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Commit 103c34d

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TheoKanningTheoKanning
authored
Add classification support (TheoKanning#11)
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‎.github/workflows/test.yml

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name: Publish
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name: Test
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on:
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‎api/src/main/java/com/theokanning/openai/classification/ClassificationRequest.java

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package com.theokanning.openai.classification;
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import lombok.*;
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import java.util.List;
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import java.util.Map;
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/**
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* A request for OpenAi to classify text based on provided examples
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* All fields are nullable.
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*
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* Documentation taken from
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* https://beta.openai.com/docs/api-reference/classifications/create
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*/
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@Builder
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@NoArgsConstructor
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@AllArgsConstructor
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@Data
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public class ClassificationRequest {
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/**
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* ID of the engine to use for completion
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*/
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@NonNull
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String model;
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/**
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* Query to be classified
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*/
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@NonNull
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String query;
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/**
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* A list of examples with labels, in the following format:
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*
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* [["The movie is so interesting.", "Positive"], ["It is quite boring.", "Negative"], ...]
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*
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* All the label strings will be normalized to be capitalized.
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*
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* You should specify either examples or file, but not both.
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*/
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List<List<String>> examples;
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/**
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* The ID of the uploaded file that contains training examples.
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* See upload file for how to upload a file of the desired format and purpose.
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*
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* You should specify either examples or file, but not both.
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*/
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String file;
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/**
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* The set of categories being classified.
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* If not specified, candidate labels will be automatically collected from the examples you provide.
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* All the label strings will be normalized to be capitalized.
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*/
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List<String> labels;
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/**
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* ID of the engine to use for Search. You can select one of ada, babbage, curie, or davinci.
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*/
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String searchModel;
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/**
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* What sampling temperature to use. Higher values means the model will take more risks.
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* Try 0.9 for more creative applications, and 0 (argmax sampling) for ones with a well-defined answer.
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*
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* We generally recommend using this or {@link top_p} but not both.
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*/
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Double temperature;
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/**
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* Include the log probabilities on the logprobs most likely tokens, as well the chosen tokens.
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* For example, if logprobs is 10, the API will return a list of the 10 most likely tokens.
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* The API will always return the logprob of the sampled token,
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* so there may be up to logprobs+1 elements in the response.
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*/
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Integer logprobs;
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/**
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* The maximum number of examples to be ranked by Search when using file.
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* Setting it to a higher value leads to improved accuracy but with increased latency and cost.
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*/
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Integer maxExamples;
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/**
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* Modify the likelihood of specified tokens appearing in the completion.
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*
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* Accepts a json object that maps tokens (specified by their token ID in the GPT tokenizer) to an
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* associated bias value from -100 to 100.
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*/
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Map<String, Double> logitBias;
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/**
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* If set to true, the returned JSON will include a "prompt" field containing the final prompt that was
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* used to request a completion. This is mainly useful for debugging purposes.
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*/
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Boolean returnPrompt;
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/**
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* A special boolean flag for showing metadata.
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* If set to true, each document entry in the returned JSON will contain a "metadata" field.
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*
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* This flag only takes effect when file is set.
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*/
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Boolean returnMetadata;
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/**
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* If an object name is in the list, we provide the full information of the object;
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* otherwise, we only provide the object ID.
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*
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* Currently we support completion and file objects for expansion.
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*/
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List<String> expand;
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/**
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* A unique identifier representing your end-user, which will help OpenAI to monitor and detect abuse.
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*/
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String user;
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}

‎api/src/main/java/com/theokanning/openai/classification/ClassificationResult.java

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package com.theokanning.openai.classification;
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import com.theokanning.openai.completion.CompletionChoice;
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import lombok.Data;
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import java.util.List;
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/**
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* An object containing a response from the classification api
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* <p>
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* https://beta.openai.com/docs/api-reference/classifications/create
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*/
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@Data
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public class ClassificationResult {
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/**
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* A unique id assigned to this completion
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*/
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String completion;
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/**
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* The predicted label for the query text.
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*/
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String label;
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/**
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* The GPT-3 model used for completion
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*/
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String model;
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/**
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* The type of object returned, should be "classification"
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*/
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String object;
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/**
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* The GPT-3 model used for search
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*/
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String searchModel;
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/**
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* A list of the most relevant examples for the query text.
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*/
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List<Example> selectedExamples;
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}

‎api/src/main/java/com/theokanning/openai/classification/Example.java

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package com.theokanning.openai.classification;
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import lombok.Data;
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/**
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* Represents an example returned by the classification api
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*
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* https://beta.openai.com/docs/api-reference/classifications/create
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*/
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@Data
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public class Example {
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/**
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* The position of this example in the example list
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*/
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Integer document;
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/**
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* The label of the example
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*/
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String label;
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/**
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* The text of the example
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*/
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String text;
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}

‎api/src/main/java/com/theokanning/openai/finetune/FineTuneRequest.java

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package com.theokanning.openai.finetune;
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import lombok.AllArgsConstructor;
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import lombok.Builder;
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import lombok.Data;
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import lombok.NoArgsConstructor;
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import lombok.*;
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import java.util.List;
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/**
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* The ID of an uploaded file that contains training data.
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*/
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@NonNull
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String trainingFile;
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/**

‎client/src/main/java/com/theokanning/openai/OpenAiApi.java

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package com.theokanning.openai;
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import com.theokanning.openai.classification.ClassificationRequest;
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import com.theokanning.openai.classification.ClassificationResult;
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import com.theokanning.openai.engine.Engine;
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import com.theokanning.openai.file.File;
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import com.theokanning.openai.finetune.FineTuneRequest;
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@POST("/v1/engines/{engine_id}/search")
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Single<OpenAiResponse<SearchResult>> search(@Path("engine_id") String engineId, @Body SearchRequest request);
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@POST("v1/classifications")
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Single<ClassificationResult> createClassification(@Body ClassificationRequest request);
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@GET("/v1/files")
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Single<OpenAiResponse<File>> listFiles();
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‎client/src/main/java/com/theokanning/openai/OpenAiService.java

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import com.fasterxml.jackson.databind.DeserializationFeature;
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import com.fasterxml.jackson.databind.ObjectMapper;
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import com.fasterxml.jackson.databind.PropertyNamingStrategy;
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import com.theokanning.openai.classification.ClassificationRequest;
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import com.theokanning.openai.classification.ClassificationResult;
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import com.theokanning.openai.file.File;
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import com.theokanning.openai.finetune.FineTuneRequest;
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import com.theokanning.openai.finetune.FineTuneEvent;
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return api.search(engineId, request).blockingGet().data;
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}
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public ClassificationResult createClassification(ClassificationRequest request) {
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return api.createClassification(request).blockingGet();
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}
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public List<File> listFiles() {
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return api.listFiles().blockingGet().data;
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}

‎client/src/test/java/com/theokanning/openai/ClassificationTest.java

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package com.theokanning.openai;
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import com.theokanning.openai.classification.ClassificationRequest;
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import com.theokanning.openai.classification.ClassificationResult;
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import com.theokanning.openai.completion.CompletionChoice;
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import com.theokanning.openai.completion.CompletionRequest;
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import org.junit.jupiter.api.Test;
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import java.util.Arrays;
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import java.util.List;
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import static org.junit.jupiter.api.Assertions.assertFalse;
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import static org.junit.jupiter.api.Assertions.assertNotNull;
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public class ClassificationTest {
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String token = System.getenv("OPENAI_TOKEN");
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OpenAiService service = new OpenAiService(token);
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@Test
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void createCompletion() {
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ClassificationRequest classificationRequest = ClassificationRequest.builder()
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.examples(Arrays.asList(
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Arrays.asList("A happy moment", "Positive"),
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Arrays.asList("I am sad.", "Negative"),
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Arrays.asList("I am feeling awesome", "Positive")
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))
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.query("It is a raining day :(")
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.model("curie")
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.searchModel("ada")
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.labels(Arrays.asList("Positive", "Negative", "Neutral"))
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.build();
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ClassificationResult result = service.createClassification(classificationRequest);
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assertNotNull(result.getCompletion());
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}
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}

‎client/src/test/java/com/theokanning/openai/FineTuneTest.java

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import org.junit.jupiter.api.*;
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import java.util.List;
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import java.util.concurrent.TimeUnit;
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import static org.junit.jupiter.api.Assertions.*;
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@BeforeAll
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static void setup() {
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static void setup() throws Exception {
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String token = System.getenv("OPENAI_TOKEN");
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service = new OpenAiService(token);
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fileId = service.uploadFile("fine-tune", "src/test/resources/fine-tuning-data.jsonl").getId();
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// wait for file to be processed
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TimeUnit.SECONDS.sleep(10);
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}
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@AfterAll

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