Index vacancies using batches

compose.yaml

@@ -1,9 +1,4 @@
 services:
-  ofelia:
-    image: mcuadros/ofelia:latest
-    command: daemon --docker -f label=com.docker.compose.project=${COMPOSE_PROJECT_NAME}
-    volumes:
-      - /var/run/docker.sock:/var/run/docker.sock:ro
   qdrant:
     image: qdrant/qdrant:latest
     restart: always
@@ -21,22 +16,3 @@ services:
       - "127.0.0.1:5432:5432"
     volumes:
       - "/srv/vision-career/postgres:/var/lib/postgresql/data"
-  bot:
-    image: vision-career:latest
-    build: .
-    command: [".venv/bin/python", "manage.py", "runbot"]
-    restart: always
-    init: true
-    network_mode: host
-    env_file:
-      - .env
-    labels:
-      ofelia.enabled: "true"
-      ofelia.job-exec.collect-vacancies-from-telegram-messages.schedule: "@every 1m"
-      ofelia.job-exec.collect-vacancies-from-telegram-messages.command: ".venv/bin/python manage.py collect_vacancies_from_telegram_messages"
-      ofelia.job-exec.generate-recommended-vacancies.schedule: "@every 1m"
-      ofelia.job-exec.generate-recommended-vacancies.command: ".venv/bin/python manage.py generate_recommended_vacancies"
-    develop:
-      watch:
-        - action: rebuild
-          path: .

vacancies/main/management/commands/collect_vacancies_from_telegram_messages.py

@@ -1,3 +1,7 @@
+import traceback
+from itertools import batched
+from concurrent.futures import ThreadPoolExecutor
+
 from django.core.management import BaseCommand
 import clickhouse_connect
 from vacancies.main.vector_store import add_vectors, extract_features, qdrant_client
@@ -21,7 +25,6 @@ WHERE timestamp >= now() - INTERVAL 30 DAY
   AND arrayCount(x -> position(lower(message), x) > 0, [
     'о себе', 'обо мне', 'умею', '#ищу'
   ]) = 0
-  AND position(message, 'Руководитель/тимлид группы ИБ АСУ ТП') > 0
   AND id NOT IN %(exist_points)s
 """
 
@@ -46,12 +49,17 @@ class Command(BaseCommand):
         exist_points_set = tuple(set(exist_points_ids))
 
         result_rows = clickhouse_client.query(query, parameters={"exist_points": exist_points_set}).result_rows
-        result_rows_len = len(result_rows)
+        batch_size = 10
+        with ThreadPoolExecutor(max_workers=batch_size) as pool:
+            pool.map(self._process_batch, batched(result_rows, batch_size))
+
+    def _process_batch(self, result_rows):
+        try:
             for index, row in enumerate(result_rows):
                 (id, chat_username, telegram_id, message, timestamp) = row
 
                 link = f"https://t.me/{chat_username}/{telegram_id}"
-            print(f"Processing {index+1}/{result_rows_len} link: {link}")
+                print(f"Processing {index+1}/{len(result_rows)} link: {link}")
                 features = extract_features(message)
 
                 add_vectors(
@@ -60,3 +68,5 @@ class Command(BaseCommand):
                     features.model_dump(),
                     {'content': message, 'features_json': features.model_dump(), "link": link, "timestamp": timestamp},
                 )
+        except Exception as exc:
+            traceback.print_exception(exc)