OpenAI: Added missing fields to API

models/spring-ai-openai/src/main/java/org/springframework/ai/openai/OpenAiChatOptions.java

@@ -59,6 +59,17 @@ public class OpenAiChatOptions implements FunctionCallingOptions, ChatOptions {
 	 * or 100 should result in a ban or exclusive selection of the relevant token.
 	 */
 	private @JsonProperty("logit_bias") Map<String, Integer> logitBias;
+	/**
+	 * Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities
+	 * of each output token returned in the 'content' of 'message'. This option is currently not available
+	 * on the 'gpt-4-vision-preview' model.
+	 */
+	private @JsonProperty("logprobs") Boolean logprobs;
+	/**
+	 * An integer between 0 and 5 specifying the number of most likely tokens to return at each token position,
+	 * each with an associated log probability. 'logprobs' must be set to 'true' if this parameter is used.
+	 */
+	private @JsonProperty("top_logprobs") Integer topLogprobs;
 	/**
 	 * The maximum number of tokens to generate in the chat completion. The total length of input
 	 * tokens and generated tokens is limited by the model's context length.
@@ -177,6 +188,16 @@ public Builder withLogitBias(Map<String, Integer> logitBias) {
 			return this;
 		}
 
+		public Builder withLogprobs(Boolean logprobs) {
+			this.options.logprobs = logprobs;
+			return this;
+		}
+
+		public Builder withTopLogprobs(Integer topLogprobs) {
+			this.options.topLogprobs = topLogprobs;
+			return this;
+		}
+
 		public Builder withMaxTokens(Integer maxTokens) {
 			this.options.maxTokens = maxTokens;
 			return this;
@@ -279,6 +300,22 @@ public void setLogitBias(Map<String, Integer> logitBias) {
 		this.logitBias = logitBias;
 	}
 
+	public Boolean getLogprobs() {
+		return this.logprobs;
+	}
+
+	public void setLogprobs(Boolean logprobs) {
+		this.logprobs = logprobs;
+	}
+
+	public Integer getTopLogprobs() {
+		return this.topLogprobs;
+	}
+
+	public void setTopLogprobs(Integer topLogprobs) {
+		this.topLogprobs = topLogprobs;
+	}
+
 	public Integer getMaxTokens() {
 		return this.maxTokens;
 	}
@@ -395,6 +432,8 @@ public int hashCode() {
 		result = prime * result + ((model == null) ? 0 : model.hashCode());
 		result = prime * result + ((frequencyPenalty == null) ? 0 : frequencyPenalty.hashCode());
 		result = prime * result + ((logitBias == null) ? 0 : logitBias.hashCode());
+		result = prime * result + ((logprobs == null) ? 0 : logprobs.hashCode());
+		result = prime * result + ((topLogprobs == null) ? 0 : topLogprobs.hashCode());
 		result = prime * result + ((maxTokens == null) ? 0 : maxTokens.hashCode());
 		result = prime * result + ((n == null) ? 0 : n.hashCode());
 		result = prime * result + ((presencePenalty == null) ? 0 : presencePenalty.hashCode());
@@ -436,6 +475,18 @@ else if (!this.frequencyPenalty.equals(other.frequencyPenalty))
 		}
 		else if (!this.logitBias.equals(other.logitBias))
 			return false;
+		if (this.logprobs == null) {
+			if (other.logprobs != null)
+				return false;
+		}
+		else if (!this.logprobs.equals(other.logprobs))
+			return false;
+		if (this.topLogprobs == null) {
+			if (other.topLogprobs != null)
+				return false;
+		}
+		else if (!this.topLogprobs.equals(other.topLogprobs))
+			return false;
 		if (this.maxTokens == null) {
 			if (other.maxTokens != null)
 				return false;

models/spring-ai-openai/src/main/java/org/springframework/ai/openai/OpenAiEmbeddingClient.java

@@ -118,7 +118,7 @@ public EmbeddingResponse call(EmbeddingRequest request) {
 			org.springframework.ai.openai.api.OpenAiApi.EmbeddingRequest<List<String>> apiRequest = (this.defaultOptions != null)
 					? new org.springframework.ai.openai.api.OpenAiApi.EmbeddingRequest<>(request.getInstructions(),
 							this.defaultOptions.getModel(), this.defaultOptions.getEncodingFormat(),
-							this.defaultOptions.getUser())
+							this.defaultOptions.getDimensions(), this.defaultOptions.getUser())
 					: new org.springframework.ai.openai.api.OpenAiApi.EmbeddingRequest<>(request.getInstructions(),
 							OpenAiApi.DEFAULT_EMBEDDING_MODEL);
 

models/spring-ai-openai/src/main/java/org/springframework/ai/openai/OpenAiEmbeddingOptions.java

@@ -37,6 +37,10 @@ public class OpenAiEmbeddingOptions implements EmbeddingOptions {
 	 * The format to return the embeddings in. Can be either float or base64.
 	 */
 	private @JsonProperty("encoding_format") String encodingFormat;
+	/**
+	 * The number of dimensions the resulting output embeddings should have. Only supported in text-embedding-3 and later models.
+	 */
+	private @JsonProperty("dimensions") Integer dimensions;
 	/**
 	 * A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.
 	 */
@@ -65,6 +69,11 @@ public Builder withEncodingFormat(String encodingFormat) {
 			return this;
 		}
 
+		public Builder withDimensions(Integer dimensions) {
+			this.options.dimensions = dimensions;
+			return this;
+		}
+
 		public Builder withUser(String user) {
 			this.options.setUser(user);
 			return this;
@@ -92,6 +101,14 @@ public void setEncodingFormat(String encodingFormat) {
 		this.encodingFormat = encodingFormat;
 	}
 
+	public Integer getDimensions() {
+		return dimensions;
+	}
+
+	public void setDimensions(Integer dimensions) {
+		this.dimensions = dimensions;
+	}
+
 	public String getUser() {
 		return user;
 	}

models/spring-ai-openai/src/main/java/org/springframework/ai/openai/api/OpenAiApi.java

@@ -265,6 +265,11 @@ public Function(String description, String name, String jsonSchema) {
 	 * Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will
 	 * vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100
 	 * or 100 should result in a ban or exclusive selection of the relevant token.
+	 * @param logprobs Whether to return log probabilities of the output tokens or not. If true, returns the log
+	 * probabilities of each output token returned in the 'content' of 'message'. This option is currently not available
+	 * on the 'gpt-4-vision-preview' model.
+	 * @param topLogprobs An integer between 0 and 5 specifying the number of most likely tokens to return at each token
+	 * position, each with an associated log probability. 'logprobs' must be set to 'true' if this parameter is used.
 	 * @param maxTokens The maximum number of tokens to generate in the chat completion. The total length of input
 	 * tokens and generated tokens is limited by the model's context length.
 	 * @param n How many chat completion choices to generate for each input message. Note that you will be charged based
@@ -302,6 +307,8 @@ public record ChatCompletionRequest (
 			@JsonProperty("model") String model,
 			@JsonProperty("frequency_penalty") Float frequencyPenalty,
 			@JsonProperty("logit_bias") Map<String, Integer> logitBias,
+			@JsonProperty("logprobs") Boolean logprobs,
+			@JsonProperty("top_logprobs") Integer topLogprobs,
 			@JsonProperty("max_tokens") Integer maxTokens,
 			@JsonProperty("n") Integer n,
 			@JsonProperty("presence_penalty") Float presencePenalty,
@@ -323,7 +330,7 @@ public record ChatCompletionRequest (
 		 * @param temperature What sampling temperature to use, between 0 and 1.
 		 */
 		public ChatCompletionRequest(List<ChatCompletionMessage> messages, String model, Float temperature) {
-			this(messages, model, null, null, null, null, null,
+			this(messages, model, null, null, null, null, null, null, null,
 					null, null, null, false, temperature, null,
 					null, null, null);
 		}
@@ -338,7 +345,7 @@ public ChatCompletionRequest(List<ChatCompletionMessage> messages, String model,
 		 * as they become available, with the stream terminated by a data: [DONE] message.
 		 */
 		public ChatCompletionRequest(List<ChatCompletionMessage> messages, String model, Float temperature, boolean stream) {
-			this(messages, model, null, null, null, null, null,
+			this(messages, model, null, null, null, null, null, null, null,
 					null, null, null, stream, temperature, null,
 					null, null, null);
 		}
@@ -354,7 +361,7 @@ public ChatCompletionRequest(List<ChatCompletionMessage> messages, String model,
 		 */
 		public ChatCompletionRequest(List<ChatCompletionMessage> messages, String model,
 				List<FunctionTool> tools, String toolChoice) {
-			this(messages, model, null, null, null, null, null,
+			this(messages, model, null, null, null, null, null, null, null,
 					null, null, null, false, 0.8f, null,
 					tools, toolChoice, null);
 		}
@@ -368,7 +375,7 @@ public ChatCompletionRequest(List<ChatCompletionMessage> messages, String model,
 		 * as they become available, with the stream terminated by a data: [DONE] message.
 		 */
 		public ChatCompletionRequest(List<ChatCompletionMessage> messages, Boolean stream) {
-			this(messages, null, null, null, null, null, null,
+			this(messages, null, null, null, null, null, null, null, null,
 					null, null, null, stream, null, null,
 					null, null, null);
 		}
@@ -869,13 +876,15 @@ public Embedding(Integer index, List<Double> embedding) {
 	 * dimensions or less.
 	 * @param model ID of the model to use.
 	 * @param encodingFormat The format to return the embeddings in. Can be either float or base64.
+	 * @param dimensions The number of dimensions the resulting output embeddings should have. Only supported in text-embedding-3 and later models.
 	 * @param user A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse.
 	 */
 	@JsonInclude(Include.NON_NULL)
 	public record EmbeddingRequest<T>(
 			@JsonProperty("input") T input,
 			@JsonProperty("model") String model,
 			@JsonProperty("encoding_format") String encodingFormat,
+			@JsonProperty("dimensions") Integer dimensions,
 			@JsonProperty("user") String user) {
 
 		/**
@@ -884,7 +893,7 @@ public record EmbeddingRequest<T>(
 		 * @param model ID of the model to use.
 		 */
 		public EmbeddingRequest(T input, String model) {
-			this(input, model, "float", null);
+			this(input, model, "float", null, null);
 		}
 
 		/**