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package io.quarkiverse.langchain4j.ollama.runtime.config;

import java.util.List;
import java.util.Optional;

import io.quarkus.runtime.annotations.ConfigDocDefault;
import io.quarkus.runtime.annotations.ConfigGroup;
import io.smallrye.config.WithDefault;

@ConfigGroup
public interface EmbeddingModelConfig {

    /**
     * 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.
     */
    @WithDefault("0.8")
    Double temperature();

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

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

    /**
     * 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
     */
    @WithDefault("0.9")
    Double topP();

    /**
     * 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
     */
    @WithDefault("40")
    Integer topK();

    /**
     * Whether embedding model requests should be logged
     */
    @ConfigDocDefault("false")
    @WithDefault("${quarkus.langchain4j.ollama.log-requests}")
    Optional logRequests();

    /**
     * Whether embedding model responses should be logged
     */
    @ConfigDocDefault("false")
    @WithDefault("${quarkus.langchain4j.ollama.log-responses}")
    Optional logResponses();
}