bb77eb5f35
Re-license Pry from full Proprietary to a dual-license model: - Core engine, extraction, templates (80+), MCP server, x402 payment rail, CLI, SDK, browser extension, WordPress plugin, Shopify app, and llm_providers: MIT (see LICENSE) - Anti-detection / stealth subset (15 files): BSL 1.1 with Change Date 2029-01-01 (see LICENSE-BSL-STEALTH) BSL files (anti-detection moat): ultimate_scraper.py, stealth_engine.py, stealth_scripts/*.js (6), camoufox_integration.py, tls_fingerprint.py, cookie_warmer.py, behavioral_biometrics.py, adaptive.py, browser_pool.py, network.py, captcha_solver.py, shadow_dom.py, lazy_load.py, signup_automator.py, auth_connector.py This enables community contributions to the core engine (templates, integrations, MCP tools) while protecting the anti-detection techniques that constitute the actual competitive moat. BSL Additional Use Grant permits free non-production use; production deployment requires a commercial license from enterprise@rugmunch.io. Changes: - Replace proprietary LICENSE with MIT LICENSE + new LICENSE-BSL-STEALTH - Add SPDX-License-Identifier headers to 300+ source files - Add docs/adr/0002-dual-licensing.md (ADR documenting the decision) - Update README.md: new License section with BSL Additional Use Grant - Update LICENSING_PRICING_STRATEGY.md: Section 3 (PryScraper) for dual license - Update AGENTS.md: license line in header + new rule 8 (PRs touching BSL rejected) - Update pyproject.toml: license = "MIT AND BSL-1.1" + classifiers + license-files - Update DECISIONS.md index with ADR-0002 - Update STATUS.md (2026-07-03) and PLAN.md sprint goals Refs: ADR-0002
72 lines
2.3 KiB
Python
72 lines
2.3 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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# SPDX-License-Identifier: MIT
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# Copyright (c) 2026 Rug Munch Media LLC
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#
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# Part of Pry — https://git.rugmunch.io/RugMunchMedia/pryscraper
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# Licensed under MIT. See LICENSE.
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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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