c6c15defee
- Add estimate_tokens() and estimate_max_tokens() to llm_client with stage-specific output ratios (0.3x segmentation, 1.2x extraction, 0.15x classification, 1.5x synthesis) - Add max_tokens override parameter to LLMClient.complete() - Wire all 4 pipeline stages to estimate max_tokens from actual prompt content with 20% buffer and 2048 floor - Add LLM_MAX_TOKENS_HARD_LIMIT=32768 config (dynamic estimator ceiling) - Log token estimates alongside every LLM request Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
79 lines
2.6 KiB
Python
79 lines
2.6 KiB
Python
"""Application configuration loaded from environment variables."""
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from functools import lru_cache
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from pydantic_settings import BaseSettings
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class Settings(BaseSettings):
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"""Chrysopedia API settings.
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Values are loaded from environment variables (or .env file via
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pydantic-settings' dotenv support).
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"""
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# Database
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database_url: str = "postgresql+asyncpg://chrysopedia:changeme@localhost:5433/chrysopedia"
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# Redis
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redis_url: str = "redis://localhost:6379/0"
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# Application
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app_env: str = "development"
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app_log_level: str = "info"
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app_secret_key: str = "changeme-generate-a-real-secret"
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# CORS
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cors_origins: list[str] = ["*"]
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# LLM endpoint (OpenAI-compatible)
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llm_api_url: str = "http://localhost:11434/v1"
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llm_api_key: str = "sk-placeholder"
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llm_model: str = "fyn-llm-agent-chat"
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llm_fallback_url: str = "http://localhost:11434/v1"
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llm_fallback_model: str = "fyn-llm-agent-chat"
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# Per-stage model overrides (optional — falls back to llm_model / "chat")
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llm_stage2_model: str | None = "fyn-llm-agent-chat" # segmentation — mechanical, fast chat
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llm_stage2_modality: str = "chat"
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llm_stage3_model: str | None = "fyn-llm-agent-think" # extraction — reasoning
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llm_stage3_modality: str = "thinking"
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llm_stage4_model: str | None = "fyn-llm-agent-chat" # classification — mechanical, fast chat
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llm_stage4_modality: str = "chat"
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llm_stage5_model: str | None = "fyn-llm-agent-think" # synthesis — reasoning
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llm_stage5_modality: str = "thinking"
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# Dynamic token estimation — each stage calculates max_tokens from input size
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llm_max_tokens_hard_limit: int = 32768 # Hard ceiling for dynamic estimator
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llm_max_tokens: int = 65536 # Fallback when no estimate is provided
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# Embedding endpoint
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embedding_api_url: str = "http://localhost:11434/v1"
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embedding_model: str = "nomic-embed-text"
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embedding_dimensions: int = 768
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# Qdrant
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qdrant_url: str = "http://localhost:6333"
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qdrant_collection: str = "chrysopedia"
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# Prompt templates
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prompts_path: str = "./prompts"
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# Review mode — when True, extracted moments go to review queue before publishing
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review_mode: bool = True
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# File storage
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transcript_storage_path: str = "/data/transcripts"
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video_metadata_path: str = "/data/video_meta"
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model_config = {
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"env_file": ".env",
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"env_file_encoding": "utf-8",
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"case_sensitive": False,
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}
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@lru_cache
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def get_settings() -> Settings:
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"""Return cached application settings (singleton)."""
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return Settings()
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