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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()
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