Add button to force send next vacancy

vacancies/main/bot.py

@@ -1,14 +1,14 @@
 import os
 import io
 import traceback
-from telegram import Update
+from telegram import InlineKeyboardButton, InlineKeyboardMarkup, Update, ReplyKeyboardMarkup, KeyboardButton
 from telegram.ext import filters, ApplicationBuilder, MessageHandler, CommandHandler, ContextTypes
 from pypdf import PdfReader
 from vacancies.main.models import Customer, CustomerCV
 from langchain.agents import create_agent
 from langchain_openai import ChatOpenAI
 from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
-from vacancies.main.vector_store import add_vectors, extract_features
+from vacancies.main.vector_store import add_vectors, extract_features, get_next_vacancy
 from vacancies.conf.settings import DB_URI
 
 SYSTEM_PROMPT = """
@@ -34,8 +34,36 @@ async def start(update: Update, context: ContextTypes.DEFAULT_TYPE):
             chat_id=update.effective_chat.id,
         ),
     )
+    keyboard = [[KeyboardButton("Получить следующую вакансию")]]
+    reply_markup = ReplyKeyboardMarkup(keyboard, resize_keyboard=True, one_time_keyboard=False)
     text = "Привет! Я карьерный копилот: помогу с работой, интервью и расскажу новости по рынку, специально для тебя. С чего начнем?"
-    await context.bot.send_message(chat_id=update.effective_chat.id, text=text)
+    await context.bot.send_message(chat_id=update.effective_chat.id, text=text, reply_markup=reply_markup)
+
+
+async def next_vacancy(update: Update, context: ContextTypes.DEFAULT_TYPE):
+    await context.bot.send_message(update.effective_chat.id, "📝 Обрабатываю твой запрос. Пожалуйста, подождите...")
+
+    customer_cv = await CustomerCV.objects.filter(customer__telegram_id=update.effective_user.id).afirst()
+    if not customer_cv:
+        message = "Пришлите мне свое резюме, чтобы я мог подобрать вам вакансии!"
+        await context.bot.send_message(chat_id=update.effective_chat.id, text=message)
+        return
+
+    result = get_next_vacancy(customer_cv)
+    if not result:
+        message = "Вакансии закончились, возвращайтесь позже!"
+        await context.bot.send_message(chat_id=update.effective_chat.id, text=message)
+        return
+
+    recommendation, vacancy_content, link = result
+
+    await context.bot.send_message(
+        chat_id=update.effective_chat.id,
+        text=vacancy_content,
+        reply_markup=InlineKeyboardMarkup([[
+            InlineKeyboardButton("Откликнуться", url=link),
+        ]]),
+    )
 
 
 async def prompt(update: Update, context: ContextTypes.DEFAULT_TYPE):
@@ -92,6 +120,7 @@ async def handle_document(update: Update, context: ContextTypes.DEFAULT_TYPE):
 
 application = ApplicationBuilder().token(os.environ["BOT_TOKEN"]).concurrent_updates(True).build()
 application.add_handler(CommandHandler('start', start, block=False))
+application.add_handler(MessageHandler(filters.Text("Получить следующую вакансию"), next_vacancy, block=False))
 application.add_handler(MessageHandler(filters.TEXT & (~filters.COMMAND), prompt, block=False))
 application.add_handler(MessageHandler((filters.Document.ALL | filters.PHOTO) & (~filters.COMMAND), handle_document, block=False))
 application.add_error_handler(error_handler)

vacancies/main/management/commands/generate_recommended_vacancies.py

@@ -1,11 +1,10 @@
 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.models import CustomerCV
 from vacancies.main.bot import application
+from vacancies.main.vector_store import get_next_vacancy
 from telegram import InlineKeyboardButton, InlineKeyboardMarkup
-from qdrant_client.models import Filter, HasIdCondition
 
 
 class Command(BaseCommand):
@@ -15,24 +14,12 @@ class Command(BaseCommand):
         asyncio.run(self.ahandle(*args, **options))
 
     async def ahandle(self, *args, **options):
-        customer_cvs = CustomerCV.objects.all()
-
-        for customer_cv in customer_cvs:
-            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)])
-            result = search_similarities(query_filter, customer_cv.id)
+        for customer_cv in CustomerCV.objects.all():
+            result = get_next_vacancy(customer_cv)
             if not result:
                 continue
 
-            search_result_id, vacancy_content, link = result
-
-            recommendation = RecommendedVacancy.objects.create(
-                customer=customer_cv.customer,
-                vacancy_id=search_result_id,
-            )
+            recommendation, vacancy_content, link = result
 
             await application.bot.send_message(
                 chat_id=recommendation.customer.chat_id,

vacancies/main/vector_store.py

@@ -5,6 +5,8 @@ from qdrant_client import QdrantClient
 from qdrant_client.models import Filter
 from vacancies.main.models import VacancyFeatures
 from vacancies.conf.settings import QDRANT_URL
+from vacancies.main.models import CustomerCV, RecommendedVacancy
+from qdrant_client.models import Filter, HasIdCondition
 
 qdrant_client = QdrantClient(url=QDRANT_URL)
 
@@ -184,3 +186,23 @@ def extract_features(content: str) -> VacancyFeatures:
     structured_llm = openai_client.with_structured_output(VacancyFeatures)
     response = structured_llm.invoke(prompt)
     return response
+
+
+def get_next_vacancy(customer_cv):
+    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)])
+    result = search_similarities(query_filter, customer_cv.id)
+    if not result:
+        return None
+
+    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