synapse.ml.cognitive.openai package
Submodules
synapse.ml.cognitive.openai.OpenAIChatCompletion module
- class synapse.ml.cognitive.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, deploymentName=None, deploymentNameCol=None, echo=None, echoCol=None, errorCol='OpenAIChatCompletion_22513703bad4_error', frequencyPenalty=None, frequencyPenaltyCol=None, handler=None, logProbs=None, logProbsCol=None, maxTokens=None, maxTokensCol=None, messagesCol=None, n=None, nCol=None, outputCol='OpenAIChatCompletion_22513703bad4_output', presencePenalty=None, presencePenaltyCol=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
CustomAuthHeader¶ (object) – A Custom Value for Authorization Header
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
concurrentTimeout¶ (float) – max number seconds to wait on futures if concurrency >= 1
echo¶ (object) – Echo back the prompt in addition to the completion
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.
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.
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.
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')
- 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.')
- 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
- getConcurrentTimeout()[source]
- Returns
max number seconds to wait on futures if concurrency >= 1
- Return type
concurrentTimeout
- getCustomAuthHeader()[source]
- Returns
A Custom Value for Authorization Header
- Return type
CustomAuthHeader
- 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
- 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
- getMessagesCol()[source]
- 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()[source]
- Returns
How many snippets to generate for each prompt. Minimum of 1 and maximum of 128 allowed.
- Return type
n
- 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
- getStop()[source]
- Returns
A sequence which indicates the end of the current document.
- Return type
stop
- 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
- 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')
- 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.')
- 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
- setConcurrentTimeout(value)[source]
- Parameters
concurrentTimeout¶ – max number seconds to wait on futures if concurrency >= 1
- setCustomAuthHeader(value)[source]
- Parameters
CustomAuthHeader¶ – A Custom Value for Authorization Header
- setCustomAuthHeaderCol(value)[source]
- Parameters
CustomAuthHeader¶ – A Custom Value for Authorization Header
- 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.
- 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.
- setMessagesCol(value)[source]
- 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)[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.
- 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, deploymentName=None, deploymentNameCol=None, echo=None, echoCol=None, errorCol='OpenAIChatCompletion_22513703bad4_error', frequencyPenalty=None, frequencyPenaltyCol=None, handler=None, logProbs=None, logProbsCol=None, maxTokens=None, maxTokensCol=None, messagesCol=None, n=None, nCol=None, outputCol='OpenAIChatCompletion_22513703bad4_output', presencePenalty=None, presencePenaltyCol=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.
- 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.
- 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.
- 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.OpenAICompletion module
- class synapse.ml.cognitive.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, deploymentName=None, deploymentNameCol=None, echo=None, echoCol=None, errorCol='OpenAICompletion_b6afd879444d_error', frequencyPenalty=None, frequencyPenaltyCol=None, handler=None, logProbs=None, logProbsCol=None, maxTokens=None, maxTokensCol=None, n=None, nCol=None, outputCol='OpenAICompletion_b6afd879444d_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
CustomAuthHeader¶ (object) – A Custom Value for Authorization Header
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
concurrentTimeout¶ (float) – max number seconds to wait on futures if concurrency >= 1
echo¶ (object) – Echo back the prompt in addition to the completion
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.
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.
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.
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')
- 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.')
- 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
- getConcurrentTimeout()[source]
- Returns
max number seconds to wait on futures if concurrency >= 1
- Return type
concurrentTimeout
- getCustomAuthHeader()[source]
- Returns
A Custom Value for Authorization Header
- Return type
CustomAuthHeader
- 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
- 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
- getN()[source]
- Returns
How many snippets to generate for each prompt. Minimum of 1 and maximum of 128 allowed.
- Return type
n
- 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
- getStop()[source]
- Returns
A sequence which indicates the end of the current document.
- Return type
stop
- 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
- 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')
- 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')
- 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
- setConcurrentTimeout(value)[source]
- Parameters
concurrentTimeout¶ – max number seconds to wait on futures if concurrency >= 1
- setCustomAuthHeader(value)[source]
- Parameters
CustomAuthHeader¶ – A Custom Value for Authorization Header
- setCustomAuthHeaderCol(value)[source]
- Parameters
CustomAuthHeader¶ – A Custom Value for Authorization Header
- 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.
- 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.
- 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.
- 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, deploymentName=None, deploymentNameCol=None, echo=None, echoCol=None, errorCol='OpenAICompletion_b6afd879444d_error', frequencyPenalty=None, frequencyPenaltyCol=None, handler=None, logProbs=None, logProbsCol=None, maxTokens=None, maxTokensCol=None, n=None, nCol=None, outputCol='OpenAICompletion_b6afd879444d_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.
- 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.
- 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.
- 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, CustomAuthHeader=None, CustomAuthHeaderCol=None, apiVersion=None, apiVersionCol=None, concurrency=1, concurrentTimeout=None, deploymentName=None, deploymentNameCol=None, errorCol='OpenAIEmbedding_b6ce4bb7dd05_error', handler=None, outputCol='OpenAIEmbedding_b6ce4bb7dd05_output', subscriptionKey=None, subscriptionKeyCol=None, text=None, textCol=None, timeout=60.0, 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
CustomAuthHeader¶ (object) – A Custom Value for Authorization Header
concurrentTimeout¶ (float) – max number seconds to wait on futures if concurrency >= 1
handler¶ (object) – Which strategy to use when handling requests
timeout¶ (float) – number of seconds to wait before closing the connection
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')
- 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')
- getConcurrentTimeout()[source]
- Returns
max number seconds to wait on futures if concurrency >= 1
- Return type
concurrentTimeout
- getCustomAuthHeader()[source]
- Returns
A Custom Value for Authorization Header
- Return type
CustomAuthHeader
- getTimeout()[source]
- Returns
number of seconds to wait before closing the connection
- Return type
timeout
- 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')
- outputCol = Param(parent='undefined', name='outputCol', doc='The name of the output column')
- setConcurrentTimeout(value)[source]
- Parameters
concurrentTimeout¶ – max number seconds to wait on futures if concurrency >= 1
- setCustomAuthHeader(value)[source]
- Parameters
CustomAuthHeader¶ – A Custom Value for Authorization Header
- setCustomAuthHeaderCol(value)[source]
- Parameters
CustomAuthHeader¶ – A Custom Value for Authorization Header
- setParams(AADToken=None, AADTokenCol=None, CustomAuthHeader=None, CustomAuthHeaderCol=None, apiVersion=None, apiVersionCol=None, concurrency=1, concurrentTimeout=None, deploymentName=None, deploymentNameCol=None, errorCol='OpenAIEmbedding_b6ce4bb7dd05_error', handler=None, outputCol='OpenAIEmbedding_b6ce4bb7dd05_output', subscriptionKey=None, subscriptionKeyCol=None, text=None, textCol=None, timeout=60.0, url=None, user=None, userCol=None)[source]
Set the (keyword only) parameters
- setTimeout(value)[source]
- Parameters
timeout¶ – number of seconds to wait before closing the connection
- 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.
- 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.cognitive.openai.OpenAIPrompt module
- class synapse.ml.cognitive.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, deploymentName=None, deploymentNameCol=None, dropPrompt=True, echo=None, echoCol=None, errorCol='OpenAIPrompt_086628fb8dd4_error', frequencyPenalty=None, frequencyPenaltyCol=None, logProbs=None, logProbsCol=None, maxTokens=None, maxTokensCol=None, n=None, nCol=None, outputCol='OpenAIPrompt_086628fb8dd4_output', postProcessing='', postProcessingOptions={}, presencePenalty=None, presencePenaltyCol=None, promptTemplate=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
CustomAuthHeader¶ (object) – A Custom Value for Authorization Header
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
concurrentTimeout¶ (float) – max number seconds to wait on futures if concurrency >= 1
dropPrompt¶ (bool) – whether to drop the column of prompts after templating
echo¶ (object) – Echo back the prompt in addition to the completion
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.
n¶ (object) – How many snippets to generate for each prompt. Minimum of 1 and maximum of 128 allowed.
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.
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.
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')
- 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')
- 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.')
- 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
- getConcurrentTimeout()[source]
- Returns
max number seconds to wait on futures if concurrency >= 1
- Return type
concurrentTimeout
- getCustomAuthHeader()[source]
- Returns
A Custom Value for Authorization Header
- Return type
CustomAuthHeader
- getDropPrompt()[source]
- Returns
whether to drop the column of prompts after templating
- Return type
dropPrompt
- 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
- 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
- getN()[source]
- Returns
How many snippets to generate for each prompt. Minimum of 1 and maximum of 128 allowed.
- Return type
n
- 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
- 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
- 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
- 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
- getUser()[source]
- 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.')
- 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}.')
- 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
- setConcurrentTimeout(value)[source]
- Parameters
concurrentTimeout¶ – max number seconds to wait on futures if concurrency >= 1
- setCustomAuthHeader(value)[source]
- Parameters
CustomAuthHeader¶ – A Custom Value for Authorization Header
- setCustomAuthHeaderCol(value)[source]
- Parameters
CustomAuthHeader¶ – A Custom Value for Authorization Header
- setDropPrompt(value)[source]
- Parameters
dropPrompt¶ – whether to drop the column of prompts after templating
- 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.
- 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.
- 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.
- 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, deploymentName=None, deploymentNameCol=None, dropPrompt=True, echo=None, echoCol=None, errorCol='OpenAIPrompt_086628fb8dd4_error', frequencyPenalty=None, frequencyPenaltyCol=None, logProbs=None, logProbsCol=None, maxTokens=None, maxTokensCol=None, n=None, nCol=None, outputCol='OpenAIPrompt_086628fb8dd4_output', postProcessing='', postProcessingOptions={}, presencePenalty=None, presencePenaltyCol=None, promptTemplate=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
- setPostProcessing(value)[source]
- Parameters
postProcessing¶ – Post processing options: csv, json, regex
- setPostProcessingOptions(value)[source]
- Parameters
postProcessingOptions¶ – Options (default): delimiter=’,’, jsonSchema, regex, regexGroup=0
- 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.
- 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.
- 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.
- 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.')
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+.