cryptorugmunch
47ba268131
Adds missing standard artifacts: - README.md (if missing) - AGENTS.md (AI agent contract) - PLAN.md (current sprint) - STATUS.md (where we are) - DEVELOPMENT.md (dev workflow) - DEPLOYMENT.md (deploy procedure) - TESTING.md (test strategy) - DECISIONS.md (ADR index + templates) - .github/CODEOWNERS - .github/workflows/ci.yml Preserves all existing artifacts. Refs: RugMunchMedia/fleet-template
66 lines
2.2 KiB
Python
66 lines
2.2 KiB
Python
"""Pry — LLM Provider abstraction with referral revenue tracking.
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Supports pluggable providers: OpenAI, Anthropic, Google, Cohere, Mistral, Ollama, OpenRouter.
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Includes referral/affiliate link tracking for revenue sharing."""
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import logging
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from abc import ABC, abstractmethod
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from dataclasses import dataclass, field
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from typing import Any
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logger = logging.getLogger(__name__)
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@dataclass
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class LLMResponse:
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"""Standard response from any LLM provider."""
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text: str
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model: str
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provider: str
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input_tokens: int = 0
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output_tokens: int = 0
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cost_usd: float = 0.0
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referral_id: str = ""
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latency_ms: int = 0
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raw: dict[str, Any] = field(default_factory=dict)
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@dataclass
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class ReferralConfig:
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"""Referral/affiliate config for revenue sharing."""
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enabled: bool = True
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program_id: str = "pry-default"
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# Provider-specific referral links (with our affiliate ID)
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referral_links: dict[str, str] = field(default_factory=dict)
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# NEW: link to the full provider catalog
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catalog: dict = field(default_factory=dict)
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def __post_init__(self):
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if not self.referral_links:
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from referrals import PROVIDER_CATALOG
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for _category, providers in PROVIDER_CATALOG.items():
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for p in providers:
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self.referral_links[p["tag"]] = p["url"]
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self.catalog = PROVIDER_CATALOG
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class LLMProvider(ABC):
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"""Abstract base class for LLM providers."""
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name: str = ""
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cost_per_1k_input: float = 0.0
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cost_per_1k_output: float = 0.0
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referral_url: str = ""
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@abstractmethod
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async def complete(self, prompt: str, system: str = "", max_tokens: int = 1024,
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temperature: float = 0.7, model: str = "") -> LLMResponse:
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"""Send completion request to provider."""
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raise NotImplementedError
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@abstractmethod
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async def embed(self, text: str, model: str = "") -> list[float]:
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"""Generate embedding for text."""
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raise NotImplementedError
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def estimate_cost(self, input_tokens: int, output_tokens: int) -> float:
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return (input_tokens / 1000) * self.cost_per_1k_input + (output_tokens / 1000) * self.cost_per_1k_output
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