Package com.oracle.bmc.generativeaiinference.model

Class CohereLlmInferenceRequest

  • @Generated(value="OracleSDKGenerator",
           comments="API Version: 20231130")
public final class CohereLlmInferenceRequest
extends LlmInferenceRequest
    Details for the text generation request for Cohere models.
    Note: Objects should always be created or deserialized using the CohereLlmInferenceRequest.Builder. This model distinguishes fields that are null because they are unset from fields that are explicitly set to null. This is done in the setter methods of the CohereLlmInferenceRequest.Builder, which maintain a set of all explicitly set fields called CohereLlmInferenceRequest.Builder.__explicitlySet__. The hashCode() and equals(Object) methods are implemented to take the explicitly set fields into account. The constructor, on the other hand, does not take the explicitly set fields into account (since the constructor cannot distinguish explicit null from unset null).

    • Method Detail

      • getPrompt

        public String getPrompt()
        Represents the prompt to be completed.

        The trailing white spaces are trimmed before completion.

        Returns:
        the value

      • getIsStream

        public Boolean getIsStream()
        Whether to stream back partial progress.

        If set, tokens are sent as data-only server-sent events as they become available.

        Returns:
        the value

      • getNumGenerations

        public Integer getNumGenerations()
        The number of generated texts that will be returned.
        Returns:
        the value

      • getIsEcho

        public Boolean getIsEcho()
        Whether or not to return the user prompt in the response.

        This option only applies to non-stream results.

        Returns:
        the value

      • getMaxTokens

        public Integer getMaxTokens()
        The maximum number of tokens to predict for each response.

        Includes input plus output tokens.

        Returns:
        the value

      • getTemperature

        public Double getTemperature()
        A number that sets the randomness of the generated output.

        A lower temperature means a less random generations.

        Use lower numbers for tasks with a correct answer such as question answering or summarizing. High temperatures can generate hallucinations or factually incorrect information. Start with temperatures lower than 1.0 and increase the temperature for more creative outputs, as you regenerate the prompts to refine the outputs.

        Returns:
        the value

      • getTopK

        public Integer getTopK()
        An integer that sets up the model to use only the top k most likely tokens in the generated output.

        A higher k introduces more randomness into the output making the output text sound more natural. Default value is 0 which disables this method and considers all tokens. To set a number for the likely tokens, choose an integer between 1 and 500.

        If also using top p, then the model considers only the top tokens whose probabilities add up to p percent and ignores the rest of the k tokens. For example, if k is 20, but the probabilities of the top 10 add up to .75, then only the top 10 tokens are chosen.

        Returns:
        the value

      • getTopP

        public Double getTopP()
        If set to a probability 0.0 < p < 1.0, it ensures that only the most likely tokens, with total probability mass of p, are considered for generation at each step.

        To eliminate tokens with low likelihood, assign p a minimum percentage for the next token's likelihood. For example, when p is set to 0.75, the model eliminates the bottom 25 percent for the next token. Set to 1.0 to consider all tokens and set to 0 to disable. If both k and p are enabled, p acts after k.

        Returns:
        the value

      • getFrequencyPenalty

        public Double getFrequencyPenalty()
        To reduce repetitiveness of generated tokens, this number penalizes new tokens based on their frequency in the generated text so far.

        Greater numbers encourage the model to use new tokens, while lower numbers encourage the model to repeat the tokens. Set to 0 to disable.

        Returns:
        the value

      • getPresencePenalty

        public Double getPresencePenalty()
        To reduce repetitiveness of generated tokens, this number penalizes new tokens based on whether they’ve appeared in the generated text so far.

        Greater numbers encourage the model to use new tokens, while lower numbers encourage the model to repeat the tokens.

        Similar to frequency penalty, a penalty is applied to previously present tokens, except that this penalty is applied equally to all tokens that have already appeared, regardless of how many times they've appeared. Set to 0 to disable.

        Returns:
        the value

      • getStopSequences

        public List<String> getStopSequences()
        The generated text is cut at the end of the earliest occurrence of this stop sequence.

        The generated text will include this stop sequence.

        Returns:
        the value

      • getTruncate

        public CohereLlmInferenceRequest.Truncate getTruncate()
        For an input that’s longer than the maximum token length, specifies which part of the input text will be truncated.
        Returns:
        the value

      • toString

        public String toString​(boolean includeByteArrayContents)
        Return a string representation of the object.
        Overrides:
        toString in class LlmInferenceRequest
        Parameters:
        includeByteArrayContents - true to include the full contents of byte arrays
        Returns:
        string representation