yolo

models/spring-ai-anthropic/src/main/java/org/springframework/ai/anthropic/AnthropicChatConnector.java

@@ -26,6 +26,7 @@ import java.util.stream.Collectors;
 
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
+import org.springframework.ai.chat.connector.ChatConnector;
 import reactor.core.publisher.Flux;
 
 import org.springframework.ai.anthropic.api.AnthropicApi;
@@ -38,7 +39,6 @@ import org.springframework.ai.anthropic.api.AnthropicApi.Role;
 import org.springframework.ai.anthropic.api.AnthropicApi.StreamResponse;
 import org.springframework.ai.anthropic.api.AnthropicApi.Usage;
 import org.springframework.ai.anthropic.metadata.AnthropicChatResponseMetadata;
-import org.springframework.ai.chat.ChatClient;
 import org.springframework.ai.chat.ChatResponse;
 import org.springframework.ai.chat.Generation;
 import org.springframework.ai.chat.StreamingChatClient;
@@ -56,16 +56,16 @@ import org.springframework.util.Assert;
 import org.springframework.util.CollectionUtils;
 
 /**
- * The {@link ChatClient} implementation for the Anthropic service.
+ * The {@link ChatConnector} implementation for the Anthropic service.
  *
  * @author Christian Tzolov
  * @since 1.0.0
  */
-public class AnthropicChatClient extends
+public class AnthropicChatConnector extends
 		AbstractFunctionCallSupport<AnthropicApi.RequestMessage, AnthropicApi.ChatCompletionRequest, ResponseEntity<AnthropicApi.ChatCompletion>>
-		implements ChatClient, StreamingChatClient {
+		implements ChatConnector, StreamingChatClient {
 
-	private static final Logger logger = LoggerFactory.getLogger(AnthropicChatClient.class);
+	private static final Logger logger = LoggerFactory.getLogger(AnthropicChatConnector.class);
 
 	public static final String DEFAULT_MODEL_NAME = AnthropicApi.ChatModel.CLAUDE_3_OPUS.getValue();
 
@@ -89,10 +89,10 @@ public class AnthropicChatClient extends
 	public final RetryTemplate retryTemplate;
 
 	/**
-	 * Construct a new {@link AnthropicChatClient} instance.
+	 * Construct a new {@link AnthropicChatConnector} instance.
 	 * @param anthropicApi the lower-level API for the Anthropic service.
 	 */
-	public AnthropicChatClient(AnthropicApi anthropicApi) {
+	public AnthropicChatConnector(AnthropicApi anthropicApi) {
 		this(anthropicApi,
 				AnthropicChatOptions.builder()
 					.withModel(DEFAULT_MODEL_NAME)
@@ -102,34 +102,34 @@ public class AnthropicChatClient extends
 	}
 
 	/**
-	 * Construct a new {@link AnthropicChatClient} instance.
+	 * Construct a new {@link AnthropicChatConnector} instance.
 	 * @param anthropicApi the lower-level API for the Anthropic service.
 	 * @param defaultOptions the default options used for the chat completion requests.
 	 */
-	public AnthropicChatClient(AnthropicApi anthropicApi, AnthropicChatOptions defaultOptions) {
+	public AnthropicChatConnector(AnthropicApi anthropicApi, AnthropicChatOptions defaultOptions) {
 		this(anthropicApi, defaultOptions, RetryUtils.DEFAULT_RETRY_TEMPLATE);
 	}
 
 	/**
-	 * Construct a new {@link AnthropicChatClient} instance.
+	 * Construct a new {@link AnthropicChatConnector} instance.
 	 * @param anthropicApi the lower-level API for the Anthropic service.
 	 * @param defaultOptions the default options used for the chat completion requests.
 	 * @param retryTemplate the retry template used to retry the Anthropic API calls.
 	 */
-	public AnthropicChatClient(AnthropicApi anthropicApi, AnthropicChatOptions defaultOptions,
+	public AnthropicChatConnector(AnthropicApi anthropicApi, AnthropicChatOptions defaultOptions,
 			RetryTemplate retryTemplate) {
 		this(anthropicApi, defaultOptions, retryTemplate, null);
 	}
 
 	/**
-	 * Construct a new {@link AnthropicChatClient} instance.
+	 * Construct a new {@link AnthropicChatConnector} instance.
 	 * @param anthropicApi the lower-level API for the Anthropic service.
 	 * @param defaultOptions the default options used for the chat completion requests.
 	 * @param retryTemplate the retry template used to retry the Anthropic API calls.
 	 * @param functionCallbackContext the function callback context used to store the
 	 * state of the function calls.
 	 */
-	public AnthropicChatClient(AnthropicApi anthropicApi, AnthropicChatOptions defaultOptions,
+	public AnthropicChatConnector(AnthropicApi anthropicApi, AnthropicChatOptions defaultOptions,
 			RetryTemplate retryTemplate, FunctionCallbackContext functionCallbackContext) {
 
 		super(functionCallbackContext);
@@ -144,7 +144,7 @@ public class AnthropicChatClient extends
 	}
 
 	@Override
-	public ChatResponse call(Prompt prompt) {
+	public ChatResponse execute(Prompt prompt) {
 
 		ChatCompletionRequest request = createRequest(prompt, false);
 

models/spring-ai-anthropic/src/main/java/org/springframework/ai/anthropic/AnthropicChatOptions.java

@@ -51,11 +51,11 @@ public class AnthropicChatOptions implements ChatOptions, FunctionCallingOptions
 	private @JsonProperty("top_k") Integer topK;
 
 	/**
-	 * Tool Function Callbacks to register with the ChatClient. For Prompt
+	 * Tool Function Callbacks to register with the ChatConnector. For Prompt
 	 * Options the functionCallbacks are automatically enabled for the duration of the
 	 * prompt execution. For Default Options the functionCallbacks are registered but
 	 * disabled by default. Use the enableFunctions to set the functions from the registry
-	 * to be used by the ChatClient chat completion requests.
+	 * to be used by the ChatConnector chat completion requests.
 	 */
 	@NestedConfigurationProperty
 	@JsonIgnore

models/spring-ai-anthropic/src/test/java/org/springframework/ai/anthropic/AnthropicChatConnectorIT.java

@@ -29,7 +29,7 @@ import org.slf4j.LoggerFactory;
 
 import org.springframework.ai.anthropic.api.AnthropicApi;
 import org.springframework.ai.anthropic.api.tool.MockWeatherService;
-import org.springframework.ai.chat.ChatClient;
+import org.springframework.ai.chat.connector.ChatConnector;
 import org.springframework.ai.chat.ChatResponse;
 import org.springframework.ai.chat.Generation;
 import org.springframework.ai.chat.StreamingChatClient;
@@ -56,12 +56,12 @@ import static org.assertj.core.api.Assertions.assertThat;
 
 @SpringBootTest(classes = AnthropicTestConfiguration.class, properties = "spring.ai.retry.on-http-codes=429")
 @EnabledIfEnvironmentVariable(named = "ANTHROPIC_API_KEY", matches = ".+")
-class AnthropicChatClientIT {
+class AnthropicChatConnectorIT {
 
-	private static final Logger logger = LoggerFactory.getLogger(AnthropicChatClientIT.class);
+	private static final Logger logger = LoggerFactory.getLogger(AnthropicChatConnectorIT.class);
 
 	@Autowired
-	protected ChatClient chatClient;
+	protected ChatConnector chatConnector;
 
 	@Autowired
 	protected StreamingChatClient streamingChatClient;
@@ -76,7 +76,7 @@ class AnthropicChatClientIT {
 		SystemPromptTemplate systemPromptTemplate = new SystemPromptTemplate(systemResource);
 		Message systemMessage = systemPromptTemplate.createMessage(Map.of("name", "Bob", "voice", "pirate"));
 		Prompt prompt = new Prompt(List.of(userMessage, systemMessage));
-		ChatResponse response = chatClient.call(prompt);
+		ChatResponse response = chatConnector.execute(prompt);
 		assertThat(response.getResults()).hasSize(1);
 		assertThat(response.getMetadata().getUsage().getGenerationTokens()).isGreaterThan(0);
 		assertThat(response.getMetadata().getUsage().getPromptTokens()).isGreaterThan(0);
@@ -102,7 +102,7 @@ class AnthropicChatClientIT {
 		PromptTemplate promptTemplate = new PromptTemplate(template,
 				Map.of("subject", "ice cream flavors", "format", format));
 		Prompt prompt = new Prompt(promptTemplate.createMessage());
-		Generation generation = this.chatClient.call(prompt).getResult();
+		Generation generation = this.chatConnector.execute(prompt).getResult();
 
 		List<String> list = listOutputConverter.convert(generation.getOutput().getContent());
 		assertThat(list).hasSize(5);
@@ -120,7 +120,7 @@ class AnthropicChatClientIT {
 		PromptTemplate promptTemplate = new PromptTemplate(template,
 				Map.of("subject", "an array of numbers from 1 to 9 under they key name 'numbers'", "format", format));
 		Prompt prompt = new Prompt(promptTemplate.createMessage());
-		Generation generation = chatClient.call(prompt).getResult();
+		Generation generation = chatConnector.execute(prompt).getResult();
 
 		Map<String, Object> result = mapOutputConverter.convert(generation.getOutput().getContent());
 		assertThat(result.get("numbers")).isEqualTo(Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8, 9));
@@ -142,7 +142,7 @@ class AnthropicChatClientIT {
 				""";
 		PromptTemplate promptTemplate = new PromptTemplate(template, Map.of("format", format));
 		Prompt prompt = new Prompt(promptTemplate.createMessage());
-		Generation generation = chatClient.call(prompt).getResult();
+		Generation generation = chatConnector.execute(prompt).getResult();
 
 		ActorsFilmsRecord actorsFilms = beanOutputConverter.convert(generation.getOutput().getContent());
 		logger.info("" + actorsFilms);
@@ -187,14 +187,14 @@ class AnthropicChatClientIT {
 		var userMessage = new UserMessage("Explain what do you see on this picture?",
 				List.of(new Media(MimeTypeUtils.IMAGE_PNG, imageData)));
 
-		var response = chatClient.call(new Prompt(List.of(userMessage)));
+		var response = chatConnector.execute(new Prompt(List.of(userMessage)));
 
 		logger.info(response.getResult().getOutput().getContent());
 		assertThat(response.getResult().getOutput().getContent()).contains("bananas", "apple", "basket");
 	}
 
 	@Test
-	void functionCallTest() {
+	void functionExecuteTest() {
 
 		UserMessage userMessage = new UserMessage(
 				"What's the weather like in San Francisco, Tokyo and Paris? Return the result in Celsius.");
@@ -209,7 +209,7 @@ class AnthropicChatClientIT {
 				.build()))
 			.build();
 
-		ChatResponse response = chatClient.call(new Prompt(messages, promptOptions));
+		ChatResponse response = chatConnector.execute(new Prompt(messages, promptOptions));
 
 		logger.info("Response: {}", response);
 

models/spring-ai-anthropic/src/test/java/org/springframework/ai/anthropic/AnthropicTestConfiguration.java

@@ -38,8 +38,8 @@ public class AnthropicTestConfiguration {
 	}
 
 	@Bean
-	public AnthropicChatClient openAiChatClient(AnthropicApi api) {
-		AnthropicChatClient anthropicChatClient = new AnthropicChatClient(api);
+	public AnthropicChatConnector openAiChatClient(AnthropicApi api) {
+		AnthropicChatConnector anthropicChatClient = new AnthropicChatConnector(api);
 		return anthropicChatClient;
 	}
 

models/spring-ai-anthropic/src/test/java/org/springframework/ai/anthropic/ChatCompletionRequestTests.java

@@ -30,7 +30,7 @@ public class ChatCompletionRequestTests {
 	@Test
 	public void createRequestWithChatOptions() {
 
-		var client = new AnthropicChatClient(new AnthropicApi("TEST"),
+		var client = new AnthropicChatConnector(new AnthropicApi("TEST"),
 				AnthropicChatOptions.builder().withModel("DEFAULT_MODEL").withTemperature(66.6f).build());
 
 		var request = client.createRequest(new Prompt("Test message content"), false);

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

@@ -38,7 +38,7 @@ import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
 import org.springframework.ai.azure.openai.metadata.AzureOpenAiChatResponseMetadata;
-import org.springframework.ai.chat.ChatClient;
+import org.springframework.ai.chat.connector.ChatConnector;
 import org.springframework.ai.chat.ChatResponse;
 import org.springframework.ai.chat.Generation;
 import org.springframework.ai.chat.StreamingChatClient;
@@ -63,7 +63,7 @@ import java.util.Set;
 import java.util.concurrent.atomic.AtomicBoolean;
 
 /**
- * {@link ChatClient} implementation for {@literal Microsoft Azure AI} backed by
+ * {@link ChatConnector} implementation for {@literal Microsoft Azure AI} backed by
  * {@link OpenAIClient}.
  *
  * @author Mark Pollack
@@ -71,12 +71,12 @@ import java.util.concurrent.atomic.AtomicBoolean;
  * @author John Blum
  * @author Christian Tzolov
  * @author Grogdunn
- * @see ChatClient
+ * @see ChatConnector
  * @see com.azure.ai.openai.OpenAIClient
  */
-public class AzureOpenAiChatClient
+public class AzureOpenAiChatConnector
 		extends AbstractFunctionCallSupport<ChatRequestMessage, ChatCompletionsOptions, ChatCompletions>
-		implements ChatClient, StreamingChatClient {
+		implements ChatConnector, StreamingChatClient {
 
 	private static final String DEFAULT_DEPLOYMENT_NAME = "gpt-35-turbo";
 
@@ -94,7 +94,7 @@ public class AzureOpenAiChatClient
 	 */
 	private final OpenAIClient openAIClient;
 
-	public AzureOpenAiChatClient(OpenAIClient microsoftOpenAiClient) {
+	public AzureOpenAiChatConnector(OpenAIClient microsoftOpenAiClient) {
 		this(microsoftOpenAiClient,
 				AzureOpenAiChatOptions.builder()
 					.withDeploymentName(DEFAULT_DEPLOYMENT_NAME)
@@ -102,11 +102,11 @@ public class AzureOpenAiChatClient
 					.build());
 	}
 
-	public AzureOpenAiChatClient(OpenAIClient microsoftOpenAiClient, AzureOpenAiChatOptions options) {
+	public AzureOpenAiChatConnector(OpenAIClient microsoftOpenAiClient, AzureOpenAiChatOptions options) {
 		this(microsoftOpenAiClient, options, null);
 	}
 
-	public AzureOpenAiChatClient(OpenAIClient microsoftOpenAiClient, AzureOpenAiChatOptions options,
+	public AzureOpenAiChatConnector(OpenAIClient microsoftOpenAiClient, AzureOpenAiChatOptions options,
 			FunctionCallbackContext functionCallbackContext) {
 		super(functionCallbackContext);
 		Assert.notNull(microsoftOpenAiClient, "com.azure.ai.openai.OpenAIClient must not be null");
@@ -117,10 +117,10 @@ public class AzureOpenAiChatClient
 
 	/**
 	 * @deprecated since 0.8.0, use
-	 * {@link #AzureOpenAiChatClient(OpenAIClient, AzureOpenAiChatOptions)} instead.
+	 * {@link #AzureOpenAiChatConnector(OpenAIClient, AzureOpenAiChatOptions)} instead.
 	 */
 	@Deprecated(forRemoval = true, since = "0.8.0")
-	public AzureOpenAiChatClient withDefaultOptions(AzureOpenAiChatOptions defaultOptions) {
+	public AzureOpenAiChatConnector withDefaultOptions(AzureOpenAiChatOptions defaultOptions) {
 		Assert.notNull(defaultOptions, "DefaultOptions must not be null");
 		this.defaultOptions = defaultOptions;
 		return this;
@@ -131,7 +131,7 @@ public class AzureOpenAiChatClient
 	}
 
 	@Override
-	public ChatResponse call(Prompt prompt) {
+	public ChatResponse execute(Prompt prompt) {
 
 		ChatCompletionsOptions options = toAzureChatCompletionsOptions(prompt);
 		options.setStream(false);

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

@@ -127,11 +127,11 @@ public class AzureOpenAiChatOptions implements FunctionCallingOptions, ChatOptio
 	private String deploymentName;
 
 	/**
-	 * OpenAI Tool Function Callbacks to register with the ChatClient. For Prompt Options
-	 * the functionCallbacks are automatically enabled for the duration of the prompt
-	 * execution. For Default Options the functionCallbacks are registered but disabled by
-	 * default. Use the enableFunctions to set the functions from the registry to be used
-	 * by the ChatClient chat completion requests.
+	 * OpenAI Tool Function Callbacks to register with the ChatConnector. For Prompt
+	 * Options the functionCallbacks are automatically enabled for the duration of the
+	 * prompt execution. For Default Options the functionCallbacks are registered but
+	 * disabled by default. Use the enableFunctions to set the functions from the registry
+	 * to be used by the ChatConnector chat completion requests.
 	 */
 	@NestedConfigurationProperty
 	@JsonIgnore

models/spring-ai-azure-openai/src/test/java/org/springframework/ai/azure/openai/AzureChatCompletionsOptionsTests.java

@@ -53,7 +53,7 @@ public class AzureChatCompletionsOptionsTests {
 			.withUser("user")
 			.build();
 
-		var client = new AzureOpenAiChatClient(mockClient, defaultOptions);
+		var client = new AzureOpenAiChatConnector(mockClient, defaultOptions);
 
 		var requestOptions = client.toAzureChatCompletionsOptions(new Prompt("Test message content"));
 

models/spring-ai-azure-openai/src/test/java/org/springframework/ai/azure/openai/AzureOpenAiChatConnectorIT.java

@@ -46,13 +46,13 @@ import org.springframework.core.convert.support.DefaultConversionService;
 
 import static org.assertj.core.api.Assertions.assertThat;
 
-@SpringBootTest(classes = AzureOpenAiChatClientIT.TestConfiguration.class)
+@SpringBootTest(classes = AzureOpenAiChatConnectorIT.TestConfiguration.class)
 @EnabledIfEnvironmentVariable(named = "AZURE_OPENAI_API_KEY", matches = ".+")
 @EnabledIfEnvironmentVariable(named = "AZURE_OPENAI_ENDPOINT", matches = ".+")
-class AzureOpenAiChatClientIT {
+class AzureOpenAiChatConnectorIT {
 
 	@Autowired
-	private AzureOpenAiChatClient chatClient;
+	private AzureOpenAiChatConnector chatClient;
 
 	record ActorsFilms(String actor, List<String> movies) {
 	}
@@ -69,7 +69,7 @@ class AzureOpenAiChatClientIT {
 		UserMessage userMessage = new UserMessage("Generate the names of 5 famous pirates.");
 
 		Prompt prompt = new Prompt(List.of(userMessage, systemMessage));
-		ChatResponse response = chatClient.call(prompt);
+		ChatResponse response = chatClient.execute(prompt);
 		assertThat(response.getResult().getOutput().getContent()).contains("Blackbeard");
 	}
 
@@ -86,7 +86,7 @@ class AzureOpenAiChatClientIT {
 		PromptTemplate promptTemplate = new PromptTemplate(template,
 				Map.of("subject", "ice cream flavors", "format", format));
 		Prompt prompt = new Prompt(promptTemplate.createMessage());
-		Generation generation = chatClient.call(prompt).getResult();
+		Generation generation = chatClient.execute(prompt).getResult();
 
 		List<String> list = outputConverter.convert(generation.getOutput().getContent());
 		assertThat(list).hasSize(5);
@@ -105,7 +105,7 @@ class AzureOpenAiChatClientIT {
 		PromptTemplate promptTemplate = new PromptTemplate(template,
 				Map.of("subject", "an array of numbers from 1 to 9 under they key name 'numbers'", "format", format));
 		Prompt prompt = new Prompt(promptTemplate.createMessage());
-		Generation generation = chatClient.call(prompt).getResult();
+		Generation generation = chatClient.execute(prompt).getResult();
 
 		Map<String, Object> result = outputConverter.convert(generation.getOutput().getContent());
 		assertThat(result.get("numbers")).isEqualTo(Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8, 9));
@@ -124,7 +124,7 @@ class AzureOpenAiChatClientIT {
 				""";
 		PromptTemplate promptTemplate = new PromptTemplate(template, Map.of("format", format));
 		Prompt prompt = new Prompt(promptTemplate.createMessage());
-		Generation generation = chatClient.call(prompt).getResult();
+		Generation generation = chatClient.execute(prompt).getResult();
 
 		ActorsFilms actorsFilms = outputConverter.convert(generation.getOutput().getContent());
 		assertThat(actorsFilms.actor()).isNotNull();
@@ -145,7 +145,7 @@ class AzureOpenAiChatClientIT {
 				""";
 		PromptTemplate promptTemplate = new PromptTemplate(template, Map.of("format", format));
 		Prompt prompt = new Prompt(promptTemplate.createMessage());
-		Generation generation = chatClient.call(prompt).getResult();
+		Generation generation = chatClient.execute(prompt).getResult();
 
 		ActorsFilmsRecord actorsFilms = outputConverter.convert(generation.getOutput().getContent());
 		System.out.println(actorsFilms);
@@ -194,8 +194,8 @@ class AzureOpenAiChatClientIT {
 		}
 
 		@Bean
-		public AzureOpenAiChatClient azureOpenAiChatClient(OpenAIClient openAIClient) {
-			return new AzureOpenAiChatClient(openAIClient,
+		public AzureOpenAiChatConnector azureOpenAiChatClient(OpenAIClient openAIClient) {
+			return new AzureOpenAiChatConnector(openAIClient,
 					AzureOpenAiChatOptions.builder().withDeploymentName("gpt-35-turbo").withMaxTokens(200).build());
 
 		}

models/spring-ai-azure-openai/src/test/java/org/springframework/ai/azure/openai/MockAzureOpenAiTestConfiguration.java

@@ -59,8 +59,8 @@ public class MockAzureOpenAiTestConfiguration {
 	}
 
 	@Bean
-	AzureOpenAiChatClient azureOpenAiChatClient(OpenAIClient microsoftAzureOpenAiClient) {
-		return new AzureOpenAiChatClient(microsoftAzureOpenAiClient);
+	AzureOpenAiChatConnector azureOpenAiChatClient(OpenAIClient microsoftAzureOpenAiClient) {
+		return new AzureOpenAiChatConnector(microsoftAzureOpenAiClient);
 	}
 
 }

models/spring-ai-azure-openai/src/test/java/org/springframework/ai/azure/openai/function/AzureOpenAiChatConnectorFunctionCallIT.java

@@ -29,7 +29,7 @@ import org.junit.jupiter.api.condition.EnabledIfEnvironmentVariable;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
-import org.springframework.ai.azure.openai.AzureOpenAiChatClient;
+import org.springframework.ai.azure.openai.AzureOpenAiChatConnector;
 import org.springframework.ai.azure.openai.AzureOpenAiChatOptions;
 import org.springframework.ai.chat.ChatResponse;
 import org.springframework.ai.chat.Generation;
@@ -46,21 +46,21 @@ import reactor.core.publisher.Flux;
 
 import static org.assertj.core.api.Assertions.assertThat;
 
-@SpringBootTest(classes = AzureOpenAiChatClientFunctionCallIT.TestConfiguration.class)
+@SpringBootTest(classes = AzureOpenAiChatConnectorFunctionCallIT.TestConfiguration.class)
 @EnabledIfEnvironmentVariable(named = "AZURE_OPENAI_API_KEY", matches = ".+")
 @EnabledIfEnvironmentVariable(named = "AZURE_OPENAI_ENDPOINT", matches = ".+")
-class AzureOpenAiChatClientFunctionCallIT {
+class AzureOpenAiChatConnectorFunctionCallIT {
 
-	private static final Logger logger = LoggerFactory.getLogger(AzureOpenAiChatClientFunctionCallIT.class);
+	private static final Logger logger = LoggerFactory.getLogger(AzureOpenAiChatConnectorFunctionCallIT.class);
 
 	@Autowired
 	private String selectedModel;
 
 	@Autowired
-	private AzureOpenAiChatClient chatClient;
+	private AzureOpenAiChatConnector chatClient;
 
 	@Test
-	void functionCallTest() {
+	void functionExecuteTest() {
 
 		UserMessage userMessage = new UserMessage("What's the weather like in San Francisco, in Tokyo, and in Paris?");
 
@@ -75,7 +75,7 @@ class AzureOpenAiChatClientFunctionCallIT {
 				.build()))
 			.build();
 
-		ChatResponse response = chatClient.call(new Prompt(messages, promptOptions));
+		ChatResponse response = chatClient.execute(new Prompt(messages, promptOptions));
 
 		logger.info("Response: {}", response);
 
@@ -85,7 +85,7 @@ class AzureOpenAiChatClientFunctionCallIT {
 	}
 
 	@Test
-	void streamFunctionCallTest() {
+	void streamFunctionExecuteTest() {
 		UserMessage userMessage = new UserMessage("What's the weather like in San Francisco, Tokyo, and Paris?");
 
 		List<Message> messages = new ArrayList<>(List.of(userMessage));
@@ -129,8 +129,8 @@ class AzureOpenAiChatClientFunctionCallIT {
 		}
 
 		@Bean
-		public AzureOpenAiChatClient azureOpenAiChatClient(OpenAIClient openAIClient, String selectedModel) {
-			return new AzureOpenAiChatClient(openAIClient,
+		public AzureOpenAiChatConnector azureOpenAiChatClient(OpenAIClient openAIClient, String selectedModel) {
+			return new AzureOpenAiChatConnector(openAIClient,
 					AzureOpenAiChatOptions.builder().withDeploymentName(selectedModel).withMaxTokens(500).build());
 		}
 

models/spring-ai-azure-openai/src/test/java/org/springframework/ai/azure/openai/metadata/AzureOpenAiChatConnectorMetadataTests.java

@@ -23,7 +23,7 @@ import com.azure.ai.openai.models.ContentFilterResultsForChoice;
 import com.azure.ai.openai.models.ContentFilterSeverity;
 import org.junit.jupiter.api.Test;
 
-import org.springframework.ai.azure.openai.AzureOpenAiChatClient;
+import org.springframework.ai.azure.openai.AzureOpenAiChatConnector;
 import org.springframework.ai.azure.openai.MockAzureOpenAiTestConfiguration;
 import org.springframework.ai.chat.ChatResponse;
 import org.springframework.ai.chat.Generation;
@@ -55,7 +55,7 @@ import org.springframework.web.context.request.WebRequest;
 import static org.assertj.core.api.Assertions.assertThat;
 
 /**
- * Unit Tests for {@link AzureOpenAiChatClient} asserting AI metadata.
+ * Unit Tests for {@link AzureOpenAiChatConnector} asserting AI metadata.
  *
  * @author John Blum
  * @author Christian Tzolov
@@ -63,19 +63,19 @@ import static org.assertj.core.api.Assertions.assertThat;
  */
 @SpringBootTest
 @ActiveProfiles("spring-ai-azure-openai-mocks")
-@ContextConfiguration(classes = AzureOpenAiChatClientMetadataTests.TestConfiguration.class)
+@ContextConfiguration(classes = AzureOpenAiChatConnectorMetadataTests.TestConfiguration.class)
 @SuppressWarnings("unused")
-class AzureOpenAiChatClientMetadataTests {
+class AzureOpenAiChatConnectorMetadataTests {
 
 	@Autowired
-	private AzureOpenAiChatClient aiClient;
+	private AzureOpenAiChatConnector aiClient;
 
 	@Test
 	void azureOpenAiMetadataCapturedDuringGeneration() {
 
 		Prompt prompt = new Prompt("Can I fly like a bird?");
 
-		ChatResponse response = this.aiClient.call(prompt);
+		ChatResponse response = this.aiClient.execute(prompt);
 
 		assertThat(response).isNotNull();
 

models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/anthropic/BedrockAnthropicChatConnector.java

@@ -17,7 +17,7 @@ package org.springframework.ai.bedrock.anthropic;
 
 import java.util.List;
 
-import org.springframework.ai.chat.ChatClient;
+import org.springframework.ai.chat.connector.ChatConnector;
 import org.springframework.ai.chat.prompt.ChatOptions;
 import org.springframework.ai.chat.ChatResponse;
 import org.springframework.ai.chat.metadata.ChatGenerationMetadata;
@@ -33,19 +33,19 @@ import org.springframework.ai.chat.prompt.Prompt;
 import org.springframework.ai.model.ModelOptionsUtils;
 
 /**
- * Java {@link ChatClient} and {@link StreamingChatClient} for the Bedrock Anthropic chat
- * generative.
+ * Java {@link ChatConnector} and {@link StreamingChatClient} for the Bedrock Anthropic
+ * chat generative.
  *
  * @author Christian Tzolov
  * @since 0.8.0
  */
-public class BedrockAnthropicChatClient implements ChatClient, StreamingChatClient {
+public class BedrockAnthropicChatConnector implements ChatConnector, StreamingChatClient {
 
 	private final AnthropicChatBedrockApi anthropicChatApi;
 
 	private final AnthropicChatOptions defaultOptions;
 
-	public BedrockAnthropicChatClient(AnthropicChatBedrockApi chatApi) {
+	public BedrockAnthropicChatConnector(AnthropicChatBedrockApi chatApi) {
 		this(chatApi,
 				AnthropicChatOptions.builder()
 					.withTemperature(0.8f)
@@ -55,13 +55,13 @@ public class BedrockAnthropicChatClient implements ChatClient, StreamingChatClie
 					.build());
 	}
 
-	public BedrockAnthropicChatClient(AnthropicChatBedrockApi chatApi, AnthropicChatOptions options) {
+	public BedrockAnthropicChatConnector(AnthropicChatBedrockApi chatApi, AnthropicChatOptions options) {
 		this.anthropicChatApi = chatApi;
 		this.defaultOptions = options;
 	}
 
 	@Override
-	public ChatResponse call(Prompt prompt) {
+	public ChatResponse execute(Prompt prompt) {
 
 		AnthropicChatRequest request = createRequest(prompt);
 

models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/anthropic3/BedrockAnthropic3ChatConnector.java

@@ -22,7 +22,7 @@ import org.springframework.ai.bedrock.anthropic3.api.Anthropic3ChatBedrockApi.An
 import org.springframework.ai.bedrock.anthropic3.api.Anthropic3ChatBedrockApi.MediaContent;
 import org.springframework.ai.bedrock.anthropic3.api.Anthropic3ChatBedrockApi.ChatCompletionMessage;
 import org.springframework.ai.bedrock.anthropic3.api.Anthropic3ChatBedrockApi.ChatCompletionMessage.Role;
-import org.springframework.ai.chat.ChatClient;
+import org.springframework.ai.chat.connector.ChatConnector;
 import org.springframework.ai.chat.ChatResponse;
 import org.springframework.ai.chat.Generation;
 import org.springframework.ai.chat.StreamingChatClient;
@@ -43,20 +43,20 @@ import java.util.concurrent.atomic.AtomicReference;
 import java.util.stream.Collectors;
 
 /**
- * Java {@link ChatClient} and {@link StreamingChatClient} for the Bedrock Anthropic chat
- * generative.
+ * Java {@link ChatConnector} and {@link StreamingChatClient} for the Bedrock Anthropic
+ * chat generative.
  *
  * @author Ben Middleton
  * @author Christian Tzolov
  * @since 1.0.0
  */
-public class BedrockAnthropic3ChatClient implements ChatClient, StreamingChatClient {
+public class BedrockAnthropic3ChatConnector implements ChatConnector, StreamingChatClient {
 
 	private final Anthropic3ChatBedrockApi anthropicChatApi;
 
 	private final Anthropic3ChatOptions defaultOptions;
 
-	public BedrockAnthropic3ChatClient(Anthropic3ChatBedrockApi chatApi) {
+	public BedrockAnthropic3ChatConnector(Anthropic3ChatBedrockApi chatApi) {
 		this(chatApi,
 				Anthropic3ChatOptions.builder()
 					.withTemperature(0.8f)
@@ -66,13 +66,13 @@ public class BedrockAnthropic3ChatClient implements ChatClient, StreamingChatCli
 					.build());
 	}
 
-	public BedrockAnthropic3ChatClient(Anthropic3ChatBedrockApi chatApi, Anthropic3ChatOptions options) {
+	public BedrockAnthropic3ChatConnector(Anthropic3ChatBedrockApi chatApi, Anthropic3ChatOptions options) {
 		this.anthropicChatApi = chatApi;
 		this.defaultOptions = options;
 	}
 
 	@Override
-	public ChatResponse call(Prompt prompt) {
+	public ChatResponse execute(Prompt prompt) {
 
 		AnthropicChatRequest request = createRequest(prompt);
 

models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/cohere/BedrockCohereChatConnector.java

@@ -17,6 +17,7 @@ package org.springframework.ai.bedrock.cohere;
 
 import java.util.List;
 
+import org.springframework.ai.chat.connector.ChatConnector;
 import reactor.core.publisher.Flux;
 
 import org.springframework.ai.bedrock.BedrockUsage;
@@ -24,7 +25,6 @@ import org.springframework.ai.bedrock.MessageToPromptConverter;
 import org.springframework.ai.bedrock.cohere.api.CohereChatBedrockApi;
 import org.springframework.ai.bedrock.cohere.api.CohereChatBedrockApi.CohereChatRequest;
 import org.springframework.ai.bedrock.cohere.api.CohereChatBedrockApi.CohereChatResponse;
-import org.springframework.ai.chat.ChatClient;
 import org.springframework.ai.chat.prompt.ChatOptions;
 import org.springframework.ai.chat.ChatResponse;
 import org.springframework.ai.chat.Generation;
@@ -39,17 +39,17 @@ import org.springframework.util.Assert;
  * @author Christian Tzolov
  * @since 0.8.0
  */
-public class BedrockCohereChatClient implements ChatClient, StreamingChatClient {
+public class BedrockCohereChatConnector implements ChatConnector, StreamingChatClient {
 
 	private final CohereChatBedrockApi chatApi;
 
 	private final BedrockCohereChatOptions defaultOptions;
 
-	public BedrockCohereChatClient(CohereChatBedrockApi chatApi) {
+	public BedrockCohereChatConnector(CohereChatBedrockApi chatApi) {
 		this(chatApi, BedrockCohereChatOptions.builder().build());
 	}
 
-	public BedrockCohereChatClient(CohereChatBedrockApi chatApi, BedrockCohereChatOptions options) {
+	public BedrockCohereChatConnector(CohereChatBedrockApi chatApi, BedrockCohereChatOptions options) {
 		Assert.notNull(chatApi, "CohereChatBedrockApi must not be null");
 		Assert.notNull(options, "BedrockCohereChatOptions must not be null");
 
@@ -58,7 +58,7 @@ public class BedrockCohereChatClient implements ChatClient, StreamingChatClient
 	}
 
 	@Override
-	public ChatResponse call(Prompt prompt) {
+	public ChatResponse execute(Prompt prompt) {
 		CohereChatResponse response = this.chatApi.chatCompletion(this.createRequest(prompt, false));
 		List<Generation> generations = response.generations().stream().map(g -> {
 			return new Generation(g.text());

models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/jurassic2/BedrockAi21Jurassic2ChatConnector.java

@@ -19,7 +19,7 @@ package org.springframework.ai.bedrock.jurassic2;
 import org.springframework.ai.bedrock.MessageToPromptConverter;
 import org.springframework.ai.bedrock.jurassic2.api.Ai21Jurassic2ChatBedrockApi;
 import org.springframework.ai.bedrock.jurassic2.api.Ai21Jurassic2ChatBedrockApi.Ai21Jurassic2ChatRequest;
-import org.springframework.ai.chat.ChatClient;
+import org.springframework.ai.chat.connector.ChatConnector;
 import org.springframework.ai.chat.ChatResponse;
 import org.springframework.ai.chat.Generation;
 import org.springframework.ai.chat.metadata.ChatGenerationMetadata;
@@ -29,18 +29,18 @@ import org.springframework.ai.model.ModelOptionsUtils;
 import org.springframework.util.Assert;
 
 /**
- * Java {@link ChatClient} for the Bedrock Jurassic2 chat generative model.
+ * Java {@link ChatConnector} for the Bedrock Jurassic2 chat generative model.
  *
  * @author Ahmed Yousri
  * @since 1.0.0
  */
-public class BedrockAi21Jurassic2ChatClient implements ChatClient {
+public class BedrockAi21Jurassic2ChatConnector implements ChatConnector {
 
 	private final Ai21Jurassic2ChatBedrockApi chatApi;
 
 	private final BedrockAi21Jurassic2ChatOptions defaultOptions;
 
-	public BedrockAi21Jurassic2ChatClient(Ai21Jurassic2ChatBedrockApi chatApi,
+	public BedrockAi21Jurassic2ChatConnector(Ai21Jurassic2ChatBedrockApi chatApi,
 			BedrockAi21Jurassic2ChatOptions options) {
 		Assert.notNull(chatApi, "Ai21Jurassic2ChatBedrockApi must not be null");
 		Assert.notNull(options, "BedrockAi21Jurassic2ChatOptions must not be null");
@@ -49,7 +49,7 @@ public class BedrockAi21Jurassic2ChatClient implements ChatClient {
 		this.defaultOptions = options;
 	}
 
-	public BedrockAi21Jurassic2ChatClient(Ai21Jurassic2ChatBedrockApi chatApi) {
+	public BedrockAi21Jurassic2ChatConnector(Ai21Jurassic2ChatBedrockApi chatApi) {
 		this(chatApi,
 				BedrockAi21Jurassic2ChatOptions.builder()
 					.withTemperature(0.8f)
@@ -59,7 +59,7 @@ public class BedrockAi21Jurassic2ChatClient implements ChatClient {
 	}
 
 	@Override
-	public ChatResponse call(Prompt prompt) {
+	public ChatResponse execute(Prompt prompt) {
 		var request = createRequest(prompt);
 		var response = this.chatApi.chatCompletion(request);
 
@@ -114,8 +114,8 @@ public class BedrockAi21Jurassic2ChatClient implements ChatClient {
 			return this;
 		}
 
-		public BedrockAi21Jurassic2ChatClient build() {
-			return new BedrockAi21Jurassic2ChatClient(chatApi,
+		public BedrockAi21Jurassic2ChatConnector build() {
+			return new BedrockAi21Jurassic2ChatConnector(chatApi,
 					options != null ? options : BedrockAi21Jurassic2ChatOptions.builder().build());
 		}
 

models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/llama/BedrockLlamaChatConnector.java

@@ -17,13 +17,13 @@ package org.springframework.ai.bedrock.llama;
 
 import java.util.List;
 
+import org.springframework.ai.chat.connector.ChatConnector;
 import reactor.core.publisher.Flux;
 
 import org.springframework.ai.bedrock.MessageToPromptConverter;
 import org.springframework.ai.bedrock.llama.api.LlamaChatBedrockApi;
 import org.springframework.ai.bedrock.llama.api.LlamaChatBedrockApi.LlamaChatRequest;
 import org.springframework.ai.bedrock.llama.api.LlamaChatBedrockApi.LlamaChatResponse;
-import org.springframework.ai.chat.ChatClient;
 import org.springframework.ai.chat.prompt.ChatOptions;
 import org.springframework.ai.chat.ChatResponse;
 import org.springframework.ai.chat.Generation;
@@ -35,25 +35,25 @@ import org.springframework.ai.model.ModelOptionsUtils;
 import org.springframework.util.Assert;
 
 /**
- * Java {@link ChatClient} and {@link StreamingChatClient} for the Bedrock Llama chat
+ * Java {@link ChatConnector} and {@link StreamingChatClient} for the Bedrock Llama chat
  * generative.
  *
  * @author Christian Tzolov
  * @author Wei Jiang
  * @since 0.8.0
  */
-public class BedrockLlamaChatClient implements ChatClient, StreamingChatClient {
+public class BedrockLlamaChatConnector implements ChatConnector, StreamingChatClient {
 
 	private final LlamaChatBedrockApi chatApi;
 
 	private final BedrockLlamaChatOptions defaultOptions;
 
-	public BedrockLlamaChatClient(LlamaChatBedrockApi chatApi) {
+	public BedrockLlamaChatConnector(LlamaChatBedrockApi chatApi) {
 		this(chatApi,
 				BedrockLlamaChatOptions.builder().withTemperature(0.8f).withTopP(0.9f).withMaxGenLen(100).build());
 	}
 
-	public BedrockLlamaChatClient(LlamaChatBedrockApi chatApi, BedrockLlamaChatOptions options) {
+	public BedrockLlamaChatConnector(LlamaChatBedrockApi chatApi, BedrockLlamaChatOptions options) {
 		Assert.notNull(chatApi, "LlamaChatBedrockApi must not be null");
 		Assert.notNull(options, "BedrockLlamaChatOptions must not be null");
 
@@ -62,7 +62,7 @@ public class BedrockLlamaChatClient implements ChatClient, StreamingChatClient {
 	}
 
 	@Override
-	public ChatResponse call(Prompt prompt) {
+	public ChatResponse execute(Prompt prompt) {
 
 		var request = createRequest(prompt);
 

models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/titan/BedrockTitanChatConnector.java

@@ -17,6 +17,7 @@ package org.springframework.ai.bedrock.titan;
 
 import java.util.List;
 
+import org.springframework.ai.chat.connector.ChatConnector;
 import reactor.core.publisher.Flux;
 
 import org.springframework.ai.bedrock.MessageToPromptConverter;
@@ -24,7 +25,6 @@ import org.springframework.ai.bedrock.titan.api.TitanChatBedrockApi;
 import org.springframework.ai.bedrock.titan.api.TitanChatBedrockApi.TitanChatRequest;
 import org.springframework.ai.bedrock.titan.api.TitanChatBedrockApi.TitanChatResponse;
 import org.springframework.ai.bedrock.titan.api.TitanChatBedrockApi.TitanChatResponseChunk;
-import org.springframework.ai.chat.ChatClient;
 import org.springframework.ai.chat.prompt.ChatOptions;
 import org.springframework.ai.chat.ChatResponse;
 import org.springframework.ai.chat.Generation;
@@ -39,17 +39,17 @@ import org.springframework.util.Assert;
  * @author Christian Tzolov
  * @since 0.8.0
  */
-public class BedrockTitanChatClient implements ChatClient, StreamingChatClient {
+public class BedrockTitanChatConnector implements ChatConnector, StreamingChatClient {
 
 	private final TitanChatBedrockApi chatApi;
 
 	private final BedrockTitanChatOptions defaultOptions;
 
-	public BedrockTitanChatClient(TitanChatBedrockApi chatApi) {
+	public BedrockTitanChatConnector(TitanChatBedrockApi chatApi) {
 		this(chatApi, BedrockTitanChatOptions.builder().withTemperature(0.8f).build());
 	}
 
-	public BedrockTitanChatClient(TitanChatBedrockApi chatApi, BedrockTitanChatOptions defaultOptions) {
+	public BedrockTitanChatConnector(TitanChatBedrockApi chatApi, BedrockTitanChatOptions defaultOptions) {
 		Assert.notNull(chatApi, "ChatApi must not be null");
 		Assert.notNull(defaultOptions, "DefaultOptions must not be null");
 		this.chatApi = chatApi;
@@ -57,7 +57,7 @@ public class BedrockTitanChatClient implements ChatClient, StreamingChatClient {
 	}
 
 	@Override
-	public ChatResponse call(Prompt prompt) {
+	public ChatResponse execute(Prompt prompt) {
 		TitanChatResponse response = this.chatApi.chatCompletion(this.createRequest(prompt));
 		List<Generation> generations = response.results().stream().map(result -> {
 			return new Generation(result.outputText());

models/spring-ai-bedrock/src/test/java/org/springframework/ai/bedrock/anthropic/BedrockAnthropicChatConnectorIT.java

@@ -55,12 +55,12 @@ import static org.assertj.core.api.Assertions.assertThat;
 @SpringBootTest
 @EnabledIfEnvironmentVariable(named = "AWS_ACCESS_KEY_ID", matches = ".*")
 @EnabledIfEnvironmentVariable(named = "AWS_SECRET_ACCESS_KEY", matches = ".*")
-class BedrockAnthropicChatClientIT {
+class BedrockAnthropicChatConnectorIT {
 
-	private static final Logger logger = LoggerFactory.getLogger(BedrockAnthropicChatClientIT.class);
+	private static final Logger logger = LoggerFactory.getLogger(BedrockAnthropicChatConnectorIT.class);
 
 	@Autowired
-	private BedrockAnthropicChatClient client;
+	private BedrockAnthropicChatConnector client;
 
 	@Value("classpath:/prompts/system-message.st")
 	private Resource systemResource;
@@ -101,7 +101,7 @@ class BedrockAnthropicChatClientIT {
 
 		Prompt prompt = new Prompt(List.of(userMessage, systemMessage));
 
-		ChatResponse response = client.call(prompt);
+		ChatResponse response = client.execute(prompt);
 
 		assertThat(response.getResult().getOutput().getContent()).contains("Blackbeard");
 	}
@@ -119,7 +119,7 @@ class BedrockAnthropicChatClientIT {
 		PromptTemplate promptTemplate = new PromptTemplate(template,
 				Map.of("subject", "ice cream flavors.", "format", format));
 		Prompt prompt = new Prompt(promptTemplate.createMessage());
-		Generation generation = this.client.call(prompt).getResult();
+		Generation generation = this.client.execute(prompt).getResult();
 
 		List<String> list = outputParser.convert(generation.getOutput().getContent());
 		assertThat(list).hasSize(5);
@@ -137,7 +137,7 @@ class BedrockAnthropicChatClientIT {
 		PromptTemplate promptTemplate = new PromptTemplate(template,
 				Map.of("subject", "an array of numbers from 1 to 9 under they key name 'numbers'", "format", format));
 		Prompt prompt = new Prompt(promptTemplate.createMessage());
-		Generation generation = client.call(prompt).getResult();
+		Generation generation = client.execute(prompt).getResult();
 
 		Map<String, Object> result = outputConverter.convert(generation.getOutput().getContent());
 		assertThat(result.get("numbers")).isEqualTo(Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8, 9));
@@ -161,7 +161,7 @@ class BedrockAnthropicChatClientIT {
 				""";
 		PromptTemplate promptTemplate = new PromptTemplate(template, Map.of("format", format));
 		Prompt prompt = new Prompt(promptTemplate.createMessage());
-		Generation generation = client.call(prompt).getResult();
+		Generation generation = client.execute(prompt).getResult();
 
 		ActorsFilmsRecord actorsFilms = outputConvert.convert(generation.getOutput().getContent());
 		assertThat(actorsFilms.actor()).isEqualTo("Tom Hanks");
@@ -209,8 +209,8 @@ class BedrockAnthropicChatClientIT {
 		}
 
 		@Bean
-		public BedrockAnthropicChatClient anthropicChatClient(AnthropicChatBedrockApi anthropicApi) {
-			return new BedrockAnthropicChatClient(anthropicApi);
+		public BedrockAnthropicChatConnector anthropicChatClient(AnthropicChatBedrockApi anthropicApi) {
+			return new BedrockAnthropicChatConnector(anthropicApi);
 		}
 
 	}

models/spring-ai-bedrock/src/test/java/org/springframework/ai/bedrock/anthropic/BedrockAnthropicCreateRequestTests.java

@@ -38,7 +38,7 @@ public class BedrockAnthropicCreateRequestTests {
 	@Test
 	public void createRequestWithChatOptions() {
 
-		var client = new BedrockAnthropicChatClient(anthropicChatApi,
+		var client = new BedrockAnthropicChatConnector(anthropicChatApi,
 				AnthropicChatOptions.builder()
 					.withTemperature(66.6f)
 					.withTopK(66)

models/spring-ai-bedrock/src/test/java/org/springframework/ai/bedrock/anthropic3/BedrockAnthropic3ChatConnectorIT.java

@@ -59,12 +59,12 @@ import static org.assertj.core.api.Assertions.assertThat;
 @SpringBootTest
 @EnabledIfEnvironmentVariable(named = "AWS_ACCESS_KEY_ID", matches = ".*")
 @EnabledIfEnvironmentVariable(named = "AWS_SECRET_ACCESS_KEY", matches = ".*")
-class BedrockAnthropic3ChatClientIT {
+class BedrockAnthropic3ChatConnectorIT {
 
-	private static final Logger logger = LoggerFactory.getLogger(BedrockAnthropic3ChatClientIT.class);
+	private static final Logger logger = LoggerFactory.getLogger(BedrockAnthropic3ChatConnectorIT.class);
 
 	@Autowired
-	private BedrockAnthropic3ChatClient client;
+	private BedrockAnthropic3ChatConnector client;
 
 	@Value("classpath:/prompts/system-message.st")
 	private Resource systemResource;
@@ -105,7 +105,7 @@ class BedrockAnthropic3ChatClientIT {
 
 		Prompt prompt = new Prompt(List.of(userMessage, systemMessage));
 
-		ChatResponse response = client.call(prompt);
+		ChatResponse response = client.execute(prompt);
 
 		assertThat(response.getResult().getOutput().getContent()).contains("Blackbeard");
 	}
@@ -123,7 +123,7 @@ class BedrockAnthropic3ChatClientIT {
 		PromptTemplate promptTemplate = new PromptTemplate(template,
 				Map.of("subject", "ice cream flavors.", "format", format));
 		Prompt prompt = new Prompt(promptTemplate.createMessage());
-		Generation generation = this.client.call(prompt).getResult();
+		Generation generation = this.client.execute(prompt).getResult();
 
 		List<String> list = outputConverter.convert(generation.getOutput().getContent());
 		assertThat(list).hasSize(5);
@@ -142,7 +142,7 @@ class BedrockAnthropic3ChatClientIT {
 		PromptTemplate promptTemplate = new PromptTemplate(template,
 				Map.of("subject", "an array of numbers from 1 to 9 under they key name 'numbers'", "format", format));
 		Prompt prompt = new Prompt(promptTemplate.createMessage());
-		Generation generation = client.call(prompt).getResult();
+		Generation generation = client.execute(prompt).getResult();
 
 		Map<String, Object> result = outputConverter.convert(generation.getOutput().getContent());
 		assertThat(result.get("numbers")).isEqualTo(Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8, 9));
@@ -166,7 +166,7 @@ class BedrockAnthropic3ChatClientIT {
 				""";
 		PromptTemplate promptTemplate = new PromptTemplate(template, Map.of("format", format));
 		Prompt prompt = new Prompt(promptTemplate.createMessage());
-		Generation generation = client.call(prompt).getResult();
+		Generation generation = client.execute(prompt).getResult();
 
 		ActorsFilmsRecord actorsFilms = outputConverter.convert(generation.getOutput().getContent());
 		assertThat(actorsFilms.actor()).isEqualTo("Tom Hanks");
@@ -211,7 +211,7 @@ class BedrockAnthropic3ChatClientIT {
 		var userMessage = new UserMessage("Explain what do you see o this picture?",
 				List.of(new Media(MimeTypeUtils.IMAGE_PNG, imageData)));
 
-		var response = client.call(new Prompt(List.of(userMessage)));
+		var response = client.execute(new Prompt(List.of(userMessage)));
 
 		logger.info(response.getResult().getOutput().getContent());
 		assertThat(response.getResult().getOutput().getContent()).contains("bananas", "apple", "basket");
@@ -228,8 +228,8 @@ class BedrockAnthropic3ChatClientIT {
 		}
 
 		@Bean
-		public BedrockAnthropic3ChatClient anthropicChatClient(Anthropic3ChatBedrockApi anthropicApi) {
-			return new BedrockAnthropic3ChatClient(anthropicApi);
+		public BedrockAnthropic3ChatConnector anthropicChatClient(Anthropic3ChatBedrockApi anthropicApi) {
+			return new BedrockAnthropic3ChatConnector(anthropicApi);
 		}
 
 	}

models/spring-ai-bedrock/src/test/java/org/springframework/ai/bedrock/anthropic3/BedrockAnthropic3CreateRequestTests.java

@@ -37,7 +37,7 @@ public class BedrockAnthropic3CreateRequestTests {
 	@Test
 	public void createRequestWithChatOptions() {
 
-		var client = new BedrockAnthropic3ChatClient(anthropicChatApi,
+		var client = new BedrockAnthropic3ChatConnector(anthropicChatApi,
 				Anthropic3ChatOptions.builder()
 					.withTemperature(66.6f)
 					.withTopK(66)

models/spring-ai-bedrock/src/test/java/org/springframework/ai/bedrock/cohere/BedrockCohereChatConnectorIT.java

@@ -54,10 +54,10 @@ import static org.assertj.core.api.Assertions.assertThat;
 @SpringBootTest
 @EnabledIfEnvironmentVariable(named = "AWS_ACCESS_KEY_ID", matches = ".*")
 @EnabledIfEnvironmentVariable(named = "AWS_SECRET_ACCESS_KEY", matches = ".*")
-class BedrockCohereChatClientIT {
+class BedrockCohereChatConnectorIT {
 
 	@Autowired
-	private BedrockCohereChatClient client;
+	private BedrockCohereChatConnector client;
 
 	@Value("classpath:/prompts/system-message.st")
 	private Resource systemResource;
@@ -98,7 +98,7 @@ class BedrockCohereChatClientIT {
 		SystemPromptTemplate systemPromptTemplate = new SystemPromptTemplate(systemResource);
 		Message systemMessage = systemPromptTemplate.createMessage(Map.of("name", name, "voice", voice));
 		Prompt prompt = new Prompt(List.of(userMessage, systemMessage));
-		ChatResponse response = client.call(prompt);
+		ChatResponse response = client.execute(prompt);
 		assertThat(response.getResult().getOutput().getContent()).contains("Blackbeard");
 	}
 
@@ -115,7 +115,7 @@ class BedrockCohereChatClientIT {
 		PromptTemplate promptTemplate = new PromptTemplate(template,
 				Map.of("subject", "ice cream flavors.", "format", format));
 		Prompt prompt = new Prompt(promptTemplate.createMessage());
-		Generation generation = this.client.call(prompt).getResult();
+		Generation generation = this.client.execute(prompt).getResult();
 
 		List<String> list = outputConverter.convert(generation.getOutput().getContent());
 		assertThat(list).hasSize(5);
@@ -134,7 +134,7 @@ class BedrockCohereChatClientIT {
 		PromptTemplate promptTemplate = new PromptTemplate(template,
 				Map.of("subject", "an array of numbers from 1 to 9 under they key name 'numbers'", "format", format));
 		Prompt prompt = new Prompt(promptTemplate.createMessage());
-		Generation generation = client.call(prompt).getResult();
+		Generation generation = client.execute(prompt).getResult();
 
 		Map<String, Object> result = outputConverter.convert(generation.getOutput().getContent());
 		assertThat(result.get("numbers")).isEqualTo(Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8, 9));
@@ -157,7 +157,7 @@ class BedrockCohereChatClientIT {
 				""";
 		PromptTemplate promptTemplate = new PromptTemplate(template, Map.of("format", format));
 		Prompt prompt = new Prompt(promptTemplate.createMessage());
-		Generation generation = client.call(prompt).getResult();
+		Generation generation = client.execute(prompt).getResult();
 
 		ActorsFilmsRecord actorsFilms = outputConverter.convert(generation.getOutput().getContent());
 		assertThat(actorsFilms.actor()).isEqualTo("Tom Hanks");
@@ -205,8 +205,8 @@ class BedrockCohereChatClientIT {
 		}
 
 		@Bean
-		public BedrockCohereChatClient cohereChatClient(CohereChatBedrockApi cohereApi) {
-			return new BedrockCohereChatClient(cohereApi);
+		public BedrockCohereChatConnector cohereChatClient(CohereChatBedrockApi cohereApi) {
+			return new BedrockCohereChatConnector(cohereApi);
 		}
 
 	}

models/spring-ai-bedrock/src/test/java/org/springframework/ai/bedrock/cohere/BedrockCohereChatCreateRequestTests.java

@@ -45,7 +45,7 @@ public class BedrockCohereChatCreateRequestTests {
 	@Test
 	public void createRequestWithChatOptions() {
 
-		var client = new BedrockCohereChatClient(chatApi,
+		var client = new BedrockCohereChatConnector(chatApi,
 				BedrockCohereChatOptions.builder()
 					.withTemperature(66.6f)
 					.withTopK(66)

models/spring-ai-bedrock/src/test/java/org/springframework/ai/bedrock/jurassic2/BedrockAi21Jurassic2ChatConnectorIT.java

@@ -49,10 +49,10 @@ import static org.assertj.core.api.Assertions.assertThat;
 @SpringBootTest
 @EnabledIfEnvironmentVariable(named = "AWS_ACCESS_KEY_ID", matches = ".*")
 @EnabledIfEnvironmentVariable(named = "AWS_SECRET_ACCESS_KEY", matches = ".*")
-class BedrockAi21Jurassic2ChatClientIT {
+class BedrockAi21Jurassic2ChatConnectorIT {
 
 	@Autowired
-	private BedrockAi21Jurassic2ChatClient client;
+	private BedrockAi21Jurassic2ChatConnector client;
 
 	@Value("classpath:/prompts/system-message.st")
 	private Resource systemResource;
@@ -66,7 +66,7 @@ class BedrockAi21Jurassic2ChatClientIT {
 
 		Prompt prompt = new Prompt(List.of(userMessage, systemMessage));
 
-		ChatResponse response = client.call(prompt);
+		ChatResponse response = client.execute(prompt);
 
 		assertThat(response.getResult().getOutput().getContent()).contains("Blackbeard");
 	}
@@ -83,7 +83,7 @@ class BedrockAi21Jurassic2ChatClientIT {
 		UserMessage userMessage = new UserMessage("Can you express happiness using an emoji like 😄 ?");
 		Prompt prompt = new Prompt(List.of(userMessage), options);
 
-		ChatResponse response = client.call(prompt);
+		ChatResponse response = client.execute(prompt);
 
 		assertThat(response.getResult().getOutput().getContent()).matches(content -> content.contains("😄"));
 	}
@@ -103,7 +103,7 @@ class BedrockAi21Jurassic2ChatClientIT {
 
 		Prompt prompt = new Prompt(List.of(userMessage, systemMessage), options);
 
-		ChatResponse response = client.call(prompt);
+		ChatResponse response = client.execute(prompt);
 
 		assertThat(response.getResult().getOutput().getContent()).doesNotContain("😄");
 	}
@@ -120,7 +120,7 @@ class BedrockAi21Jurassic2ChatClientIT {
 		PromptTemplate promptTemplate = new PromptTemplate(template,
 				Map.of("subject", "an array of numbers from 1 to 9 under they key name 'numbers'", "format", format));
 		Prompt prompt = new Prompt(promptTemplate.createMessage());
-		Generation generation = client.call(prompt).getResult();
+		Generation generation = client.execute(prompt).getResult();
 
 		Map<String, Object> result = outputConverter.convert(generation.getOutput().getContent());
 		assertThat(result.get("numbers")).isEqualTo(Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8, 9));
@@ -135,7 +135,7 @@ class BedrockAi21Jurassic2ChatClientIT {
 
 		Prompt prompt = new Prompt(List.of(userMessage, systemMessage));
 
-		ChatResponse response = client.call(prompt);
+		ChatResponse response = client.execute(prompt);
 
 		assertThat(response.getResult().getOutput().getContent()).contains("AI");
 	}
@@ -152,9 +152,9 @@ class BedrockAi21Jurassic2ChatClientIT {
 		}
 
 		@Bean
-		public BedrockAi21Jurassic2ChatClient bedrockAi21Jurassic2ChatClient(
+		public BedrockAi21Jurassic2ChatConnector bedrockAi21Jurassic2ChatClient(
 				Ai21Jurassic2ChatBedrockApi jurassic2ChatBedrockApi) {
-			return new BedrockAi21Jurassic2ChatClient(jurassic2ChatBedrockApi,
+			return new BedrockAi21Jurassic2ChatConnector(jurassic2ChatBedrockApi,
 					BedrockAi21Jurassic2ChatOptions.builder()
 						.withTemperature(0.5f)
 						.withMaxTokens(100)

models/spring-ai-bedrock/src/test/java/org/springframework/ai/bedrock/llama/BedrockLlamaChatConnectorIT.java

@@ -54,10 +54,10 @@ import static org.assertj.core.api.Assertions.assertThat;
 @SpringBootTest
 @EnabledIfEnvironmentVariable(named = "AWS_ACCESS_KEY_ID", matches = ".*")
 @EnabledIfEnvironmentVariable(named = "AWS_SECRET_ACCESS_KEY", matches = ".*")
-class BedrockLlamaChatClientIT {
+class BedrockLlamaChatConnectorIT {
 
 	@Autowired
-	private BedrockLlamaChatClient client;
+	private BedrockLlamaChatConnector client;
 
 	@Value("classpath:/prompts/system-message.st")
 	private Resource systemResource;
@@ -98,7 +98,7 @@ class BedrockLlamaChatClientIT {
 
 		Prompt prompt = new Prompt(List.of(userMessage, systemMessage));
 
-		ChatResponse response = client.call(prompt);
+		ChatResponse response = client.execute(prompt);
 
 		assertThat(response.getResult().getOutput().getContent()).contains("Blackbeard");
 	}
@@ -116,7 +116,7 @@ class BedrockLlamaChatClientIT {
 		PromptTemplate promptTemplate = new PromptTemplate(template,
 				Map.of("subject", "ice cream flavors.", "format", format));
 		Prompt prompt = new Prompt(promptTemplate.createMessage());
-		Generation generation = this.client.call(prompt).getResult();
+		Generation generation = this.client.execute(prompt).getResult();
 
 		List<String> list = outputConverter.convert(generation.getOutput().getContent());
 		assertThat(list).hasSize(5);
@@ -134,7 +134,7 @@ class BedrockLlamaChatClientIT {
 		PromptTemplate promptTemplate = new PromptTemplate(template,
 				Map.of("subject", "an array of numbers from 1 to 9 under they key name 'numbers'", "format", format));
 		Prompt prompt = new Prompt(promptTemplate.createMessage());
-		Generation generation = client.call(prompt).getResult();
+		Generation generation = client.execute(prompt).getResult();
 
 		Map<String, Object> result = outputConverter.convert(generation.getOutput().getContent());
 		assertThat(result.get("numbers")).isEqualTo(Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8, 9));
@@ -158,7 +158,7 @@ class BedrockLlamaChatClientIT {
 				""";
 		PromptTemplate promptTemplate = new PromptTemplate(template, Map.of("format", format));
 		Prompt prompt = new Prompt(promptTemplate.createMessage());
-		Generation generation = client.call(prompt).getResult();
+		Generation generation = client.execute(prompt).getResult();
 
 		ActorsFilmsRecord actorsFilms = outputConverter.convert(generation.getOutput().getContent());
 		assertThat(actorsFilms.actor()).isEqualTo("Tom Hanks");
@@ -206,8 +206,8 @@ class BedrockLlamaChatClientIT {
 		}
 
 		@Bean
-		public BedrockLlamaChatClient llamaChatClient(LlamaChatBedrockApi llamaApi) {
-			return new BedrockLlamaChatClient(llamaApi,
+		public BedrockLlamaChatConnector llamaChatClient(LlamaChatBedrockApi llamaApi) {
+			return new BedrockLlamaChatConnector(llamaApi,
 					BedrockLlamaChatOptions.builder().withTemperature(0.5f).withMaxGenLen(100).withTopP(0.9f).build());
 		}
 

models/spring-ai-bedrock/src/test/java/org/springframework/ai/bedrock/llama/BedrockLlamaCreateRequestTests.java

@@ -45,7 +45,7 @@ public class BedrockLlamaCreateRequestTests {
 	@Test
 	public void createRequestWithChatOptions() {
 
-		var client = new BedrockLlamaChatClient(api,
+		var client = new BedrockLlamaChatConnector(api,
 				BedrockLlamaChatOptions.builder().withTemperature(66.6f).withMaxGenLen(666).withTopP(0.66f).build());
 
 		var request = client.createRequest(new Prompt("Test message content"));

models/spring-ai-bedrock/src/test/java/org/springframework/ai/bedrock/titan/BedrockTitanChatConnectorIT.java

@@ -55,10 +55,10 @@ import static org.assertj.core.api.Assertions.assertThat;
 @SpringBootTest
 @EnabledIfEnvironmentVariable(named = "AWS_ACCESS_KEY_ID", matches = ".*")
 @EnabledIfEnvironmentVariable(named = "AWS_SECRET_ACCESS_KEY", matches = ".*")
-class BedrockTitanChatClientIT {
+class BedrockTitanChatConnectorIT {
 
 	@Autowired
-	private BedrockTitanChatClient client;
+	private BedrockTitanChatConnector client;
 
 	@Value("classpath:/prompts/system-message.st")
 	private Resource systemResource;
@@ -99,7 +99,7 @@ class BedrockTitanChatClientIT {
 		SystemPromptTemplate systemPromptTemplate = new SystemPromptTemplate(systemResource);
 		Message systemMessage = systemPromptTemplate.createMessage(Map.of("name", name, "voice", voice));
 		Prompt prompt = new Prompt(List.of(userMessage, systemMessage));
-		ChatResponse response = client.call(prompt);
+		ChatResponse response = client.execute(prompt);
 		assertThat(response.getResult().getOutput().getContent()).contains("Blackbeard");
 	}
 
@@ -117,7 +117,7 @@ class BedrockTitanChatClientIT {
 		PromptTemplate promptTemplate = new PromptTemplate(template,
 				Map.of("subject", "ice cream flavors.", "format", format));
 		Prompt prompt = new Prompt(promptTemplate.createMessage());
-		Generation generation = this.client.call(prompt).getResult();
+		Generation generation = this.client.execute(prompt).getResult();
 
 		List<String> list = outputConverter.convert(generation.getOutput().getContent());
 		assertThat(list).hasSize(5);
@@ -138,7 +138,7 @@ class BedrockTitanChatClientIT {
 				Map.of("subject", "an array of numbers from 1 to 9 under they key name 'numbers'", "format", format));
 		Prompt prompt = new Prompt(promptTemplate.createMessage());
 
-		Generation generation = client.call(prompt).getResult();
+		Generation generation = client.execute(prompt).getResult();
 
 		Map<String, Object> result = outputConverter.convert(generation.getOutput().getContent());
 		assertThat(result.get("numbers")).isEqualTo(Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8, 9));
@@ -162,7 +162,7 @@ class BedrockTitanChatClientIT {
 				""";
 		PromptTemplate promptTemplate = new PromptTemplate(template, Map.of("format", format));
 		Prompt prompt = new Prompt(promptTemplate.createMessage());
-		Generation generation = client.call(prompt).getResult();
+		Generation generation = client.execute(prompt).getResult();
 
 		ActorsFilmsRecord actorsFilms = outputConverter.convert(generation.getOutput().getContent());
 		assertThat(actorsFilms.actor()).isEqualTo("Tom Hanks");
@@ -211,8 +211,8 @@ class BedrockTitanChatClientIT {
 		}
 
 		@Bean
-		public BedrockTitanChatClient titanChatClient(TitanChatBedrockApi titanApi) {
-			return new BedrockTitanChatClient(titanApi);
+		public BedrockTitanChatConnector titanChatClient(TitanChatBedrockApi titanApi) {
+			return new BedrockTitanChatConnector(titanApi);
 		}
 
 	}

models/spring-ai-bedrock/src/test/java/org/springframework/ai/bedrock/titan/BedrockTitanChatCreateRequestTests.java

@@ -41,7 +41,7 @@ public class BedrockTitanChatCreateRequestTests {
 	@Test
 	public void createRequestWithChatOptions() {
 
-		var client = new BedrockTitanChatClient(api,
+		var client = new BedrockTitanChatConnector(api,
 				BedrockTitanChatOptions.builder()
 					.withTemperature(66.6f)
 					.withTopP(0.66f)

models/spring-ai-huggingface/src/main/java/org/springframework/ai/huggingface/HuggingfaceChatConnector.java

@@ -22,7 +22,7 @@ import java.util.Map;
 import com.fasterxml.jackson.core.type.TypeReference;
 import com.fasterxml.jackson.databind.ObjectMapper;
 
-import org.springframework.ai.chat.ChatClient;
+import org.springframework.ai.chat.connector.ChatConnector;
 import org.springframework.ai.chat.ChatResponse;
 import org.springframework.ai.chat.Generation;
 import org.springframework.ai.huggingface.api.TextGenerationInferenceApi;
@@ -34,12 +34,12 @@ import org.springframework.ai.huggingface.model.GenerateResponse;
 import org.springframework.ai.chat.prompt.Prompt;
 
 /**
- * An implementation of {@link ChatClient} that interfaces with HuggingFace Inference
+ * An implementation of {@link ChatConnector} that interfaces with HuggingFace Inference
  * Endpoints for text generation.
  *
  * @author Mark Pollack
  */
-public class HuggingfaceChatClient implements ChatClient {
+public class HuggingfaceChatConnector implements ChatConnector {
 
 	/**
 	 * Token required for authenticating with the HuggingFace Inference API.
@@ -68,11 +68,12 @@ public class HuggingfaceChatClient implements ChatClient {
 	private int maxNewTokens = 1000;
 
 	/**
-	 * Constructs a new HuggingfaceChatClient with the specified API token and base path.
+	 * Constructs a new HuggingfaceChatConnector with the specified API token and base
+	 * path.
 	 * @param apiToken The API token for HuggingFace.
 	 * @param basePath The base path for API requests.
 	 */
-	public HuggingfaceChatClient(final String apiToken, String basePath) {
+	public HuggingfaceChatConnector(final String apiToken, String basePath) {
 		this.apiToken = apiToken;
 		this.apiClient.setBasePath(basePath);
 		this.apiClient.addDefaultHeader("Authorization", "Bearer " + this.apiToken);
@@ -85,7 +86,7 @@ public class HuggingfaceChatClient implements ChatClient {
 	 * @return ChatResponse containing the generated text and other related details.
 	 */
 	@Override
-	public ChatResponse call(Prompt prompt) {
+	public ChatResponse execute(Prompt prompt) {
 		GenerateRequest generateRequest = new GenerateRequest();
 		generateRequest.setInputs(prompt.getContents());
 		GenerateParameters generateParameters = new GenerateParameters();

models/spring-ai-huggingface/src/test/java/org/springframework/ai/huggingface/HuggingfaceTestConfiguration.java

@@ -23,7 +23,7 @@ import org.springframework.util.StringUtils;
 public class HuggingfaceTestConfiguration {
 
 	@Bean
-	public HuggingfaceChatClient huggingfaceChatClient() {
+	public HuggingfaceChatConnector huggingfaceChatClient() {
 		String apiKey = System.getenv("HUGGINGFACE_API_KEY");
 		if (!StringUtils.hasText(apiKey)) {
 			throw new IllegalArgumentException(
@@ -31,7 +31,7 @@ public class HuggingfaceTestConfiguration {
 		}
 		// Created aws-mistral-7b-instruct-v0-1-805 via
 		// https://ui.endpoints.huggingface.co/
-		HuggingfaceChatClient huggingfaceChatClient = new HuggingfaceChatClient(apiKey,
+		HuggingfaceChatConnector huggingfaceChatClient = new HuggingfaceChatConnector(apiKey,
 				"https://f6hg7b3cvlmntp5i.us-east-1.aws.endpoints.huggingface.cloud");
 		return huggingfaceChatClient;
 	}

models/spring-ai-huggingface/src/test/java/org/springframework/ai/huggingface/client/ClientIT.java

@@ -20,7 +20,7 @@ import org.junit.jupiter.api.Test;
 import org.junit.jupiter.api.condition.EnabledIfEnvironmentVariable;
 
 import org.springframework.ai.chat.ChatResponse;
-import org.springframework.ai.huggingface.HuggingfaceChatClient;
+import org.springframework.ai.huggingface.HuggingfaceChatConnector;
 import org.springframework.ai.chat.prompt.Prompt;
 import org.springframework.beans.factory.annotation.Autowired;
 import org.springframework.boot.test.context.SpringBootTest;
@@ -33,7 +33,7 @@ import static org.assertj.core.api.Assertions.assertThat;
 public class ClientIT {
 
 	@Autowired
-	protected HuggingfaceChatClient huggingfaceChatClient;
+	protected HuggingfaceChatConnector huggingfaceChatClient;
 
 	@Test
 	void helloWorldCompletion() {
@@ -46,7 +46,7 @@ public class ClientIT {
 				[/INST]
 				 """;
 		Prompt prompt = new Prompt(mistral7bInstruct);
-		ChatResponse chatResponse = huggingfaceChatClient.call(prompt);
+		ChatResponse chatResponse = huggingfaceChatClient.execute(prompt);
 		assertThat(chatResponse.getResult().getOutput().getContent()).isNotEmpty();
 		String expectedResponse = """
 				```json

models/spring-ai-mistral-ai/src/main/java/org/springframework/ai/mistralai/MistralAiChatConnector.java

@@ -17,7 +17,7 @@ package org.springframework.ai.mistralai;
 
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
-import org.springframework.ai.chat.ChatClient;
+import org.springframework.ai.chat.connector.ChatConnector;
 import org.springframework.ai.chat.ChatResponse;
 import org.springframework.ai.chat.Generation;
 import org.springframework.ai.chat.StreamingChatClient;
@@ -50,9 +50,9 @@ import java.util.concurrent.ConcurrentHashMap;
  * @author Grogdunn
  * @since 0.8.1
  */
-public class MistralAiChatClient extends
+public class MistralAiChatConnector extends
 		AbstractFunctionCallSupport<MistralAiApi.ChatCompletionMessage, MistralAiApi.ChatCompletionRequest, ResponseEntity<MistralAiApi.ChatCompletion>>
-		implements ChatClient, StreamingChatClient {
+		implements ChatConnector, StreamingChatClient {
 
 	private final Logger log = LoggerFactory.getLogger(getClass());
 
@@ -68,7 +68,7 @@ public class MistralAiChatClient extends
 
 	private final RetryTemplate retryTemplate;
 
-	public MistralAiChatClient(MistralAiApi mistralAiApi) {
+	public MistralAiChatConnector(MistralAiApi mistralAiApi) {
 		this(mistralAiApi,
 				MistralAiChatOptions.builder()
 					.withTemperature(0.7f)
@@ -78,11 +78,11 @@ public class MistralAiChatClient extends
 					.build());
 	}
 
-	public MistralAiChatClient(MistralAiApi mistralAiApi, MistralAiChatOptions options) {
+	public MistralAiChatConnector(MistralAiApi mistralAiApi, MistralAiChatOptions options) {
 		this(mistralAiApi, options, null, RetryUtils.DEFAULT_RETRY_TEMPLATE);
 	}
 
-	public MistralAiChatClient(MistralAiApi mistralAiApi, MistralAiChatOptions options,
+	public MistralAiChatConnector(MistralAiApi mistralAiApi, MistralAiChatOptions options,
 			FunctionCallbackContext functionCallbackContext, RetryTemplate retryTemplate) {
 		super(functionCallbackContext);
 		Assert.notNull(mistralAiApi, "MistralAiApi must not be null");
@@ -94,7 +94,7 @@ public class MistralAiChatClient extends
 	}
 
 	@Override
-	public ChatResponse call(Prompt prompt) {
+	public ChatResponse execute(Prompt prompt) {
 		var request = createRequest(prompt, false);
 
 		return retryTemplate.execute(ctx -> {

models/spring-ai-mistral-ai/src/main/java/org/springframework/ai/mistralai/MistralAiChatOptions.java

@@ -101,11 +101,11 @@ public class MistralAiChatOptions implements FunctionCallingOptions, ChatOptions
 	private @JsonProperty("tool_choice") ToolChoice toolChoice;
 
 	/**
-	 * MistralAI Tool Function Callbacks to register with the ChatClient. For Prompt
+	 * MistralAI Tool Function Callbacks to register with the ChatConnector. For Prompt
 	 * Options the functionCallbacks are automatically enabled for the duration of the
 	 * prompt execution. For Default Options the functionCallbacks are registered but
 	 * disabled by default. Use the enableFunctions to set the functions from the registry
-	 * to be used by the ChatClient chat completion requests.
+	 * to be used by the ChatConnector chat completion requests.
 	 */
 	@NestedConfigurationProperty
 	@JsonIgnore

models/spring-ai-mistral-ai/src/test/java/org/springframework/ai/mistralai/MistralAiChatCompletionRequestTest.java

@@ -32,7 +32,7 @@ import static org.assertj.core.api.Assertions.assertThat;
 @EnabledIfEnvironmentVariable(named = "MISTRAL_AI_API_KEY", matches = ".+")
 public class MistralAiChatCompletionRequestTest {
 
-	MistralAiChatClient chatClient = new MistralAiChatClient(new MistralAiApi("test"));
+	MistralAiChatConnector chatClient = new MistralAiChatConnector(new MistralAiApi("test"));
 
 	@Test
 	void chatCompletionDefaultRequestTest() {

models/spring-ai-mistral-ai/src/test/java/org/springframework/ai/mistralai/MistralAiChatConnectorIT.java

@@ -27,7 +27,7 @@ import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 import reactor.core.publisher.Flux;
 
-import org.springframework.ai.chat.ChatClient;
+import org.springframework.ai.chat.connector.ChatConnector;
 import org.springframework.ai.chat.ChatResponse;
 import org.springframework.ai.chat.Generation;
 import org.springframework.ai.chat.StreamingChatClient;
@@ -56,12 +56,12 @@ import static org.assertj.core.api.Assertions.assertThat;
  */
 @SpringBootTest(classes = MistralAiTestConfiguration.class)
 @EnabledIfEnvironmentVariable(named = "MISTRAL_AI_API_KEY", matches = ".+")
-class MistralAiChatClientIT {
+class MistralAiChatConnectorIT {
 
-	private static final Logger logger = LoggerFactory.getLogger(MistralAiChatClientIT.class);
+	private static final Logger logger = LoggerFactory.getLogger(MistralAiChatConnectorIT.class);
 
 	@Autowired
-	protected ChatClient chatClient;
+	protected ChatConnector chatConnector;
 
 	@Autowired
 	protected StreamingChatClient streamingChatClient;
@@ -90,7 +90,7 @@ class MistralAiChatClientIT {
 		// NOTE: Mistral expects the system message to be before the user message or will
 		// fail with 400 error.
 		Prompt prompt = new Prompt(List.of(systemMessage, userMessage));
-		ChatResponse response = chatClient.call(prompt);
+		ChatResponse response = chatConnector.execute(prompt);
 		assertThat(response.getResults()).hasSize(1);
 		assertThat(response.getResults().get(0).getOutput().getContent()).contains("Blackbeard");
 	}
@@ -108,7 +108,7 @@ class MistralAiChatClientIT {
 		PromptTemplate promptTemplate = new PromptTemplate(template,
 				Map.of("subject", "ice cream flavors", "format", format));
 		Prompt prompt = new Prompt(promptTemplate.createMessage());
-		Generation generation = this.chatClient.call(prompt).getResult();
+		Generation generation = this.chatConnector.execute(prompt).getResult();
 
 		List<String> list = outputConverter.convert(generation.getOutput().getContent());
 		assertThat(list).hasSize(5);
@@ -126,7 +126,7 @@ class MistralAiChatClientIT {
 		PromptTemplate promptTemplate = new PromptTemplate(template,
 				Map.of("subject", "an array of numbers from 1 to 9 under they key name 'numbers'", "format", format));
 		Prompt prompt = new Prompt(promptTemplate.createMessage());
-		Generation generation = chatClient.call(prompt).getResult();
+		Generation generation = chatConnector.execute(prompt).getResult();
 
 		Map<String, Object> result = outputConverter.convert(generation.getOutput().getContent());
 		assertThat(result.get("numbers")).isEqualTo(Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8, 9));
@@ -148,7 +148,7 @@ class MistralAiChatClientIT {
 				""";
 		PromptTemplate promptTemplate = new PromptTemplate(template, Map.of("format", format));
 		Prompt prompt = new Prompt(promptTemplate.createMessage());
-		Generation generation = chatClient.call(prompt).getResult();
+		Generation generation = chatConnector.execute(prompt).getResult();
 
 		ActorsFilmsRecord actorsFilms = outputConverter.convert(generation.getOutput().getContent());
 		logger.info("" + actorsFilms);
@@ -186,7 +186,7 @@ class MistralAiChatClientIT {
 	}
 
 	@Test
-	void functionCallTest() {
+	void functionExecuteTest() {
 
 		UserMessage userMessage = new UserMessage("What's the weather like in San Francisco?");
 
@@ -201,7 +201,7 @@ class MistralAiChatClientIT {
 				.build()))
 			.build();
 
-		ChatResponse response = chatClient.call(new Prompt(messages, promptOptions));
+		ChatResponse response = chatConnector.execute(new Prompt(messages, promptOptions));
 
 		logger.info("Response: {}", response);
 
@@ -209,7 +209,7 @@ class MistralAiChatClientIT {
 	}
 
 	@Test
-	void streamFunctionCallTest() {
+	void streamFunctionExecuteTest() {
 
 		UserMessage userMessage = new UserMessage("What's the weather like in Tokyo, Japan?");
 

models/spring-ai-mistral-ai/src/test/java/org/springframework/ai/mistralai/MistralAiRetryTests.java

@@ -82,7 +82,7 @@ public class MistralAiRetryTests {
 
 	private @Mock MistralAiApi mistralAiApi;
 
-	private MistralAiChatClient chatClient;
+	private MistralAiChatConnector chatClient;
 
 	private MistralAiEmbeddingClient embeddingClient;
 
@@ -92,7 +92,7 @@ public class MistralAiRetryTests {
 		retryListener = new TestRetryListener();
 		retryTemplate.registerListener(retryListener);
 
-		chatClient = new MistralAiChatClient(mistralAiApi,
+		chatClient = new MistralAiChatConnector(mistralAiApi,
 				MistralAiChatOptions.builder()
 					.withTemperature(0.7f)
 					.withTopP(1f)
@@ -118,7 +118,7 @@ public class MistralAiRetryTests {
 			.thenThrow(new TransientAiException("Transient Error 2"))
 			.thenReturn(ResponseEntity.of(Optional.of(expectedChatCompletion)));
 
-		var result = chatClient.call(new Prompt("text"));
+		var result = chatClient.execute(new Prompt("text"));
 
 		assertThat(result).isNotNull();
 		assertThat(result.getResult().getOutput().getContent()).isSameAs("Response");
@@ -130,7 +130,7 @@ public class MistralAiRetryTests {
 	public void mistralAiChatNonTransientError() {
 		when(mistralAiApi.chatCompletionEntity(isA(ChatCompletionRequest.class)))
 				.thenThrow(new RuntimeException("Non Transient Error"));
-		assertThrows(RuntimeException.class, () -> chatClient.call(new Prompt("text")));
+		assertThrows(RuntimeException.class, () -> chatClient.execute(new Prompt("text")));
 	}
 
 	@Test

models/spring-ai-mistral-ai/src/test/java/org/springframework/ai/mistralai/MistralAiTestConfiguration.java

@@ -41,8 +41,8 @@ public class MistralAiTestConfiguration {
 	}
 
 	@Bean
-	public MistralAiChatClient mistralAiChatClient(MistralAiApi mistralAiApi) {
-		return new MistralAiChatClient(mistralAiApi,
+	public MistralAiChatConnector mistralAiChatClient(MistralAiApi mistralAiApi) {
+		return new MistralAiChatConnector(mistralAiApi,
 				MistralAiChatOptions.builder().withModel(MistralAiApi.ChatModel.MIXTRAL.getValue()).build());
 	}
 

models/spring-ai-ollama/src/main/java/org/springframework/ai/ollama/OllamaChatConnector.java

@@ -21,7 +21,7 @@ import java.util.List;
 import org.springframework.ai.ollama.metadata.OllamaChatResponseMetadata;
 import reactor.core.publisher.Flux;
 
-import org.springframework.ai.chat.ChatClient;
+import org.springframework.ai.chat.connector.ChatConnector;
 import org.springframework.ai.chat.ChatResponse;
 import org.springframework.ai.chat.Generation;
 import org.springframework.ai.chat.StreamingChatClient;
@@ -39,7 +39,7 @@ import org.springframework.util.CollectionUtils;
 import org.springframework.util.StringUtils;
 
 /**
- * {@link ChatClient} implementation for {@literal Ollama}.
+ * {@link ChatConnector} implementation for {@literal Ollama}.
  *
  * Ollama allows developers to run large language models and generate embeddings locally.
  * It supports open-source models available on [Ollama AI
@@ -52,7 +52,7 @@ import org.springframework.util.StringUtils;
  * @author Christian Tzolov
  * @since 0.8.0
  */
-public class OllamaChatClient implements ChatClient, StreamingChatClient {
+public class OllamaChatConnector implements ChatConnector, StreamingChatClient {
 
 	/**
 	 * Low-level Ollama API library.
@@ -64,11 +64,11 @@ public class OllamaChatClient implements ChatClient, StreamingChatClient {
 	 */
 	private OllamaOptions defaultOptions;
 
-	public OllamaChatClient(OllamaApi chatApi) {
+	public OllamaChatConnector(OllamaApi chatApi) {
 		this(chatApi, OllamaOptions.create().withModel(OllamaOptions.DEFAULT_MODEL));
 	}
 
-	public OllamaChatClient(OllamaApi chatApi, OllamaOptions defaultOptions) {
+	public OllamaChatConnector(OllamaApi chatApi, OllamaOptions defaultOptions) {
 		Assert.notNull(chatApi, "OllamaApi must not be null");
 		Assert.notNull(defaultOptions, "DefaultOptions must not be null");
 		this.chatApi = chatApi;
@@ -79,7 +79,7 @@ public class OllamaChatClient implements ChatClient, StreamingChatClient {
 	 * @deprecated Use {@link OllamaOptions#setModel} instead.
 	 */
 	@Deprecated
-	public OllamaChatClient withModel(String model) {
+	public OllamaChatConnector withModel(String model) {
 		this.defaultOptions.setModel(model);
 		return this;
 	}
@@ -88,13 +88,13 @@ public class OllamaChatClient implements ChatClient, StreamingChatClient {
 	 * @deprecated Use {@link OllamaOptions} constructor instead.
 	 */
 	@Deprecated
-	public OllamaChatClient withDefaultOptions(OllamaOptions options) {
+	public OllamaChatConnector withDefaultOptions(OllamaOptions options) {
 		this.defaultOptions = options;
 		return this;
 	}
 
 	@Override
-	public ChatResponse call(Prompt prompt) {
+	public ChatResponse execute(Prompt prompt) {
 
 		OllamaApi.ChatResponse response = this.chatApi.chat(ollamaChatRequest(prompt, false));
 

models/spring-ai-ollama/src/test/java/org/springframework/ai/ollama/OllamaChatConnectorIT.java

@@ -56,11 +56,11 @@ import static org.assertj.core.api.Assertions.assertThat;
 @SpringBootTest
 @Testcontainers
 @Disabled("For manual smoke testing only.")
-class OllamaChatClientIT {
+class OllamaChatConnectorIT {
 
 	private static String MODEL = "mistral";
 
-	private static final Log logger = LogFactory.getLog(OllamaChatClientIT.class);
+	private static final Log logger = LogFactory.getLog(OllamaChatConnectorIT.class);
 
 	@Container
 	static OllamaContainer ollamaContainer = new OllamaContainer("ollama/ollama:0.1.32");
@@ -77,7 +77,7 @@ class OllamaChatClientIT {
 	}
 
 	@Autowired
-	private OllamaChatClient client;
+	private OllamaChatConnector client;
 
 	@Test
 	void roleTest() {
@@ -95,13 +95,13 @@ class OllamaChatClientIT {
 
 		Prompt prompt = new Prompt(List.of(userMessage, systemMessage), portableOptions);
 
-		ChatResponse response = client.call(prompt);
+		ChatResponse response = client.execute(prompt);
 		assertThat(response.getResult().getOutput().getContent()).contains("Blackbeard");
 
 		// ollama specific options
 		var ollamaOptions = new OllamaOptions().withLowVRAM(true);
 
-		response = client.call(new Prompt(List.of(userMessage, systemMessage), ollamaOptions));
+		response = client.execute(new Prompt(List.of(userMessage, systemMessage), ollamaOptions));
 		assertThat(response.getResult().getOutput().getContent()).contains("Blackbeard");
 
 	}
@@ -109,7 +109,7 @@ class OllamaChatClientIT {
 	@Test
 	void usageTest() {
 		Prompt prompt = new Prompt("Tell me a joke");
-		ChatResponse response = client.call(prompt);
+		ChatResponse response = client.execute(prompt);
 		Usage usage = response.getMetadata().getUsage();
 
 		assertThat(usage).isNotNull();
@@ -131,7 +131,7 @@ class OllamaChatClientIT {
 		PromptTemplate promptTemplate = new PromptTemplate(template,
 				Map.of("subject", "ice cream flavors.", "format", format));
 		Prompt prompt = new Prompt(promptTemplate.createMessage());
-		Generation generation = this.client.call(prompt).getResult();
+		Generation generation = this.client.execute(prompt).getResult();
 
 		List<String> list = outputConverter.convert(generation.getOutput().getContent());
 		assertThat(list).hasSize(5);
@@ -151,7 +151,7 @@ class OllamaChatClientIT {
 				Map.of("subject", "an array of numbers from 1 to 9 under they key name 'numbers'", "format", format));
 		Prompt prompt = new Prompt(promptTemplate.createMessage());
 
-		Generation generation = client.call(prompt).getResult();
+		Generation generation = client.execute(prompt).getResult();
 
 		Map<String, Object> result = outputConverter.convert(generation.getOutput().getContent());
 		assertThat(result.get("numbers")).isEqualTo(Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8, 9));
@@ -173,7 +173,7 @@ class OllamaChatClientIT {
 				""";
 		PromptTemplate promptTemplate = new PromptTemplate(template, Map.of("format", format));
 		Prompt prompt = new Prompt(promptTemplate.createMessage());
-		Generation generation = client.call(prompt).getResult();
+		Generation generation = client.execute(prompt).getResult();
 
 		ActorsFilmsRecord actorsFilms = outputConverter.convert(generation.getOutput().getContent());
 		assertThat(actorsFilms.actor()).isEqualTo("Tom Hanks");
@@ -219,8 +219,8 @@ class OllamaChatClientIT {
 		}
 
 		@Bean
-		public OllamaChatClient ollamaChat(OllamaApi ollamaApi) {
-			return new OllamaChatClient(ollamaApi, OllamaOptions.create().withModel(MODEL).withTemperature(0.9f));
+		public OllamaChatConnector ollamaChat(OllamaApi ollamaApi) {
+			return new OllamaChatConnector(ollamaApi, OllamaOptions.create().withModel(MODEL).withTemperature(0.9f));
 		}
 
 	}

models/spring-ai-ollama/src/test/java/org/springframework/ai/ollama/OllamaChatConnectorMultimodalIT.java

@@ -44,11 +44,11 @@ import static org.assertj.core.api.Assertions.assertThat;
 @SpringBootTest
 @Testcontainers
 @Disabled("For manual smoke testing only.")
-class OllamaChatClientMultimodalIT {
+class OllamaChatConnectorMultimodalIT {
 
 	private static String MODEL = "llava";
 
-	private static final Log logger = LogFactory.getLog(OllamaChatClientIT.class);
+	private static final Log logger = LogFactory.getLog(OllamaChatConnectorIT.class);
 
 	@Container
 	static OllamaContainer ollamaContainer = new OllamaContainer("ollama/ollama:0.1.32");
@@ -65,7 +65,7 @@ class OllamaChatClientMultimodalIT {
 	}
 
 	@Autowired
-	private OllamaChatClient client;
+	private OllamaChatConnector client;
 
 	@Test
 	void multiModalityTest() throws IOException {
@@ -75,7 +75,7 @@ class OllamaChatClientMultimodalIT {
 		var userMessage = new UserMessage("Explain what do you see on this picture?",
 				List.of(new Media(MimeTypeUtils.IMAGE_PNG, imageData)));
 
-		var response = client.call(new Prompt(List.of(userMessage)));
+		var response = client.execute(new Prompt(List.of(userMessage)));
 
 		logger.info(response.getResult().getOutput().getContent());
 		assertThat(response.getResult().getOutput().getContent()).contains("bananas", "apple", "basket");
@@ -90,8 +90,8 @@ class OllamaChatClientMultimodalIT {
 		}
 
 		@Bean
-		public OllamaChatClient ollamaChat(OllamaApi ollamaApi) {
-			return new OllamaChatClient(ollamaApi, OllamaOptions.create().withModel(MODEL).withTemperature(0.9f));
+		public OllamaChatConnector ollamaChat(OllamaApi ollamaApi) {
+			return new OllamaChatConnector(ollamaApi, OllamaOptions.create().withModel(MODEL).withTemperature(0.9f));
 		}
 
 	}

models/spring-ai-ollama/src/test/java/org/springframework/ai/ollama/OllamaChatRequestTests.java

@@ -30,7 +30,7 @@ import static org.assertj.core.api.Assertions.assertThat;
  */
 public class OllamaChatRequestTests {
 
-	OllamaChatClient client = new OllamaChatClient(new OllamaApi(),
+	OllamaChatConnector client = new OllamaChatConnector(new OllamaApi(),
 			new OllamaOptions().withModel("MODEL_NAME").withTopK(99).withTemperature(66.6f).withNumGPU(1));
 
 	@Test
@@ -105,7 +105,7 @@ public class OllamaChatRequestTests {
 	@Test
 	public void createRequestWithDefaultOptionsModelOverride() {
 
-		OllamaChatClient client2 = new OllamaChatClient(new OllamaApi(),
+		OllamaChatConnector client2 = new OllamaChatConnector(new OllamaApi(),
 				new OllamaOptions().withModel("DEFAULT_OPTIONS_MODEL"));
 
 		var request = client2.ollamaChatRequest(new Prompt("Test message content"), true);

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

@@ -17,7 +17,7 @@ package org.springframework.ai.openai;
 
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
-import org.springframework.ai.chat.ChatClient;
+import org.springframework.ai.chat.connector.ChatConnector;
 import org.springframework.ai.chat.ChatResponse;
 import org.springframework.ai.chat.Generation;
 import org.springframework.ai.chat.StreamingChatClient;
@@ -58,8 +58,8 @@ import java.util.Set;
 import java.util.concurrent.ConcurrentHashMap;
 
 /**
- * {@link ChatClient} and {@link StreamingChatClient} implementation for {@literal OpenAI}
- * backed by {@link OpenAiApi}.
+ * {@link ChatConnector} and {@link StreamingChatClient} implementation for
+ * {@literal OpenAI} backed by {@link OpenAiApi}.
  *
  * @author Mark Pollack
  * @author Christian Tzolov
@@ -68,15 +68,15 @@ import java.util.concurrent.ConcurrentHashMap;
  * @author Josh Long
  * @author Jemin Huh
  * @author Grogdunn
- * @see ChatClient
+ * @see ChatConnector
  * @see StreamingChatClient
  * @see OpenAiApi
  */
-public class OpenAiChatClient extends
+public class OpenAiChatConnector extends
 		AbstractFunctionCallSupport<ChatCompletionMessage, OpenAiApi.ChatCompletionRequest, ResponseEntity<ChatCompletion>>
-		implements ChatClient, StreamingChatClient {
+		implements ChatConnector, StreamingChatClient {
 
-	private static final Logger logger = LoggerFactory.getLogger(OpenAiChatClient.class);
+	private static final Logger logger = LoggerFactory.getLogger(OpenAiChatConnector.class);
 
 	/**
 	 * The default options used for the chat completion requests.
@@ -94,35 +94,35 @@ public class OpenAiChatClient extends
 	private final OpenAiApi openAiApi;
 
 	/**
-	 * Creates an instance of the OpenAiChatClient.
+	 * Creates an instance of the OpenAiChatConnector.
 	 * @param openAiApi The OpenAiApi instance to be used for interacting with the OpenAI
 	 * Chat API.
 	 * @throws IllegalArgumentException if openAiApi is null
 	 */
-	public OpenAiChatClient(OpenAiApi openAiApi) {
+	public OpenAiChatConnector(OpenAiApi openAiApi) {
 		this(openAiApi,
 				OpenAiChatOptions.builder().withModel(OpenAiApi.DEFAULT_CHAT_MODEL).withTemperature(0.7f).build());
 	}
 
 	/**
-	 * Initializes an instance of the OpenAiChatClient.
+	 * Initializes an instance of the OpenAiChatConnector.
 	 * @param openAiApi The OpenAiApi instance to be used for interacting with the OpenAI
 	 * Chat API.
 	 * @param options The OpenAiChatOptions to configure the chat client.
 	 */
-	public OpenAiChatClient(OpenAiApi openAiApi, OpenAiChatOptions options) {
+	public OpenAiChatConnector(OpenAiApi openAiApi, OpenAiChatOptions options) {
 		this(openAiApi, options, null, RetryUtils.DEFAULT_RETRY_TEMPLATE);
 	}
 
 	/**
-	 * Initializes a new instance of the OpenAiChatClient.
+	 * Initializes a new instance of the OpenAiChatConnector.
 	 * @param openAiApi The OpenAiApi instance to be used for interacting with the OpenAI
 	 * Chat API.
 	 * @param options The OpenAiChatOptions to configure the chat client.
 	 * @param functionCallbackContext The function callback context.
 	 * @param retryTemplate The retry template.
 	 */
-	public OpenAiChatClient(OpenAiApi openAiApi, OpenAiChatOptions options,
+	public OpenAiChatConnector(OpenAiApi openAiApi, OpenAiChatOptions options,
 			FunctionCallbackContext functionCallbackContext, RetryTemplate retryTemplate) {
 		super(functionCallbackContext);
 		Assert.notNull(openAiApi, "OpenAiApi must not be null");
@@ -134,7 +134,7 @@ public class OpenAiChatClient extends
 	}
 
 	@Override
-	public ChatResponse call(Prompt prompt) {
+	public ChatResponse execute(Prompt prompt) {
 
 		ChatCompletionRequest request = createRequest(prompt, false);
 

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

@@ -134,10 +134,10 @@ public class OpenAiChatOptions implements FunctionCallingOptions, ChatOptions {
 	private @JsonProperty("user") String user;
 
 	/**
-	 * OpenAI Tool Function Callbacks to register with the ChatClient.
+	 * OpenAI Tool Function Callbacks to register with the ChatConnector.
 	 * For Prompt Options the functionCallbacks are automatically enabled for the duration of the prompt execution.
 	 * For Default Options the functionCallbacks are registered but disabled by default. Use the enableFunctions to set the functions
-	 * from the registry to be used by the ChatClient chat completion requests.
+	 * from the registry to be used by the ChatConnector chat completion requests.
 	 */
 	@NestedConfigurationProperty
 	@JsonIgnore

models/spring-ai-openai/src/test/java/org/springframework/ai/openai/ChatClientTest.java

@@ -0,0 +1,56 @@
+package org.springframework.ai.openai;
+
+import org.junit.jupiter.api.Test;
+import org.springframework.ai.chat.ChatClient;
+import org.springframework.beans.factory.annotation.Autowired;
+import org.springframework.context.annotation.Bean;
+import org.springframework.context.annotation.Configuration;
+
+import java.util.Map;
+
+class ChatClientTest {
+
+	@Configuration
+	static class ChatClientTestConfiguration {
+
+		@Bean
+		ChatClient client(OpenAiChatConnector openAiChatConnector) {
+			return ChatClient.builder(openAiChatConnector).defaultSystemPrompt("""
+					    you are customer service agent designed to answer questions
+					    about a the user, {userName}'s, orders. Here are their outstanding orders.
+
+					    {orders}
+
+					""").defaultFunctions("cancelOrder", "refundOrder").build();
+		}
+
+	}
+
+	private final ChatClient singularity;
+
+	ChatClientTest(@Autowired ChatClient singularity) {
+		this.singularity = singularity;
+	}
+
+	@Test
+	void products() throws Exception {
+		var product0 = this.client.userPrompt("tell me about this product from the merchant {merchant}")
+			.userPromptParams(Map.of("merchant", "24u92"))
+			.execute(Product.class);
+
+		/*
+		 * var product1 = this.client .build() .userPromptParam("a", "b")
+		 * .functions("cancelOrder", "refundOrder") .execute(new
+		 * ParameterizedTypeReference<Product>() { });
+		 *
+		 * var product2 = this.client
+		 * .userPrompt("tell me about this product from the merchant {merchant}",
+		 * Map.of("merchant", "232")) .execute(Product.class);
+		 */
+
+	}
+
+	record Product(String sku) {
+	}
+
+}

models/spring-ai-openai/src/test/java/org/springframework/ai/openai/ChatCompletionRequestTests.java

@@ -34,7 +34,7 @@ public class ChatCompletionRequestTests {
 	@Test
 	public void createRequestWithChatOptions() {
 
-		var client = new OpenAiChatClient(new OpenAiApi("TEST"),
+		var client = new OpenAiChatConnector(new OpenAiApi("TEST"),
 				OpenAiChatOptions.builder().withModel("DEFAULT_MODEL").withTemperature(66.6f).build());
 
 		var request = client.createRequest(new Prompt("Test message content"), false);
@@ -60,7 +60,7 @@ public class ChatCompletionRequestTests {
 
 		final String TOOL_FUNCTION_NAME = "CurrentWeather";
 
-		var client = new OpenAiChatClient(new OpenAiApi("TEST"),
+		var client = new OpenAiChatConnector(new OpenAiApi("TEST"),
 				OpenAiChatOptions.builder().withModel("DEFAULT_MODEL").build());
 
 		var request = client.createRequest(new Prompt("Test message content",
@@ -90,7 +90,7 @@ public class ChatCompletionRequestTests {
 
 		final String TOOL_FUNCTION_NAME = "CurrentWeather";
 
-		var client = new OpenAiChatClient(new OpenAiApi("TEST"),
+		var client = new OpenAiChatConnector(new OpenAiApi("TEST"),
 				OpenAiChatOptions.builder()
 					.withModel("DEFAULT_MODEL")
 					.withFunctionCallbacks(List.of(FunctionCallbackWrapper.builder(new MockWeatherService())

models/spring-ai-openai/src/test/java/org/springframework/ai/openai/OpenAiTestConfiguration.java

@@ -51,8 +51,8 @@ public class OpenAiTestConfiguration {
 	}
 
 	@Bean
-	public OpenAiChatClient openAiChatClient(OpenAiApi api) {
-		OpenAiChatClient openAiChatClient = new OpenAiChatClient(api);
+	public OpenAiChatConnector openAiChatClient(OpenAiApi api) {
+		OpenAiChatConnector openAiChatClient = new OpenAiChatConnector(api);
 		return openAiChatClient;
 	}
 

models/spring-ai-openai/src/test/java/org/springframework/ai/openai/acme/AcmeIT.java

@@ -26,8 +26,8 @@ import org.slf4j.LoggerFactory;
 
 import org.springframework.ai.chat.ChatResponse;
 import org.springframework.ai.document.Document;
+import org.springframework.ai.openai.OpenAiChatConnector;
 import org.springframework.ai.openai.OpenAiTestConfiguration;
-import org.springframework.ai.openai.OpenAiChatClient;
 import org.springframework.ai.openai.OpenAiEmbeddingClient;
 import org.springframework.ai.openai.testutils.AbstractIT;
 import org.springframework.ai.chat.prompt.Prompt;
@@ -61,7 +61,7 @@ public class AcmeIT extends AbstractIT {
 	private OpenAiEmbeddingClient embeddingClient;
 
 	@Autowired
-	private OpenAiChatClient chatClient;
+	private OpenAiChatConnector chatClient;
 
 	@Test
 	void beanTest() {
@@ -108,7 +108,7 @@ public class AcmeIT extends AbstractIT {
 		logger.info("Asking AI generative to reply to question.");
 		Prompt prompt = new Prompt(List.of(systemMessage, userMessage));
 		logger.info("AI responded.");
-		ChatResponse response = chatClient.call(prompt);
+		ChatResponse response = chatClient.execute(prompt);
 
 		evaluateQuestionAndAnswer(userQuery, response, true);
 	}

models/spring-ai-openai/src/test/java/org/springframework/ai/openai/chat/OpenAiChatClientWithChatResponseMetadataTests.java

@@ -26,8 +26,8 @@ import org.springframework.ai.chat.metadata.ChatResponseMetadata;
 import org.springframework.ai.chat.metadata.PromptMetadata;
 import org.springframework.ai.chat.metadata.RateLimit;
 import org.springframework.ai.chat.metadata.Usage;
+import org.springframework.ai.openai.OpenAiChatConnector;
 import org.springframework.ai.openai.api.OpenAiApi;
-import org.springframework.ai.openai.OpenAiChatClient;
 import org.springframework.ai.openai.metadata.support.OpenAiApiResponseHeaders;
 import org.springframework.ai.chat.prompt.Prompt;
 import org.springframework.beans.factory.annotation.Autowired;
@@ -57,7 +57,7 @@ public class OpenAiChatClientWithChatResponseMetadataTests {
 	private static String TEST_API_KEY = "sk-1234567890";
 
 	@Autowired
-	private OpenAiChatClient openAiChatClient;
+	private OpenAiChatConnector openAiChatClient;
 
 	@Autowired
 	private MockRestServiceServer server;
@@ -74,7 +74,7 @@ public class OpenAiChatClientWithChatResponseMetadataTests {
 
 		Prompt prompt = new Prompt("Reach for the sky.");
 
-		ChatResponse response = this.openAiChatClient.call(prompt);
+		ChatResponse response = this.openAiChatClient.execute(prompt);
 
 		assertThat(response).isNotNull();
 
@@ -171,8 +171,8 @@ public class OpenAiChatClientWithChatResponseMetadataTests {
 		}
 
 		@Bean
-		public OpenAiChatClient openAiClient(OpenAiApi openAiApi) {
-			return new OpenAiChatClient(openAiApi);
+		public OpenAiChatConnector openAiClient(OpenAiApi openAiApi) {
+			return new OpenAiChatConnector(openAiApi);
 		}
 
 	}

models/spring-ai-openai/src/test/java/org/springframework/ai/openai/chat/OpenAiChatConnector2IT.java

@@ -27,7 +27,7 @@ import org.slf4j.LoggerFactory;
 
 import org.springframework.ai.chat.ChatResponse;
 import org.springframework.ai.chat.prompt.Prompt;
-import org.springframework.ai.openai.OpenAiChatClient;
+import org.springframework.ai.openai.OpenAiChatConnector;
 import org.springframework.ai.openai.OpenAiChatOptions;
 import org.springframework.ai.openai.api.OpenAiApi;
 import org.springframework.ai.openai.api.OpenAiApi.ChatCompletionRequest;
@@ -41,14 +41,14 @@ import static org.assertj.core.api.Assertions.assertThat;
 /**
  * @author Christian Tzolov
  */
-@SpringBootTest(classes = OpenAiChatClient2IT.Config.class)
+@SpringBootTest(classes = OpenAiChatConnector2IT.Config.class)
 @EnabledIfEnvironmentVariable(named = "OPENAI_API_KEY", matches = ".+")
-public class OpenAiChatClient2IT {
+public class OpenAiChatConnector2IT {
 
 	private final Logger logger = LoggerFactory.getLogger(getClass());
 
 	@Autowired
-	private OpenAiChatClient openAiChatClient;
+	private OpenAiChatConnector openAiChatClient;
 
 	@Test
 	void responseFormatTest() throws JsonMappingException, JsonProcessingException {
@@ -67,7 +67,7 @@ public class OpenAiChatClient2IT {
 					.withResponseFormat(new ChatCompletionRequest.ResponseFormat("json_object"))
 					.build());
 
-		ChatResponse response = this.openAiChatClient.call(prompt);
+		ChatResponse response = this.openAiChatClient.execute(prompt);
 
 		assertThat(response).isNotNull();
 
@@ -99,8 +99,8 @@ public class OpenAiChatClient2IT {
 		}
 
 		@Bean
-		public OpenAiChatClient openAiClient(OpenAiApi openAiApi) {
-			return new OpenAiChatClient(openAiApi);
+		public OpenAiChatConnector openAiClient(OpenAiApi openAiApi) {
+			return new OpenAiChatConnector(openAiApi);
 		}
 
 	}

models/spring-ai-openai/src/test/java/org/springframework/ai/openai/chat/OpenAiChatConnectorIT.java

@@ -60,9 +60,9 @@ import static org.assertj.core.api.Assertions.assertThat;
 
 @SpringBootTest(classes = OpenAiTestConfiguration.class)
 @EnabledIfEnvironmentVariable(named = "OPENAI_API_KEY", matches = ".+")
-class OpenAiChatClientIT extends AbstractIT {
+class OpenAiChatConnectorIT extends AbstractIT {
 
-	private static final Logger logger = LoggerFactory.getLogger(OpenAiChatClientIT.class);
+	private static final Logger logger = LoggerFactory.getLogger(OpenAiChatConnectorIT.class);
 
 	@Value("classpath:/prompts/system-message.st")
 	private Resource systemResource;
@@ -74,7 +74,7 @@ class OpenAiChatClientIT extends AbstractIT {
 		SystemPromptTemplate systemPromptTemplate = new SystemPromptTemplate(systemResource);
 		Message systemMessage = systemPromptTemplate.createMessage(Map.of("name", "Bob", "voice", "pirate"));
 		Prompt prompt = new Prompt(List.of(userMessage, systemMessage));
-		ChatResponse response = chatClient.call(prompt);
+		ChatResponse response = chatConnector.execute(prompt);
 		assertThat(response.getResults()).hasSize(1);
 		assertThat(response.getResults().get(0).getOutput().getContent()).contains("Blackbeard");
 		// needs fine tuning... evaluateQuestionAndAnswer(request, response, false);
@@ -93,7 +93,7 @@ class OpenAiChatClientIT extends AbstractIT {
 		PromptTemplate promptTemplate = new PromptTemplate(template,
 				Map.of("subject", "ice cream flavors", "format", format));
 		Prompt prompt = new Prompt(promptTemplate.createMessage());
-		Generation generation = this.chatClient.call(prompt).getResult();
+		Generation generation = this.chatConnector.execute(prompt).getResult();
 
 		List<String> list = outputConverter.convert(generation.getOutput().getContent());
 		assertThat(list).hasSize(5);
@@ -112,7 +112,7 @@ class OpenAiChatClientIT extends AbstractIT {
 		PromptTemplate promptTemplate = new PromptTemplate(template,
 				Map.of("subject", "an array of numbers from 1 to 9 under they key name 'numbers'", "format", format));
 		Prompt prompt = new Prompt(promptTemplate.createMessage());
-		Generation generation = chatClient.call(prompt).getResult();
+		Generation generation = chatConnector.execute(prompt).getResult();
 
 		Map<String, Object> result = outputConverter.convert(generation.getOutput().getContent());
 		assertThat(result.get("numbers")).isEqualTo(Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8, 9));
@@ -131,7 +131,7 @@ class OpenAiChatClientIT extends AbstractIT {
 				""";
 		PromptTemplate promptTemplate = new PromptTemplate(template, Map.of("format", format));
 		Prompt prompt = new Prompt(promptTemplate.createMessage());
-		Generation generation = chatClient.call(prompt).getResult();
+		Generation generation = chatConnector.execute(prompt).getResult();
 
 		ActorsFilms actorsFilms = outputConverter.convert(generation.getOutput().getContent());
 	}
@@ -151,7 +151,7 @@ class OpenAiChatClientIT extends AbstractIT {
 				""";
 		PromptTemplate promptTemplate = new PromptTemplate(template, Map.of("format", format));
 		Prompt prompt = new Prompt(promptTemplate.createMessage());
-		Generation generation = chatClient.call(prompt).getResult();
+		Generation generation = chatConnector.execute(prompt).getResult();
 
 		ActorsFilmsRecord actorsFilms = outputConverter.convert(generation.getOutput().getContent());
 		logger.info("" + actorsFilms);
@@ -189,7 +189,7 @@ class OpenAiChatClientIT extends AbstractIT {
 	}
 
 	@Test
-	void functionCallTest() {
+	void functionExecuteTest() {
 
 		UserMessage userMessage = new UserMessage("What's the weather like in San Francisco, Tokyo, and Paris?");
 
@@ -204,7 +204,7 @@ class OpenAiChatClientIT extends AbstractIT {
 				.build()))
 			.build();
 
-		ChatResponse response = chatClient.call(new Prompt(messages, promptOptions));
+		ChatResponse response = chatConnector.execute(new Prompt(messages, promptOptions));
 
 		logger.info("Response: {}", response);
 
@@ -214,7 +214,7 @@ class OpenAiChatClientIT extends AbstractIT {
 	}
 
 	@Test
-	void streamFunctionCallTest() {
+	void streamFunctionExecuteTest() {
 
 		UserMessage userMessage = new UserMessage("What's the weather like in San Francisco, Tokyo, and Paris?");
 
@@ -255,8 +255,8 @@ class OpenAiChatClientIT extends AbstractIT {
 		var userMessage = new UserMessage("Explain what do you see on this picture?",
 				List.of(new Media(MimeTypeUtils.IMAGE_PNG, imageData)));
 
-		var response = chatClient
-			.call(new Prompt(List.of(userMessage), OpenAiChatOptions.builder().withModel(modelName).build()));
+		var response = chatConnector
+			.execute(new Prompt(List.of(userMessage), OpenAiChatOptions.builder().withModel(modelName).build()));
 
 		logger.info(response.getResult().getOutput().getContent());
 		assertThat(response.getResult().getOutput().getContent()).contains("bananas", "apple");
@@ -271,8 +271,8 @@ class OpenAiChatClientIT extends AbstractIT {
 			.of(new Media(MimeTypeUtils.IMAGE_PNG,
 					new URL("https://docs.spring.io/spring-ai/reference/1.0-SNAPSHOT/_images/multimodal.test.png"))));
 
-		ChatResponse response = chatClient
-			.call(new Prompt(List.of(userMessage), OpenAiChatOptions.builder().withModel(modelName).build()));
+		ChatResponse response = chatConnector
+			.execute(new Prompt(List.of(userMessage), OpenAiChatOptions.builder().withModel(modelName).build()));
 
 		logger.info(response.getResult().getOutput().getContent());
 		assertThat(response.getResult().getOutput().getContent()).contains("bananas", "apple");

models/spring-ai-openai/src/test/java/org/springframework/ai/openai/chat/OpenAiRetryTests.java

@@ -31,7 +31,7 @@ import org.springframework.ai.image.ImageMessage;
 import org.springframework.ai.image.ImagePrompt;
 import org.springframework.ai.openai.OpenAiAudioTranscriptionClient;
 import org.springframework.ai.openai.OpenAiAudioTranscriptionOptions;
-import org.springframework.ai.openai.OpenAiChatClient;
+import org.springframework.ai.openai.OpenAiChatConnector;
 import org.springframework.ai.openai.OpenAiChatOptions;
 import org.springframework.ai.openai.OpenAiEmbeddingClient;
 import org.springframework.ai.openai.OpenAiEmbeddingOptions;
@@ -107,7 +107,7 @@ public class OpenAiRetryTests {
 
 	private @Mock OpenAiImageApi openAiImageApi;
 
-	private OpenAiChatClient chatClient;
+	private OpenAiChatConnector chatClient;
 
 	private OpenAiEmbeddingClient embeddingClient;
 
@@ -121,7 +121,7 @@ public class OpenAiRetryTests {
 		retryListener = new TestRetryListener();
 		retryTemplate.registerListener(retryListener);
 
-		chatClient = new OpenAiChatClient(openAiApi, OpenAiChatOptions.builder().build(), null, retryTemplate);
+		chatClient = new OpenAiChatConnector(openAiApi, OpenAiChatOptions.builder().build(), null, retryTemplate);
 		embeddingClient = new OpenAiEmbeddingClient(openAiApi, MetadataMode.EMBED,
 				OpenAiEmbeddingOptions.builder().build(), retryTemplate);
 		audioTranscriptionClient = new OpenAiAudioTranscriptionClient(openAiAudioApi,
@@ -146,7 +146,7 @@ public class OpenAiRetryTests {
 			.thenThrow(new TransientAiException("Transient Error 2"))
 			.thenReturn(ResponseEntity.of(Optional.of(expectedChatCompletion)));
 
-		var result = chatClient.call(new Prompt("text"));
+		var result = chatClient.execute(new Prompt("text"));
 
 		assertThat(result).isNotNull();
 		assertThat(result.getResult().getOutput().getContent()).isSameAs("Response");
@@ -158,7 +158,7 @@ public class OpenAiRetryTests {
 	public void openAiChatNonTransientError() {
 		when(openAiApi.chatCompletionEntity(isA(ChatCompletionRequest.class)))
 				.thenThrow(new RuntimeException("Non Transient Error"));
-		assertThrows(RuntimeException.class, () -> chatClient.call(new Prompt("text")));
+		assertThrows(RuntimeException.class, () -> chatClient.execute(new Prompt("text")));
 	}
 
 	@Test

models/spring-ai-openai/src/test/java/org/springframework/ai/openai/chat/service/ChatMemoryLongTermSystemPromptIT.java

@@ -23,6 +23,7 @@ import io.qdrant.client.QdrantGrpcClient;
 import org.junit.jupiter.api.condition.EnabledIfEnvironmentVariable;
 import org.springframework.ai.chat.service.ChatService;
 import org.springframework.ai.chat.service.StreamingChatService;
+import org.springframework.ai.openai.OpenAiChatConnector;
 import org.testcontainers.junit.jupiter.Container;
 import org.testcontainers.junit.jupiter.Testcontainers;
 import org.testcontainers.qdrant.QdrantContainer;
@@ -36,7 +37,6 @@ import org.springframework.ai.chat.memory.SystemPromptChatMemoryAugmentor;
 import org.springframework.ai.embedding.EmbeddingClient;
 import org.springframework.ai.evaluation.BaseMemoryTest;
 import org.springframework.ai.evaluation.RelevancyEvaluator;
-import org.springframework.ai.openai.OpenAiChatClient;
 import org.springframework.ai.openai.OpenAiEmbeddingClient;
 import org.springframework.ai.openai.api.OpenAiApi;
 import org.springframework.ai.tokenizer.JTokkitTokenCountEstimator;
@@ -75,8 +75,8 @@ public class ChatMemoryLongTermSystemPromptIT extends BaseMemoryTest {
 		}
 
 		@Bean
-		public OpenAiChatClient openAiClient(OpenAiApi openAiApi) {
-			return new OpenAiChatClient(openAiApi);
+		public OpenAiChatConnector openAiClient(OpenAiApi openAiApi) {
+			return new OpenAiChatConnector(openAiApi);
 		}
 
 		@Bean
@@ -98,7 +98,7 @@ public class ChatMemoryLongTermSystemPromptIT extends BaseMemoryTest {
 		}
 
 		@Bean
-		public ChatService memoryChatService(OpenAiChatClient chatClient, VectorStore vectorStore,
+		public ChatService memoryChatService(OpenAiChatConnector chatClient, VectorStore vectorStore,
 				TokenCountEstimator tokenCountEstimator) {
 
 			return PromptTransformingChatService.builder(chatClient)
@@ -110,7 +110,7 @@ public class ChatMemoryLongTermSystemPromptIT extends BaseMemoryTest {
 		}
 
 		@Bean
-		public StreamingChatService memoryStreamingChatService(OpenAiChatClient streamingChatClient,
+		public StreamingChatService memoryStreamingChatService(OpenAiChatConnector streamingChatClient,
 				VectorStore vectorStore, TokenCountEstimator tokenCountEstimator) {
 
 			return StreamingPromptTransformingChatService.builder(streamingChatClient)
@@ -122,7 +122,7 @@ public class ChatMemoryLongTermSystemPromptIT extends BaseMemoryTest {
 		}
 
 		@Bean
-		public RelevancyEvaluator relevancyEvaluator(OpenAiChatClient chatClient) {
+		public RelevancyEvaluator relevancyEvaluator(OpenAiChatConnector chatClient) {
 			return new RelevancyEvaluator(chatClient);
 		}
 

models/spring-ai-openai/src/test/java/org/springframework/ai/openai/chat/service/ChatMemoryShortTermMessageListIT.java

@@ -31,7 +31,7 @@ import org.springframework.ai.chat.memory.LastMaxTokenSizeContentTransformer;
 import org.springframework.ai.chat.memory.MessageChatMemoryAugmentor;
 import org.springframework.ai.evaluation.BaseMemoryTest;
 import org.springframework.ai.evaluation.RelevancyEvaluator;
-import org.springframework.ai.openai.OpenAiChatClient;
+import org.springframework.ai.openai.OpenAiChatConnector;
 import org.springframework.ai.openai.api.OpenAiApi;
 import org.springframework.ai.tokenizer.JTokkitTokenCountEstimator;
 import org.springframework.ai.tokenizer.TokenCountEstimator;
@@ -59,8 +59,8 @@ public class ChatMemoryShortTermMessageListIT extends BaseMemoryTest {
 		}
 
 		@Bean
-		public OpenAiChatClient openAiClient(OpenAiApi openAiApi) {
-			return new OpenAiChatClient(openAiApi);
+		public OpenAiChatConnector openAiClient(OpenAiApi openAiApi) {
+			return new OpenAiChatConnector(openAiApi);
 		}
 
 		@Bean
@@ -74,7 +74,7 @@ public class ChatMemoryShortTermMessageListIT extends BaseMemoryTest {
 		}
 
 		@Bean
-		public ChatService memoryChatService(OpenAiChatClient chatClient, ChatMemory chatHistory,
+		public ChatService memoryChatService(OpenAiChatConnector chatClient, ChatMemory chatHistory,
 				TokenCountEstimator tokenCountEstimator) {
 
 			return PromptTransformingChatService.builder(chatClient)
@@ -86,7 +86,7 @@ public class ChatMemoryShortTermMessageListIT extends BaseMemoryTest {
 		}
 
 		@Bean
-		public StreamingChatService memoryStreamingChatService(OpenAiChatClient streamingChatClient,
+		public StreamingChatService memoryStreamingChatService(OpenAiChatConnector streamingChatClient,
 				ChatMemory chatHistory, TokenCountEstimator tokenCountEstimator) {
 
 			return StreamingPromptTransformingChatService.builder(streamingChatClient)
@@ -98,7 +98,7 @@ public class ChatMemoryShortTermMessageListIT extends BaseMemoryTest {
 		}
 
 		@Bean
-		public RelevancyEvaluator relevancyEvaluator(OpenAiChatClient chatClient) {
+		public RelevancyEvaluator relevancyEvaluator(OpenAiChatConnector chatClient) {
 			return new RelevancyEvaluator(chatClient);
 		}
 

models/spring-ai-openai/src/test/java/org/springframework/ai/openai/chat/service/ChatMemoryShortTermSystemPromptIT.java

@@ -32,7 +32,7 @@ import org.springframework.ai.chat.memory.LastMaxTokenSizeContentTransformer;
 import org.springframework.ai.chat.memory.SystemPromptChatMemoryAugmentor;
 import org.springframework.ai.evaluation.BaseMemoryTest;
 import org.springframework.ai.evaluation.RelevancyEvaluator;
-import org.springframework.ai.openai.OpenAiChatClient;
+import org.springframework.ai.openai.OpenAiChatConnector;
 import org.springframework.ai.openai.api.OpenAiApi;
 import org.springframework.ai.tokenizer.JTokkitTokenCountEstimator;
 import org.springframework.ai.tokenizer.TokenCountEstimator;
@@ -60,8 +60,8 @@ public class ChatMemoryShortTermSystemPromptIT extends BaseMemoryTest {
 		}
 
 		@Bean
-		public OpenAiChatClient openAiClient(OpenAiApi openAiApi) {
-			return new OpenAiChatClient(openAiApi);
+		public OpenAiChatConnector openAiClient(OpenAiApi openAiApi) {
+			return new OpenAiChatConnector(openAiApi);
 		}
 
 		@Bean
@@ -75,7 +75,7 @@ public class ChatMemoryShortTermSystemPromptIT extends BaseMemoryTest {
 		}
 
 		@Bean
-		public ChatService memoryChatService(OpenAiChatClient chatClient, ChatMemory chatHistory,
+		public ChatService memoryChatService(OpenAiChatConnector chatClient, ChatMemory chatHistory,
 				TokenCountEstimator tokenCountEstimator) {
 
 			return PromptTransformingChatService.builder(chatClient)
@@ -87,7 +87,7 @@ public class ChatMemoryShortTermSystemPromptIT extends BaseMemoryTest {
 		}
 
 		@Bean
-		public StreamingChatService memoryStreamingChatService(OpenAiChatClient streamingChatClient,
+		public StreamingChatService memoryStreamingChatService(OpenAiChatConnector streamingChatClient,
 				ChatMemory chatHistory, TokenCountEstimator tokenCountEstimator) {
 
 			return StreamingPromptTransformingChatService.builder(streamingChatClient)
@@ -99,7 +99,7 @@ public class ChatMemoryShortTermSystemPromptIT extends BaseMemoryTest {
 		}
 
 		@Bean
-		public RelevancyEvaluator relevancyEvaluator(OpenAiChatClient chatClient) {
+		public RelevancyEvaluator relevancyEvaluator(OpenAiChatConnector chatClient) {
 			return new RelevancyEvaluator(chatClient);
 		}
 

models/spring-ai-openai/src/test/java/org/springframework/ai/openai/chat/service/LongShortTermChatMemoryWithRagIT.java

@@ -50,10 +50,9 @@ import org.springframework.ai.chat.prompt.transformer.VectorStoreRetriever;
 import org.springframework.ai.document.Document;
 import org.springframework.ai.document.DocumentTransformer;
 import org.springframework.ai.embedding.EmbeddingClient;
-import org.springframework.ai.evaluation.EvaluationRequest;
 import org.springframework.ai.evaluation.EvaluationResponse;
 import org.springframework.ai.evaluation.RelevancyEvaluator;
-import org.springframework.ai.openai.OpenAiChatClient;
+import org.springframework.ai.openai.OpenAiChatConnector;
 import org.springframework.ai.openai.OpenAiEmbeddingClient;
 import org.springframework.ai.openai.api.OpenAiApi;
 import org.springframework.ai.reader.JsonReader;
@@ -164,8 +163,8 @@ public class LongShortTermChatMemoryWithRagIT {
 		}
 
 		@Bean
-		public OpenAiChatClient openAiClient(OpenAiApi openAiApi) {
-			return new OpenAiChatClient(openAiApi);
+		public OpenAiChatConnector openAiClient(OpenAiApi openAiApi) {
+			return new OpenAiChatConnector(openAiApi);
 		}
 
 		@Bean
@@ -187,7 +186,7 @@ public class LongShortTermChatMemoryWithRagIT {
 		}
 
 		@Bean
-		public ChatService memoryChatService(OpenAiChatClient chatClient, VectorStore vectorStore,
+		public ChatService memoryChatService(OpenAiChatConnector chatClient, VectorStore vectorStore,
 				TokenCountEstimator tokenCountEstimator, ChatMemory chatHistory) {
 
 			return PromptTransformingChatService.builder(chatClient)
@@ -224,7 +223,7 @@ public class LongShortTermChatMemoryWithRagIT {
 		}
 
 		// @Bean
-		// public StreamingChatService memoryStreamingChatAgent(OpenAiChatClient
+		// public StreamingChatService memoryStreamingChatAgent(OpenAiChatConnector
 		// streamingChatClient,
 		// VectorStore vectorStore, TokenCountEstimator tokenCountEstimator, ChatHistory
 		// chatHistory) {
@@ -241,7 +240,7 @@ public class LongShortTermChatMemoryWithRagIT {
 		// }
 
 		@Bean
-		public RelevancyEvaluator relevancyEvaluator(OpenAiChatClient chatClient) {
+		public RelevancyEvaluator relevancyEvaluator(OpenAiChatConnector chatClient) {
 			// Use GPT 4 as a better model for determining relevancy. gpt 3.5 makes basic
 			// mistakes
 			OpenAiChatOptions openAiChatOptions = OpenAiChatOptions.builder()

models/spring-ai-openai/src/test/java/org/springframework/ai/openai/chat/service/OpenAiPromptTransformingChatServiceIT.java

@@ -23,15 +23,16 @@ import io.qdrant.client.QdrantClient;
 import io.qdrant.client.QdrantGrpcClient;
 import org.junit.jupiter.api.Test;
 import org.junit.jupiter.api.condition.EnabledIfEnvironmentVariable;
+import org.springframework.ai.chat.connector.ChatConnector;
 import org.springframework.ai.chat.service.ChatService;
 import org.springframework.ai.chat.prompt.transformer.TransformerContentType;
 import org.springframework.ai.document.Document;
+import org.springframework.ai.openai.OpenAiChatConnector;
 import org.springframework.ai.openai.OpenAiChatOptions;
 import org.testcontainers.junit.jupiter.Container;
 import org.testcontainers.junit.jupiter.Testcontainers;
 import org.testcontainers.qdrant.QdrantContainer;
 
-import org.springframework.ai.chat.ChatClient;
 import org.springframework.ai.chat.service.PromptTransformingChatService;
 import org.springframework.ai.chat.messages.UserMessage;
 import org.springframework.ai.chat.prompt.Prompt;
@@ -39,10 +40,8 @@ import org.springframework.ai.chat.prompt.transformer.ChatServiceContext;
 import org.springframework.ai.chat.prompt.transformer.QuestionContextAugmentor;
 import org.springframework.ai.chat.prompt.transformer.VectorStoreRetriever;
 import org.springframework.ai.embedding.EmbeddingClient;
-import org.springframework.ai.evaluation.EvaluationRequest;
 import org.springframework.ai.evaluation.EvaluationResponse;
 import org.springframework.ai.evaluation.RelevancyEvaluator;
-import org.springframework.ai.openai.OpenAiChatClient;
 import org.springframework.ai.openai.OpenAiEmbeddingClient;
 import org.springframework.ai.openai.api.OpenAiApi;
 import org.springframework.ai.reader.JsonReader;
@@ -72,7 +71,7 @@ public class OpenAiPromptTransformingChatServiceIT {
 	@Container
 	static QdrantContainer qdrantContainer = new QdrantContainer("qdrant/qdrant:v1.9.2");
 
-	private final ChatClient chatClient;
+	private final ChatConnector chatConnector;
 
 	private final VectorStore vectorStore;
 
@@ -82,9 +81,9 @@ public class OpenAiPromptTransformingChatServiceIT {
 	private ChatService chatService;
 
 	@Autowired
-	public OpenAiPromptTransformingChatServiceIT(ChatClient chatClient, ChatService chatService,
+	public OpenAiPromptTransformingChatServiceIT(ChatConnector chatConnector, ChatService chatService,
 			VectorStore vectorStore) {
-		this.chatClient = chatClient;
+		this.chatConnector = chatConnector;
 		this.chatService = chatService;
 		this.vectorStore = vectorStore;
 	}
@@ -103,7 +102,7 @@ public class OpenAiPromptTransformingChatServiceIT {
 		OpenAiChatOptions openAiChatOptions = OpenAiChatOptions.builder()
 			.withModel(GPT_4_TURBO_PREVIEW.getValue())
 			.build();
-		var relevancyEvaluator = new RelevancyEvaluator(this.chatClient, openAiChatOptions);
+		var relevancyEvaluator = new RelevancyEvaluator(this.chatConnector, openAiChatOptions);
 
 		EvaluationResponse evaluationResponse = relevancyEvaluator.evaluate(chatServiceResponse.toEvaluationRequest());
 		assertTrue(evaluationResponse.isPass(), "Response is not relevant to the question");
@@ -146,8 +145,8 @@ public class OpenAiPromptTransformingChatServiceIT {
 		}
 
 		@Bean
-		public ChatClient openAiClient(OpenAiApi openAiApi) {
-			return new OpenAiChatClient(openAiApi);
+		public ChatConnector openAiClient(OpenAiApi openAiApi) {
+			return new OpenAiChatConnector(openAiApi);
 		}
 
 		@Bean
@@ -164,8 +163,8 @@ public class OpenAiPromptTransformingChatServiceIT {
 		}
 
 		@Bean
-		public ChatService chatService(ChatClient chatClient, VectorStore vectorStore) {
-			return PromptTransformingChatService.builder(chatClient)
+		public ChatService chatService(ChatConnector chatConnector, VectorStore vectorStore) {
+			return PromptTransformingChatService.builder(chatConnector)
 				.withRetrievers(List.of(new VectorStoreRetriever(vectorStore, SearchRequest.defaults())))
 				.withAugmentors(List.of(new QuestionContextAugmentor()))
 				.build();

models/spring-ai-openai/src/test/java/org/springframework/ai/openai/testutils/AbstractIT.java

@@ -21,7 +21,7 @@ import java.util.Map;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 
-import org.springframework.ai.chat.ChatClient;
+import org.springframework.ai.chat.connector.ChatConnector;
 import org.springframework.ai.chat.ChatResponse;
 import org.springframework.ai.chat.StreamingChatClient;
 import org.springframework.ai.chat.prompt.Prompt;
@@ -43,7 +43,7 @@ public abstract class AbstractIT {
 	private static final Logger logger = LoggerFactory.getLogger(AbstractIT.class);
 
 	@Autowired
-	protected ChatClient chatClient;
+	protected ChatConnector chatConnector;
 
 	@Autowired
 	protected StreamingChatClient streamingChatClient;
@@ -85,12 +85,12 @@ public abstract class AbstractIT {
 		}
 		Message userMessage = userPromptTemplate.createMessage();
 		Prompt prompt = new Prompt(List.of(userMessage, systemMessage));
-		String yesOrNo = chatClient.call(prompt).getResult().getOutput().getContent();
+		String yesOrNo = chatConnector.execute(prompt).getResult().getOutput().getContent();
 		logger.info("Is Answer related to question: " + yesOrNo);
 		if (yesOrNo.equalsIgnoreCase("no")) {
 			SystemMessage notRelatedSystemMessage = new SystemMessage(qaEvaluatorNotRelatedResource);
 			prompt = new Prompt(List.of(userMessage, notRelatedSystemMessage));
-			String reasonForFailure = chatClient.call(prompt).getResult().getOutput().getContent();
+			String reasonForFailure = chatConnector.execute(prompt).getResult().getOutput().getContent();
 			fail(reasonForFailure);
 		}
 		else {

models/spring-ai-openai/src/test/java/org/springframework/ai/openai/transformer/MetadataTransformerIT.java

@@ -24,8 +24,8 @@ import org.junit.jupiter.api.Test;
 
 import org.springframework.ai.document.DefaultContentFormatter;
 import org.springframework.ai.document.Document;
+import org.springframework.ai.openai.OpenAiChatConnector;
 import org.springframework.ai.openai.api.OpenAiApi;
-import org.springframework.ai.openai.OpenAiChatClient;
 import org.springframework.ai.transformer.ContentFormatTransformer;
 import org.springframework.ai.transformer.KeywordMetadataEnricher;
 import org.springframework.ai.transformer.SummaryMetadataEnricher;
@@ -163,18 +163,18 @@ public class MetadataTransformerIT {
 		}
 
 		@Bean
-		public OpenAiChatClient openAiChatClient(OpenAiApi openAiApi) {
-			OpenAiChatClient openAiChatClient = new OpenAiChatClient(openAiApi);
+		public OpenAiChatConnector openAiChatClient(OpenAiApi openAiApi) {
+			OpenAiChatConnector openAiChatClient = new OpenAiChatConnector(openAiApi);
 			return openAiChatClient;
 		}
 
 		@Bean
-		public KeywordMetadataEnricher keywordMetadata(OpenAiChatClient aiClient) {
+		public KeywordMetadataEnricher keywordMetadata(OpenAiChatConnector aiClient) {
 			return new KeywordMetadataEnricher(aiClient, 5);
 		}
 
 		@Bean
-		public SummaryMetadataEnricher summaryMetadata(OpenAiChatClient aiClient) {
+		public SummaryMetadataEnricher summaryMetadata(OpenAiChatConnector aiClient) {
 			return new SummaryMetadataEnricher(aiClient,
 					List.of(SummaryType.PREVIOUS, SummaryType.CURRENT, SummaryType.NEXT));
 		}

models/spring-ai-vertex-ai-gemini/src/main/java/org/springframework/ai/vertexai/gemini/VertexAiGeminiChatConnector.java

@@ -32,7 +32,7 @@ import com.google.cloud.vertexai.generativeai.PartMaker;
 import com.google.cloud.vertexai.generativeai.ResponseStream;
 import com.google.protobuf.Struct;
 import com.google.protobuf.util.JsonFormat;
-import org.springframework.ai.chat.ChatClient;
+import org.springframework.ai.chat.connector.ChatConnector;
 import org.springframework.ai.chat.ChatResponse;
 import org.springframework.ai.chat.Generation;
 import org.springframework.ai.chat.StreamingChatClient;
@@ -65,9 +65,9 @@ import java.util.stream.Collectors;
  * @author Grogdunn
  * @since 0.8.1
  */
-public class VertexAiGeminiChatClient
-		extends AbstractFunctionCallSupport<Content, VertexAiGeminiChatClient.GeminiRequest, GenerateContentResponse>
-		implements ChatClient, StreamingChatClient, DisposableBean {
+public class VertexAiGeminiChatConnector
+		extends AbstractFunctionCallSupport<Content, VertexAiGeminiChatConnector.GeminiRequest, GenerateContentResponse>
+		implements ChatConnector, StreamingChatClient, DisposableBean {
 
 	private final static boolean IS_RUNTIME_CALL = true;
 
@@ -117,7 +117,7 @@ public class VertexAiGeminiChatClient
 
 	}
 
-	public VertexAiGeminiChatClient(VertexAI vertexAI) {
+	public VertexAiGeminiChatConnector(VertexAI vertexAI) {
 		this(vertexAI,
 				VertexAiGeminiChatOptions.builder()
 					.withModel(ChatModel.GEMINI_PRO_VISION.getValue())
@@ -125,11 +125,11 @@ public class VertexAiGeminiChatClient
 					.build());
 	}
 
-	public VertexAiGeminiChatClient(VertexAI vertexAI, VertexAiGeminiChatOptions options) {
+	public VertexAiGeminiChatConnector(VertexAI vertexAI, VertexAiGeminiChatOptions options) {
 		this(vertexAI, options, null);
 	}
 
-	public VertexAiGeminiChatClient(VertexAI vertexAI, VertexAiGeminiChatOptions options,
+	public VertexAiGeminiChatConnector(VertexAI vertexAI, VertexAiGeminiChatOptions options,
 			FunctionCallbackContext functionCallbackContext) {
 
 		super(functionCallbackContext);
@@ -145,7 +145,7 @@ public class VertexAiGeminiChatClient
 
 	// https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/gemini
 	@Override
-	public ChatResponse call(Prompt prompt) {
+	public ChatResponse execute(Prompt prompt) {
 
 		var geminiRequest = createGeminiRequest(prompt);
 

models/spring-ai-vertex-ai-gemini/src/main/java/org/springframework/ai/vertexai/gemini/VertexAiGeminiChatOptions.java

@@ -77,10 +77,10 @@ public class VertexAiGeminiChatOptions implements FunctionCallingOptions, ChatOp
 	private @JsonProperty("modelName") String model;
 
 	/**
-	 * Tool Function Callbacks to register with the ChatClient.
+	 * Tool Function Callbacks to register with the ChatConnector.
 	 * For Prompt Options the functionCallbacks are automatically enabled for the duration of the prompt execution.
 	 * For Default Options the functionCallbacks are registered but disabled by default. Use the enableFunctions to set the functions
-	 * from the registry to be used by the ChatClient chat completion requests.
+	 * from the registry to be used by the ChatConnector chat completion requests.
 	 */
 	@NestedConfigurationProperty
 	@JsonIgnore

models/spring-ai-vertex-ai-gemini/src/main/java/org/springframework/ai/vertexai/gemini/aot/VertexAiGeminiRuntimeHints.java

@@ -15,7 +15,7 @@
  */
 package org.springframework.ai.vertexai.gemini.aot;
 
-import org.springframework.ai.vertexai.gemini.VertexAiGeminiChatClient;
+import org.springframework.ai.vertexai.gemini.VertexAiGeminiChatConnector;
 import org.springframework.aot.hint.MemberCategory;
 import org.springframework.aot.hint.RuntimeHints;
 import org.springframework.aot.hint.RuntimeHintsRegistrar;
@@ -34,7 +34,7 @@ public class VertexAiGeminiRuntimeHints implements RuntimeHintsRegistrar {
 	@Override
 	public void registerHints(RuntimeHints hints, ClassLoader classLoader) {
 		var mcs = MemberCategory.values();
-		for (var tr : findJsonAnnotatedClassesInPackage(VertexAiGeminiChatClient.class))
+		for (var tr : findJsonAnnotatedClassesInPackage(VertexAiGeminiChatConnector.class))
 			hints.reflection().registerType(tr, mcs);
 	}
 

models/spring-ai-vertex-ai-gemini/src/test/java/org/springframework/ai/vertexai/gemini/VertexAiGeminiChatConnectorIT.java

@@ -53,10 +53,10 @@ import static org.assertj.core.api.Assertions.assertThat;
 @SpringBootTest
 @EnabledIfEnvironmentVariable(named = "VERTEX_AI_GEMINI_PROJECT_ID", matches = ".*")
 @EnabledIfEnvironmentVariable(named = "VERTEX_AI_GEMINI_LOCATION", matches = ".*")
-class VertexAiGeminiChatClientIT {
+class VertexAiGeminiChatConnectorIT {
 
 	@Autowired
-	private VertexAiGeminiChatClient client;
+	private VertexAiGeminiChatConnector client;
 
 	@Value("classpath:/prompts/system-message.st")
 	private Resource systemResource;
@@ -70,7 +70,7 @@ class VertexAiGeminiChatClientIT {
 		SystemPromptTemplate systemPromptTemplate = new SystemPromptTemplate(systemResource);
 		Message systemMessage = systemPromptTemplate.createMessage(Map.of("name", name, "voice", voice));
 		Prompt prompt = new Prompt(List.of(userMessage, systemMessage));
-		ChatResponse response = client.call(prompt);
+		ChatResponse response = client.execute(prompt);
 		assertThat(response.getResult().getOutput().getContent()).contains("Blackbeard");
 	}
 
@@ -87,7 +87,7 @@ class VertexAiGeminiChatClientIT {
 		PromptTemplate promptTemplate = new PromptTemplate(template,
 				Map.of("subject", "ice cream flavors.", "format", format));
 		Prompt prompt = new Prompt(promptTemplate.createMessage());
-		Generation generation = this.client.call(prompt).getResult();
+		Generation generation = this.client.execute(prompt).getResult();
 
 		List<String> list = outputParser.convert(generation.getOutput().getContent());
 		assertThat(list).hasSize(5);
@@ -106,7 +106,7 @@ class VertexAiGeminiChatClientIT {
 		PromptTemplate promptTemplate = new PromptTemplate(template,
 				Map.of("subject", "an array of numbers from 1 to 9 under they key name 'numbers'", "format", format));
 		Prompt prompt = new Prompt(promptTemplate.createMessage());
-		Generation generation = client.call(prompt).getResult();
+		Generation generation = client.execute(prompt).getResult();
 
 		Map<String, Object> result = outputConverter.convert(generation.getOutput().getContent());
 		assertThat(result.get("numbers")).isEqualTo(Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8, 9));
@@ -129,7 +129,7 @@ class VertexAiGeminiChatClientIT {
 				""";
 		PromptTemplate promptTemplate = new PromptTemplate(template, Map.of("format", format));
 		Prompt prompt = new Prompt(promptTemplate.createMessage());
-		Generation generation = client.call(prompt).getResult();
+		Generation generation = client.execute(prompt).getResult();
 
 		ActorsFilmsRecord actorsFilms = outputConvert.convert(generation.getOutput().getContent());
 		assertThat(actorsFilms.actor()).isEqualTo("Tom Hanks");
@@ -191,7 +191,7 @@ class VertexAiGeminiChatClientIT {
 		var userMessage = new UserMessage("Explain what do you see o this picture?",
 				List.of(new Media(MimeTypeUtils.IMAGE_PNG, data)));
 
-		var response = client.call(new Prompt(List.of(userMessage)));
+		var response = client.execute(new Prompt(List.of(userMessage)));
 
 		// Response should contain something like:
 		// I see a bunch of bananas in a golden basket. The bananas are ripe and yellow.
@@ -231,10 +231,10 @@ class VertexAiGeminiChatClientIT {
 		}
 
 		@Bean
-		public VertexAiGeminiChatClient vertexAiEmbedding(VertexAI vertexAi) {
-			return new VertexAiGeminiChatClient(vertexAi,
+		public VertexAiGeminiChatConnector vertexAiEmbedding(VertexAI vertexAi) {
+			return new VertexAiGeminiChatConnector(vertexAi,
 					VertexAiGeminiChatOptions.builder()
-						.withModel(VertexAiGeminiChatClient.ChatModel.GEMINI_PRO_VISION.getValue())
+						.withModel(VertexAiGeminiChatConnector.ChatModel.GEMINI_PRO_VISION.getValue())
 						.build());
 		}
 

models/spring-ai-vertex-ai-gemini/src/test/java/org/springframework/ai/vertexai/gemini/aot/VertexAiGeminiRuntimeHintsTests.java

@@ -19,7 +19,7 @@ import java.util.Set;
 
 import org.junit.jupiter.api.Test;
 
-import org.springframework.ai.vertexai.gemini.VertexAiGeminiChatClient;
+import org.springframework.ai.vertexai.gemini.VertexAiGeminiChatConnector;
 import org.springframework.aot.hint.RuntimeHints;
 import org.springframework.aot.hint.TypeReference;
 
@@ -38,7 +38,7 @@ class VertexAiGeminiRuntimeHintsTests {
 		RuntimeHints runtimeHints = new RuntimeHints();
 		VertexAiGeminiRuntimeHints vertexAiGeminiRuntimeHints = new VertexAiGeminiRuntimeHints();
 		vertexAiGeminiRuntimeHints.registerHints(runtimeHints, null);
-		Set<TypeReference> jsonAnnotatedClasses = findJsonAnnotatedClassesInPackage(VertexAiGeminiChatClient.class);
+		Set<TypeReference> jsonAnnotatedClasses = findJsonAnnotatedClassesInPackage(VertexAiGeminiChatConnector.class);
 		for (TypeReference jsonAnnotatedClass : jsonAnnotatedClasses) {
 			assertThat(runtimeHints).matches(reflection().onType(jsonAnnotatedClass));
 		}

models/spring-ai-vertex-ai-gemini/src/test/java/org/springframework/ai/vertexai/gemini/function/VertexAiGeminiChatConnectorFunctionCallingIT.java

@@ -23,11 +23,11 @@ import java.util.stream.Collectors;
 import com.google.cloud.vertexai.Transport;
 import com.google.cloud.vertexai.VertexAI;
 import org.junit.jupiter.api.AfterEach;
-import org.junit.jupiter.api.Disabled;
 import org.junit.jupiter.api.Test;
 import org.junit.jupiter.api.condition.EnabledIfEnvironmentVariable;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
+import org.springframework.ai.vertexai.gemini.VertexAiGeminiChatConnector;
 import reactor.core.publisher.Flux;
 
 import org.springframework.ai.chat.ChatResponse;
@@ -38,7 +38,6 @@ import org.springframework.ai.chat.messages.UserMessage;
 import org.springframework.ai.chat.prompt.Prompt;
 import org.springframework.ai.model.function.FunctionCallbackWrapper;
 import org.springframework.ai.model.function.FunctionCallbackWrapper.Builder.SchemaType;
-import org.springframework.ai.vertexai.gemini.VertexAiGeminiChatClient;
 import org.springframework.ai.vertexai.gemini.VertexAiGeminiChatOptions;
 import org.springframework.beans.factory.annotation.Autowired;
 import org.springframework.boot.SpringBootConfiguration;
@@ -50,12 +49,12 @@ import static org.assertj.core.api.Assertions.assertThat;
 @SpringBootTest
 @EnabledIfEnvironmentVariable(named = "VERTEX_AI_GEMINI_PROJECT_ID", matches = ".*")
 @EnabledIfEnvironmentVariable(named = "VERTEX_AI_GEMINI_LOCATION", matches = ".*")
-public class VertexAiGeminiChatClientFunctionCallingIT {
+public class VertexAiGeminiChatConnectorFunctionCallingIT {
 
 	private final Logger logger = LoggerFactory.getLogger(getClass());
 
 	@Autowired
-	private VertexAiGeminiChatClient vertexGeminiClient;
+	private VertexAiGeminiChatConnector vertexGeminiClient;
 
 	@AfterEach
 	public void afterEach() {
@@ -69,7 +68,7 @@ public class VertexAiGeminiChatClientFunctionCallingIT {
 
 	@Test
 	// @Disabled("Google Vertex AI degraded support for parallel function calls")
-	public void functionCallExplicitOpenApiSchema() {
+	public void functionExecuteExplicitOpenApiSchema() {
 
 		UserMessage userMessage = new UserMessage(
 				"What's the weather like in San Francisco, in Paris and in Tokyo, Japan?"
@@ -98,8 +97,8 @@ public class VertexAiGeminiChatClientFunctionCallingIT {
 					""";
 
 		var promptOptions = VertexAiGeminiChatOptions.builder()
-			// .withModel(VertexAiGeminiChatClient.ChatModel.GEMINI_PRO.getValue())
-			.withModel(VertexAiGeminiChatClient.ChatModel.GEMINI_PRO_1_5_PRO.getValue())
+			// .withModel(VertexAiGeminiChatConnector.ChatModel.GEMINI_PRO.getValue())
+			.withModel(VertexAiGeminiChatConnector.ChatModel.GEMINI_PRO_1_5_PRO.getValue())
 			.withFunctionCallbacks(List.of(FunctionCallbackWrapper.builder(new MockWeatherService())
 				.withName("get_current_weather")
 				.withDescription("Get the current weather in a given location")
@@ -107,7 +106,7 @@ public class VertexAiGeminiChatClientFunctionCallingIT {
 				.build()))
 			.build();
 
-		ChatResponse response = vertexGeminiClient.call(new Prompt(messages, promptOptions));
+		ChatResponse response = vertexGeminiClient.execute(new Prompt(messages, promptOptions));
 
 		logger.info("Response: {}", response);
 
@@ -119,15 +118,15 @@ public class VertexAiGeminiChatClientFunctionCallingIT {
 	}
 
 	@Test
-	public void functionCallTestInferredOpenApiSchema() {
+	public void functionExecuteTestInferredOpenApiSchema() {
 
 		UserMessage userMessage = new UserMessage("What's the weather like in Paris? Use Celsius units.");
 
 		List<Message> messages = new ArrayList<>(List.of(userMessage));
 
 		var promptOptions = VertexAiGeminiChatOptions.builder()
-			.withModel(VertexAiGeminiChatClient.ChatModel.GEMINI_PRO_1_5_PRO.getValue())
-			// .withModel(VertexAiGeminiChatClient.ChatModel.GEMINI_PRO.getValue())
+			.withModel(VertexAiGeminiChatConnector.ChatModel.GEMINI_PRO_1_5_PRO.getValue())
+			// .withModel(VertexAiGeminiChatConnector.ChatModel.GEMINI_PRO.getValue())
 			.withFunctionCallbacks(List.of(
 					FunctionCallbackWrapper.builder(new MockWeatherService())
 						.withSchemaType(SchemaType.OPEN_API_SCHEMA)
@@ -142,14 +141,14 @@ public class VertexAiGeminiChatClientFunctionCallingIT {
 						.build()))
 			.build();
 
-		ChatResponse response = vertexGeminiClient.call(new Prompt(messages, promptOptions));
+		ChatResponse response = vertexGeminiClient.execute(new Prompt(messages, promptOptions));
 
 		logger.info("Response: {}", response);
 
 		assertThat(response.getResult().getOutput().getContent()).containsAnyOf("15.0", "15");
 
 		ChatResponse response2 = vertexGeminiClient
-			.call(new Prompt("What is the payment status for transaction 696?", promptOptions));
+			.execute(new Prompt("What is the payment status for transaction 696?", promptOptions));
 
 		logger.info("Response: {}", response2);
 
@@ -158,7 +157,7 @@ public class VertexAiGeminiChatClientFunctionCallingIT {
 	}
 
 	@Test
-	public void functionCallTestInferredOpenApiSchemaStream() {
+	public void functionExecuteTestInferredOpenApiSchemaStream() {
 
 		UserMessage userMessage = new UserMessage("What's the weather like in San Francisco in Celsius units?");
 		// UserMessage userMessage = new UserMessage(
@@ -168,7 +167,7 @@ public class VertexAiGeminiChatClientFunctionCallingIT {
 		List<Message> messages = new ArrayList<>(List.of(userMessage));
 
 		var promptOptions = VertexAiGeminiChatOptions.builder()
-			.withModel(VertexAiGeminiChatClient.ChatModel.GEMINI_PRO.getValue())
+			.withModel(VertexAiGeminiChatConnector.ChatModel.GEMINI_PRO.getValue())
 			.withFunctionCallbacks(List.of(FunctionCallbackWrapper.builder(new MockWeatherService())
 				.withSchemaType(SchemaType.OPEN_API_SCHEMA)
 				.withName("getCurrentWeather")
@@ -224,10 +223,10 @@ public class VertexAiGeminiChatClientFunctionCallingIT {
 		}
 
 		@Bean
-		public VertexAiGeminiChatClient vertexAiEmbedding(VertexAI vertexAi) {
-			return new VertexAiGeminiChatClient(vertexAi,
+		public VertexAiGeminiChatConnector vertexAiEmbedding(VertexAI vertexAi) {
+			return new VertexAiGeminiChatConnector(vertexAi,
 					VertexAiGeminiChatOptions.builder()
-						.withModel(VertexAiGeminiChatClient.ChatModel.GEMINI_PRO.getValue())
+						.withModel(VertexAiGeminiChatConnector.ChatModel.GEMINI_PRO.getValue())
 						.withTemperature(0.9f)
 						.build());
 		}

models/spring-ai-vertex-ai-palm2/src/main/java/org/springframework/ai/vertexai/palm2/VertexAiPaLm2ChatConnector.java

@@ -18,7 +18,7 @@ package org.springframework.ai.vertexai.palm2;
 import java.util.List;
 import java.util.stream.Collectors;
 
-import org.springframework.ai.chat.ChatClient;
+import org.springframework.ai.chat.connector.ChatConnector;
 import org.springframework.ai.chat.prompt.ChatOptions;
 import org.springframework.ai.chat.ChatResponse;
 import org.springframework.ai.chat.Generation;
@@ -35,18 +35,18 @@ import org.springframework.util.CollectionUtils;
 /**
  * @author Christian Tzolov
  */
-public class VertexAiPaLm2ChatClient implements ChatClient {
+public class VertexAiPaLm2ChatConnector implements ChatConnector {
 
 	private final VertexAiPaLm2Api vertexAiApi;
 
 	private final VertexAiPaLm2ChatOptions defaultOptions;
 
-	public VertexAiPaLm2ChatClient(VertexAiPaLm2Api vertexAiApi) {
+	public VertexAiPaLm2ChatConnector(VertexAiPaLm2Api vertexAiApi) {
 		this(vertexAiApi,
 				VertexAiPaLm2ChatOptions.builder().withTemperature(0.7f).withCandidateCount(1).withTopK(20).build());
 	}
 
-	public VertexAiPaLm2ChatClient(VertexAiPaLm2Api vertexAiApi, VertexAiPaLm2ChatOptions defaultOptions) {
+	public VertexAiPaLm2ChatConnector(VertexAiPaLm2Api vertexAiApi, VertexAiPaLm2ChatOptions defaultOptions) {
 		Assert.notNull(defaultOptions, "Default options must not be null!");
 		Assert.notNull(vertexAiApi, "VertexAiPaLm2Api must not be null!");
 
@@ -55,7 +55,7 @@ public class VertexAiPaLm2ChatClient implements ChatClient {
 	}
 
 	@Override
-	public ChatResponse call(Prompt prompt) {
+	public ChatResponse execute(Prompt prompt) {
 
 		GenerateMessageRequest request = createRequest(prompt);
 

models/spring-ai-vertex-ai-palm2/src/test/java/org/springframework/ai/vertexai/palm2/VertexAiPaLm2ChatGenerationClientIT.java

@@ -48,7 +48,7 @@ import static org.assertj.core.api.Assertions.assertThat;
 class VertexAiPaLm2ChatGenerationClientIT {
 
 	@Autowired
-	private VertexAiPaLm2ChatClient client;
+	private VertexAiPaLm2ChatConnector client;
 
 	@Value("classpath:/prompts/system-message.st")
 	private Resource systemResource;
@@ -62,7 +62,7 @@ class VertexAiPaLm2ChatGenerationClientIT {
 		SystemPromptTemplate systemPromptTemplate = new SystemPromptTemplate(systemResource);
 		Message systemMessage = systemPromptTemplate.createMessage(Map.of("name", name, "voice", voice));
 		Prompt prompt = new Prompt(List.of(userMessage, systemMessage));
-		ChatResponse response = client.call(prompt);
+		ChatResponse response = client.execute(prompt);
 		assertThat(response.getResult().getOutput().getContent()).contains("Bartholomew");
 	}
 
@@ -79,7 +79,7 @@ class VertexAiPaLm2ChatGenerationClientIT {
 		PromptTemplate promptTemplate = new PromptTemplate(template,
 				Map.of("subject", "ice cream flavors.", "format", format));
 		Prompt prompt = new Prompt(promptTemplate.createMessage());
-		Generation generation = this.client.call(prompt).getResult();
+		Generation generation = this.client.execute(prompt).getResult();
 
 		List<String> list = outputConverter.convert(generation.getOutput().getContent());
 		assertThat(list).hasSize(5);
@@ -98,7 +98,7 @@ class VertexAiPaLm2ChatGenerationClientIT {
 		PromptTemplate promptTemplate = new PromptTemplate(template,
 				Map.of("subject", "an array of numbers from 1 to 9 under they key name 'numbers'", "format", format));
 		Prompt prompt = new Prompt(promptTemplate.createMessage());
-		Generation generation = client.call(prompt).getResult();
+		Generation generation = client.execute(prompt).getResult();
 
 		Map<String, Object> result = outputConverter.convert(generation.getOutput().getContent());
 		assertThat(result.get("numbers")).isEqualTo(Arrays.asList(1, 2, 3, 4, 5, 6, 7, 8, 9));
@@ -120,7 +120,7 @@ class VertexAiPaLm2ChatGenerationClientIT {
 				""";
 		PromptTemplate promptTemplate = new PromptTemplate(template, Map.of("format", format));
 		Prompt prompt = new Prompt(promptTemplate.createMessage());
-		Generation generation = client.call(prompt).getResult();
+		Generation generation = client.execute(prompt).getResult();
 
 		ActorsFilmsRecord actorsFilms = outputConverter.convert(generation.getOutput().getContent());
 		assertThat(actorsFilms.actor()).isEqualTo("Tom Hanks");
@@ -136,8 +136,8 @@ class VertexAiPaLm2ChatGenerationClientIT {
 		}
 
 		@Bean
-		public VertexAiPaLm2ChatClient vertexAiEmbedding(VertexAiPaLm2Api vertexAiApi) {
-			return new VertexAiPaLm2ChatClient(vertexAiApi);
+		public VertexAiPaLm2ChatConnector vertexAiEmbedding(VertexAiPaLm2Api vertexAiApi) {
+			return new VertexAiPaLm2ChatConnector(vertexAiApi);
 		}
 
 	}

models/spring-ai-vertex-ai-palm2/src/test/java/org/springframework/ai/vertexai/palm2/VertexAiPaLm2ChatRequestTests.java

@@ -29,7 +29,7 @@ import static org.assertj.core.api.Assertions.assertThat;
  */
 public class VertexAiPaLm2ChatRequestTests {
 
-	VertexAiPaLm2ChatClient client = new VertexAiPaLm2ChatClient(new VertexAiPaLm2Api("bla"));
+	VertexAiPaLm2ChatConnector client = new VertexAiPaLm2ChatConnector(new VertexAiPaLm2Api("bla"));
 
 	@Test
 	public void createRequestWithDefaultOptions() {

models/spring-ai-watsonx-ai/src/main/java/org/springframework/ai/watsonx/WatsonxAiChatConnector.java

@@ -20,7 +20,7 @@ import java.util.Map;
 
 import reactor.core.publisher.Flux;
 
-import org.springframework.ai.chat.ChatClient;
+import org.springframework.ai.chat.connector.ChatConnector;
 import org.springframework.ai.chat.ChatResponse;
 import org.springframework.ai.chat.Generation;
 import org.springframework.ai.chat.StreamingChatClient;
@@ -35,7 +35,7 @@ import org.springframework.ai.watsonx.utils.MessageToPromptConverter;
 import org.springframework.util.Assert;
 
 /**
- * {@link ChatClient} implementation for {@literal watsonx.ai}.
+ * {@link ChatConnector} implementation for {@literal watsonx.ai}.
  *
  * watsonx.ai allows developers to use large language models within a SaaS service. It
  * supports multiple open-source models as well as IBM created models
@@ -48,13 +48,13 @@ import org.springframework.util.Assert;
  * @author Christian Tzolov
  * @since 1.0.0
  */
-public class WatsonxAiChatClient implements ChatClient, StreamingChatClient {
+public class WatsonxAiChatConnector implements ChatConnector, StreamingChatClient {
 
 	private final WatsonxAiApi watsonxAiApi;
 
 	private final WatsonxAiChatOptions defaultOptions;
 
-	public WatsonxAiChatClient(WatsonxAiApi watsonxAiApi) {
+	public WatsonxAiChatConnector(WatsonxAiApi watsonxAiApi) {
 		this(watsonxAiApi,
 				WatsonxAiChatOptions.builder()
 					.withTemperature(0.7f)
@@ -68,7 +68,7 @@ public class WatsonxAiChatClient implements ChatClient, StreamingChatClient {
 					.build());
 	}
 
-	public WatsonxAiChatClient(WatsonxAiApi watsonxAiApi, WatsonxAiChatOptions defaultOptions) {
+	public WatsonxAiChatConnector(WatsonxAiApi watsonxAiApi, WatsonxAiChatOptions defaultOptions) {
 		Assert.notNull(watsonxAiApi, "watsonxAiApi cannot be null");
 		Assert.notNull(defaultOptions, "defaultOptions cannot be null");
 		this.watsonxAiApi = watsonxAiApi;
@@ -76,7 +76,7 @@ public class WatsonxAiChatClient implements ChatClient, StreamingChatClient {
 	}
 
 	@Override
-	public ChatResponse call(Prompt prompt) {
+	public ChatResponse execute(Prompt prompt) {
 
 		WatsonxAiRequest request = request(prompt);
 

models/spring-ai-watsonx-ai/src/test/java/org/springframework/ai/watsonx/WatsonxAiChatConnectorTest.java

@@ -46,9 +46,9 @@ import static org.mockito.Mockito.when;
  * @author Pablo Sanchidrian Herrera
  * @author John Jairo Moreno Rojas
  */
-public class WatsonxAiChatClientTest {
+public class WatsonxAiChatConnectorTest {
 
-	WatsonxAiChatClient chatClient = new WatsonxAiChatClient(mock(WatsonxAiApi.class));
+	WatsonxAiChatConnector chatClient = new WatsonxAiChatConnector(mock(WatsonxAiApi.class));
 
 	@Test
 	public void testCreateRequestWithNoModelId() {
@@ -155,9 +155,9 @@ public class WatsonxAiChatClientTest {
 	}
 
 	@Test
-	public void testCallMethod() {
+	public void testExecuteMethod() {
 		WatsonxAiApi mockChatApi = mock(WatsonxAiApi.class);
-		WatsonxAiChatClient client = new WatsonxAiChatClient(mockChatApi);
+		WatsonxAiChatConnector client = new WatsonxAiChatConnector(mockChatApi);
 
 		Prompt prompt = new Prompt(List.of(new SystemMessage("Your prompt here")),
 				WatsonxAiChatOptions.builder().withModel("google/flan-ul2").build());
@@ -177,7 +177,7 @@ public class WatsonxAiChatClientTest {
 					Map.of("warnings", List.of(Map.of("message", "the message", "id", "disclaimer_warning")))));
 
 		ChatResponse expectedResponse = new ChatResponse(List.of(expectedGenerator));
-		ChatResponse response = client.call(prompt);
+		ChatResponse response = client.execute(prompt);
 
 		Assert.assertEquals(expectedResponse.getResults().size(), response.getResults().size());
 		Assert.assertEquals(expectedResponse.getResult().getOutput(), response.getResult().getOutput());
@@ -186,7 +186,7 @@ public class WatsonxAiChatClientTest {
 	@Test
 	public void testStreamMethod() {
 		WatsonxAiApi mockChatApi = mock(WatsonxAiApi.class);
-		WatsonxAiChatClient client = new WatsonxAiChatClient(mockChatApi);
+		WatsonxAiChatConnector client = new WatsonxAiChatConnector(mockChatApi);
 
 		Prompt prompt = new Prompt(List.of(new SystemMessage("Your prompt here")),
 				WatsonxAiChatOptions.builder().withModel("google/flan-ul2").build());

spring-ai-core/src/main/java/org/springframework/ai/chat/ChatClient.java

@@ -1,44 +1,175 @@
-/*
- * Copyright 2023 - 2024 the original author or authors.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- * https://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
 package org.springframework.ai.chat;
 
-import org.springframework.ai.chat.prompt.Prompt;
+import org.springframework.ai.chat.connector.ChatConnector;
+import org.springframework.ai.chat.messages.Media;
+import org.springframework.core.ParameterizedTypeReference;
 
-import java.util.Arrays;
+import java.util.*;
 
-import org.springframework.ai.chat.messages.Message;
-import org.springframework.ai.chat.messages.UserMessage;
-import org.springframework.ai.model.ModelClient;
 
-@FunctionalInterface
-public interface ChatClient extends ModelClient<Prompt, ChatResponse> {
+/**
+ * @author Mark Pollack
+ * @author Christian Tsolov
+ * @author Josh Long
+ */
+public class ChatClient {
+
+	private final ChatConnector connector;
+
+	private final String userPrompt, systemPrompt;
+
+	private final List<String> functions;
+
+	private final List<Media> media;
+
+	public ChatClient(ChatConnector connector, String defaultSystemPrompt, String defaultUserPrompt,
+			List<String> defaultFunctions, List<Media> defaultMedia) {
+		this.connector = connector;
+		this.userPrompt = defaultUserPrompt;
+		this.systemPrompt = defaultSystemPrompt;
+		this.functions = defaultFunctions;
+		this.media = defaultMedia;
 
-	default String call(String message) {
-		Prompt prompt = new Prompt(new UserMessage(message));
-		Generation generation = call(prompt).getResult();
-		return (generation != null) ? generation.getOutput().getContent() : "";
 	}
 
-	default String call(Message... messages) {
-		Prompt prompt = new Prompt(Arrays.asList(messages));
-		Generation generation = call(prompt).getResult();
-		return (generation != null) ? generation.getOutput().getContent() : "";
+	public ChatClientRequest build() {
+		return new ChatClientRequest(this.userPrompt, this.systemPrompt, this.functions, this.media);
 	}
 
-	@Override
-	ChatResponse call(Prompt prompt);
+	public ChatClientRequest userPrompt(String userPrompt, Map<String, String> params) {
+		var ccr = new ChatClientRequest(userPrompt, this.systemPrompt, this.functions, this.media);
+		ccr.userPromptParams(params);
+		return ccr;
+	}
+
+	public ChatClientRequest userPrompt(String userPrompt) {
+		return new ChatClientRequest(userPrompt, this.systemPrompt, this.functions, this.media);
+	}
+
+	public static class ChatClientRequest {
+
+		private String userPrompt = "";
+
+		private String systemPrompt = "";
+
+		private final List<Media> media = new ArrayList<>();
+
+		private final List<String> functions = new ArrayList<>();
+
+		private final Map<String, String> userPromptParams = new HashMap<>();
+
+		private final Map<String, String> systemPromptParams = new HashMap<>();
+
+		List<Media> media() {
+			return this.media;
+		}
+
+		String systemPrompt() {
+			return this.systemPrompt;
+		}
+
+		String userPrompt() {
+			return this.userPrompt;
+		}
+
+		List<String> functions() {
+			return this.functions;
+		}
+
+		public ChatClientRequest(String userPrompt, String systemPrompt, List<String> functions, List<Media> media) {
+			this.userPrompt = userPrompt;
+			this.systemPrompt = systemPrompt;
+			this.functions.addAll(functions);
+			this.media.addAll(media);
+		}
+
+		public ChatClientRequest userPromptParam(String key, String value) {
+			this.userPromptParams.put(key, value);
+			return this;
+		}
+
+		public ChatClientRequest systemPromptParam(String key, String value) {
+			this.systemPromptParams.put(key, value);
+			return this;
+		}
+
+		public ChatClientRequest systemPromptParams(Map<String, String> systemPromptParams) {
+			this.systemPromptParams.putAll(systemPromptParams);
+			return this;
+		}
+
+		public ChatClientRequest userPromptParams(Map<String, String> userPromptParams) {
+			this.userPromptParams.putAll(userPromptParams);
+			return this;
+		}
+
+		public ChatClientRequest userPrompt(String userPrompt) {
+			this.userPrompt = userPrompt;
+			return this;
+		}
+
+		public ChatClientRequest systemPrompt(String systemPrompt) {
+			this.systemPrompt = systemPrompt;
+			return this;
+		}
+
+		public ChatClientRequest media(Media... media) {
+			this.media.addAll(Arrays.asList(media));
+			return this;
+		}
+
+		public ChatClientRequest functions(String... functions) {
+			this.functions.addAll(Arrays.asList(functions));
+			return this;
+		}
+
+		public <T> T chat(Class<T> clzz) {
+			return null;
+		}
+
+		public <T> T chat(ParameterizedTypeReference<T> clzz) {
+			return null;
+		}
+
+	}
+
+	public static class ChatClientBuilder {
+
+		private final ChatConnector connector;
+
+		private final List<Media> defaultMedia = new ArrayList<>();
+
+		private final List<String> defaultFunctions = new ArrayList<>();
+
+		private String defaultSystemPrompt;
+
+		private String defaultUserPrompt;
+
+		ChatClientBuilder(ChatConnector connector) {
+			this.connector = connector;
+		}
+
+		public ChatClient build() {
+			return new ChatClient(this.connector, this.defaultSystemPrompt, this.defaultUserPrompt,
+					this.defaultFunctions, this.defaultMedia);
+		}
+
+		public ChatClientBuilder defaultSystemPrompt(String systemPrompt) {
+			return this;
+		}
+
+		public ChatClientBuilder defaultFunctions(String... functionNames) {
+			return this;
+		}
+
+		public ChatClientBuilder defaultUserPrompt(String userPrompt) {
+			return this;
+		}
+
+	}
+
+	public static ChatClientBuilder builder(ChatConnector connector) {
+		return new ChatClientBuilder(connector);
+	}
 
 }

spring-ai-core/src/main/java/org/springframework/ai/chat/connector/ChatConnector.java

@@ -0,0 +1,35 @@
+/*
+ * Copyright 2023 - 2024 the original author or authors.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * https://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.springframework.ai.chat.connector;
+
+import org.springframework.ai.chat.ChatResponse;
+import org.springframework.ai.chat.prompt.Prompt;
+
+public interface ChatConnector {
+
+	/*
+	 * default String call(String message) { Prompt prompt = new Prompt(new
+	 * UserMessage(message)); Generation generation = call(prompt).getResult(); return
+	 * (generation != null) ? generation.getOutput().getContent() : ""; }
+	 *
+	 * public String call(Message... messages) { Prompt prompt = new
+	 * Prompt(Arrays.asList(messages)); Generation generation = call(prompt).getResult();
+	 * return (generation != null) ? generation.getOutput().getContent() : ""; }
+	 */
+
+	ChatResponse execute(Prompt prompt);
+
+}

spring-ai-core/src/main/java/org/springframework/ai/chat/service/PromptTransformingChatService.java

@@ -15,7 +15,7 @@
  */
 package org.springframework.ai.chat.service;
 
-import org.springframework.ai.chat.ChatClient;
+import org.springframework.ai.chat.connector.ChatConnector;
 import org.springframework.ai.chat.ChatResponse;
 import org.springframework.ai.chat.prompt.transformer.ChatServiceContext;
 import org.springframework.ai.chat.prompt.transformer.PromptTransformer;
@@ -35,7 +35,7 @@ import java.util.Objects;
  */
 public class PromptTransformingChatService implements ChatService {
 
-	private ChatClient chatClient;
+	private ChatConnector chatConnector;
 
 	private List<PromptTransformer> retrievers;
 
@@ -45,19 +45,19 @@ public class PromptTransformingChatService implements ChatService {
 
 	private List<ChatServiceListener> chatServiceListeners;
 
-	public PromptTransformingChatService(ChatClient chatClient, List<PromptTransformer> retrievers,
+	public PromptTransformingChatService(ChatConnector chatConnector, List<PromptTransformer> retrievers,
 			List<PromptTransformer> documentPostProcessors, List<PromptTransformer> augmentors,
 			List<ChatServiceListener> chatServiceListeners) {
-		Objects.requireNonNull(chatClient, "chatClient must not be null");
-		this.chatClient = chatClient;
+		Objects.requireNonNull(chatConnector, "chatConnector must not be null");
+		this.chatConnector = chatConnector;
 		this.retrievers = retrievers;
 		this.documentPostProcessors = documentPostProcessors;
 		this.augmentors = augmentors;
 		this.chatServiceListeners = chatServiceListeners;
 	}
 
-	public static Builder builder(ChatClient chatClient) {
-		return new Builder().withChatClient(chatClient);
+	public static Builder builder(ChatConnector chatConnector) {
+		return new Builder().withChatClient(chatConnector);
 	}
 
 	@Override
@@ -86,7 +86,7 @@ public class PromptTransformingChatService implements ChatService {
 		}
 
 		// Perform generation
-		ChatResponse chatResponse = this.chatClient.call(chatServiceContext.getPrompt());
+		ChatResponse chatResponse = this.chatConnector.execute(chatServiceContext.getPrompt());
 
 		// Invoke Listeners onComplete
 		ChatServiceResponse chatServiceResponse = new ChatServiceResponse(chatServiceContext, chatResponse);
@@ -98,7 +98,7 @@ public class PromptTransformingChatService implements ChatService {
 
 	public static class Builder {
 
-		private ChatClient chatClient;
+		private ChatConnector chatConnector;
 
 		private List<PromptTransformer> retrievers = new ArrayList<>();
 
@@ -108,8 +108,8 @@ public class PromptTransformingChatService implements ChatService {
 
 		private List<ChatServiceListener> chatServiceListeners = new ArrayList<>();
 
-		public Builder withChatClient(ChatClient chatClient) {
-			this.chatClient = chatClient;
+		public Builder withChatClient(ChatConnector chatConnector) {
+			this.chatConnector = chatConnector;
 			return this;
 		}
 
@@ -134,7 +134,7 @@ public class PromptTransformingChatService implements ChatService {
 		}
 
 		public PromptTransformingChatService build() {
-			return new PromptTransformingChatService(chatClient, retrievers, documentPostProcessors, augmentors,
+			return new PromptTransformingChatService(chatConnector, retrievers, documentPostProcessors, augmentors,
 					chatServiceListeners);
 		}
 

spring-ai-core/src/main/java/org/springframework/ai/evaluation/RelevancyEvaluator.java

@@ -1,6 +1,6 @@
 package org.springframework.ai.evaluation;
 
-import org.springframework.ai.chat.ChatClient;
+import org.springframework.ai.chat.connector.ChatConnector;
 import org.springframework.ai.chat.ChatResponse;
 import org.springframework.ai.chat.messages.Message;
 import org.springframework.ai.chat.messages.MessageType;
@@ -31,14 +31,14 @@ public class RelevancyEvaluator implements Evaluator {
 
 	private final ChatOptions chatOptions;
 
-	private ChatClient chatClient;
+	private ChatConnector chatConnector;
 
-	public RelevancyEvaluator(ChatClient chatClient) {
-		this(chatClient, ChatOptionsBuilder.builder().build());
+	public RelevancyEvaluator(ChatConnector chatConnector) {
+		this(chatConnector, ChatOptionsBuilder.builder().build());
 	}
 
-	public RelevancyEvaluator(ChatClient chatClient, ChatOptions chatOptions) {
-		this.chatClient = chatClient;
+	public RelevancyEvaluator(ChatConnector chatConnector, ChatOptions chatOptions) {
+		this.chatConnector = chatConnector;
 		this.chatOptions = chatOptions;
 	}
 
@@ -52,7 +52,7 @@ public class RelevancyEvaluator implements Evaluator {
 		Message message = promptTemplate
 			.createMessage(Map.of("query", query, "response", response, "context", context));
 
-		ChatResponse chatResponse = this.chatClient.call(new Prompt(message, this.chatOptions));
+		ChatResponse chatResponse = this.chatConnector.execute(new Prompt(message, this.chatOptions));
 
 		var evaluationResponse = chatResponse.getResult().getOutput().getContent();
 		boolean passing = false;

spring-ai-core/src/main/java/org/springframework/ai/model/function/AbstractFunctionCallback.java

@@ -54,7 +54,7 @@ abstract class AbstractFunctionCallback<I, O> implements Function<I, O>, Functio
 	/**
 	 * Constructs a new {@link AbstractFunctionCallback} with the given name, description,
 	 * input type and default object mapper.
-	 * @param name Function name. Should be unique within the ChatClient's function
+	 * @param name Function name. Should be unique within the ChatConnector's function
 	 * registry.
 	 * @param description Function description. Used as a "system prompt" by the model to
 	 * decide if the function should be called.

spring-ai-core/src/main/java/org/springframework/ai/model/function/FunctionCallingOptions.java

@@ -24,33 +24,33 @@ import java.util.Set;
 public interface FunctionCallingOptions {
 
 	/**
-	 * Function Callbacks to be registered with the ChatClient. For Prompt Options the
+	 * Function Callbacks to be registered with the ChatConnector. For Prompt Options the
 	 * functionCallbacks are automatically enabled for the duration of the prompt
 	 * execution. For Default Options the FunctionCallbacks are registered but disabled by
 	 * default. You have to use "functions" property to list the function names from the
-	 * ChatClient registry to be used in the chat completion requests.
-	 * @return Return the Function Callbacks to be registered with the ChatClient.
+	 * ChatConnector registry to be used in the chat completion requests.
+	 * @return Return the Function Callbacks to be registered with the ChatConnector.
 	 */
 	List<FunctionCallback> getFunctionCallbacks();
 
 	/**
-	 * Set the Function Callbacks to be registered with the ChatClient.
+	 * Set the Function Callbacks to be registered with the ChatConnector.
 	 * @param functionCallbacks the Function Callbacks to be registered with the
-	 * ChatClient.
+	 * ChatConnector.
 	 */
 	void setFunctionCallbacks(List<FunctionCallback> functionCallbacks);
 
 	/**
-	 * @return List of function names from the ChatClient registry to be used in the next
-	 * chat completion requests.
+	 * @return List of function names from the ChatConnector registry to be used in the
+	 * next chat completion requests.
 	 */
 	Set<String> getFunctions();
 
 	/**
-	 * Set the list of function names from the ChatClient registry to be used in the next
-	 * chat completion requests.
-	 * @param functions the list of function names from the ChatClient registry to be used
-	 * in the next chat completion requests.
+	 * Set the list of function names from the ChatConnector registry to be used in the
+	 * next chat completion requests.
+	 * @param functions the list of function names from the ChatConnector registry to be
+	 * used in the next chat completion requests.
 	 */
 	void setFunctions(Set<String> functions);
 

spring-ai-core/src/main/java/org/springframework/ai/transformer/KeywordMetadataEnricher.java

@@ -18,7 +18,7 @@ package org.springframework.ai.transformer;
 import java.util.List;
 import java.util.Map;
 
-import org.springframework.ai.chat.ChatClient;
+import org.springframework.ai.chat.connector.ChatConnector;
 import org.springframework.ai.document.Document;
 import org.springframework.ai.document.DocumentTransformer;
 import org.springframework.ai.chat.prompt.Prompt;
@@ -43,18 +43,18 @@ public class KeywordMetadataEnricher implements DocumentTransformer {
 	/**
 	 * Model predictor
 	 */
-	private final ChatClient chatClient;
+	private final ChatConnector chatConnector;
 
 	/**
 	 * The number of keywords to extract.
 	 */
 	private final int keywordCount;
 
-	public KeywordMetadataEnricher(ChatClient chatClient, int keywordCount) {
-		Assert.notNull(chatClient, "ChatClient must not be null");
+	public KeywordMetadataEnricher(ChatConnector chatConnector, int keywordCount) {
+		Assert.notNull(chatConnector, "ChatConnector must not be null");
 		Assert.isTrue(keywordCount >= 1, "Document count must be >= 1");
 
-		this.chatClient = chatClient;
+		this.chatConnector = chatConnector;
 		this.keywordCount = keywordCount;
 	}
 
@@ -64,7 +64,7 @@ public class KeywordMetadataEnricher implements DocumentTransformer {
 
 			var template = new PromptTemplate(String.format(KEYWORDS_TEMPLATE, keywordCount));
 			Prompt prompt = template.create(Map.of(CONTEXT_STR_PLACEHOLDER, document.getContent()));
-			String keywords = this.chatClient.call(prompt).getResult().getOutput().getContent();
+			String keywords = this.chatConnector.execute(prompt).getResult().getOutput().getContent();
 			document.getMetadata().putAll(Map.of(EXCERPT_KEYWORDS_METADATA_KEY, keywords));
 		}
 		return documents;

spring-ai-core/src/main/java/org/springframework/ai/transformer/SummaryMetadataEnricher.java

@@ -20,7 +20,7 @@ import java.util.HashMap;
 import java.util.List;
 import java.util.Map;
 
-import org.springframework.ai.chat.ChatClient;
+import org.springframework.ai.chat.connector.ChatConnector;
 import org.springframework.ai.document.Document;
 import org.springframework.ai.document.DocumentTransformer;
 import org.springframework.ai.document.MetadataMode;
@@ -62,7 +62,7 @@ public class SummaryMetadataEnricher implements DocumentTransformer {
 	/**
 	 * AI client.
 	 */
-	private final ChatClient chatClient;
+	private final ChatConnector chatConnector;
 
 	/**
 	 * Number of documents from front to use for title extraction.
@@ -76,16 +76,16 @@ public class SummaryMetadataEnricher implements DocumentTransformer {
 	 */
 	private final String summaryTemplate;
 
-	public SummaryMetadataEnricher(ChatClient chatClient, List<SummaryType> summaryTypes) {
-		this(chatClient, summaryTypes, DEFAULT_SUMMARY_EXTRACT_TEMPLATE, MetadataMode.ALL);
+	public SummaryMetadataEnricher(ChatConnector chatConnector, List<SummaryType> summaryTypes) {
+		this(chatConnector, summaryTypes, DEFAULT_SUMMARY_EXTRACT_TEMPLATE, MetadataMode.ALL);
 	}
 
-	public SummaryMetadataEnricher(ChatClient chatClient, List<SummaryType> summaryTypes, String summaryTemplate,
+	public SummaryMetadataEnricher(ChatConnector chatConnector, List<SummaryType> summaryTypes, String summaryTemplate,
 			MetadataMode metadataMode) {
-		Assert.notNull(chatClient, "ChatClient must not be null");
+		Assert.notNull(chatConnector, "ChatConnector must not be null");
 		Assert.hasText(summaryTemplate, "Summary template must not be empty");
 
-		this.chatClient = chatClient;
+		this.chatConnector = chatConnector;
 		this.summaryTypes = CollectionUtils.isEmpty(summaryTypes) ? List.of(SummaryType.CURRENT) : summaryTypes;
 		this.metadataMode = metadataMode;
 		this.summaryTemplate = summaryTemplate;
@@ -101,7 +101,7 @@ public class SummaryMetadataEnricher implements DocumentTransformer {
 
 			Prompt prompt = new PromptTemplate(this.summaryTemplate)
 				.create(Map.of(CONTEXT_STR_PLACEHOLDER, documentContext));
-			documentSummaries.add(this.chatClient.call(prompt).getResult().getOutput().getContent());
+			documentSummaries.add(this.chatConnector.execute(prompt).getResult().getOutput().getContent());
 		}
 
 		for (int i = 0; i < documentSummaries.size(); i++) {

spring-ai-core/src/test/java/org/springframework/ai/chat/ChatConnectorTests.java

@@ -30,16 +30,17 @@ import static org.mockito.Mockito.when;
 import org.junit.jupiter.api.Test;
 
 import org.mockito.Mockito;
+import org.springframework.ai.chat.connector.ChatConnector;
 import org.springframework.ai.chat.messages.AssistantMessage;
 import org.springframework.ai.chat.prompt.Prompt;
 
 /**
- * Unit Tests for {@link ChatClient}.
+ * Unit Tests for {@link ChatConnector}.
  *
  * @author John Blum
  * @since 0.2.0
  */
-class ChatClientTests {
+class ChatConnectorTests {
 
 	@Test
 	void generateWithStringCallsGenerateWithPromptAndReturnsResponseCorrectly() {
@@ -47,7 +48,7 @@ class ChatClientTests {
 		String userMessage = "Zero Wing";
 		String responseMessage = "All your bases are belong to us";
 
-		ChatClient mockClient = Mockito.mock(ChatClient.class);
+		ChatConnector mockClient = Mockito.mock(ChatConnector.class);
 
 		AssistantMessage mockAssistantMessage = Mockito.mock(AssistantMessage.class);
 		when(mockAssistantMessage.getContent()).thenReturn(responseMessage);
@@ -64,7 +65,7 @@ class ChatClientTests {
 		// ChatResponse response = spy(new
 		// ChatResponse(Collections.singletonList(generation)));
 
-		doCallRealMethod().when(mockClient).call(anyString());
+		doCallRealMethod().when(mockClient).execute(anyString());
 
 		doAnswer(invocationOnMock -> {
 
@@ -75,12 +76,12 @@ class ChatClientTests {
 
 			return response;
 
-		}).when(mockClient).call(any(Prompt.class));
+		}).when(mockClient).execute(any(Prompt.class));
 
-		assertThat(mockClient.call(userMessage)).isEqualTo(responseMessage);
+		assertThat(mockClient.execute(userMessage)).isEqualTo(responseMessage);
 
-		verify(mockClient, times(1)).call(eq(userMessage));
-		verify(mockClient, times(1)).call(isA(Prompt.class));
+		verify(mockClient, times(1)).execute(eq(userMessage));
+		verify(mockClient, times(1)).execute(isA(Prompt.class));
 		verify(response, times(1)).getResult();
 		verify(generation, times(1)).getOutput();
 		verify(mockAssistantMessage, times(1)).getContent();

spring-ai-core/src/test/java/org/springframework/ai/chat/memory/ChatMemoryTests.java

@@ -25,7 +25,7 @@ import org.mockito.Captor;
 import org.mockito.Mock;
 import org.mockito.junit.jupiter.MockitoExtension;
 
-import org.springframework.ai.chat.ChatClient;
+import org.springframework.ai.chat.connector.ChatConnector;
 import org.springframework.ai.chat.ChatResponse;
 import org.springframework.ai.chat.Generation;
 import org.springframework.ai.chat.StreamingChatClient;
@@ -48,7 +48,7 @@ import static org.mockito.Mockito.when;
 public class ChatMemoryTests {
 
 	@Mock
-	ChatClient chatClient;
+	ChatConnector chatConnector;
 
 	@Mock
 	StreamingChatClient streamingChatClient;
@@ -61,7 +61,7 @@ public class ChatMemoryTests {
 
 		ChatMemory chatHistory = new InMemoryChatMemory();
 
-		PromptTransformingChatService chatService = PromptTransformingChatService.builder(chatClient)
+		PromptTransformingChatService chatService = PromptTransformingChatService.builder(chatConnector)
 			.withRetrievers(List.of(ChatMemoryRetriever.builder().withChatHistory(chatHistory).build()))
 			.withContentPostProcessors(
 					List.of(new LastMaxTokenSizeContentTransformer(new JTokkitTokenCountEstimator(), 10)))
@@ -77,7 +77,7 @@ public class ChatMemoryTests {
 
 		ChatMemory chatHistory = new InMemoryChatMemory();
 
-		PromptTransformingChatService chatService = PromptTransformingChatService.builder(chatClient)
+		PromptTransformingChatService chatService = PromptTransformingChatService.builder(chatConnector)
 			.withRetrievers(List.of(new ChatMemoryRetriever(chatHistory)))
 			.withContentPostProcessors(
 					List.of(new LastMaxTokenSizeContentTransformer(new JTokkitTokenCountEstimator(), 10)))
@@ -90,7 +90,7 @@ public class ChatMemoryTests {
 
 	public void chatClientUserMessages(PromptTransformingChatService chatService, ChatMemory chatHistory) {
 
-		when(chatClient.call(promptCaptor.capture()))
+		when(chatConnector.execute(promptCaptor.capture()))
 				.thenReturn(new ChatResponse(List.of(new Generation("assistant:1"))))
 				.thenReturn(new ChatResponse(List.of(new Generation("assistant:2"))))
 				.thenReturn(new ChatResponse(List.of(new Generation("assistant:3"))));

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/bedrock.adoc

@@ -2,7 +2,7 @@
 
 link:https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html[Amazon Bedrock] is a managed service that provides foundation models from various AI providers, available through a unified API.
 
-Spring AI supports https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids-arns.html[all the Chat and Embedding AI models] available through Amazon Bedrock by implementing the Spring interfaces `ChatClient`, `StreamingChatClient`, and  `EmbeddingClient`.
+Spring AI supports https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids-arns.html[all the Chat and Embedding AI models] available through Amazon Bedrock by implementing the Spring interfaces `ChatConnector`, `StreamingChatClient`, and  `EmbeddingClient`.
 
 Additionally, Spring AI provides Spring Auto-Configurations and Boot Starters for all clients, making it easy to bootstrap and configure for the Bedrock models.
 

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/anthropic-chat.adoc

@@ -132,7 +132,7 @@ TIP: In addition to the model specific https://github.com/spring-projects/spring
 
 == Function Calling
 
-You can register custom Java functions with the `AnthropicChatClient` and have the Anthropic Claude model intelligently choose to output a JSON object containing arguments to call one or many of the registered functions.
+You can register custom Java functions with the `AnthropicChatConnector` and have the Anthropic Claude model intelligently choose to output a JSON object containing arguments to call one or many of the registered functions.
 This is a powerful technique to connect the LLM capabilities with external tools and APIs.
 Read more about xref:api/chat/functions/anthropic-chat-functions.adoc[Anthropic Function Calling].
 
@@ -194,7 +194,7 @@ spring.ai.anthropic.chat.options.max-tokens=450
 
 TIP: replace the `api-key` with your Anthropic credentials.
 
-This will create a `AnthropicChatClient` implementation that you can inject into your class.
+This will create a `AnthropicChatConnector` implementation that you can inject into your class.
 Here is an example of a simple `@Controller` class that uses the chat client for text generations.
 
 [source,java]
@@ -224,7 +224,7 @@ public class ChatController {
 
 == Manual Configuration
 
-The https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-anthropic/src/main/java/org/springframework/ai/anthropic/AnthropicChatClient.java[AnthropicChatClient] implements the `ChatClient` and `StreamingChatClient` and uses the <<low-level-api>> to connect to the Anthropic service.
+The https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-anthropic/src/main/java/org/springframework/ai/anthropic/AnthropicChatClient.java[AnthropicChatClient] implements the `ChatConnector` and `StreamingChatClient` and uses the <<low-level-api>> to connect to the Anthropic service.
 
 Add the `spring-ai-anthropic` dependency to your project's Maven `pom.xml` file:
 
@@ -247,7 +247,7 @@ dependencies {
 
 TIP: Refer to the xref:getting-started.adoc#dependency-management[Dependency Management] section to add the Spring AI BOM to your build file.
 
-Next, create a `AnthropicChatClient` and use it for text generations:
+Next, create a `AnthropicChatConnector` and use it for text generations:
 
 [source,java]
 ----

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/azure-openai-chat.adoc

@@ -88,7 +88,7 @@ The prefix `spring.ai.azure.openai` is the property prefix to configure the conn
 | spring.ai.azure.openai.endpoint | The endpoint from the Azure AI OpenAI `Keys and Endpoint` section under `Resource Management` | -
 |====
 
-The prefix `spring.ai.azure.openai.chat` is the property prefix that configures the `ChatClient` implementation for Azure OpenAI.
+The prefix `spring.ai.azure.openai.chat` is the property prefix that configures the `ChatConnector` implementation for Azure OpenAI.
 
 [cols="3,5,3"]
 |====
@@ -157,7 +157,7 @@ spring.ai.azure.openai.chat.options.temperature=0.7
 
 TIP: replace the `api-key` and `endpoint` with your Azure OpenAI credentials.
 
-This will create a `AzureOpenAiChatClient` implementation that you can inject into your class.
+This will create a `AzureOpenAiChatConnector` implementation that you can inject into your class.
 Here is an example of a simple `@Controller` class that uses the chat client for text generations.
 
 
@@ -188,7 +188,7 @@ public class ChatController {
 
 == Manual Configuration
 
-The link:https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-azure-openai/src/main/java/org/springframework/ai/azure/openai/AzureOpenAiChatClient.java[AzureOpenAiChatClient] implements the `ChatClient` and `StreamingChatClient` and uses the link:https://learn.microsoft.com/en-us/java/api/overview/azure/ai-openai-readme?view=azure-java-preview[Azure OpenAI Java Client].
+The link:https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-azure-openai/src/main/java/org/springframework/ai/azure/openai/AzureOpenAiChatClient.java[AzureOpenAiChatClient] implements the `ChatConnector` and `StreamingChatClient` and uses the link:https://learn.microsoft.com/en-us/java/api/overview/azure/ai-openai-readme?view=azure-java-preview[Azure OpenAI Java Client].
 
 To enable it, add the `spring-ai-azure-openai` dependency to your project's Maven `pom.xml` file:
 [source, xml]
@@ -210,9 +210,9 @@ dependencies {
 
 TIP: Refer to the xref:getting-started.adoc#dependency-management[Dependency Management] section to add the Spring AI BOM to your build file.
 
-TIP: The `spring-ai-azure-openai` dependency also provide the access to the `AzureOpenAiChatClient`. For more information about the `AzureOpenAiChatClient` refer to the link:../chat/azure-openai-chat.html[Azure OpenAI Chat] section.
+TIP: The `spring-ai-azure-openai` dependency also provide the access to the `AzureOpenAiChatConnector`. For more information about the `AzureOpenAiChatConnector` refer to the link:../chat/azure-openai-chat.html[Azure OpenAI Chat] section.
 
-Next, create an `AzureOpenAiChatClient` instance and use it to generate text responses:
+Next, create an `AzureOpenAiChatConnector` instance and use it to generate text responses:
 
 [source,java]
 ----

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/bedrock/bedrock-anthropic.adoc

@@ -138,7 +138,7 @@ spring.ai.bedrock.anthropic.chat.options.top-k=15
 
 TIP: replace the `regions`, `access-key` and `secret-key` with your AWS credentials.
 
-This will create a `BedrockAnthropicChatClient` implementation that you can inject into your class.
+This will create a `BedrockAnthropicChatConnector` implementation that you can inject into your class.
 Here is an example of a simple `@Controller` class that uses the chat client for text generations.
 
 [source,java]
@@ -168,7 +168,7 @@ public class ChatController {
 
 == Manual Configuration
 
-The https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/anthropic/BedrockAnthropicChatClient.java[BedrockAnthropicChatClient] implements the `ChatClient` and `StreamingChatClient` and uses the <<low-level-api>> to connect to the Bedrock Anthropic service.
+The https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/anthropic/BedrockAnthropicChatClient.java[BedrockAnthropicChatClient] implements the `ChatConnector` and `StreamingChatClient` and uses the <<low-level-api>> to connect to the Bedrock Anthropic service.
 
 Add the `spring-ai-bedrock` dependency to your project's Maven `pom.xml` file:
 

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/bedrock/bedrock-anthropic3.adoc

@@ -179,7 +179,7 @@ spring.ai.bedrock.anthropic3.chat.options.top-k=15
 
 TIP: replace the `regions`, `access-key` and `secret-key` with your AWS credentials.
 
-This will create a `BedrockAnthropicChatClient` implementation that you can inject into your class.
+This will create a `BedrockAnthropicChatConnector` implementation that you can inject into your class.
 Here is an example of a simple `@Controller` class that uses the chat client for text generations.
 
 [source,java]
@@ -209,7 +209,7 @@ public class ChatController {
 
 == Manual Configuration
 
-The https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/anthropic3/BedrockAnthropic3ChatClient.java[BedrockAnthropic3ChatClient] implements the `ChatClient` and `StreamingChatClient` and uses the <<low-level-api>> to connect to the Bedrock Anthropic service.
+The https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/anthropic3/BedrockAnthropic3ChatClient.java[BedrockAnthropic3ChatClient] implements the `ChatConnector` and `StreamingChatClient` and uses the <<low-level-api>> to connect to the Bedrock Anthropic service.
 
 Add the `spring-ai-bedrock` dependency to your project's Maven `pom.xml` file:
 

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/bedrock/bedrock-cohere.adoc

@@ -130,7 +130,7 @@ spring.ai.bedrock.cohere.chat.options.temperature=0.8
 
 TIP: replace the `regions`, `access-key` and `secret-key` with your AWS credentials.
 
-This will create a `BedrockCohereChatClient` implementation that you can inject into your class.
+This will create a `BedrockCohereChatConnector` implementation that you can inject into your class.
 Here is an example of a simple `@Controller` class that uses the chat client for text generations.
 
 [source,java]
@@ -160,7 +160,7 @@ public class ChatController {
 
 == Manual Configuration
 
-The https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/cohere/BedrockCohereChatClient.java[BedrockCohereChatClient] implements the `ChatClient` and `StreamingChatClient` and uses the <<low-level-api>> to connect to the Bedrock Cohere service.
+The https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/cohere/BedrockCohereChatClient.java[BedrockCohereChatClient] implements the `ChatConnector` and `StreamingChatClient` and uses the <<low-level-api>> to connect to the Bedrock Cohere service.
 
 Add the `spring-ai-bedrock` dependency to your project's Maven `pom.xml` file:
 

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/bedrock/bedrock-jurassic2.adoc

@@ -123,7 +123,7 @@ spring.ai.bedrock.jurassic2.chat.options.temperature=0.8
 
 TIP: replace the `regions`, `access-key` and `secret-key` with your AWS credentials.
 
-This will create a `BedrockAi21Jurassic2ChatClient` implementation that you can inject into your class.
+This will create a `BedrockAi21Jurassic2ChatConnector` implementation that you can inject into your class.
 Here is an example of a simple `@Controller` class that uses the chat client for text generations.
 
 [source,java]
@@ -148,7 +148,7 @@ public class ChatController {
 
 == Manual Configuration
 
-The https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/jurassic2/BedrockAi21Jurassic2ChatClient.java[BedrockAi21Jurassic2ChatClient] implements the `ChatClient`  uses the <<low-level-api>> to connect to the Bedrock Jurassic-2 service.
+The https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/jurassic2/BedrockAi21Jurassic2ChatClient.java[BedrockAi21Jurassic2ChatClient] implements the `ChatConnector`  uses the <<low-level-api>> to connect to the Bedrock Jurassic-2 service.
 
 Add the `spring-ai-bedrock` dependency to your project's Maven `pom.xml` file:
 

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/bedrock/bedrock-llama.adoc

@@ -128,7 +128,7 @@ spring.ai.bedrock.llama.chat.options.temperature=0.8
 
 TIP: replace the `regions`, `access-key` and `secret-key` with your AWS credentials.
 
-This will create a `BedrockLlamaChatClient` implementation that you can inject into your class.
+This will create a `BedrockLlamaChatConnector` implementation that you can inject into your class.
 Here is an example of a simple `@Controller` class that uses the chat client for text generations.
 
 [source,java]
@@ -158,7 +158,7 @@ public class ChatController {
 
 == Manual Configuration
 
-The https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/llama/BedrockLlamaChatClient.java[BedrockLlamaChatClient] implements the `ChatClient` and `StreamingChatClient` and uses the <<low-level-api>> to connect to the Bedrock Anthropic service.
+The https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/llama/BedrockLlamaChatClient.java[BedrockLlamaChatClient] implements the `ChatConnector` and `StreamingChatClient` and uses the <<low-level-api>> to connect to the Bedrock Anthropic service.
 
 Add the `spring-ai-bedrock` dependency to your project's Maven `pom.xml` file:
 

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/bedrock/bedrock-titan.adoc

@@ -126,7 +126,7 @@ spring.ai.bedrock.titan.chat.options.temperature=0.8
 
 TIP: replace the `regions`, `access-key` and `secret-key` with your AWS credentials.
 
-This will create a `BedrockTitanChatClient` implementation that you can inject into your class.
+This will create a `BedrockTitanChatConnector` implementation that you can inject into your class.
 Here is an example of a simple `@Controller` class that uses the chat client for text generations.
 
 [source,java]
@@ -156,7 +156,7 @@ public class ChatController {
 
 == Manual Configuration
 
-The https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/titan/BedrockTitanChatClient.java[BedrockTitanChatClient] implements the `ChatClient` and `StreamingChatClient` and uses the <<low-level-api>> to connect to the Bedrock Titanic service.
+The https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/titan/BedrockTitanChatClient.java[BedrockTitanChatClient] implements the `ChatConnector` and `StreamingChatClient` and uses the <<low-level-api>> to connect to the Bedrock Titanic service.
 
 Add the `spring-ai-bedrock` dependency to your project's Maven `pom.xml` file:
 

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/functions/anthropic-chat-functions.adoc

@@ -1,6 +1,6 @@
 = Anthropic Function Calling
 
-You can register custom Java functions with the `AnthropicChatClient` and have the Anthropic models intelligently choose to output a JSON object containing arguments to call one or many of the registered functions.
+You can register custom Java functions with the `AnthropicChatConnector` and have the Anthropic models intelligently choose to output a JSON object containing arguments to call one or many of the registered functions.
 This allows you to connect the LLM capabilities with external tools and APIs.
 The `claude-3-opus`, `claude-3-sonnet` and `claude-3-haiku` link:https://docs.anthropic.com/claude/docs/tool-use#tool-use-best-practices-and-limitations[models are trained to detect when a function should be called] and to respond with JSON that adheres to the function signature.
 
@@ -15,7 +15,7 @@ The `description` helps the model to understand when to call the function.
 As a developer, you need to implement a function that takes the function call arguments sent from the AI model, and respond with the result back to the model.
 Your function can in turn invoke other 3rd party services to provide the results.
 
-Spring AI makes this as easy as defining a `@Bean` definition that returns a `java.util.Function` and supplying the bean name as an option when invoking the `ChatClient`.
+Spring AI makes this as easy as defining a `@Bean` definition that returns a `java.util.Function` and supplying the bean name as an option when invoking the `ChatConnector`.
 
 Under the hood, Spring wraps your POJO (the function) with the appropriate adapter code that enables interaction with the AI Model, saving you from writing tedious boilerplate code.
 The basis of the underlying infrastructure is the link:https://github.com/spring-projects/spring-ai/blob/main/spring-ai-core/src/main/java/org/springframework/ai/model/function/FunctionCallback.java[FunctionCallback.java] interface and the companion link:https://github.com/spring-projects/spring-ai/blob/main/spring-ai-core/src/main/java/org/springframework/ai/model/function/FunctionCallbackWrapper.java[FunctionCallbackWrapper.java] utility class to simplify the implementation and registration of Java callback functions.
@@ -70,7 +70,7 @@ We start with describing the most POJO friendly options.
 
 In this approach you define `@Beans` in your application context as you would any other Spring managed object.
 
-Internally, Spring AI `ChatClient` will create an instance of a `FunctionCallbackWrapper` wrapper that adds the logic for it being invoked via the AI model.
+Internally, Spring AI `ChatConnector` will create an instance of a `FunctionCallbackWrapper` wrapper that adds the logic for it being invoked via the AI model.
 The name of the `@Bean` is passed as a `ChatOption`.
 
 
@@ -136,7 +136,7 @@ static class Config {
 }
 ----
 
-It wraps the 3rd party `MockWeatherService` function and registers it as a `CurrentWeather` function with the `AnthropicChatClient`.
+It wraps the 3rd party `MockWeatherService` function and registers it as a `CurrentWeather` function with the `AnthropicChatConnector`.
 It also provides a description (2) and an optional response converter (3) to convert the response into a text as expected by the model.
 
 NOTE: By default, the response converter does a JSON serialization of the Response object.
@@ -159,7 +159,7 @@ ChatResponse response = chatClient.call(new Prompt(List.of(userMessage),
 logger.info("Response: {}", response);
 ----
 
-// NOTE: You can can have multiple functions registered in your `ChatClient` but only those enabled in the prompt request will be considered for the function calling.
+// NOTE: You can can have multiple functions registered in your `ChatConnector` but only those enabled in the prompt request will be considered for the function calling.
 
 Above user question will trigger 3 calls to `CurrentWeather` function (one for each city) and produce the final response.
 
@@ -187,5 +187,5 @@ NOTE: The in-prompt registered functions are enabled by default for the duration
 
 This approach allows to dynamically chose different functions to be called based on the user input.
 
-The https://github.com/spring-projects/spring-ai/blob/main/spring-ai-spring-boot-autoconfigure/src/test/java/org/springframework/ai/autoconfigure/anthropic/tool/FunctionCallWithPromptFunctionIT.java[FunctionCallWithPromptFunctionIT.java] integration test provides a complete example of how to register a function with the `AnthropicChatClient` and use it in a prompt request.
+The https://github.com/spring-projects/spring-ai/blob/main/spring-ai-spring-boot-autoconfigure/src/test/java/org/springframework/ai/autoconfigure/anthropic/tool/FunctionCallWithPromptFunctionIT.java[FunctionCallWithPromptFunctionIT.java] integration test provides a complete example of how to register a function with the `AnthropicChatConnector` and use it in a prompt request.
 

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/functions/azure-open-ai-chat-functions.adoc

@@ -2,7 +2,7 @@
 
 Function calling lets developers create a description of a function in their code, then pass that description to a language model in a request. The response from the model includes the name of a function that matches the description and the arguments to call it with.
 
-You can register custom Java functions with the `AzureOpenAiChatClient` and have the model intelligently choose to output a JSON object containing arguments to call one or many of the registered functions.
+You can register custom Java functions with the `AzureOpenAiChatConnector` and have the model intelligently choose to output a JSON object containing arguments to call one or many of the registered functions.
 This allows you to connect the LLM capabilities with external tools and APIs.
 The Azure models are trained to detect when a function should be called and to respond with JSON that adheres to the function signature.
 
@@ -16,7 +16,7 @@ In general, the custom functions need to provide a function `name`, `description
 As a developer, you need to implement a function that takes the function call arguments sent from the AI model, and respond with the result back to the model.
 Your function can in turn invoke other 3rd party services to provide the results.
 
-Spring AI makes this as easy as defining a `@Bean` definition that returns a `java.util.Function` and supplying the bean name as an option when invoking the `ChatClient`.
+Spring AI makes this as easy as defining a `@Bean` definition that returns a `java.util.Function` and supplying the bean name as an option when invoking the `ChatConnector`.
 
 Under the hood, Spring wraps your POJO (the function) with the appropriate adapter code that enables interaction with the AI Model, saving you from writing tedious boilerplate code.
 The basis of the underlying infrastructure is the link:https://github.com/spring-projects/spring-ai/blob/main/spring-ai-core/src/main/java/org/springframework/ai/model/function/FunctionCallback.java[FunctionCallback.java] interface and the companion link:https://github.com/spring-projects/spring-ai/blob/main/spring-ai-core/src/main/java/org/springframework/ai/model/function/FunctionCallbackWrapper.java[FunctionCallbackWrapper.java] utility class to simplify the implementation and registration of Java callback functions.
@@ -70,7 +70,7 @@ We start with describing the most POJO friendly options.
 
 In this approach you define `@Beans` in your application context as you would any other Spring managed object.
 
-Internally, Spring AI `ChatClient` will create an instance of a `FunctionCallbackWrapper` wrapper that adds the logic for it being invoked via the AI model.
+Internally, Spring AI `ChatConnector` will create an instance of a `FunctionCallbackWrapper` wrapper that adds the logic for it being invoked via the AI model.
 The name of the `@Bean` is passed as a `ChatOption`.
 
 
@@ -156,7 +156,7 @@ ChatResponse response = chatClient.call(new Prompt(List.of(userMessage),
 logger.info("Response: {}", response);
 ----
 
-// NOTE: You can can have multiple functions registered in your `ChatClient` but only those enabled in the prompt request will be considered for the function calling.
+// NOTE: You can can have multiple functions registered in your `ChatConnector` but only those enabled in the prompt request will be considered for the function calling.
 
 Above user question will trigger 3 calls to `CurrentWeather` function (one for each city) and the final response will be something like this:
 
@@ -194,5 +194,5 @@ NOTE: The in-prompt registered functions are enabled by default for the duration
 
 This approach allows to dynamically chose different functions to be called based on the user input.
 
-The https://github.com/spring-projects/spring-ai/blob/main/spring-ai-spring-boot-autoconfigure/src/test/java/org/springframework/ai/autoconfigure/azure/tool/FunctionCallWithPromptFunctionIT.java[FunctionCallWithPromptFunctionIT.java] integration test provides a complete example of how to register a function with the `AzureOpenAiChatClient` and use it in a prompt request.
+The https://github.com/spring-projects/spring-ai/blob/main/spring-ai-spring-boot-autoconfigure/src/test/java/org/springframework/ai/autoconfigure/azure/tool/FunctionCallWithPromptFunctionIT.java[FunctionCallWithPromptFunctionIT.java] integration test provides a complete example of how to register a function with the `AzureOpenAiChatConnector` and use it in a prompt request.
 

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/functions/mistralai-chat-functions.adoc

@@ -1,6 +1,6 @@
 = Mistral AI Function Calling
 
-You can register custom Java functions with the `MistralAiChatClient` and have the Mistral AI models intelligently choose to output a JSON object containing arguments to call one or many of the registered functions.
+You can register custom Java functions with the `MistralAiChatConnector` and have the Mistral AI models intelligently choose to output a JSON object containing arguments to call one or many of the registered functions.
 This allows you to connect the LLM capabilities with external tools and APIs.
 The `open-mixtral-8x22b`, `mistral_small_latest`, and `mistral_large_latest` models are trained to detect when a function should be called and to respond with JSON that adheres to the function signature.
 
@@ -15,7 +15,7 @@ The `description` helps the model to understand when to call the function.
 As a developer, you need to implement a function that takes the function call arguments sent from the AI model, and respond with the result back to the model.
 Your function can in turn invoke other 3rd party services to provide the results.
 
-Spring AI makes this as easy as defining a `@Bean` definition that returns a `java.util.Function` and supplying the bean name as an option when invoking the `ChatClient`.
+Spring AI makes this as easy as defining a `@Bean` definition that returns a `java.util.Function` and supplying the bean name as an option when invoking the `ChatConnector`.
 
 Under the hood, Spring wraps your POJO (the function) with the appropriate adapter code that enables interaction with the AI Model, saving you from writing tedious boilerplate code.
 The basis of the underlying infrastructure is the link:https://github.com/spring-projects/spring-ai/blob/main/spring-ai-core/src/main/java/org/springframework/ai/model/function/FunctionCallback.java[FunctionCallback.java] interface and the companion link:https://github.com/spring-projects/spring-ai/blob/main/spring-ai-core/src/main/java/org/springframework/ai/model/function/FunctionCallbackWrapper.java[FunctionCallbackWrapper.java] utility class to simplify the implementation and registration of Java callback functions.
@@ -70,7 +70,7 @@ We start with describing the most POJO friendly options.
 
 In this approach you define `@Beans` in your application context as you would any other Spring managed object.
 
-Internally, Spring AI `ChatClient` will create an instance of a `FunctionCallbackWrapper` wrapper that adds the logic for it being invoked via the AI model.
+Internally, Spring AI `ChatConnector` will create an instance of a `FunctionCallbackWrapper` wrapper that adds the logic for it being invoked via the AI model.
 The name of the `@Bean` is passed as a `ChatOption`.
 
 
@@ -139,7 +139,7 @@ static class Config {
 }
 ----
 
-It wraps the 3rd party `MockWeatherService` function and registers it as a `CurrentWeather` function with the `MistralAiChatClient`.
+It wraps the 3rd party `MockWeatherService` function and registers it as a `CurrentWeather` function with the `MistralAiChatConnector`.
 It also provides a description (2) and an optional response converter (3) to convert the response into a text as expected by the model.
 
 NOTE: By default, the response converter does a JSON serialization of the Response object.
@@ -162,7 +162,7 @@ ChatResponse response = chatClient.call(new Prompt(List.of(userMessage),
 logger.info("Response: {}", response);
 ----
 
-// NOTE: You can can have multiple functions registered in your `ChatClient` but only those enabled in the prompt request will be considered for the function calling.
+// NOTE: You can can have multiple functions registered in your `ChatConnector` but only those enabled in the prompt request will be considered for the function calling.
 
 Above user question will trigger 3 calls to `CurrentWeather` function (one for each city) and produce the final response.
 
@@ -190,7 +190,7 @@ NOTE: The in-prompt registered functions are enabled by default for the duration
 
 This approach allows to dynamically chose different functions to be called based on the user input.
 
-The https://github.com/spring-projects/spring-ai/blob/main/spring-ai-spring-boot-autoconfigure/src/test/java/org/springframework/ai/autoconfigure/mistralai/tool/PaymentStatusPromptIT.java[PaymentStatusPromptIT.java] integration test provides a complete example of how to register a function with the `MistralAiChatClient` and use it in a prompt request.
+The https://github.com/spring-projects/spring-ai/blob/main/spring-ai-spring-boot-autoconfigure/src/test/java/org/springframework/ai/autoconfigure/mistralai/tool/PaymentStatusPromptIT.java[PaymentStatusPromptIT.java] integration test provides a complete example of how to register a function with the `MistralAiChatConnector` and use it in a prompt request.
 
 
 == Appendices

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/functions/openai-chat-functions.adoc

@@ -1,6 +1,6 @@
 = Function Calling
 
-You can register custom Java functions with the `OpenAiChatClient` and have the OpenAI model intelligently choose to output a JSON object containing arguments to call one or many of the registered functions.
+You can register custom Java functions with the `OpenAiChatConnector` and have the OpenAI model intelligently choose to output a JSON object containing arguments to call one or many of the registered functions.
 This allows you to connect the LLM capabilities with external tools and APIs.
 The OpenAI models are trained to detect when a function should be called and to respond with JSON that adheres to the function signature.
 
@@ -11,12 +11,12 @@ In general, the custom functions need to provide a function `name`,  `descriptio
 
 As a developer, you need to implement a function that takes the function call arguments sent from the AI model, and respond with the result back to the model.  Your function can in turn invoke other 3rd party services to provide the results.
 
-Spring AI makes this as easy as defining a `@Bean` definition that returns a `java.util.Function` and supplying the bean name as an option when invoking the `ChatClient`.
+Spring AI makes this as easy as defining a `@Bean` definition that returns a `java.util.Function` and supplying the bean name as an option when invoking the `ChatConnector`.
 
 Under the hood, Spring wraps your POJO (the function) with the appropriate adapter code that enables interaction with the AI Model, saving you from writing tedious boilerplate code.
 The basis of the underlying infrastructure is the link:https://github.com/spring-projects/spring-ai/blob/main/spring-ai-core/src/main/java/org/springframework/ai/model/function/FunctionCallback.java[FunctionCallback.java] interface and the companion link:https://github.com/spring-projects/spring-ai/blob/main/spring-ai-core/src/main/java/org/springframework/ai/model/function/FunctionCallbackWrapper.java[FunctionCallbackWrapper.java] utility class to simplify the implementation and registration of Java callback functions.
 
-// Additionally, the Auto-Configuration provides a way to auto-register any Function<I, O> beans definition as function calling candidates in the `ChatClient`.
+// Additionally, the Auto-Configuration provides a way to auto-register any Function<I, O> beans definition as function calling candidates in the `ChatConnector`.
 
 
 == How it works
@@ -71,7 +71,7 @@ We start with describing the most POJO friendly options.
 
 In this approach you define `@Beans` in your application context as you would any other Spring managed object.
 
-Internally, Spring AI `ChatClient` will create an instance of a `FunctionCallbackWrapper` wrapper that adds the logic for it being invoked via the AI model.
+Internally, Spring AI `ChatConnector` will create an instance of a `FunctionCallbackWrapper` wrapper that adds the logic for it being invoked via the AI model.
 The name of the `@Bean` is passed as a `ChatOption`.
 
 
@@ -136,7 +136,7 @@ static class Config {
 }
 ----
 
-It wraps the 3rd party `MockWeatherService` function and registers it as a `CurrentWeather` function with the `OpenAiChatClient`.
+It wraps the 3rd party `MockWeatherService` function and registers it as a `CurrentWeather` function with the `OpenAiChatConnector`.
 It also provides a description (2) and an optional response converter (3) to convert the response into a text as expected by the model.
 
 NOTE: By default, the response converter does a JSON serialization of the Response object.
@@ -159,7 +159,7 @@ ChatResponse response = chatClient.call(new Prompt(List.of(userMessage),
 logger.info("Response: {}", response);
 ----
 
-// NOTE: You can can have multiple functions registered in your `ChatClient` but only those enabled in the prompt request will be considered for the function calling.
+// NOTE: You can can have multiple functions registered in your `ChatConnector` but only those enabled in the prompt request will be considered for the function calling.
 
 Above user question will trigger 3 calls to `CurrentWeather` function (one for each city) and the final response will be something like this:
 
@@ -197,11 +197,11 @@ NOTE: The in-prompt registered functions are enabled by default for the duration
 
 This approach allows to dynamically chose different functions to be called based on the user input.
 
-The https://github.com/spring-projects/spring-ai/blob/main/spring-ai-spring-boot-autoconfigure/src/test/java/org/springframework/ai/autoconfigure/openai/tool/FunctionCallbackInPromptIT.java[FunctionCallbackInPromptIT.java] integration test provides a complete example of how to register a function with the `OpenAiChatClient` and use it in a prompt request.
+The https://github.com/spring-projects/spring-ai/blob/main/spring-ai-spring-boot-autoconfigure/src/test/java/org/springframework/ai/autoconfigure/openai/tool/FunctionCallbackInPromptIT.java[FunctionCallbackInPromptIT.java] integration test provides a complete example of how to register a function with the `OpenAiChatConnector` and use it in a prompt request.
 //
 // === Register Functions with Default Options
 //
-// You can programmatically register functions with the `OpenAiChatClient` using the `OpenAiChatOptions#withFunctionCallbacks`:
+// You can programmatically register functions with the `OpenAiChatConnector` using the `OpenAiChatOptions#withFunctionCallbacks`:
 //
 // [source,java]
 // ----
@@ -215,7 +215,7 @@ The https://github.com/spring-projects/spring-ai/blob/main/spring-ai-spring-boot
 // 		new MockWeatherService()))) // function code
 // 	.build();
 //
-// OpenAiChatClient chatClient = new OpenAiChatClient(openaiApi, defaultOptions);
+// OpenAiChatConnector chatClient = new OpenAiChatConnector(openaiApi, defaultOptions);
 //
 // UserMessage userMessage = new UserMessage("What's the weather like in San Francisco, Tokyo, and Paris?");
 //
@@ -223,7 +223,7 @@ The https://github.com/spring-projects/spring-ai/blob/main/spring-ai-spring-boot
 // 		OpenAiChatOptions.builder().withFunction("CurrentWeather").build())); // Enable the function
 // ----
 //
-// NOTE: Functions are registered when OpenAiChatClient is created, by you must enable in the Prompt the functions to be used in the request.
+// NOTE: Functions are registered when OpenAiChatConnector is created, by you must enable in the Prompt the functions to be used in the request.
 
 
 == Appendices:

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/functions/vertexai-gemini-chat-functions.adoc

@@ -6,7 +6,7 @@ The parallel function calling is gone as well.
 
 Function calling lets developers create a description of a function in their code, then pass that description to a language model in a request. The response from the model includes the name of a function that matches the description and the arguments to call it with.
 
-You can register custom Java functions with the `VertexAiGeminiChatClient` and have the Gemini Pro model intelligently choose to output a JSON object containing arguments to call one or many of the registered functions.
+You can register custom Java functions with the `VertexAiGeminiChatConnector` and have the Gemini Pro model intelligently choose to output a JSON object containing arguments to call one or many of the registered functions.
 This allows you to connect the LLM capabilities with external tools and APIs.
 The VertexAI Gemini Pro model is trained to detect when a function should be called and to respond with JSON that adheres to the function signature.
 
@@ -18,12 +18,12 @@ In general, the custom functions need to provide a function `name`, `description
 As a developer, you need to implement a function that takes the function call arguments sent from the AI model, and respond with the result back to the model.
 Your function can in turn invoke other 3rd party services to provide the results.
 
-Spring AI makes this as easy as defining a `@Bean` definition that returns a `java.util.Function` and supplying the bean name as an option when invoking the `ChatClient`.
+Spring AI makes this as easy as defining a `@Bean` definition that returns a `java.util.Function` and supplying the bean name as an option when invoking the `ChatConnector`.
 
 Under the hood, Spring wraps your POJO (the function) with the appropriate adapter code that enables interaction with the AI Model, saving you from writing tedious boilerplate code.
 The basis of the underlying infrastructure is the link:https://github.com/spring-projects/spring-ai/blob/main/spring-ai-core/src/main/java/org/springframework/ai/model/function/FunctionCallback.java[FunctionCallback.java] interface and the companion link:https://github.com/spring-projects/spring-ai/blob/main/spring-ai-core/src/main/java/org/springframework/ai/model/function/FunctionCallbackWrapper.java[FunctionCallbackWrapper.java] utility class to simplify the implementation and registration of Java callback functions.
 
-// Additionally, the Auto-Configuration provides a way to auto-register any Function<I, O> beans definition as function calling candidates in the `ChatClient`.
+// Additionally, the Auto-Configuration provides a way to auto-register any Function<I, O> beans definition as function calling candidates in the `ChatConnector`.
 
 == How it works
 
@@ -74,7 +74,7 @@ We start with describing the most POJO friendly options.
 
 In this approach you define `@Beans` in your application context as you would any other Spring managed object.
 
-Internally, Spring AI `ChatClient` will create an instance of a `FunctionCallbackWrapper` wrapper that adds the logic for it being invoked via the AI model.
+Internally, Spring AI `ChatConnector` will create an instance of a `FunctionCallbackWrapper` wrapper that adds the logic for it being invoked via the AI model.
 The name of the `@Bean` is passed as a `ChatOption`.
 
 
@@ -139,7 +139,7 @@ static class Config {
 }
 ----
 
-It wraps the 3rd party `MockWeatherService` function and registers it as a `CurrentWeather` function with the `VertexAiGeminiChatClient`.
+It wraps the 3rd party `MockWeatherService` function and registers it as a `CurrentWeather` function with the `VertexAiGeminiChatConnector`.
 It also provides a description (2) and sets the Schema type to Open API type (3).
 
 NOTE: The default response converter does a JSON serialization of the Response object.
@@ -162,7 +162,7 @@ ChatResponse response = chatClient.call(new Prompt(List.of(userMessage),
 logger.info("Response: {}", response);
 ----
 
-// NOTE: You can can have multiple functions registered in your `ChatClient` but only those enabled in the prompt request will be considered for the function calling.
+// NOTE: You can can have multiple functions registered in your `ChatConnector` but only those enabled in the prompt request will be considered for the function calling.
 
 Above user question will trigger 3 calls to `CurrentWeather` function (one for each city) and the final response will be something like this:
 
@@ -201,5 +201,5 @@ NOTE: The in-prompt registered functions are enabled by default for the duration
 
 This approach allows to dynamically chose different functions to be called based on the user input.
 
-The https://github.com/spring-projects/spring-ai/blob/main/spring-ai-spring-boot-autoconfigure/src/test/java/org/springframework/ai/autoconfigure/gemini/tool/FunctionCallWithPromptFunctionIT.java[FunctionCallWithPromptFunctionIT.java] integration test provides a complete example of how to register a function with the `VertexAiGeminiChatClient` and use it in a prompt request.
+The https://github.com/spring-projects/spring-ai/blob/main/spring-ai-spring-boot-autoconfigure/src/test/java/org/springframework/ai/autoconfigure/gemini/tool/FunctionCallWithPromptFunctionIT.java[FunctionCallWithPromptFunctionIT.java] integration test provides a complete example of how to register a function with the `VertexAiGeminiChatConnector` and use it in a prompt request.
 

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/mistralai-chat.adoc

@@ -103,7 +103,7 @@ The prefix `spring.ai.mistralai.chat` is the property prefix that lets you confi
 | spring.ai.mistralai.chat.options.functionCallbacks | MistralAI Tool Function Callbacks to register with the ChatClient. | -
 |====
 
-NOTE: You can override the common `spring.ai.mistralai.base-url` and `spring.ai.mistralai.api-key` for the `ChatClient` and `EmbeddingClient` implementations.
+NOTE: You can override the common `spring.ai.mistralai.base-url` and `spring.ai.mistralai.api-key` for the `ChatConnector` and `EmbeddingClient` implementations.
 The `spring.ai.mistralai.chat.base-url` and `spring.ai.mistralai.chat.api-key` properties if set take precedence over the common properties.
 This is useful if you want to use different MistralAI accounts for different models and different model endpoints.
 
@@ -153,7 +153,7 @@ spring.ai.mistralai.chat.options.temperature=0.7
 
 TIP: replace the `api-key` with your OpenAI credentials.
 
-This will create a `MistralAiChatClient` implementation that you can inject into your class.
+This will create a `MistralAiChatConnector` implementation that you can inject into your class.
 Here is an example of a simple `@Controller` class that uses the chat client for text generations.
 
 [source,java]
@@ -183,7 +183,7 @@ public class ChatController {
 
 == Manual Configuration
 
-The link:https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-mistral-ai/src/main/java/org/springframework/ai/mistralai/MistralAiChatClient.java[MistralAiChatClient] implements the `ChatClient` and `StreamingChatClient` and uses the <<low-level-api>> to connect to the MistralAI service.
+The link:https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-mistral-ai/src/main/java/org/springframework/ai/mistralai/MistralAiChatClient.java[MistralAiChatClient] implements the `ChatConnector` and `StreamingChatClient` and uses the <<low-level-api>> to connect to the MistralAI service.
 
 Add the `spring-ai-mistral-ai` dependency to your project's Maven `pom.xml` file:
 
@@ -206,7 +206,7 @@ dependencies {
 
 TIP: Refer to the xref:getting-started.adoc#dependency-management[Dependency Management] section to add the Spring AI BOM to your build file.
 
-Next, create a `MistralAiChatClient` and use it for text generations:
+Next, create a `MistralAiChatConnector` and use it for text generations:
 
 [source,java]
 ----

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/ollama-chat.adoc

@@ -1,7 +1,7 @@
 = Ollama Chat
 
 With https://ollama.ai/[Ollama] you can run various Large Language Models (LLMs) locally and generate text from them.
-Spring AI supports the Ollama text generation with `OllamaChatClient`.
+Spring AI supports the Ollama text generation with `OllamaChatConnector`.
 
 == Prerequisites
 
@@ -185,7 +185,7 @@ spring.ai.ollama.chat.options.temperature=0.7
 
 TIP: replace the `base-url` with your Ollama server URL.
 
-This will create a `OllamaChatClient` implementation that you can inject into your class.
+This will create a `OllamaChatConnector` implementation that you can inject into your class.
 Here is an example of a simple `@Controller` class that uses the chat client for text generations.
 
 [source,java]
@@ -216,8 +216,8 @@ public class ChatController {
 
 == Manual Configuration
 
-If you don't want to use the Spring Boot auto-configuration, you can manually configure the `OllamaChatClient` in your application.
-The https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-ollama/src/main/java/org/springframework/ai/ollama/OllamaChatClient.java[OllamaChatClient] implements the `ChatClient` and `StreamingChatClient` and uses the <<low-level-api>> to connect to the Ollama service.
+If you don't want to use the Spring Boot auto-configuration, you can manually configure the `OllamaChatConnector` in your application.
+The https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-ollama/src/main/java/org/springframework/ai/ollama/OllamaChatClient.java[OllamaChatClient] implements the `ChatConnector` and `StreamingChatClient` and uses the <<low-level-api>> to connect to the Ollama service.
 
 To use it add the `spring-ai-ollama` dependency to your project's Maven `pom.xml` file:
 
@@ -243,7 +243,7 @@ TIP: Refer to the xref:getting-started.adoc#dependency-management[Dependency Man
 TIP: The `spring-ai-ollama` dependency provides access also to the `OllamaEmbeddingClient`.
 For more information about the `OllamaEmbeddingClient` refer to the link:../embeddings/ollama-embeddings.html[Ollama Embedding Client] section.
 
-Next, create an `OllamaChatClient` instance and use it to text generations requests:
+Next, create an `OllamaChatConnector` instance and use it to text generations requests:
 
 [source,java]
 ----
@@ -274,7 +274,7 @@ image::ollama-chat-completion-api.jpg[OllamaApi Chat Completion API Diagram, 800
 
 Here is a simple snippet showing how to use the API programmatically:
 
-NOTE: The `OllamaApi` is low level api and is not recommended for direct use. Use the `OllamaChatClient` instead.
+NOTE: The `OllamaApi` is low level api and is not recommended for direct use. Use the `OllamaChatConnector` instead.
 
 [source,java]
 ----

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/openai-chat.adoc

@@ -107,7 +107,7 @@ The prefix `spring.ai.openai.chat` is the property prefix that lets you configur
 | spring.ai.openai.chat.options.functions | List of functions, identified by their names, to enable for function calling in a single prompt requests. Functions with those names must exist in the functionCallbacks registry. | -
 |====
 
-NOTE: You can override the common `spring.ai.openai.base-url` and `spring.ai.openai.api-key` for the `ChatClient` and `EmbeddingClient` implementations.
+NOTE: You can override the common `spring.ai.openai.base-url` and `spring.ai.openai.api-key` for the `ChatConnector` and `EmbeddingClient` implementations.
 The `spring.ai.openai.chat.base-url` and `spring.ai.openai.chat.api-key` properties if set take precedence over the common properties.
 This is useful if you want to use different OpenAI accounts for different models and different model endpoints.
 
@@ -209,7 +209,7 @@ spring.ai.openai.chat.options.temperature=0.7
 
 TIP: replace the `api-key` with your OpenAI credentials.
 
-This will create a `OpenAiChatClient` implementation that you can inject into your class.
+This will create a `OpenAiChatConnector` implementation that you can inject into your class.
 Here is an example of a simple `@Controller` class that uses the chat client for text generations.
 
 [source,java]
@@ -239,7 +239,7 @@ public class ChatController {
 
 == Manual Configuration
 
-The https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-openai/src/main/java/org/springframework/ai/openai/OpenAiChatClient.java[OpenAiChatClient] implements the `ChatClient` and `StreamingChatClient` and uses the <<low-level-api>> to connect to the OpenAI service.
+The https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-openai/src/main/java/org/springframework/ai/openai/OpenAiChatClient.java[OpenAiChatClient] implements the `ChatConnector` and `StreamingChatClient` and uses the <<low-level-api>> to connect to the OpenAI service.
 
 Add the `spring-ai-openai` dependency to your project's Maven `pom.xml` file:
 
@@ -262,7 +262,7 @@ dependencies {
 
 TIP: Refer to the xref:getting-started.adoc#dependency-management[Dependency Management] section to add the Spring AI BOM to your build file.
 
-Next, create a `OpenAiChatClient` and use it for text generations:
+Next, create a `OpenAiChatConnector` and use it for text generations:
 
 [source,java]
 ----

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/vertexai-gemini-chat.adoc

@@ -150,7 +150,7 @@ spring.ai.vertex.ai.gemini.chat.options.temperature=0.5
 
 TIP: replace the `api-key` with your VertexAI credentials.
 
-This will create a `VertexAiGeminiChatClient` implementation that you can inject into your class.
+This will create a `VertexAiGeminiChatConnector` implementation that you can inject into your class.
 Here is an example of a simple `@Controller` class that uses the chat client for text generations.
 
 [source,java]
@@ -180,7 +180,7 @@ public class ChatController {
 
 == Manual Configuration
 
-The https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-vertex-ai-gemini/src/main/java/org/springframework/ai/vertexai/gemini/VertexAiGeminiChatClient.java[VertexAiGeminiChatClient] implements the `ChatClient` and uses the `VertexAI` to connect to the Vertex AI Gemini service.
+The https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-vertex-ai-gemini/src/main/java/org/springframework/ai/vertexai/gemini/VertexAiGeminiChatClient.java[VertexAiGeminiChatClient] implements the `ChatConnector` and uses the `VertexAI` to connect to the Vertex AI Gemini service.
 
 Add the `spring-ai-vertex-ai-gemini` dependency to your project's Maven `pom.xml` file:
 
@@ -203,7 +203,7 @@ dependencies {
 
 TIP: Refer to the xref:getting-started.adoc#dependency-management[Dependency Management] section to add the Spring AI BOM to your build file.
 
-Next, create a `VertexAiGeminiChatClient` and use it for text generations:
+Next, create a `VertexAiGeminiChatConnector` and use it for text generations:
 
 [source,java]
 ----

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/vertexai-palm2-chat.adoc

@@ -114,7 +114,7 @@ spring.ai.vertex.ai.chat.options.temperature=0.5
 
 TIP: replace the `api-key` with your VertexAI credentials.
 
-This will create a `VertexAiPaLm2ChatClient` implementation that you can inject into your class.
+This will create a `VertexAiPaLm2ChatConnector` implementation that you can inject into your class.
 Here is an example of a simple `@Controller` class that uses the chat client for text generations.
 
 [source,java]
@@ -144,7 +144,7 @@ public class ChatController {
 
 == Manual Configuration
 
-The https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-openai/src/main/java/org/springframework/ai/vertexai/paml2/VertexAiPaLm2ChatClient.java[VertexAiPaLm2ChatClient] implements the `ChatClient` and uses the <<low-level-api>> to connect to the VertexAI service.
+The https://github.com/spring-projects/spring-ai/blob/main/models/spring-ai-openai/src/main/java/org/springframework/ai/vertexai/paml2/VertexAiPaLm2ChatClient.java[VertexAiPaLm2ChatClient] implements the `ChatConnector` and uses the <<low-level-api>> to connect to the VertexAI service.
 
 Add the `spring-ai-vertex-ai-palm2` dependency to your project's Maven `pom.xml` file:
 
@@ -167,7 +167,7 @@ dependencies {
 
 TIP: Refer to the xref:getting-started.adoc#dependency-management[Dependency Management] section to add the Spring AI BOM to your build file.
 
-Next, create a `VertexAiPaLm2ChatClient` and use it for text generations:
+Next, create a `VertexAiPaLm2ChatConnector` and use it for text generations:
 
 [source,java]
 ----

spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/watsonx-ai-chat.adoc

@@ -1,7 +1,7 @@
 = watsonx.ai Chat
 
 With https://dataplatform.cloud.ibm.com/docs/content/wsj/getting-started/overview-wx.html?context=wx&audience=wdp[watsonx.ai] you can run various Large Language Models (LLMs) locally and generate text from them.
-Spring AI supports the watsonx.ai text generation with `WatsonxAiChatClient`.
+Spring AI supports the watsonx.ai text generation with `WatsonxAiChatConnector`.
 
 
 == Prerequisites