209 lines
7.8 KiB
Python
209 lines
7.8 KiB
Python
from qdrant_client import models
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from langchain_openai import OpenAIEmbeddings
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from langchain_openai import ChatOpenAI
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from qdrant_client import QdrantClient
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from qdrant_client.models import Filter
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from vacancies.main.models import VacancyFeatures
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from vacancies.conf.settings import QDRANT_URL
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from vacancies.main.models import CustomerCV, RecommendedVacancy
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from qdrant_client.models import Filter, HasIdCondition
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qdrant_client = QdrantClient(url=QDRANT_URL)
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FEATURE_NAMES = [
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"job_title", "employment_type", "work_format", "experience", "position_level", "industry", "tech_stack",
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"location", "salary_range", "languages", "education", "schedule", "additional_requirements"
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]
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weights = {
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"job_title": 25,
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"employment_type": 5,
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"work_format": 5,
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"experience": 8,
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"position_level": 12,
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"industry": 10,
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"tech_stack": 14,
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"location": 5,
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"salary_range": 5,
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"languages": 5,
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"education": 2,
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"schedule": 2,
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"additional_requirements": 2,
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}
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vectors_config = {
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name: models.VectorParams(size=3072, distance=models.Distance.COSINE) for name in FEATURE_NAMES
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}
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if not qdrant_client.collection_exists("vacancies"):
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qdrant_client.create_collection(
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collection_name="vacancies",
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vectors_config=vectors_config
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)
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if not qdrant_client.collection_exists("cvs"):
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qdrant_client.create_collection(
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collection_name="cvs",
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vectors_config=vectors_config
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)
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embedding = OpenAIEmbeddings(model="text-embedding-3-large")
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def _prepare_texts(features):
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"""Prepare texts for each feature from features dict."""
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texts = {}
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for name in FEATURE_NAMES:
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value = features.get(name)
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if isinstance(value, list):
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text = " ".join(value) if value else ""
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else:
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text = str(value) if value else ""
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texts[name] = text
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return texts
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def add_vectors(collection_name: str, _id: int, features: dict, payload: dict):
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"""Add vectors for a vacancy based on its features."""
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texts = _prepare_texts(features)
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vectors = {}
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for name, text in texts.items():
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vectors[name] = [0.0] * 3072
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if text:
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vec = embedding.embed_query(text)
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vectors[name] = vec
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max_similarities = {}
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for name, vec in vectors.items():
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if any(v != 0 for v in vec):
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results = qdrant_client.query_points(
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collection_name="vacancies",
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query=vec,
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using=name,
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limit=100,
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)
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for res in results.points:
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vid = res.id
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sim = res.score
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if vid not in max_similarities:
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max_similarities[vid] = {}
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max_similarities[vid][name] = sim
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scored = []
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for vid, feature_sims in max_similarities.items():
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total = sum(feature_sims[feature] * weights.get(feature, 1) for feature in feature_sims)
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scored.append({"id": vid, "score": total})
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scored.sort(key=lambda x: x["score"], reverse=True)
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if scored and scored[0]["score"] > 98: # threshold
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return
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qdrant_client.upsert(
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collection_name=collection_name,
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points=[
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models.PointStruct(
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id=_id,
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vector=vectors,
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payload=payload,
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)
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]
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)
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def search_similarities(query_filter: Filter, cv_id: int):
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cv = qdrant_client.retrieve(
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collection_name="cvs",
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ids=[cv_id],
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with_vectors=True,
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)[0]
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max_similarities = {}
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vacancies_content = {}
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for name, vec in cv.vector.items():
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if any(v != 0 for v in vec):
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results = qdrant_client.query_points(
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collection_name="vacancies",
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query=vec,
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using=name,
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limit=100,
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with_payload=True,
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query_filter=query_filter,
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)
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for res in results.points:
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vid = res.id
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sim = res.score
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if vid not in max_similarities:
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max_similarities[vid] = {}
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max_similarities[vid][name] = sim
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if vid not in vacancies_content:
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vacancies_content[vid] = {}
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vacancies_content[vid]["content"] = res.payload["content"]
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vacancies_content[vid]["link"] = res.payload["link"]
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scored = []
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for vid, feature_sims in max_similarities.items():
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total = sum(feature_sims[feature] * weights.get(feature, 1) for feature in feature_sims)
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scored.append({"id": vid, "score": total, "content": vacancies_content[vid]["content"], "link": vacancies_content[vid]["link"]})
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scored.sort(key=lambda x: x["score"], reverse=True)
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prompt = f"""
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Резюме: {cv.payload['content']}
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Среди вакансий ниже выбери одну наиболее релевантную и выведи ее индекс(от 0 до 9).
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Иногда могут попадаться чужие резюме вместо вакансий, их отдавать нельзя.
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В ответе выведи только число. Если среди вакансий нет подходящих, то верни -1.
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{scored[:10]}
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"""
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openai_client = ChatOpenAI(model_name="gpt-5-mini", reasoning_effort="minimal", temperature=0, seed=42, top_p=1)
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response = openai_client.invoke(prompt)
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index = int(response.content)
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if index == -1:
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return None
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return scored[index]["id"], scored[index]["content"], scored[index]["link"]
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def extract_features(content: str) -> VacancyFeatures:
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prompt = f"""
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Extract the following features from the job vacancy description. If a feature is not mentioned, set it to null.
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Features:
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- job_title: Должность (e.g., DevOps, Python программист)
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- employment_type: Тип занятости (e.g., Полная занятость, Частичная)
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- work_format: Формат работы (e.g., Офис, Удалённо, Гибрид)
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- experience: Опыт работы (e.g., 3-5 лет, Нет опыта)
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- position_level: Уровень позиции (e.g., Junior, Senior)
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- industry: Отрасль / Сфера деятельности (e.g., IT, Финансы)
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- tech_stack: Технологический стек / Ключевые навыки (list of strings)
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- location: География (e.g., Москва, Россия)
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- salary_range: Зарплатные ожидания / вилка (e.g., 100000-200000 руб)
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- languages: Языки (list of strings, e.g., ["Русский", "Английский"])
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- education: Образование (e.g., Высшее, Среднее специальное)
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- schedule: График работы (e.g., Полный день, Сменный)
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- additional_requirements: Дополнительные предпочтения / требования (list of strings)
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Vacancy content:
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{content}
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"""
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openai_client = ChatOpenAI(model_name="gpt-5-mini", reasoning_effort="minimal", temperature=0, seed=42, top_p=1)
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structured_llm = openai_client.with_structured_output(VacancyFeatures)
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response = structured_llm.invoke(prompt)
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return response
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def get_next_vacancy(customer_cv):
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recommended_vacancy_ids = RecommendedVacancy.objects.filter(
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customer=customer_cv.customer,
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).values_list('vacancy_id', flat=True)
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query_filter = Filter(must_not=[HasIdCondition(has_id=recommended_vacancy_ids)])
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result = search_similarities(query_filter, customer_cv.id)
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if not result:
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return None
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search_result_id, vacancy_content, link = result
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recommendation = RecommendedVacancy.objects.create(
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customer=customer_cv.customer,
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vacancy_id=search_result_id,
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)
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return recommendation, vacancy_content, link
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