generated from kgod/ai-review-template
feat: initialize resume agent with OfferPai sync
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Codex
@@ -0,0 +1,227 @@
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from __future__ import annotations
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import os
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from dataclasses import dataclass, field
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from pathlib import Path
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from dotenv import load_dotenv
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BACKEND_ROOT = Path(__file__).resolve().parents[1]
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DEFAULT_ENV_FILE = BACKEND_ROOT / ".env"
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def _as_bool(value: str | None, default: bool) -> bool:
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if value is None:
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return default
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normalized = value.strip().lower()
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if normalized in {"1", "true", "yes", "on"}:
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return True
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if normalized in {"0", "false", "no", "off"}:
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return False
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raise ValueError(f"Invalid boolean configuration value: {value!r}")
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def _as_int(name: str, value: str | None, default: int) -> int:
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if value is None:
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return default
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parsed = int(value)
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if parsed < 0:
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raise ValueError(f"{name} must be non-negative")
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return parsed
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def _as_float(name: str, value: str | None, default: float) -> float:
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if value is None:
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return default
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parsed = float(value)
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if parsed <= 0:
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raise ValueError(f"{name} must be positive")
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return parsed
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@dataclass(frozen=True, slots=True)
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class Settings:
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"""Runtime settings with the API key deliberately hidden from repr output."""
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llm_provider: str = "auto"
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openai_api_key: str | None = field(default=None, repr=False)
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openai_base_url: str | None = None
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openai_model: str = "gpt-4o-mini"
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embedding_provider: str = "tei"
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embedding_base_url: str = "http://127.0.0.1:8081"
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embedding_model: str = "BAAI/bge-m3"
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embedding_dimensions: int = 1024
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embedding_timeout_seconds: float = 30.0
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embedding_batch_size: int = 32
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openai_timeout_seconds: float = 30.0
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openai_max_retries: int = 2
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light_opt_rate_limit: int = 20
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light_opt_rate_window_seconds: float = 3600.0
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structured_output_retries: int = 1
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structured_output_mode: str = "json_schema"
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fallback_to_rules: bool = True
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intent_router_mode: str = "off"
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intent_model: str | None = None
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knowledge_admin_token: str | None = field(default=None, repr=False)
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database_url: str | None = field(default=None, repr=False)
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offerpai_auth_base_url: str = "https://test.offerpai.com.cn"
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offerpai_auth_timeout_seconds: float = 8.0
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offerpai_auth_required: bool = True
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offerpai_resume_api_base_url: str = "https://test.offerpai.com.cn/api"
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offerpai_resume_timeout_seconds: float = 8.0
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deep_max_questions: int = 6
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deep_min_questions: int = 2
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deep_gap_threshold: float = 5.0
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@property
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def use_openai(self) -> bool:
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if self.llm_provider == "openai":
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if not self.openai_api_key:
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raise ValueError("OPENAI_API_KEY is required when LLM provider is openai")
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return True
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if self.llm_provider == "volcengine":
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if not self.openai_api_key:
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raise ValueError(
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"VOLCENGINE_API_KEY is required when LLM provider is volcengine"
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)
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return True
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if self.llm_provider == "rule":
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return False
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if self.llm_provider != "auto":
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raise ValueError(
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"RESUME_AGENT_LLM_PROVIDER must be auto, openai, volcengine, or rule"
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)
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return bool(self.openai_api_key)
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def load_settings(env_file: str | Path | None = None) -> Settings:
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selected_file = Path(
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env_file or os.getenv("RESUME_AGENT_ENV_FILE", str(DEFAULT_ENV_FILE))
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)
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load_dotenv(selected_file, override=False)
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mode = os.getenv("OPENAI_STRUCTURED_OUTPUT_MODE", "json_schema").strip().lower()
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if mode not in {"json_schema", "json_object"}:
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raise ValueError(
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"OPENAI_STRUCTURED_OUTPUT_MODE must be json_schema or json_object"
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)
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provider = os.getenv("RESUME_AGENT_LLM_PROVIDER", "auto").strip().lower()
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if provider == "volcengine":
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llm_api_key = os.getenv("VOLCENGINE_API_KEY") or None
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llm_base_url = os.getenv("VOLCENGINE_BASE_URL") or None
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llm_model = os.getenv("VOLCENGINE_MODEL", "").strip()
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else:
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llm_api_key = os.getenv("OPENAI_API_KEY") or None
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llm_base_url = os.getenv("OPENAI_BASE_URL") or None
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llm_model = os.getenv("OPENAI_MODEL", "gpt-4o-mini").strip()
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intent_mode = os.getenv("RESUME_AGENT_INTENT_ROUTER_MODE", "off").strip().lower()
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if intent_mode not in {"off", "shadow", "on"}:
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raise ValueError("RESUME_AGENT_INTENT_ROUTER_MODE must be off, shadow, or on")
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settings = Settings(
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llm_provider=provider,
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openai_api_key=llm_api_key,
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openai_base_url=llm_base_url,
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openai_model=llm_model,
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embedding_provider=os.getenv("EMBEDDING_PROVIDER", "tei").strip().lower(),
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embedding_base_url=os.getenv("EMBEDDING_BASE_URL", "http://127.0.0.1:8081").rstrip("/"),
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embedding_model=os.getenv("EMBEDDING_MODEL", "BAAI/bge-m3").strip(),
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embedding_dimensions=_as_int(
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"EMBEDDING_DIMENSIONS", os.getenv("EMBEDDING_DIMENSIONS"), 1024
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),
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embedding_timeout_seconds=_as_float(
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"EMBEDDING_TIMEOUT_SECONDS", os.getenv("EMBEDDING_TIMEOUT_SECONDS"), 30.0
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),
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embedding_batch_size=_as_int(
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"EMBEDDING_BATCH_SIZE", os.getenv("EMBEDDING_BATCH_SIZE"), 32
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),
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openai_timeout_seconds=_as_float(
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"OPENAI_TIMEOUT_SECONDS", os.getenv("OPENAI_TIMEOUT_SECONDS"), 30.0
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),
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openai_max_retries=_as_int(
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"OPENAI_MAX_RETRIES", os.getenv("OPENAI_MAX_RETRIES"), 2
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),
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light_opt_rate_limit=_as_int(
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"RESUME_AGENT_LIGHT_OPT_RATE_LIMIT",
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os.getenv("RESUME_AGENT_LIGHT_OPT_RATE_LIMIT"),
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20,
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),
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light_opt_rate_window_seconds=_as_float(
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"RESUME_AGENT_LIGHT_OPT_RATE_WINDOW_SECONDS",
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os.getenv("RESUME_AGENT_LIGHT_OPT_RATE_WINDOW_SECONDS"),
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3600.0,
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),
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structured_output_retries=_as_int(
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"OPENAI_STRUCTURED_OUTPUT_RETRIES",
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os.getenv("OPENAI_STRUCTURED_OUTPUT_RETRIES"),
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1,
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),
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structured_output_mode=mode,
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fallback_to_rules=_as_bool(
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os.getenv("RESUME_AGENT_LLM_FALLBACK_TO_RULES"), True
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),
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intent_router_mode=intent_mode,
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intent_model=os.getenv("RESUME_AGENT_INTENT_MODEL", "").strip() or None,
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knowledge_admin_token=os.getenv("KNOWLEDGE_ADMIN_TOKEN") or None,
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database_url=os.getenv("DATABASE_URL") or None,
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offerpai_auth_base_url=os.getenv(
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"OFFERPAI_AUTH_BASE_URL", "https://test.offerpai.com.cn"
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).rstrip("/"),
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offerpai_auth_timeout_seconds=_as_float(
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"OFFERPAI_AUTH_TIMEOUT_SECONDS",
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os.getenv("OFFERPAI_AUTH_TIMEOUT_SECONDS"),
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8.0,
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),
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offerpai_auth_required=_as_bool(
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os.getenv("OFFERPAI_AUTH_REQUIRED"), True
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),
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offerpai_resume_api_base_url=os.getenv(
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"OFFERPAI_RESUME_API_BASE_URL", "https://test.offerpai.com.cn/api"
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).rstrip("/"),
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offerpai_resume_timeout_seconds=_as_float(
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"OFFERPAI_RESUME_TIMEOUT_SECONDS",
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os.getenv("OFFERPAI_RESUME_TIMEOUT_SECONDS"),
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8.0,
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),
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deep_max_questions=_as_int(
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"RESUME_AGENT_DEEP_MAX_QUESTIONS",
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os.getenv("RESUME_AGENT_DEEP_MAX_QUESTIONS"),
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6,
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),
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deep_min_questions=_as_int(
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"RESUME_AGENT_DEEP_MIN_QUESTIONS",
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os.getenv("RESUME_AGENT_DEEP_MIN_QUESTIONS"),
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2,
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),
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deep_gap_threshold=_as_float(
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"RESUME_AGENT_DEEP_GAP_THRESHOLD",
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os.getenv("RESUME_AGENT_DEEP_GAP_THRESHOLD"),
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5.0,
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),
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)
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if settings.embedding_provider not in {"tei", "openai", "hash"}:
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raise ValueError("EMBEDDING_PROVIDER must be tei, openai, or hash")
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if not settings.embedding_model:
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raise ValueError("EMBEDDING_MODEL cannot be blank")
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if settings.embedding_provider == "tei" and not settings.embedding_base_url:
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raise ValueError("EMBEDDING_BASE_URL cannot be blank when EMBEDDING_PROVIDER is tei")
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if settings.embedding_dimensions < 1:
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raise ValueError("EMBEDDING_DIMENSIONS must be positive")
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if settings.embedding_batch_size < 1:
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raise ValueError("EMBEDDING_BATCH_SIZE must be positive")
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if not settings.offerpai_auth_base_url.startswith(("http://", "https://")):
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raise ValueError("OFFERPAI_AUTH_BASE_URL must start with http:// or https://")
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if not settings.offerpai_resume_api_base_url.startswith(("http://", "https://")):
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raise ValueError(
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"OFFERPAI_RESUME_API_BASE_URL must start with http:// or https://"
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)
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if settings.deep_max_questions < settings.deep_min_questions:
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raise ValueError(
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"RESUME_AGENT_DEEP_MAX_QUESTIONS must be at least "
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"RESUME_AGENT_DEEP_MIN_QUESTIONS"
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)
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if settings.deep_max_questions < 1:
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raise ValueError("RESUME_AGENT_DEEP_MAX_QUESTIONS must be positive")
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if not settings.openai_model:
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model_setting = "VOLCENGINE_MODEL" if provider == "volcengine" else "OPENAI_MODEL"
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raise ValueError(f"{model_setting} cannot be blank")
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return settings
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