Interface ChatModelConfig
public interface ChatModelConfig
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Method Summary
Modifier and TypeMethodDescriptionThis property will override thequarkus.langchain4j.azure-openai.deployment-name
specifically for chat models if it is set.This property will override thequarkus.langchain4j.azure-openai.domain-name
specifically for chat models if it is set.endpoint()
This property will override thequarkus.langchain4j.azure-openai.endpoint
specifically for chat models if it is set.Number between -2.0 and 2.0.Whether chat model requests should be loggedWhether chat model responses should be loggedThe maximum number of tokens to generate in the completion.Number between -2.0 and 2.0.This property will override thequarkus.langchain4j.azure-openai.resource-name
specifically for chat models if it is set.The response format the model should use.What sampling temperature to use, with values between 0 and 2.topP()
An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with topP probability mass. 0.1 means only the tokens comprising the top 10% probability mass are considered.
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Method Details
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resourceName
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domainName
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deploymentName
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endpoint
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temperature
What sampling temperature to use, with values between 0 and 2. Higher values means the model will take more risks. A value of 0.9 is good for more creative applications, while 0 (argmax sampling) is good for ones with a well-defined answer. It is recommended to alter this or topP, but not both. -
topP
An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with topP probability mass. 0.1 means only the tokens comprising the top 10% probability mass are considered. It is recommended to alter this or temperature, but not both. -
maxTokens
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presencePenalty
Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics. -
frequencyPenalty
Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim. -
logRequests
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logResponses
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responseFormat
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