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// Copyright (c) Alibaba, Inc. and its affiliates.
package com.alibaba.dashscope.app;
import static com.alibaba.dashscope.utils.ApiKeywords.HISTORY;
import static com.alibaba.dashscope.utils.ApiKeywords.PROMPT;
import com.alibaba.dashscope.base.HalfDuplexParamBase;
import com.alibaba.dashscope.common.History;
import com.alibaba.dashscope.exception.InputRequiredException;
import com.alibaba.dashscope.utils.ApiKeywords;
import com.alibaba.dashscope.utils.JsonUtils;
import com.google.gson.JsonArray;
import com.google.gson.JsonObject;
import java.nio.ByteBuffer;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import lombok.Builder;
import lombok.Data;
import lombok.EqualsAndHashCode;
import lombok.experimental.SuperBuilder;
/**
* Title Application call input parameters.
* Description Application call input parameters.
* Created at 2024-02-23 16:15
*
* @since jdk8
*/
@EqualsAndHashCode(callSuper = true)
@Data
@SuperBuilder
public class ApplicationParam extends HalfDuplexParamBase {
@Builder.Default private Object resources = null;
/** Id of bailian application */
@lombok.NonNull private String appId;
/** prompt */
private String prompt;
/** chat history */
private List history;
/** Session id for storing chat history note: this will be ignored if history passed */
private String sessionId;
/** Flag to return rag or plugin process details */
@Builder.Default private Boolean hasThoughts = false;
/** The extra parameters for flow or plugin. */
private JsonObject bizParams;
/**
* A sampling strategy, called nucleus sampling, where the model considers the results of the
* tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10%
* probability mass are considered
*/
private Double topP;
/** A sampling strategy, the k largest elements of the given mass are considered */
private Integer topK;
/**
* When generating, the seed of the random number is used to control the randomness of the model
* generation. If you use the same seed, each run will generate the same results; you can use the
* same seed when you need to reproduce the model's generated results. The seed parameter supports
* unsigned 64-bit integer types. Default value 1234
*/
private Integer seed;
/**
* Used to control the degree of randomness and diversity. Specifically, the temperature value
* controls the degree to which the probability distribution of each candidate word is smoothed
* when generating text. A higher temperature value will reduce the peak value of the probability
* distribution, allowing more low-probability words to be selected, and the generated results
* will be more diverse; while a lower temperature value will enhance the peak value of the
* probability distribution, making it easier for high-probability words to be selected, the
* generated results are more deterministic, range(0, 2).
*/
private Float temperature;
/**
* Used to control the streaming output mode. If true, the subsequent output will include the
* previously input content by default. Otherwise, the subsequent output will not include the
* previously output content. Default: false.
*/
@Builder.Default private Boolean incrementalOutput = false;
/**
* Long term memory id is used to store long term context summary between end users and assistant.
*/
private String memoryId;
/** image list */
private List images;
/** rag options */
private RagOptions ragOptions;
@Override
public String getModel() {
return null;
}
@Override
public Map getParameters() {
Map params = new HashMap<>(0);
if (topP != null) {
params.put(AppKeywords.TOP_P, topP);
}
if (topK != null) {
params.put(AppKeywords.TOP_K, topK);
}
if (seed != null) {
params.put(AppKeywords.SEED, seed);
}
if (temperature != null) {
params.put(AppKeywords.TEMPERATURE, temperature);
}
if (hasThoughts != null) {
params.put(AppKeywords.HAS_THOUGHTS, hasThoughts);
}
if (incrementalOutput != null) {
params.put("incremental_output", incrementalOutput);
}
if (ragOptions != null) {
params.put("rag_options", ragOptions);
}
params.putAll(parameters);
return params;
}
@Override
public Map getHeaders() {
Map reqHeaders = new HashMap<>();
for (Map.Entry entry : headers.entrySet()) {
String key = entry.getKey();
String value = entry.getValue().toString();
reqHeaders.put(key, value);
}
return reqHeaders;
}
@Override
public JsonObject getHttpBody() {
JsonObject body = new JsonObject();
body.add(ApiKeywords.INPUT, getInput());
Map params = getParameters();
if (params != null && !params.isEmpty()) {
body.add(ApiKeywords.PARAMETERS, JsonUtils.parametersToJsonObject(params));
}
return body;
}
@Override
public JsonObject getInput() {
JsonObject input = new JsonObject();
input.addProperty(PROMPT, getPrompt());
input.addProperty(AppKeywords.SESSION_ID, getSessionId());
input.addProperty(AppKeywords.MEMORY_ID, memoryId);
if (history != null && !history.isEmpty()) {
JsonArray historyJson = JsonUtils.toJsonElement(history).getAsJsonArray();
input.add(HISTORY, historyJson);
}
if (bizParams != null) {
input.add(AppKeywords.BIZ_PARAMS, bizParams);
}
if (images != null && !images.isEmpty()) {
JsonArray imagesJson = JsonUtils.toJsonElement(images).getAsJsonArray();
input.add(AppKeywords.IMAGES, imagesJson);
}
return input;
}
@Override
public Object getResources() {
return resources;
}
@Override
public ByteBuffer getBinaryData() {
return null;
}
@Override
public void validate() throws InputRequiredException {
if (getPrompt() == null) {
throw new InputRequiredException("prompt must not be null");
}
}
}
