encrypted in transitYou can request that data be deletedSee detailsRatings and reviewsRatings and reviews are verifiedinfo_outlinearrow_forwardRatings and reviews are verifiedinfo_outlinephone_androidPhonetablet_androidTabletwatchWatchlaptopChromebooktvTV3.820.8M reviews54321Alex S"}, "output": "The X app is a global digital platform where users can post content, join public conversations, stay updated on breaking news, and follow their interests. It offers features such as Community Notes for extra context, Spaces for live audio or video streaming, Direct Messages for private communication, and the option to subscribe to X Premium for expanded reach and exclusive content creation. Users can also create and join Communities, upload and watch videos, and write and read long-form posts. The app prioritizes data safety and allows users to request data deletion. It has a rating of 3.8 stars based on 21.5 million reviews and has been downloaded over", "start_time": 1701749006.386894, "end_time": 1701749008.123235, "error": null, "children": [{"name": "openai.api_resources.chat_completion.ChatCompletion.create", "type": "LLM", "inputs": {"api_base": "https://gpt-test-eus.openai.azure.com/", "api_type": "azure", "api_version": "2023-07-01-preview", "engine": "gpt-35-turbo", "messages": [{"role": "system", "content": "Please summarize the following text in one paragraph. 100 words.\nDo not add any information that is not in the text."}, {"role": "user", "content": "Text: X - Apps on Google Playgoogle_logo PlayGamesAppsMovies & TVBooksKidsnonesearchhelp_outline Sign in with Googleplay_appsLibrary & devicespaymentPayments & subscriptionsreviewsMy Play activityredeemOffersPlay PasssettingsSettingsPrivacy Policy \u2022 Terms of ServiceGamesAppsMovies & TVBooksKidsXX Corp.Contains adsIn-app purchases3.8star21.5M reviews1B+DownloadsMature 17+infoInstallShareAdd to wishlistAbout this apparrow_forwardThe X app is the trusted global digital town square for everyone.With X, you can:- Post content for the world to see and join public conversations- Stay up to date on breaking news and follow your interests- Stay better informed with extra context from Community Notes- Go live with Spaces for audio or stream live video- Communicate privately with Direct Messages- Subscribe to X Premium to expand your reach, get a blue checkmark, and more- Earn a living creating exclusive content for your paid subscribers and share in the ad revenue generated in replies to your posts- Create and join Communities around topics and interests, from sports to music to technology- Upload and watch videos up to 3 hours in length- Write and read long form posts like essays and blogs- Connect directly with your customers to help your business growUpdated onDec 4, 2023#6 top grossing socialSocialData safetyarrow_forwardSafety starts with understanding how developers collect and share your data. Data privacy and security practices may vary based on your use, region, and age. The developer provided this information and may update it over time.No data shared with third partiesLearn more about how developers declare sharingThis app may collect these data typesLocation, Personal info and 9 othersData is encrypted in transitYou can request that data be deletedSee detailsRatings and reviewsRatings and reviews are verifiedinfo_outlinearrow_forwardRatings and reviews are verifiedinfo_outlinephone_androidPhonetablet_androidTabletwatchWatchlaptopChromebooktvTV3.820.8M reviews54321Alex S\nSummary: "}], "temperature": 0.2, "top_p": 1.0, "n": 1, "stream": false, "stop": null, "max_tokens": 128, "presence_penalty": 0.0, "frequency_penalty": 0.0, "logit_bias": {}, "user": ""}, "output": {"id": "chatcmpl-8SGwYyQcqIQLNvDERxnNkJ8Tv4wIf", "object": "chat.completion", "created": 1701749006, "model": "gpt-35-turbo", "prompt_filter_results": [{"prompt_index": 0, "content_filter_results": {"hate": {"filtered": false, "severity": "safe"}, "self_harm": {"filtered": false, "severity": "safe"}, "sexual": {"filtered": false, "severity": "safe"}, "violence": {"filtered": false, "severity": "safe"}}}], "choices": [{"index": 0, "finish_reason": "length", "message": {"role": "assistant", "content": "The X app is a global digital platform where users can post content, join public conversations, stay updated on breaking news, and follow their interests. It offers features such as Community Notes for extra context, Spaces for live audio or video streaming, Direct Messages for private communication, and the option to subscribe to X Premium for expanded reach and exclusive content creation. Users can also create and join Communities, upload and watch videos, and write and read long-form posts. The app prioritizes data safety and allows users to request data deletion. It has a rating of 3.8 stars based on 21.5 million reviews and has been downloaded over"}, "content_filter_results": {"hate": {"filtered": false, "severity": "safe"}, "self_harm": {"filtered": false, "severity": "safe"}, "sexual": {"filtered": false, "severity": "safe"}, "violence": {"filtered": false, "severity": "safe"}}}], "usage": {"prompt_tokens": 448, "completion_tokens": 128, "total_tokens": 576}}, "start_time": 1701749006.391002, "end_time": 1701749008.123107, "error": null, "children": null, "node_name": null}], "node_name": "summarize_text_content"}, {"name": "AzureOpenAI.chat", "type": "Tool", "inputs": {"prompt": "system:\nYour task is to classify a given url into one of the following categories:\nMovie, App, Academic, Channel, Profile, PDF or None based on the text content information.\nThe classification will be based on the url, the webpage text content summary, or both.\n\nuser:\nThe selection range of the value of \"category\" must be within \"Movie\", \"App\", \"Academic\", \"Channel\", \"Profile\", \"PDF\" and \"None\".\nThe selection range of the value of \"evidence\" must be within \"Url\", \"Text content\", and \"Both\".\nHere are a few examples:\n{% for ex in examples %}\nURL: {{ex.url}}\nText content: {{ex.text_content}}\nOUTPUT:\n{\"category\": \"{{ex.category}}\", \"evidence\": \"{{ex.evidence}}\"}\n\n{% endfor %}\n\nFor a given URL and text content, classify the url to complete the category and indicate evidence:\nURL: {{url}}\nText content: {{text_content}}.\nOUTPUT:", "deployment_name": "gpt-35-turbo", "model": "gpt-3.5-turbo", "max_tokens": 128, "temperature": 0.2, "url": "https://play.google.com/store/apps/details?id=com.twitter.android", "text_content": "The X app is a global digital platform where users can post content, join public conversations, stay updated on breaking news, and follow their interests. It offers features such as Community Notes for extra context, Spaces for live audio or video streaming, Direct Messages for private communication, and the option to subscribe to X Premium for expanded reach and exclusive content creation. Users can also create and join Communities, upload and watch videos, and write and read long-form posts. The app prioritizes data safety and allows users to request data deletion. It has a rating of 3.8 stars based on 21.5 million reviews and has been downloaded over", "examples": [{"url": "https://play.google.com/store/apps/details?id=com.spotify.music", "text_content": "Spotify is a free music and podcast streaming app with millions of songs, albums, and original podcasts. It also offers audiobooks, so users can enjoy thousands of stories. It has a variety of features such as creating and sharing music playlists, discovering new music, and listening to popular and exclusive podcasts. It also has a Premium subscription option which allows users to download and listen offline, and access ad-free music. It is available on all devices and has a variety of genres and artists to choose from.", "category": "App", "evidence": "Both"}, {"url": "https://www.youtube.com/channel/UC_x5XG1OV2P6uZZ5FSM9Ttw", "text_content": "NFL Sunday Ticket is a service offered by Google LLC that allows users to watch NFL games on YouTube. It is available in 2023 and is subject to the terms and privacy policy of Google LLC. It is also subject to YouTube's terms of use and any applicable laws.", "category": "Channel", "evidence": "URL"}, {"url": "https://arxiv.org/abs/2303.04671", "text_content": "Visual ChatGPT is a system that enables users to interact with ChatGPT by sending and receiving not only languages but also images, providing complex visual questions or visual editing instructions, and providing feedback and asking for corrected results. It incorporates different Visual Foundation Models and is publicly available. Experiments show that Visual ChatGPT opens the door to investigating the visual roles of ChatGPT with the help of Visual Foundation Models.", "category": "Academic", "evidence": "Text content"}, {"url": "https://ab.politiaromana.ro/", "text_content": "There is no content available for this text.", "category": "None", "evidence": "None"}]}, "output": "{\"category\": \"App\", \"evidence\": \"Both\"}", "start_time": 1701749008.126513, "end_time": 1701749008.469702, "error": null, "children": [{"name": "openai.api_resources.chat_completion.ChatCompletion.create", "type": "LLM", "inputs": {"api_base": "https://gpt-test-eus.openai.azure.com/", "api_type": "azure", "api_version": "2023-07-01-preview", "engine": "gpt-35-turbo", "messages": [{"role": "system", "content": "Your task is to classify a given url into one of the following categories:\nMovie, App, Academic, Channel, Profile, PDF or None based on the text content information.\nThe classification will be based on the url, the webpage text content summary, or both."}, {"role": "user", "content": "The selection range of the value of \"category\" must be within \"Movie\", \"App\", \"Academic\", \"Channel\", \"Profile\", \"PDF\" and \"None\".\nThe selection range of the value of \"evidence\" must be within \"Url\", \"Text content\", and \"Both\".\nHere are a few examples:\nURL: https://play.google.com/store/apps/details?id=com.spotify.music\nText content: Spotify is a free music and podcast streaming app with millions of songs, albums, and original podcasts. It also offers audiobooks, so users can enjoy thousands of stories. It has a variety of features such as creating and sharing music playlists, discovering new music, and listening to popular and exclusive podcasts. It also has a Premium subscription option which allows users to download and listen offline, and access ad-free music. It is available on all devices and has a variety of genres and artists to choose from.\nOUTPUT:\n{\"category\": \"App\", \"evidence\": \"Both\"}\n\nURL: https://www.youtube.com/channel/UC_x5XG1OV2P6uZZ5FSM9Ttw\nText content: NFL Sunday Ticket is a service offered by Google LLC that allows users to watch NFL games on YouTube. It is available in 2023 and is subject to the terms and privacy policy of Google LLC. It is also subject to YouTube's terms of use and any applicable laws.\nOUTPUT:\n{\"category\": \"Channel\", \"evidence\": \"URL\"}\n\nURL: https://arxiv.org/abs/2303.04671\nText content: Visual ChatGPT is a system that enables users to interact with ChatGPT by sending and receiving not only languages but also images, providing complex visual questions or visual editing instructions, and providing feedback and asking for corrected results. It incorporates different Visual Foundation Models and is publicly available. Experiments show that Visual ChatGPT opens the door to investigating the visual roles of ChatGPT with the help of Visual Foundation Models.\nOUTPUT:\n{\"category\": \"Academic\", \"evidence\": \"Text content\"}\n\nURL: https://ab.politiaromana.ro/\nText content: There is no content available for this text.\nOUTPUT:\n{\"category\": \"None\", \"evidence\": \"None\"}\n\n\nFor a given URL and text content, classify the url to complete the category and indicate evidence:\nURL: https://play.google.com/store/apps/details?id=com.twitter.android\nText content: The X app is a global digital platform where users can post content, join public conversations, stay updated on breaking news, and follow their interests. It offers features such as Community Notes for extra context, Spaces for live audio or video streaming, Direct Messages for private communication, and the option to subscribe to X Premium for expanded reach and exclusive content creation. Users can also create and join Communities, upload and watch videos, and write and read long-form posts. The app prioritizes data safety and allows users to request data deletion. It has a rating of 3.8 stars based on 21.5 million reviews and has been downloaded over.\nOUTPUT:"}], "temperature": 0.2, "top_p": 1.0, "n": 1, "stream": false, "stop": null, "max_tokens": 128, "presence_penalty": 0.0, "frequency_penalty": 0.0, "logit_bias": {}, "user": ""}, "output": {"id": "chatcmpl-8SGwaHVVKGYxKtoLEQKFdvAmblBBc", "object": "chat.completion", "created": 1701749008, "model": "gpt-35-turbo", "prompt_filter_results": [{"prompt_index": 0, "content_filter_results": {"hate": {"filtered": false, "severity": "safe"}, "self_harm": {"filtered": false, "severity": "safe"}, "sexual": {"filtered": false, "severity": "safe"}, "violence": {"filtered": false, "severity": "safe"}}}], "choices": [{"index": 0, "finish_reason": "stop", "message": {"role": "assistant", "content": "{\"category\": \"App\", \"evidence\": \"Both\"}"}, "content_filter_results": {"hate": {"filtered": false, "severity": "safe"}, "self_harm": {"filtered": false, "severity": "safe"}, "sexual": {"filtered": false, "severity": "safe"}, "violence": {"filtered": false, "severity": "safe"}}}], "usage": {"prompt_tokens": 694, "completion_tokens": 13, "total_tokens": 707}}, "start_time": 1701749008.130586, "end_time": 1701749008.469585, "error": null, "children": null, "node_name": null}], "node_name": "classify_with_llm"}, {"name": "convert_to_dict", "type": "Tool", "inputs": {"input_str": "{\"category\": \"App\", \"evidence\": \"Both\"}"}, "output": {"category": "App", "evidence": "Both"}, "start_time": 1701749008.472326, "end_time": 1701749008.472859, "error": null, "children": null, "node_name": "convert_to_dict"}], "variant_id": "", "name": "", "description": "", "tags": null, "system_metrics": {"duration": 2.742891, "total_tokens": 1283}, "result": {"category": "App", "evidence": "Both"}, "upload_metrics": false}, "start_time": "2023-12-05T04:03:25.734275", "end_time": "2023-12-05T04:03:28.477166", "name": "", "description": "", "status": "Completed", "tags": null}

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100/snapshot/data.jsonl

{"url": "https://www.youtube.com/watch?v=kYqRtjDBci8", "answer": "Channel", "evidence": "Both"} {"url": "https://arxiv.org/abs/2307.04767", "answer": "Academic", "evidence": "Both"} {"url": "https://play.google.com/store/apps/details?id=com.twitter.android", "answer": "App", "evidence": "Both"}

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100/snapshot/convert_to_dict.py

import json from promptflow import tool @tool def convert_to_dict(input_str: str): try: return json.loads(input_str) except Exception as e: print("The input is not valid, error: {}".format(e)) return {"category": "None", "evidence": "None"}

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100/snapshot/README.md

# Web Classification This is a flow demonstrating multi-class classification with LLM. Given an url, it will classify the url into one web category with just a few shots, simple summarization and classification prompts. ## Tools used in this flow - LLM Tool - Python Tool ## What you will learn In this flow, you will learn - how to compose a classification flow with LLM. - how to feed few shots to LLM classifier. ## Prerequisites Install promptflow sdk and other dependencies: ```bash pip install -r requirements.txt ``` ## Getting Started ### 1. Setup connection If you are using Azure Open AI, prepare your resource follow this [instruction](https://learn.microsoft.com/en-us/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal) and get your `api_key` if you don't have one. ```bash # Override keys with --set to avoid yaml file changes pf connection create --file ../../../connections/azure_openai.yml --set api_key=<your_api_key> api_base=<your_api_base> --name open_ai_connection ``` If you using OpenAI, sign up account [OpenAI website](https://openai.com/), login and [find personal API key](https://platform.openai.com/account/api-keys). ```shell pf connection create --file ../../../connections/openai.yml --set api_key=<your_api_key> ``` ### 2. Configure the flow with your connection `flow.dag.yaml` is already configured with connection named `open_ai_connection`. ### 3. Test flow with single line data ```bash # test with default input value in flow.dag.yaml pf flow test --flow . # test with user specified inputs pf flow test --flow . --inputs url='https://www.youtube.com/watch?v=kYqRtjDBci8' ``` ### 4. Run with multi-line data ```bash # create run using command line args pf run create --flow . --data ./data.jsonl --column-mapping url='${data.url}' --stream # (Optional) create a random run name run_name="web_classification_"$(openssl rand -hex 12) # create run using yaml file, run_name will be used in following contents, --name is optional pf run create --file run.yml --stream --name $run_name ``` You can also skip providing `column-mapping` if provided data has same column name as the flow. Reference [here](https://aka.ms/pf/column-mapping) for default behavior when `column-mapping` not provided in CLI. ```bash # list run pf run list # show run pf run show --name $run_name # show run outputs pf run show-details --name $run_name ``` ### 5. Run with classification evaluation flow create `evaluation` run: ```bash # (Optional) save previous run name into variable, and create a new random run name for further use prev_run_name=$run_name run_name="classification_accuracy_"$(openssl rand -hex 12) # create run using command line args pf run create --flow ../../evaluation/eval-classification-accuracy --data ./data.jsonl --column-mapping groundtruth='${data.answer}' prediction='${run.outputs.category}' --run $prev_run_name --stream # create run using yaml file, --name is optional pf run create --file run_evaluation.yml --run $prev_run_name --stream --name $run_name ``` ```bash pf run show-details --name $run_name pf run show-metrics --name $run_name pf run visualize --name $run_name ``` ### 6. Submit run to cloud ```bash # set default workspace az account set -s <your_subscription_id> az configure --defaults group=<your_resource_group_name> workspace=<your_workspace_name> # create run pfazure run create --flow . --data ./data.jsonl --column-mapping url='${data.url}' --stream --runtime example-runtime-ci # pfazure run create --flow . --data ./data.jsonl --column-mapping url='${data.url}' --stream # automatic runtime # (Optional) create a new random run name for further use run_name="web_classification_"$(openssl rand -hex 12) # create run using yaml file, --name is optional pfazure run create --file run.yml --runtime example-runtime-ci --name $run_name # pfazure run create --file run.yml --stream --name $run_name # automatic runtime pfazure run stream --name $run_name pfazure run show-details --name $run_name pfazure run show-metrics --name $run_name # (Optional) save previous run name into variable, and create a new random run name for further use prev_run_name=$run_name run_name="classification_accuracy_"$(openssl rand -hex 12) # create evaluation run, --name is optional pfazure run create --flow ../../evaluation/eval-classification-accuracy --data ./data.jsonl --column-mapping groundtruth='${data.answer}' prediction='${run.outputs.category}' --run $prev_run_name --runtime example-runtime-ci pfazure run create --file run_evaluation.yml --run $prev_run_name --stream --name $run_name --runtime example-runtime-ci pfazure run stream --name $run_name pfazure run show --name $run_name pfazure run show-details --name $run_name pfazure run show-metrics --name $run_name pfazure run visualize --name $run_name ```

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100/snapshot/run.yml

$schema: https://azuremlschemas.azureedge.net/promptflow/latest/Run.schema.json flow: . data: data.jsonl variant: ${summarize_text_content.variant_1} column_mapping: url: ${data.url}

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100/snapshot/fetch_text_content_from_url.py

import bs4 import requests from promptflow import tool @tool def fetch_text_content_from_url(url: str): # Send a request to the URL try: headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) " "Chrome/113.0.0.0 Safari/537.36 Edg/113.0.1774.35" } response = requests.get(url, headers=headers) if response.status_code == 200: # Parse the HTML content using BeautifulSoup soup = bs4.BeautifulSoup(response.text, "html.parser") soup.prettify() return soup.get_text()[:2000] else: msg = ( f"Get url failed with status code {response.status_code}.\nURL: {url}\nResponse: " f"{response.text[:100]}" ) print(msg) return "No available content" except Exception as e: print("Get url failed with error: {}".format(e)) return "No available content"

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100/snapshot/classify_with_llm.jinja2

system: Your task is to classify a given url into one of the following categories: Movie, App, Academic, Channel, Profile, PDF or None based on the text content information. The classification will be based on the url, the webpage text content summary, or both. user: The selection range of the value of "category" must be within "Movie", "App", "Academic", "Channel", "Profile", "PDF" and "None". The selection range of the value of "evidence" must be within "Url", "Text content", and "Both". Here are a few examples: {% for ex in examples %} URL: {{ex.url}} Text content: {{ex.text_content}} OUTPUT: {"category": "{{ex.category}}", "evidence": "{{ex.evidence}}"} {% endfor %} For a given URL and text content, classify the url to complete the category and indicate evidence: URL: {{url}} Text content: {{text_content}}. OUTPUT:

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100/snapshot/summarize_text_content__variant_1.jinja2

system: Please summarize some keywords of this paragraph and have some details of each keywords. Do not add any information that is not in the text. user: Text: {{text}} Summary:

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100/snapshot/prepare_examples.py

from promptflow import tool @tool def prepare_examples(): return [ { "url": "https://play.google.com/store/apps/details?id=com.spotify.music", "text_content": "Spotify is a free music and podcast streaming app with millions of songs, albums, and " "original podcasts. It also offers audiobooks, so users can enjoy thousands of stories. " "It has a variety of features such as creating and sharing music playlists, discovering " "new music, and listening to popular and exclusive podcasts. It also has a Premium " "subscription option which allows users to download and listen offline, and access " "ad-free music. It is available on all devices and has a variety of genres and artists " "to choose from.", "category": "App", "evidence": "Both", }, { "url": "https://www.youtube.com/channel/UC_x5XG1OV2P6uZZ5FSM9Ttw", "text_content": "NFL Sunday Ticket is a service offered by Google LLC that allows users to watch NFL " "games on YouTube. It is available in 2023 and is subject to the terms and privacy policy " "of Google LLC. It is also subject to YouTube's terms of use and any applicable laws.", "category": "Channel", "evidence": "URL", }, { "url": "https://arxiv.org/abs/2303.04671", "text_content": "Visual ChatGPT is a system that enables users to interact with ChatGPT by sending and " "receiving not only languages but also images, providing complex visual questions or " "visual editing instructions, and providing feedback and asking for corrected results. " "It incorporates different Visual Foundation Models and is publicly available. Experiments " "show that Visual ChatGPT opens the door to investigating the visual roles of ChatGPT with " "the help of Visual Foundation Models.", "category": "Academic", "evidence": "Text content", }, { "url": "https://ab.politiaromana.ro/", "text_content": "There is no content available for this text.", "category": "None", "evidence": "None", }, ]

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100/snapshot/run_evaluation.yml

$schema: https://azuremlschemas.azureedge.net/promptflow/latest/Run.schema.json flow: ../../evaluation/eval-classification-accuracy data: data.jsonl run: web_classification_variant_1_20230724_173442_973403 # replace with your run name column_mapping: groundtruth: ${data.answer} prediction: ${run.outputs.category}

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100/snapshot/requirements.txt

promptflow[azure] promptflow-tools bs4

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100/snapshot/flow.dag.yaml

inputs: url: type: string default: https://play.google.com/store/apps/details?id=com.twitter.android is_chat_input: false outputs: category: type: string reference: ${convert_to_dict.output.category} evaluation_only: false is_chat_output: false evidence: type: string reference: ${convert_to_dict.output.evidence} evaluation_only: false is_chat_output: false nodes: - name: fetch_text_content_from_url type: python source: type: code path: fetch_text_content_from_url.py inputs: url: "${inputs.url}" aggregation: false - name: prepare_examples type: python source: type: code path: prepare_examples.py inputs: {} aggregation: false - name: classify_with_llm type: llm source: type: code path: classify_with_llm.jinja2 inputs: deployment_name: "gpt-35-turbo" model: "gpt-3.5-turbo" max_tokens: 128 temperature: 0.2 url: "${inputs.url}" text_content: "${summarize_text_content.output}" examples: "${prepare_examples.output}" api: chat connection: open_ai_connection aggregation: false - name: convert_to_dict type: python source: type: code path: convert_to_dict.py inputs: input_str: "${classify_with_llm.output}" aggregation: false - name: summarize_text_content type: llm source: type: code path: summarize_text_content.jinja2 inputs: deployment_name: "gpt-35-turbo" model: "gpt-3.5-turbo" max_tokens: 128 temperature: 0.2 text: "${fetch_text_content_from_url.output}" api: chat connection: open_ai_connection aggregation: false environment: python_requirements_txt: requirements.txt

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100/snapshot/summarize_text_content.jinja2

system: Please summarize the following text in one paragraph. 100 words. Do not add any information that is not in the text. user: Text: {{text}} Summary:

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100

promptflow_repo/promptflow/src/promptflow/tests/test_configs/runs/web_classification_variant_0_20231205_120253_104100/flow_outputs/output.jsonl

{"line_number": 0, "category": "None", "evidence": "None"} {"line_number": 1, "category": "Academic", "evidence": "Both"} {"line_number": 2, "category": "App", "evidence": "Both"}

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/configs/mock_flow_empty_config

promptflow_repo/promptflow/src/promptflow/tests/test_configs/configs/mock_flow_empty_config/.azureml/config.json

{ "subscription_id": "", "resource_group": "", "workspace_name": "" }

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/configs/mock_flow1

promptflow_repo/promptflow/src/promptflow/tests/test_configs/configs/mock_flow1/.azureml/config.json

{ "subscription_id": "sub1", "resource_group": "rg1", "workspace_name": "ws1" }

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/configs/.azureml/config.json

{ "subscription_id": "sub_default", "resource_group": "rg_default", "workspace_name": "ws_default" }

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/node_recordings/node_cache.shelve.bak

'aadb0707e9a62b00df9d0d3fecb709ece90a8b67', (0, 2261) '28e341291f602cdf951239f0152fa9e26deb501b', (67584, 3359) 'e25f352dc22315a0e2c3ee2a3cd629763fc0ae5e', (9216, 3302) 'dc8625906abf86f47f2c52b461f108ab6b98a1cf', (12800, 3795) 'a30838607856c1b8efd522af93ea1edb88f57b4f', (16896, 3671) '4f7cfc5331a898e9c7abb00e2d0f6f818dd2c999', (20992, 3795) '06c8709d838389faebb00cc579c7bd1e396f5ab7', (25088, 3886) '89ef4101e31a12b70fa982b83be01a65ee3c7410', (29184, 3365) '0304f9ccf7ab8521173b43b526b26412208148b1', (32768, 509) '914c0cdebdbf3b03a21afe84f9a1af9f99729771', (33280, 1080) '3ccba72230e4213b2ae31d1df71e45377f5b9fbf', (34816, 3081) '12dcd3566273156d0071d532410670ca6a13faa8', (38400, 3028) '95aad7f462a4948f82104b8c6320ced6eeb3a59b', (41472, 3293) '2c8bece890f55462916146bb89d0df92a4210163', (45056, 3316) 'd1f105fd5701fa3ae65608e6ef732eb2c095d88d', (48640, 3371) '64b155dfa377148a12addcaabd196edd29620ede', (52224, 3258) '9a7219e5f8a5e525df2fae3a4df82eb6c6dd7837', (59392, 3870) 'bc6a4f4ae64fa01917085f3e608f0d5c7699308c', (63488, 3610) 'ed29e249e3b86abb1907bd98dc7d5368cc11bfe5', (71168, 3348) '977d4e91843e6a025d88dc10f5935f3fe50a2aa8', (74752, 3632) 'ac3d32b87a5b6271641f0fdb77dd785d07930207', (78848, 3403) '60924e7e69449f453fea01c32a84b74521f3bb56', (82432, 3080) 'd52f8bf8badc3432c05395d9f647efa2fede3121', (86016, 1938) '15b696309e5d753b6123f5521a9c8e4daded18e3', (88064, 3895) '32edf2341350a6fca9670b7ec6edc208905ec8c1', (92160, 3676) 'cc9ac3eaa4f332a204f39f050c8f83ceba89113d', (96256, 863) '2c2c8e9b4662215a00c7119e987a6b0049829e2b', (97280, 503) '9893bb77a07b7fa08f4ab85dd16368863040bc53', (97792, 1966) 'b11590a307351d8ade7df49346a826cfde9c4444', (99840, 3923) 'c8fcd047770466d76018d8b9656c18b7a87a9dcf', (103936, 2255) 'b490dc00a174b4b94e411b32b3b6df8e7291ff44', (106496, 493) '427f9902c01baef6ef8020ae5e5cb5163c6d36ac', (107008, 307) '45ea53cf0c4770788d6568a177baf123146dc20b', (107520, 924) 'ebf9f4aad38dceb848df0614195e40870dd5fe6e', (108544, 1043) '6d6d40bcf3cf2e43d640c75cba40d607f1869b9f', (110080, 1315) 'abb3142dc69818c0c7131c4642f2870ae59bd05a', (111616, 1025) '8cf128af83ea2aaf4a09890690ab16f3bb347bb3', (113152, 309) '343332ff6b96d3b1baac23c4e8394a6a965f84b1', (113664, 327) 'd7079ca5b962a7dbca1cfe90f847775652acf6b9', (114176, 961) 'b9bcb73bbe85960e4f492c6b60fd2584de421f91', (115200, 312) '71d8e363eeac4e3334679f505225acd75ef5607b', (115712, 414) '9e15e58e4a0b5ff182c0f70761c65fba081c2c2f', (132096, 3373) '1696e4d8ebc97bfec511fca7754ef717692faa70', (119808, 3623) 'bd36e2e27da8bc212f2213feb0154b6902e1b549', (123904, 3699) '91f0cff189e34431153edba55e684629b6b56487', (128000, 3808) '5bd1695cf0739697851e8ca9bd54c0305c7422c4', (135680, 3293) '33c98924b97b4eed655857dca1472bdfcf36b86a', (139264, 3446) '3dc18b56b1741937eefaa1069119cdec984e49b7', (142848, 3695) '255fbbcb72580f8c5eeed07419ed9d9fdd809433', (146944, 3113) '879388db36d150bfd7f948a8a14011ee96ee7e51', (150528, 3063) 'cf7bb24c6526295ab8e9ba4eea799d98b13dcce5', (153600, 3738) '6b05f39821053fcbc341404fa47bd2a029862637', (157696, 3725) 'a5abc1d206c9456c8230e382e04c2903756f9be2', (161792, 3690) '6c9c0aa6ecb54dcdb79b2e4dc0c09321482dde41', (165888, 400) '70f4fea54805e642e98208d6704425432e00d46d', (166400, 2975) '7c7b48d39ea68dcc368aa9ad6b0da9c30639c716', (169472, 4085) 'f80e94d98478b637304d5af02356da38b45638da', (173568, 3542) 'ce3be051617aa3d9baaabefbde10655a382725ef', (177152, 579) 'b9f991e14c162d02211acdd3737f60afbf117107', (178176, 517) '75de0fcefdd6fd5f81c2ccbb80b5e40d81c9c214', (179200, 397) 'fb43cea41f39ffa5ef98f98499c292af14cb92cd', (179712, 1305) '1af44fd75c83703dc9d960a0874fda3a2fa0e389', (181248, 502) 'ebe34b3760a5502304bb23747079e50d8515141b', (181760, 418) '83f99b7f16fc8a4548782215c97d500eed44159b', (182272, 997) '235b8b0ff5ebbf46ab52403b59346dd08717c297', (183296, 3205) '6dbf6fe28cfe055d3730b847cd603ceb498cc046', (186880, 3055) '1bc4561b56cd2944414cd994c41dc370630c814c', (189952, 3617) '29301e2df6aef321aaed0c37f5acf59efdcf0ce5', (194048, 533) 'e68e72e33a357bca30a07bd6d7157209a7ed2f46', (195072, 3385) 'a69786b6c83c69110a94fa92571d56716831b89b', (198656, 1010) '9113b6564602c0961af986fcfd51af95e7aa0d30', (199680, 2973) '34f82ce2482f5039176bfad68fea3b660cce46a2', (202752, 621) '4104c4ae613928612ddf45359338643833764a9b', (203776, 2960) '773e7701fa9b21d32135f2e0a2f80d9f3bf60ff0', (206848, 860) 'e17f414155f0f81443c79d3af49e3295fe38a3bd', (207872, 1215) '9dc92c6363cf76df823e9cc93dc3e6f961f3dfae', (209408, 3443) '24973771dad2d9beb6306802d11b103627a0c86f', (212992, 4463) '4a55d6c6e33565c8c2976ffffed0544c8f01f11f', (217600, 4079) 'c3f859fc47cf393cc3af2e7854c73143039219f9', (221696, 3541) '1c304bd9336e59ee7fe03ec0aa3ff1316accbd42', (225280, 3182) '79b019d7c272dbdfc8264e8e42b2c88d7aa7c951', (228864, 2189) 'ead9751f11bc2db068f915f8cd6a798e5d417ee1', (231424, 2224) '90e7b637f88d5ff8a781be0ca4c1682885c17e4a', (233984, 444) 'b08cfc768cc8c1c5165a02d633b7c5f3cdfdfb60', (234496, 958) '2182939be9737c58873eafa07078daccddac9b6a', (235520, 3237) '063189b9af2fa70eb9ecea7fd40adb28d9ad63a9', (239104, 3331) '45c057f3f3dbc663bc9d7661254c3df17328111c', (242688, 3248) '3a9191ae0dde33b5d4d9bc89da4b87436400f505', (246272, 3790) 'bb71bde6522266d699c50af9825a6b4f0f025d56', (250368, 3858)

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs

promptflow_repo/promptflow/src/promptflow/tests/test_configs/node_recordings/node_cache.shelve.dir

'aadb0707e9a62b00df9d0d3fecb709ece90a8b67', (0, 2261) '28e341291f602cdf951239f0152fa9e26deb501b', (67584, 3359) 'e25f352dc22315a0e2c3ee2a3cd629763fc0ae5e', (9216, 3302) 'dc8625906abf86f47f2c52b461f108ab6b98a1cf', (12800, 3795) 'a30838607856c1b8efd522af93ea1edb88f57b4f', (16896, 3671) '4f7cfc5331a898e9c7abb00e2d0f6f818dd2c999', (20992, 3795) '06c8709d838389faebb00cc579c7bd1e396f5ab7', (25088, 3886) '89ef4101e31a12b70fa982b83be01a65ee3c7410', (29184, 3365) '0304f9ccf7ab8521173b43b526b26412208148b1', (32768, 509) '914c0cdebdbf3b03a21afe84f9a1af9f99729771', (33280, 1080) '3ccba72230e4213b2ae31d1df71e45377f5b9fbf', (34816, 3081) '12dcd3566273156d0071d532410670ca6a13faa8', (38400, 3028) '95aad7f462a4948f82104b8c6320ced6eeb3a59b', (41472, 3293) '2c8bece890f55462916146bb89d0df92a4210163', (45056, 3316) 'd1f105fd5701fa3ae65608e6ef732eb2c095d88d', (48640, 3371) '64b155dfa377148a12addcaabd196edd29620ede', (52224, 3258) '9a7219e5f8a5e525df2fae3a4df82eb6c6dd7837', (59392, 3870) 'bc6a4f4ae64fa01917085f3e608f0d5c7699308c', (63488, 3610) 'ed29e249e3b86abb1907bd98dc7d5368cc11bfe5', (71168, 3348) '977d4e91843e6a025d88dc10f5935f3fe50a2aa8', (74752, 3632) 'ac3d32b87a5b6271641f0fdb77dd785d07930207', (78848, 3403) '60924e7e69449f453fea01c32a84b74521f3bb56', (82432, 3080) 'd52f8bf8badc3432c05395d9f647efa2fede3121', (86016, 1938) '15b696309e5d753b6123f5521a9c8e4daded18e3', (88064, 3895) '32edf2341350a6fca9670b7ec6edc208905ec8c1', (92160, 3676) 'cc9ac3eaa4f332a204f39f050c8f83ceba89113d', (96256, 863) '2c2c8e9b4662215a00c7119e987a6b0049829e2b', (97280, 503) '9893bb77a07b7fa08f4ab85dd16368863040bc53', (97792, 1966) 'b11590a307351d8ade7df49346a826cfde9c4444', (99840, 3923) 'c8fcd047770466d76018d8b9656c18b7a87a9dcf', (103936, 2255) 'b490dc00a174b4b94e411b32b3b6df8e7291ff44', (106496, 493) '427f9902c01baef6ef8020ae5e5cb5163c6d36ac', (107008, 307) '45ea53cf0c4770788d6568a177baf123146dc20b', (107520, 924) 'ebf9f4aad38dceb848df0614195e40870dd5fe6e', (108544, 1043) '6d6d40bcf3cf2e43d640c75cba40d607f1869b9f', (110080, 1315) 'abb3142dc69818c0c7131c4642f2870ae59bd05a', (111616, 1025) '8cf128af83ea2aaf4a09890690ab16f3bb347bb3', (113152, 309) '343332ff6b96d3b1baac23c4e8394a6a965f84b1', (113664, 327) 'd7079ca5b962a7dbca1cfe90f847775652acf6b9', (114176, 961) 'b9bcb73bbe85960e4f492c6b60fd2584de421f91', (115200, 312) '71d8e363eeac4e3334679f505225acd75ef5607b', (115712, 414) '9e15e58e4a0b5ff182c0f70761c65fba081c2c2f', (132096, 3373) '1696e4d8ebc97bfec511fca7754ef717692faa70', (119808, 3623) 'bd36e2e27da8bc212f2213feb0154b6902e1b549', (123904, 3699) '91f0cff189e34431153edba55e684629b6b56487', (128000, 3808) '5bd1695cf0739697851e8ca9bd54c0305c7422c4', (135680, 3293) '33c98924b97b4eed655857dca1472bdfcf36b86a', (139264, 3446) '3dc18b56b1741937eefaa1069119cdec984e49b7', (142848, 3695) '255fbbcb72580f8c5eeed07419ed9d9fdd809433', (146944, 3113) '879388db36d150bfd7f948a8a14011ee96ee7e51', (150528, 3063) 'cf7bb24c6526295ab8e9ba4eea799d98b13dcce5', (153600, 3738) '6b05f39821053fcbc341404fa47bd2a029862637', (157696, 3725) 'a5abc1d206c9456c8230e382e04c2903756f9be2', (161792, 3690) '6c9c0aa6ecb54dcdb79b2e4dc0c09321482dde41', (165888, 400) '70f4fea54805e642e98208d6704425432e00d46d', (166400, 2975) '7c7b48d39ea68dcc368aa9ad6b0da9c30639c716', (169472, 4085) 'f80e94d98478b637304d5af02356da38b45638da', (173568, 3542) 'ce3be051617aa3d9baaabefbde10655a382725ef', (177152, 579) 'b9f991e14c162d02211acdd3737f60afbf117107', (178176, 517) '75de0fcefdd6fd5f81c2ccbb80b5e40d81c9c214', (179200, 397) 'fb43cea41f39ffa5ef98f98499c292af14cb92cd', (179712, 1305) '1af44fd75c83703dc9d960a0874fda3a2fa0e389', (181248, 502) 'ebe34b3760a5502304bb23747079e50d8515141b', (181760, 418) '83f99b7f16fc8a4548782215c97d500eed44159b', (182272, 997) '235b8b0ff5ebbf46ab52403b59346dd08717c297', (183296, 3205) '6dbf6fe28cfe055d3730b847cd603ceb498cc046', (186880, 3055) '1bc4561b56cd2944414cd994c41dc370630c814c', (189952, 3617) '29301e2df6aef321aaed0c37f5acf59efdcf0ce5', (194048, 533) 'e68e72e33a357bca30a07bd6d7157209a7ed2f46', (195072, 3385) 'a69786b6c83c69110a94fa92571d56716831b89b', (198656, 1010) '9113b6564602c0961af986fcfd51af95e7aa0d30', (199680, 2973) '34f82ce2482f5039176bfad68fea3b660cce46a2', (202752, 621) '4104c4ae613928612ddf45359338643833764a9b', (203776, 2960) '773e7701fa9b21d32135f2e0a2f80d9f3bf60ff0', (206848, 860) 'e17f414155f0f81443c79d3af49e3295fe38a3bd', (207872, 1215) '9dc92c6363cf76df823e9cc93dc3e6f961f3dfae', (209408, 3443) '24973771dad2d9beb6306802d11b103627a0c86f', (212992, 4463) '4a55d6c6e33565c8c2976ffffed0544c8f01f11f', (217600, 4079) 'c3f859fc47cf393cc3af2e7854c73143039219f9', (221696, 3541) '1c304bd9336e59ee7fe03ec0aa3ff1316accbd42', (225280, 3182) '79b019d7c272dbdfc8264e8e42b2c88d7aa7c951', (228864, 2189) 'ead9751f11bc2db068f915f8cd6a798e5d417ee1', (231424, 2224) '90e7b637f88d5ff8a781be0ca4c1682885c17e4a', (233984, 444) 'b08cfc768cc8c1c5165a02d633b7c5f3cdfdfb60', (234496, 958) '2182939be9737c58873eafa07078daccddac9b6a', (235520, 3237) '063189b9af2fa70eb9ecea7fd40adb28d9ad63a9', (239104, 3331) '45c057f3f3dbc663bc9d7661254c3df17328111c', (242688, 3248) '3a9191ae0dde33b5d4d9bc89da4b87436400f505', (246272, 3790) 'bb71bde6522266d699c50af9825a6b4f0f025d56', (250368, 3858)

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/eval_flow_with_simple_image/pick_an_image.py

import random from promptflow.contracts.multimedia import Image from promptflow import tool @tool def pick_an_image(image_1: Image, image_2: Image) -> Image: if random.choice([True, False]): return image_1 else: return image_2

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/eval_flow_with_simple_image/inputs.jsonl

{"image": {"data:image/png;path":"logo.jpg"}} {"image": {"data:image/png;path":"logo_2.png"}}

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/eval_flow_with_simple_image/flow.dag.yaml

inputs: image: type: image default: logo.jpg outputs: output: type: image reference: ${python_node.output} nodes: - name: python_node type: python source: type: code path: pick_an_image.py inputs: image_1: ${inputs.image} image_2: data:image/png;path: logo_2.png - name: aggregate type: python source: type: code path: merge_images.py inputs: image_1: - data:image/jpg;path: logo.jpg image_2: ${inputs.image} image_3: ${python_node.output} aggregation: true

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/eval_flow_with_simple_image/merge_images.py

from promptflow import tool @tool def merge_images(image_1: list, image_2: list, image_3: list): res = set() res.add(image_1[0]) res.add(image_2[0]) res.add(image_3[0]) return list(res)

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/script_tool_with_init/script_tool_with_init.py

from promptflow import ToolProvider, tool class ScriptToolWithInit(ToolProvider): def __init__(self, init_input: str): super().__init__() self.init_input = init_input @tool def call(self, input: str): return str.join(" ", [self.init_input, input])

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/script_tool_with_init/flow.dag.yaml

inputs: input: type: string default: World outputs: output: type: string reference: ${script_tool_with_init.output} nodes: - name: script_tool_with_init type: python source: type: code path: script_tool_with_init.py inputs: init_input: Hello input: ${inputs.input}

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/async_tools/flow.dag.yaml

inputs: input_str: type: string default: Hello outputs: ouput1: type: string reference: ${async_passthrough1.output} output2: type: string reference: ${async_passthrough2.output} nodes: - name: async_passthrough type: python source: type: code path: async_passthrough.py inputs: input1: ${inputs.input_str} wait_seconds: 3 - name: async_passthrough1 type: python source: type: code path: async_passthrough.py inputs: input1: ${async_passthrough.output} wait_seconds: 3 - name: async_passthrough2 type: python source: type: code path: async_passthrough.py inputs: input1: ${async_passthrough.output} wait_seconds: 3

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/async_tools/async_passthrough.py

from promptflow import tool import asyncio @tool async def passthrough_str_and_wait(input1: str, wait_seconds=3) -> str: assert isinstance(input1, str), f"input1 should be a string, got {input1}" print(f"Wait for {wait_seconds} seconds in async function") for i in range(wait_seconds): print(i) await asyncio.sleep(1) return input1

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/chat_flow_with_image/mock_chat.py

from promptflow import tool from promptflow.contracts.multimedia import Image @tool def mock_chat(chat_history: list, question: list): ensure_image_in_list(question, "question") for item in chat_history: ensure_image_in_list(item["inputs"]["question"], "inputs of chat history") ensure_image_in_list(item["outputs"]["answer"], "outputs of chat history") res = [] for item in question: if isinstance(item, Image): res.append(item) res.append("text response") return res def ensure_image_in_list(value: list, name: str): include_image = False for item in value: if isinstance(item, Image): include_image = True if not include_image: raise Exception(f"No image found in {name}, you should include at least one image in your {name}.")

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/chat_flow_with_image/inputs.jsonl

{"chat_history":[{"inputs": {"question": ["the first question",{"data:image/jpg;path": "logo.jpg"}]},"outputs": {"answer": [{"data:image/jpg;path": "logo.jpg"}]}},{"inputs": {"question": ["the second question",{"data:image/png;path": "logo_2.png"}]},"outputs": {"answer": [{"data:image/png;path": "logo_2.png"}]}}],"question": ["the third question",{"data:image/jpg;path": "logo.jpg"},{"data:image/png;path": "logo_2.png"}]} {"chat_history":[{"inputs": {"question": ["the first question",{"data:image/jpg;path": "logo.jpg"}]},"outputs": {"answer": [{"data:image/jpg;path": "logo.jpg"}]}},{"inputs": {"question": ["the second question",{"data:image/png;path": "logo_2.png"}]},"outputs": {"answer": [{"data:image/png;path": "logo_2.png"}]}}],"question": ["the third question",{"data:image/jpg;path": "logo.jpg"},{"data:image/png;path": "logo_2.png"}]}

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/chat_flow_with_image/flow.dag.yaml

inputs: chat_history: type: list default: - inputs: question: - the first question - data:image/jpg;path: logo.jpg outputs: answer: - data:image/jpg;path: logo.jpg - inputs: question: - the second question - data:image/png;path: logo_2.png outputs: answer: - data:image/png;path: logo_2.png is_chat_history: true question: type: list default: - the third question - data:image/jpg;path: logo.jpg - data:image/png;path: logo_2.png is_chat_input: true outputs: answer: type: string reference: ${mock_chat_node.output} is_chat_output: true nodes: - name: mock_chat_node type: python source: type: code path: mock_chat.py inputs: chat_history: ${inputs.chat_history} question: ${inputs.question}

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/hello-world/hello_world.py

from promptflow import tool @tool def hello_world(name: str) -> str: return f"Hello World {name}!"

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/hello-world/flow.dag.yaml

inputs: name: type: string default: hod outputs: result: type: string reference: ${hello_world.output} nodes: - name: hello_world type: python source: type: code path: hello_world.py inputs: name: ${inputs.name}

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/hello-world

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/hello-world/.promptflow/flow.tools.json

{ "code": { "hello_world.py": { "type": "python", "inputs": { "name": { "type": [ "string" ] } }, "source": "hello_world.py", "function": "hello_world" } }, "package": { "promptflow.tools.embedding.embedding": { "name": "Embedding", "description": "Use Open AI's embedding model to create an embedding vector representing the input text.", "type": "python", "module": "promptflow.tools.embedding", "function": "embedding", "inputs": { "connection": { "type": [ "AzureOpenAIConnection", "OpenAIConnection" ] }, "deployment_name": { "type": [ "string" ], "enabled_by": "connection", "enabled_by_type": [ "AzureOpenAIConnection" ], "capabilities": { "completion": false, "chat_completion": false, "embeddings": true }, "model_list": [ "text-embedding-ada-002", "text-search-ada-doc-001", "text-search-ada-query-001" ] }, "model": { "type": [ "string" ], "enabled_by": "connection", "enabled_by_type": [ "OpenAIConnection" ], "enum": [ "text-embedding-ada-002", "text-search-ada-doc-001", "text-search-ada-query-001" ] }, "input": { "type": [ "string" ] } }, "package": "promptflow-tools", "package_version": "0.1.0b5" }, "promptflow.tools.serpapi.SerpAPI.search": { "name": "Serp API", "description": "Use Serp API to obtain search results from a specific search engine.", "inputs": { "connection": { "type": [ "SerpConnection" ] }, "engine": { "default": "google", "enum": [ "google", "bing" ], "type": [ "string" ] }, "location": { "default": "", "type": [ "string" ] }, "num": { "default": "10", "type": [ "int" ] }, "query": { "type": [ "string" ] }, "safe": { "default": "off", "enum": [ "active", "off" ], "type": [ "string" ] } }, "type": "python", "module": "promptflow.tools.serpapi", "class_name": "SerpAPI", "function": "search", "package": "promptflow-tools", "package_version": "0.1.0b5" }, "my_tool_package.tools.my_tool_1.my_tool": { "function": "my_tool", "inputs": { "connection": { "type": [ "CustomConnection" ], "custom_type": [ "MyFirstConnection", "MySecondConnection" ] }, "input_text": { "type": [ "string" ] } }, "module": "my_tool_package.tools.my_tool_1", "name": "My First Tool", "description": "This is my first tool", "type": "python", "package": "test-custom-tools", "package_version": "0.0.2" }, "my_tool_package.tools.my_tool_2.MyTool.my_tool": { "class_name": "MyTool", "function": "my_tool", "inputs": { "connection": { "type": [ "CustomConnection" ], "custom_type": [ "MySecondConnection" ] }, "input_text": { "type": [ "string" ] } }, "module": "my_tool_package.tools.my_tool_2", "name": "My Second Tool", "description": "This is my second tool", "type": "python", "package": "test-custom-tools", "package_version": "0.0.2" }, "my_tool_package.tools.my_tool_with_custom_strong_type_connection.my_tool": { "function": "my_tool", "inputs": { "connection": { "custom_type": [ "MyCustomConnection" ], "type": [ "CustomConnection" ] }, "input_param": { "type": [ "string" ] } }, "module": "my_tool_package.tools.my_tool_with_custom_strong_type_connection", "name": "Tool With Custom Strong Type Connection", "description": "This is my tool with custom strong type connection.", "type": "python", "package": "test-custom-tools", "package_version": "0.0.2" } } }

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/python_stream_tools/inputs.json

{ "text": "Hello World!" }

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/python_stream_tools/echo_input.py

from promptflow import tool @tool def my_python_tool(input: str) -> str: yield "Echo: " for word in input.split(): yield word + " "

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/python_stream_tools/flow.dag.yaml

inputs: text: type: string outputs: output_echo: type: string reference: ${echo_my_input.output} nodes: - name: echo_my_input type: python source: type: code path: echo_input.py inputs: input: ${inputs.text}

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/eval-classification-accuracy/data.jsonl

{"groundtruth": "App","prediction": "App"} {"groundtruth": "Channel","prediction": "Channel"} {"groundtruth": "Academic","prediction": "Academic"}

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/eval-classification-accuracy/calculate_accuracy.py

from typing import List from promptflow import log_metric, tool @tool def calculate_accuracy(grades: List[str]): result = [] for index in range(len(grades)): grade = grades[index] result.append(grade) # calculate accuracy for each variant accuracy = round((result.count("Correct") / len(result)), 2) log_metric("accuracy", accuracy) return result

0

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows

promptflow_repo/promptflow/src/promptflow/tests/test_configs/flows/eval-classification-accuracy/grade.py

from promptflow import tool @tool def grade(groundtruth: str, prediction: str): return "Correct" if groundtruth.lower() == prediction.lower() else "Incorrect"

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