synapse.ml.cognitive.openai package

Submodules

synapse.ml.cognitive.openai.OpenAICompletion module

class synapse.ml.cognitive.openai.OpenAICompletion.OpenAICompletion(java_obj=None, AADToken=None, AADTokenCol=None, apiVersion=None, apiVersionCol=None, batchIndexPrompt=None, batchIndexPromptCol=None, batchPrompt=None, batchPromptCol=None, bestOf=None, bestOfCol=None, cacheLevel=None, cacheLevelCol=None, concurrency=1, concurrentTimeout=None, deploymentName=None, deploymentNameCol=None, echo=None, echoCol=None, errorCol='OpenAPICompletion_9f006475244b_error', frequencyPenalty=None, frequencyPenaltyCol=None, handler=None, indexPrompt=None, indexPromptCol=None, logProbs=None, logProbsCol=None, maxTokens=None, maxTokensCol=None, model=None, modelCol=None, n=None, nCol=None, outputCol='OpenAPICompletion_9f006475244b_output', presencePenalty=None, presencePenaltyCol=None, prompt=None, promptCol=None, stop=None, stopCol=None, subscriptionKey=None, subscriptionKeyCol=None, temperature=None, temperatureCol=None, timeout=60.0, topP=None, topPCol=None, url=None, user=None, userCol=None)[source]

Bases: synapse.ml.core.schema.Utils.ComplexParamsMixin, pyspark.ml.util.JavaMLReadable, pyspark.ml.util.JavaMLWritable, pyspark.ml.wrapper.JavaTransformer

Parameters

AADToken¶ (object) – AAD Token used for authentication
apiVersion¶ (object) – version of the api
batchIndexPrompt¶ (object) – Sequence of index sequences to complete
batchPrompt¶ (object) – Sequence of prompts to complete
bestOf¶ (object) – How many generations to create server side, and display only the best. Will not stream intermediate progress if best_of > 1. Has maximum value of 128.
cacheLevel¶ (object) – can be used to disable any server-side caching, 0=no cache, 1=prompt prefix enabled, 2=full cache
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
echo¶ (object) – Echo back the prompt in addition to the completion
errorCol¶ (str) – column to hold http errors
frequencyPenalty¶ (object) – How much to penalize new tokens based on whether they appear in the text so far. Increases the likelihood of the model to talk about new topics.
handler¶ (object) – Which strategy to use when handling requests
indexPrompt¶ (object) – Sequence of indexes to complete
logProbs¶ (object) – Include the log probabilities on the logprobs most likely tokens, as well the chosen tokens. So for example, if logprobs is 10, the API will return a list of the 10 most likely tokens. If logprobs is 0, only the chosen tokens will have logprobs returned. Minimum of 0 and maximum of 100 allowed.
maxTokens¶ (object) – The maximum number of tokens to generate. Has minimum of 0.
model¶ (object) – The name of the model to use
n¶ (object) – How many snippets to generate for each prompt. Minimum of 1 and maximum of 128 allowed.
outputCol¶ (str) – The name of the output column
presencePenalty¶ (object) – How much to penalize new tokens based on their existing frequency in the text so far. Decreases the likelihood of the model to repeat the same line verbatim. Has minimum of -2 and maximum of 2.
prompt¶ (object) – The text to complete
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
topP¶ (object) – An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10 percent probability mass are considered. We generally recommend using this or temperature but not both. Minimum of 0 and maximum of 1 allowed.
url¶ (str) – Url of the service
user¶ (object) – The ID of the end-user, for use in tracking and rate-limiting.

AADToken = Param(parent='undefined', name='AADToken', doc='ServiceParam: AAD Token used for authentication')

apiVersion = Param(parent='undefined', name='apiVersion', doc='ServiceParam: version of the api')

batchIndexPrompt = Param(parent='undefined', name='batchIndexPrompt', doc='ServiceParam: Sequence of index sequences to complete')

batchPrompt = Param(parent='undefined', name='batchPrompt', doc='ServiceParam: Sequence of prompts to complete')

bestOf = Param(parent='undefined', name='bestOf', doc='ServiceParam: How many generations to create server side, and display only the best. Will not stream intermediate progress if best_of > 1. Has maximum value of 128.')

cacheLevel = Param(parent='undefined', name='cacheLevel', doc='ServiceParam: can be used to disable any server-side caching, 0=no cache, 1=prompt prefix enabled, 2=full cache')

concurrency = Param(parent='undefined', name='concurrency', doc='max number of concurrent calls')

concurrentTimeout = Param(parent='undefined', name='concurrentTimeout', doc='max number seconds to wait on futures if concurrency >= 1')

deploymentName = Param(parent='undefined', name='deploymentName', doc='ServiceParam: The name of the deployment')

echo = Param(parent='undefined', name='echo', doc='ServiceParam: Echo back the prompt in addition to the completion')

errorCol = Param(parent='undefined', name='errorCol', doc='column to hold http errors')

frequencyPenalty = Param(parent='undefined', name='frequencyPenalty', doc='ServiceParam: How much to penalize new tokens based on whether they appear in the text so far. Increases the likelihood of the model to talk about new topics.')

getAADToken()[source]

Returns: AAD Token used for authentication
Return type: AADToken

getApiVersion()[source]

Returns: version of the api
Return type: apiVersion

getBatchIndexPrompt()[source]

Returns: Sequence of index sequences to complete
Return type: batchIndexPrompt

getBatchPrompt()[source]

Returns: Sequence of prompts to complete
Return type: batchPrompt

getBestOf()[source]

Returns: How many generations to create server side, and display only the best. Will not stream intermediate progress if best_of > 1. Has maximum value of 128.
Return type: bestOf

getCacheLevel()[source]

Returns: can be used to disable any server-side caching, 0=no cache, 1=prompt prefix enabled, 2=full cache
Return type: cacheLevel

getConcurrency()[source]

Returns: max number of concurrent calls
Return type: concurrency

getConcurrentTimeout()[source]

Returns: max number seconds to wait on futures if concurrency >= 1
Return type: concurrentTimeout

getDeploymentName()[source]

Returns: The name of the deployment
Return type: deploymentName

getEcho()[source]

Returns: Echo back the prompt in addition to the completion
Return type: echo

getErrorCol()[source]

Returns: column to hold http errors
Return type: errorCol

getFrequencyPenalty()[source]

Returns: How much to penalize new tokens based on whether they appear in the text so far. Increases the likelihood of the model to talk about new topics.
Return type: frequencyPenalty

getHandler()[source]

Returns: Which strategy to use when handling requests
Return type: handler

getIndexPrompt()[source]

Returns: Sequence of indexes to complete
Return type: indexPrompt

static getJavaPackage()[source]: Returns package name String.

getLogProbs()[source]

Returns: Include the log probabilities on the logprobs most likely tokens, as well the chosen tokens. So for example, if logprobs is 10, the API will return a list of the 10 most likely tokens. If logprobs is 0, only the chosen tokens will have logprobs returned. Minimum of 0 and maximum of 100 allowed.
Return type: logProbs

getMaxTokens()[source]

Returns: The maximum number of tokens to generate. Has minimum of 0.
Return type: maxTokens

getModel()[source]

Returns: The name of the model to use
Return type: model

getN()[source]

Returns: How many snippets to generate for each prompt. Minimum of 1 and maximum of 128 allowed.
Return type: n

getOutputCol()[source]

Returns: The name of the output column
Return type: outputCol

getPresencePenalty()[source]

Returns: How much to penalize new tokens based on their existing frequency in the text so far. Decreases the likelihood of the model to repeat the same line verbatim. Has minimum of -2 and maximum of 2.
Return type: presencePenalty

getPrompt()[source]

Returns: The text to complete
Return type: prompt

getStop()[source]

Returns: A sequence which indicates the end of the current document.
Return type: stop

getSubscriptionKey()[source]

Returns: the API key to use
Return type: subscriptionKey

getTemperature()[source]

Returns: 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 type: temperature

getTimeout()[source]

Returns: number of seconds to wait before closing the connection
Return type: timeout

getTopP()[source]

Returns: An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10 percent probability mass are considered. We generally recommend using this or temperature but not both. Minimum of 0 and maximum of 1 allowed.
Return type: topP

getUrl()[source]

Returns: Url of the service
Return type: url

getUser()[source]

Returns: The ID of the end-user, for use in tracking and rate-limiting.
Return type: user

handler = Param(parent='undefined', name='handler', doc='Which strategy to use when handling requests')

indexPrompt = Param(parent='undefined', name='indexPrompt', doc='ServiceParam: Sequence of indexes to complete')

logProbs = Param(parent='undefined', name='logProbs', doc='ServiceParam: Include the log probabilities on the `logprobs` most likely tokens, as well the chosen tokens. So for example, if `logprobs` is 10, the API will return a list of the 10 most likely tokens. If `logprobs` is 0, only the chosen tokens will have logprobs returned. Minimum of 0 and maximum of 100 allowed.')

maxTokens = Param(parent='undefined', name='maxTokens', doc='ServiceParam: The maximum number of tokens to generate. Has minimum of 0.')

model = Param(parent='undefined', name='model', doc='ServiceParam: The name of the model to use')

n = Param(parent='undefined', name='n', doc='ServiceParam: How many snippets to generate for each prompt. Minimum of 1 and maximum of 128 allowed.')

outputCol = Param(parent='undefined', name='outputCol', doc='The name of the output column')

presencePenalty = Param(parent='undefined', name='presencePenalty', doc='ServiceParam: How much to penalize new tokens based on their existing frequency in the text so far. Decreases the likelihood of the model to repeat the same line verbatim. Has minimum of -2 and maximum of 2.')

prompt = Param(parent='undefined', name='prompt', doc='ServiceParam: The text to complete')

classmethod read()[source]: Returns an MLReader instance for this class.

setAADToken(value)[source]

Parameters: AADToken¶ – AAD Token used for authentication

setAADTokenCol(value)[source]

Parameters: AADToken¶ – AAD Token used for authentication

setApiVersion(value)[source]

Parameters: apiVersion¶ – version of the api

setApiVersionCol(value)[source]

Parameters: apiVersion¶ – version of the api

setBatchIndexPrompt(value)[source]

Parameters: batchIndexPrompt¶ – Sequence of index sequences to complete

setBatchIndexPromptCol(value)[source]

Parameters: batchIndexPrompt¶ – Sequence of index sequences to complete

setBatchPrompt(value)[source]

Parameters: batchPrompt¶ – Sequence of prompts to complete

setBatchPromptCol(value)[source]

Parameters: batchPrompt¶ – Sequence of prompts to complete

setBestOf(value)[source]

Parameters: bestOf¶ – How many generations to create server side, and display only the best. Will not stream intermediate progress if best_of > 1. Has maximum value of 128.

setBestOfCol(value)[source]

Parameters: bestOf¶ – How many generations to create server side, and display only the best. Will not stream intermediate progress if best_of > 1. Has maximum value of 128.

setCacheLevel(value)[source]

Parameters: cacheLevel¶ – can be used to disable any server-side caching, 0=no cache, 1=prompt prefix enabled, 2=full cache

setCacheLevelCol(value)[source]

Parameters: cacheLevel¶ – can be used to disable any server-side caching, 0=no cache, 1=prompt prefix enabled, 2=full cache

setConcurrency(value)[source]

Parameters: concurrency¶ – max number of concurrent calls

setConcurrentTimeout(value)[source]

Parameters: concurrentTimeout¶ – max number seconds to wait on futures if concurrency >= 1

setCustomServiceName(value)[source]

setDeploymentName(value)[source]

Parameters: deploymentName¶ – The name of the deployment

setDeploymentNameCol(value)[source]

Parameters: deploymentName¶ – The name of the deployment

setEcho(value)[source]

Parameters: echo¶ – Echo back the prompt in addition to the completion

setEchoCol(value)[source]

Parameters: echo¶ – Echo back the prompt in addition to the completion

setEndpoint(value)[source]

setErrorCol(value)[source]

Parameters: errorCol¶ – column to hold http errors

setFrequencyPenalty(value)[source]

Parameters: frequencyPenalty¶ – How much to penalize new tokens based on whether they appear in the text so far. Increases the likelihood of the model to talk about new topics.

setFrequencyPenaltyCol(value)[source]

Parameters: frequencyPenalty¶ – How much to penalize new tokens based on whether they appear in the text so far. Increases the likelihood of the model to talk about new topics.

setHandler(value)[source]

Parameters: handler¶ – Which strategy to use when handling requests

setIndexPrompt(value)[source]

Parameters: indexPrompt¶ – Sequence of indexes to complete

setIndexPromptCol(value)[source]

Parameters: indexPrompt¶ – Sequence of indexes to complete

setLogProbs(value)[source]

Parameters: logProbs¶ – Include the log probabilities on the logprobs most likely tokens, as well the chosen tokens. So for example, if logprobs is 10, the API will return a list of the 10 most likely tokens. If logprobs is 0, only the chosen tokens will have logprobs returned. Minimum of 0 and maximum of 100 allowed.

setLogProbsCol(value)[source]

Parameters: logProbs¶ – Include the log probabilities on the logprobs most likely tokens, as well the chosen tokens. So for example, if logprobs is 10, the API will return a list of the 10 most likely tokens. If logprobs is 0, only the chosen tokens will have logprobs returned. Minimum of 0 and maximum of 100 allowed.

setMaxTokens(value)[source]

Parameters: maxTokens¶ – The maximum number of tokens to generate. Has minimum of 0.

setMaxTokensCol(value)[source]

Parameters: maxTokens¶ – The maximum number of tokens to generate. Has minimum of 0.

setModel(value)[source]

Parameters: model¶ – The name of the model to use

setModelCol(value)[source]

Parameters: model¶ – The name of the model to use

setN(value)[source]

Parameters: n¶ – How many snippets to generate for each prompt. Minimum of 1 and maximum of 128 allowed.

setNCol(value)[source]

Parameters: n¶ – How many snippets to generate for each prompt. Minimum of 1 and maximum of 128 allowed.

setOutputCol(value)[source]

Parameters: outputCol¶ – The name of the output column

setParams(AADToken=None, AADTokenCol=None, apiVersion=None, apiVersionCol=None, batchIndexPrompt=None, batchIndexPromptCol=None, batchPrompt=None, batchPromptCol=None, bestOf=None, bestOfCol=None, cacheLevel=None, cacheLevelCol=None, concurrency=1, concurrentTimeout=None, deploymentName=None, deploymentNameCol=None, echo=None, echoCol=None, errorCol='OpenAPICompletion_9f006475244b_error', frequencyPenalty=None, frequencyPenaltyCol=None, handler=None, indexPrompt=None, indexPromptCol=None, logProbs=None, logProbsCol=None, maxTokens=None, maxTokensCol=None, model=None, modelCol=None, n=None, nCol=None, outputCol='OpenAPICompletion_9f006475244b_output', presencePenalty=None, presencePenaltyCol=None, prompt=None, promptCol=None, stop=None, stopCol=None, subscriptionKey=None, subscriptionKeyCol=None, temperature=None, temperatureCol=None, timeout=60.0, topP=None, topPCol=None, url=None, user=None, userCol=None)[source]: Set the (keyword only) parameters

setPresencePenalty(value)[source]

Parameters: presencePenalty¶ – How much to penalize new tokens based on their existing frequency in the text so far. Decreases the likelihood of the model to repeat the same line verbatim. Has minimum of -2 and maximum of 2.

setPresencePenaltyCol(value)[source]

Parameters: presencePenalty¶ – How much to penalize new tokens based on their existing frequency in the text so far. Decreases the likelihood of the model to repeat the same line verbatim. Has minimum of -2 and maximum of 2.

setPrompt(value)[source]

Parameters: prompt¶ – The text to complete

setPromptCol(value)[source]

Parameters: prompt¶ – The text to complete

setStop(value)[source]

Parameters: stop¶ – A sequence which indicates the end of the current document.

setStopCol(value)[source]

Parameters: stop¶ – A sequence which indicates the end of the current document.

setSubscriptionKey(value)[source]

Parameters: subscriptionKey¶ – the API key to use

setSubscriptionKeyCol(value)[source]

Parameters: subscriptionKey¶ – the API key to use

setTemperature(value)[source]

Parameters: 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.

setTemperatureCol(value)[source]

Parameters: 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.

setTimeout(value)[source]

Parameters: timeout¶ – number of seconds to wait before closing the connection

setTopP(value)[source]

Parameters: topP¶ – An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10 percent probability mass are considered. We generally recommend using this or temperature but not both. Minimum of 0 and maximum of 1 allowed.

setTopPCol(value)[source]

Parameters: topP¶ – An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10 percent probability mass are considered. We generally recommend using this or temperature but not both. Minimum of 0 and maximum of 1 allowed.

setUrl(value)[source]

Parameters: url¶ – Url of the service

setUser(value)[source]

Parameters: user¶ – The ID of the end-user, for use in tracking and rate-limiting.

setUserCol(value)[source]

Parameters: user¶ – The ID of the end-user, for use in tracking and rate-limiting.

stop = Param(parent='undefined', name='stop', doc='ServiceParam: A sequence which indicates the end of the current document.')

subscriptionKey = Param(parent='undefined', name='subscriptionKey', doc='ServiceParam: the API key to use')

temperature = Param(parent='undefined', name='temperature', doc='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(parent='undefined', name='timeout', doc='number of seconds to wait before closing the connection')

topP = Param(parent='undefined', name='topP', doc='ServiceParam: An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10 percent probability mass are considered. We generally recommend using this or `temperature` but not both. Minimum of 0 and maximum of 1 allowed.')

url = Param(parent='undefined', name='url', doc='Url of the service')

user = Param(parent='undefined', name='user', doc='ServiceParam: The ID of the end-user, for use in tracking and rate-limiting.')

synapse.ml.cognitive.openai.OpenAIEmbedding module

class synapse.ml.cognitive.openai.OpenAIEmbedding.OpenAIEmbedding(java_obj=None, AADToken=None, AADTokenCol=None, apiVersion=None, apiVersionCol=None, concurrency=1, concurrentTimeout=None, deploymentName=None, deploymentNameCol=None, errorCol='OpenAIEmbedding_703c909d5540_error', handler=None, outputCol='OpenAIEmbedding_703c909d5540_output', subscriptionKey=None, subscriptionKeyCol=None, text=None, textCol=None, timeout=60.0, url=None)[source]

Bases: synapse.ml.core.schema.Utils.ComplexParamsMixin, pyspark.ml.util.JavaMLReadable, pyspark.ml.util.JavaMLWritable, pyspark.ml.wrapper.JavaTransformer

Parameters

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
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

AADToken = Param(parent='undefined', name='AADToken', doc='ServiceParam: AAD Token used for authentication')

apiVersion = Param(parent='undefined', name='apiVersion', doc='ServiceParam: version of the api')

concurrency = Param(parent='undefined', name='concurrency', doc='max number of concurrent calls')

concurrentTimeout = Param(parent='undefined', name='concurrentTimeout', doc='max number seconds to wait on futures if concurrency >= 1')

deploymentName = Param(parent='undefined', name='deploymentName', doc='ServiceParam: The name of the deployment')

errorCol = Param(parent='undefined', name='errorCol', doc='column to hold http errors')

getAADToken()[source]

Returns: AAD Token used for authentication
Return type: AADToken

getApiVersion()[source]

Returns: version of the api
Return type: apiVersion

getConcurrency()[source]

Returns: max number of concurrent calls
Return type: concurrency

getConcurrentTimeout()[source]

Returns: max number seconds to wait on futures if concurrency >= 1
Return type: concurrentTimeout

getDeploymentName()[source]

Returns: The name of the deployment
Return type: deploymentName

getErrorCol()[source]

Returns: column to hold http errors
Return type: errorCol

getHandler()[source]

Returns: Which strategy to use when handling requests
Return type: handler

static getJavaPackage()[source]: Returns package name String.

getOutputCol()[source]

Returns: The name of the output column
Return type: outputCol

getSubscriptionKey()[source]

Returns: the API key to use
Return type: subscriptionKey

getText()[source]

Returns: Input text to get embeddings for.
Return type: text

getTimeout()[source]

Returns: number of seconds to wait before closing the connection
Return type: timeout

getUrl()[source]

Returns: Url of the service
Return type: url

handler = Param(parent='undefined', name='handler', doc='Which strategy to use when handling requests')

outputCol = Param(parent='undefined', name='outputCol', doc='The name of the output column')

classmethod read()[source]: Returns an MLReader instance for this class.

setAADToken(value)[source]

Parameters: AADToken¶ – AAD Token used for authentication

setAADTokenCol(value)[source]

Parameters: AADToken¶ – AAD Token used for authentication

setApiVersion(value)[source]

Parameters: apiVersion¶ – version of the api

setApiVersionCol(value)[source]

Parameters: apiVersion¶ – version of the api

setConcurrency(value)[source]

Parameters: concurrency¶ – max number of concurrent calls

setConcurrentTimeout(value)[source]

Parameters: concurrentTimeout¶ – max number seconds to wait on futures if concurrency >= 1

setCustomServiceName(value)[source]

setDeploymentName(value)[source]

Parameters: deploymentName¶ – The name of the deployment

setDeploymentNameCol(value)[source]

Parameters: deploymentName¶ – The name of the deployment

setEndpoint(value)[source]

setErrorCol(value)[source]

Parameters: errorCol¶ – column to hold http errors

setHandler(value)[source]

Parameters: handler¶ – Which strategy to use when handling requests

setOutputCol(value)[source]

Parameters: outputCol¶ – The name of the output column

setParams(AADToken=None, AADTokenCol=None, apiVersion=None, apiVersionCol=None, concurrency=1, concurrentTimeout=None, deploymentName=None, deploymentNameCol=None, errorCol='OpenAIEmbedding_703c909d5540_error', handler=None, outputCol='OpenAIEmbedding_703c909d5540_output', subscriptionKey=None, subscriptionKeyCol=None, text=None, textCol=None, timeout=60.0, url=None)[source]: Set the (keyword only) parameters

setSubscriptionKey(value)[source]

Parameters: subscriptionKey¶ – the API key to use

setSubscriptionKeyCol(value)[source]

Parameters: subscriptionKey¶ – the API key to use

setText(value)[source]

Parameters: text¶ – Input text to get embeddings for.

setTextCol(value)[source]

Parameters: text¶ – Input text to get embeddings for.

setTimeout(value)[source]

Parameters: timeout¶ – number of seconds to wait before closing the connection

setUrl(value)[source]

Parameters: url¶ – Url of the service

subscriptionKey = Param(parent='undefined', name='subscriptionKey', doc='ServiceParam: the API key to use')

text = Param(parent='undefined', name='text', doc='ServiceParam: Input text to get embeddings for.')

timeout = Param(parent='undefined', name='timeout', doc='number of seconds to wait before closing the connection')

url = Param(parent='undefined', name='url', doc='Url of the service')

synapse.ml.cognitive.openai.OpenAIPrompt module

class synapse.ml.cognitive.openai.OpenAIPrompt.OpenAIPrompt(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)[source]

Bases: synapse.ml.core.schema.Utils.ComplexParamsMixin, pyspark.ml.util.JavaMLReadable, pyspark.ml.util.JavaMLWritable, pyspark.ml.wrapper.JavaTransformer

Parameters

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(parent='undefined', name='AADToken', doc='ServiceParam: AAD Token used for authentication')

apiVersion = Param(parent='undefined', name='apiVersion', doc='ServiceParam: version of the api')

concurrency = Param(parent='undefined', name='concurrency', doc='max number of concurrent calls')

concurrentTimeout = Param(parent='undefined', name='concurrentTimeout', doc='max number seconds to wait on futures if concurrency >= 1')

deploymentName = Param(parent='undefined', name='deploymentName', doc='ServiceParam: The name of the deployment')

errorCol = Param(parent='undefined', name='errorCol', doc='column to hold http errors')

getAADToken()[source]

Returns: AAD Token used for authentication
Return type: AADToken

getApiVersion()[source]

Returns: version of the api
Return type: apiVersion

getConcurrency()[source]

Returns: max number of concurrent calls
Return type: concurrency

getConcurrentTimeout()[source]

Returns: max number seconds to wait on futures if concurrency >= 1
Return type: concurrentTimeout

getDeploymentName()[source]

Returns: The name of the deployment
Return type: deploymentName

getErrorCol()[source]

Returns: column to hold http errors
Return type: errorCol

static getJavaPackage()[source]: Returns package name String.

getMaxTokens()[source]

Returns: The maximum number of tokens to generate. Has minimum of 0.
Return type: maxTokens

getModel()[source]

Returns: The name of the model to use
Return type: model

getOutputCol()[source]

Returns: The name of the output column
Return type: outputCol

getPostProcessing()[source]

Returns: Post processing options: csv, json, regex
Return type: postProcessing

getPostProcessingOptions()[source]

Returns: Options (default): delimiter=’,’, jsonSchema, regex, regexGroup=0
Return type: postProcessingOptions

getPromptTemplate()[source]

Returns: The prompt. supports string interpolation {col1}: {col2}.
Return type: promptTemplate

getStop()[source]

Returns: A sequence which indicates the end of the current document.
Return type: stop

getSubscriptionKey()[source]

Returns: the API key to use
Return type: subscriptionKey

getTemperature()[source]

Returns: 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 type: temperature

getTimeout()[source]

Returns: number of seconds to wait before closing the connection
Return type: timeout

getUrl()[source]

Returns: Url of the service
Return type: url

maxTokens = Param(parent='undefined', name='maxTokens', doc='ServiceParam: The maximum number of tokens to generate. Has minimum of 0.')

model = Param(parent='undefined', name='model', doc='ServiceParam: The name of the model to use')

outputCol = Param(parent='undefined', name='outputCol', doc='The name of the output column')

postProcessing = Param(parent='undefined', name='postProcessing', doc='Post processing options: csv, json, regex')

postProcessingOptions = Param(parent='undefined', name='postProcessingOptions', doc="Options (default): delimiter=',', jsonSchema, regex, regexGroup=0")

promptTemplate = Param(parent='undefined', name='promptTemplate', doc='The prompt. supports string interpolation {col1}: {col2}.')

classmethod read()[source]: Returns an MLReader instance for this class.

setAADToken(value)[source]

Parameters: AADToken¶ – AAD Token used for authentication

setAADTokenCol(value)[source]

Parameters: AADToken¶ – AAD Token used for authentication

setApiVersion(value)[source]

Parameters: apiVersion¶ – version of the api

setApiVersionCol(value)[source]

Parameters: apiVersion¶ – version of the api

setConcurrency(value)[source]

Parameters: concurrency¶ – max number of concurrent calls

setConcurrentTimeout(value)[source]

Parameters: concurrentTimeout¶ – max number seconds to wait on futures if concurrency >= 1

setCustomServiceName(value)[source]

setDeploymentName(value)[source]

Parameters: deploymentName¶ – The name of the deployment

setDeploymentNameCol(value)[source]

Parameters: deploymentName¶ – The name of the deployment

setEndpoint(value)[source]

setErrorCol(value)[source]

Parameters: errorCol¶ – column to hold http errors

setMaxTokens(value)[source]

Parameters: maxTokens¶ – The maximum number of tokens to generate. Has minimum of 0.

setMaxTokensCol(value)[source]

Parameters: maxTokens¶ – The maximum number of tokens to generate. Has minimum of 0.

setModel(value)[source]

Parameters: model¶ – The name of the model to use

setModelCol(value)[source]

Parameters: model¶ – The name of the model to use

setOutputCol(value)[source]

Parameters: outputCol¶ – The name of the output column

setParams(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)[source]: Set the (keyword only) parameters

setPostProcessing(value)[source]

Parameters: postProcessing¶ – Post processing options: csv, json, regex

setPostProcessingOptions(value)[source]

Parameters: postProcessingOptions¶ – Options (default): delimiter=’,’, jsonSchema, regex, regexGroup=0

setPromptTemplate(value)[source]

Parameters: promptTemplate¶ – The prompt. supports string interpolation {col1}: {col2}.

setStop(value)[source]

Parameters: stop¶ – A sequence which indicates the end of the current document.

setStopCol(value)[source]

Parameters: stop¶ – A sequence which indicates the end of the current document.

setSubscriptionKey(value)[source]

Parameters: subscriptionKey¶ – the API key to use

setSubscriptionKeyCol(value)[source]

Parameters: subscriptionKey¶ – the API key to use

setTemperature(value)[source]

Parameters: 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.

setTemperatureCol(value)[source]

Parameters: 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.

setTimeout(value)[source]

Parameters: timeout¶ – number of seconds to wait before closing the connection

setUrl(value)[source]

Parameters: url¶ – Url of the service

stop = Param(parent='undefined', name='stop', doc='ServiceParam: A sequence which indicates the end of the current document.')

subscriptionKey = Param(parent='undefined', name='subscriptionKey', doc='ServiceParam: the API key to use')

temperature = Param(parent='undefined', name='temperature', doc='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(parent='undefined', name='timeout', doc='number of seconds to wait before closing the connection')

url = Param(parent='undefined', name='url', doc='Url of the service')

Module contents

SynapseML is an ecosystem of tools aimed towards expanding the distributed computing framework Apache Spark in several new directions. SynapseML adds many deep learning and data science tools to the Spark ecosystem, including seamless integration of Spark Machine Learning pipelines with Microsoft Cognitive Toolkit (CNTK), LightGBM and OpenCV. These tools enable powerful and highly-scalable predictive and analytical models for a variety of datasources.

SynapseML also brings new networking capabilities to the Spark Ecosystem. With the HTTP on Spark project, users can embed any web service into their SparkML models. In this vein, SynapseML provides easy to use SparkML transformers for a wide variety of Microsoft Cognitive Services. For production grade deployment, the Spark Serving project enables high throughput, sub-millisecond latency web services, backed by your Spark cluster.

SynapseML requires Scala 2.12, Spark 3.0+, and Python 3.6+.