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借鉴 odysseus 的能力设计，全程净室实现、零 AGPL 代码、不引入 AGPL 依赖。
T1 提示注入防护: pkg/promptguard 包裹外部/知识库内容为不可信数据，buildMessages 移出 system 指令区。 T2 安全 CI: .github/workflows(ci+security: govulncheck/gitleaks/actionlint/hadolint/trivy)+dependabot+.hadolint.yaml；go.mod 加 toolchain go1.25.11 修复 20 个 stdlib CVE。 T3 管理员 2FA: 迁移 000016 + RFC6238 TOTP/备份码(pkg/auth, 零依赖) + 登录流程集成(后端)。 T4 本地模型: LLM/embedding 支持本地 vLLM/Ollama(OpenAI 兼容, 鉴权头条件发送, NoAuth) + docs/local-deploy.md。 T6 深度研究: 迁移 000017 + Python research-worker(净室多步流水线, 检索避开 SearXNG) + Go research 服务/handler/路由。 T7 service 层: 新增 internal/service/{research,twofa}, 2FA 业务逻辑从胖 handler 下沉, 接口注入可单测。 T10 缓存/可观测性: internal/cache(Redis+内存, 优雅降级) 接入 store 热点列表; Prometheus 指标+/metrics; docs/openapi.yaml。 验证: go build/vet/test ./... 全绿(8 包); research-worker 12 单测过; 真实 PG 应用迁移并烟测。
54 lines
2.1 KiB
Python
54 lines
2.1 KiB
Python
"""research-worker 配置模块。"""
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import os
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try:
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from dotenv import load_dotenv
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load_dotenv()
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except Exception:
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pass
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class Config:
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# 服务
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HOST: str = os.getenv("WORKER_HOST", "0.0.0.0")
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PORT: int = int(os.getenv("RESEARCH_WORKER_PORT", "8091"))
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CONCURRENCY: int = int(os.getenv("WORKER_CONCURRENCY", "2"))
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# 数据库 / Redis
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DATABASE_URL: str = os.getenv("DATABASE_URL", "postgres://aily:aily@localhost:5432/aily_portal")
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REDIS_URL: str = os.getenv("REDIS_URL", "redis://localhost:6379/0")
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TASK_QUEUE: str = "research:tasks"
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TASK_STATUS_PREFIX: str = "research:status:"
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# LLM（OpenAI 兼容；DashScope 默认）
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LLM_PROVIDER: str = os.getenv("LLM_PROVIDER", "openai")
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OPENAI_API_KEY: str = os.getenv("OPENAI_API_KEY", "")
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OPENAI_BASE_URL: str = os.getenv("OPENAI_BASE_URL", "https://dashscope.aliyuncs.com/compatible-mode/v1")
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OPENAI_MODEL: str = os.getenv("OPENAI_MODEL", "qwen-plus")
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# 本地推理（私有化，与后端 T4 一致：vLLM/Ollama 等）
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LOCAL_LLM_BASE_URL: str = os.getenv("LOCAL_LLM_BASE_URL", "")
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LOCAL_LLM_MODEL: str = os.getenv("LOCAL_LLM_MODEL", "")
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LOCAL_LLM_API_KEY: str = os.getenv("LOCAL_LLM_API_KEY", "")
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# 检索 provider（可插拔，避开 AGPL 的 SearXNG）。留空则不联网，降级为无来源报告。
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SEARCH_PROVIDER: str = os.getenv("SEARCH_PROVIDER", "") # tavily | ""
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SEARCH_API_KEY: str = os.getenv("SEARCH_API_KEY", "")
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SEARCH_BASE_URL: str = os.getenv("SEARCH_BASE_URL", "https://api.tavily.com")
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# 研究参数
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MAX_SUBQUERIES: int = int(os.getenv("RESEARCH_MAX_SUBQUERIES", "4"))
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MAX_SOURCES: int = int(os.getenv("RESEARCH_MAX_SOURCES", "6"))
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@classmethod
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def effective_llm(cls):
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"""根据 LLM_PROVIDER 解析实际使用的 (base_url, api_key, model)。"""
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if cls.LLM_PROVIDER == "local" and cls.LOCAL_LLM_BASE_URL:
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return (cls.LOCAL_LLM_BASE_URL, cls.LOCAL_LLM_API_KEY,
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cls.LOCAL_LLM_MODEL or cls.OPENAI_MODEL)
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return (cls.OPENAI_BASE_URL, cls.OPENAI_API_KEY, cls.OPENAI_MODEL)
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config = Config()
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