Package io.quarkiverse.langchain4j.ollama.runtime.config

Interface EmbeddingModelConfig

public interface EmbeddingModelConfig

  • Method Details

    • modelId

      @WithDefault("nomic-embed-text") String modelId()
      Model to use. According to Ollama docs, the default value is nomic-embed-text

    • temperature

      @WithDefault("0.8") Double temperature()
      The temperature of the model. Increasing the temperature will make the model answer with more variability. A lower temperature will make the model answer more conservatively.

    • numPredict

      @WithDefault("128") Integer numPredict()
      Maximum number of tokens to predict when generating text

    • stop

      Optional<String> stop()
      Sets the stop sequences to use. When this pattern is encountered the LLM will stop generating text and return

    • topP

      @WithDefault("0.9") Double topP()
      Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text

    • topK

      @WithDefault("40") Integer topK()
      Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative