abe3b4960e
后端： 1. 阶段 3 notification 服务边界落地，新增 `cmd/notification`、`services/notification`、`gateway/notification`、`shared/contracts/notification` 和 notification port，按 userauth 同款最小手搓 zrpc 样板收口 2. notification outbox consumer、relay 和 retry loop 迁入独立服务入口，处理 `notification.feishu.requested`，gateway 改为通过 zrpc client 调用 notification 3. 清退旧单体 notification DAO/model/service/provider/runner 和 `service/events/notification_feishu.go`，旧实现不再作为活跃编译路径 4. 修复 outbox 路由归属、dispatch 启动扫描、Kafka topic 探测/投递超时、sending 租约恢复、毒消息 MarkDead 错误回传和 RPC timeout 边界 5. 同步调整 active-scheduler 触发通知事件、核心 outbox handler、MySQL 迁移边界和 notification 配置 文档： 1. 更新微服务迁移计划，将阶段 3 notification 标记为已完成，并明确下一阶段从 active-scheduler 开始
153 lines
3.0 KiB
YAML
153 lines
3.0 KiB
YAML
# SmartFlow 后端配置示例
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#
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# 说明：
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# 1. 请复制为 config.yaml 后按实际环境填写。
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# 2. 本示例只保留当前代码仍会读取的配置项，避免示例与运行配置持续漂移。
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# 服务启动配置。
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server:
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port: 8080
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# MySQL 主库配置。
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database:
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host: localhost
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port: 3306
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user: smartflow_user
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password: "put_your_database_password_here"
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dbname: "put_your_database_name_here"
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# 登录态与鉴权令牌配置。
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jwt:
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accessSecret: "put_your_jwt_access_secret_here"
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refreshSecret: "put_your_jwt_refresh_secret_here"
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accessTokenExpire: 15min
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refreshTokenExpire: 7d
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# Redis 配置。
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redis:
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host: localhost
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port: 6379
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password: ""
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# user/auth zrpc 独立服务与网关客户端配置。
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userauth:
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rpc:
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listenOn: "0.0.0.0:9081"
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endpoints:
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- "127.0.0.1:9081"
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timeout: 2s
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# Kafka outbox 事件总线配置。
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kafka:
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enabled: true
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brokers:
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- "localhost:9092"
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topic: "smartflow.agent.outbox"
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groupID: "smartflow-agent-outbox-consumer"
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retryScanInterval: 1s
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retryBatchSize: 100
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maxRetry: 20
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# 通知投递配置。
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notification:
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rpc:
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listenOn: "0.0.0.0:9082"
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endpoints:
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- "127.0.0.1:9082"
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timeout: 6s
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frontendBaseURL: "http://localhost:5173"
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retryScanEvery: 1m
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retryBatchSize: 50
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# 时间与学期边界配置。
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time:
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zone: "Asia/Shanghai"
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semesterStartDate: "2026-03-02"
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semesterEndDate: "2026-07-19"
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# 智能体模型配置。
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agent:
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liteModel: "doubao-seed-2-0-code-preview-260215"
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proModel: "doubao-seed-2-0-code-preview-260215"
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maxModel: "doubao-seed-2-0-code-preview-260215"
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baseURL: "https://ark.cn-beijing.volces.com/api/v3"
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thinking:
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plan: true
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execute: true
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deliver: false
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memory: false
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# 课表图片导入识别配置。
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courseImport:
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visionModel: ""
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maxImageBytes: 5242880
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maxTokens: 8192
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# 通用 RAG 配置。
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rag:
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enabled: true
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store: "milvus"
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topK: 8
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threshold: 0.55
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retrieve:
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timeoutMs: 1500
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ingest:
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chunkSize: 400
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chunkOverlap: 80
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embed:
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provider: "eino"
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model: "doubao-embedding-vision-251215"
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baseURL: "https://ark.cn-beijing.volces.com/api/v3"
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timeoutMs: 1200
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dimension: 1024
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reranker:
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enabled: false
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provider: "noop"
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timeoutMs: 1200
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milvus:
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address: "http://localhost:19530"
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token: "root:Milvus"
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dbName: ""
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collectionName: "smartflow_rag_chunks"
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metricType: "COSINE"
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requestTimeoutMs: 1500
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# 记忆模块配置。
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memory:
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enabled: true
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rag:
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enabled: true
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read:
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mode: legacy
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constraintLimit: 5
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preferenceLimit: 5
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factLimit: 5
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inject:
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renderMode: flat
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prompt:
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extract: ""
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decision: ""
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threshold: 0.55
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enableReranker: false
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llm:
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temperature: 0.1
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topP: 0.2
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job:
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maxRetry: 6
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worker:
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pollEvery: 2s
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claimBatch: 1
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decision:
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enabled: true
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candidateTopK: 5
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candidateMinScore: 0.6
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fallbackMode: legacy_add
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write:
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mode: legacy
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minConfidence: 0.5
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# 联网搜索能力配置。
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websearch:
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provider: bocha
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apiKey: ""
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