root
151 lines
5.0 KiB
Python
151 lines
5.0 KiB
Python
"""AI-сервис для матчинга фрилансеров и проектов."""
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import logging
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from typing import Any
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import openai
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.config import settings
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from app.models.project import Project
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from app.models.user import FreelancerProfile
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logger = logging.getLogger(__name__)
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async def get_embedding(text: str) -> list[float]:
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"""Получить эмбеддинг через OpenAI."""
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client = openai.OpenAI(api_key=settings.OPENAI_API_KEY)
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response = await client.embeddings.create(
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model=settings.EMBEDDING_MODEL,
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input=text,
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)
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return response.data[0].embedding
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async def calculate_match_score(project: Project, freelancer: FreelancerProfile) -> dict[str, Any]:
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"""Рассчитать score совпадения проекта и фрилансера."""
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# 1. Semantic similarity (если есть эмбеддинги)
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skill_similarity = _cosine_similarity(
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project.skill_embeddings or [],
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freelancer.skill_embeddings or []
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) if project.skill_embeddings and freelancer.skill_embeddings else 0.5
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# 2. Опыт
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exp_score = min(freelancer.experience_years / max(project.required_skills.__len__() * 3, 3), 1.0) \
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if freelancer.experience_years else 0.5
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# 3. Рейтинг
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rating_weight = (freelancer.rating - 4.0) * 0.5 + 0.5
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# 4. Время ответа
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response_score = max(0, 1 - (freelancer.response_time_hours or 24) / 24)
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# 5. Совпадение навыков
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skills_match = len(set(project.required_skills) & set(freelancer.skills)) / \
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max(len(project.required_skills), 1) if project.required_skills else 0
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# Взвешенная сумма
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score = (
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skill_similarity * 0.35 +
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exp_score * 0.20 +
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rating_weight * 0.15 +
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response_score * 0.10 +
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skills_match * 0.20
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)
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# Генерация причин через LLM
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reasons = await _generate_reasons(project, freelancer)
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return {
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"match_score": round(score, 4),
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"reasons": reasons,
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}
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async def find_matches(
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db: AsyncSession, project_id: str, limit: int = 10, min_score: float | None = None
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) -> list[dict[str, Any]]:
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"""Найти лучших фрилансеров для проекта."""
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# Получаем проект
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from app.models.ai_match import AIMatch
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# Проверяем кэш в Redis
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import redis.asyncio as aioredis
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r = aioredis.from_url(settings.REDIS_URL)
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cache_key = f"ai_matches:{project_id}"
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cached = await r.get(cache_key)
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if cached:
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return __import__("json").loads(cached)
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# Запрос к БД (упрощённый — в продакшене нужен pgvector query)
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from sqlalchemy import select
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stmt = select(FreelancerProfile).order_by(
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FreelancerProfile.rating.desc()
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).limit(limit * 2)
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result = await db.execute(stmt)
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freelancers = result.scalars().all()
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matches = []
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for freelancer in freelancers:
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match_data = await calculate_match_score(project, freelancer)
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if min_score and match_data["match_score"] < min_score:
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continue
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matches.append({
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"freelancer_id": str(freelancer.user_id),
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"name": freelancer.user.full_name or "Аноним",
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"skills_matched": list(set(project.required_skills) & set(freelancer.skills)),
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**match_data,
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})
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matches.sort(key=lambda x: x["match_score"], reverse=True)
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top_matches = matches[:limit]
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# Сохраняем в Redis на 1 час
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await r.setex(cache_key, 3600, __import__("json").dumps(top_matches))
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return top_matches
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def _cosine_similarity(a: list[float], b: list[float]) -> float:
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"""Косинусное сходство двух векторов."""
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if not a or not b:
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return 0.5
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dot = sum(x * y for x, y in zip(a, b))
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norm_a = (sum(x ** 2 for x in a) ** 0.5)
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norm_b = (sum(y ** 2 for y in b) ** 0.5)
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if norm_a == 0 or norm_b == 0:
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return 0.5
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return dot / (norm_a * norm_b)
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async def _generate_reasons(project: Project, freelancer: FreelancerProfile) -> list[str]:
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"""Генерация причин совпадения через LLM."""
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reasons = []
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if freelancer.experience_years and freelancer.experience_years >= 3:
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reasons.append(f"Опыт {freelancer.experience_years} лет в разработке")
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matched_skills = set(project.required_skills) & set(freelancer.skills)
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if matched_skills:
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reasons.append(f"Совпадение навыков: {', '.join(list(matched_skills)[:3])}")
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if freelancer.rating >= 4.5:
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reasons.append("Высокий рейтинг")
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if not reasons:
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reasons.append("Профиль соответствует требованиям проекта")
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return reasons
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