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# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import pytest
from azure.core.exceptions import HttpResponseError
from promptflow._sdk._orm import RunInfo
from promptflow.exceptions import _ErrorInfo, ErrorCategory, ErrorTarget, UserErrorException
from promptflow.executor import FlowValidator
from promptflow.executor._errors import InvalidNodeReference
FLOWS_DIR = "./tests/test_configs/flows/print_input_flow"
@pytest.mark.unittest
class TestExceptions:
def test_error_category_with_unknow_error(self, pf):
ex = None
try:
pf.run("./exceptions/flows")
except Exception as e:
ex = e
error_category, error_type, error_target, error_message, error_detail = _ErrorInfo.get_error_info(ex)
assert error_category == ErrorCategory.UNKNOWN
assert error_type == "FileNotFoundError"
assert error_target == ErrorTarget.UNKNOWN
assert error_message == ""
assert (
"module=promptflow._sdk._pf_client, "
'code=raise FileNotFoundError(f"flow path {flow} does not exist"), '
"lineno="
) in error_detail
def test_error_category_with_user_error(self, pf):
ex = None
try:
RunInfo.get("run_name")
except Exception as e:
ex = e
error_category, error_type, error_target, error_message, error_detail = _ErrorInfo.get_error_info(ex)
assert error_category == ErrorCategory.USER_ERROR
assert error_type == "RunNotFoundError"
assert error_target == ErrorTarget.CONTROL_PLANE_SDK
assert error_message == ""
assert (
"module=promptflow._sdk._orm.run_info, "
'code=raise RunNotFoundError(f"Run name {name!r} cannot be found."), '
"lineno="
) in error_detail
def test_error_category_with_system_error(self):
ex = None
try:
FlowValidator._validate_aggregation_inputs({}, {"input1": "value1"})
except Exception as e:
ex = e
error_category, error_type, error_target, error_message, error_detail = _ErrorInfo.get_error_info(ex)
assert error_category == ErrorCategory.SYSTEM_ERROR
assert error_type == "InvalidAggregationInput"
assert error_target == ErrorTarget.UNKNOWN
assert error_message == (
"The input for aggregation is incorrect. "
"The value for aggregated reference input '{input_key}' should be a list, "
"but received {value_type}. "
"Please adjust the input value to match the expected format."
)
assert (
"module=promptflow.executor.flow_validator, " "code=raise InvalidAggregationInput(, " "lineno="
) in error_detail
def test_error_category_with_http_error(self, subscription_id, resource_group_name, workspace_name):
try:
raise HttpResponseError(message="HttpResponseError")
except Exception as e:
ex = e
error_category, error_type, error_target, error_message, error_detail = _ErrorInfo.get_error_info(ex)
assert error_category == ErrorCategory.UNKNOWN
assert error_type == "HttpResponseError"
assert error_target == ErrorTarget.UNKNOWN
assert error_message == ""
assert error_detail == ""
@pytest.mark.parametrize(
"status_code, expected_error_category",
[
(203, ErrorCategory.UNKNOWN),
(304, ErrorCategory.UNKNOWN),
(400, ErrorCategory.UNKNOWN),
(401, ErrorCategory.UNKNOWN),
(429, ErrorCategory.UNKNOWN),
(500, ErrorCategory.UNKNOWN),
],
)
def test_error_category_with_status_code(self, status_code, expected_error_category):
try:
raise Exception()
except Exception as e:
e.status_code = status_code
ex = e
error_category, error_type, error_target, error_message, error_detail = _ErrorInfo.get_error_info(ex)
assert error_category == expected_error_category
assert error_type == "Exception"
assert error_target == ErrorTarget.UNKNOWN
assert error_message == ""
assert error_detail == ""
def test_error_category_with_executor_error(self):
try:
msg_format = (
"Invalid node definitions found in the flow graph. Non-aggregation node '{invalid_reference}' "
"cannot be referenced in the activate config of the aggregation node '{node_name}'. Please "
"review and rectify the node reference."
)
raise InvalidNodeReference(message_format=msg_format, invalid_reference=None, node_name="node_name")
except Exception as e:
ex = e
error_category, error_type, error_target, error_message, error_detail = _ErrorInfo.get_error_info(ex)
assert error_category == ErrorCategory.USER_ERROR
assert error_type == "InvalidNodeReference"
assert error_target == ErrorTarget.EXECUTOR
assert error_message == (
"Invalid node definitions found in the flow graph. Non-aggregation node '{invalid_reference}' "
"cannot be referenced in the activate config of the aggregation node '{node_name}'. Please "
"review and rectify the node reference."
)
assert error_detail == ""
def test_error_category_with_cause_exception1(self):
"""cause exception is PromptflowException and e is PromptflowException, recording e."""
ex = None
try:
try:
FlowValidator._validate_aggregation_inputs({}, {"input1": "value1"})
except Exception as e:
raise UserErrorException("FlowValidator._validate_aggregation_inputs failed") from e
except Exception as e:
ex = e
error_category, error_type, error_target, error_message, error_detail = _ErrorInfo.get_error_info(ex)
assert error_category == ErrorCategory.USER_ERROR
assert error_type == "InvalidAggregationInput"
assert error_target == ErrorTarget.UNKNOWN
assert error_message == ""
assert error_detail == ""
ex = None
try:
try:
FlowValidator._validate_aggregation_inputs({}, {"input1": "value1"})
except Exception as e:
raise UserErrorException(message=str(e), error=e)
except Exception as e:
ex = e
error_category, error_type, error_target, error_message, error_detail = _ErrorInfo.get_error_info(ex)
assert error_category == ErrorCategory.USER_ERROR
assert error_type == "InvalidAggregationInput"
assert error_target == ErrorTarget.UNKNOWN
assert error_message == ""
assert error_detail == ""
def test_error_category_with_cause_exception2(self):
"""cause exception is PromptflowException and e is not PromptflowException, recording cause exception."""
ex = None
try:
try:
FlowValidator._validate_aggregation_inputs({}, {"input1": "value1"})
except Exception as e:
raise Exception("FlowValidator._validate_aggregation_inputs failed") from e
except Exception as e:
ex = e
error_category, error_type, error_target, error_message, error_detail = _ErrorInfo.get_error_info(ex)
assert error_category == ErrorCategory.SYSTEM_ERROR
assert error_type == "InvalidAggregationInput"
assert error_target == ErrorTarget.UNKNOWN
assert error_message == (
"The input for aggregation is incorrect. The value for aggregated reference "
"input '{input_key}' should be a list, but received {value_type}. Please "
"adjust the input value to match the expected format."
)
assert (
"module=promptflow.executor.flow_validator, " "code=raise InvalidAggregationInput(, " "lineno="
) in error_detail
def test_error_category_with_cause_exception3(self, pf):
"""cause exception is not PromptflowException and e is not PromptflowException, recording e exception."""
ex = None
try:
try:
pf.run("./exceptions/flows")
except Exception as e:
raise Exception("pf run failed") from e
except Exception as e:
ex = e
error_category, error_type, error_target, error_message, error_detail = _ErrorInfo.get_error_info(ex)
assert error_category == ErrorCategory.UNKNOWN
assert error_type == "Exception"
assert error_target == ErrorTarget.UNKNOWN
assert error_message == ""
assert error_detail == ""
| promptflow/src/promptflow/tests/sdk_cli_test/unittests/test_exceptions.py/0 | {
"file_path": "promptflow/src/promptflow/tests/sdk_cli_test/unittests/test_exceptions.py",
"repo_id": "promptflow",
"token_count": 3605
} | 53 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import json
import tempfile
import uuid
from pathlib import Path
import mock
import pytest
from sdk_cli_azure_test.recording_utilities import is_replay
from promptflow import PFClient
from promptflow._sdk.entities import CustomConnection
from ..utils import PFSOperations, check_activity_end_telemetry
def create_custom_connection(client: PFClient) -> str:
name = str(uuid.uuid4())
connection = CustomConnection(name=name, configs={"api_base": "test"}, secrets={"api_key": "test"})
client.connections.create_or_update(connection)
return name
@pytest.mark.e2etest
class TestConnectionAPIs:
def test_list_connections(self, pf_client: PFClient, pfs_op: PFSOperations) -> None:
create_custom_connection(pf_client)
with check_activity_end_telemetry(activity_name="pf.connections.list"):
connections = pfs_op.list_connections().json
assert len(connections) >= 1
def test_get_connection(self, pf_client: PFClient, pfs_op: PFSOperations) -> None:
name = create_custom_connection(pf_client)
with check_activity_end_telemetry(activity_name="pf.connections.get"):
conn_from_pfs = pfs_op.get_connection(name=name, status_code=200).json
assert conn_from_pfs["name"] == name
assert conn_from_pfs["configs"]["api_base"] == "test"
assert "api_key" in conn_from_pfs["secrets"]
# get connection with secret
with check_activity_end_telemetry(activity_name="pf.connections.get"):
conn_from_pfs = pfs_op.get_connection_with_secret(name=name, status_code=200).json
assert not conn_from_pfs["secrets"]["api_key"].startswith("*")
def test_delete_connection(self, pf_client: PFClient, pfs_op: PFSOperations) -> None:
len_connections = len(pfs_op.list_connections().json)
name = create_custom_connection(pf_client)
with check_activity_end_telemetry(
expected_activities=[
{"activity_name": "pf.connections.delete", "first_call": True},
]
):
pfs_op.delete_connection(name=name, status_code=204)
len_connections_after = len(pfs_op.list_connections().json)
assert len_connections_after == len_connections
def test_list_connection_with_invalid_user(self, pfs_op: PFSOperations) -> None:
# TODO: should we record telemetry for this case?
with check_activity_end_telemetry(expected_activities=[]):
conn_from_pfs = pfs_op.connection_operation_with_invalid_user()
assert conn_from_pfs.status_code == 403
def test_get_connection_specs(self, pfs_op: PFSOperations) -> None:
with check_activity_end_telemetry(expected_activities=[]):
specs = pfs_op.get_connection_specs(status_code=200).json
assert len(specs) > 1
@pytest.mark.skipif(is_replay(), reason="connection provider test, skip in non-live mode.")
def test_get_connection_by_provicer(self, pfs_op, subscription_id, resource_group_name, workspace_name):
target = "promptflow._sdk._pf_client.Configuration.get_connection_provider"
provider_url_target = (
"promptflow._sdk.operations._local_azure_connection_operations."
"LocalAzureConnectionOperations._extract_workspace"
)
mock_provider_url = (subscription_id, resource_group_name, workspace_name)
with mock.patch(target) as mocked_config, mock.patch(provider_url_target) as mocked_provider_url:
mocked_config.return_value = "azureml"
mocked_provider_url.return_value = mock_provider_url
connections = pfs_op.list_connections(status_code=200).json
assert len(connections) > 0
connection = pfs_op.get_connection(name=connections[0]["name"], status_code=200).json
assert connection["name"] == connections[0]["name"]
target = "promptflow._sdk._pf_client.Configuration.get_config"
with tempfile.TemporaryDirectory() as temp:
config_file = Path(temp) / ".azureml" / "config.json"
config_file.parent.mkdir(parents=True, exist_ok=True)
with open(config_file, "w") as f:
config = {
"subscription_id": subscription_id,
"resource_group": resource_group_name,
"workspace_name": workspace_name,
}
json.dump(config, f)
with mock.patch(target) as mocked_config:
mocked_config.return_value = "azureml"
connections = pfs_op.list_connections_by_provider(working_dir=temp, status_code=200).json
assert len(connections) > 0
connection = pfs_op.get_connections_by_provider(
name=connections[0]["name"], working_dir=temp, status_code=200
).json
assert connection["name"] == connections[0]["name"]
# this test checked 2 cases:
# 1. if the working directory is not exist, it should return 400
# 2. working directory has been encoded and decoded correctly, so that previous call may pass validation
error_message = pfs_op.list_connections_by_provider(
working_dir=temp + "not exist", status_code=400
).json
assert error_message == {
"errors": {"working_directory": "Invalid working directory."},
"message": "Input payload validation failed",
}
| promptflow/src/promptflow/tests/sdk_pfs_test/e2etests/test_connection_apis.py/0 | {
"file_path": "promptflow/src/promptflow/tests/sdk_pfs_test/e2etests/test_connection_apis.py",
"repo_id": "promptflow",
"token_count": 2392
} | 54 |
$schema: https://azuremlschemas.azureedge.net/promptflow/latest/OpenAIConnection.schema.json
name: my_open_ai_connection
type: open_ai
api_key: "<to-be-replaced>"
organization: "org"
base_url: "" | promptflow/src/promptflow/tests/test_configs/connections/openai_connection.yaml/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/connections/openai_connection.yaml",
"repo_id": "promptflow",
"token_count": 78
} | 55 |
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| promptflow/src/promptflow/tests/test_configs/datas/load_data_cases/10k/5k.2.jsonl/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/datas/load_data_cases/10k/5k.2.jsonl",
"repo_id": "promptflow",
"token_count": 45023
} | 56 |
def my_flow(text: str):
raise Exception("dummy exception")
| promptflow/src/promptflow/tests/test_configs/eager_flows/dummy_flow_with_exception/entry.py/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/eager_flows/dummy_flow_with_exception/entry.py",
"repo_id": "promptflow",
"token_count": 21
} | 57 |
entry: my_func | promptflow/src/promptflow/tests/test_configs/eager_flows/invalid_no_path/flow.dag.yaml/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/eager_flows/invalid_no_path/flow.dag.yaml",
"repo_id": "promptflow",
"token_count": 5
} | 58 |
inputs:
text:
type: string
default: hello
outputs:
output:
type: string
reference: ${nodeC.output}
nodes:
- name: nodeA
type: python
source:
type: code
path: pass_through.py
inputs:
input1: ${inputs.text}
activate:
when: ${inputs.text}
is: hi
- name: nodeB
type: python
source:
type: code
path: pass_through.py
inputs:
input1: ${inputs.text}
activate:
when: ${inputs.text}
is: hi
- name: nodeC
type: python
source:
type: code
path: summary_result.py
inputs:
input1: ${nodeA.output}
input2: ${nodeB.output}
activate:
when: dummy
is: dummy
| promptflow/src/promptflow/tests/test_configs/flows/activate_condition_always_met/flow.dag.yaml/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/activate_condition_always_met/flow.dag.yaml",
"repo_id": "promptflow",
"token_count": 280
} | 59 |
[
{
"groundtruth": "Tomorrow's weather will be sunny.",
"prediction": "The weather will be sunny tomorrow."
},
{
"groundtruth": "Hello,",
"prediction": "World."
},
{
"groundtruth": "Promptflow is a super easy-to-use tool, right?",
"prediction": "Yes!"
}
] | promptflow/src/promptflow/tests/test_configs/flows/aggregation_node_failed/samples.json/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/aggregation_node_failed/samples.json",
"repo_id": "promptflow",
"token_count": 111
} | 60 |
from promptflow import tool
import asyncio
@tool
async def passthrough_str_and_wait(input1: str, wait_seconds=3) -> str:
assert isinstance(input1, str), f"input1 should be a string, got {input1}"
print(f"Wait for {wait_seconds} seconds in async function")
for i in range(wait_seconds):
print(i)
await asyncio.sleep(1)
return input1
| promptflow/src/promptflow/tests/test_configs/flows/async_tools/async_passthrough.py/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/async_tools/async_passthrough.py",
"repo_id": "promptflow",
"token_count": 137
} | 61 |
{
"incident_id_extractor.completed": 3,
"job_info_extractor.completed": 1,
"job_info_extractor.bypassed": 2,
"incident_info_extractor.completed": 2,
"incident_info_extractor.bypassed": 1,
"icm_retriever.completed": 1,
"icm_retriever.bypassed": 2,
"tsg_retriever.completed": 1,
"tsg_retriever.bypassed": 2,
"kql_tsg_retriever.completed": 1,
"kql_tsg_retriever.bypassed": 2,
"investigation_steps.completed": 2,
"investigation_steps.bypassed": 1,
"retriever_summary.completed": 2,
"retriever_summary.bypassed": 1,
"investigation_method.completed": 3
} | promptflow/src/promptflow/tests/test_configs/flows/conditional_flow_with_activate/expected_status_summary.json/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/conditional_flow_with_activate/expected_status_summary.json",
"repo_id": "promptflow",
"token_count": 282
} | 62 |
{
"square.bypassed": 2,
"double.completed": 2,
"collect_node.completed": 4,
"double.bypassed": 2,
"square.completed": 2,
"aggregation_double.completed": 1,
"aggregation_square.completed": 1
} | promptflow/src/promptflow/tests/test_configs/flows/conditional_flow_with_aggregate_bypassed/expected_status_summary.json/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/conditional_flow_with_aggregate_bypassed/expected_status_summary.json",
"repo_id": "promptflow",
"token_count": 96
} | 63 |
# syntax=docker/dockerfile:1
FROM docker.io/continuumio/miniconda3:latest
WORKDIR /
COPY ./flow /flow
# create conda environment
RUN conda create -n promptflow-serve python=3.9.16 pip=23.0.1 -q -y && \
conda run -n promptflow-serve \
pip install -r /flow/requirements_txt && \
conda run -n promptflow-serve pip install keyrings.alt && \
conda run -n promptflow-serve pip install gunicorn==20.1.0 && \
conda run -n promptflow-serve pip cache purge && \
conda clean -a -y
RUN apt-get update && apt-get install -y runit
EXPOSE 8080
COPY ./connections/* /connections/
# reset runsvdir
RUN rm -rf /var/runit
COPY ./runit /var/runit
# grant permission
RUN chmod -R +x /var/runit
COPY ./start.sh /
CMD ["bash", "./start.sh"] | promptflow/src/promptflow/tests/test_configs/flows/export/linux/Dockerfile/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/export/linux/Dockerfile",
"repo_id": "promptflow",
"token_count": 297
} | 64 |
inputs:
question:
type: string
outputs:
output:
type: string
reference: ${test_langchain_traces.output}
nodes:
- name: test_langchain_traces
type: python
source:
type: code
path: test_langchain_traces.py
inputs:
question: ${inputs.question}
conn: azure_open_ai_connection
| promptflow/src/promptflow/tests/test_configs/flows/flow_with_langchain_traces/flow.dag.yaml/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/flow_with_langchain_traces/flow.dag.yaml",
"repo_id": "promptflow",
"token_count": 123
} | 65 |
from promptflow import tool
@tool
def echo(text):
"""yield the input string."""
echo_text = "Echo - " + text
for word in echo_text.split():
yield word | promptflow/src/promptflow/tests/test_configs/flows/generator_nodes/echo.py/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/generator_nodes/echo.py",
"repo_id": "promptflow",
"token_count": 65
} | 66 |
echo Hello Promptflow!
| promptflow/src/promptflow/tests/test_configs/flows/intent-copilot/setup.sh/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/intent-copilot/setup.sh",
"repo_id": "promptflow",
"token_count": 6
} | 67 |
$schema: https://azuremlschemas.azureedge.net/latest/flow.schema.json
name: classification_accuracy_eval
type: evaluate
path: azureml://datastores/workspaceworkingdirectory/paths/Users/wanhan/my_flow_snapshot/flow.dag.yaml
| promptflow/src/promptflow/tests/test_configs/flows/meta_files/remote_fs.meta.yaml/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/meta_files/remote_fs.meta.yaml",
"repo_id": "promptflow",
"token_count": 80
} | 68 |
import openai
from openai.version import VERSION as OPENAI_VERSION
from typing import List
from promptflow import tool
from promptflow.connections import AzureOpenAIConnection
IS_LEGACY_OPENAI = OPENAI_VERSION.startswith("0.")
def get_client(connection: AzureOpenAIConnection):
api_key = connection.api_key
conn = dict(
api_key=connection.api_key,
)
if api_key.startswith("sk-"):
from openai import OpenAI as Client
else:
from openai import AzureOpenAI as Client
conn.update(
azure_endpoint=connection.api_base,
api_version=connection.api_version,
)
return Client(**conn)
def create_messages(question, chat_history):
yield {"role": "system", "content": "You are a helpful assistant."}
for chat in chat_history:
yield {"role": "user", "content": chat["inputs"]["question"]}
yield {"role": "assistant", "content": chat["outputs"]["answer"]}
yield {"role": "user", "content": question}
@tool
def chat(connection: AzureOpenAIConnection, question: str, chat_history: List, stream: bool) -> str:
if IS_LEGACY_OPENAI:
completion = openai.ChatCompletion.create(
engine="gpt-35-turbo",
messages=list(create_messages(question, chat_history)),
temperature=1.0,
top_p=1.0,
n=1,
stream=stream,
stop=None,
max_tokens=16,
**dict(connection),
)
else:
completion = get_client(connection).chat.completions.create(
model="gpt-35-turbo",
messages=list(create_messages(question, chat_history)),
temperature=1.0,
top_p=1.0,
n=1,
stream=stream,
stop=None,
max_tokens=16
)
if stream:
def generator():
for chunk in completion:
if chunk.choices:
if IS_LEGACY_OPENAI:
yield getattr(chunk.choices[0]["delta"], "content", "")
else:
yield chunk.choices[0].delta.content or ""
# We must return the generator object, not using yield directly here.
# Otherwise, the function itself will become a generator, despite whether stream is True or False.
# return generator()
return "".join(generator())
else:
# chat api may return message with no content.
if IS_LEGACY_OPENAI:
return getattr(completion.choices[0].message, "content", "")
else:
return completion.choices[0].message.content or ""
| promptflow/src/promptflow/tests/test_configs/flows/openai_chat_api_flow/chat.py/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/openai_chat_api_flow/chat.py",
"repo_id": "promptflow",
"token_count": 1207
} | 69 |
{"text": "text_0"}
{"text": "text_1"}
{"text": "text_2"}
{"text": "text_3"}
{"text": "text_4"}
{"text": "text_5"}
{"text": "text_6"}
{"text": "text_7"}
{"text": "text_8"}
{"text": "text_9"} | promptflow/src/promptflow/tests/test_configs/flows/print_input_flow/inputs.jsonl/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/print_input_flow/inputs.jsonl",
"repo_id": "promptflow",
"token_count": 89
} | 70 |
{"idx": 1, "line_number": 0}
{"idx": 2, "line_number": 1}
{"idx": 4, "line_number": 3}
{"idx": 5, "line_number": 4}
{"idx": 7, "line_number": 6}
{"idx": 8, "line_number": 7}
{"idx": 10, "line_number": 9}
| promptflow/src/promptflow/tests/test_configs/flows/python_tool_partial_failure/inputs/output.jsonl/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/python_tool_partial_failure/inputs/output.jsonl",
"repo_id": "promptflow",
"token_count": 98
} | 71 |
inputs:
image:
type: image
default: logo.jpg
outputs:
output:
type: image
reference: ${python_node_2.output}
nodes:
- name: python_node
type: python
source:
type: code
path: pick_an_image.py
inputs:
image_1: ${inputs.image}
image_2: logo_2.png
- name: python_node_2
type: python
source:
type: code
path: pick_an_image.py
inputs:
image_1: ${python_node.output}
image_2: logo_2.png
| promptflow/src/promptflow/tests/test_configs/flows/python_tool_with_simple_image/flow.dag.yaml/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/python_tool_with_simple_image/flow.dag.yaml",
"repo_id": "promptflow",
"token_count": 195
} | 72 |
id: use_functions_with_chat_models
name: Use Functions with Chat Models
inputs:
chat_history:
type: list
default:
- inputs:
question: What is the weather like in Boston?
outputs:
answer: '{"forecast":["sunny","windy"],"location":"Boston","temperature":"72","unit":"fahrenheit"}'
llm_output:
content: null
function_call:
name: get_current_weather
arguments: |-
{
"location": "Boston"
}
role: assistant
is_chat_input: false
question:
type: string
default: How about London next week?
is_chat_input: true
outputs:
answer:
type: string
reference: ${run_function.output}
is_chat_output: true
llm_output:
type: object
reference: ${use_functions_with_chat_models.output}
nodes:
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type: python
source:
type: code
path: run_function.py
inputs:
response_message: ${use_functions_with_chat_models.output}
use_variants: false
- name: use_functions_with_chat_models
type: llm
source:
type: code
path: use_functions_with_chat_models.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: ""
functions:
- name: get_current_weather
description: Get the current weather in a given location
parameters:
type: object
properties:
location:
type: string
description: The city and state, e.g. San Francisco, CA
unit:
type: string
enum:
- celsius
- fahrenheit
required:
- location
- name: get_n_day_weather_forecast
description: Get an N-day weather forecast
parameters:
type: object
properties:
location:
type: string
description: The city and state, e.g. San Francisco, CA
format:
type: string
enum:
- celsius
- fahrenheit
description: The temperature unit to use. Infer this from the users location.
num_days:
type: integer
description: The number of days to forecast
required:
- location
- format
- num_days
function_call:
name: get_current_weather
chat_history: ${inputs.chat_history}
question: ${inputs.question}
provider: AzureOpenAI
connection: azure_open_ai_connection
api: chat
module: promptflow.tools.aoai
use_variants: false
node_variants: {}
environment:
python_requirements_txt: requirements.txt
| promptflow/src/promptflow/tests/test_configs/flows/sample_flow_with_functions/flow.dag.yaml/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/sample_flow_with_functions/flow.dag.yaml",
"repo_id": "promptflow",
"token_count": 1210
} | 73 |
from aaa import bbb # noqa: F401 | promptflow/src/promptflow/tests/test_configs/flows/script_with_import/fail.py/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/script_with_import/fail.py",
"repo_id": "promptflow",
"token_count": 13
} | 74 |
[
{
"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_no_variants/samples.json/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/web_classification_no_variants/samples.json",
"repo_id": "promptflow",
"token_count": 85
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for run eval_run_name\n2024-01-12 07:57:33 +0000 3221 promptflow-runtime
INFO [49--3221] Start processing flowV2......\n2024-01-12 07:57:33 +0000 3221
promptflow-runtime INFO Runtime version: 20231204.v4. PromptFlow version:
1.2.0rc1\n2024-01-12 07:57:33 +0000 3221 promptflow-runtime INFO Setting
mlflow tracking uri...\n2024-01-12 07:57:33 +0000 3221 promptflow-runtime
INFO Validating ''AzureML Data Scientist'' user authentication...\n2024-01-12
07:57:33 +0000 3221 promptflow-runtime INFO Successfully validated
''AzureML Data Scientist'' user authentication.\n2024-01-12 07:57:33 +0000 3221
promptflow-runtime INFO Using AzureMLRunStorageV2\n2024-01-12 07:57:33
+0000 3221 promptflow-runtime INFO Setting mlflow tracking uri to ''azureml://eastus.api.azureml.ms/mlflow/v1.0/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/promptflow-eastus''\n2024-01-12
07:57:33 +0000 3221 promptflow-runtime INFO Initialized blob service
client for AzureMLRunTracker.\n2024-01-12 07:57:33 +0000 3221 promptflow-runtime
INFO Setting mlflow tracking uri to ''azureml://eastus.api.azureml.ms/mlflow/v1.0/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/00000/providers/Microsoft.MachineLearningServices/workspaces/promptflow-eastus''\n2024-01-12
07:57:34 +0000 3221 promptflow-runtime INFO Resolve data from url finished
in 0.6532626627013087 seconds\n2024-01-12 07:57:35 +0000 3221 promptflow-runtime
INFO Resolve data from url finished in 0.6382788131013513 seconds\n2024-01-12
07:57:35 +0000 3221 promptflow-runtime INFO Starting the aml run ''eval_run_name''...\n2024-01-12
07:57:35 +0000 3221 execution.bulk INFO Using fork, process count:
3\n2024-01-12 07:57:35 +0000 3264 execution.bulk INFO Process 3264
started.\n2024-01-12 07:57:35 +0000 3268 execution.bulk INFO Process
3268 started.\n2024-01-12 07:57:35 +0000 3221 execution.bulk INFO Process
name: ForkProcess-40:2, Process id: 3264, Line number: 0 start execution.\n2024-01-12
07:57:35 +0000 3273 execution.bulk INFO Process 3273 started.\n2024-01-12
07:57:35 +0000 3221 execution.bulk INFO Process name: ForkProcess-40:3,
Process id: 3268, Line number: 1 start execution.\n2024-01-12 07:57:35 +0000 3221
execution.bulk INFO Process name: ForkProcess-40:4, Process id: 3273,
Line number: 2 start execution.\n2024-01-12 07:57:35 +0000 3221 execution.bulk INFO Process
name: ForkProcess-40:2, Process id: 3264, Line number: 0 completed.\n2024-01-12
07:57:35 +0000 3221 execution.bulk INFO Finished 1 / 3 lines.\n2024-01-12
07:57:35 +0000 3221 execution.bulk INFO Average execution time
for completed lines: 0.21 seconds. Estimated time for incomplete lines: 0.42
seconds.\n2024-01-12 07:57:35 +0000 3221 execution.bulk INFO Process
name: ForkProcess-40:3, Process id: 3268, Line number: 1 completed.\n2024-01-12
07:57:35 +0000 3221 execution.bulk INFO Process name: ForkProcess-40:4,
Process id: 3273, Line number: 2 completed.\n2024-01-12 07:57:35 +0000 3221
execution.bulk INFO Finished 3 / 3 lines.\n2024-01-12 07:57:35 +0000 3221
execution.bulk INFO Finished 3 / 3 lines.\n2024-01-12 07:57:36 +0000 3221
execution.bulk INFO Average execution time for completed lines: 0.09
seconds. Estimated time for incomplete lines: 0.0 seconds.\n2024-01-12 07:57:36
+0000 3221 execution.bulk INFO Average execution time for completed
lines: 0.09 seconds. Estimated time for incomplete lines: 0.0 seconds.\n2024-01-12
07:57:37 +0000 3221 execution.bulk INFO Executing aggregation nodes...\n2024-01-12
07:57:37 +0000 3221 execution.bulk INFO Finish executing aggregation
nodes.\n2024-01-12 07:57:38 +0000 3221 execution.bulk INFO Upload
status summary metrics for run eval_run_name finished in 1.5616551944985986
seconds\n2024-01-12 07:57:39 +0000 3221 execution.bulk INFO Upload
metrics for run eval_run_name finished in 0.36921436339616776 seconds\n2024-01-12
07:57:39 +0000 3221 promptflow-runtime INFO Successfully write run
properties {\"azureml.promptflow.total_tokens\": 0, \"_azureml.evaluate_artifacts\":
\"[{\\\"path\\\": \\\"instance_results.jsonl\\\", \\\"type\\\": \\\"table\\\"}]\"}
with run id ''eval_run_name''\n2024-01-12 07:57:39 +0000 3221 execution.bulk INFO Upload
RH properties for run eval_run_name finished in 0.07229613605886698 seconds\n2024-01-12
07:57:39 +0000 3221 promptflow-runtime INFO Creating unregistered output
Asset for Run eval_run_name...\n2024-01-12 07:57:39 +0000 3221 promptflow-runtime
INFO Created debug_info Asset: azureml://locations/eastus/workspaces/00000/data/azureml_eval_run_name_output_data_debug_info/versions/1\n2024-01-12
07:57:39 +0000 3221 promptflow-runtime INFO Creating unregistered output
Asset for Run eval_run_name...\n2024-01-12 07:57:39 +0000 3221 promptflow-runtime
INFO Created flow_outputs output Asset: azureml://locations/eastus/workspaces/00000/data/azureml_eval_run_name_output_data_flow_outputs/versions/1\n2024-01-12
07:57:39 +0000 3221 promptflow-runtime INFO Creating Artifact for Run
eval_run_name...\n2024-01-12 07:57:39 +0000 3221 promptflow-runtime INFO Created
instance_results.jsonl Artifact.\n2024-01-12 07:57:39 +0000 3221 promptflow-runtime
INFO Patching eval_run_name...\n2024-01-12 07:57:40 +0000 3221 promptflow-runtime
INFO Ending the aml run ''eval_run_name'' with status ''Completed''...\n2024-01-12
07:57:41 +0000 49 promptflow-runtime INFO Process 3221 finished\n2024-01-12
07:57:41 +0000 49 promptflow-runtime INFO [49] Child process finished!\n2024-01-12
07:57:41 +0000 49 promptflow-runtime INFO [eval_run_name] End processing
bulk run\n2024-01-12 07:57:41 +0000 49 promptflow-runtime INFO Cleanup
working dir /mnt/host/service/app/39649/requests/eval_run_name for bulk run\n"'
headers:
connection:
- keep-alive
content-length:
- '10617'
content-type:
- application/json; charset=utf-8
strict-transport-security:
- max-age=15724800; includeSubDomains; preload
transfer-encoding:
- chunked
vary:
- Accept-Encoding
x-content-type-options:
- nosniff
x-request-time:
- '0.452'
status:
code: 200
message: OK
version: 1
| promptflow/src/promptflow/tests/test_configs/recordings/test_run_operations_TestFlowRun_test_show_metrics.yaml/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/recordings/test_run_operations_TestFlowRun_test_show_metrics.yaml",
"repo_id": "promptflow",
"token_count": 90735
} | 78 |
flow: ../flows/classification_accuracy_evaluation
data: not_exist
column_mapping:
groundtruth: "${data.answer}"
prediction: "${run.outputs.category}"
run: flow_run_20230629_101205 # ./sample_bulk_run.yaml
# run config: env related
environment_variables: .env
# optional
connections:
node_1:
connection: test_llm_connection
deployment_name: gpt-35-turbo
| promptflow/src/promptflow/tests/test_configs/runs/illegal/non_exist_data.yaml/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/runs/illegal/non_exist_data.yaml",
"repo_id": "promptflow",
"token_count": 139
} | 79 |
inputs:
num:
type: int
outputs:
content:
type: string
reference: ""
nodes:
- name: divide_num
type: python
source:
type: code
path: divide_num.py
inputs:
num: ${inputs.num}
| promptflow/src/promptflow/tests/test_configs/wrong_flows/flow_output_reference_invalid/flow.dag.yaml/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/wrong_flows/flow_output_reference_invalid/flow.dag.yaml",
"repo_id": "promptflow",
"token_count": 92
} | 80 |
inputs:
num:
type: int
outputs:
content:
type: string
reference: ${stringify_num.output}
nodes:
- name: stringify_num
type: python
source:
type: code
path: stringify_num.py
inputs:
num: ${inputs.num}
- name: stringify_num
type: python
source:
type: code
path: another_stringify_num.py
inputs:
num: ${inputs.num}
| promptflow/src/promptflow/tests/test_configs/wrong_flows/nodes_names_duplicated/flow.dag.yaml/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/wrong_flows/nodes_names_duplicated/flow.dag.yaml",
"repo_id": "promptflow",
"token_count": 155
} | 81 |
inputs:
text:
type: string
outputs:
output:
type: string
reference: ${search_by_text.output.search_metadata}
nodes:
- name: search_by_text
type: python
source:
type: package
tool: promptflow.tools.serpapi11.SerpAPI.search
inputs:
connection: serp_connection
query: ${inputs.text}
num: 1 | promptflow/src/promptflow/tests/test_configs/wrong_flows/wrong_package_in_package_tools/flow.dag.yaml/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/wrong_flows/wrong_package_in_package_tools/flow.dag.yaml",
"repo_id": "promptflow",
"token_count": 131
} | 82 |
# Use flow in Azure ML pipeline job
After you have developed and tested the flow in [init and test a flow](../../how-to-guides/init-and-test-a-flow.md), this guide will help you learn how to use a flow as a parallel component in a pipeline job on AzureML, so that you can integrate the created flow with existing pipelines and process a large amount of data.
:::{admonition} Pre-requirements
- Customer need to install the extension `ml>=2.21.0` to enable this feature in CLI and package `azure-ai-ml>=1.11.0` to enable this feature in SDK;
- Customer need to put `$schema` in the target `flow.dag.yaml` to enable this feature;
- `flow.dag.yaml`: `$schema`: `https://azuremlschemas.azureedge.net/promptflow/latest/Flow.schema.json`
- `run.yaml`: `$schema`: `https://azuremlschemas.azureedge.net/promptflow/latest/Run.schema.json`
- Customer need to generate `flow.tools.json` for the target flow before below usage. The generation can be done by `pf flow validate`.
:::
For more information about AzureML and component:
- [Install and set up the CLI(v2)](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-configure-cli?view=azureml-api-2&tabs=public)
- [Install and set up the SDK(v2)](https://learn.microsoft.com/en-us/python/api/overview/azure/ai-ml-readme?view=azure-python)
- [What is a pipeline](https://learn.microsoft.com/en-us/azure/machine-learning/concept-ml-pipelines?view=azureml-api-2)
- [What is a component](https://learn.microsoft.com/en-us/azure/machine-learning/concept-component?view=azureml-api-2)
## Register a flow as a component
Customer can register a flow as a component with either CLI or SDK.
::::{tab-set}
:::{tab-item} CLI
:sync: CLI
```bash
# Register flow as a component
# Default component name will be the name of flow folder, which is not a valid component name, so we override it here; default version will be "1"
az ml component create --file standard/web-classification/flow.dag.yaml --set name=web_classification
# Register flow as a component with parameters override
az ml component create --file standard/web-classification/flow.dag.yaml --version 2 --set name=web_classification_updated
```
:::
:::{tab-item} SDK
:sync: SDK
```python
from azure.ai.ml import MLClient, load_component
ml_client = MLClient()
# Register flow as a component
flow_component = load_component("standard/web-classification/flow.dag.yaml")
# Default component name will be the name of flow folder, which is not a valid component name, so we override it here; default version will be "1"
flow_component.name = "web_classification"
ml_client.components.create_or_update(flow_component)
# Register flow as a component with parameters override
ml_client.components.create_or_update(
"standard/web-classification/flow.dag.yaml",
version="2",
params_override=[
{"name": "web_classification_updated"}
]
)
```
:::
::::
After registered a flow as a component, they can be referred in a pipeline job like [regular registered components](https://github.com/Azure/azureml-examples/tree/main/cli/jobs/pipelines-with-components/basics/1b_e2e_registered_components).
## Directly use a flow in a pipeline job
Besides explicitly registering a flow as a component, customer can also directly use flow in a pipeline job:
All connections and flow inputs will be exposed as input parameters of the component. Default value can be provided in flow/run definition; they can also be set/overwrite on job submission:
::::{tab-set}
:::{tab-item} CLI
:sync: CLI
```yaml
...
jobs:
flow_node:
type: parallel
component: standard/web-classification/flow.dag.yaml
inputs:
data: ${{parent.inputs.web_classification_input}}
url: "${data.url}"
connections.summarize_text_content.connection: azure_open_ai_connection
connections.summarize_text_content.deployment_name: text-davinci-003
...
```
Above is part of the pipeline job yaml, see here for [full example](https://github.com/Azure/azureml-examples/tree/main/cli/jobs/pipelines-with-components/pipeline_job_with_flow_as_component).
:::
:::{tab-item} SDK
:sync: SDK
```python
from azure.identity import DefaultAzureCredential
from azure.ai.ml import MLClient, load_component, Input
from azure.ai.ml.dsl import pipeline
credential = DefaultAzureCredential()
ml_client = MLClient.from_config(credential=credential)
data_input = Input(path="standard/web-classification/data.jsonl", type='uri_file')
# Load flow as a component
flow_component = load_component("standard/web-classification/flow.dag.yaml")
@pipeline
def pipeline_func_with_flow(data):
flow_node = flow_component(
data=data,
url="${data.url}",
connections={
"summarize_text_content": {
"connection": "azure_open_ai_connection",
"deployment_name": "text-davinci-003",
},
},
)
flow_node.compute = "cpu-cluster"
pipeline_with_flow = pipeline_func_with_flow(data=data_input)
pipeline_job = ml_client.jobs.create_or_update(pipeline_with_flow)
ml_client.jobs.stream(pipeline_job.name)
```
Above is part of the pipeline job python code, see here for [full example](https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs/pipelines/1l_flow_in_pipeline).
:::
::::
## Difference across flow in prompt flow and pipeline job
In prompt flow, flow runs on [runtime](https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/concept-runtime), which is designed for prompt flow and easy to customize; while in pipeline job, flow runs on different types of compute, and usually compute cluster.
Given above, if your flow has logic relying on identity or environment variable, please be aware of this difference as you might run into some unexpected error(s) when the flow runs in pipeline job, and you might need some extra configurations to make it work.
| promptflow/docs/cloud/azureai/use-flow-in-azure-ml-pipeline.md/0 | {
"file_path": "promptflow/docs/cloud/azureai/use-flow-in-azure-ml-pipeline.md",
"repo_id": "promptflow",
"token_count": 1931
} | 0 |
# Deploy a flow
A flow can be deployed to multiple platforms, such as a local development service, Docker container, Kubernetes cluster, etc.
```{gallery-grid}
:grid-columns: 1 2 2 3
- image: ../../media/how-to-guides/local.png
content: "<center><b>Development server</b></center>"
website: deploy-using-dev-server.html
- image: ../../media/how-to-guides/docker.png
content: "<center><b>Docker</b></center>"
website: deploy-using-docker.html
- image: ../../media/how-to-guides/kubernetes.png
content: "<center><b>Kubernetes</b></center>"
website: deploy-using-kubernetes.html
```
We also provide guides to deploy to cloud, such as azure app service:
```{gallery-grid}
:grid-columns: 1 2 2 3
- image: ../../media/how-to-guides/appservice.png
content: "<center><b>Azure App Service</b></center>"
website: ../../cloud/azureai/deploy-to-azure-appservice.html
```
We are working on more official deployment guides for other hosting providers, and welcome user submitted guides.
```{toctree}
:maxdepth: 1
:hidden:
deploy-using-dev-server
deploy-using-docker
deploy-using-kubernetes
distribute-flow-as-executable-app
``` | promptflow/docs/how-to-guides/deploy-a-flow/index.md/0 | {
"file_path": "promptflow/docs/how-to-guides/deploy-a-flow/index.md",
"repo_id": "promptflow",
"token_count": 397
} | 1 |
# Execute flow as a function
:::{admonition} Experimental feature
This is an experimental feature, and may change at any time. Learn [more](faq.md#stable-vs-experimental).
:::
## Overview
Promptflow allows you to load a flow and use it as a function in your code.
This feature is useful when building a service on top of a flow, reference [here](https://github.com/microsoft/promptflow/tree/main/examples/tutorials/flow-deploy/create-service-with-flow) for a simple example service with flow function consumption.
## Load an invoke the flow function
To use the flow-as-function feature, you first need to load a flow using the `load_flow` function.
Then you can consume the flow object like a function by providing key-value arguments for it.
```python
f = load_flow("../../examples/flows/standard/web-classification/")
f(url="sample_url")
```
## Config the flow with context
You can overwrite some flow configs before flow function execution by setting `flow.context`.
### Load flow as a function with in-memory connection override
By providing a connection object to flow context, flow won't need to get connection in execution time, which can save time when for cases where flow function need to be called multiple times.
```python
from promptflow.entities import AzureOpenAIConnection
connection_obj = AzureOpenAIConnection(
name=conn_name,
api_key=api_key,
api_base=api_base,
api_type="azure",
api_version=api_version,
)
# no need to create the connection object.
f.context = FlowContext(
connections={"classify_with_llm": {"connection": connection_obj}}
)
```
### Local flow as a function with flow inputs override
By providing overrides, the original flow dag will be updated in execution time.
```python
f.context = FlowContext(
# node "fetch_text_content_from_url" will take inputs from the following command instead of from flow input
overrides={"nodes.fetch_text_content_from_url.inputs.url": sample_url},
)
```
**Note**, the `overrides` are only doing YAML content replacement on original `flow.dag.yaml`.
If the `flow.dag.yaml` become invalid after `overrides`, validation error will be raised when executing.
### Load flow as a function with streaming output
After set `streaming` in flow context, the flow function will return an iterator to stream the output.
```python
f = load_flow(source="../../examples/flows/chat/basic-chat/")
f.context.streaming = True
result = f(
chat_history=[
{
"inputs": {"chat_input": "Hi"},
"outputs": {"chat_output": "Hello! How can I assist you today?"},
}
],
question="How are you?",
)
answer = ""
# the result will be a generator, iterate it to get the result
for r in result["answer"]:
answer += r
```
Reference our [sample](https://github.com/microsoft/promptflow/blob/main/examples/tutorials/get-started/flow-as-function.ipynb) for usage.
## Next steps
Learn more about:
- [Flow as a function sample](https://github.com/microsoft/promptflow/blob/main/examples/tutorials/get-started/flow-as-function.ipynb)
- [Deploy a flow](./deploy-a-flow/index.md)
| promptflow/docs/how-to-guides/execute-flow-as-a-function.md/0 | {
"file_path": "promptflow/docs/how-to-guides/execute-flow-as-a-function.md",
"repo_id": "promptflow",
"token_count": 969
} | 2 |
# Custom Tools
This section contains documentation for custom tools created by the community to extend Prompt flow's capabilities for specific use cases. These tools are developed following the guide on [Creating and Using Tool Packages](../../how-to-guides/develop-a-tool/create-and-use-tool-package.md). They are not officially maintained or endorsed by the Prompt flow team. For questions or issues when using a tool, please use the support contact link in the table below.
## Tool Package Index
The table below provides an index of custom tool packages. The columns contain:
- **Package Name:** The name of the tool package. Links to the package documentation.
- **Description:** A short summary of what the tool package does.
- **Owner:** The creator/maintainer of the tool package.
- **Support Contact:** Link to contact for support and reporting new issues.
| Package Name | Description | Owner | Support Contact |
|-|-|-|-|
| promptflow-azure-ai-language | Collection of Azure AI Language Prompt flow tools. | Sean Murray | [email protected] |
```{toctree}
:maxdepth: 1
:hidden:
azure-ai-language-tool
```
| promptflow/docs/integrations/tools/index.md/0 | {
"file_path": "promptflow/docs/integrations/tools/index.md",
"repo_id": "promptflow",
"token_count": 287
} | 3 |
# Vector DB Lookup
Vector DB Lookup is a vector search tool that allows users to search top k similar vectors from vector database. This tool is a wrapper for multiple third-party vector databases. The list of current supported databases is as follows.
| Name | Description |
| --- | --- |
| Azure Cognitive Search | Microsoft's cloud search service with built-in AI capabilities that enrich all types of information to help identify and explore relevant content at scale. |
| Qdrant | Qdrant is a vector similarity search engine that provides a production-ready service with a convenient API to store, search and manage points (i.e. vectors) with an additional payload. |
| Weaviate | Weaviate is an open source vector database that stores both objects and vectors. This allows for combining vector search with structured filtering. |
This tool will support more vector databases.
## Requirements
- For AzureML users, the tool is installed in default image, you can use the tool without extra installation.
- For local users,
`pip install promptflow-vectordb`
## Prerequisites
The tool searches data from a third-party vector database. To use it, you should create resources in advance and establish connection between the tool and the resource.
- **Azure Cognitive Search:**
- Create resource [Azure Cognitive Search](https://learn.microsoft.com/en-us/azure/search/search-create-service-portal).
- Add "Cognitive search" connection. Fill "API key" field with "Primary admin key" from "Keys" section of created resource, and fill "API base" field with the URL, the URL format is `https://{your_serive_name}.search.windows.net`.
- **Qdrant:**
- Follow the [installation](https://qdrant.tech/documentation/quick-start/) to deploy Qdrant to a self-maintained cloud server.
- Add "Qdrant" connection. Fill "API base" with your self-maintained cloud server address and fill "API key" field.
- **Weaviate:**
- Follow the [installation](https://weaviate.io/developers/weaviate/installation) to deploy Weaviate to a self-maintained instance.
- Add "Weaviate" connection. Fill "API base" with your self-maintained instance address and fill "API key" field.
## Inputs
The tool accepts the following inputs:
- **Azure Cognitive Search:**
| Name | Type | Description | Required |
| ---- | ---- | ----------- | -------- |
| connection | CognitiveSearchConnection | The created connection for accessing to Cognitive Search endpoint. | Yes |
| index_name | string | The index name created in Cognitive Search resource. | Yes |
| text_field | string | The text field name. The returned text field will populate the text of output. | No |
| vector_field | string | The vector field name. The target vector is searched in this vector field. | Yes |
| search_params | dict | The search parameters. It's key-value pairs. Except for parameters in the tool input list mentioned above, additional search parameters can be formed into a JSON object as search_params. For example, use `{"select": ""}` as search_params to select the returned fields, use `{"search": ""}` to perform a [hybrid search](https://learn.microsoft.com/en-us/azure/search/search-get-started-vector#hybrid-search). | No |
| search_filters | dict | The search filters. It's key-value pairs, the input format is like `{"filter": ""}` | No |
| vector | list | The target vector to be queried, which can be generated by Embedding tool. | Yes |
| top_k | int | The count of top-scored entities to return. Default value is 3 | No |
- **Qdrant:**
| Name | Type | Description | Required |
| ---- | ---- | ----------- | -------- |
| connection | QdrantConnection | The created connection for accessing to Qdrant server. | Yes |
| collection_name | string | The collection name created in self-maintained cloud server. | Yes |
| text_field | string | The text field name. The returned text field will populate the text of output. | No |
| search_params | dict | The search parameters can be formed into a JSON object as search_params. For example, use `{"params": {"hnsw_ef": 0, "exact": false, "quantization": null}}` to set search_params. | No |
| search_filters | dict | The search filters. It's key-value pairs, the input format is like `{"filter": {"should": [{"key": "", "match": {"value": ""}}]}}` | No |
| vector | list | The target vector to be queried, which can be generated by Embedding tool. | Yes |
| top_k | int | The count of top-scored entities to return. Default value is 3 | No |
- **Weaviate:**
| Name | Type | Description | Required |
| ---- | ---- | ----------- | -------- |
| connection | WeaviateConnection | The created connection for accessing to Weaviate. | Yes |
| class_name | string | The class name. | Yes |
| text_field | string | The text field name. The returned text field will populate the text of output. | No |
| vector | list | The target vector to be queried, which can be generated by Embedding tool. | Yes |
| top_k | int | The count of top-scored entities to return. Default value is 3 | No |
## Outputs
The following is an example JSON format response returned by the tool, which includes the top-k scored entities. The entity follows a generic schema of vector search result provided by promptflow-vectordb SDK.
- **Azure Cognitive Search:**
For Azure Cognitive Search, the following fields are populated:
| Field Name | Type | Description |
| ---- | ---- | ----------- |
| original_entity | dict | the original response json from search REST API|
| score | float | @search.score from the original entity, which evaluates the similarity between the entity and the query vector |
| text | string | text of the entity|
| vector | list | vector of the entity|
<details>
<summary>Output</summary>
```json
[
{
"metadata": null,
"original_entity": {
"@search.score": 0.5099789,
"id": "",
"your_text_filed_name": "sample text1",
"your_vector_filed_name": [-0.40517663431890405, 0.5856996257406859, -0.1593078462266455, -0.9776269170785785, -0.6145604369828972],
"your_additional_field_name": ""
},
"score": 0.5099789,
"text": "sample text1",
"vector": [-0.40517663431890405, 0.5856996257406859, -0.1593078462266455, -0.9776269170785785, -0.6145604369828972]
}
]
```
</details>
- **Qdrant:**
For Qdrant, the following fields are populated:
| Field Name | Type | Description |
| ---- | ---- | ----------- |
| original_entity | dict | the original response json from search REST API|
| metadata | dict | payload from the original entity|
| score | float | score from the original entity, which evaluates the similarity between the entity and the query vector|
| text | string | text of the payload|
| vector | list | vector of the entity|
<details>
<summary>Output</summary>
```json
[
{
"metadata": {
"text": "sample text1"
},
"original_entity": {
"id": 1,
"payload": {
"text": "sample text1"
},
"score": 1,
"vector": [0.18257418, 0.36514837, 0.5477226, 0.73029673],
"version": 0
},
"score": 1,
"text": "sample text1",
"vector": [0.18257418, 0.36514837, 0.5477226, 0.73029673]
}
]
```
</details>
- **Weaviate:**
For Weaviate, the following fields are populated:
| Field Name | Type | Description |
| ---- | ---- | ----------- |
| original_entity | dict | the original response json from search REST API|
| score | float | certainty from the original entity, which evaluates the similarity between the entity and the query vector|
| text | string | text in the original entity|
| vector | list | vector of the entity|
<details>
<summary>Output</summary>
```json
[
{
"metadata": null,
"original_entity": {
"_additional": {
"certainty": 1,
"distance": 0,
"vector": [
0.58,
0.59,
0.6,
0.61,
0.62
]
},
"text": "sample text1."
},
"score": 1,
"text": "sample text1.",
"vector": [
0.58,
0.59,
0.6,
0.61,
0.62
]
}
]
```
</details> | promptflow/docs/reference/tools-reference/vector_db_lookup_tool.md/0 | {
"file_path": "promptflow/docs/reference/tools-reference/vector_db_lookup_tool.md",
"repo_id": "promptflow",
"token_count": 2697
} | 4 |
# Basic Chat
This example shows how to create a basic chat flow. It demonstrates how to create a chatbot that can remember previous interactions and use the conversation history to generate next message.
Tools used in this flow：
- `llm` tool
## Prerequisites
Install promptflow sdk and other dependencies in this folder:
```bash
pip install -r requirements.txt
```
## What you will learn
In this flow, you will learn
- how to compose a chat flow.
- prompt template format of LLM tool chat api. Message delimiter is a separate line containing role name and colon: "system:", "user:", "assistant:".
See <a href="https://platform.openai.com/docs/api-reference/chat/create#chat/create-role" target="_blank">OpenAI Chat</a> for more about message role.
```jinja
system:
You are a chatbot having a conversation with a human.
user:
{{question}}
```
- how to consume chat history in prompt.
```jinja
{% for item in chat_history %}
user:
{{item.inputs.question}}
assistant:
{{item.outputs.answer}}
{% endfor %}
```
## Getting started
### 1 Create connection for LLM tool to use
Go to "Prompt flow" "Connections" tab. Click on "Create" button, select one of LLM tool supported connection types and fill in the configurations.
Currently, there are two connection types supported by LLM tool: "AzureOpenAI" and "OpenAI". If you want to use "AzureOpenAI" connection type, you need to create an Azure OpenAI service first. Please refer to [Azure OpenAI Service](https://azure.microsoft.com/en-us/products/cognitive-services/openai-service/) for more details. If you want to use "OpenAI" connection type, you need to create an OpenAI account first. Please refer to [OpenAI](https://platform.openai.com/) for more details.
```bash
# Override keys with --set to avoid yaml file changes
pf connection create --file ../../../connections/azure_openai.yml --set api_key=<your_api_key> api_base=<your_api_base> --name open_ai_connection
```
Note in [flow.dag.yaml](flow.dag.yaml) we are using connection named `open_ai_connection`.
```bash
# show registered connection
pf connection show --name open_ai_connection
```
### 2 Start chatting
```bash
# run chat flow with default question in flow.dag.yaml
pf flow test --flow .
# run chat flow with new question
pf flow test --flow . --inputs question="What's Azure Machine Learning?"
# start a interactive chat session in CLI
pf flow test --flow . --interactive
# start a interactive chat session in CLI with verbose info
pf flow test --flow . --interactive --verbose
```
| promptflow/examples/flows/chat/basic-chat/README.md/0 | {
"file_path": "promptflow/examples/flows/chat/basic-chat/README.md",
"repo_id": "promptflow",
"token_count": 769
} | 5 |
import faiss
from jinja2 import Environment, FileSystemLoader
import os
from utils.index import FAISSIndex
from utils.oai import OAIEmbedding, render_with_token_limit
from utils.logging import log
def find_context(question: str, index_path: str):
index = FAISSIndex(index=faiss.IndexFlatL2(1536), embedding=OAIEmbedding())
index.load(path=index_path)
snippets = index.query(question, top_k=5)
template = Environment(
loader=FileSystemLoader(os.path.dirname(os.path.abspath(__file__)))
).get_template("qna_prompt.md")
token_limit = int(os.environ.get("PROMPT_TOKEN_LIMIT"))
# Try to render the template with token limit and reduce snippet count if it fails
while True:
try:
prompt = render_with_token_limit(
template, token_limit, question=question, context=enumerate(snippets)
)
break
except ValueError:
snippets = snippets[:-1]
log(f"Reducing snippet count to {len(snippets)} to fit token limit")
return prompt, snippets
| promptflow/examples/flows/chat/chat-with-pdf/chat_with_pdf/find_context.py/0 | {
"file_path": "promptflow/examples/flows/chat/chat-with-pdf/chat_with_pdf/find_context.py",
"repo_id": "promptflow",
"token_count": 422
} | 6 |
$schema: https://azuremlschemas.azureedge.net/promptflow/latest/Flow.schema.json
inputs:
groundtruth:
type: string
default: "1"
prediction:
type: string
default: "2"
outputs:
score:
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
| promptflow/examples/flows/evaluation/eval-accuracy-maths-to-code/flow.dag.yaml/0 | {
"file_path": "promptflow/examples/flows/evaluation/eval-accuracy-maths-to-code/flow.dag.yaml",
"repo_id": "promptflow",
"token_count": 228
} | 7 |
from typing import List
from promptflow import log_metric, tool
@tool
def calculate_accuracy(grades: List[str]):
result = []
for index in range(len(grades)):
grade = grades[index]
result.append(grade)
# calculate accuracy for each variant
accuracy = round((result.count("Correct") / len(result)), 2)
log_metric("accuracy", accuracy)
return result
| promptflow/examples/flows/evaluation/eval-classification-accuracy/calculate_accuracy.py/0 | {
"file_path": "promptflow/examples/flows/evaluation/eval-classification-accuracy/calculate_accuracy.py",
"repo_id": "promptflow",
"token_count": 135
} | 8 |
from promptflow import tool
from collections import Counter
@tool
def compute_f1_score(ground_truth: str, answer: str) -> str:
import string
import re
class QASplitTokenizer:
def __call__(self, line):
"""Tokenizes an input line using split() on whitespace
:param line: a segment to tokenize
:return: the tokenized line
"""
return line.split()
def normalize_text(text) -> str:
"""Lower text and remove punctuation, articles and extra whitespace."""
def remove_articles(text):
return re.sub(r"\b(a|an|the)\b", " ", text)
def white_space_fix(text):
return " ".join(text.split())
def remove_punctuation(text):
exclude = set(string.punctuation)
return "".join(ch for ch in text if ch not in exclude)
def lower(text):
return text.lower()
return white_space_fix(remove_articles(remove_punctuation(lower(text))))
prediction_tokens = normalize_text(answer)
reference_tokens = normalize_text(ground_truth)
tokenizer = QASplitTokenizer()
prediction_tokens = tokenizer(prediction_tokens)
reference_tokens = tokenizer(reference_tokens)
common_tokens = Counter(prediction_tokens) & Counter(reference_tokens)
num_common_tokens = sum(common_tokens.values())
if num_common_tokens == 0:
f1 = 0.0
else:
precision = 1.0 * num_common_tokens / len(prediction_tokens)
recall = 1.0 * num_common_tokens / len(reference_tokens)
f1 = (2.0 * precision * recall) / (precision + recall)
return f1
| promptflow/examples/flows/evaluation/eval-qna-non-rag/f1_score.py/0 | {
"file_path": "promptflow/examples/flows/evaluation/eval-qna-non-rag/f1_score.py",
"repo_id": "promptflow",
"token_count": 692
} | 9 |
from promptflow import tool
import re
@tool
def parse_grounding_output(rag_grounding_score: str) -> str:
try:
numbers_found = re.findall(r"Quality score:\s*(\d+)\/\d", rag_grounding_score)
score = float(numbers_found[0]) if len(numbers_found) > 0 else 0
except Exception:
score = float("nan")
try:
quality_reasoning, _ = rag_grounding_score.split("Quality score: ")
except Exception:
quality_reasoning = rag_grounding_score
return {"quality_score": score, "quality_reasoning": quality_reasoning}
| promptflow/examples/flows/evaluation/eval-qna-rag-metrics/parse_groundedness_score.py/0 | {
"file_path": "promptflow/examples/flows/evaluation/eval-qna-rag-metrics/parse_groundedness_score.py",
"repo_id": "promptflow",
"token_count": 214
} | 10 |
from typing import Union
from openai.version import VERSION as OPENAI_VERSION
from promptflow import tool
from promptflow.connections import CustomConnection, AzureOpenAIConnection
# The inputs section will change based on the arguments of the tool function, after you save the code
# Adding type to arguments and return value will help the system show the types properly
# Please update the function name/signature per need
def to_bool(value) -> bool:
return str(value).lower() == "true"
def get_client(connection: Union[CustomConnection, AzureOpenAIConnection]):
if OPENAI_VERSION.startswith("0."):
raise Exception(
"Please upgrade your OpenAI package to version >= 1.0.0 or using the command: pip install --upgrade openai."
)
# connection can be extract as a dict object contains the configs and secrets
connection_dict = dict(connection)
api_key = connection_dict.get("api_key")
conn = dict(
api_key=api_key,
)
if api_key.startswith("sk-"):
from openai import OpenAI as Client
else:
from openai import AzureOpenAI as Client
conn.update(
azure_endpoint=connection_dict.get("api_base"),
api_version=connection_dict.get("api_version", "2023-07-01-preview"),
)
return Client(**conn)
@tool
def my_python_tool(
prompt: str,
# for AOAI, deployment name is customized by user, not model name.
deployment_name: str,
suffix: str = None,
max_tokens: int = 120,
temperature: float = 1.0,
top_p: float = 1.0,
n: int = 1,
logprobs: int = None,
echo: bool = False,
stop: list = None,
presence_penalty: float = 0,
frequency_penalty: float = 0,
best_of: int = 1,
logit_bias: dict = {},
user: str = "",
connection: Union[CustomConnection, AzureOpenAIConnection] = None,
**kwargs,
) -> str:
# TODO: remove below type conversion after client can pass json rather than string.
echo = to_bool(echo)
response = get_client(connection).completions.create(
prompt=prompt,
model=deployment_name,
# empty string suffix should be treated as None.
suffix=suffix if suffix else None,
max_tokens=int(max_tokens),
temperature=float(temperature),
top_p=float(top_p),
n=int(n),
logprobs=int(logprobs) if logprobs else None,
echo=echo,
# fix bug "[] is not valid under any of the given schemas-'stop'"
stop=stop if stop else None,
presence_penalty=float(presence_penalty),
frequency_penalty=float(frequency_penalty),
best_of=int(best_of),
# Logit bias must be a dict if we passed it to openai api.
logit_bias=logit_bias if logit_bias else {},
user=user,
)
# get first element because prompt is single.
return response.choices[0].text
| promptflow/examples/flows/standard/basic-with-connection/hello.py/0 | {
"file_path": "promptflow/examples/flows/standard/basic-with-connection/hello.py",
"repo_id": "promptflow",
"token_count": 1109
} | 11 |
$schema: https://azuremlschemas.azureedge.net/promptflow/latest/Flow.schema.json
inputs:
question:
type: string
default: What is Prompt flow?
outputs:
answer:
type: string
reference: ${generate_result.output}
nodes:
- name: content_safety_check
type: python
source:
type: code
path: content_safety_check.py
inputs:
text: ${inputs.question}
- name: llm_result
type: python
source:
type: code
path: llm_result.py
inputs:
question: ${inputs.question}
activate:
when: ${content_safety_check.output}
is: true
- name: default_result
type: python
source:
type: code
path: default_result.py
inputs:
question: ${inputs.question}
activate:
when: ${content_safety_check.output}
is: false
- name: generate_result
type: python
source:
type: code
path: generate_result.py
inputs:
llm_result: ${llm_result.output}
default_result: ${default_result.output}
environment:
python_requirements_txt: requirements.txt
| promptflow/examples/flows/standard/conditional-flow-for-if-else/flow.dag.yaml/0 | {
"file_path": "promptflow/examples/flows/standard/conditional-flow-for-if-else/flow.dag.yaml",
"repo_id": "promptflow",
"token_count": 386
} | 12 |
CHAT_DEPLOYMENT_NAME=gpt-35-turbo
AZURE_OPENAI_API_KEY=<your_AOAI_key>
AZURE_OPENAI_API_BASE=<your_AOAI_endpoint>
| promptflow/examples/flows/standard/customer-intent-extraction/.env.example/0 | {
"file_path": "promptflow/examples/flows/standard/customer-intent-extraction/.env.example",
"repo_id": "promptflow",
"token_count": 61
} | 13 |
from promptflow import tool
from divider import Divider
from typing import List
@tool
def combine_code(divided: List[str]):
code = Divider.combine(divided)
return code
| promptflow/examples/flows/standard/gen-docstring/combine_code_tool.py/0 | {
"file_path": "promptflow/examples/flows/standard/gen-docstring/combine_code_tool.py",
"repo_id": "promptflow",
"token_count": 57
} | 14 |
from promptflow import tool
import sys
from io import StringIO
@tool
def func_exe(code_snippet: str):
if code_snippet == "JSONDecodeError" or code_snippet.startswith("Unknown Error:"):
return code_snippet
# Define the result variable before executing the code snippet
old_stdout = sys.stdout
redirected_output = sys.stdout = StringIO()
# Execute the code snippet
try:
exec(code_snippet.lstrip())
except Exception as e:
sys.stdout = old_stdout
return str(e)
sys.stdout = old_stdout
return redirected_output.getvalue().strip()
if __name__ == "__main__":
print(func_exe("print(5+3)"))
print(func_exe("count = 0\nfor i in range(100):\n if i % 8 == 0:\n count += 1\nprint(count)"))
print(func_exe("sum = 0\ni = 0\nwhile 3**i < 100:\n sum += 3**i\n i += 1\nprint(sum)"))
print(func_exe("speed_A = 80\nspeed_B = 120\ndistance = 2000\ntime = distance / (speed_A + speed_B)\nprint(time)"))
print(func_exe("Unknown Error"))
print(func_exe("JSONDecodeError"))
| promptflow/examples/flows/standard/maths-to-code/code_execution.py/0 | {
"file_path": "promptflow/examples/flows/standard/maths-to-code/code_execution.py",
"repo_id": "promptflow",
"token_count": 432
} | 15 |
include my_tool_package/yamls/*.yaml | promptflow/examples/tools/tool-package-quickstart/MANIFEST.in/0 | {
"file_path": "promptflow/examples/tools/tool-package-quickstart/MANIFEST.in",
"repo_id": "promptflow",
"token_count": 14
} | 16 |
my_tool_package.tools.my_tool_2.MyTool.my_tool:
class_name: MyTool
function: my_tool
inputs:
connection:
type:
- CustomConnection
input_text:
type:
- string
module: my_tool_package.tools.my_tool_2
name: My Second Tool
description: This is my second tool
type: python
| promptflow/examples/tools/tool-package-quickstart/my_tool_package/yamls/my_tool_2.yaml/0 | {
"file_path": "promptflow/examples/tools/tool-package-quickstart/my_tool_package/yamls/my_tool_2.yaml",
"repo_id": "promptflow",
"token_count": 126
} | 17 |
import json
import pytest
import unittest
from my_tool_package.tools.tool_with_generated_by_input import (
generate_index_json,
list_embedding_deployment,
list_fields,
list_indexes,
list_index_types,
list_semantic_configuration,
my_tool,
reverse_generate_index_json,
)
@pytest.mark.parametrize("index_type", ["Azure Cognitive Search", "Workspace MLIndex"])
def test_my_tool(index_type):
index_json = generate_index_json(index_type=index_type)
result = my_tool(index_json, "", "")
assert result == f'Hello {index_json}'
def test_generate_index_json():
index_type = "Azure Cognitive Search"
index_json = generate_index_json(index_type=index_type)
indexes = json.loads(index_json)
assert indexes["index_type"] == index_type
def test_reverse_generate_index_json():
index_type = "Workspace MLIndex"
index = list_indexes("", "", "")
inputs = {
"index_type": index_type,
"index": index,
"index_connection": "retrieved_index_connection",
"index_name": "retrieved_index_name",
"content_field": "retrieved_content_field",
"embedding_field": "retrieved_embedding_field",
"metadata_field": "retrieved_metadata_field",
"semantic_configuration": "retrieved_semantic_configuration",
"embedding_connection": "retrieved_embedding_connection",
"embedding_deployment": "retrieved_embedding_deployment"
}
input_json = json.dumps(inputs)
result = reverse_generate_index_json(input_json)
for k, v in inputs.items():
assert result[k] == v
def test_list_index_types():
result = list_index_types("", "", "")
assert isinstance(result, list)
assert len(result) == 5
def test_list_indexes():
result = list_indexes("", "", "")
assert isinstance(result, list)
assert len(result) == 10
for item in result:
assert isinstance(item, dict)
def test_list_fields():
result = list_fields("", "", "")
assert isinstance(result, list)
assert len(result) == 9
for item in result:
assert isinstance(item, dict)
def test_list_semantic_configuration():
result = list_semantic_configuration("", "", "")
assert len(result) == 1
assert isinstance(result[0], dict)
def test_list_embedding_deployment():
result = list_embedding_deployment("")
assert len(result) == 2
for item in result:
assert isinstance(item, dict)
if __name__ == "__main__":
unittest.main()
| promptflow/examples/tools/tool-package-quickstart/tests/test_tool_with_generated_by_input.py/0 | {
"file_path": "promptflow/examples/tools/tool-package-quickstart/tests/test_tool_with_generated_by_input.py",
"repo_id": "promptflow",
"token_count": 967
} | 18 |
# Flow with custom_llm tool
This is a flow demonstrating how to use a `custom_llm` tool, which enables users to seamlessly connect to a large language model with prompt tuning experience using a `PromptTemplate`.
Tools used in this flow:
- `custom_llm` Tool
Connections used in this flow:
- custom connection
## Prerequisites
Install promptflow sdk and other dependencies:
```bash
pip install -r requirements.txt
```
## Setup connection
Create connection if you haven't done that.
```bash
# Override keys with --set to avoid yaml file changes
pf connection create -f custom_connection.yml --set secrets.api_key=<your_api_key> configs.api_base=<your_api_base>
```
Ensure you have created `basic_custom_connection` connection.
```bash
pf connection show -n basic_custom_connection
```
## Run flow
- Test flow
```bash
pf flow test --flow .
```
| promptflow/examples/tools/use-cases/custom_llm_tool_showcase/README.md/0 | {
"file_path": "promptflow/examples/tools/use-cases/custom_llm_tool_showcase/README.md",
"repo_id": "promptflow",
"token_count": 249
} | 19 |
# Enforce the check of pipelines.
# This script will get the diff of the current branch and main branch, calculate the pipelines that should be triggered.
# Then it will check if the triggered pipelines are successful. This script will loop for 30*loop-times seconds at most.
# How many checks are triggered:
# 1. sdk checks: sdk_cli_tests, sdk_cli_azure_test, sdk_cli_global_config_tests are triggered.
# 2. examples checks: this script calculate the path filters and decide what should be triggered.
# Trigger checks and return the status of the checks:
# 1. If examples are not correctly generated, fail.
# 2. If required pipelines are not triggered within 6 rounds of loops, fail.
# 2.1 (special_care global variable could help on some pipelines that need to bypass the check)
# Check pipelines succeed or not:
# 1. These pipelines should return status within loop-times rounds.
# 2. If there is failed pipeline in the triggered pipelines, fail.
# Import necessary libraries
import os
import fnmatch
import subprocess
import time
import argparse
import json
import sys
# Define variables
github_repository = "microsoft/promptflow"
snippet_debug = os.getenv("SNIPPET_DEBUG", 0)
merge_commit = ""
loop_times = 30
github_workspace = os.path.expanduser("~/promptflow/")
# Special cases for pipelines that need to be triggered more or less than default value 1.
# If 0, the pipeline will not be ignored in check enforcer.
# Please notice that the key should be the Job Name in the pipeline.
special_care = {
"sdk_cli_tests": 4,
"sdk_cli_azure_test": 4,
# "samples_connections_connection": 0,
}
# Copy from original yaml pipelines
checks = {
"sdk_cli_tests": [
"src/promptflow/**",
"scripts/building/**",
".github/workflows/promptflow-sdk-cli-test.yml",
],
"sdk_cli_global_config_tests": [
"src/promptflow/**",
"scripts/building/**",
".github/workflows/promptflow-global-config-test.yml",
],
"sdk_cli_azure_test": [
"src/promptflow/**",
"scripts/building/**",
".github/workflows/promptflow-sdk-cli-azure-test.yml",
],
}
reverse_checks = {}
pipelines = {}
pipelines_count = {}
failed_reason = ""
# Define functions
def trigger_checks(valid_status_array):
global failed_reason
global github_repository
global merge_commit
global snippet_debug
global pipelines
global pipelines_count
output = subprocess.check_output(
f"gh api /repos/{github_repository}/commits/{merge_commit}/check-suites?per_page=100",
shell=True,
)
check_suites = json.loads(output)["check_suites"]
for suite in check_suites:
if snippet_debug != 0:
print(f"check-suites id {suite['id']}")
suite_id = suite["id"]
output = subprocess.check_output(
f"gh api /repos/{github_repository}/check-suites/{suite_id}/check-runs?per_page=100",
shell=True,
)
check_runs = json.loads(output)["check_runs"]
for run in check_runs:
if snippet_debug != 0:
print(f"check runs name {run['name']}")
for key in pipelines.keys():
value = pipelines[key]
if value == 0:
continue
if key in run["name"]:
pipelines_count[key] += 1
valid_status_array.append(run)
for key in pipelines.keys():
if pipelines_count[key] < pipelines[key]:
failed_reason = "Not all pipelines are triggered."
def status_checks(valid_status_array):
global failed_reason
global pipelines
global pipelines_count
# Basic fact of sdk cli checked pipelines.
failed_reason = ""
# Loop through each valid status array.
for status in valid_status_array:
# Check if the pipeline was successful.
if status["conclusion"] and status["conclusion"].lower() == "success":
# Add 1 to the count of successful pipelines.
pass
# Check if the pipeline failed.
elif status["conclusion"] and status["conclusion"].lower() == "failure":
failed_reason = "Required pipelines are not successful."
# Check if the pipeline is still running.
else:
if failed_reason == "":
failed_reason = "Required pipelines are not finished."
# Print the status of the pipeline to the console.
print(status["name"] + " is checking.")
def trigger_prepare(input_paths):
global github_workspace
global checks
global reverse_checks
global pipelines
global pipelines_count
global failed_reason
global special_care
for input_path in input_paths:
if "samples_connections_connection" in checks:
continue
# Check if the input path contains "examples" or "samples".
if "examples" in input_path or "samples" in input_path:
sys.path.append(os.path.expanduser(github_workspace + "/scripts/readme"))
from readme import main as readme_main
os.chdir(os.path.expanduser(github_workspace))
# Get the list of pipelines from the readme file.
pipelines_samples = readme_main(check=True)
git_diff_files = [
item
for item in subprocess.check_output(
["git", "diff", "--name-only", "HEAD"]
)
.decode("utf-8")
.split("\n")
if item != ""
]
for _ in git_diff_files:
failed_reason = "Run readme generation before check in"
return
# Merge the pipelines from the readme file with the original list of pipelines.
for key in pipelines_samples.keys():
value = pipelines_samples[key]
checks[key] = value
# Reverse checks.
for key in checks.keys():
value = checks[key]
for path in value:
if path in reverse_checks:
reverse_checks[path].append(key)
else:
reverse_checks[path] = [key]
# Render pipelines and pipelines_count using input_paths.
for input_path in input_paths:
# Input pattern /**: input_path should match in the middle.
# Input pattern /*: input_path should match last but one.
# Other input pattern: input_path should match last.
keys = [
key for key in reverse_checks.keys() if fnmatch.fnmatch(input_path, key)
]
# Loop through each key in the list of keys.
for key_item in keys:
# Loop through each pipeline in the list of pipelines.
for key in reverse_checks[key_item]:
# Check if the pipeline is in the list of pipelines.
if key in special_care:
pipelines[key] = special_care[key]
else:
pipelines[key] = 1
# Set the pipeline count to 0.
pipelines_count[key] = 0
def run_checks():
global github_repository
global snippet_debug
global merge_commit
global loop_times
global github_workspace
global failed_reason
if merge_commit == "":
merge_commit = (
subprocess.check_output(["git", "log", "-1"]).decode("utf-8").split("\n")
)
if snippet_debug != 0:
print(merge_commit)
for line in merge_commit:
if "Merge" in line and "into" in line:
merge_commit = line.split(" ")[-3]
break
if snippet_debug != 0:
print("MergeCommit " + merge_commit)
not_started_counter = 5
os.chdir(github_workspace)
# Get diff of current branch and main branch.
try:
git_merge_base = (
subprocess.check_output(["git", "merge-base", "origin/main", "HEAD"])
.decode("utf-8")
.rstrip()
)
git_diff = (
subprocess.check_output(
["git", "diff", "--name-only", "--diff-filter=d", f"{git_merge_base}"],
stderr=subprocess.STDOUT,
)
.decode("utf-8")
.rstrip()
.split("\n")
)
except subprocess.CalledProcessError as e:
print("Exception on process, rc=", e.returncode, "output=", e.output)
raise e
# Prepare how many pipelines should be triggered.
trigger_prepare(git_diff)
if failed_reason != "":
raise Exception(failed_reason)
# Loop for 15 minutes at most.
for i in range(loop_times):
# Wait for 30 seconds.
time.sleep(30)
# Reset the failed reason.
failed_reason = ""
# Reset the valid status array.
valid_status_array = []
# Get all triggered pipelines.
# If not all pipelines are triggered, continue.
trigger_checks(valid_status_array)
if failed_reason != "":
if not_started_counter == 0:
raise Exception(failed_reason + " for 6 times.")
print(failed_reason)
not_started_counter -= 1
continue
# Get pipeline conclusion priority:
# 1. Not successful, Fail.
# 2. Not finished, Continue.
# 3. Successful, Break.
status_checks(valid_status_array)
# Check if the failed reason contains "not successful".
if "not successful" in failed_reason.lower():
raise Exception(failed_reason)
# Check if the failed reason contains "not finished".
elif "not finished" in failed_reason.lower():
print(failed_reason)
continue
# Otherwise, print that all required pipelines are successful.
else:
print("All required pipelines are successful.")
break
# Check if the failed reason is not empty.
if failed_reason != "":
raise Exception(failed_reason)
if __name__ == "__main__":
# Run the checks.
parser = argparse.ArgumentParser()
parser.add_argument(
"-m",
"--merge-commit",
help="merge commit sha",
)
parser.add_argument(
"-n",
"--loop-times",
type=int,
help="Loop times",
)
parser.add_argument(
"-t",
"--github-workspace",
help="base path of github workspace",
)
args = parser.parse_args()
if args.merge_commit:
merge_commit = args.merge_commit
if args.loop_times:
loop_times = args.loop_times
if args.github_workspace:
github_workspace = args.github_workspace
run_checks()
| promptflow/scripts/check_enforcer/check_enforcer.py/0 | {
"file_path": "promptflow/scripts/check_enforcer/check_enforcer.py",
"repo_id": "promptflow",
"token_count": 4517
} | 20 |
#!/usr/bin/env python
# --------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# --------------------------------------------------------------------------------------------
#
# This script will install the promptflow into a directory and create an executable
# at a specified file path that is the entry point into the promptflow.
#
# The latest versions of all promptflow command packages will be installed.
#
import os
import sys
import platform
import stat
import tempfile
import shutil
import subprocess
import hashlib
PF_DISPATCH_TEMPLATE = """#!/usr/bin/env bash
export PF_INSTALLER=Script
{install_dir}/bin/python -m promptflow._cli._pf.entry "$@"
"""
PFAZURE_DISPATCH_TEMPLATE = """#!/usr/bin/env bash
{install_dir}/bin/python -m promptflow._cli._pf_azure.entry "$@"
"""
PFS_DISPATCH_TEMPLATE = """#!/usr/bin/env bash
{install_dir}/bin/python -m promptflow._sdk._service.entry "$@"
"""
DEFAULT_INSTALL_DIR = os.path.expanduser(os.path.join('~', 'lib', 'promptflow'))
DEFAULT_EXEC_DIR = os.path.expanduser(os.path.join('~', 'bin'))
PF_EXECUTABLE_NAME = 'pf'
PFAZURE_EXECUTABLE_NAME = 'pfazure'
PFS_EXECUTABLE_NAME = 'pfs'
USER_BASH_RC = os.path.expanduser(os.path.join('~', '.bashrc'))
USER_BASH_PROFILE = os.path.expanduser(os.path.join('~', '.bash_profile'))
class CLIInstallError(Exception):
pass
def print_status(msg=''):
print('-- '+msg)
def prompt_input(msg):
return input('\n===> '+msg)
def prompt_input_with_default(msg, default):
if default:
return prompt_input("{} (leave blank to use '{}'): ".format(msg, default)) or default
else:
return prompt_input('{}: '.format(msg))
def prompt_y_n(msg, default=None):
if default not in [None, 'y', 'n']:
raise ValueError("Valid values for default are 'y', 'n' or None")
y = 'Y' if default == 'y' else 'y'
n = 'N' if default == 'n' else 'n'
while True:
ans = prompt_input('{} ({}/{}): '.format(msg, y, n))
if ans.lower() == n.lower():
return False
if ans.lower() == y.lower():
return True
if default and not ans:
return default == y.lower()
def exec_command(command_list, cwd=None, env=None):
print_status('Executing: '+str(command_list))
subprocess.check_call(command_list, cwd=cwd, env=env)
def create_tmp_dir():
tmp_dir = tempfile.mkdtemp()
return tmp_dir
def create_dir(dir):
if not os.path.isdir(dir):
print_status("Creating directory '{}'.".format(dir))
os.makedirs(dir)
def is_valid_sha256sum(a_file, expected_sum):
sha256 = hashlib.sha256()
with open(a_file, 'rb') as f:
sha256.update(f.read())
computed_hash = sha256.hexdigest()
return expected_sum == computed_hash
def create_virtualenv(install_dir):
cmd = [sys.executable, '-m', 'venv', install_dir]
exec_command(cmd)
def install_cli(install_dir, tmp_dir):
path_to_pip = os.path.join(install_dir, 'bin', 'pip')
cmd = [path_to_pip, 'install', '--cache-dir', tmp_dir, 'promptflow[azure,executable,pfs,azureml-serving]',
'--upgrade']
exec_command(cmd)
cmd = [path_to_pip, 'install', '--cache-dir', tmp_dir, 'promptflow-tools', '--upgrade']
exec_command(cmd)
cmd = [path_to_pip, 'install', '--cache-dir', tmp_dir, 'keyrings.alt', '--upgrade']
exec_command(cmd)
def create_executable(exec_dir, install_dir):
create_dir(exec_dir)
exec_filepaths = []
for filename, template in [(PF_EXECUTABLE_NAME, PF_DISPATCH_TEMPLATE),
(PFAZURE_EXECUTABLE_NAME, PFAZURE_DISPATCH_TEMPLATE),
(PFS_EXECUTABLE_NAME, PFS_DISPATCH_TEMPLATE)]:
exec_filepath = os.path.join(exec_dir, filename)
with open(exec_filepath, 'w') as exec_file:
exec_file.write(template.format(install_dir=install_dir))
cur_stat = os.stat(exec_filepath)
os.chmod(exec_filepath, cur_stat.st_mode | stat.S_IXUSR | stat.S_IXGRP | stat.S_IXOTH)
print_status("The executable is available at '{}'.".format(exec_filepath))
exec_filepaths.append(exec_filepath)
return exec_filepaths
def get_install_dir():
install_dir = None
while not install_dir:
prompt_message = 'In what directory would you like to place the install?'
install_dir = prompt_input_with_default(prompt_message, DEFAULT_INSTALL_DIR)
install_dir = os.path.realpath(os.path.expanduser(install_dir))
if ' ' in install_dir:
print_status("The install directory '{}' cannot contain spaces.".format(install_dir))
install_dir = None
else:
create_dir(install_dir)
if os.listdir(install_dir):
print_status("'{}' is not empty and may contain a previous installation.".format(install_dir))
ans_yes = prompt_y_n('Remove this directory?', 'n')
if ans_yes:
shutil.rmtree(install_dir)
print_status("Deleted '{}'.".format(install_dir))
create_dir(install_dir)
else:
# User opted to not delete the directory so ask for install directory again
install_dir = None
print_status("We will install at '{}'.".format(install_dir))
return install_dir
def get_exec_dir():
exec_dir = None
while not exec_dir:
prompt_message = (f"In what directory would you like to place the "
f"'{PFS_EXECUTABLE_NAME}/{PFS_EXECUTABLE_NAME}/{PFAZURE_EXECUTABLE_NAME}' executable?")
exec_dir = prompt_input_with_default(prompt_message, DEFAULT_EXEC_DIR)
exec_dir = os.path.realpath(os.path.expanduser(exec_dir))
if ' ' in exec_dir:
print_status("The executable directory '{}' cannot contain spaces.".format(exec_dir))
exec_dir = None
create_dir(exec_dir)
print_status("The executable will be in '{}'.".format(exec_dir))
return exec_dir
def _backup_rc(rc_file):
try:
shutil.copyfile(rc_file, rc_file+'.backup')
print_status("Backed up '{}' to '{}'".format(rc_file, rc_file+'.backup'))
except (OSError, IOError):
pass
def _get_default_rc_file():
bashrc_exists = os.path.isfile(USER_BASH_RC)
bash_profile_exists = os.path.isfile(USER_BASH_PROFILE)
if not bashrc_exists and bash_profile_exists:
return USER_BASH_PROFILE
if bashrc_exists and bash_profile_exists and platform.system().lower() == 'darwin':
return USER_BASH_PROFILE
return USER_BASH_RC if bashrc_exists else None
def _default_rc_file_creation_step():
rcfile = USER_BASH_PROFILE if platform.system().lower() == 'darwin' else USER_BASH_RC
ans_yes = prompt_y_n('Could not automatically find a suitable file to use. Create {} now?'.format(rcfile),
default='y')
if ans_yes:
open(rcfile, 'a').close()
return rcfile
return None
def _find_line_in_file(file_path, search_pattern):
try:
with open(file_path, 'r', encoding="utf-8") as search_file:
for line in search_file:
if search_pattern in line:
return True
except (OSError, IOError):
pass
return False
def _modify_rc(rc_file_path, line_to_add):
if not _find_line_in_file(rc_file_path, line_to_add):
with open(rc_file_path, 'a', encoding="utf-8") as rc_file:
rc_file.write('\n'+line_to_add+'\n')
def get_rc_file_path():
rc_file = None
default_rc_file = _get_default_rc_file()
if not default_rc_file:
rc_file = _default_rc_file_creation_step()
rc_file = rc_file or prompt_input_with_default('Enter a path to an rc file to update', default_rc_file)
if rc_file:
rc_file_path = os.path.realpath(os.path.expanduser(rc_file))
if os.path.isfile(rc_file_path):
return rc_file_path
print_status("The file '{}' could not be found.".format(rc_file_path))
return None
def warn_other_azs_on_path(exec_dir, exec_filepath):
env_path = os.environ.get('PATH')
conflicting_paths = []
if env_path:
for p in env_path.split(':'):
for file in [PF_EXECUTABLE_NAME, PFAZURE_EXECUTABLE_NAME, PFS_EXECUTABLE_NAME]:
p_to_pf = os.path.join(p, file)
if p != exec_dir and os.path.isfile(p_to_pf):
conflicting_paths.append(p_to_pf)
if conflicting_paths:
print_status()
print_status(f"** WARNING: Other '{PFS_EXECUTABLE_NAME}/{PFS_EXECUTABLE_NAME}/{PFAZURE_EXECUTABLE_NAME}' "
f"executables are on your $PATH. **")
print_status("Conflicting paths: {}".format(', '.join(conflicting_paths)))
print_status("You can run this installation of the promptflow with '{}'.".format(exec_filepath))
def handle_path_and_tab_completion(exec_filepath, exec_dir):
ans_yes = prompt_y_n('Modify profile to update your $PATH now?', 'y')
if ans_yes:
rc_file_path = get_rc_file_path()
if not rc_file_path:
raise CLIInstallError('No suitable profile file found.')
_backup_rc(rc_file_path)
line_to_add = "export PATH=$PATH:{}".format(exec_dir)
_modify_rc(rc_file_path, line_to_add)
warn_other_azs_on_path(exec_dir, exec_filepath)
print_status()
print_status('** Run `exec -l $SHELL` to restart your shell. **')
print_status()
else:
print_status("You can run the promptflow with '{}'.".format(exec_filepath))
def verify_python_version():
print_status('Verifying Python version.')
v = sys.version_info
if v < (3, 8):
raise CLIInstallError('The promptflow does not support Python versions less than 3.8.')
if 'conda' in sys.version:
raise CLIInstallError("This script does not support the Python Anaconda environment. "
"Create an Anaconda virtual environment and install with 'pip'")
print_status('Python version {}.{}.{} okay.'.format(v.major, v.minor, v.micro))
def _native_dependencies_for_dist(verify_cmd_args, install_cmd_args, dep_list):
try:
print_status("Executing: '{} {}'".format(' '.join(verify_cmd_args), ' '.join(dep_list)))
subprocess.check_output(verify_cmd_args + dep_list, stderr=subprocess.STDOUT)
print_status('Native dependencies okay.')
except subprocess.CalledProcessError:
err_msg = 'One or more of the following native dependencies are not currently installed and may be required.\n'
err_msg += '"{}"'.format(' '.join(install_cmd_args + dep_list))
print_status(err_msg)
ans_yes = prompt_y_n('Missing native dependencies. Attempt to continue anyway?', 'n')
if not ans_yes:
raise CLIInstallError('Please install the native dependencies and try again.')
def _get_linux_distro():
if platform.system() != 'Linux':
return None, None
try:
with open('/etc/os-release') as lines:
tokens = [line.strip() for line in lines]
except Exception:
return None, None
release_info = {}
for token in tokens:
if '=' in token:
k, v = token.split('=', 1)
release_info[k.lower()] = v.strip('"')
return release_info.get('name', None), release_info.get('version_id', None)
def verify_install_dir_exec_path_conflict(install_dir, exec_dir):
for exec_name in [PF_EXECUTABLE_NAME, PFAZURE_EXECUTABLE_NAME, PFS_EXECUTABLE_NAME]:
exec_path = os.path.join(exec_dir, exec_name)
if install_dir == exec_path:
raise CLIInstallError("The executable file '{}' would clash with the install directory of '{}'. Choose "
"either a different install directory or directory to place the "
"executable.".format(exec_path, install_dir))
def main():
verify_python_version()
tmp_dir = create_tmp_dir()
install_dir = get_install_dir()
exec_dir = get_exec_dir()
verify_install_dir_exec_path_conflict(install_dir, exec_dir)
create_virtualenv(install_dir)
install_cli(install_dir, tmp_dir)
exec_filepath = create_executable(exec_dir, install_dir)
try:
handle_path_and_tab_completion(exec_filepath, exec_dir)
except Exception as e:
print_status("Unable to set up PATH. ERROR: {}".format(str(e)))
shutil.rmtree(tmp_dir)
print_status("Installation successful.")
print_status("Run the CLI with {} --help".format(exec_filepath))
if __name__ == '__main__':
try:
main()
except CLIInstallError as cie:
print('ERROR: '+str(cie), file=sys.stderr)
sys.exit(1)
except KeyboardInterrupt:
print('\n\nExiting...')
sys.exit(1)
# SIG # Begin signature block
# Z1F07ShfIJ7kejST2NXwW1QcFPEya4xaO2xZz6vLT847zaMzbc/PaEa1RKFlD881
# 4J+i6Au2wtbHzOXDisyH6WeLQ3gh0X2gxFRa4EzW7Nzjcvwm4+WogiTcnPVVxlk3
# qafM/oyVqs3695K7W5XttOiq2guv/yedsf/TW2BKSEKruFQh9IwDfIiBoi9Zv3wa
# iuzQulRR8KyrCtjEPDV0t4WnZVB/edQea6xJZeTlMG+uLR/miBTbPhUb/VZkVjBf
# qHBv623oLXICzoTNuaPTln9OWvL2NZpisGYvNzebKO7/Ho6AOWZNs5XOVnjs0Ax2
# aeXvlwBzIQyfyxd25487/Q==
# SIG # End signature block
| promptflow/scripts/installer/curl_install_pypi/install.py/0 | {
"file_path": "promptflow/scripts/installer/curl_install_pypi/install.py",
"repo_id": "promptflow",
"token_count": 5824
} | 21 |
{
"$schema": "http://json-schema.org/draft-07/schema#",
"definitions": {
"EagerFlowSchema": {
"properties": {
"additional_includes": {
"title": "additional_includes",
"type": "array",
"items": {
"title": "additional_includes",
"type": "string"
}
},
"description": {
"title": "description",
"type": "string"
},
"display_name": {
"title": "display_name",
"type": "string"
},
"entry": {
"title": "entry",
"type": "string"
},
"environment": {
"title": "environment",
"type": "object",
"additionalProperties": {}
},
"language": {
"title": "language",
"type": "string"
},
"path": {
"title": "path",
"type": "string"
},
"$schema": {
"title": "$schema",
"type": "string",
"readOnly": true
},
"tags": {
"title": "tags",
"type": "object",
"additionalProperties": {
"title": "tags",
"type": "string"
}
},
"type": {
"title": "type",
"type": "string",
"enum": [
"standard",
"evaluation",
"chat"
],
"enumNames": []
}
},
"type": "object",
"required": [
"entry",
"path"
],
"additionalProperties": false
}
},
"$ref": "#/definitions/EagerFlowSchema"
} | promptflow/scripts/json_schema/EagerFlow.schema.json/0 | {
"file_path": "promptflow/scripts/json_schema/EagerFlow.schema.json",
"repo_id": "promptflow",
"token_count": 1541
} | 22 |
- name: {{ step_name }}
working-directory: {{ working_dir }}
run: |
AOAI_API_KEY=${{ '{{' }} secrets.AOAI_GPT_4V_KEY }}
AOAI_API_ENDPOINT=${{ '{{' }} secrets.AOAI_GPT_4V_ENDPOINT }}
AOAI_API_ENDPOINT=$(echo ${AOAI_API_ENDPOINT//\//\\/})
cp ../../../connections/azure_openai.yml ./azure_openai.yml
sed -i -e "s/<user-input>/$AOAI_API_KEY/g" -e "s/aoai-api-endpoint/$AOAI_API_ENDPOINT/g" azure_openai.yml
| promptflow/scripts/readme/ghactions_driver/workflow_steps/step_create_env_gpt4.yml.jinja2/0 | {
"file_path": "promptflow/scripts/readme/ghactions_driver/workflow_steps/step_create_env_gpt4.yml.jinja2",
"repo_id": "promptflow",
"token_count": 218
} | 23 |
import argparse
from pathlib import Path
from functools import reduce
from ghactions_driver.readme_workflow_generate import write_readme_workflow
from ghactions_driver.readme_step import ReadmeStepsManage, ReadmeSteps
from ghactions_driver.readme_parse import readme_parser
from ghactions_driver.telemetry_obj import Telemetry
def local_filter(callback, array: [Path]):
results = []
for index, item in enumerate(array):
result = callback(item, index, array)
# if returned true, append item to results
if result:
results.append(item)
return results
def no_readme_generation_filter(item: Path, index, array) -> bool:
"""
If there is no steps in the readme, then no generation
"""
try:
if 'build' in str(item): # skip build folder
return False
full_text = readme_parser(item.relative_to(ReadmeStepsManage.git_base_dir()))
if full_text == "":
return False
else:
return True
except Exception as error:
print(error)
return False # generate readme
def main(input_glob, exclude_glob=[], output_files=[]):
def set_add(p, q):
return p | q
def set_difference(p, q):
return p - q
globs = reduce(set_add, [set(Path(ReadmeStepsManage.git_base_dir()).glob(p)) for p in input_glob], set())
globs_exclude = reduce(set_difference,
[set(Path(ReadmeStepsManage.git_base_dir()).glob(p)) for p in exclude_glob],
globs)
readme_items = sorted([i for i in globs_exclude])
readme_items = local_filter(no_readme_generation_filter, readme_items)
for readme in readme_items:
readme_telemetry = Telemetry()
workflow_name = readme.relative_to(ReadmeStepsManage.git_base_dir())
# Deal with readme
write_readme_workflow(workflow_name.resolve(), readme_telemetry)
ReadmeSteps.cleanup()
output_files.append(readme_telemetry)
if __name__ == "__main__":
# setup argparse
parser = argparse.ArgumentParser()
parser.add_argument(
"-g",
"--input-glob",
nargs="+",
help="Input Readme.md glob example 'examples/flows/**/Readme.md'",
)
args = parser.parse_args()
# call main
main(args.input_glob)
| promptflow/scripts/readme/readme_generator.py/0 | {
"file_path": "promptflow/scripts/readme/readme_generator.py",
"repo_id": "promptflow",
"token_count": 983
} | 24 |
from setuptools import find_packages, setup
PACKAGE_NAME = "{{ package_name }}"
setup(
name=PACKAGE_NAME,
version="0.0.1",
description="This is my tools package",
packages=find_packages(),
entry_points={
"package_tools": ["{{ tool_name }} = {{ package_name }}.tools.utils:list_package_tools"],
},
include_package_data=True, # This line tells setuptools to include files from MANIFEST.in
)
| promptflow/scripts/tool/templates/setup.py.j2/0 | {
"file_path": "promptflow/scripts/tool/templates/setup.py.j2",
"repo_id": "promptflow",
"token_count": 157
} | 25 |
import functools
import json
import os
import re
import requests
import sys
import time
import tempfile
from abc import abstractmethod
from datetime import datetime, timedelta
from enum import Enum
from typing import Any, Dict, List, Tuple, Optional, Union
from promptflow._core.tool import ToolProvider, tool
from promptflow._sdk._constants import ConnectionType
from promptflow.connections import CustomConnection
from promptflow.contracts.types import PromptTemplate
from promptflow.tools.common import render_jinja_template, validate_role
from promptflow.tools.exception import (
OpenModelLLMOnlineEndpointError,
OpenModelLLMUserError,
OpenModelLLMKeyValidationError,
ChatAPIInvalidRole
)
DEPLOYMENT_DEFAULT = "default"
CONNECTION_CACHE_FILE = "pf_connection_names"
VALID_LLAMA_ROLES = {"system", "user", "assistant"}
AUTH_REQUIRED_CONNECTION_TYPES = {"serverlessendpoint", "onlineendpoint", "connection"}
REQUIRED_CONFIG_KEYS = ["endpoint_url", "model_family"]
REQUIRED_SECRET_KEYS = ["endpoint_api_key"]
ENDPOINT_REQUIRED_ENV_VARS = ["AZUREML_ARM_SUBSCRIPTION", "AZUREML_ARM_RESOURCEGROUP", "AZUREML_ARM_WORKSPACE_NAME"]
def handle_online_endpoint_error(max_retries: int = 5,
initial_delay: float = 2,
exponential_base: float = 3):
def deco_retry(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
delay = initial_delay
for i in range(max_retries):
try:
return func(*args, **kwargs)
except OpenModelLLMOnlineEndpointError as e:
if i == max_retries - 1:
error_message = f"Exception hit calling Online Endpoint: {type(e).__name__}: {str(e)}"
print(error_message, file=sys.stderr)
raise OpenModelLLMOnlineEndpointError(message=error_message)
delay *= exponential_base
time.sleep(delay)
return wrapper
return deco_retry
class ConnectionCache:
def __init__(self,
use_until: datetime,
subscription_id: str,
resource_group: str,
workspace_name: str,
connection_names: List[str]):
self.use_until = use_until
self.subscription_id = subscription_id
self.resource_group = resource_group
self.workspace_name = workspace_name
self.connection_names = connection_names
@classmethod
def from_filename(self, file):
cache = json.load(file)
return self(cache['use_until'],
cache['subscription_id'],
cache['resource_group'],
cache['workspace_name'],
cache['connection_names'])
def can_use(self,
subscription_id: str,
resource_group: str,
workspace_name: str):
use_until_time = datetime.fromisoformat(self.use_until)
return (use_until_time > datetime.now()
and self.subscription_id == subscription_id
and self.resource_group == resource_group
and self.workspace_name == workspace_name)
class Endpoint:
def __init__(self,
endpoint_name: str,
endpoint_url: str,
endpoint_api_key: str):
self.deployments: List[Deployment] = []
self.default_deployment: Deployment = None
self.endpoint_url = endpoint_url
self.endpoint_api_key = endpoint_api_key
self.endpoint_name = endpoint_name
class Deployment:
def __init__(self,
deployment_name: str,
model_family: str):
self.model_family = model_family
self.deployment_name = deployment_name
class ServerlessEndpointsContainer:
API_VERSION = "2023-08-01-preview"
def _get_headers(self, token: str) -> Dict[str, str]:
headers = {
"Authorization": f"Bearer {token}",
"Content-Type": "application/json",
}
return headers
def get_serverless_arm_url(self, subscription_id, resource_group, workspace_name, suffix=None):
suffix = "" if suffix is None else f"/{suffix}"
return f"https://management.azure.com/subscriptions/{subscription_id}" \
+ f"/resourceGroups/{resource_group}/providers/Microsoft.MachineLearningServices" \
+ f"/workspaces/{workspace_name}/serverlessEndpoints{suffix}?api-version={self.API_VERSION}"
def _list(self, token: str, subscription_id: str, resource_group: str, workspace_name: str):
headers = self._get_headers(token)
url = self.get_serverless_arm_url(subscription_id, resource_group, workspace_name)
try:
response = requests.get(url, headers=headers, timeout=50)
return json.loads(response.content)['value']
except Exception as e:
print(f"Error encountered when listing serverless endpoints. Exception: {e}", file=sys.stderr)
return []
def _validate_model_family(self, serverless_endpoint):
try:
if serverless_endpoint.get('properties', {}).get('provisioningState') != "Succeeded":
return None
if (try_get_from_dict(serverless_endpoint,
['properties', 'offer', 'publisher']) == 'Meta'
and "llama" in try_get_from_dict(serverless_endpoint,
['properties', 'offer', 'offerName'])):
return ModelFamily.LLAMA
if (try_get_from_dict(serverless_endpoint,
['properties', 'marketplaceInfo', 'publisherId']) == 'metagenai'
and "llama" in try_get_from_dict(serverless_endpoint,
['properties', 'marketplaceInfo', 'offerId'])):
return ModelFamily.LLAMA
except Exception as ex:
print(f"Ignoring endpoint {serverless_endpoint['id']} due to error: {ex}", file=sys.stderr)
return None
def list_serverless_endpoints(self,
token,
subscription_id,
resource_group,
workspace_name,
return_endpoint_url: bool = False):
serverlessEndpoints = self._list(token, subscription_id, resource_group, workspace_name)
result = []
for e in serverlessEndpoints:
if (self._validate_model_family(e)):
result.append({
"value": f"serverlessEndpoint/{e['name']}",
"display_value": f"[Serverless] {e['name']}",
# "hyperlink": self.get_endpoint_url(e.endpoint_name)
"description": f"Serverless Endpoint: {e['name']}",
})
if return_endpoint_url:
result[-1]['url'] = try_get_from_dict(e, ['properties', 'inferenceEndpoint', 'uri'])
return result
def _list_endpoint_key(self,
token: str,
subscription_id: str,
resource_group: str,
workspace_name: str,
serverless_endpoint_name: str):
headers = self._get_headers(token)
url = self.get_serverless_arm_url(subscription_id,
resource_group,
workspace_name,
f"{serverless_endpoint_name}/listKeys")
try:
response = requests.post(url, headers=headers, timeout=50)
return json.loads(response.content)
except Exception as e:
print(f"Unable to get key from selected serverless endpoint. Exception: {e}", file=sys.stderr)
def get_serverless_endpoint(self,
token: str,
subscription_id: str,
resource_group: str,
workspace_name: str,
serverless_endpoint_name: str):
headers = self._get_headers(token)
url = self.get_serverless_arm_url(subscription_id, resource_group, workspace_name, serverless_endpoint_name)
try:
response = requests.get(url, headers=headers, timeout=50)
return json.loads(response.content)
except Exception as e:
print(f"Unable to get selected serverless endpoint. Exception: {e}", file=sys.stderr)
def get_serverless_endpoint_key(self,
token: str,
subscription_id: str,
resource_group: str,
workspace_name: str,
serverless_endpoint_name: str) -> Tuple[str, str, str]:
endpoint = self.get_serverless_endpoint(token,
subscription_id,
resource_group,
workspace_name,
serverless_endpoint_name)
endpoint_url = try_get_from_dict(endpoint, ['properties', 'inferenceEndpoint', 'uri'])
model_family = self._validate_model_family(endpoint)
endpoint_api_key = self._list_endpoint_key(token,
subscription_id,
resource_group,
workspace_name,
serverless_endpoint_name)['primaryKey']
return (endpoint_url,
endpoint_api_key,
model_family)
class CustomConnectionsContainer:
def get_azure_custom_connection_names(self,
credential,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
return_endpoint_url: bool = False
) -> List[Dict[str, Union[str, int, float, list, Dict]]]:
result = []
try:
from promptflow.azure import PFClient as AzurePFClient
azure_pf_client = AzurePFClient(
credential=credential,
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name)
except Exception:
message = "Skipping Azure PFClient. To connect, please ensure the following environment variables are set: "
message += ",".join(ENDPOINT_REQUIRED_ENV_VARS)
print(message, file=sys.stderr)
return result
connections = azure_pf_client._connections.list()
for c in connections:
if c.type == ConnectionType.CUSTOM and "model_family" in c.configs:
try:
validate_model_family(c.configs["model_family"])
result.append({
"value": f"connection/{c.name}",
"display_value": f"[Connection] {c.name}",
# "hyperlink": "",
"description": f"Custom Connection: {c.name}",
})
if return_endpoint_url:
result[-1]['url'] = c.configs['endpoint_url']
except Exception:
# silently ignore unsupported model family
continue
return result
def get_local_custom_connection_names(self,
return_endpoint_url: bool = False
) -> List[Dict[str, Union[str, int, float, list, Dict]]]:
result = []
try:
from promptflow import PFClient as LocalPFClient
except Exception as e:
print(f"Skipping Local PFClient. Exception: {e}", file=sys.stderr)
return result
pf = LocalPFClient()
connections = pf.connections.list()
for c in connections:
if c.type == ConnectionType.CUSTOM and "model_family" in c.configs:
try:
validate_model_family(c.configs["model_family"])
result.append({
"value": f"localConnection/{c.name}",
"display_value": f"[Local Connection] {c.name}",
# "hyperlink": "",
"description": f"Local Custom Connection: {c.name}",
})
if return_endpoint_url:
result[-1]['url'] = c.configs['endpoint_url']
except Exception:
# silently ignore unsupported model family
continue
return result
def get_endpoint_from_local_custom_connection(self, connection_name) -> Tuple[str, str, str]:
from promptflow import PFClient as LocalPFClient
pf = LocalPFClient()
connection = pf.connections.get(connection_name, with_secrets=True)
return self.get_endpoint_from_custom_connection(connection)
def get_endpoint_from_azure_custom_connection(self,
credential,
subscription_id,
resource_group_name,
workspace_name,
connection_name) -> Tuple[str, str, str]:
from promptflow.azure import PFClient as AzurePFClient
azure_pf_client = AzurePFClient(
credential=credential,
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name)
connection = azure_pf_client._arm_connections.get(connection_name)
return self.get_endpoint_from_custom_connection(connection)
def get_endpoint_from_custom_connection(self, connection: CustomConnection) -> Tuple[str, str, str]:
conn_dict = dict(connection)
for key in REQUIRED_CONFIG_KEYS:
if key not in conn_dict:
accepted_keys = ",".join([key for key in REQUIRED_CONFIG_KEYS])
raise OpenModelLLMKeyValidationError(
message=f"""Required key `{key}` not found in given custom connection.
Required keys are: {accepted_keys}."""
)
for key in REQUIRED_SECRET_KEYS:
if key not in conn_dict:
accepted_keys = ",".join([key for key in REQUIRED_SECRET_KEYS])
raise OpenModelLLMKeyValidationError(
message=f"""Required secret key `{key}` not found in given custom connection.
Required keys are: {accepted_keys}."""
)
model_family = validate_model_family(connection.configs['model_family'])
return (connection.configs['endpoint_url'],
connection.secrets['endpoint_api_key'],
model_family)
def list_custom_connection_names(self,
credential,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
return_endpoint_url: bool = False
) -> List[Dict[str, Union[str, int, float, list, Dict]]]:
azure_custom_connections = self.get_azure_custom_connection_names(credential,
subscription_id,
resource_group_name,
workspace_name,
return_endpoint_url)
local_custom_connections = self.get_local_custom_connection_names(return_endpoint_url)
return azure_custom_connections + local_custom_connections
class EndpointsContainer:
def get_ml_client(self,
credential,
subscription_id: str,
resource_group_name: str,
workspace_name: str):
try:
from azure.ai.ml import MLClient
return MLClient(
credential=credential,
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name)
except Exception as e:
message = "Unable to connect to AzureML. Please ensure the following environment variables are set: "
message += ",".join(ENDPOINT_REQUIRED_ENV_VARS)
message += "\nException: " + str(e)
raise OpenModelLLMOnlineEndpointError(message=message)
def get_endpoints_and_deployments(self,
credential,
subscription_id: str,
resource_group_name: str,
workspace_name: str) -> List[Endpoint]:
ml_client = self.get_ml_client(credential, subscription_id, resource_group_name, workspace_name)
list_of_endpoints: List[Endpoint] = []
for ep in ml_client.online_endpoints.list():
endpoint = Endpoint(
endpoint_name=ep.name,
endpoint_url=ep.scoring_uri,
endpoint_api_key=ml_client.online_endpoints.get_keys(ep.name).primary_key)
ordered_deployment_names = sorted(ep.traffic, key=lambda item: item[1])
deployments = ml_client.online_deployments.list(ep.name)
for deployment_name in ordered_deployment_names:
for d in deployments:
if d.name == deployment_name:
model_family = get_model_type(d.model)
if model_family is None:
continue
deployment = Deployment(deployment_name=d.name, model_family=model_family)
endpoint.deployments.append(deployment)
# Deployment are ordered by traffic level, first in is default
if endpoint.default_deployment is None:
endpoint.default_deployment = deployment
if len(endpoint.deployments) > 0:
list_of_endpoints.append(endpoint)
self.__endpoints_and_deployments = list_of_endpoints
return self.__endpoints_and_deployments
def get_endpoint_url(self, endpoint_name, subscription_id, resource_group_name, workspace_name):
return f"https://ml.azure.com/endpoints/realtime/{endpoint_name}" \
+ f"/detail?wsid=/subscriptions/{subscription_id}" \
+ f"/resourceGroups/{resource_group_name}" \
+ f"/providers/Microsoft.MachineLearningServices/workspaces/{workspace_name}"
def list_endpoint_names(self,
credential,
subscription_id,
resource_group_name,
workspace_name,
return_endpoint_url: bool = False
) -> List[Dict[str, Union[str, int, float, list, Dict]]]:
'''Function for listing endpoints in the UX'''
endpoints_and_deployments = self.get_endpoints_and_deployments(
credential,
subscription_id,
resource_group_name,
workspace_name)
result = []
for e in endpoints_and_deployments:
result.append({
"value": f"onlineEndpoint/{e.endpoint_name}",
"display_value": f"[Online] {e.endpoint_name}",
"hyperlink": self.get_endpoint_url(e.endpoint_name,
subscription_id,
resource_group_name,
workspace_name),
"description": f"Online Endpoint: {e.endpoint_name}",
})
if return_endpoint_url:
result[-1]['url'] = e.endpoint_url
return result
def list_deployment_names(self,
credential,
subscription_id,
resource_group_name,
workspace_name,
endpoint_name: str
) -> List[Dict[str, Union[str, int, float, list, Dict]]]:
'''Function for listing deployments in the UX'''
if endpoint_name is None:
return []
endpoints_and_deployments = self.get_endpoints_and_deployments(
credential,
subscription_id,
resource_group_name,
workspace_name)
for endpoint in endpoints_and_deployments:
if endpoint.endpoint_name == endpoint_name:
result = []
for d in endpoint.deployments:
result.append({
"value": d.deployment_name,
"display_value": d.deployment_name,
# "hyperlink": '',
"description": f"this is {d.deployment_name} item",
})
return result
return []
ENDPOINT_CONTAINER = EndpointsContainer()
CUSTOM_CONNECTION_CONTAINER = CustomConnectionsContainer()
SERVERLESS_ENDPOINT_CONTAINER = ServerlessEndpointsContainer()
def is_serverless_endpoint(endpoint_url: str) -> bool:
return "serverless.ml.azure.com" in endpoint_url or "inference.ai.azure.com" in endpoint_url
def try_get_from_dict(some_dict: Dict, key_list: List):
for key in key_list:
if some_dict is None:
return some_dict
elif key in some_dict:
some_dict = some_dict[key]
else:
return None
return some_dict
def parse_endpoint_connection_type(endpoint_connection_name: str) -> Tuple[str, str]:
endpoint_connection_details = endpoint_connection_name.split("/")
return (endpoint_connection_details[0].lower(), endpoint_connection_details[1])
def list_endpoint_names(subscription_id: str,
resource_group_name: str,
workspace_name: str,
return_endpoint_url: bool = False,
force_refresh: bool = False) -> List[Dict[str, Union[str, int, float, list, Dict]]]:
cache_file_path = None
try:
with tempfile.NamedTemporaryFile(delete=False) as temp_file:
cache_file_path = os.path.join(os.path.dirname(temp_file.name), CONNECTION_CACHE_FILE)
print(f"Attempting to read connection cache. File path: {cache_file_path}", file=sys.stdout)
if force_refresh:
print("....skipping. force_refresh is True", file=sys.stdout)
else:
with open(cache_file_path, 'r') as file:
cache = ConnectionCache.from_filename(file)
if cache.can_use(subscription_id, resource_group_name, workspace_name):
if len(cache.connection_names) > 0:
print("....using Connection Cache File", file=sys.stdout)
return cache.connection_names
else:
print("....skipping. No connections in file", file=sys.stdout)
else:
print("....skipping. File not relevant", file=sys.stdout)
except Exception as e:
print(f"....failed to find\\read connection cache file. Regenerating. Error:{e}", file=sys.stdout)
try:
from azure.identity import DefaultAzureCredential
credential = DefaultAzureCredential(exclude_interactive_browser_credential=False)
token = credential.get_token("https://management.azure.com/.default").token
except Exception as e:
print(f"Skipping list_endpoint_names. Exception: {e}", file=sys.stderr)
msg = "Exception getting token: Please retry"
return [{"value": msg, "display_value": msg, "description": msg}]
serverless_endpoints = SERVERLESS_ENDPOINT_CONTAINER.list_serverless_endpoints(token,
subscription_id,
resource_group_name,
workspace_name,
return_endpoint_url)
online_endpoints = ENDPOINT_CONTAINER.list_endpoint_names(credential,
subscription_id,
resource_group_name,
workspace_name,
return_endpoint_url)
custom_connections = CUSTOM_CONNECTION_CONTAINER.list_custom_connection_names(credential,
subscription_id,
resource_group_name,
workspace_name,
return_endpoint_url)
list_of_endpoints = custom_connections + serverless_endpoints + online_endpoints
cache = ConnectionCache(use_until=(datetime.now() + timedelta(minutes=5)).isoformat(),
subscription_id=subscription_id,
resource_group=resource_group_name,
workspace_name=workspace_name,
connection_names=list_of_endpoints)
if len(list_of_endpoints) == 0:
msg = "No endpoints found. Please add a connection."
return [{"value": msg, "display_value": msg, "description": msg}]
if cache_file_path is not None:
try:
print(f"Attempting to write connection cache. File path: {cache_file_path}", file=sys.stdout)
with open(cache_file_path, 'w') as file:
json.dump(cache, file, default=lambda obj: obj.__dict__)
print("....written", file=sys.stdout)
except Exception as e:
print(f"""....failed to write connection cache file. Will need to reload next time.
Error:{e}""", file=sys.stdout)
return list_of_endpoints
def list_deployment_names(subscription_id: str,
resource_group_name: str,
workspace_name: str,
endpoint: str = None) -> List[Dict[str, Union[str, int, float, list, Dict]]]:
deployment_default_list = [{
"value": DEPLOYMENT_DEFAULT,
"display_value": DEPLOYMENT_DEFAULT,
"description": "This will use the default deployment for the selected online endpoint."
+ "You can also manually enter a deployment name here."
}]
if endpoint is None or endpoint.strip() == "" or "/" not in endpoint:
return deployment_default_list
(endpoint_connection_type, endpoint_connection_name) = parse_endpoint_connection_type(endpoint)
if endpoint_connection_type != "onlineendpoint":
return deployment_default_list
try:
from azure.identity import DefaultAzureCredential
credential = DefaultAzureCredential(exclude_interactive_browser_credential=False)
except Exception as e:
print(f"Skipping list_deployment_names. Exception: {e}", file=sys.stderr)
return deployment_default_list
return deployment_default_list + ENDPOINT_CONTAINER.list_deployment_names(
credential,
subscription_id,
resource_group_name,
workspace_name,
endpoint_connection_name
)
def get_model_type(deployment_model: str) -> str:
m = re.match(r'azureml://registries/[^/]+/models/([^/]+)/versions/', deployment_model)
if m is None:
print(f"Unexpected model format: {deployment_model}. Skipping", file=sys.stdout)
return None
model = m[1].lower()
if model.startswith("llama-2"):
return ModelFamily.LLAMA
elif model.startswith("tiiuae-falcon"):
return ModelFamily.FALCON
elif model.startswith("databricks-dolly-v2"):
return ModelFamily.DOLLY
elif model.startswith("gpt2"):
return ModelFamily.GPT2
else:
# Not found and\or handled. Ignore this endpoint\deployment
print(f"Unexpected model type: {model} derived from deployed model: {deployment_model}")
return None
def validate_model_family(model_family: str):
try:
return ModelFamily[model_family]
except KeyError:
accepted_models = ",".join([model.name for model in ModelFamily])
raise OpenModelLLMKeyValidationError(
message=f"""Given model_family '{model_family}' not recognized.
Supported models are: {accepted_models}."""
)
class ModelFamily(str, Enum):
LLAMA = "LLaMa"
DOLLY = "Dolly"
GPT2 = "GPT-2"
FALCON = "Falcon"
@classmethod
def _missing_(cls, value):
value = value.lower()
for member in cls:
if member.lower() == value:
return member
return None
STANDARD_CONTRACT_MODELS = [ModelFamily.DOLLY, ModelFamily.GPT2, ModelFamily.FALCON]
class API(str, Enum):
CHAT = "chat"
COMPLETION = "completion"
class ContentFormatterBase:
"""Transform request and response of AzureML endpoint to match with
required schema.
"""
content_type: Optional[str] = "application/json"
"""The MIME type of the input data passed to the endpoint"""
accepts: Optional[str] = "application/json"
"""The MIME type of the response data returned from the endpoint"""
@staticmethod
def escape_special_characters(prompt: str) -> str:
"""Escapes any special characters in `prompt`"""
return re.sub(
r'\\([\\\"a-zA-Z])',
r'\\\1',
prompt)
@staticmethod
def parse_chat(chat_str: str) -> List[Dict[str, str]]:
# LLaMa only supports below roles.
separator = r"(?i)\n*(system|user|assistant)\s*:\s*\n"
chunks = re.split(separator, chat_str)
# remove any empty chunks
chunks = [c.strip() for c in chunks if c.strip()]
chat_list = []
for index in range(0, len(chunks), 2):
role = chunks[index].lower()
# Check if prompt follows chat api message format and has valid role.
try:
validate_role(role, VALID_LLAMA_ROLES)
except ChatAPIInvalidRole as e:
raise OpenModelLLMUserError(message=e.message)
if len(chunks) <= index + 1:
message = "Unexpected chat format. Please ensure the query matches the chat format of the model used."
raise OpenModelLLMUserError(message=message)
chat_list.append({
"role": role,
"content": chunks[index+1]
})
return chat_list
@abstractmethod
def format_request_payload(self, prompt: str, model_kwargs: Dict) -> str:
"""Formats the request body according to the input schema of
the model. Returns bytes or seekable file like object in the
format specified in the content_type request header.
"""
@abstractmethod
def format_response_payload(self, output: bytes) -> str:
"""Formats the response body according to the output
schema of the model. Returns the data type that is
received from the response.
"""
class MIRCompleteFormatter(ContentFormatterBase):
"""Content handler for LLMs from the HuggingFace catalog."""
def format_request_payload(self, prompt: str, model_kwargs: Dict) -> str:
input_str = json.dumps(
{
"input_data": {"input_string": [ContentFormatterBase.escape_special_characters(prompt)]},
"parameters": model_kwargs,
}
)
return input_str
def format_response_payload(self, output: bytes) -> str:
"""These models only support generation - expect a single output style"""
response_json = json.loads(output)
if len(response_json) > 0 and "0" in response_json[0]:
if "0" in response_json[0]:
return response_json[0]["0"]
elif "output" in response_json:
return response_json["output"]
error_message = f"Unexpected response format. Response: {response_json}"
print(error_message, file=sys.stderr)
raise OpenSourceLLMOnlineEndpointError(message=error_message)
class LlamaContentFormatter(ContentFormatterBase):
"""Content formatter for LLaMa"""
def __init__(self, api: API, chat_history: Optional[str] = ""):
super().__init__()
self.api = api
self.chat_history = chat_history
def format_request_payload(self, prompt: str, model_kwargs: Dict) -> str:
"""Formats the request according the the chosen api"""
if "do_sample" not in model_kwargs:
model_kwargs["do_sample"] = True
if self.api == API.CHAT:
prompt_value = ContentFormatterBase.parse_chat(self.chat_history)
else:
prompt_value = [ContentFormatterBase.escape_special_characters(prompt)]
return json.dumps(
{
"input_data":
{
"input_string": prompt_value,
"parameters": model_kwargs
}
}
)
def format_response_payload(self, output: bytes) -> str:
"""Formats response"""
response_json = json.loads(output)
if self.api == API.CHAT and "output" in response_json:
return response_json["output"]
elif self.api == API.COMPLETION and len(response_json) > 0 and "0" in response_json[0]:
return response_json[0]["0"]
else:
error_message = f"Unexpected response format. Response: {response_json}"
print(error_message, file=sys.stderr)
raise OpenModelLLMOnlineEndpointError(message=error_message)
class ServerlessLlamaContentFormatter(ContentFormatterBase):
"""Content formatter for LLaMa"""
def __init__(self, api: API, chat_history: Optional[str] = ""):
super().__init__()
self.api = api
self.chat_history = chat_history
self.model_id = "llama-2-7b-hf"
def format_request_payload(self, prompt: str, model_kwargs: Dict) -> str:
"""Formats the request according the the chosen api"""
# Modify max_tokens key for serverless
model_kwargs["max_tokens"] = model_kwargs["max_new_tokens"]
if self.api == API.CHAT:
messages = ContentFormatterBase.parse_chat(self.chat_history)
base_body = {
"model": self.model_id,
"messages": messages,
"n": 1,
}
base_body.update(model_kwargs)
else:
prompt_value = ContentFormatterBase.escape_special_characters(prompt)
base_body = {
"prompt": prompt_value,
"n": 1,
}
base_body.update(model_kwargs)
return json.dumps(base_body)
def format_response_payload(self, output: bytes) -> str:
"""Formats response"""
response_json = json.loads(output)
if self.api == API.CHAT and "choices" in response_json:
return response_json["choices"][0]["message"]["content"]
elif self.api == API.COMPLETION and "choices" in response_json:
return response_json["choices"][0]["text"]
else:
error_message = f"Unexpected response format. Response: {response_json}"
print(error_message, file=sys.stderr)
raise OpenModelLLMOnlineEndpointError(message=error_message)
class ContentFormatterFactory:
"""Factory class for supported models"""
def get_content_formatter(
model_family: ModelFamily, api: API, chat_history: Optional[List[Dict]] = [], endpoint_url: Optional[str] = ""
) -> ContentFormatterBase:
if model_family == ModelFamily.LLAMA:
if is_serverless_endpoint(endpoint_url):
return ServerlessLlamaContentFormatter(chat_history=chat_history, api=api)
else:
return LlamaContentFormatter(chat_history=chat_history, api=api)
elif model_family in STANDARD_CONTRACT_MODELS:
return MIRCompleteFormatter()
class AzureMLOnlineEndpoint:
"""Azure ML Online Endpoint models."""
endpoint_url: str = ""
"""URL of pre-existing Endpoint. Should be passed to constructor or specified as
env var `AZUREML_ENDPOINT_URL`."""
endpoint_api_key: str = ""
"""Authentication Key for Endpoint. Should be passed to constructor or specified as
env var `AZUREML_ENDPOINT_API_KEY`."""
content_formatter: Any = None
"""The content formatter that provides an input and output
transform function to handle formats between the LLM and
the endpoint"""
model_kwargs: Optional[Dict] = {}
"""Key word arguments to pass to the model."""
def __init__(
self,
endpoint_url: str,
endpoint_api_key: str,
content_formatter: ContentFormatterBase,
model_family: ModelFamily,
deployment_name: Optional[str] = None,
model_kwargs: Optional[Dict] = {},
):
self.endpoint_url = endpoint_url
self.endpoint_api_key = endpoint_api_key
self.deployment_name = deployment_name
self.content_formatter = content_formatter
self.model_kwargs = model_kwargs
self.model_family = model_family
def _call_endpoint(self, request_body: str) -> str:
"""call."""
headers = {
"Content-Type": "application/json",
"Authorization": ("Bearer " + self.endpoint_api_key),
"x-ms-user-agent": "PromptFlow/OpenModelLLM/" + self.model_family
}
# If this is not set it'll use the default deployment on the endpoint.
if self.deployment_name is not None:
headers["azureml-model-deployment"] = self.deployment_name
result = requests.post(self.endpoint_url, data=request_body, headers=headers)
if result.status_code != 200:
error_message = f"""Request failure while calling Online Endpoint Status:{result.status_code}
Error:{result.text}"""
print(error_message, file=sys.stderr)
raise OpenModelLLMOnlineEndpointError(message=error_message)
return result.text
def __call__(
self,
prompt: str
) -> str:
"""Call out to an AzureML Managed Online endpoint.
Args:
prompt: The prompt to pass into the model.
Returns:
The string generated by the model.
Example:
.. code-block:: python
response = azureml_model("Tell me a joke.")
"""
request_body = self.content_formatter.format_request_payload(prompt, self.model_kwargs)
endpoint_response = self._call_endpoint(request_body)
response = self.content_formatter.format_response_payload(endpoint_response)
return response
class OpenModelLLM(ToolProvider):
def __init__(self):
super().__init__()
def get_deployment_from_endpoint(self,
credential,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
endpoint_name: str,
deployment_name: str = None) -> Tuple[str, str, str]:
endpoints_and_deployments = ENDPOINT_CONTAINER.get_endpoints_and_deployments(
credential,
subscription_id,
resource_group_name,
workspace_name)
for ep in endpoints_and_deployments:
if ep.endpoint_name == endpoint_name:
if deployment_name is None:
return (ep.endpoint_url,
ep.endpoint_api_key,
ep.default_deployment.model_family)
for d in ep.deployments:
if d.deployment_name == deployment_name:
return (ep.endpoint_url,
ep.endpoint_api_key,
d.model_family)
message = f"""Invalid endpoint and deployment values.
Please ensure endpoint name and deployment names are correct, and the deployment was successfull.
Could not find endpoint: {endpoint_name} and deployment: {deployment_name}"""
raise OpenModelLLMUserError(message=message)
def sanitize_endpoint_url(self,
endpoint_url: str,
api_type: API):
if is_serverless_endpoint(endpoint_url):
if api_type == API.CHAT:
if not endpoint_url.endswith("/v1/chat/completions"):
return endpoint_url + "/v1/chat/completions"
else:
if not endpoint_url.endswith("/v1/completions"):
return endpoint_url + "/v1/completions"
return endpoint_url
def get_endpoint_details(self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
endpoint: str,
api_type: API,
deployment_name: str = None,
**kwargs) -> Tuple[str, str, str]:
if self.endpoint_values_in_kwargs(**kwargs):
endpoint_url = kwargs["endpoint_url"]
endpoint_api_key = kwargs["endpoint_api_key"]
model_family = kwargs["model_family"]
# clean these up, aka don't send them to MIR
del kwargs["endpoint_url"]
del kwargs["endpoint_api_key"]
del kwargs["model_family"]
return (endpoint_url, endpoint_api_key, model_family)
(endpoint_connection_type, endpoint_connection_name) = parse_endpoint_connection_type(endpoint)
print(f"endpoint_connection_type: {endpoint_connection_type} name: {endpoint_connection_name}", file=sys.stdout)
con_type = endpoint_connection_type.lower()
if con_type in AUTH_REQUIRED_CONNECTION_TYPES:
try:
from azure.identity import DefaultAzureCredential
credential = DefaultAzureCredential(exclude_interactive_browser_credential=False)
token = credential.get_token("https://management.azure.com/.default").token
except Exception as e:
message = f"""Error encountered while attempting to Authorize access to {endpoint}.
Exception: {e}"""
print(message, file=sys.stderr)
raise OpenModelLLMUserError(message=message)
if con_type == "serverlessendpoint":
(endpoint_url, endpoint_api_key, model_family) = SERVERLESS_ENDPOINT_CONTAINER.get_serverless_endpoint_key(
token,
subscription_id,
resource_group_name,
workspace_name,
endpoint_connection_name)
elif con_type == "onlineendpoint":
(endpoint_url, endpoint_api_key, model_family) = self.get_deployment_from_endpoint(
credential,
subscription_id,
resource_group_name,
workspace_name,
endpoint_connection_name,
deployment_name)
elif con_type == "connection":
(endpoint_url,
endpoint_api_key,
model_family) = CUSTOM_CONNECTION_CONTAINER.get_endpoint_from_azure_custom_connection(
credential,
subscription_id,
resource_group_name,
workspace_name,
endpoint_connection_name)
elif con_type == "localconnection":
(endpoint_url,
endpoint_api_key,
model_family) = CUSTOM_CONNECTION_CONTAINER.get_endpoint_from_local_custom_connection(
endpoint_connection_name)
else:
raise OpenModelLLMUserError(message=f"Invalid endpoint connection type: {endpoint_connection_type}")
return (self.sanitize_endpoint_url(endpoint_url, api_type), endpoint_api_key, model_family)
def endpoint_values_in_kwargs(self, **kwargs):
# This is mostly for testing, suggest not using this since security\privacy concerns for the endpoint key
if 'endpoint_url' not in kwargs and 'endpoint_api_key' not in kwargs and 'model_family' not in kwargs:
return False
if 'endpoint_url' not in kwargs or 'endpoint_api_key' not in kwargs or 'model_family' not in kwargs:
message = """Endpoint connection via kwargs not fully set.
If using kwargs, the following values must be set: endpoint_url, endpoint_api_key, and model_family"""
raise OpenModelLLMKeyValidationError(message=message)
return True
@tool
@handle_online_endpoint_error()
def call(
self,
prompt: PromptTemplate,
api: API,
endpoint_name: str,
deployment_name: Optional[str] = None,
temperature: Optional[float] = 1.0,
max_new_tokens: Optional[int] = 500,
top_p: Optional[float] = 1.0,
model_kwargs: Optional[Dict] = {},
**kwargs
) -> str:
# Sanitize deployment name. Empty deployment name is the same as None.
if deployment_name is not None:
deployment_name = deployment_name.strip()
if not deployment_name or deployment_name == DEPLOYMENT_DEFAULT:
deployment_name = None
print(f"Executing Open Model LLM Tool for endpoint: '{endpoint_name}', deployment: '{deployment_name}'",
file=sys.stdout)
(endpoint_url, endpoint_api_key, model_family) = self.get_endpoint_details(
subscription_id=os.getenv("AZUREML_ARM_SUBSCRIPTION", None),
resource_group_name=os.getenv("AZUREML_ARM_RESOURCEGROUP", None),
workspace_name=os.getenv("AZUREML_ARM_WORKSPACE_NAME", None),
endpoint=endpoint_name,
api_type=api,
deployment_name=deployment_name,
**kwargs)
prompt = render_jinja_template(prompt, trim_blocks=True, keep_trailing_newline=True, **kwargs)
model_kwargs["top_p"] = top_p
model_kwargs["temperature"] = temperature
model_kwargs["max_new_tokens"] = max_new_tokens
content_formatter = ContentFormatterFactory.get_content_formatter(
model_family=model_family,
api=api,
chat_history=prompt,
endpoint_url=endpoint_url
)
llm = AzureMLOnlineEndpoint(
endpoint_url=endpoint_url,
endpoint_api_key=endpoint_api_key,
model_family=model_family,
content_formatter=content_formatter,
deployment_name=deployment_name,
model_kwargs=model_kwargs
)
return llm(prompt)
| promptflow/src/promptflow-tools/promptflow/tools/open_model_llm.py/0 | {
"file_path": "promptflow/src/promptflow-tools/promptflow/tools/open_model_llm.py",
"repo_id": "promptflow",
"token_count": 23948
} | 26 |
[pytest]
markers =
skip_if_no_api_key: skip the test if actual api key is not provided. | promptflow/src/promptflow-tools/tests/pytest.ini/0 | {
"file_path": "promptflow/src/promptflow-tools/tests/pytest.ini",
"repo_id": "promptflow",
"token_count": 33
} | 27 |
import pytest
from promptflow.exceptions import UserErrorException
from promptflow.tools.serpapi import Engine, SafeMode, search
import tests.utils as utils
@pytest.mark.usefixtures("use_secrets_config_file")
@pytest.mark.skip_if_no_api_key("serp_connection")
class TestSerpAPI:
def test_engine(self, serp_connection):
query = "cute cats"
num = 2
result_dict = search(
connection=serp_connection, query=query, num=num, safe=SafeMode.ACTIVE, engine=Engine.GOOGLE.value)
utils.is_json_serializable(result_dict, "serp api search()")
assert result_dict["search_metadata"]["google_url"] is not None
assert int(result_dict["search_parameters"]["num"]) == num
assert result_dict["search_parameters"]["safe"].lower() == "active"
result_dict = search(
connection=serp_connection, query=query, num=num, safe=SafeMode.ACTIVE, engine=Engine.BING.value)
utils.is_json_serializable(result_dict, "serp api search()")
assert int(result_dict["search_parameters"]["count"]) == num
assert result_dict["search_parameters"]["safe_search"].lower() == "strict"
def test_invalid_api_key(self, serp_connection):
serp_connection.api_key = "hello"
query = "cute cats"
num = 2
engine = Engine.GOOGLE.value
error_msg = "Invalid API key. Your API key should be here: https://serpapi.com/manage-api-key"
with pytest.raises(UserErrorException) as exc_info:
search(connection=serp_connection, query=query, num=num, engine=engine)
assert error_msg == exc_info.value.args[0]
@pytest.mark.parametrize("engine", [Engine.GOOGLE.value, Engine.BING.value])
def test_invalid_query(self, serp_connection, engine):
query = ""
num = 2
error_msg = "Missing query `q` parameter."
with pytest.raises(UserErrorException) as exc_info:
search(connection=serp_connection, query=query, num=num, engine=engine)
assert error_msg == exc_info.value.args[0]
| promptflow/src/promptflow-tools/tests/test_serpapi.py/0 | {
"file_path": "promptflow/src/promptflow-tools/tests/test_serpapi.py",
"repo_id": "promptflow",
"token_count": 832
} | 28 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import argparse
import json
from functools import partial
from promptflow._cli._params import (
add_param_all_results,
add_param_max_results,
add_param_set,
add_param_yes,
base_params,
)
from promptflow._cli._utils import activate_action, confirm, exception_handler, get_secret_input, print_yellow_warning
from promptflow._sdk._constants import MAX_LIST_CLI_RESULTS
from promptflow._sdk._load_functions import load_connection
from promptflow._sdk._pf_client import PFClient
from promptflow._sdk.entities._connection import _Connection
from promptflow._utils.logger_utils import get_cli_sdk_logger
from promptflow._utils.yaml_utils import load_yaml
logger = get_cli_sdk_logger()
_client = None
def _get_pf_client():
global _client
if _client is None:
_client = PFClient()
return _client
def add_param_file(parser):
parser.add_argument("--file", "-f", type=str, help="File path of the connection yaml.", required=True)
def add_param_name(parser, required=False):
parser.add_argument("--name", "-n", type=str, help="Name of the connection.", required=required)
def add_connection_parser(subparsers):
connection_parser = subparsers.add_parser(
"connection",
description="""A CLI tool to manage connections for promptflow.
Your secrets will be encrypted using AES(Advanced Encryption Standard) technology.""", # noqa: E501
help="pf connection",
)
subparsers = connection_parser.add_subparsers()
add_connection_create(subparsers)
add_connection_update(subparsers)
add_connection_show(subparsers)
add_connection_list(subparsers)
add_connection_delete(subparsers)
connection_parser.set_defaults(action="connection")
def add_connection_create(subparsers):
# Do not change the indent of epilog
epilog = """
Examples:
# Creating a connection with yaml file:
pf connection create -f connection.yaml
# Creating a connection with yaml file and overrides:
pf connection create -f connection.yaml --set api_key="my_api_key"
# Creating a custom connection with .env file, note that overrides specified by --set will be ignored:
pf connection create -f .env --name custom
"""
activate_action(
name="create",
description="Create a connection.",
epilog=epilog,
add_params=[add_param_set, add_param_file, add_param_name] + base_params,
subparsers=subparsers,
help_message="Create a connection.",
action_param_name="sub_action",
)
def add_connection_update(subparsers):
epilog = """
Examples:
# Updating a connection:
pf connection update -n my_connection --set api_key="my_api_key"
"""
activate_action(
name="update",
description="Update a connection.",
epilog=epilog,
add_params=[add_param_set, partial(add_param_name, required=True)] + base_params,
subparsers=subparsers,
help_message="Update a connection.",
action_param_name="sub_action",
)
def add_connection_show(subparsers):
epilog = """
Examples:
# Get and show a connection:
pf connection show -n my_connection_name
"""
activate_action(
name="show",
description="Show a connection for promptflow.",
epilog=epilog,
add_params=[partial(add_param_name, required=True)] + base_params,
subparsers=subparsers,
help_message="Show a connection for promptflow.",
action_param_name="sub_action",
)
def add_connection_delete(subparsers):
epilog = """
Examples:
# Delete a connection:
pf connection delete -n my_connection_name
"""
activate_action(
name="delete",
description="Delete a connection with specific name.",
epilog=epilog,
add_params=[partial(add_param_name, required=True), add_param_yes] + base_params,
subparsers=subparsers,
help_message="Delete a connection with specific name.",
action_param_name="sub_action",
)
def add_connection_list(subparsers):
epilog = """
Examples:
# List all connections:
pf connection list
"""
activate_action(
name="list",
description="List all connections.",
epilog=epilog,
add_params=[add_param_max_results, add_param_all_results] + base_params,
subparsers=subparsers,
help_message="List all connections.",
action_param_name="sub_action",
)
def validate_and_interactive_get_secrets(connection, is_update=False):
"""Validate the connection and interactive get secrets if no secrets is provided."""
prompt = "=================== Please input required secrets ==================="
missing_secrets_prompt = False
for name, val in connection.secrets.items():
if not _Connection._is_scrubbed_value(val) and not _Connection._is_user_input_value(val):
# Not scrubbed value, not require user input.
continue
if is_update and _Connection._is_scrubbed_value(val):
# Scrubbed value, will use existing, not require user input.
continue
if not missing_secrets_prompt:
print(prompt)
missing_secrets_prompt = True
while True:
secret = get_secret_input(prompt=f"{name}: ")
if secret:
break
print_yellow_warning("Secret can't be empty.")
connection.secrets[name] = secret
if missing_secrets_prompt:
print("=================== Required secrets collected ===================")
return connection
# Note the connection secrets value behaviors:
# --------------------------------------------------------------------------------
# | secret value | CLI create | CLI update | SDK create_or_update |
# --------------------------------------------------------------------------------
# | empty or all "*" | prompt input | use existing values | use existing values |
# | <no-change> | prompt input | use existing values | use existing values |
# | <user-input> | prompt input | prompt input | raise error |
# --------------------------------------------------------------------------------
@exception_handler("Connection create")
def create_connection(file_path, params_override=None, name=None):
params_override = params_override or []
if name:
params_override.append({"name": name})
connection = load_connection(source=file_path, params_override=params_override)
existing_connection = _get_pf_client().connections.get(connection.name, raise_error=False)
if existing_connection:
logger.warning(f"Connection with name {connection.name} already exists. Updating it.")
# Note: We don't set the existing secret back here, let user input the secrets.
validate_and_interactive_get_secrets(connection)
connection = _get_pf_client().connections.create_or_update(connection)
print(json.dumps(connection._to_dict(), indent=4))
@exception_handler("Connection show")
def show_connection(name):
connection = _get_pf_client().connections.get(name)
print(json.dumps(connection._to_dict(), indent=4))
@exception_handler("Connection list")
def list_connection(max_results=MAX_LIST_CLI_RESULTS, all_results=False):
connections = _get_pf_client().connections.list(max_results, all_results)
print(json.dumps([connection._to_dict() for connection in connections], indent=4))
def _upsert_connection_from_file(file, params_override=None):
# Note: This function is used for pfutil, do not edit it.
params_override = params_override or []
params_override.append(load_yaml(file))
connection = load_connection(source=file, params_override=params_override)
existing_connection = _get_pf_client().connections.get(connection.name, raise_error=False)
if existing_connection:
connection = _Connection._load(data=existing_connection._to_dict(), params_override=params_override)
validate_and_interactive_get_secrets(connection, is_update=True)
# Set the secrets not scrubbed, as _to_dict() dump scrubbed connections.
connection._secrets = existing_connection._secrets
else:
validate_and_interactive_get_secrets(connection)
connection = _get_pf_client().connections.create_or_update(connection)
return connection
@exception_handler("Connection update")
def update_connection(name, params_override=None):
params_override = params_override or []
existing_connection = _get_pf_client().connections.get(name)
connection = _Connection._load(data=existing_connection._to_dict(), params_override=params_override)
validate_and_interactive_get_secrets(connection, is_update=True)
# Set the secrets not scrubbed, as _to_dict() dump scrubbed connections.
connection._secrets = existing_connection._secrets
connection = _get_pf_client().connections.create_or_update(connection)
print(json.dumps(connection._to_dict(), indent=4))
@exception_handler("Connection delete")
def delete_connection(name, skip_confirm: bool = False):
if confirm("Are you sure you want to perform this operation?", skip_confirm):
_get_pf_client().connections.delete(name)
else:
print("The delete operation was canceled.")
def dispatch_connection_commands(args: argparse.Namespace):
if args.sub_action == "create":
create_connection(args.file, args.params_override, args.name)
elif args.sub_action == "show":
show_connection(args.name)
elif args.sub_action == "list":
list_connection(args.max_results, args.all_results)
elif args.sub_action == "update":
update_connection(args.name, args.params_override)
elif args.sub_action == "delete":
delete_connection(args.name, args.yes)
| promptflow/src/promptflow/promptflow/_cli/_pf/_connection.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_cli/_pf/_connection.py",
"repo_id": "promptflow",
"token_count": 3450
} | 29 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import argparse
import contextlib
import json
import os
import shutil
import sys
import traceback
from collections import namedtuple
from configparser import ConfigParser
from functools import wraps
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import pydash
from dotenv import load_dotenv
from tabulate import tabulate
from promptflow._sdk._constants import CLIListOutputFormat
from promptflow._sdk._utils import print_red_error, print_yellow_warning
from promptflow._utils.exception_utils import ExceptionPresenter
from promptflow._utils.logger_utils import get_cli_sdk_logger
from promptflow._utils.utils import is_in_ci_pipeline
from promptflow.exceptions import ErrorTarget, PromptflowException, UserErrorException
AzureMLWorkspaceTriad = namedtuple("AzureMLWorkspace", ["subscription_id", "resource_group_name", "workspace_name"])
logger = get_cli_sdk_logger()
def _set_workspace_argument_for_subparsers(subparser, required=False):
"""Add workspace arguments to subparsers."""
# Make these arguments optional so that user can use local azure cli context
subparser.add_argument(
"--subscription", required=required, type=str, help="Subscription id, required when pass run id."
)
subparser.add_argument(
"--resource-group", "-g", required=required, type=str, help="Resource group name, required when pass run id."
)
subparser.add_argument(
"--workspace-name", "-w", required=required, type=str, help="Workspace name, required when pass run id."
)
def dump_connection_file(dot_env_file: str):
for key in ["AZURE_OPENAI_API_KEY", "AZURE_OPENAI_API_BASE", "CHAT_DEPLOYMENT_NAME"]:
if key not in os.environ:
# skip dump connection file if not all required environment variables are set
return
connection_file_path = "./connection.json"
os.environ["PROMPTFLOW_CONNECTIONS"] = connection_file_path
load_dotenv(dot_env_file)
connection_dict = {
"custom_connection": {
"type": "CustomConnection",
"value": {
"AZURE_OPENAI_API_KEY": os.environ["AZURE_OPENAI_API_KEY"],
"AZURE_OPENAI_API_BASE": os.environ["AZURE_OPENAI_API_BASE"],
"CHAT_DEPLOYMENT_NAME": os.environ["CHAT_DEPLOYMENT_NAME"],
},
"module": "promptflow.connections",
}
}
with open(connection_file_path, "w") as f:
json.dump(connection_dict, f)
def get_workspace_triad_from_local() -> AzureMLWorkspaceTriad:
subscription_id = None
resource_group_name = None
workspace_name = None
azure_config_path = Path.home() / ".azure"
config_parser = ConfigParser()
# subscription id
try:
config_parser.read_file(open(azure_config_path / "clouds.config"))
subscription_id = config_parser["AzureCloud"]["subscription"]
except Exception: # pylint: disable=broad-except
pass
# resource group name & workspace name
try:
config_parser.read_file(open(azure_config_path / "config"))
resource_group_name = config_parser["defaults"]["group"]
workspace_name = config_parser["defaults"]["workspace"]
except Exception: # pylint: disable=broad-except
pass
return AzureMLWorkspaceTriad(subscription_id, resource_group_name, workspace_name)
def get_credentials_for_cli():
"""
This function is part of mldesigner.dsl._dynamic_executor.DynamicExecutor._get_ml_client with
some local imports.
"""
from azure.ai.ml.identity import AzureMLOnBehalfOfCredential
from azure.identity import AzureCliCredential, DefaultAzureCredential, ManagedIdentityCredential
# May return a different one if executing in local
# credential priority: OBO > managed identity > default
# check OBO via environment variable, the referenced code can be found from below search:
# https://msdata.visualstudio.com/Vienna/_search?text=AZUREML_OBO_ENABLED&type=code&pageSize=25&filters=ProjectFilters%7BVienna%7D&action=contents
if os.getenv(IdentityEnvironmentVariable.OBO_ENABLED_FLAG):
logger.debug("User identity is configured, use OBO credential.")
credential = AzureMLOnBehalfOfCredential()
else:
client_id_from_env = os.getenv(IdentityEnvironmentVariable.DEFAULT_IDENTITY_CLIENT_ID)
if client_id_from_env:
# use managed identity when client id is available from environment variable.
# reference code:
# https://learn.microsoft.com/en-us/azure/machine-learning/how-to-identity-based-service-authentication?tabs=cli#compute-cluster
logger.debug("Use managed identity credential.")
credential = ManagedIdentityCredential(client_id=client_id_from_env)
elif is_in_ci_pipeline():
# use managed identity when executing in CI pipeline.
logger.debug("Use azure cli credential.")
credential = AzureCliCredential()
else:
# use default Azure credential to handle other cases.
logger.debug("Use default credential.")
credential = DefaultAzureCredential()
return credential
def get_client_info_for_cli(subscription_id: str = None, resource_group_name: str = None, workspace_name: str = None):
if not (subscription_id and resource_group_name and workspace_name):
workspace_triad = get_workspace_triad_from_local()
subscription_id = subscription_id or workspace_triad.subscription_id
resource_group_name = resource_group_name or workspace_triad.resource_group_name
workspace_name = workspace_name or workspace_triad.workspace_name
if not (subscription_id and resource_group_name and workspace_name):
workspace_name = workspace_name or os.getenv("AZUREML_ARM_WORKSPACE_NAME")
subscription_id = subscription_id or os.getenv("AZUREML_ARM_SUBSCRIPTION")
resource_group_name = resource_group_name or os.getenv("AZUREML_ARM_RESOURCEGROUP")
return subscription_id, resource_group_name, workspace_name
def get_client_for_cli(*, subscription_id: str = None, resource_group_name: str = None, workspace_name: str = None):
from azure.ai.ml import MLClient
subscription_id, resource_group_name, workspace_name = get_client_info_for_cli(
subscription_id=subscription_id, resource_group_name=resource_group_name, workspace_name=workspace_name
)
missing_fields = []
for key in ["workspace_name", "subscription_id", "resource_group_name"]:
if not locals()[key]:
missing_fields.append(key)
if missing_fields:
raise UserErrorException(
"Please provide all required fields to work on specific workspace: {}".format(", ".join(missing_fields)),
target=ErrorTarget.CONTROL_PLANE_SDK,
)
return MLClient(
credential=get_credentials_for_cli(),
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
)
def confirm(question, skip_confirm) -> bool:
if skip_confirm:
return True
answer = input(f"{question} [y/n]")
while answer.lower() not in ["y", "n"]:
answer = input("Please input 'y' or 'n':")
return answer.lower() == "y"
@contextlib.contextmanager
def inject_sys_path(path):
original_sys_path = sys.path.copy()
sys.path.insert(0, str(path))
try:
yield
finally:
sys.path = original_sys_path
def activate_action(name, description, epilog, add_params, subparsers, help_message, action_param_name="action"):
parser = subparsers.add_parser(
name,
description=description,
epilog=epilog,
formatter_class=argparse.RawDescriptionHelpFormatter,
help=help_message,
)
if add_params:
for add_param_func in add_params:
add_param_func(parser)
parser.set_defaults(**{action_param_name: name})
return parser
class IdentityEnvironmentVariable:
"""This class is copied from mldesigner._constants.IdentityEnvironmentVariable."""
DEFAULT_IDENTITY_CLIENT_ID = "DEFAULT_IDENTITY_CLIENT_ID"
OBO_ENABLED_FLAG = "AZUREML_OBO_ENABLED"
def _dump_entity_with_warnings(entity) -> Dict:
if not entity:
return
if isinstance(entity, Dict):
return entity
try:
return entity._to_dict() # type: ignore
except Exception as err:
logger.warning("Failed to deserialize response: " + str(err))
logger.warning(str(entity))
logger.debug(traceback.format_exc())
def list_of_dict_to_dict(obj: list):
if not isinstance(obj, list):
return {}
result = {}
for item in obj:
result.update(item)
return result
def list_of_dict_to_nested_dict(obj: list):
result = {}
for item in obj:
for keys, value in item.items():
keys = keys.split(".")
pydash.set_(result, keys, value)
return result
def _build_sorted_column_widths_tuple_list(
columns: List[str],
values1: Dict[str, int],
values2: Dict[str, int],
margins: Dict[str, int],
) -> List[Tuple[str, int]]:
res = []
for column in columns:
value = max(values1[column], values2[column]) + margins[column]
res.append((column, value))
res.sort(key=lambda x: x[1], reverse=True)
return res
def _assign_available_width(
column_expected_widths: List[Tuple[str, int]],
available_width: int,
column_assigned_widths: Dict[str, int],
average_width: Optional[int] = None,
) -> Tuple[int, Dict[str, int]]:
for column, expected_width in column_expected_widths:
if available_width <= 0:
break
target = average_width if average_width is not None else column_assigned_widths[column]
delta = expected_width - target
if delta <= available_width:
column_assigned_widths[column] = expected_width
available_width -= delta
else:
column_assigned_widths[column] += available_width
available_width = 0
return available_width, column_assigned_widths
def _calculate_column_widths(df: "DataFrame", terminal_width: int) -> List[int]:
num_rows, num_columns = len(df), len(df.columns)
index_column_width = max(len(str(num_rows)) + 2, 4) # tabulate index column min width is 4
terminal_width_buffer = 10
available_width = terminal_width - terminal_width_buffer - index_column_width - (num_columns + 2)
avg_available_width = available_width // num_columns
header_widths, content_avg_widths, content_max_widths, column_margin = {}, {}, {}, {}
for column in df.columns:
header_widths[column] = len(column)
contents = []
for value in df[column]:
contents.append(len(str(value)))
content_avg_widths[column] = sum(contents) // len(contents)
content_max_widths[column] = max(contents)
# if header is longer than the longest content, the margin is 4; otherwise is 2
# so we need to record this for every column
if header_widths[column] >= content_max_widths[column]:
column_margin[column] = 4
else:
column_margin[column] = 2
column_widths = {}
# first round: try to meet the average(or column header) width
# record columns that need more width, we will deal with them in second round if we still have width
round_one_left_columns = []
for column in df.columns:
expected_width = max(header_widths[column], content_avg_widths[column]) + column_margin[column]
if avg_available_width <= expected_width:
column_widths[column] = avg_available_width
round_one_left_columns.append(column)
else:
column_widths[column] = expected_width
current_available_width = available_width - sum(column_widths.values())
if current_available_width > 0:
# second round: assign left available width to those columns that need more
# assign with greedy, sort recorded columns first from longest to shortest;
# iterate and try to meet each column's expected width
column_avg_tuples = _build_sorted_column_widths_tuple_list(
round_one_left_columns, header_widths, content_avg_widths, column_margin
)
current_available_width, column_widths = _assign_available_width(
column_avg_tuples, current_available_width, column_widths, avg_available_width
)
if current_available_width > 0:
# third round: if there are still left available width, assign to try to meet the max width
# still use greedy, sort first and iterate through all columns
column_max_tuples = _build_sorted_column_widths_tuple_list(
df.columns, header_widths, content_max_widths, column_margin
)
current_available_width, column_widths = _assign_available_width(
column_max_tuples, current_available_width, column_widths
)
max_col_widths = [index_column_width] # index column
max_col_widths += [max(column_widths[column] - column_margin[column], 1) for column in df.columns] # sub margin
return max_col_widths
def pretty_print_dataframe_as_table(df: "DataFrame") -> None:
# try to get terminal window width
try:
terminal_width = shutil.get_terminal_size().columns
except Exception: # pylint: disable=broad-except
terminal_width = 120 # default value for Windows Terminal launch size columns
column_widths = _calculate_column_widths(df, terminal_width)
print(tabulate(df, headers="keys", tablefmt="grid", maxcolwidths=column_widths, maxheadercolwidths=column_widths))
def is_format_exception():
if os.environ.get("PROMPTFLOW_STRUCTURE_EXCEPTION_OUTPUT", "false").lower() == "true":
return True
return False
def exception_handler(command: str):
"""Catch known cli exceptions."""
def decorator(func):
@wraps(func)
def wrapper(*args, **kwargs):
try:
return func(*args, **kwargs)
except Exception as e:
if is_format_exception():
# When the flag format_exception is set in command,
# it will write a json with exception info and command to stderr.
error_msg = ExceptionPresenter.create(e).to_dict(include_debug_info=True)
error_msg["command"] = " ".join(sys.argv)
sys.stderr.write(json.dumps(error_msg))
if isinstance(e, PromptflowException):
print_red_error(f"{command} failed with {e.__class__.__name__}: {str(e)}")
exit(1)
else:
raise e
return wrapper
return decorator
def get_secret_input(prompt, mask="*"):
"""Get secret input with mask printed on screen in CLI.
Provide better handling for control characters:
- Handle Ctrl-C as KeyboardInterrupt
- Ignore control characters and print warning message.
"""
if not isinstance(prompt, str):
raise TypeError(f"prompt must be a str, not ${type(prompt).__name__}")
if not isinstance(mask, str):
raise TypeError(f"mask argument must be a one-character str, not ${type(mask).__name__}")
if len(mask) != 1:
raise ValueError("mask argument must be a one-character str")
if sys.platform == "win32":
# For some reason, mypy reports that msvcrt doesn't have getch, ignore this warning:
from msvcrt import getch # type: ignore
else: # macOS and Linux
import termios
import tty
def getch():
fd = sys.stdin.fileno()
old_settings = termios.tcgetattr(fd)
try:
tty.setraw(sys.stdin.fileno())
ch = sys.stdin.read(1)
finally:
termios.tcsetattr(fd, termios.TCSADRAIN, old_settings)
return ch
secret_input = []
sys.stdout.write(prompt)
sys.stdout.flush()
while True:
key = ord(getch())
if key == 13: # Enter key pressed.
sys.stdout.write("\n")
return "".join(secret_input)
elif key == 3: # Ctrl-C pressed.
raise KeyboardInterrupt()
elif key in (8, 127): # Backspace/Del key erases previous output.
if len(secret_input) > 0:
# Erases previous character.
sys.stdout.write("\b \b") # \b doesn't erase the character, it just moves the cursor back.
sys.stdout.flush()
secret_input = secret_input[:-1]
elif 0 <= key <= 31:
msg = "\nThe last user input got ignored as it is control character."
print_yellow_warning(msg)
sys.stdout.write(prompt + mask * len(secret_input))
sys.stdout.flush()
else:
# display the mask character.
char = chr(key)
sys.stdout.write(mask)
sys.stdout.flush()
secret_input.append(char)
def _copy_to_flow(flow_path, source_file):
target = flow_path / source_file.name
action = "Overwriting" if target.exists() else "Creating"
if source_file.is_file():
print(f"{action} {source_file.name}...")
shutil.copy2(source_file, target)
else:
print(f"{action} {source_file.name} folder...")
shutil.copytree(source_file, target, dirs_exist_ok=True)
def _output_result_list_with_format(result_list: List[Dict], output_format: CLIListOutputFormat) -> None:
import pandas as pd
if output_format == CLIListOutputFormat.TABLE:
df = pd.DataFrame(result_list)
df.fillna("", inplace=True)
pretty_print_dataframe_as_table(df)
elif output_format == CLIListOutputFormat.JSON:
print(json.dumps(result_list, indent=4))
else:
warning_message = (
f"Unknown output format {output_format!r}, accepted values are 'json' and 'table';"
"will print using 'json'."
)
logger.warning(warning_message)
print(json.dumps(result_list, indent=4))
def _get_cli_activity_name(cli, args):
activity_name = cli
if getattr(args, "action", None):
activity_name += f".{args.action}"
if getattr(args, "sub_action", None):
activity_name += f".{args.sub_action}"
return activity_name
def _try_delete_existing_run_record(run_name: str):
from promptflow._sdk._errors import RunNotFoundError
from promptflow._sdk._orm import RunInfo as ORMRun
try:
ORMRun.delete(run_name)
except RunNotFoundError:
pass
| promptflow/src/promptflow/promptflow/_cli/_utils.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_cli/_utils.py",
"repo_id": "promptflow",
"token_count": 7526
} | 30 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from promptflow import tool
@tool
def line_process(groundtruth: str, prediction: str):
"""
This tool processes the prediction of a single line and returns the processed result.
:param groundtruth: the groundtruth of a single line.
:param prediction: the prediction of a single line.
"""
# Add your line processing logic here
processed_result = "Correct" if groundtruth.lower() == prediction.lower() else "Incorrect"
return processed_result
| promptflow/src/promptflow/promptflow/_cli/data/evaluation_flow/line_process.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_cli/data/evaluation_flow/line_process.py",
"repo_id": "promptflow",
"token_count": 156
} | 31 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import copy
import json
import os
from dataclasses import fields, is_dataclass
from pathlib import Path
from typing import Any, Dict, List
from promptflow._constants import CONNECTION_NAME_PROPERTY, CONNECTION_SECRET_KEYS, PROMPTFLOW_CONNECTIONS
from promptflow._sdk._constants import CustomStrongTypeConnectionConfigs
from promptflow._utils.utils import try_import
from promptflow.contracts.tool import ConnectionType
from promptflow.contracts.types import Secret
class ConnectionManager:
"""This class will be used for construction mode to run flow. Do not include it into tool code."""
instance = None
def __init__(self, _dict: Dict[str, dict] = None):
if _dict is None and PROMPTFLOW_CONNECTIONS in os.environ:
# !!! Important !!!: Do not leverage this environment variable in any production code, this is test only.
if PROMPTFLOW_CONNECTIONS not in os.environ:
raise ValueError(f"Required environment variable {PROMPTFLOW_CONNECTIONS!r} not set.")
connection_path = Path(os.environ[PROMPTFLOW_CONNECTIONS]).resolve().absolute()
if not connection_path.exists():
raise ValueError(f"Connection file not exists. Path {connection_path.as_posix()}.")
_dict = json.loads(open(connection_path).read())
self._connections_dict = _dict or {}
self._connections = self._build_connections(self._connections_dict)
@classmethod
def _build_connections(cls, _dict: Dict[str, dict]):
"""Build connection dict."""
from promptflow._core.tools_manager import connections as cls_mapping
cls.import_requisites(_dict)
connections = {} # key to connection object
for key, connection_dict in _dict.items():
typ = connection_dict.get("type")
if typ not in cls_mapping:
supported = [key for key in cls_mapping.keys() if not key.startswith("_")]
raise ValueError(f"Unknown connection {key!r} type {typ!r}, supported are {supported}.")
value = connection_dict.get("value", {})
connection_class = cls_mapping[typ]
from promptflow.connections import CustomConnection
if connection_class is CustomConnection:
# Note: CustomConnection definition can not be got, secret keys will be provided in connection dict.
secret_keys = connection_dict.get("secret_keys", [])
secrets = {k: v for k, v in value.items() if k in secret_keys}
configs = {k: v for k, v in value.items() if k not in secrets}
connection_value = connection_class(configs=configs, secrets=secrets)
if CustomStrongTypeConnectionConfigs.PROMPTFLOW_TYPE_KEY in configs:
connection_value.custom_type = configs[CustomStrongTypeConnectionConfigs.PROMPTFLOW_TYPE_KEY]
else:
"""
Note: Ignore non exists keys of connection class,
because there are some keys just used by UX like resource id, while not used by backend.
"""
if is_dataclass(connection_class):
# Do not delete this branch, as promptflow_vectordb.connections is dataclass type.
cls_fields = {f.name: f for f in fields(connection_class)}
connection_value = connection_class(**{k: v for k, v in value.items() if k in cls_fields})
secret_keys = [f.name for f in cls_fields.values() if f.type == Secret]
else:
connection_value = connection_class(**{k: v for k, v in value.items()})
secrets = getattr(connection_value, "secrets", {})
secret_keys = list(secrets.keys()) if isinstance(secrets, dict) else []
# Set secret keys for log scrubbing
setattr(connection_value, CONNECTION_SECRET_KEYS, secret_keys)
# Use this hack to make sure serialization works
setattr(connection_value, CONNECTION_NAME_PROPERTY, key)
connections[key] = connection_value
return connections
@classmethod
def init_from_env(cls):
return ConnectionManager()
def get(self, connection_info: Any) -> Any:
"""Get Connection by connection info.
connection_info:
connection name as string or connection object
"""
if isinstance(connection_info, str):
return self._connections.get(connection_info)
elif ConnectionType.is_connection_value(connection_info):
return connection_info
return None
def get_secret_list(self) -> List[str]:
def secrets():
for connection in self._connections.values():
secret_keys = getattr(connection, CONNECTION_SECRET_KEYS, [])
for secret_key in secret_keys:
yield getattr(connection, secret_key)
return list(secrets())
@classmethod
def import_requisites(cls, _dict: Dict[str, dict]):
"""Import connection required modules."""
modules = set()
for key, connection_dict in _dict.items():
module = connection_dict.get("module")
if module:
modules.add(module)
for module in modules:
# Suppress import error, as we have legacy module promptflow.tools.connections.
try_import(module, f"Import connection module {module!r} failed.", raise_error=False)
@staticmethod
def is_legacy_connections(_dict: Dict[str, dict]):
"""Detect if is legacy connections. Legacy connections dict doesn't have module and type.
So import requisites can not be performed. Only request from MT will hit this.
Legacy connection example: {"aoai_config": {"api_key": "..."}}
"""
has_module = any(isinstance(v, dict) and "module" in v for k, v in _dict.items())
return not has_module
def to_connections_dict(self) -> dict:
"""Get all connections and reformat to key-values format."""
# Value returned: {"aoai_config": {"api_key": "..."}}
return copy.deepcopy(self._connections_dict)
| promptflow/src/promptflow/promptflow/_core/connection_manager.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_core/connection_manager.py",
"repo_id": "promptflow",
"token_count": 2536
} | 32 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import logging
import os.path
import uuid
from itertools import product
from os import PathLike
from pathlib import Path
from typing import Optional, Union
import pydash
from promptflow._sdk._constants import (
DEFAULT_ENCODING,
FLOW_DIRECTORY_MACRO_IN_CONFIG,
HOME_PROMPT_FLOW_DIR,
SERVICE_CONFIG_FILE,
ConnectionProvider,
)
from promptflow._sdk._utils import call_from_extension, read_write_by_user
from promptflow._utils.logger_utils import get_cli_sdk_logger
from promptflow._utils.yaml_utils import dump_yaml, load_yaml
from promptflow.exceptions import ErrorTarget, ValidationException
logger = get_cli_sdk_logger()
class ConfigFileNotFound(ValidationException):
pass
class InvalidConfigFile(ValidationException):
pass
class InvalidConfigValue(ValidationException):
pass
class Configuration(object):
CONFIG_PATH = Path(HOME_PROMPT_FLOW_DIR) / SERVICE_CONFIG_FILE
COLLECT_TELEMETRY = "telemetry.enabled"
EXTENSION_COLLECT_TELEMETRY = "extension.telemetry_enabled"
INSTALLATION_ID = "cli.installation_id"
CONNECTION_PROVIDER = "connection.provider"
RUN_OUTPUT_PATH = "run.output_path"
USER_AGENT = "user_agent"
ENABLE_INTERNAL_FEATURES = "enable_internal_features"
_instance = None
def __init__(self, overrides=None):
if not os.path.exists(self.CONFIG_PATH.parent):
os.makedirs(self.CONFIG_PATH.parent, exist_ok=True)
if not os.path.exists(self.CONFIG_PATH):
self.CONFIG_PATH.touch(mode=read_write_by_user(), exist_ok=True)
with open(self.CONFIG_PATH, "w", encoding=DEFAULT_ENCODING) as f:
dump_yaml({}, f)
self._config = load_yaml(self.CONFIG_PATH)
if not self._config:
self._config = {}
# Allow config override by kwargs
overrides = overrides or {}
for key, value in overrides.items():
self._validate(key, value)
pydash.set_(self._config, key, value)
@property
def config(self):
return self._config
@classmethod
def get_instance(cls):
"""Use this to get instance to avoid multiple copies of same global config."""
if cls._instance is None:
cls._instance = Configuration()
return cls._instance
def set_config(self, key, value):
"""Store config in file to avoid concurrent write."""
self._validate(key, value)
pydash.set_(self._config, key, value)
with open(self.CONFIG_PATH, "w", encoding=DEFAULT_ENCODING) as f:
dump_yaml(self._config, f)
def get_config(self, key):
try:
return pydash.get(self._config, key, None)
except Exception: # pylint: disable=broad-except
return None
def get_all(self):
return self._config
@classmethod
def _get_workspace_from_config(
cls,
*,
path: Union[PathLike, str] = None,
) -> str:
"""Return a workspace arm id from an existing Azure Machine Learning Workspace.
Reads workspace configuration from a file. Throws an exception if the config file can't be found.
:param path: The path to the config file or starting directory to search.
The parameter defaults to starting the search in the current directory.
:type path: str
:return: The workspace arm id for an existing Azure ML Workspace.
:rtype: ~str
"""
from azure.ai.ml import MLClient
from azure.ai.ml._file_utils.file_utils import traverse_up_path_and_find_file
from azure.ai.ml.constants._common import AZUREML_RESOURCE_PROVIDER, RESOURCE_ID_FORMAT
path = Path(".") if path is None else Path(path)
if path.is_file():
found_path = path
else:
# Based on priority
# Look in config dirs like .azureml or plain directory
# with None
directories_to_look = [".azureml", None]
files_to_look = ["config.json"]
found_path = None
for curr_dir, curr_file in product(directories_to_look, files_to_look):
logging.debug(
"No config file directly found, starting search from %s "
"directory, for %s file name to be present in "
"%s subdirectory",
path,
curr_file,
curr_dir,
)
found_path = traverse_up_path_and_find_file(
path=path,
file_name=curr_file,
directory_name=curr_dir,
num_levels=20,
)
if found_path:
break
if not found_path:
msg = (
"We could not find config.json in: {} or in its parent directories. "
"Please provide the full path to the config file or ensure that "
"config.json exists in the parent directories."
)
raise ConfigFileNotFound(
message=msg.format(path),
no_personal_data_message=msg.format("[path]"),
target=ErrorTarget.CONTROL_PLANE_SDK,
)
subscription_id, resource_group, workspace_name = MLClient._get_workspace_info(found_path)
if not (subscription_id and resource_group and workspace_name):
raise InvalidConfigFile(
"The subscription_id, resource_group and workspace_name can not be empty. Got: "
f"subscription_id: {subscription_id}, resource_group: {resource_group}, "
f"workspace_name: {workspace_name} from file {found_path}."
)
return RESOURCE_ID_FORMAT.format(subscription_id, resource_group, AZUREML_RESOURCE_PROVIDER, workspace_name)
def get_connection_provider(self, path=None) -> Optional[str]:
"""Get the current connection provider. Default to local if not configured."""
provider = self.get_config(key=self.CONNECTION_PROVIDER)
return self.resolve_connection_provider(provider, path=path)
@classmethod
def resolve_connection_provider(cls, provider, path=None) -> Optional[str]:
if provider is None:
return ConnectionProvider.LOCAL
if provider == ConnectionProvider.AZUREML.value:
# Note: The below function has azure-ai-ml dependency.
return "azureml:" + cls._get_workspace_from_config(path=path)
# If provider not None and not Azure, return it directly.
# It can be the full path of a workspace.
return provider
def get_telemetry_consent(self) -> Optional[bool]:
"""Get the current telemetry consent value. Return None if not configured."""
if call_from_extension():
return self.get_config(key=self.EXTENSION_COLLECT_TELEMETRY)
return self.get_config(key=self.COLLECT_TELEMETRY)
def set_telemetry_consent(self, value):
"""Set the telemetry consent value and store in local."""
self.set_config(key=self.COLLECT_TELEMETRY, value=value)
def get_or_set_installation_id(self):
"""Get user id if exists, otherwise set installation id and return it."""
user_id = self.get_config(key=self.INSTALLATION_ID)
if user_id:
return user_id
else:
user_id = str(uuid.uuid4())
self.set_config(key=self.INSTALLATION_ID, value=user_id)
return user_id
def get_run_output_path(self) -> Optional[str]:
"""Get the run output path in local."""
return self.get_config(key=self.RUN_OUTPUT_PATH)
def _to_dict(self):
return self._config
@staticmethod
def _validate(key: str, value: str) -> None:
if key == Configuration.RUN_OUTPUT_PATH:
if value.rstrip("/").endswith(FLOW_DIRECTORY_MACRO_IN_CONFIG):
raise InvalidConfigValue(
"Cannot specify flow directory as run output path; "
"if you want to specify run output path under flow directory, "
"please use its child folder, e.g. '${flow_directory}/.runs'."
)
return
def get_user_agent(self) -> Optional[str]:
"""Get customer set user agent. If set, will add prefix `PFCustomer_`"""
user_agent = self.get_config(key=self.USER_AGENT)
if user_agent:
return f"PFCustomer_{user_agent}"
return user_agent
def is_internal_features_enabled(self) -> Optional[bool]:
"""Get enable_preview_features"""
result = self.get_config(key=self.ENABLE_INTERNAL_FEATURES)
if isinstance(result, str):
return result.lower() == "true"
return result is True
| promptflow/src/promptflow/promptflow/_sdk/_configuration.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_sdk/_configuration.py",
"repo_id": "promptflow",
"token_count": 3891
} | 33 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import flask
from jinja2 import Template
from pathlib import Path
from flask import Blueprint, request, url_for, current_app as app
def construct_staticweb_blueprint(static_folder):
"""Construct static web blueprint."""
staticweb_blueprint = Blueprint('staticweb_blueprint', __name__, static_folder=static_folder)
@staticweb_blueprint.route("/", methods=["GET", "POST"])
def home():
"""Show the home page."""
index_path = Path(static_folder) / "index.html" if static_folder else None
if index_path and index_path.exists():
template = Template(open(index_path, "r", encoding="UTF-8").read())
return flask.render_template(template, url_for=url_for)
else:
return "<h1>Welcome to promptflow app.</h1>"
@staticweb_blueprint.route("/<path:path>", methods=["GET", "POST", "PUT", "DELETE", "PATCH"])
def notfound(path):
rules = {rule.rule: rule.methods for rule in app.url_map.iter_rules()}
if path not in rules or request.method not in rules[path]:
unsupported_message = (
f"The requested api {path!r} with {request.method} is not supported by current app, "
f"if you entered the URL manually please check your spelling and try again."
)
return unsupported_message, 404
return staticweb_blueprint
| promptflow/src/promptflow/promptflow/_sdk/_serving/blueprint/static_web_blueprint.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_sdk/_serving/blueprint/static_web_blueprint.py",
"repo_id": "promptflow",
"token_count": 545
} | 34 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import json
import os
import time
import base64
import zlib
from flask import jsonify, request
from promptflow._sdk._serving._errors import (
JsonPayloadRequiredForMultipleInputFields,
MissingRequiredFlowInput,
NotAcceptable,
)
from promptflow._utils.exception_utils import ErrorResponse, ExceptionPresenter
from promptflow.contracts.flow import Flow as FlowContract
from promptflow.exceptions import ErrorTarget
def load_request_data(flow, raw_data, logger):
try:
data = json.loads(raw_data)
except Exception:
input = None
if flow.inputs.keys().__len__() > 1:
# this should only work if there's only 1 input field, otherwise it will fail
# TODO: add a check to make sure there's only 1 input field
message = (
"Promptflow executor received non json data, but there's more than 1 input fields, "
"please use json request data instead."
)
raise JsonPayloadRequiredForMultipleInputFields(message, target=ErrorTarget.SERVING_APP)
if isinstance(raw_data, bytes) or isinstance(raw_data, bytearray):
input = str(raw_data, "UTF-8")
elif isinstance(raw_data, str):
input = raw_data
default_key = list(flow.inputs.keys())[0]
logger.debug(f"Promptflow executor received non json data: {input}, default key: {default_key}")
data = {default_key: input}
return data
def validate_request_data(flow, data):
"""Validate required request data is provided."""
# TODO: Note that we don't have default flow input presently, all of the default is None.
required_inputs = [k for k, v in flow.inputs.items() if v.default is None]
missing_inputs = [k for k in required_inputs if k not in data]
if missing_inputs:
raise MissingRequiredFlowInput(
f"Required input fields {missing_inputs} are missing in request data {data!r}",
target=ErrorTarget.SERVING_APP,
)
def streaming_response_required():
"""Check if streaming response is required."""
return "text/event-stream" in request.accept_mimetypes.values()
def get_sample_json(project_path, logger):
# load swagger sample if exists
sample_file = os.path.join(project_path, "samples.json")
if not os.path.exists(sample_file):
return None
logger.info("Promptflow sample file detected.")
with open(sample_file, "r", encoding="UTF-8") as f:
sample = json.load(f)
return sample
# get evaluation only fields
def get_output_fields_to_remove(flow: FlowContract, logger) -> list:
"""get output fields to remove."""
included_outputs = os.getenv("PROMPTFLOW_RESPONSE_INCLUDED_FIELDS", None)
if included_outputs:
logger.info(f"Response included fields: {included_outputs}")
res = json.loads(included_outputs)
return [k for k, v in flow.outputs.items() if k not in res]
return [k for k, v in flow.outputs.items() if v.evaluation_only]
def handle_error_to_response(e, logger):
presenter = ExceptionPresenter.create(e)
logger.error(f"Promptflow serving app error: {presenter.to_dict()}")
logger.error(f"Promptflow serving error traceback: {presenter.formatted_traceback}")
resp = ErrorResponse(presenter.to_dict())
response_code = resp.response_code
# The http response code for NotAcceptable is 406.
# Currently the error framework does not allow response code overriding,
# we add a check here to override the response code.
# TODO: Consider how to embed this logic into the error framework.
if isinstance(e, NotAcceptable):
response_code = 406
return jsonify(resp.to_simplified_dict()), response_code
def get_pf_serving_env(env_key: str):
if len(env_key) == 0:
return None
value = os.getenv(env_key, None)
if value is None and env_key.startswith("PROMPTFLOW_"):
value = os.getenv(env_key.replace("PROMPTFLOW_", "PF_"), None)
return value
def get_cost_up_to_now(start_time: float):
return (time.time() - start_time) * 1000
def enable_monitoring(func):
func._enable_monitoring = True
return func
def normalize_connection_name(connection_name: str):
return connection_name.replace(" ", "_")
def decode_dict(data: str) -> dict:
# str -> bytes
data = data.encode()
zipped_conns = base64.b64decode(data)
# gzip decode
conns_data = zlib.decompress(zipped_conns, 16 + zlib.MAX_WBITS)
return json.loads(conns_data.decode())
def encode_dict(data: dict) -> str:
# json encode
data = json.dumps(data)
# gzip compress
gzip_compress = zlib.compressobj(9, zlib.DEFLATED, zlib.MAX_WBITS | 16)
zipped_data = gzip_compress.compress(data.encode()) + gzip_compress.flush()
# base64 encode
b64_data = base64.b64encode(zipped_data)
# bytes -> str
return b64_data.decode()
| promptflow/src/promptflow/promptflow/_sdk/_serving/utils.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_sdk/_serving/utils.py",
"repo_id": "promptflow",
"token_count": 1868
} | 35 |
import json
import os
from pathlib import Path
from PIL import Image
import streamlit as st
from streamlit_quill import st_quill
from promptflow._sdk._serving.flow_invoker import FlowInvoker
from utils import dict_iter_render_message, parse_list_from_html, parse_image_content
invoker = None
{% set indent_level = 4 %}
def start():
def clear_chat() -> None:
st.session_state.messages = []
def render_message(role, message_items):
with st.chat_message(role):
dict_iter_render_message(message_items)
def show_conversation() -> None:
if "messages" not in st.session_state:
st.session_state.messages = []
st.session_state.history = []
if st.session_state.messages:
for role, message_items in st.session_state.messages:
render_message(role, message_items)
def get_chat_history_from_session():
if "history" in st.session_state:
return st.session_state.history
return []
def submit(**kwargs) -> None:
st.session_state.messages.append(("user", kwargs))
session_state_history = dict()
session_state_history.update({"inputs": kwargs})
with container:
render_message("user", kwargs)
# Force append chat history to kwargs
{% if is_chat_flow %}
{{ ' ' * indent_level * 2 }}response = run_flow({'{{chat_history_input_name}}': get_chat_history_from_session(), **kwargs})
{% else %}
{{ ' ' * indent_level * 2 }}response = run_flow(kwargs)
{% endif %}
st.session_state.messages.append(("assistant", response))
session_state_history.update({"outputs": response})
st.session_state.history.append(session_state_history)
with container:
render_message("assistant", response)
def run_flow(data: dict) -> dict:
global invoker
if not invoker:
{% if flow_path %}
{{ ' ' * indent_level * 3 }}flow = Path('{{flow_path}}')
{{ ' ' * indent_level * 3 }}dump_path = Path('{{flow_path}}').parent
{% else %}
{{ ' ' * indent_level * 3 }}flow = Path(__file__).parent / "flow"
{{ ' ' * indent_level * 3 }}dump_path = flow.parent
{% endif %}
if flow.is_dir():
os.chdir(flow)
else:
os.chdir(flow.parent)
invoker = FlowInvoker(flow, connection_provider="local", dump_to=dump_path)
result = invoker.invoke(data)
return result
image = Image.open(Path(__file__).parent / "logo.png")
st.set_page_config(
layout="wide",
page_title="{{flow_name}} - Promptflow App",
page_icon=image,
menu_items={
'About': """
# This is a Promptflow App.
You can refer to [promptflow](https://github.com/microsoft/promptflow) for more information.
"""
}
)
# Set primary button color here since button color of the same form need to be identical in streamlit, but we only need Run/Chat button to be blue.
st.config.set_option("theme.primaryColor", "#0F6CBD")
st.title("{{flow_name}}")
st.divider()
st.chat_message("assistant").write("Hello, please input following flow inputs.")
container = st.container()
with container:
show_conversation()
with st.form(key='input_form', clear_on_submit=True):
settings_path = os.path.join(os.path.dirname(__file__), "settings.json")
if os.path.exists(settings_path):
with open(settings_path, "r", encoding="utf-8") as file:
json_data = json.load(file)
environment_variables = list(json_data.keys())
for environment_variable in environment_variables:
secret_input = st.sidebar.text_input(label=environment_variable, type="password", placeholder=f"Please input {environment_variable} here. If you input before, you can leave it blank.")
if secret_input != "":
os.environ[environment_variable] = secret_input
{% for flow_input, (default_value, value_type) in flow_inputs.items() %}
{% if value_type == "list" %}
{{ ' ' * indent_level * 2 }}st.text('{{flow_input}}')
{{ ' ' * indent_level * 2 }}{{flow_input}} = st_quill(html=True, toolbar=["image"], key='{{flow_input}}', placeholder='Please enter the list values and use the image icon to upload a picture. Make sure to format each list item correctly with line breaks')
{% elif value_type == "image" %}
{{ ' ' * indent_level * 2 }}{{flow_input}} = st.file_uploader(label='{{flow_input}}')
{% elif value_type == "string" %}
{{ ' ' * indent_level * 2 }}{{flow_input}} = st.text_input(label='{{flow_input}}', placeholder='{{default_value}}')
{% else %}
{{ ' ' * indent_level * 2 }}{{flow_input}} = st.text_input(label='{{flow_input}}', placeholder={{default_value}})
{% endif %}
{% endfor %}
cols = st.columns(7)
submit_bt = cols[0].form_submit_button(label='{{label}}', type='primary')
clear_bt = cols[1].form_submit_button(label='Clear')
if submit_bt:
with st.spinner("Loading..."):
{% for flow_input, (default_value, value_type) in flow_inputs.items() %}
{% if value_type == "list" %}
{{ ' ' * indent_level * 4 }}{{flow_input}} = parse_list_from_html({{flow_input}})
{% elif value_type == "image" %}
{{ ' ' * indent_level * 4 }}{{flow_input}} = parse_image_content({{flow_input}}, {{flow_input}}.type if {{flow_input}} else None)
{% endif %}
{% endfor %}
submit({{flow_inputs_params}})
if clear_bt:
with st.spinner("Cleaning..."):
clear_chat()
st.rerun()
if __name__ == "__main__":
start()
| promptflow/src/promptflow/promptflow/_sdk/data/executable/main.py.jinja2/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_sdk/data/executable/main.py.jinja2",
"repo_id": "promptflow",
"token_count": 2320
} | 36 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from datetime import datetime
from typing import List
from promptflow._sdk._constants import MAX_LIST_CLI_RESULTS
from promptflow._sdk._orm import Connection as ORMConnection
from promptflow._sdk._telemetry import ActivityType, TelemetryMixin, monitor_operation
from promptflow._sdk._utils import safe_parse_object_list
from promptflow._sdk.entities._connection import _Connection
class ConnectionOperations(TelemetryMixin):
"""ConnectionOperations."""
def __init__(self, **kwargs):
super().__init__(**kwargs)
@monitor_operation(activity_name="pf.connections.list", activity_type=ActivityType.PUBLICAPI)
def list(
self,
max_results: int = MAX_LIST_CLI_RESULTS,
all_results: bool = False,
) -> List[_Connection]:
"""List connections.
:param max_results: Max number of results to return.
:type max_results: int
:param all_results: Return all results.
:type all_results: bool
:return: List of run objects.
:rtype: List[~promptflow.sdk.entities._connection._Connection]
"""
orm_connections = ORMConnection.list(max_results=max_results, all_results=all_results)
return safe_parse_object_list(
obj_list=orm_connections,
parser=_Connection._from_orm_object,
message_generator=lambda x: f"Failed to load connection {x.connectionName}, skipped.",
)
@monitor_operation(activity_name="pf.connections.get", activity_type=ActivityType.PUBLICAPI)
def get(self, name: str, **kwargs) -> _Connection:
"""Get a connection entity.
:param name: Name of the connection.
:type name: str
:return: connection object retrieved from the database.
:rtype: ~promptflow.sdk.entities._connection._Connection
"""
return self._get(name, **kwargs)
def _get(self, name: str, **kwargs) -> _Connection:
with_secrets = kwargs.get("with_secrets", False)
raise_error = kwargs.get("raise_error", True)
orm_connection = ORMConnection.get(name, raise_error)
if orm_connection is None:
return None
if with_secrets:
return _Connection._from_orm_object_with_secrets(orm_connection)
return _Connection._from_orm_object(orm_connection)
@monitor_operation(activity_name="pf.connections.delete", activity_type=ActivityType.PUBLICAPI)
def delete(self, name: str) -> None:
"""Delete a connection entity.
:param name: Name of the connection.
:type name: str
"""
ORMConnection.delete(name)
@monitor_operation(activity_name="pf.connections.create_or_update", activity_type=ActivityType.PUBLICAPI)
def create_or_update(self, connection: _Connection, **kwargs):
"""Create or update a connection.
:param connection: Run object to create or update.
:type connection: ~promptflow.sdk.entities._connection._Connection
"""
orm_object = connection._to_orm_object()
now = datetime.now().isoformat()
if orm_object.createdDate is None:
orm_object.createdDate = now
orm_object.lastModifiedDate = now
ORMConnection.create_or_update(orm_object)
return self.get(connection.name)
| promptflow/src/promptflow/promptflow/_sdk/operations/_connection_operations.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_sdk/operations/_connection_operations.py",
"repo_id": "promptflow",
"token_count": 1297
} | 37 |
from promptflow.exceptions import SystemErrorException, UserErrorException, ValidationException
class InvalidImageInput(ValidationException):
pass
class LoadMultimediaDataError(UserErrorException):
pass
class YamlParseError(SystemErrorException):
"""Exception raised when yaml parse failed."""
pass
| promptflow/src/promptflow/promptflow/_utils/_errors.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_utils/_errors.py",
"repo_id": "promptflow",
"token_count": 85
} | 38 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import contextvars
import logging
import threading
from promptflow._utils.utils import set_context
class RepeatLogTimer(threading.Timer):
"""Repeat to log message every interval seconds until it is cancelled."""
def __init__(
self, interval_seconds: float, logger: logging.Logger, level: int, log_message_function, args: tuple = None
):
self._logger = logger
self._level = level
self._log_message_function = log_message_function
self._function_args = args if args else tuple()
self._context = contextvars.copy_context()
super().__init__(interval_seconds, function=None)
def __enter__(self):
self.start()
return self
def __exit__(self, *args):
self.cancel()
def run(self):
"""Override Timer.run method."""
# Set context variables from parent context.
set_context(self._context)
while not self.finished.wait(self.interval):
if not self.finished.is_set():
msgs = self._log_message_function(*self._function_args)
for msg in msgs:
self._logger.log(self._level, msg)
self.finished.set()
| promptflow/src/promptflow/promptflow/_utils/thread_utils.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/_utils/thread_utils.py",
"repo_id": "promptflow",
"token_count": 505
} | 39 |
# How to automatically generate the REST client code
Rest client code in this folder are not manually written, but generated by autorest.
## Setup
+ install [nodejs](https://nodejs.org/en)
+ install autorest
+ run `npm install -g autorest`
## Download swagger.json
Download swagger.json from [here](https://int.api.azureml-test.ms/flow/swagger/v1.0/swagger.json) to
[promptflow/azure/_restclient](../promptflow/azure/_restclient)
## Update code
+ cd to [promptflow/azure/_restclient](../promptflow/azure/_restclient)
+ run `autorest --v3 --python --track2 --version=3.8.0 --use=@autorest/[email protected] --input-file=swagger.json --output-folder=. --namespace=flow --modelerfour.lenient-model-deduplication`
+ don't change `--use`. latest version of `autorest/python` will generate code following different pattern, which is not compatible with our code.
## Update the generation history
- 2023.11.13 - [Update SDK restclient](https://github.com/microsoft/promptflow/pull/1101).
- 2023.12.18 - [Remove data portal url from the result of pfazure run show](https://github.com/microsoft/promptflow/pull/1497)
## Troubleshooting
### Duplicate object schemas with "xxx" name detected.
This may be caused by the duplicate generated class names.
```json
"FlowFeature": {
"type": "object",
"properties": {
"name": {
"type": "string",
"nullable": true
},
"description": {
"type": "string",
"nullable": true
},
"state": {
"type": "object",
"properties": {
"Runtime": {
"$ref": "#/components/schemas/FlowFeatureState"
},
"Executor": {
"$ref": "#/components/schemas/FlowFeatureState"
},
"PFS": {
"$ref": "#/components/schemas/FlowFeatureState"
}
},
"additionalProperties": false,
"nullable": true
}
},
"additionalProperties": false
},
"FlowFeatureState": {
"enum": [
"Ready",
"E2ETest"
],
"type": "string"
},
```
`FlowFeature` has a nested object field `state`, which will be generated to a new class named `FlowFeatureState`, and it duplicates with the enum `FlowFeatureState`.
To fix this, server side needs to change the class name in the schema, in this case, server side changed the object `state` to `states` and the problem is resolved.
| promptflow/src/promptflow/promptflow/azure/_restclient/README.md/0 | {
"file_path": "promptflow/src/promptflow/promptflow/azure/_restclient/README.md",
"repo_id": "promptflow",
"token_count": 837
} | 40 |
# 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._flow_runtimes_operations import build_check_ci_availability_request, build_check_mir_availability_request, build_check_runtime_upgrade_request, build_create_runtime_request, build_delete_runtime_request, build_get_runtime_capability_request, build_get_runtime_latest_config_request, build_get_runtime_request, build_list_runtimes_request, build_update_runtime_request
T = TypeVar('T')
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]
class FlowRuntimesOperations:
"""FlowRuntimesOperations 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 create_runtime(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
runtime_name: str,
async_call: Optional[bool] = False,
msi_token: Optional[bool] = False,
skip_port_check: Optional[bool] = False,
body: Optional["_models.CreateFlowRuntimeRequest"] = None,
**kwargs: Any
) -> "_models.FlowRuntimeDto":
"""create_runtime.
: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 runtime_name:
:type runtime_name: str
:param async_call:
:type async_call: bool
:param msi_token:
:type msi_token: bool
:param skip_port_check:
:type skip_port_check: bool
:param body:
:type body: ~flow.models.CreateFlowRuntimeRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowRuntimeDto, or the result of cls(response)
:rtype: ~flow.models.FlowRuntimeDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowRuntimeDto"]
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, 'CreateFlowRuntimeRequest')
else:
_json = None
request = build_create_runtime_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
runtime_name=runtime_name,
content_type=content_type,
json=_json,
async_call=async_call,
msi_token=msi_token,
skip_port_check=skip_port_check,
template_url=self.create_runtime.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('FlowRuntimeDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
create_runtime.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}'} # type: ignore
@distributed_trace_async
async def update_runtime(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
runtime_name: str,
async_call: Optional[bool] = False,
msi_token: Optional[bool] = False,
skip_port_check: Optional[bool] = False,
body: Optional["_models.UpdateFlowRuntimeRequest"] = None,
**kwargs: Any
) -> "_models.FlowRuntimeDto":
"""update_runtime.
: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 runtime_name:
:type runtime_name: str
:param async_call:
:type async_call: bool
:param msi_token:
:type msi_token: bool
:param skip_port_check:
:type skip_port_check: bool
:param body:
:type body: ~flow.models.UpdateFlowRuntimeRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowRuntimeDto, or the result of cls(response)
:rtype: ~flow.models.FlowRuntimeDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowRuntimeDto"]
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, 'UpdateFlowRuntimeRequest')
else:
_json = None
request = build_update_runtime_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
runtime_name=runtime_name,
content_type=content_type,
json=_json,
async_call=async_call,
msi_token=msi_token,
skip_port_check=skip_port_check,
template_url=self.update_runtime.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('FlowRuntimeDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
update_runtime.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}'} # type: ignore
@distributed_trace_async
async def get_runtime(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
runtime_name: str,
**kwargs: Any
) -> "_models.FlowRuntimeDto":
"""get_runtime.
: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 runtime_name:
:type runtime_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowRuntimeDto, or the result of cls(response)
:rtype: ~flow.models.FlowRuntimeDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowRuntimeDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_runtime_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
runtime_name=runtime_name,
template_url=self.get_runtime.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('FlowRuntimeDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_runtime.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}'} # type: ignore
@distributed_trace_async
async def delete_runtime(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
runtime_name: str,
async_call: Optional[bool] = False,
msi_token: Optional[bool] = False,
**kwargs: Any
) -> "_models.FlowRuntimeDto":
"""delete_runtime.
: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 runtime_name:
:type runtime_name: str
:param async_call:
:type async_call: bool
:param msi_token:
:type msi_token: bool
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowRuntimeDto, or the result of cls(response)
:rtype: ~flow.models.FlowRuntimeDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowRuntimeDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_delete_runtime_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
runtime_name=runtime_name,
async_call=async_call,
msi_token=msi_token,
template_url=self.delete_runtime.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('FlowRuntimeDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
delete_runtime.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}'} # type: ignore
@distributed_trace_async
async def check_ci_availability(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
compute_instance_name: str,
custom_app_name: str,
**kwargs: Any
) -> "_models.AvailabilityResponse":
"""check_ci_availability.
: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 compute_instance_name:
:type compute_instance_name: str
:param custom_app_name:
:type custom_app_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: AvailabilityResponse, or the result of cls(response)
:rtype: ~flow.models.AvailabilityResponse
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.AvailabilityResponse"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_check_ci_availability_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
compute_instance_name=compute_instance_name,
custom_app_name=custom_app_name,
template_url=self.check_ci_availability.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('AvailabilityResponse', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
check_ci_availability.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/checkCiAvailability'} # type: ignore
@distributed_trace_async
async def check_mir_availability(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
endpoint_name: str,
deployment_name: str,
**kwargs: Any
) -> "_models.AvailabilityResponse":
"""check_mir_availability.
: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 endpoint_name:
:type endpoint_name: str
:param deployment_name:
:type deployment_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: AvailabilityResponse, or the result of cls(response)
:rtype: ~flow.models.AvailabilityResponse
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.AvailabilityResponse"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_check_mir_availability_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
endpoint_name=endpoint_name,
deployment_name=deployment_name,
template_url=self.check_mir_availability.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('AvailabilityResponse', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
check_mir_availability.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/checkMirAvailability'} # type: ignore
@distributed_trace_async
async def check_runtime_upgrade(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
runtime_name: str,
**kwargs: Any
) -> bool:
"""check_runtime_upgrade.
: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 runtime_name:
:type runtime_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: bool, or the result of cls(response)
:rtype: bool
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[bool]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_check_runtime_upgrade_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
runtime_name=runtime_name,
template_url=self.check_runtime_upgrade.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('bool', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
check_runtime_upgrade.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}/needUpgrade'} # type: ignore
@distributed_trace_async
async def get_runtime_capability(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
runtime_name: str,
**kwargs: Any
) -> "_models.FlowRuntimeCapability":
"""get_runtime_capability.
: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 runtime_name:
:type runtime_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowRuntimeCapability, or the result of cls(response)
:rtype: ~flow.models.FlowRuntimeCapability
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowRuntimeCapability"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_runtime_capability_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
runtime_name=runtime_name,
template_url=self.get_runtime_capability.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('FlowRuntimeCapability', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_runtime_capability.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}/capability'} # type: ignore
@distributed_trace_async
async def get_runtime_latest_config(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
**kwargs: Any
) -> "_models.RuntimeConfiguration":
"""get_runtime_latest_config.
: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: RuntimeConfiguration, or the result of cls(response)
:rtype: ~flow.models.RuntimeConfiguration
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.RuntimeConfiguration"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_runtime_latest_config_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
template_url=self.get_runtime_latest_config.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('RuntimeConfiguration', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_runtime_latest_config.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/latestConfig'} # type: ignore
@distributed_trace_async
async def list_runtimes(
self,
subscription_id: str,
resource_group_name: str,
workspace_name: str,
**kwargs: Any
) -> List["_models.FlowRuntimeDto"]:
"""list_runtimes.
: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: list of FlowRuntimeDto, or the result of cls(response)
:rtype: list[~flow.models.FlowRuntimeDto]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[List["_models.FlowRuntimeDto"]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_list_runtimes_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
template_url=self.list_runtimes.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('[FlowRuntimeDto]', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
list_runtimes.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes'} # type: ignore
| promptflow/src/promptflow/promptflow/azure/_restclient/flow/aio/operations/_flow_runtimes_operations.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/azure/_restclient/flow/aio/operations/_flow_runtimes_operations.py",
"repo_id": "promptflow",
"token_count": 11768
} | 41 |
# 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_create_runtime_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
runtime_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
content_type = kwargs.pop('content_type', None) # type: Optional[str]
async_call = kwargs.pop('async_call', False) # type: Optional[bool]
msi_token = kwargs.pop('msi_token', False) # type: Optional[bool]
skip_port_check = kwargs.pop('skip_port_check', False) # type: Optional[bool]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}')
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'),
"runtimeName": _SERIALIZER.url("runtime_name", runtime_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if async_call is not None:
query_parameters['asyncCall'] = _SERIALIZER.query("async_call", async_call, 'bool')
if msi_token is not None:
query_parameters['msiToken'] = _SERIALIZER.query("msi_token", msi_token, 'bool')
if skip_port_check is not None:
query_parameters['skipPortCheck'] = _SERIALIZER.query("skip_port_check", skip_port_check, 'bool')
# 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_update_runtime_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
runtime_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
content_type = kwargs.pop('content_type', None) # type: Optional[str]
async_call = kwargs.pop('async_call', False) # type: Optional[bool]
msi_token = kwargs.pop('msi_token', False) # type: Optional[bool]
skip_port_check = kwargs.pop('skip_port_check', False) # type: Optional[bool]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}')
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'),
"runtimeName": _SERIALIZER.url("runtime_name", runtime_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if async_call is not None:
query_parameters['asyncCall'] = _SERIALIZER.query("async_call", async_call, 'bool')
if msi_token is not None:
query_parameters['msiToken'] = _SERIALIZER.query("msi_token", msi_token, 'bool')
if skip_port_check is not None:
query_parameters['skipPortCheck'] = _SERIALIZER.query("skip_port_check", skip_port_check, 'bool')
# 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="PUT",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_get_runtime_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
runtime_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}/FlowRuntimes/{runtimeName}')
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'),
"runtimeName": _SERIALIZER.url("runtime_name", runtime_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_delete_runtime_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
runtime_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
async_call = kwargs.pop('async_call', False) # type: Optional[bool]
msi_token = kwargs.pop('msi_token', False) # type: Optional[bool]
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}')
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'),
"runtimeName": _SERIALIZER.url("runtime_name", runtime_name, 'str'),
}
url = _format_url_section(url, **path_format_arguments)
# Construct parameters
query_parameters = kwargs.pop("params", {}) # type: Dict[str, Any]
if async_call is not None:
query_parameters['asyncCall'] = _SERIALIZER.query("async_call", async_call, 'bool')
if msi_token is not None:
query_parameters['msiToken'] = _SERIALIZER.query("msi_token", msi_token, 'bool')
# Construct headers
header_parameters = kwargs.pop("headers", {}) # type: Dict[str, Any]
header_parameters['Accept'] = _SERIALIZER.header("accept", accept, 'str')
return HttpRequest(
method="DELETE",
url=url,
params=query_parameters,
headers=header_parameters,
**kwargs
)
def build_check_ci_availability_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
compute_instance_name = kwargs.pop('compute_instance_name') # type: str
custom_app_name = kwargs.pop('custom_app_name') # type: str
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/checkCiAvailability')
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['computeInstanceName'] = _SERIALIZER.query("compute_instance_name", compute_instance_name, 'str')
query_parameters['customAppName'] = _SERIALIZER.query("custom_app_name", custom_app_name, '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_check_mir_availability_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> HttpRequest
endpoint_name = kwargs.pop('endpoint_name') # type: str
deployment_name = kwargs.pop('deployment_name') # type: str
accept = "application/json"
# Construct URL
url = kwargs.pop("template_url", '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/checkMirAvailability')
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['endpointName'] = _SERIALIZER.query("endpoint_name", endpoint_name, 'str')
query_parameters['deploymentName'] = _SERIALIZER.query("deployment_name", deployment_name, '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_check_runtime_upgrade_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
runtime_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}/FlowRuntimes/{runtimeName}/needUpgrade')
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'),
"runtimeName": _SERIALIZER.url("runtime_name", runtime_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_runtime_capability_request(
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
runtime_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}/FlowRuntimes/{runtimeName}/capability')
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'),
"runtimeName": _SERIALIZER.url("runtime_name", runtime_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_runtime_latest_config_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}/FlowRuntimes/latestConfig')
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_list_runtimes_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}/FlowRuntimes')
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
)
# fmt: on
class FlowRuntimesOperations(object):
"""FlowRuntimesOperations 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 create_runtime(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
runtime_name, # type: str
async_call=False, # type: Optional[bool]
msi_token=False, # type: Optional[bool]
skip_port_check=False, # type: Optional[bool]
body=None, # type: Optional["_models.CreateFlowRuntimeRequest"]
**kwargs # type: Any
):
# type: (...) -> "_models.FlowRuntimeDto"
"""create_runtime.
: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 runtime_name:
:type runtime_name: str
:param async_call:
:type async_call: bool
:param msi_token:
:type msi_token: bool
:param skip_port_check:
:type skip_port_check: bool
:param body:
:type body: ~flow.models.CreateFlowRuntimeRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowRuntimeDto, or the result of cls(response)
:rtype: ~flow.models.FlowRuntimeDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowRuntimeDto"]
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, 'CreateFlowRuntimeRequest')
else:
_json = None
request = build_create_runtime_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
runtime_name=runtime_name,
content_type=content_type,
json=_json,
async_call=async_call,
msi_token=msi_token,
skip_port_check=skip_port_check,
template_url=self.create_runtime.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('FlowRuntimeDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
create_runtime.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}'} # type: ignore
@distributed_trace
def update_runtime(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
runtime_name, # type: str
async_call=False, # type: Optional[bool]
msi_token=False, # type: Optional[bool]
skip_port_check=False, # type: Optional[bool]
body=None, # type: Optional["_models.UpdateFlowRuntimeRequest"]
**kwargs # type: Any
):
# type: (...) -> "_models.FlowRuntimeDto"
"""update_runtime.
: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 runtime_name:
:type runtime_name: str
:param async_call:
:type async_call: bool
:param msi_token:
:type msi_token: bool
:param skip_port_check:
:type skip_port_check: bool
:param body:
:type body: ~flow.models.UpdateFlowRuntimeRequest
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowRuntimeDto, or the result of cls(response)
:rtype: ~flow.models.FlowRuntimeDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowRuntimeDto"]
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, 'UpdateFlowRuntimeRequest')
else:
_json = None
request = build_update_runtime_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
runtime_name=runtime_name,
content_type=content_type,
json=_json,
async_call=async_call,
msi_token=msi_token,
skip_port_check=skip_port_check,
template_url=self.update_runtime.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('FlowRuntimeDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
update_runtime.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}'} # type: ignore
@distributed_trace
def get_runtime(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
runtime_name, # type: str
**kwargs # type: Any
):
# type: (...) -> "_models.FlowRuntimeDto"
"""get_runtime.
: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 runtime_name:
:type runtime_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowRuntimeDto, or the result of cls(response)
:rtype: ~flow.models.FlowRuntimeDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowRuntimeDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_runtime_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
runtime_name=runtime_name,
template_url=self.get_runtime.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('FlowRuntimeDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_runtime.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}'} # type: ignore
@distributed_trace
def delete_runtime(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
runtime_name, # type: str
async_call=False, # type: Optional[bool]
msi_token=False, # type: Optional[bool]
**kwargs # type: Any
):
# type: (...) -> "_models.FlowRuntimeDto"
"""delete_runtime.
: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 runtime_name:
:type runtime_name: str
:param async_call:
:type async_call: bool
:param msi_token:
:type msi_token: bool
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowRuntimeDto, or the result of cls(response)
:rtype: ~flow.models.FlowRuntimeDto
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowRuntimeDto"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_delete_runtime_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
runtime_name=runtime_name,
async_call=async_call,
msi_token=msi_token,
template_url=self.delete_runtime.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('FlowRuntimeDto', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
delete_runtime.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}'} # type: ignore
@distributed_trace
def check_ci_availability(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
compute_instance_name, # type: str
custom_app_name, # type: str
**kwargs # type: Any
):
# type: (...) -> "_models.AvailabilityResponse"
"""check_ci_availability.
: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 compute_instance_name:
:type compute_instance_name: str
:param custom_app_name:
:type custom_app_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: AvailabilityResponse, or the result of cls(response)
:rtype: ~flow.models.AvailabilityResponse
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.AvailabilityResponse"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_check_ci_availability_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
compute_instance_name=compute_instance_name,
custom_app_name=custom_app_name,
template_url=self.check_ci_availability.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('AvailabilityResponse', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
check_ci_availability.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/checkCiAvailability'} # type: ignore
@distributed_trace
def check_mir_availability(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
endpoint_name, # type: str
deployment_name, # type: str
**kwargs # type: Any
):
# type: (...) -> "_models.AvailabilityResponse"
"""check_mir_availability.
: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 endpoint_name:
:type endpoint_name: str
:param deployment_name:
:type deployment_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: AvailabilityResponse, or the result of cls(response)
:rtype: ~flow.models.AvailabilityResponse
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.AvailabilityResponse"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_check_mir_availability_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
endpoint_name=endpoint_name,
deployment_name=deployment_name,
template_url=self.check_mir_availability.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('AvailabilityResponse', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
check_mir_availability.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/checkMirAvailability'} # type: ignore
@distributed_trace
def check_runtime_upgrade(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
runtime_name, # type: str
**kwargs # type: Any
):
# type: (...) -> bool
"""check_runtime_upgrade.
: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 runtime_name:
:type runtime_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: bool, or the result of cls(response)
:rtype: bool
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[bool]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_check_runtime_upgrade_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
runtime_name=runtime_name,
template_url=self.check_runtime_upgrade.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('bool', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
check_runtime_upgrade.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}/needUpgrade'} # type: ignore
@distributed_trace
def get_runtime_capability(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
runtime_name, # type: str
**kwargs # type: Any
):
# type: (...) -> "_models.FlowRuntimeCapability"
"""get_runtime_capability.
: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 runtime_name:
:type runtime_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: FlowRuntimeCapability, or the result of cls(response)
:rtype: ~flow.models.FlowRuntimeCapability
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.FlowRuntimeCapability"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_runtime_capability_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
runtime_name=runtime_name,
template_url=self.get_runtime_capability.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('FlowRuntimeCapability', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_runtime_capability.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/{runtimeName}/capability'} # type: ignore
@distributed_trace
def get_runtime_latest_config(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> "_models.RuntimeConfiguration"
"""get_runtime_latest_config.
: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: RuntimeConfiguration, or the result of cls(response)
:rtype: ~flow.models.RuntimeConfiguration
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType["_models.RuntimeConfiguration"]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_get_runtime_latest_config_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
template_url=self.get_runtime_latest_config.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('RuntimeConfiguration', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get_runtime_latest_config.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes/latestConfig'} # type: ignore
@distributed_trace
def list_runtimes(
self,
subscription_id, # type: str
resource_group_name, # type: str
workspace_name, # type: str
**kwargs # type: Any
):
# type: (...) -> List["_models.FlowRuntimeDto"]
"""list_runtimes.
: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: list of FlowRuntimeDto, or the result of cls(response)
:rtype: list[~flow.models.FlowRuntimeDto]
:raises: ~azure.core.exceptions.HttpResponseError
"""
cls = kwargs.pop('cls', None) # type: ClsType[List["_models.FlowRuntimeDto"]]
error_map = {
401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError
}
error_map.update(kwargs.pop('error_map', {}))
request = build_list_runtimes_request(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
workspace_name=workspace_name,
template_url=self.list_runtimes.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('[FlowRuntimeDto]', pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
list_runtimes.metadata = {'url': '/flow/api/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/FlowRuntimes'} # type: ignore
| promptflow/src/promptflow/promptflow/azure/_restclient/flow/operations/_flow_runtimes_operations.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/azure/_restclient/flow/operations/_flow_runtimes_operations.py",
"repo_id": "promptflow",
"token_count": 17964
} | 42 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
from dataclasses import dataclass
from datetime import datetime
from itertools import chain
from typing import Any, List, Mapping
from promptflow._utils.exception_utils import RootErrorCode
from promptflow._utils.openai_metrics_calculator import OpenAIMetricsCalculator
from promptflow.contracts.run_info import RunInfo, Status
from promptflow.executor._result import AggregationResult, LineResult
@dataclass
class LineError:
"""The error of a line in a batch run.
It contains the line number and the error dict of a failed line in the batch run.
The error dict is gengerated by ExceptionPresenter.to_dict().
"""
line_number: int
error: Mapping[str, Any]
def to_dict(self):
return {
"line_number": self.line_number,
"error": self.error,
}
@dataclass
class ErrorSummary:
"""The summary of errors in a batch run.
:param failed_user_error_lines: The number of lines that failed with user error.
:type failed_user_error_lines: int
:param failed_system_error_lines: The number of lines that failed with system error.
:type failed_system_error_lines: int
:param error_list: The line number and error dict of failed lines in the line results.
:type error_list: List[~promptflow.batch._result.LineError]
:param aggr_error_dict: The dict of node name and error dict of failed nodes in the aggregation result.
:type aggr_error_dict: Mapping[str, Any]
"""
failed_user_error_lines: int
failed_system_error_lines: int
error_list: List[LineError]
aggr_error_dict: Mapping[str, Any]
@staticmethod
def create(line_results: List[LineResult], aggr_result: AggregationResult):
failed_user_error_lines = 0
failed_system_error_lines = 0
error_list: List[LineError] = []
for line_result in line_results:
if line_result.run_info.status != Status.Failed:
continue
flow_run = line_result.run_info
if flow_run.error.get("code", "") == RootErrorCode.USER_ERROR:
failed_user_error_lines += 1
else:
failed_system_error_lines += 1
line_error = LineError(
line_number=flow_run.index,
error=flow_run.error,
)
error_list.append(line_error)
error_summary = ErrorSummary(
failed_user_error_lines=failed_user_error_lines,
failed_system_error_lines=failed_system_error_lines,
error_list=sorted(error_list, key=lambda x: x.line_number),
aggr_error_dict={
node_name: node_run_info.error
for node_name, node_run_info in aggr_result.node_run_infos.items()
if node_run_info.status == Status.Failed
},
)
return error_summary
@dataclass
class SystemMetrics:
"""The system metrics of a batch run."""
total_tokens: int
prompt_tokens: int
completion_tokens: int
duration: float # in seconds
@staticmethod
def create(
start_time: datetime, end_time: datetime, line_results: List[LineResult], aggr_results: AggregationResult
):
openai_metrics = SystemMetrics._get_openai_metrics(line_results, aggr_results)
return SystemMetrics(
total_tokens=openai_metrics.get("total_tokens", 0),
prompt_tokens=openai_metrics.get("prompt_tokens", 0),
completion_tokens=openai_metrics.get("completion_tokens", 0),
duration=(end_time - start_time).total_seconds(),
)
@staticmethod
def _get_openai_metrics(line_results: List[LineResult], aggr_results: AggregationResult):
node_run_infos = _get_node_run_infos(line_results, aggr_results)
total_metrics = {}
calculator = OpenAIMetricsCalculator()
for run_info in node_run_infos:
metrics = SystemMetrics._try_get_openai_metrics(run_info)
if metrics:
calculator.merge_metrics_dict(total_metrics, metrics)
else:
api_calls = run_info.api_calls or []
for call in api_calls:
metrics = calculator.get_openai_metrics_from_api_call(call)
calculator.merge_metrics_dict(total_metrics, metrics)
return total_metrics
def _try_get_openai_metrics(run_info: RunInfo):
openai_metrics = {}
if run_info.system_metrics:
for metric in ["total_tokens", "prompt_tokens", "completion_tokens"]:
if metric not in run_info.system_metrics:
return False
openai_metrics[metric] = run_info.system_metrics[metric]
return openai_metrics
def to_dict(self):
return {
"total_tokens": self.total_tokens,
"prompt_tokens": self.prompt_tokens,
"completion_tokens": self.completion_tokens,
"duration": self.duration,
}
@dataclass
class BatchResult:
"""The result of a batch run."""
status: Status
total_lines: int
completed_lines: int
failed_lines: int
node_status: Mapping[str, int]
start_time: datetime
end_time: datetime
metrics: Mapping[str, str]
system_metrics: SystemMetrics
error_summary: ErrorSummary
@classmethod
def create(
cls,
start_time: datetime,
end_time: datetime,
line_results: List[LineResult],
aggr_result: AggregationResult,
status: Status = Status.Completed,
) -> "BatchResult":
total_lines = len(line_results)
completed_lines = sum(line_result.run_info.status == Status.Completed for line_result in line_results)
failed_lines = total_lines - completed_lines
return cls(
status=status,
total_lines=total_lines,
completed_lines=completed_lines,
failed_lines=failed_lines,
node_status=BatchResult._get_node_status(line_results, aggr_result),
start_time=start_time,
end_time=end_time,
metrics=aggr_result.metrics,
system_metrics=SystemMetrics.create(start_time, end_time, line_results, aggr_result),
error_summary=ErrorSummary.create(line_results, aggr_result),
)
@staticmethod
def _get_node_status(line_results: List[LineResult], aggr_result: AggregationResult):
node_run_infos = _get_node_run_infos(line_results, aggr_result)
node_status = {}
for node_run_info in node_run_infos:
key = f"{node_run_info.node}.{node_run_info.status.value.lower()}"
node_status[key] = node_status.get(key, 0) + 1
return node_status
def _get_node_run_infos(line_results: List[LineResult], aggr_result: AggregationResult):
line_node_run_infos = (
node_run_info for line_result in line_results for node_run_info in line_result.node_run_infos.values()
)
aggr_node_run_infos = (node_run_info for node_run_info in aggr_result.node_run_infos.values())
return chain(line_node_run_infos, aggr_node_run_infos)
| promptflow/src/promptflow/promptflow/batch/_result.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/batch/_result.py",
"repo_id": "promptflow",
"token_count": 3127
} | 43 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import asyncio
import contextvars
import inspect
import os
import signal
import threading
import time
import traceback
from asyncio import Task
from concurrent.futures import ThreadPoolExecutor
from typing import Any, Dict, List, Tuple
from promptflow._core.flow_execution_context import FlowExecutionContext
from promptflow._core.tools_manager import ToolsManager
from promptflow._utils.logger_utils import flow_logger
from promptflow._utils.utils import extract_user_frame_summaries, set_context
from promptflow.contracts.flow import Node
from promptflow.executor._dag_manager import DAGManager
from promptflow.executor._errors import NoNodeExecutedError
PF_ASYNC_NODE_SCHEDULER_EXECUTE_TASK_NAME = "_pf_async_nodes_scheduler.execute"
DEFAULT_TASK_LOGGING_INTERVAL = 60
ASYNC_DAG_MANAGER_COMPLETED = False
class AsyncNodesScheduler:
def __init__(
self,
tools_manager: ToolsManager,
node_concurrency: int,
) -> None:
self._tools_manager = tools_manager
self._node_concurrency = node_concurrency
self._task_start_time = {}
self._task_last_log_time = {}
self._dag_manager_completed_event = threading.Event()
async def execute(
self,
nodes: List[Node],
inputs: Dict[str, Any],
context: FlowExecutionContext,
) -> Tuple[dict, dict]:
# TODO: Provide cancel API
if threading.current_thread() is threading.main_thread():
signal.signal(signal.SIGINT, signal_handler)
signal.signal(signal.SIGTERM, signal_handler)
else:
flow_logger.info(
"Current thread is not main thread, skip signal handler registration in AsyncNodesScheduler."
)
# Semaphore should be created in the loop, otherwise it will not work.
loop = asyncio.get_running_loop()
self._semaphore = asyncio.Semaphore(self._node_concurrency)
monitor = threading.Thread(
target=monitor_long_running_coroutine,
args=(loop, self._task_start_time, self._task_last_log_time, self._dag_manager_completed_event),
daemon=True,
)
monitor.start()
# Set the name of scheduler tasks to avoid monitoring its duration
task = asyncio.current_task()
task.set_name(PF_ASYNC_NODE_SCHEDULER_EXECUTE_TASK_NAME)
parent_context = contextvars.copy_context()
executor = ThreadPoolExecutor(
max_workers=self._node_concurrency, initializer=set_context, initargs=(parent_context,)
)
# Note that we must not use `with` statement to manage the executor.
# This is because it will always call `executor.shutdown()` when exiting the `with` block.
# Then the event loop will wait for all tasks to be completed before raising the cancellation error.
# See reference: https://docs.python.org/3/library/concurrent.futures.html#concurrent.futures.Executor
outputs = await self._execute_with_thread_pool(executor, nodes, inputs, context)
executor.shutdown()
return outputs
async def _execute_with_thread_pool(
self,
executor: ThreadPoolExecutor,
nodes: List[Node],
inputs: Dict[str, Any],
context: FlowExecutionContext,
) -> Tuple[dict, dict]:
flow_logger.info(f"Start to run {len(nodes)} nodes with the current event loop.")
dag_manager = DAGManager(nodes, inputs)
task2nodes = self._execute_nodes(dag_manager, context, executor)
while not dag_manager.completed():
task2nodes = await self._wait_and_complete_nodes(task2nodes, dag_manager)
submitted_tasks2nodes = self._execute_nodes(dag_manager, context, executor)
task2nodes.update(submitted_tasks2nodes)
# Set the event to notify the monitor thread to exit
# Ref: https://docs.python.org/3/library/threading.html#event-objects
self._dag_manager_completed_event.set()
for node in dag_manager.bypassed_nodes:
dag_manager.completed_nodes_outputs[node] = None
return dag_manager.completed_nodes_outputs, dag_manager.bypassed_nodes
async def _wait_and_complete_nodes(self, task2nodes: Dict[Task, Node], dag_manager: DAGManager) -> Dict[Task, Node]:
if not task2nodes:
raise NoNodeExecutedError("No nodes are ready for execution, but the flow is not completed.")
tasks = [task for task in task2nodes]
for task in tasks:
self._task_start_time[task] = time.time()
done, _ = await asyncio.wait(tasks, return_when=asyncio.FIRST_COMPLETED)
dag_manager.complete_nodes({task2nodes[task].name: task.result() for task in done})
for task in done:
del task2nodes[task]
return task2nodes
def _execute_nodes(
self,
dag_manager: DAGManager,
context: FlowExecutionContext,
executor: ThreadPoolExecutor,
) -> Dict[Task, Node]:
# Bypass nodes and update node run info until there are no nodes to bypass
nodes_to_bypass = dag_manager.pop_bypassable_nodes()
while nodes_to_bypass:
for node in nodes_to_bypass:
context.bypass_node(node)
nodes_to_bypass = dag_manager.pop_bypassable_nodes()
# Create tasks for ready nodes
return {
self._create_node_task(node, dag_manager, context, executor): node for node in dag_manager.pop_ready_nodes()
}
async def run_task_with_semaphore(self, coroutine):
async with self._semaphore:
return await coroutine
def _create_node_task(
self,
node: Node,
dag_manager: DAGManager,
context: FlowExecutionContext,
executor: ThreadPoolExecutor,
) -> Task:
f = self._tools_manager.get_tool(node.name)
kwargs = dag_manager.get_node_valid_inputs(node, f)
if inspect.iscoroutinefunction(f):
# For async task, it will not be executed before calling create_task.
task = context.invoke_tool_async(node, f, kwargs)
else:
# For sync task, convert it to async task and run it in executor thread.
# Even though the task is put to the thread pool, thread.start will only be triggered after create_task.
task = self._sync_function_to_async_task(executor, context, node, f, kwargs)
# Set the name of the task to the node name for debugging purpose
# It does not need to be unique by design.
# Wrap the coroutine in a task with asyncio.create_task to schedule it for event loop execution
# The task is created and added to the event loop, but the exact execution depends on loop's scheduling
return asyncio.create_task(self.run_task_with_semaphore(task), name=node.name)
@staticmethod
async def _sync_function_to_async_task(
executor: ThreadPoolExecutor,
context: FlowExecutionContext,
node,
f,
kwargs,
):
# The task will not be executed before calling create_task.
return await asyncio.get_running_loop().run_in_executor(executor, context.invoke_tool, node, f, kwargs)
def signal_handler(sig, frame):
"""
Start a thread to monitor coroutines after receiving signal.
"""
flow_logger.info(f"Received signal {sig}({signal.Signals(sig).name}), start coroutine monitor thread.")
loop = asyncio.get_running_loop()
monitor = threading.Thread(target=monitor_coroutine_after_cancellation, args=(loop,))
monitor.start()
raise KeyboardInterrupt
def log_stack_recursively(task: asyncio.Task, elapse_time: float):
"""Recursively log the frame of a task or coroutine.
Traditional stacktrace would stop at the first awaited nested inside the coroutine.
:param task: Task to log
:type task_or_coroutine: asyncio.Task
:param elapse_time: Seconds elapsed since the task started
:type elapse_time: float
"""
# We cannot use task.get_stack() to get the stack, because only one stack frame is
# returned for a suspended coroutine because of the implementation of CPython
# Ref: https://github.com/python/cpython/blob/main/Lib/asyncio/tasks.py
# "only one stack frame is returned for a suspended coroutine."
task_or_coroutine = task
frame_summaries = []
# Collect frame_summaries along async call chain
while True:
if isinstance(task_or_coroutine, asyncio.Task):
# For a task, get the coroutine it's running
coroutine: asyncio.coroutine = task_or_coroutine.get_coro()
elif asyncio.iscoroutine(task_or_coroutine):
coroutine = task_or_coroutine
else:
break
frame = coroutine.cr_frame
stack_summary: traceback.StackSummary = traceback.extract_stack(frame)
frame_summaries.extend(stack_summary)
task_or_coroutine = coroutine.cr_await
# Format the frame summaries to warning message
if frame_summaries:
user_frame_summaries = extract_user_frame_summaries(frame_summaries)
stack_messages = traceback.format_list(user_frame_summaries)
all_stack_message = "".join(stack_messages)
task_msg = (
f"Task {task.get_name()} has been running for {elapse_time:.0f} seconds,"
f" stacktrace:\n{all_stack_message}"
)
flow_logger.warning(task_msg)
def monitor_long_running_coroutine(
loop: asyncio.AbstractEventLoop,
task_start_time: dict,
task_last_log_time: dict,
dag_manager_completed_event: threading.Event,
):
flow_logger.info("monitor_long_running_coroutine started")
logging_interval = DEFAULT_TASK_LOGGING_INTERVAL
logging_interval_in_env = os.environ.get("PF_TASK_PEEKING_INTERVAL")
if logging_interval_in_env:
try:
value = int(logging_interval_in_env)
if value <= 0:
raise ValueError
logging_interval = value
flow_logger.info(
f"Using value of PF_TASK_PEEKING_INTERVAL in environment variable as "
f"logging interval: {logging_interval_in_env}"
)
except ValueError:
flow_logger.warning(
f"Value of PF_TASK_PEEKING_INTERVAL in environment variable ('{logging_interval_in_env}') "
f"is invalid, use default value {DEFAULT_TASK_LOGGING_INTERVAL}"
)
while not dag_manager_completed_event.is_set():
running_tasks = [task for task in asyncio.all_tasks(loop) if not task.done()]
# get duration of running tasks
for task in running_tasks:
# Do not monitor the scheduler task
if task.get_name() == PF_ASYNC_NODE_SCHEDULER_EXECUTE_TASK_NAME:
continue
# Do not monitor sync tools, since they will run in executor thread and will
# be monitored by RepeatLogTimer.
task_stacks = task.get_stack()
if (
task_stacks
and task_stacks[-1].f_code
and task_stacks[-1].f_code.co_name == AsyncNodesScheduler._sync_function_to_async_task.__name__
):
continue
if task_start_time.get(task) is None:
flow_logger.warning(f"task {task.get_name()} has no start time, which should not happen")
else:
duration = time.time() - task_start_time[task]
if duration > logging_interval:
if (
task_last_log_time.get(task) is None
or time.time() - task_last_log_time[task] > logging_interval
):
log_stack_recursively(task, duration)
task_last_log_time[task] = time.time()
time.sleep(1)
def monitor_coroutine_after_cancellation(loop: asyncio.AbstractEventLoop):
"""Exit the process when all coroutines are done.
We add this function because if a sync tool is running in async mode,
the task will be cancelled after receiving SIGINT,
but the thread will not be terminated and blocks the program from exiting.
:param loop: event loop of main thread
:type loop: asyncio.AbstractEventLoop
"""
# TODO: Use environment variable to ensure it is flow test scenario to avoid unexpected exit.
# E.g. Customer is integrating Promptflow in their own code, and they want to handle SIGINT by themselves.
max_wait_seconds = os.environ.get("PF_WAIT_SECONDS_AFTER_CANCELLATION", 30)
all_tasks_are_done = False
exceeded_wait_seconds = False
thread_start_time = time.time()
flow_logger.info(f"Start to monitor coroutines after cancellation, max wait seconds: {max_wait_seconds}s")
while not all_tasks_are_done and not exceeded_wait_seconds:
# For sync tool running in async mode, the task will be cancelled,
# but the thread will not be terminated, we exit the program despite of it.
# TODO: Detect whether there is any sync tool running in async mode,
# if there is none, avoid sys.exit and let the program exit gracefully.
all_tasks_are_done = all(task.done() for task in asyncio.all_tasks(loop))
if all_tasks_are_done:
flow_logger.info("All coroutines are done. Exiting.")
# We cannot ensure persist_flow_run is called before the process exits in the case that there is
# non-daemon thread running, sleep for 3 seconds as a best effort.
# If the caller wants to ensure flow status is cancelled in storage, it should check the flow status
# after timeout and set the flow status to Cancelled.
time.sleep(3)
# Use os._exit instead of sys.exit, so that the process can stop without
# waiting for the thread created by run_in_executor to finish.
# sys.exit: https://docs.python.org/3/library/sys.html#sys.exit
# Raise a SystemExit exception, signaling an intention to exit the interpreter.
# Specifically, it does not exit non-daemon thread
# os._exit https://docs.python.org/3/library/os.html#os._exit
# Exit the process with status n, without calling cleanup handlers, flushing stdio buffers, etc.
# Specifically, it stops process without waiting for non-daemon thread.
os._exit(0)
exceeded_wait_seconds = time.time() - thread_start_time > max_wait_seconds
time.sleep(1)
if exceeded_wait_seconds:
if not all_tasks_are_done:
flow_logger.info(
f"Not all coroutines are done within {max_wait_seconds}s"
" after cancellation. Exiting the process despite of them."
" Please config the environment variable"
" PF_WAIT_SECONDS_AFTER_CANCELLATION if your tool needs"
" more time to clean up after cancellation."
)
remaining_tasks = [task for task in asyncio.all_tasks(loop) if not task.done()]
flow_logger.info(f"Remaining tasks: {[task.get_name() for task in remaining_tasks]}")
time.sleep(3)
os._exit(0)
| promptflow/src/promptflow/promptflow/executor/_async_nodes_scheduler.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/executor/_async_nodes_scheduler.py",
"repo_id": "promptflow",
"token_count": 6253
} | 44 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
__path__ = __import__("pkgutil").extend_path(__path__, __name__) # type: ignore
from promptflow._sdk.operations._connection_operations import ConnectionOperations
from promptflow._sdk.operations._flow_operations import FlowOperations
from promptflow._sdk.operations._run_operations import RunOperations
__all__ = ["ConnectionOperations", "FlowOperations", "RunOperations"]
| promptflow/src/promptflow/promptflow/operations/__init__.py/0 | {
"file_path": "promptflow/src/promptflow/promptflow/operations/__init__.py",
"repo_id": "promptflow",
"token_count": 133
} | 45 |
from pathlib import Path
from tempfile import mkdtemp
import pytest
from promptflow._utils.exception_utils import ExceptionPresenter
from promptflow.batch import BatchEngine
from promptflow.batch._result import BatchResult, LineError
from promptflow.contracts.run_info import Status
from promptflow.executor._errors import BatchExecutionTimeoutError, LineExecutionTimeoutError
from ..utils import MemoryRunStorage, get_flow_folder, get_flow_inputs_file, get_yaml_file
SAMPLE_FLOW = "web_classification_no_variants"
ONE_LINE_OF_BULK_TEST_TIMEOUT = "one_line_of_bulktest_timeout"
@pytest.mark.usefixtures("use_secrets_config_file", "dev_connections")
@pytest.mark.e2etest
class TestBatchTimeout:
@pytest.mark.parametrize(
"flow_folder",
[
ONE_LINE_OF_BULK_TEST_TIMEOUT,
],
)
def test_batch_with_line_timeout(self, flow_folder, dev_connections):
# set line timeout to 1 second for testing
mem_run_storage = MemoryRunStorage()
batch_engine = BatchEngine(
get_yaml_file(flow_folder),
get_flow_folder(flow_folder),
connections=dev_connections,
storage=mem_run_storage,
)
batch_engine._line_timeout_sec = 5
# prepare input file and output dir
input_dirs = {"data": get_flow_inputs_file(flow_folder, file_name="samples_all_timeout.json")}
output_dir = Path(mkdtemp())
inputs_mapping = {"idx": "${data.idx}"}
batch_results = batch_engine.run(input_dirs, inputs_mapping, output_dir)
assert isinstance(batch_results, BatchResult)
assert batch_results.completed_lines == 0
assert batch_results.failed_lines == 2
assert batch_results.total_lines == 2
assert batch_results.node_status == {
"my_python_tool_with_failed_line.canceled": 2,
"my_python_tool.completed": 2,
}
# assert mem_run_storage persists run infos correctly
assert len(mem_run_storage._flow_runs) == 2, "Flow runs are persisted in memory storage."
assert len(mem_run_storage._node_runs) == 4, "Node runs are persisted in memory storage."
msg = "Tool execution is canceled because of the error: Line execution timeout after 5 seconds."
for run in mem_run_storage._node_runs.values():
if run.node == "my_python_tool_with_failed_line":
assert run.status == Status.Canceled
assert run.error["message"] == msg
else:
assert run.status == Status.Completed
assert batch_results.status == Status.Completed
assert batch_results.total_lines == 2
assert batch_results.completed_lines == 0
assert batch_results.failed_lines == 2
assert batch_results.error_summary.failed_user_error_lines == 2
assert batch_results.error_summary.failed_system_error_lines == 0
for i, line_error in enumerate(batch_results.error_summary.error_list):
assert isinstance(line_error, LineError)
assert line_error.error["message"] == f"Line {i} execution timeout for exceeding 5 seconds"
assert line_error.error["code"] == "UserError"
@pytest.mark.parametrize(
"flow_folder",
[
ONE_LINE_OF_BULK_TEST_TIMEOUT,
],
)
def test_batch_with_one_line_timeout(self, flow_folder, dev_connections):
mem_run_storage = MemoryRunStorage()
batch_engine = BatchEngine(
get_yaml_file(flow_folder),
get_flow_folder(flow_folder),
connections=dev_connections,
storage=mem_run_storage,
)
batch_engine._line_timeout_sec = 5
# set line timeout to 5 seconds for testing
# prepare input file and output dir
input_dirs = {"data": get_flow_inputs_file(flow_folder, file_name="samples.json")}
output_dir = Path(mkdtemp())
inputs_mapping = {"idx": "${data.idx}"}
batch_results = batch_engine.run(input_dirs, inputs_mapping, output_dir)
assert isinstance(batch_results, BatchResult)
# assert the line status in batch result
assert batch_results.status == Status.Completed
assert batch_results.total_lines == 3
assert batch_results.completed_lines == 2
assert batch_results.failed_lines == 1
assert batch_results.node_status == {
"my_python_tool_with_failed_line.completed": 2,
"my_python_tool_with_failed_line.canceled": 1,
"my_python_tool.completed": 3,
}
# assert the error summary in batch result
assert batch_results.error_summary.failed_user_error_lines == 1
assert batch_results.error_summary.failed_system_error_lines == 0
assert isinstance(batch_results.error_summary.error_list[0], LineError)
assert batch_results.error_summary.error_list[0].line_number == 2
assert batch_results.error_summary.error_list[0].error["code"] == "UserError"
assert batch_results.error_summary.error_list[0].error["referenceCode"] == "Executor"
assert batch_results.error_summary.error_list[0].error["innerError"]["code"] == "LineExecutionTimeoutError"
assert (
batch_results.error_summary.error_list[0].error["message"]
== "Line 2 execution timeout for exceeding 5 seconds"
)
# assert mem_run_storage persists run infos correctly
assert len(mem_run_storage._flow_runs) == 3, "Flow runs are persisted in memory storage."
assert len(mem_run_storage._node_runs) == 6, "Node runs are persisted in memory storage."
@pytest.mark.parametrize(
"flow_folder, line_timeout_sec, batch_timeout_sec, expected_error",
[
(ONE_LINE_OF_BULK_TEST_TIMEOUT, 600, 5, BatchExecutionTimeoutError(2, 5)),
(ONE_LINE_OF_BULK_TEST_TIMEOUT, 3, 600, LineExecutionTimeoutError(2, 3)),
(ONE_LINE_OF_BULK_TEST_TIMEOUT, 3, 5, LineExecutionTimeoutError(2, 3)),
# TODO: Will change to BatchExecutionTimeoutError after refining the implementation of batch timeout.
# (ONE_LINE_OF_BULK_TEST_TIMEOUT, 3, 3, LineExecutionTimeoutError(2, 3)),
],
)
def test_batch_timeout(self, flow_folder, line_timeout_sec, batch_timeout_sec, expected_error):
mem_run_storage = MemoryRunStorage()
batch_engine = BatchEngine(
get_yaml_file(flow_folder),
get_flow_folder(flow_folder),
connections={},
storage=mem_run_storage,
)
batch_engine._line_timeout_sec = line_timeout_sec
batch_engine._batch_timeout_sec = batch_timeout_sec
input_dirs = {"data": get_flow_inputs_file(flow_folder, file_name="samples.json")}
output_dir = Path(mkdtemp())
inputs_mapping = {"idx": "${data.idx}"}
batch_results = batch_engine.run(input_dirs, inputs_mapping, output_dir)
assert isinstance(batch_results, BatchResult)
# assert the line status in batch result
assert batch_results.status == Status.Completed
assert batch_results.total_lines == 3
assert batch_results.completed_lines == 2
assert batch_results.failed_lines == 1
assert batch_results.node_status == {
"my_python_tool_with_failed_line.completed": 2,
"my_python_tool_with_failed_line.canceled": 1,
"my_python_tool.completed": 3,
}
# assert the error summary in batch result
assert batch_results.error_summary.failed_user_error_lines == 1
assert batch_results.error_summary.failed_system_error_lines == 0
assert isinstance(batch_results.error_summary.error_list[0], LineError)
assert batch_results.error_summary.error_list[0].line_number == 2
actual_error_dict = batch_results.error_summary.error_list[0].error
expected_error_dict = ExceptionPresenter.create(expected_error).to_dict()
assert actual_error_dict["code"] == expected_error_dict["code"]
assert actual_error_dict["message"] == expected_error_dict["message"]
assert actual_error_dict["referenceCode"] == expected_error_dict["referenceCode"]
assert actual_error_dict["innerError"]["code"] == expected_error_dict["innerError"]["code"]
# assert mem_run_storage persists run infos correctly
assert len(mem_run_storage._flow_runs) == 3, "Flow runs are persisted in memory storage."
# TODO: Currently, the node status is incomplete.
# We will assert the correct result after refining the implementation of batch timeout.
assert len(mem_run_storage._node_runs) == 6, "Node runs are persisted in memory storage."
| promptflow/src/promptflow/tests/executor/e2etests/test_batch_timeout.py/0 | {
"file_path": "promptflow/src/promptflow/tests/executor/e2etests/test_batch_timeout.py",
"repo_id": "promptflow",
"token_count": 3518
} | 46 |
import pytest
from promptflow._core.connection_manager import ConnectionManager
from promptflow.connections import AzureOpenAIConnection
from promptflow.contracts.tool import ConnectionType
def get_connection_dict():
return {
"azure_open_ai_connection": {
"type": "AzureOpenAIConnection",
"value": {
"api_key": "<azure-openai-key>",
"api_base": "<api-base>",
"api_type": "azure",
"api_version": "2023-07-01-preview",
},
},
"custom_connection": {
"type": "CustomConnection",
"value": {
"api_key": "<your-key>",
"url": "https://api.bing.microsoft.com/v7.0/search",
},
"module": "promptflow.connections",
"secret_keys": ["api_key"],
},
}
@pytest.mark.unittest
class TestConnectionManager:
def test_build_connections(self):
new_connection = get_connection_dict()
# Add not exist key
new_connection["azure_open_ai_connection"]["value"]["not_exist"] = "test"
connection_manager = ConnectionManager(new_connection)
assert len(connection_manager._connections) == 2
assert isinstance(connection_manager.get("azure_open_ai_connection"), AzureOpenAIConnection)
assert connection_manager.to_connections_dict() == new_connection
def test_serialize(self):
new_connection = get_connection_dict()
connection_manager = ConnectionManager(new_connection)
assert (
ConnectionType.serialize_conn(connection_manager.get("azure_open_ai_connection"))
== "azure_open_ai_connection"
)
assert ConnectionType.serialize_conn(connection_manager.get("custom_connection")) == "custom_connection"
def test_get_secret_list(self):
new_connection = get_connection_dict()
connection_manager = ConnectionManager(new_connection)
expected_list = ["<azure-openai-key>", "<your-key>"]
assert set(connection_manager.get_secret_list()) == set(expected_list)
def test_is_secret(self):
new_connection = get_connection_dict()
connection_manager = ConnectionManager(new_connection)
connection = connection_manager.get("custom_connection")
assert connection.is_secret("api_key") is True
assert connection.is_secret("url") is False
| promptflow/src/promptflow/tests/executor/unittests/_core/test_connection_manager.py/0 | {
"file_path": "promptflow/src/promptflow/tests/executor/unittests/_core/test_connection_manager.py",
"repo_id": "promptflow",
"token_count": 1015
} | 47 |
import os
import re
import sys
from multiprocessing import Pool
from pathlib import Path
from unittest.mock import patch
import pytest
from promptflow._core.tool_meta_generator import (
JinjaParsingError,
MultipleToolsDefined,
NoToolDefined,
PythonLoadError,
PythonParsingError,
generate_prompt_meta,
generate_python_meta,
generate_tool_meta_dict_by_file,
)
from promptflow._utils.exception_utils import ExceptionPresenter
from ...utils import FLOW_ROOT, load_json
TEST_ROOT = Path(__file__).parent.parent.parent.parent
TOOLS_ROOT = TEST_ROOT / "test_configs/wrong_tools"
def cd_and_run(working_dir, source_path, tool_type):
os.chdir(working_dir)
sys.path.insert(0, working_dir)
try:
return generate_tool_meta_dict_by_file(source_path, tool_type)
except Exception as e:
return f"({e.__class__.__name__}) {e}"
def cd_and_run_with_read_text_error(working_dir, source_path, tool_type):
def mock_read_text_error(self: Path, *args, **kwargs):
raise Exception("Mock read text error.")
os.chdir(working_dir)
sys.path.insert(0, working_dir)
try:
with patch("promptflow._core.tool_meta_generator.Path.read_text", new=mock_read_text_error):
return generate_tool_meta_dict_by_file(source_path, tool_type)
except Exception as e:
return f"({e.__class__.__name__}) {e}"
def cd_and_run_with_bad_function_interface(working_dir, source_path, tool_type):
def mock_function_to_interface(*args, **kwargs):
raise Exception("Mock function to interface error.")
os.chdir(working_dir)
sys.path.insert(0, working_dir)
try:
with patch("promptflow._core.tool_meta_generator.function_to_interface", new=mock_function_to_interface):
return generate_tool_meta_dict_by_file(source_path, tool_type)
except Exception as e:
return f"({e.__class__.__name__}) {e}"
def generate_tool_meta_dict_by_file_with_cd(wd, tool_path, tool_type, func):
with Pool(1) as pool:
return pool.apply(func, (wd, tool_path, tool_type))
@pytest.mark.unittest
class TestToolMetaUtils:
@pytest.mark.parametrize(
"flow_dir, tool_path, tool_type",
[
("prompt_tools", "summarize_text_content_prompt.jinja2", "prompt"),
("prompt_tools", "summarize_text_content_prompt.jinja2", "llm"),
("script_with_import", "dummy_utils/main.py", "python"),
("script_with___file__", "script_with___file__.py", "python"),
("script_with_special_character", "script_with_special_character.py", "python"),
],
)
def test_generate_tool_meta_dict_by_file(self, flow_dir, tool_path, tool_type):
wd = str((FLOW_ROOT / flow_dir).resolve())
meta_dict = generate_tool_meta_dict_by_file_with_cd(wd, tool_path, tool_type, cd_and_run)
assert isinstance(meta_dict, dict), "Call cd_and_run failed:\n" + meta_dict
target_file = (Path(wd) / tool_path).with_suffix(".meta.json")
expected_dict = load_json(target_file)
if tool_type == "llm":
expected_dict["type"] = "llm" # We use prompt as default for jinja2
assert meta_dict == expected_dict
@pytest.mark.parametrize(
"flow_dir, tool_path, tool_type, func, msg_pattern",
[
pytest.param(
"prompt_tools",
"summarize_text_content_prompt.jinja2",
"python",
cd_and_run,
r"\(PythonLoaderNotFound\) Failed to load python file '.*summarize_text_content_prompt.jinja2'. "
r"Please make sure it is a valid .py file.",
id="PythonLoaderNotFound",
),
pytest.param(
"script_with_import",
"fail.py",
"python",
cd_and_run,
r"\(PythonLoadError\) Failed to load python module from file '.*fail.py': "
r"\(ModuleNotFoundError\) No module named 'aaa'",
id="PythonLoadError",
),
pytest.param(
"simple_flow_with_python_tool",
"divide_num.py",
"python",
cd_and_run_with_bad_function_interface,
r"\(BadFunctionInterface\) Parse interface for tool 'divide_num' failed: "
r"\(Exception\) Mock function to interface error.",
id="BadFunctionInterface",
),
pytest.param(
"script_with_import",
"aaa.py",
"python",
cd_and_run,
r"\(MetaFileNotFound\) Generate tool meta failed for python tool. "
r"Meta file 'aaa.py' can not be found.",
id="MetaFileNotFound",
),
pytest.param(
"simple_flow_with_python_tool",
"divide_num.py",
"python",
cd_and_run_with_read_text_error,
r"\(MetaFileReadError\) Generate tool meta failed for python tool. "
r"Read meta file 'divide_num.py' failed: \(Exception\) Mock read text error.",
id="MetaFileReadError",
),
pytest.param(
"simple_flow_with_python_tool",
"divide_num.py",
"action",
cd_and_run,
r"\(NotSupported\) Generate tool meta failed. The type 'action' is currently unsupported. "
r"Please choose from available types: python,llm,prompt and try again.",
id="NotSupported",
),
],
)
def test_generate_tool_meta_dict_by_file_exception(self, flow_dir, tool_path, tool_type, func, msg_pattern):
wd = str((FLOW_ROOT / flow_dir).resolve())
ret = generate_tool_meta_dict_by_file_with_cd(wd, tool_path, tool_type, func)
assert isinstance(ret, str), "Call cd_and_run should fail but succeeded:\n" + str(ret)
assert re.match(msg_pattern, ret)
@pytest.mark.parametrize(
"content, error_code, message",
[
pytest.param(
"zzz",
PythonParsingError,
"Failed to load python module. Python parsing failed: (NameError) name 'zzz' is not defined",
id="PythonParsingError_NameError",
),
pytest.param(
"# Nothing",
NoToolDefined,
"No tool found in the python script. "
"Please make sure you have one and only one tool definition in your script.",
id="NoToolDefined",
),
pytest.param(
"multiple_tools.py",
MultipleToolsDefined,
"Expected 1 but collected 2 tools: tool1, tool2. "
"Please make sure you have one and only one tool definition in your script.",
id="MultipleToolsDefined",
),
pytest.param(
"{% zzz",
PythonParsingError,
"Failed to load python module. Python parsing failed: "
"(SyntaxError) invalid syntax (<string>, line 1)",
id="PythonParsingError_SyntaxError",
),
],
)
def test_custom_python_meta(self, content, error_code, message) -> None:
if content.endswith(".py"):
source = TOOLS_ROOT / content
with open(source, "r") as f:
code = f.read()
else:
code = content
source = None
with pytest.raises(error_code) as ex:
generate_python_meta("some_tool", code, source)
assert message == str(ex.value)
@pytest.mark.parametrize(
"content, error_code, message",
[
pytest.param(
"{% zzz",
JinjaParsingError,
"Generate tool meta failed for llm tool. Jinja parsing failed at line 1: "
"(TemplateSyntaxError) Encountered unknown tag 'zzz'.",
id="JinjaParsingError_Code",
),
pytest.param(
"no_end.jinja2",
JinjaParsingError,
"Generate tool meta failed for llm tool. Jinja parsing failed at line 2: "
"(TemplateSyntaxError) Unexpected end of template. Jinja was looking for the following tags: "
"'endfor' or 'else'. The innermost block that needs to be closed is 'for'.",
id="JinjaParsingError_File",
),
],
)
def test_custom_llm_meta(self, content, error_code, message) -> None:
if content.endswith(".jinja2"):
with open(TOOLS_ROOT / content, "r") as f:
code = f.read()
else:
code = content
with pytest.raises(error_code) as ex:
generate_prompt_meta("some_tool", code)
assert message == str(ex.value)
@pytest.mark.parametrize(
"content, error_code, message",
[
pytest.param(
"{% zzz",
JinjaParsingError,
"Generate tool meta failed for prompt tool. Jinja parsing failed at line 1: "
"(TemplateSyntaxError) Encountered unknown tag 'zzz'.",
id="JinjaParsingError_Code",
),
pytest.param(
"no_end.jinja2",
JinjaParsingError,
"Generate tool meta failed for prompt tool. Jinja parsing failed at line 2: "
"(TemplateSyntaxError) Unexpected end of template. Jinja was looking for the following tags: "
"'endfor' or 'else'. The innermost block that needs to be closed is 'for'.",
id="JinjaParsingError_File",
),
],
)
def test_custom_prompt_meta(self, content, error_code, message) -> None:
if content.endswith(".jinja2"):
with open(TOOLS_ROOT / content, "r") as f:
code = f.read()
else:
code = content
with pytest.raises(error_code) as ex:
generate_prompt_meta("some_tool", code, prompt_only=True)
assert message == str(ex.value)
@pytest.mark.unittest
class TestPythonLoadError:
def test_additional_info(self):
source = TOOLS_ROOT / "load_error.py"
with open(source, "r") as f:
code = f.read()
with pytest.raises(PythonLoadError) as ex:
generate_python_meta("some_tool", code, source)
additional_info = ExceptionPresenter.create(ex.value).to_dict().get("additionalInfo")
assert len(additional_info) == 1
info_0 = additional_info[0]
assert info_0["type"] == "UserCodeStackTrace"
info_0_value = info_0["info"]
assert info_0_value.get("type") == "ZeroDivisionError"
assert info_0_value.get("message") == "division by zero"
assert re.match(r".*load_error.py", info_0_value["filename"])
assert info_0_value.get("lineno") == 3
assert info_0_value.get("name") == "<module>"
assert re.search(
r"Traceback \(most recent call last\):\n"
r' File ".*load_error.py", line .*, in <module>\n'
r" 1 / 0\n"
r"(.*\n)?" # Python >= 3.11 add extra line here like a pointer.
r"ZeroDivisionError: division by zero\n",
info_0_value.get("traceback"),
)
def test_additional_info_for_empty_inner_error(self):
ex = PythonLoadError(message_format="Test empty error")
additional_info = ExceptionPresenter.create(ex).to_dict().get("additionalInfo")
assert additional_info is None
| promptflow/src/promptflow/tests/executor/unittests/_utils/test_generate_tool_meta_utils.py/0 | {
"file_path": "promptflow/src/promptflow/tests/executor/unittests/_utils/test_generate_tool_meta_utils.py",
"repo_id": "promptflow",
"token_count": 5664
} | 48 |
from datetime import datetime
import pytest
from promptflow.contracts.run_info import FlowRunInfo, RunInfo, Status
@pytest.mark.unittest
class TestStatus:
@pytest.mark.parametrize(
"status,expected",
[
(Status.Completed, True),
(Status.Failed, True),
(Status.Bypassed, True),
(Status.Canceled, True),
(Status.Running, False),
(Status.Preparing, False),
(Status.NotStarted, False),
(Status.CancelRequested, False),
(123, False),
],
)
def test_status_is_terminated(self, status, expected):
assert Status.is_terminated(status) == expected
@pytest.mark.unittest
class TestRunInfo:
def test_creation(self):
run_info = RunInfo(
node="node1",
flow_run_id="123",
run_id="123:456",
status=Status.Running,
inputs=[],
output={},
metrics={},
error={},
parent_run_id="789",
start_time=datetime.now(),
end_time=datetime.now(),
system_metrics={},
)
assert run_info.node == "node1"
assert run_info.flow_run_id == "123"
assert run_info.run_id == "123:456"
assert run_info.status == Status.Running
def test_deserialize(self):
run_info_dict = {
"node": "get_answer",
"flow_run_id": "",
"run_id": "dummy_run_id",
"status": "Completed",
"inputs": {"question": "string"},
"output": "Hello world: What's promptflow?",
"metrics": None,
"error": None,
"parent_run_id": "dummy_flow_run_id",
"start_time": "2023-11-24T06:03:20.2688262Z",
"end_time": "2023-11-24T06:03:20.268858Z",
"index": 0,
"api_calls": None,
"variant_id": "",
"cached_run_id": None,
"cached_flow_run_id": None,
"logs": None,
"system_metrics": {"duration": "00:00:00.0000318", "total_tokens": 0},
"result": "Hello world: What's promptflow?",
}
run_info = RunInfo.deserialize(run_info_dict)
assert run_info.index == 0
assert isinstance(run_info.start_time, datetime) and isinstance(run_info.end_time, datetime)
assert run_info.status == Status.Completed
assert run_info.run_id == "dummy_run_id"
assert run_info.api_calls is None
assert run_info.system_metrics == {"duration": "00:00:00.0000318", "total_tokens": 0}
assert run_info.output == "Hello world: What's promptflow?"
@pytest.mark.unittest
class TestFlowRunInfo:
def test_creation(self):
flow_run_info = FlowRunInfo(
run_id="123:456",
status=Status.Running,
error={},
inputs={},
output={},
metrics={},
request={},
parent_run_id="789",
root_run_id="123",
source_run_id="456",
flow_id="flow1",
start_time=datetime.now(),
end_time=datetime.now(),
system_metrics={},
upload_metrics=False,
)
assert flow_run_info.run_id == "123:456"
assert flow_run_info.status == Status.Running
assert flow_run_info.flow_id == "flow1"
def test_deserialize(self):
flow_run_info_dict = {
"run_id": "dummy_run_id",
"status": "Completed",
"error": None,
"inputs": {"question": "What's promptflow?"},
"output": {"answer": "Hello world: What's promptflow?"},
"metrics": None,
"request": None,
"parent_run_id": None,
"root_run_id": None,
"source_run_id": None,
"flow_id": "Flow",
"start_time": "2023-11-23T10:58:37.9436245Z",
"end_time": "2023-11-23T10:58:37.9590789Z",
"index": 0,
"api_calls": None,
"variant_id": "",
"name": "",
"description": "",
"tags": None,
"system_metrics": {"duration": "00:00:00.0154544", "total_tokens": 0},
"result": {"answer": "Hello world: What's promptflow?"},
"upload_metrics": False,
}
flow_run_info = FlowRunInfo.deserialize(flow_run_info_dict)
assert flow_run_info.index == 0
assert isinstance(flow_run_info.start_time, datetime) and isinstance(flow_run_info.end_time, datetime)
assert flow_run_info.status == Status.Completed
assert flow_run_info.run_id == "dummy_run_id"
assert flow_run_info.api_calls is None
assert flow_run_info.system_metrics == {"duration": "00:00:00.0154544", "total_tokens": 0}
assert flow_run_info.output == {"answer": "Hello world: What's promptflow?"}
| promptflow/src/promptflow/tests/executor/unittests/contracts/test_run_info.py/0 | {
"file_path": "promptflow/src/promptflow/tests/executor/unittests/contracts/test_run_info.py",
"repo_id": "promptflow",
"token_count": 2527
} | 49 |
import re
from pathlib import Path
import pydash
import pytest
from promptflow._utils.yaml_utils import dump_yaml, load_yaml_string
from promptflow.connections import AzureOpenAIConnection
from .._azure_utils import DEFAULT_TEST_TIMEOUT, PYTEST_TIMEOUT_METHOD
from ..recording_utilities import is_live
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()
def assert_dict_equals_with_skip_fields(item1, item2, skip_fields):
for fot_key in skip_fields:
pydash.set_(item1, fot_key, None)
pydash.set_(item2, fot_key, None)
assert item1 == item2
def normalize_arm_id(origin_value: str):
if origin_value:
m = re.match(
r"(.*)/subscriptions/[a-z0-9\-]+/resourceGroups/[a-z0-9\-]+/providers/"
r"Microsoft.MachineLearningServices/workspaces/[a-z0-9\-]+/([a-z]+)/[^/]+/versions/([a-z0-9\-]+)",
origin_value,
)
if m:
prefix, asset_type, _ = m.groups()
return (
f"{prefix}/subscriptions/xxx/resourceGroups/xxx/providers/"
f"Microsoft.MachineLearningServices/workspaces/xxx/{asset_type}/xxx/versions/xxx"
)
return None
def update_saved_spec(component, saved_spec_path: str):
yaml_text = component._to_yaml()
saved_spec_path = Path(saved_spec_path)
yaml_content = load_yaml_string(yaml_text)
if yaml_content.get("creation_context"):
for key in yaml_content.get("creation_context"):
yaml_content["creation_context"][key] = "xxx"
for key in ["task.code", "task.environment", "id"]:
target_value = normalize_arm_id(pydash.get(yaml_content, key))
if target_value:
pydash.set_(yaml_content, key, target_value)
yaml_text = dump_yaml(yaml_content)
if saved_spec_path.is_file():
current_spec_text = saved_spec_path.read_text()
if current_spec_text == yaml_text:
return
saved_spec_path.parent.mkdir(parents=True, exist_ok=True)
saved_spec_path.write_text(yaml_text)
@pytest.mark.skipif(
condition=not is_live(),
reason="flow in pipeline tests require secrets config file, only run in live mode.",
)
@pytest.mark.usefixtures("use_secrets_config_file")
@pytest.mark.timeout(timeout=DEFAULT_TEST_TIMEOUT, method=PYTEST_TIMEOUT_METHOD)
@pytest.mark.e2etest
class TestFlowInAzureML:
@pytest.mark.parametrize(
"load_params, expected_spec_attrs",
[
pytest.param(
{
"name": "web_classification_4",
"version": "1.0.0",
"description": "Create flows that use large language models to "
"classify URLs into multiple categories.",
"environment_variables": {
"verbose": "true",
},
},
{
"name": "web_classification_4",
"version": "1.0.0",
"description": "Create flows that use large language models to "
"classify URLs into multiple categories.",
"type": "parallel",
},
id="parallel",
),
],
)
def test_flow_as_component(
self,
azure_open_ai_connection: AzureOpenAIConnection,
temp_output_dir,
ml_client,
load_params: dict,
expected_spec_attrs: dict,
request,
) -> None:
# keep the simplest test here, more tests are in azure-ai-ml
from azure.ai.ml import load_component
flows_dir = "./tests/test_configs/flows"
flow_func: Component = load_component(
f"{flows_dir}/web_classification/flow.dag.yaml", params_override=[load_params]
)
# TODO: snapshot of flow component changed every time?
created_component = ml_client.components.create_or_update(flow_func, is_anonymous=True)
update_saved_spec(
created_component, f"./tests/test_configs/flows/saved_component_spec/{request.node.callspec.id}.yaml"
)
component_dict = created_component._to_dict()
slimmed_created_component_attrs = {key: pydash.get(component_dict, key) for key in expected_spec_attrs.keys()}
assert slimmed_created_component_attrs == expected_spec_attrs
| promptflow/src/promptflow/tests/sdk_cli_azure_test/e2etests/test_flow_in_azure_ml.py/0 | {
"file_path": "promptflow/src/promptflow/tests/sdk_cli_azure_test/e2etests/test_flow_in_azure_ml.py",
"repo_id": "promptflow",
"token_count": 2108
} | 50 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import shutil
import tempfile
import uuid
from pathlib import Path
import pytest
from mock.mock import Mock
from promptflow._sdk._load_functions import load_run
from promptflow._sdk._vendor import get_upload_files_from_folder
from promptflow._utils.flow_utils import load_flow_dag
from promptflow.azure._constants._flow import ENVIRONMENT, PYTHON_REQUIREMENTS_TXT
from promptflow.azure._entities._flow import Flow
tests_root_dir = Path(__file__).parent.parent.parent
FLOWS_DIR = (tests_root_dir / "test_configs/flows").resolve()
RUNS_DIR = (tests_root_dir / "test_configs/runs").resolve()
def load_flow(source):
from promptflow.azure._load_functions import load_flow
return load_flow(source=source)
@pytest.mark.unittest
class TestFlow:
@pytest.mark.skip(reason="TODO: add back when we bring back meta.yaml")
def test_load_flow(self):
local_file = tests_root_dir / "test_configs/flows/meta_files/flow.meta.yaml"
flow = load_flow(source=local_file)
assert flow._to_dict() == {
"name": "web_classificiation_flow_3",
"description": "Create flows that use large language models to classify URLs into multiple categories.",
"display_name": "Web Classification",
"type": "default",
"path": "./flow.dag.yaml",
}
rest_dict = flow._to_rest_object().as_dict()
assert rest_dict == {
"description": "Create flows that use large language models to classify URLs into multiple categories.",
"flow_name": "Web Classification",
"flow_run_settings": {},
"flow_type": "default",
"is_archived": True,
"flow_definition_file_path": "./flow.dag.yaml",
}
@pytest.mark.skip(reason="TODO: add back when we bring back meta.yaml")
def test_load_flow_from_remote_storage(self):
from promptflow.azure.operations._flow_operations import FlowOperations
local_file = tests_root_dir / "test_configs/flows/meta_files/remote_fs.meta.yaml"
flow = load_flow(source=local_file)
assert flow._to_dict() == {
"name": "classification_accuracy_eval",
"path": "azureml://datastores/workspaceworkingdirectory/paths/Users/wanhan/my_flow_snapshot/flow.dag.yaml",
"type": "evaluation",
}
FlowOperations._try_resolve_code_for_flow(flow, Mock())
rest_dict = flow._to_rest_object().as_dict()
assert rest_dict == {
"flow_definition_file_path": "Users/wanhan/my_flow_snapshot/flow.dag.yaml",
"flow_run_settings": {},
"flow_type": "evaluation",
"is_archived": True,
}
def test_ignore_files_in_flow(self):
local_file = tests_root_dir / "test_configs/flows/web_classification"
with tempfile.TemporaryDirectory() as temp:
flow_path = Path(temp) / "flow"
shutil.copytree(local_file, flow_path)
assert (Path(temp) / "flow/.promptflow/flow.tools.json").exists()
(Path(flow_path) / ".runs").mkdir(parents=True)
(Path(flow_path) / ".runs" / "mock.file").touch()
flow = load_flow(source=flow_path)
with flow._build_code() as code:
assert code is not None
upload_paths = get_upload_files_from_folder(
path=code.path,
ignore_file=code._ignore_file,
)
flow_files = list(sorted([item[1] for item in upload_paths]))
# assert that .runs/mock.file are ignored
assert ".runs/mock.file" not in flow_files
# Web classification may be executed and include flow.detail.json, flow.logs, flow.outputs.json
assert all(
file in flow_files
for file in [
".promptflow/flow.tools.json",
"classify_with_llm.jinja2",
"convert_to_dict.py",
"fetch_text_content_from_url.py",
"fetch_text_content_from_url_input.jsonl",
"flow.dag.yaml",
"prepare_examples.py",
"samples.json",
"summarize_text_content.jinja2",
"summarize_text_content__variant_1.jinja2",
"webClassification20.csv",
]
)
def test_load_yaml_run_with_resources(self):
source = f"{RUNS_DIR}/sample_bulk_run_with_resources.yaml"
run = load_run(source=source, params_override=[{"name": str(uuid.uuid4())}])
assert run._resources["instance_type"] == "Standard_D2"
assert run._resources["idle_time_before_shutdown_minutes"] == 60
def test_flow_with_additional_includes(self):
flow_folder = FLOWS_DIR / "web_classification_with_additional_include"
flow = load_flow(source=flow_folder)
with flow._build_code() as code:
assert code is not None
_, temp_flow = load_flow_dag(code.path)
assert "additional_includes" not in temp_flow
upload_paths = get_upload_files_from_folder(
path=code.path,
ignore_file=code._ignore_file,
)
flow_files = list(sorted([item[1] for item in upload_paths]))
target_additional_includes = [
"convert_to_dict.py",
"fetch_text_content_from_url.py",
"summarize_text_content.jinja2",
"external_files/convert_to_dict.py",
"external_files/fetch_text_content_from_url.py",
"external_files/summarize_text_content.jinja2",
]
# assert all additional includes are included
for file in target_additional_includes:
assert file in flow_files
def test_flow_with_ignore_file(self):
flow_folder = FLOWS_DIR / "flow_with_ignore_file"
flow = load_flow(source=flow_folder)
with flow._build_code() as code:
assert code is not None
upload_paths = get_upload_files_from_folder(
path=code.path,
ignore_file=code._ignore_file,
)
flow_files = list(sorted([item[1] for item in upload_paths]))
assert len(flow_files) > 0
target_ignored_files = ["ignored_folder/1.txt", "random.ignored"]
# assert all ignored files are ignored
for file in target_ignored_files:
assert file not in flow_files
def test_resolve_requirements(self):
flow_dag = {}
# Test when requirements.txt does not exist
assert not Flow._resolve_requirements(flow_path=FLOWS_DIR / "flow_with_ignore_file", flow_dag=flow_dag)
# Test when requirements.txt exists but already added to flow_dag
flow_dag[ENVIRONMENT] = {PYTHON_REQUIREMENTS_TXT: "another_requirements.txt"}
assert not Flow._resolve_requirements(flow_path=FLOWS_DIR / "flow_with_requirements_txt", flow_dag=flow_dag)
# Test when requirements.txt exists and not added to flow_dag
flow_dag = {}
assert Flow._resolve_requirements(flow_path=FLOWS_DIR / "flow_with_requirements_txt", flow_dag=flow_dag)
def test_resolve_requirements_for_flow(self):
with tempfile.TemporaryDirectory() as temp:
temp = Path(temp)
# flow without environment section
flow_folder = FLOWS_DIR / "flow_with_requirements_txt"
shutil.copytree(flow_folder, temp / "flow_with_requirements_txt")
flow_folder = temp / "flow_with_requirements_txt"
flow = load_flow(source=flow_folder)
with flow._build_code():
_, flow_dag = load_flow_dag(flow_path=flow_folder)
assert flow_dag[ENVIRONMENT] == {"python_requirements_txt": "requirements.txt"}
_, flow_dag = load_flow_dag(flow_path=flow_folder)
assert ENVIRONMENT not in flow_dag
# flow with environment section
flow_folder = FLOWS_DIR / "flow_with_requirements_txt_and_env"
shutil.copytree(flow_folder, temp / "flow_with_requirements_txt_and_env")
flow_folder = temp / "flow_with_requirements_txt_and_env"
flow = load_flow(source=flow_folder)
with flow._build_code():
_, flow_dag = load_flow_dag(flow_path=flow_folder)
assert flow_dag[ENVIRONMENT] == {
"image": "python:3.8-slim",
"python_requirements_txt": "requirements.txt",
}
_, flow_dag = load_flow_dag(flow_path=flow_folder)
assert flow_dag[ENVIRONMENT] == {"image": "python:3.8-slim"}
| promptflow/src/promptflow/tests/sdk_cli_azure_test/unittests/test_flow_entity.py/0 | {
"file_path": "promptflow/src/promptflow/tests/sdk_cli_azure_test/unittests/test_flow_entity.py",
"repo_id": "promptflow",
"token_count": 4148
} | 51 |
import contextlib
import io
import multiprocessing
import os
import sys
import tempfile
import timeit
import uuid
from pathlib import Path
from unittest import mock
import pytest
from promptflow._cli._user_agent import USER_AGENT as CLI_USER_AGENT # noqa: E402
from promptflow._sdk._telemetry import log_activity
from promptflow._sdk._utils import ClientUserAgentUtil
FLOWS_DIR = "./tests/test_configs/flows"
CONNECTIONS_DIR = "./tests/test_configs/connections"
DATAS_DIR = "./tests/test_configs/datas"
def mock_log_activity(*args, **kwargs):
custom_message = "github run: https://github.com/microsoft/promptflow/actions/runs/{0}".format(
os.environ.get("GITHUB_RUN_ID")
)
if len(args) == 4:
if args[3] is not None:
args[3]["custom_message"] = custom_message
else:
args = list(args)
args[3] = {"custom_message": custom_message}
elif "custom_dimensions" in kwargs and kwargs["custom_dimensions"] is not None:
kwargs["custom_dimensions"]["custom_message"] = custom_message
else:
kwargs["custom_dimensions"] = {"custom_message": custom_message}
return log_activity(*args, **kwargs)
def run_cli_command(cmd, time_limit=3600, result_queue=None):
from promptflow._cli._pf.entry import main
sys.argv = list(cmd)
output = io.StringIO()
st = timeit.default_timer()
with contextlib.redirect_stdout(output), mock.patch.object(
ClientUserAgentUtil, "get_user_agent"
) as get_user_agent_fun, mock.patch(
"promptflow._sdk._telemetry.activity.log_activity", side_effect=mock_log_activity
), mock.patch(
"promptflow._cli._pf.entry.log_activity", side_effect=mock_log_activity
):
# Client side will modify user agent only through ClientUserAgentUtil to avoid impact executor/runtime.
get_user_agent_fun.return_value = f"{CLI_USER_AGENT} perf_monitor/1.0"
user_agent = ClientUserAgentUtil.get_user_agent()
assert user_agent == f"{CLI_USER_AGENT} perf_monitor/1.0"
main()
ed = timeit.default_timer()
print(f"{cmd}, \n Total time: {ed - st}s")
assert ed - st < time_limit, f"The time limit is {time_limit}s, but it took {ed - st}s."
res_value = output.getvalue()
if result_queue:
result_queue.put(res_value)
return res_value
def subprocess_run_cli_command(cmd, time_limit=3600):
result_queue = multiprocessing.Queue()
process = multiprocessing.Process(
target=run_cli_command, args=(cmd,), kwargs={"time_limit": time_limit, "result_queue": result_queue}
)
process.start()
process.join()
assert process.exitcode == 0
return result_queue.get_nowait()
@pytest.mark.usefixtures("use_secrets_config_file", "setup_local_connection")
@pytest.mark.perf_monitor_test
class TestCliPerf:
def test_pf_run_create(self, time_limit=20) -> None:
res = subprocess_run_cli_command(
cmd=(
"pf",
"run",
"create",
"--flow",
f"{FLOWS_DIR}/print_input_flow",
"--data",
f"{DATAS_DIR}/print_input_flow.jsonl",
),
time_limit=time_limit,
)
assert "Completed" in res
def test_pf_run_update(self, time_limit=10) -> None:
run_name = str(uuid.uuid4())
run_cli_command(
cmd=(
"pf",
"run",
"create",
"--flow",
f"{FLOWS_DIR}/print_input_flow",
"--data",
f"{DATAS_DIR}/print_input_flow.jsonl",
"--name",
run_name,
)
)
res = subprocess_run_cli_command(
cmd=("pf", "run", "update", "--name", run_name, "--set", "description=test pf run update"),
time_limit=time_limit,
)
assert "Completed" in res
def test_pf_flow_test(self, time_limit=10):
subprocess_run_cli_command(
cmd=(
"pf",
"flow",
"test",
"--flow",
f"{FLOWS_DIR}/print_input_flow",
"--inputs",
"text=https://www.youtube.com/watch?v=o5ZQyXaAv1g",
),
time_limit=time_limit,
)
output_path = Path(FLOWS_DIR) / "print_input_flow" / ".promptflow" / "flow.output.json"
assert output_path.exists()
def test_pf_flow_build(self, time_limit=20):
with tempfile.TemporaryDirectory() as temp_dir:
subprocess_run_cli_command(
cmd=(
"pf",
"flow",
"build",
"--source",
f"{FLOWS_DIR}/print_input_flow/flow.dag.yaml",
"--output",
temp_dir,
"--format",
"docker",
),
time_limit=time_limit,
)
def test_pf_connection_create(self, time_limit=10):
name = f"Connection_{str(uuid.uuid4())[:4]}"
res = subprocess_run_cli_command(
cmd=(
"pf",
"connection",
"create",
"--file",
f"{CONNECTIONS_DIR}/azure_openai_connection.yaml",
"--name",
f"{name}",
),
time_limit=time_limit,
)
assert "api_type" in res
def test_pf_connection_list(self, time_limit=10):
name = "connection_list"
res = run_cli_command(
cmd=(
"pf",
"connection",
"create",
"--file",
f"{CONNECTIONS_DIR}/azure_openai_connection.yaml",
"--name",
f"{name}",
)
)
assert "api_type" in res
res = subprocess_run_cli_command(cmd=("pf", "connection", "list"), time_limit=time_limit)
assert "api_type" in res
| promptflow/src/promptflow/tests/sdk_cli_test/e2etests/test_cli_perf.py/0 | {
"file_path": "promptflow/src/promptflow/tests/sdk_cli_test/e2etests/test_cli_perf.py",
"repo_id": "promptflow",
"token_count": 3111
} | 52 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import hashlib
import json
import os
import shelve
from pathlib import Path
from typing import Dict
from filelock import FileLock
from promptflow.exceptions import PromptflowException
from .constants import ENVIRON_TEST_MODE, RecordMode
class RecordItemMissingException(PromptflowException):
"""Exception raised when record item missing."""
pass
class RecordFileMissingException(PromptflowException):
"""Exception raised when record file missing or invalid."""
pass
class RecordStorage(object):
"""
RecordStorage is used to store the record of node run.
File often stored in .promptflow/node_cache.shelve
Currently only text input/output could be recorded.
Example of cached items:
{
"/record/file/resolved": {
"hash_value": { # hash_value is sha1 of dict, accelerate the search
"input": {
"key1": "value1", # Converted to string, type info dropped
},
"output": "output_convert_to_string",
"output_type": "output_type" # Currently support only simple strings.
}
}
}
"""
_standard_record_folder = ".promptflow"
_standard_record_name = "node_cache.shelve"
_instance = None
def __init__(self, record_file: str = None):
"""
RecordStorage is used to store the record of node run.
"""
self._record_file: Path = None
self.cached_items: Dict[str, Dict[str, Dict[str, object]]] = {}
self.record_file = record_file
@property
def record_file(self) -> Path:
return self._record_file
@record_file.setter
def record_file(self, record_file_input) -> None:
"""
Will load record_file if exist.
"""
if record_file_input == self._record_file:
return
if isinstance(record_file_input, str):
self._record_file = Path(record_file_input).resolve()
elif isinstance(record_file_input, Path):
self._record_file = record_file_input.resolve()
else:
return
if not self._record_file.parts[-1].endswith(RecordStorage._standard_record_name):
record_folder = self._record_file / RecordStorage._standard_record_folder
self._record_file = record_folder / RecordStorage._standard_record_name
else:
record_folder = self._record_file.parent
self._record_file_str = str(self._record_file.resolve())
# cache folder we could create if not exist.
if not record_folder.exists():
record_folder.mkdir(parents=True, exist_ok=True)
# if file exist, load file
if self.exists_record_file(record_folder, self._record_file.parts[-1]):
self._load_file()
else:
self.cached_items = {
self._record_file_str: {},
}
def exists_record_file(self, record_folder, file_name) -> bool:
files = os.listdir(record_folder)
for file in files:
if file.startswith(file_name):
return True
return False
def _write_file(self, hashkey) -> None:
file_content = self.cached_items.get(self._record_file_str, None)
if file_content is not None:
file_content_line = file_content.get(hashkey, None)
if file_content_line is not None:
lock = FileLock(self.record_file.parent / "record_file.lock")
with lock:
saved_dict = shelve.open(self._record_file_str, "c", writeback=False)
saved_dict[hashkey] = file_content_line
saved_dict.close()
else:
raise RecordItemMissingException(f"Record item not found in cache with hashkey {hashkey}.")
else:
raise RecordFileMissingException(
f"This exception should not happen here, but record file is not found {self._record_file_str}."
)
def _load_file(self) -> None:
local_content = self.cached_items.get(self._record_file_str, None)
if not local_content:
if RecordStorage.is_recording_mode():
lock = FileLock(self.record_file.parent / "record_file.lock")
with lock:
if not self.exists_record_file(self.record_file.parent, self.record_file.parts[-1]):
return
self.cached_items[self._record_file_str] = {}
saved_dict = shelve.open(self._record_file_str, "r", writeback=False)
for key, value in saved_dict.items():
self.cached_items[self._record_file_str][key] = value
saved_dict.close()
else:
if not self.exists_record_file(self.record_file.parent, self.record_file.parts[-1]):
return
self.cached_items[self._record_file_str] = {}
saved_dict = shelve.open(self._record_file_str, "r", writeback=False)
for key, value in saved_dict.items():
self.cached_items[self._record_file_str][key] = value
saved_dict.close()
def delete_lock_file(self):
lock_file = self.record_file.parent / "record_file.lock"
if lock_file.exists():
os.remove(lock_file)
def get_record(self, input_dict: Dict) -> object:
"""
Get record from local storage.
:param input_dict: input dict of critical AOAI inputs
:type input_dict: Dict
:raises RecordFileMissingException: Record file not exist
:raises RecordItemMissingException: Record item not exist in record file
:return: original output of node run
:rtype: object
"""
input_dict = self._recursive_create_hashable_args(input_dict)
hash_value: str = hashlib.sha1(str(sorted(input_dict.items())).encode("utf-8")).hexdigest()
current_saved_records: Dict[str, str] = self.cached_items.get(self._record_file_str, None)
if current_saved_records is None:
raise RecordFileMissingException(f"Record file not found {self.record_file}.")
saved_output = current_saved_records.get(hash_value, None)
if saved_output is None:
raise RecordItemMissingException(
f"Record item not found in file {self.record_file}.\n" f"values: {json.dumps(input_dict)}\n"
)
# not all items are reserved in the output dict.
output = saved_output["output"]
output_type = saved_output["output_type"]
if "generator" in output_type:
return self._create_output_generator(output, output_type)
else:
return output
def _recursive_create_hashable_args(self, item):
if isinstance(item, tuple):
return [self._recursive_create_hashable_args(i) for i in item]
if isinstance(item, list):
return [self._recursive_create_hashable_args(i) for i in item]
if isinstance(item, dict):
return {k: self._recursive_create_hashable_args(v) for k, v in item.items()}
elif "module: promptflow.connections" in str(item) or "object at" in str(item):
return []
else:
return item
def _parse_output_generator(self, output):
"""
Special handling for generator type. Since pickle will not work for generator.
Returns the real list for reocrding, and create a generator for original output.
Parse output has a simplified hypothesis: output is simple dict, list or generator,
because a full schema of output is too heavy to handle.
Example: {"answer": <generator>, "a": "b"}, <generator>
"""
output_type = ""
output_value = None
output_generator = None
if isinstance(output, dict):
output_value = {}
output_generator = {}
for item in output.items():
k, v = item
if type(v).__name__ == "generator":
vlist = list(v)
def vgenerator():
for vitem in vlist:
yield vitem
output_value[k] = vlist
output_generator[k] = vgenerator()
output_type = "dict[generator]"
else:
output_value[k] = v
elif type(output).__name__ == "generator":
output_value = list(output)
def generator():
for item in output_value:
yield item
output_generator = generator()
output_type = "generator"
else:
output_value = output
output_generator = None
output_type = type(output).__name__
return output_value, output_generator, output_type
def _create_output_generator(self, output, output_type):
"""
Special handling for generator type.
Returns a generator for original output.
Create output has a simplified hypothesis:
All list with output type generator is treated as generator.
"""
output_generator = None
if output_type == "dict[generator]":
output_generator = {}
for k, v in output.items():
if type(v).__name__ == "list":
def vgenerator():
for item in v:
yield item
output_generator[k] = vgenerator()
else:
output_generator[k] = v
elif output_type == "generator":
def generator():
for item in output:
yield item
output_generator = generator()
return output_generator
def set_record(self, input_dict: Dict, output):
"""
Set record to local storage, always override the old record.
:param input_dict: input dict of critical AOAI inputs
:type input_dict: OrderedDict
:param output: original output of node run
:type output: object
"""
# filter args, object at will not hash
input_dict = self._recursive_create_hashable_args(input_dict)
hash_value: str = hashlib.sha1(str(sorted(input_dict.items())).encode("utf-8")).hexdigest()
current_saved_records: Dict[str, str] = self.cached_items.get(self._record_file_str, None)
output_value, output_generator, output_type = self._parse_output_generator(output)
if current_saved_records is None:
current_saved_records = {}
current_saved_records[hash_value] = {
"input": input_dict,
"output": output_value,
"output_type": output_type,
}
else:
saved_output = current_saved_records.get(hash_value, None)
if saved_output is not None:
if saved_output["output"] == output_value and saved_output["output_type"] == output_type:
if "generator" in output_type:
return output_generator
else:
return output_value
else:
current_saved_records[hash_value] = {
"input": input_dict,
"output": output_value,
"output_type": output_type,
}
else:
current_saved_records[hash_value] = {
"input": input_dict,
"output": output_value,
"output_type": output_type,
}
self.cached_items[self._record_file_str] = current_saved_records
self._write_file(hash_value)
if "generator" in output_type:
return output_generator
else:
return output_value
@classmethod
def get_test_mode_from_environ(cls) -> str:
return os.getenv(ENVIRON_TEST_MODE, RecordMode.LIVE)
@classmethod
def is_recording_mode(cls) -> bool:
return RecordStorage.get_test_mode_from_environ() == RecordMode.RECORD
@classmethod
def is_replaying_mode(cls) -> bool:
return RecordStorage.get_test_mode_from_environ() == RecordMode.REPLAY
@classmethod
def is_live_mode(cls) -> bool:
return RecordStorage.get_test_mode_from_environ() == RecordMode.LIVE
@classmethod
def get_instance(cls, record_file=None) -> "RecordStorage":
"""
Use this to get instance to avoid multiple copies of same record storage.
:param record_file: initiate at first entrance, defaults to None in the first call will raise exception.
:type record_file: str or Path, optional
:return: instance of RecordStorage
:rtype: RecordStorage
"""
# if not in recording mode, return None
if not (RecordStorage.is_recording_mode() or RecordStorage.is_replaying_mode()):
return None
# Create instance if not exist
if cls._instance is None:
if record_file is None:
raise RecordFileMissingException("record_file is value None")
cls._instance = RecordStorage(record_file)
if record_file is not None:
cls._instance.record_file = record_file
return cls._instance
| promptflow/src/promptflow/tests/sdk_cli_test/recording_utilities/record_storage.py/0 | {
"file_path": "promptflow/src/promptflow/tests/sdk_cli_test/recording_utilities/record_storage.py",
"repo_id": "promptflow",
"token_count": 6211
} | 53 |
[run]
source =
*/promptflow/_sdk/_service/*
omit =
*/promptflow/_cli/*
*/promptflow/azure/*
*/promptflow/entities/*
*/promptflow/operations/*
*__init__.py*
| promptflow/src/promptflow/tests/sdk_pfs_test/.coveragerc/0 | {
"file_path": "promptflow/src/promptflow/tests/sdk_pfs_test/.coveragerc",
"repo_id": "promptflow",
"token_count": 83
} | 54 |
$schema: https://azuremlschemas.azureedge.net/promptflow/latest/CognitiveSearchConnection.schema.json
name: my_cognitive_search_connection
type: cognitive_search # snake case
api_key: "<to-be-replaced>"
api_base: "endpoint"
api_version: "2023-07-01-Preview"
| promptflow/src/promptflow/tests/test_configs/connections/cognitive_search_connection.yaml/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/connections/cognitive_search_connection.yaml",
"repo_id": "promptflow",
"token_count": 93
} | 55 |
{"image": {"data:image/png;path":"logo_1.png"}}
{"image": {"data:image/png;path":"logo_2.png"}} | promptflow/src/promptflow/tests/test_configs/datas/image_inputs/inputs.jsonl/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/datas/image_inputs/inputs.jsonl",
"repo_id": "promptflow",
"token_count": 41
} | 56 |
{"text": "https://www.youtube.com/watch?v=o5ZQyXaAv1g"}
| promptflow/src/promptflow/tests/test_configs/datas/print_input_flow.jsonl/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/datas/print_input_flow.jsonl",
"repo_id": "promptflow",
"token_count": 28
} | 57 |
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
def my_flow(input_val: str):
"""Simple flow without yaml."""
print(f"Hello world! {input_val}")
| promptflow/src/promptflow/tests/test_configs/eager_flows/simple_without_yaml/entry.py/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/eager_flows/simple_without_yaml/entry.py",
"repo_id": "promptflow",
"token_count": 65
} | 58 |
from typing import List
from promptflow import tool
@tool
def aggregate(processed_results: List[str]):
aggregated_results = processed_results
# raise error to test aggregation node failed
num = 1/0
return aggregated_results
| promptflow/src/promptflow/tests/test_configs/flows/aggregation_node_failed/aggregate.py/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/aggregation_node_failed/aggregate.py",
"repo_id": "promptflow",
"token_count": 71
} | 59 |
version: 2
inputs:
assistant_input:
type: list
default:
- type: text
text: The provided file contains end-of-day (EOD) stock prices for companies A
and B across various dates in March. However, it does not include the
EOD stock prices for Company C.
- type: file_path
file_path:
path: ./stock_price.csv
- type: text
text: Please draw a line chart with the stock price of the company A, B and C
and return a CVS file with the data.
assistant_id:
type: string
default: asst_eHO2rwEYqGH3pzzHHov2kBCG
thread_id:
type: string
default: ""
outputs:
assistant_output:
type: string
reference: ${add_message_and_run.output}
thread_id:
type: string
reference: ${get_or_create_thread.output}
nodes:
- name: get_or_create_thread
type: python
source:
type: code
path: get_or_create_thread.py
inputs:
conn: chw_openai
thread_id: ${inputs.thread_id}
- name: add_message_and_run
type: python
source:
type: code
path: add_message_and_run.py
inputs:
conn: chw_openai
message: ${inputs.assistant_input}
assistant_id: ${inputs.assistant_id}
thread_id: ${get_or_create_thread.output}
assistant_definition: assistant_definition.yaml
download_images: true
| promptflow/src/promptflow/tests/test_configs/flows/assistant-with-file/flow.dag.yaml/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/assistant-with-file/flow.dag.yaml",
"repo_id": "promptflow",
"token_count": 517
} | 60 |
inputs:
chat_history:
type: list
question:
type: string
is_chat_input: true
default: What is ChatGPT?
outputs:
answer:
type: string
reference: ${chat_node.output}
is_chat_output: true
multi_answer:
type: string
reference: ${chat_node.output}
is_chat_output: true
nodes:
- inputs:
deployment_name: gpt-35-turbo
max_tokens: "256"
temperature: "0.7"
chat_history: ${inputs.chat_history}
question: ${inputs.question}
name: chat_node
type: llm
source:
type: code
path: chat.jinja2
api: chat
provider: AzureOpenAI
connection: azure_open_ai_connection | promptflow/src/promptflow/tests/test_configs/flows/chat_flow_with_multi_output_invalid/flow.dag.yaml/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/chat_flow_with_multi_output_invalid/flow.dag.yaml",
"repo_id": "promptflow",
"token_count": 260
} | 61 |
name: TestPythonToolLongWaitTime
inputs:
input1:
type: bool
input2:
type: bool
input3:
type: bool
input4:
type: bool
outputs:
output:
type: int
reference: ${wait_long_1.output}
nodes:
- name: wait_1
type: python
source:
type: code
path: wait_short.py
inputs:
throw_exception: ${inputs.input1}
- name: wait_2
type: python
source:
type: code
path: wait_short.py
inputs:
throw_exception: ${inputs.input2}
- name: wait_3
type: python
source:
type: code
path: wait_short.py
inputs:
throw_exception: ${inputs.input3}
- name: wait_4
type: python
source:
type: code
path: wait_short.py
inputs:
throw_exception: ${inputs.input4}
- name: wait_long_1
type: python
source:
type: code
path: wait_long.py
inputs:
text_1: ${wait_1.output}
text_2: ${wait_2.output}
text_3: ${wait_3.output}
text_4: ${wait_4.output}
| promptflow/src/promptflow/tests/test_configs/flows/concurrent_execution_flow/flow.dag.yaml/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/concurrent_execution_flow/flow.dag.yaml",
"repo_id": "promptflow",
"token_count": 427
} | 62 |
from promptflow import tool
@tool
def tsg_retriever(content: str) -> str:
return "TSG: " + content | promptflow/src/promptflow/tests/test_configs/flows/conditional_flow_with_activate/tsg_retriever.py/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/conditional_flow_with_activate/tsg_retriever.py",
"repo_id": "promptflow",
"token_count": 35
} | 63 |
inputs:
input_str:
type: string
default: input value from default
input_bool:
type: bool
default: False
input_list:
type: list
default: []
input_dict:
type: object
default: {}
outputs:
output:
type: string
reference: ${test_print_input.output}
nodes:
- name: test_print_input
type: python
source:
type: code
path: test_print_input.py
inputs:
input_str: ${inputs.input_str}
input_bool: ${inputs.input_bool}
input_list: ${inputs.input_list}
input_dict: ${inputs.input_dict}
- name: aggregate_node
type: python
source:
type: code
path: test_print_aggregation.py
inputs:
input_str: ${inputs.input_str}
input_bool: ${inputs.input_bool}
input_list: ${inputs.input_list}
input_dict: ${inputs.input_dict}
aggregation: true
use_variants: false
| promptflow/src/promptflow/tests/test_configs/flows/default_input/flow.dag.yaml/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/default_input/flow.dag.yaml",
"repo_id": "promptflow",
"token_count": 346
} | 64 |
#!/bin/bash
echo "$(date -uIns) - promptflow-serve/finish $@"
# stop all gunicorn processes
echo "$(date -uIns) - Stopping all Gunicorn processes"
pkill gunicorn
while pgrep gunicorn >/dev/null; do
echo "$(date -uIns) - Gunicorn process is still running, waiting for 1s"
sleep 1
done
echo "$(date -uIns) - Stopped all Gunicorn processes" | promptflow/src/promptflow/tests/test_configs/flows/export/linux/runit/promptflow-serve/finish/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/export/linux/runit/promptflow-serve/finish",
"repo_id": "promptflow",
"token_count": 123
} | 65 |
inputs:
key:
type: object
outputs:
output:
type: string
reference: ${get_dict_val.output.value}
nodes:
- name: get_dict_val
type: python
source:
type: code
path: get_dict_val.py
inputs:
key: ${inputs.key}
- name: print_val
type: python
source:
type: code
path: print_val.py
inputs:
val: ${get_dict_val.output.value}
origin_val: ${get_dict_val.output.origin_value}
| promptflow/src/promptflow/tests/test_configs/flows/flow_with_dict_input/flow.dag.yaml/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/flow_with_dict_input/flow.dag.yaml",
"repo_id": "promptflow",
"token_count": 181
} | 66 |
{"text": "Hello World!"}
| promptflow/src/promptflow/tests/test_configs/flows/flow_with_package_tool_with_custom_strong_type_connection/data.jsonl/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/flow_with_package_tool_with_custom_strong_type_connection/data.jsonl",
"repo_id": "promptflow",
"token_count": 9
} | 67 |
inputs: {}
outputs:
output:
type: string
reference: ${long_run_node.output}
nodes:
- name: long_run_node
type: python
inputs: {}
source:
type: code
path: long_run.py
| promptflow/src/promptflow/tests/test_configs/flows/long_run/flow.dag.yaml/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/long_run/flow.dag.yaml",
"repo_id": "promptflow",
"token_count": 80
} | 68 |
inputs:
idx:
type: int
outputs:
output:
type: int
reference: ${my_python_tool_with_failed_line.output}
nodes:
- name: my_python_tool
type: python
source:
type: code
path: my_python_tool.py
inputs:
idx: ${inputs.idx}
- name: my_python_tool_with_failed_line
type: python
source:
type: code
path: my_python_tool_with_failed_line.py
inputs:
idx: ${my_python_tool.output} | promptflow/src/promptflow/tests/test_configs/flows/one_line_of_bulktest_timeout/flow.dag.yaml/0 | {
"file_path": "promptflow/src/promptflow/tests/test_configs/flows/one_line_of_bulktest_timeout/flow.dag.yaml",
"repo_id": "promptflow",
"token_count": 181
} | 69 |
Subsets and Splits