Add CV vector store

vacancies/main/bot.py

@@ -8,6 +8,7 @@ from vacancies.main.models import Customer, CustomerCV
 from langchain.agents import create_agent
 from langchain_openai import ChatOpenAI
 from langgraph.checkpoint.memory import InMemorySaver
+from vacancies.main.vector_store import add_vectors, extract_features
 
 SYSTEM_PROMPT = """
 Ты — карьерный копилот для ИТ. Ты можешь отвечать на любые вопросы по тематике карьеры.
@@ -63,7 +64,7 @@ async def error_handler(update: object, context: ContextTypes.DEFAULT_TYPE) -> N
 async def handle_document(update: Update, context: ContextTypes.DEFAULT_TYPE):
     if not update.message.document:
         await context.bot.send_message(chat_id=update.effective_chat.id, text="Не удалось прочитать информацию из файла! Попробуйте другой формат.")
-        return 
+        return
 
     buffer = io.BytesIO()
     file = await update.message.document.get_file()
@@ -75,6 +76,13 @@ async def handle_document(update: Update, context: ContextTypes.DEFAULT_TYPE):
     customer_cv, _ = await CustomerCV.objects.aupdate_or_create(customer=customer, defaults=dict(
         content=resume,
     ))
+    features = extract_features(customer_cv.content)
+    add_vectors(
+        "cvs",
+        customer_cv.id,
+        features.model_dump(),
+        {'content': customer_cv.content, 'features_json': features.model_dump()},
+    )
 
     await context.bot.send_message(chat_id=update.effective_chat.id, text="Отлично! Запомнил Ваше резюме.")
 

vacancies/main/management/commands/collect_vacancies_from_telegram_messages.py

@@ -1,7 +1,7 @@
 from django.core.management import BaseCommand
 from vacancies.main.models import Vacancy
 import clickhouse_connect
-from vacancies.main.vector_store import add_vacancy_vectors, extract_vacancy_features
+from vacancies.main.vector_store import add_vectors, extract_features
 
 clickhouse_client = clickhouse_connect.create_client(host="127.0.0.1", port=18123)
 
@@ -24,15 +24,28 @@ class Command(BaseCommand):
     help = "Collect vacancies from telegram messages"
 
     def handle(self, *args, **options):
-        for index, row in enumerate(clickhouse_client.query(query).result_rows):
+        import time
+        start_time = time.time()
+        result_rows = clickhouse_client.query(query).result_rows
+        print(f"query time: {time.time() - start_time:.4f}")
+        result_rows_len = len(result_rows)
+        for index, row in enumerate(result_rows):
             (id, chat_username, telegram_id, message, timestamp) = row
 
             link = f"https://t.me/{chat_username}/{telegram_id}"
-            features = extract_vacancy_features(message)
+            print(f"Processing {index}/{result_rows_len} link: {link}")
+            start_time = time.time()
+            features = extract_features(message)
+            print(f"ai time: {time.time() - start_time:.4f}")
             vacancy, created = Vacancy.objects.get_or_create(
                 link=link,
-                defaults={'content': message, 'features_json': features.model_dump()}
             )
 
-            print(index, link)
+            start_time = time.time()
-            add_vacancy_vectors(vacancy.id, features.model_dump(), {"link": link})
+            add_vectors(
+                "vacancies",
+                vacancy.id,
+                features.model_dump(),
+                {'content': message, 'features_json': features.model_dump()},
+            )
+            print(f"write vector time: {time.time() - start_time:.4f}")

vacancies/main/management/commands/generate_recommended_vacancies.py

@@ -1,8 +1,8 @@
+import asyncio
 from django.core.management import BaseCommand
 from vacancies.main.vector_store import search_similarities
 from vacancies.main.models import CustomerCV, RecommendedVacancy
 from vacancies.main.bot import application
-from vacancies.main.features_extractor import extract_vacancy_features
 from telegram import InlineKeyboardButton, InlineKeyboardMarkup
 from qdrant_client.models import Filter, HasIdCondition
 
@@ -14,23 +14,22 @@ class Command(BaseCommand):
         customer_cvs = CustomerCV.objects.all()
 
         for customer_cv in customer_cvs:
-            features = extract_vacancy_features(customer_cv.content)
             recommended_vacancy_ids = RecommendedVacancy.objects.filter(
                 customer=customer_cv.customer,
             ).values_list('vacancy_id', flat=True)
 
             query_filter = Filter(must_not=[HasIdCondition(has_id=recommended_vacancy_ids)])
-            search_result_id = search_similarities(features.model_dump(), query_filter)
+            search_result_id = search_similarities(query_filter, customer_cv.id)
 
             recommendation = RecommendedVacancy.objects.create(
                 customer=customer_cv.customer,
                 vacancy_id=search_result_id,
             )
 
-            application.bot.send_message(
+            asyncio.run(application.bot.send_message(
                 chat_id=recommendation.customer.chat_id,
                 text=recommendation.vacancy.content,
                 reply_markup=InlineKeyboardMarkup([[
                     InlineKeyboardButton("Откликнуться", url=recommendation.vacancy.link),
                 ]]),
-            )
+            ))

vacancies/main/migrations/0006_remove_vacancy_features_json.py

@@ -0,0 +1,17 @@
+# Generated by Django 5.2.7 on 2025-10-26 16:14
+
+from django.db import migrations
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('main', '0005_remove_vacancy_is_processed'),
+    ]
+
+    operations = [
+        migrations.RemoveField(
+            model_name='vacancy',
+            name='features_json',
+        ),
+    ]

vacancies/main/models.py

@@ -34,7 +34,6 @@ class CustomerCV(models.Model):
 class Vacancy(models.Model):
     content = models.TextField()
     link = models.URLField(unique=True)
-    features_json = models.JSONField(null=True, blank=True)
     created_at = models.DateTimeField(auto_now_add=True)
 
     objects = models.Manager()

vacancies/main/vector_store.py

@@ -17,10 +17,14 @@ vectors_config = {
     name: models.VectorParams(size=3072, distance=models.Distance.COSINE) for name in FEATURE_NAMES
 }
 
-collection_name = "vacancies"
+if not client.collection_exists("vacancies"):
-if not client.collection_exists(collection_name):
     client.create_collection(
-        collection_name=collection_name,
+        collection_name="vacancies",
+        vectors_config=vectors_config
+    )
+if not client.collection_exists("cvs"):
+    client.create_collection(
+        collection_name="cvs",
         vectors_config=vectors_config
     )
 
@@ -39,7 +43,7 @@ def _prepare_texts(features):
     return texts
 
 
-def add_vacancy_vectors(vacancy_id: int, features: dict, payload: dict):
+def add_vectors(collection_name: str, _id: int, features: dict, payload: dict):
     """Add vectors for a vacancy based on its features."""
     texts = _prepare_texts(features)
     vectors = {}
@@ -52,7 +56,7 @@ def add_vacancy_vectors(vacancy_id: int, features: dict, payload: dict):
         collection_name=collection_name,
         points=[
             models.PointStruct(
-                id=vacancy_id,
+                id=_id,
                 vector=vectors,
                 payload=payload,
             )
@@ -60,20 +64,18 @@ def add_vacancy_vectors(vacancy_id: int, features: dict, payload: dict):
     )
 
 
-def search_similarities(query_features: dict, query_filter: Filter) -> list[dict]:
+def search_similarities(query_filter: Filter, cv_id: int) -> list[dict]:
-    texts = _prepare_texts(query_features)
+    vectors = client.retrieve(
-    vectors = {}
+        collection_name="cvs",
-    for name, text in texts.items():
+        ids=[cv_id],
-        vectors[name] = [0.0] * 3072
+        with_vectors=True,
-        if text:
+    )[0].vector
-            vec = embedding.embed_query(text)
-            vectors[name] = vec
 
     max_similarities = {}
     for name, vec in vectors.items():
         if any(v != 0 for v in vec):
             results = client.search(
-                collection_name=collection_name,
+                collection_name="vacancies",
                 query_vector=(name, vec),
                 limit=1000,
                 with_payload=True,
@@ -95,7 +97,7 @@ def search_similarities(query_features: dict, query_filter: Filter) -> list[dict
     return scored[0]["id"]
 
 
-def extract_vacancy_features(content: str) -> VacancyFeatures:
+def extract_features(content: str) -> VacancyFeatures:
     prompt = f"""
     Extract the following features from the job vacancy description. If a feature is not mentioned, set it to null.
     Features: