1dcdcf23fd
- strawberry-graphql[asgi] dependency added - GeneratedReport and LessonPlanStatus @key types with resolve_reference - RouterAuthMiddleware validates Router-Authorization header on /graphql - GraphQL endpoint mounted at /graphql in FastAPI app - WorkflowStateStore injected for lesson plan status resolution
109 lines
3.3 KiB
Python
109 lines
3.3 KiB
Python
"""配置管理（pydantic-settings，12-factor 合规）."""
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from pydantic_settings import BaseSettings
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class Settings(BaseSettings):
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"""应用配置.
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配置优先级：环境变量 > .env 文件 > 默认值.
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"""
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# 服务
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service_name: str = "ai"
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http_port: int = 3008
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grpc_port: int = 50058
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dev_mode: bool = False
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# 可观测性
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otel_endpoint: str = "http://localhost:4318"
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log_level: str = "info"
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# LLM Provider 配置
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openai_api_key: str = ""
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openai_base_url: str = "https://api.openai.com/v1"
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anthropic_api_key: str = ""
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anthropic_base_url: str = "https://api.anthropic.com"
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baichuan_api_key: str = ""
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baichuan_base_url: str = "https://api.baichuan-ai.com/v1"
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ollama_base_url: str = "" # 本地 Ollama，如 http://localhost:11434
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# Provider 优先级（按顺序 failover）
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llm_provider_priority: str = "openai,anthropic,baichuan,local_ollama"
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# LLM 调用参数
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llm_timeout_seconds: float = 30.0
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llm_stream_connect_timeout: float = 30.0
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llm_stream_read_timeout: float = 60.0
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llm_max_retries: int = 3
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# 默认模型
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default_chat_model: str = "gpt-4o-mini"
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default_question_model: str = "gpt-4o-mini"
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# Redis（限流 + 缓存 + 工作流状态）
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redis_url: str = "redis://localhost:6379/0"
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redis_rate_limit_user_per_min: int = 10
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redis_rate_limit_ip_per_min: int = 30
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redis_rate_limit_school_per_min: int = 100
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# Kafka（用量事件发布，派生数据豁免 Outbox）
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kafka_bootstrap_servers: str = "localhost:9092"
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kafka_ai_usage_topic: str = "edu.ai.usage"
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kafka_producer_transactional_id: str = "ai-service-producer"
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# 下游 gRPC
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content_grpc_endpoint: str = "localhost:50054"
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data_ana_grpc_endpoint: str = "localhost:50055"
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iam_grpc_endpoint: str = "localhost:50052"
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# GraphQL Federation 2 子图（v2.1 ADR-036 Router-Authorization 信任凭证）
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router_auth_secret: str = ""
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# 备课工作流
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workflow_ttl_seconds: int = 86400 # 24h
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workflow_max_retries: int = 3
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# 评估
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evaluation_pass_threshold: float = 0.7
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evaluation_excellent_threshold: float = 0.85
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# 配额（月度 token 预算）
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default_school_monthly_budget: int = 1_000_000
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default_teacher_monthly_budget: int = 100_000
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model_config = {"env_file": ".env", "env_prefix": "", "extra": "ignore"}
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@property
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def is_dev(self) -> bool:
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"""是否处于开发模式."""
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return self.dev_mode
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@property
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def llm_available(self) -> bool:
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"""LLM 是否可用（至少一个 provider 配置了 API key）."""
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return bool(
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self.openai_api_key
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or self.anthropic_api_key
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or self.baichuan_api_key
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or self.ollama_base_url,
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)
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@property
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def provider_priority_list(self) -> list[str]:
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"""Provider 优先级列表."""
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return [p.strip() for p in self.llm_provider_priority.split(",") if p.strip()]
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@property
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def providers_status(self) -> dict[str, bool]:
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"""各 Provider 配置状态."""
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return {
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"openai": bool(self.openai_api_key),
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"anthropic": bool(self.anthropic_api_key),
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"baichuan": bool(self.baichuan_api_key),
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"local_ollama": bool(self.ollama_base_url),
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}
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settings = Settings()
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