1 line
352 KiB
JSON
1 line
352 KiB
JSON
{"repo": "chenguojun06-star/fz66666", "files": {"cloudbaserc.json": {"92136382831779e22f23b72a08f4e137bfa17dd3": {"t": 1785745313.773528, "n": 3154, "h": "d7faedb3f54e9911", "c": "{\n \"version\": \"2.0\",\n \"envId\": \"{{TCB_ENV_ID}}\",\n \"framework\": {\n \"name\": \"fashion-supplychain\",\n \"plugins\": {\n \"backend\": {\n \"use\": \"@cloudbase/framework-plugin-container\",\n \"inputs\": {\n \"serviceName\": \"fashion-backend\",\n \"servicePath\": \"/api\",\n \"localPath\": \"./backend\",\n \"dockerfilePath\": \"./backend/Dockerfile\",\n \"containerPort\": 8088,\n \"initialDelaySeconds\": 300,\n \"minNum\": 1,\n \"maxNum\": 5,\n \"cpu\": 1,\n \"mem\": 2,\n \"envVariables\": {\n \"SPRING_PROFILES_ACTIVE\": \"prod\",\n \"WECHAT_TRUST_ALL_CERTS\": \"true\",\n \"APP_AUTH_JWT_SECRET\": \"{{JWT_SECRET}}\",\n \"APP_DB_HOST\": \"{{DB_HOST}}\",\n \"APP_DB_NAME\": \"{{DB_NAME}}\",\n \"APP_DB_USERNAME\": \"{{DB_USERNAME}}\",\n \"APP_DB_PASSWORD\": \"{{DB_PASSWORD}}\",\n \"APP_COS_SECRET_ID\": \"{{COS_SECRET_ID}}\",\n \"APP_COS_SECRET_KEY\": \"{{COS_SECRET_KEY}}\",\n \"APP_COS_BUCKET\": \"{{COS_BUCKET}}\",\n \"APP_COS_REGION\": \"{{COS_REGION}}\",\n \"DEEPSEEK_API_KEY\": \"{{DEEPSEEK_API_KEY}}\",\n \"AGNES_API_KEY\": \"{{AGNES_API_KEY}}\",\n \"AGNES_MODEL\": \"{{AGNES_MODEL:agnes-2.5-flash}}\",\n \"AGNES2_MODEL\": \"{{AGNES2_MODEL:agnes-2.5-flash}}\",\n \"AI_MODEL_VISION\": \"{{AI_MODEL_VISION:agnes-2.5-flash}}\",\n \"VOYAGE_API_KEY\": \"{{VOYAGE_API_KEY}}\",\n \"QDRANT_URL\": \"{{QDRANT_URL}}\",\n \"QDRANT_API_KEY\": \"{{QDRANT_API_KEY}}\",\n \"WECHAT_MP_APPID\": \"{{WECHAT_MP_APPID}}\",\n \"WECHAT_MP_SECRET\": \"{{WECHAT_MP_SECRET}}\",\n \"APP_SECURITY_PII_ENCRYPTION_KEY\": \"{{PII_ENCRYPTION_KEY}}\",\n \"AI_OPENAI_COMPAT_ENABLED\": \"{{AI_OPENAI_COMPAT_ENABLED}}\",\n \"AI_OPENAI_COMPAT_API_KEY\": \"{{AI_OPENAI_COMPAT_API_KEY}}\",\n \"AI_OPENAI_COMPAT_TENANT_ID\": \"{{AI_OPENAI_COMPAT_TENANT_ID}}\",\n \"AI_IM_AI_FEISHU_ENABLED\": \"true\",\n 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\"{{WECHAT_MP_SECRET}}\",\n \"APP_SECURITY_PII_ENCRYPTION_KEY\": \"{{PII_ENCRYPTION_KEY}}\",\n \"AI_OPENAI_COMPAT_ENABLED\": \"{{AI_OPENAI_COMPAT_ENABLED}}\",\n \"AI_OPENAI_COMPAT_API_KEY\": \"{{AI_OPENAI_COMPAT_API_KEY}}\",\n \"AI_OPENAI_COMPAT_TENANT_ID\": \"{{AI_OPENAI_COMPAT_TENANT_ID}}\",\n \"AI_IM_AI_FEISHU_ENABLED\": \"true\",\n \"AI_IM_AI_FEISHU_VERIFICATION_TOKEN\": \"dxZCfyFE816hLOrPbpE3pzdHJpmcXAMQ\",\n \"AI_IM_AI_FEISHU_ENCRYPT_KEY\": \"7qZik8wobG3FJVt8rrg97esIHfRRV6dc\"\n }\n }\n ],\n \"static\": [\n {\n \"name\": \"fashion-h5\",\n \"localPath\": \"./h5-web/dist\",\n \"cloudPath\": \"/h5\"\n },\n {\n \"name\": \"fashion-admin\",\n \"localPath\": \"./frontend/dist\",\n \"cloudPath\": \"/\"\n }\n ]\n}\n"}, "49b31a363b2a1b237b97d53d638aad2dbcd3279f": {"t": 1785749959.2831678, "n": 1966, "h": "8b9aaf48e647f684", "c": "{\n \"version\": \"2.0\",\n \"envId\": \"{{TCB_ENV_ID}}\",\n \"services\": [\n {\n \"name\": \"fashion-backend\",\n \"dockerfile\": \"./backend/Dockerfile\",\n 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\"mem\": 1,\n \"minNum\": 1,\n \"maxNum\": 2,\n \"envVariables\": {\n \"BACKEND_URL\": \"http://backend:8088\"\n }\n }\n }\n }\n }\n}\n"}, "ad51ee6c6a3f767ca4208e39dffd66fb54532203": {"t": 1785749985.343798, "n": 2211, "h": "67574c62768cce8f", "c": "{\n \"envId\": \"{{ENV_ID}}\",\n \"framework\": {\n \"name\": \"fashion-supplychain\",\n \"plugins\": {\n \"backend\": {\n \"use\": \"@cloudbase/framework-plugin-container\",\n \"inputs\": {\n \"serviceName\": \"backend\",\n \"servicePath\": \"/backend\",\n \"localPath\": \"./backend\",\n \"containerPort\": 8088,\n \"cpu\": 2,\n \"mem\": 4,\n \"minNum\": 1,\n \"maxNum\": 4,\n \"policyType\": \"cpu\",\n \"policyThreshold\": 60,\n \"envVariables\": {\n \"PORT\": \"8088\",\n \"SPRING_DATASOURCE_URL\": \"jdbc:mysql://{{MYSQL_HOST}}:3306/fashion_supplychain?useUnicode=true&characterEncoding=UTF-8&useSSL=false&serverTimezone=Asia/Shanghai&allowPublicKeyRetrieval=true\",\n \"SPRING_DATASOURCE_USERNAME\": \"{{MYSQL_USER}}\",\n \"SPRING_DATASOURCE_PASSWORD\": \"{{MYSQL_PASSWORD}}\",\n \"SPRING_REDIS_HOST\": \"{{REDIS_HOST}}\",\n \"SPRING_REDIS_PORT\": \"6379\",\n \"APP_AUTH_JWT_SECRET\": \"{{JWT_SECRET}}\",\n \"WECHAT_MP_APPID\": \"{{WECHAT_APPID}}\",\n \"WECHAT_MP_SECRET\": \"{{WECHAT_SECRET}}\",\n \"WECHAT_MINI_PROGRAM_MOCK_ENABLED\": \"false\",\n \"FLYWAY_ENABLED\": \"true\",\n \"SPRING_PROFILES_ACTIVE\": \"prod\",\n \"FASHION_DB_INITIALIZER_ENABLED\": \"false\",\n \"APP_CORS_ALLOWED_ORIGIN_PATTERNS\": \"https://*.tcloudbase.com,https://*.service.tcloudbase.com,https://*.ap-shanghai.service.tcloudbase.com\",\n \"COS_SECRET_ID\": \"{{COS_SECRET_ID}}\",\n \"COS_SECRET_KEY\": \"{{COS_SECRET_KEY}}\",\n \"COS_REGION\": \"{{COS_REGION}}\",\n \"COS_BUCKET\": \"{{COS_BUCKET}}\"\n },\n \"customLogs\": \"stdout\",\n \"healthPath\": \"/actuator/health/liveness\"\n }\n },\n \"frontend\": {\n \"use\": \"@cloudbase/framework-plugin-container\",\n \"inputs\": {\n \"serviceName\": \"frontend\",\n \"servicePath\": \"/\",\n \"localPath\": \"./frontend\",\n \"containerPort\": 80,\n \"cpu\": 0.5,\n \"mem\": 1,\n \"minNum\": 1,\n \"maxNum\": 2,\n \"envVariables\": {\n \"BACKEND_URL\": \"http://backend:8088\"\n }\n }\n }\n }\n }\n}\n"}, "601715e5cbc4c33900ddcd2a3df14884eaf406b0": {"t": 1785749986.407472, "n": 2165, "h": "8cf96518164031d4", "c": "{\n \"envId\": \"{{ENV_ID}}\",\n \"framework\": {\n \"name\": \"fashion-supplychain\",\n \"plugins\": {\n \"backend\": {\n \"use\": \"@cloudbase/framework-plugin-container\",\n \"inputs\": {\n \"serviceName\": \"backend\",\n \"servicePath\": \"/backend\",\n \"localPath\": \"./backend\",\n \"containerPort\": 8088,\n \"cpu\": 2,\n \"mem\": 4,\n \"minNum\": 1,\n \"maxNum\": 4,\n \"policyType\": \"cpu\",\n \"policyThreshold\": 60,\n \"envVariables\": {\n \"PORT\": \"8088\",\n \"SPRING_DATASOURCE_URL\": \"jdbc:mysql://{{MYSQL_HOST}}:3306/fashion_supplychain?useUnicode=true&characterEncoding=UTF-8&useSSL=false&serverTimezone=Asia/Shanghai&allowPublicKeyRetrieval=true\",\n \"SPRING_DATASOURCE_USERNAME\": \"{{MYSQL_USER}}\",\n \"SPRING_DATASOURCE_PASSWORD\": \"{{MYSQL_PASSWORD}}\",\n \"SPRING_REDIS_HOST\": \"{{REDIS_HOST}}\",\n \"SPRING_REDIS_PORT\": \"6379\",\n \"APP_AUTH_JWT_SECRET\": \"{{JWT_SECRET}}\",\n \"WECHAT_MP_APPID\": \"{{WECHAT_APPID}}\",\n \"WECHAT_MP_SECRET\": \"{{WECHAT_SECRET}}\",\n \"WECHAT_MINI_PROGRAM_MOCK_ENABLED\": \"false\",\n \"FLYWAY_ENABLED\": \"true\",\n \"FASHION_DB_INITIALIZER_ENABLED\": \"false\",\n \"APP_CORS_ALLOWED_ORIGIN_PATTERNS\": \"https://*.tcloudbase.com,https://*.service.tcloudbase.com,https://*.ap-shanghai.service.tcloudbase.com\",\n \"COS_SECRET_ID\": \"{{COS_SECRET_ID}}\",\n \"COS_SECRET_KEY\": \"{{COS_SECRET_KEY}}\",\n \"COS_REGION\": \"{{COS_REGION}}\",\n \"COS_BUCKET\": \"{{COS_BUCKET}}\"\n },\n \"customLogs\": \"stdout\",\n \"healthPath\": \"/actuator/health/liveness\"\n }\n },\n \"frontend\": {\n \"use\": \"@cloudbase/framework-plugin-container\",\n \"inputs\": {\n \"serviceName\": \"frontend\",\n \"servicePath\": \"/\",\n \"localPath\": \"./frontend\",\n \"containerPort\": 80,\n \"cpu\": 0.5,\n \"mem\": 1,\n \"minNum\": 1,\n \"maxNum\": 2,\n \"envVariables\": {\n \"BACKEND_URL\": \"http://backend:8088\"\n }\n }\n }\n }\n }\n}\n"}, "40ac69d8bb188db9ff3a7e76bcbef748accb06a1": {"t": 1785749987.5192716, "n": 2156, "h": "2bfc268bdbb7fd0f", "c": "{\n \"envId\": \"{{ENV_ID}}\",\n \"framework\": {\n \"name\": \"fashion-supplychain\",\n \"plugins\": {\n \"backend\": {\n \"use\": \"@cloudbase/framework-plugin-container\",\n \"inputs\": {\n \"serviceName\": \"backend\",\n \"servicePath\": \"/backend\",\n \"localPath\": \"./backend\",\n \"containerPort\": 8088,\n \"cpu\": 2,\n \"mem\": 4,\n \"minNum\": 1,\n \"maxNum\": 4,\n \"policyType\": \"cpu\",\n \"policyThreshold\": 60,\n \"envVariables\": {\n \"PORT\": \"8088\",\n \"SPRING_DATASOURCE_URL\": \"jdbc:mysql://{{MYSQL_HOST}}:3306/fashion_supplychain?useUnicode=true&characterEncoding=UTF-8&useSSL=false&serverTimezone=Asia/Shanghai&allowPublicKeyRetrieval=true\",\n \"SPRING_DATASOURCE_USERNAME\": \"{{MYSQL_USER}}\",\n \"SPRING_DATASOURCE_PASSWORD\": \"{{MYSQL_PASSWORD}}\",\n \"SPRING_REDIS_HOST\": \"{{REDIS_HOST}}\",\n \"SPRING_REDIS_PORT\": \"6379\",\n \"APP_AUTH_JWT_SECRET\": \"{{JWT_SECRET}}\",\n \"WECHAT_MP_APPID\": \"{{WECHAT_APPID}}\",\n \"WECHAT_MP_SECRET\": \"{{WECHAT_SECRET}}\",\n \"WECHAT_MINI_PROGRAM_MOCK_ENABLED\": \"false\",\n \"FLYWAY_ENABLED\": \"true\",\n \"FASHION_DB_INITIALIZER_ENABLED\": \"false\",\n \"APP_CORS_ALLOWED_ORIGIN_PATTERNS\": \"https://*.tcloudbase.com,https://*.service.tcloudbase.com,https://*.ap-shanghai.service.tcloudbase.com\",\n \"COS_SECRET_ID\": \"{{COS_SECRET_ID}}\",\n \"COS_SECRET_KEY\": \"{{COS_SECRET_KEY}}\",\n \"COS_REGION\": \"{{COS_REGION}}\",\n \"COS_BUCKET\": \"{{COS_BUCKET}}\"\n },\n \"customLogs\": \"stdout\",\n \"healthPath\": \"/actuator/health\"\n }\n },\n \"frontend\": {\n \"use\": \"@cloudbase/framework-plugin-container\",\n \"inputs\": {\n \"serviceName\": \"frontend\",\n \"servicePath\": \"/\",\n \"localPath\": \"./frontend\",\n \"containerPort\": 80,\n \"cpu\": 0.5,\n \"mem\": 1,\n \"minNum\": 1,\n \"maxNum\": 2,\n \"envVariables\": {\n \"BACKEND_URL\": \"http://backend:8088\"\n }\n }\n }\n }\n }\n}\n"}, "270bc97bb3d0f62e5eded3e08752b3677c7c64af": {"t": 1785749988.746784, "n": 1966, "h": "c22de3d22a3029a3", "c": "{\n \"envId\": \"{{ENV_ID}}\",\n \"framework\": {\n \"name\": \"fashion-supplychain\",\n \"plugins\": {\n \"backend\": {\n \"use\": \"@cloudbase/framework-plugin-container\",\n \"inputs\": {\n \"serviceName\": \"backend\",\n \"servicePath\": \"/backend\",\n \"localPath\": \"./backend\",\n \"containerPort\": 8088,\n \"cpu\": 2,\n \"mem\": 4,\n \"minNum\": 1,\n \"maxNum\": 4,\n \"policyType\": \"cpu\",\n \"policyThreshold\": 60,\n \"envVariables\": {\n \"PORT\": \"8088\",\n \"SPRING_DATASOURCE_URL\": \"jdbc:mysql://{{MYSQL_HOST}}:3306/fashion_supplychain?useUnicode=true&characterEncoding=UTF-8&useSSL=false&serverTimezone=Asia/Shanghai&allowPublicKeyRetrieval=true\",\n \"SPRING_DATASOURCE_USERNAME\": \"{{MYSQL_USER}}\",\n \"SPRING_DATASOURCE_PASSWORD\": \"{{MYSQL_PASSWORD}}\",\n \"SPRING_REDIS_HOST\": \"{{REDIS_HOST}}\",\n \"SPRING_REDIS_PORT\": \"6379\",\n \"APP_AUTH_JWT_SECRET\": \"{{JWT_SECRET}}\",\n \"WECHAT_MP_APPID\": \"{{WECHAT_APPID}}\",\n \"WECHAT_MP_SECRET\": \"{{WECHAT_SECRET}}\",\n \"WECHAT_MINI_PROGRAM_MOCK_ENABLED\": \"false\",\n \"FLYWAY_ENABLED\": \"true\",\n \"FASHION_DB_INITIALIZER_ENABLED\": \"false\",\n \"APP_CORS_ALLOWED_ORIGIN_PATTERNS\": \"https://*.tcloudbase.com,https://*.service.tcloudbase.com,https://*.ap-shanghai.service.tcloudbase.com\"\n },\n \"customLogs\": \"stdout\",\n \"healthPath\": \"/actuator/health\"\n }\n },\n \"frontend\": {\n \"use\": \"@cloudbase/framework-plugin-container\",\n \"inputs\": {\n \"serviceName\": \"frontend\",\n \"servicePath\": \"/\",\n \"localPath\": \"./frontend\",\n \"containerPort\": 80,\n \"cpu\": 0.5,\n \"mem\": 1,\n \"minNum\": 1,\n \"maxNum\": 2,\n \"envVariables\": {\n \"BACKEND_URL\": \"http://backend:8088\"\n }\n }\n }\n }\n }\n}\n"}}, "backend/src/main/java/com/fashion/supplychain/intelligence/gateway/AiInferenceRouter.java": {"92136382831779e22f23b72a08f4e137bfa17dd3": {"t": 1785745687.3106582, "n": 20750, "h": "31e72f3762438f68", "c": "package com.fashion.supplychain.intelligence.gateway;\n\nimport com.fashion.supplychain.intelligence.agent.AiMessage;\nimport com.fashion.supplychain.intelligence.agent.AiTool;\nimport com.fashion.supplychain.intelligence.dto.IntelligenceInferenceResult;\nimport com.fashion.supplychain.intelligence.orchestration.AiCostTrackingOrchestrator;\nimport com.fashion.supplychain.intelligence.orchestration.AiOperationAuditOrchestrator;\nimport com.fashion.supplychain.intelligence.service.CostExplosionGuard;\nimport com.fashion.supplychain.intelligence.service.ModelSelectionRouter;\nimport com.fashion.supplychain.common.UserContext;\nimport java.util.List;\nimport java.util.Map;\nimport java.util.concurrent.atomic.AtomicInteger;\nimport java.util.concurrent.atomic.AtomicLong;\nimport lombok.extern.slf4j.Slf4j;\nimport org.springframework.beans.factory.annotation.Autowired;\nimport org.springframework.beans.factory.annotation.Value;\nimport org.springframework.context.annotation.Primary;\nimport org.springframework.stereotype.Service;\nimport org.springframework.context.annotation.Lazy;\n\n@Slf4j\n@Service\n@Lazy\n@Primary\npublic class AiInferenceRouter implements AiInferenceGateway {\n\n private static final int CIRCUIT_OPEN_THRESHOLD = 3;\n private static final long CIRCUIT_RESET_MS = 30_000L;\n\n @Value(\"${ai.gateway.routing-strategy:failover}\")\n private String routingStrategy;\n\n /**\n * 【P1-10修复】启动时校验 DEEPSEEK_API_KEY 非空。\n *\n * <p>原行为：key 为空时启动正常，但所有 AI 功能（对话/视觉/工具调用）都会失败，\n * 用户排查困难（错误散落在各 Service 中）。\n *\n * <p>现行为：\n * <ul>\n * <li>fail-fast-on-empty=true（默认 false）：启动失败，明确提示缺失 key</li>\n * <li>fail-fast-on-empty=false（默认）：启动成功，但打印 ERROR 级别告警 + 列出受影响功能</li>\n * </ul>\n *\n * <p>受影响功能：DeepSeek 对话、Spring AI 工具调用、AiAdvisor 日常建议、\n * SelfCritic 自评分、EvolutionPipeline 自进化、Qdrant 向量检索（embedding 降级）。\n */\n @Value(\"${ai.api-key.fail-fast-on-empty:false}\")\n private boolean failFastOnEmptyApiKey;\n\n @Value(\"${ai.deepseek.api-key:${DEEPSEEK_API_KEY:}}\")\n private String deepseekApiKey;\n\n @Autowired\n private LegacyInferenceAdapter legacyAdapter;\n\n @Autowired(required = false)\n private SpringAiInferenceAdapter springAiAdapter;\n\n @Autowired(required = false)\n private AiCostTrackingOrchestrator aiCostTrackingOrchestrator;\n\n @Autowired(required = false)\n private AiOperationAuditOrchestrator aiOperationAuditOrchestrator;\n\n @Autowired(required = false)\n private ModelConsortiumRouter modelConsortiumRouter;\n\n @Autowired(required = false)\n private ModelSelectionRouter modelSelectionRouter;\n\n @Autowired(required = false)\n private CostExplosionGuard costExplosionGuard;\n\n private final AtomicInteger springAiConsecutiveFailures = new AtomicInteger(0);\n private final AtomicLong springAiCircuitOpenSince = new AtomicLong(0);\n\n /**\n * 【P1-10修复】启动时校验 DEEPSEEK_API_KEY。\n * 默认仅告警不阻止启动，开启 fail-fast-on-empty=true 可强制阻止启动。\n */\n @jakarta.annotation.PostConstruct\n public void validateApiKey() {\n boolean keyMissing = deepseekApiKey == null || deepseekApiKey.isBlank();\n if (!keyMissing) {\n log.info(\"[AiInferenceRouter] DEEPSEEK_API_KEY 已配置（长度={}），AI 功能正常可用\",\n deepseekApiKey.length());\n return;\n }\n\n String affectedFeatures = \"DeepSeek 对话 / Spring AI 工具调用 / AiAdvisor 日常建议 / \"\n + \"SelfCritic 自评分 / EvolutionPipeline 自进化 / Qdrant 向量检索（embedding 降级）\";\n\n if (failFastOnEmptyApiKey) {\n String msg = \"[AiInferenceRouter] DEEPSEEK_API_KEY 为空且 ai.api-key.fail-fast-on-empty=true，\"\n + \"应用启动被阻止。受影响功能：\" + affectedFeatures\n + \"。解决：设置环境变量 DEEPSEEK_API_KEY 或在 yml 配置 ai.deepseek.api-key\";\n log.error(msg);\n throw new IllegalStateException(msg);\n }\n\n log.error(\"[AiInferenceRouter] ⚠️ DEEPSEEK_API_KEY 为空！AI 功能将全部失败。受影响功能：{}\", affectedFeatures);\n log.error(\"[AiInferenceRouter] 解决：设置环境变量 DEEPSEEK_API_KEY 或在 yml 配置 ai.deepseek.api-key\");\n log.error(\"[AiInferenceRouter] 如需启动失败而非降级，设置 ai.api-key.fail-fast-on-empty=true\");\n }\n\n @Override\n public IntelligenceInferenceResult chat(String scene, String systemPrompt, String userMessage) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chat(scene, systemPrompt, userMessage);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chat(scene, systemPrompt, userMessage);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chat(String scene, List<AiMessage> messages, List<AiTool> tools) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chat(scene, messages, tools);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chat(scene, messages, tools);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chatStream(String scene, List<AiMessage> messages,\n List<AiTool> tools,\n StreamChunkConsumer chunkConsumer) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chatStream(scene, messages, tools, chunkConsumer);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chatStream(scene, messages, tools, chunkConsumer);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chatWithVision(String scene, String systemPrompt, String userMessage, String imageUrl) {\n // 视觉调用优先走 legacy adapter — Agnes视觉模型支持真正的image_url格式，\n // 比Spring AI适配器的文本嵌入方式更可靠\n if (legacyAdapter != null && legacyAdapter.isAvailable()) {\n try {\n IntelligenceInferenceResult result =\n legacyAdapter.chatWithVision(scene, systemPrompt, userMessage, imageUrl);\n if (result != null && result.isSuccess() && hasText(result.getContent())) {\n recordCostAndAudit(scene, result);\n return result;\n }\n log.warn(\"[AiInferenceRouter] legacy视觉调用未返回有效内容，fallback到Spring AI\");\n } catch (Exception e) {\n log.warn(\"[AiInferenceRouter] legacy视觉调用异常，fallback到Spring AI: {}\", e.getMessage());\n }\n }\n // Spring AI 作为第二路径\n if (springAiAdapter != null && !isSpringAiCircuitOpen() && springAiAdapter.isAvailable()) {\n IntelligenceInferenceResult result = springAiAdapter.chatWithVision(scene, systemPrompt, userMessage, imageUrl);\n trackSpringAiHealth(result);\n recordCostAndAudit(scene, result);\n return result;\n }\n // 最后兜底返回空结果\n IntelligenceInferenceResult empty = new IntelligenceInferenceResult();\n empty.setSuccess(false);\n empty.setProvider(\"none\");\n empty.setErrorMessage(\"没有可用的视觉模型（请配置AGNES_API_KEY或DEEPSEEK_API_KEY）\");\n recordCostAndAudit(scene, empty);\n return empty;\n }\n\n @Override\n public boolean isAvailable() {\n return resolveGateway(null).isAvailable();\n }\n\n @Override\n public boolean isVisionAvailable() {\n if (legacyAdapter != null && legacyAdapter.isVisionAvailable()) {\n return true;\n }\n if (springAiAdapter != null && !isSpringAiCircuitOpen() && springAiAdapter.isVisionAvailable()) {\n return true;\n }\n return false;\n }\n\n @Override\n public String getProviderName() {\n return resolveGateway(null).getProviderName();\n }\n\n private AiInferenceGateway resolveGateway(String scene) {\n return switch (routingStrategy) {\n case \"spring-ai\" -> resolveSpringAi();\n case \"failover\" -> resolveFailover(scene);\n case \"legacy\" -> legacyAdapter;\n default -> resolveFailover(scene);\n };\n }\n\n private AiInferenceGateway resolveSpringAi() {\n if (isSpringAiCircuitOpen()) {\n log.warn(\"[AiInferenceRouter] Spring AI 熔断中，降级到 legacy\");\n return legacyAdapter;\n }\n if (springAiAdapter != null && springAiAdapter.isAvailable()) {\n return springAiAdapter;\n }\n log.warn(\"[AiInferenceRouter] spring-ai unavailable, falling back to legacy\");\n return legacyAdapter;\n }\n\n private AiInferenceGateway resolveFailover(String scene) {\n // 短查询/非Agent场景优先走 Spring AI（更快、工具调用更新）\n boolean preferSpringAi = isShortQueryScene(scene);\n if (preferSpringAi && !isSpringAiCircuitOpen()\n && springAiAdapter != null && springAiAdapter.isAvailable()) {\n log.debug(\"[AiInferenceRouter] 短查询场景={}，优先使用 Spring AI\", scene);\n return springAiAdapter;\n }\n // Agent主循环优先Spring AI（流式+tool_calls更好），熔断时降级\n if (!isSpringAiCircuitOpen() && springAiAdapter != null && springAiAdapter.isAvailable()) {\n return springAiAdapter;\n }\n if (legacyAdapter.isAvailable()) {\n return legacyAdapter;\n }\n if (!isSpringAiCircuitOpen() && springAiAdapter != null) {\n return springAiAdapter;\n }\n return legacyAdapter;\n }\n\n private boolean isShortQueryScene(String scene) {\n return scene != null && (\n \"nl-intent\".equals(scene) || \"daily-brief\".equals(scene) ||\n \"critic_review\".equals(scene) || scene.startsWith(\"memory\"));\n }\n\n private boolean isSpringAiCircuitOpen() {\n if (springAiConsecutiveFailures.get() < CIRCUIT_OPEN_THRESHOLD) return false;\n long openSince = springAiCircuitOpenSince.get();\n if (openSince > 0 && System.currentTimeMillis() - openSince > CIRCUIT_RESET_MS) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断进入半开状态，允许探测请求\");\n return false;\n }\n return openSince > 0;\n }\n\n private void trackSpringAiHealth(IntelligenceInferenceResult result) {\n if (result == null || !\"spring-ai\".equals(result.getProvider())) return;\n if (result.isSuccess()) {\n if (springAiConsecutiveFailures.get() >= CIRCUIT_OPEN_THRESHOLD) {\n log.info(\"[AiInferenceRouter] Spring AI 半开探测成功，熔断恢复\");\n }\n springAiConsecutiveFailures.set(0);\n springAiCircuitOpenSince.set(0);\n } else {\n int failures = springAiConsecutiveFailures.incrementAndGet();\n if (failures == CIRCUIT_OPEN_THRESHOLD && springAiCircuitOpenSince.get() == 0) {\n springAiCircuitOpenSince.set(System.currentTimeMillis());\n log.warn(\"[AiInferenceRouter] Spring AI 连续{}次失败，熔断开启（{}秒后进入半开探测）\", failures, CIRCUIT_RESET_MS / 1000);\n } else if (springAiCircuitOpenSince.get() > 0\n && System.currentTimeMillis() - springAiCircuitOpenSince.get() > CIRCUIT_RESET_MS) {\n log.warn(\"[AiInferenceRouter] Spring AI 半开探测失败，重新熔断 {} 秒\", CIRCUIT_RESET_MS / 1000);\n springAiCircuitOpenSince.set(System.currentTimeMillis());\n }\n }\n }\n\n private void recordCostAndAudit(String scene, IntelligenceInferenceResult result) {\n if (result == null) return;\n // 统一性能日志：方便排查双AI引擎表现\n log.info(\"[AiInference] engine={}, scene={}, success={}, latency={}ms, tokens={}/{}, fallback={}\",\n result.getProvider() != null ? result.getProvider() : \"unknown\",\n scene != null ? scene : \"default\",\n result.isSuccess(),\n result.getLatencyMs(),\n result.getPromptTokens(), result.getCompletionTokens(),\n Boolean.TRUE.equals(result.isFallbackUsed()));\n if (!result.isSuccess()) {\n log.warn(\"[AiInference] engine={}, scene={}, error={}\", result.getProvider(), scene, result.getErrorMessage());\n }\n try {\n if (aiCostTrackingOrchestrator != null) {\n aiCostTrackingOrchestrator.recordAsync(\n result.getModel() != null ? result.getModel() : result.getProvider(),\n scene,\n result.getPromptTokens(),\n result.getCompletionTokens(),\n (int) result.getLatencyMs(),\n result.isSuccess(),\n result.getErrorMessage()\n );\n }\n } catch (Exception e) {\n log.debug(\"[AiInferenceRouter] cost tracking failed: {}\", e.getMessage());\n }\n try {\n if (aiOperationAuditOrchestrator != null && result.getTraceId() != null) {\n aiOperationAuditOrchestrator.recordAudit(\n result.getTraceId(),\n \"inference:\" + scene,\n result.getPromptChars() + \" chars\",\n result.getResponseChars() + \" chars\",\n result.getLatencyMs(),\n result.isSuccess(),\n result.getErrorMessage()\n );\n }\n } catch (Exception e) {\n log.debug(\"[AiInferenceRouter] audit recording failed: {}\", e.getMessage());\n }\n }\n\n public Map<String, Object> getRoutingStatus() {\n Map<String, Object> status = new java.util.LinkedHashMap<>();\n status.put(\"strategy\", routingStrategy);\n status.put(\"legacyAvailable\", legacyAdapter.isAvailable());\n status.put(\"springAiAvailable\", springAiAdapter != null && springAiAdapter.isAvailable());\n status.put(\"springAiCircuitOpen\", isSpringAiCircuitOpen());\n status.put(\"springAiConsecutiveFailures\", springAiConsecutiveFailures.get());\n status.put(\"activeProvider\", resolveGateway(null).getProviderName());\n if (modelConsortiumRouter != null) {\n status.put(\"consortium\", modelConsortiumRouter.getConfig());\n }\n return status;\n }\n\n /**\n * 模型智能路由 — 根据用户查询复杂度选择最优模型。\n * 可在上层（AiAgentOrchestrator）调用此方法获取推荐模型，传递给推理适配器。\n */\n public String recommendModel(String userMessage, boolean hasImage, int toolCount) {\n if (modelConsortiumRouter != null) {\n return modelConsortiumRouter.selectModel(userMessage, hasImage, toolCount);\n }\n return \"deepseek-v4-flash\";\n }\n\n public String chatSimple(String prompt) {\n if (prompt == null || prompt.isBlank()) {\n log.warn(\"[AiInferenceRouter] chatSimple 收到空 prompt，跳过 LLM 调用\");\n return \"\";\n }\n IntelligenceInferenceResult result = chat(\"reflection\", null, prompt);\n return result != null ? result.getContent() : \"\";\n }\n\n /**\n * 带模型选择的聊天接口实现（per-call model selection）。\n *\n * <p>路由到具体的底层 gateway（SpringAiInferenceAdapter / LegacyInferenceAdapter），\n * 由底层 adapter 真正实现 per-call model 覆盖。\n * 同时记录 LLM 调用成本到 CostExplosionGuard（用于成本爆炸防御）。\n */\n @Override\n public String chatWithModel(String prompt, Long tenantId, Long userId, String modelId) {\n long start = System.currentTimeMillis();\n AiInferenceGateway gateway = resolveGateway(\"model-selection\");\n String content;\n try {\n content = gateway.chatWithModel(prompt, tenantId, userId, modelId);\n } catch (Exception e) {\n log.warn(\"[AiInferenceRouter] chatWithModel failed, fallback to legacy: {}\", e.getMessage());\n content = legacyAdapter.chatWithModel(prompt, tenantId, userId, modelId);\n }\n long elapsed = System.currentTimeMillis() - start;\n\n // 记录 LLM 调用成本到 CostExplosionGuard（用于成本爆炸防御 + 重复检测）\n if (costExplosionGuard != null && tenantId != null) {\n try {\n // 用 \"llm_call\" 作为 toolName，prompt 哈希作为 paramsHash\n String paramsHash = costExplosionGuard.hashParams(prompt);\n costExplosionGuard.recordToolCall(tenantId, \"llm_call\", paramsHash,\n content != null ? content : \"\");\n log.debug(\"[AiInferenceRouter] LLM 调用成本已记录 tenantId={} modelId={} elapsed={}ms\",\n tenantId, modelId, elapsed);\n } catch (Exception e) {\n log.debug(\"[AiInferenceRouter] 记录 LLM 调用成本失败（不影响主流程）: {}\", e.getMessage());\n }\n }\n\n log.info(\"[AiInferenceRouter] chatWithModel: gateway={} modelId={} elapsed={}ms contentLen={}\",\n gateway.getProviderName(), modelId != null ? modelId : \"default\",\n elapsed, content != null ? content.length() : 0);\n return content;\n }\n\n /**\n * Per-call 模型选择调用（借鉴 Claude Agent SDK per-call model selection）。\n *\n * <p>根据用户消息复杂度、预估工具调用数、多域标识自动选择模型分级：\n * <ul>\n * <li>ECONOMY — 简单查询 → 便宜模型</li>\n * <li>STANDARD — 普通对话 → 标准模型</li>\n * <li>PREMIUM — 复杂排产 → 强模型</li>\n * </ul>\n *\n * <p>★ 接入点3：真正调用 gateway.chatWithModel 传递 modelId，而非仅用 scene 路由。\n *\n * @param prompt 完整提示词\n * @param userMessage 用户原始消息（用于复杂度评估）\n * @param estimatedToolCalls 预估工具调用数\n * @param isMultiDomain 是否多域任务\n * @return LLM 回答文本\n */\n public String chatWithModelSelection(String prompt, String userMessage,\n int estimatedToolCalls, boolean isMultiDomain) {\n if (modelSelectionRouter == null || !modelSelectionRouter.isEnabled()) {\n return chatSimple(prompt);\n }\n ModelSelectionRouter.ModelTier tier = modelSelectionRouter.selectModel(\n userMessage, estimatedToolCalls, isMultiDomain);\n String modelId = modelSelectionRouter.resolveModelId(tier);\n log.info(\"[AiInferenceRouter] per-call model selection: tier={} modelId={}\", tier, modelId);\n\n // 从 UserContext 获取 tenantId/userId（多租户隔离 + 成本追踪）\n Long tenantId = UserContext.tenantId();\n String userIdStr = UserContext.userId();\n\n // ★ 真正调用 gateway.chatWithModel 传递 modelId（接入点3 核心）\n try {\n return chatWithModel(prompt, tenantId, userIdStr != null ? userIdStr.hashCode() : 0L, modelId);\n } catch (Exception e) {\n log.warn(\"[AiInferenceRouter] chatWithModelSelection failed, fallback to chatSimple: {}\", e.getMessage());\n return chatSimple(prompt);\n }\n }\n\n /**\n * 强制使用 PREMIUM 模型调用（高风险场景，如复杂排产优化）。\n *\n * @param prompt 完整提示词\n * @return LLM 回答文本\n */\n public String chatPremium(String prompt) {\n if (modelSelectionRouter == null || !modelSelectionRouter.isEnabled()) {\n return chatSimple(prompt);\n }\n String modelId = modelSelectionRouter.resolveModelId(ModelSelectionRouter.ModelTier.PREMIUM);\n log.info(\"[AiInferenceRouter] forced premium model: modelId={}\", modelId);\n IntelligenceInferenceResult result = chat(\"premium-reasoning\", null, prompt);\n return result != null ? result.getContent() : \"\";\n }\n\n /** 根据模型分级映射到 scene（用于网关路由 hint） */\n private String resolveSceneByTier(ModelSelectionRouter.ModelTier tier) {\n return switch (tier) {\n case ECONOMY -> \"economy-query\";\n case STANDARD -> \"reflection\";\n case PREMIUM -> \"premium-reasoning\";\n };\n }\n\n /**\n * 获取模型推荐参数（temperature、maxTokens、timeout）。\n */\n public ModelConsortiumRouter.ModelParams recommendModelParams(String userMessage, boolean hasImage, int toolCount) {\n if (modelConsortiumRouter != null) {\n ModelConsortiumRouter.Complexity complexity = modelConsortiumRouter.classifyComplexity(\n userMessage, hasImage, toolCount);\n return modelConsortiumRouter.getModelParams(complexity);\n }\n return new ModelConsortiumRouter.ModelParams(0.5, 1024, 30);\n }\n\n private boolean hasText(String value) {\n return value != null && !value.trim().isEmpty();\n }\n}\n"}, "92b7fd9578d448a1537b900a5ee7fb0da73abc84": {"t": 1785749954.3668191, "n": 20750, "h": "31e72f3762438f68", "c": "package com.fashion.supplychain.intelligence.gateway;\n\nimport com.fashion.supplychain.intelligence.agent.AiMessage;\nimport com.fashion.supplychain.intelligence.agent.AiTool;\nimport com.fashion.supplychain.intelligence.dto.IntelligenceInferenceResult;\nimport com.fashion.supplychain.intelligence.orchestration.AiCostTrackingOrchestrator;\nimport com.fashion.supplychain.intelligence.orchestration.AiOperationAuditOrchestrator;\nimport com.fashion.supplychain.intelligence.service.CostExplosionGuard;\nimport com.fashion.supplychain.intelligence.service.ModelSelectionRouter;\nimport com.fashion.supplychain.common.UserContext;\nimport java.util.List;\nimport java.util.Map;\nimport java.util.concurrent.atomic.AtomicInteger;\nimport java.util.concurrent.atomic.AtomicLong;\nimport lombok.extern.slf4j.Slf4j;\nimport org.springframework.beans.factory.annotation.Autowired;\nimport org.springframework.beans.factory.annotation.Value;\nimport org.springframework.context.annotation.Primary;\nimport org.springframework.stereotype.Service;\nimport org.springframework.context.annotation.Lazy;\n\n@Slf4j\n@Service\n@Lazy\n@Primary\npublic class AiInferenceRouter implements AiInferenceGateway {\n\n private static final int CIRCUIT_OPEN_THRESHOLD = 3;\n private static final long CIRCUIT_RESET_MS = 30_000L;\n\n @Value(\"${ai.gateway.routing-strategy:failover}\")\n private String routingStrategy;\n\n /**\n * 【P1-10修复】启动时校验 DEEPSEEK_API_KEY 非空。\n *\n * <p>原行为：key 为空时启动正常，但所有 AI 功能（对话/视觉/工具调用）都会失败，\n * 用户排查困难（错误散落在各 Service 中）。\n *\n * <p>现行为：\n * <ul>\n * <li>fail-fast-on-empty=true（默认 false）：启动失败，明确提示缺失 key</li>\n * <li>fail-fast-on-empty=false（默认）：启动成功，但打印 ERROR 级别告警 + 列出受影响功能</li>\n * </ul>\n *\n * <p>受影响功能：DeepSeek 对话、Spring AI 工具调用、AiAdvisor 日常建议、\n * SelfCritic 自评分、EvolutionPipeline 自进化、Qdrant 向量检索（embedding 降级）。\n */\n @Value(\"${ai.api-key.fail-fast-on-empty:false}\")\n private boolean failFastOnEmptyApiKey;\n\n @Value(\"${ai.deepseek.api-key:${DEEPSEEK_API_KEY:}}\")\n private String deepseekApiKey;\n\n @Autowired\n private LegacyInferenceAdapter legacyAdapter;\n\n @Autowired(required = false)\n private SpringAiInferenceAdapter springAiAdapter;\n\n @Autowired(required = false)\n private AiCostTrackingOrchestrator aiCostTrackingOrchestrator;\n\n @Autowired(required = false)\n private AiOperationAuditOrchestrator aiOperationAuditOrchestrator;\n\n @Autowired(required = false)\n private ModelConsortiumRouter modelConsortiumRouter;\n\n @Autowired(required = false)\n private ModelSelectionRouter modelSelectionRouter;\n\n @Autowired(required = false)\n private CostExplosionGuard costExplosionGuard;\n\n private final AtomicInteger springAiConsecutiveFailures = new AtomicInteger(0);\n private final AtomicLong springAiCircuitOpenSince = new AtomicLong(0);\n\n /**\n * 【P1-10修复】启动时校验 DEEPSEEK_API_KEY。\n * 默认仅告警不阻止启动，开启 fail-fast-on-empty=true 可强制阻止启动。\n */\n @jakarta.annotation.PostConstruct\n public void validateApiKey() {\n boolean keyMissing = deepseekApiKey == null || deepseekApiKey.isBlank();\n if (!keyMissing) {\n log.info(\"[AiInferenceRouter] DEEPSEEK_API_KEY 已配置（长度={}），AI 功能正常可用\",\n deepseekApiKey.length());\n return;\n }\n\n String affectedFeatures = \"DeepSeek 对话 / Spring AI 工具调用 / AiAdvisor 日常建议 / \"\n + \"SelfCritic 自评分 / EvolutionPipeline 自进化 / Qdrant 向量检索（embedding 降级）\";\n\n if (failFastOnEmptyApiKey) {\n String msg = \"[AiInferenceRouter] DEEPSEEK_API_KEY 为空且 ai.api-key.fail-fast-on-empty=true，\"\n + \"应用启动被阻止。受影响功能：\" + affectedFeatures\n + \"。解决：设置环境变量 DEEPSEEK_API_KEY 或在 yml 配置 ai.deepseek.api-key\";\n log.error(msg);\n throw new IllegalStateException(msg);\n }\n\n log.error(\"[AiInferenceRouter] ⚠️ DEEPSEEK_API_KEY 为空！AI 功能将全部失败。受影响功能：{}\", affectedFeatures);\n log.error(\"[AiInferenceRouter] 解决：设置环境变量 DEEPSEEK_API_KEY 或在 yml 配置 ai.deepseek.api-key\");\n log.error(\"[AiInferenceRouter] 如需启动失败而非降级，设置 ai.api-key.fail-fast-on-empty=true\");\n }\n\n @Override\n public IntelligenceInferenceResult chat(String scene, String systemPrompt, String userMessage) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chat(scene, systemPrompt, userMessage);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chat(scene, systemPrompt, userMessage);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chat(String scene, List<AiMessage> messages, List<AiTool> tools) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chat(scene, messages, tools);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chat(scene, messages, tools);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chatStream(String scene, List<AiMessage> messages,\n List<AiTool> tools,\n StreamChunkConsumer chunkConsumer) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chatStream(scene, messages, tools, chunkConsumer);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chatStream(scene, messages, tools, chunkConsumer);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chatWithVision(String scene, String systemPrompt, String userMessage, String imageUrl) {\n // 视觉调用优先走 legacy adapter — Agnes视觉模型支持真正的image_url格式，\n // 比Spring AI适配器的文本嵌入方式更可靠\n if (legacyAdapter != null && legacyAdapter.isAvailable()) {\n try {\n IntelligenceInferenceResult result =\n legacyAdapter.chatWithVision(scene, systemPrompt, userMessage, imageUrl);\n if (result != null && result.isSuccess() && hasText(result.getContent())) {\n recordCostAndAudit(scene, result);\n return result;\n }\n log.warn(\"[AiInferenceRouter] legacy视觉调用未返回有效内容，fallback到Spring AI\");\n } catch (Exception e) {\n log.warn(\"[AiInferenceRouter] legacy视觉调用异常，fallback到Spring AI: {}\", e.getMessage());\n }\n }\n // Spring AI 作为第二路径\n if (springAiAdapter != null && !isSpringAiCircuitOpen() && springAiAdapter.isAvailable()) {\n IntelligenceInferenceResult result = springAiAdapter.chatWithVision(scene, systemPrompt, userMessage, imageUrl);\n trackSpringAiHealth(result);\n recordCostAndAudit(scene, result);\n return result;\n }\n // 最后兜底返回空结果\n IntelligenceInferenceResult empty = new IntelligenceInferenceResult();\n empty.setSuccess(false);\n empty.setProvider(\"none\");\n empty.setErrorMessage(\"没有可用的视觉模型（请配置AGNES_API_KEY或DEEPSEEK_API_KEY）\");\n recordCostAndAudit(scene, empty);\n return empty;\n }\n\n @Override\n public boolean isAvailable() {\n return resolveGateway(null).isAvailable();\n }\n\n @Override\n public boolean isVisionAvailable() {\n if (legacyAdapter != null && legacyAdapter.isVisionAvailable()) {\n return true;\n }\n if (springAiAdapter != null && !isSpringAiCircuitOpen() && springAiAdapter.isVisionAvailable()) {\n return true;\n }\n return false;\n }\n\n @Override\n public String getProviderName() {\n return resolveGateway(null).getProviderName();\n }\n\n private AiInferenceGateway resolveGateway(String scene) {\n return switch (routingStrategy) {\n case \"spring-ai\" -> resolveSpringAi();\n case \"failover\" -> resolveFailover(scene);\n case \"legacy\" -> legacyAdapter;\n default -> resolveFailover(scene);\n };\n }\n\n private AiInferenceGateway resolveSpringAi() {\n if (isSpringAiCircuitOpen()) {\n log.warn(\"[AiInferenceRouter] Spring AI 熔断中，降级到 legacy\");\n return legacyAdapter;\n }\n if (springAiAdapter != null && springAiAdapter.isAvailable()) {\n return springAiAdapter;\n }\n log.warn(\"[AiInferenceRouter] spring-ai unavailable, falling back to legacy\");\n return legacyAdapter;\n }\n\n private AiInferenceGateway resolveFailover(String scene) {\n // 短查询/非Agent场景优先走 Spring AI（更快、工具调用更新）\n boolean preferSpringAi = isShortQueryScene(scene);\n if (preferSpringAi && !isSpringAiCircuitOpen()\n && springAiAdapter != null && springAiAdapter.isAvailable()) {\n log.debug(\"[AiInferenceRouter] 短查询场景={}，优先使用 Spring AI\", scene);\n return springAiAdapter;\n }\n // Agent主循环优先Spring AI（流式+tool_calls更好），熔断时降级\n if (!isSpringAiCircuitOpen() && springAiAdapter != null && springAiAdapter.isAvailable()) {\n return springAiAdapter;\n }\n if (legacyAdapter.isAvailable()) {\n return legacyAdapter;\n }\n if (!isSpringAiCircuitOpen() && springAiAdapter != null) {\n return springAiAdapter;\n }\n return legacyAdapter;\n }\n\n private boolean isShortQueryScene(String scene) {\n return scene != null && (\n \"nl-intent\".equals(scene) || \"daily-brief\".equals(scene) ||\n \"critic_review\".equals(scene) || scene.startsWith(\"memory\"));\n }\n\n private boolean isSpringAiCircuitOpen() {\n if (springAiConsecutiveFailures.get() < CIRCUIT_OPEN_THRESHOLD) return false;\n long openSince = springAiCircuitOpenSince.get();\n if (openSince > 0 && System.currentTimeMillis() - openSince > CIRCUIT_RESET_MS) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断进入半开状态，允许探测请求\");\n return false;\n }\n return openSince > 0;\n }\n\n private void trackSpringAiHealth(IntelligenceInferenceResult result) {\n if (result == null || !\"spring-ai\".equals(result.getProvider())) return;\n if (result.isSuccess()) {\n if (springAiConsecutiveFailures.get() >= CIRCUIT_OPEN_THRESHOLD) {\n log.info(\"[AiInferenceRouter] Spring AI 半开探测成功，熔断恢复\");\n }\n springAiConsecutiveFailures.set(0);\n springAiCircuitOpenSince.set(0);\n } else {\n int failures = springAiConsecutiveFailures.incrementAndGet();\n if (failures == CIRCUIT_OPEN_THRESHOLD && springAiCircuitOpenSince.get() == 0) {\n springAiCircuitOpenSince.set(System.currentTimeMillis());\n log.warn(\"[AiInferenceRouter] Spring AI 连续{}次失败，熔断开启（{}秒后进入半开探测）\", failures, CIRCUIT_RESET_MS / 1000);\n } else if (springAiCircuitOpenSince.get() > 0\n && System.currentTimeMillis() - springAiCircuitOpenSince.get() > CIRCUIT_RESET_MS) {\n log.warn(\"[AiInferenceRouter] Spring AI 半开探测失败，重新熔断 {} 秒\", CIRCUIT_RESET_MS / 1000);\n springAiCircuitOpenSince.set(System.currentTimeMillis());\n }\n }\n }\n\n private void recordCostAndAudit(String scene, IntelligenceInferenceResult result) {\n if (result == null) return;\n // 统一性能日志：方便排查双AI引擎表现\n log.info(\"[AiInference] engine={}, scene={}, success={}, latency={}ms, tokens={}/{}, fallback={}\",\n result.getProvider() != null ? result.getProvider() : \"unknown\",\n scene != null ? scene : \"default\",\n result.isSuccess(),\n result.getLatencyMs(),\n result.getPromptTokens(), result.getCompletionTokens(),\n Boolean.TRUE.equals(result.isFallbackUsed()));\n if (!result.isSuccess()) {\n log.warn(\"[AiInference] engine={}, scene={}, error={}\", result.getProvider(), scene, result.getErrorMessage());\n }\n try {\n if (aiCostTrackingOrchestrator != null) {\n aiCostTrackingOrchestrator.recordAsync(\n result.getModel() != null ? result.getModel() : result.getProvider(),\n scene,\n result.getPromptTokens(),\n result.getCompletionTokens(),\n (int) result.getLatencyMs(),\n result.isSuccess(),\n result.getErrorMessage()\n );\n }\n } catch (Exception e) {\n log.debug(\"[AiInferenceRouter] cost tracking failed: {}\", e.getMessage());\n }\n try {\n if (aiOperationAuditOrchestrator != null && result.getTraceId() != null) {\n aiOperationAuditOrchestrator.recordAudit(\n result.getTraceId(),\n \"inference:\" + scene,\n result.getPromptChars() + \" chars\",\n result.getResponseChars() + \" chars\",\n result.getLatencyMs(),\n result.isSuccess(),\n result.getErrorMessage()\n );\n }\n } catch (Exception e) {\n log.debug(\"[AiInferenceRouter] audit recording failed: {}\", e.getMessage());\n }\n }\n\n public Map<String, Object> getRoutingStatus() {\n Map<String, Object> status = new java.util.LinkedHashMap<>();\n status.put(\"strategy\", routingStrategy);\n status.put(\"legacyAvailable\", legacyAdapter.isAvailable());\n status.put(\"springAiAvailable\", springAiAdapter != null && springAiAdapter.isAvailable());\n status.put(\"springAiCircuitOpen\", isSpringAiCircuitOpen());\n status.put(\"springAiConsecutiveFailures\", springAiConsecutiveFailures.get());\n status.put(\"activeProvider\", resolveGateway(null).getProviderName());\n if (modelConsortiumRouter != null) {\n status.put(\"consortium\", modelConsortiumRouter.getConfig());\n }\n return status;\n }\n\n /**\n * 模型智能路由 — 根据用户查询复杂度选择最优模型。\n * 可在上层（AiAgentOrchestrator）调用此方法获取推荐模型，传递给推理适配器。\n */\n public String recommendModel(String userMessage, boolean hasImage, int toolCount) {\n if (modelConsortiumRouter != null) {\n return modelConsortiumRouter.selectModel(userMessage, hasImage, toolCount);\n }\n return \"deepseek-v4-flash\";\n }\n\n public String chatSimple(String prompt) {\n if (prompt == null || prompt.isBlank()) {\n log.warn(\"[AiInferenceRouter] chatSimple 收到空 prompt，跳过 LLM 调用\");\n return \"\";\n }\n IntelligenceInferenceResult result = chat(\"reflection\", null, prompt);\n return result != null ? result.getContent() : \"\";\n }\n\n /**\n * 带模型选择的聊天接口实现（per-call model selection）。\n *\n * <p>路由到具体的底层 gateway（SpringAiInferenceAdapter / LegacyInferenceAdapter），\n * 由底层 adapter 真正实现 per-call model 覆盖。\n * 同时记录 LLM 调用成本到 CostExplosionGuard（用于成本爆炸防御）。\n */\n @Override\n public String chatWithModel(String prompt, Long tenantId, Long userId, String modelId) {\n long start = System.currentTimeMillis();\n AiInferenceGateway gateway = resolveGateway(\"model-selection\");\n String content;\n try {\n content = gateway.chatWithModel(prompt, tenantId, userId, modelId);\n } catch (Exception e) {\n log.warn(\"[AiInferenceRouter] chatWithModel failed, fallback to legacy: {}\", e.getMessage());\n content = legacyAdapter.chatWithModel(prompt, tenantId, userId, modelId);\n }\n long elapsed = System.currentTimeMillis() - start;\n\n // 记录 LLM 调用成本到 CostExplosionGuard（用于成本爆炸防御 + 重复检测）\n if (costExplosionGuard != null && tenantId != null) {\n try {\n // 用 \"llm_call\" 作为 toolName，prompt 哈希作为 paramsHash\n String paramsHash = costExplosionGuard.hashParams(prompt);\n costExplosionGuard.recordToolCall(tenantId, \"llm_call\", paramsHash,\n content != null ? content : \"\");\n log.debug(\"[AiInferenceRouter] LLM 调用成本已记录 tenantId={} modelId={} elapsed={}ms\",\n tenantId, modelId, elapsed);\n } catch (Exception e) {\n log.debug(\"[AiInferenceRouter] 记录 LLM 调用成本失败（不影响主流程）: {}\", e.getMessage());\n }\n }\n\n log.info(\"[AiInferenceRouter] chatWithModel: gateway={} modelId={} elapsed={}ms contentLen={}\",\n gateway.getProviderName(), modelId != null ? modelId : \"default\",\n elapsed, content != null ? content.length() : 0);\n return content;\n }\n\n /**\n * Per-call 模型选择调用（借鉴 Claude Agent SDK per-call model selection）。\n *\n * <p>根据用户消息复杂度、预估工具调用数、多域标识自动选择模型分级：\n * <ul>\n * <li>ECONOMY — 简单查询 → 便宜模型</li>\n * <li>STANDARD — 普通对话 → 标准模型</li>\n * <li>PREMIUM — 复杂排产 → 强模型</li>\n * </ul>\n *\n * <p>★ 接入点3：真正调用 gateway.chatWithModel 传递 modelId，而非仅用 scene 路由。\n *\n * @param prompt 完整提示词\n * @param userMessage 用户原始消息（用于复杂度评估）\n * @param estimatedToolCalls 预估工具调用数\n * @param isMultiDomain 是否多域任务\n * @return LLM 回答文本\n */\n public String chatWithModelSelection(String prompt, String userMessage,\n int estimatedToolCalls, boolean isMultiDomain) {\n if (modelSelectionRouter == null || !modelSelectionRouter.isEnabled()) {\n return chatSimple(prompt);\n }\n ModelSelectionRouter.ModelTier tier = modelSelectionRouter.selectModel(\n userMessage, estimatedToolCalls, isMultiDomain);\n String modelId = modelSelectionRouter.resolveModelId(tier);\n log.info(\"[AiInferenceRouter] per-call model selection: tier={} modelId={}\", tier, modelId);\n\n // 从 UserContext 获取 tenantId/userId（多租户隔离 + 成本追踪）\n Long tenantId = UserContext.tenantId();\n String userIdStr = UserContext.userId();\n\n // ★ 真正调用 gateway.chatWithModel 传递 modelId（接入点3 核心）\n try {\n return chatWithModel(prompt, tenantId, userIdStr != null ? userIdStr.hashCode() : 0L, modelId);\n } catch (Exception e) {\n log.warn(\"[AiInferenceRouter] chatWithModelSelection failed, fallback to chatSimple: {}\", e.getMessage());\n return chatSimple(prompt);\n }\n }\n\n /**\n * 强制使用 PREMIUM 模型调用（高风险场景，如复杂排产优化）。\n *\n * @param prompt 完整提示词\n * @return LLM 回答文本\n */\n public String chatPremium(String prompt) {\n if (modelSelectionRouter == null || !modelSelectionRouter.isEnabled()) {\n return chatSimple(prompt);\n }\n String modelId = modelSelectionRouter.resolveModelId(ModelSelectionRouter.ModelTier.PREMIUM);\n log.info(\"[AiInferenceRouter] forced premium model: modelId={}\", modelId);\n IntelligenceInferenceResult result = chat(\"premium-reasoning\", null, prompt);\n return result != null ? result.getContent() : \"\";\n }\n\n /** 根据模型分级映射到 scene（用于网关路由 hint） */\n private String resolveSceneByTier(ModelSelectionRouter.ModelTier tier) {\n return switch (tier) {\n case ECONOMY -> \"economy-query\";\n case STANDARD -> \"reflection\";\n case PREMIUM -> \"premium-reasoning\";\n };\n }\n\n /**\n * 获取模型推荐参数（temperature、maxTokens、timeout）。\n */\n public ModelConsortiumRouter.ModelParams recommendModelParams(String userMessage, boolean hasImage, int toolCount) {\n if (modelConsortiumRouter != null) {\n ModelConsortiumRouter.Complexity complexity = modelConsortiumRouter.classifyComplexity(\n userMessage, hasImage, toolCount);\n return modelConsortiumRouter.getModelParams(complexity);\n }\n return new ModelConsortiumRouter.ModelParams(0.5, 1024, 30);\n }\n\n private boolean hasText(String value) {\n return value != null && !value.trim().isEmpty();\n }\n}\n"}, "6fe4a586649c80cfffa2ec60afd3daca815705ed": {"t": 1785749955.5275457, "n": 18783, "h": "d1c9ce85273937de", "c": "package com.fashion.supplychain.intelligence.gateway;\n\nimport com.fashion.supplychain.intelligence.agent.AiMessage;\nimport com.fashion.supplychain.intelligence.agent.AiTool;\nimport com.fashion.supplychain.intelligence.dto.IntelligenceInferenceResult;\nimport com.fashion.supplychain.intelligence.orchestration.AiCostTrackingOrchestrator;\nimport com.fashion.supplychain.intelligence.orchestration.AiOperationAuditOrchestrator;\nimport com.fashion.supplychain.intelligence.service.CostExplosionGuard;\nimport com.fashion.supplychain.intelligence.service.ModelSelectionRouter;\nimport com.fashion.supplychain.common.UserContext;\nimport java.util.List;\nimport java.util.Map;\nimport java.util.concurrent.atomic.AtomicInteger;\nimport java.util.concurrent.atomic.AtomicLong;\nimport lombok.extern.slf4j.Slf4j;\nimport org.springframework.beans.factory.annotation.Autowired;\nimport org.springframework.beans.factory.annotation.Value;\nimport org.springframework.context.annotation.Primary;\nimport org.springframework.stereotype.Service;\nimport org.springframework.context.annotation.Lazy;\n\n@Slf4j\n@Service\n@Lazy\n@Primary\npublic class AiInferenceRouter implements AiInferenceGateway {\n\n private static final int CIRCUIT_OPEN_THRESHOLD = 3;\n private static final long CIRCUIT_RESET_MS = 30_000L;\n\n @Value(\"${ai.gateway.routing-strategy:failover}\")\n private String routingStrategy;\n\n @Autowired\n private LegacyInferenceAdapter legacyAdapter;\n\n @Autowired(required = false)\n private SpringAiInferenceAdapter springAiAdapter;\n\n @Autowired(required = false)\n private AiCostTrackingOrchestrator aiCostTrackingOrchestrator;\n\n @Autowired(required = false)\n private AiOperationAuditOrchestrator aiOperationAuditOrchestrator;\n\n @Autowired(required = false)\n private ModelConsortiumRouter modelConsortiumRouter;\n\n @Autowired(required = false)\n private ModelSelectionRouter modelSelectionRouter;\n\n @Autowired(required = false)\n private CostExplosionGuard costExplosionGuard;\n\n private final AtomicInteger springAiConsecutiveFailures = new AtomicInteger(0);\n private final AtomicLong springAiCircuitOpenSince = new AtomicLong(0);\n\n @Override\n public IntelligenceInferenceResult chat(String scene, String systemPrompt, String userMessage) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chat(scene, systemPrompt, userMessage);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chat(scene, systemPrompt, userMessage);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chat(String scene, List<AiMessage> messages, List<AiTool> tools) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chat(scene, messages, tools);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chat(scene, messages, tools);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chatStream(String scene, List<AiMessage> messages,\n List<AiTool> tools,\n StreamChunkConsumer chunkConsumer) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chatStream(scene, messages, tools, chunkConsumer);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chatStream(scene, messages, tools, chunkConsumer);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chatWithVision(String scene, String systemPrompt, String userMessage, String imageUrl) {\n // 视觉调用优先走 legacy adapter — Agnes视觉模型支持真正的image_url格式，\n // 比Spring AI适配器的文本嵌入方式更可靠\n if (legacyAdapter != null && legacyAdapter.isAvailable()) {\n try {\n IntelligenceInferenceResult result =\n legacyAdapter.chatWithVision(scene, systemPrompt, userMessage, imageUrl);\n if (result != null && result.isSuccess() && hasText(result.getContent())) {\n recordCostAndAudit(scene, result);\n return result;\n }\n log.warn(\"[AiInferenceRouter] legacy视觉调用未返回有效内容，fallback到Spring AI\");\n } catch (Exception e) {\n log.warn(\"[AiInferenceRouter] legacy视觉调用异常，fallback到Spring AI: {}\", e.getMessage());\n }\n }\n // Spring AI 作为第二路径\n if (springAiAdapter != null && !isSpringAiCircuitOpen() && springAiAdapter.isAvailable()) {\n IntelligenceInferenceResult result = springAiAdapter.chatWithVision(scene, systemPrompt, userMessage, imageUrl);\n trackSpringAiHealth(result);\n recordCostAndAudit(scene, result);\n return result;\n }\n // 最后兜底返回空结果\n IntelligenceInferenceResult empty = new IntelligenceInferenceResult();\n empty.setSuccess(false);\n empty.setProvider(\"none\");\n empty.setErrorMessage(\"没有可用的视觉模型（请配置AGNES_API_KEY或DEEPSEEK_API_KEY）\");\n recordCostAndAudit(scene, empty);\n return empty;\n }\n\n @Override\n public boolean isAvailable() {\n return resolveGateway(null).isAvailable();\n }\n\n @Override\n public boolean isVisionAvailable() {\n if (legacyAdapter != null && legacyAdapter.isVisionAvailable()) {\n return true;\n }\n if (springAiAdapter != null && !isSpringAiCircuitOpen() && springAiAdapter.isVisionAvailable()) {\n return true;\n }\n return false;\n }\n\n @Override\n public String getProviderName() {\n return resolveGateway(null).getProviderName();\n }\n\n private AiInferenceGateway resolveGateway(String scene) {\n return switch (routingStrategy) {\n case \"spring-ai\" -> resolveSpringAi();\n case \"failover\" -> resolveFailover(scene);\n case \"legacy\" -> legacyAdapter;\n default -> resolveFailover(scene);\n };\n }\n\n private AiInferenceGateway resolveSpringAi() {\n if (isSpringAiCircuitOpen()) {\n log.warn(\"[AiInferenceRouter] Spring AI 熔断中，降级到 legacy\");\n return legacyAdapter;\n }\n if (springAiAdapter != null && springAiAdapter.isAvailable()) {\n return springAiAdapter;\n }\n log.warn(\"[AiInferenceRouter] spring-ai unavailable, falling back to legacy\");\n return legacyAdapter;\n }\n\n private AiInferenceGateway resolveFailover(String scene) {\n // 短查询/非Agent场景优先走 Spring AI（更快、工具调用更新）\n boolean preferSpringAi = isShortQueryScene(scene);\n if (preferSpringAi && !isSpringAiCircuitOpen()\n && springAiAdapter != null && springAiAdapter.isAvailable()) {\n log.debug(\"[AiInferenceRouter] 短查询场景={}，优先使用 Spring AI\", scene);\n return springAiAdapter;\n }\n // Agent主循环优先Spring AI（流式+tool_calls更好），熔断时降级\n if (!isSpringAiCircuitOpen() && springAiAdapter != null && springAiAdapter.isAvailable()) {\n return springAiAdapter;\n }\n if (legacyAdapter.isAvailable()) {\n return legacyAdapter;\n }\n if (!isSpringAiCircuitOpen() && springAiAdapter != null) {\n return springAiAdapter;\n }\n return legacyAdapter;\n }\n\n private boolean isShortQueryScene(String scene) {\n return scene != null && (\n \"nl-intent\".equals(scene) || \"daily-brief\".equals(scene) ||\n \"critic_review\".equals(scene) || scene.startsWith(\"memory\"));\n }\n\n private boolean isSpringAiCircuitOpen() {\n if (springAiConsecutiveFailures.get() < CIRCUIT_OPEN_THRESHOLD) return false;\n long openSince = springAiCircuitOpenSince.get();\n if (openSince > 0 && System.currentTimeMillis() - openSince > CIRCUIT_RESET_MS) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断进入半开状态，允许探测请求\");\n return false;\n }\n return openSince > 0;\n }\n\n private void trackSpringAiHealth(IntelligenceInferenceResult result) {\n if (result == null || !\"spring-ai\".equals(result.getProvider())) return;\n if (result.isSuccess()) {\n if (springAiConsecutiveFailures.get() >= CIRCUIT_OPEN_THRESHOLD) {\n log.info(\"[AiInferenceRouter] Spring AI 半开探测成功，熔断恢复\");\n }\n springAiConsecutiveFailures.set(0);\n springAiCircuitOpenSince.set(0);\n } else {\n int failures = springAiConsecutiveFailures.incrementAndGet();\n if (failures == CIRCUIT_OPEN_THRESHOLD && springAiCircuitOpenSince.get() == 0) {\n springAiCircuitOpenSince.set(System.currentTimeMillis());\n log.warn(\"[AiInferenceRouter] Spring AI 连续{}次失败，熔断开启（{}秒后进入半开探测）\", failures, CIRCUIT_RESET_MS / 1000);\n } else if (springAiCircuitOpenSince.get() > 0\n && System.currentTimeMillis() - springAiCircuitOpenSince.get() > CIRCUIT_RESET_MS) {\n log.warn(\"[AiInferenceRouter] Spring AI 半开探测失败，重新熔断 {} 秒\", CIRCUIT_RESET_MS / 1000);\n springAiCircuitOpenSince.set(System.currentTimeMillis());\n }\n }\n }\n\n private void recordCostAndAudit(String scene, IntelligenceInferenceResult result) {\n if (result == null) return;\n // 统一性能日志：方便排查双AI引擎表现\n log.info(\"[AiInference] engine={}, scene={}, success={}, latency={}ms, tokens={}/{}, fallback={}\",\n result.getProvider() != null ? result.getProvider() : \"unknown\",\n scene != null ? scene : \"default\",\n result.isSuccess(),\n result.getLatencyMs(),\n result.getPromptTokens(), result.getCompletionTokens(),\n Boolean.TRUE.equals(result.isFallbackUsed()));\n if (!result.isSuccess()) {\n log.warn(\"[AiInference] engine={}, scene={}, error={}\", result.getProvider(), scene, result.getErrorMessage());\n }\n try {\n if (aiCostTrackingOrchestrator != null) {\n aiCostTrackingOrchestrator.recordAsync(\n result.getModel() != null ? result.getModel() : result.getProvider(),\n scene,\n result.getPromptTokens(),\n result.getCompletionTokens(),\n (int) result.getLatencyMs(),\n result.isSuccess(),\n result.getErrorMessage()\n );\n }\n } catch (Exception e) {\n log.debug(\"[AiInferenceRouter] cost tracking failed: {}\", e.getMessage());\n }\n try {\n if (aiOperationAuditOrchestrator != null && result.getTraceId() != null) {\n aiOperationAuditOrchestrator.recordAudit(\n result.getTraceId(),\n \"inference:\" + scene,\n result.getPromptChars() + \" chars\",\n result.getResponseChars() + \" chars\",\n result.getLatencyMs(),\n result.isSuccess(),\n result.getErrorMessage()\n );\n }\n } catch (Exception e) {\n log.debug(\"[AiInferenceRouter] audit recording failed: {}\", e.getMessage());\n }\n }\n\n public Map<String, Object> getRoutingStatus() {\n Map<String, Object> status = new java.util.LinkedHashMap<>();\n status.put(\"strategy\", routingStrategy);\n status.put(\"legacyAvailable\", legacyAdapter.isAvailable());\n status.put(\"springAiAvailable\", springAiAdapter != null && springAiAdapter.isAvailable());\n status.put(\"springAiCircuitOpen\", isSpringAiCircuitOpen());\n status.put(\"springAiConsecutiveFailures\", springAiConsecutiveFailures.get());\n status.put(\"activeProvider\", resolveGateway(null).getProviderName());\n if (modelConsortiumRouter != null) {\n status.put(\"consortium\", modelConsortiumRouter.getConfig());\n }\n return status;\n }\n\n /**\n * 模型智能路由 — 根据用户查询复杂度选择最优模型。\n * 可在上层（AiAgentOrchestrator）调用此方法获取推荐模型，传递给推理适配器。\n */\n public String recommendModel(String userMessage, boolean hasImage, int toolCount) {\n if (modelConsortiumRouter != null) {\n return modelConsortiumRouter.selectModel(userMessage, hasImage, toolCount);\n }\n return \"deepseek-v4-flash\";\n }\n\n public String chatSimple(String prompt) {\n if (prompt == null || prompt.isBlank()) {\n log.warn(\"[AiInferenceRouter] chatSimple 收到空 prompt，跳过 LLM 调用\");\n return \"\";\n }\n IntelligenceInferenceResult result = chat(\"reflection\", null, prompt);\n return result != null ? result.getContent() : \"\";\n }\n\n /**\n * 带模型选择的聊天接口实现（per-call model selection）。\n *\n * <p>路由到具体的底层 gateway（SpringAiInferenceAdapter / LegacyInferenceAdapter），\n * 由底层 adapter 真正实现 per-call model 覆盖。\n * 同时记录 LLM 调用成本到 CostExplosionGuard（用于成本爆炸防御）。\n */\n @Override\n public String chatWithModel(String prompt, Long tenantId, Long userId, String modelId) {\n long start = System.currentTimeMillis();\n AiInferenceGateway gateway = resolveGateway(\"model-selection\");\n String content;\n try {\n content = gateway.chatWithModel(prompt, tenantId, userId, modelId);\n } catch (Exception e) {\n log.warn(\"[AiInferenceRouter] chatWithModel failed, fallback to legacy: {}\", e.getMessage());\n content = legacyAdapter.chatWithModel(prompt, tenantId, userId, modelId);\n }\n long elapsed = System.currentTimeMillis() - start;\n\n // 记录 LLM 调用成本到 CostExplosionGuard（用于成本爆炸防御 + 重复检测）\n if (costExplosionGuard != null && tenantId != null) {\n try {\n // 用 \"llm_call\" 作为 toolName，prompt 哈希作为 paramsHash\n String paramsHash = costExplosionGuard.hashParams(prompt);\n costExplosionGuard.recordToolCall(tenantId, \"llm_call\", paramsHash,\n content != null ? content : \"\");\n log.debug(\"[AiInferenceRouter] LLM 调用成本已记录 tenantId={} modelId={} elapsed={}ms\",\n tenantId, modelId, elapsed);\n } catch (Exception e) {\n log.debug(\"[AiInferenceRouter] 记录 LLM 调用成本失败（不影响主流程）: {}\", e.getMessage());\n }\n }\n\n log.info(\"[AiInferenceRouter] chatWithModel: gateway={} modelId={} elapsed={}ms contentLen={}\",\n gateway.getProviderName(), modelId != null ? modelId : \"default\",\n elapsed, content != null ? content.length() : 0);\n return content;\n }\n\n /**\n * Per-call 模型选择调用（借鉴 Claude Agent SDK per-call model selection）。\n *\n * <p>根据用户消息复杂度、预估工具调用数、多域标识自动选择模型分级：\n * <ul>\n * <li>ECONOMY — 简单查询 → 便宜模型</li>\n * <li>STANDARD — 普通对话 → 标准模型</li>\n * <li>PREMIUM — 复杂排产 → 强模型</li>\n * </ul>\n *\n * <p>★ 接入点3：真正调用 gateway.chatWithModel 传递 modelId，而非仅用 scene 路由。\n *\n * @param prompt 完整提示词\n * @param userMessage 用户原始消息（用于复杂度评估）\n * @param estimatedToolCalls 预估工具调用数\n * @param isMultiDomain 是否多域任务\n * @return LLM 回答文本\n */\n public String chatWithModelSelection(String prompt, String userMessage,\n int estimatedToolCalls, boolean isMultiDomain) {\n if (modelSelectionRouter == null || !modelSelectionRouter.isEnabled()) {\n return chatSimple(prompt);\n }\n ModelSelectionRouter.ModelTier tier = modelSelectionRouter.selectModel(\n userMessage, estimatedToolCalls, isMultiDomain);\n String modelId = modelSelectionRouter.resolveModelId(tier);\n log.info(\"[AiInferenceRouter] per-call model selection: tier={} modelId={}\", tier, modelId);\n\n // 从 UserContext 获取 tenantId/userId（多租户隔离 + 成本追踪）\n Long tenantId = UserContext.tenantId();\n String userIdStr = UserContext.userId();\n\n // ★ 真正调用 gateway.chatWithModel 传递 modelId（接入点3 核心）\n try {\n return chatWithModel(prompt, tenantId, userIdStr != null ? userIdStr.hashCode() : 0L, modelId);\n } catch (Exception e) {\n log.warn(\"[AiInferenceRouter] chatWithModelSelection failed, fallback to chatSimple: {}\", e.getMessage());\n return chatSimple(prompt);\n }\n }\n\n /**\n * 强制使用 PREMIUM 模型调用（高风险场景，如复杂排产优化）。\n *\n * @param prompt 完整提示词\n * @return LLM 回答文本\n */\n public String chatPremium(String prompt) {\n if (modelSelectionRouter == null || !modelSelectionRouter.isEnabled()) {\n return chatSimple(prompt);\n }\n String modelId = modelSelectionRouter.resolveModelId(ModelSelectionRouter.ModelTier.PREMIUM);\n log.info(\"[AiInferenceRouter] forced premium model: modelId={}\", modelId);\n IntelligenceInferenceResult result = chat(\"premium-reasoning\", null, prompt);\n return result != null ? result.getContent() : \"\";\n }\n\n /** 根据模型分级映射到 scene（用于网关路由 hint） */\n private String resolveSceneByTier(ModelSelectionRouter.ModelTier tier) {\n return switch (tier) {\n case ECONOMY -> \"economy-query\";\n case STANDARD -> \"reflection\";\n case PREMIUM -> \"premium-reasoning\";\n };\n }\n\n /**\n * 获取模型推荐参数（temperature、maxTokens、timeout）。\n */\n public ModelConsortiumRouter.ModelParams recommendModelParams(String userMessage, boolean hasImage, int toolCount) {\n if (modelConsortiumRouter != null) {\n ModelConsortiumRouter.Complexity complexity = modelConsortiumRouter.classifyComplexity(\n userMessage, hasImage, toolCount);\n return modelConsortiumRouter.getModelParams(complexity);\n }\n return new ModelConsortiumRouter.ModelParams(0.5, 1024, 30);\n }\n\n private boolean hasText(String value) {\n return value != null && !value.trim().isEmpty();\n }\n}\n"}, "0fc1de77cd3cb0bcbf5f816d748f22c7ffbf316f": {"t": 1785749956.6754286, "n": 18449, "h": "15ced3ba90c9e71c", "c": "package com.fashion.supplychain.intelligence.gateway;\n\nimport com.fashion.supplychain.intelligence.agent.AiMessage;\nimport com.fashion.supplychain.intelligence.agent.AiTool;\nimport com.fashion.supplychain.intelligence.dto.IntelligenceInferenceResult;\nimport com.fashion.supplychain.intelligence.orchestration.AiCostTrackingOrchestrator;\nimport com.fashion.supplychain.intelligence.orchestration.AiOperationAuditOrchestrator;\nimport com.fashion.supplychain.intelligence.service.CostExplosionGuard;\nimport com.fashion.supplychain.intelligence.service.ModelSelectionRouter;\nimport com.fashion.supplychain.common.UserContext;\nimport java.util.List;\nimport java.util.Map;\nimport java.util.concurrent.atomic.AtomicInteger;\nimport java.util.concurrent.atomic.AtomicLong;\nimport lombok.extern.slf4j.Slf4j;\nimport org.springframework.beans.factory.annotation.Autowired;\nimport org.springframework.beans.factory.annotation.Value;\nimport org.springframework.context.annotation.Primary;\nimport org.springframework.stereotype.Service;\nimport org.springframework.context.annotation.Lazy;\n\n@Slf4j\n@Service\n@Lazy\n@Primary\npublic class AiInferenceRouter implements AiInferenceGateway {\n\n private static final int CIRCUIT_OPEN_THRESHOLD = 3;\n private static final long CIRCUIT_RESET_MS = 30_000L;\n\n @Value(\"${ai.gateway.routing-strategy:failover}\")\n private String routingStrategy;\n\n @Autowired\n private LegacyInferenceAdapter legacyAdapter;\n\n @Autowired(required = false)\n private SpringAiInferenceAdapter springAiAdapter;\n\n @Autowired(required = false)\n private AiCostTrackingOrchestrator aiCostTrackingOrchestrator;\n\n @Autowired(required = false)\n private AiOperationAuditOrchestrator aiOperationAuditOrchestrator;\n\n @Autowired(required = false)\n private ModelConsortiumRouter modelConsortiumRouter;\n\n @Autowired(required = false)\n private ModelSelectionRouter modelSelectionRouter;\n\n @Autowired(required = false)\n private CostExplosionGuard costExplosionGuard;\n\n private final AtomicInteger springAiConsecutiveFailures = new AtomicInteger(0);\n private final AtomicLong springAiCircuitOpenSince = new AtomicLong(0);\n\n @Override\n public IntelligenceInferenceResult chat(String scene, String systemPrompt, String userMessage) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chat(scene, systemPrompt, userMessage);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chat(scene, systemPrompt, userMessage);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chat(String scene, List<AiMessage> messages, List<AiTool> tools) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chat(scene, messages, tools);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chat(scene, messages, tools);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chatStream(String scene, List<AiMessage> messages,\n List<AiTool> tools,\n StreamChunkConsumer chunkConsumer) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chatStream(scene, messages, tools, chunkConsumer);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chatStream(scene, messages, tools, chunkConsumer);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chatWithVision(String scene, String systemPrompt, String userMessage, String imageUrl) {\n // 视觉调用优先走 legacy adapter — Agnes视觉模型支持真正的image_url格式，\n // 比Spring AI适配器的文本嵌入方式更可靠\n if (legacyAdapter != null && legacyAdapter.isAvailable()) {\n try {\n IntelligenceInferenceResult result =\n legacyAdapter.chatWithVision(scene, systemPrompt, userMessage, imageUrl);\n if (result != null && result.isSuccess() && hasText(result.getContent())) {\n recordCostAndAudit(scene, result);\n return result;\n }\n log.warn(\"[AiInferenceRouter] legacy视觉调用未返回有效内容，fallback到Spring AI\");\n } catch (Exception e) {\n log.warn(\"[AiInferenceRouter] legacy视觉调用异常，fallback到Spring AI: {}\", e.getMessage());\n }\n }\n // Spring AI 作为第二路径\n if (springAiAdapter != null && !isSpringAiCircuitOpen() && springAiAdapter.isAvailable()) {\n IntelligenceInferenceResult result = springAiAdapter.chatWithVision(scene, systemPrompt, userMessage, imageUrl);\n trackSpringAiHealth(result);\n recordCostAndAudit(scene, result);\n return result;\n }\n // 最后兜底返回空结果\n IntelligenceInferenceResult empty = new IntelligenceInferenceResult();\n empty.setSuccess(false);\n empty.setProvider(\"none\");\n empty.setErrorMessage(\"没有可用的视觉模型（请配置AGNES_API_KEY或DEEPSEEK_API_KEY）\");\n recordCostAndAudit(scene, empty);\n return empty;\n }\n\n @Override\n public boolean isAvailable() {\n return resolveGateway(null).isAvailable();\n }\n\n @Override\n public String getProviderName() {\n return resolveGateway(null).getProviderName();\n }\n\n private AiInferenceGateway resolveGateway(String scene) {\n return switch (routingStrategy) {\n case \"spring-ai\" -> resolveSpringAi();\n case \"failover\" -> resolveFailover(scene);\n case \"legacy\" -> legacyAdapter;\n default -> resolveFailover(scene);\n };\n }\n\n private AiInferenceGateway resolveSpringAi() {\n if (isSpringAiCircuitOpen()) {\n log.warn(\"[AiInferenceRouter] Spring AI 熔断中，降级到 legacy\");\n return legacyAdapter;\n }\n if (springAiAdapter != null && springAiAdapter.isAvailable()) {\n return springAiAdapter;\n }\n log.warn(\"[AiInferenceRouter] spring-ai unavailable, falling back to legacy\");\n return legacyAdapter;\n }\n\n private AiInferenceGateway resolveFailover(String scene) {\n // 短查询/非Agent场景优先走 Spring AI（更快、工具调用更新）\n boolean preferSpringAi = isShortQueryScene(scene);\n if (preferSpringAi && !isSpringAiCircuitOpen()\n && springAiAdapter != null && springAiAdapter.isAvailable()) {\n log.debug(\"[AiInferenceRouter] 短查询场景={}，优先使用 Spring AI\", scene);\n return springAiAdapter;\n }\n // Agent主循环优先Spring AI（流式+tool_calls更好），熔断时降级\n if (!isSpringAiCircuitOpen() && springAiAdapter != null && springAiAdapter.isAvailable()) {\n return springAiAdapter;\n }\n if (legacyAdapter.isAvailable()) {\n return legacyAdapter;\n }\n if (!isSpringAiCircuitOpen() && springAiAdapter != null) {\n return springAiAdapter;\n }\n return legacyAdapter;\n }\n\n private boolean isShortQueryScene(String scene) {\n return scene != null && (\n \"nl-intent\".equals(scene) || \"daily-brief\".equals(scene) ||\n \"critic_review\".equals(scene) || scene.startsWith(\"memory\"));\n }\n\n private boolean isSpringAiCircuitOpen() {\n if (springAiConsecutiveFailures.get() < CIRCUIT_OPEN_THRESHOLD) return false;\n long openSince = springAiCircuitOpenSince.get();\n if (openSince > 0 && System.currentTimeMillis() - openSince > CIRCUIT_RESET_MS) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断进入半开状态，允许探测请求\");\n return false;\n }\n return openSince > 0;\n }\n\n private void trackSpringAiHealth(IntelligenceInferenceResult result) {\n if (result == null || !\"spring-ai\".equals(result.getProvider())) return;\n if (result.isSuccess()) {\n if (springAiConsecutiveFailures.get() >= CIRCUIT_OPEN_THRESHOLD) {\n log.info(\"[AiInferenceRouter] Spring AI 半开探测成功，熔断恢复\");\n }\n springAiConsecutiveFailures.set(0);\n springAiCircuitOpenSince.set(0);\n } else {\n int failures = springAiConsecutiveFailures.incrementAndGet();\n if (failures == CIRCUIT_OPEN_THRESHOLD && springAiCircuitOpenSince.get() == 0) {\n springAiCircuitOpenSince.set(System.currentTimeMillis());\n log.warn(\"[AiInferenceRouter] Spring AI 连续{}次失败，熔断开启（{}秒后进入半开探测）\", failures, CIRCUIT_RESET_MS / 1000);\n } else if (springAiCircuitOpenSince.get() > 0\n && System.currentTimeMillis() - springAiCircuitOpenSince.get() > CIRCUIT_RESET_MS) {\n log.warn(\"[AiInferenceRouter] Spring AI 半开探测失败，重新熔断 {} 秒\", CIRCUIT_RESET_MS / 1000);\n springAiCircuitOpenSince.set(System.currentTimeMillis());\n }\n }\n }\n\n private void recordCostAndAudit(String scene, IntelligenceInferenceResult result) {\n if (result == null) return;\n // 统一性能日志：方便排查双AI引擎表现\n log.info(\"[AiInference] engine={}, scene={}, success={}, latency={}ms, tokens={}/{}, fallback={}\",\n result.getProvider() != null ? result.getProvider() : \"unknown\",\n scene != null ? scene : \"default\",\n result.isSuccess(),\n result.getLatencyMs(),\n result.getPromptTokens(), result.getCompletionTokens(),\n Boolean.TRUE.equals(result.isFallbackUsed()));\n if (!result.isSuccess()) {\n log.warn(\"[AiInference] engine={}, scene={}, error={}\", result.getProvider(), scene, result.getErrorMessage());\n }\n try {\n if (aiCostTrackingOrchestrator != null) {\n aiCostTrackingOrchestrator.recordAsync(\n result.getModel() != null ? result.getModel() : result.getProvider(),\n scene,\n result.getPromptTokens(),\n result.getCompletionTokens(),\n (int) result.getLatencyMs(),\n result.isSuccess(),\n result.getErrorMessage()\n );\n }\n } catch (Exception e) {\n log.debug(\"[AiInferenceRouter] cost tracking failed: {}\", e.getMessage());\n }\n try {\n if (aiOperationAuditOrchestrator != null && result.getTraceId() != null) {\n aiOperationAuditOrchestrator.recordAudit(\n result.getTraceId(),\n \"inference:\" + scene,\n result.getPromptChars() + \" chars\",\n result.getResponseChars() + \" chars\",\n result.getLatencyMs(),\n result.isSuccess(),\n result.getErrorMessage()\n );\n }\n } catch (Exception e) {\n log.debug(\"[AiInferenceRouter] audit recording failed: {}\", e.getMessage());\n }\n }\n\n public Map<String, Object> getRoutingStatus() {\n Map<String, Object> status = new java.util.LinkedHashMap<>();\n status.put(\"strategy\", routingStrategy);\n status.put(\"legacyAvailable\", legacyAdapter.isAvailable());\n status.put(\"springAiAvailable\", springAiAdapter != null && springAiAdapter.isAvailable());\n status.put(\"springAiCircuitOpen\", isSpringAiCircuitOpen());\n status.put(\"springAiConsecutiveFailures\", springAiConsecutiveFailures.get());\n status.put(\"activeProvider\", resolveGateway(null).getProviderName());\n if (modelConsortiumRouter != null) {\n status.put(\"consortium\", modelConsortiumRouter.getConfig());\n }\n return status;\n }\n\n /**\n * 模型智能路由 — 根据用户查询复杂度选择最优模型。\n * 可在上层（AiAgentOrchestrator）调用此方法获取推荐模型，传递给推理适配器。\n */\n public String recommendModel(String userMessage, boolean hasImage, int toolCount) {\n if (modelConsortiumRouter != null) {\n return modelConsortiumRouter.selectModel(userMessage, hasImage, toolCount);\n }\n return \"deepseek-v4-flash\";\n }\n\n public String chatSimple(String prompt) {\n if (prompt == null || prompt.isBlank()) {\n log.warn(\"[AiInferenceRouter] chatSimple 收到空 prompt，跳过 LLM 调用\");\n return \"\";\n }\n IntelligenceInferenceResult result = chat(\"reflection\", null, prompt);\n return result != null ? result.getContent() : \"\";\n }\n\n /**\n * 带模型选择的聊天接口实现（per-call model selection）。\n *\n * <p>路由到具体的底层 gateway（SpringAiInferenceAdapter / LegacyInferenceAdapter），\n * 由底层 adapter 真正实现 per-call model 覆盖。\n * 同时记录 LLM 调用成本到 CostExplosionGuard（用于成本爆炸防御）。\n */\n @Override\n public String chatWithModel(String prompt, Long tenantId, Long userId, String modelId) {\n long start = System.currentTimeMillis();\n AiInferenceGateway gateway = resolveGateway(\"model-selection\");\n String content;\n try {\n content = gateway.chatWithModel(prompt, tenantId, userId, modelId);\n } catch (Exception e) {\n log.warn(\"[AiInferenceRouter] chatWithModel failed, fallback to legacy: {}\", e.getMessage());\n content = legacyAdapter.chatWithModel(prompt, tenantId, userId, modelId);\n }\n long elapsed = System.currentTimeMillis() - start;\n\n // 记录 LLM 调用成本到 CostExplosionGuard（用于成本爆炸防御 + 重复检测）\n if (costExplosionGuard != null && tenantId != null) {\n try {\n // 用 \"llm_call\" 作为 toolName，prompt 哈希作为 paramsHash\n String paramsHash = costExplosionGuard.hashParams(prompt);\n costExplosionGuard.recordToolCall(tenantId, \"llm_call\", paramsHash,\n content != null ? content : \"\");\n log.debug(\"[AiInferenceRouter] LLM 调用成本已记录 tenantId={} modelId={} elapsed={}ms\",\n tenantId, modelId, elapsed);\n } catch (Exception e) {\n log.debug(\"[AiInferenceRouter] 记录 LLM 调用成本失败（不影响主流程）: {}\", e.getMessage());\n }\n }\n\n log.info(\"[AiInferenceRouter] chatWithModel: gateway={} modelId={} elapsed={}ms contentLen={}\",\n gateway.getProviderName(), modelId != null ? modelId : \"default\",\n elapsed, content != null ? content.length() : 0);\n return content;\n }\n\n /**\n * Per-call 模型选择调用（借鉴 Claude Agent SDK per-call model selection）。\n *\n * <p>根据用户消息复杂度、预估工具调用数、多域标识自动选择模型分级：\n * <ul>\n * <li>ECONOMY — 简单查询 → 便宜模型</li>\n * <li>STANDARD — 普通对话 → 标准模型</li>\n * <li>PREMIUM — 复杂排产 → 强模型</li>\n * </ul>\n *\n * <p>★ 接入点3：真正调用 gateway.chatWithModel 传递 modelId，而非仅用 scene 路由。\n *\n * @param prompt 完整提示词\n * @param userMessage 用户原始消息（用于复杂度评估）\n * @param estimatedToolCalls 预估工具调用数\n * @param isMultiDomain 是否多域任务\n * @return LLM 回答文本\n */\n public String chatWithModelSelection(String prompt, String userMessage,\n int estimatedToolCalls, boolean isMultiDomain) {\n if (modelSelectionRouter == null || !modelSelectionRouter.isEnabled()) {\n return chatSimple(prompt);\n }\n ModelSelectionRouter.ModelTier tier = modelSelectionRouter.selectModel(\n userMessage, estimatedToolCalls, isMultiDomain);\n String modelId = modelSelectionRouter.resolveModelId(tier);\n log.info(\"[AiInferenceRouter] per-call model selection: tier={} modelId={}\", tier, modelId);\n\n // 从 UserContext 获取 tenantId/userId（多租户隔离 + 成本追踪）\n Long tenantId = UserContext.tenantId();\n String userIdStr = UserContext.userId();\n\n // ★ 真正调用 gateway.chatWithModel 传递 modelId（接入点3 核心）\n try {\n return chatWithModel(prompt, tenantId, userIdStr != null ? userIdStr.hashCode() : 0L, modelId);\n } catch (Exception e) {\n log.warn(\"[AiInferenceRouter] chatWithModelSelection failed, fallback to chatSimple: {}\", e.getMessage());\n return chatSimple(prompt);\n }\n }\n\n /**\n * 强制使用 PREMIUM 模型调用（高风险场景，如复杂排产优化）。\n *\n * @param prompt 完整提示词\n * @return LLM 回答文本\n */\n public String chatPremium(String prompt) {\n if (modelSelectionRouter == null || !modelSelectionRouter.isEnabled()) {\n return chatSimple(prompt);\n }\n String modelId = modelSelectionRouter.resolveModelId(ModelSelectionRouter.ModelTier.PREMIUM);\n log.info(\"[AiInferenceRouter] forced premium model: modelId={}\", modelId);\n IntelligenceInferenceResult result = chat(\"premium-reasoning\", null, prompt);\n return result != null ? result.getContent() : \"\";\n }\n\n /** 根据模型分级映射到 scene（用于网关路由 hint） */\n private String resolveSceneByTier(ModelSelectionRouter.ModelTier tier) {\n return switch (tier) {\n case ECONOMY -> \"economy-query\";\n case STANDARD -> \"reflection\";\n case PREMIUM -> \"premium-reasoning\";\n };\n }\n\n /**\n * 获取模型推荐参数（temperature、maxTokens、timeout）。\n */\n public ModelConsortiumRouter.ModelParams recommendModelParams(String userMessage, boolean hasImage, int toolCount) {\n if (modelConsortiumRouter != null) {\n ModelConsortiumRouter.Complexity complexity = modelConsortiumRouter.classifyComplexity(\n userMessage, hasImage, toolCount);\n return modelConsortiumRouter.getModelParams(complexity);\n }\n return new ModelConsortiumRouter.ModelParams(0.5, 1024, 30);\n }\n\n private boolean hasText(String value) {\n return value != null && !value.trim().isEmpty();\n }\n}\n"}, "dd96c9df2639c5b031479024e45d7cf36378fc96": {"t": 1785749957.8133483, "n": 18289, "h": "f37704f23d2abafb", "c": "package com.fashion.supplychain.intelligence.gateway;\n\nimport com.fashion.supplychain.intelligence.agent.AiMessage;\nimport com.fashion.supplychain.intelligence.agent.AiTool;\nimport com.fashion.supplychain.intelligence.dto.IntelligenceInferenceResult;\nimport com.fashion.supplychain.intelligence.orchestration.AiCostTrackingOrchestrator;\nimport com.fashion.supplychain.intelligence.orchestration.AiOperationAuditOrchestrator;\nimport com.fashion.supplychain.intelligence.service.CostExplosionGuard;\nimport com.fashion.supplychain.intelligence.service.ModelSelectionRouter;\nimport com.fashion.supplychain.common.UserContext;\nimport java.util.List;\nimport java.util.Map;\nimport java.util.concurrent.atomic.AtomicInteger;\nimport java.util.concurrent.atomic.AtomicLong;\nimport lombok.extern.slf4j.Slf4j;\nimport org.springframework.beans.factory.annotation.Autowired;\nimport org.springframework.beans.factory.annotation.Value;\nimport org.springframework.context.annotation.Primary;\nimport org.springframework.stereotype.Service;\nimport org.springframework.context.annotation.Lazy;\n\n@Slf4j\n@Service\n@Lazy\n@Primary\npublic class AiInferenceRouter implements AiInferenceGateway {\n\n private static final int CIRCUIT_OPEN_THRESHOLD = 3;\n private static final long CIRCUIT_RESET_MS = 30_000L;\n\n @Value(\"${ai.gateway.routing-strategy:failover}\")\n private String routingStrategy;\n\n @Autowired\n private LegacyInferenceAdapter legacyAdapter;\n\n @Autowired(required = false)\n private SpringAiInferenceAdapter springAiAdapter;\n\n @Autowired(required = false)\n private AiCostTrackingOrchestrator aiCostTrackingOrchestrator;\n\n @Autowired(required = false)\n private AiOperationAuditOrchestrator aiOperationAuditOrchestrator;\n\n @Autowired(required = false)\n private ModelConsortiumRouter modelConsortiumRouter;\n\n @Autowired(required = false)\n private ModelSelectionRouter modelSelectionRouter;\n\n @Autowired(required = false)\n private CostExplosionGuard costExplosionGuard;\n\n private final AtomicInteger springAiConsecutiveFailures = new AtomicInteger(0);\n private final AtomicLong springAiCircuitOpenSince = new AtomicLong(0);\n\n @Override\n public IntelligenceInferenceResult chat(String scene, String systemPrompt, String userMessage) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chat(scene, systemPrompt, userMessage);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chat(scene, systemPrompt, userMessage);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chat(String scene, List<AiMessage> messages, List<AiTool> tools) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chat(scene, messages, tools);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chat(scene, messages, tools);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chatStream(String scene, List<AiMessage> messages,\n List<AiTool> tools,\n StreamChunkConsumer chunkConsumer) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chatStream(scene, messages, tools, chunkConsumer);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chatStream(scene, messages, tools, chunkConsumer);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chatWithVision(String scene, String systemPrompt, String userMessage, String imageUrl) {\n // 视觉调用优先走 legacy adapter — Agnes视觉模型支持真正的image_url格式，\n // 比Spring AI适配器的文本嵌入方式更可靠\n if (legacyAdapter != null && legacyAdapter.isAvailable()) {\n try {\n IntelligenceInferenceResult result =\n legacyAdapter.chatWithVision(scene, systemPrompt, userMessage, imageUrl);\n if (result != null && result.isSuccess() && hasText(result.getContent())) {\n recordCostAndAudit(scene, result);\n return result;\n }\n log.warn(\"[AiInferenceRouter] legacy视觉调用未返回有效内容，fallback到Spring AI\");\n } catch (Exception e) {\n log.warn(\"[AiInferenceRouter] legacy视觉调用异常，fallback到Spring AI: {}\", e.getMessage());\n }\n }\n // Spring AI 作为第二路径\n if (springAiAdapter != null && !isSpringAiCircuitOpen() && springAiAdapter.isAvailable()) {\n IntelligenceInferenceResult result = springAiAdapter.chatWithVision(scene, systemPrompt, userMessage, imageUrl);\n trackSpringAiHealth(result);\n recordCostAndAudit(scene, result);\n return result;\n }\n // 最后兜底返回空结果\n IntelligenceInferenceResult empty = new IntelligenceInferenceResult();\n empty.setSuccess(false);\n empty.setProvider(\"none\");\n empty.setErrorMessage(\"没有可用的视觉模型（请配置AGNES_API_KEY或DEEPSEEK_API_KEY）\");\n recordCostAndAudit(scene, empty);\n return empty;\n }\n\n @Override\n public boolean isAvailable() {\n return resolveGateway(null).isAvailable();\n }\n\n @Override\n public String getProviderName() {\n return resolveGateway(null).getProviderName();\n }\n\n private AiInferenceGateway resolveGateway(String scene) {\n return switch (routingStrategy) {\n case \"spring-ai\" -> resolveSpringAi();\n case \"failover\" -> resolveFailover(scene);\n case \"legacy\" -> legacyAdapter;\n default -> resolveFailover(scene);\n };\n }\n\n private AiInferenceGateway resolveSpringAi() {\n if (isSpringAiCircuitOpen()) {\n log.warn(\"[AiInferenceRouter] Spring AI 熔断中，降级到 legacy\");\n return legacyAdapter;\n }\n if (springAiAdapter != null && springAiAdapter.isAvailable()) {\n return springAiAdapter;\n }\n log.warn(\"[AiInferenceRouter] spring-ai unavailable, falling back to legacy\");\n return legacyAdapter;\n }\n\n private AiInferenceGateway resolveFailover(String scene) {\n // 短查询/非Agent场景优先走 Spring AI（更快、工具调用更新）\n boolean preferSpringAi = isShortQueryScene(scene);\n if (preferSpringAi && !isSpringAiCircuitOpen()\n && springAiAdapter != null && springAiAdapter.isAvailable()) {\n log.debug(\"[AiInferenceRouter] 短查询场景={}，优先使用 Spring AI\", scene);\n return springAiAdapter;\n }\n // Agent主循环优先Spring AI（流式+tool_calls更好），熔断时降级\n if (!isSpringAiCircuitOpen() && springAiAdapter != null && springAiAdapter.isAvailable()) {\n return springAiAdapter;\n }\n if (legacyAdapter.isAvailable()) {\n return legacyAdapter;\n }\n if (!isSpringAiCircuitOpen() && springAiAdapter != null) {\n return springAiAdapter;\n }\n return legacyAdapter;\n }\n\n private boolean isShortQueryScene(String scene) {\n return scene != null && (\n \"nl-intent\".equals(scene) || \"daily-brief\".equals(scene) ||\n \"critic_review\".equals(scene) || scene.startsWith(\"memory\"));\n }\n\n private boolean isSpringAiCircuitOpen() {\n if (springAiConsecutiveFailures.get() < CIRCUIT_OPEN_THRESHOLD) return false;\n long openSince = springAiCircuitOpenSince.get();\n if (openSince > 0 && System.currentTimeMillis() - openSince > CIRCUIT_RESET_MS) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断进入半开状态，允许探测请求\");\n return false;\n }\n return openSince > 0;\n }\n\n private void trackSpringAiHealth(IntelligenceInferenceResult result) {\n if (result == null || !\"spring-ai\".equals(result.getProvider())) return;\n if (result.isSuccess()) {\n if (springAiConsecutiveFailures.get() >= CIRCUIT_OPEN_THRESHOLD) {\n log.info(\"[AiInferenceRouter] Spring AI 半开探测成功，熔断恢复\");\n }\n springAiConsecutiveFailures.set(0);\n springAiCircuitOpenSince.set(0);\n } else {\n int failures = springAiConsecutiveFailures.incrementAndGet();\n if (failures == CIRCUIT_OPEN_THRESHOLD && springAiCircuitOpenSince.get() == 0) {\n springAiCircuitOpenSince.set(System.currentTimeMillis());\n log.warn(\"[AiInferenceRouter] Spring AI 连续{}次失败，熔断开启（{}秒后进入半开探测）\", failures, CIRCUIT_RESET_MS / 1000);\n } else if (springAiCircuitOpenSince.get() > 0\n && System.currentTimeMillis() - springAiCircuitOpenSince.get() > CIRCUIT_RESET_MS) {\n log.warn(\"[AiInferenceRouter] Spring AI 半开探测失败，重新熔断 {} 秒\", CIRCUIT_RESET_MS / 1000);\n springAiCircuitOpenSince.set(System.currentTimeMillis());\n }\n }\n }\n\n private void recordCostAndAudit(String scene, IntelligenceInferenceResult result) {\n if (result == null) return;\n // 统一性能日志：方便排查双AI引擎表现\n log.info(\"[AiInference] engine={}, scene={}, success={}, latency={}ms, tokens={}/{}, fallback={}\",\n result.getProvider() != null ? result.getProvider() : \"unknown\",\n scene != null ? scene : \"default\",\n result.isSuccess(),\n result.getLatencyMs(),\n result.getPromptTokens(), result.getCompletionTokens(),\n Boolean.TRUE.equals(result.isFallbackUsed()));\n if (!result.isSuccess()) {\n log.warn(\"[AiInference] engine={}, scene={}, error={}\", result.getProvider(), scene, result.getErrorMessage());\n }\n try {\n if (aiCostTrackingOrchestrator != null) {\n aiCostTrackingOrchestrator.recordAsync(\n result.getModel() != null ? result.getModel() : result.getProvider(),\n scene,\n result.getPromptTokens(),\n result.getCompletionTokens(),\n (int) result.getLatencyMs(),\n result.isSuccess(),\n result.getErrorMessage()\n );\n }\n } catch (Exception e) {\n log.debug(\"[AiInferenceRouter] cost tracking failed: {}\", e.getMessage());\n }\n try {\n if (aiOperationAuditOrchestrator != null && result.getTraceId() != null) {\n aiOperationAuditOrchestrator.recordAudit(\n result.getTraceId(),\n \"inference:\" + scene,\n result.getPromptChars() + \" chars\",\n result.getResponseChars() + \" chars\",\n result.getLatencyMs(),\n result.isSuccess(),\n result.getErrorMessage()\n );\n }\n } catch (Exception e) {\n log.debug(\"[AiInferenceRouter] audit recording failed: {}\", e.getMessage());\n }\n }\n\n public Map<String, Object> getRoutingStatus() {\n Map<String, Object> status = new java.util.LinkedHashMap<>();\n status.put(\"strategy\", routingStrategy);\n status.put(\"legacyAvailable\", legacyAdapter.isAvailable());\n status.put(\"springAiAvailable\", springAiAdapter != null && springAiAdapter.isAvailable());\n status.put(\"springAiCircuitOpen\", isSpringAiCircuitOpen());\n status.put(\"springAiConsecutiveFailures\", springAiConsecutiveFailures.get());\n status.put(\"activeProvider\", resolveGateway(null).getProviderName());\n if (modelConsortiumRouter != null) {\n status.put(\"consortium\", modelConsortiumRouter.getConfig());\n }\n return status;\n }\n\n /**\n * 模型智能路由 — 根据用户查询复杂度选择最优模型。\n * 可在上层（AiAgentOrchestrator）调用此方法获取推荐模型，传递给推理适配器。\n */\n public String recommendModel(String userMessage, boolean hasImage, int toolCount) {\n if (modelConsortiumRouter != null) {\n return modelConsortiumRouter.selectModel(userMessage, hasImage, toolCount);\n }\n return \"deepseek-v4-flash\";\n }\n\n public String chatSimple(String prompt) {\n IntelligenceInferenceResult result = chat(\"reflection\", null, prompt);\n return result != null ? result.getContent() : \"\";\n }\n\n /**\n * 带模型选择的聊天接口实现（per-call model selection）。\n *\n * <p>路由到具体的底层 gateway（SpringAiInferenceAdapter / LegacyInferenceAdapter），\n * 由底层 adapter 真正实现 per-call model 覆盖。\n * 同时记录 LLM 调用成本到 CostExplosionGuard（用于成本爆炸防御）。\n */\n @Override\n public String chatWithModel(String prompt, Long tenantId, Long userId, String modelId) {\n long start = System.currentTimeMillis();\n AiInferenceGateway gateway = resolveGateway(\"model-selection\");\n String content;\n try {\n content = gateway.chatWithModel(prompt, tenantId, userId, modelId);\n } catch (Exception e) {\n log.warn(\"[AiInferenceRouter] chatWithModel failed, fallback to legacy: {}\", e.getMessage());\n content = legacyAdapter.chatWithModel(prompt, tenantId, userId, modelId);\n }\n long elapsed = System.currentTimeMillis() - start;\n\n // 记录 LLM 调用成本到 CostExplosionGuard（用于成本爆炸防御 + 重复检测）\n if (costExplosionGuard != null && tenantId != null) {\n try {\n // 用 \"llm_call\" 作为 toolName，prompt 哈希作为 paramsHash\n String paramsHash = costExplosionGuard.hashParams(prompt);\n costExplosionGuard.recordToolCall(tenantId, \"llm_call\", paramsHash,\n content != null ? content : \"\");\n log.debug(\"[AiInferenceRouter] LLM 调用成本已记录 tenantId={} modelId={} elapsed={}ms\",\n tenantId, modelId, elapsed);\n } catch (Exception e) {\n log.debug(\"[AiInferenceRouter] 记录 LLM 调用成本失败（不影响主流程）: {}\", e.getMessage());\n }\n }\n\n log.info(\"[AiInferenceRouter] chatWithModel: gateway={} modelId={} elapsed={}ms contentLen={}\",\n gateway.getProviderName(), modelId != null ? modelId : \"default\",\n elapsed, content != null ? content.length() : 0);\n return content;\n }\n\n /**\n * Per-call 模型选择调用（借鉴 Claude Agent SDK per-call model selection）。\n *\n * <p>根据用户消息复杂度、预估工具调用数、多域标识自动选择模型分级：\n * <ul>\n * <li>ECONOMY — 简单查询 → 便宜模型</li>\n * <li>STANDARD — 普通对话 → 标准模型</li>\n * <li>PREMIUM — 复杂排产 → 强模型</li>\n * </ul>\n *\n * <p>★ 接入点3：真正调用 gateway.chatWithModel 传递 modelId，而非仅用 scene 路由。\n *\n * @param prompt 完整提示词\n * @param userMessage 用户原始消息（用于复杂度评估）\n * @param estimatedToolCalls 预估工具调用数\n * @param isMultiDomain 是否多域任务\n * @return LLM 回答文本\n */\n public String chatWithModelSelection(String prompt, String userMessage,\n int estimatedToolCalls, boolean isMultiDomain) {\n if (modelSelectionRouter == null || !modelSelectionRouter.isEnabled()) {\n return chatSimple(prompt);\n }\n ModelSelectionRouter.ModelTier tier = modelSelectionRouter.selectModel(\n userMessage, estimatedToolCalls, isMultiDomain);\n String modelId = modelSelectionRouter.resolveModelId(tier);\n log.info(\"[AiInferenceRouter] per-call model selection: tier={} modelId={}\", tier, modelId);\n\n // 从 UserContext 获取 tenantId/userId（多租户隔离 + 成本追踪）\n Long tenantId = UserContext.tenantId();\n String userIdStr = UserContext.userId();\n\n // ★ 真正调用 gateway.chatWithModel 传递 modelId（接入点3 核心）\n try {\n return chatWithModel(prompt, tenantId, userIdStr != null ? userIdStr.hashCode() : 0L, modelId);\n } catch (Exception e) {\n log.warn(\"[AiInferenceRouter] chatWithModelSelection failed, fallback to chatSimple: {}\", e.getMessage());\n return chatSimple(prompt);\n }\n }\n\n /**\n * 强制使用 PREMIUM 模型调用（高风险场景，如复杂排产优化）。\n *\n * @param prompt 完整提示词\n * @return LLM 回答文本\n */\n public String chatPremium(String prompt) {\n if (modelSelectionRouter == null || !modelSelectionRouter.isEnabled()) {\n return chatSimple(prompt);\n }\n String modelId = modelSelectionRouter.resolveModelId(ModelSelectionRouter.ModelTier.PREMIUM);\n log.info(\"[AiInferenceRouter] forced premium model: modelId={}\", modelId);\n IntelligenceInferenceResult result = chat(\"premium-reasoning\", null, prompt);\n return result != null ? result.getContent() : \"\";\n }\n\n /** 根据模型分级映射到 scene（用于网关路由 hint） */\n private String resolveSceneByTier(ModelSelectionRouter.ModelTier tier) {\n return switch (tier) {\n case ECONOMY -> \"economy-query\";\n case STANDARD -> \"reflection\";\n case PREMIUM -> \"premium-reasoning\";\n };\n }\n\n /**\n * 获取模型推荐参数（temperature、maxTokens、timeout）。\n */\n public ModelConsortiumRouter.ModelParams recommendModelParams(String userMessage, boolean hasImage, int toolCount) {\n if (modelConsortiumRouter != null) {\n ModelConsortiumRouter.Complexity complexity = modelConsortiumRouter.classifyComplexity(\n userMessage, hasImage, toolCount);\n return modelConsortiumRouter.getModelParams(complexity);\n }\n return new ModelConsortiumRouter.ModelParams(0.5, 1024, 30);\n }\n\n private boolean hasText(String value) {\n return value != null && !value.trim().isEmpty();\n }\n}\n"}, "5096c8449e8798402e701944701dd0cc7033e1b7": {"t": 1785749959.105322, "n": 13376, "h": "ea0aae905f5cd560", "c": "package com.fashion.supplychain.intelligence.gateway;\n\nimport com.fashion.supplychain.intelligence.agent.AiMessage;\nimport com.fashion.supplychain.intelligence.agent.AiTool;\nimport com.fashion.supplychain.intelligence.dto.IntelligenceInferenceResult;\nimport com.fashion.supplychain.intelligence.orchestration.AiCostTrackingOrchestrator;\nimport com.fashion.supplychain.intelligence.orchestration.AiOperationAuditOrchestrator;\nimport java.util.List;\nimport java.util.Map;\nimport java.util.concurrent.atomic.AtomicInteger;\nimport java.util.concurrent.atomic.AtomicLong;\nimport lombok.extern.slf4j.Slf4j;\nimport org.springframework.beans.factory.annotation.Autowired;\nimport org.springframework.beans.factory.annotation.Value;\nimport org.springframework.context.annotation.Primary;\nimport org.springframework.stereotype.Service;\nimport org.springframework.context.annotation.Lazy;\n\n@Slf4j\n@Service\n@Lazy\n@Primary\npublic class AiInferenceRouter implements AiInferenceGateway {\n\n private static final int CIRCUIT_OPEN_THRESHOLD = 3;\n private static final long CIRCUIT_RESET_MS = 30_000L;\n\n @Value(\"${ai.gateway.routing-strategy:failover}\")\n private String routingStrategy;\n\n @Autowired\n private LegacyInferenceAdapter legacyAdapter;\n\n @Autowired(required = false)\n private SpringAiInferenceAdapter springAiAdapter;\n\n @Autowired(required = false)\n private AiCostTrackingOrchestrator aiCostTrackingOrchestrator;\n\n @Autowired(required = false)\n private AiOperationAuditOrchestrator aiOperationAuditOrchestrator;\n\n @Autowired(required = false)\n private ModelConsortiumRouter modelConsortiumRouter;\n\n private final AtomicInteger springAiConsecutiveFailures = new AtomicInteger(0);\n private final AtomicLong springAiCircuitOpenSince = new AtomicLong(0);\n\n @Override\n public IntelligenceInferenceResult chat(String scene, String systemPrompt, String userMessage) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chat(scene, systemPrompt, userMessage);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chat(scene, systemPrompt, userMessage);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chat(String scene, List<AiMessage> messages, List<AiTool> tools) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chat(scene, messages, tools);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chat(scene, messages, tools);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chatStream(String scene, List<AiMessage> messages,\n List<AiTool> tools,\n StreamChunkConsumer chunkConsumer) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chatStream(scene, messages, tools, chunkConsumer);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chatStream(scene, messages, tools, chunkConsumer);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chatWithVision(String scene, String systemPrompt, String userMessage, String imageUrl) {\n // 视觉调用优先走 legacy adapter — Agnes视觉模型支持真正的image_url格式，\n // 比Spring AI适配器的文本嵌入方式更可靠\n if (legacyAdapter != null && legacyAdapter.isAvailable()) {\n try {\n IntelligenceInferenceResult result =\n legacyAdapter.chatWithVision(scene, systemPrompt, userMessage, imageUrl);\n if (result != null && result.isSuccess() && hasText(result.getContent())) {\n recordCostAndAudit(scene, result);\n return result;\n }\n log.warn(\"[AiInferenceRouter] legacy视觉调用未返回有效内容，fallback到Spring AI\");\n } catch (Exception e) {\n log.warn(\"[AiInferenceRouter] legacy视觉调用异常，fallback到Spring AI: {}\", e.getMessage());\n }\n }\n // Spring AI 作为第二路径\n if (springAiAdapter != null && !isSpringAiCircuitOpen() && springAiAdapter.isAvailable()) {\n IntelligenceInferenceResult result = springAiAdapter.chatWithVision(scene, systemPrompt, userMessage, imageUrl);\n trackSpringAiHealth(result);\n recordCostAndAudit(scene, result);\n return result;\n }\n // 最后兜底返回空结果\n IntelligenceInferenceResult empty = new IntelligenceInferenceResult();\n empty.setSuccess(false);\n empty.setProvider(\"none\");\n empty.setErrorMessage(\"没有可用的视觉模型（请配置AGNES_API_KEY或DEEPSEEK_API_KEY）\");\n recordCostAndAudit(scene, empty);\n return empty;\n }\n\n @Override\n public boolean isAvailable() {\n return resolveGateway(null).isAvailable();\n }\n\n @Override\n public String getProviderName() {\n return resolveGateway(null).getProviderName();\n }\n\n private AiInferenceGateway resolveGateway(String scene) {\n return switch (routingStrategy) {\n case \"spring-ai\" -> resolveSpringAi();\n case \"failover\" -> resolveFailover(scene);\n case \"legacy\" -> legacyAdapter;\n default -> resolveFailover(scene);\n };\n }\n\n private AiInferenceGateway resolveSpringAi() {\n if (isSpringAiCircuitOpen()) {\n log.warn(\"[AiInferenceRouter] Spring AI 熔断中，降级到 legacy\");\n return legacyAdapter;\n }\n if (springAiAdapter != null && springAiAdapter.isAvailable()) {\n return springAiAdapter;\n }\n log.warn(\"[AiInferenceRouter] spring-ai unavailable, falling back to legacy\");\n return legacyAdapter;\n }\n\n private AiInferenceGateway resolveFailover(String scene) {\n // 短查询/非Agent场景优先走 Spring AI（更快、工具调用更新）\n boolean preferSpringAi = isShortQueryScene(scene);\n if (preferSpringAi && !isSpringAiCircuitOpen()\n && springAiAdapter != null && springAiAdapter.isAvailable()) {\n log.debug(\"[AiInferenceRouter] 短查询场景={}，优先使用 Spring AI\", scene);\n return springAiAdapter;\n }\n // Agent主循环优先Spring AI（流式+tool_calls更好），熔断时降级\n if (!isSpringAiCircuitOpen() && springAiAdapter != null && springAiAdapter.isAvailable()) {\n return springAiAdapter;\n }\n if (legacyAdapter.isAvailable()) {\n return legacyAdapter;\n }\n if (!isSpringAiCircuitOpen() && springAiAdapter != null) {\n return springAiAdapter;\n }\n return legacyAdapter;\n }\n\n private boolean isShortQueryScene(String scene) {\n return scene != null && (\n \"nl-intent\".equals(scene) || \"daily-brief\".equals(scene) ||\n \"critic_review\".equals(scene) || scene.startsWith(\"memory\"));\n }\n\n private boolean isSpringAiCircuitOpen() {\n if (springAiConsecutiveFailures.get() < CIRCUIT_OPEN_THRESHOLD) return false;\n long openSince = springAiCircuitOpenSince.get();\n if (openSince > 0 && System.currentTimeMillis() - openSince > CIRCUIT_RESET_MS) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断进入半开状态，允许探测请求\");\n return false;\n }\n return openSince > 0;\n }\n\n private void trackSpringAiHealth(IntelligenceInferenceResult result) {\n if (result == null || !\"spring-ai\".equals(result.getProvider())) return;\n if (result.isSuccess()) {\n if (springAiConsecutiveFailures.get() >= CIRCUIT_OPEN_THRESHOLD) {\n log.info(\"[AiInferenceRouter] Spring AI 半开探测成功，熔断恢复\");\n }\n springAiConsecutiveFailures.set(0);\n springAiCircuitOpenSince.set(0);\n } else {\n int failures = springAiConsecutiveFailures.incrementAndGet();\n if (failures == CIRCUIT_OPEN_THRESHOLD && springAiCircuitOpenSince.get() == 0) {\n springAiCircuitOpenSince.set(System.currentTimeMillis());\n log.warn(\"[AiInferenceRouter] Spring AI 连续{}次失败，熔断开启（{}秒后进入半开探测）\", failures, CIRCUIT_RESET_MS / 1000);\n } else if (springAiCircuitOpenSince.get() > 0\n && System.currentTimeMillis() - springAiCircuitOpenSince.get() > CIRCUIT_RESET_MS) {\n log.warn(\"[AiInferenceRouter] Spring AI 半开探测失败，重新熔断 {} 秒\", CIRCUIT_RESET_MS / 1000);\n springAiCircuitOpenSince.set(System.currentTimeMillis());\n }\n }\n }\n\n private void recordCostAndAudit(String scene, IntelligenceInferenceResult result) {\n if (result == null) return;\n // 统一性能日志：方便排查双AI引擎表现\n log.info(\"[AiInference] engine={}, scene={}, success={}, latency={}ms, tokens={}/{}, fallback={}\",\n result.getProvider() != null ? result.getProvider() : \"unknown\",\n scene != null ? scene : \"default\",\n result.isSuccess(),\n result.getLatencyMs(),\n result.getPromptTokens(), result.getCompletionTokens(),\n Boolean.TRUE.equals(result.isFallbackUsed()));\n if (!result.isSuccess()) {\n log.warn(\"[AiInference] engine={}, scene={}, error={}\", result.getProvider(), scene, result.getErrorMessage());\n }\n try {\n if (aiCostTrackingOrchestrator != null) {\n aiCostTrackingOrchestrator.recordAsync(\n result.getModel() != null ? result.getModel() : result.getProvider(),\n scene,\n result.getPromptTokens(),\n result.getCompletionTokens(),\n (int) result.getLatencyMs(),\n result.isSuccess(),\n result.getErrorMessage()\n );\n }\n } catch (Exception e) {\n log.debug(\"[AiInferenceRouter] cost tracking failed: {}\", e.getMessage());\n }\n try {\n if (aiOperationAuditOrchestrator != null && result.getTraceId() != null) {\n aiOperationAuditOrchestrator.recordAudit(\n result.getTraceId(),\n \"inference:\" + scene,\n result.getPromptChars() + \" chars\",\n result.getResponseChars() + \" chars\",\n result.getLatencyMs(),\n result.isSuccess(),\n result.getErrorMessage()\n );\n }\n } catch (Exception e) {\n log.debug(\"[AiInferenceRouter] audit recording failed: {}\", e.getMessage());\n }\n }\n\n public Map<String, Object> getRoutingStatus() {\n Map<String, Object> status = new java.util.LinkedHashMap<>();\n status.put(\"strategy\", routingStrategy);\n status.put(\"legacyAvailable\", legacyAdapter.isAvailable());\n status.put(\"springAiAvailable\", springAiAdapter != null && springAiAdapter.isAvailable());\n status.put(\"springAiCircuitOpen\", isSpringAiCircuitOpen());\n status.put(\"springAiConsecutiveFailures\", springAiConsecutiveFailures.get());\n status.put(\"activeProvider\", resolveGateway(null).getProviderName());\n if (modelConsortiumRouter != null) {\n status.put(\"consortium\", modelConsortiumRouter.getConfig());\n }\n return status;\n }\n\n /**\n * 模型智能路由 — 根据用户查询复杂度选择最优模型。\n * 可在上层（AiAgentOrchestrator）调用此方法获取推荐模型，传递给推理适配器。\n */\n public String recommendModel(String userMessage, boolean hasImage, int toolCount) {\n if (modelConsortiumRouter != null) {\n return modelConsortiumRouter.selectModel(userMessage, hasImage, toolCount);\n }\n return \"deepseek-v4-flash\";\n }\n\n public String chatSimple(String prompt) {\n IntelligenceInferenceResult result = chat(\"reflection\", null, prompt);\n return result != null ? result.getContent() : \"\";\n }\n\n /**\n * 获取模型推荐参数（temperature、maxTokens、timeout）。\n */\n public ModelConsortiumRouter.ModelParams recommendModelParams(String userMessage, boolean hasImage, int toolCount) {\n if (modelConsortiumRouter != null) {\n ModelConsortiumRouter.Complexity complexity = modelConsortiumRouter.classifyComplexity(\n userMessage, hasImage, toolCount);\n return modelConsortiumRouter.getModelParams(complexity);\n }\n return new ModelConsortiumRouter.ModelParams(0.5, 1024, 30);\n }\n\n private boolean hasText(String value) {\n return value != null && !value.trim().isEmpty();\n }\n}\n"}, "fc10d435e3d8429461ad7e59b9cdd2179a093756": {"t": 1785749960.1908967, "n": 12396, "h": "f2fd10483f74da71", "c": "package com.fashion.supplychain.intelligence.gateway;\n\nimport com.fashion.supplychain.intelligence.agent.AiMessage;\nimport com.fashion.supplychain.intelligence.agent.AiTool;\nimport com.fashion.supplychain.intelligence.dto.IntelligenceInferenceResult;\nimport com.fashion.supplychain.intelligence.orchestration.AiCostTrackingOrchestrator;\nimport com.fashion.supplychain.intelligence.orchestration.AiOperationAuditOrchestrator;\nimport java.util.List;\nimport java.util.Map;\nimport java.util.concurrent.atomic.AtomicInteger;\nimport java.util.concurrent.atomic.AtomicLong;\nimport lombok.extern.slf4j.Slf4j;\nimport org.springframework.beans.factory.annotation.Autowired;\nimport org.springframework.beans.factory.annotation.Value;\nimport org.springframework.context.annotation.Primary;\nimport org.springframework.stereotype.Service;\nimport org.springframework.context.annotation.Lazy;\n\n@Slf4j\n@Service\n@Lazy\n@Primary\npublic class AiInferenceRouter implements AiInferenceGateway {\n\n private static final int CIRCUIT_OPEN_THRESHOLD = 3;\n private static final long CIRCUIT_RESET_MS = 30_000L;\n\n @Value(\"${ai.gateway.routing-strategy:failover}\")\n private String routingStrategy;\n\n @Autowired\n private LegacyInferenceAdapter legacyAdapter;\n\n @Autowired(required = false)\n private SpringAiInferenceAdapter springAiAdapter;\n\n @Autowired(required = false)\n private AiCostTrackingOrchestrator aiCostTrackingOrchestrator;\n\n @Autowired(required = false)\n private AiOperationAuditOrchestrator aiOperationAuditOrchestrator;\n\n @Autowired(required = false)\n private ModelConsortiumRouter modelConsortiumRouter;\n\n private final AtomicInteger springAiConsecutiveFailures = new AtomicInteger(0);\n private final AtomicLong springAiCircuitOpenSince = new AtomicLong(0);\n\n @Override\n public IntelligenceInferenceResult chat(String scene, String systemPrompt, String userMessage) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chat(scene, systemPrompt, userMessage);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chat(scene, systemPrompt, userMessage);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chat(String scene, List<AiMessage> messages, List<AiTool> tools) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chat(scene, messages, tools);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chat(scene, messages, tools);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chatStream(String scene, List<AiMessage> messages,\n List<AiTool> tools,\n StreamChunkConsumer chunkConsumer) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chatStream(scene, messages, tools, chunkConsumer);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chatStream(scene, messages, tools, chunkConsumer);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chatWithVision(String scene, String systemPrompt, String userMessage, String imageUrl) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chatWithVision(scene, systemPrompt, userMessage, imageUrl);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chatWithVision(scene, systemPrompt, userMessage, imageUrl);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public boolean isAvailable() {\n return resolveGateway(null).isAvailable();\n }\n\n @Override\n public String getProviderName() {\n return resolveGateway(null).getProviderName();\n }\n\n private AiInferenceGateway resolveGateway(String scene) {\n return switch (routingStrategy) {\n case \"spring-ai\" -> resolveSpringAi();\n case \"failover\" -> resolveFailover(scene);\n case \"legacy\" -> legacyAdapter;\n default -> resolveFailover(scene);\n };\n }\n\n private AiInferenceGateway resolveSpringAi() {\n if (isSpringAiCircuitOpen()) {\n log.warn(\"[AiInferenceRouter] Spring AI 熔断中，降级到 legacy\");\n return legacyAdapter;\n }\n if (springAiAdapter != null && springAiAdapter.isAvailable()) {\n return springAiAdapter;\n }\n log.warn(\"[AiInferenceRouter] spring-ai unavailable, falling back to legacy\");\n return legacyAdapter;\n }\n\n private AiInferenceGateway resolveFailover(String scene) {\n // 短查询/非Agent场景优先走 Spring AI（更快、工具调用更新）\n boolean preferSpringAi = isShortQueryScene(scene);\n if (preferSpringAi && !isSpringAiCircuitOpen()\n && springAiAdapter != null && springAiAdapter.isAvailable()) {\n log.debug(\"[AiInferenceRouter] 短查询场景={}，优先使用 Spring AI\", scene);\n return springAiAdapter;\n }\n // Agent主循环优先Spring AI（流式+tool_calls更好），熔断时降级\n if (!isSpringAiCircuitOpen() && springAiAdapter != null && springAiAdapter.isAvailable()) {\n return springAiAdapter;\n }\n if (legacyAdapter.isAvailable()) {\n return legacyAdapter;\n }\n if (!isSpringAiCircuitOpen() && springAiAdapter != null) {\n return springAiAdapter;\n }\n return legacyAdapter;\n }\n\n private boolean isShortQueryScene(String scene) {\n return scene != null && (\n \"nl-intent\".equals(scene) || \"daily-brief\".equals(scene) ||\n \"critic_review\".equals(scene) || scene.startsWith(\"memory\"));\n }\n\n private boolean isSpringAiCircuitOpen() {\n if (springAiConsecutiveFailures.get() < CIRCUIT_OPEN_THRESHOLD) return false;\n long openSince = springAiCircuitOpenSince.get();\n if (openSince > 0 && System.currentTimeMillis() - openSince > CIRCUIT_RESET_MS) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断进入半开状态，允许探测请求\");\n return false;\n }\n return openSince > 0;\n }\n\n private void trackSpringAiHealth(IntelligenceInferenceResult result) {\n if (result == null || !\"spring-ai\".equals(result.getProvider())) return;\n if (result.isSuccess()) {\n if (springAiConsecutiveFailures.get() >= CIRCUIT_OPEN_THRESHOLD) {\n log.info(\"[AiInferenceRouter] Spring AI 半开探测成功，熔断恢复\");\n }\n springAiConsecutiveFailures.set(0);\n springAiCircuitOpenSince.set(0);\n } else {\n int failures = springAiConsecutiveFailures.incrementAndGet();\n if (failures == CIRCUIT_OPEN_THRESHOLD && springAiCircuitOpenSince.get() == 0) {\n springAiCircuitOpenSince.set(System.currentTimeMillis());\n log.warn(\"[AiInferenceRouter] Spring AI 连续{}次失败，熔断开启（{}秒后进入半开探测）\", failures, CIRCUIT_RESET_MS / 1000);\n } else if (springAiCircuitOpenSince.get() > 0\n && System.currentTimeMillis() - springAiCircuitOpenSince.get() > CIRCUIT_RESET_MS) {\n log.warn(\"[AiInferenceRouter] Spring AI 半开探测失败，重新熔断 {} 秒\", CIRCUIT_RESET_MS / 1000);\n springAiCircuitOpenSince.set(System.currentTimeMillis());\n }\n }\n }\n\n private void recordCostAndAudit(String scene, IntelligenceInferenceResult result) {\n if (result == null) return;\n // 统一性能日志：方便排查双AI引擎表现\n log.info(\"[AiInference] engine={}, scene={}, success={}, latency={}ms, tokens={}/{}, fallback={}\",\n result.getProvider() != null ? result.getProvider() : \"unknown\",\n scene != null ? scene : \"default\",\n result.isSuccess(),\n result.getLatencyMs(),\n result.getPromptTokens(), result.getCompletionTokens(),\n Boolean.TRUE.equals(result.isFallbackUsed()));\n if (!result.isSuccess()) {\n log.warn(\"[AiInference] engine={}, scene={}, error={}\", result.getProvider(), scene, result.getErrorMessage());\n }\n try {\n if (aiCostTrackingOrchestrator != null) {\n aiCostTrackingOrchestrator.recordAsync(\n result.getModel() != null ? result.getModel() : result.getProvider(),\n scene,\n result.getPromptTokens(),\n result.getCompletionTokens(),\n (int) result.getLatencyMs(),\n result.isSuccess(),\n result.getErrorMessage()\n );\n }\n } catch (Exception e) {\n log.debug(\"[AiInferenceRouter] cost tracking failed: {}\", e.getMessage());\n }\n try {\n if (aiOperationAuditOrchestrator != null && result.getTraceId() != null) {\n aiOperationAuditOrchestrator.recordAudit(\n result.getTraceId(),\n \"inference:\" + scene,\n result.getPromptChars() + \" chars\",\n result.getResponseChars() + \" chars\",\n result.getLatencyMs(),\n result.isSuccess(),\n result.getErrorMessage()\n );\n }\n } catch (Exception e) {\n log.debug(\"[AiInferenceRouter] audit recording failed: {}\", e.getMessage());\n }\n }\n\n public Map<String, Object> getRoutingStatus() {\n Map<String, Object> status = new java.util.LinkedHashMap<>();\n status.put(\"strategy\", routingStrategy);\n status.put(\"legacyAvailable\", legacyAdapter.isAvailable());\n status.put(\"springAiAvailable\", springAiAdapter != null && springAiAdapter.isAvailable());\n status.put(\"springAiCircuitOpen\", isSpringAiCircuitOpen());\n status.put(\"springAiConsecutiveFailures\", springAiConsecutiveFailures.get());\n status.put(\"activeProvider\", resolveGateway(null).getProviderName());\n if (modelConsortiumRouter != null) {\n status.put(\"consortium\", modelConsortiumRouter.getConfig());\n }\n return status;\n }\n\n /**\n * 模型智能路由 — 根据用户查询复杂度选择最优模型。\n * 可在上层（AiAgentOrchestrator）调用此方法获取推荐模型，传递给推理适配器。\n */\n public String recommendModel(String userMessage, boolean hasImage, int toolCount) {\n if (modelConsortiumRouter != null) {\n return modelConsortiumRouter.selectModel(userMessage, hasImage, toolCount);\n }\n return \"deepseek-v4-flash\";\n }\n\n public String chatSimple(String prompt) {\n IntelligenceInferenceResult result = chat(\"reflection\", null, prompt);\n return result != null ? result.getContent() : \"\";\n }\n\n /**\n * 获取模型推荐参数（temperature、maxTokens、timeout）。\n */\n public ModelConsortiumRouter.ModelParams recommendModelParams(String userMessage, boolean hasImage, int toolCount) {\n if (modelConsortiumRouter != null) {\n ModelConsortiumRouter.Complexity complexity = modelConsortiumRouter.classifyComplexity(\n userMessage, hasImage, toolCount);\n return modelConsortiumRouter.getModelParams(complexity);\n }\n return new ModelConsortiumRouter.ModelParams(0.5, 1024, 30);\n }\n}\n"}, "90c4cf9babe331e23a41125a6d92eb5c22f87e67": {"t": 1785749962.3397975, "n": 12338, "h": "29bcfb47b5e08689", "c": "package com.fashion.supplychain.intelligence.gateway;\n\nimport com.fashion.supplychain.intelligence.agent.AiMessage;\nimport com.fashion.supplychain.intelligence.agent.AiTool;\nimport com.fashion.supplychain.intelligence.dto.IntelligenceInferenceResult;\nimport com.fashion.supplychain.intelligence.orchestration.AiCostTrackingOrchestrator;\nimport com.fashion.supplychain.intelligence.orchestration.AiOperationAuditOrchestrator;\nimport java.util.List;\nimport java.util.Map;\nimport java.util.concurrent.atomic.AtomicInteger;\nimport java.util.concurrent.atomic.AtomicLong;\nimport lombok.extern.slf4j.Slf4j;\nimport org.springframework.beans.factory.annotation.Autowired;\nimport org.springframework.beans.factory.annotation.Value;\nimport org.springframework.context.annotation.Primary;\nimport org.springframework.stereotype.Service;\n\n@Slf4j\n@Service\n@Primary\npublic class AiInferenceRouter implements AiInferenceGateway {\n\n private static final int CIRCUIT_OPEN_THRESHOLD = 3;\n private static final long CIRCUIT_RESET_MS = 30_000L;\n\n @Value(\"${ai.gateway.routing-strategy:failover}\")\n private String routingStrategy;\n\n @Autowired\n private LegacyInferenceAdapter legacyAdapter;\n\n @Autowired(required = false)\n private SpringAiInferenceAdapter springAiAdapter;\n\n @Autowired(required = false)\n private AiCostTrackingOrchestrator aiCostTrackingOrchestrator;\n\n @Autowired(required = false)\n private AiOperationAuditOrchestrator aiOperationAuditOrchestrator;\n\n @Autowired(required = false)\n private ModelConsortiumRouter modelConsortiumRouter;\n\n private final AtomicInteger springAiConsecutiveFailures = new AtomicInteger(0);\n private final AtomicLong springAiCircuitOpenSince = new AtomicLong(0);\n\n @Override\n public IntelligenceInferenceResult chat(String scene, String systemPrompt, String userMessage) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chat(scene, systemPrompt, userMessage);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chat(scene, systemPrompt, userMessage);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chat(String scene, List<AiMessage> messages, List<AiTool> tools) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chat(scene, messages, tools);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chat(scene, messages, tools);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chatStream(String scene, List<AiMessage> messages,\n List<AiTool> tools,\n StreamChunkConsumer chunkConsumer) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chatStream(scene, messages, tools, chunkConsumer);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chatStream(scene, messages, tools, chunkConsumer);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chatWithVision(String scene, String systemPrompt, String userMessage, String imageUrl) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chatWithVision(scene, systemPrompt, userMessage, imageUrl);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chatWithVision(scene, systemPrompt, userMessage, imageUrl);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public boolean isAvailable() {\n return resolveGateway(null).isAvailable();\n }\n\n @Override\n public String getProviderName() {\n return resolveGateway(null).getProviderName();\n }\n\n private AiInferenceGateway resolveGateway(String scene) {\n return switch (routingStrategy) {\n case \"spring-ai\" -> resolveSpringAi();\n case \"failover\" -> resolveFailover(scene);\n case \"legacy\" -> legacyAdapter;\n default -> resolveFailover(scene);\n };\n }\n\n private AiInferenceGateway resolveSpringAi() {\n if (isSpringAiCircuitOpen()) {\n log.warn(\"[AiInferenceRouter] Spring AI 熔断中，降级到 legacy\");\n return legacyAdapter;\n }\n if (springAiAdapter != null && springAiAdapter.isAvailable()) {\n return springAiAdapter;\n }\n log.warn(\"[AiInferenceRouter] spring-ai unavailable, falling back to legacy\");\n return legacyAdapter;\n }\n\n private AiInferenceGateway resolveFailover(String scene) {\n // 短查询/非Agent场景优先走 Spring AI（更快、工具调用更新）\n boolean preferSpringAi = isShortQueryScene(scene);\n if (preferSpringAi && !isSpringAiCircuitOpen()\n && springAiAdapter != null && springAiAdapter.isAvailable()) {\n log.debug(\"[AiInferenceRouter] 短查询场景={}，优先使用 Spring AI\", scene);\n return springAiAdapter;\n }\n // Agent主循环优先Spring AI（流式+tool_calls更好），熔断时降级\n if (!isSpringAiCircuitOpen() && springAiAdapter != null && springAiAdapter.isAvailable()) {\n return springAiAdapter;\n }\n if (legacyAdapter.isAvailable()) {\n return legacyAdapter;\n }\n if (!isSpringAiCircuitOpen() && springAiAdapter != null) {\n return springAiAdapter;\n }\n return legacyAdapter;\n }\n\n private boolean isShortQueryScene(String scene) {\n return scene != null && (\n \"nl-intent\".equals(scene) || \"daily-brief\".equals(scene) ||\n \"critic_review\".equals(scene) || scene.startsWith(\"memory\"));\n }\n\n private boolean isSpringAiCircuitOpen() {\n if (springAiConsecutiveFailures.get() < CIRCUIT_OPEN_THRESHOLD) return false;\n long openSince = springAiCircuitOpenSince.get();\n if (openSince > 0 && System.currentTimeMillis() - openSince > CIRCUIT_RESET_MS) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断进入半开状态，允许探测请求\");\n return false;\n }\n return openSince > 0;\n }\n\n private void trackSpringAiHealth(IntelligenceInferenceResult result) {\n if (result == null || !\"spring-ai\".equals(result.getProvider())) return;\n if (result.isSuccess()) {\n if (springAiConsecutiveFailures.get() >= CIRCUIT_OPEN_THRESHOLD) {\n log.info(\"[AiInferenceRouter] Spring AI 半开探测成功，熔断恢复\");\n }\n springAiConsecutiveFailures.set(0);\n springAiCircuitOpenSince.set(0);\n } else {\n int failures = springAiConsecutiveFailures.incrementAndGet();\n if (failures == CIRCUIT_OPEN_THRESHOLD && springAiCircuitOpenSince.get() == 0) {\n springAiCircuitOpenSince.set(System.currentTimeMillis());\n log.warn(\"[AiInferenceRouter] Spring AI 连续{}次失败，熔断开启（{}秒后进入半开探测）\", failures, CIRCUIT_RESET_MS / 1000);\n } else if (springAiCircuitOpenSince.get() > 0\n && System.currentTimeMillis() - springAiCircuitOpenSince.get() > CIRCUIT_RESET_MS) {\n log.warn(\"[AiInferenceRouter] Spring AI 半开探测失败，重新熔断 {} 秒\", CIRCUIT_RESET_MS / 1000);\n springAiCircuitOpenSince.set(System.currentTimeMillis());\n }\n }\n }\n\n private void recordCostAndAudit(String scene, IntelligenceInferenceResult result) {\n if (result == null) return;\n // 统一性能日志：方便排查双AI引擎表现\n log.info(\"[AiInference] engine={}, scene={}, success={}, latency={}ms, tokens={}/{}, fallback={}\",\n result.getProvider() != null ? result.getProvider() : \"unknown\",\n scene != null ? scene : \"default\",\n result.isSuccess(),\n result.getLatencyMs(),\n result.getPromptTokens(), result.getCompletionTokens(),\n Boolean.TRUE.equals(result.isFallbackUsed()));\n if (!result.isSuccess()) {\n log.warn(\"[AiInference] engine={}, scene={}, error={}\", result.getProvider(), scene, result.getErrorMessage());\n }\n try {\n if (aiCostTrackingOrchestrator != null) {\n aiCostTrackingOrchestrator.recordAsync(\n result.getModel() != null ? result.getModel() : result.getProvider(),\n scene,\n result.getPromptTokens(),\n result.getCompletionTokens(),\n (int) result.getLatencyMs(),\n result.isSuccess(),\n result.getErrorMessage()\n );\n }\n } catch (Exception e) {\n log.debug(\"[AiInferenceRouter] cost tracking failed: {}\", e.getMessage());\n }\n try {\n if (aiOperationAuditOrchestrator != null && result.getTraceId() != null) {\n aiOperationAuditOrchestrator.recordAudit(\n result.getTraceId(),\n \"inference:\" + scene,\n result.getPromptChars() + \" chars\",\n result.getResponseChars() + \" chars\",\n result.getLatencyMs(),\n result.isSuccess(),\n result.getErrorMessage()\n );\n }\n } catch (Exception e) {\n log.debug(\"[AiInferenceRouter] audit recording failed: {}\", e.getMessage());\n }\n }\n\n public Map<String, Object> getRoutingStatus() {\n Map<String, Object> status = new java.util.LinkedHashMap<>();\n status.put(\"strategy\", routingStrategy);\n status.put(\"legacyAvailable\", legacyAdapter.isAvailable());\n status.put(\"springAiAvailable\", springAiAdapter != null && springAiAdapter.isAvailable());\n status.put(\"springAiCircuitOpen\", isSpringAiCircuitOpen());\n status.put(\"springAiConsecutiveFailures\", springAiConsecutiveFailures.get());\n status.put(\"activeProvider\", resolveGateway(null).getProviderName());\n if (modelConsortiumRouter != null) {\n status.put(\"consortium\", modelConsortiumRouter.getConfig());\n }\n return status;\n }\n\n /**\n * 模型智能路由 — 根据用户查询复杂度选择最优模型。\n * 可在上层（AiAgentOrchestrator）调用此方法获取推荐模型，传递给推理适配器。\n */\n public String recommendModel(String userMessage, boolean hasImage, int toolCount) {\n if (modelConsortiumRouter != null) {\n return modelConsortiumRouter.selectModel(userMessage, hasImage, toolCount);\n }\n return \"deepseek-v4-flash\";\n }\n\n public String chatSimple(String prompt) {\n IntelligenceInferenceResult result = chat(\"reflection\", null, prompt);\n return result != null ? result.getContent() : \"\";\n }\n\n /**\n * 获取模型推荐参数（temperature、maxTokens、timeout）。\n */\n public ModelConsortiumRouter.ModelParams recommendModelParams(String userMessage, boolean hasImage, int toolCount) {\n if (modelConsortiumRouter != null) {\n ModelConsortiumRouter.Complexity complexity = modelConsortiumRouter.classifyComplexity(\n userMessage, hasImage, toolCount);\n return modelConsortiumRouter.getModelParams(complexity);\n }\n return new ModelConsortiumRouter.ModelParams(0.5, 1024, 30);\n }\n}\n"}, "8984df30bfe77bcc304b28856cde70285a8310aa": {"t": 1785749963.4729555, "n": 11590, "h": "dfc25be17d3265ed", "c": "package com.fashion.supplychain.intelligence.gateway;\n\nimport com.fashion.supplychain.intelligence.agent.AiMessage;\nimport com.fashion.supplychain.intelligence.agent.AiTool;\nimport com.fashion.supplychain.intelligence.dto.IntelligenceInferenceResult;\nimport com.fashion.supplychain.intelligence.orchestration.AiCostTrackingOrchestrator;\nimport com.fashion.supplychain.intelligence.orchestration.AiOperationAuditOrchestrator;\nimport java.util.List;\nimport java.util.Map;\nimport java.util.concurrent.atomic.AtomicInteger;\nimport java.util.concurrent.atomic.AtomicLong;\nimport lombok.extern.slf4j.Slf4j;\nimport org.springframework.beans.factory.annotation.Autowired;\nimport org.springframework.beans.factory.annotation.Value;\nimport org.springframework.context.annotation.Primary;\nimport org.springframework.stereotype.Service;\n\n@Slf4j\n@Service\n@Primary\npublic class AiInferenceRouter implements AiInferenceGateway {\n\n private static final int CIRCUIT_OPEN_THRESHOLD = 3;\n private static final long CIRCUIT_RESET_MS = 30_000L;\n\n @Value(\"${ai.gateway.routing-strategy:failover}\")\n private String routingStrategy;\n\n @Autowired\n private LegacyInferenceAdapter legacyAdapter;\n\n @Autowired(required = false)\n private SpringAiInferenceAdapter springAiAdapter;\n\n @Autowired(required = false)\n private AiCostTrackingOrchestrator aiCostTrackingOrchestrator;\n\n @Autowired(required = false)\n private AiOperationAuditOrchestrator aiOperationAuditOrchestrator;\n\n @Autowired(required = false)\n private ModelConsortiumRouter modelConsortiumRouter;\n\n private final AtomicInteger springAiConsecutiveFailures = new AtomicInteger(0);\n private final AtomicLong springAiCircuitOpenSince = new AtomicLong(0);\n\n @Override\n public IntelligenceInferenceResult chat(String scene, String systemPrompt, String userMessage) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chat(scene, systemPrompt, userMessage);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chat(scene, systemPrompt, userMessage);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chat(String scene, List<AiMessage> messages, List<AiTool> tools) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chat(scene, messages, tools);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chat(scene, messages, tools);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chatStream(String scene, List<AiMessage> messages,\n List<AiTool> tools,\n StreamChunkConsumer chunkConsumer) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chatStream(scene, messages, tools, chunkConsumer);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chatStream(scene, messages, tools, chunkConsumer);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public boolean isAvailable() {\n return resolveGateway(null).isAvailable();\n }\n\n @Override\n public String getProviderName() {\n return resolveGateway(null).getProviderName();\n }\n\n private AiInferenceGateway resolveGateway(String scene) {\n return switch (routingStrategy) {\n case \"spring-ai\" -> resolveSpringAi();\n case \"failover\" -> resolveFailover(scene);\n case \"legacy\" -> legacyAdapter;\n default -> resolveFailover(scene);\n };\n }\n\n private AiInferenceGateway resolveSpringAi() {\n if (isSpringAiCircuitOpen()) {\n log.warn(\"[AiInferenceRouter] Spring AI 熔断中，降级到 legacy\");\n return legacyAdapter;\n }\n if (springAiAdapter != null && springAiAdapter.isAvailable()) {\n return springAiAdapter;\n }\n log.warn(\"[AiInferenceRouter] spring-ai unavailable, falling back to legacy\");\n return legacyAdapter;\n }\n\n private AiInferenceGateway resolveFailover(String scene) {\n // 短查询/非Agent场景优先走 Spring AI（更快、工具调用更新）\n boolean preferSpringAi = isShortQueryScene(scene);\n if (preferSpringAi && !isSpringAiCircuitOpen()\n && springAiAdapter != null && springAiAdapter.isAvailable()) {\n log.debug(\"[AiInferenceRouter] 短查询场景={}，优先使用 Spring AI\", scene);\n return springAiAdapter;\n }\n // Agent主循环优先Spring AI（流式+tool_calls更好），熔断时降级\n if (!isSpringAiCircuitOpen() && springAiAdapter != null && springAiAdapter.isAvailable()) {\n return springAiAdapter;\n }\n if (legacyAdapter.isAvailable()) {\n return legacyAdapter;\n }\n if (!isSpringAiCircuitOpen() && springAiAdapter != null) {\n return springAiAdapter;\n }\n return legacyAdapter;\n }\n\n private boolean isShortQueryScene(String scene) {\n return scene != null && (\n \"nl-intent\".equals(scene) || \"daily-brief\".equals(scene) ||\n \"critic_review\".equals(scene) || scene.startsWith(\"memory\"));\n }\n\n private boolean isSpringAiCircuitOpen() {\n if (springAiConsecutiveFailures.get() < CIRCUIT_OPEN_THRESHOLD) return false;\n long openSince = springAiCircuitOpenSince.get();\n if (openSince > 0 && System.currentTimeMillis() - openSince > CIRCUIT_RESET_MS) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断进入半开状态，允许探测请求\");\n return false;\n }\n return openSince > 0;\n }\n\n private void trackSpringAiHealth(IntelligenceInferenceResult result) {\n if (result == null || !\"spring-ai\".equals(result.getProvider())) return;\n if (result.isSuccess()) {\n if (springAiConsecutiveFailures.get() >= CIRCUIT_OPEN_THRESHOLD) {\n log.info(\"[AiInferenceRouter] Spring AI 半开探测成功，熔断恢复\");\n }\n springAiConsecutiveFailures.set(0);\n springAiCircuitOpenSince.set(0);\n } else {\n int failures = springAiConsecutiveFailures.incrementAndGet();\n if (failures == CIRCUIT_OPEN_THRESHOLD && springAiCircuitOpenSince.get() == 0) {\n springAiCircuitOpenSince.set(System.currentTimeMillis());\n log.warn(\"[AiInferenceRouter] Spring AI 连续{}次失败，熔断开启（{}秒后进入半开探测）\", failures, CIRCUIT_RESET_MS / 1000);\n } else if (springAiCircuitOpenSince.get() > 0\n && System.currentTimeMillis() - springAiCircuitOpenSince.get() > CIRCUIT_RESET_MS) {\n log.warn(\"[AiInferenceRouter] Spring AI 半开探测失败，重新熔断 {} 秒\", CIRCUIT_RESET_MS / 1000);\n springAiCircuitOpenSince.set(System.currentTimeMillis());\n }\n }\n }\n\n private void recordCostAndAudit(String scene, IntelligenceInferenceResult result) {\n if (result == null) return;\n // 统一性能日志：方便排查双AI引擎表现\n log.info(\"[AiInference] engine={}, scene={}, success={}, latency={}ms, tokens={}/{}, fallback={}\",\n result.getProvider() != null ? result.getProvider() : \"unknown\",\n scene != null ? scene : \"default\",\n result.isSuccess(),\n result.getLatencyMs(),\n result.getPromptTokens(), result.getCompletionTokens(),\n Boolean.TRUE.equals(result.isFallbackUsed()));\n if (!result.isSuccess()) {\n log.warn(\"[AiInference] engine={}, scene={}, error={}\", result.getProvider(), scene, result.getErrorMessage());\n }\n try {\n if (aiCostTrackingOrchestrator != null) {\n aiCostTrackingOrchestrator.recordAsync(\n result.getModel() != null ? result.getModel() : result.getProvider(),\n scene,\n result.getPromptTokens(),\n result.getCompletionTokens(),\n (int) result.getLatencyMs(),\n result.isSuccess(),\n result.getErrorMessage()\n );\n }\n } catch (Exception e) {\n log.debug(\"[AiInferenceRouter] cost tracking failed: {}\", e.getMessage());\n }\n try {\n if (aiOperationAuditOrchestrator != null && result.getTraceId() != null) {\n aiOperationAuditOrchestrator.recordAudit(\n result.getTraceId(),\n \"inference:\" + scene,\n result.getPromptChars() + \" chars\",\n result.getResponseChars() + \" chars\",\n result.getLatencyMs(),\n result.isSuccess(),\n result.getErrorMessage()\n );\n }\n } catch (Exception e) {\n log.debug(\"[AiInferenceRouter] audit recording failed: {}\", e.getMessage());\n }\n }\n\n public Map<String, Object> getRoutingStatus() {\n Map<String, Object> status = new java.util.LinkedHashMap<>();\n status.put(\"strategy\", routingStrategy);\n status.put(\"legacyAvailable\", legacyAdapter.isAvailable());\n status.put(\"springAiAvailable\", springAiAdapter != null && springAiAdapter.isAvailable());\n status.put(\"springAiCircuitOpen\", isSpringAiCircuitOpen());\n status.put(\"springAiConsecutiveFailures\", springAiConsecutiveFailures.get());\n status.put(\"activeProvider\", resolveGateway(null).getProviderName());\n if (modelConsortiumRouter != null) {\n status.put(\"consortium\", modelConsortiumRouter.getConfig());\n }\n return status;\n }\n\n /**\n * 模型智能路由 — 根据用户查询复杂度选择最优模型。\n * 可在上层（AiAgentOrchestrator）调用此方法获取推荐模型，传递给推理适配器。\n */\n public String recommendModel(String userMessage, boolean hasImage, int toolCount) {\n if (modelConsortiumRouter != null) {\n return modelConsortiumRouter.selectModel(userMessage, hasImage, toolCount);\n }\n return \"deepseek-v4-flash\";\n }\n\n public String chatSimple(String prompt) {\n IntelligenceInferenceResult result = chat(\"reflection\", null, prompt);\n return result != null ? result.getContent() : \"\";\n }\n\n /**\n * 获取模型推荐参数（temperature、maxTokens、timeout）。\n */\n public ModelConsortiumRouter.ModelParams recommendModelParams(String userMessage, boolean hasImage, int toolCount) {\n if (modelConsortiumRouter != null) {\n ModelConsortiumRouter.Complexity complexity = modelConsortiumRouter.classifyComplexity(\n userMessage, hasImage, toolCount);\n return modelConsortiumRouter.getModelParams(complexity);\n }\n return new ModelConsortiumRouter.ModelParams(0.5, 1024, 30);\n }\n}\n"}, "01152878ccaac8d964b541b9cfaad7850ed2f39a": {"t": 1785749969.0422065, "n": 11400, "h": "545ebcd37a4241d1", "c": "package com.fashion.supplychain.intelligence.gateway;\n\nimport com.fashion.supplychain.intelligence.agent.AiMessage;\nimport com.fashion.supplychain.intelligence.agent.AiTool;\nimport com.fashion.supplychain.intelligence.dto.IntelligenceInferenceResult;\nimport com.fashion.supplychain.intelligence.orchestration.AiCostTrackingOrchestrator;\nimport com.fashion.supplychain.intelligence.orchestration.AiOperationAuditOrchestrator;\nimport java.util.List;\nimport java.util.Map;\nimport java.util.concurrent.atomic.AtomicInteger;\nimport java.util.concurrent.atomic.AtomicLong;\nimport lombok.extern.slf4j.Slf4j;\nimport org.springframework.beans.factory.annotation.Autowired;\nimport org.springframework.beans.factory.annotation.Value;\nimport org.springframework.context.annotation.Primary;\nimport org.springframework.stereotype.Service;\n\n@Slf4j\n@Service\n@Primary\npublic class AiInferenceRouter implements AiInferenceGateway {\n\n private static final int CIRCUIT_OPEN_THRESHOLD = 3;\n private static final long CIRCUIT_RESET_MS = 30_000L;\n\n @Value(\"${ai.gateway.routing-strategy:failover}\")\n private String routingStrategy;\n\n @Autowired\n private LegacyInferenceAdapter legacyAdapter;\n\n @Autowired(required = false)\n private SpringAiInferenceAdapter springAiAdapter;\n\n @Autowired(required = false)\n private AiCostTrackingOrchestrator aiCostTrackingOrchestrator;\n\n @Autowired(required = false)\n private AiOperationAuditOrchestrator aiOperationAuditOrchestrator;\n\n @Autowired(required = false)\n private ModelConsortiumRouter modelConsortiumRouter;\n\n private final AtomicInteger springAiConsecutiveFailures = new AtomicInteger(0);\n private final AtomicLong springAiCircuitOpenSince = new AtomicLong(0);\n\n @Override\n public IntelligenceInferenceResult chat(String scene, String systemPrompt, String userMessage) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chat(scene, systemPrompt, userMessage);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chat(scene, systemPrompt, userMessage);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chat(String scene, List<AiMessage> messages, List<AiTool> tools) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chat(scene, messages, tools);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chat(scene, messages, tools);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chatStream(String scene, List<AiMessage> messages,\n List<AiTool> tools,\n StreamChunkConsumer chunkConsumer) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chatStream(scene, messages, tools, chunkConsumer);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chatStream(scene, messages, tools, chunkConsumer);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public boolean isAvailable() {\n return resolveGateway(null).isAvailable();\n }\n\n @Override\n public String getProviderName() {\n return resolveGateway(null).getProviderName();\n }\n\n private AiInferenceGateway resolveGateway(String scene) {\n return switch (routingStrategy) {\n case \"spring-ai\" -> resolveSpringAi();\n case \"failover\" -> resolveFailover(scene);\n case \"legacy\" -> legacyAdapter;\n default -> resolveFailover(scene);\n };\n }\n\n private AiInferenceGateway resolveSpringAi() {\n if (isSpringAiCircuitOpen()) {\n log.warn(\"[AiInferenceRouter] Spring AI 熔断中，降级到 legacy\");\n return legacyAdapter;\n }\n if (springAiAdapter != null && springAiAdapter.isAvailable()) {\n return springAiAdapter;\n }\n log.warn(\"[AiInferenceRouter] spring-ai unavailable, falling back to legacy\");\n return legacyAdapter;\n }\n\n private AiInferenceGateway resolveFailover(String scene) {\n // 短查询/非Agent场景优先走 Spring AI（更快、工具调用更新）\n boolean preferSpringAi = isShortQueryScene(scene);\n if (preferSpringAi && !isSpringAiCircuitOpen()\n && springAiAdapter != null && springAiAdapter.isAvailable()) {\n log.debug(\"[AiInferenceRouter] 短查询场景={}，优先使用 Spring AI\", scene);\n return springAiAdapter;\n }\n // Agent主循环优先Spring AI（流式+tool_calls更好），熔断时降级\n if (!isSpringAiCircuitOpen() && springAiAdapter != null && springAiAdapter.isAvailable()) {\n return springAiAdapter;\n }\n if (legacyAdapter.isAvailable()) {\n return legacyAdapter;\n }\n if (!isSpringAiCircuitOpen() && springAiAdapter != null) {\n return springAiAdapter;\n }\n return legacyAdapter;\n }\n\n private boolean isShortQueryScene(String scene) {\n return scene != null && (\n \"nl-intent\".equals(scene) || \"daily-brief\".equals(scene) ||\n \"critic_review\".equals(scene) || scene.startsWith(\"memory\"));\n }\n\n private boolean isSpringAiCircuitOpen() {\n if (springAiConsecutiveFailures.get() < CIRCUIT_OPEN_THRESHOLD) return false;\n long openSince = springAiCircuitOpenSince.get();\n if (openSince > 0 && System.currentTimeMillis() - openSince > CIRCUIT_RESET_MS) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断进入半开状态，允许探测请求\");\n return false;\n }\n return openSince > 0;\n }\n\n private void trackSpringAiHealth(IntelligenceInferenceResult result) {\n if (result == null || !\"spring-ai\".equals(result.getProvider())) return;\n if (result.isSuccess()) {\n if (springAiConsecutiveFailures.get() >= CIRCUIT_OPEN_THRESHOLD) {\n log.info(\"[AiInferenceRouter] Spring AI 半开探测成功，熔断恢复\");\n }\n springAiConsecutiveFailures.set(0);\n springAiCircuitOpenSince.set(0);\n } else {\n int failures = springAiConsecutiveFailures.incrementAndGet();\n if (failures == CIRCUIT_OPEN_THRESHOLD && springAiCircuitOpenSince.get() == 0) {\n springAiCircuitOpenSince.set(System.currentTimeMillis());\n log.warn(\"[AiInferenceRouter] Spring AI 连续{}次失败，熔断开启（{}秒后进入半开探测）\", failures, CIRCUIT_RESET_MS / 1000);\n } else if (springAiCircuitOpenSince.get() > 0\n && System.currentTimeMillis() - springAiCircuitOpenSince.get() > CIRCUIT_RESET_MS) {\n log.warn(\"[AiInferenceRouter] Spring AI 半开探测失败，重新熔断 {} 秒\", CIRCUIT_RESET_MS / 1000);\n springAiCircuitOpenSince.set(System.currentTimeMillis());\n }\n }\n }\n\n private void recordCostAndAudit(String scene, IntelligenceInferenceResult result) {\n if (result == null) return;\n // 统一性能日志：方便排查双AI引擎表现\n log.info(\"[AiInference] engine={}, scene={}, success={}, latency={}ms, tokens={}/{}, fallback={}\",\n result.getProvider() != null ? result.getProvider() : \"unknown\",\n scene != null ? scene : \"default\",\n result.isSuccess(),\n result.getLatencyMs(),\n result.getPromptTokens(), result.getCompletionTokens(),\n Boolean.TRUE.equals(result.isFallbackUsed()));\n if (!result.isSuccess()) {\n log.warn(\"[AiInference] engine={}, scene={}, error={}\", result.getProvider(), scene, result.getErrorMessage());\n }\n try {\n if (aiCostTrackingOrchestrator != null) {\n aiCostTrackingOrchestrator.recordAsync(\n result.getModel() != null ? result.getModel() : result.getProvider(),\n scene,\n result.getPromptTokens(),\n result.getCompletionTokens(),\n (int) result.getLatencyMs(),\n result.isSuccess(),\n result.getErrorMessage()\n );\n }\n } catch (Exception e) {\n log.debug(\"[AiInferenceRouter] cost tracking failed: {}\", e.getMessage());\n }\n try {\n if (aiOperationAuditOrchestrator != null && result.getTraceId() != null) {\n aiOperationAuditOrchestrator.recordAudit(\n result.getTraceId(),\n \"inference:\" + scene,\n result.getPromptChars() + \" chars\",\n result.getResponseChars() + \" chars\",\n result.getLatencyMs(),\n result.isSuccess(),\n result.getErrorMessage()\n );\n }\n } catch (Exception e) {\n log.debug(\"[AiInferenceRouter] audit recording failed: {}\", e.getMessage());\n }\n }\n\n public Map<String, Object> getRoutingStatus() {\n Map<String, Object> status = new java.util.LinkedHashMap<>();\n status.put(\"strategy\", routingStrategy);\n status.put(\"legacyAvailable\", legacyAdapter.isAvailable());\n status.put(\"springAiAvailable\", springAiAdapter != null && springAiAdapter.isAvailable());\n status.put(\"springAiCircuitOpen\", isSpringAiCircuitOpen());\n status.put(\"springAiConsecutiveFailures\", springAiConsecutiveFailures.get());\n status.put(\"activeProvider\", resolveGateway(null).getProviderName());\n if (modelConsortiumRouter != null) {\n status.put(\"consortium\", modelConsortiumRouter.getConfig());\n }\n return status;\n }\n\n /**\n * 模型智能路由 — 根据用户查询复杂度选择最优模型。\n * 可在上层（AiAgentOrchestrator）调用此方法获取推荐模型，传递给推理适配器。\n */\n public String recommendModel(String userMessage, boolean hasImage, int toolCount) {\n if (modelConsortiumRouter != null) {\n return modelConsortiumRouter.selectModel(userMessage, hasImage, toolCount);\n }\n return \"deepseek-v4-flash\";\n }\n\n /**\n * 获取模型推荐参数（temperature、maxTokens、timeout）。\n */\n public ModelConsortiumRouter.ModelParams recommendModelParams(String userMessage, boolean hasImage, int toolCount) {\n if (modelConsortiumRouter != null) {\n ModelConsortiumRouter.Complexity complexity = modelConsortiumRouter.classifyComplexity(\n userMessage, hasImage, toolCount);\n return modelConsortiumRouter.getModelParams(complexity);\n }\n return new ModelConsortiumRouter.ModelParams(0.5, 1024, 30);\n }\n}\n"}, "79b8c850437e336d9253403986bf1d477c67887b": {"t": 1785749970.164954, "n": 10968, "h": "41acbf9c7acd51d6", "c": "package com.fashion.supplychain.intelligence.gateway;\n\nimport com.fashion.supplychain.intelligence.agent.AiMessage;\nimport com.fashion.supplychain.intelligence.agent.AiTool;\nimport com.fashion.supplychain.intelligence.dto.IntelligenceInferenceResult;\nimport com.fashion.supplychain.intelligence.orchestration.AiCostTrackingOrchestrator;\nimport com.fashion.supplychain.intelligence.orchestration.AiOperationAuditOrchestrator;\nimport java.util.List;\nimport java.util.Map;\nimport java.util.concurrent.atomic.AtomicInteger;\nimport java.util.concurrent.atomic.AtomicLong;\nimport lombok.extern.slf4j.Slf4j;\nimport org.springframework.beans.factory.annotation.Autowired;\nimport org.springframework.beans.factory.annotation.Value;\nimport org.springframework.context.annotation.Primary;\nimport org.springframework.stereotype.Service;\n\n@Slf4j\n@Service\n@Primary\npublic class AiInferenceRouter implements AiInferenceGateway {\n\n private static final int CIRCUIT_OPEN_THRESHOLD = 3;\n private static final long CIRCUIT_RESET_MS = 30_000L;\n\n @Value(\"${ai.gateway.routing-strategy:failover}\")\n private String routingStrategy;\n\n @Autowired\n private LegacyInferenceAdapter legacyAdapter;\n\n @Autowired(required = false)\n private SpringAiInferenceAdapter springAiAdapter;\n\n @Autowired(required = false)\n private AiCostTrackingOrchestrator aiCostTrackingOrchestrator;\n\n @Autowired(required = false)\n private AiOperationAuditOrchestrator aiOperationAuditOrchestrator;\n\n @Autowired(required = false)\n private ModelConsortiumRouter modelConsortiumRouter;\n\n private final AtomicInteger springAiConsecutiveFailures = new AtomicInteger(0);\n private final AtomicLong springAiCircuitOpenSince = new AtomicLong(0);\n\n @Override\n public IntelligenceInferenceResult chat(String scene, String systemPrompt, String userMessage) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chat(scene, systemPrompt, userMessage);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chat(scene, systemPrompt, userMessage);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chat(String scene, List<AiMessage> messages, List<AiTool> tools) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chat(scene, messages, tools);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chat(scene, messages, tools);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chatStream(String scene, List<AiMessage> messages,\n List<AiTool> tools,\n StreamChunkConsumer chunkConsumer) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chatStream(scene, messages, tools, chunkConsumer);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chatStream(scene, messages, tools, chunkConsumer);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public boolean isAvailable() {\n return resolveGateway(null).isAvailable();\n }\n\n @Override\n public String getProviderName() {\n return resolveGateway(null).getProviderName();\n }\n\n private AiInferenceGateway resolveGateway(String scene) {\n return switch (routingStrategy) {\n case \"spring-ai\" -> resolveSpringAi();\n case \"failover\" -> resolveFailover(scene);\n case \"legacy\" -> legacyAdapter;\n default -> resolveFailover(scene);\n };\n }\n\n private AiInferenceGateway resolveSpringAi() {\n if (isSpringAiCircuitOpen()) {\n log.warn(\"[AiInferenceRouter] Spring AI 熔断中，降级到 legacy\");\n return legacyAdapter;\n }\n if (springAiAdapter != null && springAiAdapter.isAvailable()) {\n return springAiAdapter;\n }\n log.warn(\"[AiInferenceRouter] spring-ai unavailable, falling back to legacy\");\n return legacyAdapter;\n }\n\n private AiInferenceGateway resolveFailover(String scene) {\n // 短查询/非Agent场景优先走 Spring AI（更快、工具调用更新）\n boolean preferSpringAi = isShortQueryScene(scene);\n if (preferSpringAi && !isSpringAiCircuitOpen()\n && springAiAdapter != null && springAiAdapter.isAvailable()) {\n log.debug(\"[AiInferenceRouter] 短查询场景={}，优先使用 Spring AI\", scene);\n return springAiAdapter;\n }\n // Agent主循环优先Spring AI（流式+tool_calls更好），熔断时降级\n if (!isSpringAiCircuitOpen() && springAiAdapter != null && springAiAdapter.isAvailable()) {\n return springAiAdapter;\n }\n if (legacyAdapter.isAvailable()) {\n return legacyAdapter;\n }\n if (!isSpringAiCircuitOpen() && springAiAdapter != null) {\n return springAiAdapter;\n }\n return legacyAdapter;\n }\n\n private boolean isShortQueryScene(String scene) {\n return scene != null && (\n \"nl-intent\".equals(scene) || \"daily-brief\".equals(scene) ||\n \"critic_review\".equals(scene) || scene.startsWith(\"memory\"));\n }\n\n private boolean isSpringAiCircuitOpen() {\n if (springAiConsecutiveFailures.get() < CIRCUIT_OPEN_THRESHOLD) return false;\n long openSince = springAiCircuitOpenSince.get();\n if (openSince > 0 && System.currentTimeMillis() - openSince > CIRCUIT_RESET_MS) {\n springAiConsecutiveFailures.set(0);\n springAiCircuitOpenSince.set(0);\n log.info(\"[AiInferenceRouter] Spring AI 熔断恢复（{}秒冷却期已过）\", CIRCUIT_RESET_MS / 1000);\n return false;\n }\n return true;\n }\n\n private void trackSpringAiHealth(IntelligenceInferenceResult result) {\n if (result == null || !\"spring-ai\".equals(result.getProvider())) return;\n if (result.isSuccess()) {\n springAiConsecutiveFailures.set(0);\n springAiCircuitOpenSince.set(0);\n } else {\n int failures = springAiConsecutiveFailures.incrementAndGet();\n if (failures >= CIRCUIT_OPEN_THRESHOLD && springAiCircuitOpenSince.compareAndSet(0, System.currentTimeMillis())) {\n log.warn(\"[AiInferenceRouter] Spring AI 连续{}次失败，熔断开启（{}秒后自动恢复尝试）\", failures, CIRCUIT_RESET_MS / 1000);\n }\n }\n }\n\n private void recordCostAndAudit(String scene, IntelligenceInferenceResult result) {\n if (result == null) return;\n // 统一性能日志：方便排查双AI引擎表现\n log.info(\"[AiInference] engine={}, scene={}, success={}, latency={}ms, tokens={}/{}, fallback={}\",\n result.getProvider() != null ? result.getProvider() : \"unknown\",\n scene != null ? scene : \"default\",\n result.isSuccess(),\n result.getLatencyMs(),\n result.getPromptTokens(), result.getCompletionTokens(),\n Boolean.TRUE.equals(result.isFallbackUsed()));\n if (!result.isSuccess()) {\n log.warn(\"[AiInference] engine={}, scene={}, error={}\", result.getProvider(), scene, result.getErrorMessage());\n }\n try {\n if (aiCostTrackingOrchestrator != null) {\n aiCostTrackingOrchestrator.recordAsync(\n result.getModel() != null ? result.getModel() : result.getProvider(),\n scene,\n result.getPromptTokens(),\n result.getCompletionTokens(),\n (int) result.getLatencyMs(),\n result.isSuccess(),\n result.getErrorMessage()\n );\n }\n } catch (Exception e) {\n log.debug(\"[AiInferenceRouter] cost tracking failed: {}\", e.getMessage());\n }\n try {\n if (aiOperationAuditOrchestrator != null && result.getTraceId() != null) {\n aiOperationAuditOrchestrator.recordAudit(\n result.getTraceId(),\n \"inference:\" + scene,\n result.getPromptChars() + \" chars\",\n result.getResponseChars() + \" chars\",\n result.getLatencyMs(),\n result.isSuccess(),\n result.getErrorMessage()\n );\n }\n } catch (Exception e) {\n log.debug(\"[AiInferenceRouter] audit recording failed: {}\", e.getMessage());\n }\n }\n\n public Map<String, Object> getRoutingStatus() {\n Map<String, Object> status = new java.util.LinkedHashMap<>();\n status.put(\"strategy\", routingStrategy);\n status.put(\"legacyAvailable\", legacyAdapter.isAvailable());\n status.put(\"springAiAvailable\", springAiAdapter != null && springAiAdapter.isAvailable());\n status.put(\"springAiCircuitOpen\", isSpringAiCircuitOpen());\n status.put(\"springAiConsecutiveFailures\", springAiConsecutiveFailures.get());\n status.put(\"activeProvider\", resolveGateway(null).getProviderName());\n if (modelConsortiumRouter != null) {\n status.put(\"consortium\", modelConsortiumRouter.getConfig());\n }\n return status;\n }\n\n /**\n * 模型智能路由 — 根据用户查询复杂度选择最优模型。\n * 可在上层（AiAgentOrchestrator）调用此方法获取推荐模型，传递给推理适配器。\n */\n public String recommendModel(String userMessage, boolean hasImage, int toolCount) {\n if (modelConsortiumRouter != null) {\n return modelConsortiumRouter.selectModel(userMessage, hasImage, toolCount);\n }\n return \"deepseek-chat\"; // 默认\n }\n\n /**\n * 获取模型推荐参数（temperature、maxTokens、timeout）。\n */\n public ModelConsortiumRouter.ModelParams recommendModelParams(String userMessage, boolean hasImage, int toolCount) {\n if (modelConsortiumRouter != null) {\n ModelConsortiumRouter.Complexity complexity = modelConsortiumRouter.classifyComplexity(\n userMessage, hasImage, toolCount);\n return modelConsortiumRouter.getModelParams(complexity);\n }\n return new ModelConsortiumRouter.ModelParams(0.5, 1024, 30);\n }\n}\n"}, "ba4fdd20657f2c6fc9b5b95efce4d1123dd72f7a": {"t": 1785749971.2633975, "n": 9722, "h": "53ffd1286a57b186", "c": "package com.fashion.supplychain.intelligence.gateway;\n\nimport com.fashion.supplychain.intelligence.agent.AiMessage;\nimport com.fashion.supplychain.intelligence.agent.AiTool;\nimport com.fashion.supplychain.intelligence.dto.IntelligenceInferenceResult;\nimport com.fashion.supplychain.intelligence.orchestration.AiCostTrackingOrchestrator;\nimport com.fashion.supplychain.intelligence.orchestration.AiOperationAuditOrchestrator;\nimport java.util.List;\nimport java.util.Map;\nimport java.util.concurrent.atomic.AtomicInteger;\nimport java.util.concurrent.atomic.AtomicLong;\nimport lombok.extern.slf4j.Slf4j;\nimport org.springframework.beans.factory.annotation.Autowired;\nimport org.springframework.beans.factory.annotation.Value;\nimport org.springframework.context.annotation.Primary;\nimport org.springframework.stereotype.Service;\n\n@Slf4j\n@Service\n@Primary\npublic class AiInferenceRouter implements AiInferenceGateway {\n\n private static final int CIRCUIT_OPEN_THRESHOLD = 3;\n private static final long CIRCUIT_RESET_MS = 30_000L;\n\n @Value(\"${ai.gateway.routing-strategy:failover}\")\n private String routingStrategy;\n\n @Autowired\n private LegacyInferenceAdapter legacyAdapter;\n\n @Autowired(required = false)\n private SpringAiInferenceAdapter springAiAdapter;\n\n @Autowired(required = false)\n private AiCostTrackingOrchestrator aiCostTrackingOrchestrator;\n\n @Autowired(required = false)\n private AiOperationAuditOrchestrator aiOperationAuditOrchestrator;\n\n private final AtomicInteger springAiConsecutiveFailures = new AtomicInteger(0);\n private final AtomicLong springAiCircuitOpenSince = new AtomicLong(0);\n\n @Override\n public IntelligenceInferenceResult chat(String scene, String systemPrompt, String userMessage) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chat(scene, systemPrompt, userMessage);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chat(scene, systemPrompt, userMessage);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chat(String scene, List<AiMessage> messages, List<AiTool> tools) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chat(scene, messages, tools);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chat(scene, messages, tools);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chatStream(String scene, List<AiMessage> messages,\n List<AiTool> tools,\n StreamChunkConsumer chunkConsumer) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chatStream(scene, messages, tools, chunkConsumer);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chatStream(scene, messages, tools, chunkConsumer);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public boolean isAvailable() {\n return resolveGateway(null).isAvailable();\n }\n\n @Override\n public String getProviderName() {\n return resolveGateway(null).getProviderName();\n }\n\n private AiInferenceGateway resolveGateway(String scene) {\n return switch (routingStrategy) {\n case \"spring-ai\" -> resolveSpringAi();\n case \"failover\" -> resolveFailover(scene);\n case \"legacy\" -> legacyAdapter;\n default -> resolveFailover(scene);\n };\n }\n\n private AiInferenceGateway resolveSpringAi() {\n if (isSpringAiCircuitOpen()) {\n log.warn(\"[AiInferenceRouter] Spring AI 熔断中，降级到 legacy\");\n return legacyAdapter;\n }\n if (springAiAdapter != null && springAiAdapter.isAvailable()) {\n return springAiAdapter;\n }\n log.warn(\"[AiInferenceRouter] spring-ai unavailable, falling back to legacy\");\n return legacyAdapter;\n }\n\n private AiInferenceGateway resolveFailover(String scene) {\n // 短查询/非Agent场景优先走 Spring AI（更快、工具调用更新）\n boolean preferSpringAi = isShortQueryScene(scene);\n if (preferSpringAi && !isSpringAiCircuitOpen()\n && springAiAdapter != null && springAiAdapter.isAvailable()) {\n log.debug(\"[AiInferenceRouter] 短查询场景={}，优先使用 Spring AI\", scene);\n return springAiAdapter;\n }\n // Agent主循环优先Spring AI（流式+tool_calls更好），熔断时降级\n if (!isSpringAiCircuitOpen() && springAiAdapter != null && springAiAdapter.isAvailable()) {\n return springAiAdapter;\n }\n if (legacyAdapter.isAvailable()) {\n return legacyAdapter;\n }\n if (!isSpringAiCircuitOpen() && springAiAdapter != null) {\n return springAiAdapter;\n }\n return legacyAdapter;\n }\n\n private boolean isShortQueryScene(String scene) {\n return scene != null && (\n \"nl-intent\".equals(scene) || \"daily-brief\".equals(scene) ||\n \"critic_review\".equals(scene) || scene.startsWith(\"memory\"));\n }\n\n private boolean isSpringAiCircuitOpen() {\n if (springAiConsecutiveFailures.get() < CIRCUIT_OPEN_THRESHOLD) return false;\n long openSince = springAiCircuitOpenSince.get();\n if (openSince > 0 && System.currentTimeMillis() - openSince > CIRCUIT_RESET_MS) {\n springAiConsecutiveFailures.set(0);\n springAiCircuitOpenSince.set(0);\n log.info(\"[AiInferenceRouter] Spring AI 熔断恢复（{}秒冷却期已过）\", CIRCUIT_RESET_MS / 1000);\n return false;\n }\n return true;\n }\n\n private void trackSpringAiHealth(IntelligenceInferenceResult result) {\n if (result == null || !\"spring-ai\".equals(result.getProvider())) return;\n if (result.isSuccess()) {\n springAiConsecutiveFailures.set(0);\n springAiCircuitOpenSince.set(0);\n } else {\n int failures = springAiConsecutiveFailures.incrementAndGet();\n if (failures >= CIRCUIT_OPEN_THRESHOLD && springAiCircuitOpenSince.compareAndSet(0, System.currentTimeMillis())) {\n log.warn(\"[AiInferenceRouter] Spring AI 连续{}次失败，熔断开启（{}秒后自动恢复尝试）\", failures, CIRCUIT_RESET_MS / 1000);\n }\n }\n }\n\n private void recordCostAndAudit(String scene, IntelligenceInferenceResult result) {\n if (result == null) return;\n // 统一性能日志：方便排查双AI引擎表现\n log.info(\"[AiInference] engine={}, scene={}, success={}, latency={}ms, tokens={}/{}, fallback={}\",\n result.getProvider() != null ? result.getProvider() : \"unknown\",\n scene != null ? scene : \"default\",\n result.isSuccess(),\n result.getLatencyMs(),\n result.getPromptTokens(), result.getCompletionTokens(),\n Boolean.TRUE.equals(result.isFallbackUsed()));\n if (!result.isSuccess()) {\n log.warn(\"[AiInference] engine={}, scene={}, error={}\", result.getProvider(), scene, result.getErrorMessage());\n }\n try {\n if (aiCostTrackingOrchestrator != null) {\n aiCostTrackingOrchestrator.recordAsync(\n result.getModel() != null ? result.getModel() : result.getProvider(),\n scene,\n result.getPromptTokens(),\n result.getCompletionTokens(),\n (int) result.getLatencyMs(),\n result.isSuccess(),\n result.getErrorMessage()\n );\n }\n } catch (Exception e) {\n log.debug(\"[AiInferenceRouter] cost tracking failed: {}\", e.getMessage());\n }\n try {\n if (aiOperationAuditOrchestrator != null && result.getTraceId() != null) {\n aiOperationAuditOrchestrator.recordAudit(\n result.getTraceId(),\n \"inference:\" + scene,\n result.getPromptChars() + \" chars\",\n result.getResponseChars() + \" chars\",\n result.getLatencyMs(),\n result.isSuccess(),\n result.getErrorMessage()\n );\n }\n } catch (Exception e) {\n log.debug(\"[AiInferenceRouter] audit recording failed: {}\", e.getMessage());\n }\n }\n\n public Map<String, Object> getRoutingStatus() {\n return Map.of(\n \"strategy\", routingStrategy,\n \"legacyAvailable\", legacyAdapter.isAvailable(),\n \"springAiAvailable\", springAiAdapter != null && springAiAdapter.isAvailable(),\n \"springAiCircuitOpen\", isSpringAiCircuitOpen(),\n \"springAiConsecutiveFailures\", springAiConsecutiveFailures.get(),\n \"activeProvider\", resolveGateway(null).getProviderName()\n );\n }\n}\n"}, "c40d5ea3a741db9d441460401c55605395c9f249": {"t": 1785749972.3551986, "n": 8367, "h": "cbc60b275b5af521", "c": "package com.fashion.supplychain.intelligence.gateway;\n\nimport com.fashion.supplychain.intelligence.agent.AiMessage;\nimport com.fashion.supplychain.intelligence.agent.AiTool;\nimport com.fashion.supplychain.intelligence.dto.IntelligenceInferenceResult;\nimport com.fashion.supplychain.intelligence.orchestration.AiCostTrackingOrchestrator;\nimport com.fashion.supplychain.intelligence.orchestration.AiOperationAuditOrchestrator;\nimport java.util.List;\nimport java.util.Map;\nimport java.util.concurrent.atomic.AtomicInteger;\nimport java.util.concurrent.atomic.AtomicLong;\nimport lombok.extern.slf4j.Slf4j;\nimport org.springframework.beans.factory.annotation.Autowired;\nimport org.springframework.beans.factory.annotation.Value;\nimport org.springframework.context.annotation.Primary;\nimport org.springframework.stereotype.Service;\n\n@Slf4j\n@Service\n@Primary\npublic class AiInferenceRouter implements AiInferenceGateway {\n\n private static final int CIRCUIT_OPEN_THRESHOLD = 5;\n private static final long CIRCUIT_RESET_MS = 60_000L;\n\n @Value(\"${ai.gateway.routing-strategy:legacy}\")\n private String routingStrategy;\n\n @Autowired\n private LegacyInferenceAdapter legacyAdapter;\n\n @Autowired(required = false)\n private SpringAiInferenceAdapter springAiAdapter;\n\n @Autowired(required = false)\n private AiCostTrackingOrchestrator aiCostTrackingOrchestrator;\n\n @Autowired(required = false)\n private AiOperationAuditOrchestrator aiOperationAuditOrchestrator;\n\n private final AtomicInteger springAiConsecutiveFailures = new AtomicInteger(0);\n private final AtomicLong springAiCircuitOpenSince = new AtomicLong(0);\n\n @Override\n public IntelligenceInferenceResult chat(String scene, String systemPrompt, String userMessage) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chat(scene, systemPrompt, userMessage);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chat(scene, systemPrompt, userMessage);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chat(String scene, List<AiMessage> messages, List<AiTool> tools) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chat(scene, messages, tools);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chat(scene, messages, tools);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chatStream(String scene, List<AiMessage> messages,\n List<AiTool> tools,\n StreamChunkConsumer chunkConsumer) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chatStream(scene, messages, tools, chunkConsumer);\n trackSpringAiHealth(result);\n if (isSpringAiCircuitOpen() && !\"legacy\".equals(gateway.getProviderName())) {\n log.info(\"[AiInferenceRouter] Spring AI 熔断触发，降级到 legacy 重试 scene={}\", scene);\n result = legacyAdapter.chatStream(scene, messages, tools, chunkConsumer);\n result.setFallbackUsed(true);\n }\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public boolean isAvailable() {\n return resolveGateway(null).isAvailable();\n }\n\n @Override\n public String getProviderName() {\n return resolveGateway(null).getProviderName();\n }\n\n private AiInferenceGateway resolveGateway(String scene) {\n return switch (routingStrategy) {\n case \"spring-ai\" -> resolveSpringAi();\n case \"failover\" -> resolveFailover();\n case \"legacy\" -> legacyAdapter;\n default -> legacyAdapter;\n };\n }\n\n private AiInferenceGateway resolveSpringAi() {\n if (isSpringAiCircuitOpen()) {\n log.warn(\"[AiInferenceRouter] Spring AI 熔断中，降级到 legacy\");\n return legacyAdapter;\n }\n if (springAiAdapter != null && springAiAdapter.isAvailable()) {\n return springAiAdapter;\n }\n log.warn(\"[AiInferenceRouter] spring-ai unavailable, falling back to legacy\");\n return legacyAdapter;\n }\n\n private AiInferenceGateway resolveFailover() {\n if (legacyAdapter.isAvailable()) {\n return legacyAdapter;\n }\n if (!isSpringAiCircuitOpen() && springAiAdapter != null && springAiAdapter.isAvailable()) {\n log.warn(\"[AiInferenceRouter] legacy unavailable, using spring-ai as failover\");\n return springAiAdapter;\n }\n return legacyAdapter;\n }\n\n private boolean isSpringAiCircuitOpen() {\n if (springAiConsecutiveFailures.get() < CIRCUIT_OPEN_THRESHOLD) return false;\n long openSince = springAiCircuitOpenSince.get();\n if (openSince > 0 && System.currentTimeMillis() - openSince > CIRCUIT_RESET_MS) {\n springAiConsecutiveFailures.set(0);\n springAiCircuitOpenSince.set(0);\n log.info(\"[AiInferenceRouter] Spring AI 熔断恢复（{}秒冷却期已过）\", CIRCUIT_RESET_MS / 1000);\n return false;\n }\n return true;\n }\n\n private void trackSpringAiHealth(IntelligenceInferenceResult result) {\n if (result == null || !\"spring-ai\".equals(result.getProvider())) return;\n if (result.isSuccess()) {\n springAiConsecutiveFailures.set(0);\n springAiCircuitOpenSince.set(0);\n } else {\n int failures = springAiConsecutiveFailures.incrementAndGet();\n if (failures >= CIRCUIT_OPEN_THRESHOLD && springAiCircuitOpenSince.compareAndSet(0, System.currentTimeMillis())) {\n log.warn(\"[AiInferenceRouter] Spring AI 连续{}次失败，熔断开启（{}秒后自动恢复尝试）\", failures, CIRCUIT_RESET_MS / 1000);\n }\n }\n }\n\n private void recordCostAndAudit(String scene, IntelligenceInferenceResult result) {\n if (result == null) return;\n try {\n if (aiCostTrackingOrchestrator != null) {\n aiCostTrackingOrchestrator.recordAsync(\n result.getModel() != null ? result.getModel() : result.getProvider(),\n scene,\n result.getPromptTokens(),\n result.getCompletionTokens(),\n (int) result.getLatencyMs(),\n result.isSuccess(),\n result.getErrorMessage()\n );\n }\n } catch (Exception e) {\n log.debug(\"[AiInferenceRouter] cost tracking failed: {}\", e.getMessage());\n }\n try {\n if (aiOperationAuditOrchestrator != null && result.getTraceId() != null) {\n aiOperationAuditOrchestrator.recordAudit(\n result.getTraceId(),\n \"inference:\" + scene,\n result.getPromptChars() + \" chars\",\n result.getResponseChars() + \" chars\",\n result.getLatencyMs(),\n result.isSuccess(),\n result.getErrorMessage()\n );\n }\n } catch (Exception e) {\n log.debug(\"[AiInferenceRouter] audit recording failed: {}\", e.getMessage());\n }\n }\n\n public Map<String, Object> getRoutingStatus() {\n return Map.of(\n \"strategy\", routingStrategy,\n \"legacyAvailable\", legacyAdapter.isAvailable(),\n \"springAiAvailable\", springAiAdapter != null && springAiAdapter.isAvailable(),\n \"springAiCircuitOpen\", isSpringAiCircuitOpen(),\n \"springAiConsecutiveFailures\", springAiConsecutiveFailures.get(),\n \"activeProvider\", resolveGateway(null).getProviderName()\n );\n }\n}\n"}, "224b8fcea85eeb056d0d0a23657208a2ead7c393": {"t": 1785749974.4750092, "n": 5472, "h": "d7ab29a3a989ef56", "c": "package com.fashion.supplychain.intelligence.gateway;\n\nimport com.fashion.supplychain.intelligence.agent.AiMessage;\nimport com.fashion.supplychain.intelligence.agent.AiTool;\nimport com.fashion.supplychain.intelligence.dto.IntelligenceInferenceResult;\nimport com.fashion.supplychain.intelligence.orchestration.AiCostTrackingOrchestrator;\nimport com.fashion.supplychain.intelligence.orchestration.AiOperationAuditOrchestrator;\nimport java.util.List;\nimport java.util.Map;\nimport lombok.extern.slf4j.Slf4j;\nimport org.springframework.beans.factory.annotation.Autowired;\nimport org.springframework.beans.factory.annotation.Value;\nimport org.springframework.context.annotation.Primary;\nimport org.springframework.stereotype.Service;\n\n@Slf4j\n@Service\n@Primary\npublic class AiInferenceRouter implements AiInferenceGateway {\n\n @Value(\"${ai.gateway.routing-strategy:legacy}\")\n private String routingStrategy;\n\n @Autowired\n private LegacyInferenceAdapter legacyAdapter;\n\n @Autowired(required = false)\n private SpringAiInferenceAdapter springAiAdapter;\n\n @Autowired(required = false)\n private AiCostTrackingOrchestrator aiCostTrackingOrchestrator;\n\n @Autowired(required = false)\n private AiOperationAuditOrchestrator aiOperationAuditOrchestrator;\n\n @Override\n public IntelligenceInferenceResult chat(String scene, String systemPrompt, String userMessage) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chat(scene, systemPrompt, userMessage);\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chat(String scene, List<AiMessage> messages, List<AiTool> tools) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chat(scene, messages, tools);\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chatStream(String scene, List<AiMessage> messages,\n List<AiTool> tools,\n StreamChunkConsumer chunkConsumer) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chatStream(scene, messages, tools, chunkConsumer);\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public boolean isAvailable() {\n return resolveGateway(null).isAvailable();\n }\n\n @Override\n public String getProviderName() {\n return resolveGateway(null).getProviderName();\n }\n\n private AiInferenceGateway resolveGateway(String scene) {\n return switch (routingStrategy) {\n case \"spring-ai\" -> resolveSpringAi();\n case \"failover\" -> resolveFailover();\n case \"legacy\" -> legacyAdapter;\n default -> legacyAdapter;\n };\n }\n\n private AiInferenceGateway resolveSpringAi() {\n if (springAiAdapter != null && springAiAdapter.isAvailable()) {\n return springAiAdapter;\n }\n log.warn(\"[AiInferenceRouter] spring-ai unavailable, falling back to legacy\");\n return legacyAdapter;\n }\n\n private AiInferenceGateway resolveFailover() {\n if (legacyAdapter.isAvailable()) {\n return legacyAdapter;\n }\n if (springAiAdapter != null && springAiAdapter.isAvailable()) {\n log.warn(\"[AiInferenceRouter] legacy unavailable, using spring-ai as failover\");\n return springAiAdapter;\n }\n return legacyAdapter;\n }\n\n private void recordCostAndAudit(String scene, IntelligenceInferenceResult result) {\n if (result == null) return;\n try {\n if (aiCostTrackingOrchestrator != null) {\n aiCostTrackingOrchestrator.recordAsync(\n result.getModel() != null ? result.getModel() : result.getProvider(),\n scene,\n result.getPromptTokens(),\n result.getCompletionTokens(),\n (int) result.getLatencyMs(),\n result.isSuccess(),\n result.getErrorMessage()\n );\n }\n } catch (Exception e) {\n log.debug(\"[AiInferenceRouter] cost tracking failed: {}\", e.getMessage());\n }\n try {\n if (aiOperationAuditOrchestrator != null && result.getTraceId() != null) {\n aiOperationAuditOrchestrator.recordAudit(\n result.getTraceId(),\n \"inference:\" + scene,\n result.getPromptChars() + \" chars\",\n result.getResponseChars() + \" chars\",\n result.getLatencyMs(),\n result.isSuccess(),\n result.getErrorMessage()\n );\n }\n } catch (Exception e) {\n log.debug(\"[AiInferenceRouter] audit recording failed: {}\", e.getMessage());\n }\n }\n\n public Map<String, Object> getRoutingStatus() {\n return Map.of(\n \"strategy\", routingStrategy,\n \"legacyAvailable\", legacyAdapter.isAvailable(),\n \"springAiAvailable\", springAiAdapter != null && springAiAdapter.isAvailable(),\n \"activeProvider\", resolveGateway(null).getProviderName()\n );\n }\n}\n"}, "9ff702bfd86152d27bcb7ab8788c73ff521c091b": {"t": 1785749975.545124, "n": 4441, "h": "17bf3af9d40deb40", "c": "package com.fashion.supplychain.intelligence.gateway;\n\nimport com.fashion.supplychain.intelligence.agent.AiMessage;\nimport com.fashion.supplychain.intelligence.agent.AiTool;\nimport com.fashion.supplychain.intelligence.dto.IntelligenceInferenceResult;\nimport com.fashion.supplychain.intelligence.orchestration.AiCostTrackingOrchestrator;\nimport com.fashion.supplychain.intelligence.orchestration.AiOperationAuditOrchestrator;\nimport java.util.List;\nimport java.util.Map;\nimport lombok.extern.slf4j.Slf4j;\nimport org.springframework.beans.factory.annotation.Autowired;\nimport org.springframework.beans.factory.annotation.Value;\nimport org.springframework.context.annotation.Primary;\nimport org.springframework.stereotype.Service;\n\n@Slf4j\n@Service\n@Primary\npublic class AiInferenceRouter implements AiInferenceGateway {\n\n @Value(\"${ai.gateway.routing-strategy:legacy}\")\n private String routingStrategy;\n\n @Autowired\n private LegacyInferenceAdapter legacyAdapter;\n\n @Autowired(required = false)\n private AiCostTrackingOrchestrator aiCostTrackingOrchestrator;\n\n @Autowired(required = false)\n private AiOperationAuditOrchestrator aiOperationAuditOrchestrator;\n\n @Override\n public IntelligenceInferenceResult chat(String scene, String systemPrompt, String userMessage) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chat(scene, systemPrompt, userMessage);\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chat(String scene, List<AiMessage> messages, List<AiTool> tools) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chat(scene, messages, tools);\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chatStream(String scene, List<AiMessage> messages,\n List<AiTool> tools,\n StreamChunkConsumer chunkConsumer) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chatStream(scene, messages, tools, chunkConsumer);\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public boolean isAvailable() {\n return resolveGateway(null).isAvailable();\n }\n\n @Override\n public String getProviderName() {\n return resolveGateway(null).getProviderName();\n }\n\n private AiInferenceGateway resolveGateway(String scene) {\n return legacyAdapter;\n }\n\n private void recordCostAndAudit(String scene, IntelligenceInferenceResult result) {\n if (result == null) return;\n try {\n if (aiCostTrackingOrchestrator != null) {\n aiCostTrackingOrchestrator.recordAsync(\n result.getModel() != null ? result.getModel() : result.getProvider(),\n scene,\n result.getPromptTokens(),\n result.getCompletionTokens(),\n (int) result.getLatencyMs(),\n result.isSuccess(),\n result.getErrorMessage()\n );\n }\n } catch (Exception e) {\n log.debug(\"[AiInferenceRouter] cost tracking failed: {}\", e.getMessage());\n }\n try {\n if (aiOperationAuditOrchestrator != null && result.getTraceId() != null) {\n aiOperationAuditOrchestrator.recordAudit(\n result.getTraceId(),\n \"inference:\" + scene,\n result.getPromptChars() + \" chars\",\n result.getResponseChars() + \" chars\",\n result.getLatencyMs(),\n result.isSuccess(),\n result.getErrorMessage()\n );\n }\n } catch (Exception e) {\n log.debug(\"[AiInferenceRouter] audit recording failed: {}\", e.getMessage());\n }\n }\n\n public Map<String, Object> getRoutingStatus() {\n return Map.of(\n \"strategy\", routingStrategy,\n \"legacyAvailable\", legacyAdapter.isAvailable(),\n \"springAiAvailable\", false,\n \"activeProvider\", legacyAdapter.getProviderName()\n );\n }\n}\n"}, "3e9c97d347fac7e1e28d906154ecb80603ffc273": {"t": 1785749976.6258533, "n": 5472, "h": "d7ab29a3a989ef56", "c": "package com.fashion.supplychain.intelligence.gateway;\n\nimport com.fashion.supplychain.intelligence.agent.AiMessage;\nimport com.fashion.supplychain.intelligence.agent.AiTool;\nimport com.fashion.supplychain.intelligence.dto.IntelligenceInferenceResult;\nimport com.fashion.supplychain.intelligence.orchestration.AiCostTrackingOrchestrator;\nimport com.fashion.supplychain.intelligence.orchestration.AiOperationAuditOrchestrator;\nimport java.util.List;\nimport java.util.Map;\nimport lombok.extern.slf4j.Slf4j;\nimport org.springframework.beans.factory.annotation.Autowired;\nimport org.springframework.beans.factory.annotation.Value;\nimport org.springframework.context.annotation.Primary;\nimport org.springframework.stereotype.Service;\n\n@Slf4j\n@Service\n@Primary\npublic class AiInferenceRouter implements AiInferenceGateway {\n\n @Value(\"${ai.gateway.routing-strategy:legacy}\")\n private String routingStrategy;\n\n @Autowired\n private LegacyInferenceAdapter legacyAdapter;\n\n @Autowired(required = false)\n private SpringAiInferenceAdapter springAiAdapter;\n\n @Autowired(required = false)\n private AiCostTrackingOrchestrator aiCostTrackingOrchestrator;\n\n @Autowired(required = false)\n private AiOperationAuditOrchestrator aiOperationAuditOrchestrator;\n\n @Override\n public IntelligenceInferenceResult chat(String scene, String systemPrompt, String userMessage) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chat(scene, systemPrompt, userMessage);\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chat(String scene, List<AiMessage> messages, List<AiTool> tools) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chat(scene, messages, tools);\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chatStream(String scene, List<AiMessage> messages,\n List<AiTool> tools,\n StreamChunkConsumer chunkConsumer) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chatStream(scene, messages, tools, chunkConsumer);\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public boolean isAvailable() {\n return resolveGateway(null).isAvailable();\n }\n\n @Override\n public String getProviderName() {\n return resolveGateway(null).getProviderName();\n }\n\n private AiInferenceGateway resolveGateway(String scene) {\n return switch (routingStrategy) {\n case \"spring-ai\" -> resolveSpringAi();\n case \"failover\" -> resolveFailover();\n case \"legacy\" -> legacyAdapter;\n default -> legacyAdapter;\n };\n }\n\n private AiInferenceGateway resolveSpringAi() {\n if (springAiAdapter != null && springAiAdapter.isAvailable()) {\n return springAiAdapter;\n }\n log.warn(\"[AiInferenceRouter] spring-ai unavailable, falling back to legacy\");\n return legacyAdapter;\n }\n\n private AiInferenceGateway resolveFailover() {\n if (legacyAdapter.isAvailable()) {\n return legacyAdapter;\n }\n if (springAiAdapter != null && springAiAdapter.isAvailable()) {\n log.warn(\"[AiInferenceRouter] legacy unavailable, using spring-ai as failover\");\n return springAiAdapter;\n }\n return legacyAdapter;\n }\n\n private void recordCostAndAudit(String scene, IntelligenceInferenceResult result) {\n if (result == null) return;\n try {\n if (aiCostTrackingOrchestrator != null) {\n aiCostTrackingOrchestrator.recordAsync(\n result.getModel() != null ? result.getModel() : result.getProvider(),\n scene,\n result.getPromptTokens(),\n result.getCompletionTokens(),\n (int) result.getLatencyMs(),\n result.isSuccess(),\n result.getErrorMessage()\n );\n }\n } catch (Exception e) {\n log.debug(\"[AiInferenceRouter] cost tracking failed: {}\", e.getMessage());\n }\n try {\n if (aiOperationAuditOrchestrator != null && result.getTraceId() != null) {\n aiOperationAuditOrchestrator.recordAudit(\n result.getTraceId(),\n \"inference:\" + scene,\n result.getPromptChars() + \" chars\",\n result.getResponseChars() + \" chars\",\n result.getLatencyMs(),\n result.isSuccess(),\n result.getErrorMessage()\n );\n }\n } catch (Exception e) {\n log.debug(\"[AiInferenceRouter] audit recording failed: {}\", e.getMessage());\n }\n }\n\n public Map<String, Object> getRoutingStatus() {\n return Map.of(\n \"strategy\", routingStrategy,\n \"legacyAvailable\", legacyAdapter.isAvailable(),\n \"springAiAvailable\", springAiAdapter != null && springAiAdapter.isAvailable(),\n \"activeProvider\", resolveGateway(null).getProviderName()\n );\n }\n}\n"}, "1d8263154937b9e693cf726a990b9a264c4debef": {"t": 1785749977.6948547, "n": 5408, "h": "b7989e14d577f458", "c": "package com.fashion.supplychain.intelligence.gateway;\n\nimport com.fashion.supplychain.intelligence.agent.AiMessage;\nimport com.fashion.supplychain.intelligence.agent.AiTool;\nimport com.fashion.supplychain.intelligence.dto.IntelligenceInferenceResult;\nimport com.fashion.supplychain.intelligence.orchestration.AiCostTrackingOrchestrator;\nimport com.fashion.supplychain.intelligence.orchestration.AiOperationAuditOrchestrator;\nimport java.util.List;\nimport java.util.Map;\nimport lombok.extern.slf4j.Slf4j;\nimport org.springframework.beans.factory.annotation.Autowired;\nimport org.springframework.beans.factory.annotation.Value;\nimport org.springframework.stereotype.Service;\n\n@Slf4j\n@Service\npublic class AiInferenceRouter implements AiInferenceGateway {\n\n @Value(\"${ai.gateway.routing-strategy:legacy}\")\n private String routingStrategy;\n\n @Autowired\n private LegacyInferenceAdapter legacyAdapter;\n\n @Autowired(required = false)\n private SpringAiInferenceAdapter springAiAdapter;\n\n @Autowired(required = false)\n private AiCostTrackingOrchestrator aiCostTrackingOrchestrator;\n\n @Autowired(required = false)\n private AiOperationAuditOrchestrator aiOperationAuditOrchestrator;\n\n @Override\n public IntelligenceInferenceResult chat(String scene, String systemPrompt, String userMessage) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chat(scene, systemPrompt, userMessage);\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chat(String scene, List<AiMessage> messages, List<AiTool> tools) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chat(scene, messages, tools);\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public IntelligenceInferenceResult chatStream(String scene, List<AiMessage> messages,\n List<AiTool> tools,\n StreamChunkConsumer chunkConsumer) {\n AiInferenceGateway gateway = resolveGateway(scene);\n IntelligenceInferenceResult result = gateway.chatStream(scene, messages, tools, chunkConsumer);\n recordCostAndAudit(scene, result);\n return result;\n }\n\n @Override\n public boolean isAvailable() {\n return resolveGateway(null).isAvailable();\n }\n\n @Override\n public String getProviderName() {\n return resolveGateway(null).getProviderName();\n }\n\n private AiInferenceGateway resolveGateway(String scene) {\n return switch (routingStrategy) {\n case \"spring-ai\" -> resolveSpringAi();\n case \"failover\" -> resolveFailover();\n case \"legacy\" -> legacyAdapter;\n default -> legacyAdapter;\n };\n }\n\n private AiInferenceGateway resolveSpringAi() {\n if (springAiAdapter != null && springAiAdapter.isAvailable()) {\n return springAiAdapter;\n }\n log.warn(\"[AiInferenceRouter] spring-ai unavailable, falling back to legacy\");\n return legacyAdapter;\n }\n\n private AiInferenceGateway resolveFailover() {\n if (legacyAdapter.isAvailable()) {\n return legacyAdapter;\n }\n if (springAiAdapter != null && springAiAdapter.isAvailable()) {\n log.warn(\"[AiInferenceRouter] legacy unavailable, using spring-ai as failover\");\n return springAiAdapter;\n }\n return legacyAdapter;\n }\n\n private void recordCostAndAudit(String scene, IntelligenceInferenceResult result) {\n if (result == null) return;\n try {\n if (aiCostTrackingOrchestrator != null) {\n aiCostTrackingOrchestrator.recordAsync(\n result.getModel() != null ? result.getModel() : result.getProvider(),\n scene,\n result.getPromptTokens(),\n result.getCompletionTokens(),\n (int) result.getLatencyMs(),\n result.isSuccess(),\n result.getErrorMessage()\n );\n }\n } catch (Exception e) {\n log.debug(\"[AiInferenceRouter] cost tracking failed: {}\", e.getMessage());\n }\n try {\n if (aiOperationAuditOrchestrator != null && result.getTraceId() != null) {\n aiOperationAuditOrchestrator.recordAudit(\n result.getTraceId(),\n \"inference:\" + scene,\n result.getPromptChars() + \" chars\",\n result.getResponseChars() + \" chars\",\n result.getLatencyMs(),\n result.isSuccess(),\n result.getErrorMessage()\n );\n }\n } catch (Exception e) {\n log.debug(\"[AiInferenceRouter] audit recording failed: {}\", e.getMessage());\n }\n }\n\n public Map<String, Object> getRoutingStatus() {\n return Map.of(\n \"strategy\", routingStrategy,\n \"legacyAvailable\", legacyAdapter.isAvailable(),\n \"springAiAvailable\", springAiAdapter != null && springAiAdapter.isAvailable(),\n \"activeProvider\", resolveGateway(null).getProviderName()\n );\n }\n}\n"}}, "backend/src/main/java/com/fashion/supplychain/intelligence/service/AiAdvisorService.java": {"92136382831779e22f23b72a08f4e137bfa17dd3": {"t": 1785745701.6783586, "n": 5838, "h": "0c462b9a466a0964", "c": "package com.fashion.supplychain.intelligence.service;\n\nimport com.fashion.supplychain.intelligence.dto.IntelligenceInferenceResult;\nimport com.fashion.supplychain.intelligence.orchestration.IntelligenceInferenceOrchestrator;\nimport lombok.extern.slf4j.Slf4j;\nimport org.springframework.beans.factory.annotation.Autowired;\nimport org.springframework.beans.factory.annotation.Value;\nimport org.springframework.cache.annotation.Cacheable;\nimport org.springframework.scheduling.annotation.Scheduled;\nimport org.springframework.stereotype.Service;\nimport org.springframework.context.annotation.Lazy;\n\nimport java.time.LocalDate;\nimport java.util.concurrent.ConcurrentHashMap;\nimport java.util.concurrent.atomic.AtomicInteger;\n\n/**\n * AI 顾问服务。\n *\n * <p>职责：保留配额与启用判断，对外提供 AI 顾问语义能力。\n * 真实推理由独立的 {@link IntelligenceInferenceOrchestrator} 执行。</p>\n *\n * <p>默认直连 DeepSeek，也支持通过 LiteLLM 网关接入，配置方式（环境变量）：\n * <pre>\n * DEEPSEEK_API_KEY = sk-xxxxxxxxxxxxxxxx\n * AI_LITELLM_ENABLED = true/false\n * AI_LITELLM_BASE_URL = http://localhost:4000\n * AI_LITELLM_API_KEY = sk-virtual-key\n * </pre>\n *\n * <p>若未配置 AI_API_KEY，所有调用会立即返回 null，系统继续走本地规则引擎，\n * 不影响任何现有功能。\n */\n@Service\n@Lazy\n@Slf4j\npublic class AiAdvisorService {\n\n @Value(\"${ai.deepseek.api-key:}\")\n private String apiKey;\n\n /** 每个租户每天最多允许调用 DeepSeek 的次数（0 = 不限制） */\n @Value(\"${ai.deepseek.daily-quota-per-tenant:50}\")\n private int dailyQuotaPerTenant;\n\n @Autowired\n private IntelligenceInferenceOrchestrator intelligenceInferenceOrchestrator;\n\n /**\n * 每租户每日调用计数器\n * key = \"tenantId_yyyyMMdd\"，value = 当日已调用次数\n * 不同日期的 key 自动过期（不再被读写），内存占用可忽略不计\n */\n private final ConcurrentHashMap<String, AtomicInteger> dailyCounters = new ConcurrentHashMap<>();\n\n @Scheduled(fixedRate = 3600000)\n public void cleanupStaleCounters() {\n String todaySuffix = \"_\" + LocalDate.now();\n dailyCounters.keySet().removeIf(key -> !key.endsWith(todaySuffix));\n }\n\n /**\n * 是否已启用 AI（有 API Key）\n */\n public boolean isEnabled() {\n return intelligenceInferenceOrchestrator.isAnyModelEnabled();\n }\n\n /**\n * 检查当前租户今日配额是否已用完\n *\n * @param tenantId 租户ID（null 视为系统内部调用，不限制）\n * @return true = 配额充足可以调用；false = 今日配额已用完\n */\n public boolean checkAndConsumeQuota(Long tenantId) {\n if (dailyQuotaPerTenant <= 0 || tenantId == null) return true; // 0=不限\n String key = tenantId + \"_\" + LocalDate.now();\n AtomicInteger count = dailyCounters.computeIfAbsent(key, k -> new AtomicInteger(0));\n int used = count.incrementAndGet();\n if (used > dailyQuotaPerTenant) {\n count.decrementAndGet(); // 回滚，不消耗\n log.warn(\"[AiAdvisor] 租户 {} 今日AI调用已达配额上限 {}\", tenantId, dailyQuotaPerTenant);\n return false;\n }\n log.debug(\"[AiAdvisor] 租户 {} 今日AI调用 {}/{}\", tenantId, used, dailyQuotaPerTenant);\n return true;\n }\n\n /**\n * 查询当前租户今日已用配额\n */\n public int getTodayUsage(Long tenantId) {\n if (tenantId == null) return 0;\n String key = tenantId + \"_\" + LocalDate.now();\n AtomicInteger count = dailyCounters.get(key);\n return count == null ? 0 : count.get();\n }\n\n /**\n * 发送单轮对话请求\n *\n * @param systemPrompt 系统角色描述（如：你是服装供应链分析师）\n * @param userMessage 用户消息\n * @return AI 回复文本，未配置 Key 或调用失败时返回 null\n */\n public String chat(String systemPrompt, String userMessage) {\n IntelligenceInferenceResult result = invoke(\"ai-advisor\", systemPrompt, userMessage);\n if (!result.isSuccess()) {\n log.warn(\"[AiAdvisor] AI 调用失败 provider={} error={}\", result.getProvider(), result.getErrorMessage());\n return null;\n }\n return result.getContent();\n }\n\n /**\n * 智能建议：给定业务数据摘要，返回简短建议文案（用于运营日报）\n *\n * <p>结果按 contextSummary 内容缓存 5 分钟，相同摘要（同一租户当日数据未变化）\n * 直接返回缓存值，避免重复调用 DeepSeek API（每次约 2~3 秒延迟）。\n * Redis 不可用时自动降级为直接调用 AI（见 RedisConfig.errorHandler）。</p>\n *\n * @param contextSummary 业务摘要（如：今日逾期3单，高风险2单，停滞1单）\n * @return 1~3句建议文案，未启用时返回 null\n */\n @Cacheable(value = \"daily-brief\", key = \"T(com.fashion.supplychain.common.UserContext).tenantId() + ':' + T(java.time.LocalDate).now()\")\n public String getDailyAdvice(String contextSummary) {\n String systemPrompt = \"你是一名服装供应链管理顾问，根据工厂今日生产数据，\" +\n \"用简洁中文给出1~3条可执行的管理建议。每条建议一行，不超过30字。不要废话。\";\n IntelligenceInferenceResult result = invoke(\"daily-brief\", systemPrompt, contextSummary);\n return result.isSuccess() ? result.getContent() : null;\n }\n\n /**\n * 自然语言转结构化查询意图分析\n * 当 NlQueryOrchestrator 本地规则无法匹配时，调用此方法让 AI 理解意图\n *\n * @param question 用户问题\n * @return JSON 字符串，格式：{\"intent\":\"xxx\",\"params\":{\"key\":\"value\"}}\n * 失败返回 null\n */\n public String parseNlIntent(String question) {\n String systemPrompt = \"你是服装供应链系统的 NLP 解析器。\" +\n \"将用户问题解析为 JSON：{\\\"intent\\\":\\\"意图\\\",\\\"params\\\":{\\\"参数\\\":\\\"值\\\"}}。\" +\n \"可能的 intent: query_order_status, query_material_stock, query_factory_capacity, \" +\n \"query_finance_settlement, query_worker_efficiency, query_delivery_risk。\" +\n \"只输出 JSON，不要解释。\";\n IntelligenceInferenceResult result = invoke(\"nl-intent\", systemPrompt, question);\n return result.isSuccess() ? result.getContent() : null;\n }\n\n private IntelligenceInferenceResult invoke(String scene, String systemPrompt, String userMessage) {\n if (!isEnabled()) {\n IntelligenceInferenceResult result = new IntelligenceInferenceResult();\n result.setSuccess(false);\n result.setProvider(\"none\");\n result.setErrorMessage(\"ai-disabled\");\n return result;\n }\n return intelligenceInferenceOrchestrator.chat(scene, systemPrompt, userMessage);\n }\n}\n"}, "fc10d435e3d8429461ad7e59b9cdd2179a093756": {"t": 1785749953.3053546, "n": 5838, "h": "0c462b9a466a0964", "c": "package com.fashion.supplychain.intelligence.service;\n\nimport com.fashion.supplychain.intelligence.dto.IntelligenceInferenceResult;\nimport com.fashion.supplychain.intelligence.orchestration.IntelligenceInferenceOrchestrator;\nimport lombok.extern.slf4j.Slf4j;\nimport org.springframework.beans.factory.annotation.Autowired;\nimport org.springframework.beans.factory.annotation.Value;\nimport org.springframework.cache.annotation.Cacheable;\nimport org.springframework.scheduling.annotation.Scheduled;\nimport org.springframework.stereotype.Service;\nimport org.springframework.context.annotation.Lazy;\n\nimport java.time.LocalDate;\nimport java.util.concurrent.ConcurrentHashMap;\nimport java.util.concurrent.atomic.AtomicInteger;\n\n/**\n * AI 顾问服务。\n *\n * <p>职责：保留配额与启用判断，对外提供 AI 顾问语义能力。\n * 真实推理由独立的 {@link IntelligenceInferenceOrchestrator} 执行。</p>\n *\n * <p>默认直连 DeepSeek，也支持通过 LiteLLM 网关接入，配置方式（环境变量）：\n * <pre>\n * DEEPSEEK_API_KEY = sk-xxxxxxxxxxxxxxxx\n * AI_LITELLM_ENABLED = true/false\n * AI_LITELLM_BASE_URL = http://localhost:4000\n * AI_LITELLM_API_KEY = sk-virtual-key\n * </pre>\n *\n * <p>若未配置 AI_API_KEY，所有调用会立即返回 null，系统继续走本地规则引擎，\n * 不影响任何现有功能。\n */\n@Service\n@Lazy\n@Slf4j\npublic class AiAdvisorService {\n\n @Value(\"${ai.deepseek.api-key:}\")\n private String apiKey;\n\n /** 每个租户每天最多允许调用 DeepSeek 的次数（0 = 不限制） */\n @Value(\"${ai.deepseek.daily-quota-per-tenant:50}\")\n private int dailyQuotaPerTenant;\n\n @Autowired\n private IntelligenceInferenceOrchestrator intelligenceInferenceOrchestrator;\n\n /**\n * 每租户每日调用计数器\n * key = \"tenantId_yyyyMMdd\"，value = 当日已调用次数\n * 不同日期的 key 自动过期（不再被读写），内存占用可忽略不计\n */\n private final ConcurrentHashMap<String, AtomicInteger> dailyCounters = new ConcurrentHashMap<>();\n\n @Scheduled(fixedRate = 3600000)\n public void cleanupStaleCounters() {\n String todaySuffix = \"_\" + LocalDate.now();\n dailyCounters.keySet().removeIf(key -> !key.endsWith(todaySuffix));\n }\n\n /**\n * 是否已启用 AI（有 API Key）\n */\n public boolean isEnabled() {\n return intelligenceInferenceOrchestrator.isAnyModelEnabled();\n }\n\n /**\n * 检查当前租户今日配额是否已用完\n *\n * @param tenantId 租户ID（null 视为系统内部调用，不限制）\n * @return true = 配额充足可以调用；false = 今日配额已用完\n */\n public boolean checkAndConsumeQuota(Long tenantId) {\n if (dailyQuotaPerTenant <= 0 || tenantId == null) return true; // 0=不限\n String key = tenantId + \"_\" + LocalDate.now();\n AtomicInteger count = dailyCounters.computeIfAbsent(key, k -> new AtomicInteger(0));\n int used = count.incrementAndGet();\n if (used > dailyQuotaPerTenant) {\n count.decrementAndGet(); // 回滚，不消耗\n log.warn(\"[AiAdvisor] 租户 {} 今日AI调用已达配额上限 {}\", tenantId, dailyQuotaPerTenant);\n return false;\n }\n log.debug(\"[AiAdvisor] 租户 {} 今日AI调用 {}/{}\", tenantId, used, dailyQuotaPerTenant);\n return true;\n }\n\n /**\n * 查询当前租户今日已用配额\n */\n public int getTodayUsage(Long tenantId) {\n if (tenantId == null) return 0;\n String key = tenantId + \"_\" + LocalDate.now();\n AtomicInteger count = dailyCounters.get(key);\n return count == null ? 0 : count.get();\n }\n\n /**\n * 发送单轮对话请求\n *\n * @param systemPrompt 系统角色描述（如：你是服装供应链分析师）\n * @param userMessage 用户消息\n * @return AI 回复文本，未配置 Key 或调用失败时返回 null\n */\n public String chat(String systemPrompt, String userMessage) {\n IntelligenceInferenceResult result = invoke(\"ai-advisor\", systemPrompt, userMessage);\n if (!result.isSuccess()) {\n log.warn(\"[AiAdvisor] AI 调用失败 provider={} error={}\", result.getProvider(), result.getErrorMessage());\n return null;\n }\n return result.getContent();\n }\n\n /**\n * 智能建议：给定业务数据摘要，返回简短建议文案（用于运营日报）\n *\n * <p>结果按 contextSummary 内容缓存 5 分钟，相同摘要（同一租户当日数据未变化）\n * 直接返回缓存值，避免重复调用 DeepSeek API（每次约 2~3 秒延迟）。\n * Redis 不可用时自动降级为直接调用 AI（见 RedisConfig.errorHandler）。</p>\n *\n * @param contextSummary 业务摘要（如：今日逾期3单，高风险2单，停滞1单）\n * @return 1~3句建议文案，未启用时返回 null\n */\n @Cacheable(value = \"daily-brief\", key = \"T(com.fashion.supplychain.common.UserContext).tenantId() + ':' + T(java.time.LocalDate).now()\")\n public String getDailyAdvice(String contextSummary) {\n String systemPrompt = \"你是一名服装供应链管理顾问，根据工厂今日生产数据，\" +\n \"用简洁中文给出1~3条可执行的管理建议。每条建议一行，不超过30字。不要废话。\";\n IntelligenceInferenceResult result = invoke(\"daily-brief\", systemPrompt, contextSummary);\n return result.isSuccess() ? result.getContent() : null;\n }\n\n /**\n * 自然语言转结构化查询意图分析\n * 当 NlQueryOrchestrator 本地规则无法匹配时，调用此方法让 AI 理解意图\n *\n * @param question 用户问题\n * @return JSON 字符串，格式：{\"intent\":\"xxx\",\"params\":{\"key\":\"value\"}}\n * 失败返回 null\n */\n public String parseNlIntent(String question) {\n String systemPrompt = \"你是服装供应链系统的 NLP 解析器。\" +\n \"将用户问题解析为 JSON：{\\\"intent\\\":\\\"意图\\\",\\\"params\\\":{\\\"参数\\\":\\\"值\\\"}}。\" +\n \"可能的 intent: query_order_status, query_material_stock, query_factory_capacity, \" +\n \"query_finance_settlement, query_worker_efficiency, query_delivery_risk。\" +\n \"只输出 JSON，不要解释。\";\n IntelligenceInferenceResult result = invoke(\"nl-intent\", systemPrompt, question);\n return result.isSuccess() ? result.getContent() : null;\n }\n\n private IntelligenceInferenceResult invoke(String scene, String systemPrompt, String userMessage) {\n if (!isEnabled()) {\n IntelligenceInferenceResult result = new IntelligenceInferenceResult();\n result.setSuccess(false);\n result.setProvider(\"none\");\n result.setErrorMessage(\"ai-disabled\");\n return result;\n }\n return intelligenceInferenceOrchestrator.chat(scene, systemPrompt, userMessage);\n }\n}\n"}, "cc55c6b31d802f926aac252da09310cf26df12ee": {"t": 1785749954.580515, "n": 5780, "h": "d524bdf2a0c2d6de", "c": "package com.fashion.supplychain.intelligence.service;\n\nimport com.fashion.supplychain.intelligence.dto.IntelligenceInferenceResult;\nimport com.fashion.supplychain.intelligence.orchestration.IntelligenceInferenceOrchestrator;\nimport lombok.extern.slf4j.Slf4j;\nimport org.springframework.beans.factory.annotation.Autowired;\nimport org.springframework.beans.factory.annotation.Value;\nimport org.springframework.cache.annotation.Cacheable;\nimport org.springframework.scheduling.annotation.Scheduled;\nimport org.springframework.stereotype.Service;\n\nimport java.time.LocalDate;\nimport java.util.concurrent.ConcurrentHashMap;\nimport java.util.concurrent.atomic.AtomicInteger;\n\n/**\n * AI 顾问服务。\n *\n * <p>职责：保留配额与启用判断，对外提供 AI 顾问语义能力。\n * 真实推理由独立的 {@link IntelligenceInferenceOrchestrator} 执行。</p>\n *\n * <p>默认直连 DeepSeek，也支持通过 LiteLLM 网关接入，配置方式（环境变量）：\n * <pre>\n * DEEPSEEK_API_KEY = sk-xxxxxxxxxxxxxxxx\n * AI_LITELLM_ENABLED = true/false\n * AI_LITELLM_BASE_URL = http://localhost:4000\n * AI_LITELLM_API_KEY = sk-virtual-key\n * </pre>\n *\n * <p>若未配置 AI_API_KEY，所有调用会立即返回 null，系统继续走本地规则引擎，\n * 不影响任何现有功能。\n */\n@Service\n@Slf4j\npublic class AiAdvisorService {\n\n @Value(\"${ai.deepseek.api-key:}\")\n private String apiKey;\n\n /** 每个租户每天最多允许调用 DeepSeek 的次数（0 = 不限制） */\n @Value(\"${ai.deepseek.daily-quota-per-tenant:50}\")\n private int dailyQuotaPerTenant;\n\n @Autowired\n private IntelligenceInferenceOrchestrator intelligenceInferenceOrchestrator;\n\n /**\n * 每租户每日调用计数器\n * key = \"tenantId_yyyyMMdd\"，value = 当日已调用次数\n * 不同日期的 key 自动过期（不再被读写），内存占用可忽略不计\n */\n private final ConcurrentHashMap<String, AtomicInteger> dailyCounters = new ConcurrentHashMap<>();\n\n @Scheduled(fixedRate = 3600000)\n public void cleanupStaleCounters() {\n String todaySuffix = \"_\" + LocalDate.now();\n dailyCounters.keySet().removeIf(key -> !key.endsWith(todaySuffix));\n }\n\n /**\n * 是否已启用 AI（有 API Key）\n */\n public boolean isEnabled() {\n return intelligenceInferenceOrchestrator.isAnyModelEnabled();\n }\n\n /**\n * 检查当前租户今日配额是否已用完\n *\n * @param tenantId 租户ID（null 视为系统内部调用，不限制）\n * @return true = 配额充足可以调用；false = 今日配额已用完\n */\n public boolean checkAndConsumeQuota(Long tenantId) {\n if (dailyQuotaPerTenant <= 0 || tenantId == null) return true; // 0=不限\n String key = tenantId + \"_\" + LocalDate.now();\n AtomicInteger count = dailyCounters.computeIfAbsent(key, k -> new AtomicInteger(0));\n int used = count.incrementAndGet();\n if (used > dailyQuotaPerTenant) {\n count.decrementAndGet(); // 回滚，不消耗\n log.warn(\"[AiAdvisor] 租户 {} 今日AI调用已达配额上限 {}\", tenantId, dailyQuotaPerTenant);\n return false;\n }\n log.debug(\"[AiAdvisor] 租户 {} 今日AI调用 {}/{}\", tenantId, used, dailyQuotaPerTenant);\n return true;\n }\n\n /**\n * 查询当前租户今日已用配额\n */\n public int getTodayUsage(Long tenantId) {\n if (tenantId == null) return 0;\n String key = tenantId + \"_\" + LocalDate.now();\n AtomicInteger count = dailyCounters.get(key);\n return count == null ? 0 : count.get();\n }\n\n /**\n * 发送单轮对话请求\n *\n * @param systemPrompt 系统角色描述（如：你是服装供应链分析师）\n * @param userMessage 用户消息\n * @return AI 回复文本，未配置 Key 或调用失败时返回 null\n */\n public String chat(String systemPrompt, String userMessage) {\n IntelligenceInferenceResult result = invoke(\"ai-advisor\", systemPrompt, userMessage);\n if (!result.isSuccess()) {\n log.warn(\"[AiAdvisor] AI 调用失败 provider={} error={}\", result.getProvider(), result.getErrorMessage());\n return null;\n }\n return result.getContent();\n }\n\n /**\n * 智能建议：给定业务数据摘要，返回简短建议文案（用于运营日报）\n *\n * <p>结果按 contextSummary 内容缓存 5 分钟，相同摘要（同一租户当日数据未变化）\n * 直接返回缓存值，避免重复调用 DeepSeek API（每次约 2~3 秒延迟）。\n * Redis 不可用时自动降级为直接调用 AI（见 RedisConfig.errorHandler）。</p>\n *\n * @param contextSummary 业务摘要（如：今日逾期3单，高风险2单，停滞1单）\n * @return 1~3句建议文案，未启用时返回 null\n */\n @Cacheable(value = \"daily-brief\", key = \"T(com.fashion.supplychain.common.UserContext).tenantId() + ':' + T(java.time.LocalDate).now()\")\n public String getDailyAdvice(String contextSummary) {\n String systemPrompt = \"你是一名服装供应链管理顾问，根据工厂今日生产数据，\" +\n \"用简洁中文给出1~3条可执行的管理建议。每条建议一行，不超过30字。不要废话。\";\n IntelligenceInferenceResult result = invoke(\"daily-brief\", systemPrompt, contextSummary);\n return result.isSuccess() ? result.getContent() : null;\n }\n\n /**\n * 自然语言转结构化查询意图分析\n * 当 NlQueryOrchestrator 本地规则无法匹配时，调用此方法让 AI 理解意图\n *\n * @param question 用户问题\n * @return JSON 字符串，格式：{\"intent\":\"xxx\",\"params\":{\"key\":\"value\"}}\n * 失败返回 null\n */\n public String parseNlIntent(String question) {\n String systemPrompt = \"你是服装供应链系统的 NLP 解析器。\" +\n \"将用户问题解析为 JSON：{\\\"intent\\\":\\\"意图\\\",\\\"params\\\":{\\\"参数\\\":\\\"值\\\"}}。\" +\n \"可能的 intent: query_order_status, query_material_stock, query_factory_capacity, \" +\n \"query_finance_settlement, query_worker_efficiency, query_delivery_risk。\" +\n \"只输出 JSON，不要解释。\";\n IntelligenceInferenceResult result = invoke(\"nl-intent\", systemPrompt, question);\n return result.isSuccess() ? result.getContent() : null;\n }\n\n private IntelligenceInferenceResult invoke(String scene, String systemPrompt, String userMessage) {\n if (!isEnabled()) {\n IntelligenceInferenceResult result = new IntelligenceInferenceResult();\n result.setSuccess(false);\n result.setProvider(\"none\");\n result.setErrorMessage(\"ai-disabled\");\n return result;\n }\n return intelligenceInferenceOrchestrator.chat(scene, systemPrompt, userMessage);\n }\n}\n"}, "5f3b3d1c5d9e1263d4af0f790474284a920c8a8f": {"t": 1785749957.7036502, "n": 5495, "h": "60fab315a6e32af6", "c": "package com.fashion.supplychain.intelligence.service;\n\nimport com.fashion.supplychain.intelligence.dto.IntelligenceInferenceResult;\nimport com.fashion.supplychain.intelligence.orchestration.IntelligenceInferenceOrchestrator;\nimport lombok.extern.slf4j.Slf4j;\nimport org.springframework.beans.factory.annotation.Autowired;\nimport org.springframework.beans.factory.annotation.Value;\nimport org.springframework.cache.annotation.Cacheable;\nimport org.springframework.stereotype.Service;\n\nimport java.time.LocalDate;\nimport java.util.concurrent.ConcurrentHashMap;\nimport java.util.concurrent.atomic.AtomicInteger;\n\n/**\n * AI 顾问服务。\n *\n * <p>职责：保留配额与启用判断，对外提供 AI 顾问语义能力。\n * 真实推理由独立的 {@link IntelligenceInferenceOrchestrator} 执行。</p>\n *\n * <p>默认直连 DeepSeek，也支持通过 LiteLLM 网关接入，配置方式（环境变量）：\n * <pre>\n * DEEPSEEK_API_KEY = sk-xxxxxxxxxxxxxxxx\n * AI_LITELLM_ENABLED = true/false\n * AI_LITELLM_BASE_URL = http://localhost:4000\n * AI_LITELLM_API_KEY = sk-virtual-key\n * </pre>\n *\n * <p>若未配置 AI_API_KEY，所有调用会立即返回 null，系统继续走本地规则引擎，\n * 不影响任何现有功能。\n */\n@Service\n@Slf4j\npublic class AiAdvisorService {\n\n @Value(\"${ai.deepseek.api-key:}\")\n private String apiKey;\n\n /** 每个租户每天最多允许调用 DeepSeek 的次数（0 = 不限制） */\n @Value(\"${ai.deepseek.daily-quota-per-tenant:50}\")\n private int dailyQuotaPerTenant;\n\n @Autowired\n private IntelligenceInferenceOrchestrator intelligenceInferenceOrchestrator;\n\n /**\n * 每租户每日调用计数器\n * key = \"tenantId_yyyyMMdd\"，value = 当日已调用次数\n * 不同日期的 key 自动过期（不再被读写），内存占用可忽略不计\n */\n private final ConcurrentHashMap<String, AtomicInteger> dailyCounters = new ConcurrentHashMap<>();\n\n /**\n * 是否已启用 AI（有 API Key）\n */\n public boolean isEnabled() {\n return intelligenceInferenceOrchestrator.isAnyModelEnabled();\n }\n\n /**\n * 检查当前租户今日配额是否已用完\n *\n * @param tenantId 租户ID（null 视为系统内部调用，不限制）\n * @return true = 配额充足可以调用；false = 今日配额已用完\n */\n public boolean checkAndConsumeQuota(Long tenantId) {\n if (dailyQuotaPerTenant <= 0 || tenantId == null) return true; // 0=不限\n String key = tenantId + \"_\" + LocalDate.now();\n AtomicInteger count = dailyCounters.computeIfAbsent(key, k -> new AtomicInteger(0));\n int used = count.incrementAndGet();\n if (used > dailyQuotaPerTenant) {\n count.decrementAndGet(); // 回滚，不消耗\n log.warn(\"[AiAdvisor] 租户 {} 今日AI调用已达配额上限 {}\", tenantId, dailyQuotaPerTenant);\n return false;\n }\n log.debug(\"[AiAdvisor] 租户 {} 今日AI调用 {}/{}\", tenantId, used, dailyQuotaPerTenant);\n return true;\n }\n\n /**\n * 查询当前租户今日已用配额\n */\n public int getTodayUsage(Long tenantId) {\n if (tenantId == null) return 0;\n String key = tenantId + \"_\" + LocalDate.now();\n AtomicInteger count = dailyCounters.get(key);\n return count == null ? 0 : count.get();\n }\n\n /**\n * 发送单轮对话请求\n *\n * @param systemPrompt 系统角色描述（如：你是服装供应链分析师）\n * @param userMessage 用户消息\n * @return AI 回复文本，未配置 Key 或调用失败时返回 null\n */\n public String chat(String systemPrompt, String userMessage) {\n IntelligenceInferenceResult result = invoke(\"ai-advisor\", systemPrompt, userMessage);\n if (!result.isSuccess()) {\n log.warn(\"[AiAdvisor] AI 调用失败 provider={} error={}\", result.getProvider(), result.getErrorMessage());\n return null;\n }\n return result.getContent();\n }\n\n /**\n * 智能建议：给定业务数据摘要，返回简短建议文案（用于运营日报）\n *\n * <p>结果按 contextSummary 内容缓存 5 分钟，相同摘要（同一租户当日数据未变化）\n * 直接返回缓存值，避免重复调用 DeepSeek API（每次约 2~3 秒延迟）。\n * Redis 不可用时自动降级为直接调用 AI（见 RedisConfig.errorHandler）。</p>\n *\n * @param contextSummary 业务摘要（如：今日逾期3单，高风险2单，停滞1单）\n * @return 1~3句建议文案，未启用时返回 null\n */\n @Cacheable(value = \"daily-brief\", key = \"T(com.fashion.supplychain.common.UserContext).tenantId() + ':' + #contextSummary\")\n public String getDailyAdvice(String contextSummary) {\n String systemPrompt = \"你是一名服装供应链管理顾问，根据工厂今日生产数据，\" +\n \"用简洁中文给出1~3条可执行的管理建议。每条建议一行，不超过30字。不要废话。\";\n IntelligenceInferenceResult result = invoke(\"daily-brief\", systemPrompt, contextSummary);\n return result.isSuccess() ? result.getContent() : null;\n }\n\n /**\n * 自然语言转结构化查询意图分析\n * 当 NlQueryOrchestrator 本地规则无法匹配时，调用此方法让 AI 理解意图\n *\n * @param question 用户问题\n * @return JSON 字符串，格式：{\"intent\":\"xxx\",\"params\":{\"key\":\"value\"}}\n * 失败返回 null\n */\n public String parseNlIntent(String question) {\n String systemPrompt = \"你是服装供应链系统的 NLP 解析器。\" +\n \"将用户问题解析为 JSON：{\\\"intent\\\":\\\"意图\\\",\\\"params\\\":{\\\"参数\\\":\\\"值\\\"}}。\" +\n \"可能的 intent: query_order_status, query_material_stock, query_factory_capacity, \" +\n \"query_finance_settlement, query_worker_efficiency, query_delivery_risk。\" +\n \"只输出 JSON，不要解释。\";\n IntelligenceInferenceResult result = invoke(\"nl-intent\", systemPrompt, question);\n return result.isSuccess() ? result.getContent() : null;\n }\n\n private IntelligenceInferenceResult invoke(String scene, String systemPrompt, String userMessage) {\n if (!isEnabled()) {\n IntelligenceInferenceResult result = new IntelligenceInferenceResult();\n result.setSuccess(false);\n result.setProvider(\"none\");\n result.setErrorMessage(\"ai-disabled\");\n return result;\n }\n return intelligenceInferenceOrchestrator.chat(scene, systemPrompt, userMessage);\n }\n}\n"}, "64c4685b5bf2577e8ed6f8aff55fa2b7fa6cf319": {"t": 1785749958.7763484, "n": 5543, "h": "15755135f48490ef", "c": "package com.fashion.supplychain.intelligence.service;\n\nimport com.fasterxml.jackson.databind.JsonNode;\nimport com.fashion.supplychain.intelligence.dto.IntelligenceInferenceResult;\nimport com.fashion.supplychain.intelligence.orchestration.IntelligenceInferenceOrchestrator;\nimport lombok.extern.slf4j.Slf4j;\nimport org.springframework.beans.factory.annotation.Autowired;\nimport org.springframework.beans.factory.annotation.Value;\nimport org.springframework.cache.annotation.Cacheable;\nimport org.springframework.stereotype.Service;\n\nimport java.time.LocalDate;\nimport java.util.concurrent.ConcurrentHashMap;\nimport java.util.concurrent.atomic.AtomicInteger;\n\n/**\n * AI 顾问服务。\n *\n * <p>职责：保留配额与启用判断，对外提供 AI 顾问语义能力。\n * 真实推理由独立的 {@link IntelligenceInferenceOrchestrator} 执行。</p>\n *\n * <p>默认直连 DeepSeek，也支持通过 LiteLLM 网关接入，配置方式（环境变量）：\n * <pre>\n * DEEPSEEK_API_KEY = sk-xxxxxxxxxxxxxxxx\n * AI_LITELLM_ENABLED = true/false\n * AI_LITELLM_BASE_URL = http://localhost:4000\n * AI_LITELLM_API_KEY = sk-virtual-key\n * </pre>\n *\n * <p>若未配置 AI_API_KEY，所有调用会立即返回 null，系统继续走本地规则引擎，\n * 不影响任何现有功能。\n */\n@Service\n@Slf4j\npublic class AiAdvisorService {\n\n @Value(\"${ai.deepseek.api-key:}\")\n private String apiKey;\n\n /** 每个租户每天最多允许调用 DeepSeek 的次数（0 = 不限制） */\n @Value(\"${ai.deepseek.daily-quota-per-tenant:50}\")\n private int dailyQuotaPerTenant;\n\n @Autowired\n private IntelligenceInferenceOrchestrator intelligenceInferenceOrchestrator;\n\n /**\n * 每租户每日调用计数器\n * key = \"tenantId_yyyyMMdd\"，value = 当日已调用次数\n * 不同日期的 key 自动过期（不再被读写），内存占用可忽略不计\n */\n private final ConcurrentHashMap<String, AtomicInteger> dailyCounters = new ConcurrentHashMap<>();\n\n /**\n * 是否已启用 AI（有 API Key）\n */\n public boolean isEnabled() {\n return intelligenceInferenceOrchestrator.isAnyModelEnabled();\n }\n\n /**\n * 检查当前租户今日配额是否已用完\n *\n * @param tenantId 租户ID（null 视为系统内部调用，不限制）\n * @return true = 配额充足可以调用；false = 今日配额已用完\n */\n public boolean checkAndConsumeQuota(Long tenantId) {\n if (dailyQuotaPerTenant <= 0 || tenantId == null) return true; // 0=不限\n String key = tenantId + \"_\" + LocalDate.now();\n AtomicInteger count = dailyCounters.computeIfAbsent(key, k -> new AtomicInteger(0));\n int used = count.incrementAndGet();\n if (used > dailyQuotaPerTenant) {\n count.decrementAndGet(); // 回滚，不消耗\n log.warn(\"[AiAdvisor] 租户 {} 今日AI调用已达配额上限 {}\", tenantId, dailyQuotaPerTenant);\n return false;\n }\n log.debug(\"[AiAdvisor] 租户 {} 今日AI调用 {}/{}\", tenantId, used, dailyQuotaPerTenant);\n return true;\n }\n\n /**\n * 查询当前租户今日已用配额\n */\n public int getTodayUsage(Long tenantId) {\n if (tenantId == null) return 0;\n String key = tenantId + \"_\" + LocalDate.now();\n AtomicInteger count = dailyCounters.get(key);\n return count == null ? 0 : count.get();\n }\n\n /**\n * 发送单轮对话请求\n *\n * @param systemPrompt 系统角色描述（如：你是服装供应链分析师）\n * @param userMessage 用户消息\n * @return AI 回复文本，未配置 Key 或调用失败时返回 null\n */\n public String chat(String systemPrompt, String userMessage) {\n IntelligenceInferenceResult result = invoke(\"ai-advisor\", systemPrompt, userMessage);\n if (!result.isSuccess()) {\n log.warn(\"[AiAdvisor] AI 调用失败 provider={} error={}\", result.getProvider(), result.getErrorMessage());\n return null;\n }\n return result.getContent();\n }\n\n /**\n * 智能建议：给定业务数据摘要，返回简短建议文案（用于运营日报）\n *\n * <p>结果按 contextSummary 内容缓存 5 分钟，相同摘要（同一租户当日数据未变化）\n * 直接返回缓存值，避免重复调用 DeepSeek API（每次约 2~3 秒延迟）。\n * Redis 不可用时自动降级为直接调用 AI（见 RedisConfig.errorHandler）。</p>\n *\n * @param contextSummary 业务摘要（如：今日逾期3单，高风险2单，停滞1单）\n * @return 1~3句建议文案，未启用时返回 null\n */\n @Cacheable(value = \"daily-brief\", key = \"T(com.fashion.supplychain.common.UserContext).tenantId() + ':' + #contextSummary\")\n public String getDailyAdvice(String contextSummary) {\n String systemPrompt = \"你是一名服装供应链管理顾问，根据工厂今日生产数据，\" +\n \"用简洁中文给出1~3条可执行的管理建议。每条建议一行，不超过30字。不要废话。\";\n IntelligenceInferenceResult result = invoke(\"daily-brief\", systemPrompt, contextSummary);\n return result.isSuccess() ? result.getContent() : null;\n }\n\n /**\n * 自然语言转结构化查询意图分析\n * 当 NlQueryOrchestrator 本地规则无法匹配时，调用此方法让 AI 理解意图\n *\n * @param question 用户问题\n * @return JSON 字符串，格式：{\"intent\":\"xxx\",\"params\":{\"key\":\"value\"}}\n * 失败返回 null\n */\n public String parseNlIntent(String question) {\n String systemPrompt = \"你是服装供应链系统的 NLP 解析器。\" +\n \"将用户问题解析为 JSON：{\\\"intent\\\":\\\"意图\\\",\\\"params\\\":{\\\"参数\\\":\\\"值\\\"}}。\" +\n \"可能的 intent: query_order_status, query_material_stock, query_factory_capacity, \" +\n \"query_finance_settlement, query_worker_efficiency, query_delivery_risk。\" +\n \"只输出 JSON，不要解释。\";\n IntelligenceInferenceResult result = invoke(\"nl-intent\", systemPrompt, question);\n return result.isSuccess() ? result.getContent() : null;\n }\n\n private IntelligenceInferenceResult invoke(String scene, String systemPrompt, String userMessage) {\n if (!isEnabled()) {\n IntelligenceInferenceResult result = new IntelligenceInferenceResult();\n result.setSuccess(false);\n result.setProvider(\"none\");\n result.setErrorMessage(\"ai-disabled\");\n return result;\n }\n return intelligenceInferenceOrchestrator.chat(scene, systemPrompt, userMessage);\n }\n}\n"}, "34014ba5d2031264de5c960eaad707590ce3b729": {"t": 1785749959.8803394, "n": 5478, "h": "3cfa15a117d66e9f", "c": "package com.fashion.supplychain.intelligence.service;\n\nimport com.fasterxml.jackson.databind.JsonNode;\nimport com.fashion.supplychain.intelligence.dto.IntelligenceInferenceResult;\nimport com.fashion.supplychain.intelligence.orchestration.IntelligenceInferenceOrchestrator;\nimport lombok.extern.slf4j.Slf4j;\nimport org.springframework.beans.factory.annotation.Autowired;\nimport org.springframework.beans.factory.annotation.Value;\nimport org.springframework.cache.annotation.Cacheable;\nimport org.springframework.stereotype.Service;\n\nimport java.time.LocalDate;\nimport java.util.concurrent.ConcurrentHashMap;\nimport java.util.concurrent.atomic.AtomicInteger;\n\n/**\n * AI 顾问服务。\n *\n * <p>职责：保留配额与启用判断，对外提供 AI 顾问语义能力。\n * 真实推理由独立的 {@link IntelligenceInferenceOrchestrator} 执行。</p>\n *\n * <p>默认直连 DeepSeek，也支持通过 LiteLLM 网关接入，配置方式（环境变量）：\n * <pre>\n * DEEPSEEK_API_KEY = sk-xxxxxxxxxxxxxxxx\n * AI_LITELLM_ENABLED = true/false\n * AI_LITELLM_BASE_URL = http://localhost:4000\n * AI_LITELLM_API_KEY = sk-virtual-key\n * </pre>\n *\n * <p>若未配置 AI_API_KEY，所有调用会立即返回 null，系统继续走本地规则引擎，\n * 不影响任何现有功能。\n */\n@Service\n@Slf4j\npublic class AiAdvisorService {\n\n @Value(\"${ai.deepseek.api-key:}\")\n private String apiKey;\n\n /** 每个租户每天最多允许调用 DeepSeek 的次数（0 = 不限制） */\n @Value(\"${ai.deepseek.daily-quota-per-tenant:50}\")\n private int dailyQuotaPerTenant;\n\n @Autowired\n private IntelligenceInferenceOrchestrator intelligenceInferenceOrchestrator;\n\n /**\n * 每租户每日调用计数器\n * key = \"tenantId_yyyyMMdd\"，value = 当日已调用次数\n * 不同日期的 key 自动过期（不再被读写），内存占用可忽略不计\n */\n private final ConcurrentHashMap<String, AtomicInteger> dailyCounters = new ConcurrentHashMap<>();\n\n /**\n * 是否已启用 AI（有 API Key）\n */\n public boolean isEnabled() {\n return intelligenceInferenceOrchestrator.isAnyModelEnabled();\n }\n\n /**\n * 检查当前租户今日配额是否已用完\n *\n * @param tenantId 租户ID（null 视为系统内部调用，不限制）\n * @return true = 配额充足可以调用；false = 今日配额已用完\n */\n public boolean checkAndConsumeQuota(Long tenantId) {\n if (dailyQuotaPerTenant <= 0 || tenantId == null) return true; // 0=不限\n String key = tenantId + \"_\" + LocalDate.now();\n AtomicInteger count = dailyCounters.computeIfAbsent(key, k -> new AtomicInteger(0));\n int used = count.incrementAndGet();\n if (used > dailyQuotaPerTenant) {\n count.decrementAndGet(); // 回滚，不消耗\n log.warn(\"[AiAdvisor] 租户 {} 今日AI调用已达配额上限 {}\", tenantId, dailyQuotaPerTenant);\n return false;\n }\n log.debug(\"[AiAdvisor] 租户 {} 今日AI调用 {}/{}\", tenantId, used, dailyQuotaPerTenant);\n return true;\n }\n\n /**\n * 查询当前租户今日已用配额\n */\n public int getTodayUsage(Long tenantId) {\n if (tenantId == null) return 0;\n String key = tenantId + \"_\" + LocalDate.now();\n AtomicInteger count = dailyCounters.get(key);\n return count == null ? 0 : count.get();\n }\n\n /**\n * 发送单轮对话请求\n *\n * @param systemPrompt 系统角色描述（如：你是服装供应链分析师）\n * @param userMessage 用户消息\n * @return AI 回复文本，未配置 Key 或调用失败时返回 null\n */\n public String chat(String systemPrompt, String userMessage) {\n IntelligenceInferenceResult result = invoke(\"ai-advisor\", systemPrompt, userMessage);\n if (!result.isSuccess()) {\n log.warn(\"[AiAdvisor] AI 调用失败 provider={} error={}\", result.getProvider(), result.getErrorMessage());\n return null;\n }\n return result.getContent();\n }\n\n /**\n * 智能建议：给定业务数据摘要，返回简短建议文案（用于运营日报）\n *\n * <p>结果按 contextSummary 内容缓存 5 分钟，相同摘要（同一租户当日数据未变化）\n * 直接返回缓存值，避免重复调用 DeepSeek API（每次约 2~3 秒延迟）。\n * Redis 不可用时自动降级为直接调用 AI（见 RedisConfig.errorHandler）。</p>\n *\n * @param contextSummary 业务摘要（如：今日逾期3单，高风险2单，停滞1单）\n * @return 1~3句建议文案，未启用时返回 null\n */\n @Cacheable(value = \"daily-brief\", key = \"#contextSummary\")\n public String getDailyAdvice(String contextSummary) {\n String systemPrompt = \"你是一名服装供应链管理顾问，根据工厂今日生产数据，\" +\n \"用简洁中文给出1~3条可执行的管理建议。每条建议一行，不超过30字。不要废话。\";\n IntelligenceInferenceResult result = invoke(\"daily-brief\", systemPrompt, contextSummary);\n return result.isSuccess() ? result.getContent() : null;\n }\n\n /**\n * 自然语言转结构化查询意图分析\n * 当 NlQueryOrchestrator 本地规则无法匹配时，调用此方法让 AI 理解意图\n *\n * @param question 用户问题\n * @return JSON 字符串，格式：{\"intent\":\"xxx\",\"params\":{\"key\":\"value\"}}\n * 失败返回 null\n */\n public String parseNlIntent(String question) {\n String systemPrompt = \"你是服装供应链系统的 NLP 解析器。\" +\n \"将用户问题解析为 JSON：{\\\"intent\\\":\\\"意图\\\",\\\"params\\\":{\\\"参数\\\":\\\"值\\\"}}。\" +\n \"可能的 intent: query_order_status, query_material_stock, query_factory_capacity, \" +\n \"query_finance_settlement, query_worker_efficiency, query_delivery_risk。\" +\n \"只输出 JSON，不要解释。\";\n IntelligenceInferenceResult result = invoke(\"nl-intent\", systemPrompt, question);\n return result.isSuccess() ? result.getContent() : null;\n }\n\n private IntelligenceInferenceResult invoke(String scene, String systemPrompt, String userMessage) {\n if (!isEnabled()) {\n IntelligenceInferenceResult result = new IntelligenceInferenceResult();\n result.setSuccess(false);\n result.setProvider(\"none\");\n result.setErrorMessage(\"ai-disabled\");\n return result;\n }\n return intelligenceInferenceOrchestrator.chat(scene, systemPrompt, userMessage);\n }\n}\n"}, "ee39636df0ebdc4a1f9923cc34d0684344a4bf4b": {"t": 1785749960.9941413, "n": 5184, "h": "f1a7f3b07fb8ce2d", "c": "package com.fashion.supplychain.intelligence.service;\n\nimport com.fasterxml.jackson.databind.JsonNode;\nimport com.fashion.supplychain.intelligence.dto.IntelligenceInferenceResult;\nimport com.fashion.supplychain.intelligence.orchestration.IntelligenceInferenceOrchestrator;\nimport lombok.extern.slf4j.Slf4j;\nimport org.springframework.beans.factory.annotation.Autowired;\nimport org.springframework.beans.factory.annotation.Value;\nimport org.springframework.stereotype.Service;\n\nimport java.time.LocalDate;\nimport java.util.concurrent.ConcurrentHashMap;\nimport java.util.concurrent.atomic.AtomicInteger;\n\n/**\n * AI 顾问服务。\n *\n * <p>职责：保留配额与启用判断，对外提供 AI 顾问语义能力。\n * 真实推理由独立的 {@link IntelligenceInferenceOrchestrator} 执行。</p>\n *\n * <p>默认直连 DeepSeek，也支持通过 LiteLLM 网关接入，配置方式（环境变量）：\n * <pre>\n * DEEPSEEK_API_KEY = sk-xxxxxxxxxxxxxxxx\n * AI_LITELLM_ENABLED = true/false\n * AI_LITELLM_BASE_URL = http://localhost:4000\n * AI_LITELLM_API_KEY = sk-virtual-key\n * </pre>\n *\n * <p>若未配置 AI_API_KEY，所有调用会立即返回 null，系统继续走本地规则引擎，\n * 不影响任何现有功能。\n */\n@Service\n@Slf4j\npublic class AiAdvisorService {\n\n @Value(\"${ai.deepseek.api-key:}\")\n private String apiKey;\n\n /** 每个租户每天最多允许调用 DeepSeek 的次数（0 = 不限制） */\n @Value(\"${ai.deepseek.daily-quota-per-tenant:50}\")\n private int dailyQuotaPerTenant;\n\n @Autowired\n private IntelligenceInferenceOrchestrator intelligenceInferenceOrchestrator;\n\n /**\n * 每租户每日调用计数器\n * key = \"tenantId_yyyyMMdd\"，value = 当日已调用次数\n * 不同日期的 key 自动过期（不再被读写），内存占用可忽略不计\n */\n private final ConcurrentHashMap<String, AtomicInteger> dailyCounters = new ConcurrentHashMap<>();\n\n /**\n * 是否已启用 AI（有 API Key）\n */\n public boolean isEnabled() {\n return intelligenceInferenceOrchestrator.isAnyModelEnabled();\n }\n\n /**\n * 检查当前租户今日配额是否已用完\n *\n * @param tenantId 租户ID（null 视为系统内部调用，不限制）\n * @return true = 配额充足可以调用；false = 今日配额已用完\n */\n public boolean checkAndConsumeQuota(Long tenantId) {\n if (dailyQuotaPerTenant <= 0 || tenantId == null) return true; // 0=不限\n String key = tenantId + \"_\" + LocalDate.now();\n AtomicInteger count = dailyCounters.computeIfAbsent(key, k -> new AtomicInteger(0));\n int used = count.incrementAndGet();\n if (used > dailyQuotaPerTenant) {\n count.decrementAndGet(); // 回滚，不消耗\n log.warn(\"[AiAdvisor] 租户 {} 今日AI调用已达配额上限 {}\", tenantId, dailyQuotaPerTenant);\n return false;\n }\n log.debug(\"[AiAdvisor] 租户 {} 今日AI调用 {}/{}\", tenantId, used, dailyQuotaPerTenant);\n return true;\n }\n\n /**\n * 查询当前租户今日已用配额\n */\n public int getTodayUsage(Long tenantId) {\n if (tenantId == null) return 0;\n String key = tenantId + \"_\" + LocalDate.now();\n AtomicInteger count = dailyCounters.get(key);\n return count == null ? 0 : count.get();\n }\n\n /**\n * 发送单轮对话请求\n *\n * @param systemPrompt 系统角色描述（如：你是服装供应链分析师）\n * @param userMessage 用户消息\n * @return AI 回复文本，未配置 Key 或调用失败时返回 null\n */\n public String chat(String systemPrompt, String userMessage) {\n IntelligenceInferenceResult result = invoke(\"ai-advisor\", systemPrompt, userMessage);\n if (!result.isSuccess()) {\n log.warn(\"[AiAdvisor] AI 调用失败 provider={} error={}\", result.getProvider(), result.getErrorMessage());\n return null;\n }\n return result.getContent();\n }\n\n /**\n * 智能建议：给定业务数据摘要，返回简短建议文案（用于运营日报）\n *\n * @param contextSummary 业务摘要（如：今日逾期3单，高风险2单，停滞1单）\n * @return 1~3句建议文案，未启用时返回 null\n */\n public String getDailyAdvice(String contextSummary) {\n String systemPrompt = \"你是一名服装供应链管理顾问，根据工厂今日生产数据，\" +\n \"用简洁中文给出1~3条可执行的管理建议。每条建议一行，不超过30字。不要废话。\";\n IntelligenceInferenceResult result = invoke(\"daily-brief\", systemPrompt, contextSummary);\n return result.isSuccess() ? result.getContent() : null;\n }\n\n /**\n * 自然语言转结构化查询意图分析\n * 当 NlQueryOrchestrator 本地规则无法匹配时，调用此方法让 AI 理解意图\n *\n * @param question 用户问题\n * @return JSON 字符串，格式：{\"intent\":\"xxx\",\"params\":{\"key\":\"value\"}}\n * 失败返回 null\n */\n public String parseNlIntent(String question) {\n String systemPrompt = \"你是服装供应链系统的 NLP 解析器。\" +\n \"将用户问题解析为 JSON：{\\\"intent\\\":\\\"意图\\\",\\\"params\\\":{\\\"参数\\\":\\\"值\\\"}}。\" +\n \"可能的 intent: query_order_status, query_material_stock, query_factory_capacity, \" +\n \"query_finance_settlement, query_worker_efficiency, query_delivery_risk。\" +\n \"只输出 JSON，不要解释。\";\n IntelligenceInferenceResult result = invoke(\"nl-intent\", systemPrompt, question);\n return result.isSuccess() ? result.getContent() : null;\n }\n\n private IntelligenceInferenceResult invoke(String scene, String systemPrompt, String userMessage) {\n if (!isEnabled()) {\n IntelligenceInferenceResult result = new IntelligenceInferenceResult();\n result.setSuccess(false);\n result.setProvider(\"none\");\n result.setErrorMessage(\"ai-disabled\");\n return result;\n }\n return intelligenceInferenceOrchestrator.chat(scene, systemPrompt, userMessage);\n }\n}\n"}, "6f4ed6d1a104aecf5e94fb4632c129e2a83d710e": {"t": 1785749963.079358, "n": 6578, "h": "164114db2d13a06e", "c": "package com.fashion.supplychain.intelligence.service;\n\nimport com.fasterxml.jackson.databind.JsonNode;\nimport com.fasterxml.jackson.databind.ObjectMapper;\nimport lombok.extern.slf4j.Slf4j;\nimport org.springframework.beans.factory.annotation.Value;\nimport org.springframework.stereotype.Service;\n\nimport java.net.URI;\nimport java.net.http.HttpClient;\nimport java.net.http.HttpRequest;\nimport java.net.http.HttpResponse;\nimport java.time.Duration;\nimport java.time.LocalDate;\nimport java.util.concurrent.ConcurrentHashMap;\nimport java.util.concurrent.atomic.AtomicInteger;\n\n/**\n * AI 顾问服务 — 统一的 AI API 接入层\n *\n * <p>默认接 DeepSeek，配置方式（环境变量）：\n * <pre>\n * AI_API_KEY = sk-xxxxxxxxxxxxxxxx （必填，有此值才会真实调用 AI）\n * AI_API_URL = https://api.deepseek.com/v1/chat/completions （可选，默认 DeepSeek）\n * AI_MODEL = deepseek-chat （可选，默认 deepseek-chat）\n * </pre>\n *\n * <p>若未配置 AI_API_KEY，所有调用会立即返回 null，系统继续走本地规则引擎，\n * 不影响任何现有功能。\n *\n * <p>切换 OpenAI / 其他兼容 API 只需改 AI_API_URL 和 AI_MODEL。\n */\n@Service\n@Slf4j\npublic class AiAdvisorService {\n\n @Value(\"${ai.deepseek.api-key:}\")\n private String apiKey;\n\n @Value(\"${ai.deepseek.api-url:https://api.deepseek.com/v1/chat/completions}\")\n private String apiUrl;\n\n @Value(\"${ai.deepseek.model:deepseek-chat}\")\n private String model;\n\n /** 每个租户每天最多允许调用 DeepSeek 的次数（0 = 不限制） */\n @Value(\"${ai.deepseek.daily-quota-per-tenant:50}\")\n private int dailyQuotaPerTenant;\n\n private static final int TIMEOUT_SECONDS = 30;\n private static final ObjectMapper MAPPER = new ObjectMapper();\n\n /**\n * 每租户每日调用计数器\n * key = \"tenantId_yyyyMMdd\"，value = 当日已调用次数\n * 不同日期的 key 自动过期（不再被读写），内存占用可忽略不计\n */\n private final ConcurrentHashMap<String, AtomicInteger> dailyCounters = new ConcurrentHashMap<>();\n\n /**\n * 是否已启用 AI（有 API Key）\n */\n public boolean isEnabled() {\n return apiKey != null && !apiKey.isBlank();\n }\n\n /**\n * 检查当前租户今日配额是否已用完\n *\n * @param tenantId 租户ID（null 视为系统内部调用，不限制）\n * @return true = 配额充足可以调用；false = 今日配额已用完\n */\n public boolean checkAndConsumeQuota(Long tenantId) {\n if (dailyQuotaPerTenant <= 0 || tenantId == null) return true; // 0=不限\n String key = tenantId + \"_\" + LocalDate.now();\n AtomicInteger count = dailyCounters.computeIfAbsent(key, k -> new AtomicInteger(0));\n int used = count.incrementAndGet();\n if (used > dailyQuotaPerTenant) {\n count.decrementAndGet(); // 回滚，不消耗\n log.warn(\"[AiAdvisor] 租户 {} 今日AI调用已达配额上限 {}\", tenantId, dailyQuotaPerTenant);\n return false;\n }\n log.debug(\"[AiAdvisor] 租户 {} 今日AI调用 {}/{}\", tenantId, used, dailyQuotaPerTenant);\n return true;\n }\n\n /**\n * 查询当前租户今日已用配额\n */\n public int getTodayUsage(Long tenantId) {\n if (tenantId == null) return 0;\n String key = tenantId + \"_\" + LocalDate.now();\n AtomicInteger count = dailyCounters.get(key);\n return count == null ? 0 : count.get();\n }\n\n /**\n * 发送单轮对话请求\n *\n * @param systemPrompt 系统角色描述（如：你是服装供应链分析师）\n * @param userMessage 用户消息\n * @return AI 回复文本，未配置 Key 或调用失败时返回 null\n */\n public String chat(String systemPrompt, String userMessage) {\n if (!isEnabled()) {\n log.debug(\"[AiAdvisor] AI_API_KEY 未配置，跳过 AI 调用\");\n return null;\n }\n\n try {\n String body = buildRequestBody(systemPrompt, userMessage);\n HttpClient client = HttpClient.newBuilder()\n .connectTimeout(Duration.ofSeconds(10))\n .build();\n HttpRequest request = HttpRequest.newBuilder()\n .uri(URI.create(apiUrl))\n .header(\"Content-Type\", \"application/json\")\n .header(\"Authorization\", \"Bearer \" + apiKey)\n .POST(HttpRequest.BodyPublishers.ofString(body))\n .timeout(Duration.ofSeconds(TIMEOUT_SECONDS))\n .build();\n\n HttpResponse<String> response = client.send(request, HttpResponse.BodyHandlers.ofString());\n\n if (response.statusCode() == 200) {\n return extractContent(response.body());\n } else {\n log.warn(\"[AiAdvisor] API 返回非200: status={}, body={}\", response.statusCode(),\n response.body().substring(0, Math.min(200, response.body().length())));\n return null;\n }\n } catch (Exception e) {\n log.warn(\"[AiAdvisor] AI 调用失败: {}\", e.getMessage());\n return null;\n }\n }\n\n /**\n * 智能建议：给定业务数据摘要，返回简短建议文案（用于运营日报）\n *\n * @param contextSummary 业务摘要（如：今日逾期3单，高风险2单，停滞1单）\n * @return 1~3句建议文案，未启用时返回 null\n */\n public String getDailyAdvice(String contextSummary) {\n String systemPrompt = \"你是一名服装供应链管理顾问，根据工厂今日生产数据，\" +\n \"用简洁中文给出1~3条可执行的管理建议。每条建议一行，不超过30字。不要废话。\";\n return chat(systemPrompt, contextSummary);\n }\n\n /**\n * 自然语言转结构化查询意图分析\n * 当 NlQueryOrchestrator 本地规则无法匹配时，调用此方法让 AI 理解意图\n *\n * @param question 用户问题\n * @return JSON 字符串，格式：{\"intent\":\"xxx\",\"params\":{\"key\":\"value\"}}\n * 失败返回 null\n */\n public String parseNlIntent(String question) {\n String systemPrompt = \"你是服装供应链系统的 NLP 解析器。\" +\n \"将用户问题解析为 JSON：{\\\"intent\\\":\\\"意图\\\",\\\"params\\\":{\\\"参数\\\":\\\"值\\\"}}。\" +\n \"可能的 intent: query_order_status, query_material_stock, query_factory_capacity, \" +\n \"query_finance_settlement, query_worker_efficiency, query_delivery_risk。\" +\n \"只输出 JSON，不要解释。\";\n return chat(systemPrompt, question);\n }\n\n // ────────────────── 私有方法 ──────────────────\n\n private String buildRequestBody(String systemPrompt, String userMessage) throws Exception {\n var root = MAPPER.createObjectNode();\n root.put(\"model\", model);\n root.put(\"temperature\", 0.3);\n root.put(\"max_tokens\", 512);\n var messages = root.putArray(\"messages\");\n messages.addObject().put(\"role\", \"system\").put(\"content\", systemPrompt);\n messages.addObject().put(\"role\", \"user\").put(\"content\", userMessage);\n return MAPPER.writeValueAsString(root);\n }\n\n private String extractContent(String responseBody) throws Exception {\n JsonNode root = MAPPER.readTree(responseBody);\n JsonNode choices = root.path(\"choices\");\n if (choices.isArray() && choices.size() > 0) {\n return choices.get(0).path(\"message\").path(\"content\").asText(null);\n }\n return null;\n }\n}\n"}, "fdd7d49b70ea84d9b24ec29bc480b05a726ff117": {"t": 1785749969.0282362, "n": 4998, "h": "61094422963d0eb0", "c": "package com.fashion.supplychain.intelligence.service;\n\nimport com.fasterxml.jackson.databind.JsonNode;\nimport com.fasterxml.jackson.databind.ObjectMapper;\nimport lombok.extern.slf4j.Slf4j;\nimport org.springframework.beans.factory.annotation.Value;\nimport org.springframework.stereotype.Service;\n\nimport java.net.URI;\nimport java.net.http.HttpClient;\nimport java.net.http.HttpRequest;\nimport java.net.http.HttpResponse;\nimport java.time.Duration;\n\n/**\n * AI 顾问服务 — 统一的 AI API 接入层\n *\n * <p>默认接 DeepSeek，配置方式（环境变量）：\n * <pre>\n * AI_API_KEY = sk-xxxxxxxxxxxxxxxx （必填，有此值才会真实调用 AI）\n * AI_API_URL = https://api.deepseek.com/v1/chat/completions （可选，默认 DeepSeek）\n * AI_MODEL = deepseek-chat （可选，默认 deepseek-chat）\n * </pre>\n *\n * <p>若未配置 AI_API_KEY，所有调用会立即返回 null，系统继续走本地规则引擎，\n * 不影响任何现有功能。\n *\n * <p>切换 OpenAI / 其他兼容 API 只需改 AI_API_URL 和 AI_MODEL。\n */\n@Service\n@Slf4j\npublic class AiAdvisorService {\n\n @Value(\"${ai.deepseek.api-key:}\")\n private String apiKey;\n\n @Value(\"${ai.deepseek.api-url:https://api.deepseek.com/v1/chat/completions}\")\n private String apiUrl;\n\n @Value(\"${ai.deepseek.model:deepseek-chat}\")\n private String model;\n\n private static final int TIMEOUT_SECONDS = 30;\n private static final ObjectMapper MAPPER = new ObjectMapper();\n\n /**\n * 是否已启用 AI（有 API Key）\n */\n public boolean isEnabled() {\n return apiKey != null && !apiKey.isBlank();\n }\n\n /**\n * 发送单轮对话请求\n *\n * @param systemPrompt 系统角色描述（如：你是服装供应链分析师）\n * @param userMessage 用户消息\n * @return AI 回复文本，未配置 Key 或调用失败时返回 null\n */\n public String chat(String systemPrompt, String userMessage) {\n if (!isEnabled()) {\n log.debug(\"[AiAdvisor] AI_API_KEY 未配置，跳过 AI 调用\");\n return null;\n }\n\n try {\n String body = buildRequestBody(systemPrompt, userMessage);\n HttpClient client = HttpClient.newBuilder()\n .connectTimeout(Duration.ofSeconds(10))\n .build();\n HttpRequest request = HttpRequest.newBuilder()\n .uri(URI.create(apiUrl))\n .header(\"Content-Type\", \"application/json\")\n .header(\"Authorization\", \"Bearer \" + apiKey)\n .POST(HttpRequest.BodyPublishers.ofString(body))\n .timeout(Duration.ofSeconds(TIMEOUT_SECONDS))\n .build();\n\n HttpResponse<String> response = client.send(request, HttpResponse.BodyHandlers.ofString());\n\n if (response.statusCode() == 200) {\n return extractContent(response.body());\n } else {\n log.warn(\"[AiAdvisor] API 返回非200: status={}, body={}\", response.statusCode(),\n response.body().substring(0, Math.min(200, response.body().length())));\n return null;\n }\n } catch (Exception e) {\n log.warn(\"[AiAdvisor] AI 调用失败: {}\", e.getMessage());\n return null;\n }\n }\n\n /**\n * 智能建议：给定业务数据摘要，返回简短建议文案（用于运营日报）\n *\n * @param contextSummary 业务摘要（如：今日逾期3单，高风险2单，停滞1单）\n * @return 1~3句建议文案，未启用时返回 null\n */\n public String getDailyAdvice(String contextSummary) {\n String systemPrompt = \"你是一名服装供应链管理顾问，根据工厂今日生产数据，\" +\n \"用简洁中文给出1~3条可执行的管理建议。每条建议一行，不超过30字。不要废话。\";\n return chat(systemPrompt, contextSummary);\n }\n\n /**\n * 自然语言转结构化查询意图分析\n * 当 NlQueryOrchestrator 本地规则无法匹配时，调用此方法让 AI 理解意图\n *\n * @param question 用户问题\n * @return JSON 字符串，格式：{\"intent\":\"xxx\",\"params\":{\"key\":\"value\"}}\n * 失败返回 null\n */\n public String parseNlIntent(String question) {\n String systemPrompt = \"你是服装供应链系统的 NLP 解析器。\" +\n \"将用户问题解析为 JSON：{\\\"intent\\\":\\\"意图\\\",\\\"params\\\":{\\\"参数\\\":\\\"值\\\"}}。\" +\n \"可能的 intent: query_order_status, query_material_stock, query_factory_capacity, \" +\n \"query_finance_settlement, query_worker_efficiency, query_delivery_risk。\" +\n \"只输出 JSON，不要解释。\";\n return chat(systemPrompt, question);\n }\n\n // ────────────────── 私有方法 ──────────────────\n\n private String buildRequestBody(String systemPrompt, String userMessage) throws Exception {\n var root = MAPPER.createObjectNode();\n root.put(\"model\", model);\n root.put(\"temperature\", 0.3);\n root.put(\"max_tokens\", 512);\n var messages = root.putArray(\"messages\");\n messages.addObject().put(\"role\", \"system\").put(\"content\", systemPrompt);\n messages.addObject().put(\"role\", \"user\").put(\"content\", userMessage);\n return MAPPER.writeValueAsString(root);\n }\n\n private String extractContent(String responseBody) throws Exception {\n JsonNode root = MAPPER.readTree(responseBody);\n JsonNode choices = root.path(\"choices\");\n if (choices.isArray() && choices.size() > 0) {\n return choices.get(0).path(\"message\").path(\"content\").asText(null);\n }\n return null;\n }\n}\n"}}}} |