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发布 v1.10 #5

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czm merged 147 commits from develop into main 2026-08-20 11:36:27 +08:00
Conversation 0 Commits 147 Files Changed 1488 +175 -9
2 changed files with 175 additions and 9 deletions
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85
easyflow-modules/easyflow-module-agent/src/main/java/tech/easyflow/agent/runtime/AgentScopeChatModelConnectivityVerifier.java
View File

@@ -51,8 +51,10 @@ public class AgentScopeChatModelConnectivityVerifier implements ChatModelConnect
private static final String PROBE_NONCE_FIELD = "nonce";
private static final String PROBE_IMAGE_FIELD = "imageCode";
private static final String VERIFICATION_SYSTEM_PROMPT = "You are a model connectivity verification assistant.";
/** 为未识别关闭思考扩展参数的推理模型保留足够的小额输出预算。 */
/** 首次能力探测使用的低成本输出预算。 */
private static final int MAX_TOKENS = 128;
/** 首次响应被输出上限截断时使用的单次重试预算。 */
private static final int TRUNCATION_RETRY_MAX_TOKENS = 512;
private static final Duration DEFAULT_PHASE_TIMEOUT = Duration.ofSeconds(60);
/** AgentScope 模型工厂。 */
@@ -158,11 +160,23 @@ public class AgentScopeChatModelConnectivityVerifier implements ChatModelConnect
*/
private boolean verifyProbeRequest(AgentModelSpec modelSpec, String nonce) {
AgentMessage message = buildProbeMessage(modelSpec.isSupportImage(), nonce);
List<ToolSchema> tools = List.of(buildProbeToolSchema(modelSpec.isSupportImage()));
ToolChoice toolChoice = new ToolChoice.Specific(PROBE_TOOL_NAME);
List<ChatResponse> responses = request(
modelSpec,
message,
List.of(buildProbeToolSchema(modelSpec.isSupportImage())),
new ToolChoice.Specific(PROBE_TOOL_NAME));
tools,
toolChoice);
if (hasLengthLimitedResponse(responses)) {
LOG.info("Model probe response reached token limit, retry with larger budget, modelName={}, maxTokens={}",
modelSpec.getModelName(), TRUNCATION_RETRY_MAX_TOKENS);
responses = request(
modelSpec,
message,
tools,
toolChoice,
TRUNCATION_RETRY_MAX_TOKENS);
}
boolean validToolCall = hasValidProbeToolCall(
responses, nonce, modelSpec.isSupportImage());
validateProbeResponse(modelSpec.isSupportImage(), responses, validToolCall);
@@ -197,11 +211,29 @@ public class AgentScopeChatModelConnectivityVerifier implements ChatModelConnect
AgentMessage verificationMessage,
List<ToolSchema> tools,
ToolChoice toolChoice) {
return request(modelSpec, verificationMessage, tools, toolChoice, MAX_TOKENS);
}
/**
* 使用指定输出预算执行一次非流式模型请求。
*
* @param modelSpec 运行时模型声明
* @param verificationMessage 验证消息
* @param tools 工具 Schema
* @param toolChoice 工具选择策略
* @param maxTokens 最大输出 Token 数
* @return 模型响应片段
*/
private List<ChatResponse> request(AgentModelSpec modelSpec,
AgentMessage verificationMessage,
List<ToolSchema> tools,
ToolChoice toolChoice,
int maxTokens) {
AgentGenerationOptions generationOptions = new AgentGenerationOptions();
generationOptions.setStream(false);
generationOptions.setThinkingEnabled(false);
disableOpenAiCompatibleThinking(modelSpec, generationOptions);
generationOptions.setMaxTokens(MAX_TOKENS);
generationOptions.setMaxTokens(maxTokens);
io.agentscope.core.model.Model agentScopeModel = modelFactory.create(modelSpec, generationOptions);
List<Msg> messages = List.of(
@@ -210,7 +242,7 @@ public class AgentScopeChatModelConnectivityVerifier implements ChatModelConnect
messageAdapter.toMsg(verificationMessage));
GenerateOptions.Builder requestBuilder = GenerateOptions.builder()
.stream(false)
.maxTokens(MAX_TOKENS)
.maxTokens(maxTokens)
.executionConfig(ExecutionConfig.builder()
.timeout(phaseTimeout)
.maxAttempts(1)
@@ -245,15 +277,34 @@ public class AgentScopeChatModelConnectivityVerifier implements ChatModelConnect
generationOptions.getAdditionalBodyParams().put("enable_thinking", false);
}
Object sourceProviderType = modelSpec.getMetadata().get("sourceProviderType");
if (sourceProviderType != null
&& "gpustack".equalsIgnoreCase(String.valueOf(sourceProviderType))) {
// GPUStack 托管的 vLLM 模型通过 chat_template_kwargs 控制 Qwen 思考模式。
if (usesChatTemplateThinkingControl(sourceProviderType)) {
// vLLM 与 SGLang 通过聊天模板参数控制 Qwen 思考模式。
generationOptions.getAdditionalBodyParams().put(
"chat_template_kwargs",
Map.of("enable_thinking", false));
}
}
/**
* 判断供应商是否通过聊天模板参数控制思考模式。
*
* @param sourceProviderType 模型配置中的原始供应商类型
* @return GPUStack 或自部署 vLLM/SGLang 入口返回 true
*/
private boolean usesChatTemplateThinkingControl(Object sourceProviderType) {
if (sourceProviderType == null) {
return false;
}
String normalizedProviderType = String.valueOf(sourceProviderType)
.trim()
.toLowerCase(Locale.ROOT)
.replace('_', '-');
return "gpustack".equals(normalizedProviderType)
|| "self-hosted".equals(normalizedProviderType)
|| "vllm".equals(normalizedProviderType)
|| "sglang".equals(normalizedProviderType);
}
/**
* 创建同时验证连接、视觉和工具调用的消息。
*
@@ -371,6 +422,24 @@ public class AgentScopeChatModelConnectivityVerifier implements ChatModelConnect
return false;
}
/**
* 判断响应是否因为达到输出 Token 上限而结束。
*
* @param responses AgentScope 响应片段
* @return 任一响应的结束原因为 length 时返回 true
*/
private boolean hasLengthLimitedResponse(List<ChatResponse> responses) {
if (responses == null) {
return false;
}
for (ChatResponse response : responses) {
if (response != null && "length".equalsIgnoreCase(response.getFinishReason())) {
return true;
}
}
return false;
}
/**
* 校验探测响应是否足以证明连接和视觉能力。
*

99
easyflow-modules/easyflow-module-agent/src/test/java/tech/easyflow/agent/runtime/AgentScopeChatModelConnectivityVerifierTest.java
View File

@@ -61,6 +61,43 @@ public class AgentScopeChatModelConnectivityVerifierTest {
Assert.assertEquals(MsgRole.USER, factory.getMessageBatches().get(0).get(1).getRole());
}
/**
* 验证能力探测达到输出上限时使用更大预算重试一次。
*/
@Test
public void shouldRetryProbeWithLargerBudgetWhenResponseIsLengthLimited() {
RecordingModelFactory factory = new RecordingModelFactory(
Flux.just(lengthLimitedResponse()),
Flux.just(toolResponse(TEST_NONCE, null)));
ChatModelVerificationResult result = verifier(factory).verify(model(false));
Assert.assertEquals(ModelVerificationStatus.PASSED, result.getStatus());
Assert.assertEquals(Boolean.TRUE, result.getSupportTool());
Assert.assertEquals(List.of(1, 1), factory.getToolCounts());
Assert.assertEquals(List.of(128, 512), factory.getFactoryMaxTokens());
Assert.assertEquals(List.of(128, 512), factory.getRequestMaxTokens());
}
/**
* 验证自部署 vLLM/SGLang 入口通过聊天模板参数关闭思考。
*/
@Test
public void shouldDisableThinkingThroughChatTemplateKwargsForSelfHostedEndpoint() {
RecordingModelFactory factory = new RecordingModelFactory(
Flux.just(toolResponse(TEST_NONCE, null)));
ChatModelVerificationResult result = verifier(factory).verify(
model(false, "self-hosted"));
Assert.assertEquals(ModelVerificationStatus.PASSED, result.getStatus());
Map<String, Object> bodyParams = factory.getFactoryAdditionalBodyParams().get(0);
Assert.assertEquals(Boolean.FALSE, bodyParams.get("enable_thinking"));
Assert.assertEquals(
Map.of("enable_thinking", false),
bodyParams.get("chat_template_kwargs"));
}
/**
* 验证模型返回普通文本时连接通过但工具能力保持关闭。
*/
@@ -143,6 +180,17 @@ public class AgentScopeChatModelConnectivityVerifierTest {
* @return 测试模型
*/
private Model model(boolean supportImage) {
return model(supportImage, "gpustack");
}
/**
* 创建携带指定供应商类型的待验证模型。
*
* @param supportImage 是否支持图片
* @param providerType 供应商类型
* @return 测试模型
*/
private Model model(boolean supportImage, String providerType) {
Model model = new Model();
model.setId(BigInteger.TEN);
model.setModelName("test-model");
@@ -151,7 +199,7 @@ public class AgentScopeChatModelConnectivityVerifierTest {
model.setApiKey("test-key");
model.setSupportImage(supportImage);
ModelProvider provider = new ModelProvider();
provider.setProviderType("gpustack");
provider.setProviderType(providerType);
model.setModelProvider(provider);
return model;
}
@@ -182,6 +230,18 @@ public class AgentScopeChatModelConnectivityVerifierTest {
.build();
}
/**
* 创建达到输出上限的响应。
*
* @return 输出被截断的 AgentScope 响应
*/
private ChatResponse lengthLimitedResponse() {
return ChatResponse.builder()
.content(List.of(TextBlock.builder().text("incomplete").build()))
.finishReason("length")
.build();
}
/**
* 创建探测工具调用响应。
*
@@ -212,8 +272,14 @@ public class AgentScopeChatModelConnectivityVerifierTest {
private final List<Flux<ChatResponse>> responses;
/** 模型工厂收到的流式参数。 */
private final List<Boolean> factoryStreams = new ArrayList<>();
/** 模型工厂收到的最大输出 Token 数。 */
private final List<Integer> factoryMaxTokens = new ArrayList<>();
/** 模型工厂收到的额外请求体参数。 */
private final List<Map<String, Object>> factoryAdditionalBodyParams = new ArrayList<>();
/** 模型请求收到的流式参数。 */
private final List<Boolean> requestStreams = new ArrayList<>();
/** 模型请求收到的最大输出 Token 数。 */
private final List<Integer> requestMaxTokens = new ArrayList<>();
/** 模型请求收到的消息批次。 */
private final List<List<Msg>> messageBatches = new ArrayList<>();
/** 每次请求携带的工具数量。 */
@@ -244,6 +310,9 @@ public class AgentScopeChatModelConnectivityVerifierTest {
AgentGenerationOptions generationOptions) {
int requestIndex = factoryStreams.size();
factoryStreams.add(Boolean.TRUE.equals(generationOptions.getStream()));
factoryMaxTokens.add(generationOptions.getMaxTokens());
factoryAdditionalBodyParams.add(Map.copyOf(
generationOptions.getAdditionalBodyParams()));
Flux<ChatResponse> response = responses.get(requestIndex);
return new io.agentscope.core.model.Model() {
/**
@@ -260,6 +329,7 @@ public class AgentScopeChatModelConnectivityVerifierTest {
List<ToolSchema> tools,
GenerateOptions options) {
requestStreams.add(Boolean.TRUE.equals(options.getStream()));
requestMaxTokens.add(options.getMaxTokens());
messageBatches.add(List.copyOf(inputMessages));
toolCounts.add(tools.size());
toolChoices.add(options.getToolChoice());
@@ -287,6 +357,24 @@ public class AgentScopeChatModelConnectivityVerifierTest {
return factoryStreams;
}
/**
* 获取模型工厂收到的最大输出 Token 数。
*
* @return 最大输出 Token 数列表
*/
private List<Integer> getFactoryMaxTokens() {
return factoryMaxTokens;
}
/**
* 获取模型工厂收到的额外请求体参数。
*
* @return 额外请求体参数列表
*/
private List<Map<String, Object>> getFactoryAdditionalBodyParams() {
return factoryAdditionalBodyParams;
}
/**
* 获取请求流式参数。
*
@@ -296,6 +384,15 @@ public class AgentScopeChatModelConnectivityVerifierTest {
return requestStreams;
}
/**
* 获取模型请求收到的最大输出 Token 数。
*
* @return 最大输出 Token 数列表
*/
private List<Integer> getRequestMaxTokens() {
return requestMaxTokens;
}
/**
* 获取每次请求的验证消息批次。
*