vacancies/main/management/commands/collect_vacancies_from_telegram_messages.py

@@ -1,27 +1,11 @@
 from django.core.management import BaseCommand
 from vacancies.main.models import Vacancy
 import clickhouse_connect
-from vacancies.main.vector_store import add_vectors, extract_features, client as qdrant
+from vacancies.main.vector_store import add_vectors, extract_features
 
 clickhouse_client = clickhouse_connect.create_client(host="127.0.0.1", port=18123)
 
-next_page_offset = 0
+query = """
-exist_points_ids = []
-while next_page_offset is not None:
-    response = qdrant.scroll(
-        collection_name="vacancies",
-        limit=100_000,
-        offset=next_page_offset,
-        with_payload=False,
-        with_vectors=False,
-        timeout=30,
-    )
-    exist_points_ids.extend([point.id for point in response[0]])
-    next_page_offset = response[1]
-exist_points_set = tuple(exist_points_ids)
-print("qdrant vacancies points count:", len(exist_points_set))
-
-query = f"""
 SELECT id, chat_username, telegram_id, message, timestamp
 FROM telegram_parser_chatmessage
 WHERE timestamp >= now() - INTERVAL 30 DAY
@@ -33,7 +17,6 @@ WHERE timestamp >= now() - INTERVAL 30 DAY
     'удаленно', 'гибкий график', 'полный день', 'частичная занятость',
     'резюме', 'собеседование', 'junior', 'middle', 'senior'
   ]) >= 5 AND position(message, 'О себе') = 0 AND position(message, 'Обо мне') = 0 AND position(message, '#ищу') = 0
-  AND id NOT IN {exist_points_set}
 """
 
 

vacancies/main/management/commands/generate_recommended_vacancies.py

@@ -1,6 +1,6 @@
 import asyncio
 from django.core.management import BaseCommand
-from vacancies.main.vector_store import search_similarities, client
+from vacancies.main.vector_store import search_similarities
 from vacancies.main.models import CustomerCV, RecommendedVacancy
 from vacancies.main.bot import application
 from telegram import InlineKeyboardButton, InlineKeyboardMarkup
@@ -21,11 +21,6 @@ class Command(BaseCommand):
             query_filter = Filter(must_not=[HasIdCondition(has_id=recommended_vacancy_ids)])
             search_result_id = search_similarities(query_filter, customer_cv.id)
 
-            vacancy_content = client.retrieve(
-                collection_name="vacancies",
-                ids=[search_result_id],
-            )[0].payload["content"]
-
             recommendation = RecommendedVacancy.objects.create(
                 customer=customer_cv.customer,
                 vacancy_id=search_result_id,
@@ -33,7 +28,7 @@ class Command(BaseCommand):
 
             asyncio.run(application.bot.send_message(
                 chat_id=recommendation.customer.chat_id,
-                text=vacancy_content,
+                text=recommendation.vacancy.content,
                 reply_markup=InlineKeyboardMarkup([[
                     InlineKeyboardButton("Откликнуться", url=recommendation.vacancy.link),
                 ]]),

vacancies/main/vector_store.py

@@ -5,29 +5,14 @@ from qdrant_client import QdrantClient
 from qdrant_client.models import Filter
 from vacancies.main.models import VacancyFeatures
 
-# client = QdrantClient(path="./embeddings")
+client = QdrantClient(path="./embeddings")
-client = QdrantClient(url="http://localhost:6333")
+#client = QdrantClient(url="http://localhost:6333")
 
 FEATURE_NAMES = [
     "employment_type", "work_format", "experience", "position_level", "industry", "tech_stack",
     "location", "salary_range", "languages", "education", "schedule", "additional_requirements"
 ]
 
-weights = {
-    "employment_type": 2,
-    "work_format": 2,
-    "experience": 4,
-    "position_level": 4,
-    "industry": 4,
-    "tech_stack": 5,
-    "location": 2,
-    "salary_range": 2,
-    "languages": 2,
-    "education": 1,
-    "schedule": 1,
-    "additional_requirements": 1,
-}
-
 vectors_config = {
     name: models.VectorParams(size=3072, distance=models.Distance.COSINE) for name in FEATURE_NAMES
 }
@@ -105,7 +90,7 @@ def search_similarities(query_filter: Filter, cv_id: int) -> list[dict]:
 
     scored = []
     for vid, feature_sims in max_similarities.items():
-        total = sum(feature_sims[feature] * weights.get(feature, 1) for feature in feature_sims)
+        total = sum(feature_sims.values())
         scored.append({"id": vid, "score": total})
 
     scored.sort(key=lambda x: x["score"], reverse=True)