Windows
74b7d00dff
交付落点
- 新增 GET /api/v1/users/me/advisor-contents（客户读**自己**已发布方案）：
「发送给客户」原先只改数据状态、客户端没有任何页面或接口能读到它
- 客户端新增「我的投顾方案」页与导航入口
可视化（投顾结果区与客户页**共用** common/advisor-plan-view.js，避免两处漂移）
- 净值折线图（带坐标轴与网格）、组合业绩等权合成曲线（含区间收益与最大回撤）、
资产配置环形图与图例、组合构成条
- 修 num(null)=0 的假 0：Number(null)/Number('') 会得 0，导致「没数据」被渲染成 0.00%；
现一律显示「--」。同理管理费/起投未维护时按没数据处理，不显示 0
- 涨跌口径为「涨红跌绿」（A 股习惯），由 CSS 变量 --plan-up / --plan-down 集中定义
推荐依据接入大模型（可选，失败即回退）
- 新增 AdvisorReasonService：**只改文案，不参与选品**（候选池与排序在它之前已固定）
- 输入只允许是已算出的真实参数（风险等级、排序得分、区间收益、最大回撤、期限与流动性）
- 命中收益承诺词（保本/保证收益/稳赚/无风险…）整条丢弃并回退规则文案
- 未启用 / 缺密钥 / 超时 / 解析失败一律回退，推荐主流程不因模型不可用而失败
- 前端标注来源（AI 生成 / 规则生成）
数据与权限
- 客户角色补齐：绑 customer 角色、补建缺失的账户与交易段权限码（9060-9065）
- 净值全量同步（20 只产品），行情同步脚本按 --codes 分块（全量一次会被超时终止）
测试
- 新增 tests/unit/service/test_advisor_reason_service.py（10 项，专测三条合规边界）
- 前端模块自检纳入 service-request-module；补「两处共用同一渲染」回归测试
161 lines
8.7 KiB
Python
161 lines
8.7 KiB
Python
from functools import lru_cache
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from dotenv import load_dotenv
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from pydantic import Field
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from pydantic_settings import BaseSettings, SettingsConfigDict
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# 把 `.env` 显式注入进程环境变量。pydantic-settings 只把 `.env` 读进 Settings 对象，
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# **不会**写 `os.environ`；而模型密钥解析（`EnvironmentSecretResolver` 要求 secret_ref
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# 形如 `env:VAR`）读的正是 `os.getenv`。两者不打通就会出现"按文档在 .env 里配好密钥、
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# 运行时却读不到"，且症状伪装成"没有可用的模型端点"，排查方向被带偏。
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# `override=False`：真实进程环境变量优先，`.env` 只作补充（生产用容器注入时不会被覆盖）。
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load_dotenv(override=False)
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class Settings(BaseSettings):
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app_env: str = "local"
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app_name: str = "jr-agent-platform"
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log_level: str = "INFO"
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timezone: str = "Asia/Shanghai"
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cors_allowed_origins: str = "http://127.0.0.1:5173,http://localhost:5173"
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jwt_issuer: str
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jwt_audience: str
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jwt_algorithm: str = "RS256"
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# 默认指向**开发专用**密钥（tools/generate_jwt_keys.py 生成，config/jwt/ 不进版本库）。
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# 生产环境必须用环境变量覆盖为生产机上单独生成的那一套，不要复用开发密钥。
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jwt_private_key_path: str = "config/jwt/dev/jwt-private.pem"
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jwt_public_key_path: str = "config/jwt/dev/jwt-public.pem"
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jwt_clock_skew_seconds: int = Field(default=30, ge=0)
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visitor_token_ttl_seconds: int = Field(default=900, ge=60, le=3600)
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mysql_dsn: str
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mysql_pool_size: int = Field(default=5, ge=1)
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mysql_max_overflow: int = Field(default=10, ge=0)
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mysql_pool_pre_ping: bool = False
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mysql_tx_isolation: str = "READ COMMITTED"
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redis_url: str
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redis_connect_timeout_seconds: float = Field(default=2, gt=0)
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# 限流（文档 §3.5 的 429 / §3.6 的 RATE_LIMITED）。默认值刻意宽松：
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# 每用户每接口每分钟 600 次（10 QPS）。取值依据是"本地验收与集成测试的量级"——
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# 两个验收脚本与 tests/integration 里同一身份对同一接口最多几十次调用，
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# 600 留了一个数量级余量，绝不可能误伤；同时 10 QPS 已足以拦住单个客户端
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# 把底座打爆（正常业务交互远低于 1 QPS）。生产按真实容量收紧即可，无需改代码。
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rate_limit_enabled: bool = True
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rate_limit_window_seconds: int = Field(default=60, gt=0)
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rate_limit_max_requests: int = Field(default=600, ge=1)
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rate_limit_key_prefix: str = "jr:rate_limit"
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message_broker_url: str = ""
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message_broker_run_topic: str = "agent.run"
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message_broker_outbox_topic: str = "agent.outbox"
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message_broker_dlq_topic: str = "agent.dlq"
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milvus_uri: str
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milvus_local_uri: str = ""
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milvus_token: str = ""
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# 长期记忆向量集合名**刻意不做成配置项**：它是写（投影）、读（召回）、删（清理）
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# 三侧共用的代码级契约，集合 schema 与向量维度（1024）也由代码定义
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# （`app/infrastructure/milvus_profile_projection.py` 的 `PROFILE_COLLECTION`）。
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# 这里原本有个 `milvus_collection`（`jr_memory`），写路径却从没读过它 ——
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# 只有读/删两侧读，于是"写进 A、从 B 查、从 B 删"，语义召回恒空、清理恒报成功却不删。
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# 一个只在契约一侧生效的配置项比没有配置项更危险，故直接删除。
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neo4j_uri: str
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neo4j_database: str = "neo4j"
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neo4j_username: str = "neo4j"
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neo4j_password: str = ""
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model_router_config_ref: str = "local"
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model_default_endpoint: str = ""
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model_fallback_endpoint: str = ""
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knowledge_embedding_endpoint_code: str = ""
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knowledge_embedding_timeout_ms: int = Field(default=15000, gt=0)
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sse_heartbeat_seconds: int = Field(default=15, gt=0)
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sse_chunk_characters: int = Field(default=256, ge=1, le=4096)
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sse_max_connection_seconds: int = Field(default=300, gt=0)
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worker_lease_seconds: int = Field(default=60, gt=0)
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worker_retry_limit: int = Field(default=3, ge=0)
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worker_poll_seconds: float = Field(default=1, gt=0)
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offsite_mailbox: str = "yuan80818843@163.com"
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offsite_allowed_senders: tuple[str, ...] = ("15273589815@163.com",)
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offsite_risk_receiver_id: str = ""
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offsite_settlement_receiver_id: str = ""
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offsite_mail_return_receiver: str = "yuan80818843@163.com"
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offsite_max_retry_count: int = Field(default=3, ge=0)
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offsite_imap_enabled: bool = False
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offsite_imap_host: str = ""
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offsite_imap_port: int = Field(default=993, gt=0)
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offsite_imap_username: str = ""
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offsite_imap_password: str = ""
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offsite_imap_use_ssl: bool = True
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offsite_imap_idle_enabled: bool = False
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offsite_imap_idle_timeout_seconds: float = Field(default=120, gt=0)
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offsite_mail_worker_enabled: bool = False
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offsite_mail_worker_batch_size: int = Field(default=20, ge=1, le=100)
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offsite_worker_user_id: str = ""
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offsite_notification_sending_timeout_seconds: int = Field(default=900, gt=0)
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offsite_mail_storage_dir: str = "data/offsite_mail"
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offsite_ocr_enabled: bool = False
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offsite_ocr_timeout_seconds: float = Field(default=30, gt=0)
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offsite_aliyun_ocr_endpoint: str = "https://docmind-api.cn-hangzhou.aliyuncs.com"
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offsite_aliyun_region_id: str = "cn-hangzhou"
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offsite_aliyun_poll_interval_seconds: float = Field(default=1, gt=0)
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offsite_aliyun_access_key_id: str = ""
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offsite_aliyun_access_key_secret: str = ""
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offsite_deepseek_enabled: bool = False
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offsite_deepseek_base_url: str = "https://api.deepseek.com"
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offsite_deepseek_api_key: str = ""
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offsite_deepseek_model: str = "deepseek-v4-flash"
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offsite_deepseek_timeout_seconds: float = Field(default=30, gt=0)
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#: 平台级 DeepSeek 密钥。`model_endpoint_config.secret_ref` 走 `env:DEEPSEEK_API_KEY`，
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#: 投顾「推荐依据」的 LLM 增强也复用它 —— 密钥只有一个存放点，不按特性各配一把。
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deepseek_api_key: str = ""
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#: 投顾「推荐依据」的 LLM 增强（可选）。未启用、或取不到密钥/调用失败时，
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#: **自动回退**到确定性的数据化文案：推荐流程绝不因为模型不可用而失败。
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advisor_reason_llm_enabled: bool = False
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advisor_reason_llm_base_url: str = "https://api.deepseek.com"
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#: 实测 `api.deepseek.com`：`deepseek-chat` 正常返回文本；`deepseek-v4-flash` /
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#: `deepseek-reasoner` 返回 200 但 `content` 为空（推理型内容在 `reasoning_content`），
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#: 客户端已做兜底读取，但默认仍用最稳的 `deepseek-chat`。
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advisor_reason_llm_model: str = "deepseek-chat"
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advisor_reason_llm_timeout_seconds: float = Field(default=20, gt=0)
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offsite_smtp_enabled: bool = False
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offsite_smtp_dry_run: bool = True
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offsite_smtp_host: str = ""
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offsite_smtp_port: int = Field(default=465, gt=0)
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offsite_smtp_username: str = ""
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offsite_smtp_password: str = ""
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offsite_smtp_sender: str = ""
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offsite_smtp_use_ssl: bool = True
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offsite_smtp_timeout_seconds: float = Field(default=30, gt=0)
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promotion_material_storage_dir: str = "data/promotion_materials"
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promotion_pdf_enabled: bool = False
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promotion_pdf_converter_path: str = ""
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promotion_max_photo_size_bytes: int = Field(default=10 * 1024 * 1024, gt=0)
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promotion_max_performance_file_size_bytes: int = Field(default=20 * 1024 * 1024, gt=0)
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promotion_image_enabled: bool = False
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promotion_image_base_url: str = "https://dashscope.aliyuncs.com"
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promotion_image_model: str = "wan2.2-t2i-flash"
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promotion_image_timeout_seconds: float = Field(default=90, gt=0)
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promotion_image_poll_interval_seconds: float = Field(default=2, gt=0)
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risk_scan_schedule_enabled: bool = False
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risk_scan_interval_minutes: int = Field(default=5, ge=1, le=1440)
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risk_scan_run_immediately: bool = False
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risk_scan_retry_limit: int = Field(default=2, ge=0, le=5)
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risk_scan_poll_seconds: float = Field(default=30, gt=0)
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risk_alert_mail_enabled: bool = False
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risk_alert_mail_dry_run: bool = True
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risk_alert_mail_recipients: str = ""
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# 投顾灰度默认关闭，只有显式开启并配置客户白名单后才限制客户流量。
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# 管理员角色始终可进入，便于审核、发布和故障处置。
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advisor_rollout_enabled: bool = False
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advisor_rollout_customer_ids: str = ""
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@property
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def resolved_milvus_uri(self) -> str:
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"""本地开发优先使用 Lite 文件；部署环境使用标准 Milvus URI。"""
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return self.milvus_local_uri or self.milvus_uri
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model_config = SettingsConfigDict(env_file=".env", env_file_encoding="utf-8", extra="ignore")
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@lru_cache(maxsize=1)
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def get_settings() -> Settings:
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return Settings() # type: ignore[call-arg]
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