final case class CreateAnswerRequest(model: String, question: Question, examples: NonEmptyChunk[Chunk[ExamplesItemItem]], examplesContext: String, documents: Optional[Chunk[String]] = Optional.Absent, file: Optional[String] = Optional.Absent, searchModel: Optional[String] = Optional.Absent, maxRerank: Optional[Int] = Optional.Absent, temperature: Optional[Double] = Optional.Absent, logprobs: Optional[Logprobs] = Optional.Absent, maxTokens: Optional[Int] = Optional.Absent, stop: Optional[Stop] = Optional.Absent, n: Optional[CreateAnswerRequest.N] = Optional.Absent, logitBias: Optional[LogitBias] = Optional.Absent, returnMetadata: Optional[Boolean] = Optional.Absent, returnPrompt: Optional[Boolean] = Optional.Absent, expand: Optional[Chunk[ExpandItem]] = Optional.Absent, user: Optional[String] = Optional.Absent) extends Product with Serializable

CreateAnswerRequest model

model

ID of the model to use for completion. You can select one of ada, babbage, curie, or davinci.

question

Question to get answered.

examples

List of (question, answer) pairs that will help steer the model towards the tone and answer format you'd like. We recommend adding 2 to 3 examples.

examplesContext

A text snippet containing the contextual information used to generate the answers for the examples you provide.

documents

List of documents from which the answer for the input question should be derived. If this is an empty list, the question will be answered based on the question-answer examples. You should specify either documents or a file, but not both.

file

The ID of an uploaded file that contains documents to search over. See [upload file](/docs/api-reference/files/upload) for how to upload a file of the desired format and purpose. You should specify either documents or a file, but not both.

searchModel

ID of the model to use for [Search](/docs/api-reference/searches/create). You can select one of ada, babbage, curie, or davinci.

maxRerank

The maximum number of documents to be ranked by [Search](/docs/api-reference/searches/create) when using file. Setting it to a higher value leads to improved accuracy but with increased latency and cost.

temperature

What [sampling temperature](https://towardsdatascience.com/how-to-sample-from-language-models-682bceb97277) to use. Higher values mean the model will take more risks and value 0 (argmax sampling) works better for scenarios with a well-defined answer.

logprobs

Include the log probabilities on the logprobs most likely tokens, as well the chosen tokens. For example, if logprobs is 5, the API will return a list of the 5 most likely tokens. The API will always return the logprob of the sampled token, so there may be up to logprobs+1 elements in the response. The maximum value for logprobs is 5. If you need more than this, please contact us through our [Help center](https://help.openai.com) and describe your use case. When logprobs is set, completion will be automatically added into expand to get the logprobs.

maxTokens

The maximum number of tokens allowed for the generated answer

stop

Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.

n

How many answers to generate for each question.

logitBias

Modify the likelihood of specified tokens appearing in the completion. Accepts a json object that maps tokens (specified by their token ID in the GPT tokenizer) to an associated bias value from -100 to 100. You can use this [tokenizer tool](/tokenizer?view=bpe) (which works for both GPT-2 and GPT-3) to convert text to token IDs. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. As an example, you can pass {"50256": -100} to prevent the <|endoftext|> token from being generated.

returnMetadata

A special boolean flag for showing metadata. If set to true, each document entry in the returned JSON will contain a "metadata" field. This flag only takes effect when file is set.

returnPrompt

If set to true, the returned JSON will include a "prompt" field containing the final prompt that was used to request a completion. This is mainly useful for debugging purposes.

expand

If an object name is in the list, we provide the full information of the object; otherwise, we only provide the object ID. Currently we support completion and file objects for expansion.

user

A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. [Learn more](/docs/guides/safety-best-practices/end-user-ids).

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Instance Constructors

  1. new CreateAnswerRequest(model: String, question: Question, examples: NonEmptyChunk[Chunk[ExamplesItemItem]], examplesContext: String, documents: Optional[Chunk[String]] = Optional.Absent, file: Optional[String] = Optional.Absent, searchModel: Optional[String] = Optional.Absent, maxRerank: Optional[Int] = Optional.Absent, temperature: Optional[Double] = Optional.Absent, logprobs: Optional[Logprobs] = Optional.Absent, maxTokens: Optional[Int] = Optional.Absent, stop: Optional[Stop] = Optional.Absent, n: Optional[CreateAnswerRequest.N] = Optional.Absent, logitBias: Optional[LogitBias] = Optional.Absent, returnMetadata: Optional[Boolean] = Optional.Absent, returnPrompt: Optional[Boolean] = Optional.Absent, expand: Optional[Chunk[ExpandItem]] = Optional.Absent, user: Optional[String] = Optional.Absent)

    model

    ID of the model to use for completion. You can select one of ada, babbage, curie, or davinci.

    question

    Question to get answered.

    examples

    List of (question, answer) pairs that will help steer the model towards the tone and answer format you'd like. We recommend adding 2 to 3 examples.

    examplesContext

    A text snippet containing the contextual information used to generate the answers for the examples you provide.

    documents

    List of documents from which the answer for the input question should be derived. If this is an empty list, the question will be answered based on the question-answer examples. You should specify either documents or a file, but not both.

    file

    The ID of an uploaded file that contains documents to search over. See [upload file](/docs/api-reference/files/upload) for how to upload a file of the desired format and purpose. You should specify either documents or a file, but not both.

    searchModel

    ID of the model to use for [Search](/docs/api-reference/searches/create). You can select one of ada, babbage, curie, or davinci.

    maxRerank

    The maximum number of documents to be ranked by [Search](/docs/api-reference/searches/create) when using file. Setting it to a higher value leads to improved accuracy but with increased latency and cost.

    temperature

    What [sampling temperature](https://towardsdatascience.com/how-to-sample-from-language-models-682bceb97277) to use. Higher values mean the model will take more risks and value 0 (argmax sampling) works better for scenarios with a well-defined answer.

    logprobs

    Include the log probabilities on the logprobs most likely tokens, as well the chosen tokens. For example, if logprobs is 5, the API will return a list of the 5 most likely tokens. The API will always return the logprob of the sampled token, so there may be up to logprobs+1 elements in the response. The maximum value for logprobs is 5. If you need more than this, please contact us through our [Help center](https://help.openai.com) and describe your use case. When logprobs is set, completion will be automatically added into expand to get the logprobs.

    maxTokens

    The maximum number of tokens allowed for the generated answer

    stop

    Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.

    n

    How many answers to generate for each question.

    logitBias

    Modify the likelihood of specified tokens appearing in the completion. Accepts a json object that maps tokens (specified by their token ID in the GPT tokenizer) to an associated bias value from -100 to 100. You can use this [tokenizer tool](/tokenizer?view=bpe) (which works for both GPT-2 and GPT-3) to convert text to token IDs. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token. As an example, you can pass {"50256": -100} to prevent the <|endoftext|> token from being generated.

    returnMetadata

    A special boolean flag for showing metadata. If set to true, each document entry in the returned JSON will contain a "metadata" field. This flag only takes effect when file is set.

    returnPrompt

    If set to true, the returned JSON will include a "prompt" field containing the final prompt that was used to request a completion. This is mainly useful for debugging purposes.

    expand

    If an object name is in the list, we provide the full information of the object; otherwise, we only provide the object ID. Currently we support completion and file objects for expansion.

    user

    A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. [Learn more](/docs/guides/safety-best-practices/end-user-ids).

Value Members

  1. final def !=(arg0: Any): Boolean
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  2. final def ##: Int
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  3. final def ==(arg0: Any): Boolean
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  5. def clone(): AnyRef
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    @throws(classOf[java.lang.CloneNotSupportedException]) @native()
  6. val documents: Optional[Chunk[String]]
  7. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  8. val examples: NonEmptyChunk[Chunk[ExamplesItemItem]]
  9. val examplesContext: String
  10. val expand: Optional[Chunk[ExpandItem]]
  11. val file: Optional[String]
  12. def finalize(): Unit
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    @throws(classOf[java.lang.Throwable])
  13. final def getClass(): Class[_ <: AnyRef]
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    @native()
  14. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  15. val logitBias: Optional[LogitBias]
  16. val logprobs: Optional[Logprobs]
  17. val maxRerank: Optional[Int]
  18. val maxTokens: Optional[Int]
  19. val model: String
  20. val n: Optional[CreateAnswerRequest.N]
  21. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  22. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  23. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  24. def productElementNames: Iterator[String]
    Definition Classes
    Product
  25. val question: Question
  26. val returnMetadata: Optional[Boolean]
  27. val returnPrompt: Optional[Boolean]
  28. val searchModel: Optional[String]
  29. val stop: Optional[Stop]
  30. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  31. val temperature: Optional[Double]
  32. val user: Optional[String]
  33. final def wait(): Unit
    Definition Classes
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    @throws(classOf[java.lang.InterruptedException])
  34. final def wait(arg0: Long, arg1: Int): Unit
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  35. final def wait(arg0: Long): Unit
    Definition Classes
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    @throws(classOf[java.lang.InterruptedException]) @native()

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