Implement simplified recommendations
This commit is contained in:
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908384a118
@ -36,6 +36,5 @@ uv run --env-file .env manage.py runbot
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Production port-forwards:
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```bash
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KUBECONFIG=production-kubeconfig.yaml kubectl port-forward svc/qdrant -n qdrant 6333:6333
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KUBECONFIG=production-kubeconfig.yaml kubectl port-forward svc/main-cluster-rw -n postgresql-cluster 5432
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```
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@ -1,11 +1,4 @@
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services:
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qdrant:
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image: qdrant/qdrant:latest
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restart: always
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ports:
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- "127.0.0.1:6333:6333"
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volumes:
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- "/srv/vision-career/qdrant:/qdrant/storage"
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postgres:
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image: postgres:17-alpine3.20
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restart: always
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@ -10,7 +10,6 @@ dependencies = [
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"gunicorn>=23.0.0",
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"langchain>=0.3.27",
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"langchain-openai>=0.3.35",
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"langchain-qdrant>=1.1.0",
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"langgraph-checkpoint-postgres>=3.0.0",
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"psycopg[binary]>=3.2.12",
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"pydantic>=2.0",
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@ -1,16 +1,27 @@
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from django.contrib import admin
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from vacancies.main import models
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@admin.register(models.Customer)
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class CustomerAdmin(admin.ModelAdmin):
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pass
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@admin.register(models.CustomerCV)
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class CustomerCVADMIN(admin.ModelAdmin):
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class CustomerCVAdmin(admin.ModelAdmin):
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pass
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@admin.register(models.RecommendedVacancy)
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class RecommendedVacancyAdmin(admin.ModelAdmin):
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pass
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@admin.register(models.Vacancy)
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class VacancyAdmin(admin.ModelAdmin):
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pass
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@admin.register(models.JobTitle)
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class JobTitleAdmin(admin.ModelAdmin):
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pass
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@ -1,5 +1,4 @@
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import io
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import asyncio
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import os
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import traceback
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@ -22,14 +21,11 @@ from telegram.ext import (
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filters,
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)
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from pydantic import BaseModel
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from typing import Literal
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from vacancies.conf.settings import DB_URI
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from vacancies.main.models import Customer, CustomerCV
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from vacancies.main.vector_store import (
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add_vectors,
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batch_extract_features,
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get_next_vacancy,
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embed_features,
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)
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from vacancies.main.models import Customer, CustomerCV, JobTitle
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from vacancies.main.vector_store import get_next_vacancy
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SYSTEM_PROMPT = """
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Ты — карьерный копилот для ИТ. Ты можешь отвечать на любые вопросы по тематике карьеры.
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@ -69,19 +65,17 @@ async def next_vacancy(update: Update, context: ContextTypes.DEFAULT_TYPE):
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await context.bot.send_message(chat_id=update.effective_chat.id, text=message)
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return
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result = get_next_vacancy(customer_cv)
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if not result:
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vacancy = get_next_vacancy(customer_cv)
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if not vacancy:
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message = "Вакансии закончились, возвращайтесь позже!"
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await context.bot.send_message(chat_id=update.effective_chat.id, text=message)
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return
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recommendation, vacancy_content, link = result
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await context.bot.send_message(
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chat_id=update.effective_chat.id,
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text=vacancy_content,
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text=vacancy.content,
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reply_markup=InlineKeyboardMarkup([[
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InlineKeyboardButton("Откликнуться", url=link),
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InlineKeyboardButton("Откликнуться", url=vacancy.link),
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]]),
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)
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@ -123,23 +117,28 @@ async def handle_document(update: Update, context: ContextTypes.DEFAULT_TYPE):
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reader = PdfReader(buffer)
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resume = "\n".join(page.extract_text() for page in reader.pages)
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job_titles = JobTitle.objects.values_list('title', flat=True)
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job_title_map = dict(JobTitle.objects.values_list('title', 'id'))
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class Structure(BaseModel):
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job_title: Literal[tuple(job_titles)]
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min_salary_rub: int | None
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max_salary_rub: int | None
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openai_client = ChatOpenAI(model_name="gpt-5-mini", temperature=0, seed=42, top_p=1)
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structured_llm = openai_client.with_structured_output(Structure)
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prompt = f"Extract fields from following CV. Carefully choice job title.\nCV: {resume}"
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response = await structured_llm.ainvoke(prompt)
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customer = await Customer.objects.aget(telegram_id=update.effective_user.id)
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customer_cv, _ = await CustomerCV.objects.aupdate_or_create(customer=customer, defaults=dict(
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content=resume,
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job_title_id=job_title_map[response.job_title],
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min_salary_rub=response.min_salary_rub,
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max_salary_rub=response.max_salary_rub,
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))
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def upload_vectors():
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features = batch_extract_features([customer_cv.content])[0]
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add_vectors(
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"cvs",
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customer_cv.id,
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features.model_dump(),
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{'content': customer_cv.content, 'features_json': features.model_dump()},
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embed_features(features.model_dump()),
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)
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await asyncio.to_thread(upload_vectors)
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await context.bot.editMessageText("Отлично! Запомнил Ваше резюме.", update.effective_chat.id, message.id)
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@ -1,18 +1,14 @@
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from concurrent.futures import ThreadPoolExecutor
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from datetime import datetime, timedelta
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from itertools import batched
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from datetime import timedelta
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from django.utils import timezone
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from pydantic import BaseModel
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from typing import Literal
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from vacancies.main.models import Vacancy, JobTitle
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from langchain_openai import ChatOpenAI
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import clickhouse_connect
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from django.core.management import BaseCommand
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from django.conf import settings
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from qdrant_client.models import OrderBy
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from vacancies.main.vector_store import (
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add_vectors,
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batch_extract_features,
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embed_features,
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qdrant_client,
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)
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query = """
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SELECT DISTINCT ON (message) id, chat_username, telegram_id, message, timestamp
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@ -38,23 +34,38 @@ class Command(BaseCommand):
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help = "Collect vacancies from telegram messages"
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def handle(self, *args, **options):
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response = qdrant_client.scroll(collection_name="vacancies", limit=1, order_by=OrderBy(key="timestamp", direction="desc"))
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last_point_timestamp = datetime.now() - timedelta(days=30)
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if response[0]:
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last_point_timestamp = response[0][0].payload["timestamp"]
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job_titles = JobTitle.objects.values_list('title', flat=True)
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job_title_map = dict(JobTitle.objects.values_list('title', 'id'))
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class Structure(BaseModel):
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job_title: Literal[tuple(job_titles)]
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min_salary_rub: int | None
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max_salary_rub: int | None
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openai_client = ChatOpenAI(model_name="gpt-5-mini", temperature=0, seed=42, top_p=1)
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structured_llm = openai_client.with_structured_output(Structure)
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last_timestamp = timezone.now() - timedelta(days=30)
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if last_vacancy := Vacancy.objects.order_by("-timestamp").first():
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last_timestamp = last_vacancy.timestamp
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clickhouse_client = clickhouse_connect.create_client(host=settings.CLICKHOUSE_HOST, port=settings.CLICKHOUSE_PORT)
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result_rows = clickhouse_client.query(query, parameters={"timestamp": last_point_timestamp}).result_rows
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result_rows = clickhouse_client.query(query, parameters={"timestamp": last_timestamp}).result_rows
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for index, rows in enumerate(batched(result_rows, settings.COLLECT_VACANCIES_BATCH_SIZE)):
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vacancies_features = batch_extract_features([row[3] for row in rows])
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print(f"Processing {index+1}/{len(result_rows)//settings.COLLECT_VACANCIES_BATCH_SIZE}")
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with ThreadPoolExecutor() as pool:
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vacancies_vectors = pool.map(embed_features, [vacancy_features.model_dump() for vacancy_features in vacancies_features])
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for row, vacancy_features, vacancy_vectors in zip(rows, vacancies_features, vacancies_vectors):
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prompts = [f"Extract fields from vacancy. Carefully choice job title.\nVacancy: {row[3]}" for row in rows]
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responses = structured_llm.batch(prompts)
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vacancies = []
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for row, response in zip(rows, responses):
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print(response)
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(id, chat_username, telegram_id, message, timestamp) = row
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link = f"https://t.me/{chat_username}/{telegram_id}"
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payload = {'content': message, 'features_json': vacancy_features.model_dump(), "link": link, "timestamp": timestamp}
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add_vectors("vacancies", id, vacancy_features.model_dump(), payload, vacancy_vectors)
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vacancies.append(Vacancy(
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external_id=id,
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job_title_id=job_title_map[response.job_title],
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min_salary_rub=response.min_salary_rub,
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max_salary_rub=response.max_salary_rub,
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content=message,
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timestamp=timestamp,
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link=f"https://t.me/{chat_username}/{telegram_id}",
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))
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print(Vacancy.objects.bulk_create(vacancies, ignore_conflicts=True))
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@ -15,16 +15,14 @@ class Command(BaseCommand):
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async def ahandle(self, *args, **options):
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for customer_cv in CustomerCV.objects.all():
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result = get_next_vacancy(customer_cv)
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if not result:
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vacancy = get_next_vacancy(customer_cv)
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if not vacancy:
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continue
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recommendation, vacancy_content, link = result
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await application.bot.send_message(
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chat_id=recommendation.customer.chat_id,
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text=vacancy_content,
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chat_id=customer_cv.customer.chat_id,
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text=vacancy.content,
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reply_markup=InlineKeyboardMarkup([[
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InlineKeyboardButton("Откликнуться", url=link),
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InlineKeyboardButton("Откликнуться", url=vacancy.link),
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]]),
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)
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@ -0,0 +1,55 @@
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# Generated by Django 5.2.7 on 2025-11-08 19:11
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import django.db.models.deletion
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from django.db import migrations, models
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class Migration(migrations.Migration):
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dependencies = [
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('main', '0008_alter_recommendedvacancy_vacancy_id'),
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]
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operations = [
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migrations.CreateModel(
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name='JobTitle',
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fields=[
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('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
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('title', models.CharField(max_length=255, unique=True)),
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],
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),
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migrations.AddField(
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model_name='customercv',
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name='max_salary_rub',
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field=models.PositiveIntegerField(blank=True, default=None, null=True),
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),
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migrations.AddField(
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model_name='customercv',
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name='min_salary_rub',
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field=models.PositiveIntegerField(blank=True, default=None, null=True),
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),
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migrations.AddField(
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model_name='customercv',
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name='job_title',
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field=models.ForeignKey(default=0, on_delete=django.db.models.deletion.CASCADE, to='main.jobtitle'),
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preserve_default=False,
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),
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migrations.CreateModel(
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name='Vacancy',
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fields=[
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('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
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('external_id', models.CharField(max_length=255, unique=True)),
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('min_salary_rub', models.PositiveIntegerField(blank=True, default=None, null=True)),
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('max_salary_rub', models.PositiveIntegerField(blank=True, default=None, null=True)),
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('content', models.TextField()),
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('timestamp', models.DateTimeField()),
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('link', models.URLField()),
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('job_title', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='main.jobtitle')),
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],
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),
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migrations.AlterField(
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model_name='recommendedvacancy',
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name='vacancy_id',
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field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='main.vacancy'),
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),
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]
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@ -0,0 +1,18 @@
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# Generated by Django 5.2.7 on 2025-11-09 08:06
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from django.db import migrations
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class Migration(migrations.Migration):
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dependencies = [
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('main', '0009_jobtitle_customercv_max_salary_rub_and_more'),
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]
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operations = [
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migrations.RenameField(
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model_name='recommendedvacancy',
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old_name='vacancy_id',
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new_name='vacancy',
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),
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]
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@ -1,5 +1,4 @@
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from django.db import models
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from pydantic import BaseModel
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class Customer(models.Model):
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@ -17,8 +16,18 @@ class Customer(models.Model):
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db_table = "customers"
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class JobTitle(models.Model):
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title = models.CharField(max_length=255, unique=True)
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def __str__(self):
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return self.title
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class CustomerCV(models.Model):
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customer = models.OneToOneField(Customer, on_delete=models.CASCADE)
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job_title = models.ForeignKey(JobTitle, on_delete=models.CASCADE)
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min_salary_rub = models.PositiveIntegerField(null=True, blank=True, default=None)
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max_salary_rub = models.PositiveIntegerField(null=True, blank=True, default=None)
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content = models.TextField()
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created_at = models.DateTimeField(auto_now_add=True)
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@ -31,9 +40,22 @@ class CustomerCV(models.Model):
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db_table = "customer_vcs"
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class Vacancy(models.Model):
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job_title = models.ForeignKey(JobTitle, on_delete=models.CASCADE)
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external_id = models.CharField(max_length=255, unique=True)
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min_salary_rub = models.PositiveIntegerField(null=True, blank=True, default=None)
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max_salary_rub = models.PositiveIntegerField(null=True, blank=True, default=None)
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content = models.TextField()
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timestamp = models.DateTimeField()
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link = models.URLField()
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def __str__(self):
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return self.job_title.title
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class RecommendedVacancy(models.Model):
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customer = models.ForeignKey(Customer, on_delete=models.CASCADE)
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vacancy_id = models.BigIntegerField()
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customer = models.ForeignKey(Customer, on_delete=models.CASCADE, related_name="recommended_vacancies")
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vacancy = models.ForeignKey(Vacancy, on_delete=models.CASCADE, related_name="recommended_vacancies")
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created_at = models.DateTimeField(auto_now_add=True)
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objects = models.Manager()
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@ -44,19 +66,3 @@ class RecommendedVacancy(models.Model):
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class Meta:
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verbose_name_plural = 'Recommended Vacancies'
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db_table = "recommended_vacancies"
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class VacancyFeatures(BaseModel):
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job_title: str | None = None # Должность
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employment_type: str | None = None # Тип занятости
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work_format: str | None = None # Формат работы
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experience: str | None = None # Опыт работы
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position_level: str | None = None # Уровень позиции
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industry: str | None = None # Отрасль / Сфера деятельности
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tech_stack: list[str] | None = None # Технологический стек / Ключевые навыки
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location: str | None = None # География
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salary_range: str | None = None # Зарплатные ожидания / вилка
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languages: list[str] | None = None # Языки
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education: str | None = None # Образование
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schedule: str | None = None # График работы
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additional_requirements: list[str] | None = None # Дополнительные предпочтения / требования
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@ -1,171 +1,14 @@
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from langchain_openai import ChatOpenAI, OpenAIEmbeddings
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from qdrant_client import QdrantClient, models
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from qdrant_client.models import Filter, HasIdCondition
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from vacancies.conf.settings import QDRANT_URL
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from vacancies.main.models import RecommendedVacancy, VacancyFeatures
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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": 70,
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"tech_stack": 10,
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"salary_range": 10,
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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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qdrant_client.create_payload_index(
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collection_name="vacancies",
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field_name="timestamp",
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field_schema="datetime",
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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")
|
||||
|
||||
def _prepare_texts(features):
|
||||
texts = {}
|
||||
for name in FEATURE_NAMES:
|
||||
value = features.get(name)
|
||||
if isinstance(value, list):
|
||||
text = " ".join(value) if value else ""
|
||||
else:
|
||||
text = str(value) if value else ""
|
||||
texts[name] = text
|
||||
return texts
|
||||
|
||||
|
||||
def embed_features(features):
|
||||
features = {key: value for key, value in features.items() if value}
|
||||
features_texts = _prepare_texts(features)
|
||||
names, texts = features_texts.keys(), features_texts.values()
|
||||
vectors = dict(zip(names, embedding.embed_documents(texts)))
|
||||
return vectors
|
||||
|
||||
|
||||
def add_vectors(collection_name: str, _id: int, features: dict, payload: dict, vectors):
|
||||
max_similarities = {}
|
||||
for name, vec in vectors.items():
|
||||
results = qdrant_client.query_points(collection_name="vacancies", query=vec, using=name, limit=100)
|
||||
for res in results.points:
|
||||
max_similarities.setdefault(res.id, {})
|
||||
max_similarities[res.id][name] = res.score
|
||||
|
||||
scored = []
|
||||
for vid, feature_sims in max_similarities.items():
|
||||
total = sum(feature_sims[feature] * weights.get(feature, 1) for feature in feature_sims)
|
||||
scored.append({"id": vid, "score": total})
|
||||
|
||||
scored.sort(key=lambda x: x["score"], reverse=True)
|
||||
if scored and scored[0]["score"] > 80: # threshold
|
||||
return
|
||||
|
||||
qdrant_client.upsert(
|
||||
collection_name=collection_name,
|
||||
points=[models.PointStruct(id=_id, vector=vectors, payload=payload)]
|
||||
)
|
||||
|
||||
|
||||
def search_similarities(query_filter: Filter, cv_id: int):
|
||||
cv = qdrant_client.retrieve(collection_name="cvs", ids=[cv_id], with_vectors=True)[0]
|
||||
|
||||
max_similarities, vacancies_content = {}, {}
|
||||
for name, vec in cv.vector.items():
|
||||
results = qdrant_client.query_points(
|
||||
collection_name="vacancies",
|
||||
query=vec,
|
||||
using=name,
|
||||
limit=100000,
|
||||
with_payload=True,
|
||||
query_filter=query_filter,
|
||||
)
|
||||
for res in results.points:
|
||||
max_similarities.setdefault(res.id, {})
|
||||
vacancies_content.setdefault(res.id, {})
|
||||
|
||||
max_similarities[res.id][name] = res.score
|
||||
vacancies_content[res.id]["content"] = res.payload["content"]
|
||||
vacancies_content[res.id]["features_json"] = res.payload["features_json"]
|
||||
vacancies_content[res.id]["link"] = res.payload["link"]
|
||||
|
||||
scored = []
|
||||
for vid, feature_sims in max_similarities.items():
|
||||
total = sum(feature_sims[feature] * weights.get(feature, 1) for feature in feature_sims)
|
||||
scored.append({
|
||||
"id": vid,
|
||||
"score": total,
|
||||
"content": vacancies_content[vid]["content"],
|
||||
"features_json": vacancies_content[vid]["features_json"],
|
||||
"link": vacancies_content[vid]["link"],
|
||||
"sims": feature_sims,
|
||||
})
|
||||
|
||||
scored.sort(key=lambda x: x["score"], reverse=True)
|
||||
|
||||
return scored[0]["id"], scored[0]["content"], scored[0]["link"]
|
||||
|
||||
|
||||
def batch_extract_features(contents: list[str]) -> list[VacancyFeatures]:
|
||||
prompts = [
|
||||
f"""
|
||||
Extract the following features from the job vacancy description. If a feature is not mentioned, set it to null.
|
||||
Features:
|
||||
- job_title: Должность (e.g., DevOps, Python программист)
|
||||
- employment_type: Тип занятости (e.g., Полная занятость, Частичная)
|
||||
- work_format: Формат работы (e.g., Офис, Удалённо, Гибрид)
|
||||
- experience: Опыт работы (e.g., 3-5 лет, Нет опыта)
|
||||
- position_level: Уровень позиции (e.g., Junior, Senior)
|
||||
- industry: Отрасль / Сфера деятельности (e.g., IT, Финансы)
|
||||
- tech_stack: Технологический стек / Ключевые навыки (list of strings)
|
||||
- location: География (e.g., Москва, Россия)
|
||||
- salary_range: Зарплатные ожидания / вилка (e.g., 100000-200000 руб)
|
||||
- languages: Языки (list of strings, e.g., ["Русский", "Английский"])
|
||||
- education: Образование (e.g., Высшее, Среднее специальное)
|
||||
- schedule: График работы (e.g., Полный день, Сменный)
|
||||
- additional_requirements: Дополнительные предпочтения / требования (list of strings)
|
||||
Vacancy content:
|
||||
{content}
|
||||
"""
|
||||
for content in contents
|
||||
]
|
||||
openai_client = ChatOpenAI(model_name="gpt-5-mini", reasoning_effort="minimal", temperature=0, seed=42, top_p=1)
|
||||
structured_llm = openai_client.with_structured_output(VacancyFeatures)
|
||||
response = structured_llm.batch(prompts)
|
||||
return response
|
||||
from vacancies.main.models import Vacancy
|
||||
|
||||
|
||||
def get_next_vacancy(customer_cv):
|
||||
recommended_vacancy_ids = RecommendedVacancy.objects.filter(
|
||||
customer=customer_cv.customer,
|
||||
).values_list('vacancy_id', flat=True)
|
||||
recommended_vacancy_ids = customer_cv.customer.recommended_vacancies.values_list("vacancy_id", flat=True)
|
||||
|
||||
query_filter = Filter(must_not=[HasIdCondition(has_id=recommended_vacancy_ids)])
|
||||
result = search_similarities(query_filter, customer_cv.id)
|
||||
if not result:
|
||||
return None
|
||||
vacancy = Vacancy.objects.exclude(id__in=recommended_vacancy_ids).filter(
|
||||
job_title=customer_cv.job_title,
|
||||
min_salary_rub__gt=customer_cv.min_salary_rub,
|
||||
).first()
|
||||
if vacancy:
|
||||
customer_cv.customer.recommended_vacancies.create(vacancy=vacancy)
|
||||
|
||||
search_result_id, vacancy_content, link = result
|
||||
|
||||
recommendation = RecommendedVacancy.objects.create(
|
||||
customer=customer_cv.customer,
|
||||
vacancy_id=search_result_id,
|
||||
)
|
||||
|
||||
return recommendation, vacancy_content, link
|
||||
return vacancy
|
||||
|
||||
Loading…
Reference in New Issue
Block a user