Source code for synapse.ml.services.openai.OpenAIEmbedding

# 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


@inherit_doc
class OpenAIEmbedding(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaTransformer):
    """
    Args:
        AADToken (object): AAD Token used for authentication
        CustomAuthHeader (object): A Custom Value for Authorization Header
        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
        customHeaders (object): Map of Custom Header Key-Value Tuples.
        customUrlRoot (str): The custom URL root for the service. This will not append OpenAI specific model path completions (i.e. /chat/completions) to the URL.
        deploymentName (object): The name of the deployment
        dimensions (object): Number of dimensions for output embeddings.
        errorCol (str): column to hold http errors
        handler (object): Which strategy to use when handling requests
        outputCol (str): The name of the output column
        subscriptionKey (object): the API key to use
        text (object): Input text to get embeddings for.
        timeout (float): number of seconds to wait before closing the connection
        url (str): Url of the service
        user (object): The ID of the end-user, for use in tracking and rate-limiting.
    """

    AADToken = Param(Params._dummy(), "AADToken", "ServiceParam: AAD Token used for authentication")
    
    CustomAuthHeader = Param(Params._dummy(), "CustomAuthHeader", "ServiceParam: A Custom Value for Authorization Header")
    
    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)
    
    customHeaders = Param(Params._dummy(), "customHeaders", "ServiceParam: Map of Custom Header Key-Value Tuples.")
    
    customUrlRoot = Param(Params._dummy(), "customUrlRoot", "The custom URL root for the service. This will not append OpenAI specific model path completions (i.e. /chat/completions) to the URL.", typeConverter=TypeConverters.toString)
    
    deploymentName = Param(Params._dummy(), "deploymentName", "ServiceParam: The name of the deployment")
    
    dimensions = Param(Params._dummy(), "dimensions", "ServiceParam: Number of dimensions for output embeddings.")
    
    errorCol = Param(Params._dummy(), "errorCol", "column to hold http errors", typeConverter=TypeConverters.toString)
    
    handler = Param(Params._dummy(), "handler", "Which strategy to use when handling requests")
    
    outputCol = Param(Params._dummy(), "outputCol", "The name of the output column", typeConverter=TypeConverters.toString)
    
    subscriptionKey = Param(Params._dummy(), "subscriptionKey", "ServiceParam: the API key to use")
    
    text = Param(Params._dummy(), "text", "ServiceParam: Input text to get embeddings for.")
    
    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)
    
    user = Param(Params._dummy(), "user", "ServiceParam: The ID of the end-user, for use in tracking and rate-limiting.")

    
    @keyword_only
    def __init__(
        self,
        java_obj=None,
        AADToken=None,
        AADTokenCol=None,
        CustomAuthHeader=None,
        CustomAuthHeaderCol=None,
        apiVersion=None,
        apiVersionCol=None,
        concurrency=1,
        concurrentTimeout=None,
        customHeaders=None,
        customHeadersCol=None,
        customUrlRoot=None,
        deploymentName=None,
        deploymentNameCol=None,
        dimensions=None,
        dimensionsCol=None,
        errorCol="OpenAIEmbedding_531aa802682f_error",
        handler=None,
        outputCol="OpenAIEmbedding_531aa802682f_output",
        subscriptionKey=None,
        subscriptionKeyCol=None,
        text=None,
        textCol=None,
        timeout=360.0,
        url=None,
        user=None,
        userCol=None
        ):
        super(OpenAIEmbedding, self).__init__()
        if java_obj is None:
            self._java_obj = self._new_java_obj("com.microsoft.azure.synapse.ml.services.openai.OpenAIEmbedding", self.uid)
        else:
            self._java_obj = java_obj
        self._setDefault(concurrency=1)
        self._setDefault(errorCol="OpenAIEmbedding_531aa802682f_error")
        self._setDefault(outputCol="OpenAIEmbedding_531aa802682f_output")
        self._setDefault(timeout=360.0)
        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)

    @keyword_only
    def setParams(
        self,
        AADToken=None,
        AADTokenCol=None,
        CustomAuthHeader=None,
        CustomAuthHeaderCol=None,
        apiVersion=None,
        apiVersionCol=None,
        concurrency=1,
        concurrentTimeout=None,
        customHeaders=None,
        customHeadersCol=None,
        customUrlRoot=None,
        deploymentName=None,
        deploymentNameCol=None,
        dimensions=None,
        dimensionsCol=None,
        errorCol="OpenAIEmbedding_531aa802682f_error",
        handler=None,
        outputCol="OpenAIEmbedding_531aa802682f_output",
        subscriptionKey=None,
        subscriptionKeyCol=None,
        text=None,
        textCol=None,
        timeout=360.0,
        url=None,
        user=None,
        userCol=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)

    @classmethod
    def read(cls):
        """ Returns an MLReader instance for this class. """
        return JavaMMLReader(cls)

    @staticmethod
    def getJavaPackage():
        """ Returns package name String. """
        return "com.microsoft.azure.synapse.ml.services.openai.OpenAIEmbedding"

    @staticmethod
    def _from_java(java_stage):
        module_name=OpenAIEmbedding.__module__
        module_name=module_name.rsplit(".", 1)[0] + ".OpenAIEmbedding"
        return from_java(java_stage, module_name)

    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
    
    def setAADTokenCol(self, value):
        """
        Args:
            AADToken: AAD Token used for authentication
        """
        self._java_obj = self._java_obj.setAADTokenCol(value)
        return self
    
    def setCustomAuthHeader(self, value):
        """
        Args:
            CustomAuthHeader: A Custom Value for Authorization Header
        """
        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.setCustomAuthHeader(value)
        return self
    
    def setCustomAuthHeaderCol(self, value):
        """
        Args:
            CustomAuthHeader: A Custom Value for Authorization Header
        """
        self._java_obj = self._java_obj.setCustomAuthHeaderCol(value)
        return self
    
    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
    
    def setApiVersionCol(self, value):
        """
        Args:
            apiVersion: version of the api
        """
        self._java_obj = self._java_obj.setApiVersionCol(value)
        return self
    
    def setConcurrency(self, value):
        """
        Args:
            concurrency: max number of concurrent calls
        """
        self._set(concurrency=value)
        return self
    
    def setConcurrentTimeout(self, value):
        """
        Args:
            concurrentTimeout: max number seconds to wait on futures if concurrency >= 1
        """
        self._set(concurrentTimeout=value)
        return self
    
    def setCustomHeaders(self, value):
        """
        Args:
            customHeaders: Map of Custom Header Key-Value Tuples.
        """
        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.setCustomHeaders(value)
        return self
    
    def setCustomHeadersCol(self, value):
        """
        Args:
            customHeaders: Map of Custom Header Key-Value Tuples.
        """
        self._java_obj = self._java_obj.setCustomHeadersCol(value)
        return self
    
    def setCustomUrlRoot(self, value):
        """
        Args:
            customUrlRoot: The custom URL root for the service. This will not append OpenAI specific model path completions (i.e. /chat/completions) to the URL.
        """
        self._set(customUrlRoot=value)
        return self
    
    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
    
    def setDeploymentNameCol(self, value):
        """
        Args:
            deploymentName: The name of the deployment
        """
        self._java_obj = self._java_obj.setDeploymentNameCol(value)
        return self
    
    def setDimensions(self, value):
        """
        Args:
            dimensions: Number of dimensions for output embeddings.
        """
        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.setDimensions(value)
        return self
    
    def setDimensionsCol(self, value):
        """
        Args:
            dimensions: Number of dimensions for output embeddings.
        """
        self._java_obj = self._java_obj.setDimensionsCol(value)
        return self
    
    def setErrorCol(self, value):
        """
        Args:
            errorCol: column to hold http errors
        """
        self._set(errorCol=value)
        return self
    
    def setHandler(self, value):
        """
        Args:
            handler: Which strategy to use when handling requests
        """
        self._set(handler=value)
        return self
    
    def setOutputCol(self, value):
        """
        Args:
            outputCol: The name of the output column
        """
        self._set(outputCol=value)
        return self
    
    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
    
    def setSubscriptionKeyCol(self, value):
        """
        Args:
            subscriptionKey: the API key to use
        """
        self._java_obj = self._java_obj.setSubscriptionKeyCol(value)
        return self
    
    def setText(self, value):
        """
        Args:
            text: Input text to get embeddings for.
        """
        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.setText(value)
        return self
    
    def setTextCol(self, value):
        """
        Args:
            text: Input text to get embeddings for.
        """
        self._java_obj = self._java_obj.setTextCol(value)
        return self
    
    def setTimeout(self, value):
        """
        Args:
            timeout: number of seconds to wait before closing the connection
        """
        self._set(timeout=value)
        return self
    
    def setUrl(self, value):
        """
        Args:
            url: Url of the service
        """
        self._set(url=value)
        return self
    
    def setUser(self, value):
        """
        Args:
            user: The ID of the end-user, for use in tracking and rate-limiting.
        """
        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.setUser(value)
        return self
    
    def setUserCol(self, value):
        """
        Args:
            user: The ID of the end-user, for use in tracking and rate-limiting.
        """
        self._java_obj = self._java_obj.setUserCol(value)
        return self

    
    def getAADToken(self):
        """
        Returns:
            AADToken: AAD Token used for authentication
        """
        return self._java_obj.getAADToken()
    
    
    def getCustomAuthHeader(self):
        """
        Returns:
            CustomAuthHeader: A Custom Value for Authorization Header
        """
        return self._java_obj.getCustomAuthHeader()
    
    
    def getApiVersion(self):
        """
        Returns:
            apiVersion: version of the api
        """
        return self._java_obj.getApiVersion()
    
    
    def getConcurrency(self):
        """
        Returns:
            concurrency: max number of concurrent calls
        """
        return self.getOrDefault(self.concurrency)
    
    
    def getConcurrentTimeout(self):
        """
        Returns:
            concurrentTimeout: max number seconds to wait on futures if concurrency >= 1
        """
        return self.getOrDefault(self.concurrentTimeout)
    
    
    def getCustomHeaders(self):
        """
        Returns:
            customHeaders: Map of Custom Header Key-Value Tuples.
        """
        return self._java_obj.getCustomHeaders()
    
    
    def getCustomUrlRoot(self):
        """
        Returns:
            customUrlRoot: The custom URL root for the service. This will not append OpenAI specific model path completions (i.e. /chat/completions) to the URL.
        """
        return self.getOrDefault(self.customUrlRoot)
    
    
    def getDeploymentName(self):
        """
        Returns:
            deploymentName: The name of the deployment
        """
        return self._java_obj.getDeploymentName()
    
    
    def getDimensions(self):
        """
        Returns:
            dimensions: Number of dimensions for output embeddings.
        """
        return self._java_obj.getDimensions()
    
    
    def getErrorCol(self):
        """
        Returns:
            errorCol: column to hold http errors
        """
        return self.getOrDefault(self.errorCol)
    
    
    def getHandler(self):
        """
        Returns:
            handler: Which strategy to use when handling requests
        """
        return self.getOrDefault(self.handler)
    
    
    def getOutputCol(self):
        """
        Returns:
            outputCol: The name of the output column
        """
        return self.getOrDefault(self.outputCol)
    
    
    def getSubscriptionKey(self):
        """
        Returns:
            subscriptionKey: the API key to use
        """
        return self._java_obj.getSubscriptionKey()
    
    
    def getText(self):
        """
        Returns:
            text: Input text to get embeddings for.
        """
        return self._java_obj.getText()
    
    
    def getTimeout(self):
        """
        Returns:
            timeout: number of seconds to wait before closing the connection
        """
        return self.getOrDefault(self.timeout)
    
    
    def getUrl(self):
        """
        Returns:
            url: Url of the service
        """
        return self.getOrDefault(self.url)
    
    
    def getUser(self):
        """
        Returns:
            user: The ID of the end-user, for use in tracking and rate-limiting.
        """
        return self._java_obj.getUser()

    

    def setCustomServiceName(self, value):
        self._java_obj = self._java_obj.setCustomServiceName(value)
        return self
    
    def setEndpoint(self, value):
        self._java_obj = self._java_obj.setEndpoint(value)
        return self
    
    def setDefaultInternalEndpoint(self, value):
        self._java_obj = self._java_obj.setDefaultInternalEndpoint(value)
        return self
    
    def _transform(self, dataset: DataFrame) -> DataFrame:
        return super()._transform(dataset)