Praxis CI
111 lines
4.1 KiB
Python
111 lines
4.1 KiB
Python
"""Cost logging — per-session cost derivation (REQ-NFR-COST-01, D-012, TASK-04-04).
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Counts LLM input/output tokens (gemma4 + deepseek-v4-flash), Deepgram audio
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minutes, Cartesia/Piper characters; derives an estimated cost in cents using
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cost_rates.yaml. No enforced ceiling (D-012 — pilot). The derived cost +
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breakdown are stored in sessions.cost_estimated_cents / cost_breakdown_json.
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v0.1 logged costs are NOT representative of at-scale per-learner cost (G-005):
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Ollama tier-based pricing + Canada cloud + low volume = the most expensive
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configuration. The $3/learner target requires self-hosted gemma4:e4b + Piper
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(post-pilot). The logging infrastructure is the v0.1 contribution.
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from pathlib import Path
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from typing import Any
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import yaml
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_DEFAULT_RATES_PATH = Path(__file__).resolve().parent.parent / "scenarios" / "cost_rates.yaml"
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@dataclass
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class CostBreakdown:
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"""Per-session cost inputs + derived cents."""
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llm_input_tokens: int = 0
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llm_output_tokens: int = 0
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deepgram_audio_minutes: float = 0.0
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tts_characters: int = 0
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debrief_input_tokens: int = 0
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debrief_output_tokens: int = 0
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rates: dict[str, float] = field(default_factory=dict)
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derived_cents: int = 0
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def as_dict(self) -> dict[str, Any]:
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return {
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"llm_input_tokens": self.llm_input_tokens,
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"llm_output_tokens": self.llm_output_tokens,
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"deepgram_audio_minutes": round(self.deepgram_audio_minutes, 3),
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"tts_characters": self.tts_characters,
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"debrief_input_tokens": self.debrief_input_tokens,
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"debrief_output_tokens": self.debrief_output_tokens,
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"rates": self.rates,
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"derived_cents": self.derived_cents,
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}
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def load_rates(path: Path | None = None) -> dict[str, float]:
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"""Load cost rates from cost_rates.yaml (or defaults if absent)."""
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p = path or _DEFAULT_RATES_PATH
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if p.exists():
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with p.open("r", encoding="utf-8") as f:
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return yaml.safe_load(f) or {}
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# Defaults — vendor-list prices, per-unit (pilot estimates, G-005).
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return {
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"gemma4_cloud_per_1k_tokens_cents": 0.5, # Ollama tier (pro plan amortized)
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"deepseek_v4_flash_per_1k_tokens_cents": 1.0, # Ollama tier
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"deepgram_per_audio_minute_cents": 0.43, # $0.0043/min
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"cartesia_per_1k_chars_cents": 3.0, # per-char pricing
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"piper_per_1k_chars_cents": 0.0, # self-hosted, $0
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}
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def derive_cost(
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llm_input_tokens: int = 0,
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llm_output_tokens: int = 0,
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deepgram_audio_minutes: float = 0.0,
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tts_characters: int = 0,
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debrief_input_tokens: int = 0,
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debrief_output_tokens: int = 0,
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tts_provider: str = "cartesia",
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rates: dict[str, float] | None = None,
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) -> CostBreakdown:
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"""Derive the per-session cost in cents from the usage inputs + rates."""
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r = rates or load_rates()
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# LLM role-play (gemma4:cloud).
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rp_tokens = llm_input_tokens + llm_output_tokens
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rp_cents = (rp_tokens / 1000.0) * r.get("gemma4_cloud_per_1k_tokens_cents", 0.5)
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# Debrief (deepseek-v4-flash:cloud).
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db_tokens = debrief_input_tokens + debrief_output_tokens
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db_cents = (db_tokens / 1000.0) * r.get("deepseek_v4_flash_per_1k_tokens_cents", 1.0)
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# ASR (Deepgram).
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asr_cents = deepgram_audio_minutes * r.get("deepgram_per_audio_minute_cents", 0.43)
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# TTS (Cartesia or Piper).
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tts_rate_key = (
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"piper_per_1k_chars_cents" if tts_provider == "piper"
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else "cartesia_per_1k_chars_cents"
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)
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tts_cents = (tts_characters / 1000.0) * r.get(tts_rate_key, 3.0)
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total = int(round(rp_cents + db_cents + asr_cents + tts_cents))
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return CostBreakdown(
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llm_input_tokens=llm_input_tokens,
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llm_output_tokens=llm_output_tokens,
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deepgram_audio_minutes=deepgram_audio_minutes,
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tts_characters=tts_characters,
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debrief_input_tokens=debrief_input_tokens,
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debrief_output_tokens=debrief_output_tokens,
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rates=r,
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derived_cents=total,
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)
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__all__ = ["CostBreakdown", "derive_cost", "load_rates"] |