# Copyright (C) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See LICENSE in project root for information.
import sys
if sys.version >= '3':
basestring = str
from pyspark import SparkContext, SQLContext
from pyspark.sql import DataFrame
from pyspark.ml.param.shared import *
from pyspark import keyword_only
from pyspark.ml.util import JavaMLReadable, JavaMLWritable
from synapse.ml.core.platform import running_on_synapse_internal
from synapse.ml.core.serialize.java_params_patch import *
from pyspark.ml.wrapper import JavaTransformer, JavaEstimator, JavaModel
from pyspark.ml.evaluation import JavaEvaluator
from pyspark.ml.common import inherit_doc
from synapse.ml.core.schema.Utils import *
from pyspark.ml.param import TypeConverters
from synapse.ml.core.schema.TypeConversionUtils import generateTypeConverter, complexTypeConverter
[docs]@inherit_doc
class OpenAIPrompt(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaTransformer):
"""
Args:
AADToken (object): AAD Token used for authentication
apiVersion (object): version of the api
concurrency (int): max number of concurrent calls
concurrentTimeout (float): max number seconds to wait on futures if concurrency >= 1
deploymentName (object): The name of the deployment
errorCol (str): column to hold http errors
maxTokens (object): The maximum number of tokens to generate. Has minimum of 0.
model (object): The name of the model to use
outputCol (str): The name of the output column
postProcessing (str): Post processing options: csv, json, regex
postProcessingOptions (dict): Options (default): delimiter=',', jsonSchema, regex, regexGroup=0
promptTemplate (str): The prompt. supports string interpolation {col1}: {col2}.
stop (object): A sequence which indicates the end of the current document.
subscriptionKey (object): the API key to use
temperature (object): What sampling temperature to use. Higher values means the model will take more risks. Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer. We generally recommend using this or `top_p` but not both. Minimum of 0 and maximum of 2 allowed.
timeout (float): number of seconds to wait before closing the connection
url (str): Url of the service
"""
AADToken = Param(Params._dummy(), "AADToken", "ServiceParam: AAD Token used for authentication")
apiVersion = Param(Params._dummy(), "apiVersion", "ServiceParam: version of the api")
concurrency = Param(Params._dummy(), "concurrency", "max number of concurrent calls", typeConverter=TypeConverters.toInt)
concurrentTimeout = Param(Params._dummy(), "concurrentTimeout", "max number seconds to wait on futures if concurrency >= 1", typeConverter=TypeConverters.toFloat)
deploymentName = Param(Params._dummy(), "deploymentName", "ServiceParam: The name of the deployment")
errorCol = Param(Params._dummy(), "errorCol", "column to hold http errors", typeConverter=TypeConverters.toString)
maxTokens = Param(Params._dummy(), "maxTokens", "ServiceParam: The maximum number of tokens to generate. Has minimum of 0.")
model = Param(Params._dummy(), "model", "ServiceParam: The name of the model to use")
outputCol = Param(Params._dummy(), "outputCol", "The name of the output column", typeConverter=TypeConverters.toString)
postProcessing = Param(Params._dummy(), "postProcessing", "Post processing options: csv, json, regex", typeConverter=TypeConverters.toString)
postProcessingOptions = Param(Params._dummy(), "postProcessingOptions", "Options (default): delimiter=',', jsonSchema, regex, regexGroup=0")
promptTemplate = Param(Params._dummy(), "promptTemplate", "The prompt. supports string interpolation {col1}: {col2}.", typeConverter=TypeConverters.toString)
stop = Param(Params._dummy(), "stop", "ServiceParam: A sequence which indicates the end of the current document.")
subscriptionKey = Param(Params._dummy(), "subscriptionKey", "ServiceParam: the API key to use")
temperature = Param(Params._dummy(), "temperature", "ServiceParam: What sampling temperature to use. Higher values means the model will take more risks. Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer. We generally recommend using this or `top_p` but not both. Minimum of 0 and maximum of 2 allowed.")
timeout = Param(Params._dummy(), "timeout", "number of seconds to wait before closing the connection", typeConverter=TypeConverters.toFloat)
url = Param(Params._dummy(), "url", "Url of the service", typeConverter=TypeConverters.toString)
@keyword_only
def __init__(
self,
java_obj=None,
AADToken=None,
AADTokenCol=None,
apiVersion=None,
apiVersionCol=None,
concurrency=1,
concurrentTimeout=None,
deploymentName=None,
deploymentNameCol=None,
errorCol="Error",
maxTokens=None,
maxTokensCol=None,
model=None,
modelCol=None,
outputCol="out",
postProcessing="",
postProcessingOptions={},
promptTemplate=None,
stop=None,
stopCol=None,
subscriptionKey=None,
subscriptionKeyCol=None,
temperature=None,
temperatureCol=None,
timeout=60.0,
url=None
):
super(OpenAIPrompt, self).__init__()
if java_obj is None:
self._java_obj = self._new_java_obj("com.microsoft.azure.synapse.ml.cognitive.openai.OpenAIPrompt", self.uid)
else:
self._java_obj = java_obj
self._setDefault(concurrency=1)
self._setDefault(errorCol="Error")
self._setDefault(outputCol="out")
self._setDefault(postProcessing="")
self._setDefault(postProcessingOptions={})
self._setDefault(timeout=60.0)
if hasattr(self, "_input_kwargs"):
kwargs = self._input_kwargs
else:
kwargs = self.__init__._input_kwargs
if java_obj is None:
for k,v in kwargs.items():
if v is not None:
getattr(self, "set" + k[0].upper() + k[1:])(v)
[docs] @keyword_only
def setParams(
self,
AADToken=None,
AADTokenCol=None,
apiVersion=None,
apiVersionCol=None,
concurrency=1,
concurrentTimeout=None,
deploymentName=None,
deploymentNameCol=None,
errorCol="Error",
maxTokens=None,
maxTokensCol=None,
model=None,
modelCol=None,
outputCol="out",
postProcessing="",
postProcessingOptions={},
promptTemplate=None,
stop=None,
stopCol=None,
subscriptionKey=None,
subscriptionKeyCol=None,
temperature=None,
temperatureCol=None,
timeout=60.0,
url=None
):
"""
Set the (keyword only) parameters
"""
if hasattr(self, "_input_kwargs"):
kwargs = self._input_kwargs
else:
kwargs = self.__init__._input_kwargs
return self._set(**kwargs)
[docs] @classmethod
def read(cls):
""" Returns an MLReader instance for this class. """
return JavaMMLReader(cls)
[docs] @staticmethod
def getJavaPackage():
""" Returns package name String. """
return "com.microsoft.azure.synapse.ml.cognitive.openai.OpenAIPrompt"
@staticmethod
def _from_java(java_stage):
module_name=OpenAIPrompt.__module__
module_name=module_name.rsplit(".", 1)[0] + ".OpenAIPrompt"
return from_java(java_stage, module_name)
[docs] def setAADToken(self, value):
"""
Args:
AADToken: AAD Token used for authentication
"""
if isinstance(value, list):
value = SparkContext._active_spark_context._jvm.com.microsoft.azure.synapse.ml.param.ServiceParam.toSeq(value)
self._java_obj = self._java_obj.setAADToken(value)
return self
[docs] def setAADTokenCol(self, value):
"""
Args:
AADToken: AAD Token used for authentication
"""
self._java_obj = self._java_obj.setAADTokenCol(value)
return self
[docs] def setApiVersion(self, value):
"""
Args:
apiVersion: version of the api
"""
if isinstance(value, list):
value = SparkContext._active_spark_context._jvm.com.microsoft.azure.synapse.ml.param.ServiceParam.toSeq(value)
self._java_obj = self._java_obj.setApiVersion(value)
return self
[docs] def setApiVersionCol(self, value):
"""
Args:
apiVersion: version of the api
"""
self._java_obj = self._java_obj.setApiVersionCol(value)
return self
[docs] def setConcurrency(self, value):
"""
Args:
concurrency: max number of concurrent calls
"""
self._set(concurrency=value)
return self
[docs] def setConcurrentTimeout(self, value):
"""
Args:
concurrentTimeout: max number seconds to wait on futures if concurrency >= 1
"""
self._set(concurrentTimeout=value)
return self
[docs] def setDeploymentName(self, value):
"""
Args:
deploymentName: The name of the deployment
"""
if isinstance(value, list):
value = SparkContext._active_spark_context._jvm.com.microsoft.azure.synapse.ml.param.ServiceParam.toSeq(value)
self._java_obj = self._java_obj.setDeploymentName(value)
return self
[docs] def setDeploymentNameCol(self, value):
"""
Args:
deploymentName: The name of the deployment
"""
self._java_obj = self._java_obj.setDeploymentNameCol(value)
return self
[docs] def setErrorCol(self, value):
"""
Args:
errorCol: column to hold http errors
"""
self._set(errorCol=value)
return self
[docs] def setMaxTokens(self, value):
"""
Args:
maxTokens: The maximum number of tokens to generate. Has minimum of 0.
"""
if isinstance(value, list):
value = SparkContext._active_spark_context._jvm.com.microsoft.azure.synapse.ml.param.ServiceParam.toSeq(value)
self._java_obj = self._java_obj.setMaxTokens(value)
return self
[docs] def setMaxTokensCol(self, value):
"""
Args:
maxTokens: The maximum number of tokens to generate. Has minimum of 0.
"""
self._java_obj = self._java_obj.setMaxTokensCol(value)
return self
[docs] def setModel(self, value):
"""
Args:
model: The name of the model to use
"""
if isinstance(value, list):
value = SparkContext._active_spark_context._jvm.com.microsoft.azure.synapse.ml.param.ServiceParam.toSeq(value)
self._java_obj = self._java_obj.setModel(value)
return self
[docs] def setModelCol(self, value):
"""
Args:
model: The name of the model to use
"""
self._java_obj = self._java_obj.setModelCol(value)
return self
[docs] def setOutputCol(self, value):
"""
Args:
outputCol: The name of the output column
"""
self._set(outputCol=value)
return self
[docs] def setPostProcessing(self, value):
"""
Args:
postProcessing: Post processing options: csv, json, regex
"""
self._set(postProcessing=value)
return self
[docs] def setPostProcessingOptions(self, value):
"""
Args:
postProcessingOptions: Options (default): delimiter=',', jsonSchema, regex, regexGroup=0
"""
self._set(postProcessingOptions=value)
return self
[docs] def setPromptTemplate(self, value):
"""
Args:
promptTemplate: The prompt. supports string interpolation {col1}: {col2}.
"""
self._set(promptTemplate=value)
return self
[docs] def setStop(self, value):
"""
Args:
stop: A sequence which indicates the end of the current document.
"""
if isinstance(value, list):
value = SparkContext._active_spark_context._jvm.com.microsoft.azure.synapse.ml.param.ServiceParam.toSeq(value)
self._java_obj = self._java_obj.setStop(value)
return self
[docs] def setStopCol(self, value):
"""
Args:
stop: A sequence which indicates the end of the current document.
"""
self._java_obj = self._java_obj.setStopCol(value)
return self
[docs] def setSubscriptionKey(self, value):
"""
Args:
subscriptionKey: the API key to use
"""
if isinstance(value, list):
value = SparkContext._active_spark_context._jvm.com.microsoft.azure.synapse.ml.param.ServiceParam.toSeq(value)
self._java_obj = self._java_obj.setSubscriptionKey(value)
return self
[docs] def setSubscriptionKeyCol(self, value):
"""
Args:
subscriptionKey: the API key to use
"""
self._java_obj = self._java_obj.setSubscriptionKeyCol(value)
return self
[docs] def setTemperature(self, value):
"""
Args:
temperature: What sampling temperature to use. Higher values means the model will take more risks. Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer. We generally recommend using this or `top_p` but not both. Minimum of 0 and maximum of 2 allowed.
"""
if isinstance(value, list):
value = SparkContext._active_spark_context._jvm.com.microsoft.azure.synapse.ml.param.ServiceParam.toSeq(value)
self._java_obj = self._java_obj.setTemperature(value)
return self
[docs] def setTemperatureCol(self, value):
"""
Args:
temperature: What sampling temperature to use. Higher values means the model will take more risks. Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer. We generally recommend using this or `top_p` but not both. Minimum of 0 and maximum of 2 allowed.
"""
self._java_obj = self._java_obj.setTemperatureCol(value)
return self
[docs] def setTimeout(self, value):
"""
Args:
timeout: number of seconds to wait before closing the connection
"""
self._set(timeout=value)
return self
[docs] def setUrl(self, value):
"""
Args:
url: Url of the service
"""
self._set(url=value)
return self
[docs] def getAADToken(self):
"""
Returns:
AADToken: AAD Token used for authentication
"""
return self._java_obj.getAADToken()
[docs] def getApiVersion(self):
"""
Returns:
apiVersion: version of the api
"""
return self._java_obj.getApiVersion()
[docs] def getConcurrency(self):
"""
Returns:
concurrency: max number of concurrent calls
"""
return self.getOrDefault(self.concurrency)
[docs] def getConcurrentTimeout(self):
"""
Returns:
concurrentTimeout: max number seconds to wait on futures if concurrency >= 1
"""
return self.getOrDefault(self.concurrentTimeout)
[docs] def getDeploymentName(self):
"""
Returns:
deploymentName: The name of the deployment
"""
return self._java_obj.getDeploymentName()
[docs] def getErrorCol(self):
"""
Returns:
errorCol: column to hold http errors
"""
return self.getOrDefault(self.errorCol)
[docs] def getMaxTokens(self):
"""
Returns:
maxTokens: The maximum number of tokens to generate. Has minimum of 0.
"""
return self._java_obj.getMaxTokens()
[docs] def getModel(self):
"""
Returns:
model: The name of the model to use
"""
return self._java_obj.getModel()
[docs] def getOutputCol(self):
"""
Returns:
outputCol: The name of the output column
"""
return self.getOrDefault(self.outputCol)
[docs] def getPostProcessing(self):
"""
Returns:
postProcessing: Post processing options: csv, json, regex
"""
return self.getOrDefault(self.postProcessing)
[docs] def getPostProcessingOptions(self):
"""
Returns:
postProcessingOptions: Options (default): delimiter=',', jsonSchema, regex, regexGroup=0
"""
return self.getOrDefault(self.postProcessingOptions)
[docs] def getPromptTemplate(self):
"""
Returns:
promptTemplate: The prompt. supports string interpolation {col1}: {col2}.
"""
return self.getOrDefault(self.promptTemplate)
[docs] def getStop(self):
"""
Returns:
stop: A sequence which indicates the end of the current document.
"""
return self._java_obj.getStop()
[docs] def getSubscriptionKey(self):
"""
Returns:
subscriptionKey: the API key to use
"""
return self._java_obj.getSubscriptionKey()
[docs] def getTemperature(self):
"""
Returns:
temperature: What sampling temperature to use. Higher values means the model will take more risks. Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer. We generally recommend using this or `top_p` but not both. Minimum of 0 and maximum of 2 allowed.
"""
return self._java_obj.getTemperature()
[docs] def getTimeout(self):
"""
Returns:
timeout: number of seconds to wait before closing the connection
"""
return self.getOrDefault(self.timeout)
[docs] def getUrl(self):
"""
Returns:
url: Url of the service
"""
return self.getOrDefault(self.url)
[docs] def setCustomServiceName(self, value):
self._java_obj = self._java_obj.setCustomServiceName(value)
return self
[docs] def setEndpoint(self, value):
self._java_obj = self._java_obj.setEndpoint(value)
return self
def _transform(self, dataset: DataFrame) -> DataFrame:
if running_on_synapse_internal():
from synapse.ml.mlflow import get_mlflow_env_config
mlflow_env_configs = get_mlflow_env_config()
self.setAADToken(mlflow_env_configs.driver_aad_token)
self.setEndpoint(mlflow_env_configs.workload_endpoint + "/cognitive/api/")
return super()._transform(dataset)