src/config.py

"""
VentureForge Configuration
==========================
Pydantic v2 settings loaded from environment variables.
Single LLM_BASE_URL switches between OpenAI/OpenRouter/AMD vLLM.

Usage:
    from src.config import settings
    print(settings.llm_base_url)
"""

from functools import lru_cache

from pydantic import Field, field_validator
from pydantic_settings import BaseSettings, SettingsConfigDict


class Settings(BaseSettings):
    """All application settings loaded from .env or environment."""

    model_config = SettingsConfigDict(
        env_file=".env",
        env_file_encoding="utf-8",
        extra="ignore",
    )

    # ------------------------------------------------------------------
    # LLM Provider — REASONING tier (scorer, critic)
    # ------------------------------------------------------------------
    llm_base_url: str = Field(
        default="https://api.openai.com/v1",
        description="OpenAI-compatible API base URL for reasoning tasks",
    )
    llm_api_key: str = Field(
        default="",
        description="API key for the reasoning LLM provider",
    )
    llm_model: str = Field(
        default="gpt-4o-mini",
        description="Model name for reasoning tasks (scorer, critic)",
    )

    # ------------------------------------------------------------------
    # LLM Provider — FAST tier (pain_point_miner, idea_generator, pitch_writer)
    # Falls back to reasoning tier if unset.
    # ------------------------------------------------------------------
    fast_llm_base_url: str | None = Field(
        default=None,
        description="Base URL for fast/non-reasoning LLM. Defaults to llm_base_url.",
    )
    fast_llm_api_key: str | None = Field(
        default=None,
        description="API key for fast LLM. Defaults to llm_api_key.",
    )
    fast_llm_model: str | None = Field(
        default=None,
        description="Model name for fast tasks. Defaults to llm_model.",
    )

    # Optional: separate OpenRouter config (falls back to llm_* if unset)
    openrouter_api_key: str | None = Field(default=None)
    openrouter_base_url: str = Field(default="https://openrouter.ai/api/v1")

    # ------------------------------------------------------------------
    # LLM Generation Parameters (per-agent overrides possible)
    # ------------------------------------------------------------------
    default_temperature: float = Field(default=0.2, ge=0.0, le=2.0)
    max_tokens: int = Field(default=4096, ge=1)
    request_timeout: int = Field(default=120, ge=1)

    # ------------------------------------------------------------------
    # Reddit — NOT required.  We use public `.json` endpoints (no PRAW).
    # Only set these if you later want PRAW features.
    # ------------------------------------------------------------------
    reddit_client_id: str | None = Field(default=None)
    reddit_client_secret: str | None = Field(default=None)
    reddit_user_agent: str = Field(
        default="ventureforge:v1.0 by u/username",
        description="Optional PRAW user agent string",
    )

    # ------------------------------------------------------------------
    # Tavily — used for community-discovery fallback
    # ------------------------------------------------------------------
    tavily_api_key: str | None = Field(default=None)

    # ------------------------------------------------------------------
    # Product Hunt — for scraping product comments and feedback
    # Get key at: https://api.producthunt.com/v1/oauth/authorize
    # ------------------------------------------------------------------
    product_hunt_api_key: str | None = Field(default=None)

    # ------------------------------------------------------------------
    # YouTube Data API v3 — for scraping video comments
    # Get key at: https://console.cloud.google.com/apis/credentials
    # Free quota: 10,000 units/day
    # ------------------------------------------------------------------
    youtube_api_key: str | None = Field(default=None)

    # ------------------------------------------------------------------
    # HuggingFace (for AMD vLLM model download)
    # ------------------------------------------------------------------
    hf_token: str | None = Field(default=None)

    # ------------------------------------------------------------------
    # Pipeline Defaults
    # ------------------------------------------------------------------
    max_pain_points: int = Field(default=30, ge=5, le=100)
    # TEMPORARY: Reduced from 5 to 2 due to vLLM server max_tokens limit (~2048)
    # The server truncates responses at ~1600 tokens, so we generate fewer ideas per call
    # TODO: Increase back to 5 once server --max-tokens is increased to 16384
    ideas_per_run: int = Field(default=2, ge=1, le=20)
    top_n_pitches: int = Field(default=3, ge=1, le=10)
    max_revisions: int = Field(default=2, ge=0, le=5)
    lookback_days: int = Field(default=90, ge=7, le=365)

    # ------------------------------------------------------------------
    # Caching
    # ------------------------------------------------------------------
    cache_dir: str = Field(default=".cache")
    cache_ttl_hours: int = Field(default=24, ge=1)

    # ------------------------------------------------------------------
    # UI
    # ------------------------------------------------------------------
    gradio_port: int = Field(default=7860, ge=1024, le=65535)
    gradio_host: str = Field(default="0.0.0.0")

    # ------------------------------------------------------------------
    # Validators
    # ------------------------------------------------------------------
    @field_validator("reddit_user_agent", mode="after")
    @classmethod
    def _warn_default_agent(cls, v: str) -> str:
        if "username" in v:
            # Allow it but it's clearly a placeholder
            pass
        return v

    @property
    def tavily_enabled(self) -> bool:
        return bool(self.tavily_api_key)

    @property
    def product_hunt_enabled(self) -> bool:
        return bool(self.product_hunt_api_key)

    @property
    def effective_llm_config(self) -> dict:
        """Return the active LLM configuration as a dict."""
        return {
            "base_url": self.llm_base_url,
            "api_key": self.llm_api_key,
            "model": self.llm_model,
            "timeout": self.request_timeout,
        }

    def get_llm_config(self, *, reasoning: bool = False) -> dict:
        """Return LLM config for a given tier.

        reasoning=True  → scorer, critic (large model)
        reasoning=False → pain_point_miner, idea_generator, pitch_writer (fast model)
        """
        if reasoning:
            return self.effective_llm_config
        return {
            "base_url": self.fast_llm_base_url or self.llm_base_url,
            "api_key": self.fast_llm_api_key or self.llm_api_key,
            "model": self.fast_llm_model or self.llm_model,
            "timeout": self.request_timeout,
        }


@lru_cache
def get_settings() -> Settings:
    """Return cached settings instance."""
    return Settings()


settings = get_settings()