synapse.ml.services.openai package

Submodules

synapse.ml.services.openai.OpenAIChatCompletion module

class synapse.ml.services.openai.OpenAIChatCompletion.OpenAIChatCompletion(java_obj=None, AADToken=None, AADTokenCol=None, CustomAuthHeader=None, CustomAuthHeaderCol=None, apiVersion=None, apiVersionCol=None, bestOf=None, bestOfCol=None, cacheLevel=None, cacheLevelCol=None, concurrency=1, concurrentTimeout=None, customHeaders=None, customHeadersCol=None, customUrlRoot=None, deploymentName=None, deploymentNameCol=None, echo=None, echoCol=None, errorCol='OpenAIChatCompletion_173a4934c2e6_error', frequencyPenalty=None, frequencyPenaltyCol=None, handler=None, logProbs=None, logProbsCol=None, maxTokens=None, maxTokensCol=None, messagesCol=None, n=None, nCol=None, outputCol='OpenAIChatCompletion_173a4934c2e6_output', presencePenalty=None, presencePenaltyCol=None, stop=None, stopCol=None, subscriptionKey=None, subscriptionKeyCol=None, temperature=None, temperatureCol=None, timeout=360.0, topP=None, topPCol=None, url=None, user=None, userCol=None)

Bases: ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaTransformer

Parameters:

• AADToken (object) – AAD Token used for authentication

• CustomAuthHeader (object) – A Custom Value for Authorization Header

• apiVersion (object) – version of the api

• 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

• 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

• 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

• 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.

• messagesCol (str) – The column messages to generate chat completions for, in the chat format. This column should have type Array(Struct(role: String, content: String)).

• 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.

• 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')

CustomAuthHeader = Param(parent='undefined', name='CustomAuthHeader', doc='ServiceParam: A Custom Value for Authorization Header')

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

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')

customHeaders = Param(parent='undefined', name='customHeaders', doc='ServiceParam: Map of Custom Header Key-Value Tuples.')

customUrlRoot = Param(parent='undefined', name='customUrlRoot', doc='The custom URL root for the service. This will not append OpenAI specific model path completions (i.e. /chat/completions) to the URL.')

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()

Returns:

AAD Token used for authentication

Return type:

AADToken

getApiVersion()

Returns:

version of the api

Return type:

apiVersion

getBestOf()

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()

Returns:

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

Return type:

cacheLevel

getConcurrency()

Returns:

max number of concurrent calls

Return type:

concurrency

getConcurrentTimeout()

Returns:

max number seconds to wait on futures if concurrency >= 1

Return type:

concurrentTimeout

getCustomAuthHeader()

Returns:

A Custom Value for Authorization Header

Return type:

CustomAuthHeader

getCustomHeaders()

Returns:

Map of Custom Header Key-Value Tuples.

Return type:

customHeaders

getCustomUrlRoot()

Returns:

The custom URL root for the service. This will not append OpenAI specific model path completions (i.e. /chat/completions) to the URL.

Return type:

customUrlRoot

getDeploymentName()

Returns:

The name of the deployment

Return type:

deploymentName

getEcho()

Returns:

Echo back the prompt in addition to the completion

Return type:

echo

getErrorCol()

Returns:

column to hold http errors

Return type:

errorCol

getFrequencyPenalty()

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()

Returns:

Which strategy to use when handling requests

Return type:

handler

static getJavaPackage()

Returns package name String.

getLogProbs()

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()

Returns:

The maximum number of tokens to generate. Has minimum of 0.

Return type:

maxTokens

getMessagesCol()

Returns:

The column messages to generate chat completions for, in the chat format. This column should have type Array(Struct(role: String, content: String)).

Return type:

messagesCol

getN()

Returns:

How many snippets to generate for each prompt. Minimum of 1 and maximum of 128 allowed.

Return type:

n

getOutputCol()

Returns:

The name of the output column

Return type:

outputCol

getPresencePenalty()

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

getStop()

Returns:

A sequence which indicates the end of the current document.

Return type:

stop

getSubscriptionKey()

Returns:

the API key to use

Return type:

subscriptionKey

getTemperature()

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()

Returns:

number of seconds to wait before closing the connection

Return type:

timeout

getTopP()

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()

Returns:

Url of the service

Return type:

url

getUser()

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')

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.')

messagesCol = Param(parent='undefined', name='messagesCol', doc='The column messages to generate chat completions for, in the chat format. This column should have type Array(Struct(role: String, content: String)).')

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.')

classmethod read()

Returns an MLReader instance for this class.

setAADToken(value)

Parameters:

AADToken – AAD Token used for authentication

setAADTokenCol(value)

Parameters:

AADToken – AAD Token used for authentication

setApiVersion(value)

Parameters:

apiVersion – version of the api

setApiVersionCol(value)

Parameters:

apiVersion – version of the api

setBestOf(value)

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)

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)

Parameters:

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

setCacheLevelCol(value)

Parameters:

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

setConcurrency(value)

Parameters:

concurrency – max number of concurrent calls

setConcurrentTimeout(value)

Parameters:

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

setCustomAuthHeader(value)

Parameters:

CustomAuthHeader – A Custom Value for Authorization Header

setCustomAuthHeaderCol(value)

Parameters:

CustomAuthHeader – A Custom Value for Authorization Header

setCustomHeaders(value)

Parameters:

customHeaders – Map of Custom Header Key-Value Tuples.

setCustomHeadersCol(value)

Parameters:

customHeaders – Map of Custom Header Key-Value Tuples.

setCustomServiceName(value)

setCustomUrlRoot(value)

Parameters:

customUrlRoot – The custom URL root for the service. This will not append OpenAI specific model path completions (i.e. /chat/completions) to the URL.

setDefaultInternalEndpoint(value)

setDeploymentName(value)

Parameters:

deploymentName – The name of the deployment

setDeploymentNameCol(value)

Parameters:

deploymentName – The name of the deployment

setEcho(value)

Parameters:

echo – Echo back the prompt in addition to the completion

setEchoCol(value)

Parameters:

echo – Echo back the prompt in addition to the completion

setEndpoint(value)

setErrorCol(value)

Parameters:

errorCol – column to hold http errors

setFrequencyPenalty(value)

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)

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)

Parameters:

handler – Which strategy to use when handling requests

setLogProbs(value)

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)

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)

Parameters:

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

setMaxTokensCol(value)

Parameters:

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

setMessagesCol(value)

Parameters:

messagesCol – The column messages to generate chat completions for, in the chat format. This column should have type Array(Struct(role: String, content: String)).

setN(value)

Parameters:

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

setNCol(value)

Parameters:

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

setOutputCol(value)

Parameters:

outputCol – The name of the output column

setParams(AADToken=None, AADTokenCol=None, CustomAuthHeader=None, CustomAuthHeaderCol=None, apiVersion=None, apiVersionCol=None, bestOf=None, bestOfCol=None, cacheLevel=None, cacheLevelCol=None, concurrency=1, concurrentTimeout=None, customHeaders=None, customHeadersCol=None, customUrlRoot=None, deploymentName=None, deploymentNameCol=None, echo=None, echoCol=None, errorCol='OpenAIChatCompletion_173a4934c2e6_error', frequencyPenalty=None, frequencyPenaltyCol=None, handler=None, logProbs=None, logProbsCol=None, maxTokens=None, maxTokensCol=None, messagesCol=None, n=None, nCol=None, outputCol='OpenAIChatCompletion_173a4934c2e6_output', presencePenalty=None, presencePenaltyCol=None, stop=None, stopCol=None, subscriptionKey=None, subscriptionKeyCol=None, temperature=None, temperatureCol=None, timeout=360.0, topP=None, topPCol=None, url=None, user=None, userCol=None)

Set the (keyword only) parameters

setPresencePenalty(value)

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)

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.

setStop(value)

Parameters:

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

setStopCol(value)

Parameters:

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

setSubscriptionKey(value)

Parameters:

subscriptionKey – the API key to use

setSubscriptionKeyCol(value)

Parameters:

subscriptionKey – the API key to use

setTemperature(value)

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)

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)

Parameters:

timeout – number of seconds to wait before closing the connection

setTopP(value)

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)

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)

Parameters:

url – Url of the service

setUser(value)

Parameters:

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

setUserCol(value)

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.services.openai.OpenAICompletion module

class synapse.ml.services.openai.OpenAICompletion.OpenAICompletion(java_obj=None, AADToken=None, AADTokenCol=None, CustomAuthHeader=None, CustomAuthHeaderCol=None, apiVersion=None, apiVersionCol=None, batchPrompt=None, batchPromptCol=None, bestOf=None, bestOfCol=None, cacheLevel=None, cacheLevelCol=None, concurrency=1, concurrentTimeout=None, customHeaders=None, customHeadersCol=None, customUrlRoot=None, deploymentName=None, deploymentNameCol=None, echo=None, echoCol=None, errorCol='OpenAICompletion_413b9f8e6738_error', frequencyPenalty=None, frequencyPenaltyCol=None, handler=None, logProbs=None, logProbsCol=None, maxTokens=None, maxTokensCol=None, n=None, nCol=None, outputCol='OpenAICompletion_413b9f8e6738_output', presencePenalty=None, presencePenaltyCol=None, prompt=None, promptCol=None, stop=None, stopCol=None, subscriptionKey=None, subscriptionKeyCol=None, temperature=None, temperatureCol=None, timeout=360.0, topP=None, topPCol=None, url=None, user=None, userCol=None)

Bases: ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaTransformer

Parameters:

• AADToken (object) – AAD Token used for authentication

• CustomAuthHeader (object) – A Custom Value for Authorization Header

• apiVersion (object) – version of the api

• 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

• 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

• 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

• 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.

• 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')

CustomAuthHeader = Param(parent='undefined', name='CustomAuthHeader', doc='ServiceParam: A Custom Value for Authorization Header')

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

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')

customHeaders = Param(parent='undefined', name='customHeaders', doc='ServiceParam: Map of Custom Header Key-Value Tuples.')

customUrlRoot = Param(parent='undefined', name='customUrlRoot', doc='The custom URL root for the service. This will not append OpenAI specific model path completions (i.e. /chat/completions) to the URL.')

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()

Returns:

AAD Token used for authentication

Return type:

AADToken

getApiVersion()

Returns:

version of the api

Return type:

apiVersion

getBatchPrompt()

Returns:

Sequence of prompts to complete

Return type:

batchPrompt

getBestOf()

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()

Returns:

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

Return type:

cacheLevel

getConcurrency()

Returns:

max number of concurrent calls

Return type:

concurrency

getConcurrentTimeout()

Returns:

max number seconds to wait on futures if concurrency >= 1

Return type:

concurrentTimeout

getCustomAuthHeader()

Returns:

A Custom Value for Authorization Header

Return type:

CustomAuthHeader

getCustomHeaders()

Returns:

Map of Custom Header Key-Value Tuples.

Return type:

customHeaders

getCustomUrlRoot()

Returns:

The custom URL root for the service. This will not append OpenAI specific model path completions (i.e. /chat/completions) to the URL.

Return type:

customUrlRoot

getDeploymentName()

Returns:

The name of the deployment

Return type:

deploymentName

getEcho()

Returns:

Echo back the prompt in addition to the completion

Return type:

echo

getErrorCol()

Returns:

column to hold http errors

Return type:

errorCol

getFrequencyPenalty()

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()

Returns:

Which strategy to use when handling requests

Return type:

handler

static getJavaPackage()

Returns package name String.

getLogProbs()

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()

Returns:

The maximum number of tokens to generate. Has minimum of 0.

Return type:

maxTokens

getN()

Returns:

How many snippets to generate for each prompt. Minimum of 1 and maximum of 128 allowed.

Return type:

n

getOutputCol()

Returns:

The name of the output column

Return type:

outputCol

getPresencePenalty()

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()

Returns:

The text to complete

Return type:

prompt

getStop()

Returns:

A sequence which indicates the end of the current document.

Return type:

stop

getSubscriptionKey()

Returns:

the API key to use

Return type:

subscriptionKey

getTemperature()

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()

Returns:

number of seconds to wait before closing the connection

Return type:

timeout

getTopP()

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()

Returns:

Url of the service

Return type:

url

getUser()

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')

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.')

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()

Returns an MLReader instance for this class.

setAADToken(value)

Parameters:

AADToken – AAD Token used for authentication

setAADTokenCol(value)

Parameters:

AADToken – AAD Token used for authentication

setApiVersion(value)

Parameters:

apiVersion – version of the api

setApiVersionCol(value)

Parameters:

apiVersion – version of the api

setBatchPrompt(value)

Parameters:

batchPrompt – Sequence of prompts to complete

setBatchPromptCol(value)

Parameters:

batchPrompt – Sequence of prompts to complete

setBestOf(value)

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)

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)

Parameters:

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

setCacheLevelCol(value)

Parameters:

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

setConcurrency(value)

Parameters:

concurrency – max number of concurrent calls

setConcurrentTimeout(value)

Parameters:

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

setCustomAuthHeader(value)

Parameters:

CustomAuthHeader – A Custom Value for Authorization Header

setCustomAuthHeaderCol(value)

Parameters:

CustomAuthHeader – A Custom Value for Authorization Header

setCustomHeaders(value)

Parameters:

customHeaders – Map of Custom Header Key-Value Tuples.

setCustomHeadersCol(value)

Parameters:

customHeaders – Map of Custom Header Key-Value Tuples.

setCustomServiceName(value)

setCustomUrlRoot(value)

Parameters:

customUrlRoot – The custom URL root for the service. This will not append OpenAI specific model path completions (i.e. /chat/completions) to the URL.

setDefaultInternalEndpoint(value)

setDeploymentName(value)

Parameters:

deploymentName – The name of the deployment

setDeploymentNameCol(value)

Parameters:

deploymentName – The name of the deployment

setEcho(value)

Parameters:

echo – Echo back the prompt in addition to the completion

setEchoCol(value)

Parameters:

echo – Echo back the prompt in addition to the completion

setEndpoint(value)

setErrorCol(value)

Parameters:

errorCol – column to hold http errors

setFrequencyPenalty(value)

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)

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)

Parameters:

handler – Which strategy to use when handling requests

setLogProbs(value)

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)

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)

Parameters:

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

setMaxTokensCol(value)

Parameters:

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

setN(value)

Parameters:

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

setNCol(value)

Parameters:

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

setOutputCol(value)

Parameters:

outputCol – The name of the output column

setParams(AADToken=None, AADTokenCol=None, CustomAuthHeader=None, CustomAuthHeaderCol=None, apiVersion=None, apiVersionCol=None, batchPrompt=None, batchPromptCol=None, bestOf=None, bestOfCol=None, cacheLevel=None, cacheLevelCol=None, concurrency=1, concurrentTimeout=None, customHeaders=None, customHeadersCol=None, customUrlRoot=None, deploymentName=None, deploymentNameCol=None, echo=None, echoCol=None, errorCol='OpenAICompletion_413b9f8e6738_error', frequencyPenalty=None, frequencyPenaltyCol=None, handler=None, logProbs=None, logProbsCol=None, maxTokens=None, maxTokensCol=None, n=None, nCol=None, outputCol='OpenAICompletion_413b9f8e6738_output', presencePenalty=None, presencePenaltyCol=None, prompt=None, promptCol=None, stop=None, stopCol=None, subscriptionKey=None, subscriptionKeyCol=None, temperature=None, temperatureCol=None, timeout=360.0, topP=None, topPCol=None, url=None, user=None, userCol=None)

Set the (keyword only) parameters

setPresencePenalty(value)

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)

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)

Parameters:

prompt – The text to complete

setPromptCol(value)

Parameters:

prompt – The text to complete

setStop(value)

Parameters:

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

setStopCol(value)

Parameters:

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

setSubscriptionKey(value)

Parameters:

subscriptionKey – the API key to use

setSubscriptionKeyCol(value)

Parameters:

subscriptionKey – the API key to use

setTemperature(value)

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)

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)

Parameters:

timeout – number of seconds to wait before closing the connection

setTopP(value)

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)

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)

Parameters:

url – Url of the service

setUser(value)

Parameters:

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

setUserCol(value)

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.services.openai.OpenAIEmbedding module

class synapse.ml.services.openai.OpenAIEmbedding.OpenAIEmbedding(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_0e709ac3fcf2_error', handler=None, outputCol='OpenAIEmbedding_0e709ac3fcf2_output', subscriptionKey=None, subscriptionKeyCol=None, text=None, textCol=None, timeout=360.0, url=None, user=None, userCol=None)

Bases: ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaTransformer

Parameters:

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

CustomAuthHeader = Param(parent='undefined', name='CustomAuthHeader', doc='ServiceParam: A Custom Value for Authorization Header')

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')

customHeaders = Param(parent='undefined', name='customHeaders', doc='ServiceParam: Map of Custom Header Key-Value Tuples.')

customUrlRoot = Param(parent='undefined', name='customUrlRoot', doc='The custom URL root for the service. This will not append OpenAI specific model path completions (i.e. /chat/completions) to the URL.')

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

dimensions = Param(parent='undefined', name='dimensions', doc='ServiceParam: Number of dimensions for output embeddings.')

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

getAADToken()

Returns:

AAD Token used for authentication

Return type:

AADToken

getApiVersion()

Returns:

version of the api

Return type:

apiVersion

getConcurrency()

Returns:

max number of concurrent calls

Return type:

concurrency

getConcurrentTimeout()

Returns:

max number seconds to wait on futures if concurrency >= 1

Return type:

concurrentTimeout

getCustomAuthHeader()

Returns:

A Custom Value for Authorization Header

Return type:

CustomAuthHeader

getCustomHeaders()

Returns:

Map of Custom Header Key-Value Tuples.

Return type:

customHeaders

getCustomUrlRoot()

Returns:

The custom URL root for the service. This will not append OpenAI specific model path completions (i.e. /chat/completions) to the URL.

Return type:

customUrlRoot

getDeploymentName()

Returns:

The name of the deployment

Return type:

deploymentName

getDimensions()

Returns:

Number of dimensions for output embeddings.

Return type:

dimensions

getErrorCol()

Returns:

column to hold http errors

Return type:

errorCol

getHandler()

Returns:

Which strategy to use when handling requests

Return type:

handler

static getJavaPackage()

Returns package name String.

getOutputCol()

Returns:

The name of the output column

Return type:

outputCol

getSubscriptionKey()

Returns:

the API key to use

Return type:

subscriptionKey

getText()

Returns:

Input text to get embeddings for.

Return type:

text

getTimeout()

Returns:

number of seconds to wait before closing the connection

Return type:

timeout

getUrl()

Returns:

Url of the service

Return type:

url

getUser()

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')

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

classmethod read()

Returns an MLReader instance for this class.

setAADToken(value)

Parameters:

AADToken – AAD Token used for authentication

setAADTokenCol(value)

Parameters:

AADToken – AAD Token used for authentication

setApiVersion(value)

Parameters:

apiVersion – version of the api

setApiVersionCol(value)

Parameters:

apiVersion – version of the api

setConcurrency(value)

Parameters:

concurrency – max number of concurrent calls

setConcurrentTimeout(value)

Parameters:

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

setCustomAuthHeader(value)

Parameters:

CustomAuthHeader – A Custom Value for Authorization Header

setCustomAuthHeaderCol(value)

Parameters:

CustomAuthHeader – A Custom Value for Authorization Header

setCustomHeaders(value)

Parameters:

customHeaders – Map of Custom Header Key-Value Tuples.

setCustomHeadersCol(value)

Parameters:

customHeaders – Map of Custom Header Key-Value Tuples.

setCustomServiceName(value)

setCustomUrlRoot(value)

Parameters:

customUrlRoot – The custom URL root for the service. This will not append OpenAI specific model path completions (i.e. /chat/completions) to the URL.

setDefaultInternalEndpoint(value)

setDeploymentName(value)

Parameters:

deploymentName – The name of the deployment

setDeploymentNameCol(value)

Parameters:

deploymentName – The name of the deployment

setDimensions(value)

Parameters:

dimensions – Number of dimensions for output embeddings.

setDimensionsCol(value)

Parameters:

dimensions – Number of dimensions for output embeddings.

setEndpoint(value)

setErrorCol(value)

Parameters:

errorCol – column to hold http errors

setHandler(value)

Parameters:

handler – Which strategy to use when handling requests

setOutputCol(value)

Parameters:

outputCol – The name of the output column

setParams(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_0e709ac3fcf2_error', handler=None, outputCol='OpenAIEmbedding_0e709ac3fcf2_output', subscriptionKey=None, subscriptionKeyCol=None, text=None, textCol=None, timeout=360.0, url=None, user=None, userCol=None)

Set the (keyword only) parameters

setSubscriptionKey(value)

Parameters:

subscriptionKey – the API key to use

setSubscriptionKeyCol(value)

Parameters:

subscriptionKey – the API key to use

setText(value)

Parameters:

text – Input text to get embeddings for.

setTextCol(value)

Parameters:

text – Input text to get embeddings for.

setTimeout(value)

Parameters:

timeout – number of seconds to wait before closing the connection

setUrl(value)

Parameters:

url – Url of the service

setUser(value)

Parameters:

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

setUserCol(value)

Parameters:

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

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')

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

synapse.ml.services.openai.OpenAIPrompt module

class synapse.ml.services.openai.OpenAIPrompt.OpenAIPrompt(java_obj=None, AADToken=None, AADTokenCol=None, CustomAuthHeader=None, CustomAuthHeaderCol=None, apiVersion=None, apiVersionCol=None, bestOf=None, bestOfCol=None, cacheLevel=None, cacheLevelCol=None, concurrency=1, concurrentTimeout=None, customHeaders=None, customHeadersCol=None, customUrlRoot=None, deploymentName=None, deploymentNameCol=None, dropPrompt=True, echo=None, echoCol=None, errorCol='OpenAIPrompt_77c6a4d3cf1f_error', frequencyPenalty=None, frequencyPenaltyCol=None, logProbs=None, logProbsCol=None, maxTokens=None, maxTokensCol=None, messagesCol='OpenAIPrompt_77c6a4d3cf1f_messages', n=None, nCol=None, outputCol='OpenAIPrompt_77c6a4d3cf1f_output', postProcessing='', postProcessingOptions={}, presencePenalty=None, presencePenaltyCol=None, promptTemplate=None, stop=None, stopCol=None, subscriptionKey=None, subscriptionKeyCol=None, systemPrompt="You are an AI chatbot who wants to answer user's questions and complete tasks. Follow their instructions carefully and be brief if they don't say otherwise.", temperature=None, temperatureCol=None, timeout=360.0, topP=None, topPCol=None, url=None, user=None, userCol=None)

Bases: ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaTransformer

Parameters:

• AADToken (object) – AAD Token used for authentication

• CustomAuthHeader (object) – A Custom Value for Authorization Header

• apiVersion (object) – version of the api

• 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

• 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

• dropPrompt (bool) – whether to drop the column of prompts after templating (when using legacy models)

• 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.

• 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.

• messagesCol (str) – The column messages to generate chat completions for, in the chat format. This column should have type Array(Struct(role: String, content: String)).

• 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

• postProcessing (str) – Post processing options: csv, json, regex

• postProcessingOptions (dict) – Options (default): delimiter=’,’, jsonSchema, regex, regexGroup=0

• 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.

• 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

• systemPrompt (str) – The initial system prompt to be used.

• 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')

CustomAuthHeader = Param(parent='undefined', name='CustomAuthHeader', doc='ServiceParam: A Custom Value for Authorization Header')

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

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')

customHeaders = Param(parent='undefined', name='customHeaders', doc='ServiceParam: Map of Custom Header Key-Value Tuples.')

customUrlRoot = Param(parent='undefined', name='customUrlRoot', doc='The custom URL root for the service. This will not append OpenAI specific model path completions (i.e. /chat/completions) to the URL.')

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

dropPrompt = Param(parent='undefined', name='dropPrompt', doc='whether to drop the column of prompts after templating (when using legacy models)')

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()

Returns:

AAD Token used for authentication

Return type:

AADToken

getApiVersion()

Returns:

version of the api

Return type:

apiVersion

getBestOf()

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()

Returns:

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

Return type:

cacheLevel

getConcurrency()

Returns:

max number of concurrent calls

Return type:

concurrency

getConcurrentTimeout()

Returns:

max number seconds to wait on futures if concurrency >= 1

Return type:

concurrentTimeout

getCustomAuthHeader()

Returns:

A Custom Value for Authorization Header

Return type:

CustomAuthHeader

getCustomHeaders()

Returns:

Map of Custom Header Key-Value Tuples.

Return type:

customHeaders

getCustomUrlRoot()

Returns:

The custom URL root for the service. This will not append OpenAI specific model path completions (i.e. /chat/completions) to the URL.

Return type:

customUrlRoot

getDeploymentName()

Returns:

The name of the deployment

Return type:

deploymentName

getDropPrompt()

Returns:

whether to drop the column of prompts after templating (when using legacy models)

Return type:

dropPrompt

getEcho()

Returns:

Echo back the prompt in addition to the completion

Return type:

echo

getErrorCol()

Returns:

column to hold http errors

Return type:

errorCol

getFrequencyPenalty()

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

static getJavaPackage()

Returns package name String.

getLogProbs()

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()

Returns:

The maximum number of tokens to generate. Has minimum of 0.

Return type:

maxTokens

getMessagesCol()

Returns:

The column messages to generate chat completions for, in the chat format. This column should have type Array(Struct(role: String, content: String)).

Return type:

messagesCol

getN()

Returns:

How many snippets to generate for each prompt. Minimum of 1 and maximum of 128 allowed.

Return type:

n

getOutputCol()

Returns:

The name of the output column

Return type:

outputCol

getPostProcessing()

Returns:

Post processing options: csv, json, regex

Return type:

postProcessing

getPostProcessingOptions()

Returns:

Options (default): delimiter=’,’, jsonSchema, regex, regexGroup=0

Return type:

postProcessingOptions

getPresencePenalty()

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

getPromptTemplate()

Returns:

The prompt. supports string interpolation {col1}: {col2}.

Return type:

promptTemplate

getStop()

Returns:

A sequence which indicates the end of the current document.

Return type:

stop

getSubscriptionKey()

Returns:

the API key to use

Return type:

subscriptionKey

getSystemPrompt()

Returns:

The initial system prompt to be used.

Return type:

systemPrompt

getTemperature()

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()

Returns:

number of seconds to wait before closing the connection

Return type:

timeout

getTopP()

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()

Returns:

Url of the service

Return type:

url

getUser()

Returns:

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

Return type:

user

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.')

messagesCol = Param(parent='undefined', name='messagesCol', doc='The column messages to generate chat completions for, in the chat format. This column should have type Array(Struct(role: String, content: String)).')

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')

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")

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.')

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

classmethod read()

Returns an MLReader instance for this class.

setAADToken(value)

Parameters:

AADToken – AAD Token used for authentication

setAADTokenCol(value)

Parameters:

AADToken – AAD Token used for authentication

setApiVersion(value)

Parameters:

apiVersion – version of the api

setApiVersionCol(value)

Parameters:

apiVersion – version of the api

setBestOf(value)

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)

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)

Parameters:

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

setCacheLevelCol(value)

Parameters:

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

setConcurrency(value)

Parameters:

concurrency – max number of concurrent calls

setConcurrentTimeout(value)

Parameters:

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

setCustomAuthHeader(value)

Parameters:

CustomAuthHeader – A Custom Value for Authorization Header

setCustomAuthHeaderCol(value)

Parameters:

CustomAuthHeader – A Custom Value for Authorization Header

setCustomHeaders(value)

Parameters:

customHeaders – Map of Custom Header Key-Value Tuples.

setCustomHeadersCol(value)

Parameters:

customHeaders – Map of Custom Header Key-Value Tuples.

setCustomServiceName(value)

setCustomUrlRoot(value)

Parameters:

customUrlRoot – The custom URL root for the service. This will not append OpenAI specific model path completions (i.e. /chat/completions) to the URL.

setDefaultInternalEndpoint(value)

setDeploymentName(value)

Parameters:

deploymentName – The name of the deployment

setDeploymentNameCol(value)

Parameters:

deploymentName – The name of the deployment

setDropPrompt(value)

Parameters:

dropPrompt – whether to drop the column of prompts after templating (when using legacy models)

setEcho(value)

Parameters:

echo – Echo back the prompt in addition to the completion

setEchoCol(value)

Parameters:

echo – Echo back the prompt in addition to the completion

setEndpoint(value)

setErrorCol(value)

Parameters:

errorCol – column to hold http errors

setFrequencyPenalty(value)

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)

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.

setLogProbs(value)

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)

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)

Parameters:

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

setMaxTokensCol(value)

Parameters:

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

setMessagesCol(value)

Parameters:

messagesCol – The column messages to generate chat completions for, in the chat format. This column should have type Array(Struct(role: String, content: String)).

setN(value)

Parameters:

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

setNCol(value)

Parameters:

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

setOutputCol(value)

Parameters:

outputCol – The name of the output column

setParams(AADToken=None, AADTokenCol=None, CustomAuthHeader=None, CustomAuthHeaderCol=None, apiVersion=None, apiVersionCol=None, bestOf=None, bestOfCol=None, cacheLevel=None, cacheLevelCol=None, concurrency=1, concurrentTimeout=None, customHeaders=None, customHeadersCol=None, customUrlRoot=None, deploymentName=None, deploymentNameCol=None, dropPrompt=True, echo=None, echoCol=None, errorCol='OpenAIPrompt_77c6a4d3cf1f_error', frequencyPenalty=None, frequencyPenaltyCol=None, logProbs=None, logProbsCol=None, maxTokens=None, maxTokensCol=None, messagesCol='OpenAIPrompt_77c6a4d3cf1f_messages', n=None, nCol=None, outputCol='OpenAIPrompt_77c6a4d3cf1f_output', postProcessing='', postProcessingOptions={}, presencePenalty=None, presencePenaltyCol=None, promptTemplate=None, stop=None, stopCol=None, subscriptionKey=None, subscriptionKeyCol=None, systemPrompt="You are an AI chatbot who wants to answer user's questions and complete tasks. Follow their instructions carefully and be brief if they don't say otherwise.", temperature=None, temperatureCol=None, timeout=360.0, topP=None, topPCol=None, url=None, user=None, userCol=None)

Set the (keyword only) parameters

setPostProcessing(value)

Parameters:

postProcessing – Post processing options: csv, json, regex

setPostProcessingOptions(value)

Parameters:

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

setPresencePenalty(value)

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)

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.

setPromptTemplate(value)

Parameters:

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

setStop(value)

Parameters:

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

setStopCol(value)

Parameters:

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

setSubscriptionKey(value)

Parameters:

subscriptionKey – the API key to use

setSubscriptionKeyCol(value)

Parameters:

subscriptionKey – the API key to use

setSystemPrompt(value)

Parameters:

systemPrompt – The initial system prompt to be used.

setTemperature(value)

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)

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)

Parameters:

timeout – number of seconds to wait before closing the connection

setTopP(value)

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)

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)

Parameters:

url – Url of the service

setUser(value)

Parameters:

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

setUserCol(value)

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')

systemPrompt = Param(parent='undefined', name='systemPrompt', doc='The initial system prompt to be used.')

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.')

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+.