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Commit 0ae8868

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TheoKanningTheoKanning
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Add comments explaining the api object fields
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‎openai-api/src/main/java/openai/OpenAiResponse.java

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import java.util.List;
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/**
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* A wrapper class to fit the OpenAI engine and search endpoints
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*/
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@Data
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public class OpenAiResponse<T> {
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/**
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* A list containing the actual results
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*/
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public List<T> data;
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/**
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* The type of object returned, should be "list"
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*/
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public String object;
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}

‎openai-api/src/main/java/openai/completion/CompletionChoice.java

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import lombok.Data;
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/**
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* A completion generated by GPT-3
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*
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* https://beta.openai.com/docs/api-reference/create-completion
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*/
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@Data
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public class CompletionChoice {
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/**
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* The generated text. Will include the prompt if {@link CompletionRequest#echo } is true
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*/
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String text;
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/**
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* This index of this completion in the returned list.
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*/
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Integer index;
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// todo add logprobs
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/**
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* The reason why GPT-3 stopped generating, for example "length".
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*/
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String finish_reason;
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}

‎openai-api/src/main/java/openai/completion/CompletionRequest.java

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import java.util.List;
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/**
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* A request for OpenAi to generate a predicted completion for a prompt.
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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/create-completion
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*/
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@Data
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public class CompletionRequest {
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/**
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* An optional prompt to complete from
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*/
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String prompt;
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/**
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* The maximum number of tokens to generate.
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* Requests can use up to 2048 tokens shared between prompt and completion.
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* (One token is roughly 4 characters for normal English text)
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*/
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Integer maxTokens;
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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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* An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of
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* the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are
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* considered.
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*
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* We generally recommend using this or {@link temperature} but not both.
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*/
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Double topP;
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/**
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* How many completions to generate for each prompt.
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*
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* Because this parameter generates many completions, it can quickly consume your token quota.
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* Use carefully and ensure that you have reasonable settings for {@link max_tokens} and {@link stop}.
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*/
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Integer n;
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/**
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* Whether to stream back partial progress.
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* If set, tokens will be sent as data-only server-sent events as they become available,
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* with the stream terminated by a data: DONE message.
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*/
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Boolean stream;
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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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* Echo back the prompt in addition to the completion
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*/
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Boolean echo;
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/**
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* Up to 4 sequences where the API will stop generating further tokens.
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* The returned text will not contain the stop sequence.
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*/
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List<String> stop; //todo test this
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/**
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* Number between 0 and 1 (default 0) that penalizes new tokens based on whether they appear in the text so far.
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* Increases the model's likelihood to talk about new topics.
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*/
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Double presencePenalty;
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/**
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* Number between 0 and 1 (default 0) that penalizes new tokens based on their existing frequency in the text so far.
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* Decreases the model's likelihood to repeat the same line verbatim.
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*/
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Double frequencyPenalty;
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/**
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* Generates best_of completions server-side and returns the "best"
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* (the one with the lowest log probability per token).
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* Results cannot be streamed.
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*
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* When used with {@link n}, best_of controls the number of candidate completions and n specifies how many to return,
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* best_of must be greater than n.
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*/
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Integer bestOf;
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}

‎openai-api/src/main/java/openai/completion/CompletionResult.java

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import java.util.List;
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/**
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* An object containing a response from the completion api
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*
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* https://beta.openai.com/docs/api-reference/create-completion
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*/
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@Data
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public class CompletionResult {
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/**
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* A unique id assigned to this completion
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*/
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String id;
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/**
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* The type of object returned, should be "text_completion"
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*/
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String object;
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/**
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* The creation time in epoch milliseconds.
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*/
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long created;
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/**
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* The GPT-3 model used
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*/
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String model;
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/**
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* A list of generated completions
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*/
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List<CompletionChoice> choices;
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}

‎openai-api/src/main/java/openai/engine/Engine.java

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import lombok.Data;
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/**
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* GPT-3 engine details
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*
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* https://beta.openai.com/docs/api-reference/retrieve-engine
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*/
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@Data
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public class Engine {
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/**
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* An identifier for this engine, used to specify an engine for completions or searching.
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*/
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public String id;
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/**
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* The type of object returned, should be "engine"
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*/
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public String object;
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/**
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* The owner of the GPT-3 engine, typically "openai"
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*/
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public String owner;
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/**
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* Whether the engine is ready to process requests or not
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*/
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public boolean ready;
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}

‎openai-api/src/main/java/openai/search/SearchRequest.java

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import java.util.List;
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/**
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* A request to the document search api.
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* GPT-3 will perform a semantic search over the documents and score them based on how related they are to the query.
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* Higher scores indicate a stronger relation.
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*
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* https://beta.openai.com/docs/api-reference/search
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*/
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@Data
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public class SearchRequest {
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/**
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* Documents to search over
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*/
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List<String> documents;
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/**
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* Search query
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*/
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String query;
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}

‎openai-api/src/main/java/openai/search/SearchResult.java

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import lombok.Data;
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/**
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* A search result for a single document.
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*
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* https://beta.openai.com/docs/api-reference/search
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*/
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@Data
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public class SearchResult {
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/**
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* The position of this document in the request list
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*/
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Integer document;
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/**
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* The type of object returned, should be "search_result"
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*/
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String object;
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/**
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* A number measuring the document's correlation with the query.
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* A higher score means a stronger relationship.
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*/
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Double score;
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}

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