Interface ChatModelConfig

public interface ChatModelConfig

  • Method Details

    • modelName

      @WithDefault("gpt-4o-mini") String modelName()
      Model name to use

    • temperature

      @WithDefault("${quarkus.langchain4j.temperature:1.0}") Double 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

      @WithDefault("1.0") Double 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

      Optional<Integer> maxTokens()
      The maximum number of tokens to generate in the completion. The token count of your prompt plus max_tokens can't exceed the model's context length. Most models have a context length of 2048 tokens (except for the newest models, which support 4096).

    • presencePenalty

      @WithDefault("0") Double 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

      @WithDefault("0") Double 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

      @ConfigDocDefault("false") Optional<Boolean> logRequests()
      Whether chat model requests should be logged

    • logResponses

      @ConfigDocDefault("false") Optional<Boolean> logResponses()
      Whether chat model responses should be logged

    • responseFormat

      Optional<String> responseFormat()
      The response format the model should use. Some models are not compatible with some response formats, make sure to review OpenAI documentation.

    • strictJsonSchema

      Optional<Boolean> strictJsonSchema()
      Whether responses follow JSON Schema for Structured Outputs

    • stop

      Optional<List<String>> stop()
      The list of stop words to use.
      Returns: