#3996

Committed 06 Sep 2024 01:11PM UTC coverage: 71.703% (-0.7%) from 72.441%

Build # #3996

Build Type

push

github

Committed by

web-flow

Commit Message

feat: Support AI Agent (#1265)

Run Details

248 of 452 new or added lines in 14 files covered. (54.87%)

3 existing lines in 3 files now uncovered.

7921 of 11047 relevant lines covered (71.7%)

0.72 hits per line

Source File
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52.17

/src/main/java/com/box/sdk/BoxAIAgentLLMEndpointParamsOpenAI.java

package com.box.sdk;

import com.box.sdk.internal.utils.JsonUtils;
import com.eclipsesource.json.JsonObject;

/**
 * Represents the AI LLM endpoint params OpenAI object.
 */
public class BoxAIAgentLLMEndpointParamsOpenAI extends BoxAIAgentLLMEndpointParams {

    /**
     * The type of the LLM endpoint parameters.
     */
    public static final String TYPE = "openai_params";

    /**
     * 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.
     */
    private double frequencyPenalty;
    /**
     * 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.
     */
    private double presencePenalty;
    /**
     * Up to 4 sequences where the API will stop generating further tokens.
     */
    private String stop;
    /**
     * What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random,
     * while lower values like 0.2 will make it more focused and deterministic.
     * We generally recommend altering this or top_p but not both.
     */
    private double temperature;
    /**
     * An alternative to sampling with temperature, 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. We generally recommend altering this or temperature but not both.
     */
    private double topP;

    /**
     * Constructs an AI agent with default settings.
     * @param 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.
     * @param 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.
     * @param stop Up to 4 sequences where the API will stop generating further tokens.
     * @param temperature What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random,
     *                   while lower values like 0.2 will make it more focused and deterministic.
     *                   We generally recommend altering this or top_p but not both.
     * @param topP An alternative to sampling with temperature, 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. We generally recommend altering this or temperature but not both.
     */
    public BoxAIAgentLLMEndpointParamsOpenAI(double frequencyPenalty,
                                             double presencePenalty,
                                             String stop,
                                             double temperature,
                                             double topP) {
        super(TYPE);
        this.frequencyPenalty = frequencyPenalty;
        this.presencePenalty = presencePenalty;
        this.stop = stop;
        this.temperature = temperature;
        this.topP = topP;
    }

    /**
     * Constructs an AI agent with default settings.
     * @param jsonObject JSON object representing the AI agent.
     */
    public BoxAIAgentLLMEndpointParamsOpenAI(JsonObject jsonObject) {
        super(jsonObject);
    }

    /**
     * Gets the frequency penalty.
     * @return The frequency penalty.
     */
    public double getFrequencyPenalty() {
        return frequencyPenalty;
    }

    /**
     * Sets the frequency penalty.
     * @param frequencyPenalty The frequency penalty.
     */
    public void setFrequencyPenalty(double frequencyPenalty) {
        this.frequencyPenalty = frequencyPenalty;
    }

    /**
     * Gets the presence penalty.
     * @return The presence penalty.
     */
    public double getPresencePenalty() {
        return presencePenalty;
    }

    /**
     * Sets the presence penalty.
     * @param presencePenalty The presence penalty.
     */
    public void setPresencePenalty(double presencePenalty) {
        this.presencePenalty = presencePenalty;
    }

    /**
     * Gets the stop.
     * @return The stop.
     */
    public String getStop() {
        return stop;
    }

    /**
     * Sets the stop.
     * @param stop The stop.
     */
    public void setStop(String stop) {
        this.stop = stop;
    }

    /**
     * Gets the temperature.
     * @return The temperature.
     */
    public double getTemperature() {
        return temperature;
    }

    /**
     * Sets the temperature.
     * @param temperature The temperature.
     */
    public void setTemperature(double temperature) {
        this.temperature = temperature;
    }

    /**
     * Gets the top-P.
     * @return The top-P.
     */
    public double getTopP() {
        return topP;
    }

    /**
     * Sets the top-P.
     * @param topP The top-P.
     */
    public void setTopP(double topP) {
        this.topP = topP;
    }

    @Override
    void parseJSONMember(JsonObject.Member member) {
        super.parseJSONMember(member);
        String memberName = member.getName();
        switch (memberName) {
            case "frequency_penalty":
                this.frequencyPenalty = member.getValue().asDouble();
                break;
            case "presence_penalty":
                this.presencePenalty = member.getValue().asDouble();
                break;
            case "stop":
                this.stop = member.getValue().asString();
                break;
            case "temperature":
                this.temperature = member.getValue().asDouble();
                break;
            case "top_p":
                this.topP = member.getValue().asDouble();
                break;
            default:
                break;
        }
    }

    public JsonObject getJSONObject() {
        JsonObject jsonObject = new JsonObject();
        JsonUtils.addIfNotNull(jsonObject, "type", this.getType());
        JsonUtils.addIfNotNull(jsonObject, "frequency_penalty", this.frequencyPenalty);
        JsonUtils.addIfNotNull(jsonObject, "presence_penalty", this.presencePenalty);
        JsonUtils.addIfNotNull(jsonObject, "stop", this.stop);
        JsonUtils.addIfNotNull(jsonObject, "temperature", this.temperature);
        JsonUtils.addIfNotNull(jsonObject, "top_p", this.topP);
        return jsonObject;
    }
}

1	package com.box.sdk;
2
3	import com.box.sdk.internal.utils.JsonUtils;
4	import com.eclipsesource.json.JsonObject;
5
6	/**
7	* Represents the AI LLM endpoint params OpenAI object.
8	*/
9	public class BoxAIAgentLLMEndpointParamsOpenAI extends BoxAIAgentLLMEndpointParams {
10
11	/**
12	* The type of the LLM endpoint parameters.
13	*/
14	public static final String TYPE = "openai_params";
15
16	/**
17	* Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text
18	* so far, decreasing the model's likelihood to repeat the same line verbatim.
19	*/
20	private double frequencyPenalty;
21	/**
22	* Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far,
23	* increasing the model's likelihood to talk about new topics.
24	*/
25	private double presencePenalty;
26	/**
27	* Up to 4 sequences where the API will stop generating further tokens.
28	*/
29	private String stop;
30	/**
31	* What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random,
32	* while lower values like 0.2 will make it more focused and deterministic.
33	* We generally recommend altering this or top_p but not both.
34	*/
35	private double temperature;
36	/**
37	* An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of
38	* the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass
39	* are considered. We generally recommend altering this or temperature but not both.
40	*/
41	private double topP;
42
43	/**
44	* Constructs an AI agent with default settings.
45	* @param frequencyPenalty Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text
46	* so far, decreasing the model's likelihood to repeat the same line verbatim.
47	* @param presencePenalty Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far,
48	* increasing the model's likelihood to talk about new topics.
49	* @param stop Up to 4 sequences where the API will stop generating further tokens.
50	* @param temperature What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random,
51	* while lower values like 0.2 will make it more focused and deterministic.
52	* We generally recommend altering this or top_p but not both.
53	* @param topP An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of
54	* the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass
55	* are considered. We generally recommend altering this or temperature but not both.
56	*/
57	public BoxAIAgentLLMEndpointParamsOpenAI(double frequencyPenalty,
58	double presencePenalty,
59	String stop,
60	double temperature,
61	double topP) {
NEW 62	super(TYPE);	×
NEW 63	this.frequencyPenalty = frequencyPenalty;	×
NEW 64	this.presencePenalty = presencePenalty;	×
NEW 65	this.stop = stop;	×
NEW 66	this.temperature = temperature;	×
NEW 67	this.topP = topP;	×
NEW 68	}	×
69
70	/**
71	* Constructs an AI agent with default settings.
72	* @param jsonObject JSON object representing the AI agent.
73	*/
74	public BoxAIAgentLLMEndpointParamsOpenAI(JsonObject jsonObject) {
75	super(jsonObject);	1✔
76	}	1✔
77
78	/**
79	* Gets the frequency penalty.
80	* @return The frequency penalty.
81	*/
82	public double getFrequencyPenalty() {
NEW 83	return frequencyPenalty;	×
84	}
85
86	/**
87	* Sets the frequency penalty.
88	* @param frequencyPenalty The frequency penalty.
89	*/
90	public void setFrequencyPenalty(double frequencyPenalty) {
NEW 91	this.frequencyPenalty = frequencyPenalty;	×
NEW 92	}	×
93
94	/**
95	* Gets the presence penalty.
96	* @return The presence penalty.
97	*/
98	public double getPresencePenalty() {
NEW 99	return presencePenalty;	×
100	}
101
102	/**
103	* Sets the presence penalty.
104	* @param presencePenalty The presence penalty.
105	*/
106	public void setPresencePenalty(double presencePenalty) {
NEW 107	this.presencePenalty = presencePenalty;	×
NEW 108	}	×
109
110	/**
111	* Gets the stop.
112	* @return The stop.
113	*/
114	public String getStop() {
NEW 115	return stop;	×
116	}
117
118	/**
119	* Sets the stop.
120	* @param stop The stop.
121	*/
122	public void setStop(String stop) {
NEW 123	this.stop = stop;	×
NEW 124	}	×
125
126	/**
127	* Gets the temperature.
128	* @return The temperature.
129	*/
130	public double getTemperature() {
NEW 131	return temperature;	×
132	}
133
134	/**
135	* Sets the temperature.
136	* @param temperature The temperature.
137	*/
138	public void setTemperature(double temperature) {
NEW 139	this.temperature = temperature;	×
NEW 140	}	×
141
142	/**
143	* Gets the top-P.
144	* @return The top-P.
145	*/
146	public double getTopP() {
NEW 147	return topP;	×
148	}
149
150	/**
151	* Sets the top-P.
152	* @param topP The top-P.
153	*/
154	public void setTopP(double topP) {
NEW 155	this.topP = topP;	×
NEW 156	}	×
157
158	@Override
159	void parseJSONMember(JsonObject.Member member) {
160	super.parseJSONMember(member);	1✔
161	String memberName = member.getName();	1✔
162	switch (memberName) {	1✔
163	case "frequency_penalty":
164	this.frequencyPenalty = member.getValue().asDouble();	1✔
165	break;	1✔
166	case "presence_penalty":
167	this.presencePenalty = member.getValue().asDouble();	1✔
168	break;	1✔
169	case "stop":
170	this.stop = member.getValue().asString();	1✔
171	break;	1✔
172	case "temperature":
173	this.temperature = member.getValue().asDouble();	1✔
174	break;	1✔
175	case "top_p":
176	this.topP = member.getValue().asDouble();	1✔
177	break;	1✔
178	default:
179	break;
180	}
181	}	1✔
182
183	public JsonObject getJSONObject() {
184	JsonObject jsonObject = new JsonObject();	1✔
185	JsonUtils.addIfNotNull(jsonObject, "type", this.getType());	1✔
186	JsonUtils.addIfNotNull(jsonObject, "frequency_penalty", this.frequencyPenalty);	1✔
187	JsonUtils.addIfNotNull(jsonObject, "presence_penalty", this.presencePenalty);	1✔
188	JsonUtils.addIfNotNull(jsonObject, "stop", this.stop);	1✔
189	JsonUtils.addIfNotNull(jsonObject, "temperature", this.temperature);	1✔
190	JsonUtils.addIfNotNull(jsonObject, "top_p", this.topP);	1✔
191	return jsonObject;	1✔
192	}
193	}

box / box-java-sdk / #3996

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