treezy254/hug_stack · Datasets at Hugging Face

import os from promptflow import tool @tool def get_env_var(key: str): if key == "raise": raise Exception("expected raise!") print(os.environ.get(key)) # get from env var return {"value": os.environ.get(key)}

promptflow/src/promptflow/tests/test_configs/flows/partial_fail/print_env.py/0

{ "file_path": "promptflow/src/promptflow/tests/test_configs/flows/partial_fail/print_env.py", "repo_id": "promptflow", "token_count": 92 }

70

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}

promptflow/src/promptflow/tests/test_configs/flows/python_stream_tools/flow.dag.yaml/0

{ "file_path": "promptflow/src/promptflow/tests/test_configs/flows/python_stream_tools/flow.dag.yaml", "repo_id": "promptflow", "token_count": 103 }

71

from promptflow import tool @tool def passthrough(image, call_passthrough: bool = True): if call_passthrough: image = passthrough(image, False) return image

promptflow/src/promptflow/tests/test_configs/flows/python_tool_with_image_nested_api_calls/passthrough.py/0

{ "file_path": "promptflow/src/promptflow/tests/test_configs/flows/python_tool_with_image_nested_api_calls/passthrough.py", "repo_id": "promptflow", "token_count": 63 }

72

{ "name": "script_with___file__", "type": "python", "inputs": { "input1": { "type": [ "string" ] } }, "source": "script_with___file__.py", "function": "my_python_tool" }

promptflow/src/promptflow/tests/test_configs/flows/script_with___file__/script_with___file__.meta.json/0

{ "file_path": "promptflow/src/promptflow/tests/test_configs/flows/script_with___file__/script_with___file__.meta.json", "repo_id": "promptflow", "token_count": 103 }

73

inputs: message: type: string default: Hello World! outputs: output: type: object reference: ${test_assistant_definition.output} nodes: - name: test_assistant_definition type: python source: type: code path: test_assistant_definition.py inputs: message: ${inputs.message} assistant_definition: assistant_definition.yaml

promptflow/src/promptflow/tests/test_configs/flows/tool_with_assistant_definition/flow.dag.yaml/0

{ "file_path": "promptflow/src/promptflow/tests/test_configs/flows/tool_with_assistant_definition/flow.dag.yaml", "repo_id": "promptflow", "token_count": 129 }

74

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promptflow/src/promptflow/tests/test_configs/node_recordings/node_cache.shelve.bak/0

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"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": 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raise!", "traceback": "Traceback (most recent call last):\n File \"/mnt/host/service/app/39415/requests/name/print_env.py\", line 9, in get_env_var\n raise Exception(\"expected raise!\")\nException: expected raise!\n", "filename": "/mnt/host/service/app/39415/requests/name/print_env.py", "lineno": 9, "name": "get_env_var"}}], "debugInfo": {"type": "ToolExecutionError", "message": "Execution failure in ''print_env'': (Exception) expected raise!", "stackTrace": "\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/executor/flow_executor.py\", line 804, in _exec\n output, nodes_outputs = self._traverse_nodes(inputs, context)\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/executor/flow_executor.py\", line 890, in _traverse_nodes\n nodes_outputs, bypassed_nodes = self._submit_to_scheduler(context, inputs, batch_nodes)\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/executor/flow_executor.py\", line 910, in _submit_to_scheduler\n return FlowNodesScheduler(self._tools_manager, inputs, nodes, self._node_concurrency, context).execute()\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/executor/_flow_nodes_scheduler.py\", line 69, in execute\n raise e\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/executor/_flow_nodes_scheduler.py\", line 58, in execute\n self._dag_manager.complete_nodes(self._collect_outputs(completed_futures))\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/executor/_flow_nodes_scheduler.py\", line 90, in _collect_outputs\n each_node_result = each_future.result()\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/concurrent/futures/_base.py\", line 451, in result\n return self.__get_result()\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/concurrent/futures/_base.py\", line 403, in __get_result\n raise self._exception\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/concurrent/futures/thread.py\", line 58, in run\n result = self.fn(*self.args, **self.kwargs)\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/executor/_flow_nodes_scheduler.py\", line 111, in _exec_single_node_in_thread\n result = context.invoke_tool(node, f, kwargs=kwargs)\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/_core/flow_execution_context.py\", line 89, in invoke_tool\n result = self._invoke_tool_with_timer(node, f, kwargs)\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/_core/flow_execution_context.py\", line 196, in _invoke_tool_with_timer\n raise ToolExecutionError(node_name=node_name, module=module) from e\n", "innerException": {"type": "Exception", "message": "expected raise!", "stackTrace": "Traceback (most recent call last):\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/_core/flow_execution_context.py\", line 185, in _invoke_tool_with_timer\n return f(**kwargs)\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/_core/tool.py\", line 106, in decorated_tool\n output = func(*args, **kwargs)\n File \"/mnt/host/service/app/39415/requests/name/print_env.py\", line 9, in get_env_var\n raise Exception(\"expected raise!\")\n", "innerException": null}}}, "inputs": {"key": "raise", "line_number": 1}, "output": null, "metrics": null, "request": null, "parent_run_id": "name", "root_run_id": "name", "source_run_id": null, "flow_id": "default_flow_id", "start_time": "2024-01-12T08:50:26.77979Z", "end_time": "2024-01-12T08:50:26.953981Z", "index": 1, "api_calls": [{"name": "get_env_var", "type": "Tool", "inputs": {"key": "raise"}, "output": null, "start_time": 1705049426.782672, "end_time": 1705049426.783439, "error": {"message": "expected raise!", "type": "Exception"}, "children": null, "node_name": "print_env"}], "variant_id": "", "name": "", "description": "", "tags": null, "system_metrics": {"duration": 0.174191, "prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0}, "result": null, "upload_metrics": false}]' headers: connection: - keep-alive content-length: - '6638' 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: - '0.729' 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/name/childRuns?endIndex=49&startIndex=25 response: body: string: '[]' headers: connection: - keep-alive content-length: - '2' content-type: - application/json; charset=utf-8 strict-transport-security: - max-age=15724800; includeSubDomains; preload x-content-type-options: - nosniff x-request-time: - '1.083' 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": 1705049404, "rootRunId": "name", "createdUtc": "2024-01-12T08:50:04.3467507+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": "Execution failure in ''print_env'': (Exception) expected raise!", "messageFormat": "{\"totalChildRuns\": 3, \"userErrorChildRuns\": 1, \"systemErrorChildRuns\": 0, \"errorDetails\": [{\"code\": \"UserError/ToolExecutionError\", \"messageFormat\": \"Execution failure in ''{node_name}'': {error_type_and_message}\", \"count\": 1}]}", "messageParameters": {"node_name": "print_env", "error_type_and_message": "(Exception) expected raise!"}, "referenceCode": "Tool/__pf_main__", "detailsUri": null, "target": null, "details": [], "innerError": {"code": "ToolExecutionError", "innerError": null}, "debugInfo": {"type": "ToolExecutionError", "message": "Execution failure in ''print_env'': (Exception) expected raise!", "stackTrace": "\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/executor/flow_executor.py\", line 804, in _exec\n output, nodes_outputs = self._traverse_nodes(inputs, context)\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/executor/flow_executor.py\", line 890, in _traverse_nodes\n nodes_outputs, bypassed_nodes = self._submit_to_scheduler(context, inputs, batch_nodes)\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/executor/flow_executor.py\", line 910, in _submit_to_scheduler\n return FlowNodesScheduler(self._tools_manager, inputs, nodes, self._node_concurrency, context).execute()\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/executor/_flow_nodes_scheduler.py\", line 69, in execute\n raise e\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/executor/_flow_nodes_scheduler.py\", line 58, in execute\n self._dag_manager.complete_nodes(self._collect_outputs(completed_futures))\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/executor/_flow_nodes_scheduler.py\", line 90, in _collect_outputs\n each_node_result = each_future.result()\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/concurrent/futures/_base.py\", line 451, in result\n return self.__get_result()\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/concurrent/futures/_base.py\", line 403, in __get_result\n raise self._exception\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/concurrent/futures/thread.py\", line 58, in run\n result = self.fn(*self.args, **self.kwargs)\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/executor/_flow_nodes_scheduler.py\", line 111, in _exec_single_node_in_thread\n result = context.invoke_tool(node, f, kwargs=kwargs)\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/_core/flow_execution_context.py\", line 89, in invoke_tool\n result = self._invoke_tool_with_timer(node, f, kwargs)\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/_core/flow_execution_context.py\", line 196, in _invoke_tool_with_timer\n raise ToolExecutionError(node_name=node_name, module=module) from e\n", "innerException": {"type": "Exception", "message": "expected raise!", "stackTrace": "Traceback (most recent call last):\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/_core/flow_execution_context.py\", line 185, in _invoke_tool_with_timer\n return f(**kwargs)\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/_core/tool.py\", line 106, in decorated_tool\n output = func(*args, **kwargs)\n File \"/mnt/host/service/app/39415/requests/name/print_env.py\", line 9, in get_env_var\n raise Exception(\"expected raise!\")\n", "innerException": null, "data": null, "errorResponse": null}, "data": null, "errorResponse": null}, "additionalInfo": [{"type": "ToolExecutionErrorDetails", "info": {"type": "Exception", "message": "expected raise!", "traceback": "Traceback (most recent call last):\n File \"/mnt/host/service/app/39415/requests/name/print_env.py\", line 9, in get_env_var\n raise Exception(\"expected raise!\")\nException: expected raise!\n", "filename": "/mnt/host/service/app/39415/requests/name/print_env.py", "lineno": 9, "name": "get_env_var"}}]}, "correlation": null, "environment": null, "location": null, "time": "2024-01-12T08:50:30.744399+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": "d689f844-6eae-4a78-a210-4779ec098e1e", "parentRunUuid": null, "rootRunUuid": "d689f844-6eae-4a78-a210-4779ec098e1e", "lastStartTimeUtc": null, "currentComputeTime": null, "computeDuration": "00:00:04.8547843", "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": "2024-01-12T08:50:30.0347672+00:00", "duration": "00:00:04.8547843", "cancelationReason": null, "currentAttemptId": 1, "runId": "name", "parentRunId": null, "experimentId": "1848033e-509f-4c52-92ee-f0a0121fe99e", "status": "Completed", "startTimeUtc": "2024-01-12T08:50:26.1204745+00:00", "endTimeUtc": "2024-01-12T08:50:30.9752588+00:00", "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": "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/bbbb2b4cfb3d236b4f9b6110fd82264c/data.jsonl", "_azureml.evaluation_run": "promptflow.BatchRun", "azureml.promptflow.snapshot_id": "f36c2e06-b2b3-4ee5-9ed4-127ae490ffa8", "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_name_output_data_debug_info/versions/1", "type": "UriFolder"}, "flow_outputs": {"assetId": "azureml://locations/eastus/workspaces/00000/data/azureml_name_output_data_flow_outputs/versions/1", "type": "UriFolder"}}}, "runDefinition": null, "jobSpecification": null, "systemSettings": null}' headers: connection: - keep-alive content-length: - '9873' 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: - '0.037' 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/name/logContent response: body: string: '"2024-01-12 08:50:11 +0000 78 promptflow-runtime INFO [name] Receiving v2 bulk run request afd6522c-26b0-4e2e-966c-43176d74cb1f: {\"flow_id\": \"name\", \"flow_run_id\": \"name\", \"flow_source\": {\"flow_source_type\": 1, \"flow_source_info\": {\"snapshot_id\": \"f36c2e06-b2b3-4ee5-9ed4-127ae490ffa8\"}, \"flow_dag_file\": \"flow.dag.yaml\"}, \"log_path\": \"https://promptfloweast4063704120.blob.core.windows.net/azureml/ExperimentRun/dcid.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=2024-01-12T08%3A40%3A10Z&se=2024-01-12T16%3A50%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/bbbb2b4cfb3d236b4f9b6110fd82264c/data.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/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%3A50%3A11Z&ske=2024-01-19T08%3A50%3A11Z&sks=b&skv=2019-07-07&se=2024-01-19T08%3A50%3A11Z&sp=racwl\", \"output_datastore_name\": \"workspaceblobstore\"}}\n2024-01-12 08:50:11 +0000 78 promptflow-runtime INFO Runtime version: 20231204.v4. PromptFlow version: 1.2.0rc1\n2024-01-12 08:50:11 +0000 78 promptflow-runtime INFO Updating name to Status.Preparing...\n2024-01-12 08:50:11 +0000 78 promptflow-runtime INFO Downloading snapshot to /mnt/host/service/app/39415/requests/name\n2024-01-12 08:50:11 +0000 78 promptflow-runtime INFO Get snapshot sas url for f36c2e06-b2b3-4ee5-9ed4-127ae490ffa8...\n2024-01-12 08:50:18 +0000 78 promptflow-runtime INFO Downloading snapshot f36c2e06-b2b3-4ee5-9ed4-127ae490ffa8 from uri https://promptfloweast4063704120.blob.core.windows.net/snapshotzips/promptflow-eastus:3e123da1-f9a5-4c91-9234-8d9ffbb39ff5:snapshotzip/f36c2e06-b2b3-4ee5-9ed4-127ae490ffa8.zip...\n2024-01-12 08:50:18 +0000 78 promptflow-runtime INFO Downloaded file /mnt/host/service/app/39415/requests/name/f36c2e06-b2b3-4ee5-9ed4-127ae490ffa8.zip with size 701 for snapshot f36c2e06-b2b3-4ee5-9ed4-127ae490ffa8.\n2024-01-12 08:50:18 +0000 78 promptflow-runtime INFO Download snapshot f36c2e06-b2b3-4ee5-9ed4-127ae490ffa8 completed.\n2024-01-12 08:50:18 +0000 78 promptflow-runtime INFO Successfully download snapshot to /mnt/host/service/app/39415/requests/name\n2024-01-12 08:50:18 +0000 78 promptflow-runtime INFO About to execute a python flow.\n2024-01-12 08:50:18 +0000 78 promptflow-runtime INFO Use spawn method to start child process.\n2024-01-12 08:50:18 +0000 78 promptflow-runtime INFO Starting to check process 5940 status for run name\n2024-01-12 08:50:18 +0000 78 promptflow-runtime INFO Start checking run status for run name\n2024-01-12 08:50:22 +0000 5940 promptflow-runtime INFO [78--5940] Start processing flowV2......\n2024-01-12 08:50:22 +0000 5940 promptflow-runtime INFO Runtime version: 20231204.v4. PromptFlow version: 1.2.0rc1\n2024-01-12 08:50:22 +0000 5940 promptflow-runtime INFO Setting mlflow tracking uri...\n2024-01-12 08:50:23 +0000 5940 promptflow-runtime INFO Validating ''AzureML Data Scientist'' user authentication...\n2024-01-12 08:50:25 +0000 5940 promptflow-runtime INFO Successfully validated ''AzureML Data Scientist'' user authentication.\n2024-01-12 08:50:25 +0000 5940 promptflow-runtime INFO Using AzureMLRunStorageV2\n2024-01-12 08:50:25 +0000 5940 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 08:50:25 +0000 5940 promptflow-runtime INFO Initialized blob service client for AzureMLRunTracker.\n2024-01-12 08:50:25 +0000 5940 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 08:50:25 +0000 5940 promptflow-runtime INFO Resolve data from url finished in 0.47867807000875473 seconds\n2024-01-12 08:50:25 +0000 5940 promptflow-runtime INFO Starting the aml run ''name''...\n2024-01-12 08:50:26 +0000 5940 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 08:50:26 +0000 5940 execution.bulk INFO Using fork, process count: 3\n2024-01-12 08:50:26 +0000 5987 execution.bulk INFO Process 5987 started.\n2024-01-12 08:50:26 +0000 5992 execution.bulk INFO Process 5992 started.\n2024-01-12 08:50:26 +0000 5940 execution.bulk INFO Process name: ForkProcess-74:3, Process id: 5987, Line number: 0 start execution.\n2024-01-12 08:50:26 +0000 5940 execution.bulk INFO Process name: ForkProcess-74:4, Process id: 5992, Line number: 1 start execution.\n2024-01-12 08:50:26 +0000 5981 execution.bulk INFO Process 5981 started.\n2024-01-12 08:50:26 +0000 5940 execution.bulk INFO Process name: ForkProcess-74:2, Process id: 5981, Line number: 2 start execution.\n2024-01-12 08:50:26 +0000 5940 execution.bulk INFO Process name: ForkProcess-74:3, Process id: 5987, Line number: 0 completed.\n2024-01-12 08:50:26 +0000 5940 execution.bulk INFO Finished 1 / 3 lines.\n2024-01-12 08:50:26 +0000 5992 execution ERROR Node print_env in line 1 failed. Exception: Execution failure in ''print_env'': (Exception) expected raise!.\nTraceback (most recent call last):\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/_core/flow_execution_context.py\", line 185, in _invoke_tool_with_timer\n return f(**kwargs)\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/_core/tool.py\", line 106, in decorated_tool\n output = func(*args, **kwargs)\n File \"/mnt/host/service/app/39415/requests/name/print_env.py\", line 9, in get_env_var\n raise Exception(\"expected raise!\")\nException: expected raise!\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/_core/flow_execution_context.py\", line 89, in invoke_tool\n result = self._invoke_tool_with_timer(node, f, kwargs)\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/_core/flow_execution_context.py\", line 196, in _invoke_tool_with_timer\n raise ToolExecutionError(node_name=node_name, module=module) from e\npromptflow._core._errors.ToolExecutionError: Execution failure in ''print_env'': (Exception) expected raise!\n2024-01-12 08:50:26 +0000 5940 execution.bulk INFO Average execution time for completed lines: 0.24 seconds. Estimated time for incomplete lines: 0.48 seconds.\n2024-01-12 08:50:26 +0000 5992 execution ERROR Execution of one node has failed. Cancelling all running nodes: print_env.\n2024-01-12 08:50:26 +0000 5940 execution.bulk INFO Process name: ForkProcess-74:2, Process id: 5981, Line number: 2 completed.\n2024-01-12 08:50:26 +0000 5940 execution.bulk INFO Finished 2 / 3 lines.\n2024-01-12 08:50:27 +0000 5940 execution.bulk INFO Average execution time for completed lines: 0.16 seconds. Estimated time for incomplete lines: 0.16 seconds.\n2024-01-12 08:50:27 +0000 5940 execution.bulk INFO Process name: ForkProcess-74:4, Process id: 5992, Line number: 1 completed.\n2024-01-12 08:50:27 +0000 5940 execution.bulk INFO Finished 3 / 3 lines.\n2024-01-12 08:50:27 +0000 5940 execution.bulk INFO Average execution time for completed lines: 0.18 seconds. Estimated time for incomplete lines: 0.0 seconds.\n2024-01-12 08:50:28 +0000 5940 execution ERROR 1/3 flow run failed, indexes: [1], exception of index 1: Execution failure in ''print_env'': (Exception) expected raise!\n2024-01-12 08:50:29 +0000 5940 execution.bulk INFO Upload status summary metrics for run name finished in 1.7115172129124403 seconds\n2024-01-12 08:50:30 +0000 5940 promptflow-runtime INFO Successfully write run properties {\"azureml.promptflow.total_tokens\": 0, \"_azureml.evaluate_artifacts\": \"[{\\\"path\\\": \\\"instance_results.jsonl\\\", \\\"type\\\": \\\"table\\\"}]\"} with run id ''name''\n2024-01-12 08:50:30 +0000 5940 execution.bulk INFO Upload RH properties for run name finished in 0.08352885954082012 seconds\n2024-01-12 08:50:30 +0000 5940 promptflow-runtime INFO Creating unregistered output Asset for Run name...\n2024-01-12 08:50:30 +0000 5940 promptflow-runtime INFO Created debug_info Asset: azureml://locations/eastus/workspaces/00000/data/azureml_name_output_data_debug_info/versions/1\n2024-01-12 08:50:30 +0000 5940 promptflow-runtime INFO Creating unregistered output Asset for Run name...\n2024-01-12 08:50:30 +0000 5940 promptflow-runtime INFO Created flow_outputs output Asset: azureml://locations/eastus/workspaces/00000/data/azureml_name_output_data_flow_outputs/versions/1\n2024-01-12 08:50:30 +0000 5940 promptflow-runtime INFO Creating Artifact for Run name...\n2024-01-12 08:50:30 +0000 5940 promptflow-runtime INFO Created instance_results.jsonl Artifact.\n2024-01-12 08:50:30 +0000 5940 promptflow-runtime INFO Patching name...\n2024-01-12 08:50:30 +0000 5940 promptflow-runtime WARNING [name] Run failed. Execution stackTrace: Traceback (most recent call last):\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/_core/flow_execution_context.py\", line 185, in _invoke_tool_with_timer\n return f(**kwargs)\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/_core/tool.py\", line 106, in decorated_tool\n output = func(*args, **kwargs)\n [REDACTED: External StackTrace]\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/executor/flow_executor.py\", line 804, in _exec\n output, nodes_outputs = self._traverse_nodes(inputs, context)\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/executor/flow_executor.py\", line 890, in _traverse_nodes\n nodes_outputs, bypassed_nodes = self._submit_to_scheduler(context, inputs, batch_nodes)\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/executor/flow_executor.py\", line 910, in _submit_to_scheduler\n return FlowNodesScheduler(self._tools_manager, inputs, nodes, self._node_concurrency, context).execute()\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/executor/_flow_nodes_scheduler.py\", line 69, in execute\n raise e\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/executor/_flow_nodes_scheduler.py\", line 58, in execute\n self._dag_manager.complete_nodes(self._collect_outputs(completed_futures))\n File \"/azureml-envs/prompt-flow/runtime/lib/python3.10/site-packages/promptflow/executor/_flow_nodes_scheduler.py\", line 90, in _collect_outputs\n each_node_result = each_future.result()\n [REDACTED: External StackTrace]\n\n2024-01-12 08:50:30 +0000 5940 promptflow-runtime INFO Ending the aml run ''name'' with status ''Completed''...\n2024-01-12 08:50:32 +0000 78 promptflow-runtime INFO Process 5940 finished\n2024-01-12 08:50:32 +0000 78 promptflow-runtime INFO [78] Child process finished!\n2024-01-12 08:50:32 +0000 78 promptflow-runtime INFO [name] End processing bulk run\n2024-01-12 08:50:32 +0000 78 promptflow-runtime INFO Cleanup working dir /mnt/host/service/app/39415/requests/name for bulk run\n"' headers: connection: - keep-alive content-length: - '13657' 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: - '0.694' status: code: 200 message: OK version: 1

promptflow/src/promptflow/tests/test_configs/recordings/test_run_operations_TestFlowRun_test_get_detail_against_partial_fail_run.yaml/0

{ "file_path": "promptflow/src/promptflow/tests/test_configs/recordings/test_run_operations_TestFlowRun_test_get_detail_against_partial_fail_run.yaml", "repo_id": "promptflow", "token_count": 54461 }

77

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

promptflow/src/promptflow/tests/test_configs/runs/bulk_run_invalid_flow_path.yaml/0

{ "file_path": "promptflow/src/promptflow/tests/test_configs/runs/bulk_run_invalid_flow_path.yaml", "repo_id": "promptflow", "token_count": 115 }

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from jinja2 import Template from promptflow.connections import CustomConnection from promptflow import ToolProvider, tool from promptflow.connections import AzureOpenAIConnection from promptflow.contracts.types import PromptTemplate class TestCustomLLMTool(ToolProvider): def __init__(self, connection: AzureOpenAIConnection): super().__init__() self.connection = connection @tool( name="My Custom LLM Tool", type="custom_llm", description="This is a tool to demonstrate the custom_llm tool type", ) def tool_func(self, api: str, template: PromptTemplate, **kwargs): pass @tool( name="My Custom LLM Tool", type="custom_llm", description="This is a tool to demonstrate the custom_llm tool type", ) def my_tool(connection: CustomConnection, prompt: PromptTemplate, **kwargs) -> str: # Replace with your tool code, customise your own code to handle and use the prompt here. # Usually connection contains configs to connect to an API. # Not all tools need a connection. You can remove it if you don't need it. rendered_prompt = Template(prompt, trim_blocks=True, keep_trailing_newline=True).render(**kwargs) return rendered_prompt

promptflow/src/promptflow/tests/test_configs/tools/custom_llm_tool.py/0

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inputs: text: type: string outputs: output: type: string reference: ${summarize_text_content.output} nodes: - name: summarize_text_content type: llm source: type: code path: summarize_text_content__variant_1.jinja2 inputs: deployment_name: text-davinci-003 suffix: '' max_tokens: '256' temperature: '0.2' top_p: '1.0' logprobs: '' echo: 'False' stop: '' presence_penalty: '0' frequency_penalty: '0' best_of: '1' logit_bias: '' text: ${inputs.text} provider: AzureOpenAI connection: azure_open_ai_connection api: completion_1 module: promptflow.tools.aoai

promptflow/src/promptflow/tests/test_configs/wrong_flows/wrong_api/flow.dag.yaml/0

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# Prompt flow [![Python package](https://img.shields.io/pypi/v/promptflow)](https://pypi.org/project/promptflow/) [![Python](https://img.shields.io/pypi/pyversions/promptflow.svg?maxAge=2592000)](https://pypi.python.org/pypi/promptflow/) [![PyPI - Downloads](https://img.shields.io/pypi/dm/promptflow)](https://pypi.org/project/promptflow/) [![CLI](https://img.shields.io/badge/CLI-reference-blue)](https://microsoft.github.io/promptflow/reference/pf-command-reference.html) [![vsc extension](https://img.shields.io/visual-studio-marketplace/i/prompt-flow.prompt-flow?logo=Visual%20Studio&label=Extension%20)](https://marketplace.visualstudio.com/items?itemName=prompt-flow.prompt-flow) [![Doc](https://img.shields.io/badge/Doc-online-green)](https://microsoft.github.io/promptflow/index.html) [![Issue](https://img.shields.io/github/issues/microsoft/promptflow)](https://github.com/microsoft/promptflow/issues/new/choose) [![Discussions](https://img.shields.io/github/discussions/microsoft/promptflow)](https://github.com/microsoft/promptflow/issues/new/choose) [![CONTRIBUTING](https://img.shields.io/badge/Contributing-8A2BE2)](https://github.com/microsoft/promptflow/blob/main/CONTRIBUTING.md) [![License: MIT](https://img.shields.io/github/license/microsoft/promptflow)](https://github.com/microsoft/promptflow/blob/main/LICENSE) > Welcome to join us to make prompt flow better by > participating [discussions](https://github.com/microsoft/promptflow/discussions), > opening [issues](https://github.com/microsoft/promptflow/issues/new/choose), > submitting [PRs](https://github.com/microsoft/promptflow/pulls). **Prompt flow** is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality. With prompt flow, you will be able to: - **Create and iteratively develop flow** - Create executable [flows](https://microsoft.github.io/promptflow/concepts/concept-flows.html) that link LLMs, prompts, Python code and other [tools](https://microsoft.github.io/promptflow/concepts/concept-tools.html) together. - Debug and iterate your flows, especially the [interaction with LLMs](https://microsoft.github.io/promptflow/concepts/concept-connections.html) with ease. - **Evaluate flow quality and performance** - Evaluate your flow's quality and performance with larger datasets. - Integrate the testing and evaluation into your CI/CD system to ensure quality of your flow. - **Streamlined development cycle for production** - Deploy your flow to the serving platform you choose or integrate into your app's code base easily. - (Optional but highly recommended) Collaborate with your team by leveraging the cloud version of [Prompt flow in Azure AI](https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/overview-what-is-prompt-flow?view=azureml-api-2). ------ ## Installation To get started quickly, you can use a pre-built development environment. **Click the button below** to open the repo in GitHub Codespaces, and then continue the readme! [![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/microsoft/promptflow?quickstart=1) If you want to get started in your local environment, first install the packages: Ensure you have a python environment, `python=3.9` is recommended. ```sh pip install promptflow promptflow-tools ``` ## Quick Start ⚡ **Create a chatbot with prompt flow** Run the command to initiate a prompt flow from a chat template, it creates folder named `my_chatbot` and generates required files within it: ```sh pf flow init --flow ./my_chatbot --type chat ``` **Setup a connection for your API key** For OpenAI key, establish a connection by running the command, using the `openai.yaml` file in the `my_chatbot` folder, which stores your OpenAI key (override keys and name with --set to avoid yaml file changes): ```sh pf connection create --file ./my_chatbot/openai.yaml --set api_key=<your_api_key> --name open_ai_connection ``` For Azure OpenAI key, establish the connection by running the command, using the `azure_openai.yaml` file: ```sh pf connection create --file ./my_chatbot/azure_openai.yaml --set api_key=<your_api_key> api_base=<your_api_base> --name open_ai_connection ``` **Chat with your flow** In the `my_chatbot` folder, there's a `flow.dag.yaml` file that outlines the flow, including inputs/outputs, nodes, connection, and the LLM model, etc > Note that in the `chat` node, we're using a connection named `open_ai_connection` (specified in `connection` field) and the `gpt-35-turbo` model (specified in `deployment_name` field). The deployment_name filed is to specify the OpenAI model, or the Azure OpenAI deployment resource. Interact with your chatbot by running: (press `Ctrl + C` to end the session) ```sh pf flow test --flow ./my_chatbot --interactive ``` **Core value: ensuring "High Quality” from prototype to production** Explore our [**15-minute tutorial**](examples/tutorials/flow-fine-tuning-evaluation/promptflow-quality-improvement.md) that guides you through prompt tuning ➡ batch testing ➡ evaluation, all designed to ensure high quality ready for production. Next Step! Continue with the **Tutorial** 👇 section to delve deeper into prompt flow. ## Tutorial 🏃‍♂️ Prompt flow is a tool designed to **build high quality LLM apps**, the development process in prompt flow follows these steps: develop a flow, improve the flow quality, deploy the flow to production. ### Develop your own LLM apps #### VS Code Extension We also offer a VS Code extension (a flow designer) for an interactive flow development experience with UI. <img src="examples/tutorials/quick-start/media/vsc.png" alt="vsc" width="1000"/> You can install it from the <a href="https://marketplace.visualstudio.com/items?itemName=prompt-flow.prompt-flow">visualstudio marketplace</a>. #### Deep delve into flow development [Getting started with prompt flow](https://microsoft.github.io/promptflow/how-to-guides/quick-start.html): A step by step guidance to invoke your first flow run. ### Learn from use cases [Tutorial: Chat with PDF](https://github.com/microsoft/promptflow/blob/main/examples/tutorials/e2e-development/chat-with-pdf.md): An end-to-end tutorial on how to build a high quality chat application with prompt flow, including flow development and evaluation with metrics. > More examples can be found [here](https://microsoft.github.io/promptflow/tutorials/index.html#samples). We welcome contributions of new use cases! ### Setup for contributors If you're interested in contributing, please start with our dev setup guide: [dev_setup.md](./docs/dev/dev_setup.md). Next Step! Continue with the **Contributing** 👇 section to contribute to prompt flow. ## Contributing This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com. When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA. This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact [[email protected]](mailto:[email protected]) with any additional questions or comments. ## Trademarks This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies. ## Code of Conduct This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact [[email protected]](mailto:[email protected]) with any additional questions or comments. ## Data Collection The software may collect information about you and your use of the software and send it to Microsoft if configured to enable telemetry. Microsoft may use this information to provide services and improve our products and services. You may turn on the telemetry as described in the repository. There are also some features in the software that may enable you and Microsoft to collect data from users of your applications. If you use these features, you must comply with applicable law, including providing appropriate notices to users of your applications together with a copy of Microsoft's privacy statement. Our privacy statement is located at https://go.microsoft.com/fwlink/?LinkID=824704. You can learn more about data collection and use in the help documentation and our privacy statement. Your use of the software operates as your consent to these practices. ### Telemetry Configuration Telemetry collection is on by default. To opt out, please run `pf config set telemetry.enabled=false` to turn it off. ## License Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the [MIT](LICENSE) license.

promptflow/README.md/0

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# Adding a Tool Icon A tool icon serves as a graphical representation of your tool in the user interface (UI). Follow this guidance to add a custom tool icon when developing your own tool package. Adding a custom tool icon is optional. If you do not provide one, the system uses a default icon. ## Prerequisites - Please ensure that your [Prompt flow for VS Code](https://marketplace.visualstudio.com/items?itemName=prompt-flow.prompt-flow) is updated to version 1.4.2 or later. - Create a tool package as described in [Create and Use Tool Package](create-and-use-tool-package.md). - Prepare custom icon image that meets these requirements: - Use PNG, JPG or BMP format. - 16x16 pixels to prevent distortion when resizing. - Avoid complex images with lots of detail or contrast, as they may not resize well. See [this example](https://github.com/microsoft/promptflow/blob/main/examples/tools/tool-package-quickstart/my_tool_package/icons/custom-tool-icon.png) as a reference. - Install dependencies to generate icon data URI: ``` pip install pillow ``` ## Add tool icon with _icon_ parameter Run the command below in your tool project directory to automatically generate your tool YAML, use _-i_ or _--icon_ parameter to add a custom tool icon: ``` python <promptflow github repo>\scripts\tool\generate_package_tool_meta.py -m <tool_module> -o <tool_yaml_path> -i <tool-icon-path> ``` Here we use [an existing tool project](https://github.com/microsoft/promptflow/tree/main/examples/tools/tool-package-quickstart) as an example. ``` cd D:\proj\github\promptflow\examples\tools\tool-package-quickstart python D:\proj\github\promptflow\scripts\tool\generate_package_tool_meta.py -m my_tool_package.tools.my_tool_1 -o my_tool_package\yamls\my_tool_1.yaml -i my_tool_package\icons\custom-tool-icon.png ``` In the auto-generated tool YAML file, the custom tool icon data URI is added in the `icon` field: ```yaml my_tool_package.tools.my_tool_1.my_tool: function: my_tool icon: data:image/png;base64,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 inputs: connection: type: - CustomConnection input_text: type: - string module: my_tool_package.tools.my_tool_1 name: my_tool type: python ``` ## Verify the tool icon in VS Code extension Follow [steps](create-and-use-tool-package.md#use-your-tool-from-vscode-extension) to use your tool from VS Code extension. Your tool displays with the custom icon: ![custom-tool-with-icon-in-extension](../../media/how-to-guides/develop-a-tool/custom-tool-with-icon-in-extension.png) ## FAQ ### Can I preview the tool icon image before adding it to a tool? Yes, you could run below command under the root folder to generate a data URI for your custom tool icon. Make sure the output file has an `.html` extension. ``` python <path-to-scripts>\tool\convert_image_to_data_url.py --image-path <image_input_path> -o <html_output_path> ``` For example: ``` python D:\proj\github\promptflow\scripts\tool\convert_image_to_data_url.py --image-path D:\proj\github\promptflow\examples\tools\tool-package-quickstart\my_tool_package\icons\custom-tool-icon.png -o output.html ``` The content of `output.html` looks like the following, open it in a web browser to preview the icon. ```html <html> <body> <img src="data:image/png;base64,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" alt="My Image"> </body> </html> ``` ### Can I add a tool icon to an existing tool package? Yes, you can refer to the [preview icon](add-a-tool-icon.md#can-i-preview-the-tool-icon-image-before-adding-it-to-a-tool) section to generate the data URI and manually add the data URI to the tool's YAML file. ### Can I add tool icons for dark and light mode separately? Yes, you can add the tool icon data URIs manually or run the command below in your tool project directory to automatically generate your tool YAML, use _--icon-light_ to add a custom tool icon for the light mode and use _--icon-dark_ to add a custom tool icon for the dark mode: ``` python <promptflow github repo>\scripts\tool\generate_package_tool_meta.py -m <tool_module> -o <tool_yaml_path> --icon-light <light-tool-icon-path> --icon-dark <dark-tool-icon-path> ``` Here we use [an existing tool project](https://github.com/microsoft/promptflow/tree/main/examples/tools/tool-package-quickstart) as an example. ``` cd D:\proj\github\promptflow\examples\tools\tool-package-quickstart python D:\proj\github\promptflow\scripts\tool\generate_package_tool_meta.py -m my_tool_package.tools.my_tool_1 -o my_tool_package\yamls\my_tool_1.yaml --icon-light my_tool_package\icons\custom-tool-icon-light.png --icon-dark my_tool_package\icons\custom-tool-icon-dark.png ``` In the auto-generated tool YAML file, the light and dark tool icon data URIs are added in the `icon` field: ```yaml my_tool_package.tools.my_tool_1.my_tool: function: my_tool icon: dark: data:image/png;base64,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 light: data:image/png;base64,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 inputs: connection: type: - CustomConnection input_text: type: - string module: my_tool_package.tools.my_tool_1 name: my_tool type: python ``` Note: Both light and dark icons are optional. If you set either a light or dark icon, it will be used in its respective mode, and the system default icon will be used in the other mode.

promptflow/docs/how-to-guides/develop-a-tool/add-a-tool-icon.md/0

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# Process image in flow PromptFlow defines a contract to represent image data. ## Data class `promptflow.contracts.multimedia.Image` Image class is a subclass of `bytes`, thus you can access the binary data by directly using the object. It has an extra attribute `source_url` to store the origin url of the image, which would be useful if you want to pass the url instead of content of image to APIs like GPT-4V model. ## Data type in flow input Set the type of flow input to `image` and promptflow will treat it as an image. ## Reference image in prompt template In prompt templates that support image (e.g. in OpenAI GPT-4V tool), using markdown syntax to denote that a template input is an image: `![image]({{test_image}})`. In this case, `test_image` will be substituted with base64 or source_url (if set) before sending to LLM model. ## Serialization/Deserialization Promptflow uses a special dict to represent image. `{"data:image/<mime-type>;<representation>": "<value>"}` - `<mime-type>` can be html standard [mime](https://developer.mozilla.org/en-US/docs/Web/HTTP/Basics_of_HTTP/MIME_types/Common_types) image types. Setting it to specific type can help previewing the image correctly, or it can be `*` for unknown type. - `<representation>` is the image serialized representation, there are 3 supported types: - url It can point to a public accessable web url. E.g. {"data:image/png;url": "https://developer.microsoft.com/_devcom/images/logo-ms-social.png"} - base64 It can be the base64 encoding of the image. E.g. {"data:image/png;base64": "iVBORw0KGgoAAAANSUhEUgAAAGQAAABLAQMAAAC81rD0AAAABGdBTUEAALGPC/xhBQAAACBjSFJNAAB6JgAAgIQAAPoAAACA6AAAdTAAAOpgAAA6mAAAF3CculE8AAAABlBMVEUAAP7////DYP5JAAAAAWJLR0QB/wIt3gAAAAlwSFlzAAALEgAACxIB0t1+/AAAAAd0SU1FB+QIGBcKN7/nP/UAAAASSURBVDjLY2AYBaNgFIwCdAAABBoAAaNglfsAAAAZdEVYdGNvbW1lbnQAQ3JlYXRlZCB3aXRoIEdJTVDnr0DLAAAAJXRFWHRkYXRlOmNyZWF0ZQAyMDIwLTA4LTI0VDIzOjEwOjU1KzAzOjAwkHdeuQAAACV0RVh0ZGF0ZTptb2RpZnkAMjAyMC0wOC0yNFQyMzoxMDo1NSswMzowMOEq5gUAAAAASUVORK5CYII="} - path It can reference an image file on local disk. Both absolute path and relative path are supported, but in the cases where the serialized image representation is stored in a file, relative to the containing folder of that file is recommended, as in the case of flow IO data. E.g. {"data:image/png;path": "./my-image.png"} Please note that `path` representation is not supported in Deployment scenario. ## Batch Input data Batch input data containing image can be of 2 formats: 1. The same jsonl format of regular batch input, except that some column may be seriliazed image data or composite data type (dict/list) containing images. The serialized images can only be Url or Base64. E.g. ```json {"question": "How many colors are there in the image?", "input_image": {"data:image/png;url": "https://developer.microsoft.com/_devcom/images/logo-ms-social.png"}} {"question": "What's this image about?", "input_image": {"data:image/png;url": "https://developer.microsoft.com/_devcom/images/404.png"}} ``` 2. A folder containing a jsonl file under root path, which contains serialized image in File Reference format. The referenced file are stored in the folder and their relative path to the root path is used as path in the file reference. Here is a sample batch input, note that the name of `input.jsonl` is arbitrary as long as it's a jsonl file: ``` BatchInputFolder |----input.jsonl |----image1.png |----image2.png ``` Content of `input.jsonl` ```json {"question": "How many colors are there in the image?", "input_image": {"data:image/png;path": "image1.png"}} {"question": "What's this image about?", "input_image": {"data:image/png;path": "image2.png"}} ```

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# Azure OpenAI GPT-4 Turbo with Vision ## Introduction Azure OpenAI GPT-4 Turbo with Vision tool enables you to leverage your AzureOpenAI GPT-4 Turbo with Vision model deployment to analyze images and provide textual responses to questions about them. ## Prerequisites - Create AzureOpenAI resources Create Azure OpenAI resources with [instruction](https://learn.microsoft.com/en-us/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal) - Create a GPT-4 Turbo with Vision deployment Browse to [Azure OpenAI Studio](https://oai.azure.com/) and sign in with the credentials associated with your Azure OpenAI resource. During or after the sign-in workflow, select the appropriate directory, Azure subscription, and Azure OpenAI resource. Under Management select Deployments and Create a GPT-4 Turbo with Vision deployment by selecting model name: `gpt-4` and model version `vision-preview`. ## Connection Setup connections to provisioned resources in prompt flow. | Type | Name | API KEY | API Type | API Version | |-------------|----------|----------|----------|-------------| | AzureOpenAI | Required | Required | Required | Required | ## Inputs | Name | Type | Description | Required | |------------------------|-------------|------------------------------------------------------------------------------------------------|----------| | connection | AzureOpenAI | the AzureOpenAI connection to be used in the tool | Yes | | deployment\_name | string | the language model to use | Yes | | prompt | string | The text prompt that the language model will use to generate it's response. | Yes | | max\_tokens | integer | the maximum number of tokens to generate in the response. Default is 512. | No | | temperature | float | the randomness of the generated text. Default is 1. | No | | stop | list | the stopping sequence for the generated text. Default is null. | No | | top_p | float | the probability of using the top choice from the generated tokens. Default is 1. | No | | presence\_penalty | float | value that controls the model's behavior with regards to repeating phrases. Default is 0. | No | | frequency\_penalty | float | value that controls the model's behavior with regards to generating rare phrases. Default is 0. | No | ## Outputs | Return Type | Description | |-------------|------------------------------------------| | string | The text of one response of conversation |

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# Working with Connection This folder contains example `YAML` files for creating `connection` using `pf` cli. Learn more on all the [connections types](https://microsoft.github.io/promptflow/concepts/concept-connections.html). ## Prerequisites - Install promptflow sdk and other dependencies: ```bash pip install -r requirements.txt ``` ## Get started - To create a connection using any of the sample `YAML` files provided in this directory, execute following command: ```bash # Override keys with --set to avoid yaml file changes pf connection create -f custom.yml --set configs.key1='<your_api_key>' pf connection create -f azure_openai.yml --set api_key='<your_api_key>' ``` - To create a custom connection using an `.env` file, execute following command: ```bash pf connection create -f .env --name custom_connection ``` - To list the created connection, execute following command: ```bash pf connection list ``` - To show one connection details, execute following command: ```bash pf connection show --name custom_connection ``` - To update a connection that in workspace, execute following command. Currently only a few fields(description, display_name) support update: ```bash # Update an existing connection with --set to override values # Update an azure open ai connection with a new api base pf connection update -n open_ai_connection --set api_base='<your_api_base>' # Update a custom connection pf connection update -n custom_connection --set configs.key1='<your_new_key>' secrets.key2='<your_another_key>' ``` - To delete a connection: ```bash pf connection delete -n custom_connection ```

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{ "package": {}, "code": { "chat.jinja2": { "type": "llm", "inputs": { "chat_history": { "type": [ "string" ] }, "question": { "type": [ "string" ] } }, "source": "chat.jinja2" }, "chat_variant_1.jinja2": { "type": "llm", "inputs": { "chat_history": { "type": [ "string" ] }, "question": { "type": [ "string" ] } }, "source": "chat_variant_1.jinja2" }, "chat_variant_2.jinja2": { "type": "llm", "inputs": { "chat_history": { "type": [ "string" ] }, "question": { "type": [ "string" ] } }, "source": "chat_variant_2.jinja2" }, "extract_result.py": { "type": "python", "inputs": { "input1": { "type": [ "string" ] } }, "source": "extract_result.py", "function": "my_python_tool" } } }

promptflow/examples/flows/chat/chat-math-variant/.promptflow/flow.tools.json/0

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<jupyter_start><jupyter_code>from main import chat_with_pdf, print_stream_and_return_full_answer from dotenv import load_dotenv load_dotenv() bert_paper_url = "https://arxiv.org/pdf/1810.04805.pdf" questions = [ "what is BERT?", "what NLP tasks does it perform well?", "is BERT suitable for NER?", "is it better than GPT", "when was GPT come up?", "when was BERT come up?", "so about same time?", ] history = [] for q in questions: stream, context = chat_with_pdf(q, bert_paper_url, history) print("User: " + q, flush=True) print("Bot: ", end="", flush=True) answer = print_stream_and_return_full_answer(stream) history = history + [ {"role": "user", "content": q}, {"role": "assistant", "content": answer}, ]<jupyter_output><empty_output>

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inputs: chat_history: type: list default: [] pdf_url: type: string default: https://arxiv.org/pdf/1810.04805.pdf question: type: string is_chat_input: true default: what NLP tasks does it perform well? outputs: answer: type: string is_chat_output: true reference: ${qna_tool.output.answer} context: type: string reference: ${qna_tool.output.context} nodes: - name: setup_env type: python source: type: code path: setup_env.py inputs: conn: my_custom_connection - name: download_tool type: python source: type: code path: download_tool.py inputs: url: ${inputs.pdf_url} env_ready_signal: ${setup_env.output} - name: build_index_tool type: python source: type: code path: build_index_tool.py inputs: pdf_path: ${download_tool.output} - name: qna_tool type: python source: type: code path: qna_tool.py inputs: question: ${rewrite_question_tool.output} index_path: ${build_index_tool.output} history: ${inputs.chat_history} - name: rewrite_question_tool type: python source: type: code path: rewrite_question_tool.py inputs: question: ${inputs.question} history: ${inputs.chat_history}

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from promptflow import tool @tool def process_search_result(search_result): def format(doc: dict): return f"Content: {doc['Content']}\nSource: {doc['Source']}" try: context = [] for url, content in search_result: context.append({"Content": content, "Source": url}) context_str = "\n\n".join([format(c) for c in context]) return context_str except Exception as e: print(f"Error: {e}") return ""

promptflow/examples/flows/chat/chat-with-wikipedia/process_search_result.py/0

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$schema: https://azuremlschemas.azureedge.net/promptflow/latest/Flow.schema.json inputs: groundtruth: type: string default: groundtruth prediction: type: string default: prediction outputs: results: type: string reference: ${line_process.output} nodes: - name: line_process type: python source: type: code path: line_process.py inputs: groundtruth: ${inputs.groundtruth} prediction: ${inputs.prediction} - name: aggregate type: python source: type: code path: aggregate.py inputs: processed_results: ${line_process.output} aggregation: true environment: python_requirements_txt: requirements.txt

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system: You are an AI assistant. You will be given the definition of an evaluation metric for assessing the quality of an answer in a question-answering task. Your job is to compute an accurate evaluation score using the provided evaluation metric. user: Equivalence, as a metric, measures the similarity between the predicted answer and the correct answer. If the information and content in the predicted answer is similar or equivalent to the correct answer, then the value of the Equivalence metric should be high, else it should be low. Given the question, correct answer, and predicted answer, determine the value of Equivalence metric using the following rating scale: One star: the predicted answer is not at all similar to the correct answer Two stars: the predicted answer is mostly not similar to the correct answer Three stars: the predicted answer is somewhat similar to the correct answer Four stars: the predicted answer is mostly similar to the correct answer Five stars: the predicted answer is completely similar to the correct answer This rating value should always be an integer between 1 and 5. So the rating produced should be 1 or 2 or 3 or 4 or 5. The examples below show the Equivalence score for a question, a correct answer, and a predicted answer. question: What is the role of ribosomes? correct answer: Ribosomes are cellular structures responsible for protein synthesis. They interpret the genetic information carried by messenger RNA (mRNA) and use it to assemble amino acids into proteins. predicted answer: Ribosomes participate in carbohydrate breakdown by removing nutrients from complex sugar molecules. stars: 1 question: Why did the Titanic sink? correct answer: The Titanic sank after it struck an iceberg during its maiden voyage in 1912. The impact caused the ship's hull to breach, allowing water to flood into the vessel. The ship's design, lifeboat shortage, and lack of timely rescue efforts contributed to the tragic loss of life. predicted answer: The sinking of the Titanic was a result of a large iceberg collision. This caused the ship to take on water and eventually sink, leading to the death of many passengers due to a shortage of lifeboats and insufficient rescue attempts. stars: 2 question: What causes seasons on Earth? correct answer: Seasons on Earth are caused by the tilt of the Earth's axis and its revolution around the Sun. As the Earth orbits the Sun, the tilt causes different parts of the planet to receive varying amounts of sunlight, resulting in changes in temperature and weather patterns. predicted answer: Seasons occur because of the Earth's rotation and its elliptical orbit around the Sun. The tilt of the Earth's axis causes regions to be subjected to different sunlight intensities, which leads to temperature fluctuations and alternating weather conditions. stars: 3 question: How does photosynthesis work? correct answer: Photosynthesis is a process by which green plants and some other organisms convert light energy into chemical energy. This occurs as light is absorbed by chlorophyll molecules, and then carbon dioxide and water are converted into glucose and oxygen through a series of reactions. predicted answer: In photosynthesis, sunlight is transformed into nutrients by plants and certain microorganisms. Light is captured by chlorophyll molecules, followed by the conversion of carbon dioxide and water into sugar and oxygen through multiple reactions. stars: 4 question: What are the health benefits of regular exercise? correct answer: Regular exercise can help maintain a healthy weight, increase muscle and bone strength, and reduce the risk of chronic diseases. It also promotes mental well-being by reducing stress and improving overall mood. predicted answer: Routine physical activity can contribute to maintaining ideal body weight, enhancing muscle and bone strength, and preventing chronic illnesses. In addition, it supports mental health by alleviating stress and augmenting general mood. stars: 5 question: {{question}} correct answer:{{ground_truth}} predicted answer: {{answer}} stars:

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from promptflow import tool @tool def select_metrics(metrics: str) -> str: supported_metrics = ('gpt_relevance', 'gpt_groundedness', 'gpt_retrieval_score') user_selected_metrics = [metric.strip() for metric in metrics.split(',') if metric] metric_selection_dict = {} for metric in supported_metrics: if metric in user_selected_metrics: metric_selection_dict[metric] = True else: metric_selection_dict[metric] = False return metric_selection_dict

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$schema: https://azuremlschemas.azureedge.net/promptflow/latest/Flow.schema.json environment: python_requirements_txt: requirements.txt inputs: chat_history: type: list is_chat_history: true utterance: type: string is_chat_input: true default: Play BB King and increase the volume. outputs: intents: type: string reference: ${Conversational_Language_Understanding.output} is_chat_output: true nodes: - name: LLM_Rewrite type: llm source: type: code path: chat.jinja2 inputs: deployment_name: cluGPTTurbo max_tokens: 256 temperature: 0.7 question: ${inputs.utterance} connection: CLUGPTModel api: chat - name: Conversational_Language_Understanding type: python source: type: package tool: language_tools.tools.conversational_language_understanding.get_conversational_language_understanding inputs: connection: azure_ai_language_connection language: en-us utterances: ${LLM_Rewrite.output} project_name: MediaPlayer deployment_name: adv parse_response: false

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# Basic flow with builtin llm tool A basic standard flow that calls Azure OpenAI with builtin llm tool. Tools used in this flow： - `prompt` tool - built-in `llm` tool Connections used in this flow: - `azure_open_ai` connection ## Prerequisites Install promptflow sdk and other dependencies: ```bash pip install -r requirements.txt ``` ## Setup connection Prepare your Azure Open AI 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. Note in this example, we are using [chat api](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/chatgpt?pivots=programming-language-chat-completions), please use `gpt-35-turbo` or `gpt-4` model deployment. Ensure you have created `open_ai_connection` connection before. ```bash pf connection show -n open_ai_connection ``` Create connection if you haven't done that. Ensure you have put your azure open ai endpoint key in [azure_openai.yml](../../../connections/azure_openai.yml) file. ```bash # Override keys with --set to avoid yaml file changes pf connection create -f ../../../connections/azure_openai.yml --name open_ai_connection --set api_key=<your_api_key> api_base=<your_api_base> ``` ## Run flow ### Run with single line input ```bash # test with default input value in flow.dag.yaml pf flow test --flow . # test with inputs pf flow test --flow . --inputs text="Python Hello World!" ``` ### run with multiple lines data - create run ```bash pf run create --flow . --data ./data.jsonl --column-mapping text='${data.text}' --stream ``` You can also skip providing `column-mapping` if provided data has same column name as the flow. Reference [here](https://aka.ms/pf/column-mapping) for default behavior when `column-mapping` not provided in CLI. - list and show run meta ```bash # list created run pf run list # get a sample run name name=$(pf run list -r 10 | jq '.[] | select(.name | contains("basic_with_builtin_llm")) | .name'| head -n 1 | tr -d '"') # show specific run detail pf run show --name $name # show output pf run show-details --name $name # visualize run in browser pf run visualize --name $name ```

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system: There is a search bar in the mall APP and users can enter any query in the search bar. The user may want to search for orders, view product information, or seek recommended products. Therefore, please classify user intentions into the following three types according to the query: product_recommendation, order_search, product_info Please note that only the above three situations can be returned, and try not to include other return values. user: The user's query is {{query}}

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import logging import os from urllib.parse import urlparse import requests class File: def __init__(self, source: str): self._source = source self._is_url = source.startswith("http://") or source.startswith("https://") if self._is_url: parsed_url = urlparse(source) path = parsed_url.path else: path = source self._path = os.path.normpath(os.path.abspath(path)) self._dirname = os.path.dirname(self._path) self._filename = os.path.basename(self._path).split(".")[0] self._language = os.path.basename(self._path).split(".")[1] def _read_content(self): if self._is_url: response = requests.get(self.source) if response.status_code == 200: content = response.text return content else: print(f"Failed to retrieve content from URL: {self.source}") return None else: try: with open(self._path, "r") as file: content = file.read() return content except FileNotFoundError: print(f"File not found: {self.source}") return None @property def content(self) -> str: if not hasattr(self, "_text"): self._content = self._read_content() return self._content @property def language(self) -> str: return self._language @property def filename(self) -> str: return self._filename @property def dirname(self) -> str: return self._dirname @property def source(self) -> str: return self._source def override_origin_file(self, content: str) -> None: if not self._is_url: with open(self._path, "w") as f: f.write(content) else: logging.warning("Cannot override origin file from URL, create a new file instead.") self.create_new_file(content) def create_new_file(self, content: str) -> None: if self._is_url: path = os.path.join( './', self.filename + f"_doc.{self.language}", ) else: path = os.path.join( self.dirname, self.filename + f"_doc.{self.language}", ) with open(path, "w") as f: f.write(content)

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my_tool_package.tools.tool_with_generated_by_input.my_tool: function: my_tool inputs: index_json: type: - string generated_by: func_path: my_tool_package.tools.tool_with_generated_by_input.generate_index_json func_kwargs: - name: index_type type: - string reference: ${inputs.index_type} - name: index type: - string optional: true reference: ${inputs.index} - name: index_connection type: [CognitiveSearchConnection] optional: true reference: ${inputs.index_connection} - name: index_name type: - string optional: true reference: ${inputs.index_name} - name: content_field type: - string optional: true reference: ${inputs.content_field} - name: embedding_field type: - string optional: true reference: ${inputs.embedding_field} - name: metadata_field type: - string optional: true reference: ${inputs.metadata_field} - name: semantic_configuration type: - string optional: true reference: ${inputs.semantic_configuration} - name: embedding_connection type: [AzureOpenAIConnection, OpenAIConnection] optional: true reference: ${inputs.embedding_connection} - name: embedding_deployment type: - string optional: true reference: ${inputs.embedding_deployment} reverse_func_path: my_tool_package.tools.tool_with_generated_by_input.reverse_generate_index_json queries: type: - string top_k: type: - int index_type: type: - string dynamic_list: func_path: my_tool_package.tools.tool_with_generated_by_input.list_index_types input_type: uionly_hidden index: type: - string enabled_by: index_type enabled_by_value: ["Workspace MLIndex"] dynamic_list: func_path: my_tool_package.tools.tool_with_generated_by_input.list_indexes input_type: uionly_hidden index_connection: type: [CognitiveSearchConnection] enabled_by: index_type enabled_by_value: ["Azure Cognitive Search"] input_type: uionly_hidden index_name: type: - string enabled_by: index_type enabled_by_value: ["Azure Cognitive Search"] input_type: uionly_hidden content_field: type: - string enabled_by: index_type enabled_by_value: ["Azure Cognitive Search"] dynamic_list: func_path: my_tool_package.tools.tool_with_generated_by_input.list_fields input_type: uionly_hidden embedding_field: type: - string enabled_by: index_type enabled_by_value: ["Azure Cognitive Search"] dynamic_list: func_path: my_tool_package.tools.tool_with_generated_by_input.list_fields input_type: uionly_hidden metadata_field: type: - string enabled_by: index_type enabled_by_value: ["Azure Cognitive Search"] dynamic_list: func_path: my_tool_package.tools.tool_with_generated_by_input.list_fields input_type: uionly_hidden semantic_configuration: type: - string enabled_by: index_type enabled_by_value: ["Azure Cognitive Search"] dynamic_list: func_path: my_tool_package.tools.tool_with_generated_by_input.list_semantic_configuration input_type: uionly_hidden embedding_connection: type: [AzureOpenAIConnection, OpenAIConnection] enabled_by: index_type enabled_by_value: ["Azure Cognitive Search"] input_type: uionly_hidden embedding_deployment: type: - string enabled_by: index_type enabled_by_value: ["Azure Cognitive Search"] dynamic_list: func_path: my_tool_package.tools.tool_with_generated_by_input.list_embedding_deployment func_kwargs: - name: embedding_connection type: - string reference: ${inputs.embedding_connection} input_type: uionly_hidden module: my_tool_package.tools.tool_with_generated_by_input name: Tool with Generated By Input description: This is a tool with generated by input type: python

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$schema: https://azuremlschemas.azureedge.net/promptflow/latest/Flow.schema.json inputs: {} outputs: output: type: string reference: ${My_Tool_with_Dynamic_List_Input_cywc.output} nodes: - name: My_Tool_with_Dynamic_List_Input_cywc type: python source: type: package tool: my_tool_package.tools.tool_with_dynamic_list_input.my_tool inputs: input_prefix: hi input_text: - grape3 - elderberry5 endpoint_name: my_endpoint

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$schema: https://azuremlschemas.azureedge.net/promptflow/latest/Flow.schema.json inputs: flow_input: type: string outputs: output: type: object reference: ${echo_connection.output} nodes: - name: echo_connection type: python source: type: code path: echo_connection.py inputs: flow_input: ${inputs.flow_input} node_input: "dummy_node_input" connection: open_ai_connection

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<jupyter_start><jupyter_text>Run prompt flow in Azure AI**Requirements** - In order to benefit from this tutorial, you will need:- An Azure account with an active subscription - [Create an account for free](https://azure.microsoft.com/free/?WT.mc_id=A261C142F)- An Azure ML workspace - [Configure workspace](../../configuration.ipynb)- A python environment- Installed prompt flow SDK**Learning Objectives** - By the end of this tutorial, you should be able to:- Connect to your Azure AI workspace from the Python SDK- Create and develop a new promptflow run- Evaluate the run with a evaluation flow**Motivations** - This guide will walk you through the main user journey of prompt flow code-first experience. You will learn how to create and develop your first prompt flow, test and evaluate it. 0. Install dependent packages<jupyter_code>%pip install -r ../../requirements.txt<jupyter_output><empty_output><jupyter_text>1. Connect to Azure Machine Learning WorkspaceThe [workspace](https://docs.microsoft.com/en-us/azure/machine-learning/concept-workspace) is the top-level resource for Azure Machine Learning, providing a centralized place to work with all the artifacts you create when you use Azure Machine Learning. In this section we will connect to the workspace in which the job will be run. 1.1 Import the required libraries<jupyter_code>import json # Import required libraries from azure.identity import DefaultAzureCredential, InteractiveBrowserCredential # azure version promptflow apis from promptflow.azure import PFClient<jupyter_output><empty_output><jupyter_text>1.2 Configure credentialWe are using `DefaultAzureCredential` to get access to workspace. `DefaultAzureCredential` should be capable of handling most Azure SDK authentication scenarios. Reference for more available credentials if it does not work for you: [configure credential example](../../configuration.ipynb), [azure-identity reference doc](https://docs.microsoft.com/en-us/python/api/azure-identity/azure.identity?view=azure-python).<jupyter_code>try: credential = DefaultAzureCredential() # Check if given credential can get token successfully. credential.get_token("https://management.azure.com/.default") except Exception as ex: # Fall back to InteractiveBrowserCredential in case DefaultAzureCredential not work credential = InteractiveBrowserCredential()<jupyter_output><empty_output><jupyter_text>1.3 Get a handle to the workspaceWe use config file to connect to a workspace. The Azure ML workspace should be configured with computer cluster. [Check this notebook for configure a workspace](../../configuration.ipynb)<jupyter_code># Get a handle to workspace pf = PFClient.from_config(credential=credential)<jupyter_output><empty_output><jupyter_text>1.4 Create necessary connectionsConnection helps securely store and manage secret keys or other sensitive credentials required for interacting with LLM and other external tools for example Azure Content Safety.In this notebook, we will use flow `web-classification` which uses connection `azure_open_ai_connection` inside, we need to set up the connection if we haven't added it before.Prepare your Azure Open AI 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.Please go to [workspace portal](https://ml.azure.com/), click `Prompt flow` -> `Connections` -> `Create`, then follow the instruction to create your own connections. Learn more on [connections](https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/concept-connections?view=azureml-api-2). 2. Create a new run`web-classification` 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. Set flow path and input data<jupyter_code># load flow flow = "../../flows/standard/web-classification" data = "../../flows/standard/web-classification/data.jsonl"<jupyter_output><empty_output><jupyter_text>Submit run<jupyter_code># create run base_run = pf.run( flow=flow, data=data, ) print(base_run) pf.stream(base_run) details = pf.get_details(base_run) details.head(10) pf.visualize(base_run)<jupyter_output><empty_output><jupyter_text>3. Evaluate your flow run resultThen you can use an evaluation method to evaluate your flow. The evaluation methods are also flows which use Python or LLM etc., to calculate metrics like accuracy, relevance score.In this notebook, we use `eval-classification-accuracy` flow to evaluate. This is a flow illustrating how to evaluate the performance of a classification system. It involves comparing each prediction to the groundtruth and assigns a "Correct" or "Incorrect" grade, and aggregating the results to produce metrics such as accuracy, which reflects how good the system is at classifying the data.<jupyter_code>eval_run = pf.run( flow="../../flows/evaluation/eval-classification-accuracy", data=data, run=base_run, column_mapping={ "groundtruth": "${data.answer}", "prediction": "${run.outputs.category}", }, ) pf.stream(eval_run) details = pf.get_details(eval_run) details.head(10) metrics = pf.get_metrics(eval_run) print(json.dumps(metrics, indent=4)) pf.visualize([base_run, eval_run])<jupyter_output><empty_output><jupyter_text>Create another run with different variant nodeIn this example, `web-classification`'s node `summarize_text_content` has two variants: `variant_0` and `variant_1`. The difference between them is the inputs parameters:variant_0: - inputs: - deployment_name: gpt-35-turbo - max_tokens: '128' - temperature: '0.2' - text: ${fetch_text_content_from_url.output}variant_1: - inputs: - deployment_name: gpt-35-turbo - max_tokens: '256' - temperature: '0.3' - text: ${fetch_text_content_from_url.output}You can check the whole flow definition at [flow.dag.yaml](../../flows/standard/web-classification/flow.dag.yaml)<jupyter_code># use the variant1 of the summarize_text_content node. variant_run = pf.run( flow=flow, data=data, variant="${summarize_text_content.variant_1}", # here we specify node "summarize_text_content" to use variant 1 version. ) pf.stream(variant_run) details = pf.get_details(variant_run) details.head(10)<jupyter_output><empty_output><jupyter_text>Run evaluation against variant run<jupyter_code>eval_flow = "../../flows/evaluation/eval-classification-accuracy" eval_run_variant = pf.run( flow=eval_flow, data="../../flows/standard/web-classification/data.jsonl", # path to the data file run=variant_run, # use run as the variant column_mapping={ # reference data "groundtruth": "${data.answer}", # reference the run's output "prediction": "${run.outputs.category}", }, ) pf.stream(eval_run_variant) details = pf.get_details(eval_run_variant) details.head(10) metrics = pf.get_metrics(eval_run_variant) print(json.dumps(metrics, indent=4)) pf.visualize([eval_run, eval_run_variant])<jupyter_output><empty_output>

promptflow/examples/tutorials/get-started/quickstart-azure.ipynb/0

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.title { font-weight:700; } .sd-card-header { font-weight:700; font-size: 16px; } .bd-page-width { max-width: 100rem; } .bd-sidebar-primary { flex: 0 0 20%; } .bd-main .bd-content .bd-article-container { max-width: 70em; } html[data-theme="light"] { --header-announcement-color: #fff070; } html[data-theme="dark"] { --header-announcement-color: #4d4d00; } .bd-header-announcement { background: var(--header-announcement-color); } /* (A) LIGHTBOX BACKGROUND */ #lightbox { /* (A1) COVERS FULLSCREEN */ position: fixed; z-index: 1060; top: 0; left: 0; width: 100%; height: 100%; /* (A2) BACKGROUND */ background: rgba(0, 0, 0, 0.5); /* (A3) CENTER IMAGE ON SCREEN */ display: flex; align-items: center; align-items: center; /* (A4) HIDDEN BY DEFAULT */ visibility: hidden; opacity: 0; /* (A5) SHOW/HIDE ANIMATION */ transition: opacity ease 0.4s; } /* (A6) TOGGLE VISIBILITY */ #lightbox.show { visibility: visible; opacity: 1; } /* (B) LIGHTBOX IMAGE */ #lightbox img { /* (B1) DIMENSIONS */ width: 100%; height: 100%; /* (B2) IMAGE FIT */ /* contain | cover | fill | scale-down */ object-fit: contain; }

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- name: {{ step_name }} working-directory: examples run: | python -m pip install --upgrade pip pip install -r dev_requirements.txt

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$schema: https://azuremlschemas.azureedge.net/latest/environment.schema.json name: chat-with-pdf build: path: context inference_config: liveness_route: port: 8080 path: /health readiness_route: port: 8080 path: /health scoring_route: port: 8080 path: /score

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{{ package_name }}.tools.{{ tool_name }}.{{ class_name }}.{{ function_name }}: class_name: {{ class_name }} function: {{ function_name }} inputs: url: type: - string query: type: - string module: {{ package_name }}.tools.{{ tool_name }} name: Hello World Tool description: This is hello world tool type: python

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{ "file_path": "promptflow/scripts/tool/templates/tool2.yaml.j2", "repo_id": "promptflow", "token_count": 133 }

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promptflow.tools.azure_content_safety.analyze_text: module: promptflow.tools.azure_content_safety function: analyze_text inputs: connection: type: - AzureContentSafetyConnection hate_category: default: medium_sensitivity enum: - disable - low_sensitivity - medium_sensitivity - high_sensitivity type: - string self_harm_category: default: medium_sensitivity enum: - disable - low_sensitivity - medium_sensitivity - high_sensitivity type: - string sexual_category: default: medium_sensitivity enum: - disable - low_sensitivity - medium_sensitivity - high_sensitivity type: - string text: type: - string violence_category: default: medium_sensitivity enum: - disable - low_sensitivity - medium_sensitivity - high_sensitivity type: - string name: Content Safety (Text Analyze) description: Use Azure Content Safety to detect harmful content. type: python deprecated_tools: - content_safety_text.tools.content_safety_text_tool.analyze_text

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# Prompt flow [![Python package](https://img.shields.io/pypi/v/promptflow)](https://pypi.org/project/promptflow/) [![Python](https://img.shields.io/pypi/pyversions/promptflow.svg?maxAge=2592000)](https://pypi.python.org/pypi/promptflow/) [![PyPI - Downloads](https://img.shields.io/pypi/dm/promptflow)](https://pypi.org/project/promptflow/) [![CLI](https://img.shields.io/badge/CLI-reference-blue)](https://microsoft.github.io/promptflow/reference/pf-command-reference.html) [![vsc extension](https://img.shields.io/visual-studio-marketplace/i/prompt-flow.prompt-flow?logo=Visual%20Studio&label=Extension%20)](https://marketplace.visualstudio.com/items?itemName=prompt-flow.prompt-flow) [![Doc](https://img.shields.io/badge/Doc-online-green)](https://microsoft.github.io/promptflow/index.html) [![Issue](https://img.shields.io/github/issues/microsoft/promptflow)](https://github.com/microsoft/promptflow/issues/new/choose) [![Discussions](https://img.shields.io/github/discussions/microsoft/promptflow)](https://github.com/microsoft/promptflow/issues/new/choose) [![CONTRIBUTING](https://img.shields.io/badge/Contributing-8A2BE2)](https://github.com/microsoft/promptflow/blob/main/CONTRIBUTING.md) [![License: MIT](https://img.shields.io/github/license/microsoft/promptflow)](https://github.com/microsoft/promptflow/blob/main/LICENSE) > Welcome to join us to make prompt flow better by > participating [discussions](https://github.com/microsoft/promptflow/discussions), > opening [issues](https://github.com/microsoft/promptflow/issues/new/choose), > submitting [PRs](https://github.com/microsoft/promptflow/pulls). **Prompt flow** is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality. With prompt flow, you will be able to: - **Create and iteratively develop flow** - Create executable [flows](https://microsoft.github.io/promptflow/concepts/concept-flows.html) that link LLMs, prompts, Python code and other [tools](https://microsoft.github.io/promptflow/concepts/concept-tools.html) together. - Debug and iterate your flows, especially the [interaction with LLMs](https://microsoft.github.io/promptflow/concepts/concept-connections.html) with ease. - **Evaluate flow quality and performance** - Evaluate your flow's quality and performance with larger datasets. - Integrate the testing and evaluation into your CI/CD system to ensure quality of your flow. - **Streamlined development cycle for production** - Deploy your flow to the serving platform you choose or integrate into your app's code base easily. - (Optional but highly recommended) Collaborate with your team by leveraging the cloud version of [prompt flow in Azure AI](https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/overview-what-is-prompt-flow?view=azureml-api-2). ------ ## Installation Ensure you have a python environment, `python=3.9` is recommended. ```sh pip install promptflow promptflow-tools ``` ## Quick Start ⚡ **Create a chatbot with prompt flow** Run the command to initiate a prompt flow from a chat template, it creates folder named `my_chatbot` and generates required files within it: ```sh pf flow init --flow ./my_chatbot --type chat ``` **Setup a connection for your API key** For OpenAI key, establish a connection by running the command, using the `openai.yaml` file in the `my_chatbot` folder, which stores your OpenAI key: ```sh # Override keys with --set to avoid yaml file changes pf connection create --file ./my_chatbot/openai.yaml --set api_key=<your_api_key> --name open_ai_connection ``` For Azure OpenAI key, establish the connection by running the command, using the `azure_openai.yaml` file: ```sh pf connection create --file ./my_chatbot/azure_openai.yaml --set api_key=<your_api_key> api_base=<your_api_base> --name open_ai_connection ``` **Chat with your flow** In the `my_chatbot` folder, there's a `flow.dag.yaml` file that outlines the flow, including inputs/outputs, nodes, connection, and the LLM model, etc > Note that in the `chat` node, we're using a connection named `open_ai_connection` (specified in `connection` field) and the `gpt-35-turbo` model (specified in `deployment_name` field). The deployment_name filed is to specify the OpenAI model, or the Azure OpenAI deployment resource. Interact with your chatbot by running: (press `Ctrl + C` to end the session) ```sh pf flow test --flow ./my_chatbot --interactive ``` #### Continue to delve deeper into [prompt flow](https://github.com/microsoft/promptflow).

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import os from promptflow._cli._params import ( add_param_yes, base_params, ) from promptflow._cli._utils import activate_action, get_cli_sdk_logger from promptflow._utils.utils import prompt_y_n from promptflow.exceptions import UserErrorException logger = get_cli_sdk_logger() UPGRADE_MSG = 'Not able to upgrade automatically' def add_upgrade_parser(subparsers): """Add upgrade parser to the pf subparsers.""" epilog = """ Examples: # Upgrade prompt flow without prompt and run non-interactively: pf upgrade --yes """ # noqa: E501 add_params = [ add_param_yes, ] + base_params activate_action( name="upgrade", description="Upgrade prompt flow CLI.", epilog=epilog, add_params=add_params, subparsers=subparsers, help_message="pf upgrade", action_param_name="action", ) def upgrade_version(args): import platform import sys import subprocess from promptflow._constants import _ENV_PF_INSTALLER, CLI_PACKAGE_NAME from promptflow._version import VERSION as local_version from packaging.version import parse from promptflow._utils.version_hint_utils import get_latest_version_from_pypi latest_version = get_latest_version_from_pypi(CLI_PACKAGE_NAME) if not latest_version: logger.warning("Failed to get the latest prompt flow version.") return elif parse(latest_version) <= parse(local_version): logger.warning("You already have the latest prompt flow version: %s", local_version) return yes = args.yes exit_code = 0 installer = os.getenv(_ENV_PF_INSTALLER) or '' installer = installer.upper() print(f"installer: {installer}") latest_version_msg = 'Upgrading prompt flow CLI version to {}.'.format(latest_version) if yes \ else 'Latest version available is {}.'.format(latest_version) logger.warning("Your current prompt flow CLI version is %s. %s", local_version, latest_version_msg) if not yes: logger.warning("Please check the release notes first") if not sys.stdin.isatty(): logger.debug('No tty available.') raise UserErrorException("No tty available. Please run command with --yes.") confirmation = prompt_y_n("Do you want to continue?", default='y') if not confirmation: logger.debug("Upgrade stopped by user") return if installer == 'MSI': _upgrade_on_windows(yes) elif installer == 'PIP': pip_args = [sys.executable, '-m', 'pip', 'install', '--upgrade', 'promptflow[azure,executable,pfs,azureml-serving]', '-vv', '--disable-pip-version-check', '--no-cache-dir'] logger.debug("Update prompt flow with '%s'", " ".join(pip_args)) exit_code = subprocess.call(pip_args, shell=platform.system() == 'Windows') elif installer == 'SCRIPT': command = "curl https://promptflowartifact.blob.core.windows.net/linux-install-scripts/install | bash" logger.warning(f"{UPGRADE_MSG}, you can try to run {command} in your terminal directly to upgrade package.") return else: logger.warning(UPGRADE_MSG) return if exit_code: err_msg = "CLI upgrade failed." logger.warning(err_msg) sys.exit(exit_code) import importlib import json importlib.reload(subprocess) importlib.reload(json) version_result = subprocess.check_output(['pf', 'version'], shell=platform.system() == 'Windows') version_json = json.loads(version_result) new_version = version_json['promptflow'] if new_version == local_version: err_msg = f"CLI upgrade to version {latest_version} failed or aborted." logger.warning(err_msg) sys.exit(1) logger.warning("Upgrade finished.") def _upgrade_on_windows(yes): """Download MSI to a temp folder and install it with msiexec.exe. Directly installing from URL may be blocked by policy: https://github.com/Azure/azure-cli/issues/19171 This also gives the user a chance to manually install the MSI in case of msiexec.exe failure. """ import subprocess import sys import tempfile msi_url = 'https://aka.ms/installpromptflowwindowsx64' logger.warning("Updating prompt flow CLI with MSI from %s", msi_url) # Save MSI to ~\AppData\Local\Temp\promptflow-msi, clean up the folder first msi_dir = os.path.join(tempfile.gettempdir(), 'promptflow-msi') try: import shutil shutil.rmtree(msi_dir) except FileNotFoundError: # The folder has already been deleted. No further retry is needed. # errno: 2, winerror: 3, strerror: 'The system cannot find the path specified' pass except OSError as err: logger.warning("Failed to delete '%s': %s. You may try to delete it manually.", msi_dir, err) os.makedirs(msi_dir, exist_ok=True) msi_path = _download_from_url(msi_url, msi_dir) if yes: subprocess.Popen(['msiexec.exe', '/i', msi_path, '/qn']) else: subprocess.call(['msiexec.exe', '/i', msi_path]) logger.warning("Installation started. Please complete the upgrade in the opened window.") sys.exit(0) def _download_from_url(url, target_dir): import requests r = requests.get(url, stream=True) if r.status_code != 200: raise UserErrorException("Request to {} failed with {}".format(url, r.status_code)) # r.url is the real path of the msi, like # 'https://promptflowartifact.blob.core.windows.net/msi-installer/promptflow.msi' file_name = r.url.rsplit('/')[-1] msi_path = os.path.join(target_dir, file_name) logger.warning("Downloading MSI to %s", msi_path) with open(msi_path, 'wb') as f: for chunk in r.iter_content(chunk_size=1024): f.write(chunk) return msi_path

promptflow/src/promptflow/promptflow/_cli/_pf/_upgrade.py/0

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$schema: https://azuremlschemas.azureedge.net/promptflow/latest/OpenAIConnection.schema.json name: {{ connection }} type: open_ai api_key: "<user-input>"

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# --------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # --------------------------------------------------------- from contextvars import ContextVar from typing import Dict, Mapping from promptflow._version import VERSION class OperationContext(Dict): """The OperationContext class. This class is used to store the context information for the current operation. It is a dictionary-like class that can be used to store any primitive context information. The object is a context variable that can be accessed from anywhere in the current context. The context information is used to provide additional information to the service for logging and telemetry purposes. """ _CONTEXT_KEY = "operation_context" _current_context = ContextVar(_CONTEXT_KEY, default=None) USER_AGENT_KEY = "user_agent" @classmethod def get_instance(cls): """Get the OperationContext instance. This method returns the OperationContext instance from the current context. If there is no instance in the current context, it creates a new one and sets it in the current context. Returns: OperationContext: The OperationContext instance. """ # get the OperationContext instance from the current context instance = cls._current_context.get() if instance is None: # create a new instance and set it in the current context instance = OperationContext() cls._current_context.set(instance) return instance def __setattr__(self, name, value): """Set the attribute. This method sets an attribute with the given name and value in the OperationContext instance. The name must be a string and the value must be a primitive. Args: name (str): The name of the attribute. value (int, float, str, bool, or None): The value of the attribute. Raises: TypeError: If name is not a string or value is not a primitive. """ # check that name is a string if not isinstance(name, str): raise TypeError("Name must be a string") # check that value is a primitive if value is not None and not isinstance(value, (int, float, str, bool)): raise TypeError("Value must be a primitive") # set the item in the data attribute self[name] = value def __getattr__(self, name): """Get the attribute. This method returns the attribute with the given name from the OperationContext instance. If there is no such attribute, it returns the default attribute from the super class. Args: name (str): The name of the attribute. Returns: int, float, str, bool, or None: The value of the attribute. """ if name in self: return self[name] else: super().__getattribute__(name) def __delattr__(self, name): """Delete the attribute. This method deletes the attribute with the given name from the OperationContext instance. If there is no such attribute, it deletes the default attribute from the super class. Args: name (str): The name of the attribute. """ if name in self: del self[name] else: super().__delattr__(name) def get_user_agent(self): """Get the user agent string. This method returns the user agent string for the OperationContext instance. The user agent string consists of the promptflow-sdk version and any additional user agent information stored in the user_agent attribute. Returns: str: The user agent string. """ def parts(): if OperationContext.USER_AGENT_KEY in self: yield self.get(OperationContext.USER_AGENT_KEY) yield f"promptflow/{VERSION}" # strip to avoid leading or trailing spaces, which may cause error when sending request ua = " ".join(parts()).strip() return ua def append_user_agent(self, user_agent: str): """Append the user agent string. This method appends user agent information to the user_agent attribute of the OperationContext instance. If there is no user_agent attribute, it creates one with the given user agent information. Args: user_agent (str): The user agent information to append. """ if OperationContext.USER_AGENT_KEY in self: if user_agent not in self.user_agent: self.user_agent = f"{self.user_agent.strip()} {user_agent.strip()}" else: self.user_agent = user_agent def set_batch_input_source_from_inputs_mapping(self, inputs_mapping: Mapping[str, str]): """Infer the batch input source from the input mapping and set it in the OperationContext instance. This method analyzes the `inputs_mapping` to ascertain the origin of the inputs for a batch operation. The `inputs_mapping` should be a dictionary with keys representing input names and values specifying the sources of these inputs. Inputs can originate from direct data or from the outputs of a previous run. The `inputs_mapping` is dictated entirely by the external caller. For more details on column mapping, refer to https://aka.ms/pf/column-mapping. The mapping can include references to both the inputs and outputs of previous runs, using a reserved source name 'run' to indicate such references. However, this method specifically checks for references to outputs of previous runs, which are denoted by values starting with "${run.outputs". When such a reference is found, the `batch_input_source` attribute of the OperationContext instance is set to "Run" to reflect that the batch operation is utilizing outputs from a prior run. If no values in the `inputs_mapping` start with "${run.outputs", it is inferred that the inputs do not derive from a previous run, and the `batch_input_source` is set to "Data". Examples of `inputs_mapping`: - Referencing a previous run's output: {'input1': '${run.outputs.some_output}', 'input2': 'direct_data'} In this case, 'input1' is sourced from a prior run's output, and 'input2' is from direct data. The `batch_input_source` would be set to "Run". - Sourcing directly from data: {'input1': 'data_source1', 'input2': 'data_source2'} Since no values start with "${run.outputs", the `batch_input_source` is set to "Data". Args: inputs_mapping (Mapping[str, str]): A dictionary mapping input names to their sources, where the sources can be either direct data or outputs from a previous run. The structure and content of this mapping are entirely under the control of the external caller. Returns: None """ if inputs_mapping and any( isinstance(value, str) and value.startswith("${run.outputs") for value in inputs_mapping.values() ): self.batch_input_source = "Run" else: self.batch_input_source = "Data" def get_context_dict(self): """Get the context dictionary. This method returns the context dictionary for the OperationContext instance. The context dictionary is a dictionary that contains all the context information stored in the OperationContext instance. Returns: dict: The context dictionary. """ return dict(self)

promptflow/src/promptflow/promptflow/_core/operation_context.py/0

{ "file_path": "promptflow/src/promptflow/promptflow/_core/operation_context.py", "repo_id": "promptflow", "token_count": 2764 }

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# --------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # --------------------------------------------------------- from typing import List, Optional from sqlalchemy import TEXT, Column from sqlalchemy.exc import IntegrityError from sqlalchemy.orm import declarative_base from promptflow._sdk._constants import CONNECTION_TABLE_NAME from promptflow._sdk._orm.retry import sqlite_retry from .._errors import ConnectionNotFoundError from .session import mgmt_db_session Base = declarative_base() class Connection(Base): __tablename__ = CONNECTION_TABLE_NAME connectionName = Column(TEXT, primary_key=True) connectionType = Column(TEXT, nullable=False) configs = Column(TEXT, nullable=False) # For custom connection, configs can be customConfigs = Column(TEXT, nullable=False) # For strong type connection, custom configs is an empty dict createdDate = Column(TEXT, nullable=False) # ISO8601("YYYY-MM-DD HH:MM:SS.SSS"), string lastModifiedDate = Column(TEXT, nullable=False) # ISO8601("YYYY-MM-DD HH:MM:SS.SSS"), string expiryTime = Column(TEXT) # ISO8601("YYYY-MM-DD HH:MM:SS.SSS"), string @staticmethod @sqlite_retry def create_or_update(connection: "Connection") -> None: session = mgmt_db_session() name = connection.connectionName try: session.add(connection) session.commit() except IntegrityError: session = mgmt_db_session() # Remove the _sa_instance_state update_dict = {k: v for k, v in connection.__dict__.items() if not k.startswith("_")} update_dict.pop("createdDate") session.query(Connection).filter(Connection.connectionName == name).update(update_dict) session.commit() @staticmethod @sqlite_retry def get(name: str, raise_error=True) -> "Connection": with mgmt_db_session() as session: connection = session.query(Connection).filter(Connection.connectionName == name).first() if connection is None and raise_error: raise ConnectionNotFoundError(f"Connection {name!r} is not found.") return connection @staticmethod @sqlite_retry def list(max_results: Optional[int] = None, all_results: bool = False) -> List["Connection"]: with mgmt_db_session() as session: if all_results: return [run_info for run_info in session.query(Connection).all()] else: return [run_info for run_info in session.query(Connection).limit(max_results)] @staticmethod @sqlite_retry def delete(name: str) -> None: with mgmt_db_session() as session: session.query(Connection).filter(Connection.connectionName == name).delete() session.commit()

promptflow/src/promptflow/promptflow/_sdk/_orm/connection.py/0

{ "file_path": "promptflow/src/promptflow/promptflow/_sdk/_orm/connection.py", "repo_id": "promptflow", "token_count": 1061 }

29

packageName: Promptflow.Core.PfsClient packageVersion: 0.0.1 targetFramework: netstandard2.0 optionalProjectFile: false

promptflow/src/promptflow/promptflow/_sdk/_service/generator_configs/csharp.yaml/0

{ "file_path": "promptflow/src/promptflow/promptflow/_sdk/_service/generator_configs/csharp.yaml", "repo_id": "promptflow", "token_count": 37 }

30

# --------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # --------------------------------------------------------- from dataclasses import dataclass from typing import Mapping, Any from promptflow.contracts.run_info import FlowRunInfo from promptflow.contracts.run_info import RunInfo as NodeRunInfo @dataclass class FlowResult: """The result of a flow call.""" output: Mapping[str, Any] # trace info of the flow run. run_info: FlowRunInfo node_run_infos: Mapping[str, NodeRunInfo]

promptflow/src/promptflow/promptflow/_sdk/_serving/flow_result.py/0

{ "file_path": "promptflow/src/promptflow/promptflow/_sdk/_serving/flow_result.py", "repo_id": "promptflow", "token_count": 154 }

31

# --------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # --------------------------------------------------------- from .activity import ( # noqa: F401 ActivityCompletionStatus, ActivityType, log_activity, monitor_operation, request_id_context, ) from .logging_handler import PromptFlowSDKLogHandler, get_appinsights_log_handler # noqa: F401 from .telemetry import TelemetryMixin, WorkspaceTelemetryMixin, get_telemetry_logger, is_telemetry_enabled # noqa: F401

promptflow/src/promptflow/promptflow/_sdk/_telemetry/__init__.py/0

{ "file_path": "promptflow/src/promptflow/promptflow/_sdk/_telemetry/__init__.py", "repo_id": "promptflow", "token_count": 150 }

32

# syntax=docker/dockerfile:1 FROM mcr.microsoft.com/dotnet/sdk:6.0 AS build WORKDIR / COPY ./flow /flow COPY ./connections /connections COPY ./start.sh /start.sh FROM mcr.microsoft.com/dotnet/aspnet:6.0 AS runtime COPY --from=build / / ENV IS_IN_DOCKER="true" EXPOSE 8080 RUN apt-get update && apt-get install -y runit # reset runsvdir RUN rm -rf /var/runit COPY ./runit /var/runit # grant permission RUN chmod -R +x /var/runit CMD ["bash", "./start.sh"]

promptflow/src/promptflow/promptflow/_sdk/data/docker_csharp/Dockerfile.jinja2/0

{ "file_path": "promptflow/src/promptflow/promptflow/_sdk/data/docker_csharp/Dockerfile.jinja2", "repo_id": "promptflow", "token_count": 194 }

33

# --------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # --------------------------------------------------------- from os import PathLike from pathlib import Path from typing import Union from promptflow._constants import LANGUAGE_KEY, FlowLanguage from promptflow._sdk._constants import BASE_PATH_CONTEXT_KEY from promptflow._sdk.entities._flow import FlowBase from promptflow.exceptions import UserErrorException class EagerFlow(FlowBase): """This class is used to represent an eager flow.""" def __init__( self, path: Union[str, PathLike], entry: str, data: dict, **kwargs, ): self.path = Path(path) self.code = self.path.parent self.entry = entry self._data = data super().__init__(**kwargs) @property def language(self) -> str: return self._data.get(LANGUAGE_KEY, FlowLanguage.Python) @classmethod def _create_schema_for_validation(cls, context): # import here to avoid circular import from ..schemas._flow import EagerFlowSchema return EagerFlowSchema(context=context) @classmethod def _load(cls, path: Path, entry: str = None, data: dict = None, **kwargs): data = data or {} # schema validation on unknown fields if path.suffix in [".yaml", ".yml"]: data = cls._create_schema_for_validation(context={BASE_PATH_CONTEXT_KEY: path.parent}).load(data) path = data["path"] if entry: raise UserErrorException("Specifying entry function is not allowed when YAML file is provided.") else: entry = data["entry"] if entry is None: raise UserErrorException(f"Entry function is not specified for flow {path}") return cls(path=path, entry=entry, data=data, **kwargs)

promptflow/src/promptflow/promptflow/_sdk/entities/_eager_flow.py/0

{ "file_path": "promptflow/src/promptflow/promptflow/_sdk/entities/_eager_flow.py", "repo_id": "promptflow", "token_count": 724 }

34

# --------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # --------------------------------------------------------- import copy import os.path import sys import time from dataclasses import asdict from typing import Any, Dict, List, Optional, Union from promptflow._constants import LANGUAGE_KEY, AvailableIDE, FlowLanguage from promptflow._sdk._constants import ( MAX_RUN_LIST_RESULTS, MAX_SHOW_DETAILS_RESULTS, FlowRunProperties, ListViewType, RunInfoSources, RunStatus, ) from promptflow._sdk._errors import InvalidRunStatusError, RunExistsError, RunNotFoundError, RunOperationParameterError from promptflow._sdk._orm import RunInfo as ORMRun from promptflow._sdk._telemetry import ActivityType, TelemetryMixin, monitor_operation from promptflow._sdk._utils import incremental_print, print_red_error, safe_parse_object_list from promptflow._sdk._visualize_functions import dump_html, generate_html_string from promptflow._sdk.entities import Run from promptflow._sdk.operations._local_storage_operations import LocalStorageOperations from promptflow._utils.logger_utils import get_cli_sdk_logger from promptflow._utils.yaml_utils import load_yaml_string from promptflow.contracts._run_management import RunDetail, RunMetadata, RunVisualization, VisualizationConfig from promptflow.exceptions import UserErrorException RUNNING_STATUSES = RunStatus.get_running_statuses() logger = get_cli_sdk_logger() class RunOperations(TelemetryMixin): """RunOperations.""" def __init__(self, **kwargs): super().__init__(**kwargs) @monitor_operation(activity_name="pf.runs.list", activity_type=ActivityType.PUBLICAPI) def list( self, max_results: Optional[int] = MAX_RUN_LIST_RESULTS, *, list_view_type: ListViewType = ListViewType.ACTIVE_ONLY, ) -> List[Run]: """List runs. :param max_results: Max number of results to return. Default: MAX_RUN_LIST_RESULTS. :type max_results: Optional[int] :param list_view_type: View type for including/excluding (for example) archived runs. Default: ACTIVE_ONLY. :type include_archived: Optional[ListViewType] :return: List of run objects. :rtype: List[~promptflow.entities.Run] """ orm_runs = ORMRun.list(max_results=max_results, list_view_type=list_view_type) return safe_parse_object_list( obj_list=orm_runs, parser=Run._from_orm_object, message_generator=lambda x: f"Error parsing run {x.name!r}, skipped.", ) @monitor_operation(activity_name="pf.runs.get", activity_type=ActivityType.PUBLICAPI) def get(self, name: str) -> Run: """Get a run entity. :param name: Name of the run. :type name: str :return: run object retrieved from the database. :rtype: ~promptflow.entities.Run """ return self._get(name) def _get(self, name: str) -> Run: name = Run._validate_and_return_run_name(name) try: return Run._from_orm_object(ORMRun.get(name)) except RunNotFoundError as e: raise e @monitor_operation(activity_name="pf.runs.create_or_update", activity_type=ActivityType.PUBLICAPI) def create_or_update(self, run: Run, **kwargs) -> Run: """Create or update a run. :param run: Run object to create or update. :type run: ~promptflow.entities.Run :return: Run object created or updated. :rtype: ~promptflow.entities.Run """ # create run from an existing run folder if run._run_source == RunInfoSources.EXISTING_RUN: return self._create_run_from_existing_run_folder(run=run, **kwargs) # TODO: change to async stream = kwargs.pop("stream", False) try: from promptflow._sdk._submitter import RunSubmitter created_run = RunSubmitter(run_operations=self).submit(run=run, **kwargs) if stream: self.stream(created_run) return created_run except RunExistsError: raise RunExistsError(f"Run {run.name!r} already exists.") def _create_run_from_existing_run_folder(self, run: Run, **kwargs) -> Run: """Create run from existing run folder.""" try: self.get(run.name) except RunNotFoundError: pass else: raise RunExistsError(f"Run {run.name!r} already exists.") try: run._dump() return run except Exception as e: raise UserErrorException( f"Failed to create run {run.name!r} from existing run folder {run.source!r}: {str(e)}" ) from e def _print_run_summary(self, run: Run) -> None: print("======= Run Summary =======\n") duration = str(run._end_time - run._created_on) print( f'Run name: "{run.name}"\n' f'Run status: "{run.status}"\n' f'Start time: "{run._created_on}"\n' f'Duration: "{duration}"\n' f'Output path: "{run._output_path}"\n' ) @monitor_operation(activity_name="pf.runs.stream", activity_type=ActivityType.PUBLICAPI) def stream(self, name: Union[str, Run], raise_on_error: bool = True) -> Run: """Stream run logs to the console. :param name: Name of the run, or run object. :type name: Union[str, ~promptflow.sdk.entities.Run] :param raise_on_error: Raises an exception if a run fails or canceled. :type raise_on_error: bool :return: Run object. :rtype: ~promptflow.entities.Run """ name = Run._validate_and_return_run_name(name) run = self.get(name=name) local_storage = LocalStorageOperations(run=run) file_handler = sys.stdout try: printed = 0 run = self.get(run.name) while run.status in RUNNING_STATUSES or run.status == RunStatus.FINALIZING: file_handler.flush() available_logs = local_storage.logger.get_logs() printed = incremental_print(available_logs, printed, file_handler) time.sleep(10) run = self.get(run.name) # ensure all logs are printed file_handler.flush() available_logs = local_storage.logger.get_logs() incremental_print(available_logs, printed, file_handler) self._print_run_summary(run) except KeyboardInterrupt: error_message = "The output streaming for the run was interrupted, but the run is still executing." print(error_message) if run.status == RunStatus.FAILED or run.status == RunStatus.CANCELED: if run.status == RunStatus.FAILED: error_message = local_storage.load_exception().get("message", "Run fails with unknown error.") else: error_message = "Run is canceled." if raise_on_error: raise InvalidRunStatusError(error_message) else: print_red_error(error_message) return run @monitor_operation(activity_name="pf.runs.archive", activity_type=ActivityType.PUBLICAPI) def archive(self, name: Union[str, Run]) -> Run: """Archive a run. :param name: Name of the run. :type name: str :return: archived run object. :rtype: ~promptflow._sdk.entities._run.Run """ name = Run._validate_and_return_run_name(name) ORMRun.get(name).archive() return self.get(name) @monitor_operation(activity_name="pf.runs.restore", activity_type=ActivityType.PUBLICAPI) def restore(self, name: Union[str, Run]) -> Run: """Restore a run. :param name: Name of the run. :type name: str :return: restored run object. :rtype: ~promptflow._sdk.entities._run.Run """ name = Run._validate_and_return_run_name(name) ORMRun.get(name).restore() return self.get(name) @monitor_operation(activity_name="pf.runs.update", activity_type=ActivityType.PUBLICAPI) def update( self, name: Union[str, Run], display_name: Optional[str] = None, description: Optional[str] = None, tags: Optional[Dict[str, str]] = None, **kwargs, ) -> Run: """Update run status. :param name: run name :param display_name: display name to update :param description: description to update :param tags: tags to update :param kwargs: other fields to update, fields not supported will be directly dropped. :return: updated run object :rtype: ~promptflow._sdk.entities._run.Run """ name = Run._validate_and_return_run_name(name) # the kwargs is to support update run status scenario but keep it private ORMRun.get(name).update(display_name=display_name, description=description, tags=tags, **kwargs) return self.get(name) @monitor_operation(activity_name="pf.runs.delete", activity_type=ActivityType.PUBLICAPI) def delete( self, name: str, ) -> None: """Delete run permanently. Caution: This operation will delete the run permanently from your local disk. Both run entity and output data will be deleted. :param name: run name to delete :return: None """ valid_run = self.get(name) LocalStorageOperations(valid_run).delete() ORMRun.delete(name) @monitor_operation(activity_name="pf.runs.get_details", activity_type=ActivityType.PUBLICAPI) def get_details( self, name: Union[str, Run], max_results: int = MAX_SHOW_DETAILS_RESULTS, all_results: bool = False ) -> "DataFrame": """Get the details from the run. .. note:: If `all_results` is set to True, `max_results` will be overwritten to sys.maxsize. :param name: The run name or run object :type name: Union[str, ~promptflow.sdk.entities.Run] :param max_results: The max number of runs to return, defaults to 100 :type max_results: int :param all_results: Whether to return all results, defaults to False :type all_results: bool :raises RunOperationParameterError: If `max_results` is not a positive integer. :return: The details data frame. :rtype: pandas.DataFrame """ from pandas import DataFrame # if all_results is True, set max_results to sys.maxsize if all_results: max_results = sys.maxsize if not isinstance(max_results, int) or max_results < 1: raise RunOperationParameterError(f"'max_results' must be a positive integer, got {max_results!r}") name = Run._validate_and_return_run_name(name) run = self.get(name=name) local_storage = LocalStorageOperations(run=run) inputs, outputs = local_storage.load_inputs_and_outputs() inputs = inputs.to_dict("list") outputs = outputs.to_dict("list") data = {} columns = [] for k in inputs: new_k = f"inputs.{k}" data[new_k] = copy.deepcopy(inputs[k]) columns.append(new_k) for k in outputs: new_k = f"outputs.{k}" data[new_k] = copy.deepcopy(outputs[k]) columns.append(new_k) df = DataFrame(data).head(max_results).reindex(columns=columns) return df @monitor_operation(activity_name="pf.runs.get_metrics", activity_type=ActivityType.PUBLICAPI) def get_metrics(self, name: Union[str, Run]) -> Dict[str, Any]: """Get run metrics. :param name: name of the run. :type name: str :return: Run metrics. :rtype: Dict[str, Any] """ name = Run._validate_and_return_run_name(name) run = self.get(name=name) run._check_run_status_is_completed() local_storage = LocalStorageOperations(run=run) return local_storage.load_metrics() def _visualize(self, runs: List[Run], html_path: Optional[str] = None) -> None: details: List[RunDetail] = [] metadatas: List[RunMetadata] = [] configs: List[VisualizationConfig] = [] for run in runs: # check run status first # if run status is not compeleted, there might be unexpected error during parse data # so we directly raise error if there is any incomplete run run._check_run_status_is_completed() local_storage = LocalStorageOperations(run) # nan, inf and -inf are not JSON serializable, which will lead to JavaScript parse error # so specify `parse_const_as_str` as True to parse them as string detail = local_storage.load_detail(parse_const_as_str=True) # ad-hoc step: make logs field empty to avoid too big HTML file # we don't provide logs view in visualization page for now # when we enable, we will save those big data (e.g. logs) in separate file(s) # JS load can be faster than static HTML for i in range(len(detail["node_runs"])): detail["node_runs"][i]["logs"] = {"stdout": "", "stderr": ""} metadata = RunMetadata( name=run.name, display_name=run.display_name, create_time=run.created_on, flow_path=run.properties.get(FlowRunProperties.FLOW_PATH, None), output_path=run.properties[FlowRunProperties.OUTPUT_PATH], tags=run.tags, lineage=run.run, metrics=self.get_metrics(name=run.name), dag=local_storage.load_dag_as_string(), flow_tools_json=local_storage.load_flow_tools_json(), mode="eager" if local_storage.eager_mode else "", ) details.append(copy.deepcopy(detail)) metadatas.append(asdict(metadata)) # TODO: add language to run metadata flow_dag = load_yaml_string(metadata.dag) or {} configs.append( VisualizationConfig( [AvailableIDE.VS_CODE] if flow_dag.get(LANGUAGE_KEY, FlowLanguage.Python) == FlowLanguage.Python else [AvailableIDE.VS] ) ) data_for_visualize = RunVisualization( detail=details, metadata=metadatas, config=configs, ) html_string = generate_html_string(asdict(data_for_visualize)) # if html_path is specified, not open it in webbrowser(as it comes from VSC) dump_html(html_string, html_path=html_path, open_html=html_path is None) @monitor_operation(activity_name="pf.runs.visualize", activity_type=ActivityType.PUBLICAPI) def visualize(self, runs: Union[str, Run, List[str], List[Run]], **kwargs) -> None: """Visualize run(s). :param runs: List of run objects, or names of the runs. :type runs: Union[str, ~promptflow.sdk.entities.Run, List[str], List[~promptflow.sdk.entities.Run]] """ if not isinstance(runs, list): runs = [runs] validated_runs = [] for run in runs: run_name = Run._validate_and_return_run_name(run) validated_runs.append(self.get(name=run_name)) html_path = kwargs.pop("html_path", None) try: self._visualize(validated_runs, html_path=html_path) except InvalidRunStatusError as e: error_message = f"Cannot visualize non-completed run. {str(e)}" logger.error(error_message) def _get_outputs(self, run: Union[str, Run]) -> List[Dict[str, Any]]: """Get the outputs of the run, load from local storage.""" local_storage = self._get_local_storage(run) return local_storage.load_outputs() def _get_inputs(self, run: Union[str, Run]) -> List[Dict[str, Any]]: """Get the outputs of the run, load from local storage.""" local_storage = self._get_local_storage(run) return local_storage.load_inputs() def _get_outputs_path(self, run: Union[str, Run]) -> str: """Get the outputs file path of the run.""" local_storage = self._get_local_storage(run) return local_storage._outputs_path if local_storage.load_outputs() else None def _get_data_path(self, run: Union[str, Run]) -> str: """Get the outputs file path of the run.""" local_storage = self._get_local_storage(run) # TODO: what if the data is deleted? if local_storage._data_path and not os.path.exists(local_storage._data_path): raise UserErrorException( f"Data path {local_storage._data_path} for run {run.name} does not exist. " "Please make sure it exists and not deleted." ) return local_storage._data_path def _get_local_storage(self, run: Union[str, Run]) -> LocalStorageOperations: """Get the local storage of the run.""" if isinstance(run, str): run = self.get(name=run) return LocalStorageOperations(run)

promptflow/src/promptflow/promptflow/_sdk/operations/_run_operations.py/0

{ "file_path": "promptflow/src/promptflow/promptflow/_sdk/operations/_run_operations.py", "repo_id": "promptflow", "token_count": 7452 }

35

# --------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # --------------------------------------------------------- import json import os from datetime import datetime from enum import Enum from traceback import TracebackException, format_tb from types import TracebackType, FrameType from promptflow.exceptions import PromptflowException, SystemErrorException, UserErrorException, ValidationException ADDITIONAL_INFO_USER_EXECUTION_ERROR = "ToolExecutionErrorDetails" ADDITIONAL_INFO_USER_CODE_STACKTRACE = "UserCodeStackTrace" CAUSE_MESSAGE = "\nThe above exception was the direct cause of the following exception:\n\n" CONTEXT_MESSAGE = "\nDuring handling of the above exception, another exception occurred:\n\n" TRACEBACK_MESSAGE = "Traceback (most recent call last):\n" class RootErrorCode: USER_ERROR = "UserError" SYSTEM_ERROR = "SystemError" class ResponseCode(str, Enum): SUCCESS = "200" ACCEPTED = "202" REDIRECTION = "300" CLIENT_ERROR = "400" SERVICE_ERROR = "500" UNKNOWN = "0" class ErrorResponse: """A class that represents the response body when an error occurs. It follows the following specification: https://github.com/microsoft/api-guidelines/blob/vNext/Guidelines.md#7102-error-condition-responses """ def __init__(self, error_dict): self._error_dict = error_dict @staticmethod def from_error_dict(error_dict): """Create an ErrorResponse from an error dict. The error dict which usually is generated by ExceptionPresenter.create(exception).to_dict() """ return ErrorResponse(error_dict) @staticmethod def from_exception(ex: Exception, *, include_debug_info=False): presenter = ExceptionPresenter.create(ex) error_dict = presenter.to_dict(include_debug_info=include_debug_info) return ErrorResponse(error_dict) @property def message(self): return self._error_dict.get("message", "") @property def response_code(self): """Given the error code, return the corresponding http response code.""" root_error_code = self._error_dict.get("code") return ResponseCode.CLIENT_ERROR if root_error_code == RootErrorCode.USER_ERROR else ResponseCode.SERVICE_ERROR @property def additional_info(self): """Return the additional info of the error. The additional info is defined in the error response. It is stored as a list of dict, each of which contains a "type" and "info" field. We change the list of dict to a dict of dict for easier access. """ result = {} list_of_dict = self._error_dict.get("additionalInfo") if not list_of_dict or not isinstance(list_of_dict, list): return result for item in list_of_dict: # We just ignore the item if it is not a dict or does not contain the required fields. if not isinstance(item, dict): continue name = item.get("type") info = item.get("info") if not name or not info: continue result[name] = info return result def get_additional_info(self, name): """Get the additional info by name.""" return self.additional_info.get(name) def get_user_execution_error_info(self): """Get user tool execution error info from additional info.""" user_execution_error_info = self.get_additional_info(ADDITIONAL_INFO_USER_EXECUTION_ERROR) if not user_execution_error_info or not isinstance(user_execution_error_info, dict): return {} return user_execution_error_info def to_dict(self): from promptflow._core.operation_context import OperationContext return { "error": self._error_dict, "correlation": None, # TODO: to be implemented "environment": None, # TODO: to be implemented "location": None, # TODO: to be implemented "componentName": OperationContext.get_instance().get_user_agent(), "time": datetime.utcnow().isoformat(), } def to_simplified_dict(self): return { "error": { "code": self._error_dict.get("code"), "message": self._error_dict.get("message"), } } @property def error_codes(self): error = self._error_dict error_codes = [] while error is not None: code = error.get("code") if code is not None: error_codes.append(code) error = error.get("innerError") else: break return error_codes @property def error_code_hierarchy(self): """Get the code hierarchy from error dict.""" return "/".join(self.error_codes) @property def innermost_error_code(self): error_codes = self.error_codes if error_codes: return error_codes[-1] return None class ExceptionPresenter: """A class that can extract information from the exception instance. It is designed to work for both PromptflowException and other exceptions. """ def __init__(self, ex: Exception): self._ex = ex @staticmethod def create(ex: Exception): if isinstance(ex, PromptflowException): return PromptflowExceptionPresenter(ex) return ExceptionPresenter(ex) @property def formatted_traceback(self): te = TracebackException.from_exception(self._ex) return "".join(te.format()) @property def debug_info(self): return self.build_debug_info(self._ex) def build_debug_info(self, ex: Exception): inner_exception: dict = None stack_trace = TRACEBACK_MESSAGE + "".join(format_tb(ex.__traceback__)) if ex.__cause__ is not None: inner_exception = self.build_debug_info(ex.__cause__) stack_trace = CAUSE_MESSAGE + stack_trace elif ex.__context__ is not None and not ex.__suppress_context__: inner_exception = self.build_debug_info(ex.__context__) stack_trace = CONTEXT_MESSAGE + stack_trace return { "type": ex.__class__.__qualname__, "message": str(ex), "stackTrace": stack_trace, "innerException": inner_exception, } @property def error_codes(self): """The hierarchy of the error codes. We follow the "Microsoft REST API Guidelines" to define error codes in a hierarchy style. See the below link for details: https://github.com/microsoft/api-guidelines/blob/vNext/Guidelines.md#7102-error-condition-responses This method returns the error codes in a list. It will be converted into a nested json format by error_code_recursed. """ return [infer_error_code_from_class(SystemErrorException), self._ex.__class__.__name__] @property def error_code_recursed(self): """Returns a dict of the error codes for this exception. It is populated in a recursive manner, using the source from `error_codes` property. i.e. For PromptflowException, such as ToolExcutionError which inherits from UserErrorException, The result would be: { "code": "UserError", "innerError": { "code": "ToolExecutionError", "innerError": None, }, } For other exception types, such as ValueError, the result would be: { "code": "SystemError", "innerError": { "code": "ValueError", "innerError": None, }, } """ current_error = None reversed_error_codes = reversed(self.error_codes) if self.error_codes else [] for code in reversed_error_codes: current_error = { "code": code, "innerError": current_error, } return current_error def to_dict(self, *, include_debug_info=False): """Return a dict representation of the exception. This dict specification corresponds to the specification of the Microsoft API Guidelines: https://github.com/microsoft/api-guidelines/blob/vNext/Guidelines.md#7102-error-condition-responses Note that this dict represents the "error" field in the response body of the API. The whole error response is then populated in another place outside of this class. """ if isinstance(self._ex, JsonSerializedPromptflowException): return self._ex.to_dict(include_debug_info=include_debug_info) # Otherwise, return general dict representation of the exception. result = {"message": str(self._ex), "messageFormat": "", "messageParameters": {}} result.update(self.error_code_recursed) if include_debug_info: result["debugInfo"] = self.debug_info return result class PromptflowExceptionPresenter(ExceptionPresenter): @property def error_codes(self): """The hierarchy of the error codes. We follow the "Microsoft REST API Guidelines" to define error codes in a hierarchy style. See the below link for details: https://github.com/microsoft/api-guidelines/blob/vNext/Guidelines.md#7102-error-condition-responses For subclass of PromptflowException, use the ex.error_codes directly. For PromptflowException (not a subclass), the ex.error_code is None. The result should be: ["SystemError", {inner_exception type name if exist}] """ if self._ex.error_codes: return self._ex.error_codes # For PromptflowException (not a subclass), the ex.error_code is None. # Handle this case specifically. error_codes = [infer_error_code_from_class(SystemErrorException)] if self._ex.inner_exception: error_codes.append(infer_error_code_from_class(self._ex.inner_exception.__class__)) return error_codes def to_dict(self, *, include_debug_info=False): result = { "message": self._ex.message, "messageFormat": self._ex.message_format, "messageParameters": self._ex.serializable_message_parameters, "referenceCode": self._ex.reference_code, } result.update(self.error_code_recursed) if self._ex.additional_info: result["additionalInfo"] = [{"type": k, "info": v} for k, v in self._ex.additional_info.items()] if include_debug_info: result["debugInfo"] = self.debug_info return result class JsonSerializedPromptflowException(Exception): """Json serialized PromptflowException. This exception only has one argument message to avoid the argument missing error when load/dump with pickle in multiprocessing. Ref: https://bugs.python.org/issue32696 :param message: A Json serialized message describing the error. :type message: str """ def __init__(self, message): self.message = message super().__init__(self.message) def __str__(self): return self.message def to_dict(self, *, include_debug_info=False): # Return a dict representation of the inner exception. error_dict = json.loads(self.message) # The original serialized error might contain debugInfo. # We pop it out if include_debug_info is set to False. if not include_debug_info and "debugInfo" in error_dict: error_dict.pop("debugInfo") return error_dict def get_tb_next(tb: TracebackType, next_cnt: int): """Return the nth tb_next of input tb. If the tb does not have n tb_next, return the last tb which has a value. n = next_cnt """ while tb.tb_next and next_cnt > 0: tb = tb.tb_next next_cnt -= 1 return tb def last_frame_info(ex: Exception): """Return the line number where the error occurred.""" if ex: tb = TracebackException.from_exception(ex) last_frame = tb.stack[-1] if tb.stack else None if last_frame: return { "filename": last_frame.filename, "lineno": last_frame.lineno, "name": last_frame.name, } return {} def infer_error_code_from_class(cls): # Python has a built-in SystemError if cls == SystemErrorException: return RootErrorCode.SYSTEM_ERROR if cls == UserErrorException: return RootErrorCode.USER_ERROR if cls == ValidationException: return "ValidationError" return cls.__name__ def is_pf_core_frame(frame: FrameType): """Check if the frame is from promptflow core code.""" from promptflow import _core folder_of_core = os.path.dirname(_core.__file__) return folder_of_core in frame.f_code.co_filename def remove_suffix(text: str, suffix: str = None): """ Given a string, removes specified suffix, if it has. >>> remove_suffix('hello world', 'world') 'hello ' >>> remove_suffix('hello world', 'hello ') 'hello world' >>> remove_suffix('NoColumnFoundError', 'Error') 'NoColumnFound' :param text: string from which prefix will be removed. :param suffix: suffix to be removed. :return: string removed suffix. """ if not text or not suffix: return text if not text.endswith(suffix): return text return text[:-len(suffix)]

promptflow/src/promptflow/promptflow/_utils/exception_utils.py/0

{ "file_path": "promptflow/src/promptflow/promptflow/_utils/exception_utils.py", "repo_id": "promptflow", "token_count": 5410 }

36

# --------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # --------------------------------------------------------- __path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore from ._pf_client import PFClient __all__ = ["PFClient"]

promptflow/src/promptflow/promptflow/azure/__init__.py/0

{ "file_path": "promptflow/src/promptflow/promptflow/azure/__init__.py", "repo_id": "promptflow", "token_count": 75 }

37

# -------------------------------------------------------------------------- # Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.8.0, generator: @autorest/[email protected]) # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- from azure.core.pipeline.transport import HttpRequest def _convert_request(request, files=None): data = request.content if not files else None request = HttpRequest(method=request.method, url=request.url, headers=request.headers, data=data) if files: request.set_formdata_body(files) return request def _format_url_section(template, **kwargs): components = template.split("/") while components: try: return template.format(**kwargs) except KeyError as key: formatted_components = template.split("/") components = [ c for c in formatted_components if "{}".format(key.args[0]) not in c ] template = "/".join(components)

promptflow/src/promptflow/promptflow/azure/_restclient/flow/_vendor.py/0

{ "file_path": "promptflow/src/promptflow/promptflow/azure/_restclient/flow/_vendor.py", "repo_id": "promptflow", "token_count": 365 }

38

# coding=utf-8 # -------------------------------------------------------------------------- # Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.8.0, generator: @autorest/[email protected]) # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- import functools from typing import Any, Callable, Dict, Generic, List, Optional, TypeVar import warnings from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error from azure.core.pipeline import PipelineResponse from azure.core.pipeline.transport import AsyncHttpResponse from azure.core.rest import HttpRequest from azure.core.tracing.decorator_async import distributed_trace_async from ... import models as _models from ..._vendor import _convert_request from ...operations._tools_operations import build_get_dynamic_list_request, build_get_package_tools_request, build_get_samples_request, build_get_tool_meta_request, build_get_tool_meta_v2_request, build_get_tool_setting_request, build_retrieve_tool_func_result_request T = TypeVar('T') ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]] class ToolsOperations: """ToolsOperations async operations. You should not instantiate this class directly. Instead, you should create a Client instance that instantiates it for you and attaches it as an attribute. :ivar models: Alias to model classes used in this operation group. :type models: ~flow.models :param client: Client for service requests. :param config: Configuration of service client. :param serializer: An object model serializer. :param deserializer: An object model deserializer. """ models = _models def __init__(self, client, config, serializer, deserializer) -> None: self._client = client self._serialize = serializer self._deserialize = deserializer self._config = config @distributed_trace_async async def get_tool_setting( self, subscription_id: str, resource_group_name: str, workspace_name: str, **kwargs: Any ) -> "_models.ToolSetting": """get_tool_setting. :param subscription_id: The Azure Subscription ID. :type subscription_id: str :param resource_group_name: The Name of the resource group in which the workspace is located. :type resource_group_name: str :param workspace_name: The name of the workspace. :type workspace_name: str :keyword callable cls: A custom type or function that will be passed the direct response :return: ToolSetting, or the result of cls(response) :rtype: ~flow.models.ToolSetting :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["_models.ToolSetting"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) request = build_get_tool_setting_request( subscription_id=subscription_id, resource_group_name=resource_group_name, workspace_name=workspace_name, template_url=self.get_tool_setting.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) raise HttpResponseError(response=response, model=error) deserialized = self._deserialize('ToolSetting', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_tool_setting.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/setting'} # type: ignore @distributed_trace_async async def get_samples( self, subscription_id: str, resource_group_name: str, workspace_name: str, **kwargs: Any ) -> Dict[str, "_models.Tool"]: """get_samples. :param subscription_id: The Azure Subscription ID. :type subscription_id: str :param resource_group_name: The Name of the resource group in which the workspace is located. :type resource_group_name: str :param workspace_name: The name of the workspace. :type workspace_name: str :keyword callable cls: A custom type or function that will be passed the direct response :return: dict mapping str to Tool, or the result of cls(response) :rtype: dict[str, ~flow.models.Tool] :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[Dict[str, "_models.Tool"]] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) request = build_get_samples_request( subscription_id=subscription_id, resource_group_name=resource_group_name, workspace_name=workspace_name, template_url=self.get_samples.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) raise HttpResponseError(response=response, model=error) deserialized = self._deserialize('{Tool}', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_samples.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/samples'} # type: ignore @distributed_trace_async async def get_tool_meta( self, subscription_id: str, resource_group_name: str, workspace_name: str, tool_name: str, tool_type: str, endpoint_name: Optional[str] = None, flow_runtime_name: Optional[str] = None, flow_id: Optional[str] = None, data: Optional[str] = None, **kwargs: Any ) -> str: """get_tool_meta. :param subscription_id: The Azure Subscription ID. :type subscription_id: str :param resource_group_name: The Name of the resource group in which the workspace is located. :type resource_group_name: str :param workspace_name: The name of the workspace. :type workspace_name: str :param tool_name: :type tool_name: str :param tool_type: :type tool_type: str :param endpoint_name: :type endpoint_name: str :param flow_runtime_name: :type flow_runtime_name: str :param flow_id: :type flow_id: str :param data: :type data: str :keyword callable cls: A custom type or function that will be passed the direct response :return: str, or the result of cls(response) :rtype: str :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[str] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) content_type = kwargs.pop('content_type', "text/plain") # type: Optional[str] _content = data request = build_get_tool_meta_request( subscription_id=subscription_id, resource_group_name=resource_group_name, workspace_name=workspace_name, content_type=content_type, tool_name=tool_name, tool_type=tool_type, content=_content, endpoint_name=endpoint_name, flow_runtime_name=flow_runtime_name, flow_id=flow_id, template_url=self.get_tool_meta.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) raise HttpResponseError(response=response, model=error) deserialized = self._deserialize('str', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_tool_meta.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/meta'} # type: ignore @distributed_trace_async async def get_tool_meta_v2( self, subscription_id: str, resource_group_name: str, workspace_name: str, flow_runtime_name: Optional[str] = None, flow_id: Optional[str] = None, body: Optional["_models.GenerateToolMetaRequest"] = None, **kwargs: Any ) -> "_models.ToolMetaDto": """get_tool_meta_v2. :param subscription_id: The Azure Subscription ID. :type subscription_id: str :param resource_group_name: The Name of the resource group in which the workspace is located. :type resource_group_name: str :param workspace_name: The name of the workspace. :type workspace_name: str :param flow_runtime_name: :type flow_runtime_name: str :param flow_id: :type flow_id: str :param body: :type body: ~flow.models.GenerateToolMetaRequest :keyword callable cls: A custom type or function that will be passed the direct response :return: ToolMetaDto, or the result of cls(response) :rtype: ~flow.models.ToolMetaDto :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["_models.ToolMetaDto"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] if body is not None: _json = self._serialize.body(body, 'GenerateToolMetaRequest') else: _json = None request = build_get_tool_meta_v2_request( subscription_id=subscription_id, resource_group_name=resource_group_name, workspace_name=workspace_name, content_type=content_type, json=_json, flow_runtime_name=flow_runtime_name, flow_id=flow_id, template_url=self.get_tool_meta_v2.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) raise HttpResponseError(response=response, model=error) deserialized = self._deserialize('ToolMetaDto', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_tool_meta_v2.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/meta-v2'} # type: ignore @distributed_trace_async async def get_package_tools( self, subscription_id: str, resource_group_name: str, workspace_name: str, flow_runtime_name: Optional[str] = None, flow_id: Optional[str] = None, **kwargs: Any ) -> Dict[str, "_models.Tool"]: """get_package_tools. :param subscription_id: The Azure Subscription ID. :type subscription_id: str :param resource_group_name: The Name of the resource group in which the workspace is located. :type resource_group_name: str :param workspace_name: The name of the workspace. :type workspace_name: str :param flow_runtime_name: :type flow_runtime_name: str :param flow_id: :type flow_id: str :keyword callable cls: A custom type or function that will be passed the direct response :return: dict mapping str to Tool, or the result of cls(response) :rtype: dict[str, ~flow.models.Tool] :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[Dict[str, "_models.Tool"]] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) request = build_get_package_tools_request( subscription_id=subscription_id, resource_group_name=resource_group_name, workspace_name=workspace_name, flow_runtime_name=flow_runtime_name, flow_id=flow_id, template_url=self.get_package_tools.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) raise HttpResponseError(response=response, model=error) deserialized = self._deserialize('{Tool}', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_package_tools.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/packageTools'} # type: ignore @distributed_trace_async async def get_dynamic_list( self, subscription_id: str, resource_group_name: str, workspace_name: str, flow_runtime_name: Optional[str] = None, flow_id: Optional[str] = None, body: Optional["_models.GetDynamicListRequest"] = None, **kwargs: Any ) -> List[Any]: """get_dynamic_list. :param subscription_id: The Azure Subscription ID. :type subscription_id: str :param resource_group_name: The Name of the resource group in which the workspace is located. :type resource_group_name: str :param workspace_name: The name of the workspace. :type workspace_name: str :param flow_runtime_name: :type flow_runtime_name: str :param flow_id: :type flow_id: str :param body: :type body: ~flow.models.GetDynamicListRequest :keyword callable cls: A custom type or function that will be passed the direct response :return: list of any, or the result of cls(response) :rtype: list[any] :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[List[Any]] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] if body is not None: _json = self._serialize.body(body, 'GetDynamicListRequest') else: _json = None request = build_get_dynamic_list_request( subscription_id=subscription_id, resource_group_name=resource_group_name, workspace_name=workspace_name, content_type=content_type, json=_json, flow_runtime_name=flow_runtime_name, flow_id=flow_id, template_url=self.get_dynamic_list.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) raise HttpResponseError(response=response, model=error) deserialized = self._deserialize('[object]', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_dynamic_list.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/dynamicList'} # type: ignore @distributed_trace_async async def retrieve_tool_func_result( self, subscription_id: str, resource_group_name: str, workspace_name: str, flow_runtime_name: Optional[str] = None, flow_id: Optional[str] = None, body: Optional["_models.RetrieveToolFuncResultRequest"] = None, **kwargs: Any ) -> "_models.ToolFuncResponse": """retrieve_tool_func_result. :param subscription_id: The Azure Subscription ID. :type subscription_id: str :param resource_group_name: The Name of the resource group in which the workspace is located. :type resource_group_name: str :param workspace_name: The name of the workspace. :type workspace_name: str :param flow_runtime_name: :type flow_runtime_name: str :param flow_id: :type flow_id: str :param body: :type body: ~flow.models.RetrieveToolFuncResultRequest :keyword callable cls: A custom type or function that will be passed the direct response :return: ToolFuncResponse, or the result of cls(response) :rtype: ~flow.models.ToolFuncResponse :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["_models.ToolFuncResponse"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] if body is not None: _json = self._serialize.body(body, 'RetrieveToolFuncResultRequest') else: _json = None request = build_retrieve_tool_func_result_request( subscription_id=subscription_id, resource_group_name=resource_group_name, workspace_name=workspace_name, content_type=content_type, json=_json, flow_runtime_name=flow_runtime_name, flow_id=flow_id, template_url=self.retrieve_tool_func_result.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) raise HttpResponseError(response=response, model=error) deserialized = self._deserialize('ToolFuncResponse', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized retrieve_tool_func_result.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/RetrieveToolFuncResult'} # type: ignore

promptflow/src/promptflow/promptflow/azure/_restclient/flow/aio/operations/_tools_operations.py/0

{ "file_path": "promptflow/src/promptflow/promptflow/azure/_restclient/flow/aio/operations/_tools_operations.py", "repo_id": "promptflow", "token_count": 8893 }

39

# coding=utf-8 # -------------------------------------------------------------------------- # Code generated by Microsoft (R) AutoRest Code Generator (autorest: 3.8.0, generator: @autorest/[email protected]) # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- import functools from typing import TYPE_CHECKING import warnings from azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error from azure.core.pipeline import PipelineResponse from azure.core.pipeline.transport import HttpResponse from azure.core.rest import HttpRequest from azure.core.tracing.decorator import distributed_trace from msrest import Serializer from .. import models as _models from .._vendor import _convert_request, _format_url_section if TYPE_CHECKING: # pylint: disable=unused-import,ungrouped-imports from typing import Any, Callable, Dict, Generic, List, Optional, TypeVar T = TypeVar('T') ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]] _SERIALIZER = Serializer() _SERIALIZER.client_side_validation = False # fmt: off def build_get_tool_setting_request( subscription_id, # type: str resource_group_name, # type: str workspace_name, # type: str **kwargs # type: Any ): # type: (...) -> HttpRequest accept = "application/json" # Construct URL url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/setting') path_format_arguments = { "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), } url = _format_url_section(url, **path_format_arguments) # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="GET", url=url, headers=header_parameters, **kwargs ) def build_get_samples_request( subscription_id, # type: str resource_group_name, # type: str workspace_name, # type: str **kwargs # type: Any ): # type: (...) -> HttpRequest accept = "application/json" # Construct URL url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/samples') path_format_arguments = { "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), } url = _format_url_section(url, **path_format_arguments) # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="GET", url=url, headers=header_parameters, **kwargs ) def build_get_tool_meta_request( subscription_id, # type: str resource_group_name, # type: str workspace_name, # type: str **kwargs # type: Any ): # type: (...) -> HttpRequest content_type = kwargs.pop('content_type', None) # type: Optional[str] tool_name = kwargs.pop('tool_name') # type: str tool_type = kwargs.pop('tool_type') # type: str endpoint_name = kwargs.pop('endpoint_name', None) # type: Optional[str] flow_runtime_name = kwargs.pop('flow_runtime_name', None) # type: Optional[str] flow_id = kwargs.pop('flow_id', None) # type: Optional[str] accept = "application/json" # Construct URL url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/meta') path_format_arguments = { "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), } url = _format_url_section(url, **path_format_arguments) # Construct parameters query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] query_parameters['toolName'] = _SERIALIZER.query("tool_name", tool_name, 'str') query_parameters['toolType'] = _SERIALIZER.query("tool_type", tool_type, 'str') if endpoint_name is not None: query_parameters['endpointName'] = _SERIALIZER.query("endpoint_name", endpoint_name, 'str') if flow_runtime_name is not None: query_parameters['flowRuntimeName'] = _SERIALIZER.query("flow_runtime_name", flow_runtime_name, 'str') if flow_id is not None: query_parameters['flowId'] = _SERIALIZER.query("flow_id", flow_id, 'str') # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] if content_type is not None: header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str') header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="POST", url=url, params=query_parameters, headers=header_parameters, **kwargs ) def build_get_tool_meta_v2_request( subscription_id, # type: str resource_group_name, # type: str workspace_name, # type: str **kwargs # type: Any ): # type: (...) -> HttpRequest content_type = kwargs.pop('content_type', None) # type: Optional[str] flow_runtime_name = kwargs.pop('flow_runtime_name', None) # type: Optional[str] flow_id = kwargs.pop('flow_id', None) # type: Optional[str] accept = "application/json" # Construct URL url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/meta-v2') path_format_arguments = { "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), } url = _format_url_section(url, **path_format_arguments) # Construct parameters query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] if flow_runtime_name is not None: query_parameters['flowRuntimeName'] = _SERIALIZER.query("flow_runtime_name", flow_runtime_name, 'str') if flow_id is not None: query_parameters['flowId'] = _SERIALIZER.query("flow_id", flow_id, 'str') # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] if content_type is not None: header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str') header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="POST", url=url, params=query_parameters, headers=header_parameters, **kwargs ) def build_get_package_tools_request( subscription_id, # type: str resource_group_name, # type: str workspace_name, # type: str **kwargs # type: Any ): # type: (...) -> HttpRequest flow_runtime_name = kwargs.pop('flow_runtime_name', None) # type: Optional[str] flow_id = kwargs.pop('flow_id', None) # type: Optional[str] accept = "application/json" # Construct URL url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/packageTools') path_format_arguments = { "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), } url = _format_url_section(url, **path_format_arguments) # Construct parameters query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] if flow_runtime_name is not None: query_parameters['flowRuntimeName'] = _SERIALIZER.query("flow_runtime_name", flow_runtime_name, 'str') if flow_id is not None: query_parameters['flowId'] = _SERIALIZER.query("flow_id", flow_id, 'str') # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="GET", url=url, params=query_parameters, headers=header_parameters, **kwargs ) def build_get_dynamic_list_request( subscription_id, # type: str resource_group_name, # type: str workspace_name, # type: str **kwargs # type: Any ): # type: (...) -> HttpRequest content_type = kwargs.pop('content_type', None) # type: Optional[str] flow_runtime_name = kwargs.pop('flow_runtime_name', None) # type: Optional[str] flow_id = kwargs.pop('flow_id', None) # type: Optional[str] accept = "application/json" # Construct URL url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/dynamicList') path_format_arguments = { "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), } url = _format_url_section(url, **path_format_arguments) # Construct parameters query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] if flow_runtime_name is not None: query_parameters['flowRuntimeName'] = _SERIALIZER.query("flow_runtime_name", flow_runtime_name, 'str') if flow_id is not None: query_parameters['flowId'] = _SERIALIZER.query("flow_id", flow_id, 'str') # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] if content_type is not None: header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str') header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="POST", url=url, params=query_parameters, headers=header_parameters, **kwargs ) def build_retrieve_tool_func_result_request( subscription_id, # type: str resource_group_name, # type: str workspace_name, # type: str **kwargs # type: Any ): # type: (...) -> HttpRequest content_type = kwargs.pop('content_type', None) # type: Optional[str] flow_runtime_name = kwargs.pop('flow_runtime_name', None) # type: Optional[str] flow_id = kwargs.pop('flow_id', None) # type: Optional[str] accept = "application/json" # Construct URL url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/RetrieveToolFuncResult') path_format_arguments = { "subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, 'str'), "resourceGroupName": _SERIALIZER.url("resource_group_name", resource_group_name, 'str'), "workspaceName": _SERIALIZER.url("workspace_name", workspace_name, 'str'), } url = _format_url_section(url, **path_format_arguments) # Construct parameters query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any] if flow_runtime_name is not None: query_parameters['flowRuntimeName'] = _SERIALIZER.query("flow_runtime_name", flow_runtime_name, 'str') if flow_id is not None: query_parameters['flowId'] = _SERIALIZER.query("flow_id", flow_id, 'str') # Construct headers header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any] if content_type is not None: header_parameters['Content-Type'] = _SERIALIZER.header("content_type", content_type, 'str') header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str') return HttpRequest( method="POST", url=url, params=query_parameters, headers=header_parameters, **kwargs ) # fmt: on class ToolsOperations(object): """ToolsOperations operations. You should not instantiate this class directly. Instead, you should create a Client instance that instantiates it for you and attaches it as an attribute. :ivar models: Alias to model classes used in this operation group. :type models: ~flow.models :param client: Client for service requests. :param config: Configuration of service client. :param serializer: An object model serializer. :param deserializer: An object model deserializer. """ models = _models def __init__(self, client, config, serializer, deserializer): self._client = client self._serialize = serializer self._deserialize = deserializer self._config = config @distributed_trace def get_tool_setting( self, subscription_id, # type: str resource_group_name, # type: str workspace_name, # type: str **kwargs # type: Any ): # type: (...) -> "_models.ToolSetting" """get_tool_setting. :param subscription_id: The Azure Subscription ID. :type subscription_id: str :param resource_group_name: The Name of the resource group in which the workspace is located. :type resource_group_name: str :param workspace_name: The name of the workspace. :type workspace_name: str :keyword callable cls: A custom type or function that will be passed the direct response :return: ToolSetting, or the result of cls(response) :rtype: ~flow.models.ToolSetting :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["_models.ToolSetting"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) request = build_get_tool_setting_request( subscription_id=subscription_id, resource_group_name=resource_group_name, workspace_name=workspace_name, template_url=self.get_tool_setting.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) raise HttpResponseError(response=response, model=error) deserialized = self._deserialize('ToolSetting', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_tool_setting.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/setting'} # type: ignore @distributed_trace def get_samples( self, subscription_id, # type: str resource_group_name, # type: str workspace_name, # type: str **kwargs # type: Any ): # type: (...) -> Dict[str, "_models.Tool"] """get_samples. :param subscription_id: The Azure Subscription ID. :type subscription_id: str :param resource_group_name: The Name of the resource group in which the workspace is located. :type resource_group_name: str :param workspace_name: The name of the workspace. :type workspace_name: str :keyword callable cls: A custom type or function that will be passed the direct response :return: dict mapping str to Tool, or the result of cls(response) :rtype: dict[str, ~flow.models.Tool] :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[Dict[str, "_models.Tool"]] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) request = build_get_samples_request( subscription_id=subscription_id, resource_group_name=resource_group_name, workspace_name=workspace_name, template_url=self.get_samples.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) raise HttpResponseError(response=response, model=error) deserialized = self._deserialize('{Tool}', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_samples.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/samples'} # type: ignore @distributed_trace def get_tool_meta( self, subscription_id, # type: str resource_group_name, # type: str workspace_name, # type: str tool_name, # type: str tool_type, # type: str endpoint_name=None, # type: Optional[str] flow_runtime_name=None, # type: Optional[str] flow_id=None, # type: Optional[str] data=None, # type: Optional[str] **kwargs # type: Any ): # type: (...) -> str """get_tool_meta. :param subscription_id: The Azure Subscription ID. :type subscription_id: str :param resource_group_name: The Name of the resource group in which the workspace is located. :type resource_group_name: str :param workspace_name: The name of the workspace. :type workspace_name: str :param tool_name: :type tool_name: str :param tool_type: :type tool_type: str :param endpoint_name: :type endpoint_name: str :param flow_runtime_name: :type flow_runtime_name: str :param flow_id: :type flow_id: str :param data: :type data: str :keyword callable cls: A custom type or function that will be passed the direct response :return: str, or the result of cls(response) :rtype: str :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[str] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) content_type = kwargs.pop('content_type', "text/plain") # type: Optional[str] _content = data request = build_get_tool_meta_request( subscription_id=subscription_id, resource_group_name=resource_group_name, workspace_name=workspace_name, content_type=content_type, tool_name=tool_name, tool_type=tool_type, content=_content, endpoint_name=endpoint_name, flow_runtime_name=flow_runtime_name, flow_id=flow_id, template_url=self.get_tool_meta.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) raise HttpResponseError(response=response, model=error) deserialized = self._deserialize('str', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_tool_meta.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/meta'} # type: ignore @distributed_trace def get_tool_meta_v2( self, subscription_id, # type: str resource_group_name, # type: str workspace_name, # type: str flow_runtime_name=None, # type: Optional[str] flow_id=None, # type: Optional[str] body=None, # type: Optional["_models.GenerateToolMetaRequest"] **kwargs # type: Any ): # type: (...) -> "_models.ToolMetaDto" """get_tool_meta_v2. :param subscription_id: The Azure Subscription ID. :type subscription_id: str :param resource_group_name: The Name of the resource group in which the workspace is located. :type resource_group_name: str :param workspace_name: The name of the workspace. :type workspace_name: str :param flow_runtime_name: :type flow_runtime_name: str :param flow_id: :type flow_id: str :param body: :type body: ~flow.models.GenerateToolMetaRequest :keyword callable cls: A custom type or function that will be passed the direct response :return: ToolMetaDto, or the result of cls(response) :rtype: ~flow.models.ToolMetaDto :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["_models.ToolMetaDto"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] if body is not None: _json = self._serialize.body(body, 'GenerateToolMetaRequest') else: _json = None request = build_get_tool_meta_v2_request( subscription_id=subscription_id, resource_group_name=resource_group_name, workspace_name=workspace_name, content_type=content_type, json=_json, flow_runtime_name=flow_runtime_name, flow_id=flow_id, template_url=self.get_tool_meta_v2.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) raise HttpResponseError(response=response, model=error) deserialized = self._deserialize('ToolMetaDto', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_tool_meta_v2.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/meta-v2'} # type: ignore @distributed_trace def get_package_tools( self, subscription_id, # type: str resource_group_name, # type: str workspace_name, # type: str flow_runtime_name=None, # type: Optional[str] flow_id=None, # type: Optional[str] **kwargs # type: Any ): # type: (...) -> Dict[str, "_models.Tool"] """get_package_tools. :param subscription_id: The Azure Subscription ID. :type subscription_id: str :param resource_group_name: The Name of the resource group in which the workspace is located. :type resource_group_name: str :param workspace_name: The name of the workspace. :type workspace_name: str :param flow_runtime_name: :type flow_runtime_name: str :param flow_id: :type flow_id: str :keyword callable cls: A custom type or function that will be passed the direct response :return: dict mapping str to Tool, or the result of cls(response) :rtype: dict[str, ~flow.models.Tool] :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[Dict[str, "_models.Tool"]] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) request = build_get_package_tools_request( subscription_id=subscription_id, resource_group_name=resource_group_name, workspace_name=workspace_name, flow_runtime_name=flow_runtime_name, flow_id=flow_id, template_url=self.get_package_tools.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) raise HttpResponseError(response=response, model=error) deserialized = self._deserialize('{Tool}', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_package_tools.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/packageTools'} # type: ignore @distributed_trace def get_dynamic_list( self, subscription_id, # type: str resource_group_name, # type: str workspace_name, # type: str flow_runtime_name=None, # type: Optional[str] flow_id=None, # type: Optional[str] body=None, # type: Optional["_models.GetDynamicListRequest"] **kwargs # type: Any ): # type: (...) -> List[Any] """get_dynamic_list. :param subscription_id: The Azure Subscription ID. :type subscription_id: str :param resource_group_name: The Name of the resource group in which the workspace is located. :type resource_group_name: str :param workspace_name: The name of the workspace. :type workspace_name: str :param flow_runtime_name: :type flow_runtime_name: str :param flow_id: :type flow_id: str :param body: :type body: ~flow.models.GetDynamicListRequest :keyword callable cls: A custom type or function that will be passed the direct response :return: list of any, or the result of cls(response) :rtype: list[any] :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType[List[Any]] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] if body is not None: _json = self._serialize.body(body, 'GetDynamicListRequest') else: _json = None request = build_get_dynamic_list_request( subscription_id=subscription_id, resource_group_name=resource_group_name, workspace_name=workspace_name, content_type=content_type, json=_json, flow_runtime_name=flow_runtime_name, flow_id=flow_id, template_url=self.get_dynamic_list.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) raise HttpResponseError(response=response, model=error) deserialized = self._deserialize('[object]', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_dynamic_list.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/dynamicList'} # type: ignore @distributed_trace def retrieve_tool_func_result( self, subscription_id, # type: str resource_group_name, # type: str workspace_name, # type: str flow_runtime_name=None, # type: Optional[str] flow_id=None, # type: Optional[str] body=None, # type: Optional["_models.RetrieveToolFuncResultRequest"] **kwargs # type: Any ): # type: (...) -> "_models.ToolFuncResponse" """retrieve_tool_func_result. :param subscription_id: The Azure Subscription ID. :type subscription_id: str :param resource_group_name: The Name of the resource group in which the workspace is located. :type resource_group_name: str :param workspace_name: The name of the workspace. :type workspace_name: str :param flow_runtime_name: :type flow_runtime_name: str :param flow_id: :type flow_id: str :param body: :type body: ~flow.models.RetrieveToolFuncResultRequest :keyword callable cls: A custom type or function that will be passed the direct response :return: ToolFuncResponse, or the result of cls(response) :rtype: ~flow.models.ToolFuncResponse :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["_models.ToolFuncResponse"] error_map = { 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError } error_map.update(kwargs.pop('error_map', {})) content_type = kwargs.pop('content_type', "application/json") # type: Optional[str] if body is not None: _json = self._serialize.body(body, 'RetrieveToolFuncResultRequest') else: _json = None request = build_retrieve_tool_func_result_request( subscription_id=subscription_id, resource_group_name=resource_group_name, workspace_name=workspace_name, content_type=content_type, json=_json, flow_runtime_name=flow_runtime_name, flow_id=flow_id, template_url=self.retrieve_tool_func_result.metadata['url'], ) request = _convert_request(request) request.url = self._client.format_url(request.url) pipeline_response = self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize.failsafe_deserialize(_models.ErrorResponse, pipeline_response) raise HttpResponseError(response=response, model=error) deserialized = self._deserialize('ToolFuncResponse', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized retrieve_tool_func_result.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/Tools/RetrieveToolFuncResult'} # type: ignore

promptflow/src/promptflow/promptflow/azure/_restclient/flow/operations/_tools_operations.py/0

{ "file_path": "promptflow/src/promptflow/promptflow/azure/_restclient/flow/operations/_tools_operations.py", "repo_id": "promptflow", "token_count": 13696 }

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# --------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # --------------------------------------------------------- # pylint: disable=protected-access import copy import json import os import re from datetime import datetime from functools import cached_property from pathlib import Path from typing import Dict, List, Optional, Union import requests from azure.ai.ml._artifacts._artifact_utilities import _check_and_upload_path from azure.ai.ml._scope_dependent_operations import ( OperationConfig, OperationsContainer, OperationScope, _ScopeDependentOperations, ) from azure.ai.ml.constants._common import SHORT_URI_FORMAT from azure.ai.ml.entities import Workspace from azure.ai.ml.operations._operation_orchestrator import OperationOrchestrator from azure.core.exceptions import HttpResponseError from promptflow._sdk._constants import ( CLIENT_FLOW_TYPE_2_SERVICE_FLOW_TYPE, DAG_FILE_NAME, MAX_LIST_CLI_RESULTS, WORKSPACE_LINKED_DATASTORE_NAME, FlowType, ListViewType, ) from promptflow._sdk._errors import FlowOperationError from promptflow._sdk._telemetry import ActivityType, WorkspaceTelemetryMixin, monitor_operation from promptflow._sdk._utils import PromptflowIgnoreFile from promptflow._sdk._vendor._asset_utils import traverse_directory from promptflow._utils.logger_utils import get_cli_sdk_logger from promptflow.azure._constants._flow import DEFAULT_STORAGE from promptflow.azure._entities._flow import Flow from promptflow.azure._load_functions import load_flow from promptflow.azure._restclient.flow_service_caller import FlowServiceCaller from promptflow.azure.operations._artifact_utilities import _get_datastore_name, get_datastore_info from promptflow.azure.operations._fileshare_storeage_helper import FlowFileStorageClient from promptflow.exceptions import SystemErrorException, UserErrorException logger = get_cli_sdk_logger() class FlowOperations(WorkspaceTelemetryMixin, _ScopeDependentOperations): """FlowOperations that can manage flows. You should not instantiate this class directly. Instead, you should create a :class:`~promptflow.azure.PFClient` instance and this operation is available as the instance's attribute. """ _FLOW_RESOURCE_PATTERN = re.compile(r"azureml:.*?/workspaces/(?P<experiment_id>.*?)/flows/(?P<flow_id>.*?)$") def __init__( self, operation_scope: OperationScope, operation_config: OperationConfig, all_operations: OperationsContainer, credential, service_caller: FlowServiceCaller, workspace: Workspace, **kwargs: Dict, ): super().__init__( operation_scope=operation_scope, operation_config=operation_config, workspace_name=operation_scope.workspace_name, subscription_id=operation_scope.subscription_id, resource_group_name=operation_scope.resource_group_name, ) self._all_operations = all_operations self._service_caller = service_caller self._credential = credential self._workspace = workspace @cached_property def _workspace_id(self): return self._workspace._workspace_id @cached_property def _index_service_endpoint_url(self): """Get the endpoint url for the workspace.""" endpoint = self._service_caller._service_endpoint return endpoint + "index/v1.0" + self._service_caller._common_azure_url_pattern @monitor_operation(activity_name="pfazure.flows.create_or_update", activity_type=ActivityType.PUBLICAPI) def create_or_update(self, flow: Union[str, Path], display_name=None, type=None, **kwargs) -> Flow: """Create a flow to remote from local source, or update the metadata of an existing flow. .. note:: Functionality of updating flow metadata is yet to be supported. :param flow: The source of the flow to create. :type flow: Union[str, Path] :param display_name: The display name of the flow to create. Default to be flow folder name + timestamp if not specified. e.g. "web-classification-10-27-2023-14-19-10" :type display_name: str :param type: The type of the flow to create. One of ["standard", evaluation", "chat"]. Default to be "standard" if not specified. :type type: str :param description: The description of the flow to create. Default to be the description in flow yaml file. :type description: str :param tags: The tags of the flow to create. Default to be the tags in flow yaml file. :type tags: Dict[str, str] """ # validate the parameters azure_flow, flow_display_name, flow_type, kwargs = FlowOperations._validate_flow_creation_parameters( flow, display_name, type, **kwargs ) # upload to file share file_share_flow_path = self._resolve_flow_code_and_upload_to_file_share(flow=azure_flow) if not file_share_flow_path: raise FlowOperationError(f"File share path should not be empty, got {file_share_flow_path!r}.") # create flow to remote flow_definition_file_path = f"{file_share_flow_path}/{DAG_FILE_NAME}" rest_flow = self._create_remote_flow_via_file_share_path( flow_display_name=flow_display_name, flow_type=flow_type, flow_definition_file_path=flow_definition_file_path, **kwargs, ) result_flow = Flow._from_pf_service(rest_flow) flow_dict = result_flow._to_dict() print(f"Flow created successfully:\n{json.dumps(flow_dict, indent=4)}") return result_flow @staticmethod def _validate_flow_creation_parameters(source, flow_display_name=None, flow_type=None, **kwargs): """Validate the parameters for flow creation operation.""" # validate the source folder logger.info("Validating flow source.") if not Path(source, DAG_FILE_NAME).exists(): raise UserErrorException( f"Flow source must be a directory with flow definition yaml '{DAG_FILE_NAME}'. " f"Got {Path(source).resolve().as_posix()!r}." ) # validate flow source with flow schema logger.info("Validating flow schema.") flow_dict = FlowOperations._validate_flow_schema(source, flow_display_name, flow_type, **kwargs) logger.info("Validating flow creation parameters.") flow = load_flow(source) # if no flow name specified, use "flow name + timestamp" flow_display_name = flow_dict.get("display_name", None) if not flow_display_name: flow_display_name = f"{Path(source).name}-{datetime.now().strftime('%m-%d-%Y-%H-%M-%S')}" # if no flow type specified, use default flow type "standard" flow_type = flow_dict.get("type", None) if not flow_type: flow_type = FlowType.STANDARD # update description and tags to be the final value description = flow_dict.get("description", None) if isinstance(description, str): kwargs["description"] = description tags = flow_dict.get("tags", None) if tags: kwargs["tags"] = tags return flow, flow_display_name, flow_type, kwargs @staticmethod def _validate_flow_schema(source, display_name=None, type=None, **kwargs): """Validate the flow schema.""" from promptflow._sdk.entities._flow import ProtectedFlow params_override = copy.deepcopy(kwargs) if display_name is not None: params_override["display_name"] = display_name if type is not None: params_override["type"] = type flow_entity = ProtectedFlow.load(source=source, params_override=params_override) flow_entity._validate(raise_error=True) # raise error if validation failed flow_dict = flow_entity._dump_for_validation() return flow_dict def _resolve_flow_code_and_upload_to_file_share(self, flow: Flow, ignore_tools_json=False) -> str: remote_file_share_folder_name = f"{Path(flow.code).name}-{datetime.now().strftime('%m-%d-%Y-%H-%M-%S')}" ops = OperationOrchestrator(self._all_operations, self._operation_scope, self._operation_config) file_share_flow_path = "" logger.info("Building flow code.") with flow._build_code() as code: if code is None: raise FlowOperationError("Failed to build flow code.") # ignore flow.tools.json if needed (e.g. for flow run scenario) if ignore_tools_json: ignore_file = code._ignore_file if isinstance(ignore_file, PromptflowIgnoreFile): ignore_file._ignore_tools_json = ignore_tools_json else: raise FlowOperationError( message=f"Flow code should have PromptflowIgnoreFile, got {type(ignore_file)}" ) code.datastore = DEFAULT_STORAGE datastore_name = _get_datastore_name(datastore_name=DEFAULT_STORAGE) datastore_operation = ops._code_assets._datastore_operation datastore_info = get_datastore_info(datastore_operation, datastore_name) logger.debug("Creating storage client for uploading flow to file share.") storage_client = FlowFileStorageClient( credential=datastore_info["credential"], file_share_name=datastore_info["container_name"], account_url=datastore_info["account_url"], azure_cred=datastore_operation._credential, ) # set storage client to flow operation, can be used in test case self._storage_client = storage_client # check if the file share directory exists logger.debug("Checking if the file share directory exists.") if storage_client._check_file_share_directory_exist(remote_file_share_folder_name): raise FlowOperationError( f"Remote flow folder {remote_file_share_folder_name!r} already exists under " f"'{storage_client.file_share_prefix}'. Please change the flow folder name and try again." ) try: logger.info("Uploading flow directory to file share.") storage_client.upload_dir( source=code.path, dest=remote_file_share_folder_name, msg="test", ignore_file=code._ignore_file, show_progress=False, ) except Exception as e: raise FlowOperationError(f"Failed to upload flow to file share due to: {str(e)}.") from e file_share_flow_path = f"{storage_client.file_share_prefix}/{remote_file_share_folder_name}" logger.info(f"Successfully uploaded flow to file share path {file_share_flow_path!r}.") return file_share_flow_path def _create_remote_flow_via_file_share_path( self, flow_display_name, flow_type, flow_definition_file_path, **kwargs ): """Create a flow to remote from file share path.""" service_flow_type = CLIENT_FLOW_TYPE_2_SERVICE_FLOW_TYPE[flow_type] description = kwargs.get("description", None) tags = kwargs.get("tags", None) body = { "flow_name": flow_display_name, "flow_definition_file_path": flow_definition_file_path, "flow_type": service_flow_type, "description": description, "tags": tags, } rest_flow_result = self._service_caller.create_flow( subscription_id=self._operation_scope.subscription_id, resource_group_name=self._operation_scope.resource_group_name, workspace_name=self._operation_scope.workspace_name, body=body, ) return rest_flow_result def get(self, name: str) -> Flow: """Get a flow from azure. :param name: The name of the flow to get. :type name: str :return: The flow. :rtype: ~promptflow.azure.entities.Flow """ try: rest_flow = self._service_caller.get_flow( subscription_id=self._operation_scope.subscription_id, resource_group_name=self._operation_scope.resource_group_name, workspace_name=self._operation_scope.workspace_name, flow_id=name, experiment_id=self._workspace_id, # for flow operations, current experiment id is workspace id ) except HttpResponseError as e: if e.status_code == 404: raise FlowOperationError(f"Flow {name!r} not found.") from e else: raise FlowOperationError(f"Failed to get flow {name!r} due to: {str(e)}.") from e flow = Flow._from_pf_service(rest_flow) return flow @monitor_operation(activity_name="pfazure.flows.list", activity_type=ActivityType.PUBLICAPI) def list( self, max_results: int = MAX_LIST_CLI_RESULTS, flow_type: Optional[FlowType] = None, list_view_type: ListViewType = ListViewType.ACTIVE_ONLY, include_others: bool = False, **kwargs, ) -> List[Flow]: """List flows from azure. :param max_results: The max number of runs to return, defaults to 50, max is 100 :type max_results: int :param flow_type: The flow type, defaults to None, which means all flow types. Other supported flow types are ["standard", "evaluation", "chat"]. :type flow_type: Optional[FlowType] :param list_view_type: The list view type, defaults to ListViewType.ACTIVE_ONLY :type list_view_type: ListViewType :param include_others: Whether to list flows owned by other users in the remote workspace, defaults to False :type include_others: bool :return: The list of flows. :rtype: List[~promptflow.azure.entities.Flow] """ if not isinstance(max_results, int) or max_results < 1: raise FlowOperationError(f"'max_results' must be a positive integer, got {max_results!r}") normalized_flow_type = str(flow_type).lower() if flow_type is not None and normalized_flow_type not in FlowType.get_all_values(): raise FlowOperationError(f"'flow_type' must be one of {FlowType.get_all_values()}, got {flow_type!r}.") headers = self._service_caller._get_headers() if list_view_type == ListViewType.ACTIVE_ONLY: filter_archived = ["false"] elif list_view_type == ListViewType.ARCHIVED_ONLY: filter_archived = ["true"] elif list_view_type == ListViewType.ALL: filter_archived = ["true", "false"] else: raise FlowOperationError( f"Invalid list view type: {list_view_type!r}, expecting one of ['ActiveOnly', 'ArchivedOnly', 'All']" ) user_object_id, user_tenant_id = self._service_caller._get_user_identity_info() payload = { "filters": [ {"field": "type", "operator": "eq", "values": ["flows"]}, {"field": "annotations/isArchived", "operator": "eq", "values": filter_archived}, { "field": "properties/creationContext/createdBy/userTenantId", "operator": "eq", "values": [user_tenant_id], }, ], "freeTextSearch": "", "order": [{"direction": "Desc", "field": "properties/creationContext/createdTime"}], # index service can return 100 results at most "pageSize": min(max_results, 100), "skip": 0, "includeTotalResultCount": True, "searchBuilder": "AppendPrefix", } # add flow filter to only list flows from current user if not include_others: payload["filters"].append( { "field": "properties/creationContext/createdBy/userObjectId", "operator": "eq", "values": [user_object_id], } ) endpoint = self._index_service_endpoint_url url = endpoint + "/entities" response = requests.post(url, headers=headers, json=payload) if response.status_code == 200: entities = json.loads(response.text) flow_entities = entities["value"] else: raise FlowOperationError( f"Failed to get flows from index service. Code: {response.status_code}, text: {response.text}" ) # transform to flow instances flow_instances = [] for entity in flow_entities: flow = Flow._from_index_service(entity) flow_instances.append(flow) return flow_instances def _download(self, source, dest): # TODO: support download flow raise NotImplementedError("Not implemented yet") def _resolve_arm_id_or_upload_dependencies(self, flow: Flow, ignore_tools_json=False) -> None: ops = OperationOrchestrator(self._all_operations, self._operation_scope, self._operation_config) # resolve flow's code self._try_resolve_code_for_flow(flow=flow, ops=ops, ignore_tools_json=ignore_tools_json) @classmethod def _try_resolve_code_for_flow(cls, flow: Flow, ops: OperationOrchestrator, ignore_tools_json=False) -> None: if flow.path: # remote path if flow.path.startswith("azureml://datastores"): flow._code_uploaded = True return else: raise ValueError("Path is required for flow.") with flow._build_code() as code: if code is None: return if flow._code_uploaded: return # TODO(2917889): generate flow meta for eager flow if ignore_tools_json: ignore_file = code._ignore_file if isinstance(ignore_file, PromptflowIgnoreFile): ignore_file._ignore_tools_json = ignore_tools_json else: raise SystemErrorException( message=f"Flow code should have PromptflowIgnoreFile, got {type(ignore_file)}" ) # flow directory per file upload summary # as the upload logic locates in azure-ai-ml, we cannot touch during the upload # copy the logic here to print per file upload summary ignore_file = code._ignore_file upload_paths = [] source_path = Path(code.path).resolve() prefix = os.path.basename(source_path) + "/" for root, _, files in os.walk(source_path, followlinks=True): upload_paths += list( traverse_directory( root, files, prefix=prefix, ignore_file=ignore_file, ) ) ignore_files = code._ignore_file._get_ignore_list() for file_path in ignore_files: logger.debug(f"will ignore file: {file_path}...") for file_path, _ in upload_paths: logger.debug(f"will upload file: {file_path}...") code.datastore = WORKSPACE_LINKED_DATASTORE_NAME # NOTE: For flow directory upload, we prefer to upload it to the workspace linked datastore, # therefore we will directly use _check_and_upload_path, instead of v2 SDK public API # CodeOperations.create_or_update, as later one will upload the code asset to another # container in the storage account, which may fail with vnet for MT. # However, we might run into list secret permission error(especially in Heron workspace), # in this case, we will leverage v2 SDK public API, which has solution for Heron, # and request MT with the blob url; # refer to except block for more details. try: uploaded_code_asset, _ = _check_and_upload_path( artifact=code, asset_operations=ops._code_assets, artifact_type="Code", datastore_name=WORKSPACE_LINKED_DATASTORE_NAME, # actually not work at all show_progress=True, ) path = uploaded_code_asset.path path = path[path.find("LocalUpload") :] # path on container flow.code = path # azureml://datastores/workspaceblobstore/paths/<path-to-flow-dag-yaml> flow.path = SHORT_URI_FORMAT.format( WORKSPACE_LINKED_DATASTORE_NAME, (Path(path) / flow.path).as_posix() ) except HttpResponseError as e: # catch authorization error for list secret on datastore if "AuthorizationFailed" in str(e) and "datastores/listSecrets/action" in str(e): uploaded_code_asset = ops._code_assets.create_or_update(code) path = uploaded_code_asset.path path = path.replace(".blob.core.windows.net:443/", ".blob.core.windows.net/") # remove :443 port flow.code = path # https://<storage-account-name>.blob.core.windows.net/<container-name>/<path-to-flow-dag-yaml> flow.path = f"{path}/{flow.path}" else: raise flow._code_uploaded = True # region deprecated but keep for runtime test dependencies def _resolve_arm_id_or_upload_dependencies_to_file_share(self, flow: Flow) -> None: ops = OperationOrchestrator(self._all_operations, self._operation_scope, self._operation_config) # resolve flow's code self._try_resolve_code_for_flow_to_file_share(flow=flow, ops=ops) @classmethod def _try_resolve_code_for_flow_to_file_share(cls, flow: Flow, ops: OperationOrchestrator) -> None: from azure.ai.ml._utils._storage_utils import AzureMLDatastorePathUri from ._artifact_utilities import _check_and_upload_path if flow.path: if flow.path.startswith("azureml://datastores"): # remote path path_uri = AzureMLDatastorePathUri(flow.path) if path_uri.datastore != DEFAULT_STORAGE: raise ValueError(f"Only {DEFAULT_STORAGE} is supported as remote storage for now.") flow.path = path_uri.path flow._code_uploaded = True return else: raise ValueError("Path is required for flow.") with flow._build_code() as code: if code is None: return if flow._code_uploaded: return code.datastore = DEFAULT_STORAGE uploaded_code_asset = _check_and_upload_path( artifact=code, asset_operations=ops._code_assets, artifact_type="Code", show_progress=False, ) if "remote_path" in uploaded_code_asset: path = uploaded_code_asset["remote_path"] elif "remote path" in uploaded_code_asset: path = uploaded_code_asset["remote path"] flow.code = path flow.path = (Path(path) / flow.path).as_posix() flow._code_uploaded = True # endregion

promptflow/src/promptflow/promptflow/azure/operations/_flow_operations.py/0

{ "file_path": "promptflow/src/promptflow/promptflow/azure/operations/_flow_operations.py", "repo_id": "promptflow", "token_count": 10428 }

41

import base64 import filetype import hashlib from typing import Callable, Optional class PFBytes(bytes): """This class is used to represent a bytes object in PromptFlow. It has all the functionalities of a bytes object, and also has some additional methods to help with serialization and deserialization. """ def __new__(cls, value: bytes, *args, **kwargs): # Here we must only pass the value to the bytes constructor, # otherwise we will get a type error that the constructor doesn't take such args. # See https://docs.python.org/3/reference/datamodel.html#object.__new__ return super().__new__(cls, value) def __init__(self, value: bytes, mime_type: str, source_url: Optional[str] = None): # Here the first argument should also be "value", the same as __new__. # Otherwise we will get error when initialize the object. super().__init__() # Use this hash to identify this bytes. self._hash = hashlib.sha1(value).hexdigest()[:8] self._mime_type = mime_type.lower() self._source_url = source_url @property def source_url(self): return self._source_url def to_base64(self, with_type: bool = False, dict_type: bool = False): """Returns the base64 representation of the PFBytes.""" if with_type: if not dict_type: return f"data:{self._mime_type};base64," + base64.b64encode(self).decode("utf-8") return {f"data:{self._mime_type};base64": base64.b64encode(self).decode("utf-8")} return base64.b64encode(self).decode("utf-8") class Image(PFBytes): """This class is used to represent an image in PromptFlow. It is a subclass of ~promptflow.contracts.multimedia.PFBytes. """ def __init__(self, value: bytes, mime_type: str = None, source_url: Optional[str] = None): if mime_type is None: mime_type = filetype.guess_mime(value) if mime_type is None or not mime_type.startswith("image/"): mime_type = "image/*" return super().__init__(value, mime_type, source_url) def __str__(self): return f"Image({self._hash})" def __repr__(self) -> str: return f"Image({self._hash})" def serialize(self, encoder: Callable = None): """Serialize the image to a dictionary.""" if encoder is None: return self.__str__() return encoder(self)

promptflow/src/promptflow/promptflow/contracts/multimedia.py/0

{ "file_path": "promptflow/src/promptflow/promptflow/contracts/multimedia.py", "repo_id": "promptflow", "token_count": 977 }

42

import contextvars import multiprocessing import os import queue import signal import sys import threading import time from datetime import datetime from functools import partial from logging import INFO from multiprocessing import Manager, Queue from multiprocessing.pool import ThreadPool from typing import List, Optional, Union import psutil from promptflow._constants import LINE_NUMBER_KEY, LINE_TIMEOUT_SEC from promptflow._core._errors import ProcessPoolError, UnexpectedError from promptflow._core.operation_context import OperationContext from promptflow._core.run_tracker import RunTracker from promptflow._utils.dataclass_serializer import convert_eager_flow_output_to_dict from promptflow._utils.exception_utils import ExceptionPresenter from promptflow._utils.logger_utils import bulk_logger from promptflow._utils.multimedia_utils import _process_recursively, persist_multimedia_data from promptflow._utils.thread_utils import RepeatLogTimer from promptflow._utils.utils import get_int_env_var, log_progress, set_context from promptflow.contracts.multimedia import Image from promptflow.contracts.run_info import FlowRunInfo from promptflow.contracts.run_info import RunInfo as NodeRunInfo from promptflow.contracts.run_info import Status from promptflow.exceptions import ErrorTarget, PromptflowException from promptflow.executor._errors import ( BatchExecutionTimeoutError, LineExecutionTimeoutError, ProcessCrashError, ProcessInfoObtainedTimeout, ProcessTerminatedTimeout, ) from promptflow.executor._process_manager import ForkProcessManager, SpawnProcessManager from promptflow.executor._result import LineResult from promptflow.executor._script_executor import ScriptExecutor from promptflow.executor.flow_executor import DEFAULT_CONCURRENCY_BULK, FlowExecutor from promptflow.storage import AbstractRunStorage def signal_handler(signum, frame): signame = signal.Signals(signum).name bulk_logger.info("Execution stopping. Handling signal %s (%s)", signame, signum) try: process = psutil.Process(os.getpid()) bulk_logger.info("Successfully terminated process with pid %s", process.pid) process.terminate() except Exception: bulk_logger.warning("Error when handling execution stop signal", exc_info=True) finally: sys.exit(1) class QueueRunStorage(AbstractRunStorage): """This storage persists run info by putting it into a queue.""" def __init__(self, queue: Queue): self.queue = queue def persist_node_run(self, run_info: NodeRunInfo): self.queue.put(run_info) def persist_flow_run(self, run_info: FlowRunInfo): self.queue.put(run_info) def format_current_process_info(process_name, pid, line_number: int): return f"Process name({process_name})-Process id({pid})-Line number({line_number})" def log_process_status(process_name, pid, line_number: int, is_completed=False, is_failed=False): process_info = format_current_process_info(process_name, pid, line_number) if is_completed: bulk_logger.info(f"{process_info} completed.") elif is_failed: bulk_logger.info(f"{process_info} failed.") else: bulk_logger.info(f"{process_info} start execution.") class LineExecutionProcessPool: _DEFAULT_WORKER_COUNT = 4 _PROCESS_TERMINATED_TIMEOUT = 60 _PROCESS_INFO_OBTAINED_TIMEOUT = 60 def __init__( self, flow_executor: FlowExecutor, nlines, run_id, output_dir, batch_timeout_sec: Optional[int] = None, line_timeout_sec: Optional[int] = None, ): self._nlines = nlines self._run_id = run_id multiprocessing_start_method = os.environ.get("PF_BATCH_METHOD", multiprocessing.get_start_method()) sys_start_methods = multiprocessing.get_all_start_methods() if multiprocessing_start_method not in sys_start_methods: bulk_logger.warning( f"Failed to set start method to '{multiprocessing_start_method}', " f"start method {multiprocessing_start_method} is not in: {sys_start_methods}." ) bulk_logger.info(f"Set start method to default {multiprocessing.get_start_method()}.") multiprocessing_start_method = multiprocessing.get_start_method() use_fork = multiprocessing_start_method in ["fork", "forkserver"] self._flow_file = flow_executor._flow_file self._connections = flow_executor._connections self._working_dir = flow_executor._working_dir self._use_fork = use_fork if isinstance(flow_executor, ScriptExecutor): self._storage = flow_executor._storage else: self._storage = flow_executor._run_tracker._storage self._flow_id = flow_executor._flow_id self._log_interval = flow_executor._log_interval self._line_timeout_sec = line_timeout_sec or LINE_TIMEOUT_SEC self._batch_timeout_sec = batch_timeout_sec self._output_dir = output_dir self._flow_create_kwargs = { "flow_file": flow_executor._flow_file, "connections": flow_executor._connections, "working_dir": flow_executor._working_dir, "entry": flow_executor._entry, "line_timeout_sec": self._line_timeout_sec, "raise_ex": False, } def __enter__(self): manager = Manager() self._processing_idx = manager.dict() self._completed_idx = manager.dict() self._task_queue = Queue() self._n_process = self._determine_worker_count() # When using fork, we first spawn a sub process, the SemLock created in fork context (multiprocessing.Queue()） # can't used in a spawn context. Since spawn does not share memory, synchronization primitives created by # fork cannot be used directly. It will cause an error: "A SemLock created in a fork context is being # shared with a process in a spawn context. This is not supported". # So use multiprocessing.Manager().Queue() instead of multiprocessing.Queue(). # Manager().Queue() operates through a manager server process, which passes messages between different # processes without directly sharing memory state, which makes it safe to use in a spawn context. self._input_queues = [manager.Queue() for _ in range(self._n_process)] self._output_queues = [manager.Queue() for _ in range(self._n_process)] self._control_signal_queue = manager.Queue() self._process_info = manager.dict() # when using fork, we first create a process with spawn method to establish a clean environment # Then fork the subprocess in this environment to avoid some deadlock problems common_kwargs = { "input_queues": self._input_queues, "output_queues": self._output_queues, "process_info": self._process_info, "process_target_func": _process_wrapper, } if self._use_fork: # 1. Create input_queue, output_queue, control_signal_queue and _process_info in the main process. # 2. Pass the above queue/dict as parameters to spawn and fork processes to transfer information # between processes. self._processes_manager = ForkProcessManager( self._control_signal_queue, self._flow_create_kwargs, **common_kwargs, ) else: executor_creation_func = partial(FlowExecutor.create, **self._flow_create_kwargs) # 1. Create input_queue, output_queue, and _process_info in the main process. # 2. Spawn _n_process sub-process and pass the above queue/dict to these sub-process to transfer information # between main process and sub process. self._processes_manager = SpawnProcessManager(executor_creation_func, **common_kwargs) self._processes_manager.start_processes() self._processes_manager.ensure_healthy() monitor_pool = ThreadPool(self._n_process, initializer=set_context, initargs=(contextvars.copy_context(),)) self._monitor_pool = monitor_pool return self def __exit__(self, exc_type, exc_val, exc_tb): if self._monitor_pool is not None: self._monitor_pool.close() self._monitor_pool.join() def _get_process_info(self, index): start_time = time.time() while True: self._processes_manager.ensure_healthy() try: if time.time() - start_time > self._PROCESS_INFO_OBTAINED_TIMEOUT: raise ProcessInfoObtainedTimeout(self._PROCESS_INFO_OBTAINED_TIMEOUT) # Try to get process id and name from the process_info process_id = self._process_info[index].process_id process_name = self._process_info[index].process_name return (index, process_id, process_name) except KeyError: # If the process_info does not exist for the given index, it means the process have not ready yet, # try again. time.sleep(1) continue except Exception as e: raise Exception(f"Unexpected error occurred while get process info. Exception: {e}") def _ensure_process_terminated_within_timeout(self, process_id): start_time = time.time() while psutil.pid_exists(process_id): if time.time() - start_time > self._PROCESS_TERMINATED_TIMEOUT: raise ProcessTerminatedTimeout(self._PROCESS_TERMINATED_TIMEOUT) time.sleep(1) def _is_process_alive(self, process_id): return psutil.pid_exists(process_id) def _handle_output_queue_messages(self, output_queue: Queue, result_list): try: message = output_queue.get(timeout=1) if isinstance(message, LineResult): message = self._process_multimedia(message) result_list.append(message) return message elif isinstance(message, FlowRunInfo): self._storage.persist_flow_run(message) return message elif isinstance(message, NodeRunInfo): self._storage.persist_node_run(message) return message except queue.Empty: pass return None def _monitor_workers_and_process_tasks_in_thread( self, task_queue: Queue, result_list: List[LineResult], index: int, input_queue: Queue, output_queue: Queue, batch_start_time: datetime, ): index, process_id, process_name = self._get_process_info(index) # Entering the while loop requires two conditions: # 1. The task queue is not empty, meaning there are lines yet to be executed. # 2. The batch run has not reached the batch timeout limit. while not self._batch_timeout_expired(batch_start_time): self._processes_manager.ensure_healthy() try: # Get task from task_queue inputs, line_number, run_id = task_queue.get(timeout=1) except queue.Empty: break # Calculate the line timeout for the current line. line_timeout_sec = self._line_timeout_sec if self._batch_timeout_sec: remaining_execution_time = ( self._batch_timeout_sec - (datetime.utcnow() - batch_start_time).total_seconds() ) if remaining_execution_time <= 0: break line_timeout_sec = min(line_timeout_sec, remaining_execution_time) # Put task into input_queue args = (inputs, line_number, run_id, line_timeout_sec) input_queue.put(args) self._processing_idx[line_number] = format_current_process_info(process_name, process_id, line_number) log_process_status(process_name, process_id, line_number) start_time = datetime.utcnow() completed = False crashed = False returned_node_run_infos = {} # Responsible for checking the output queue messages and processing them within a specified timeout period. while not self._batch_timeout_expired(batch_start_time) and not self._line_timeout_expired(start_time): # Monitor process aliveness. crashed = not self._is_process_alive(process_id) if crashed: break # Handle output queue message. message = self._handle_output_queue_messages(output_queue, result_list) if isinstance(message, LineResult): completed = True break if isinstance(message, NodeRunInfo): returned_node_run_infos[message.node] = message # Handle line execution completed. if completed: self._completed_idx[line_number] = format_current_process_info(process_name, process_id, line_number) log_process_status(process_name, process_id, line_number, is_completed=True) # Handle line execution is not completed. else: ex = None # Handle process crashed. if crashed: bulk_logger.warning(f"Process crashed while executing line {line_number}.") ex = ProcessCrashError(line_number) # Handle line execution timeout. elif self._line_timeout_expired(start_time): bulk_logger.warning(f"Line {line_number} timeout after {self._line_timeout_sec} seconds.") ex = LineExecutionTimeoutError(line_number, self._line_timeout_sec) # Handle batch execution timeout. elif self._batch_timeout_expired(batch_start_time): bulk_logger.warning( f"Line {line_number} execution terminated due to the total " f"batch run exceeding the batch timeout ({self._batch_timeout_sec}s)." ) ex = BatchExecutionTimeoutError(line_number, self._batch_timeout_sec) else: # This branch should not be reached, add this warning for the case. msg = f"Unexpected error occurred while monitoring line execution at line {line_number}." bulk_logger.warning(msg) ex = UnexpectedError(msg) result = self._generate_line_result_for_exception( inputs, run_id, line_number, self._flow_id, start_time, ex, returned_node_run_infos, ) result_list.append(result) self._completed_idx[line_number] = format_current_process_info(process_name, process_id, line_number) log_process_status(process_name, process_id, line_number, is_failed=True) # If there are still tasks in the task_queue and the batch run does not exceed the batch timeout, # restart a new process to execute the task. run_finished = task_queue.empty() or self._batch_timeout_expired(batch_start_time) if not run_finished: self._processes_manager.restart_process(index) # We need to ensure the process has been killed before continuing to execute. # Otherwise the process will receive new task, and during the execution, the process # is killed, which will result in the 'ProcessCrashError'. self._ensure_process_terminated_within_timeout(process_id) index, process_id, process_name = self._get_process_info(index) self._processing_idx.pop(line_number) # End the process when the batch timeout is exceeded or when all lines have been executed. self._processes_manager.end_process(index) # In fork mode, the main process and the sub spawn process communicate through _process_info. # We need to ensure the process has been killed before returning. Otherwise, it may cause # the main process have exited but the spawn process is still alive. # At this time, a connection error will be reported. self._ensure_process_terminated_within_timeout(process_id) def _batch_timeout_expired(self, start_time: datetime) -> bool: if self._batch_timeout_sec is None: return False return (datetime.utcnow() - start_time).total_seconds() > self._batch_timeout_sec + 10 def _line_timeout_expired(self, start_time: datetime) -> bool: # Here we add more seconds because of the following reasons: # 1. At the last second, there would be several timeout message from exec_line. # 2. It may take time to create worker so actual timeout time may be longer. return (datetime.utcnow() - start_time).total_seconds() > self._line_timeout_sec + 10 def _process_multimedia(self, result: LineResult) -> LineResult: """Replace multimedia data in line result with string place holder to prevent OOM and persist multimedia data in output when batch running.""" if not self._output_dir: return result self._process_multimedia_in_flow_run(result.run_info) for node_name, node_run_info in result.node_run_infos.items(): result.node_run_infos[node_name] = self._process_multimedia_in_node_run(node_run_info) result.output = persist_multimedia_data(result.output, self._output_dir) return result def _process_multimedia_in_run_info(self, run_info: Union[FlowRunInfo, NodeRunInfo]): # Persist and convert images in inputs to path dictionaries. # This replaces any image objects with their corresponding file path dictionaries. if run_info.inputs: run_info.inputs = self._persist_and_convert_images_to_path_dicts(run_info.inputs) # Persist and convert images in output to path dictionaries. # This replaces any image objects with their corresponding file path dictionaries. if run_info.output: serialized_output = self._persist_and_convert_images_to_path_dicts(run_info.output) run_info.output = serialized_output run_info.result = None # Persist and convert images in api_calls to path dictionaries. # The `inplace=True` parameter is used here to ensure that the original list structure holding generator outputs # is maintained. This allows us to keep tracking the list as it dynamically changes when the generator is # consumed. It is crucial to process the api_calls list in place to avoid losing the reference to the list that # holds the generator items, which is essential for tracing generator execution. if run_info.api_calls: run_info.api_calls = self._persist_and_convert_images_to_path_dicts(run_info.api_calls, inplace=True) return run_info def _process_multimedia_in_flow_run(self, run_info: FlowRunInfo): self._process_multimedia_in_run_info(run_info) def _process_multimedia_in_node_run(self, run_info: NodeRunInfo): run_info = self._process_multimedia_in_run_info(run_info) return run_info def _persist_and_convert_images_to_path_dicts(self, value, inplace=False): serialization_funcs = {Image: partial(Image.serialize, **{"encoder": None})} return _process_recursively(value, process_funcs=serialization_funcs, inplace=inplace) def _generate_line_result_for_exception( self, inputs, run_id, line_number, flow_id, start_time, ex, node_run_infos={}, ) -> LineResult: bulk_logger.error(f"Line {line_number}, Process {os.getpid()} failed with exception: {ex}") run_info = FlowRunInfo( run_id=f"{run_id}_{line_number}", status=Status.Failed, error=ExceptionPresenter.create(ex).to_dict(include_debug_info=True), inputs=inputs, output=None, metrics=None, request=None, parent_run_id=run_id, root_run_id=run_id, source_run_id=None, flow_id=flow_id, start_time=start_time, end_time=datetime.utcnow(), index=line_number, ) result = LineResult( output={}, aggregation_inputs={}, run_info=run_info, node_run_infos=node_run_infos, ) # TODO: There is a corner case that the run info is persisted in the subprocess when timeouted, # while we also persist the run info here. This may cause duplicate run info in the storage. # We need to find a way to avoid this. self._storage.persist_flow_run(result.run_info) return result def run(self, batch_inputs): for index, inputs in batch_inputs: self._task_queue.put( ( inputs, index, self._run_id, ) ) result_list = [] run_start_time = datetime.utcnow() with RepeatLogTimer( interval_seconds=self._log_interval, logger=bulk_logger, level=INFO, log_message_function=self._generate_thread_status_messages, args=( self._monitor_pool, self._nlines, ), ): try: batch_start_time = datetime.utcnow() args_list = [ ( self._task_queue, # Shared task queue for all sub processes to read the input data. result_list, # Line result list of the batch run. i, # Index of the sub process. # Specific input queue for sub process, used to send input data to it. self._input_queues[i], # Specific output queue for the sub process, used to receive results from it. self._output_queues[i], batch_start_time, ) for i in range(self._n_process) ] # The variable 'async_result' here is not the actual result of the batch run # but an AsyncResult object that can be used to check if the execution are finished # The actual results of the batch run are stored in 'result_list' # Create _n_process monitoring threads, mainly used to assign tasks and receive line result. # When task_queue is empty, end the process. # When line execution timeout or process crash, restart the process. async_result = self._monitor_pool.starmap_async( self._monitor_workers_and_process_tasks_in_thread, args_list ) try: # Only log when the number of results changes to avoid duplicate logging. last_log_count = 0 # Wait for batch run to complete or KeyboardInterrupt while not async_result.ready(): current_result_count = len(result_list) if current_result_count != last_log_count: log_progress( run_start_time=run_start_time, logger=bulk_logger, count=len(result_list), total_count=self._nlines, ) last_log_count = current_result_count # Check every 1 second async_result.wait(1) # To ensure exceptions in thread-pool calls are propagated to the main process for proper handling # The exceptions raised will be re-raised by the get() method. # Related link: # https://docs.python.org/3/library/multiprocessing.html#multiprocessing.pool.AsyncResult async_result.get() except KeyboardInterrupt: raise except PromptflowException: raise except Exception as e: bulk_logger.error(f"ProcessPool failed with exception: {e}") raise ProcessPoolError( message_format=f"ProcessPool failed with exception: {e}", target=ErrorTarget.EXECUTOR, ) from e return result_list def _generate_thread_status_messages(self, pool: ThreadPool, total_count: int): msgs = [] active_threads = sum(thread.is_alive() for thread in pool._pool) msgs.append(f"[Process Pool] [Active processes: {active_threads} / {len(pool._pool)}]") processing_lines_copy = self._processing_idx.copy() completed_lines_copy = self._completed_idx.copy() msgs.append( f"[Lines] [Finished: {len(completed_lines_copy)}] [Processing: {len(processing_lines_copy)}] " f"[Pending: {total_count - len(processing_lines_copy) - len(completed_lines_copy)}]" ) lines = [] for idx, thread_name in sorted(processing_lines_copy.items()): lines.append(f"line {idx} ({thread_name})") if len(lines) > 0: msgs.append("Processing Lines: " + ", ".join(lines) + ".") return msgs def _determine_worker_count(self): worker_count = get_int_env_var("PF_WORKER_COUNT") # Starting a new process in non-fork mode requires to allocate memory. Calculate the maximum number of processes # based on available memory to avoid memory bursting. estimated_available_worker_count = get_available_max_worker_count() if not self._use_fork else None # If the environment variable PF_WORKER_COUNT exists and valid, use the value as the worker_count. if worker_count is not None and worker_count > 0: self._log_set_worker_count(worker_count, estimated_available_worker_count) return worker_count # If the environment variable PF_WORKER_COUNT is not set or invalid, take the minimum value among the # factors: default_worker_count, row_count and estimated_worker_count_based_on_memory_usage factors = { "default_worker_count": self._DEFAULT_WORKER_COUNT, "row_count": self._nlines, "estimated_worker_count_based_on_memory_usage": estimated_available_worker_count, } valid_factors = {k: v for k, v in factors.items() if v is not None and v > 0} # Take the minimum value as the result worker_count = min(valid_factors.values()) bulk_logger.info( f"Set process count to {worker_count} by taking the minimum value among the factors of {valid_factors}." ) return worker_count def _log_set_worker_count(self, worker_count, estimated_available_worker_count): bulk_logger.info(f"Set process count to {worker_count} with the environment variable 'PF_WORKER_COUNT'.") if estimated_available_worker_count is not None and estimated_available_worker_count < worker_count: bulk_logger.warning( f"The current process count ({worker_count}) is larger than recommended process count " f"({estimated_available_worker_count}) that estimated by system available memory. This may " f"cause memory exhaustion" ) def _exec_line( executor: FlowExecutor, output_queue: Queue, *, inputs: dict, run_id: str, index: int, line_timeout_sec: int ): try: line_result = executor.exec_line( inputs=inputs, run_id=run_id, index=index, node_concurrency=DEFAULT_CONCURRENCY_BULK, line_timeout_sec=line_timeout_sec, ) if line_result is not None: # For eager flow, the output may be a dataclass which is not picklable, we need to convert it to dict. if not isinstance(line_result.output, dict): line_result.output = convert_eager_flow_output_to_dict(line_result.output) line_result.output.pop(LINE_NUMBER_KEY, None) # TODO: Put serialized line result into queue to catch serialization error beforehand. # Otherwise it might cause the process to hang, e.g, line failed because output is not seralizable. if line_result is not None and line_result.run_info.status == Status.Failed: line_result.output = {} return line_result except Exception as e: bulk_logger.error(f"Line {index}, Process {os.getpid()} failed with exception: {e}") flow_id = executor._flow_id line_run_id = run_id if index is None else f"{run_id}_{index}" # If line execution failed before start, there is no flow information in the run_tracker. # So we call start_flow_run before handling exception to make sure the run_tracker has flow info. if isinstance(executor, ScriptExecutor): run_tracker = RunTracker(executor._storage) else: run_tracker = executor._run_tracker run_tracker.start_flow_run(flow_id, run_id, line_run_id, run_id) run_info = run_tracker.end_run(f"{run_id}_{index}", ex=e) output_queue.put(run_info) result = LineResult( output={}, aggregation_inputs={}, run_info=run_info, node_run_infos={}, ) return result def _process_wrapper( executor_creation_func, input_queue: Queue, output_queue: Queue, log_context_initialization_func, operation_contexts_dict: dict, ): if threading.current_thread() is threading.main_thread(): signal.signal(signal.SIGINT, signal_handler) else: bulk_logger.info("Current thread is not main thread, skip signal handler registration in batch process pool.") OperationContext.get_instance().update(operation_contexts_dict) # Update the operation context for the new process. if log_context_initialization_func: with log_context_initialization_func(): exec_line_for_queue(executor_creation_func, input_queue, output_queue) else: exec_line_for_queue(executor_creation_func, input_queue, output_queue) def create_executor_fork(*, flow_executor: FlowExecutor, storage: AbstractRunStorage): if isinstance(flow_executor, ScriptExecutor): return ScriptExecutor( flow_file=flow_executor._flow_file, entry=flow_executor._entry, connections=flow_executor._connections, working_dir=flow_executor._working_dir, storage=storage, ) else: run_tracker = RunTracker(run_storage=storage) return FlowExecutor( flow=flow_executor._flow, connections=flow_executor._connections, run_tracker=run_tracker, cache_manager=flow_executor._cache_manager, loaded_tools=flow_executor._loaded_tools, raise_ex=False, line_timeout_sec=flow_executor._line_timeout_sec, ) def exec_line_for_queue(executor_creation_func, input_queue: Queue, output_queue: Queue): run_storage = QueueRunStorage(output_queue) executor: FlowExecutor = executor_creation_func(storage=run_storage) while True: try: inputs, line_number, run_id, line_timeout_sec = input_queue.get(timeout=1) result = _exec_line( executor=executor, output_queue=output_queue, inputs=inputs, run_id=run_id, index=line_number, line_timeout_sec=line_timeout_sec, ) output_queue.put(result) except queue.Empty: # Do nothing until the input_queue have content or process is killed # TODO: Exit the process more gracefully. pass def get_available_max_worker_count(): pid = os.getpid() mem_info = psutil.virtual_memory() available_memory = mem_info.available / (1024 * 1024) # in MB process = psutil.Process(pid) process_memory_info = process.memory_info() process_memory = process_memory_info.rss / (1024 * 1024) # in MB estimated_available_worker_count = int(available_memory // process_memory) if estimated_available_worker_count < 1: # TODO: For the case of vector db, Optimize execution logic # 1. Let the main process not consume memory because it does not actually invoke # 2. When the degree of parallelism is 1, main process executes the task directly and not # create the child process bulk_logger.warning( f"Current system's available memory is {available_memory}MB, less than the memory " f"{process_memory}MB required by the process. The maximum available worker count is 1." ) estimated_available_worker_count = 1 else: bulk_logger.info( f"Current system's available memory is {available_memory}MB, " f"memory consumption of current process is {process_memory}MB, " f"estimated available worker count is {available_memory}/{process_memory} " f"= {estimated_available_worker_count}" ) return estimated_available_worker_count

promptflow/src/promptflow/promptflow/executor/_line_execution_process_pool.py/0

{ "file_path": "promptflow/src/promptflow/promptflow/executor/_line_execution_process_pool.py", "repo_id": "promptflow", "token_count": 14550 }

43

[tool.black] line-length = 120 [tool.pytest.ini_options] markers = [ "sdk_test", "cli_test", "unittest", "e2etest", "flaky", "endpointtest", "mt_endpointtest", ] [tool.coverage.run] omit = [ # omit anything in a _restclient directory anywhere "*/_restclient/*", ]

promptflow/src/promptflow/pyproject.toml/0

{ "file_path": "promptflow/src/promptflow/pyproject.toml", "repo_id": "promptflow", "token_count": 139 }

44

import json from pathlib import Path from tempfile import mkdtemp import pytest from promptflow._core._errors import FlowOutputUnserializable, InvalidSource from promptflow._core.tools_manager import APINotFound from promptflow._sdk._constants import DAG_FILE_NAME from promptflow._utils.utils import dump_list_to_jsonl from promptflow.batch import BatchEngine from promptflow.contracts._errors import FailedToImportModule from promptflow.executor import FlowExecutor from promptflow.executor._errors import ( ConnectionNotFound, DuplicateNodeName, EmptyOutputReference, InputNotFound, InputReferenceNotFound, InputTypeError, InvalidConnectionType, NodeCircularDependency, NodeInputValidationError, NodeReferenceNotFound, OutputReferenceNotFound, ResolveToolError, SingleNodeValidationError, ) from ..utils import FLOW_ROOT, WRONG_FLOW_ROOT, get_flow_folder, get_flow_inputs_file, get_yaml_file @pytest.mark.usefixtures("use_secrets_config_file", "dev_connections") @pytest.mark.e2etest class TestValidation: @pytest.mark.parametrize( "flow_folder, yml_file, error_class, inner_class, error_msg", [ ( "flow_llm_with_wrong_conn", "flow.dag.yaml", ResolveToolError, InvalidConnectionType, ( "Tool load failed in 'wrong_llm': " "(InvalidConnectionType) Connection type CustomConnection is not supported for LLM." ), ), ( "nodes_names_duplicated", "flow.dag.yaml", DuplicateNodeName, None, ( "Invalid node definitions found in the flow graph. Node with name 'stringify_num' appears more " "than once in the node definitions in your flow, which is not allowed. To " "address this issue, please review your flow and either rename or remove " "nodes with identical names." ), ), ( "source_file_missing", "flow.dag.jinja.yaml", ResolveToolError, InvalidSource, ( "Tool load failed in 'summarize_text_content': (InvalidSource) " "Node source path 'summarize_text_content__variant_1.jinja2' is invalid on node " "'summarize_text_content'." ), ), ( "node_reference_not_found", "flow.dag.yaml", NodeReferenceNotFound, None, ( "Invalid node definitions found in the flow graph. Node 'divide_num_2' references a non-existent " "node 'divide_num_3' in your flow. Please review your flow to ensure that the " "node name is accurately specified." ), ), ( "node_circular_dependency", "flow.dag.yaml", NodeCircularDependency, None, ( "Invalid node definitions found in the flow graph. Node circular dependency has been detected " "among the nodes in your flow. Kindly review the reference relationships for " "the nodes ['divide_num', 'divide_num_1', 'divide_num_2'] and resolve the " "circular reference issue in the flow." ), ), ( "flow_input_reference_invalid", "flow.dag.yaml", InputReferenceNotFound, None, ( "Invalid node definitions found in the flow graph. Node 'divide_num' references flow input 'num_1' " "which is not defined in your flow. To resolve this issue, please review your " "flow, ensuring that you either add the missing flow inputs or adjust node " "reference to the correct flow input." ), ), ( "flow_output_reference_invalid", "flow.dag.yaml", EmptyOutputReference, None, ( "The output 'content' for flow is incorrect. The reference is not specified for the output " "'content' in the flow. To rectify this, ensure that you accurately specify " "the reference in the flow." ), ), ( "outputs_reference_not_valid", "flow.dag.yaml", OutputReferenceNotFound, None, ( "The output 'content' for flow is incorrect. The output 'content' references non-existent " "node 'another_stringify_num' in your flow. To resolve this issue, please " "carefully review your flow and correct the reference definition for the " "output in question." ), ), ( "outputs_with_invalid_flow_inputs_ref", "flow.dag.yaml", OutputReferenceNotFound, None, ( "The output 'num' for flow is incorrect. The output 'num' references non-existent flow " "input 'num11' in your flow. Please carefully review your flow and correct " "the reference definition for the output in question." ), ), ], ) def test_executor_create_failure_type_and_message( self, flow_folder, yml_file, error_class, inner_class, error_msg, dev_connections ): with pytest.raises(error_class) as exc_info: FlowExecutor.create(get_yaml_file(flow_folder, WRONG_FLOW_ROOT, yml_file), dev_connections) if isinstance(exc_info.value, ResolveToolError): assert isinstance(exc_info.value.inner_exception, inner_class) assert error_msg == exc_info.value.message @pytest.mark.parametrize( "flow_folder, yml_file, error_class, inner_class", [ ("source_file_missing", "flow.dag.python.yaml", ResolveToolError, InvalidSource), ], ) def test_executor_create_failure_type(self, flow_folder, yml_file, error_class, inner_class, dev_connections): with pytest.raises(error_class) as e: FlowExecutor.create(get_yaml_file(flow_folder, WRONG_FLOW_ROOT, yml_file), dev_connections) if isinstance(e.value, ResolveToolError): assert isinstance(e.value.inner_exception, inner_class) @pytest.mark.parametrize( "ordered_flow_folder, unordered_flow_folder", [ ("web_classification_no_variants", "web_classification_no_variants_unordered"), ], ) def test_node_topology_in_order(self, ordered_flow_folder, unordered_flow_folder, dev_connections): ordered_executor = FlowExecutor.create(get_yaml_file(ordered_flow_folder), dev_connections) unordered_executor = FlowExecutor.create(get_yaml_file(unordered_flow_folder), dev_connections) for node1, node2 in zip(ordered_executor._flow.nodes, unordered_executor._flow.nodes): assert node1.name == node2.name @pytest.mark.parametrize( "flow_folder, error_class, inner_class", [ ("invalid_connection", ResolveToolError, ConnectionNotFound), ("tool_type_missing", ResolveToolError, NotImplementedError), ("wrong_module", FailedToImportModule, None), ("wrong_api", ResolveToolError, APINotFound), ("wrong_provider", ResolveToolError, APINotFound), ], ) def test_invalid_flow_dag(self, flow_folder, error_class, inner_class, dev_connections): with pytest.raises(error_class) as e: FlowExecutor.create(get_yaml_file(flow_folder, WRONG_FLOW_ROOT), dev_connections) if isinstance(e.value, ResolveToolError): assert isinstance(e.value.inner_exception, inner_class) @pytest.mark.parametrize( "flow_folder, line_input, error_class", [ ("simple_flow_with_python_tool", {"num11": "22"}, InputNotFound), ("simple_flow_with_python_tool", {"num": "hello"}, InputTypeError), ("python_tool_with_simple_image_without_default", {}, InputNotFound), ], ) def test_flow_run_input_type_invalid(self, flow_folder, line_input, error_class, dev_connections): # Flow run - the input is from get_partial_line_inputs() executor = FlowExecutor.create(get_yaml_file(flow_folder, FLOW_ROOT), dev_connections) with pytest.raises(error_class): executor.exec_line(line_input) @pytest.mark.parametrize( "flow_folder, line_input, error_class, error_msg", [ ( "flow_output_unserializable", {"num": "22"}, FlowOutputUnserializable, ( "The output 'content' for flow is incorrect. The output value is not JSON serializable. " "JSON dump failed: (TypeError) Object of type UnserializableClass is not JSON serializable. " "Please verify your flow output and make sure the value serializable." ), ), ], ) def test_flow_run_execution_errors(self, flow_folder, line_input, error_class, error_msg, dev_connections): executor = FlowExecutor.create(get_yaml_file(flow_folder, WRONG_FLOW_ROOT), dev_connections) # For now, there exception is designed to be swallowed in executor. But Run Info would have the error details res = executor.exec_line(line_input) assert error_msg == res.run_info.error["message"] @pytest.mark.parametrize( "flow_folder, inputs_mapping, error_message, error_class", [ ( "simple_flow_with_python_tool", {"num": "${data.num}"}, ( "The input for flow is incorrect. The value for flow input 'num' in line 0 of input data does not " "match the expected type 'int'. Please change flow input type or adjust the input value in " "your input data." ), "InputTypeError", ), ], ) def test_batch_run_input_type_invalid( self, flow_folder, inputs_mapping, error_message, error_class, dev_connections ): # Bulk run - the input is from sample.json batch_engine = BatchEngine( get_yaml_file(flow_folder), get_flow_folder(flow_folder), connections=dev_connections ) input_dirs = {"data": get_flow_inputs_file(flow_folder)} output_dir = Path(mkdtemp()) batch_results = batch_engine.run(input_dirs, inputs_mapping, output_dir) assert error_message in str( batch_results.error_summary.error_list[0].error ), f"Expected message {error_message} but got {str(batch_results.error_summary.error_list[0].error)}" assert error_class in str( batch_results.error_summary.error_list[0].error ), f"Expected message {error_class} but got {str(batch_results.error_summary.error_list[0].error)}" @pytest.mark.parametrize( "path_root, flow_folder, node_name, line_input, error_class, error_msg", [ ( FLOW_ROOT, "simple_flow_with_python_tool", "divide_num", {"num11": "22"}, InputNotFound, ( "The input for node is incorrect. Node input 'num' is not found in input data " "for node 'divide_num'. Please verify the inputs data for the node." ), ), ( FLOW_ROOT, "simple_flow_with_python_tool", "divide_num", {"num": "hello"}, InputTypeError, ( "The input for node is incorrect. Value for input 'num' of node 'divide_num' " "is not type 'int'. Please review and rectify the input data." ), ), ( WRONG_FLOW_ROOT, "flow_input_reference_invalid", "divide_num", {"num": "22"}, InputNotFound, ( "The input for node is incorrect. Node input 'num_1' is not found from flow " "inputs of node 'divide_num'. Please review the node definition in your flow." ), ), ( FLOW_ROOT, "simple_flow_with_python_tool", "bad_node_name", {"num": "22"}, SingleNodeValidationError, ( "Validation failed when attempting to execute the node. Node 'bad_node_name' is not found in flow " "'flow.dag.yaml'. Please change node name or correct the flow file." ), ), ( WRONG_FLOW_ROOT, "node_missing_type_or_source", "divide_num", {"num": "22"}, SingleNodeValidationError, ( "Validation failed when attempting to execute the node. Properties 'source' or 'type' are not " "specified for Node 'divide_num' in flow 'flow.dag.yaml'. Please make sure " "these properties are in place and try again." ), ), ], ) def test_single_node_input_type_invalid( self, path_root: str, flow_folder, node_name, line_input, error_class, error_msg, dev_connections ): # Single Node run - the inputs are from flow_inputs + dependency_nodes_outputs with pytest.raises(error_class) as exe_info: FlowExecutor.load_and_exec_node( flow_file=DAG_FILE_NAME, node_name=node_name, flow_inputs=line_input, dependency_nodes_outputs={}, connections=dev_connections, working_dir=Path(path_root) / flow_folder, raise_ex=True, ) assert error_msg == exe_info.value.message @pytest.mark.parametrize( "flow_folder, msg", [ ( "prompt_tool_with_duplicated_inputs", "Invalid inputs {'template'} in prompt template of node prompt_tool_with_duplicated_inputs. " "These inputs are duplicated with the reserved parameters of prompt tool.", ), ( "llm_tool_with_duplicated_inputs", "Invalid inputs {'prompt'} in prompt template of node llm_tool_with_duplicated_inputs. " "These inputs are duplicated with the parameters of AzureOpenAI.completion.", ), ], ) def test_flow_run_with_duplicated_inputs(self, flow_folder, msg, dev_connections): with pytest.raises(ResolveToolError, match=msg) as e: FlowExecutor.create(get_yaml_file(flow_folder, FLOW_ROOT), dev_connections) assert isinstance(e.value.inner_exception, NodeInputValidationError) @pytest.mark.parametrize( "flow_folder, batch_input, raise_on_line_failure, error_class", [ ("simple_flow_with_python_tool", [{"num": "hello"}], True, Exception), ("simple_flow_with_python_tool", [{"num": "hello"}], False, InputTypeError), ("simple_flow_with_python_tool", [{"num": "22"}], True, None), ("simple_flow_with_python_tool", [{"num": "22"}], False, None), ], ) def test_batch_run_raise_on_line_failure( self, flow_folder, batch_input, raise_on_line_failure, error_class, dev_connections ): # Bulk run - the input is from sample.json batch_engine = BatchEngine( get_yaml_file(flow_folder), get_flow_folder(flow_folder), connections=dev_connections ) # prepare input file and output dir input_file = Path(mkdtemp()) / "inputs.jsonl" dump_list_to_jsonl(input_file, batch_input) input_dirs = {"data": input_file} output_dir = Path(mkdtemp()) inputs_mapping = {"num": "${data.num}"} if error_class is None: batch_result = batch_engine.run( input_dirs, inputs_mapping, output_dir, raise_on_line_failure=raise_on_line_failure ) assert batch_result.total_lines == 1 assert batch_result.completed_lines == 1 assert batch_result.error_summary.error_list == [] else: if raise_on_line_failure: with pytest.raises(error_class): batch_engine.run( input_dirs, inputs_mapping, output_dir, raise_on_line_failure=raise_on_line_failure ) else: batch_result = batch_engine.run( input_dirs, inputs_mapping, output_dir, raise_on_line_failure=raise_on_line_failure ) assert batch_result.total_lines == 1 assert batch_result.failed_lines == 1 assert error_class.__name__ in json.dumps(batch_result.error_summary.error_list[0].error)

promptflow/src/promptflow/tests/executor/e2etests/test_executor_validation.py/0

{ "file_path": "promptflow/src/promptflow/tests/executor/e2etests/test_executor_validation.py", "repo_id": "promptflow", "token_count": 8603 }

45

promptflow/src/promptflow/tests/executor/package_tools/custom_llm_tool_with_duplicated_inputs/prompt_with_duplicated_inputs.jinja2/0

{ "file_path": "promptflow/src/promptflow/tests/executor/package_tools/custom_llm_tool_with_duplicated_inputs/prompt_with_duplicated_inputs.jinja2", "repo_id": "promptflow", "token_count": 4 }

46

import inspect import pytest from promptflow import tool from promptflow._core.tool import InputSetting, ToolType from promptflow._core.tracer import Tracer, TraceType from promptflow.exceptions import UserErrorException @tool def decorated_without_parentheses(a: int): return a @tool() def decorated_with_parentheses(a: int): return a @tool async def decorated_without_parentheses_async(a: int): return a @tool() async def decorated_with_parentheses_async(a: int): return a @tool( name="tool_with_attributes", description="Sample tool with a lot of attributes", type=ToolType.LLM, input_settings=InputSetting(), streaming_option_parameter="stream", extra_a="a", extra_b="b", ) def tool_with_attributes(stream: bool, a: int, b: int): return stream, a, b @pytest.mark.unittest class TestTool: """This class tests the `tool` decorator.""" @pytest.mark.asyncio @pytest.mark.parametrize( "func", [ decorated_with_parentheses, decorated_without_parentheses, decorated_with_parentheses_async, decorated_without_parentheses_async, ], ) async def test_traces_are_created_correctly(self, func): Tracer.start_tracing("test_run_id") if inspect.iscoroutinefunction(func): result = await func(1) else: result = func(1) assert result == 1 traces = Tracer.end_tracing() assert len(traces) == 1 trace = traces[0] assert trace["name"] == func.__qualname__ assert trace["type"] == TraceType.TOOL assert trace["inputs"] == {"a": 1} assert trace["output"] == 1 assert trace["error"] is None assert trace["children"] == [] assert isinstance(trace["start_time"], float) assert isinstance(trace["end_time"], float) def test_attributes_are_set_to_the_tool_function(self): stream, a, b = tool_with_attributes(True, 1, 2) # Check the results are as expected assert stream is True assert a == 1 assert b == 2 # Check the attributes are set to the function assert getattr(tool_with_attributes, "__tool") is None assert getattr(tool_with_attributes, "__name") == "tool_with_attributes" assert getattr(tool_with_attributes, "__description") == "Sample tool with a lot of attributes" assert getattr(tool_with_attributes, "__type") == ToolType.LLM assert getattr(tool_with_attributes, "__input_settings") == InputSetting() assert getattr(tool_with_attributes, "__extra_info") == {"extra_a": "a", "extra_b": "b"} assert getattr(tool_with_attributes, "_streaming_option_parameter") == "stream" def test_invalid_tool_type_should_raise_error(self): with pytest.raises(UserErrorException, match="Tool type invalid_type is not supported yet."): @tool(type="invalid_type") def invalid_tool_type(): pass

promptflow/src/promptflow/tests/executor/unittests/_core/test_tool.py/0

{ "file_path": "promptflow/src/promptflow/tests/executor/unittests/_core/test_tool.py", "repo_id": "promptflow", "token_count": 1222 }

47

import pytest import os from unittest.mock import patch from datetime import datetime from promptflow._utils.utils import is_json_serializable, get_int_env_var, log_progress class MyObj: pass @pytest.mark.unittest class TestUtils: @pytest.mark.parametrize("value, expected_res", [(None, True), (1, True), ("", True), (MyObj(), False)]) def test_is_json_serializable(self, value, expected_res): assert is_json_serializable(value) == expected_res @pytest.mark.parametrize( "env_var, env_value, default_value, expected_result", [ ("TEST_VAR", "10", None, 10), # Valid integer string ("TEST_VAR", "invalid", None, None), # Invalid integer strings ("TEST_VAR", None, 5, 5), # Environment variable does not exist ("TEST_VAR", "10", 5, 10), # Valid integer string with a default value ("TEST_VAR", "invalid", 5, 5), # Invalid integer string with a default value ]) def test_get_int_env_var(self, env_var, env_value, default_value, expected_result): with patch.dict(os.environ, {env_var: env_value} if env_value is not None else {}): assert get_int_env_var(env_var, default_value) == expected_result @pytest.mark.parametrize( "env_var, env_value, expected_result", [ ("TEST_VAR", "10", 10), # Valid integer string ("TEST_VAR", "invalid", None), # Invalid integer strings ("TEST_VAR", None, None), # Environment variable does not exist ]) def test_get_int_env_var_without_default_vaue(self, env_var, env_value, expected_result): with patch.dict(os.environ, {env_var: env_value} if env_value is not None else {}): assert get_int_env_var(env_var) == expected_result @patch('promptflow.executor._line_execution_process_pool.bulk_logger', autospec=True) def test_log_progress(self, mock_logger): run_start_time = datetime.utcnow() count = 1 # Tests do not log when not specified at specified intervals (interval = 2) total_count = 20 log_progress(run_start_time, mock_logger, count, total_count) mock_logger.info.assert_not_called() # Test logging at specified intervals (interval = 2) count = 8 log_progress(run_start_time, mock_logger, count, total_count) mock_logger.info.assert_any_call("Finished 8 / 20 lines.") mock_logger.reset_mock() # Test logging using last_log_count parameter (conut - last_log_count > interval(2)) log_progress(run_start_time, mock_logger, count, total_count, last_log_count=5) mock_logger.info.assert_any_call("Finished 8 / 20 lines.") mock_logger.reset_mock() # Test don't log using last_log_count parameter ((conut - last_log_count < interval(2)) log_progress(run_start_time, mock_logger, count, total_count, last_log_count=7) mock_logger.info.assert_not_called()

promptflow/src/promptflow/tests/executor/unittests/_utils/test_utils.py/0

{ "file_path": "promptflow/src/promptflow/tests/executor/unittests/_utils/test_utils.py", "repo_id": "promptflow", "token_count": 1286 }

48

import pytest from pathlib import Path from typing import Callable from promptflow import tool from promptflow.executor._assistant_tool_invoker import AssistantToolInvoker from promptflow.executor._errors import UnsupportedAssistantToolType @pytest.mark.unittest class TestAssistantToolInvoker: @pytest.fixture def tool_definitions(self): return [ {"type": "code_interpreter"}, {"type": "retrieval"}, { "type": "function", "tool_type": "python", "source": {"type": "code", "path": "test_assistant_tool_invoker.py"}, } ] @pytest.mark.parametrize( "predefined_inputs", [({}), ({"input_int": 1})] ) def test_load_tools(self, predefined_inputs): input_int = 1 input_str = "test" tool_definitions = [ {"type": "code_interpreter"}, {"type": "retrieval"}, { "type": "function", "tool_type": "python", "source": {"type": "code", "path": "test_assistant_tool_invoker.py"}, "predefined_inputs": predefined_inputs } ] # Test load tools invoker = AssistantToolInvoker.init(tool_definitions, working_dir=Path(__file__).parent) for tool_name, assistant_tool in invoker._assistant_tools.items(): assert tool_name in ("code_interpreter", "retrieval", "sample_tool") assert assistant_tool.name == tool_name assert isinstance(assistant_tool.openai_definition, dict) if tool_name in ("code_interpreter", "retrieval"): assert assistant_tool.func is None else: assert isinstance(assistant_tool.func, Callable) # Test to_openai_tools descriptions = invoker.to_openai_tools() assert len(descriptions) == 3 properties = { "input_int": {"description": "This is a sample input int.", "type": "number"}, "input_str": {"description": "This is a sample input str.", "type": "string"} } required = ["input_int", "input_str"] self._remove_predefined_inputs(properties, predefined_inputs.keys()) self._remove_predefined_inputs(required, predefined_inputs.keys()) for description in descriptions: if description["type"] in ("code_interpreter", "retrieval"): assert description == {"type": description["type"]} else: assert description == { "type": "function", "function": { "name": "sample_tool", "description": "This is a sample tool.", "parameters": { "type": "object", "properties": properties, "required": required } } } # Test invoke tool kwargs = {"input_int": input_int, "input_str": input_str} self._remove_predefined_inputs(kwargs, predefined_inputs.keys()) result = invoker.invoke_tool(func_name="sample_tool", kwargs=kwargs) assert result == (input_int, input_str) def test_load_tools_with_invalid_case(self): tool_definitions = [{"type": "invalid_type"}] with pytest.raises(UnsupportedAssistantToolType) as exc_info: AssistantToolInvoker.init(tool_definitions) assert "Unsupported assistant tool type" in exc_info.value.message def _remove_predefined_inputs(self, value: any, predefined_inputs: list): for input in predefined_inputs: if input in value: if isinstance(value, dict): value.pop(input) elif isinstance(value, list): value.remove(input) @tool def sample_tool(input_int: int, input_str: str): """This is a sample tool. :param input_int: This is a sample input int. :type input_int: int :param input_str: This is a sample input str. :type input_str: str """ return input_int, input_str

promptflow/src/promptflow/tests/executor/unittests/executor/test_assistant_tool_invoker.py/0

{ "file_path": "promptflow/src/promptflow/tests/executor/unittests/executor/test_assistant_tool_invoker.py", "repo_id": "promptflow", "token_count": 1969 }

49

# --------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # --------------------------------------------------------- import copy import inspect import json from pathlib import Path from typing import Dict, List import vcr from vcr import matchers from vcr.request import Request from .constants import FILTER_HEADERS, TEST_CLASSES_FOR_RUN_INTEGRATION_TEST_RECORDING, SanitizedValues from .processors import ( AzureMLExperimentIDProcessor, AzureOpenAIConnectionProcessor, AzureResourceProcessor, AzureWorkspaceTriadProcessor, DropProcessor, EmailProcessor, IndexServiceProcessor, PFSProcessor, RecordingProcessor, StorageProcessor, UserInfoProcessor, ) from .utils import ( is_httpx_response, is_json_payload_request, is_live, is_record, is_replay, sanitize_automatic_runtime_request_path, sanitize_azure_workspace_triad, sanitize_file_share_flow_path, sanitize_pfs_request_body, sanitize_upload_hash, ) from .variable_recorder import VariableRecorder class PFAzureIntegrationTestRecording: def __init__( self, test_class, test_func_name: str, user_object_id: str, tenant_id: str, variable_recorder: VariableRecorder, ): self.test_class = test_class self.test_func_name = test_func_name self.user_object_id = user_object_id self.tenant_id = tenant_id self.recording_file = self._get_recording_file() self.recording_processors = self._get_recording_processors() self.vcr = self._init_vcr() self._cm = None # context manager from VCR self.cassette = None self.variable_recorder = variable_recorder @staticmethod def from_test_case(test_class, test_func_name: str, **kwargs) -> "PFAzureIntegrationTestRecording": test_class_name = test_class.__name__ if test_class_name in TEST_CLASSES_FOR_RUN_INTEGRATION_TEST_RECORDING: return PFAzureRunIntegrationTestRecording( test_class=test_class, test_func_name=test_func_name, user_object_id=kwargs["user_object_id"], tenant_id=kwargs["tenant_id"], variable_recorder=kwargs["variable_recorder"], ) else: return PFAzureIntegrationTestRecording( test_class=test_class, test_func_name=test_func_name, user_object_id=kwargs["user_object_id"], tenant_id=kwargs["tenant_id"], variable_recorder=kwargs["variable_recorder"], ) def _get_recording_file(self) -> Path: # recording files are expected to be located at "tests/test_configs/recordings" # test file path should locate at "tests/sdk_cli_azure_test/e2etests" test_file_path = Path(inspect.getfile(self.test_class)).resolve() recording_dir = (test_file_path.parent.parent.parent / "test_configs" / "recordings").resolve() recording_dir.mkdir(exist_ok=True) test_file_name = test_file_path.stem test_class_name = self.test_class.__name__ if "[" in self.test_func_name: # for tests that use pytest.mark.parametrize, there will be "[]" in test function name # recording filename pattern: # {test_file_name}_{test_class_name}_{test_func_name}/{parameter_id}.yaml test_func_name, parameter_id = self.test_func_name.split("[") parameter_id = parameter_id.rstrip("]") test_func_dir = (recording_dir / f"{test_file_name}_{test_class_name}_{test_func_name}").resolve() test_func_dir.mkdir(exist_ok=True) recording_file = (test_func_dir / f"{parameter_id}.yaml").resolve() else: # for most remaining tests # recording filename pattern: {test_file_name}_{test_class_name}_{test_func_name}.yaml recording_filename = f"{test_file_name}_{test_class_name}_{self.test_func_name}.yaml" recording_file = (recording_dir / recording_filename).resolve() if is_record() and recording_file.is_file(): recording_file.unlink() return recording_file def _init_vcr(self) -> vcr.VCR: _vcr = vcr.VCR( cassette_library_dir=self.recording_file.parent.as_posix(), before_record_request=self._process_request_recording, before_record_response=self._process_response_recording, decode_compressed_response=True, record_mode="none" if is_replay() else "all", filter_headers=FILTER_HEADERS, ) _vcr.match_on += ("body",) return _vcr def enter_vcr(self): self._cm = self.vcr.use_cassette(self.recording_file.as_posix()) self.cassette = self._cm.__enter__() def exit_vcr(self): if is_record(): self._postprocess_recording() self._cm.__exit__() def _process_request_recording(self, request: Request) -> Request: if is_live(): return request if is_record(): for processor in self.recording_processors: request = processor.process_request(request) return request def _process_response_recording(self, response: Dict) -> Dict: if is_live(): return response # httpx and non-httpx responses have different structure # non-httpx has .body.string, while httpx has .content # in our sanitizers (processors) logic, we only handle .body.string # so make httpx align non-httpx for less code change is_httpx = is_httpx_response(response) if is_httpx: body_string = response.pop("content") response["body"] = {"string": body_string} else: response["body"]["string"] = response["body"]["string"].decode("utf-8") if is_record(): # lower and filter some headers headers = {} for k in response["headers"]: if k.lower() not in FILTER_HEADERS: headers[k.lower()] = response["headers"][k] response["headers"] = headers for processor in self.recording_processors: response = processor.process_response(response) if is_httpx: response["content"] = response["body"]["string"] if not is_replay(): response.pop("body") if isinstance(response["content"], bytes): response["content"] = response["content"].decode("utf-8") else: # vcrpy does not handle well with httpx, so we need some transformations # otherwise, replay tests will break during init VCR response instance response["status"] = {"code": response["status_code"], "message": ""} if isinstance(response["body"]["string"], str): response["body"]["string"] = response["body"]["string"].encode("utf-8") else: response["body"]["string"] = response["body"]["string"].encode("utf-8") return response def _get_recording_processors(self) -> List[RecordingProcessor]: return [ AzureMLExperimentIDProcessor(), AzureOpenAIConnectionProcessor(), AzureResourceProcessor(), AzureWorkspaceTriadProcessor(), DropProcessor(), EmailProcessor(), IndexServiceProcessor(), PFSProcessor(), StorageProcessor(), UserInfoProcessor(user_object_id=self.user_object_id, tenant_id=self.tenant_id), ] def _postprocess_recording(self) -> None: self._apply_replacement_for_recordings() return def _apply_replacement_for_recordings(self) -> None: for i in range(len(self.cassette.data)): req, resp = self.cassette.data[i] req = self.variable_recorder.sanitize_request(req) resp = self.variable_recorder.sanitize_response(resp) self.cassette.data[i] = (req, resp) return class PFAzureRunIntegrationTestRecording(PFAzureIntegrationTestRecording): """Test class for run operations in Prompt Flow Azure. Different from other operations, run operations have: - duplicate network requests for stream run - blob storage requests contain upload hash - Submit and get run data API requests are indistinguishable without run name in body Use a separate class with more pre/post recording processing method or request matchers to handle above cases. """ def _init_vcr(self) -> vcr.VCR: _vcr = super(PFAzureRunIntegrationTestRecording, self)._init_vcr() _vcr.register_matcher("path", self._custom_request_path_matcher) _vcr.register_matcher("body", self._custom_request_body_matcher) return _vcr def enter_vcr(self): self._cm = self.vcr.use_cassette( self.recording_file.as_posix(), allow_playback_repeats=True, filter_query_parameters=["api-version"], ) self.cassette = self._cm.__enter__() def _postprocess_recording(self) -> None: self._drop_duplicate_recordings() super(PFAzureRunIntegrationTestRecording, self)._postprocess_recording() def _drop_duplicate_recordings(self) -> None: # stream run operation contains two requests: # 1. get status; 2. get logs # before the run is terminated, there will be many duplicate requests # getting status/logs, which leads to infinite loop during replay # therefore apply such post process to drop those duplicate recordings dropped_recordings = [] run_data_requests = dict() log_content_requests = dict() for req, resp in self.cassette.data: # run hisotry's rundata API if str(req.path).endswith("/rundata"): body = req.body.decode("utf-8") body_dict = json.loads(body) name = body_dict["runId"] run_data_requests[name] = (req, resp) continue if str(req.path).endswith("/logContent"): log_content_requests[req.uri] = (req, resp) continue dropped_recordings.append((req, resp)) # append rundata recording(s) for req, resp in run_data_requests.values(): dropped_recordings.append((req, resp)) for req, resp in log_content_requests.values(): dropped_recordings.append((req, resp)) self.cassette.data = dropped_recordings return def _custom_request_path_matcher(self, r1: Request, r2: Request) -> bool: # NOTE: orders of below conditions matter, please modify with caution # in run download scenario, observed below wired path: https://<xxx>/https://<yyy>/<remaining> # as we don't have append/replace logic, it might result from Azure blob client, # which is hard to patch; therefore, hack this in matcher (here) # https:// should appear in path, so it's safe to use this as a condition if "https://" in r1.path: _path = str(r1.path) endpoint = ".blob.core.windows.net/" duplicate_path = _path[_path.index(endpoint) + len(endpoint) :] path_for_compare = _path[: _path.index("https://")] + duplicate_path[duplicate_path.index("/") + 1 :] return path_for_compare == r2.path # for blob storage request, sanitize the upload hash in path if r1.host == r2.host and r1.host == SanitizedValues.BLOB_STORAGE_REQUEST_HOST: return sanitize_upload_hash(r1.path) == r2.path # for file share request, mainly target pytest fixture "created_flow" if r1.host == r2.host and r1.host == SanitizedValues.FILE_SHARE_REQUEST_HOST: return sanitize_file_share_flow_path(r1.path) == r2.path # for automatic runtime, sanitize flow session id in path if r1.host == r2.host and ("FlowSessions" in r1.path and "FlowSessions" in r2.path): path1 = sanitize_automatic_runtime_request_path(r1.path) path2 = sanitize_automatic_runtime_request_path(r2.path) return sanitize_azure_workspace_triad(path1) == path2 return r1.path == r2.path def _custom_request_body_matcher(self, r1: Request, r2: Request) -> bool: if is_json_payload_request(r1) and r1.body is not None: # note that `sanitize_upload_hash` is not idempotent # so we should not modify r1 directly # otherwise it will be sanitized multiple times with many zeros _r1 = copy.deepcopy(r1) body1 = _r1.body.decode("utf-8") body1 = sanitize_pfs_request_body(body1) body1 = sanitize_upload_hash(body1) _r1.body = body1.encode("utf-8") try: return matchers.body(_r1, r2) except AssertionError: # if not match, extra sanitize flow file share path (if exists) # for potential pytest fixture "created_flow" scenario body_dict = json.loads(body1) if "flowDefinitionFilePath" in body_dict: body_dict["flowDefinitionFilePath"] = "Users/unknown_user/promptflow/flow_name/flow.dag.yaml" body1 = json.dumps(body_dict) _r1.body = body1.encode("utf-8") return matchers.body(_r1, r2) else: return False else: return matchers.body(r1, r2)

promptflow/src/promptflow/tests/sdk_cli_azure_test/recording_utilities/bases.py/0

{ "file_path": "promptflow/src/promptflow/tests/sdk_cli_azure_test/recording_utilities/bases.py", "repo_id": "promptflow", "token_count": 6082 }

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# --------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # --------------------------------------------------------- from unittest.mock import MagicMock import pytest from promptflow.exceptions import UserErrorException @pytest.mark.unittest class TestUtils: def test_url_parse(self): from promptflow.azure._utils._url_utils import BulkRunId, BulkRunURL flow_id = ( "azureml://experiment/3e123da1-f9a5-4c91-9234-8d9ffbb39ff5/flow/" "0ab9d2dd-3bac-4b68-bb28-12af959b1165/bulktest/715efeaf-b0b4-4778-b94a-2538152b8766/" "run/f88faee6-e510-45b7-9e63-08671b30b3a2" ) flow_id = BulkRunId(flow_id) assert flow_id.experiment_id == "3e123da1-f9a5-4c91-9234-8d9ffbb39ff5" assert flow_id.flow_id == "0ab9d2dd-3bac-4b68-bb28-12af959b1165" assert flow_id.bulk_test_id == "715efeaf-b0b4-4778-b94a-2538152b8766" flow_run_url = ( "https://ml.azure.com/prompts/flow/3e123da1-f9a5-4c91-9234-8d9ffbb39ff5/" "0ab9d2dd-3bac-4b68-bb28-12af959b1165/bulktest/715efeaf-b0b4-4778-b94a-2538152b8766/" "details?wsid=/subscriptions/96aede12-2f73-41cb-b983-6d11a904839b/resourcegroups/promptflow/" "providers/Microsoft.MachineLearningServices/workspaces/promptflow-eastus" ) flow_url = BulkRunURL(flow_run_url) assert flow_url.experiment_id == "3e123da1-f9a5-4c91-9234-8d9ffbb39ff5" assert flow_url.flow_id == "0ab9d2dd-3bac-4b68-bb28-12af959b1165" assert flow_url.bulk_test_id == "715efeaf-b0b4-4778-b94a-2538152b8766" def test_forbidden_new_caller(self): from promptflow.azure._restclient.flow_service_caller import FlowServiceCaller with pytest.raises(UserErrorException) as e: FlowServiceCaller(MagicMock(), MagicMock(), MagicMock()) assert "_FlowServiceCallerFactory" in str(e.value)

promptflow/src/promptflow/tests/sdk_cli_azure_test/unittests/test_utils.py/0

{ "file_path": "promptflow/src/promptflow/tests/sdk_cli_azure_test/unittests/test_utils.py", "repo_id": "promptflow", "token_count": 953 }

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import copy import os.path import re import shutil import tempfile from pathlib import Path import mock import pytest from promptflow._sdk._constants import FLOW_TOOLS_JSON, NODE_VARIANTS, PROMPT_FLOW_DIR_NAME, USE_VARIANTS from promptflow._utils.yaml_utils import load_yaml from promptflow.connections import AzureOpenAIConnection PROMOTFLOW_ROOT = Path(__file__) / "../../../.." TEST_ROOT = Path(__file__).parent.parent.parent MODEL_ROOT = TEST_ROOT / "test_configs/e2e_samples" CONNECTION_FILE = (PROMOTFLOW_ROOT / "connections.json").resolve().absolute().as_posix() FLOWS_DIR = "./tests/test_configs/flows" DATAS_DIR = "./tests/test_configs/datas" def e2e_test_docker_build_and_run(output_path): """Build and run the docker image locally. This function is for adhoc local test and need to run on a dev machine with docker installed. """ import subprocess subprocess.check_output(["docker", "build", ".", "-t", "test"], cwd=output_path) subprocess.check_output(["docker", "tag", "test", "elliotz/promptflow-export-result:latest"], cwd=output_path) subprocess.check_output( [ "docker", "run", "-e", "CUSTOM_CONNECTION_AZURE_OPENAI_API_KEY='xxx'" "elliotz/promptflow-export-result:latest", ], cwd=output_path, ) @pytest.fixture def setup_connections(azure_open_ai_connection: AzureOpenAIConnection): _ = { "azure_open_ai_connection": azure_open_ai_connection, } from promptflow._sdk._pf_client import PFClient from promptflow._sdk.entities._connection import _Connection _client = PFClient() _client.connections.create_or_update( _Connection._load( data={ "name": "custom_connection", "type": "custom", "configs": { "CHAT_DEPLOYMENT_NAME": "gpt-35-turbo", "AZURE_OPENAI_API_BASE": azure_open_ai_connection.api_base, }, "secrets": { "AZURE_OPENAI_API_KEY": azure_open_ai_connection.api_key, }, } ) ) _client.connections.create_or_update( _Connection._load( data={ "name": "azure_open_ai_connection", "type": "azure_open_ai", "api_type": azure_open_ai_connection.api_type, "api_base": azure_open_ai_connection.api_base, "api_version": azure_open_ai_connection.api_version, "api_key": azure_open_ai_connection.api_key, } ) ) @pytest.mark.usefixtures("use_secrets_config_file", "setup_connections") @pytest.mark.sdk_test @pytest.mark.e2etest class TestFlowLocalOperations: def test_flow_build_as_docker(self, pf) -> None: source = f"{FLOWS_DIR}/intent-copilot" output_path = f"{FLOWS_DIR}/export/linux" shutil.rmtree(output_path, ignore_errors=True) (Path(source) / ".runs").mkdir(exist_ok=True) (Path(source) / ".runs" / "dummy_run_file").touch() with mock.patch("promptflow._sdk.operations._flow_operations.generate_random_string") as mock_random_string: mock_random_string.return_value = "dummy1" pf.flows.build( flow=source, output=output_path, format="docker", ) assert mock_random_string.call_count == 1 # check if .amlignore works assert os.path.isdir(f"{source}/data") assert not (Path(output_path) / "flow" / "data").exists() # check if .runs is ignored by default assert os.path.isfile(f"{source}/.runs/dummy_run_file") assert not (Path(output_path) / "flow" / ".runs" / "dummy_run_file").exists() # e2e_test_docker_build_and_run(output_path) def test_flow_build_as_docker_with_additional_includes(self, pf) -> None: source = f"{FLOWS_DIR}/web_classification_with_additional_include" with tempfile.TemporaryDirectory() as temp_dir: pf.flows.build( flow=source, output=temp_dir, format="docker", ) for additional_include in [ "../external_files/convert_to_dict.py", "../external_files/fetch_text_content_from_url.py", "../external_files/summarize_text_content.jinja2", ]: additional_include_path = Path(source, additional_include) target_path = Path(temp_dir, "flow", additional_include_path.name) assert target_path.is_file() assert target_path.read_text() == additional_include_path.read_text() def test_flow_build_flow_only(self, pf) -> None: source = f"{FLOWS_DIR}/web_classification_with_additional_include" with tempfile.TemporaryDirectory() as temp_dir: pf.flows.build( flow=source, output=temp_dir, format="docker", flow_only=True, ) for additional_include in [ "../external_files/convert_to_dict.py", "../external_files/fetch_text_content_from_url.py", "../external_files/summarize_text_content.jinja2", ]: additional_include_path = Path(source, additional_include) target_path = Path(temp_dir, additional_include_path.name) assert target_path.is_file() assert target_path.read_text() == additional_include_path.read_text() assert Path(temp_dir, PROMPT_FLOW_DIR_NAME, FLOW_TOOLS_JSON).is_file() with open(Path(temp_dir, "flow.dag.yaml"), "r", encoding="utf-8") as f: flow_dag_content = load_yaml(f) assert NODE_VARIANTS not in flow_dag_content assert "additional_includes" not in flow_dag_content assert not any([USE_VARIANTS in node for node in flow_dag_content["nodes"]]) def test_flow_build_as_docker_with_variant(self, pf) -> None: source = f"{FLOWS_DIR}/web_classification_with_additional_include" flow_dag_path = Path(source, "flow.dag.yaml") flow_dag = load_yaml(flow_dag_path) with tempfile.TemporaryDirectory() as temp_dir: pf.flows.build( flow=source, output=temp_dir, format="docker", variant="${summarize_text_content.variant_0}", ) new_flow_dag_path = Path(temp_dir, "flow", "flow.dag.yaml") new_flow_dag = load_yaml(new_flow_dag_path) target_node = next(filter(lambda x: x["name"] == "summarize_text_content", new_flow_dag["nodes"])) target_node.pop("name") assert target_node == flow_dag["node_variants"]["summarize_text_content"]["variants"]["variant_0"]["node"] def test_flow_build_generate_flow_tools_json(self, pf) -> None: source = f"{FLOWS_DIR}/web_classification_with_additional_include" with tempfile.TemporaryDirectory() as temp_dir: pf.flows.build( flow=source, output=temp_dir, variant="${summarize_text_content.variant_0}", ) flow_tools_path = Path(temp_dir) / "flow" / PROMPT_FLOW_DIR_NAME / FLOW_TOOLS_JSON assert flow_tools_path.is_file() # package in flow.tools.json is not determined by the flow, so we don't check it here assert load_yaml(flow_tools_path)["code"] == { "classify_with_llm.jinja2": { "inputs": { "examples": {"type": ["string"]}, "text_content": {"type": ["string"]}, "url": {"type": ["string"]}, }, "source": "classify_with_llm.jinja2", "type": "llm", }, "convert_to_dict.py": { "function": "convert_to_dict", "inputs": {"input_str": {"type": ["string"]}}, "source": "convert_to_dict.py", "type": "python", }, "fetch_text_content_from_url.py": { "function": "fetch_text_content_from_url", "inputs": {"url": {"type": ["string"]}}, "source": "fetch_text_content_from_url.py", "type": "python", }, "prepare_examples.py": { "function": "prepare_examples", "source": "prepare_examples.py", "type": "python", }, "summarize_text_content.jinja2": { "inputs": {"text": {"type": ["string"]}}, "source": "summarize_text_content.jinja2", "type": "llm", }, } def test_flow_validate_generate_flow_tools_json(self, pf) -> None: source = f"{FLOWS_DIR}/web_classification_with_additional_include" flow_tools_path = Path(source) / PROMPT_FLOW_DIR_NAME / FLOW_TOOLS_JSON flow_tools_path.unlink(missing_ok=True) validation_result = pf.flows.validate(flow=source) assert validation_result.passed assert flow_tools_path.is_file() # package in flow.tools.json is not determined by the flow, so we don't check it here assert load_yaml(flow_tools_path)["code"] == { "classify_with_llm.jinja2": { "inputs": { "examples": {"type": ["string"]}, "text_content": {"type": ["string"]}, "url": {"type": ["string"]}, }, "source": "classify_with_llm.jinja2", "type": "llm", }, "convert_to_dict.py": { "function": "convert_to_dict", "inputs": {"input_str": {"type": ["string"]}}, "source": os.path.join("..", "external_files", "convert_to_dict.py"), "type": "python", }, "fetch_text_content_from_url.py": { "function": "fetch_text_content_from_url", "inputs": {"url": {"type": ["string"]}}, "source": os.path.join("..", "external_files", "fetch_text_content_from_url.py"), "type": "python", }, "prepare_examples.py": { "function": "prepare_examples", "source": "prepare_examples.py", "type": "python", }, "summarize_text_content.jinja2": { "inputs": {"text": {"type": ["string"]}}, "source": os.path.join("..", "external_files", "summarize_text_content.jinja2"), "type": "llm", }, "summarize_text_content__variant_1.jinja2": { "inputs": {"text": {"type": ["string"]}}, "source": "summarize_text_content__variant_1.jinja2", "type": "llm", }, } def test_flow_validation_failed(self, pf) -> None: source = f"{FLOWS_DIR}/web_classification_invalid" flow_tools_path = Path(source) / PROMPT_FLOW_DIR_NAME / FLOW_TOOLS_JSON flow_tools_path.unlink(missing_ok=True) validation_result = pf.flows.validate(flow=source) error_messages = copy.deepcopy(validation_result.error_messages) assert "Failed to load python module from file" in error_messages.pop("nodes.2.source.path", "") for yaml_path in [ "node_variants.summarize_text_content.variants.variant_0.node.source.path", "nodes.1.source.path", ]: assert re.search(r"Meta file '.*' can not be found.", error_messages.pop(yaml_path, "")) assert error_messages == { "inputs.url.type": "Missing data for required field.", "outputs.category.type": "Missing data for required field.", } assert "line 22" in repr(validation_result) assert flow_tools_path.is_file() flow_tools = load_yaml(flow_tools_path) assert "code" in flow_tools assert flow_tools["code"] == { "classify_with_llm.jinja2": { "inputs": { "examples": {"type": ["string"]}, "text_content": {"type": ["string"]}, "url": {"type": ["string"]}, }, "source": "classify_with_llm.jinja2", "type": "prompt", }, "./classify_with_llm.jinja2": { "inputs": { "examples": {"type": ["string"]}, "text_content": {"type": ["string"]}, "url": {"type": ["string"]}, }, "source": "./classify_with_llm.jinja2", "type": "llm", }, "convert_to_dict.py": { "function": "convert_to_dict", "inputs": {"input_str": {"type": ["string"]}}, "source": "convert_to_dict.py", "type": "python", }, "fetch_text_content_from_url.py": { "function": "fetch_text_content_from_url", "inputs": {"url": {"type": ["string"]}}, "source": os.path.join("..", "external_files", "fetch_text_content_from_url.py"), "type": "python", }, "summarize_text_content__variant_1.jinja2": { "inputs": {"text": {"type": ["string"]}}, "source": "summarize_text_content__variant_1.jinja2", "type": "llm", }, } def test_flow_generate_tools_meta(self, pf) -> None: source = f"{FLOWS_DIR}/web_classification_invalid" tools_meta, tools_error = pf.flows._generate_tools_meta(source) assert tools_meta["code"] == { "classify_with_llm.jinja2": { "inputs": { "examples": {"type": ["string"]}, "text_content": {"type": ["string"]}, "url": {"type": ["string"]}, }, "source": "classify_with_llm.jinja2", "type": "prompt", }, "./classify_with_llm.jinja2": { "inputs": { "examples": {"type": ["string"]}, "text_content": {"type": ["string"]}, "url": {"type": ["string"]}, }, "source": "./classify_with_llm.jinja2", "type": "llm", }, "convert_to_dict.py": { "function": "convert_to_dict", "inputs": {"input_str": {"type": ["string"]}}, "source": "convert_to_dict.py", "type": "python", }, "fetch_text_content_from_url.py": { "function": "fetch_text_content_from_url", "inputs": {"url": {"type": ["string"]}}, "source": os.path.join("..", "external_files", "fetch_text_content_from_url.py"), "type": "python", }, "summarize_text_content__variant_1.jinja2": { "inputs": {"text": {"type": ["string"]}}, "source": "summarize_text_content__variant_1.jinja2", "type": "llm", }, } # promptflow-tools is not installed in ci # assert list(tools_meta["package"]) == ["promptflow.tools.azure_translator.get_translation"] assert "Failed to load python module from file" in tools_error.pop("prepare_examples.py", "") assert re.search(r"Meta file '.*' can not be found.", tools_error.pop("summarize_text_content.jinja2", "")) assert tools_error == {} tools_meta, tools_error = pf.flows._generate_tools_meta(source, source_name="summarize_text_content.jinja2") assert tools_meta == {"code": {}, "package": {}} assert re.search(r"Meta file '.*' can not be found.", tools_error.pop("summarize_text_content.jinja2", "")) assert tools_error == {} tools_meta, tools_error = pf.flows._generate_tools_meta(source, source_name="fetch_text_content_from_url.py") assert tools_meta == { "code": { "fetch_text_content_from_url.py": { "function": "fetch_text_content_from_url", "inputs": {"url": {"type": ["string"]}}, "source": os.path.join("..", "external_files", "fetch_text_content_from_url.py"), "type": "python", }, }, "package": {}, } assert tools_error == {} @pytest.mark.skip(reason="It will fail in CI for some reasons. Still need to investigate.") def test_flow_generate_tools_meta_timeout(self, pf) -> None: source = f"{FLOWS_DIR}/web_classification_invalid" for tools_meta, tools_error in [ pf.flows._generate_tools_meta(source, timeout=1), # There is no built-in method to forcefully stop a running thread in Python # because abruptly stopping a thread can cause issues like resource leaks, # deadlocks, or inconsistent states. # Caller (VSCode extension) will handle the timeout error. # pf.flows._generate_tools_meta(source, source_name="convert_to_dict.py", timeout=1), ]: assert tools_meta == {"code": {}, "package": {}} assert tools_error for error in tools_error.values(): assert "timeout" in error def test_flow_generate_tools_meta_with_pkg_tool_with_custom_strong_type_connection(self, pf) -> None: source = f"{FLOWS_DIR}/flow_with_package_tool_with_custom_strong_type_connection" tools_meta, tools_error = pf.flows._generate_tools_meta(source) assert tools_error == {} assert tools_meta["code"] == {} assert tools_meta["package"] == { "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", }, } def test_flow_generate_tools_meta_with_script_tool_with_custom_strong_type_connection(self, pf) -> None: source = f"{FLOWS_DIR}/flow_with_script_tool_with_custom_strong_type_connection" tools_meta, tools_error = pf.flows._generate_tools_meta(source) assert tools_error == {} assert tools_meta["package"] == {} assert tools_meta["code"] == { "my_script_tool.py": { "function": "my_tool", "inputs": {"connection": {"type": ["CustomConnection"]}, "input_param": {"type": ["string"]}}, "source": "my_script_tool.py", "type": "python", } }

promptflow/src/promptflow/tests/sdk_cli_test/e2etests/test_flow_local_operations.py/0

{ "file_path": "promptflow/src/promptflow/tests/sdk_cli_test/e2etests/test_flow_local_operations.py", "repo_id": "promptflow", "token_count": 10437 }

52

# --------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # --------------------------------------------------------- from pathlib import Path import pytest from marshmallow import ValidationError from promptflow import load_flow from promptflow._sdk.entities._eager_flow import EagerFlow from promptflow._sdk.entities._flow import ProtectedFlow from promptflow.exceptions import UserErrorException FLOWS_DIR = Path("./tests/test_configs/flows") EAGER_FLOWS_DIR = Path("./tests/test_configs/eager_flows") @pytest.mark.sdk_test @pytest.mark.unittest class TestRun: @pytest.mark.parametrize( "kwargs", [ {"source": EAGER_FLOWS_DIR / "simple_with_yaml"}, {"source": EAGER_FLOWS_DIR / "simple_with_yaml" / "flow.dag.yaml"}, {"source": EAGER_FLOWS_DIR / "simple_without_yaml" / "entry.py", "entry": "my_flow"}, {"source": EAGER_FLOWS_DIR / "multiple_entries" / "entry1.py", "entry": "my_flow1"}, {"source": EAGER_FLOWS_DIR / "multiple_entries" / "entry1.py", "entry": "my_flow2"}, ], ) def test_eager_flow_load(self, kwargs): flow = load_flow(**kwargs) assert isinstance(flow, EagerFlow) @pytest.mark.parametrize( "kwargs", [ {"source": FLOWS_DIR / "print_input_flow"}, {"source": FLOWS_DIR / "print_input_flow" / "flow.dag.yaml"}, ], ) def test_dag_flow_load(self, kwargs): flow = load_flow(**kwargs) assert isinstance(flow, ProtectedFlow) def test_flow_load_advanced(self): flow = load_flow(source=EAGER_FLOWS_DIR / "flow_with_environment") assert isinstance(flow, EagerFlow) assert flow._data["environment"] == {"python_requirements_txt": "requirements.txt"} @pytest.mark.parametrize( "kwargs, error_message, exception_type", [ ( { "source": EAGER_FLOWS_DIR / "multiple_entries" / "entry1.py", }, "Entry function is not specified", UserErrorException, ), ( { "source": EAGER_FLOWS_DIR / "multiple_entries" / "not_exist.py", }, "does not exist", UserErrorException, ), ( { "source": EAGER_FLOWS_DIR / "invalid_no_path", }, "{'path': ['Missing data for required field.']}", ValidationError, ), ( { "source": EAGER_FLOWS_DIR / "invalid_illegal_path", }, "Can't find directory or file in resolved absolute path:", ValidationError, ), ( {"source": EAGER_FLOWS_DIR / "invalid_extra_fields_nodes"}, "{'nodes': ['Unknown field.']}", ValidationError, ), ], ) def test_flow_load_invalid(self, kwargs, error_message, exception_type): with pytest.raises(exception_type) as e: load_flow(**kwargs) assert error_message in str(e.value)

promptflow/src/promptflow/tests/sdk_cli_test/unittests/test_flow.py/0

{ "file_path": "promptflow/src/promptflow/tests/sdk_cli_test/unittests/test_flow.py", "repo_id": "promptflow", "token_count": 1607 }

53

# --------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # --------------------------------------------------------- import pytest from promptflow._sdk._utils import get_promptflow_sdk_version from ..utils import PFSOperations @pytest.mark.e2etest class TestGeneralAPIs: def test_heartbeat(self, pfs_op: PFSOperations) -> None: response = pfs_op.heartbeat() assert response.status_code == 200 response_json = response.json assert isinstance(response_json, dict) assert "promptflow" in response_json assert response_json["promptflow"] == get_promptflow_sdk_version()

promptflow/src/promptflow/tests/sdk_pfs_test/e2etests/test_general_apis.py/0

{ "file_path": "promptflow/src/promptflow/tests/sdk_pfs_test/e2etests/test_general_apis.py", "repo_id": "promptflow", "token_count": 217 }

54

path: ./entry.py entry: my_flow

promptflow/src/promptflow/tests/test_configs/eager_flows/dummy_flow_with_exception/flow.dag.yaml/0

{ "file_path": "promptflow/src/promptflow/tests/test_configs/eager_flows/dummy_flow_with_exception/flow.dag.yaml", "repo_id": "promptflow", "token_count": 12 }

55

# --------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # --------------------------------------------------------- import time def my_flow(input_val) -> str: """Simple flow with yaml.""" time.sleep(100) print(f"Hello world! {input_val}") return f"Hello world! {input_val}"

promptflow/src/promptflow/tests/test_configs/eager_flows/long_running/entry.py/0

{ "file_path": "promptflow/src/promptflow/tests/test_configs/eager_flows/long_running/entry.py", "repo_id": "promptflow", "token_count": 94 }

56

{ "text": "hello" }

promptflow/src/promptflow/tests/test_configs/flows/activate_condition_always_met/inputs.json/0

{ "file_path": "promptflow/src/promptflow/tests/test_configs/flows/activate_condition_always_met/inputs.json", "repo_id": "promptflow", "token_count": 13 }

57

[ { "expected_node_count": 2, "expected_outputs": { "output": "Execution" }, "expected_bypassed_nodes": [ "nodeA" ] } ]

promptflow/src/promptflow/tests/test_configs/flows/all_depedencies_bypassed_with_activate_met/expected_result.json/0

{ "file_path": "promptflow/src/promptflow/tests/test_configs/flows/all_depedencies_bypassed_with_activate_met/expected_result.json", "repo_id": "promptflow", "token_count": 117 }

58

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

promptflow/src/promptflow/tests/test_configs/flows/async_tools/flow.dag.yaml/0

{ "file_path": "promptflow/src/promptflow/tests/test_configs/flows/async_tools/flow.dag.yaml", "repo_id": "promptflow", "token_count": 296 }

59

model: gpt-4-1106-preview instructions: You are a helpful assistant. tools: - type: code_interpreter - type: function source: type: code path: get_calorie_by_jogging.py tool_type: python - type: function source: type: code path: get_calorie_by_swimming.py tool_type: python - type: function source: type: code path: get_current_city.py tool_type: python - type: function source: type: code path: get_temperature.py tool_type: python

promptflow/src/promptflow/tests/test_configs/flows/chat-with-assistant-no-file/assistant_definition.yaml/0

{ "file_path": "promptflow/src/promptflow/tests/test_configs/flows/chat-with-assistant-no-file/assistant_definition.yaml", "repo_id": "promptflow", "token_count": 224 }

60

id: template_standard_flow name: Template Standard Flow inputs: incident_content: type: string incident_id: type: int outputs: investigation_method: type: string reference: ${investigation_method.output} nodes: - name: incident_id_extractor type: python source: type: code path: incident_id_extractor.py inputs: incident_content: ${inputs.incident_content} incident_id: ${inputs.incident_id} - name: job_info_extractor type: python source: type: code path: job_info_extractor.py inputs: incident_content: ${incident_id_extractor.output.incident_content} activate: when: ${incident_id_extractor.output.has_incident_id} is: false - name: incident_info_extractor type: python source: type: code path: incident_info_extractor.py inputs: incident: ${incident_id_extractor.output} activate: when: ${incident_id_extractor.output.has_incident_id} is: true - name: tsg_retriever type: python source: type: code path: tsg_retriever.py inputs: content: ${incident_info_extractor.output.incident_content} activate: when: ${incident_info_extractor.output.retriever} is: tsg - name: icm_retriever type: python source: type: code path: icm_retriever.py inputs: content: ${incident_info_extractor.output.incident_content} activate: when: ${incident_info_extractor.output.retriever} is: icm - name: kql_tsg_retriever type: python source: type: code path: kql_tsg_retriever.py inputs: content: ${incident_info_extractor.output.incident_content} activate: when: ${incident_info_extractor.output.retriever} is: tsg - name: investigation_steps type: llm source: type: code path: investigation_steps.jinja2 inputs: deployment_name: gpt-35-turbo temperature: 0.7 top_p: 1 stop: "" max_tokens: 256 presence_penalty: 0 frequency_penalty: 0 logit_bias: "" first_method: ${icm_retriever.output} second_method: ${tsg_retriever.output} third_method: ${kql_tsg_retriever.output} provider: AzureOpenAI connection: azure_open_ai_connection api: chat module: promptflow.tools.aoai - name: retriever_summary type: python source: type: code path: retriever_summary.py inputs: summary: ${investigation_steps.output} - name: investigation_method type: python source: type: code path: investigation_method.py inputs: method1: ${job_info_extractor.output} method2: ${retriever_summary.output}

promptflow/src/promptflow/tests/test_configs/flows/conditional_flow_with_activate/flow.dag.yaml/0

{ "file_path": "promptflow/src/promptflow/tests/test_configs/flows/conditional_flow_with_activate/flow.dag.yaml", "repo_id": "promptflow", "token_count": 999 }

61

inputs: case: type: string default: double is_chat_input: false value: type: int default: 1 outputs: output: type: string reference: ${collect_node.output} evaluation_only: false is_chat_output: false nodes: - name: double type: python source: type: code path: double.py inputs: input: ${inputs.value} activate: when: ${inputs.case} is: double aggregation: false - name: square type: python source: type: code path: square.py inputs: input: ${inputs.value} activate: when: ${inputs.case} is: square aggregation: false - name: aggregation_double type: python source: type: code path: aggregation_node.py inputs: input: ${double.output} aggregation: true - name: aggregation_square type: python source: type: code path: aggregation_node.py inputs: input: ${square.output} aggregation: true - name: collect_node type: python source: type: code path: collect_node.py inputs: input1: ${double.output} input2: ${square.output} aggregation: false

promptflow/src/promptflow/tests/test_configs/flows/conditional_flow_with_aggregate_bypassed/flow.dag.yaml/0

{ "file_path": "promptflow/src/promptflow/tests/test_configs/flows/conditional_flow_with_aggregate_bypassed/flow.dag.yaml", "repo_id": "promptflow", "token_count": 426 }

62

{"question": "What is 2 to the 10th power?"} {"question": "What is the sum of 2 and 2?"}

promptflow/src/promptflow/tests/test_configs/flows/flow_with_langchain_traces/inputs.jsonl/0

{ "file_path": "promptflow/src/promptflow/tests/test_configs/flows/flow_with_langchain_traces/inputs.jsonl", "repo_id": "promptflow", "token_count": 29 }

63

inputs: text: type: string outputs: answer: type: string reference: ${echo_generator.output} nodes: - name: echo_generator type: python source: type: code path: echo.py inputs: text: ${inputs.text}

promptflow/src/promptflow/tests/test_configs/flows/generator_nodes/flow.dag.yaml/0

{ "file_path": "promptflow/src/promptflow/tests/test_configs/flows/generator_nodes/flow.dag.yaml", "repo_id": "promptflow", "token_count": 97 }

64

inputs: question: type: string chat_history: type: list stream: type: bool outputs: answer: type: string reference: ${chat.output} nodes: - name: chat type: python source: type: code path: chat.py inputs: question: ${inputs.question} chat_history: ${inputs.chat_history} connection: azure_open_ai_connection stream: ${inputs.stream}

promptflow/src/promptflow/tests/test_configs/flows/openai_chat_api_flow/flow.dag.yaml/0

{ "file_path": "promptflow/src/promptflow/tests/test_configs/flows/openai_chat_api_flow/flow.dag.yaml", "repo_id": "promptflow", "token_count": 157 }

65

from promptflow import tool import sys @tool def print_inputs( text: str = None, ): print(f"STDOUT: {text}") print(f"STDERR: {text}", file=sys.stderr) return text

promptflow/src/promptflow/tests/test_configs/flows/print_input_flow/print_input.py/0

{ "file_path": "promptflow/src/promptflow/tests/test_configs/flows/print_input_flow/print_input.py", "repo_id": "promptflow", "token_count": 75 }

66

from promptflow import tool @tool def my_python_tool_with_failed_line(idx: int, mod) -> int: if idx % mod == 0: raise Exception("Failed") return idx

promptflow/src/promptflow/tests/test_configs/flows/python_tool_partial_failure/my_python_tool_with_failed_line.py/0

{ "file_path": "promptflow/src/promptflow/tests/test_configs/flows/python_tool_partial_failure/my_python_tool_with_failed_line.py", "repo_id": "promptflow", "token_count": 66 }

67

{"image": {"data:image/png;path":"logo_1.png"}} {"image": {"data:image/png;path":"logo_2.png"}}

promptflow/src/promptflow/tests/test_configs/flows/python_tool_with_simple_image/image_inputs/inputs.jsonl/0

{ "file_path": "promptflow/src/promptflow/tests/test_configs/flows/python_tool_with_simple_image/image_inputs/inputs.jsonl", "repo_id": "promptflow", "token_count": 41 }

68

inputs: text: type: string outputs: output: type: string reference: ${node1.output} nodes: - name: node1 type: python source: type: code path: dummy_utils/main.py inputs: x: ${inputs.text}

promptflow/src/promptflow/tests/test_configs/flows/script_with_import/flow.dag.yaml/0

{ "file_path": "promptflow/src/promptflow/tests/test_configs/flows/script_with_import/flow.dag.yaml", "repo_id": "promptflow", "token_count": 97 }

69

id: template_standard_flow name: Template Standard Flow inputs: input: type: string is_chat_input: false index: type: int is_chat_input: false outputs: output: type: string reference: ${python_node.output} nodes: - name: python_node type: python source: type: code path: python_node.py inputs: index: ${inputs.index} input: ${inputs.input} use_variants: false node_variants: {}

promptflow/src/promptflow/tests/test_configs/flows/simple_flow_with_ten_inputs/flow.dag.yaml/0

{ "file_path": "promptflow/src/promptflow/tests/test_configs/flows/simple_flow_with_ten_inputs/flow.dag.yaml", "repo_id": "promptflow", "token_count": 171 }

70

[ { "url": "https://www.microsoft.com/en-us/d/xbox-wireless-controller-stellar-shift-special-edition/94fbjc7h0h6h" }, { "url": "https://www.microsoft.com/en-us/windows/" } ]

promptflow/src/promptflow/tests/test_configs/flows/web_classification_v1/samples.json/0

{ "file_path": "promptflow/src/promptflow/tests/test_configs/flows/web_classification_v1/samples.json", "repo_id": "promptflow", "token_count": 86 }

71

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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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'{"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": "${grade.output}"}, "tool": "calculate_accuracy.py", "reduce": true}], "tools": [{"name": "Content Safety (Text Analyze)", "type": "python", "inputs": {"connection": {"type": ["AzureContentSafetyConnection"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "hate_category": {"type": ["string"], "default": "medium_sensitivity", "enum": ["disable", "low_sensitivity", "medium_sensitivity", "high_sensitivity"], "allow_manual_entry": false, "is_multi_select": false, "input_type": "default"}, "self_harm_category": {"type": ["string"], "default": "medium_sensitivity", "enum": ["disable", 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"calculate_accuracy", "type": "python", "source": {"type": "code", "path": "calculate_accuracy.py"}, "inputs": {"grades": "${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, 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"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": "${grade.output}", "evaluation_only": false, "is_chat_output": false}}}, "flowRunResourceId": "azureml://locations/eastus/workspaces/00000/flows/eval_run_name/flowRuns/eval_run_name", "flowRunId": "eval_run_name", "flowRunDisplayName": "eval_run_name", "batchDataInput": {"dataUri": "azureml://datastores/workspaceblobstore/paths/LocalUpload/74c11bba717480b2d6b04b8e746d09d7/webClassification3.jsonl"}, "flowRunType": "FlowRun", "flowType": "Default", "runtimeName": "test-runtime-ci", "inputsMapping": {"groundtruth": "${data.answer}", "prediction": "${run.outputs.category}"}, "outputDatastoreName": "workspaceblobstore", "childRunBasePath": "promptflow/PromptFlowArtifacts/eval_run_name/flow_artifacts", "flowDagFileRelativePath": "flow.dag.yaml", "flowSnapshotId": "73d0c061-880c-466c-8fb0-b29e9ae7ab66", "studioPortalEndpoint": "https://ml.azure.com/runs/eval_run_name?wsid=/subscriptions/00000000-0000-0000-0000-000000000000/resourcegroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000"}' headers: connection: - keep-alive content-length: - '13869' content-type: - application/json; charset=utf-8 strict-transport-security: - max-age=15724800; includeSubDomains; preload transfer-encoding: - chunked vary: - Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.270' status: code: 200 message: OK - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - promptflow-sdk/0.0.1 azsdk-python-azuremachinelearningdesignerserviceclient/unknown Python/3.10.13 (Windows-10-10.0.22631-SP0) method: GET uri: https://eastus.api.azureml.ms/flow/api/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/BulkRuns/eval_run_name response: body: string: '{"flowGraph": {"nodes": [{"name": "grade", "type": "python", "source": {"type": "code", "path": "grade.py"}, "inputs": {"groundtruth": "${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": 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PromptFlow version: 1.2.0rc1\n2024-01-12 08:16:40 +0000 134 promptflow-runtime INFO Updating eval_run_name to Status.Preparing...\n2024-01-12 08:16:41 +0000 134 promptflow-runtime INFO Downloading snapshot to /mnt/host/service/app/38343/requests/eval_run_name\n2024-01-12 08:16:41 +0000 134 promptflow-runtime INFO Get snapshot sas url for 73d0c061-880c-466c-8fb0-b29e9ae7ab66...\n2024-01-12 08:16:47 +0000 134 promptflow-runtime INFO Downloading snapshot 73d0c061-880c-466c-8fb0-b29e9ae7ab66 from uri https://promptfloweast4063704120.blob.core.windows.net/snapshotzips/promptflow-eastus:3e123da1-f9a5-4c91-9234-8d9ffbb39ff5:snapshotzip/73d0c061-880c-466c-8fb0-b29e9ae7ab66.zip...\n2024-01-12 08:16:47 +0000 134 promptflow-runtime INFO Downloaded file /mnt/host/service/app/38343/requests/eval_run_name/73d0c061-880c-466c-8fb0-b29e9ae7ab66.zip with size 1243 for snapshot 73d0c061-880c-466c-8fb0-b29e9ae7ab66.\n2024-01-12 08:16:47 +0000 134 promptflow-runtime INFO Download snapshot 73d0c061-880c-466c-8fb0-b29e9ae7ab66 completed.\n2024-01-12 08:16:47 +0000 134 promptflow-runtime INFO Successfully download snapshot to /mnt/host/service/app/38343/requests/eval_run_name\n2024-01-12 08:16:47 +0000 134 promptflow-runtime INFO About to execute a python flow.\n2024-01-12 08:16:47 +0000 134 promptflow-runtime INFO Use spawn method to start child process.\n2024-01-12 08:16:47 +0000 134 promptflow-runtime INFO Starting to check process 4041 status for run eval_run_name\n2024-01-12 08:16:47 +0000 134 promptflow-runtime INFO Start checking run status for run eval_run_name\n2024-01-12 08:16:51 +0000 4041 promptflow-runtime INFO [134--4041] Start processing flowV2......\n2024-01-12 08:16:51 +0000 4041 promptflow-runtime INFO Runtime version: 20231204.v4. PromptFlow version: 1.2.0rc1\n2024-01-12 08:16:51 +0000 4041 promptflow-runtime INFO Setting mlflow tracking uri...\n2024-01-12 08:16:52 +0000 4041 promptflow-runtime INFO Validating ''AzureML Data Scientist'' user authentication...\n2024-01-12 08:16:52 +0000 4041 promptflow-runtime INFO Successfully validated ''AzureML Data Scientist'' user authentication.\n2024-01-12 08:16:52 +0000 4041 promptflow-runtime INFO Using AzureMLRunStorageV2\n2024-01-12 08:16:52 +0000 4041 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 08:16:52 +0000 4041 promptflow-runtime INFO Initialized blob service client for AzureMLRunTracker.\n2024-01-12 08:16:52 +0000 4041 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 08:16:52 +0000 4041 promptflow-runtime INFO Resolve data from url finished in 0.45952979754656553 seconds\n2024-01-12 08:16:53 +0000 4041 promptflow-runtime INFO Resolve data from url finished in 0.7092651706188917 seconds\n2024-01-12 08:16:53 +0000 4041 promptflow-runtime INFO Starting the aml run ''eval_run_name''...\n2024-01-12 08:16:54 +0000 4041 execution.bulk INFO Using fork, process count: 3\n2024-01-12 08:16:54 +0000 4083 execution.bulk INFO Process 4083 started.\n2024-01-12 08:16:54 +0000 4093 execution.bulk INFO Process 4093 started.\n2024-01-12 08:16:54 +0000 4041 execution.bulk INFO Process name: ForkProcess-38:2, Process id: 4083, Line number: 0 start execution.\n2024-01-12 08:16:54 +0000 4041 execution.bulk INFO Process name: ForkProcess-38:4, Process id: 4093, Line number: 1 start execution.\n2024-01-12 08:16:54 +0000 4041 execution.bulk INFO Process name: ForkProcess-38:2, Process id: 4083, Line number: 0 completed.\n2024-01-12 08:16:54 +0000 4088 execution.bulk INFO Process 4088 started.\n2024-01-12 08:16:54 +0000 4041 execution.bulk INFO Finished 1 / 3 lines.\n2024-01-12 08:16:54 +0000 4041 execution.bulk INFO Process name: ForkProcess-38:4, Process id: 4093, Line number: 1 completed.\n2024-01-12 08:16:54 +0000 4041 execution.bulk INFO Process name: ForkProcess-38:3, Process id: 4088, Line number: 2 start execution.\n2024-01-12 08:16:54 +0000 4041 execution.bulk INFO Average execution time for completed lines: 0.23 seconds. Estimated time for incomplete lines: 0.46 seconds.\n2024-01-12 08:16:54 +0000 4041 execution.bulk INFO Finished 2 / 3 lines.\n2024-01-12 08:16:54 +0000 4041 execution.bulk INFO Average execution time for completed lines: 0.13 seconds. Estimated time for incomplete lines: 0.13 seconds.\n2024-01-12 08:16:54 +0000 4041 execution.bulk INFO Process name: ForkProcess-38:3, Process id: 4088, Line number: 2 completed.\n2024-01-12 08:16:54 +0000 4041 execution.bulk INFO Finished 3 / 3 lines.\n2024-01-12 08:16:54 +0000 4041 execution.bulk INFO Average execution time for completed lines: 0.13 seconds. 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promptflow/src/promptflow/tests/test_configs/recordings/test_run_operations_TestFlowRun_test_basic_evaluation.yaml/0

{ "file_path": "promptflow/src/promptflow/tests/test_configs/recordings/test_run_operations_TestFlowRun_test_basic_evaluation.yaml", "repo_id": "promptflow", "token_count": 88949 }

72

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promptflow/src/promptflow/tests/test_configs/recordings/test_run_operations_TestFlowRun_test_request_id_when_making_http_requests.yaml/0

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["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": "${run.outputs.result}"}, "outputDatastoreName": "workspaceblobstore", "childRunBasePath": "promptflow/PromptFlowArtifacts/eval_run_name/flow_artifacts", "flowDagFileRelativePath": "flow.dag.yaml", "flowSnapshotId": "1885cd85-1969-4fca-8c21-d8826ed5d886", "studioPortalEndpoint": "https://ml.azure.com/runs/eval_run_name?wsid=/subscriptions/00000000-0000-0000-0000-000000000000/resourcegroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000"}' headers: connection: - keep-alive content-length: - '13872' content-type: - application/json; charset=utf-8 strict-transport-security: - max-age=15724800; includeSubDomains; preload transfer-encoding: - chunked vary: - Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.547' status: code: 200 message: OK - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - promptflow-sdk/0.0.1 azsdk-python-azuremachinelearningdesignerserviceclient/unknown Python/3.10.13 (Windows-10-10.0.22631-SP0) method: GET uri: https://eastus.api.azureml.ms/flow/api/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/BulkRuns/eval_run_name response: body: string: '{"flowGraph": {"nodes": [{"name": "grade", "type": "python", "source": {"type": "code", "path": "grade.py"}, "inputs": {"groundtruth": "${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": "${run.outputs.result}"}, "outputDatastoreName": "workspaceblobstore", "childRunBasePath": "promptflow/PromptFlowArtifacts/eval_run_name/flow_artifacts", "flowDagFileRelativePath": "flow.dag.yaml", "flowSnapshotId": "1885cd85-1969-4fca-8c21-d8826ed5d886", "studioPortalEndpoint": "https://ml.azure.com/runs/eval_run_name?wsid=/subscriptions/00000000-0000-0000-0000-000000000000/resourcegroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000"}' headers: connection: - keep-alive content-length: - '13872' content-type: - application/json; charset=utf-8 strict-transport-security: - max-age=15724800; includeSubDomains; preload transfer-encoding: - chunked vary: - Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.526' status: code: 200 message: OK - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - promptflow-sdk/0.0.1 azsdk-python-azuremachinelearningdesignerserviceclient/unknown Python/3.10.13 (Windows-10-10.0.22631-SP0) method: GET uri: https://eastus.api.azureml.ms/flow/api/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/BulkRuns/eval_run_name response: body: string: '{"flowGraph": {"nodes": [{"name": "grade", "type": "python", "source": {"type": "code", "path": "grade.py"}, "inputs": {"groundtruth": "${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": "${run.outputs.result}"}, "outputDatastoreName": "workspaceblobstore", "childRunBasePath": "promptflow/PromptFlowArtifacts/eval_run_name/flow_artifacts", "flowDagFileRelativePath": "flow.dag.yaml", "flowSnapshotId": "1885cd85-1969-4fca-8c21-d8826ed5d886", "studioPortalEndpoint": "https://ml.azure.com/runs/eval_run_name?wsid=/subscriptions/00000000-0000-0000-0000-000000000000/resourcegroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000"}' headers: connection: - keep-alive content-length: - '13872' content-type: - application/json; charset=utf-8 strict-transport-security: - max-age=15724800; includeSubDomains; preload transfer-encoding: - chunked vary: - Accept-Encoding x-content-type-options: - nosniff x-request-time: - '0.270' status: code: 200 message: OK - request: body: null headers: Accept: - application/json Accept-Encoding: - gzip, deflate Connection: - keep-alive User-Agent: - promptflow-sdk/0.0.1 azsdk-python-azuremachinelearningdesignerserviceclient/unknown Python/3.10.13 (Windows-10-10.0.22631-SP0) method: GET uri: https://eastus.api.azureml.ms/flow/api/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/00000/BulkRuns/eval_run_name response: body: string: '{"flowGraph": {"nodes": [{"name": "grade", "type": "python", "source": {"type": "code", "path": "grade.py"}, "inputs": {"groundtruth": "${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,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAACgElEQVR4nGWSz2vcVRTFP/e9NzOZ1KDGohASslLEH6VLV0ak4l/QpeDCrfQPcNGliODKnVm4EBdBsIjQIlhciKW0ycKFVCSNbYnjdDLtmPnmO/nO9917XcxMkjYX3uLx7nnn3HOuMK2Nix4fP78ZdrYXVkLVWjf3l3B1B+HpcjzGFtmqa6cePz7/x0dnn1n5qhj3iBJPYREIURAJuCtpY8PjReDbrf9WG7H1fuefwQU9qKztTcMJT+PNnEFvjGVDBDlSsH6p/9MLzy6+NxwVqI8RAg4IPmWedMckdLYP6O6UpIaQfvyyXG012+e79/ZfHukoS1ISMT2hGTB1RkUmNgQ5QZ0w+a2VWDq73MbdEWmfnnv6UWe7oNzPaLapl5CwuLTXK9WUGBuCjqekzhP+z52ZXOrKMD3OJg0Hh778aiOuvpnYvp05d6GJO4iAO4QAe/eV36/X5LFRV4Zmn+AdkqlL8Vjp3oVioOz+WTPzzYEgsN+fgPLYyJVheSbPPVl2ikeGZRjtG52/8rHuaV9VOlpP2OtKyVndcRVCSqOhsvxa4vW359i6OuKdD+aP8Q4SYPdOzS/flGjt1JUSaMqZ5nwa1Y8qWb/Ud/eZZkHisYezEM0m+fcelDr8F1SqW2LNK6r1jXQwyLzy1hxvrLXZulry7ocL+FS6G4QIu3fG/Px1gdYeW7LIgXU2P/115TOA5G7e3Rmj2aS/m7l5pThiZzrCcE/d1XHzbln373nw7y6veeoUm5KCNKT/IPPwbiY1hYd/l5MIT65BMFt87sU4v9D7/JMflr44uV6hGh1+L4RCkg6z5iK2tAhNLeLsNGwYA4fDYnC/drvuuFxe86NV/x+Ut27g0FvykgAAAABJRU5ErkJggg==", "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": 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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"], 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"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-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": 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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: - '0.829' 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 07:52:45 +0000 49 promptflow-runtime INFO [eval_run_name] Receiving v2 bulk run request 9ada5207-1c07-4048-8721-8a87560f52bf: {\"flow_id\": \"eval_run_name\", \"flow_run_id\": \"eval_run_name\", \"flow_source\": {\"flow_source_type\": 1, \"flow_source_info\": {\"snapshot_id\": \"1885cd85-1969-4fca-8c21-d8826ed5d886\"}, \"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%3A41%3A49Z&ske=2024-01-13T15%3A51%3A49Z&sks=b&skv=2019-07-07&st=2024-01-12T07%3A42%3A44Z&se=2024-01-12T15%3A52%3A44Z&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%3A52%3A45Z&ske=2024-01-19T07%3A52%3A45Z&sks=b&skv=2019-07-07&se=2024-01-19T07%3A52%3A45Z&sp=racwl\", \"output_datastore_name\": \"workspaceblobstore\"}}\n2024-01-12 07:52:45 +0000 49 promptflow-runtime INFO Runtime version: 20231204.v4. PromptFlow version: 1.2.0rc1\n2024-01-12 07:52:45 +0000 49 promptflow-runtime INFO Updating eval_run_name to Status.Preparing...\n2024-01-12 07:52:45 +0000 49 promptflow-runtime INFO Downloading snapshot to /mnt/host/service/app/39649/requests/eval_run_name\n2024-01-12 07:52:45 +0000 49 promptflow-runtime INFO Get snapshot sas url for 1885cd85-1969-4fca-8c21-d8826ed5d886...\n2024-01-12 07:52:52 +0000 49 promptflow-runtime INFO Downloading snapshot 1885cd85-1969-4fca-8c21-d8826ed5d886 from uri https://promptfloweast4063704120.blob.core.windows.net/snapshotzips/promptflow-eastus:3e123da1-f9a5-4c91-9234-8d9ffbb39ff5:snapshotzip/1885cd85-1969-4fca-8c21-d8826ed5d886.zip...\n2024-01-12 07:52:52 +0000 49 promptflow-runtime INFO Downloaded file /mnt/host/service/app/39649/requests/eval_run_name/1885cd85-1969-4fca-8c21-d8826ed5d886.zip with size 1243 for snapshot 1885cd85-1969-4fca-8c21-d8826ed5d886.\n2024-01-12 07:52:52 +0000 49 promptflow-runtime INFO Download snapshot 1885cd85-1969-4fca-8c21-d8826ed5d886 completed.\n2024-01-12 07:52:52 +0000 49 promptflow-runtime INFO Successfully download snapshot to /mnt/host/service/app/39649/requests/eval_run_name\n2024-01-12 07:52:52 +0000 49 promptflow-runtime INFO About to execute a python flow.\n2024-01-12 07:52:52 +0000 49 promptflow-runtime INFO Use spawn method to start child process.\n2024-01-12 07:52:52 +0000 49 promptflow-runtime INFO Starting to check process 2911 status for run eval_run_name\n2024-01-12 07:52:52 +0000 49 promptflow-runtime INFO Start checking run status for run eval_run_name\n2024-01-12 07:52:56 +0000 2911 promptflow-runtime INFO [49--2911] Start processing flowV2......\n2024-01-12 07:52:56 +0000 2911 promptflow-runtime INFO Runtime version: 20231204.v4. PromptFlow version: 1.2.0rc1\n2024-01-12 07:52:56 +0000 2911 promptflow-runtime INFO Setting mlflow tracking uri...\n2024-01-12 07:52:56 +0000 2911 promptflow-runtime INFO Validating ''AzureML Data Scientist'' user authentication...\n2024-01-12 07:52:56 +0000 2911 promptflow-runtime INFO Successfully validated ''AzureML Data Scientist'' user authentication.\n2024-01-12 07:52:56 +0000 2911 promptflow-runtime INFO Using AzureMLRunStorageV2\n2024-01-12 07:52:56 +0000 2911 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 07:52:56 +0000 2911 promptflow-runtime INFO Initialized blob service client for AzureMLRunTracker.\n2024-01-12 07:52:57 +0000 2911 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 07:52:57 +0000 2911 promptflow-runtime INFO Resolve data from url finished in 0.5648300694301724 seconds\n2024-01-12 07:52:58 +0000 2911 promptflow-runtime INFO Resolve data from url finished in 0.6205331217497587 seconds\n2024-01-12 07:52:58 +0000 2911 promptflow-runtime INFO Starting the aml run ''eval_run_name''...\n2024-01-12 07:52:59 +0000 2911 execution.bulk INFO Using fork, process count: 3\n2024-01-12 07:52:59 +0000 2954 execution.bulk INFO Process 2954 started.\n2024-01-12 07:52:59 +0000 2958 execution.bulk INFO Process 2958 started.\n2024-01-12 07:52:59 +0000 2911 execution.bulk INFO Process name: ForkProcess-34:2, Process id: 2954, Line number: 0 start execution.\n2024-01-12 07:52:59 +0000 2911 execution.bulk INFO Process name: ForkProcess-34:3, Process id: 2958, Line number: 1 start execution.\n2024-01-12 07:52:59 +0000 2911 execution.bulk INFO Process name: ForkProcess-34:2, Process id: 2954, Line number: 0 completed.\n2024-01-12 07:52:59 +0000 2911 execution.bulk INFO Finished 1 / 3 lines.\n2024-01-12 07:52:59 +0000 2911 execution.bulk INFO Average execution time for completed lines: 0.21 seconds. Estimated time for incomplete lines: 0.42 seconds.\n2024-01-12 07:52:59 +0000 2911 execution.bulk INFO Process name: ForkProcess-34:2, Process id: 2954, Line number: 2 start execution.\n2024-01-12 07:52:59 +0000 2911 execution.bulk INFO Process name: ForkProcess-34:3, Process id: 2958, Line number: 1 completed.\n2024-01-12 07:52:59 +0000 2911 execution.bulk INFO Finished 2 / 3 lines.\n2024-01-12 07:52:59 +0000 2961 execution.bulk INFO Process 2961 started.\n2024-01-12 07:52:59 +0000 2911 execution.bulk INFO Average execution time for completed lines: 0.13 seconds. Estimated time for incomplete lines: 0.13 seconds.\n2024-01-12 07:52:59 +0000 2911 execution.bulk INFO Process name: ForkProcess-34:2, Process id: 2954, Line number: 2 completed.\n2024-01-12 07:52:59 +0000 2911 execution.bulk INFO Finished 3 / 3 lines.\n2024-01-12 07:52:59 +0000 2911 execution.bulk INFO Average execution time for completed lines: 0.12 seconds. Estimated time for incomplete lines: 0.0 seconds.\n2024-01-12 07:53:00 +0000 2911 execution.bulk INFO Executing aggregation nodes...\n2024-01-12 07:53:00 +0000 2911 execution.bulk INFO Finish executing aggregation nodes.\n2024-01-12 07:53:02 +0000 2911 execution.bulk INFO Upload status summary metrics for run eval_run_name finished in 1.4952670317143202 seconds\n2024-01-12 07:53:02 +0000 2911 execution.bulk INFO Upload metrics for run eval_run_name finished in 0.3417100487276912 seconds\n2024-01-12 07:53:02 +0000 2911 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 07:53:02 +0000 2911 execution.bulk INFO Upload RH properties for run eval_run_name finished in 0.09185165446251631 seconds\n2024-01-12 07:53:02 +0000 2911 promptflow-runtime INFO Creating unregistered output Asset for Run eval_run_name...\n2024-01-12 07:53:02 +0000 2911 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 07:53:02 +0000 2911 promptflow-runtime INFO Creating unregistered output Asset for Run eval_run_name...\n2024-01-12 07:53:03 +0000 2911 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 07:53:03 +0000 2911 promptflow-runtime INFO Creating Artifact for Run eval_run_name...\n2024-01-12 07:53:03 +0000 2911 promptflow-runtime INFO Created instance_results.jsonl Artifact.\n2024-01-12 07:53:03 +0000 2911 promptflow-runtime INFO Patching eval_run_name...\n2024-01-12 07:53:03 +0000 2911 promptflow-runtime INFO Ending the aml run ''eval_run_name'' with status ''Completed''...\n2024-01-12 07:53:04 +0000 49 promptflow-runtime INFO Process 2911 finished\n2024-01-12 07:53:04 +0000 49 promptflow-runtime INFO [49] Child process finished!\n2024-01-12 07:53:04 +0000 49 promptflow-runtime INFO [eval_run_name] End processing bulk run\n2024-01-12 07:53:04 +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: - '0.503' status: code: 200 message: OK version: 1

promptflow/src/promptflow/tests/test_configs/recordings/test_run_operations_TestFlowRun_test_show_run.yaml/0

{ "file_path": "promptflow/src/promptflow/tests/test_configs/recordings/test_run_operations_TestFlowRun_test_show_run.yaml", "repo_id": "promptflow", "token_count": 89116 }

74

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

promptflow/src/promptflow/tests/test_configs/runs/input_with_dict_val.yaml/0

{ "file_path": "promptflow/src/promptflow/tests/test_configs/runs/input_with_dict_val.yaml", "repo_id": "promptflow", "token_count": 70 }

75

from promptflow import tool @tool def divide_num(num: int): return UnserializableClass(num=(int)(num / 2)) class UnserializableClass: def __init__(self, num: int): self.num = num def __str__(self): return str(self.num)

promptflow/src/promptflow/tests/test_configs/wrong_flows/flow_output_unserializable/divide_num.py/0

{ "file_path": "promptflow/src/promptflow/tests/test_configs/wrong_flows/flow_output_unserializable/divide_num.py", "repo_id": "promptflow", "token_count": 99 }

76