634a9fb926
后端： 1. Memory 写入链路新增"召回→比对→汇总"去重决策层 - 新增决策流程：Runner 根据decision.enabled 配置走决策路径（语义召回候选 → Hash 精确命中 → LLM 逐对比对 → 汇总决策 → 执行 ADD/UPDATE/DELETE/NONE），默认关闭，旧路径完全保留 - 新增 LLMDecisionOrchestrator：单对关系判断编排器，输出 duplicate/update/conflict/unrelated 四种关系 - 新增 decision_flow / apply_actions：决策流程主循环与动作落地（新增、更新内容、软删除、跳过） - 新增 aggregate_decision / decision_validate：汇总规则（按优先级判定动作）与 LLM 输出校验 - 新增 decision model：CandidateSnapshot / ComparisonResult / FinalDecision 等决策层核心类型 - ItemRepo 新增 FindActiveByHash / UpdateContentByID / SoftDeleteByID 三个决策层专用方法 - RAG Runtime / Pipeline / Service 新增 DeleteMemory 向量删除能力，MilvusStore 补充 duplicate collection 错误识别 - Runner 新增 syncVectorDeletes 处理决策层 DELETE 动作的向量清理 - config 新增 decision（enabled/candidateTopK/candidateMinScore/fallbackMode）和 write.mode 配置项，config_loader 增加默认值兜底 - 删除 HANDOFF-RAG复用后续实施计划.md 和旧 log.txt，新增 Log.txt 记录决策流程调试日志 - normalize_facts 导出 HashContent 供决策层复用，audit 新增 update 操作常量 前端：无 仓库：无
118 lines
3.6 KiB
YAML
118 lines
3.6 KiB
YAML
# 应用配置文件示例
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# 包含服务器、数据库等基础配置
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# 请根据实际情况修改并保存为 config.yaml
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server:
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port: 8080
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mode: debug
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timeout: 30s
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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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charset: utf8mb4
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parseTime: true
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loc: Local
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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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log:
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level: info
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path: logs/
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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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db: 0
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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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time:
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zone: "Asia/Shanghai"
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semesterStartDate: "2026-03-02" #学期开始日期，一定要设定为周一，以便于计算周数
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semesterEndDate: "2026-07-19" #学期结束日期，一定要设定为周日，确保最后一周完整
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agent:
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workerModel: "doubao-seed-2-0-code-preview-260215" # 智能体使用的Worker模型，需根据实际情况调整
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strategistModel: "doubao-seed-2-0-code-preview-260215" # 策略师使用的Worker模型，需根据实际情况调整
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baseURL: "https://ark.cn-beijing.volces.com/api/v3" # Worker服务的基础URL，需根据实际情况调整
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dailyRefineConcurrency: 7 # 日内并发优化并发度，建议按模型配额调整
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weeklyAdjustBudget: 5 # 周级跨天配平额度上限，防止过度调整
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rag:
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enabled: true
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store: "milvus" # 可选：inmemory / 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" # 可选：mock / eino
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model: "doubao-embedding-vision-251215" # 例如 Ark/OpenAI 兼容 embedding 模型名
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baseURL: "https://ark.cn-beijing.volces.com/api/v3" # 这里填服务根路径，SDK 会自动拼接 /embeddings；API Key 统一从环境变量 ARK_API_KEY 读取
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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" # 当前默认 noop，后续可扩展
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milvus:
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address: "http://localhost:19530" # Milvus REST 入口，当前联调确认不要填 9091 健康检查口
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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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memory:
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enabled: true
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rag:
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enabled: true
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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: false # 决策层总开关，默认关闭。开启后写入链路走"召回→比对→汇总"流程
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candidateTopK: 5 # Milvus 语义召回候选数上限
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candidateMinScore: 0.6 # Milvus 语义召回最低相似度阈值
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fallbackMode: legacy_add # 决策流程整体失败时的降级策略：legacy_add（退回旧路径）/ drop（丢弃）
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write:
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mode: legacy # 写入模式：legacy（旧路径）/ decision（决策流程），仅 decision.enabled=true 时生效
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websearch:
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provider: bocha # 可选：mock | bocha（mock 为空实现，跑通链路用）
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apiKey: "" # 搜索供应商 API Key（bocha 模式必填，否则降级为 mock）
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timeout: 10s # 单次搜索请求超时
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fetchTimeout: 15s # 单次 URL 抓取超时
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fetchMaxChars: 4000 # 抓取正文最大字符数
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rag:
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enabled: false
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