Модуль оптимизации ставок

import os import json import numpy as np from openai import OpenAI OPENAI_KEY = os.getenv("OPENAI_API_KEY", "your_openai_key") openai_client = OpenAI(api_key=OPENAI_KEY) HISTORY_FILE = "bid_history.json" class SelfLearningBidOptimizer: def __init__(self): self.history = self._load_history() def _load_history(self) -> list: """Загружает историю успешных и неуспешных ставок (локальная память)""" if os.path.exists(HISTORY_FILE): try: with open(HISTORY_FILE, "r", encoding="utf-8") as f: return json.load(f) except Exception: return [] return [] def save_deal_outcome(self, job_id: str, bid_data: dict, is_won: bool): """Сохраняет исход сделки в память для дальнейшего самообучения ИИ""" record = { "job_id": job_id, "proposed_price": bid_data["proposed_price"], "proposed_days": bid_data["proposed_days"], "proposal_text": bid_data["proposal_text"], "is_won": is_won } self.history.append(record) # Храним только последние 100 сделок, чтобы не перегружать контекст LLM if len(self.history) > 100: self.history.pop(0) with open(HISTORY_FILE, "w", encoding="utf-8") as f: json.dump(self.history, f, indent=2, ensure_ascii=False) print(f"[🧠 Memory Updated] Recorded {'WIN' if is_won else 'LOSS'} for job {job_id}.") def _get_calibration_examples(self) -> str: """Формирует текстовый блок Few-Shot примеров для обучения ИИ на его ошибках""" if not self.history: return "No historical data yet. Rely on general bidding best-practices." examples_text = "Here is the historical record of your previous bids and their outcomes:\n" for i, record in enumerate(self.history[-10:]): # Берем последние 10 для контекста status = "SUCCESS (Won the job)" if record["is_won"] else "FAILED (Rejected)" examples_text += ( f"Example {i+1}:\n" f"- Proposed Price: {record['proposed_price']} USDC\n" f"- Delivery: {record['proposed_days']} days\n" f"- Proposal Letter: \"{record['proposal_text']}\"\n" f"- Outcome: {status}\n" f"-------------------\n" ) return examples_text def optimize_and_generate_bid(self, job_data: dict) -> dict: """Анализирует задачу, обучается на прошлых сделках и генерирует идеальный отклик""" print(f"[📊 Optimizer] Analyzing job {job_data['id']} to hit 50%+ win-rate target...") market_budget = job_data.get("budget", 100) job_spec = job_data.get("spec", {}) # Шаг 1: Извлекаем опыт из памяти experience_context = self._get_calibration_examples() # Шаг 2: Жесткая JSON-схема для предсказания оптимальных параметров ставки json_schema = { "name": "optimized_bid_structure", "strict": True, "schema": { "type": "object", "properties": { "proposed_price": { "type": "number", "description": "Optimized bid amount in USDC. Slightly undercut or match market based on complexity." }, "proposed_days": { "type": "integer", "description": "Optimized delivery time in days. Speed increases win-rate." }, "proposal_text": { "type": "string", "description": "A laser-focused, custom proposal letter. Address the acceptance criteria directly. No fluff." }, "win_probability_estimation": { "type": "number", "description": "Your internal confidence score (0.0 to 1.0) that this bid will hit the 50% target." } }, "required": ["proposed_price", "proposed_days", "proposal_text", "win_probability_estimation"], "additionalProperties": False } } system_prompt = ( "You are a Self-Optimizing Game-Theoretic Media Buyer on MoltJobs. Your KPI is a 50% win-rate. " "Analyze the client's job criteria, look at your historical success/failure examples, " "and calibrate your price, timeline, and pitch to win. Be highly specific about the technical implementation." ) user_prompt = f""" {experience_context} Target Job to Bid On: - Job ID: {job_data['id']} - Client Max Budget: {market_budget} USDC - Desired Outcome: {job_spec.get('outcome')} - Acceptance Criteria: {job_spec.get('acceptance_criteria')} """ # Запуск LLM-оптимизатора response = openai_client.chat.completions.create( model="gpt-4o", messages=[ {"role": "system", "content": system_prompt}, {"role": "user", "content": user_prompt} ], response_format={"type": "json_schema", "json_schema": json_schema}, temperature=0.3 # Небольшая креативность для текста, но строгость в цифрах ) optimized_bid = json.loads(response.choices.message.content) print(f"[🎯 Bid Calibrated] Price: {optimized_bid['proposed_price']} USDC | Est. Win Rate: {optimized_bid['win_probability_estimation']*100}%") return optimized_bid