Spring/spring-ai
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

polish the building of messages

Browse Source
This commit is contained in:
Josh Long
2024-05-18 18:08:36 +02:00
parent 2c5ae0837f
commit 703e3d3a8f
157 changed files with 708 additions and 682 deletions
Download Patch File Download Diff File Expand all files Collapse all files

4
models/spring-ai-anthropic/src/main/java/org/springframework/ai/anthropic/AnthropicChatOptions.java
View File

@@ -51,11 +51,11 @@ public class AnthropicChatOptions implements ChatOptions, FunctionCallingOptions
private @JsonProperty("top_k") Integer topK;
/**
* Tool Function Callbacks to register with the ChatConnector. For Prompt
* Tool Function Callbacks to register with the ModelCall. 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.
* to be used by the ModelCall chat completion requests.
*/
@NestedConfigurationProperty
@JsonIgnore

26
models/spring-ai-anthropic/src/main/java/org/springframework/ai/anthropic/AnthropicChatConnector.java → models/spring-ai-anthropic/src/main/java/org/springframework/ai/anthropic/AnthropicModelCall.java
View File

@@ -26,7 +26,7 @@ import java.util.stream.Collectors;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.ai.chat.connector.ChatConnector;
import org.springframework.ai.chat.connector.ModelCall;
import reactor.core.publisher.Flux;
import org.springframework.ai.anthropic.api.AnthropicApi;
@@ -56,16 +56,16 @@ import org.springframework.util.Assert;
import org.springframework.util.CollectionUtils;
/**
* The {@link ChatConnector} implementation for the Anthropic service.
* The {@link ModelCall} implementation for the Anthropic service.
*
* @author Christian Tzolov
* @since 1.0.0
*/
public class AnthropicChatConnector extends
public class AnthropicModelCall extends
AbstractFunctionCallSupport<AnthropicApi.RequestMessage, AnthropicApi.ChatCompletionRequest, ResponseEntity<AnthropicApi.ChatCompletion>>
implements ChatConnector, StreamingChatClient {
implements ModelCall, StreamingChatClient {
private static final Logger logger = LoggerFactory.getLogger(AnthropicChatConnector.class);
private static final Logger logger = LoggerFactory.getLogger(AnthropicModelCall.class);
public static final String DEFAULT_MODEL_NAME = AnthropicApi.ChatModel.CLAUDE_3_OPUS.getValue();
@@ -89,10 +89,10 @@ public class AnthropicChatConnector extends
public final RetryTemplate retryTemplate;
/**
* Construct a new {@link AnthropicChatConnector} instance.
* Construct a new {@link AnthropicModelCall} instance.
* @param anthropicApi the lower-level API for the Anthropic service.
*/
public AnthropicChatConnector(AnthropicApi anthropicApi) {
public AnthropicModelCall(AnthropicApi anthropicApi) {
this(anthropicApi,
AnthropicChatOptions.builder()
.withModel(DEFAULT_MODEL_NAME)
@@ -102,34 +102,34 @@ public class AnthropicChatConnector extends
}
/**
* Construct a new {@link AnthropicChatConnector} instance.
* Construct a new {@link AnthropicModelCall} instance.
* @param anthropicApi the lower-level API for the Anthropic service.
* @param defaultOptions the default options used for the chat completion requests.
*/
public AnthropicChatConnector(AnthropicApi anthropicApi, AnthropicChatOptions defaultOptions) {
public AnthropicModelCall(AnthropicApi anthropicApi, AnthropicChatOptions defaultOptions) {
this(anthropicApi, defaultOptions, RetryUtils.DEFAULT_RETRY_TEMPLATE);
}
/**
* Construct a new {@link AnthropicChatConnector} instance.
* Construct a new {@link AnthropicModelCall} 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 AnthropicChatConnector(AnthropicApi anthropicApi, AnthropicChatOptions defaultOptions,
public AnthropicModelCall(AnthropicApi anthropicApi, AnthropicChatOptions defaultOptions,
RetryTemplate retryTemplate) {
this(anthropicApi, defaultOptions, retryTemplate, null);
}
/**
* Construct a new {@link AnthropicChatConnector} instance.
* Construct a new {@link AnthropicModelCall} 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 AnthropicChatConnector(AnthropicApi anthropicApi, AnthropicChatOptions defaultOptions,
public AnthropicModelCall(AnthropicApi anthropicApi, AnthropicChatOptions defaultOptions,
RetryTemplate retryTemplate, FunctionCallbackContext functionCallbackContext) {
super(functionCallbackContext);

20
models/spring-ai-anthropic/src/test/java/org/springframework/ai/anthropic/AnthropicChatConnectorIT.java → models/spring-ai-anthropic/src/test/java/org/springframework/ai/anthropic/AnthropicModelCallIT.java
View File

@@ -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.connector.ChatConnector;
import org.springframework.ai.chat.connector.ModelCall;
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 AnthropicChatConnectorIT {
class AnthropicModelCallIT {
private static final Logger logger = LoggerFactory.getLogger(AnthropicChatConnectorIT.class);
private static final Logger logger = LoggerFactory.getLogger(AnthropicModelCallIT.class);
@Autowired
protected ChatConnector chatConnector;
protected ModelCall modelCall;
@Autowired
protected StreamingChatClient streamingChatClient;
@@ -76,7 +76,7 @@ class AnthropicChatConnectorIT {
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 = chatConnector.call(prompt);
ChatResponse response = modelCall.call(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 AnthropicChatConnectorIT {
PromptTemplate promptTemplate = new PromptTemplate(template,
Map.of("subject", "ice cream flavors", "format", format));
Prompt prompt = new Prompt(promptTemplate.createMessage());
Generation generation = this.chatConnector.call(prompt).getResult();
Generation generation = this.modelCall.call(prompt).getResult();
List<String> list = listOutputConverter.convert(generation.getOutput().getContent());
assertThat(list).hasSize(5);
@@ -120,7 +120,7 @@ class AnthropicChatConnectorIT {
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 = chatConnector.call(prompt).getResult();
Generation generation = modelCall.call(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 AnthropicChatConnectorIT {
""";
PromptTemplate promptTemplate = new PromptTemplate(template, Map.of("format", format));
Prompt prompt = new Prompt(promptTemplate.createMessage());
Generation generation = chatConnector.call(prompt).getResult();
Generation generation = modelCall.call(prompt).getResult();
ActorsFilmsRecord actorsFilms = beanOutputConverter.convert(generation.getOutput().getContent());
logger.info("" + actorsFilms);
@@ -187,7 +187,7 @@ class AnthropicChatConnectorIT {
var userMessage = new UserMessage("Explain what do you see on this picture?",
List.of(new Media(MimeTypeUtils.IMAGE_PNG, imageData)));
var response = chatConnector.call(new Prompt(List.of(userMessage)));
var response = modelCall.call(new Prompt(List.of(userMessage)));
logger.info(response.getResult().getOutput().getContent());
assertThat(response.getResult().getOutput().getContent()).contains("bananas", "apple", "basket");
@@ -209,7 +209,7 @@ class AnthropicChatConnectorIT {
.build()))
.build();
ChatResponse response = chatConnector.call(new Prompt(messages, promptOptions));
ChatResponse response = modelCall.call(new Prompt(messages, promptOptions));
logger.info("Response: {}", response);

4
models/spring-ai-anthropic/src/test/java/org/springframework/ai/anthropic/AnthropicTestConfiguration.java
View File

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

2
models/spring-ai-anthropic/src/test/java/org/springframework/ai/anthropic/ChatCompletionRequestTests.java
View File

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

10
models/spring-ai-azure-openai/src/main/java/org/springframework/ai/azure/openai/AzureOpenAiChatOptions.java
View File

@@ -127,11 +127,11 @@ public class AzureOpenAiChatOptions implements FunctionCallingOptions, ChatOptio
private String deploymentName;
/**
* 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.
* OpenAI Tool Function Callbacks to register with the ModelCall. 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 ModelCall chat completion requests.
*/
@NestedConfigurationProperty
@JsonIgnore

20
models/spring-ai-azure-openai/src/main/java/org/springframework/ai/azure/openai/AzureOpenAiChatConnector.java → models/spring-ai-azure-openai/src/main/java/org/springframework/ai/azure/openai/AzureOpenAiModelCall.java
View File

@@ -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.connector.ChatConnector;
import org.springframework.ai.chat.connector.ModelCall;
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 ChatConnector} implementation for {@literal Microsoft Azure AI} backed by
* {@link ModelCall} 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 ChatConnector
* @see ModelCall
* @see com.azure.ai.openai.OpenAIClient
*/
public class AzureOpenAiChatConnector
public class AzureOpenAiModelCall
extends AbstractFunctionCallSupport<ChatRequestMessage, ChatCompletionsOptions, ChatCompletions>
implements ChatConnector, StreamingChatClient {
implements ModelCall, StreamingChatClient {
private static final String DEFAULT_DEPLOYMENT_NAME = "gpt-35-turbo";
@@ -94,7 +94,7 @@ public class AzureOpenAiChatConnector
*/
private final OpenAIClient openAIClient;
public AzureOpenAiChatConnector(OpenAIClient microsoftOpenAiClient) {
public AzureOpenAiModelCall(OpenAIClient microsoftOpenAiClient) {
this(microsoftOpenAiClient,
AzureOpenAiChatOptions.builder()
.withDeploymentName(DEFAULT_DEPLOYMENT_NAME)
@@ -102,11 +102,11 @@ public class AzureOpenAiChatConnector
.build());
}
public AzureOpenAiChatConnector(OpenAIClient microsoftOpenAiClient, AzureOpenAiChatOptions options) {
public AzureOpenAiModelCall(OpenAIClient microsoftOpenAiClient, AzureOpenAiChatOptions options) {
this(microsoftOpenAiClient, options, null);
}
public AzureOpenAiChatConnector(OpenAIClient microsoftOpenAiClient, AzureOpenAiChatOptions options,
public AzureOpenAiModelCall(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 AzureOpenAiChatConnector
/**
* @deprecated since 0.8.0, use
* {@link #AzureOpenAiChatConnector(OpenAIClient, AzureOpenAiChatOptions)} instead.
* {@link #AzureOpenAiModelCall(OpenAIClient, AzureOpenAiChatOptions)} instead.
*/
@Deprecated(forRemoval = true, since = "0.8.0")
public AzureOpenAiChatConnector withDefaultOptions(AzureOpenAiChatOptions defaultOptions) {
public AzureOpenAiModelCall withDefaultOptions(AzureOpenAiChatOptions defaultOptions) {
Assert.notNull(defaultOptions, "DefaultOptions must not be null");
this.defaultOptions = defaultOptions;
return this;

2
models/spring-ai-azure-openai/src/test/java/org/springframework/ai/azure/openai/AzureChatCompletionsOptionsTests.java
View File

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

10
models/spring-ai-azure-openai/src/test/java/org/springframework/ai/azure/openai/AzureOpenAiChatConnectorIT.java → models/spring-ai-azure-openai/src/test/java/org/springframework/ai/azure/openai/AzureOpenAiModelCallIT.java
View File

@@ -46,13 +46,13 @@ import org.springframework.core.convert.support.DefaultConversionService;
import static org.assertj.core.api.Assertions.assertThat;
@SpringBootTest(classes = AzureOpenAiChatConnectorIT.TestConfiguration.class)
@SpringBootTest(classes = AzureOpenAiModelCallIT.TestConfiguration.class)
@EnabledIfEnvironmentVariable(named = "AZURE_OPENAI_API_KEY", matches = ".+")
@EnabledIfEnvironmentVariable(named = "AZURE_OPENAI_ENDPOINT", matches = ".+")
class AzureOpenAiChatConnectorIT {
class AzureOpenAiModelCallIT {
@Autowired
private AzureOpenAiChatConnector chatClient;
private AzureOpenAiModelCall chatClient;
record ActorsFilms(String actor, List<String> movies) {
}
@@ -194,8 +194,8 @@ class AzureOpenAiChatConnectorIT {
}
@Bean
public AzureOpenAiChatConnector azureOpenAiChatClient(OpenAIClient openAIClient) {
return new AzureOpenAiChatConnector(openAIClient,
public AzureOpenAiModelCall azureOpenAiChatClient(OpenAIClient openAIClient) {
return new AzureOpenAiModelCall(openAIClient,
AzureOpenAiChatOptions.builder().withDeploymentName("gpt-35-turbo").withMaxTokens(200).build());
}

4
models/spring-ai-azure-openai/src/test/java/org/springframework/ai/azure/openai/MockAzureOpenAiTestConfiguration.java
View File

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

14
models/spring-ai-azure-openai/src/test/java/org/springframework/ai/azure/openai/function/AzureOpenAiChatConnectorFunctionCallIT.java → models/spring-ai-azure-openai/src/test/java/org/springframework/ai/azure/openai/function/AzureOpenAiModelCallFunctionCallIT.java
View File

@@ -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.AzureOpenAiChatConnector;
import org.springframework.ai.azure.openai.AzureOpenAiModelCall;
import org.springframework.ai.azure.openai.AzureOpenAiChatOptions;
import org.springframework.ai.chat.ChatResponse;
import org.springframework.ai.chat.Generation;
@@ -46,18 +46,18 @@ import reactor.core.publisher.Flux;
import static org.assertj.core.api.Assertions.assertThat;
@SpringBootTest(classes = AzureOpenAiChatConnectorFunctionCallIT.TestConfiguration.class)
@SpringBootTest(classes = AzureOpenAiModelCallFunctionCallIT.TestConfiguration.class)
@EnabledIfEnvironmentVariable(named = "AZURE_OPENAI_API_KEY", matches = ".+")
@EnabledIfEnvironmentVariable(named = "AZURE_OPENAI_ENDPOINT", matches = ".+")
class AzureOpenAiChatConnectorFunctionCallIT {
class AzureOpenAiModelCallFunctionCallIT {
private static final Logger logger = LoggerFactory.getLogger(AzureOpenAiChatConnectorFunctionCallIT.class);
private static final Logger logger = LoggerFactory.getLogger(AzureOpenAiModelCallFunctionCallIT.class);
@Autowired
private String selectedModel;
@Autowired
private AzureOpenAiChatConnector chatClient;
private AzureOpenAiModelCall chatClient;
@Test
void functionCallTest() {
@@ -129,8 +129,8 @@ class AzureOpenAiChatConnectorFunctionCallIT {
}
@Bean
public AzureOpenAiChatConnector azureOpenAiChatClient(OpenAIClient openAIClient, String selectedModel) {
return new AzureOpenAiChatConnector(openAIClient,
public AzureOpenAiModelCall azureOpenAiChatClient(OpenAIClient openAIClient, String selectedModel) {
return new AzureOpenAiModelCall(openAIClient,
AzureOpenAiChatOptions.builder().withDeploymentName(selectedModel).withMaxTokens(500).build());
}

10
models/spring-ai-azure-openai/src/test/java/org/springframework/ai/azure/openai/metadata/AzureOpenAiChatConnectorMetadataTests.java → models/spring-ai-azure-openai/src/test/java/org/springframework/ai/azure/openai/metadata/AzureOpenAiModelCallMetadataTests.java
View File

@@ -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.AzureOpenAiChatConnector;
import org.springframework.ai.azure.openai.AzureOpenAiModelCall;
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 AzureOpenAiChatConnector} asserting AI metadata.
* Unit Tests for {@link AzureOpenAiModelCall} asserting AI metadata.
*
* @author John Blum
* @author Christian Tzolov
@@ -63,12 +63,12 @@ import static org.assertj.core.api.Assertions.assertThat;
*/
@SpringBootTest
@ActiveProfiles("spring-ai-azure-openai-mocks")
@ContextConfiguration(classes = AzureOpenAiChatConnectorMetadataTests.TestConfiguration.class)
@ContextConfiguration(classes = AzureOpenAiModelCallMetadataTests.TestConfiguration.class)
@SuppressWarnings("unused")
class AzureOpenAiChatConnectorMetadataTests {
class AzureOpenAiModelCallMetadataTests {
@Autowired
private AzureOpenAiChatConnector aiClient;
private AzureOpenAiModelCall aiClient;
@Test
void azureOpenAiMetadataCapturedDuringGeneration() {

12
models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/anthropic/BedrockAnthropicChatConnector.java → models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/anthropic/BedrockAnthropicModelCall.java
View File

@@ -17,7 +17,7 @@ package org.springframework.ai.bedrock.anthropic;
import java.util.List;
import org.springframework.ai.chat.connector.ChatConnector;
import org.springframework.ai.chat.connector.ModelCall;
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 ChatConnector} and {@link StreamingChatClient} for the Bedrock Anthropic
* chat generative.
* Java {@link ModelCall} and {@link StreamingChatClient} for the Bedrock Anthropic chat
* generative.
*
* @author Christian Tzolov
* @since 0.8.0
*/
public class BedrockAnthropicChatConnector implements ChatConnector, StreamingChatClient {
public class BedrockAnthropicModelCall implements ModelCall, StreamingChatClient {
private final AnthropicChatBedrockApi anthropicChatApi;
private final AnthropicChatOptions defaultOptions;
public BedrockAnthropicChatConnector(AnthropicChatBedrockApi chatApi) {
public BedrockAnthropicModelCall(AnthropicChatBedrockApi chatApi) {
this(chatApi,
AnthropicChatOptions.builder()
.withTemperature(0.8f)
@@ -55,7 +55,7 @@ public class BedrockAnthropicChatConnector implements ChatConnector, StreamingCh
.build());
}
public BedrockAnthropicChatConnector(AnthropicChatBedrockApi chatApi, AnthropicChatOptions options) {
public BedrockAnthropicModelCall(AnthropicChatBedrockApi chatApi, AnthropicChatOptions options) {
this.anthropicChatApi = chatApi;
this.defaultOptions = options;
}

12
models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/anthropic3/BedrockAnthropic3ChatConnector.java → models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/anthropic3/BedrockAnthropic3ModelCall.java
View File

@@ -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.connector.ChatConnector;
import org.springframework.ai.chat.connector.ModelCall;
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 ChatConnector} and {@link StreamingChatClient} for the Bedrock Anthropic
* chat generative.
* Java {@link ModelCall} and {@link StreamingChatClient} for the Bedrock Anthropic chat
* generative.
*
* @author Ben Middleton
* @author Christian Tzolov
* @since 1.0.0
*/
public class BedrockAnthropic3ChatConnector implements ChatConnector, StreamingChatClient {
public class BedrockAnthropic3ModelCall implements ModelCall, StreamingChatClient {
private final Anthropic3ChatBedrockApi anthropicChatApi;
private final Anthropic3ChatOptions defaultOptions;
public BedrockAnthropic3ChatConnector(Anthropic3ChatBedrockApi chatApi) {
public BedrockAnthropic3ModelCall(Anthropic3ChatBedrockApi chatApi) {
this(chatApi,
Anthropic3ChatOptions.builder()
.withTemperature(0.8f)
@@ -66,7 +66,7 @@ public class BedrockAnthropic3ChatConnector implements ChatConnector, StreamingC
.build());
}
public BedrockAnthropic3ChatConnector(Anthropic3ChatBedrockApi chatApi, Anthropic3ChatOptions options) {
public BedrockAnthropic3ModelCall(Anthropic3ChatBedrockApi chatApi, Anthropic3ChatOptions options) {
this.anthropicChatApi = chatApi;
this.defaultOptions = options;
}

8
models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/cohere/BedrockCohereChatConnector.java → models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/cohere/BedrockCohereModelCall.java
View File

@@ -17,7 +17,7 @@ package org.springframework.ai.bedrock.cohere;
import java.util.List;
import org.springframework.ai.chat.connector.ChatConnector;
import org.springframework.ai.chat.connector.ModelCall;
import reactor.core.publisher.Flux;
import org.springframework.ai.bedrock.BedrockUsage;
@@ -39,17 +39,17 @@ import org.springframework.util.Assert;
* @author Christian Tzolov
* @since 0.8.0
*/
public class BedrockCohereChatConnector implements ChatConnector, StreamingChatClient {
public class BedrockCohereModelCall implements ModelCall, StreamingChatClient {
private final CohereChatBedrockApi chatApi;
private final BedrockCohereChatOptions defaultOptions;
public BedrockCohereChatConnector(CohereChatBedrockApi chatApi) {
public BedrockCohereModelCall(CohereChatBedrockApi chatApi) {
this(chatApi, BedrockCohereChatOptions.builder().build());
}
public BedrockCohereChatConnector(CohereChatBedrockApi chatApi, BedrockCohereChatOptions options) {
public BedrockCohereModelCall(CohereChatBedrockApi chatApi, BedrockCohereChatOptions options) {
Assert.notNull(chatApi, "CohereChatBedrockApi must not be null");
Assert.notNull(options, "BedrockCohereChatOptions must not be null");

15
models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/jurassic2/BedrockAi21Jurassic2ChatConnector.java → models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/jurassic2/BedrockAi21Jurassic2ModelCall.java
View File

@@ -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.connector.ChatConnector;
import org.springframework.ai.chat.connector.ModelCall;
import org.springframework.ai.chat.ChatResponse;
import org.springframework.ai.chat.Generation;
import org.springframework.ai.chat.metadata.ChatGenerationMetadata;
@@ -29,19 +29,18 @@ import org.springframework.ai.model.ModelOptionsUtils;
import org.springframework.util.Assert;
/**
* Java {@link ChatConnector} for the Bedrock Jurassic2 chat generative model.
* Java {@link ModelCall} for the Bedrock Jurassic2 chat generative model.
*
* @author Ahmed Yousri
* @since 1.0.0
*/
public class BedrockAi21Jurassic2ChatConnector implements ChatConnector {
public class BedrockAi21Jurassic2ModelCall implements ModelCall {
private final Ai21Jurassic2ChatBedrockApi chatApi;
private final BedrockAi21Jurassic2ChatOptions defaultOptions;
public BedrockAi21Jurassic2ChatConnector(Ai21Jurassic2ChatBedrockApi chatApi,
BedrockAi21Jurassic2ChatOptions options) {
public BedrockAi21Jurassic2ModelCall(Ai21Jurassic2ChatBedrockApi chatApi, BedrockAi21Jurassic2ChatOptions options) {
Assert.notNull(chatApi, "Ai21Jurassic2ChatBedrockApi must not be null");
Assert.notNull(options, "BedrockAi21Jurassic2ChatOptions must not be null");
@@ -49,7 +48,7 @@ public class BedrockAi21Jurassic2ChatConnector implements ChatConnector {
this.defaultOptions = options;
}
public BedrockAi21Jurassic2ChatConnector(Ai21Jurassic2ChatBedrockApi chatApi) {
public BedrockAi21Jurassic2ModelCall(Ai21Jurassic2ChatBedrockApi chatApi) {
this(chatApi,
BedrockAi21Jurassic2ChatOptions.builder()
.withTemperature(0.8f)
@@ -114,8 +113,8 @@ public class BedrockAi21Jurassic2ChatConnector implements ChatConnector {
return this;
}
public BedrockAi21Jurassic2ChatConnector build() {
return new BedrockAi21Jurassic2ChatConnector(chatApi,
public BedrockAi21Jurassic2ModelCall build() {
return new BedrockAi21Jurassic2ModelCall(chatApi,
options != null ? options : BedrockAi21Jurassic2ChatOptions.builder().build());
}

10
models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/llama/BedrockLlamaChatConnector.java → models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/llama/BedrockLlamaModelCall.java
View File

@@ -17,7 +17,7 @@ package org.springframework.ai.bedrock.llama;
import java.util.List;
import org.springframework.ai.chat.connector.ChatConnector;
import org.springframework.ai.chat.connector.ModelCall;
import reactor.core.publisher.Flux;
import org.springframework.ai.bedrock.MessageToPromptConverter;
@@ -35,25 +35,25 @@ import org.springframework.ai.model.ModelOptionsUtils;
import org.springframework.util.Assert;
/**
* Java {@link ChatConnector} and {@link StreamingChatClient} for the Bedrock Llama chat
* Java {@link ModelCall} and {@link StreamingChatClient} for the Bedrock Llama chat
* generative.
*
* @author Christian Tzolov
* @author Wei Jiang
* @since 0.8.0
*/
public class BedrockLlamaChatConnector implements ChatConnector, StreamingChatClient {
public class BedrockLlamaModelCall implements ModelCall, StreamingChatClient {
private final LlamaChatBedrockApi chatApi;
private final BedrockLlamaChatOptions defaultOptions;
public BedrockLlamaChatConnector(LlamaChatBedrockApi chatApi) {
public BedrockLlamaModelCall(LlamaChatBedrockApi chatApi) {
this(chatApi,
BedrockLlamaChatOptions.builder().withTemperature(0.8f).withTopP(0.9f).withMaxGenLen(100).build());
}
public BedrockLlamaChatConnector(LlamaChatBedrockApi chatApi, BedrockLlamaChatOptions options) {
public BedrockLlamaModelCall(LlamaChatBedrockApi chatApi, BedrockLlamaChatOptions options) {
Assert.notNull(chatApi, "LlamaChatBedrockApi must not be null");
Assert.notNull(options, "BedrockLlamaChatOptions must not be null");

8
models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/titan/BedrockTitanChatConnector.java → models/spring-ai-bedrock/src/main/java/org/springframework/ai/bedrock/titan/BedrockTitanModelCall.java
View File

@@ -17,7 +17,7 @@ package org.springframework.ai.bedrock.titan;
import java.util.List;
import org.springframework.ai.chat.connector.ChatConnector;
import org.springframework.ai.chat.connector.ModelCall;
import reactor.core.publisher.Flux;
import org.springframework.ai.bedrock.MessageToPromptConverter;
@@ -39,17 +39,17 @@ import org.springframework.util.Assert;
* @author Christian Tzolov
* @since 0.8.0
*/
public class BedrockTitanChatConnector implements ChatConnector, StreamingChatClient {
public class BedrockTitanModelCall implements ModelCall, StreamingChatClient {
private final TitanChatBedrockApi chatApi;
private final BedrockTitanChatOptions defaultOptions;
public BedrockTitanChatConnector(TitanChatBedrockApi chatApi) {
public BedrockTitanModelCall(TitanChatBedrockApi chatApi) {
this(chatApi, BedrockTitanChatOptions.builder().withTemperature(0.8f).build());
}
public BedrockTitanChatConnector(TitanChatBedrockApi chatApi, BedrockTitanChatOptions defaultOptions) {
public BedrockTitanModelCall(TitanChatBedrockApi chatApi, BedrockTitanChatOptions defaultOptions) {
Assert.notNull(chatApi, "ChatApi must not be null");
Assert.notNull(defaultOptions, "DefaultOptions must not be null");
this.chatApi = chatApi;

2
models/spring-ai-bedrock/src/test/java/org/springframework/ai/bedrock/anthropic/BedrockAnthropicCreateRequestTests.java
View File

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

10
models/spring-ai-bedrock/src/test/java/org/springframework/ai/bedrock/anthropic/BedrockAnthropicChatConnectorIT.java → models/spring-ai-bedrock/src/test/java/org/springframework/ai/bedrock/anthropic/BedrockAnthropicModelCallIT.java
View File

@@ -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 BedrockAnthropicChatConnectorIT {
class BedrockAnthropicModelCallIT {
private static final Logger logger = LoggerFactory.getLogger(BedrockAnthropicChatConnectorIT.class);
private static final Logger logger = LoggerFactory.getLogger(BedrockAnthropicModelCallIT.class);
@Autowired
private BedrockAnthropicChatConnector client;
private BedrockAnthropicModelCall client;
@Value("classpath:/prompts/system-message.st")
private Resource systemResource;
@@ -209,8 +209,8 @@ class BedrockAnthropicChatConnectorIT {
}
@Bean
public BedrockAnthropicChatConnector anthropicChatClient(AnthropicChatBedrockApi anthropicApi) {
return new BedrockAnthropicChatConnector(anthropicApi);
public BedrockAnthropicModelCall anthropicChatClient(AnthropicChatBedrockApi anthropicApi) {
return new BedrockAnthropicModelCall(anthropicApi);
}
}

2
models/spring-ai-bedrock/src/test/java/org/springframework/ai/bedrock/anthropic3/BedrockAnthropic3CreateRequestTests.java
View File

@@ -37,7 +37,7 @@ public class BedrockAnthropic3CreateRequestTests {
@Test
public void createRequestWithChatOptions() {
var client = new BedrockAnthropic3ChatConnector(anthropicChatApi,
var client = new BedrockAnthropic3ModelCall(anthropicChatApi,
Anthropic3ChatOptions.builder()
.withTemperature(66.6f)
.withTopK(66)

10
models/spring-ai-bedrock/src/test/java/org/springframework/ai/bedrock/anthropic3/BedrockAnthropic3ChatConnectorIT.java → models/spring-ai-bedrock/src/test/java/org/springframework/ai/bedrock/anthropic3/BedrockAnthropic3ModelCallIT.java
View File

@@ -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 BedrockAnthropic3ChatConnectorIT {
class BedrockAnthropic3ModelCallIT {
private static final Logger logger = LoggerFactory.getLogger(BedrockAnthropic3ChatConnectorIT.class);
private static final Logger logger = LoggerFactory.getLogger(BedrockAnthropic3ModelCallIT.class);
@Autowired
private BedrockAnthropic3ChatConnector client;
private BedrockAnthropic3ModelCall client;
@Value("classpath:/prompts/system-message.st")
private Resource systemResource;
@@ -228,8 +228,8 @@ class BedrockAnthropic3ChatConnectorIT {
}
@Bean
public BedrockAnthropic3ChatConnector anthropicChatClient(Anthropic3ChatBedrockApi anthropicApi) {
return new BedrockAnthropic3ChatConnector(anthropicApi);
public BedrockAnthropic3ModelCall anthropicChatClient(Anthropic3ChatBedrockApi anthropicApi) {
return new BedrockAnthropic3ModelCall(anthropicApi);
}
}

2
models/spring-ai-bedrock/src/test/java/org/springframework/ai/bedrock/cohere/BedrockCohereChatCreateRequestTests.java
View File

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

8
models/spring-ai-bedrock/src/test/java/org/springframework/ai/bedrock/cohere/BedrockCohereChatConnectorIT.java → models/spring-ai-bedrock/src/test/java/org/springframework/ai/bedrock/cohere/BedrockCohereModelCallIT.java
View File

@@ -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 BedrockCohereChatConnectorIT {
class BedrockCohereModelCallIT {
@Autowired
private BedrockCohereChatConnector client;
private BedrockCohereModelCall client;
@Value("classpath:/prompts/system-message.st")
private Resource systemResource;
@@ -205,8 +205,8 @@ class BedrockCohereChatConnectorIT {
}
@Bean
public BedrockCohereChatConnector cohereChatClient(CohereChatBedrockApi cohereApi) {
return new BedrockCohereChatConnector(cohereApi);
public BedrockCohereModelCall cohereChatClient(CohereChatBedrockApi cohereApi) {
return new BedrockCohereModelCall(cohereApi);
}
}

8
models/spring-ai-bedrock/src/test/java/org/springframework/ai/bedrock/jurassic2/BedrockAi21Jurassic2ChatConnectorIT.java → models/spring-ai-bedrock/src/test/java/org/springframework/ai/bedrock/jurassic2/BedrockAi21Jurassic2ModelCallIT.java
View File

@@ -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 BedrockAi21Jurassic2ChatConnectorIT {
class BedrockAi21Jurassic2ModelCallIT {
@Autowired
private BedrockAi21Jurassic2ChatConnector client;
private BedrockAi21Jurassic2ModelCall client;
@Value("classpath:/prompts/system-message.st")
private Resource systemResource;
@@ -152,9 +152,9 @@ class BedrockAi21Jurassic2ChatConnectorIT {
}
@Bean
public BedrockAi21Jurassic2ChatConnector bedrockAi21Jurassic2ChatClient(
public BedrockAi21Jurassic2ModelCall bedrockAi21Jurassic2ChatClient(
Ai21Jurassic2ChatBedrockApi jurassic2ChatBedrockApi) {
return new BedrockAi21Jurassic2ChatConnector(jurassic2ChatBedrockApi,
return new BedrockAi21Jurassic2ModelCall(jurassic2ChatBedrockApi,
BedrockAi21Jurassic2ChatOptions.builder()
.withTemperature(0.5f)
.withMaxTokens(100)

2
models/spring-ai-bedrock/src/test/java/org/springframework/ai/bedrock/llama/BedrockLlamaCreateRequestTests.java
View File

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

8
models/spring-ai-bedrock/src/test/java/org/springframework/ai/bedrock/llama/BedrockLlamaChatConnectorIT.java → models/spring-ai-bedrock/src/test/java/org/springframework/ai/bedrock/llama/BedrockLlamaModelCallIT.java
View File

@@ -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 BedrockLlamaChatConnectorIT {
class BedrockLlamaModelCallIT {
@Autowired
private BedrockLlamaChatConnector client;
private BedrockLlamaModelCall client;
@Value("classpath:/prompts/system-message.st")
private Resource systemResource;
@@ -206,8 +206,8 @@ class BedrockLlamaChatConnectorIT {
}
@Bean
public BedrockLlamaChatConnector llamaChatClient(LlamaChatBedrockApi llamaApi) {
return new BedrockLlamaChatConnector(llamaApi,
public BedrockLlamaModelCall llamaChatClient(LlamaChatBedrockApi llamaApi) {
return new BedrockLlamaModelCall(llamaApi,
BedrockLlamaChatOptions.builder().withTemperature(0.5f).withMaxGenLen(100).withTopP(0.9f).build());
}

2
models/spring-ai-bedrock/src/test/java/org/springframework/ai/bedrock/titan/BedrockTitanChatCreateRequestTests.java
View File

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

8
models/spring-ai-bedrock/src/test/java/org/springframework/ai/bedrock/titan/BedrockTitanChatConnectorIT.java → models/spring-ai-bedrock/src/test/java/org/springframework/ai/bedrock/titan/BedrockTitanModelCallIT.java
View File

@@ -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 BedrockTitanChatConnectorIT {
class BedrockTitanModelCallIT {
@Autowired
private BedrockTitanChatConnector client;
private BedrockTitanModelCall client;
@Value("classpath:/prompts/system-message.st")
private Resource systemResource;
@@ -211,8 +211,8 @@ class BedrockTitanChatConnectorIT {
}
@Bean
public BedrockTitanChatConnector titanChatClient(TitanChatBedrockApi titanApi) {
return new BedrockTitanChatConnector(titanApi);
public BedrockTitanModelCall titanChatClient(TitanChatBedrockApi titanApi) {
return new BedrockTitanModelCall(titanApi);
}
}

11
models/spring-ai-huggingface/src/main/java/org/springframework/ai/huggingface/HuggingfaceChatConnector.java → models/spring-ai-huggingface/src/main/java/org/springframework/ai/huggingface/HuggingfaceModelCall.java
View File

@@ -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.connector.ChatConnector;
import org.springframework.ai.chat.connector.ModelCall;
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 ChatConnector} that interfaces with HuggingFace Inference
* An implementation of {@link ModelCall} that interfaces with HuggingFace Inference
* Endpoints for text generation.
*
* @author Mark Pollack
*/
public class HuggingfaceChatConnector implements ChatConnector {
public class HuggingfaceModelCall implements ModelCall {
/**
* Token required for authenticating with the HuggingFace Inference API.
@@ -68,12 +68,11 @@ public class HuggingfaceChatConnector implements ChatConnector {
private int maxNewTokens = 1000;
/**
* Constructs a new HuggingfaceChatConnector with the specified API token and base
* path.
* Constructs a new HuggingfaceModelCall with the specified API token and base path.
* @param apiToken The API token for HuggingFace.
* @param basePath The base path for API requests.
*/
public HuggingfaceChatConnector(final String apiToken, String basePath) {
public HuggingfaceModelCall(final String apiToken, String basePath) {
this.apiToken = apiToken;
this.apiClient.setBasePath(basePath);
this.apiClient.addDefaultHeader("Authorization", "Bearer " + this.apiToken);

4
models/spring-ai-huggingface/src/test/java/org/springframework/ai/huggingface/HuggingfaceTestConfiguration.java
View File

@@ -23,7 +23,7 @@ import org.springframework.util.StringUtils;
public class HuggingfaceTestConfiguration {
@Bean
public HuggingfaceChatConnector huggingfaceChatClient() {
public HuggingfaceModelCall 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/
HuggingfaceChatConnector huggingfaceChatClient = new HuggingfaceChatConnector(apiKey,
HuggingfaceModelCall huggingfaceChatClient = new HuggingfaceModelCall(apiKey,
"https://f6hg7b3cvlmntp5i.us-east-1.aws.endpoints.huggingface.cloud");
return huggingfaceChatClient;
}

4
models/spring-ai-huggingface/src/test/java/org/springframework/ai/huggingface/client/ClientIT.java
View File

@@ -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.HuggingfaceChatConnector;
import org.springframework.ai.huggingface.HuggingfaceModelCall;
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 HuggingfaceChatConnector huggingfaceChatClient;
protected HuggingfaceModelCall huggingfaceChatClient;
@Test
void helloWorldCompletion() {

4
models/spring-ai-mistral-ai/src/main/java/org/springframework/ai/mistralai/MistralAiChatOptions.java
View File

@@ -101,11 +101,11 @@ public class MistralAiChatOptions implements FunctionCallingOptions, ChatOptions
private @JsonProperty("tool_choice") ToolChoice toolChoice;
/**
* MistralAI Tool Function Callbacks to register with the ChatConnector. For Prompt
* MistralAI Tool Function Callbacks to register with the ModelCall. 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.
* to be used by the ModelCall chat completion requests.
*/
@NestedConfigurationProperty
@JsonIgnore

12
models/spring-ai-mistral-ai/src/main/java/org/springframework/ai/mistralai/MistralAiChatConnector.java → models/spring-ai-mistral-ai/src/main/java/org/springframework/ai/mistralai/MistralAiModelCall.java
View File

@@ -17,7 +17,7 @@ package org.springframework.ai.mistralai;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.ai.chat.connector.ChatConnector;
import org.springframework.ai.chat.connector.ModelCall;
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 MistralAiChatConnector extends
public class MistralAiModelCall extends
AbstractFunctionCallSupport<MistralAiApi.ChatCompletionMessage, MistralAiApi.ChatCompletionRequest, ResponseEntity<MistralAiApi.ChatCompletion>>
implements ChatConnector, StreamingChatClient {
implements ModelCall, StreamingChatClient {
private final Logger log = LoggerFactory.getLogger(getClass());
@@ -68,7 +68,7 @@ public class MistralAiChatConnector extends
private final RetryTemplate retryTemplate;
public MistralAiChatConnector(MistralAiApi mistralAiApi) {
public MistralAiModelCall(MistralAiApi mistralAiApi) {
this(mistralAiApi,
MistralAiChatOptions.builder()
.withTemperature(0.7f)
@@ -78,11 +78,11 @@ public class MistralAiChatConnector extends
.build());
}
public MistralAiChatConnector(MistralAiApi mistralAiApi, MistralAiChatOptions options) {
public MistralAiModelCall(MistralAiApi mistralAiApi, MistralAiChatOptions options) {
this(mistralAiApi, options, null, RetryUtils.DEFAULT_RETRY_TEMPLATE);
}
public MistralAiChatConnector(MistralAiApi mistralAiApi, MistralAiChatOptions options,
public MistralAiModelCall(MistralAiApi mistralAiApi, MistralAiChatOptions options,
FunctionCallbackContext functionCallbackContext, RetryTemplate retryTemplate) {
super(functionCallbackContext);
Assert.notNull(mistralAiApi, "MistralAiApi must not be null");

2
models/spring-ai-mistral-ai/src/test/java/org/springframework/ai/mistralai/MistralAiChatCompletionRequestTest.java
View File

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

18
models/spring-ai-mistral-ai/src/test/java/org/springframework/ai/mistralai/MistralAiChatConnectorIT.java → models/spring-ai-mistral-ai/src/test/java/org/springframework/ai/mistralai/MistralAiModelCallIT.java
View File

@@ -25,7 +25,7 @@ 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.chat.connector.ChatConnector;
import org.springframework.ai.chat.connector.ModelCall;
import reactor.core.publisher.Flux;
import org.springframework.ai.chat.ChatResponse;
@@ -56,12 +56,12 @@ import static org.assertj.core.api.Assertions.assertThat;
*/
@SpringBootTest(classes = MistralAiTestConfiguration.class)
@EnabledIfEnvironmentVariable(named = "MISTRAL_AI_API_KEY", matches = ".+")
class MistralAiChatConnectorIT {
class MistralAiModelCallIT {
private static final Logger logger = LoggerFactory.getLogger(MistralAiChatConnectorIT.class);
private static final Logger logger = LoggerFactory.getLogger(MistralAiModelCallIT.class);
@Autowired
protected ChatConnector chatConnector;
protected ModelCall modelCall;
@Autowired
protected StreamingChatClient streamingChatClient;
@@ -90,7 +90,7 @@ class MistralAiChatConnectorIT {
// 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 = chatConnector.call(prompt);
ChatResponse response = modelCall.call(prompt);
assertThat(response.getResults()).hasSize(1);
assertThat(response.getResults().get(0).getOutput().getContent()).contains("Blackbeard");
}
@@ -108,7 +108,7 @@ class MistralAiChatConnectorIT {
PromptTemplate promptTemplate = new PromptTemplate(template,
Map.of("subject", "ice cream flavors", "format", format));
Prompt prompt = new Prompt(promptTemplate.createMessage());
Generation generation = this.chatConnector.call(prompt).getResult();
Generation generation = this.modelCall.call(prompt).getResult();
List<String> list = outputConverter.convert(generation.getOutput().getContent());
assertThat(list).hasSize(5);
@@ -126,7 +126,7 @@ class MistralAiChatConnectorIT {
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 = chatConnector.call(prompt).getResult();
Generation generation = modelCall.call(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 MistralAiChatConnectorIT {
""";
PromptTemplate promptTemplate = new PromptTemplate(template, Map.of("format", format));
Prompt prompt = new Prompt(promptTemplate.createMessage());
Generation generation = chatConnector.call(prompt).getResult();
Generation generation = modelCall.call(prompt).getResult();
ActorsFilmsRecord actorsFilms = outputConverter.convert(generation.getOutput().getContent());
logger.info("" + actorsFilms);
@@ -201,7 +201,7 @@ class MistralAiChatConnectorIT {
.build()))
.build();
ChatResponse response = chatConnector.call(new Prompt(messages, promptOptions));
ChatResponse response = modelCall.call(new Prompt(messages, promptOptions));
logger.info("Response: {}", response);

4
models/spring-ai-mistral-ai/src/test/java/org/springframework/ai/mistralai/MistralAiRetryTests.java
View File

@@ -82,7 +82,7 @@ public class MistralAiRetryTests {
private @Mock MistralAiApi mistralAiApi;
private MistralAiChatConnector chatClient;
private MistralAiModelCall chatClient;
private MistralAiEmbeddingClient embeddingClient;
@@ -92,7 +92,7 @@ public class MistralAiRetryTests {
retryListener = new TestRetryListener();
retryTemplate.registerListener(retryListener);
chatClient = new MistralAiChatConnector(mistralAiApi,
chatClient = new MistralAiModelCall(mistralAiApi,
MistralAiChatOptions.builder()
.withTemperature(0.7f)
.withTopP(1f)

4
models/spring-ai-mistral-ai/src/test/java/org/springframework/ai/mistralai/MistralAiTestConfiguration.java
View File

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

14
models/spring-ai-ollama/src/main/java/org/springframework/ai/ollama/OllamaChatConnector.java → models/spring-ai-ollama/src/main/java/org/springframework/ai/ollama/OllamaModelCall.java
View File

@@ -18,7 +18,7 @@ package org.springframework.ai.ollama;
import java.util.Base64;
import java.util.List;
import org.springframework.ai.chat.connector.ChatConnector;
import org.springframework.ai.chat.connector.ModelCall;
import org.springframework.ai.ollama.metadata.OllamaChatResponseMetadata;
import reactor.core.publisher.Flux;
@@ -39,7 +39,7 @@ import org.springframework.util.CollectionUtils;
import org.springframework.util.StringUtils;
/**
* {@link ChatConnector} implementation for {@literal Ollama}.
* {@link ModelCall} 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 OllamaChatConnector implements ChatConnector, StreamingChatClient {
public class OllamaModelCall implements ModelCall, StreamingChatClient {
/**
* Low-level Ollama API library.
@@ -64,11 +64,11 @@ public class OllamaChatConnector implements ChatConnector, StreamingChatClient {
*/
private OllamaOptions defaultOptions;
public OllamaChatConnector(OllamaApi chatApi) {
public OllamaModelCall(OllamaApi chatApi) {
this(chatApi, OllamaOptions.create().withModel(OllamaOptions.DEFAULT_MODEL));
}
public OllamaChatConnector(OllamaApi chatApi, OllamaOptions defaultOptions) {
public OllamaModelCall(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 OllamaChatConnector implements ChatConnector, StreamingChatClient {
* @deprecated Use {@link OllamaOptions#setModel} instead.
*/
@Deprecated
public OllamaChatConnector withModel(String model) {
public OllamaModelCall withModel(String model) {
this.defaultOptions.setModel(model);
return this;
}
@@ -88,7 +88,7 @@ public class OllamaChatConnector implements ChatConnector, StreamingChatClient {
* @deprecated Use {@link OllamaOptions} constructor instead.
*/
@Deprecated
public OllamaChatConnector withDefaultOptions(OllamaOptions options) {
public OllamaModelCall withDefaultOptions(OllamaOptions options) {
this.defaultOptions = options;
return this;
}

4
models/spring-ai-ollama/src/test/java/org/springframework/ai/ollama/OllamaChatRequestTests.java
View File

@@ -30,7 +30,7 @@ import static org.assertj.core.api.Assertions.assertThat;
*/
public class OllamaChatRequestTests {
OllamaChatConnector client = new OllamaChatConnector(new OllamaApi(),
OllamaModelCall client = new OllamaModelCall(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() {
OllamaChatConnector client2 = new OllamaChatConnector(new OllamaApi(),
OllamaModelCall client2 = new OllamaModelCall(new OllamaApi(),
new OllamaOptions().withModel("DEFAULT_OPTIONS_MODEL"));
var request = client2.ollamaChatRequest(new Prompt("Test message content"), true);

10
models/spring-ai-ollama/src/test/java/org/springframework/ai/ollama/OllamaChatConnectorIT.java → models/spring-ai-ollama/src/test/java/org/springframework/ai/ollama/OllamaModelCallIT.java
View File

@@ -56,11 +56,11 @@ import static org.assertj.core.api.Assertions.assertThat;
@SpringBootTest
@Testcontainers
@Disabled("For manual smoke testing only.")
class OllamaChatConnectorIT {
class OllamaModelCallIT {
private static String MODEL = "mistral";
private static final Log logger = LogFactory.getLog(OllamaChatConnectorIT.class);
private static final Log logger = LogFactory.getLog(OllamaModelCallIT.class);
@Container
static OllamaContainer ollamaContainer = new OllamaContainer("ollama/ollama:0.1.32");
@@ -77,7 +77,7 @@ class OllamaChatConnectorIT {
}
@Autowired
private OllamaChatConnector client;
private OllamaModelCall client;
@Test
void roleTest() {
@@ -219,8 +219,8 @@ class OllamaChatConnectorIT {
}
@Bean
public OllamaChatConnector ollamaChat(OllamaApi ollamaApi) {
return new OllamaChatConnector(ollamaApi, OllamaOptions.create().withModel(MODEL).withTemperature(0.9f));
public OllamaModelCall ollamaChat(OllamaApi ollamaApi) {
return new OllamaModelCall(ollamaApi, OllamaOptions.create().withModel(MODEL).withTemperature(0.9f));
}
}

10
models/spring-ai-ollama/src/test/java/org/springframework/ai/ollama/OllamaChatConnectorMultimodalIT.java → models/spring-ai-ollama/src/test/java/org/springframework/ai/ollama/OllamaModelCallMultimodalIT.java
View File

@@ -44,11 +44,11 @@ import static org.assertj.core.api.Assertions.assertThat;
@SpringBootTest
@Testcontainers
@Disabled("For manual smoke testing only.")
class OllamaChatConnectorMultimodalIT {
class OllamaModelCallMultimodalIT {
private static String MODEL = "llava";
private static final Log logger = LogFactory.getLog(OllamaChatConnectorIT.class);
private static final Log logger = LogFactory.getLog(OllamaModelCallIT.class);
@Container
static OllamaContainer ollamaContainer = new OllamaContainer("ollama/ollama:0.1.32");
@@ -65,7 +65,7 @@ class OllamaChatConnectorMultimodalIT {
}
@Autowired
private OllamaChatConnector client;
private OllamaModelCall client;
@Test
void multiModalityTest() throws IOException {
@@ -90,8 +90,8 @@ class OllamaChatConnectorMultimodalIT {
}
@Bean
public OllamaChatConnector ollamaChat(OllamaApi ollamaApi) {
return new OllamaChatConnector(ollamaApi, OllamaOptions.create().withModel(MODEL).withTemperature(0.9f));
public OllamaModelCall ollamaChat(OllamaApi ollamaApi) {
return new OllamaModelCall(ollamaApi, OllamaOptions.create().withModel(MODEL).withTemperature(0.9f));
}
}

4
models/spring-ai-openai/src/main/java/org/springframework/ai/openai/OpenAiChatOptions.java
View File

@@ -134,10 +134,10 @@ public class OpenAiChatOptions implements FunctionCallingOptions, ChatOptions {
private @JsonProperty("user") String user;
/**
* OpenAI Tool Function Callbacks to register with the ChatConnector.
* OpenAI Tool Function Callbacks to register with the ModelCall.
* 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.
* from the registry to be used by the ModelCall chat completion requests.
*/
@NestedConfigurationProperty
@JsonIgnore

26
models/spring-ai-openai/src/main/java/org/springframework/ai/openai/OpenAiChatConnector.java → models/spring-ai-openai/src/main/java/org/springframework/ai/openai/OpenAiModelCall.java
View File

@@ -17,7 +17,7 @@ package org.springframework.ai.openai;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.ai.chat.connector.ChatConnector;
import org.springframework.ai.chat.connector.ModelCall;
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 ChatConnector} and {@link StreamingChatClient} implementation for
* {@literal OpenAI} backed by {@link OpenAiApi}.
* {@link ModelCall} 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 ChatConnector
* @see ModelCall
* @see StreamingChatClient
* @see OpenAiApi
*/
public class OpenAiChatConnector extends
public class OpenAiModelCall extends
AbstractFunctionCallSupport<ChatCompletionMessage, OpenAiApi.ChatCompletionRequest, ResponseEntity<ChatCompletion>>
implements ChatConnector, StreamingChatClient {
implements ModelCall, StreamingChatClient {
private static final Logger logger = LoggerFactory.getLogger(OpenAiChatConnector.class);
private static final Logger logger = LoggerFactory.getLogger(OpenAiModelCall.class);
/**
* The default options used for the chat completion requests.
@@ -94,35 +94,35 @@ public class OpenAiChatConnector extends
private final OpenAiApi openAiApi;
/**
* Creates an instance of the OpenAiChatConnector.
* Creates an instance of the OpenAiModelCall.
* @param openAiApi The OpenAiApi instance to be used for interacting with the OpenAI
* Chat API.
* @throws IllegalArgumentException if openAiApi is null
*/
public OpenAiChatConnector(OpenAiApi openAiApi) {
public OpenAiModelCall(OpenAiApi openAiApi) {
this(openAiApi,
OpenAiChatOptions.builder().withModel(OpenAiApi.DEFAULT_CHAT_MODEL).withTemperature(0.7f).build());
}
/**
* Initializes an instance of the OpenAiChatConnector.
* Initializes an instance of the OpenAiModelCall.
* @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 OpenAiChatConnector(OpenAiApi openAiApi, OpenAiChatOptions options) {
public OpenAiModelCall(OpenAiApi openAiApi, OpenAiChatOptions options) {
this(openAiApi, options, null, RetryUtils.DEFAULT_RETRY_TEMPLATE);
}
/**
* Initializes a new instance of the OpenAiChatConnector.
* Initializes a new instance of the OpenAiModelCall.
* @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 OpenAiChatConnector(OpenAiApi openAiApi, OpenAiChatOptions options,
public OpenAiModelCall(OpenAiApi openAiApi, OpenAiChatOptions options,
FunctionCallbackContext functionCallbackContext, RetryTemplate retryTemplate) {
super(functionCallbackContext);
Assert.notNull(openAiApi, "OpenAiApi must not be null");

6
models/spring-ai-openai/src/test/java/org/springframework/ai/openai/ChatCompletionRequestTests.java
View File

@@ -34,7 +34,7 @@ public class ChatCompletionRequestTests {
@Test
public void createRequestWithChatOptions() {
var client = new OpenAiChatConnector(new OpenAiApi("TEST"),
var client = new OpenAiModelCall(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 OpenAiChatConnector(new OpenAiApi("TEST"),
var client = new OpenAiModelCall(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 OpenAiChatConnector(new OpenAiApi("TEST"),
var client = new OpenAiModelCall(new OpenAiApi("TEST"),
OpenAiChatOptions.builder()
.withModel("DEFAULT_MODEL")
.withFunctionCallbacks(List.of(FunctionCallbackWrapper.builder(new MockWeatherService())

4
models/spring-ai-openai/src/test/java/org/springframework/ai/openai/OpenAiTestConfiguration.java
View File

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

4
models/spring-ai-openai/src/test/java/org/springframework/ai/openai/acme/AcmeIT.java
View File

@@ -26,7 +26,7 @@ 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.OpenAiModelCall;
import org.springframework.ai.openai.OpenAiTestConfiguration;
import org.springframework.ai.openai.OpenAiEmbeddingClient;
import org.springframework.ai.openai.testutils.AbstractIT;
@@ -61,7 +61,7 @@ public class AcmeIT extends AbstractIT {
private OpenAiEmbeddingClient embeddingClient;
@Autowired
private OpenAiChatConnector chatClient;
private OpenAiModelCall chatClient;
@Test
void beanTest() {

8
models/spring-ai-openai/src/test/java/org/springframework/ai/openai/chat/OpenAiChatClientWithChatResponseMetadataTests.java
View File

@@ -26,7 +26,7 @@ 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.OpenAiModelCall;
import org.springframework.ai.openai.api.OpenAiApi;
import org.springframework.ai.openai.metadata.support.OpenAiApiResponseHeaders;
import org.springframework.ai.chat.prompt.Prompt;
@@ -57,7 +57,7 @@ public class OpenAiChatClientWithChatResponseMetadataTests {
private static String TEST_API_KEY = "sk-1234567890";
@Autowired
private OpenAiChatConnector openAiChatClient;
private OpenAiModelCall openAiChatClient;
@Autowired
private MockRestServiceServer server;
@@ -171,8 +171,8 @@ public class OpenAiChatClientWithChatResponseMetadataTests {
}
@Bean
public OpenAiChatConnector openAiClient(OpenAiApi openAiApi) {
return new OpenAiChatConnector(openAiApi);
public OpenAiModelCall openAiClient(OpenAiApi openAiApi) {
return new OpenAiModelCall(openAiApi);
}
}

12
models/spring-ai-openai/src/test/java/org/springframework/ai/openai/chat/OpenAiChatConnector2IT.java → models/spring-ai-openai/src/test/java/org/springframework/ai/openai/chat/OpenAiModelCall2IT.java
View File

@@ -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.OpenAiChatConnector;
import org.springframework.ai.openai.OpenAiModelCall;
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 = OpenAiChatConnector2IT.Config.class)
@SpringBootTest(classes = OpenAiModelCall2IT.Config.class)
@EnabledIfEnvironmentVariable(named = "OPENAI_API_KEY", matches = ".+")
public class OpenAiChatConnector2IT {
public class OpenAiModelCall2IT {
private final Logger logger = LoggerFactory.getLogger(getClass());
@Autowired
private OpenAiChatConnector openAiChatClient;
private OpenAiModelCall openAiChatClient;
@Test
void responseFormatTest() throws JsonMappingException, JsonProcessingException {
@@ -99,8 +99,8 @@ public class OpenAiChatConnector2IT {
}
@Bean
public OpenAiChatConnector openAiClient(OpenAiApi openAiApi) {
return new OpenAiChatConnector(openAiApi);
public OpenAiModelCall openAiClient(OpenAiApi openAiApi) {
return new OpenAiModelCall(openAiApi);
}
}

20
models/spring-ai-openai/src/test/java/org/springframework/ai/openai/chat/OpenAiChatConnectorIT.java → models/spring-ai-openai/src/test/java/org/springframework/ai/openai/chat/OpenAiModelCallIT.java
View File

@@ -60,9 +60,9 @@ import static org.assertj.core.api.Assertions.assertThat;
@SpringBootTest(classes = OpenAiTestConfiguration.class)
@EnabledIfEnvironmentVariable(named = "OPENAI_API_KEY", matches = ".+")
class OpenAiChatConnectorIT extends AbstractIT {
class OpenAiModelCallIT extends AbstractIT {
private static final Logger logger = LoggerFactory.getLogger(OpenAiChatConnectorIT.class);
private static final Logger logger = LoggerFactory.getLogger(OpenAiModelCallIT.class);
@Value("classpath:/prompts/system-message.st")
private Resource systemResource;
@@ -74,7 +74,7 @@ class OpenAiChatConnectorIT 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 = chatConnector.call(prompt);
ChatResponse response = modelCall.call(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 OpenAiChatConnectorIT extends AbstractIT {
PromptTemplate promptTemplate = new PromptTemplate(template,
Map.of("subject", "ice cream flavors", "format", format));
Prompt prompt = new Prompt(promptTemplate.createMessage());
Generation generation = this.chatConnector.call(prompt).getResult();
Generation generation = this.modelCall.call(prompt).getResult();
List<String> list = outputConverter.convert(generation.getOutput().getContent());
assertThat(list).hasSize(5);
@@ -112,7 +112,7 @@ class OpenAiChatConnectorIT 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 = chatConnector.call(prompt).getResult();
Generation generation = modelCall.call(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 OpenAiChatConnectorIT extends AbstractIT {
""";
PromptTemplate promptTemplate = new PromptTemplate(template, Map.of("format", format));
Prompt prompt = new Prompt(promptTemplate.createMessage());
Generation generation = chatConnector.call(prompt).getResult();
Generation generation = modelCall.call(prompt).getResult();
ActorsFilms actorsFilms = outputConverter.convert(generation.getOutput().getContent());
}
@@ -151,7 +151,7 @@ class OpenAiChatConnectorIT extends AbstractIT {
""";
PromptTemplate promptTemplate = new PromptTemplate(template, Map.of("format", format));
Prompt prompt = new Prompt(promptTemplate.createMessage());
Generation generation = chatConnector.call(prompt).getResult();
Generation generation = modelCall.call(prompt).getResult();
ActorsFilmsRecord actorsFilms = outputConverter.convert(generation.getOutput().getContent());
logger.info("" + actorsFilms);
@@ -204,7 +204,7 @@ class OpenAiChatConnectorIT extends AbstractIT {
.build()))
.build();
ChatResponse response = chatConnector.call(new Prompt(messages, promptOptions));
ChatResponse response = modelCall.call(new Prompt(messages, promptOptions));
logger.info("Response: {}", response);
@@ -255,7 +255,7 @@ class OpenAiChatConnectorIT extends AbstractIT {
var userMessage = new UserMessage("Explain what do you see on this picture?",
List.of(new Media(MimeTypeUtils.IMAGE_PNG, imageData)));
var response = chatConnector
var response = modelCall
.call(new Prompt(List.of(userMessage), OpenAiChatOptions.builder().withModel(modelName).build()));
logger.info(response.getResult().getOutput().getContent());
@@ -271,7 +271,7 @@ class OpenAiChatConnectorIT 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 = chatConnector
ChatResponse response = modelCall
.call(new Prompt(List.of(userMessage), OpenAiChatOptions.builder().withModel(modelName).build()));
logger.info(response.getResult().getOutput().getContent());

6
models/spring-ai-openai/src/test/java/org/springframework/ai/openai/chat/OpenAiRetryTests.java
View File

@@ -30,7 +30,7 @@ import org.springframework.ai.chat.prompt.Prompt;
import org.springframework.ai.document.MetadataMode;
import org.springframework.ai.image.ImageMessage;
import org.springframework.ai.image.ImagePrompt;
import org.springframework.ai.openai.OpenAiChatConnector;
import org.springframework.ai.openai.OpenAiModelCall;
import org.springframework.ai.openai.api.OpenAiApi;
import org.springframework.ai.openai.api.OpenAiApi.ChatCompletion;
import org.springframework.ai.openai.api.OpenAiApi.ChatCompletionChunk;
@@ -101,7 +101,7 @@ public class OpenAiRetryTests {
private @Mock OpenAiImageApi openAiImageApi;
private OpenAiChatConnector chatClient;
private OpenAiModelCall chatClient;
private OpenAiEmbeddingClient embeddingClient;
@@ -115,7 +115,7 @@ public class OpenAiRetryTests {
retryListener = new TestRetryListener();
retryTemplate.registerListener(retryListener);
chatClient = new OpenAiChatConnector(openAiApi, OpenAiChatOptions.builder().build(), null, retryTemplate);
chatClient = new OpenAiModelCall(openAiApi, OpenAiChatOptions.builder().build(), null, retryTemplate);
embeddingClient = new OpenAiEmbeddingClient(openAiApi, MetadataMode.EMBED,
OpenAiEmbeddingOptions.builder().build(), retryTemplate);
audioTranscriptionClient = new OpenAiAudioTranscriptionClient(openAiAudioApi,

12
models/spring-ai-openai/src/test/java/org/springframework/ai/openai/chat/service/ChatMemoryLongTermSystemPromptIT.java
View File

@@ -23,7 +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.springframework.ai.openai.OpenAiModelCall;
import org.testcontainers.junit.jupiter.Container;
import org.testcontainers.junit.jupiter.Testcontainers;
import org.testcontainers.qdrant.QdrantContainer;
@@ -75,8 +75,8 @@ public class ChatMemoryLongTermSystemPromptIT extends BaseMemoryTest {
}
@Bean
public OpenAiChatConnector openAiClient(OpenAiApi openAiApi) {
return new OpenAiChatConnector(openAiApi);
public OpenAiModelCall openAiClient(OpenAiApi openAiApi) {
return new OpenAiModelCall(openAiApi);
}
@Bean
@@ -98,7 +98,7 @@ public class ChatMemoryLongTermSystemPromptIT extends BaseMemoryTest {
}
@Bean
public ChatService memoryChatService(OpenAiChatConnector chatClient, VectorStore vectorStore,
public ChatService memoryChatService(OpenAiModelCall chatClient, VectorStore vectorStore,
TokenCountEstimator tokenCountEstimator) {
return PromptTransformingChatService.builder(chatClient)
@@ -110,7 +110,7 @@ public class ChatMemoryLongTermSystemPromptIT extends BaseMemoryTest {
}
@Bean
public StreamingChatService memoryStreamingChatService(OpenAiChatConnector streamingChatClient,
public StreamingChatService memoryStreamingChatService(OpenAiModelCall streamingChatClient,
VectorStore vectorStore, TokenCountEstimator tokenCountEstimator) {
return StreamingPromptTransformingChatService.builder(streamingChatClient)
@@ -122,7 +122,7 @@ public class ChatMemoryLongTermSystemPromptIT extends BaseMemoryTest {
}
@Bean
public RelevancyEvaluator relevancyEvaluator(OpenAiChatConnector chatClient) {
public RelevancyEvaluator relevancyEvaluator(OpenAiModelCall chatClient) {
return new RelevancyEvaluator(chatClient);
}

12
models/spring-ai-openai/src/test/java/org/springframework/ai/openai/chat/service/ChatMemoryShortTermMessageListIT.java
View File

@@ -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.OpenAiChatConnector;
import org.springframework.ai.openai.OpenAiModelCall;
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 OpenAiChatConnector openAiClient(OpenAiApi openAiApi) {
return new OpenAiChatConnector(openAiApi);
public OpenAiModelCall openAiClient(OpenAiApi openAiApi) {
return new OpenAiModelCall(openAiApi);
}
@Bean
@@ -74,7 +74,7 @@ public class ChatMemoryShortTermMessageListIT extends BaseMemoryTest {
}
@Bean
public ChatService memoryChatService(OpenAiChatConnector chatClient, ChatMemory chatHistory,
public ChatService memoryChatService(OpenAiModelCall chatClient, ChatMemory chatHistory,
TokenCountEstimator tokenCountEstimator) {
return PromptTransformingChatService.builder(chatClient)
@@ -86,7 +86,7 @@ public class ChatMemoryShortTermMessageListIT extends BaseMemoryTest {
}
@Bean
public StreamingChatService memoryStreamingChatService(OpenAiChatConnector streamingChatClient,
public StreamingChatService memoryStreamingChatService(OpenAiModelCall streamingChatClient,
ChatMemory chatHistory, TokenCountEstimator tokenCountEstimator) {
return StreamingPromptTransformingChatService.builder(streamingChatClient)
@@ -98,7 +98,7 @@ public class ChatMemoryShortTermMessageListIT extends BaseMemoryTest {
}
@Bean
public RelevancyEvaluator relevancyEvaluator(OpenAiChatConnector chatClient) {
public RelevancyEvaluator relevancyEvaluator(OpenAiModelCall chatClient) {
return new RelevancyEvaluator(chatClient);
}

12
models/spring-ai-openai/src/test/java/org/springframework/ai/openai/chat/service/ChatMemoryShortTermSystemPromptIT.java
View File

@@ -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.OpenAiChatConnector;
import org.springframework.ai.openai.OpenAiModelCall;
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 OpenAiChatConnector openAiClient(OpenAiApi openAiApi) {
return new OpenAiChatConnector(openAiApi);
public OpenAiModelCall openAiClient(OpenAiApi openAiApi) {
return new OpenAiModelCall(openAiApi);
}
@Bean
@@ -75,7 +75,7 @@ public class ChatMemoryShortTermSystemPromptIT extends BaseMemoryTest {
}
@Bean
public ChatService memoryChatService(OpenAiChatConnector chatClient, ChatMemory chatHistory,
public ChatService memoryChatService(OpenAiModelCall chatClient, ChatMemory chatHistory,
TokenCountEstimator tokenCountEstimator) {
return PromptTransformingChatService.builder(chatClient)
@@ -87,7 +87,7 @@ public class ChatMemoryShortTermSystemPromptIT extends BaseMemoryTest {
}
@Bean
public StreamingChatService memoryStreamingChatService(OpenAiChatConnector streamingChatClient,
public StreamingChatService memoryStreamingChatService(OpenAiModelCall streamingChatClient,
ChatMemory chatHistory, TokenCountEstimator tokenCountEstimator) {
return StreamingPromptTransformingChatService.builder(streamingChatClient)
@@ -99,7 +99,7 @@ public class ChatMemoryShortTermSystemPromptIT extends BaseMemoryTest {
}
@Bean
public RelevancyEvaluator relevancyEvaluator(OpenAiChatConnector chatClient) {
public RelevancyEvaluator relevancyEvaluator(OpenAiModelCall chatClient) {
return new RelevancyEvaluator(chatClient);
}

12
models/spring-ai-openai/src/test/java/org/springframework/ai/openai/chat/service/LongShortTermChatMemoryWithRagIT.java
View File

@@ -29,7 +29,7 @@ import org.slf4j.LoggerFactory;
import org.springframework.ai.chat.prompt.transformer.ChatServiceContext;
import org.springframework.ai.chat.service.ChatService;
import org.springframework.ai.chat.service.PromptTransformingChatService;
import org.springframework.ai.openai.OpenAiChatConnector;
import org.springframework.ai.openai.OpenAiModelCall;
import org.springframework.ai.openai.OpenAiChatOptions;
import org.testcontainers.junit.jupiter.Container;
import org.testcontainers.junit.jupiter.Testcontainers;
@@ -163,8 +163,8 @@ public class LongShortTermChatMemoryWithRagIT {
}
@Bean
public OpenAiChatConnector openAiClient(OpenAiApi openAiApi) {
return new OpenAiChatConnector(openAiApi);
public OpenAiModelCall openAiClient(OpenAiApi openAiApi) {
return new OpenAiModelCall(openAiApi);
}
@Bean
@@ -186,7 +186,7 @@ public class LongShortTermChatMemoryWithRagIT {
}
@Bean
public ChatService memoryChatService(OpenAiChatConnector chatClient, VectorStore vectorStore,
public ChatService memoryChatService(OpenAiModelCall chatClient, VectorStore vectorStore,
TokenCountEstimator tokenCountEstimator, ChatMemory chatHistory) {
return PromptTransformingChatService.builder(chatClient)
@@ -223,7 +223,7 @@ public class LongShortTermChatMemoryWithRagIT {
}
// @Bean
// public StreamingChatService memoryStreamingChatAgent(OpenAiChatConnector
// public StreamingChatService memoryStreamingChatAgent(OpenAiModelCall
// streamingChatClient,
// VectorStore vectorStore, TokenCountEstimator tokenCountEstimator, ChatHistory
// chatHistory) {
@@ -240,7 +240,7 @@ public class LongShortTermChatMemoryWithRagIT {
// }
@Bean
public RelevancyEvaluator relevancyEvaluator(OpenAiChatConnector chatClient) {
public RelevancyEvaluator relevancyEvaluator(OpenAiModelCall chatClient) {
// Use GPT 4 as a better model for determining relevancy. gpt 3.5 makes basic
// mistakes
OpenAiChatOptions openAiChatOptions = OpenAiChatOptions.builder()

20
models/spring-ai-openai/src/test/java/org/springframework/ai/openai/chat/service/OpenAiPromptTransformingChatServiceIT.java
View File

@@ -23,11 +23,11 @@ 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.connector.ModelCall;
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.OpenAiModelCall;
import org.springframework.ai.openai.OpenAiChatOptions;
import org.testcontainers.junit.jupiter.Container;
import org.testcontainers.junit.jupiter.Testcontainers;
@@ -71,7 +71,7 @@ public class OpenAiPromptTransformingChatServiceIT {
@Container
static QdrantContainer qdrantContainer = new QdrantContainer("qdrant/qdrant:v1.9.2");
private final ChatConnector chatConnector;
private final ModelCall modelCall;
private final VectorStore vectorStore;
@@ -81,9 +81,9 @@ public class OpenAiPromptTransformingChatServiceIT {
private ChatService chatService;
@Autowired
public OpenAiPromptTransformingChatServiceIT(ChatConnector chatConnector, ChatService chatService,
public OpenAiPromptTransformingChatServiceIT(ModelCall modelCall, ChatService chatService,
VectorStore vectorStore) {
this.chatConnector = chatConnector;
this.modelCall = modelCall;
this.chatService = chatService;
this.vectorStore = vectorStore;
}
@@ -102,7 +102,7 @@ public class OpenAiPromptTransformingChatServiceIT {
OpenAiChatOptions openAiChatOptions = OpenAiChatOptions.builder()
.withModel(GPT_4_TURBO_PREVIEW.getValue())
.build();
var relevancyEvaluator = new RelevancyEvaluator(this.chatConnector, openAiChatOptions);
var relevancyEvaluator = new RelevancyEvaluator(this.modelCall, openAiChatOptions);
EvaluationResponse evaluationResponse = relevancyEvaluator.evaluate(chatServiceResponse.toEvaluationRequest());
assertTrue(evaluationResponse.isPass(), "Response is not relevant to the question");
@@ -145,8 +145,8 @@ public class OpenAiPromptTransformingChatServiceIT {
}
@Bean
public ChatConnector openAiClient(OpenAiApi openAiApi) {
return new OpenAiChatConnector(openAiApi);
public ModelCall openAiClient(OpenAiApi openAiApi) {
return new OpenAiModelCall(openAiApi);
}
@Bean
@@ -163,8 +163,8 @@ public class OpenAiPromptTransformingChatServiceIT {
}
@Bean
public ChatService chatService(ChatConnector chatConnector, VectorStore vectorStore) {
return PromptTransformingChatService.builder(chatConnector)
public ChatService chatService(ModelCall modelCall, VectorStore vectorStore) {
return PromptTransformingChatService.builder(modelCall)
.withRetrievers(List.of(new VectorStoreRetriever(vectorStore, SearchRequest.defaults())))
.withAugmentors(List.of(new QuestionContextAugmentor()))
.build();

8
models/spring-ai-openai/src/test/java/org/springframework/ai/openai/testutils/AbstractIT.java
View File

@@ -21,7 +21,7 @@ import java.util.Map;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.springframework.ai.chat.connector.ChatConnector;
import org.springframework.ai.chat.connector.ModelCall;
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 ChatConnector chatConnector;
protected ModelCall modelCall;
@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 = chatConnector.call(prompt).getResult().getOutput().getContent();
String yesOrNo = modelCall.call(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 = chatConnector.call(prompt).getResult().getOutput().getContent();
String reasonForFailure = modelCall.call(prompt).getResult().getOutput().getContent();
fail(reasonForFailure);
}
else {

10
models/spring-ai-openai/src/test/java/org/springframework/ai/openai/transformer/MetadataTransformerIT.java
View File

@@ -24,7 +24,7 @@ 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.OpenAiModelCall;
import org.springframework.ai.openai.api.OpenAiApi;
import org.springframework.ai.transformer.ContentFormatTransformer;
import org.springframework.ai.transformer.KeywordMetadataEnricher;
@@ -163,18 +163,18 @@ public class MetadataTransformerIT {
}
@Bean
public OpenAiChatConnector openAiChatClient(OpenAiApi openAiApi) {
OpenAiChatConnector openAiChatClient = new OpenAiChatConnector(openAiApi);
public OpenAiModelCall openAiChatClient(OpenAiApi openAiApi) {
OpenAiModelCall openAiChatClient = new OpenAiModelCall(openAiApi);
return openAiChatClient;
}
@Bean
public KeywordMetadataEnricher keywordMetadata(OpenAiChatConnector aiClient) {
public KeywordMetadataEnricher keywordMetadata(OpenAiModelCall aiClient) {
return new KeywordMetadataEnricher(aiClient, 5);
}
@Bean
public SummaryMetadataEnricher summaryMetadata(OpenAiChatConnector aiClient) {
public SummaryMetadataEnricher summaryMetadata(OpenAiModelCall aiClient) {
return new SummaryMetadataEnricher(aiClient,
List.of(SummaryType.PREVIOUS, SummaryType.CURRENT, SummaryType.NEXT));
}

4
models/spring-ai-vertex-ai-gemini/src/main/java/org/springframework/ai/vertexai/gemini/VertexAiGeminiChatOptions.java
View File

@@ -77,10 +77,10 @@ public class VertexAiGeminiChatOptions implements FunctionCallingOptions, ChatOp
private @JsonProperty("modelName") String model;
/**
* Tool Function Callbacks to register with the ChatConnector.
* Tool Function Callbacks to register with the ModelCall.
* 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.
* from the registry to be used by the ModelCall chat completion requests.
*/
@NestedConfigurationProperty
@JsonIgnore

14
models/spring-ai-vertex-ai-gemini/src/main/java/org/springframework/ai/vertexai/gemini/VertexAiGeminiChatConnector.java → models/spring-ai-vertex-ai-gemini/src/main/java/org/springframework/ai/vertexai/gemini/VertexAiGeminiModelCall.java
View File

@@ -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.connector.ChatConnector;
import org.springframework.ai.chat.connector.ModelCall;
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 VertexAiGeminiChatConnector
extends AbstractFunctionCallSupport<Content, VertexAiGeminiChatConnector.GeminiRequest, GenerateContentResponse>
implements ChatConnector, StreamingChatClient, DisposableBean {
public class VertexAiGeminiModelCall
extends AbstractFunctionCallSupport<Content, VertexAiGeminiModelCall.GeminiRequest, GenerateContentResponse>
implements ModelCall, StreamingChatClient, DisposableBean {
private final static boolean IS_RUNTIME_CALL = true;
@@ -117,7 +117,7 @@ public class VertexAiGeminiChatConnector
}
public VertexAiGeminiChatConnector(VertexAI vertexAI) {
public VertexAiGeminiModelCall(VertexAI vertexAI) {
this(vertexAI,
VertexAiGeminiChatOptions.builder()
.withModel(ChatModel.GEMINI_PRO_VISION.getValue())
@@ -125,11 +125,11 @@ public class VertexAiGeminiChatConnector
.build());
}
public VertexAiGeminiChatConnector(VertexAI vertexAI, VertexAiGeminiChatOptions options) {
public VertexAiGeminiModelCall(VertexAI vertexAI, VertexAiGeminiChatOptions options) {
this(vertexAI, options, null);
}
public VertexAiGeminiChatConnector(VertexAI vertexAI, VertexAiGeminiChatOptions options,
public VertexAiGeminiModelCall(VertexAI vertexAI, VertexAiGeminiChatOptions options,
FunctionCallbackContext functionCallbackContext) {
super(functionCallbackContext);

4
models/spring-ai-vertex-ai-gemini/src/main/java/org/springframework/ai/vertexai/gemini/aot/VertexAiGeminiRuntimeHints.java
View File

@@ -15,7 +15,7 @@
*/
package org.springframework.ai.vertexai.gemini.aot;
import org.springframework.ai.vertexai.gemini.VertexAiGeminiChatConnector;
import org.springframework.ai.vertexai.gemini.VertexAiGeminiModelCall;
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(VertexAiGeminiChatConnector.class))
for (var tr : findJsonAnnotatedClassesInPackage(VertexAiGeminiModelCall.class))
hints.reflection().registerType(tr, mcs);
}

10
models/spring-ai-vertex-ai-gemini/src/test/java/org/springframework/ai/vertexai/gemini/VertexAiGeminiChatConnectorIT.java → models/spring-ai-vertex-ai-gemini/src/test/java/org/springframework/ai/vertexai/gemini/VertexAiGeminiModelCallIT.java
View File

@@ -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 VertexAiGeminiChatConnectorIT {
class VertexAiGeminiModelCallIT {
@Autowired
private VertexAiGeminiChatConnector client;
private VertexAiGeminiModelCall client;
@Value("classpath:/prompts/system-message.st")
private Resource systemResource;
@@ -231,10 +231,10 @@ class VertexAiGeminiChatConnectorIT {
}
@Bean
public VertexAiGeminiChatConnector vertexAiEmbedding(VertexAI vertexAi) {
return new VertexAiGeminiChatConnector(vertexAi,
public VertexAiGeminiModelCall vertexAiEmbedding(VertexAI vertexAi) {
return new VertexAiGeminiModelCall(vertexAi,
VertexAiGeminiChatOptions.builder()
.withModel(VertexAiGeminiChatConnector.ChatModel.GEMINI_PRO_VISION.getValue())
.withModel(VertexAiGeminiModelCall.ChatModel.GEMINI_PRO_VISION.getValue())
.build());
}

4
models/spring-ai-vertex-ai-gemini/src/test/java/org/springframework/ai/vertexai/gemini/aot/VertexAiGeminiRuntimeHintsTests.java
View File

@@ -19,7 +19,7 @@ import java.util.Set;
import org.junit.jupiter.api.Test;
import org.springframework.ai.vertexai.gemini.VertexAiGeminiChatConnector;
import org.springframework.ai.vertexai.gemini.VertexAiGeminiModelCall;
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(VertexAiGeminiChatConnector.class);
Set<TypeReference> jsonAnnotatedClasses = findJsonAnnotatedClassesInPackage(VertexAiGeminiModelCall.class);
for (TypeReference jsonAnnotatedClass : jsonAnnotatedClasses) {
assertThat(runtimeHints).matches(reflection().onType(jsonAnnotatedClass));
}

22
models/spring-ai-vertex-ai-gemini/src/test/java/org/springframework/ai/vertexai/gemini/function/VertexAiGeminiChatConnectorFunctionCallingIT.java → models/spring-ai-vertex-ai-gemini/src/test/java/org/springframework/ai/vertexai/gemini/function/VertexAiGeminiModelCallFunctionCallingIT.java
View File

@@ -27,7 +27,7 @@ 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 org.springframework.ai.vertexai.gemini.VertexAiGeminiModelCall;
import reactor.core.publisher.Flux;
import org.springframework.ai.chat.ChatResponse;
@@ -49,12 +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 VertexAiGeminiChatConnectorFunctionCallingIT {
public class VertexAiGeminiModelCallFunctionCallingIT {
private final Logger logger = LoggerFactory.getLogger(getClass());
@Autowired
private VertexAiGeminiChatConnector vertexGeminiClient;
private VertexAiGeminiModelCall vertexGeminiClient;
@AfterEach
public void afterEach() {
@@ -97,8 +97,8 @@ public class VertexAiGeminiChatConnectorFunctionCallingIT {
""";
var promptOptions = VertexAiGeminiChatOptions.builder()
// .withModel(VertexAiGeminiChatConnector.ChatModel.GEMINI_PRO.getValue())
.withModel(VertexAiGeminiChatConnector.ChatModel.GEMINI_PRO_1_5_PRO.getValue())
// .withModel(VertexAiGeminiModelCall.ChatModel.GEMINI_PRO.getValue())
.withModel(VertexAiGeminiModelCall.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")
@@ -125,8 +125,8 @@ public class VertexAiGeminiChatConnectorFunctionCallingIT {
List<Message> messages = new ArrayList<>(List.of(userMessage));
var promptOptions = VertexAiGeminiChatOptions.builder()
.withModel(VertexAiGeminiChatConnector.ChatModel.GEMINI_PRO_1_5_PRO.getValue())
// .withModel(VertexAiGeminiChatConnector.ChatModel.GEMINI_PRO.getValue())
.withModel(VertexAiGeminiModelCall.ChatModel.GEMINI_PRO_1_5_PRO.getValue())
// .withModel(VertexAiGeminiModelCall.ChatModel.GEMINI_PRO.getValue())
.withFunctionCallbacks(List.of(
FunctionCallbackWrapper.builder(new MockWeatherService())
.withSchemaType(SchemaType.OPEN_API_SCHEMA)
@@ -167,7 +167,7 @@ public class VertexAiGeminiChatConnectorFunctionCallingIT {
List<Message> messages = new ArrayList<>(List.of(userMessage));
var promptOptions = VertexAiGeminiChatOptions.builder()
.withModel(VertexAiGeminiChatConnector.ChatModel.GEMINI_PRO.getValue())
.withModel(VertexAiGeminiModelCall.ChatModel.GEMINI_PRO.getValue())
.withFunctionCallbacks(List.of(FunctionCallbackWrapper.builder(new MockWeatherService())
.withSchemaType(SchemaType.OPEN_API_SCHEMA)
.withName("getCurrentWeather")
@@ -223,10 +223,10 @@ public class VertexAiGeminiChatConnectorFunctionCallingIT {
}
@Bean
public VertexAiGeminiChatConnector vertexAiEmbedding(VertexAI vertexAi) {
return new VertexAiGeminiChatConnector(vertexAi,
public VertexAiGeminiModelCall vertexAiEmbedding(VertexAI vertexAi) {
return new VertexAiGeminiModelCall(vertexAi,
VertexAiGeminiChatOptions.builder()
.withModel(VertexAiGeminiChatConnector.ChatModel.GEMINI_PRO.getValue())
.withModel(VertexAiGeminiModelCall.ChatModel.GEMINI_PRO.getValue())
.withTemperature(0.9f)
.build());
}

8
models/spring-ai-vertex-ai-palm2/src/main/java/org/springframework/ai/vertexai/palm2/VertexAiPaLm2ChatConnector.java → models/spring-ai-vertex-ai-palm2/src/main/java/org/springframework/ai/vertexai/palm2/VertexAiPaLm2ModelCall.java
View File

@@ -18,7 +18,7 @@ package org.springframework.ai.vertexai.palm2;
import java.util.List;
import java.util.stream.Collectors;
import org.springframework.ai.chat.connector.ChatConnector;
import org.springframework.ai.chat.connector.ModelCall;
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 VertexAiPaLm2ChatConnector implements ChatConnector {
public class VertexAiPaLm2ModelCall implements ModelCall {
private final VertexAiPaLm2Api vertexAiApi;
private final VertexAiPaLm2ChatOptions defaultOptions;
public VertexAiPaLm2ChatConnector(VertexAiPaLm2Api vertexAiApi) {
public VertexAiPaLm2ModelCall(VertexAiPaLm2Api vertexAiApi) {
this(vertexAiApi,
VertexAiPaLm2ChatOptions.builder().withTemperature(0.7f).withCandidateCount(1).withTopK(20).build());
}
public VertexAiPaLm2ChatConnector(VertexAiPaLm2Api vertexAiApi, VertexAiPaLm2ChatOptions defaultOptions) {
public VertexAiPaLm2ModelCall(VertexAiPaLm2Api vertexAiApi, VertexAiPaLm2ChatOptions defaultOptions) {
Assert.notNull(defaultOptions, "Default options must not be null!");
Assert.notNull(vertexAiApi, "VertexAiPaLm2Api must not be null!");

6
models/spring-ai-vertex-ai-palm2/src/test/java/org/springframework/ai/vertexai/palm2/VertexAiPaLm2ChatGenerationClientIT.java
View File

@@ -48,7 +48,7 @@ import static org.assertj.core.api.Assertions.assertThat;
class VertexAiPaLm2ChatGenerationClientIT {
@Autowired
private VertexAiPaLm2ChatConnector client;
private VertexAiPaLm2ModelCall client;
@Value("classpath:/prompts/system-message.st")
private Resource systemResource;
@@ -136,8 +136,8 @@ class VertexAiPaLm2ChatGenerationClientIT {
}
@Bean
public VertexAiPaLm2ChatConnector vertexAiEmbedding(VertexAiPaLm2Api vertexAiApi) {
return new VertexAiPaLm2ChatConnector(vertexAiApi);
public VertexAiPaLm2ModelCall vertexAiEmbedding(VertexAiPaLm2Api vertexAiApi) {
return new VertexAiPaLm2ModelCall(vertexAiApi);
}
}

2
models/spring-ai-vertex-ai-palm2/src/test/java/org/springframework/ai/vertexai/palm2/VertexAiPaLm2ChatRequestTests.java
View File

@@ -29,7 +29,7 @@ import static org.assertj.core.api.Assertions.assertThat;
*/
public class VertexAiPaLm2ChatRequestTests {
VertexAiPaLm2ChatConnector client = new VertexAiPaLm2ChatConnector(new VertexAiPaLm2Api("bla"));
VertexAiPaLm2ModelCall client = new VertexAiPaLm2ModelCall(new VertexAiPaLm2Api("bla"));
@Test
public void createRequestWithDefaultOptions() {

10
models/spring-ai-watsonx-ai/src/main/java/org/springframework/ai/watsonx/WatsonxAiChatConnector.java → models/spring-ai-watsonx-ai/src/main/java/org/springframework/ai/watsonx/WatsonxAiModelCall.java
View File

@@ -18,7 +18,7 @@ package org.springframework.ai.watsonx;
import java.util.List;
import java.util.Map;
import org.springframework.ai.chat.connector.ChatConnector;
import org.springframework.ai.chat.connector.ModelCall;
import reactor.core.publisher.Flux;
import org.springframework.ai.chat.ChatResponse;
@@ -35,7 +35,7 @@ import org.springframework.ai.watsonx.utils.MessageToPromptConverter;
import org.springframework.util.Assert;
/**
* {@link ChatConnector} implementation for {@literal watsonx.ai}.
* {@link ModelCall} 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 WatsonxAiChatConnector implements ChatConnector, StreamingChatClient {
public class WatsonxAiModelCall implements ModelCall, StreamingChatClient {
private final WatsonxAiApi watsonxAiApi;
private final WatsonxAiChatOptions defaultOptions;
public WatsonxAiChatConnector(WatsonxAiApi watsonxAiApi) {
public WatsonxAiModelCall(WatsonxAiApi watsonxAiApi) {
this(watsonxAiApi,
WatsonxAiChatOptions.builder()
.withTemperature(0.7f)
@@ -68,7 +68,7 @@ public class WatsonxAiChatConnector implements ChatConnector, StreamingChatClien
.build());
}
public WatsonxAiChatConnector(WatsonxAiApi watsonxAiApi, WatsonxAiChatOptions defaultOptions) {
public WatsonxAiModelCall(WatsonxAiApi watsonxAiApi, WatsonxAiChatOptions defaultOptions) {
Assert.notNull(watsonxAiApi, "watsonxAiApi cannot be null");
Assert.notNull(defaultOptions, "defaultOptions cannot be null");
this.watsonxAiApi = watsonxAiApi;

8
models/spring-ai-watsonx-ai/src/test/java/org/springframework/ai/watsonx/WatsonxAiChatConnectorTest.java → models/spring-ai-watsonx-ai/src/test/java/org/springframework/ai/watsonx/WatsonxAiModelCallTest.java
View File

@@ -46,9 +46,9 @@ import static org.mockito.Mockito.when;
* @author Pablo Sanchidrian Herrera
* @author John Jairo Moreno Rojas
*/
public class WatsonxAiChatConnectorTest {
public class WatsonxAiModelCallTest {
WatsonxAiChatConnector chatClient = new WatsonxAiChatConnector(mock(WatsonxAiApi.class));
WatsonxAiModelCall chatClient = new WatsonxAiModelCall(mock(WatsonxAiApi.class));
@Test
public void testCreateRequestWithNoModelId() {
@@ -157,7 +157,7 @@ public class WatsonxAiChatConnectorTest {
@Test
public void testCallMethod() {
WatsonxAiApi mockChatApi = mock(WatsonxAiApi.class);
WatsonxAiChatConnector client = new WatsonxAiChatConnector(mockChatApi);
WatsonxAiModelCall client = new WatsonxAiModelCall(mockChatApi);
Prompt prompt = new Prompt(List.of(new SystemMessage("Your prompt here")),
WatsonxAiChatOptions.builder().withModel("google/flan-ul2").build());
@@ -186,7 +186,7 @@ public class WatsonxAiChatConnectorTest {
@Test
public void testStreamMethod() {
WatsonxAiApi mockChatApi = mock(WatsonxAiApi.class);
WatsonxAiChatConnector client = new WatsonxAiChatConnector(mockChatApi);
WatsonxAiModelCall client = new WatsonxAiModelCall(mockChatApi);
Prompt prompt = new Prompt(List.of(new SystemMessage("Your prompt here")),
WatsonxAiChatOptions.builder().withModel("google/flan-ul2").build());

87
spring-ai-core/src/main/java/org/springframework/ai/chat/ChatClient.java
View File

@@ -1,6 +1,6 @@
package org.springframework.ai.chat;
import org.springframework.ai.chat.connector.ChatConnector;
import org.springframework.ai.chat.connector.ModelCall;
import org.springframework.ai.chat.messages.Media;
import org.springframework.ai.chat.messages.Message;
import org.springframework.ai.chat.messages.SystemMessage;
@@ -31,7 +31,7 @@ import java.util.function.Consumer;
// todo rebase to use the latest code
// todo can the fluid DSL be designed in such a way that calling .user() or .system()
// returns an object that wont let u call .messages(), and vice versa?
// todo make it so that i can reuse the specs when setting up defaults: defaultUser( spec->spec.text("..").
/*
* @author Mark Pollack
* @author Christian Tzolov
@@ -40,7 +40,7 @@ import java.util.function.Consumer;
*/
public interface ChatClient {
static ChatClientBuilder builder(ChatConnector connector) {
static ChatClientBuilder builder(ModelCall connector) {
return new ChatClientBuilder(connector);
}
@@ -156,7 +156,7 @@ public interface ChatClient {
class ChatClientRequest {
private final ChatConnector connector;
private final ModelCall connector;
private String userText = "";
@@ -176,8 +176,8 @@ public interface ChatClient {
private final Map<String, Object> systemParams = new HashMap<>();
public ChatClientRequest(ChatConnector connector, String userText, String systemText,
List<String> functionNames, List<Media> media, ChatOptions chatOptions) {
public ChatClientRequest(ModelCall connector, String userText, String systemText, List<String> functionNames,
List<Media> media, ChatOptions chatOptions) {
this.userText = userText;
this.systemText = systemText;
this.connector = connector;
@@ -233,10 +233,10 @@ public interface ChatClient {
private final ChatClientRequest request;
private final ChatConnector chatConnector;
private final ModelCall modelCall;
public ChatResponseSpec(ChatConnector chatConnector, ChatClientRequest request) {
this.chatConnector = chatConnector;
public ChatResponseSpec(ModelCall modelCall, ChatClientRequest request) {
this.modelCall = modelCall;
this.request = request;
}
@@ -296,7 +296,7 @@ public interface ChatClient {
}
}
var prompt = new Prompt(messages, this.request.chatOptions);
return this.chatConnector.call(prompt);
return this.modelCall.call(prompt);
}
public ChatResponse chatResponse() {
@@ -343,38 +343,73 @@ public interface ChatClient {
class ChatClientBuilder {
private final ChatConnector connector;
private final ModelCall modelCall;
private final List<Media> defaultMedia = new ArrayList<>();
private final List<String> defaultFunctions = new ArrayList<>();
private final List<String> defaultFunctionsNames = new ArrayList<>();
private final List<FunctionCallback> defaultFunctionCallbacks = new ArrayList<>();
private String defaultSystem;
private String defaultUser;
ChatClientBuilder(ChatConnector connector) {
Assert.notNull(connector, "the " + ChatConnector.class.getName() + " must be non-null!");
this.connector = connector;
ChatClientBuilder(ModelCall modelCall) {
Assert.notNull(modelCall, "the " + ModelCall.class.getName() + " must be non-null");
this.modelCall = modelCall;
}
public ChatClient build() {
return new DefaultChatClient(this.connector, this.defaultSystem, this.defaultUser, this.defaultFunctions,
this.defaultMedia);
return new DefaultChatClient(this.modelCall, this.defaultSystem, this.defaultUser,
this.defaultFunctionsNames, this.defaultMedia);
}
public ChatClientBuilder defaultSystem(String systemPrompt) {
this.defaultSystem = systemPrompt;
public ChatClientBuilder defaultSystem(Resource resource) {
return this.defaultSystem(resource, Charset.defaultCharset());
}
public ChatClientBuilder defaultSystem(Resource resource, Charset charset) {
try {
this.defaultSystem = resource.getContentAsString(charset);
}
catch (IOException e) {
throw new RuntimeException(e);
}
return this;
}
public ChatClientBuilder defaultSystem(String systemText) {
this.defaultSystem = systemText;
return this;
}
public ChatClientBuilder defaultFunctions(String... functionNames) {
this.defaultFunctions.addAll(List.of(functionNames));
this.defaultFunctionsNames.addAll(List.of(functionNames));
return this;
}
public ChatClientBuilder defaultUser(String userPrompt) {
this.defaultUser = userPrompt;
public ChatClientBuilder defaultFunctions(List<FunctionCallback> functions) {
this.defaultFunctionCallbacks.addAll(functions);
return this;
}
public ChatClientBuilder defaultUser(Resource userText) {
return this.defaultUser(userText, Charset.defaultCharset());
}
public ChatClientBuilder defaultUser(Resource userText, Charset charset) {
try {
this.defaultUser = userText.getContentAsString(charset);
}
catch (IOException e) {
throw new RuntimeException(e);
}
return this;
}
public ChatClientBuilder defaultUser(String userText) {
this.defaultUser = userText;
return this;
}
@@ -382,15 +417,15 @@ public interface ChatClient {
@Deprecated(since = "1.0.0 M1", forRemoval = true)
default String call(String message) {
Prompt prompt = new Prompt(new UserMessage(message));
Generation generation = call(prompt).getResult();
var prompt = new Prompt(new UserMessage(message));
var generation = call(prompt).getResult();
return (generation != null) ? generation.getOutput().getContent() : "";
}
@Deprecated(since = "1.0.0 M1", forRemoval = true)
default String call(Message... messages) {
Prompt prompt = new Prompt(Arrays.asList(messages));
Generation generation = call(prompt).getResult();
var prompt = new Prompt(Arrays.asList(messages));
var generation = call(prompt).getResult();
return (generation != null) ? generation.getOutput().getContent() : "";
}

12
spring-ai-core/src/main/java/org/springframework/ai/chat/DefaultChatClient.java
View File

@@ -1,6 +1,6 @@
package org.springframework.ai.chat;
import org.springframework.ai.chat.connector.ChatConnector;
import org.springframework.ai.chat.connector.ModelCall;
import org.springframework.ai.chat.messages.Media;
import org.springframework.ai.chat.prompt.Prompt;
@@ -14,7 +14,7 @@ import java.util.List;
*/
class DefaultChatClient implements ChatClient {
private final ChatConnector connector;
private final ModelCall modelCall;
private final String userText, systemText;
@@ -22,9 +22,9 @@ class DefaultChatClient implements ChatClient {
private final List<Media> media;
public DefaultChatClient(ChatConnector connector, String defaultSystemPrompt, String defaultUserPrompt,
public DefaultChatClient(ModelCall modelCall, String defaultSystemPrompt, String defaultUserPrompt,
List<String> defaultFunctions, List<Media> defaultMedia) {
this.connector = connector;
this.modelCall = modelCall;
this.userText = defaultUserPrompt;
this.systemText = defaultSystemPrompt;
this.functionNames = defaultFunctions;
@@ -34,7 +34,7 @@ class DefaultChatClient implements ChatClient {
@Override
public ChatClientRequest call() {
return new ChatClientRequest(this.connector, this.userText, this.systemText, this.functionNames, this.media,
return new ChatClientRequest(this.modelCall, this.userText, this.systemText, this.functionNames, this.media,
null);
}
@@ -46,7 +46,7 @@ class DefaultChatClient implements ChatClient {
@Deprecated(forRemoval = true, since = "1.0.0 M1")
@Override
public ChatResponse call(Prompt prompt) {
return this.connector.call(prompt);
return this.modelCall.call(prompt);
}
}

7
spring-ai-core/src/main/java/org/springframework/ai/chat/connector/ChatConnector.java → spring-ai-core/src/main/java/org/springframework/ai/chat/connector/ModelCall.java
View File

@@ -16,14 +16,9 @@
package org.springframework.ai.chat.connector;
import org.springframework.ai.chat.ChatResponse;
import org.springframework.ai.chat.Generation;
import org.springframework.ai.chat.messages.Message;
import org.springframework.ai.chat.messages.UserMessage;
import org.springframework.ai.chat.prompt.Prompt;
import java.util.Arrays;
public interface ChatConnector {
public interface ModelCall {
ChatResponse call(Prompt prompt);

24
spring-ai-core/src/main/java/org/springframework/ai/chat/service/PromptTransformingChatService.java
View File

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

14
spring-ai-core/src/main/java/org/springframework/ai/evaluation/RelevancyEvaluator.java
View File

@@ -1,6 +1,6 @@
package org.springframework.ai.evaluation;
import org.springframework.ai.chat.connector.ChatConnector;
import org.springframework.ai.chat.connector.ModelCall;
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 ChatConnector chatConnector;
private ModelCall modelCall;
public RelevancyEvaluator(ChatConnector chatConnector) {
this(chatConnector, ChatOptionsBuilder.builder().build());
public RelevancyEvaluator(ModelCall modelCall) {
this(modelCall, ChatOptionsBuilder.builder().build());
}
public RelevancyEvaluator(ChatConnector chatConnector, ChatOptions chatOptions) {
this.chatConnector = chatConnector;
public RelevancyEvaluator(ModelCall modelCall, ChatOptions chatOptions) {
this.modelCall = modelCall;
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.chatConnector.call(new Prompt(message, this.chatOptions));
ChatResponse chatResponse = this.modelCall.call(new Prompt(message, this.chatOptions));
var evaluationResponse = chatResponse.getResult().getOutput().getContent();
boolean passing = false;

2
spring-ai-core/src/main/java/org/springframework/ai/model/function/AbstractFunctionCallback.java
View File

@@ -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 ChatConnector's function
* @param name Function name. Should be unique within the ModelCall's function
* registry.
* @param description Function description. Used as a "system prompt" by the model to
* decide if the function should be called.

22
spring-ai-core/src/main/java/org/springframework/ai/model/function/FunctionCallingOptions.java
View File

@@ -24,33 +24,33 @@ import java.util.Set;
public interface FunctionCallingOptions {
/**
* Function Callbacks to be registered with the ChatConnector. For Prompt Options the
* Function Callbacks to be registered with the ModelCall. 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
* ChatConnector registry to be used in the chat completion requests.
* @return Return the Function Callbacks to be registered with the ChatConnector.
* ModelCall registry to be used in the chat completion requests.
* @return Return the Function Callbacks to be registered with the ModelCall.
*/
List<FunctionCallback> getFunctionCallbacks();
/**
* Set the Function Callbacks to be registered with the ChatConnector.
* Set the Function Callbacks to be registered with the ModelCall.
* @param functionCallbacks the Function Callbacks to be registered with the
* ChatConnector.
* ModelCall.
*/
void setFunctionCallbacks(List<FunctionCallback> functionCallbacks);
/**
* @return List of function names from the ChatConnector registry to be used in the
* next chat completion requests.
* @return List of function names from the ModelCall registry to be used in the next
* chat completion requests.
*/
Set<String> getFunctions();
/**
* 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.
* Set the list of function names from the ModelCall registry to be used in the next
* chat completion requests.
* @param functions the list of function names from the ModelCall registry to be used
* in the next chat completion requests.
*/
void setFunctions(Set<String> functions);

12
spring-ai-core/src/main/java/org/springframework/ai/transformer/KeywordMetadataEnricher.java
View File

@@ -18,7 +18,7 @@ package org.springframework.ai.transformer;
import java.util.List;
import java.util.Map;
import org.springframework.ai.chat.connector.ChatConnector;
import org.springframework.ai.chat.connector.ModelCall;
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 ChatConnector chatConnector;
private final ModelCall modelCall;
/**
* The number of keywords to extract.
*/
private final int keywordCount;
public KeywordMetadataEnricher(ChatConnector chatConnector, int keywordCount) {
Assert.notNull(chatConnector, "ChatConnector must not be null");
public KeywordMetadataEnricher(ModelCall modelCall, int keywordCount) {
Assert.notNull(modelCall, "ModelCall must not be null");
Assert.isTrue(keywordCount >= 1, "Document count must be >= 1");
this.chatConnector = chatConnector;
this.modelCall = modelCall;
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.chatConnector.call(prompt).getResult().getOutput().getContent();
String keywords = this.modelCall.call(prompt).getResult().getOutput().getContent();
document.getMetadata().putAll(Map.of(EXCERPT_KEYWORDS_METADATA_KEY, keywords));
}
return documents;

16
spring-ai-core/src/main/java/org/springframework/ai/transformer/SummaryMetadataEnricher.java
View File

@@ -20,7 +20,7 @@ import java.util.HashMap;
import java.util.List;
import java.util.Map;
import org.springframework.ai.chat.connector.ChatConnector;
import org.springframework.ai.chat.connector.ModelCall;
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 ChatConnector chatConnector;
private final ModelCall modelCall;
/**
* 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(ChatConnector chatConnector, List<SummaryType> summaryTypes) {
this(chatConnector, summaryTypes, DEFAULT_SUMMARY_EXTRACT_TEMPLATE, MetadataMode.ALL);
public SummaryMetadataEnricher(ModelCall modelCall, List<SummaryType> summaryTypes) {
this(modelCall, summaryTypes, DEFAULT_SUMMARY_EXTRACT_TEMPLATE, MetadataMode.ALL);
}
public SummaryMetadataEnricher(ChatConnector chatConnector, List<SummaryType> summaryTypes, String summaryTemplate,
public SummaryMetadataEnricher(ModelCall modelCall, List<SummaryType> summaryTypes, String summaryTemplate,
MetadataMode metadataMode) {
Assert.notNull(chatConnector, "ChatConnector must not be null");
Assert.notNull(modelCall, "ModelCall must not be null");
Assert.hasText(summaryTemplate, "Summary template must not be empty");
this.chatConnector = chatConnector;
this.modelCall = modelCall;
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.chatConnector.call(prompt).getResult().getOutput().getContent());
documentSummaries.add(this.modelCall.call(prompt).getResult().getOutput().getContent());
}
for (int i = 0; i < documentSummaries.size(); i++) {

6
spring-ai-core/src/test/java/org/springframework/ai/chat/ChatConnectorTests.java → spring-ai-core/src/test/java/org/springframework/ai/chat/ModelCallTests.java
View File

@@ -30,17 +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.connector.ModelCall;
import org.springframework.ai.chat.messages.AssistantMessage;
import org.springframework.ai.chat.prompt.Prompt;
/**
* Unit Tests for {@link ChatConnector}.
* Unit Tests for {@link ModelCall}.
*
* @author John Blum
* @since 0.2.0
*/
class ChatConnectorTests {
class ModelCallTests {
@Test
void generateWithStringCallsGenerateWithPromptAndReturnsResponseCorrectly() {

10
spring-ai-core/src/test/java/org/springframework/ai/chat/memory/ChatMemoryTests.java
View File

@@ -25,7 +25,7 @@ import org.mockito.Captor;
import org.mockito.Mock;
import org.mockito.junit.jupiter.MockitoExtension;
import org.springframework.ai.chat.connector.ChatConnector;
import org.springframework.ai.chat.connector.ModelCall;
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
ChatConnector chatConnector;
ModelCall modelCall;
@Mock
StreamingChatClient streamingChatClient;
@@ -61,7 +61,7 @@ public class ChatMemoryTests {
ChatMemory chatHistory = new InMemoryChatMemory();
PromptTransformingChatService chatService = PromptTransformingChatService.builder(chatConnector)
PromptTransformingChatService chatService = PromptTransformingChatService.builder(modelCall)
.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(chatConnector)
PromptTransformingChatService chatService = PromptTransformingChatService.builder(modelCall)
.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(chatConnector.call(promptCaptor.capture()))
when(modelCall.call(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"))));

2
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/bedrock.adoc
View File

@@ -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 `ChatConnector`, `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 `ModelCall`, `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.

8
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/anthropic-chat.adoc
View File

@@ -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 `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.
You can register custom Java functions with the `AnthropicModelCall` 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 `AnthropicChatConnector` implementation that you can inject into your class.
This will create a `AnthropicModelCall` 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 `ChatConnector` 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 `ModelCall` 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 `AnthropicChatConnector` and use it for text generations:
Next, create a `AnthropicModelCall` and use it for text generations:
[source,java]
----

10
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/azure-openai-chat.adoc
View File

@@ -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 `ChatConnector` implementation for Azure OpenAI.
The prefix `spring.ai.azure.openai.chat` is the property prefix that configures the `ModelCall` 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 `AzureOpenAiChatConnector` implementation that you can inject into your class.
This will create a `AzureOpenAiModelCall` 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 `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].
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 `ModelCall` 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 `AzureOpenAiChatConnector`. For more information about the `AzureOpenAiChatConnector` 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 `AzureOpenAiModelCall`. For more information about the `AzureOpenAiModelCall` refer to the link:../chat/azure-openai-chat.html[Azure OpenAI Chat] section.
Next, create an `AzureOpenAiChatConnector` instance and use it to generate text responses:
Next, create an `AzureOpenAiModelCall` instance and use it to generate text responses:
[source,java]
----

4
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/bedrock/bedrock-anthropic.adoc
View File

@@ -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 `BedrockAnthropicChatConnector` implementation that you can inject into your class.
This will create a `BedrockAnthropicModelCall` 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 `ChatConnector` 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 `ModelCall` 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:

4
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/bedrock/bedrock-anthropic3.adoc
View File

@@ -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 `BedrockAnthropicChatConnector` implementation that you can inject into your class.
This will create a `BedrockAnthropicModelCall` 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 `ChatConnector` 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 `ModelCall` 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:

4
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/bedrock/bedrock-cohere.adoc
View File

@@ -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 `BedrockCohereChatConnector` implementation that you can inject into your class.
This will create a `BedrockCohereModelCall` 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 `ChatConnector` 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 `ModelCall` 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:

4
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/bedrock/bedrock-jurassic2.adoc
View File

@@ -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 `BedrockAi21Jurassic2ChatConnector` implementation that you can inject into your class.
This will create a `BedrockAi21Jurassic2ModelCall` 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 `ChatConnector` 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 `ModelCall` 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:

4
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/bedrock/bedrock-llama.adoc
View File

@@ -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 `BedrockLlamaChatConnector` implementation that you can inject into your class.
This will create a `BedrockLlamaModelCall` 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 `ChatConnector` 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 `ModelCall` 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:

4
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/bedrock/bedrock-titan.adoc
View File

@@ -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 `BedrockTitanChatConnector` implementation that you can inject into your class.
This will create a `BedrockTitanModelCall` 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 `ChatConnector` 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 `ModelCall` 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:

12
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/functions/anthropic-chat-functions.adoc
View File

@@ -1,6 +1,6 @@
= Anthropic Function Calling
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.
You can register custom Java functions with the `AnthropicModelCall` 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 `ChatConnector`.
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 `ModelCall`.
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 `ChatConnector` will create an instance of a `FunctionCallbackWrapper` wrapper that adds the logic for it being invoked via the AI model.
Internally, Spring AI `ModelCall` 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 `AnthropicChatConnector`.
It wraps the 3rd party `MockWeatherService` function and registers it as a `CurrentWeather` function with the `AnthropicModelCall`.
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 `ChatConnector` 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 `ModelCall` 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 `AnthropicChatConnector` 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 `AnthropicModelCall` and use it in a prompt request.

10
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/functions/azure-open-ai-chat-functions.adoc
View File

@@ -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 `AzureOpenAiChatConnector` 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 `AzureOpenAiModelCall` 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 `ChatConnector`.
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 `ModelCall`.
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 `ChatConnector` will create an instance of a `FunctionCallbackWrapper` wrapper that adds the logic for it being invoked via the AI model.
Internally, Spring AI `ModelCall` 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 `ChatConnector` 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 `ModelCall` 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 `AzureOpenAiChatConnector` 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 `AzureOpenAiModelCall` and use it in a prompt request.

12
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/functions/mistralai-chat-functions.adoc
View File

@@ -1,6 +1,6 @@
= Mistral AI Function Calling
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.
You can register custom Java functions with the `MistralAiModelCall` 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 `ChatConnector`.
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 `ModelCall`.
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 `ChatConnector` will create an instance of a `FunctionCallbackWrapper` wrapper that adds the logic for it being invoked via the AI model.
Internally, Spring AI `ModelCall` 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 `MistralAiChatConnector`.
It wraps the 3rd party `MockWeatherService` function and registers it as a `CurrentWeather` function with the `MistralAiModelCall`.
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 `ChatConnector` 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 `ModelCall` 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 `MistralAiChatConnector` 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 `MistralAiModelCall` and use it in a prompt request.
== Appendices

20
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/functions/openai-chat-functions.adoc
View File

@@ -1,6 +1,6 @@
= Function Calling
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.
You can register custom Java functions with the `OpenAiModelCall` 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 `ChatConnector`.
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 `ModelCall`.
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 `ChatConnector`.
// Additionally, the Auto-Configuration provides a way to auto-register any Function<I, O> beans definition as function calling candidates in the `ModelCall`.
== 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 `ChatConnector` will create an instance of a `FunctionCallbackWrapper` wrapper that adds the logic for it being invoked via the AI model.
Internally, Spring AI `ModelCall` 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 `OpenAiChatConnector`.
It wraps the 3rd party `MockWeatherService` function and registers it as a `CurrentWeather` function with the `OpenAiModelCall`.
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 `ChatConnector` 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 `ModelCall` 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 `OpenAiChatConnector` 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 `OpenAiModelCall` and use it in a prompt request.
//
// === Register Functions with Default Options
//
// You can programmatically register functions with the `OpenAiChatConnector` using the `OpenAiChatOptions#withFunctionCallbacks`:
// You can programmatically register functions with the `OpenAiModelCall` 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();
//
// OpenAiChatConnector chatClient = new OpenAiChatConnector(openaiApi, defaultOptions);
// OpenAiModelCall chatClient = new OpenAiModelCall(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 OpenAiChatConnector is created, by you must enable in the Prompt the functions to be used in the request.
// NOTE: Functions are registered when OpenAiModelCall is created, by you must enable in the Prompt the functions to be used in the request.
== Appendices:

14
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/functions/vertexai-gemini-chat-functions.adoc
View File

@@ -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 `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.
You can register custom Java functions with the `VertexAiGeminiModelCall` 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 `ChatConnector`.
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 `ModelCall`.
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 `ChatConnector`.
// Additionally, the Auto-Configuration provides a way to auto-register any Function<I, O> beans definition as function calling candidates in the `ModelCall`.
== 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 `ChatConnector` will create an instance of a `FunctionCallbackWrapper` wrapper that adds the logic for it being invoked via the AI model.
Internally, Spring AI `ModelCall` 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 `VertexAiGeminiChatConnector`.
It wraps the 3rd party `MockWeatherService` function and registers it as a `CurrentWeather` function with the `VertexAiGeminiModelCall`.
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 `ChatConnector` 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 `ModelCall` 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 `VertexAiGeminiChatConnector` 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 `VertexAiGeminiModelCall` and use it in a prompt request.

8
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/mistralai-chat.adoc
View File

@@ -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 `ChatConnector` and `EmbeddingClient` implementations.
NOTE: You can override the common `spring.ai.mistralai.base-url` and `spring.ai.mistralai.api-key` for the `ModelCall` 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 `MistralAiChatConnector` implementation that you can inject into your class.
This will create a `MistralAiModelCall` 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 `ChatConnector` 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 `ModelCall` 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 `MistralAiChatConnector` and use it for text generations:
Next, create a `MistralAiModelCall` and use it for text generations:
[source,java]
----

12
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/ollama-chat.adoc
View File

@@ -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 `OllamaChatConnector`.
Spring AI supports the Ollama text generation with `OllamaModelCall`.
== 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 `OllamaChatConnector` implementation that you can inject into your class.
This will create a `OllamaModelCall` 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 `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.
If you don't want to use the Spring Boot auto-configuration, you can manually configure the `OllamaModelCall` 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 `ModelCall` 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 `OllamaChatConnector` instance and use it to text generations requests:
Next, create an `OllamaModelCall` 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 `OllamaChatConnector` instead.
NOTE: The `OllamaApi` is low level api and is not recommended for direct use. Use the `OllamaModelCall` instead.
[source,java]
----

8
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/openai-chat.adoc
View File

@@ -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 `ChatConnector` and `EmbeddingClient` implementations.
NOTE: You can override the common `spring.ai.openai.base-url` and `spring.ai.openai.api-key` for the `ModelCall` 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 `OpenAiChatConnector` implementation that you can inject into your class.
This will create a `OpenAiModelCall` 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 `ChatConnector` 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 `ModelCall` 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 `OpenAiChatConnector` and use it for text generations:
Next, create a `OpenAiModelCall` and use it for text generations:
[source,java]
----

6
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/vertexai-gemini-chat.adoc
View File

@@ -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 `VertexAiGeminiChatConnector` implementation that you can inject into your class.
This will create a `VertexAiGeminiModelCall` 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 `ChatConnector` 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 `ModelCall` 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 `VertexAiGeminiChatConnector` and use it for text generations:
Next, create a `VertexAiGeminiModelCall` and use it for text generations:
[source,java]
----

6
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/vertexai-palm2-chat.adoc
View File

@@ -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 `VertexAiPaLm2ChatConnector` implementation that you can inject into your class.
This will create a `VertexAiPaLm2ModelCall` 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 `ChatConnector` 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 `ModelCall` 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 `VertexAiPaLm2ChatConnector` and use it for text generations:
Next, create a `VertexAiPaLm2ModelCall` and use it for text generations:
[source,java]
----

2
spring-ai-docs/src/main/antora/modules/ROOT/pages/api/chat/watsonx-ai-chat.adoc
View File

@@ -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 `WatsonxAiChatConnector`.
Spring AI supports the watsonx.ai text generation with `WatsonxAiModelCall`.
== Prerequisites

Some files were not shown because too many files have changed in this diff Show More