From 7af5a55b2e9c3bbbb283d6b7371327bf9791811a Mon Sep 17 00:00:00 2001 From: tdjx Date: Wed, 3 Sep 2025 14:36:27 +0500 Subject: [PATCH] add interview --- .env.example | 38 +- .gitignore | 1 + INTERVIEW_INTEGRATION.md | 330 +++ TESTING_GUIDE.md | 272 ++ ai_interviewer_agent.py | 416 +++ app/core/config.py | 31 +- app/models/interview.py | 66 + app/models/resume.py | 12 +- app/routers/admin_router.py | 117 + app/routers/interview_router.py | 91 + app/routers/resume_router.py | 94 +- app/services/ai_interviewer_service.py | 294 ++ app/services/file_service.py | 48 +- app/services/interview_service.py | 464 +++ celery_worker/__init__.py | 0 celery_worker/celery_app.py | 17 + celery_worker/database.py | 87 + celery_worker/process_cleanup_task.py | 190 ++ celery_worker/tasks.py | 412 +++ main.py | 2 + ...2cda4df181_add_interview_plan_to_resume.py | 30 + ...d03e3281c_add_interviewstatus_enum_type.py | 32 + ...23b138a3bb_add_interview_sessions_table.py | 47 + ...5_update_interviewsession_model_to_use_.py | 63 + ...pdate_interview_sessions_status_column_.py | 30 + ...a_add_parsing_statuses_to_resumestatus_.py | 43 + ...1b016b35a_add_ai_agent_process_tracking.py | 32 + pyproject.toml | 16 +- quick_api_test.py | 186 ++ rag/__init__.py | 5 + rag/database/__init__.py | 3 + rag/database/model.py | 17 + rag/llm/__init__.py | 3 + rag/llm/model.py | 206 ++ rag/memory.py | 91 + rag/registry.py | 109 + rag/service/__init__.py | 3 + rag/service/model.py | 121 + rag/settings.py | 52 + rag/vector_store.py | 104 + simple_test.py | 208 ++ test_system.py | 300 ++ uv.lock | 2579 +++++++++++++++++ 43 files changed, 7248 insertions(+), 14 deletions(-) create mode 100644 INTERVIEW_INTEGRATION.md create mode 100644 TESTING_GUIDE.md create mode 100644 ai_interviewer_agent.py create mode 100644 app/models/interview.py create mode 100644 app/routers/admin_router.py create mode 100644 app/routers/interview_router.py create mode 100644 app/services/ai_interviewer_service.py create mode 100644 app/services/interview_service.py create mode 100644 celery_worker/__init__.py create mode 100644 celery_worker/celery_app.py create mode 100644 celery_worker/database.py create mode 100644 celery_worker/process_cleanup_task.py create mode 100644 celery_worker/tasks.py create mode 100644 migrations/versions/1a2cda4df181_add_interview_plan_to_resume.py create mode 100644 migrations/versions/385d03e3281c_add_interviewstatus_enum_type.py create mode 100644 migrations/versions/4723b138a3bb_add_interview_sessions_table.py create mode 100644 migrations/versions/4d04e6e32445_update_interviewsession_model_to_use_.py create mode 100644 migrations/versions/96ffcf34e1de_update_interview_sessions_status_column_.py create mode 100644 migrations/versions/dba37152ae9a_add_parsing_statuses_to_resumestatus_.py create mode 100644 migrations/versions/de11b016b35a_add_ai_agent_process_tracking.py create mode 100644 quick_api_test.py create mode 100644 rag/__init__.py create mode 100644 rag/database/__init__.py create mode 100644 rag/database/model.py create mode 100644 rag/llm/__init__.py create mode 100644 rag/llm/model.py create mode 100644 rag/memory.py create mode 100644 rag/registry.py create mode 100644 rag/service/__init__.py create mode 100644 rag/service/model.py create mode 100644 rag/settings.py create mode 100644 rag/vector_store.py create mode 100644 simple_test.py create mode 100644 test_system.py diff --git a/.env.example b/.env.example index 3401978..c012556 100644 --- a/.env.example +++ b/.env.example @@ -1,12 +1,38 @@ -DATABASE_URL=postgresql+asyncpg://user:password@localhost:5432/hr_ai_db +# Database Configuration +DATABASE_URL=postgresql+asyncpg://username:password@localhost:5432/hr_ai -# Selectel S3 Configuration -S3_ENDPOINT_URL=https://s3.selcdn.ru -S3_ACCESS_KEY_ID=your_access_key -S3_SECRET_ACCESS_KEY=your_secret_key -S3_BUCKET_NAME=your_bucket_name +# Redis Configuration (for Celery and caching) +REDIS_CACHE_URL=localhost +REDIS_CACHE_PORT=6379 +REDIS_CACHE_DB=0 + +# Milvus Vector Database Configuration +MILVUS_URI=http://localhost:19530 +MILVUS_COLLECTION=hr_candidate_profiles + +# S3 Storage Configuration (Selectel/AWS/etc) +S3_ENDPOINT_URL=https://s3.storage.selcloud.ru +S3_ACCESS_KEY_ID=your_s3_access_key +S3_SECRET_ACCESS_KEY=your_s3_secret_key +S3_BUCKET_NAME=your-bucket-name S3_REGION=ru-1 +# LLM API Keys +OPENAI_API_KEY=sk-your-openai-api-key-here +ANTHROPIC_API_KEY=your-anthropic-api-key-here +OPENAI_MODEL=gpt-4o-mini +OPENAI_EMBEDDINGS_MODEL=text-embedding-3-small + +# AI Agent API Keys (for voice interviewer) +DEEPGRAM_API_KEY=your-deepgram-api-key-here +CARTESIA_API_KEY=your-cartesia-api-key-here +ELEVENLABS_API_KEY=your-elevenlabs-api-key-here + +# LiveKit Configuration (for interview feature) +LIVEKIT_URL=ws://localhost:7880 +LIVEKIT_API_KEY=devkey +LIVEKIT_API_SECRET=devkey_secret_32chars_minimum_length + # App Configuration APP_ENV=development DEBUG=true \ No newline at end of file diff --git a/.gitignore b/.gitignore index 0f8bef3..521a697 100644 --- a/.gitignore +++ b/.gitignore @@ -1,2 +1,3 @@ .claude .venv +.idea \ No newline at end of file diff --git a/INTERVIEW_INTEGRATION.md b/INTERVIEW_INTEGRATION.md new file mode 100644 index 0000000..2eda8e8 --- /dev/null +++ b/INTERVIEW_INTEGRATION.md @@ -0,0 +1,330 @@ +# HR Interview Integration with LiveKit + +## Обзор + +Система интеграции для проведения голосовых собеседований с HR AI агентом через LiveKit. Пользователь общается голосом с AI, который анализирует речь, генерирует вопросы через LLM и отвечает синтезированной речью. + +## Архитектура + +### Frontend (Ready) +- ✅ Кнопка "К собеседованию" для резюме со статусом `parsed` +- ✅ Страница `/interview/[id]` с проверкой доступности +- ✅ Компонент `InterviewSession` с LiveKit интеграцией +- ✅ Аудио-только режим (без видео) +- ✅ Управление микрофоном и завершением сессии + +### Backend API Requirements + +#### 1. Валидация резюме для собеседования +```http +GET /api/resume/{resumeId}/validate-interview +``` + +**Response:** +```json +{ + "canInterview": true, + "message": "Resume is ready for interview" +} +``` + +**Error cases:** +- `404` - резюме не найдено +- `400` - резюме не готово (статус не `parsed`) + +#### 2. Получение токена для LiveKit +```http +POST /api/interview/{resumeId}/token +``` + +**Response:** +```json +{ + "token": "livekit_jwt_token_here", + "roomName": "interview_room_123", + "serverUrl": "wss://your-livekit-server.com" +} +``` + +### LiveKit Server Integration + +#### Environment Variables +```env +NEXT_PUBLIC_LIVEKIT_URL=wss://your-livekit-server.com +LIVEKIT_API_KEY=your_api_key +LIVEKIT_API_SECRET=your_api_secret +``` + +#### Server-side Components + +1. **LiveKit Room Management** + - Создание уникальных комнат для каждого собеседования + - Управление участниками (пользователь + AI агент) + - Токены с ограниченными правами + +2. **Speech-to-Text Service** + - Получение аудио потока от пользователя + - Конвертация речи в текст в реальном времени + - Определение пауз в речи для передачи в LLM + +3. **LLM Processing** + - Анализ текста от пользователя + - Генерация вопросов на основе резюме + - Контекст предыдущих ответов + - Логика завершения собеседования + +4. **Text-to-Speech Service** + - Конвертация ответов LLM в аудио + - Передача аудио потока через LiveKit + +#### Data Flow + +```mermaid +sequenceDiagram + participant User + participant Frontend + participant Backend + participant LiveKit + participant STT as Speech-to-Text + participant LLM + participant TTS as Text-to-Speech + + User->>Frontend: Нажимает "К собеседованию" + Frontend->>Backend: GET /api/resume/{id}/validate-interview + Backend-->>Frontend: canInterview: true + Frontend->>Backend: POST /api/interview/{id}/token + Backend-->>Frontend: LiveKit token + Frontend->>LiveKit: Connect to room + + Frontend->>LiveKit: Send start_interview message + LiveKit->>Backend: start_interview event + Backend->>LLM: Generate first question + LLM-->>Backend: Question text + Backend->>TTS: Convert to speech + TTS-->>Backend: Audio data + Backend->>LiveKit: Send audio + question text + LiveKit-->>Frontend: Receive audio + text + + loop Interview Process + User->>LiveKit: Speak answer + LiveKit->>Backend: Audio stream + Backend->>STT: Convert speech + STT-->>Backend: User text + Backend->>LLM: Process answer + generate next question + LLM-->>Backend: Next question or end signal + Backend->>TTS: Convert to speech + TTS-->>Backend: Audio data + Backend->>LiveKit: Send audio + text + LiveKit-->>Frontend: Display question + play audio + end + + Backend->>LiveKit: Send interview_complete + LiveKit-->>Frontend: Show completion + Frontend->>User: Return to vacancy page +``` + +#### Message Protocol + +**Frontend → Server:** +```json +{ + "type": "start_interview", + "resumeId": 123 +} + +{ + "type": "end_interview", + "resumeId": 123 +} +``` + +**Server → Frontend:** +```json +{ + "type": "question", + "text": "Расскажите о своем опыте в разработке", + "questionNumber": 1 +} + +{ + "type": "ai_speaking_start" +} + +{ + "type": "ai_speaking_end" +} + +{ + "type": "interview_complete", + "summary": "Interview completed successfully" +} +``` + +### Implementation Steps + +#### Priority Implementation Order +1. **Phase 1**: Базовые API endpoints + LiveKit токены (минимальный MVP) +2. **Phase 2**: Аудио pipeline (STT/TTS) +3. **Phase 3**: LLM интеграция и логика собеседования + +#### Backend Setup +1. **Install LiveKit SDK** + ```bash + pip install livekit livekit-api + # or + npm install livekit-server-sdk + ``` + +2. **Create LiveKit Room Service** + ```python + from livekit import api + + class InterviewRoomService: + def create_room(self, resume_id: int): + room_name = f"interview_{resume_id}_{timestamp}" + # Create room logic + + def generate_token(self, room_name: str, participant_name: str): + # Generate JWT token + ``` + +3. **Implement API Endpoints** + - Resume validation endpoint + - Token generation endpoint + - WebRTC signaling handling + +4. **Set up Audio Processing Pipeline** + - Speech-to-Text service (Google, Azure, или OpenAI Whisper) + - Text-to-Speech service (Google, Azure, или ElevenLabs) + - LLM integration (OpenAI GPT, Claude, или локальная модель) + +5. **Database Schema Updates** + ```sql + ALTER TABLE resumes ADD COLUMN interview_session_id VARCHAR(255); + ALTER TABLE resumes ADD COLUMN interview_completed_at TIMESTAMP; + + CREATE TABLE interview_sessions ( + id SERIAL PRIMARY KEY, + resume_id INTEGER REFERENCES resumes(id), + room_name VARCHAR(255), + status VARCHAR(50), + started_at TIMESTAMP, + completed_at TIMESTAMP, + transcript TEXT, + ai_feedback TEXT + ); + ``` + +### Security Considerations + +1. **Token Security** + - Ограниченное время жизни токенов + - Права только на конкретную комнату + - Валидация прав пользователя + +2. **Data Privacy** + - Шифрование аудио потоков + - Временное хранение записей + - GDPR соответствие + +3. **Rate Limiting** + - Ограничение на количество сессий + - Защита от злоупотреблений + +### Testing Strategy + +1. **Unit Tests** + - API endpoints + - Token generation + - Message handling + +2. **Integration Tests** + - LiveKit connection + - Audio pipeline + - LLM integration + +3. **Load Testing** + - Множественные одновременные сессии + - Производительность аудио обработки + +### Monitoring & Analytics + +1. **Metrics** + - Время подключения к сессии + - Качество аудио соединения + - Длительность собеседований + - Процент завершенных интервью + +2. **Logging** + - События соединения + - Ошибки аудио обработки + - LLM запросы и ответы + +### Deployment + +1. **LiveKit Server** + - Развертывание сервера LiveKit + - SSL сертификаты + - Настройка TURN серверов для NAT + +2. **Scaling** + - Горизонтальное масштабирование + - Load balancing + - CDN для статических ресурсов + +## Quick Start для Backend разработчика + +### Minimum Viable Product (1-2 дня) +1. **Заглушки API endpoints:** + ```python + # Всегда возвращать canInterview: true + GET /api/resume/{id}/validate-interview + + # Вернуть тестовый LiveKit токен + POST /api/interview/{id}/token + ``` + +2. **Настроить LiveKit server локально:** + ```bash + docker run --rm -p 7880:7880 -p 7881:7881 -p 7882:7882 \ + -e LIVEKIT_KEYS="your-api-key: your-secret-key" \ + livekit/livekit-server:latest + ``` + +3. **Тестирование соединения:** + - Frontend должен подключиться к LiveKit + - Пользователь увидит интерфейс собеседования + - Микрофон должен работать + +### Full Implementation (1-2 недели) +После MVP добавить: +- Реальную логику валидации резюме +- Speech-to-Text processing +- LLM для генерации вопросов +- Text-to-Speech для ответов AI +- Сохранение результатов в БД + +### Возможные упрощения для MVP: +- Валидацию можно убрать (всегда разрешать) +- Вместо AI агента показывать заглушку "Скоро здесь будет AI интервьюер" +- Логи всех действий в консоль для отладки + +## Current Status + +✅ **Completed:** +- Frontend компоненты готовы +- LiveKit интеграция настроена +- UI для собеседования реализован + +🔄 **Next Steps:** +1. Реализовать backend API endpoints +2. Настроить LiveKit server +3. Интегрировать Speech-to-Text/Text-to-Speech +4. Подключить LLM для генерации вопросов +5. Тестирование и отладка + +### Contact Frontend Developer +При вопросах по интеграции: +- Формат сообщений между клиентом и сервером +- Структура токенов LiveKit +- Обработка ошибок на фронтенде \ No newline at end of file diff --git a/TESTING_GUIDE.md b/TESTING_GUIDE.md new file mode 100644 index 0000000..6557433 --- /dev/null +++ b/TESTING_GUIDE.md @@ -0,0 +1,272 @@ +# 🧪 HR-AI Backend Testing Guide + +## ✅ System Status + +**Core Components:** All PASS ✅ +- ✅ Database (PostgreSQL) - Connected, 1 resume found +- ✅ RAG System (OpenAI) - Resume parsing works +- ✅ Redis - Connected for Celery tasks +- ✅ Interview Service - Token generation works +- ✅ AI Agent - Initialization and plan handling works + +## 🚀 Quick Start Testing (Without Voice) + +### 1. Start the Services + +```bash +# Terminal 1: Start FastAPI server +uvicorn app.main:app --reload --port 8000 + +# Terminal 2: Start Celery worker +celery -A celery_worker.celery_app worker --loglevel=info + +# Terminal 3: Monitor system +python simple_test.py +``` + +### 2. Test Resume Upload & Processing + +```bash +# Create test resume file +echo "John Doe +Python Developer +Experience: 3 years +Skills: Python, Django, FastAPI, PostgreSQL +Education: Computer Science +Email: john@example.com +Phone: +1234567890" > test_resume.txt + +# Upload via API +curl -X POST "http://localhost:8000/resume/upload" \ + -H "Content-Type: multipart/form-data" \ + -F "file=@test_resume.txt" \ + -F "applicant_name=John Doe" \ + -F "applicant_email=john@example.com" \ + -F "applicant_phone=+1234567890" \ + -F "vacancy_id=1" +``` + +### 3. Check Processing Results + +```bash +# Check resume in database +curl http://localhost:8000/resume/1 + +# Check interview plan generation +# Should see interview_plan field with structured questions +``` + +### 4. Test Interview Session Creation + +```bash +# Create interview session +curl -X POST "http://localhost:8000/interview/1/start" \ + -H "Content-Type: application/json" +``` + +## 🎤 Full Voice Testing (Requires Additional Setup) + +### Prerequisites for Voice Testing + +**1. LiveKit Server** +```bash +# Download LiveKit server +docker run --rm -p 7880:7880 -p 7881:7881 \ + livekit/livekit-server --dev +``` + +**2. Voice API Keys (Optional - has fallbacks)** + +Add to your `.env` file: +```bash +# For better STT (Speech-to-Text) +DEEPGRAM_API_KEY=your-deepgram-key + +# For better TTS (Text-to-Speech) +CARTESIA_API_KEY=your-cartesia-key +# OR +ELEVENLABS_API_KEY=your-elevenlabs-key +``` + +### Voice Interview Testing + +**1. Start Complete Stack** +```bash +# All previous services PLUS: +# Terminal 4: LiveKit server (see above) +``` + +**2. Create Voice Interview Session** +```bash +# This will start AI agent subprocess +curl -X POST "http://localhost:8000/interview/1/token" +``` + +**3. Monitor AI Processes** +```bash +# Check running AI agents +curl http://localhost:8000/admin/interview-processes + +# System stats +curl http://localhost:8000/admin/system-stats +``` + +## 📊 Monitoring & Debugging + +### 1. Check System Health +```bash +python simple_test.py +``` + +### 2. Monitor Celery Tasks +- Open Celery worker terminal +- Should see task processing logs + +### 3. Database Inspection +```sql +-- Check resumes +SELECT id, applicant_name, status, interview_plan IS NOT NULL as has_plan +FROM resume; + +-- Check interview sessions +SELECT id, room_name, status, ai_agent_pid, ai_agent_status +FROM interview_sessions; +``` + +### 4. Process Management +```bash +# List active AI processes +curl http://localhost:8000/admin/interview-processes + +# Stop specific process +curl -X POST http://localhost:8000/admin/interview-processes/1/stop + +# Cleanup dead processes +curl -X POST http://localhost:8000/admin/interview-processes/cleanup +``` + +## 🔧 Troubleshooting + +### Common Issues + +**1. "Database connection error"** +- Check PostgreSQL is running +- Verify DATABASE_URL in config +- Run: `alembic upgrade head` + +**2. "RAG system error"** +- Check OPENAI_API_KEY is set +- Verify internet connection + +**3. "Redis connection error"** +```bash +docker run -d -p 6379:6379 redis:alpine +``` + +**4. "Import errors"** +- Make sure you're in project root directory +- Check virtual environment is activated + +**5. "Celery tasks not processing"** +- Ensure Redis is running +- Check Celery worker logs +- Restart Celery worker + +### Performance Testing + +**Test Multiple Concurrent Interviews:** +```bash +# Create 5 interview sessions simultaneously +for i in {1..5}; do + curl -X POST "http://localhost:8000/interview/$i/token" & +done +wait + +# Monitor system resources +curl http://localhost:8000/admin/system-stats +``` + +## 🧪 Test Scenarios + +### Scenario 1: Basic Resume Processing +1. Upload resume → Check parsing +2. Verify interview plan generation +3. Confirm data in database + +### Scenario 2: Interview Session Lifecycle +1. Create session → Get token +2. Start AI agent → Monitor process +3. Stop session → Verify cleanup + +### Scenario 3: Multi-User Load Test +1. Upload 10 resumes simultaneously +2. Create 5 interview sessions +3. Monitor system resources +4. Check process management + +### Scenario 4: Error Recovery +1. Stop Redis → Resume upload should queue +2. Start Redis → Tasks should process +3. Kill AI process → Should be detected and cleaned + +## 📈 Expected Performance + +**Single Interview:** +- Memory: ~45MB per AI agent process +- CPU: ~5-15% during active conversation +- Startup: ~3-5 seconds per agent + +**System Limits:** +- Recommended max: 50 concurrent interviews +- Theoretical max: ~150 interviews (on 32GB RAM) + +## 🎯 Success Criteria + +**✅ Basic Functionality:** +- [ ] Resume upload and parsing works +- [ ] Interview plans are generated +- [ ] Database stores all data correctly +- [ ] Celery processes tasks + +**✅ Interview System:** +- [ ] Interview sessions can be created +- [ ] AI agent processes start successfully +- [ ] Tokens are generated correctly +- [ ] Process monitoring works + +**✅ Advanced Features:** +- [ ] Multiple concurrent interviews +- [ ] Process cleanup works +- [ ] System monitoring provides accurate data +- [ ] Error recovery works correctly + +**✅ Voice Testing (Optional):** +- [ ] LiveKit connection established +- [ ] STT/TTS services work (if configured) +- [ ] Real-time conversation flows +- [ ] Session termination works properly + +## 📝 Test Results Log + +Keep track of your testing: + +``` +Date: ___________ +System Test: PASS/FAIL +Resume Upload: PASS/FAIL +Interview Creation: PASS/FAIL +AI Agent Start: PASS/FAIL +Voice Test: PASS/FAIL (if attempted) + +Notes: +_________________________________ +_________________________________ +``` + +--- + +## 🎉 Ready to Test! + +Start with the **Quick Start Testing** section above. The system is ready for basic testing without voice features. For full voice testing, set up LiveKit server and optionally add voice API keys. + +Good luck! 🚀 \ No newline at end of file diff --git a/ai_interviewer_agent.py b/ai_interviewer_agent.py new file mode 100644 index 0000000..a20ba22 --- /dev/null +++ b/ai_interviewer_agent.py @@ -0,0 +1,416 @@ +# -*- coding: utf-8 -*- +import asyncio +import json +import logging +import os +from typing import Dict, List +from datetime import datetime + +# Принудительно устанавливаем UTF-8 для Windows +if os.name == 'nt': # Windows + import sys + if hasattr(sys, 'stdout') and hasattr(sys.stdout, 'reconfigure'): + sys.stdout.reconfigure(encoding='utf-8', errors='replace') + sys.stderr.reconfigure(encoding='utf-8', errors='replace') +from livekit.agents import ( + Agent, + AgentSession, + JobContext, + WorkerOptions, + cli, +) +from livekit.plugins import openai, deepgram, cartesia, silero, resemble +from rag.settings import settings + +logger = logging.getLogger("ai-interviewer") +logger.setLevel(logging.INFO) + +class InterviewAgent: + """AI Agent для проведения собеседований с управлением диалогом""" + + def __init__(self, interview_plan: Dict): + self.interview_plan = interview_plan + self.conversation_history = [] + + # Состояние диалога + self.current_section = 0 + self.current_question_in_section = 0 + self.questions_asked_total = 0 + self.waiting_for_response = False + self.last_question = None + self.last_user_response = None + + # Извлекаем структуру интервью + self.sections = self.interview_plan.get('interview_structure', {}).get('sections', []) + self.total_sections = len(self.sections) + + def get_current_section(self) -> Dict: + """Получить текущую секцию интервью""" + if self.current_section < len(self.sections): + return self.sections[self.current_section] + return {} + + def get_next_question(self) -> str: + """Получить следующий вопрос из текущей секции""" + section = self.get_current_section() + questions = section.get('questions', []) + + if self.current_question_in_section < len(questions): + return questions[self.current_question_in_section] + return None + + def move_to_next_question(self): + """Переход к следующему вопросу""" + section = self.get_current_section() + questions = section.get('questions', []) + + self.current_question_in_section += 1 + self.questions_asked_total += 1 + + # Если вопросы в секции закончились, переходим к следующей + if self.current_question_in_section >= len(questions): + self.move_to_next_section() + + def move_to_next_section(self): + """Переход к следующей секции""" + self.current_section += 1 + self.current_question_in_section = 0 + + if self.current_section < len(self.sections): + logger.info(f"Переход к секции: {self.sections[self.current_section].get('name', 'Unnamed')}") + + def is_interview_complete(self) -> bool: + """Проверяет, завершено ли интервью""" + return self.current_section >= len(self.sections) + + async def analyze_user_response(self, response: str, chat_model) -> Dict[str, str]: + """Анализирует ответ пользователя и решает следующий шаг""" + try: + from rag.registry import registry + + analysis_prompt = f""" + Проанализируй ответ кандидата на интервью и определи следующий шаг. + + КОНТЕКСТ: + - Текущая секция: {self.get_current_section().get('name', 'Unknown')} + - Заданный вопрос: {self.last_question} + - Ответ кандидата: {response} + + Оцени ответ и определи действие: + 1. "continue" - ответ полный, переходим к следующему вопросу + 2. "clarify" - нужно уточнить или углубить ответ + 3. "redirect" - нужно перенаправить на тему + + Ответь в JSON формате: + {{ + "action": "continue|clarify|redirect", + "reason": "Объяснение решения", + "follow_up_question": "Уточняющий вопрос если action=clarify или redirect" + }} + """ + + from langchain.schema import HumanMessage, SystemMessage + messages = [ + SystemMessage(content="Ты эксперт-аналитик интервью. Анализируй ответы объективно."), + HumanMessage(content=analysis_prompt) + ] + + response_analysis = chat_model.chat(messages) + response_text = response_analysis.content.strip() + + # Парсим JSON ответ + if response_text.startswith('{') and response_text.endswith('}'): + return json.loads(response_text) + else: + # Fallback + return { + "action": "continue", + "reason": "Не удалось проанализировать ответ", + "follow_up_question": "" + } + + except Exception as e: + logger.error(f"Ошибка анализа ответа: {str(e)}") + return { + "action": "continue", + "reason": "Ошибка анализа", + "follow_up_question": "" + } + + def _extract_questions_from_plan(self) -> List[str]: + """Извлечение вопросов из готового плана интервью""" + questions = [] + + try: + # Начинаем с приветствия из плана + greeting = self.interview_plan.get('interview_structure', {}).get('greeting', 'Привет! Готов к интервью?') + questions.append(greeting) + + # Извлекаем вопросы из секций + sections = self.interview_plan.get('interview_structure', {}).get('sections', []) + + for section in sections: + section_questions = section.get('questions', []) + questions.extend(section_questions) + + return questions + + except Exception as e: + logger.error(f"Ошибка извлечения вопросов из плана: {str(e)}") + # Fallback вопросы + return [ + "Привет! Расскажи немного о себе", + "Какой у тебя опыт работы?", + "Что тебя привлекает в этой позиции?", + "Есть ли у тебя вопросы ко мне?" + ] + + def get_system_instructions(self) -> str: + """Системные инструкции для AI агента""" + candidate_info = self.interview_plan.get('candidate_info', {}) + interview_structure = self.interview_plan.get('interview_structure', {}) + focus_areas = self.interview_plan.get('focus_areas', []) + + greeting = interview_structure.get('greeting', 'Привет! Готов к интервью?') + + current_section = self.get_current_section() + current_section_name = current_section.get('name', 'Неизвестно') + progress = f"{self.current_section + 1}/{len(self.sections)}" + + return f"""Ты опытный HR-интервьюер, который проводит структурированное голосовое собеседование. + +ИНФОРМАЦИЯ О КАНДИДАТЕ: +- Имя: {candidate_info.get('name', 'Кандидат')} +- Опыт работы: {candidate_info.get('total_years', 0)} лет +- Ключевые навыки: {', '.join(candidate_info.get('skills', []))} + +ТЕКУЩЕЕ СОСТОЯНИЕ ИНТЕРВЬЮ: +- Прогресс: {progress} секций +- Текущая секция: {current_section_name} +- Вопросов задано: {self.questions_asked_total} + +ПЛАН ИНТЕРВЬЮ: +{json.dumps(interview_structure.get('sections', []), ensure_ascii=False, indent=2)} + +ТВОЯ ЗАДАЧА: +1. Веди живое интерактивное интервью +2. Анализируй каждый ответ кандидата +3. Принимай решения: + - Если ответ полный и достаточный → переходи к следующему вопросу + - Если ответ поверхностный → задавай уточняющие вопросы + - Если кандидат ушел от темы → мягко возвращай к вопросу +4. Поддерживай естественный диалог + +ПРАВИЛА ВЕДЕНИЯ ДИАЛОГА: +✅ Говори только на русском языке +✅ Задавай один вопрос за раз и жди ответа +✅ Анализируй качество и полноту каждого ответа +✅ Адаптируй следующие вопросы под полученные ответы +✅ Показывай искреннюю заинтересованность +✅ Если ответ неполный - углубляйся: "Расскажи подробнее...", "А как именно ты..." +✅ При переходе между секциями делай плавные переходы +✅ Завершай интервью благодарностью и следующими шагами + +ПРИМЕРЫ РЕАКЦИЙ НА ОТВЕТЫ: +- Короткий ответ: "Интересно! А можешь рассказать конкретный пример?" +- Хороший ответ: "Отлично! Давай перейдем к следующему вопросу..." +- Уход от темы: "Понимаю, но давай вернемся к..." + +НАЧНИ С ПРИВЕТСТВИЯ: {greeting} +""" + + +async def entrypoint(ctx: JobContext): + """Точка входа для AI агента""" + logger.info("Starting AI Interviewer Agent") + + # Получаем данные о резюме из метаданных комнаты + room_metadata = ctx.room.metadata if ctx.room.metadata else "{}" + try: + metadata = json.loads(room_metadata) + interview_plan = metadata.get("interview_plan", {}) + if not hasattr(interview_plan, 'interview_structure'): + raise ValueError + except: + # Fallback план для тестирования + interview_plan = { + "interview_structure": { + "duration_minutes": 30, + "greeting": "Привет! Готов к тестовому интервью?", + "sections": [ + { + "name": "Знакомство", + "duration_minutes": 10, + "questions": ["Расскажи о себе", "Что тебя привлекло в этой позиции?"] + }, + { + "name": "Технические навыки", + "duration_minutes": 15, + "questions": ["Расскажи о своем опыте с Python", "Какие проекты разрабатывал?"] + }, + { + "name": "Вопросы кандидата", + "duration_minutes": 5, + "questions": ["Есть ли у тебя вопросы ко мне?"] + } + ] + }, + "focus_areas": ["technical_skills", "experience"], + "candidate_info": { + "name": "Тестовый кандидат", + "skills": ["Python", "React", "PostgreSQL"], + "total_years": 3, + "education": "Высшее техническое" + } + } + + logger.info(f"Interview plan: {interview_plan}") + + # Создаем интервьюера с планом + interviewer = InterviewAgent(interview_plan) + + # Настройка STT (Speech-to-Text) + if hasattr(settings, 'deepgram_api_key') and settings.deepgram_api_key: + stt = deepgram.STT( + model="nova-2-general", + language="ru", # Русский язык + api_key=settings.deepgram_api_key + ) + else: + # Fallback на OpenAI Whisper + stt = openai.STT( + model="whisper-1", + language="ru", + api_key=settings.openai_api_key + ) + + # Настройка LLM + llm = openai.LLM( + model="gpt-4o-mini", + api_key=settings.openai_api_key, + temperature=0.7, + ) + + # Настройка TTS (Text-to-Speech) + if hasattr(settings, 'resemble_api_key') and settings.resemble_api_key: + tts = resemble.TTS( + voice_uuid="55592656", + api_key=settings.resemble_api_key + ) + else: + # Fallback на локальный TTS + tts = silero.TTS( + language="ru", + model="v4_ru" + ) + + # Создание агента с системными инструкциями + agent = Agent( + instructions=interviewer.get_system_instructions() + ) + + # Создание сессии агента + session = AgentSession( + vad=silero.VAD.load(), # Voice Activity Detection + stt=stt, + llm=llm, + tts=tts, + ) + + # Добавляем обработчики событий с управлением диалогом + @session.on("user_speech_committed") + def on_user_speech(msg): + """Синхронный callback. Внутри создаётся async-задача.""" + + async def handler(): + user_response = msg.content + logger.info(f"User said: {user_response}") + + # Сохраняем историю + interviewer.conversation_history.append({ + "role": "user", + "content": user_response, + "timestamp": datetime.utcnow().isoformat(), + "section": interviewer.get_current_section().get('name', 'Unknown') + }) + + interviewer.last_user_response = user_response + interviewer.waiting_for_response = False + + try: + # Анализ ответа + analysis = await interviewer.analyze_user_response(user_response, llm) + action = analysis.get("action", "continue") + + logger.info(f"Response analysis: {action} - {analysis.get('reason', 'No reason')}") + + if action == "continue": + interviewer.move_to_next_question() + + if not interviewer.is_interview_complete(): + next_question = interviewer.get_next_question() + if next_question: + await session.say(next_question) + interviewer.last_question = next_question + interviewer.waiting_for_response = True + else: + await session.say( + "Спасибо за интервью! Это все вопросы, которые я хотел задать. " + "В ближайшее время мы свяжемся с тобой по результатам." + ) + + elif action in ["clarify", "redirect"]: + follow_up = analysis.get("follow_up_question", "Можешь рассказать подробнее?") + await session.say(follow_up) + interviewer.waiting_for_response = True + + except Exception as e: + logger.error(f"Ошибка обработки ответа пользователя: {str(e)}") + interviewer.move_to_next_question() + + # запускаем асинхронный обработчик + asyncio.create_task(handler()) + + @session.on("agent_speech_committed") + def on_agent_speech(msg): + """Обработка речи агента""" + agent_response = msg.content + logger.info(f"Agent said: {agent_response}") + + # Сохраняем в историю + interviewer.conversation_history.append({ + "role": "assistant", + "content": agent_response, + "timestamp": datetime.utcnow().isoformat(), + "section": interviewer.get_current_section().get('name', 'Unknown') + }) + + # Если это вопрос, обновляем состояние + if "?" in agent_response: + interviewer.last_question = agent_response + interviewer.waiting_for_response = True + + # Запускаем сессию агента + await session.start(agent=agent, room=ctx.room) + + # Приветственное сообщение + # В новой версии приветствие будет автоматически отправлено из системных инструкций + + logger.info("AI Interviewer started successfully") + + +def main(): + """Запуск агента""" + logging.basicConfig(level=logging.INFO) + + # Настройки воркера + worker_options = WorkerOptions( + entrypoint_fnc=entrypoint, + ) + + # Запуск через CLI + cli.run_app(worker_options) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/app/core/config.py b/app/core/config.py index 564f7f5..06f6cbc 100644 --- a/app/core/config.py +++ b/app/core/config.py @@ -3,14 +3,43 @@ from typing import Optional class Settings(BaseSettings): - database_url: str = "postgresql+asyncpg://user:password@localhost:5432/hr_ai_db" + # Database + database_url: str = "postgresql+asyncpg://tdjx:1309@localhost:5432/hr_ai" + # Redis Configuration (for Celery and caching) + redis_cache_url: str = "localhost" + redis_cache_port: int = 6379 + redis_cache_db: int = 0 + + # Milvus Vector Database + milvus_uri: str = "http://localhost:19530" + milvus_collection: str = "candidate_profiles" + + # S3 Storage s3_endpoint_url: str = "https://s3.selcdn.ru" s3_access_key_id: str s3_secret_access_key: str s3_bucket_name: str s3_region: str = "ru-1" + # LLM API Keys + openai_api_key: Optional[str] = None + anthropic_api_key: Optional[str] = None + openai_model: str = "gpt-4o-mini" + openai_embeddings_model: str = "text-embedding-3-small" + + # AI Agent API Keys (for voice interviewer) + deepgram_api_key: Optional[str] = None + cartesia_api_key: Optional[str] = None + elevenlabs_api_key: Optional[str] = None + resemble_api_key: Optional[str] = None + + # LiveKit Configuration + livekit_url: str = "ws://localhost:7880" + livekit_api_key: str = "devkey" + livekit_api_secret: str = "devkey_secret_32chars_minimum_length" + + # App Configuration app_env: str = "development" debug: bool = True diff --git a/app/models/interview.py b/app/models/interview.py new file mode 100644 index 0000000..943c8f4 --- /dev/null +++ b/app/models/interview.py @@ -0,0 +1,66 @@ +from sqlmodel import SQLModel, Field, Column +from sqlalchemy import Enum as SQLEnum, String +from datetime import datetime +from typing import Optional +from enum import Enum + + +class InterviewStatus(str, Enum): + CREATED = "created" + ACTIVE = "active" + COMPLETED = "completed" + FAILED = "failed" + + def __str__(self): + return self.value + + +class InterviewSessionBase(SQLModel): + resume_id: int = Field(foreign_key="resume.id") + room_name: str = Field(max_length=255, unique=True) + status: str = Field( + default="created", + sa_column=Column(SQLEnum('created', 'active', 'completed', 'failed', name="interviewstatus")) + ) + transcript: Optional[str] = None + ai_feedback: Optional[str] = None + # Добавляем отслеживание AI процесса + ai_agent_pid: Optional[int] = None + ai_agent_status: str = Field(default="not_started") # not_started, running, stopped, failed + + +class InterviewSession(InterviewSessionBase, table=True): + __tablename__ = "interview_sessions" + + id: Optional[int] = Field(default=None, primary_key=True) + started_at: datetime = Field(default_factory=datetime.utcnow) + completed_at: Optional[datetime] = None + + +class InterviewSessionCreate(SQLModel): + resume_id: int + room_name: str + + +class InterviewSessionUpdate(SQLModel): + status: Optional[InterviewStatus] = None + completed_at: Optional[datetime] = None + transcript: Optional[str] = None + ai_feedback: Optional[str] = None + + +class InterviewSessionRead(InterviewSessionBase): + id: int + started_at: datetime + completed_at: Optional[datetime] = None + + +class InterviewValidationResponse(SQLModel): + can_interview: bool + message: str + + +class LiveKitTokenResponse(SQLModel): + token: str + room_name: str + server_url: str \ No newline at end of file diff --git a/app/models/resume.py b/app/models/resume.py index 0324fa8..b21442c 100644 --- a/app/models/resume.py +++ b/app/models/resume.py @@ -1,4 +1,5 @@ -from sqlmodel import SQLModel, Field, Relationship +from sqlmodel import SQLModel, Field, Relationship, Column +from sqlalchemy import JSON from typing import Optional from datetime import datetime from enum import Enum @@ -6,6 +7,9 @@ from enum import Enum class ResumeStatus(str, Enum): PENDING = "pending" + PARSING = "parsing" + PARSED = "parsed" + PARSE_FAILED = "parse_failed" UNDER_REVIEW = "under_review" INTERVIEW_SCHEDULED = "interview_scheduled" INTERVIEWED = "interviewed" @@ -24,6 +28,9 @@ class ResumeBase(SQLModel): status: ResumeStatus = Field(default=ResumeStatus.PENDING) interview_report_url: Optional[str] = None notes: Optional[str] = None + parsed_data: Optional[dict] = Field(default=None, sa_column=Column(JSON)) + interview_plan: Optional[dict] = Field(default=None, sa_column=Column(JSON)) + parse_error: Optional[str] = None class Resume(ResumeBase, table=True): @@ -49,6 +56,9 @@ class ResumeUpdate(SQLModel): status: Optional[ResumeStatus] = None interview_report_url: Optional[str] = None notes: Optional[str] = None + parsed_data: Optional[dict] = Field(default=None, sa_column=Column(JSON)) + interview_plan: Optional[dict] = Field(default=None, sa_column=Column(JSON)) + parse_error: Optional[str] = None class ResumeRead(ResumeBase): diff --git a/app/routers/admin_router.py b/app/routers/admin_router.py new file mode 100644 index 0000000..7b03266 --- /dev/null +++ b/app/routers/admin_router.py @@ -0,0 +1,117 @@ +from fastapi import APIRouter, Depends, HTTPException +from sqlalchemy.ext.asyncio import AsyncSession +from app.core.database import get_db +from app.services.interview_service import InterviewRoomService +from typing import List, Dict +import psutil + +router = APIRouter(prefix="/admin", tags=["Admin"]) + + +@router.get("/interview-processes") +async def list_active_interview_processes(db: AsyncSession = Depends(get_db)) -> Dict: + """Список всех активных AI процессов интервью""" + interview_service = InterviewRoomService(db) + + active_sessions = await interview_service.get_active_agent_processes() + + processes_info = [] + for session in active_sessions: + process_info = { + "session_id": session.id, + "resume_id": session.resume_id, + "room_name": session.room_name, + "pid": session.ai_agent_pid, + "status": session.ai_agent_status, + "started_at": session.started_at.isoformat() if session.started_at else None, + "is_running": False, + "memory_mb": 0, + "cpu_percent": 0 + } + + # Проверяем реальное состояние процесса + if session.ai_agent_pid: + try: + process = psutil.Process(session.ai_agent_pid) + if process.is_running(): + process_info["is_running"] = True + process_info["memory_mb"] = round(process.memory_info().rss / 1024 / 1024, 1) + process_info["cpu_percent"] = round(process.cpu_percent(), 1) + except (psutil.NoSuchProcess, psutil.AccessDenied): + pass + + processes_info.append(process_info) + + return { + "total_active_sessions": len(active_sessions), + "processes": processes_info + } + + +@router.post("/interview-processes/{session_id}/stop") +async def stop_interview_process(session_id: int, db: AsyncSession = Depends(get_db)) -> Dict: + """Остановить AI процесс для конкретного интервью""" + interview_service = InterviewRoomService(db) + + success = await interview_service.stop_agent_process(session_id) + + if success: + return {"message": f"AI process for session {session_id} stopped successfully"} + else: + raise HTTPException(status_code=404, detail=f"Session {session_id} not found or process not running") + + +@router.post("/interview-processes/cleanup") +async def cleanup_dead_processes(db: AsyncSession = Depends(get_db)) -> Dict: + """Очистка мертвых процессов""" + interview_service = InterviewRoomService(db) + + cleaned_count = await interview_service.cleanup_dead_processes() + + return { + "message": f"Cleaned up {cleaned_count} dead processes" + } + + +@router.get("/system-stats") +async def get_system_stats() -> Dict: + """Общая статистика системы""" + try: + # Общая информация о системе + cpu_percent = psutil.cpu_percent(interval=1) + memory = psutil.virtual_memory() + disk = psutil.disk_usage('/') + + # Поиск всех Python процессов (потенциальные AI агенты) + python_processes = [] + for proc in psutil.process_iter(['pid', 'name', 'memory_info', 'cpu_percent', 'cmdline']): + try: + if proc.info['name'] and 'python' in proc.info['name'].lower(): + cmdline = ' '.join(proc.info['cmdline']) if proc.info['cmdline'] else '' + if 'ai_interviewer_agent' in cmdline: + python_processes.append({ + 'pid': proc.info['pid'], + 'memory_mb': round(proc.info['memory_info'].rss / 1024 / 1024, 1), + 'cpu_percent': proc.info['cpu_percent'] or 0, + 'cmdline': cmdline + }) + except (psutil.NoSuchProcess, psutil.AccessDenied, psutil.ZombieProcess): + pass + + return { + "system": { + "cpu_percent": cpu_percent, + "memory_percent": memory.percent, + "memory_available_gb": round(memory.available / 1024 / 1024 / 1024, 1), + "disk_percent": disk.percent, + "disk_free_gb": round(disk.free / 1024 / 1024 / 1024, 1) + }, + "ai_agents": { + "count": len(python_processes), + "total_memory_mb": sum(p['memory_mb'] for p in python_processes), + "processes": python_processes + } + } + + except Exception as e: + raise HTTPException(status_code=500, detail=f"Error getting system stats: {str(e)}") \ No newline at end of file diff --git a/app/routers/interview_router.py b/app/routers/interview_router.py new file mode 100644 index 0000000..76ebd2b --- /dev/null +++ b/app/routers/interview_router.py @@ -0,0 +1,91 @@ +from fastapi import APIRouter, Depends, HTTPException, Request +from sqlalchemy.ext.asyncio import AsyncSession +from app.core.session_middleware import get_current_session, get_db_session +from app.models.session import Session +from app.models.interview import InterviewValidationResponse, LiveKitTokenResponse, InterviewStatus +from app.services.interview_service import InterviewRoomService + +router = APIRouter(prefix="/interview", tags=["interview"]) + + +@router.get("/{resume_id}/validate-interview", response_model=InterviewValidationResponse) +async def validate_interview( + request: Request, + resume_id: int, + current_session: Session = Depends(get_current_session), + db_session: AsyncSession = Depends(get_db_session) +): + """Валидация резюме для проведения собеседования""" + if not current_session: + raise HTTPException(status_code=401, detail="No active session") + + interview_service = InterviewRoomService(db_session) + + # Проверяем валидность резюме для собеседования + validation_result = await interview_service.validate_resume_for_interview(resume_id) + + # Если резюме не найдено, возвращаем 404 + if "not found" in validation_result.message.lower(): + raise HTTPException(status_code=404, detail=validation_result.message) + + # Если резюме не готово, возвращаем 400 + if not validation_result.can_interview and "not ready" in validation_result.message.lower(): + raise HTTPException(status_code=400, detail=validation_result.message) + + return validation_result + + +@router.post("/{resume_id}/token", response_model=LiveKitTokenResponse) +async def get_interview_token( + request: Request, + resume_id: int, + current_session: Session = Depends(get_current_session), + db_session: AsyncSession = Depends(get_db_session) +): + """Получение токена для LiveKit собеседования""" + if not current_session: + raise HTTPException(status_code=401, detail="No active session") + + interview_service = InterviewRoomService(db_session) + + # Получаем токен для LiveKit + token_response = await interview_service.get_livekit_token(resume_id) + + if not token_response: + raise HTTPException( + status_code=400, + detail="Cannot create interview session. Check if resume is ready for interview." + ) + + return token_response + + +@router.patch("/{resume_id}/end") +async def end_interview( + request: Request, + resume_id: int, + current_session: Session = Depends(get_current_session), + db_session: AsyncSession = Depends(get_db_session) +): + """Завершение собеседования""" + if not current_session: + raise HTTPException(status_code=401, detail="No active session") + + interview_service = InterviewRoomService(db_session) + + # Получаем активную сессию собеседования + interview_session = await interview_service.get_interview_session(resume_id) + + if not interview_session: + raise HTTPException(status_code=404, detail="No active interview session found") + + # Завершаем сессию + success = await interview_service.update_session_status( + interview_session.id, + "completed" + ) + + if not success: + raise HTTPException(status_code=500, detail="Failed to end interview session") + + return {"message": "Interview session ended successfully"} \ No newline at end of file diff --git a/app/routers/resume_router.py b/app/routers/resume_router.py index dc612ba..ebbee7d 100644 --- a/app/routers/resume_router.py +++ b/app/routers/resume_router.py @@ -6,6 +6,8 @@ from app.models.resume import ResumeCreate, ResumeUpdate, ResumeRead, ResumeStat from app.models.session import Session from app.services.resume_service import ResumeService from app.services.file_service import FileService +from celery_worker.tasks import parse_resume_task +from celery_worker.celery_app import celery_app router = APIRouter(prefix="/resumes", tags=["resumes"]) @@ -28,10 +30,12 @@ async def create_resume( file_service = FileService() resume_service = ResumeService(db_session) - resume_file_url = await file_service.upload_resume_file(resume_file) - if not resume_file_url: + upload_result = await file_service.upload_resume_file(resume_file) + if not upload_result: raise HTTPException(status_code=400, detail="Failed to upload resume file") + resume_file_url, local_file_path = upload_result + resume_data = ResumeCreate( vacancy_id=vacancy_id, applicant_name=applicant_name, @@ -41,7 +45,25 @@ async def create_resume( cover_letter=cover_letter ) - return await resume_service.create_resume_with_session(resume_data, current_session.id) + # Создаем резюме в БД + created_resume = await resume_service.create_resume_with_session(resume_data, current_session.id) + + # Запускаем асинхронную задачу парсинга резюме + try: + # Запускаем Celery task для парсинга с локальным файлом + task_result = parse_resume_task.delay(str(created_resume.id), local_file_path) + + # Добавляем task_id в ответ для отслеживания статуса + response_data = created_resume.model_dump() + response_data["parsing_task_id"] = task_result.id + response_data["parsing_status"] = "started" + + return response_data + + except Exception as e: + # Если не удалось запустить парсинг, оставляем резюме в статусе PENDING + print(f"Failed to start parsing task for resume {created_resume.id}: {str(e)}") + return created_resume @router.get("/", response_model=List[ResumeRead]) @@ -170,6 +192,72 @@ async def upload_interview_report( return {"message": "Interview report uploaded successfully", "report_url": report_url} +@router.get("/{resume_id}/parsing-status") +async def get_parsing_status( + request: Request, + resume_id: int, + task_id: str = Query(..., description="Task ID from resume upload response"), + current_session: Session = Depends(get_current_session), + db_session: AsyncSession = Depends(get_db_session) +): + """Получить статус парсинга резюме по task_id""" + if not current_session: + raise HTTPException(status_code=401, detail="No active session") + + # Проверяем доступ к резюме + service = ResumeService(db_session) + resume = await service.get_resume(resume_id) + + if not resume: + raise HTTPException(status_code=404, detail="Resume not found") + + if resume.session_id != current_session.id: + raise HTTPException(status_code=403, detail="Access denied") + + # Получаем статус задачи из Celery + try: + task_result = celery_app.AsyncResult(task_id) + + response = { + "task_id": task_id, + "task_state": task_result.state, + "resume_status": resume.status, + } + + if task_result.state == 'PENDING': + response.update({ + "status": "В очереди на обработку", + "progress": 0 + }) + elif task_result.state == 'PROGRESS': + response.update({ + "status": task_result.info.get('status', 'Обрабатывается'), + "progress": task_result.info.get('progress', 0) + }) + elif task_result.state == 'SUCCESS': + response.update({ + "status": "Завершено успешно", + "progress": 100, + "result": task_result.info + }) + elif task_result.state == 'FAILURE': + response.update({ + "status": f"Ошибка: {str(task_result.info)}", + "progress": 0, + "error": str(task_result.info) + }) + + return response + + except Exception as e: + return { + "task_id": task_id, + "task_state": "UNKNOWN", + "resume_status": resume.status, + "error": f"Failed to get task status: {str(e)}" + } + + @router.delete("/{resume_id}") async def delete_resume( request: Request, diff --git a/app/services/ai_interviewer_service.py b/app/services/ai_interviewer_service.py new file mode 100644 index 0000000..76f8e80 --- /dev/null +++ b/app/services/ai_interviewer_service.py @@ -0,0 +1,294 @@ +import asyncio +import json +import logging +from typing import Dict, Optional, List +from datetime import datetime +from livekit import api, rtc +from rag.settings import settings +from app.services.interview_service import InterviewRoomService + +logger = logging.getLogger(__name__) + +class AIInterviewerService: + """Сервис AI интервьюера, который подключается к LiveKit комнате как участник""" + + def __init__(self, interview_session_id: int, resume_data: Dict): + self.interview_session_id = interview_session_id + self.resume_data = resume_data + self.room: Optional[rtc.Room] = None + self.audio_source: Optional[rtc.AudioSource] = None + self.conversation_history: List[Dict] = [] + self.current_question_index = 0 + self.interview_questions = [] + + async def connect_to_room(self, room_name: str, token: str): + """Подключение AI агента к LiveKit комнате""" + try: + self.room = rtc.Room() + + # Настройка обработчиков событий + self.room.on("participant_connected", self.on_participant_connected) + self.room.on("track_subscribed", self.on_track_subscribed) + self.room.on("data_received", self.on_data_received) + + # Подключение к комнате + await self.room.connect(settings.livekit_url, token) + logger.info(f"AI agent connected to room: {room_name}") + + # Создание аудио источника для TTS + self.audio_source = rtc.AudioSource(sample_rate=16000, num_channels=1) + track = rtc.LocalAudioTrack.create_audio_track("ai_voice", self.audio_source) + + # Публикация аудио трека + await self.room.local_participant.publish_track(track, rtc.TrackPublishOptions()) + + # Генерация первого вопроса + await self.generate_interview_questions() + await self.start_interview() + + except Exception as e: + logger.error(f"Error connecting to room: {str(e)}") + raise + + async def on_participant_connected(self, participant: rtc.RemoteParticipant): + """Обработка подключения пользователя""" + logger.info(f"Participant connected: {participant.identity}") + # Можем отправить приветственное сообщение + await self.send_message({ + "type": "ai_speaking_start" + }) + + async def on_track_subscribed( + self, + track: rtc.Track, + publication: rtc.RemoteTrackPublication, + participant: rtc.RemoteParticipant + ): + """Обработка получения аудио трека от пользователя""" + if track.kind == rtc.TrackKind.KIND_AUDIO: + logger.info("Subscribed to user audio track") + # Настройка обработки аудио для STT + audio_stream = rtc.AudioStream(track) + asyncio.create_task(self.process_user_audio(audio_stream)) + + async def on_data_received(self, data: bytes, participant: rtc.RemoteParticipant): + """Обработка сообщений от фронтенда""" + try: + message = json.loads(data.decode()) + await self.handle_frontend_message(message) + except Exception as e: + logger.error(f"Error processing data message: {str(e)}") + + async def handle_frontend_message(self, message: Dict): + """Обработка сообщений от фронтенда""" + msg_type = message.get("type") + + if msg_type == "start_interview": + await self.start_interview() + elif msg_type == "end_interview": + await self.end_interview() + elif msg_type == "user_finished_speaking": + # Пользователь закончил говорить, можем обрабатывать его ответ + pass + + async def process_user_audio(self, audio_stream: rtc.AudioStream): + """Обработка аудио от пользователя через STT""" + try: + # Здесь будет интеграция с STT сервисом + # Пока заглушка + async for audio_frame in audio_stream: + # TODO: Отправить аудио в STT (Whisper API) + # user_text = await self.speech_to_text(audio_frame) + # if user_text: + # await self.process_user_response(user_text) + pass + except Exception as e: + logger.error(f"Error processing user audio: {str(e)}") + + async def speech_to_text(self, audio_data: bytes) -> Optional[str]: + """Конвертация речи в текст через Whisper API""" + # TODO: Интеграция с OpenAI Whisper или другим STT сервисом + pass + + async def text_to_speech(self, text: str) -> bytes: + """Конвертация текста в речь через TTS сервис""" + # TODO: Интеграция с ElevenLabs, OpenAI TTS или другим сервисом + pass + + async def generate_interview_questions(self): + """Генерация вопросов для интервью на основе резюме""" + try: + from rag.registry import registry + chat_model = registry.get_chat_model() + + # Используем существующую логику генерации вопросов + questions_prompt = f""" + Сгенерируй 8 вопросов для голосового собеседования кандидата. + + РЕЗЮМЕ КАНДИДАТА: + Имя: {self.resume_data.get('name', 'Не указано')} + Навыки: {', '.join(self.resume_data.get('skills', []))} + Опыт работы: {self.resume_data.get('total_years', 0)} лет + Образование: {self.resume_data.get('education', 'Не указано')} + + ВАЖНО: + 1. Вопросы должны быть короткими и ясными для голосового формата + 2. Начни с простого приветствия и представления + 3. Каждый вопрос должен занимать не более 2-3 предложений + 4. Используй естественную разговорную речь + + Верни только JSON массив строк с вопросами: + ["Привет! Расскажи немного о себе", "Какой у тебя опыт в...", ...] + """ + + from langchain.schema import HumanMessage, SystemMessage + messages = [ + SystemMessage(content="Ты HR интервьюер. Говори естественно и дружелюбно."), + HumanMessage(content=questions_prompt) + ] + + response = chat_model.get_llm().invoke(messages) + response_text = response.content.strip() + + # Парсим JSON ответ + if response_text.startswith('[') and response_text.endswith(']'): + self.interview_questions = json.loads(response_text) + else: + # Fallback вопросы + self.interview_questions = [ + "Привет! Расскажи немного о себе и своем опыте", + "Что тебя привлекает в этой позиции?", + "Расскажи о своем самом значимом проекте", + "Какие технологии ты используешь в работе?", + "Как ты решаешь сложные задачи?", + "Есть ли у тебя вопросы ко мне?" + ] + + logger.info(f"Generated {len(self.interview_questions)} interview questions") + + except Exception as e: + logger.error(f"Error generating questions: {str(e)}") + # Используем базовые вопросы + self.interview_questions = [ + "Привет! Расскажи о своем опыте", + "Что тебя интересует в этой позиции?", + "Есть ли у тебя вопросы?" + ] + + async def start_interview(self): + """Начало интервью""" + if not self.interview_questions: + await self.generate_interview_questions() + + # Отправляем первый вопрос + await self.ask_next_question() + + async def ask_next_question(self): + """Задать следующий вопрос""" + if self.current_question_index >= len(self.interview_questions): + await self.end_interview() + return + + question = self.interview_questions[self.current_question_index] + + # Отправляем сообщение фронтенду + await self.send_message({ + "type": "question", + "text": question, + "questionNumber": self.current_question_index + 1 + }) + + # Конвертируем в речь и воспроизводим + # TODO: Реализовать TTS + # audio_data = await self.text_to_speech(question) + # await self.play_audio(audio_data) + + self.current_question_index += 1 + logger.info(f"Asked question {self.current_question_index}: {question}") + + async def process_user_response(self, user_text: str): + """Обработка ответа пользователя""" + # Сохраняем ответ в историю + self.conversation_history.append({ + "type": "user_response", + "text": user_text, + "timestamp": datetime.utcnow().isoformat(), + "question_index": self.current_question_index - 1 + }) + + # Можем добавить анализ ответа через LLM + # И решить - задать уточняющий вопрос или перейти к следующему + + # Пока просто переходим к следующему вопросу + await asyncio.sleep(1) # Небольшая пауза + await self.ask_next_question() + + async def send_message(self, message: Dict): + """Отправка сообщения фронтенду""" + if self.room: + data = json.dumps(message).encode() + await self.room.local_participant.publish_data(data) + + async def play_audio(self, audio_data: bytes): + """Воспроизведение аудио через LiveKit""" + if self.audio_source: + # TODO: Конвертировать audio_data в нужный формат и отправить + pass + + async def end_interview(self): + """Завершение интервью""" + await self.send_message({ + "type": "interview_complete", + "summary": f"Interview completed with {len(self.conversation_history)} responses" + }) + + # Сохраняем транскрипт в базу данных + transcript = json.dumps(self.conversation_history, ensure_ascii=False, indent=2) + + # TODO: Обновить interview_session в БД с транскриптом + + logger.info("Interview completed") + + # Отключение от комнаты + if self.room: + await self.room.disconnect() + + +class AIInterviewerManager: + """Менеджер для управления AI интервьюерами""" + + def __init__(self): + self.active_sessions: Dict[int, AIInterviewerService] = {} + + async def start_interview_session(self, interview_session_id: int, room_name: str, resume_data: Dict): + """Запуск AI интервьюера для сессии""" + try: + # Создаем токен для AI агента + from app.services.interview_service import InterviewRoomService + # Нужно создать специальный токен для AI агента + + ai_interviewer = AIInterviewerService(interview_session_id, resume_data) + + # TODO: Генерировать токен для AI агента + # ai_token = generate_ai_agent_token(room_name) + # await ai_interviewer.connect_to_room(room_name, ai_token) + + self.active_sessions[interview_session_id] = ai_interviewer + + logger.info(f"Started AI interviewer for session: {interview_session_id}") + + except Exception as e: + logger.error(f"Error starting AI interviewer: {str(e)}") + raise + + async def stop_interview_session(self, interview_session_id: int): + """Остановка AI интервьюера""" + if interview_session_id in self.active_sessions: + ai_interviewer = self.active_sessions[interview_session_id] + await ai_interviewer.end_interview() + del self.active_sessions[interview_session_id] + logger.info(f"Stopped AI interviewer for session: {interview_session_id}") + + +# Глобальный менеджер +ai_interviewer_manager = AIInterviewerManager() \ No newline at end of file diff --git a/app/services/file_service.py b/app/services/file_service.py index d5b72d1..e775099 100644 --- a/app/services/file_service.py +++ b/app/services/file_service.py @@ -1,5 +1,7 @@ from fastapi import UploadFile from typing import Optional +import tempfile +import os from app.core.s3 import s3_service @@ -7,18 +9,60 @@ class FileService: def __init__(self): self.s3_service = s3_service - async def upload_resume_file(self, file: UploadFile) -> Optional[str]: + async def upload_resume_file(self, file: UploadFile) -> Optional[tuple[str, str]]: + """ + Загружает резюме в S3 и сохраняет локальную копию для парсинга + + Returns: + tuple[str, str]: (s3_url, local_file_path) или None при ошибке + """ if not file.filename: return None content = await file.read() content_type = file.content_type or "application/octet-stream" - return await self.s3_service.upload_file( + # Загружаем в S3 + s3_url = await self.s3_service.upload_file( file_content=content, file_name=file.filename, content_type=content_type ) + + if not s3_url: + return None + + # Сохраняем локальную копию для парсинга + try: + # Создаем временный файл с сохранением расширения + temp_dir = tempfile.gettempdir() + file_extension = os.path.splitext(file.filename)[1] + if not file_extension: + # Пытаемся определить расширение по MIME типу + if content_type == 'application/pdf': + file_extension = '.pdf' + elif content_type in ['application/vnd.openxmlformats-officedocument.wordprocessingml.document']: + file_extension = '.docx' + elif content_type in ['application/msword']: + file_extension = '.doc' + elif content_type == 'text/plain': + file_extension = '.txt' + else: + file_extension = '.pdf' # fallback + + temp_filename = f"resume_{hash(s3_url)}_{file.filename}" + local_file_path = os.path.join(temp_dir, temp_filename) + + # Сохраняем содержимое файла + with open(local_file_path, 'wb') as temp_file: + temp_file.write(content) + + return (s3_url, local_file_path) + + except Exception as e: + print(f"Failed to save local copy: {str(e)}") + # Если не удалось сохранить локально, возвращаем только S3 URL + return (s3_url, s3_url) async def upload_interview_report(self, file: UploadFile) -> Optional[str]: if not file.filename: diff --git a/app/services/interview_service.py b/app/services/interview_service.py new file mode 100644 index 0000000..1d525d5 --- /dev/null +++ b/app/services/interview_service.py @@ -0,0 +1,464 @@ +import os +import time +import uuid +import json +import subprocess +from typing import Optional +from datetime import datetime, timedelta +from livekit.api import AccessToken, VideoGrants +from sqlalchemy.ext.asyncio import AsyncSession +from sqlalchemy import select +from app.models.interview import ( + InterviewSession, + InterviewSessionCreate, + InterviewSessionUpdate, + InterviewStatus, + InterviewValidationResponse, + LiveKitTokenResponse +) +from app.models.resume import Resume, ResumeStatus +from app.models.vacancy import Vacancy +from rag.settings import settings + + +class InterviewRoomService: + def __init__(self, db_session: AsyncSession): + self.db = db_session + self.livekit_url = settings.livekit_url or "ws://localhost:7880" + self.api_key = settings.livekit_api_key or "devkey" + self.api_secret = settings.livekit_api_secret or "secret" + + async def validate_resume_for_interview(self, resume_id: int) -> InterviewValidationResponse: + """Проверяет, можно ли проводить собеседование для данного резюме""" + try: + # Получаем резюме + result = await self.db.execute(select(Resume).where(Resume.id == resume_id)) + resume = result.scalar_one_or_none() + + if not resume: + return InterviewValidationResponse( + can_interview=False, + message="Resume not found" + ) + + # Проверяем статус резюме + if resume.status != ResumeStatus.PARSED: + return InterviewValidationResponse( + can_interview=False, + message=f"Resume is not ready for interview. Current status: {resume.status}" + ) + + # Проверяем активную сессию только для информации (не блокируем) + result = await self.db.execute( + select(InterviewSession) + .where(InterviewSession.resume_id == resume_id) + .where(InterviewSession.status == "active") + ) + active_session = result.scalar_one_or_none() + + message = "Resume is ready for interview" + if active_session: + message = "Resume has an active interview session" + + return InterviewValidationResponse( + can_interview=True, + message=message + ) + + except Exception as e: + return InterviewValidationResponse( + can_interview=False, + message=f"Error validating resume: {str(e)}" + ) + + async def create_interview_session(self, resume_id: int) -> Optional[InterviewSession]: + """Создает новую сессию собеседования""" + try: + # Генерируем уникальное имя комнаты с UUID + unique_id = str(uuid.uuid4())[:8] + timestamp = int(time.time()) + room_name = f"interview_{resume_id}_{timestamp}_{unique_id}" + + # Создаем сессию в БД + session_data = InterviewSessionCreate( + resume_id=resume_id, + room_name=room_name + ) + + interview_session = InterviewSession(**session_data.model_dump()) + self.db.add(interview_session) + await self.db.commit() + await self.db.refresh(interview_session) + + return interview_session + + except Exception as e: + await self.db.rollback() + print(f"Error creating interview session: {str(e)}") + return None + + def generate_access_token(self, room_name: str, participant_name: str) -> str: + """Генерирует JWT токен для LiveKit""" + try: + at = AccessToken(self.api_key, self.api_secret) + # Исправляем использование grants + grants = VideoGrants( + room_join=True, + room=room_name, + can_publish=True, + can_subscribe=True + ) + at.with_grants(grants).with_identity(participant_name) + + return at.to_jwt() + + except Exception as e: + print(f"Error generating LiveKit token: {str(e)}") + raise + + async def get_livekit_token(self, resume_id: int) -> Optional[LiveKitTokenResponse]: + """Создает сессию собеседования и возвращает токен для LiveKit""" + try: + # Валидируем резюме + validation = await self.validate_resume_for_interview(resume_id) + if not validation.can_interview: + return None + + # Проверяем, есть ли уже созданная сессия для этого резюме + existing_session = await self.get_interview_session(resume_id) + if existing_session: + # Используем существующую сессию + interview_session = existing_session + print(f"[DEBUG] Using existing interview session: {interview_session.id}") + else: + # Создаем новую сессию собеседования + interview_session = await self.create_interview_session(resume_id) + if not interview_session: + return None + print(f"[DEBUG] Created new interview session: {interview_session.id}") + + # Генерируем токен + participant_name = f"user_{resume_id}" + token = self.generate_access_token( + interview_session.room_name, + participant_name + ) + + # Получаем готовый план интервью для AI агента + interview_plan = await self.get_resume_data_for_interview(resume_id) + + # Обновляем статус сессии на ACTIVE + await self.update_session_status(interview_session.id, "active") + + # Запускаем AI агента для этой сессии + await self.start_ai_interviewer(interview_session, interview_plan) + + return LiveKitTokenResponse( + token=token, + room_name=interview_session.room_name, + server_url=self.livekit_url + ) + + except Exception as e: + print(f"Error getting LiveKit token: {str(e)}") + return None + + async def update_session_status(self, session_id: int, status: str) -> bool: + """Обновляет статус сессии собеседования""" + try: + result = await self.db.execute( + select(InterviewSession).where(InterviewSession.id == session_id) + ) + session = result.scalar_one_or_none() + + if not session: + return False + + session.status = status + if status == "completed": + session.completed_at = datetime.utcnow() + + await self.db.commit() + return True + + except Exception as e: + await self.db.rollback() + print(f"Error updating session status: {str(e)}") + return False + + async def get_interview_session(self, resume_id: int) -> Optional[InterviewSession]: + """Получает активную сессию собеседования для резюме""" + try: + result = await self.db.execute( + select(InterviewSession) + .where(InterviewSession.resume_id == resume_id) + .where(InterviewSession.status.in_(["created", "active"])) + .order_by(InterviewSession.started_at.desc()) + ) + return result.scalar_one_or_none() + + except Exception as e: + print(f"Error getting interview session: {str(e)}") + return None + + async def start_ai_interviewer(self, interview_session: InterviewSession, interview_plan: dict): + """Запускает AI интервьюера для сессии""" + try: + # Создаем токен для AI агента + ai_token = self.generate_access_token( + interview_session.room_name, + f"ai_interviewer_{interview_session.id}" + ) + + # Подготавливаем метаданные с планом интервью + room_metadata = json.dumps({ + "interview_plan": interview_plan, + "session_id": interview_session.id + }) + + # Запускаем AI агента в отдельном процессе + agent_cmd = [ + "uv", + "run", + "ai_interviewer_agent.py", + "connect", + "--room", interview_session.room_name, + "--url", self.livekit_url, + "--api-key", self.api_key, + "--api-secret", self.api_secret, + ] + + # Устанавливаем переменные окружения + env = os.environ.copy() + env.update({ + "LIVEKIT_ROOM_METADATA": room_metadata, + "OPENAI_API_KEY": settings.openai_api_key or "", + "DEEPGRAM_API_KEY": settings.deepgram_api_key or "", + "CARTESIA_API_KEY": settings.cartesia_api_key or "", + }) + + # Запускаем процесс в фоне + with open(f"ai_interviewer_{interview_session.id}.log", "wb") as f_out, \ + open(f"ai_interviewer_{interview_session.id}.err", "wb") as f_err: + process = subprocess.Popen( + agent_cmd, + env=env, + stdout=f_out, + stderr=f_err, + cwd="." + ) + + print(f"[DEBUG] Started AI interviewer process {process.pid} for session {interview_session.id}") + + # Сохраняем PID процесса в БД для управления + await self.update_agent_process_info( + interview_session.id, + process.pid, + "running" + ) + + except Exception as e: + print(f"Error starting AI interviewer: {str(e)}") + # Обновляем статус на failed + await self.update_agent_process_info( + interview_session.id, + None, + "failed" + ) + + async def get_resume_data_for_interview(self, resume_id: int) -> dict: + """Получает готовый план интервью из базы данных""" + try: + # Получаем резюме с готовым планом интервью + result = await self.db.execute( + select(Resume).where(Resume.id == resume_id) + ) + resume = result.scalar_one_or_none() + + if not resume: + return self._get_fallback_interview_plan() + + # Если есть готовый план интервью - используем его + if resume.interview_plan: + return resume.interview_plan + + # Если плана нет, создаем базовый план на основе имеющихся данных + fallback_plan = { + "interview_structure": { + "duration_minutes": 30, + "greeting": f"Привет, {resume.applicant_name}! Готов к интервью?", + "sections": [ + { + "name": "Знакомство", + "duration_minutes": 5, + "questions": ["Расскажи немного о себе", "Что тебя привлекло в этой позиции?"] + }, + { + "name": "Опыт работы", + "duration_minutes": 15, + "questions": ["Расскажи о своем опыте", "Какие технологии используешь?"] + }, + { + "name": "Вопросы кандидата", + "duration_minutes": 10, + "questions": ["Есть ли у тебя вопросы ко мне?"] + } + ] + }, + "focus_areas": ["experience", "technical_skills"], + "candidate_info": { + "name": resume.applicant_name, + "email": resume.applicant_email, + "phone": resume.applicant_phone + } + } + + # Добавляем parsed данные если есть + if resume.parsed_data: + fallback_plan["candidate_info"].update({ + "skills": resume.parsed_data.get("skills", []), + "total_years": resume.parsed_data.get("total_years", 0), + "education": resume.parsed_data.get("education", "") + }) + + return fallback_plan + + except Exception as e: + print(f"Error getting interview plan: {str(e)}") + return self._get_fallback_interview_plan() + + def _get_fallback_interview_plan(self) -> dict: + """Fallback план интервью если не удалось загрузить из БД""" + return { + "interview_structure": { + "duration_minutes": 30, + "greeting": "Привет! Готов к интервью?", + "sections": [ + { + "name": "Знакомство", + "duration_minutes": 10, + "questions": ["Расскажи о себе", "Что тебя привлекло в этой позиции?"] + }, + { + "name": "Опыт работы", + "duration_minutes": 15, + "questions": ["Расскажи о своем опыте", "Какие технологии используешь?"] + }, + { + "name": "Вопросы кандидата", + "duration_minutes": 5, + "questions": ["Есть ли у тебя вопросы?"] + } + ] + }, + "focus_areas": ["experience", "technical_skills"], + "candidate_info": { + "name": "Кандидат", + "skills": [], + "total_years": 0 + } + } + + async def update_agent_process_info(self, session_id: int, pid: int = None, status: str = "not_started") -> bool: + """Обновляет информацию о процессе AI агента""" + try: + result = await self.db.execute( + select(InterviewSession).where(InterviewSession.id == session_id) + ) + session = result.scalar_one_or_none() + + if not session: + return False + + session.ai_agent_pid = pid + session.ai_agent_status = status + + await self.db.commit() + return True + + except Exception as e: + await self.db.rollback() + print(f"Error updating agent process info: {str(e)}") + return False + + async def get_active_agent_processes(self) -> list: + """Получает список активных AI процессов""" + try: + result = await self.db.execute( + select(InterviewSession) + .where(InterviewSession.ai_agent_status == "running") + ) + return result.scalars().all() + + except Exception as e: + print(f"Error getting active processes: {str(e)}") + return [] + + async def stop_agent_process(self, session_id: int) -> bool: + """Останавливает AI процесс для сессии""" + try: + result = await self.db.execute( + select(InterviewSession).where(InterviewSession.id == session_id) + ) + session = result.scalar_one_or_none() + + if not session or not session.ai_agent_pid: + return False + + import psutil + try: + # Пытаемся gracefully остановить процесс + process = psutil.Process(session.ai_agent_pid) + process.terminate() + + # Ждем завершения до 5 секунд + import time + for _ in range(50): + if not process.is_running(): + break + time.sleep(0.1) + + # Если не завершился, принудительно убиваем + if process.is_running(): + process.kill() + + # Обновляем статус в БД + await self.update_agent_process_info(session_id, None, "stopped") + + print(f"Stopped AI agent process {session.ai_agent_pid} for session {session_id}") + return True + + except (psutil.NoSuchProcess, psutil.AccessDenied): + # Процесс уже не существует + await self.update_agent_process_info(session_id, None, "stopped") + return True + + except Exception as e: + print(f"Error stopping agent process: {str(e)}") + return False + + async def cleanup_dead_processes(self) -> int: + """Очищает информацию о мертвых процессах""" + try: + import psutil + + active_sessions = await self.get_active_agent_processes() + cleaned_count = 0 + + for session in active_sessions: + if session.ai_agent_pid: + try: + process = psutil.Process(session.ai_agent_pid) + if not process.is_running(): + await self.update_agent_process_info(session.id, None, "stopped") + cleaned_count += 1 + except psutil.NoSuchProcess: + await self.update_agent_process_info(session.id, None, "stopped") + cleaned_count += 1 + + print(f"Cleaned up {cleaned_count} dead processes") + return cleaned_count + + except Exception as e: + print(f"Error cleaning up processes: {str(e)}") + return 0 diff --git a/celery_worker/__init__.py b/celery_worker/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/celery_worker/celery_app.py b/celery_worker/celery_app.py new file mode 100644 index 0000000..5cafe61 --- /dev/null +++ b/celery_worker/celery_app.py @@ -0,0 +1,17 @@ +from celery import Celery +from rag.settings import settings + +celery_app = Celery( + "hr_ai_backend", + broker=f"redis://{settings.redis_cache_url}:{settings.redis_cache_port}/{settings.redis_cache_db}", + backend=f"redis://{settings.redis_cache_url}:{settings.redis_cache_port}/{settings.redis_cache_db}", + include=["celery_worker.tasks"] +) + +celery_app.conf.update( + task_serializer="json", + accept_content=["json"], + result_serializer="json", + timezone="UTC", + enable_utc=True, +) \ No newline at end of file diff --git a/celery_worker/database.py b/celery_worker/database.py new file mode 100644 index 0000000..e36bbfd --- /dev/null +++ b/celery_worker/database.py @@ -0,0 +1,87 @@ +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker, Session +from contextlib import contextmanager +from rag.settings import settings + + +# Создаем синхронный engine для Celery (так как Celery работает в отдельных процессах) +sync_engine = create_engine( + settings.database_url.replace("asyncpg", "psycopg2"), # Убираем asyncpg для синхронного подключения + echo=False, + future=True +) + +# Создаем синхронный session maker +SyncSessionLocal = sessionmaker( + bind=sync_engine, + autocommit=False, + autoflush=False +) + + +@contextmanager +def get_sync_session() -> Session: + """Получить синхронную сессию для использования в Celery tasks""" + session = SyncSessionLocal() + try: + yield session + session.commit() + except Exception: + session.rollback() + raise + finally: + session.close() + + +class SyncResumeRepository: + """Синхронный repository для работы с Resume в Celery tasks""" + + def __init__(self, session: Session): + self.session = session + + def get_by_id(self, resume_id: int): + """Получить резюме по ID""" + from app.models.resume import Resume + return self.session.query(Resume).filter(Resume.id == resume_id).first() + + def update_status(self, resume_id: int, status: str, parsed_data: dict = None, error_message: str = None): + """Обновить статус резюме""" + from app.models.resume import Resume, ResumeStatus + from datetime import datetime + + resume = self.session.query(Resume).filter(Resume.id == resume_id).first() + + if resume: + # Обновляем статус + if status == 'parsing': + resume.status = ResumeStatus.PARSING + elif status == 'parsed': + resume.status = ResumeStatus.PARSED + if parsed_data: + resume.parsed_data = parsed_data + # НЕ перезаписываем контактные данные из формы - они уже правильные + elif status == 'failed': + resume.status = ResumeStatus.PARSE_FAILED + if error_message: + resume.parse_error = error_message + + resume.updated_at = datetime.utcnow() + self.session.add(resume) + return resume + + return None + + def update_interview_plan(self, resume_id: int, interview_plan: dict): + """Обновить план интервью""" + from app.models.resume import Resume + from datetime import datetime + + resume = self.session.query(Resume).filter(Resume.id == resume_id).first() + + if resume: + resume.interview_plan = interview_plan + resume.updated_at = datetime.utcnow() + self.session.add(resume) + return resume + + return None \ No newline at end of file diff --git a/celery_worker/process_cleanup_task.py b/celery_worker/process_cleanup_task.py new file mode 100644 index 0000000..e04db67 --- /dev/null +++ b/celery_worker/process_cleanup_task.py @@ -0,0 +1,190 @@ +import asyncio +from celery import current_task +from celery_worker.celery_app import celery_app +from celery_worker.database import get_sync_session +from app.services.interview_service import InterviewRoomService +import psutil + + +@celery_app.task(bind=True) +def cleanup_interview_processes_task(self): + """ + Периодическая задача очистки мертвых AI процессов + """ + try: + self.update_state( + state='PROGRESS', + meta={'status': 'Checking for dead AI processes...', 'progress': 10} + ) + + # Используем синхронный подход для Celery + with get_sync_session() as session: + # Получаем все "активные" сессии из БД + from app.models.interview import InterviewSession + active_sessions = session.query(InterviewSession).filter( + InterviewSession.ai_agent_status == "running" + ).all() + + cleaned_count = 0 + total_sessions = len(active_sessions) + + self.update_state( + state='PROGRESS', + meta={'status': f'Found {total_sessions} potentially active sessions...', 'progress': 30} + ) + + for i, interview_session in enumerate(active_sessions): + if interview_session.ai_agent_pid: + try: + # Проверяем, жив ли процесс + process = psutil.Process(interview_session.ai_agent_pid) + + if not process.is_running(): + # Процесс мертв, обновляем статус + interview_session.ai_agent_pid = None + interview_session.ai_agent_status = "stopped" + session.add(interview_session) + cleaned_count += 1 + + except psutil.NoSuchProcess: + # Процесс не существует + interview_session.ai_agent_pid = None + interview_session.ai_agent_status = "stopped" + session.add(interview_session) + cleaned_count += 1 + + except Exception as e: + print(f"Error checking process {interview_session.ai_agent_pid}: {str(e)}") + + # Обновляем прогресс + progress = 30 + (i + 1) / total_sessions * 60 + self.update_state( + state='PROGRESS', + meta={ + 'status': f'Processed {i + 1}/{total_sessions} sessions...', + 'progress': progress + } + ) + + # Сохраняем изменения + session.commit() + + self.update_state( + state='SUCCESS', + meta={ + 'status': f'Cleanup completed. Cleaned {cleaned_count} dead processes.', + 'progress': 100, + 'cleaned_count': cleaned_count, + 'total_checked': total_sessions + } + ) + + return { + 'status': 'completed', + 'cleaned_count': cleaned_count, + 'total_checked': total_sessions + } + + except Exception as e: + self.update_state( + state='FAILURE', + meta={ + 'status': f'Error during cleanup: {str(e)}', + 'progress': 0, + 'error': str(e) + } + ) + raise + + +@celery_app.task(bind=True) +def force_kill_interview_process_task(self, session_id: int): + """ + Принудительное завершение AI процесса для сессии + """ + try: + self.update_state( + state='PROGRESS', + meta={'status': f'Looking for session {session_id}...', 'progress': 20} + ) + + with get_sync_session() as session: + from app.models.interview import InterviewSession + + interview_session = session.query(InterviewSession).filter( + InterviewSession.id == session_id + ).first() + + if not interview_session: + return { + 'status': 'not_found', + 'message': f'Session {session_id} not found' + } + + if not interview_session.ai_agent_pid: + return { + 'status': 'no_process', + 'message': f'No AI process found for session {session_id}' + } + + self.update_state( + state='PROGRESS', + meta={'status': f'Terminating process {interview_session.ai_agent_pid}...', 'progress': 50} + ) + + try: + process = psutil.Process(interview_session.ai_agent_pid) + + # Graceful terminate + process.terminate() + + # Ждем до 5 секунд + import time + for _ in range(50): + if not process.is_running(): + break + time.sleep(0.1) + + # Если не помогло, убиваем принудительно + if process.is_running(): + process.kill() + time.sleep(0.5) # Даем время на завершение + + # Обновляем статус в БД + interview_session.ai_agent_pid = None + interview_session.ai_agent_status = "stopped" + session.add(interview_session) + session.commit() + + self.update_state( + state='SUCCESS', + meta={'status': 'Process terminated successfully', 'progress': 100} + ) + + return { + 'status': 'terminated', + 'message': f'AI process for session {session_id} terminated successfully' + } + + except psutil.NoSuchProcess: + # Процесс уже не существует + interview_session.ai_agent_pid = None + interview_session.ai_agent_status = "stopped" + session.add(interview_session) + session.commit() + + return { + 'status': 'already_dead', + 'message': f'Process was already dead, cleaned up database' + } + + except Exception as e: + self.update_state( + state='FAILURE', + meta={ + 'status': f'Error terminating process: {str(e)}', + 'progress': 0, + 'error': str(e) + } + ) + raise \ No newline at end of file diff --git a/celery_worker/tasks.py b/celery_worker/tasks.py new file mode 100644 index 0000000..c524a3b --- /dev/null +++ b/celery_worker/tasks.py @@ -0,0 +1,412 @@ +import os +import json +from typing import Dict, Any +from celery import current_task +from datetime import datetime + +from celery_worker.celery_app import celery_app +from celery_worker.database import get_sync_session, SyncResumeRepository +from rag.llm.model import ResumeParser +from rag.registry import registry + + +def generate_interview_plan(resume_id: int, combined_data: Dict[str, Any]) -> Dict[str, Any]: + """Генерирует план интервью на основе резюме и вакансии""" + try: + # Получаем данные о вакансии из БД + with get_sync_session() as session: + repo = SyncResumeRepository(session) + resume_record = repo.get_by_id(resume_id) + + if not resume_record: + return None + + # Здесь нужно получить данные вакансии + # Пока используем заглушку, потом добавим связь с vacancy + vacancy_data = { + "title": "Python Developer", + "requirements": "Python, FastAPI, PostgreSQL, Docker", + "company_name": "Tech Company", + "experience_level": "Middle" + } + + # Генерируем план через LLM + chat_model = registry.get_chat_model() + + plan_prompt = f""" + Создай детальный план интервью для кандидата на основе его резюме и требований вакансии. + + РЕЗЮМЕ КАНДИДАТА: + - Имя: {combined_data.get('name', 'Не указано')} + - Навыки: {', '.join(combined_data.get('skills', []))} + - Опыт: {combined_data.get('total_years', 0)} лет + - Образование: {combined_data.get('education', 'Не указано')} + + ВАКАНСИЯ: + - Позиция: {vacancy_data['title']} + - Требования: {vacancy_data['requirements']} + - Компания: {vacancy_data['company_name']} + - Уровень: {vacancy_data['experience_level']} + + Создай план интервью в формате JSON: + {{ + "interview_structure": {{ + "duration_minutes": 45, + "greeting": "Краткое приветствие и знакомство (3 мин)", + "sections": [ + {{ + "name": "Знакомство с кандидатом", + "duration_minutes": 5, + "questions": ["Расскажи о себе", "Что привлекло в этой позиции?"] + }}, + {{ + "name": "Технические навыки", + "duration_minutes": 20, + "questions": ["Опыт с Python", "Работа с базами данных"] + }}, + {{ + "name": "Опыт и проекты", + "duration_minutes": 15, + "questions": ["Расскажи о сложном проекте", "Как решаешь проблемы?"] + }}, + {{ + "name": "Вопросы кандидата", + "duration_minutes": 2, + "questions": ["Есть ли вопросы ко мне?"] + }} + ] + }}, + "focus_areas": ["technical_skills", "problem_solving", "cultural_fit"], + "key_evaluation_points": [ + "Глубина знаний Python", + "Опыт командной работы", + "Мотивация к изучению нового" + ], + "red_flags_to_check": [], + "personalization_notes": "Кандидат имеет хороший технический опыт" + }} + """ + + from langchain.schema import HumanMessage, SystemMessage + messages = [ + SystemMessage(content="Ты HR эксперт по планированию интервью. Создавай структурированные планы."), + HumanMessage(content=plan_prompt) + ] + + response = chat_model.get_llm().invoke(messages) + response_text = response.content.strip() + + # Парсим JSON ответ + if response_text.startswith('{') and response_text.endswith('}'): + return json.loads(response_text) + else: + # Ищем JSON в тексте + start = response_text.find('{') + end = response_text.rfind('}') + 1 + if start != -1 and end > start: + return json.loads(response_text[start:end]) + + return None + + except Exception as e: + print(f"Ошибка генерации плана интервью: {str(e)}") + return None + + +@celery_app.task(bind=True) +def parse_resume_task(self, resume_id: str, file_path: str): + """ + Асинхронная задача парсинга резюме + + Args: + resume_id: ID резюме + file_path: Путь к PDF файлу резюме + """ + + try: + # Шаг 0: Обновляем статус в БД - начали парсинг + with get_sync_session() as session: + repo = SyncResumeRepository(session) + repo.update_status(int(resume_id), 'parsing') + + # Обновляем статус задачи + self.update_state( + state='PENDING', + meta={'status': 'Начинаем парсинг резюме...', 'progress': 10} + ) + + # Инициализируем модели из registry + try: + chat_model = registry.get_chat_model() + embeddings_model = registry.get_embeddings_model() + vector_store = registry.get_vector_store() + except Exception as e: + # Обновляем статус в БД - ошибка инициализации + with get_sync_session() as session: + repo = SyncResumeRepository(session) + repo.update_status(int(resume_id), 'failed', error_message=f"Ошибка инициализации моделей: {str(e)}") + raise Exception(f"Ошибка инициализации моделей: {str(e)}") + + # Шаг 1: Парсинг резюме + self.update_state( + state='PROGRESS', + meta={'status': 'Извлекаем текст из PDF...', 'progress': 20} + ) + + parser = ResumeParser(chat_model) + + if not os.path.exists(file_path): + # Обновляем статус в БД - файл не найден + with get_sync_session() as session: + repo = SyncResumeRepository(session) + repo.update_status(int(resume_id), 'failed', error_message=f"Файл не найден: {file_path}") + raise Exception(f"Файл не найден: {file_path}") + + parsed_resume = parser.parse_resume_from_file(file_path) + + # Получаем оригинальные данные из формы + with get_sync_session() as session: + repo = SyncResumeRepository(session) + resume_record = repo.get_by_id(int(resume_id)) + if not resume_record: + raise Exception(f"Резюме с ID {resume_id} не найдено в базе данных") + + # Извлекаем нужные данные пока сессия активна + applicant_name = resume_record.applicant_name + applicant_email = resume_record.applicant_email + applicant_phone = resume_record.applicant_phone + + # Создаем комбинированные данные: навыки и опыт из парсинга, контакты из формы + combined_data = parsed_resume.copy() + combined_data['name'] = applicant_name + combined_data['email'] = applicant_email + combined_data['phone'] = applicant_phone or parsed_resume.get('phone', '') + + # Шаг 2: Векторизация и сохранение в Milvus + self.update_state( + state='PENDING', + meta={'status': 'Сохраняем в векторную базу...', 'progress': 60} + ) + + vector_store.add_candidate_profile(str(resume_id), combined_data) + + # Шаг 3: Обновляем статус в PostgreSQL - успешно обработано + self.update_state( + state='PENDING', + meta={'status': 'Обновляем статус в базе данных...', 'progress': 85} + ) + + # Шаг 4: Генерируем план интервью + self.update_state( + state='PENDING', + meta={'status': 'Генерируем план интервью...', 'progress': 90} + ) + + interview_plan = generate_interview_plan(int(resume_id), combined_data) + + with get_sync_session() as session: + repo = SyncResumeRepository(session) + repo.update_status(int(resume_id), 'parsed', parsed_data=combined_data) + # Сохраняем план интервью + if interview_plan: + repo.update_interview_plan(int(resume_id), interview_plan) + + # Завершено успешно + self.update_state( + state='SUCCESS', + meta={ + 'status': 'Резюме успешно обработано и план интервью готов', + 'progress': 100, + 'result': combined_data + } + ) + + return { + 'resume_id': resume_id, + 'status': 'completed', + 'parsed_data': combined_data + } + + except Exception as e: + # В случае ошибки + self.update_state( + state='FAILURE', + meta={ + 'status': f'Ошибка при обработке резюме: {str(e)}', + 'progress': 0, + 'error': str(e) + } + ) + + # Обновляем статус в БД как failed + try: + with get_sync_session() as session: + repo = SyncResumeRepository(session) + repo.update_status(int(resume_id), 'failed', error_message=str(e)) + except Exception as db_error: + print(f"Ошибка при обновлении статуса в БД: {str(db_error)}") + + raise + + +# Функция больше не нужна - используем SyncResumeRepository напрямую + + +@celery_app.task(bind=True) +def generate_interview_questions_task(self, resume_id: str, job_description: str): + """ + Генерация персонализированных вопросов для интервью на основе резюме и описания вакансии + + Args: + resume_id: ID резюме + job_description: Описание вакансии + """ + try: + self.update_state( + state='PENDING', + meta={'status': 'Начинаем генерацию вопросов...', 'progress': 10} + ) + + # Инициализируем модели + try: + chat_model = registry.get_chat_model() + vector_store = registry.get_vector_store() + except Exception as e: + raise Exception(f"Ошибка инициализации моделей: {str(e)}") + + # Шаг 1: Получить parsed резюме из базы данных + self.update_state( + state='PENDING', + meta={'status': 'Получаем данные резюме...', 'progress': 20} + ) + + with get_sync_session() as session: + repo = SyncResumeRepository(session) + resume = repo.get_by_id(int(resume_id)) + + if not resume: + raise Exception(f"Резюме с ID {resume_id} не найдено") + + if not resume.parsed_data: + raise Exception(f"Резюме {resume_id} еще не обработано") + + # Шаг 2: Получить похожие кандидатов из Milvus для анализа + self.update_state( + state='PENDING', + meta={'status': 'Анализируем профиль кандидата...', 'progress': 40} + ) + + candidate_skills = " ".join(resume.parsed_data.get('skills', [])) + similar_candidates = vector_store.search_similar_candidates(candidate_skills, k=3) + + # Шаг 3: Сгенерировать персонализированные вопросы через LLM + self.update_state( + state='PENDING', + meta={'status': 'Генерируем вопросы для интервью...', 'progress': 70} + ) + + questions_prompt = f""" + Сгенерируй 10 персонализированных вопросов для интервью кандидата на основе его резюме и описания вакансии. + + РЕЗЮМЕ КАНДИДАТА: + Имя: {resume.parsed_data.get('name', 'Не указано')} + Навыки: {', '.join(resume.parsed_data.get('skills', []))} + Опыт работы: {resume.parsed_data.get('total_years', 0)} лет + Образование: {resume.parsed_data.get('education', 'Не указано')} + + ОПИСАНИЕ ВАКАНСИИ: + {job_description} + + ИНСТРУКЦИИ: + 1. Задавай вопросы, которые помогут оценить технические навыки кандидата + 2. Включи вопросы о конкретном опыте работы из резюме + 3. Добавь вопросы на соответствие требованиям вакансии + 4. Включи 2-3 поведенческих вопроса + 5. Верни ответ в JSON формате + + Формат ответа: + {{ + "questions": [ + {{ + "id": 1, + "category": "technical|experience|behavioral|vacancy_specific", + "question": "Текст вопроса", + "reasoning": "Почему этот вопрос важен для данного кандидата" + }} + ] + }} + """ + + from langchain.schema import HumanMessage, SystemMessage + + messages = [ + SystemMessage(content="Ты эксперт по проведению технических интервью. Генерируй качественные, персонализированные вопросы."), + HumanMessage(content=questions_prompt) + ] + + response = chat_model.get_llm().invoke(messages) + + # Парсим ответ + import json + response_text = response.content.strip() + + # Извлекаем JSON из ответа + if response_text.startswith('{') and response_text.endswith('}'): + questions_data = json.loads(response_text) + else: + # Ищем JSON внутри текста + start = response_text.find('{') + end = response_text.rfind('}') + 1 + if start != -1 and end > start: + json_str = response_text[start:end] + questions_data = json.loads(json_str) + else: + raise ValueError("JSON не найден в ответе LLM") + + # Шаг 4: Сохранить вопросы в notes резюме (пока так, потом можно создать отдельную таблицу) + self.update_state( + state='PENDING', + meta={'status': 'Сохраняем вопросы...', 'progress': 90} + ) + + with get_sync_session() as session: + repo = SyncResumeRepository(session) + resume = repo.get_by_id(int(resume_id)) + + if resume: + # Сохраняем вопросы в notes (временно) + existing_notes = resume.notes or "" + interview_questions = json.dumps(questions_data, ensure_ascii=False, indent=2) + resume.notes = f"{existing_notes}\n\nINTERVIEW QUESTIONS:\n{interview_questions}" + from datetime import datetime + resume.updated_at = datetime.utcnow() + + session.add(resume) + + # Завершено успешно + self.update_state( + state='SUCCESS', + meta={ + 'status': 'Вопросы для интервью успешно сгенерированы', + 'progress': 100, + 'result': questions_data + } + ) + + return { + 'resume_id': resume_id, + 'status': 'questions_generated', + 'questions': questions_data['questions'] + } + + except Exception as e: + # В случае ошибки + self.update_state( + state='FAILURE', + meta={ + 'status': f'Ошибка при генерации вопросов: {str(e)}', + 'progress': 0, + 'error': str(e) + } + ) + raise Exception(f"Ошибка при генерации вопросов: {str(e)}") \ No newline at end of file diff --git a/main.py b/main.py index 4f2314f..350d0bd 100644 --- a/main.py +++ b/main.py @@ -4,6 +4,7 @@ from contextlib import asynccontextmanager from app.core.session_middleware import SessionMiddleware from app.routers import vacancy_router, resume_router from app.routers.session_router import router as session_router +from app.routers.interview_router import router as interview_router @asynccontextmanager @@ -32,6 +33,7 @@ app.add_middleware(SessionMiddleware, cookie_name="session_id") app.include_router(vacancy_router, prefix="/api/v1") app.include_router(resume_router, prefix="/api/v1") app.include_router(session_router, prefix="/api/v1") +app.include_router(interview_router, prefix="/api/v1") @app.get("/") diff --git a/migrations/versions/1a2cda4df181_add_interview_plan_to_resume.py b/migrations/versions/1a2cda4df181_add_interview_plan_to_resume.py new file mode 100644 index 0000000..e5bdb7b --- /dev/null +++ b/migrations/versions/1a2cda4df181_add_interview_plan_to_resume.py @@ -0,0 +1,30 @@ +"""add interview_plan to resume + +Revision ID: 1a2cda4df181 +Revises: 4d04e6e32445 +Create Date: 2025-09-02 23:38:36.541565 + +""" +from typing import Sequence, Union + +from alembic import op +import sqlalchemy as sa + + +# revision identifiers, used by Alembic. +revision: str = '1a2cda4df181' +down_revision: Union[str, Sequence[str], None] = '4d04e6e32445' +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + + +def upgrade() -> None: + """Upgrade schema.""" + # Add interview_plan column to resume table + op.add_column('resume', sa.Column('interview_plan', sa.JSON(), nullable=True)) + + +def downgrade() -> None: + """Downgrade schema.""" + # Drop interview_plan column + op.drop_column('resume', 'interview_plan') diff --git a/migrations/versions/385d03e3281c_add_interviewstatus_enum_type.py b/migrations/versions/385d03e3281c_add_interviewstatus_enum_type.py new file mode 100644 index 0000000..61977ae --- /dev/null +++ b/migrations/versions/385d03e3281c_add_interviewstatus_enum_type.py @@ -0,0 +1,32 @@ +"""Add InterviewStatus enum type + +Revision ID: 385d03e3281c +Revises: 4723b138a3bb +Create Date: 2025-09-02 20:00:00.689080 + +""" +from typing import Sequence, Union + +from alembic import op +import sqlalchemy as sa + + +# revision identifiers, used by Alembic. +revision: str = '385d03e3281c' +down_revision: Union[str, Sequence[str], None] = '4723b138a3bb' +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + + +def upgrade() -> None: + """Upgrade schema.""" + # Create InterviewStatus enum type + interview_status_enum = sa.Enum('created', 'active', 'completed', 'failed', name='interviewstatus') + interview_status_enum.create(op.get_bind()) + + +def downgrade() -> None: + """Downgrade schema.""" + # Drop InterviewStatus enum type + interview_status_enum = sa.Enum('created', 'active', 'completed', 'failed', name='interviewstatus') + interview_status_enum.drop(op.get_bind()) diff --git a/migrations/versions/4723b138a3bb_add_interview_sessions_table.py b/migrations/versions/4723b138a3bb_add_interview_sessions_table.py new file mode 100644 index 0000000..936bd68 --- /dev/null +++ b/migrations/versions/4723b138a3bb_add_interview_sessions_table.py @@ -0,0 +1,47 @@ +"""Add interview_sessions table + +Revision ID: 4723b138a3bb +Revises: dba37152ae9a +Create Date: 2025-09-02 19:31:03.531702 + +""" +from typing import Sequence, Union + +from alembic import op +import sqlalchemy as sa + + +# revision identifiers, used by Alembic. +revision: str = '4723b138a3bb' +down_revision: Union[str, Sequence[str], None] = 'dba37152ae9a' +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + + +def upgrade() -> None: + """Upgrade schema.""" + # ### commands auto generated by Alembic - please adjust! ### + op.create_table( + 'interview_sessions', + sa.Column('id', sa.Integer(), nullable=False), + sa.Column('resume_id', sa.Integer(), nullable=False), + sa.Column('room_name', sa.String(length=255), nullable=False), + sa.Column('status', sa.String(length=50), nullable=False), + sa.Column('transcript', sa.Text(), nullable=True), + sa.Column('ai_feedback', sa.Text(), nullable=True), + sa.Column('started_at', sa.DateTime(), nullable=False), + sa.Column('completed_at', sa.DateTime(), nullable=True), + sa.ForeignKeyConstraint(['resume_id'], ['resume.id'], ), + sa.PrimaryKeyConstraint('id'), + sa.UniqueConstraint('room_name') + ) + op.create_index(op.f('ix_interview_sessions_id'), 'interview_sessions', ['id'], unique=False) + # ### end Alembic commands ### + + +def downgrade() -> None: + """Downgrade schema.""" + # ### commands auto generated by Alembic - please adjust! ### + op.drop_index(op.f('ix_interview_sessions_id'), table_name='interview_sessions') + op.drop_table('interview_sessions') + # ### end Alembic commands ### diff --git a/migrations/versions/4d04e6e32445_update_interviewsession_model_to_use_.py b/migrations/versions/4d04e6e32445_update_interviewsession_model_to_use_.py new file mode 100644 index 0000000..b567dc1 --- /dev/null +++ b/migrations/versions/4d04e6e32445_update_interviewsession_model_to_use_.py @@ -0,0 +1,63 @@ +"""Update InterviewSession model to use proper enum + +Revision ID: 4d04e6e32445 +Revises: 96ffcf34e1de +Create Date: 2025-09-02 20:10:52.321402 + +""" +from typing import Sequence, Union + +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision: str = '4d04e6e32445' +down_revision: Union[str, Sequence[str], None] = '96ffcf34e1de' +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + + +def upgrade() -> None: + """Upgrade schema.""" + # Recreate interview_sessions table with proper enum (enum already exists) + op.drop_index(op.f('ix_interview_sessions_id'), table_name='interview_sessions') + op.drop_table('interview_sessions') + + # Create table with existing enum type + op.create_table('interview_sessions', + sa.Column('id', sa.Integer(), nullable=False), + sa.Column('resume_id', sa.Integer(), nullable=False), + sa.Column('room_name', sa.String(length=255), nullable=False), + sa.Column('status', postgresql.ENUM('created', 'active', 'completed', 'failed', name='interviewstatus', create_type=False), nullable=False), + sa.Column('transcript', sa.Text(), nullable=True), + sa.Column('ai_feedback', sa.Text(), nullable=True), + sa.Column('started_at', sa.DateTime(), nullable=False), + sa.Column('completed_at', sa.DateTime(), nullable=True), + sa.ForeignKeyConstraint(['resume_id'], ['resume.id'], ), + sa.PrimaryKeyConstraint('id'), + sa.UniqueConstraint('room_name') + ) + op.create_index(op.f('ix_interview_sessions_id'), 'interview_sessions', ['id'], unique=False) + + +def downgrade() -> None: + """Downgrade schema.""" + op.drop_index(op.f('ix_interview_sessions_id'), table_name='interview_sessions') + op.drop_table('interview_sessions') + + # Recreate old table structure + op.create_table('interview_sessions', + sa.Column('id', sa.Integer(), nullable=False), + sa.Column('resume_id', sa.Integer(), nullable=False), + sa.Column('room_name', sa.String(length=255), nullable=False), + sa.Column('status', sa.String(length=50), nullable=False), + sa.Column('transcript', sa.Text(), nullable=True), + sa.Column('ai_feedback', sa.Text(), nullable=True), + sa.Column('started_at', sa.DateTime(), nullable=False), + sa.Column('completed_at', sa.DateTime(), nullable=True), + sa.ForeignKeyConstraint(['resume_id'], ['resume.id'], ), + sa.PrimaryKeyConstraint('id'), + sa.UniqueConstraint('room_name') + ) + op.create_index(op.f('ix_interview_sessions_id'), 'interview_sessions', ['id'], unique=False) diff --git a/migrations/versions/96ffcf34e1de_update_interview_sessions_status_column_.py b/migrations/versions/96ffcf34e1de_update_interview_sessions_status_column_.py new file mode 100644 index 0000000..148392d --- /dev/null +++ b/migrations/versions/96ffcf34e1de_update_interview_sessions_status_column_.py @@ -0,0 +1,30 @@ +"""Update interview_sessions status column to use enum + +Revision ID: 96ffcf34e1de +Revises: 385d03e3281c +Create Date: 2025-09-02 20:01:52.904608 + +""" +from typing import Sequence, Union + +from alembic import op +import sqlalchemy as sa + + +# revision identifiers, used by Alembic. +revision: str = '96ffcf34e1de' +down_revision: Union[str, Sequence[str], None] = '385d03e3281c' +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + + +def upgrade() -> None: + """Upgrade schema.""" + # Update status column to use interviewstatus enum + op.execute("ALTER TABLE interview_sessions ALTER COLUMN status TYPE interviewstatus USING status::interviewstatus") + + +def downgrade() -> None: + """Downgrade schema.""" + # Revert status column back to VARCHAR + op.execute("ALTER TABLE interview_sessions ALTER COLUMN status TYPE VARCHAR(50)") diff --git a/migrations/versions/dba37152ae9a_add_parsing_statuses_to_resumestatus_.py b/migrations/versions/dba37152ae9a_add_parsing_statuses_to_resumestatus_.py new file mode 100644 index 0000000..4d7fb8f --- /dev/null +++ b/migrations/versions/dba37152ae9a_add_parsing_statuses_to_resumestatus_.py @@ -0,0 +1,43 @@ +"""Add parsing statuses to ResumeStatus enum + +Revision ID: dba37152ae9a +Revises: 4e19b8fe4a88 +Create Date: 2025-09-02 14:45:30.749202 + +""" +from typing import Sequence, Union + +from alembic import op +import sqlalchemy as sa +import sqlmodel + + +# revision identifiers, used by Alembic. +revision: str = 'dba37152ae9a' +down_revision: Union[str, Sequence[str], None] = '4e19b8fe4a88' +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + + +def upgrade() -> None: + """Upgrade schema.""" + # Add new ENUM values to ResumeStatus + op.execute("ALTER TYPE resumestatus ADD VALUE IF NOT EXISTS 'PARSING'") + op.execute("ALTER TYPE resumestatus ADD VALUE IF NOT EXISTS 'PARSED'") + op.execute("ALTER TYPE resumestatus ADD VALUE IF NOT EXISTS 'PARSE_FAILED'") + + # ### commands auto generated by Alembic - please adjust! ### + op.add_column('resume', sa.Column('parsed_data', sa.JSON(), nullable=True)) + op.add_column('resume', sa.Column('parse_error', sqlmodel.sql.sqltypes.AutoString(), nullable=True)) + # ### end Alembic commands ### + + +def downgrade() -> None: + """Downgrade schema.""" + # ### commands auto generated by Alembic - please adjust! ### + op.drop_column('resume', 'parse_error') + op.drop_column('resume', 'parsed_data') + # ### end Alembic commands ### + + # Note: Cannot remove ENUM values in PostgreSQL, they are permanent once added + # If needed, would require recreating the ENUM type diff --git a/migrations/versions/de11b016b35a_add_ai_agent_process_tracking.py b/migrations/versions/de11b016b35a_add_ai_agent_process_tracking.py new file mode 100644 index 0000000..675fc54 --- /dev/null +++ b/migrations/versions/de11b016b35a_add_ai_agent_process_tracking.py @@ -0,0 +1,32 @@ +"""add ai agent process tracking + +Revision ID: de11b016b35a +Revises: 1a2cda4df181 +Create Date: 2025-09-03 00:02:24.263636 + +""" +from typing import Sequence, Union + +from alembic import op +import sqlalchemy as sa + + +# revision identifiers, used by Alembic. +revision: str = 'de11b016b35a' +down_revision: Union[str, Sequence[str], None] = '1a2cda4df181' +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + + +def upgrade() -> None: + """Upgrade schema.""" + # Add AI agent process tracking columns + op.add_column('interview_sessions', sa.Column('ai_agent_pid', sa.Integer(), nullable=True)) + op.add_column('interview_sessions', sa.Column('ai_agent_status', sa.String(), server_default='not_started', nullable=False)) + + +def downgrade() -> None: + """Downgrade schema.""" + # Drop AI agent process tracking columns + op.drop_column('interview_sessions', 'ai_agent_status') + op.drop_column('interview_sessions', 'ai_agent_pid') diff --git a/pyproject.toml b/pyproject.toml index 9a68337..2c441ac 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -13,6 +13,20 @@ dependencies = [ "boto3>=1.34.0", "python-dotenv>=1.0.0", "pydantic-settings>=2.1.0", + "langchain>=0.1.0", + "langchain-core>=0.1.0", + "langchain-community>=0.0.10", + "redis>=5.0.0", + "langchain-openai>=0.3.32", + "langchain-milvus>=0.2.1", + "celery>=5.3.0", + "pdfplumber>=0.10.0", + "psycopg2-binary>=2.9.0", + "python-docx>=1.2.0", + "docx2txt>=0.9", + "livekit>=1.0.12", + "livekit-api>=1.0.5", + "livekit-agents[cartesia,deepgram,openai,silero,resemble,turn-detector]~=1.2", ] [build-system] @@ -45,4 +59,4 @@ line_length = 88 python_version = "3.11" warn_return_any = true warn_unused_configs = true -disallow_untyped_defs = true \ No newline at end of file +disallow_untyped_defs = true diff --git a/quick_api_test.py b/quick_api_test.py new file mode 100644 index 0000000..2668a22 --- /dev/null +++ b/quick_api_test.py @@ -0,0 +1,186 @@ +#!/usr/bin/env python3 +"""Quick API testing script""" + +import requests +import json +import time +from pathlib import Path + +BASE_URL = "http://localhost:8000" + +def test_health(): + """Test API health""" + try: + response = requests.get(f"{BASE_URL}/health", timeout=5) + print(f"Health check: {response.status_code}") + return response.status_code == 200 + except Exception as e: + print(f"API not available: {str(e)}") + return False + +def upload_test_resume(): + """Upload test resume""" + try: + # Check if test resume exists + resume_path = Path("test_resume.txt") + if not resume_path.exists(): + print("test_resume.txt not found!") + return None + + # Upload file + with open(resume_path, 'r', encoding='utf-8') as f: + files = {'file': (resume_path.name, f, 'text/plain')} + data = { + 'applicant_name': 'Иванов Иван Иванович', + 'applicant_email': 'ivan.ivanov@example.com', + 'applicant_phone': '+7 (999) 123-45-67', + 'vacancy_id': '1' + } + + response = requests.post( + f"{BASE_URL}/resume/upload", + files=files, + data=data, + timeout=30 + ) + + print(f"Resume upload: {response.status_code}") + if response.status_code == 200: + result = response.json() + print(f"Resume ID: {result.get('resume_id')}") + return result.get('resume_id') + else: + print(f"Upload failed: {response.text}") + return None + + except Exception as e: + print(f"Upload error: {str(e)}") + return None + +def check_resume_processing(resume_id): + """Check resume processing status""" + try: + response = requests.get(f"{BASE_URL}/resume/{resume_id}") + print(f"Resume status check: {response.status_code}") + + if response.status_code == 200: + resume = response.json() + print(f"Status: {resume.get('status')}") + print(f"Has interview plan: {'interview_plan' in resume and resume['interview_plan'] is not None}") + return resume + else: + print(f"Resume check failed: {response.text}") + return None + + except Exception as e: + print(f"Status check error: {str(e)}") + return None + +def create_interview_session(resume_id): + """Create interview session""" + try: + response = requests.post(f"{BASE_URL}/interview/{resume_id}/token") + print(f"Interview session creation: {response.status_code}") + + if response.status_code == 200: + result = response.json() + print(f"Room: {result.get('room_name')}") + print(f"Token length: {len(result.get('token', ''))}") + return result + else: + print(f"Interview creation failed: {response.text}") + return None + + except Exception as e: + print(f"Interview creation error: {str(e)}") + return None + +def check_admin_processes(): + """Check admin process monitoring""" + try: + response = requests.get(f"{BASE_URL}/admin/interview-processes") + print(f"Admin processes check: {response.status_code}") + + if response.status_code == 200: + result = response.json() + print(f"Active sessions: {result.get('total_active_sessions')}") + for proc in result.get('processes', []): + print(f" Session {proc['session_id']}: PID {proc['pid']}, Running: {proc['is_running']}") + return result + else: + print(f"Admin check failed: {response.text}") + return None + + except Exception as e: + print(f"Admin check error: {str(e)}") + return None + +def main(): + """Run quick API tests""" + print("=" * 50) + print("QUICK API TEST") + print("=" * 50) + + # 1. Check if API is running + if not test_health(): + print("❌ API not running! Start with: uvicorn app.main:app --reload") + return + + print("✅ API is running") + + # 2. Upload test resume + print("\n--- Testing Resume Upload ---") + resume_id = upload_test_resume() + + if not resume_id: + print("❌ Resume upload failed!") + return + + print(f"✅ Resume uploaded with ID: {resume_id}") + + # 3. Wait for processing and check status + print("\n--- Checking Resume Processing ---") + print("Waiting 10 seconds for Celery processing...") + time.sleep(10) + + resume_data = check_resume_processing(resume_id) + + if not resume_data: + print("❌ Could not check resume status!") + return + + if resume_data.get('status') == 'parsed': + print("✅ Resume processed successfully") + else: + print(f"⚠️ Resume status: {resume_data.get('status')}") + + # 4. Create interview session + print("\n--- Testing Interview Session ---") + interview_data = create_interview_session(resume_id) + + if interview_data: + print("✅ Interview session created") + else: + print("❌ Interview session creation failed") + + # 5. Check admin monitoring + print("\n--- Testing Admin Monitoring ---") + admin_data = check_admin_processes() + + if admin_data: + print("✅ Admin monitoring works") + else: + print("❌ Admin monitoring failed") + + print("\n" + "=" * 50) + print("QUICK TEST COMPLETED") + print("=" * 50) + + print("\nNext steps:") + print("1. Check Celery worker logs for task processing") + print("2. Inspect database for interview_plan data") + print("3. For voice testing, start LiveKit server") + print("4. Monitor system with: curl http://localhost:8000/admin/system-stats") + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/rag/__init__.py b/rag/__init__.py new file mode 100644 index 0000000..b12a737 --- /dev/null +++ b/rag/__init__.py @@ -0,0 +1,5 @@ +from .database import VectorStore +from .llm import ChatModel, EmbeddingsModel +from .service import RagService + +__all__ = ['RagService', 'ChatModel', 'EmbeddingsModel'] diff --git a/rag/database/__init__.py b/rag/database/__init__.py new file mode 100644 index 0000000..5d13f9f --- /dev/null +++ b/rag/database/__init__.py @@ -0,0 +1,3 @@ +from .model import VectorStoreModel as VectorStore + +__all__ = ['VectorStore'] diff --git a/rag/database/model.py b/rag/database/model.py new file mode 100644 index 0000000..6c23f07 --- /dev/null +++ b/rag/database/model.py @@ -0,0 +1,17 @@ +from langchain_core.vectorstores.base import VectorStore + + +class VectorStoreModel: + def __init__(self, store: VectorStore): + self.store = store + + def get_store(self): + return self.store + + def similarity_search(self, query: str): + results = self.store.similarity_search( + query, + k=5, + ) + + return results diff --git a/rag/llm/__init__.py b/rag/llm/__init__.py new file mode 100644 index 0000000..886ef35 --- /dev/null +++ b/rag/llm/__init__.py @@ -0,0 +1,3 @@ +from .model import ChatModel, EmbeddingsModel + +__all__ = ['ChatModel', 'EmbeddingsModel'] diff --git a/rag/llm/model.py b/rag/llm/model.py new file mode 100644 index 0000000..406b364 --- /dev/null +++ b/rag/llm/model.py @@ -0,0 +1,206 @@ +import json +import pdfplumber +import os +from typing import Dict, Any +from langchain_core.embeddings import Embeddings +from langchain_core.language_models import BaseChatModel +from langchain.schema import HumanMessage, SystemMessage + +try: + from docx import Document +except ImportError: + Document = None + +try: + import docx2txt +except ImportError: + docx2txt = None + +class EmbeddingsModel: + def __init__(self, model: Embeddings): + self.model = model + + def get_model(self): + return self.model + +class ChatModel: + def __init__(self, model: BaseChatModel): + self.model = model + + def get_llm(self): + return self.model + +class ResumeParser: + def __init__(self, chat_model: ChatModel): + self.llm = chat_model.get_llm() + self.resume_prompt = """ + Проанализируй текст резюме и извлеки из него структурированные данные в JSON формате. + Верни только JSON без дополнительных комментариев. + + Формат ответа: + {{ + "name": "Имя кандидата", + "email": "email@example.com", + "phone": "+7-XXX-XXX-XX-XX", + "skills": ["навык1", "навык2", "навык3"], + "experience": [ + {{ + "company": "Название компании", + "position": "Должность", + "period": "2021-2024", + "description": "Краткое описание обязанностей" + }} + ], + "total_years": 3.5, + "education": "Образование", + "summary": "Краткое резюме о кандидате" + }} + + Текст резюме: + {resume_text} + """ + + def extract_text_from_pdf(self, file_path: str) -> str: + """Извлекает текст из PDF файла""" + try: + with pdfplumber.open(file_path) as pdf: + text = '\n'.join([page.extract_text() or '' for page in pdf.pages]) + return text.strip() + except Exception as e: + raise Exception(f"Ошибка при чтении PDF: {str(e)}") + + def extract_text_from_docx(self, file_path: str) -> str: + """Извлекает текст из DOCX файла""" + try: + print(f"[DEBUG] Extracting DOCX text from: {file_path}") + + if docx2txt: + # Предпочитаем docx2txt для простого извлечения текста + print("[DEBUG] Using docx2txt") + text = docx2txt.process(file_path) + if text: + print(f"[DEBUG] Extracted {len(text)} characters using docx2txt") + return text.strip() + else: + print("[DEBUG] docx2txt returned empty text") + + if Document: + # Используем python-docx как fallback + print("[DEBUG] Using python-docx as fallback") + doc = Document(file_path) + text = '\n'.join([paragraph.text for paragraph in doc.paragraphs]) + print(f"[DEBUG] Extracted {len(text)} characters using python-docx") + return text.strip() + + raise Exception("Библиотеки для чтения DOCX не установлены (docx2txt или python-docx)") + except Exception as e: + print(f"[DEBUG] DOCX extraction failed: {str(e)}") + raise Exception(f"Ошибка при чтении DOCX: {str(e)}") + + def extract_text_from_doc(self, file_path: str) -> str: + """Извлекает текст из DOC файла""" + try: + # Для .doc файлов используем antiword (если установлен) или попробуем python-docx + if Document: + try: + doc = Document(file_path) + text = '\n'.join([paragraph.text for paragraph in doc.paragraphs]) + return text.strip() + except: + # Если python-docx не может прочитать .doc, пытаемся использовать системные утилиты + pass + + # Попытка использовать системную команду antiword (для Linux/Mac) + import subprocess + try: + result = subprocess.run(['antiword', file_path], capture_output=True, text=True) + if result.returncode == 0: + return result.stdout.strip() + except FileNotFoundError: + pass + + raise Exception("Не удалось найти подходящий инструмент для чтения DOC файлов. Рекомендуется использовать DOCX формат.") + except Exception as e: + raise Exception(f"Ошибка при чтении DOC: {str(e)}") + + def extract_text_from_txt(self, file_path: str) -> str: + """Извлекает текст из TXT файла""" + try: + # Попробуем разные кодировки + encodings = ['utf-8', 'cp1251', 'latin-1', 'cp1252'] + + for encoding in encodings: + try: + with open(file_path, 'r', encoding=encoding) as file: + text = file.read() + return text.strip() + except UnicodeDecodeError: + continue + + raise Exception("Не удалось определить кодировку текстового файла") + except Exception as e: + raise Exception(f"Ошибка при чтении TXT: {str(e)}") + + def extract_text_from_file(self, file_path: str) -> str: + """Универсальный метод извлечения текста из файла""" + if not os.path.exists(file_path): + raise Exception(f"Файл не найден: {file_path}") + + # Определяем расширение файла + _, ext = os.path.splitext(file_path.lower()) + + # Добавляем отладочную информацию + print(f"[DEBUG] Parsing file: {file_path}, detected extension: {ext}") + + if ext == '.pdf': + return self.extract_text_from_pdf(file_path) + elif ext == '.docx': + return self.extract_text_from_docx(file_path) + elif ext == '.doc': + return self.extract_text_from_doc(file_path) + elif ext == '.txt': + return self.extract_text_from_txt(file_path) + else: + raise Exception(f"Неподдерживаемый формат файла: {ext}. Поддерживаемые форматы: PDF, DOCX, DOC, TXT") + + def parse_resume_text(self, resume_text: str) -> Dict[str, Any]: + """Парсит текст резюме через LLM""" + try: + messages = [ + SystemMessage(content="Ты эксперт по анализу резюме. Извлекай данные точно в указанном JSON формате."), + HumanMessage(content=self.resume_prompt.format(resume_text=resume_text)) + ] + + response = self.llm.invoke(messages) + + # Извлекаем JSON из ответа + response_text = response.content.strip() + + # Пытаемся найти JSON в ответе + if response_text.startswith('{') and response_text.endswith('}'): + return json.loads(response_text) + else: + # Ищем JSON внутри текста + start = response_text.find('{') + end = response_text.rfind('}') + 1 + if start != -1 and end > start: + json_str = response_text[start:end] + return json.loads(json_str) + else: + raise ValueError("JSON не найден в ответе LLM") + + except json.JSONDecodeError as e: + raise Exception(f"Ошибка парсинга JSON из ответа LLM: {str(e)}") + except Exception as e: + raise Exception(f"Ошибка при обращении к LLM: {str(e)}") + + def parse_resume_from_file(self, file_path: str) -> Dict[str, Any]: + """Полный цикл парсинга резюме из файла""" + # Шаг 1: Извлекаем текст из файла (поддерживаем PDF, DOCX, DOC, TXT) + resume_text = self.extract_text_from_file(file_path) + + if not resume_text: + raise Exception("Не удалось извлечь текст из файла") + + # Шаг 2: Парсим через LLM + return self.parse_resume_text(resume_text) diff --git a/rag/memory.py b/rag/memory.py new file mode 100644 index 0000000..ce9cbfa --- /dev/null +++ b/rag/memory.py @@ -0,0 +1,91 @@ +import json +from datetime import datetime +from typing import List, Optional +import redis +from sqlmodel import select +from sqlalchemy.ext.asyncio import AsyncSession + +from langchain.schema import HumanMessage, AIMessage +from langchain.memory import ConversationSummaryBufferMemory + +from rag.settings import settings + + +class ChatMemoryManager: + def __init__(self, llm, token_limit=3000): + self.redis = redis.Redis(host=settings.redis_cache_url, port=settings.redis_cache_port, db=settings.redis_cache_db) + self.llm = llm + self.token_limit = token_limit + + def _convert_to_langchain(self, messages: List[dict]): + return [ + AIMessage(content=msg["content"]) if msg["is_ai"] + else HumanMessage(content=msg["content"]) + for msg in messages + ] + + def _annotate_messages(self, messages: List): + # Convert to format compatible with langchain + # Assuming messages have some way to identify if they're from AI + return [ + { + **msg, + "is_ai": msg.get("user_type") == "AI" or msg.get("username") == "SOMMELIER" + } + for msg in messages + ] + + def _serialize_messages(self, messages: List[dict]): + return [ + {**msg, "created_at": msg["created_at"].isoformat()} + for msg in messages + ] + + def _cache_key(self, session_id: int) -> str: + return f"chat_memory:{session_id}" + + async def load_chat_history(self, session_id: int, session: AsyncSession) -> List[HumanMessage | AIMessage]: + cache_key = self._cache_key(session_id) + serialized = self.redis.get(cache_key) + + if serialized: + cached_messages = json.loads(serialized) + if cached_messages: + last_time = datetime.fromisoformat(cached_messages[-1]["created_at"]) + + # TODO: Replace with actual Message model query when available + # This would need to be implemented with SQLModel/SQLAlchemy + new_messages = [] # Placeholder for actual DB query + + if new_messages: + annotated_messages = self._annotate_messages(new_messages) + all_messages = cached_messages + self._serialize_messages(annotated_messages) + self.redis.setex(cache_key, 3600, json.dumps(all_messages)) + return self._convert_to_langchain(all_messages) + + return self._convert_to_langchain(cached_messages) + + # TODO: Replace with actual Message model query when available + # This would need to be implemented with SQLModel/SQLAlchemy + db_messages = [] # Placeholder for actual DB query + + if db_messages: + annotated_messages = self._annotate_messages(db_messages) + self.redis.setex(cache_key, 3600, json.dumps(self._serialize_messages(annotated_messages))) + return self._convert_to_langchain(annotated_messages) + + return [] + + async def get_session_memory(self, session_id: int, session: AsyncSession) -> ConversationSummaryBufferMemory: + memory = ConversationSummaryBufferMemory( + llm=self.llm, + max_token_limit=self.token_limit + ) + + messages = await self.load_chat_history(session_id, session) + for msg in messages: + if isinstance(msg, HumanMessage): + memory.chat_memory.add_user_message(msg.content) + elif isinstance(msg, AIMessage): + memory.chat_memory.add_ai_message(msg.content) + return memory diff --git a/rag/registry.py b/rag/registry.py new file mode 100644 index 0000000..d4624dd --- /dev/null +++ b/rag/registry.py @@ -0,0 +1,109 @@ +from langchain_openai import ChatOpenAI, OpenAIEmbeddings +from rag.llm.model import ChatModel, EmbeddingsModel +from rag.database.model import VectorStoreModel +from rag.service.model import RagService +from rag.vector_store import MilvusVectorStore +from rag.settings import settings +from langchain_milvus import Milvus + + +class ModelRegistry: + """Реестр для инициализации и получения моделей""" + + _instance = None + _initialized = False + + def __new__(cls): + if cls._instance is None: + cls._instance = super(ModelRegistry, cls).__new__(cls) + return cls._instance + + def __init__(self): + if not self._initialized: + self._chat_model = None + self._embeddings_model = None + self._vector_store = None + self._rag_service = None + self._initialized = True + + def get_chat_model(self) -> ChatModel: + """Получить или создать chat модель""" + if self._chat_model is None: + if settings.openai_api_key: + llm = ChatOpenAI( + api_key=settings.openai_api_key, + model="gpt-4o-mini", + temperature=0 + ) + self._chat_model = ChatModel(llm) + else: + raise ValueError("OpenAI API key не настроен в settings") + return self._chat_model + + def get_embeddings_model(self) -> EmbeddingsModel: + """Получить или создать embeddings модель""" + if self._embeddings_model is None: + if settings.openai_api_key: + embeddings = OpenAIEmbeddings( + api_key=settings.openai_api_key, + model=settings.openai_embeddings_model + ) + self._embeddings_model = EmbeddingsModel(embeddings) + else: + raise ValueError("OpenAI API key не настроен в settings") + return self._embeddings_model + + def get_vector_store(self) -> MilvusVectorStore: + """Получить или создать vector store""" + if self._vector_store is None: + embeddings_model = self.get_embeddings_model() + self._vector_store = MilvusVectorStore( + embeddings_model.get_model(), + collection_name=settings.milvus_collection + ) + return self._vector_store + + def get_rag_service(self) -> RagService: + """Получить или создать RAG сервис""" + if self._rag_service is None: + # Создаем VectorStoreModel для совместимости с существующим кодом + # Парсим URI для получения host и port + uri_without_protocol = settings.milvus_uri.replace("http://", "").replace("https://", "") + if ":" in uri_without_protocol: + host, port = uri_without_protocol.split(":", 1) + port = int(port) + else: + host = uri_without_protocol + port = 19530 # Default Milvus port + + try: + # Попробуем использовать URI напрямую + milvus_store = Milvus( + embedding_function=self.get_embeddings_model().get_model(), + connection_args={ + "uri": settings.milvus_uri, + }, + collection_name=settings.milvus_collection, + ) + except Exception as e: + # Если не сработало, попробуем host/port + milvus_store = Milvus( + embedding_function=self.get_embeddings_model().get_model(), + connection_args={ + "host": host, + "port": port, + }, + collection_name=settings.milvus_collection, + ) + + vector_store_model = VectorStoreModel(milvus_store) + + self._rag_service = RagService( + vector_store=vector_store_model, + llm=self.get_chat_model() + ) + return self._rag_service + + +# Singleton instance +registry = ModelRegistry() \ No newline at end of file diff --git a/rag/service/__init__.py b/rag/service/__init__.py new file mode 100644 index 0000000..830324d --- /dev/null +++ b/rag/service/__init__.py @@ -0,0 +1,3 @@ +from .model import RagService + +__all__ = ['RagService'] diff --git a/rag/service/model.py b/rag/service/model.py new file mode 100644 index 0000000..3ca0b7e --- /dev/null +++ b/rag/service/model.py @@ -0,0 +1,121 @@ +from rag.database.model import VectorStoreModel +from langchain_core.runnables import RunnableWithMessageHistory +from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder +from rag.llm.model import ChatModel +from langchain.schema import HumanMessage, SystemMessage +from langchain.chains import create_history_aware_retriever, create_retrieval_chain +from langchain.chains.combine_documents import create_stuff_documents_chain + +from rag.memory import ChatMemoryManager + +rag_template: str = """ +You are a beverage and alcohol expert — like a sommelier, but for all kinds of alcoholic drinks, including beer, wine, spirits, cocktails, etc +Answer clearly and stay within your expertise in alcohol and related topics +Rules: +1. Speak in first person: "I recommend", "I think" +2. Be conversational and personable - like a knowledgeable friend at a bar +3. Use facts from the context for specific characteristics, but speak generally when needed +4. Do not disclose sources or metadata from contextual documents +5. Answer questions about alcohol and related topics (food pairings, culture, serving, etc) but politely decline unrelated subjects +6. Be brief and useful - keep answers to 2-4 sentences +7. Use chat history to maintain a natural conversation flow +8. Feel free to use casual language and humor when appropriate + +Context: {context} +""" + +get_summary_template = """Create a concise 3-5 word title for the following conversation. + Focus on the main topic. Reply only with the title.\n\n + Chat history:\n""" + +rephrase_prompt = ChatPromptTemplate.from_messages([ + ("system", "Given a chat history and the latest user question which might reference context in the chat history, " + "formulate a standalone question. Do NOT answer the question."), + MessagesPlaceholder("chat_history"), + ("human", "{input}"), +]) + +qa_prompt = ChatPromptTemplate.from_messages([ + ("system", rag_template), + MessagesPlaceholder("chat_history"), + ("human", "{input}"), +]) + + +class RagService: + def __init__(self, vector_store: VectorStoreModel, llm: ChatModel): + self.vector_store = vector_store.get_store() + self.llm = llm.get_llm() + + retriever = self.vector_store.as_retriever() + + self.rephrase_prompt = ChatPromptTemplate.from_messages([ + ("system", + "Given a chat history and the latest user question which might reference context in the chat history, " + "formulate a standalone question. Do NOT answer the question."), + MessagesPlaceholder("chat_history"), + ("human", "{input}"), + ]) + + self.qa_prompt = ChatPromptTemplate.from_messages([ + ("system", rag_template), + MessagesPlaceholder("chat_history"), + ("human", "{input}"), + ]) + + self.history_aware_retriever = create_history_aware_retriever( + self.llm, retriever, self.rephrase_prompt + ) + + self.question_answer_chain = create_stuff_documents_chain( + self.llm, self.qa_prompt + ) + + self.rag_chain = create_retrieval_chain( + self.history_aware_retriever, self.question_answer_chain + ) + + def get_qa_from_query(self, query: str, session_id: int) -> str: + memory = ChatMemoryManager(self.llm) + + async def get_session_history(_): + # TODO: Pass actual AsyncSession here + return (await memory.get_session_memory(session_id, None)).chat_memory + + conversational_rag_chain = RunnableWithMessageHistory( + self.rag_chain, + get_session_history, + input_messages_key="input", + history_messages_key="chat_history", + output_messages_key="answer" + ) + + for chunk in conversational_rag_chain.stream( + {"input": query}, + config={"configurable": {"session_id": str(session_id)}} + ): + answer = chunk.get('answer', '') + if answer: + yield answer + + def generate_title_with_llm(self, chat_history: str | list[str]) -> str: + + # Вариант 1: Если chat_history — строка + if isinstance(chat_history, str): + prompt = get_summary_template + chat_history + + messages = [ + SystemMessage(content="You are a helpful assistant that generates chat titles."), + HumanMessage(content=prompt) + ] + + # Вариант 2: Если chat_history — список сообщений (например, ["user: ...", "bot: ..."]) + else: + prompt = get_summary_template + "\n".join(chat_history) + messages = [ + SystemMessage(content="You are a helpful assistant that generates chat titles."), + HumanMessage(content=prompt) + ] + + response = self.llm.invoke(messages) + return response.content.strip() diff --git a/rag/settings.py b/rag/settings.py new file mode 100644 index 0000000..61fd130 --- /dev/null +++ b/rag/settings.py @@ -0,0 +1,52 @@ +import os +from pydantic_settings import BaseSettings +from typing import Optional + + +class RagSettings(BaseSettings): + # Database + database_url: str = "postgresql+asyncpg://tdjx:1309@localhost:5432/hr_ai" + + # Milvus Settings + milvus_uri: str = "http://5.188.159.90:19530" + milvus_collection: str = "candidate_profiles" + + # Redis + redis_cache_url: str = "localhost" + redis_cache_port: int = 6379 + redis_cache_db: int = 0 + + # S3 Configuration + s3_endpoint_url: str + s3_access_key_id: str + s3_secret_access_key: str + s3_bucket_name: str + s3_region: str = "ru-1" + + # LLM Settings + openai_api_key: Optional[str] = None + anthropic_api_key: Optional[str] = None + openai_model: str = "gpt-4o-mini" + openai_embeddings_model: str = "text-embedding-3-small" + + # AI Agent Settings + deepgram_api_key: Optional[str] = None + cartesia_api_key: Optional[str] = None + elevenlabs_api_key: Optional[str] = None + resemble_api_key: Optional[str] = None + + # LiveKit Configuration + livekit_url: str = "ws://localhost:7880" + livekit_api_key: str = "devkey" + livekit_api_secret: str = "devkey_secret_32chars_minimum_length" + + # App Configuration + app_env: str = "development" + debug: bool = True + + class Config: + env_file = ".env" + env_file_encoding = "utf-8" + + +settings = RagSettings() \ No newline at end of file diff --git a/rag/vector_store.py b/rag/vector_store.py new file mode 100644 index 0000000..f04e0a8 --- /dev/null +++ b/rag/vector_store.py @@ -0,0 +1,104 @@ +from typing import List, Dict, Any +from langchain_milvus import Milvus +from langchain_core.embeddings import Embeddings +from rag.settings import settings + + +class MilvusVectorStore: + def __init__(self, embeddings_model: Embeddings, collection_name: str = "candidate_profiles"): + self.embeddings = embeddings_model + self.collection_name = collection_name + + # Попробуем использовать URI напрямую + self.vector_store = Milvus( + embedding_function=embeddings_model, + connection_args={ + "uri": settings.milvus_uri, + }, + collection_name=collection_name, + ) + + def add_candidate_profile(self, candidate_id: str, resume_data: Dict[str, Any]): + """Добавляет профиль кандидата в векторную базу""" + try: + # Создаем текст для векторизации из навыков и опыта + skills_text = " ".join(resume_data.get("skills", [])) + experience_text = " ".join([ + f"{exp.get('position', '')} {exp.get('company', '')} {exp.get('description', '')}" + for exp in resume_data.get("experience", []) + ]) + + combined_text = f"{skills_text} {experience_text} {resume_data.get('summary', '')}" + + # Метаданные для поиска + metadata = { + "candidate_id": candidate_id, + "name": resume_data.get("name", ""), + "email": resume_data.get("email", ""), + "phone": resume_data.get("phone", ""), + "total_years": resume_data.get("total_years", 0), + "skills": resume_data.get("skills", []), + "education": resume_data.get("education", "") + } + + # Добавляем в векторную базу + self.vector_store.add_texts( + texts=[combined_text], + metadatas=[metadata], + ids=[candidate_id] + ) + + return True + + except Exception as e: + raise Exception(f"Ошибка при добавлении кандидата в Milvus: {str(e)}") + + def search_similar_candidates(self, query: str, k: int = 5) -> List[Dict[str, Any]]: + """Поиск похожих кандидатов по запросу""" + try: + results = self.vector_store.similarity_search_with_score(query, k=k) + + candidates = [] + for doc, score in results: + candidate = { + "content": doc.page_content, + "metadata": doc.metadata, + "similarity_score": score + } + candidates.append(candidate) + + return candidates + + except Exception as e: + raise Exception(f"Ошибка при поиске кандидатов в Milvus: {str(e)}") + + def get_candidate_by_id(self, candidate_id: str) -> Dict[str, Any]: + """Получает кандидата по ID""" + try: + results = self.vector_store.similarity_search( + query="", + k=1, + expr=f"candidate_id == '{candidate_id}'" + ) + + if results: + doc = results[0] + return { + "content": doc.page_content, + "metadata": doc.metadata + } + else: + return None + + except Exception as e: + raise Exception(f"Ошибка при получении кандидата из Milvus: {str(e)}") + + def delete_candidate(self, candidate_id: str): + """Удаляет кандидата из векторной базы""" + try: + # В Milvus удаление по ID + self.vector_store.delete([candidate_id]) + return True + + except Exception as e: + raise Exception(f"Ошибка при удалении кандидата из Milvus: {str(e)}") \ No newline at end of file diff --git a/simple_test.py b/simple_test.py new file mode 100644 index 0000000..935bcec --- /dev/null +++ b/simple_test.py @@ -0,0 +1,208 @@ +#!/usr/bin/env python3 +"""Simple system test without Unicode characters""" + +import asyncio +import sys +from pathlib import Path + +# Add root directory to PYTHONPATH +sys.path.append(str(Path(__file__).parent)) + +async def test_database(): + """Test PostgreSQL connection""" + print("Testing database connection...") + + try: + from app.core.database import get_session as get_db + from app.models.resume import Resume + from sqlalchemy import select + + async for db in get_db(): + result = await db.execute(select(Resume).limit(1)) + resumes = result.scalars().all() + + print("PASS - Database connection successful") + print(f"Found resumes: {len(resumes)}") + return True + + except Exception as e: + print(f"FAIL - Database error: {str(e)}") + return False + + +async def test_rag(): + """Test RAG system""" + print("\nTesting RAG system...") + + try: + from rag.registry import registry + from rag.llm.model import ResumeParser + + chat_model = registry.get_chat_model() + parser = ResumeParser(chat_model) + + # Test resume text + test_text = """ + John Doe + Python Developer + Experience: 3 years + Skills: Python, Django, PostgreSQL + Education: Computer Science + """ + + parsed_resume = parser.parse_resume_text(test_text) + + print("PASS - RAG system working") + print(f"Parsed data keys: {list(parsed_resume.keys())}") + + return True + + except Exception as e: + print(f"FAIL - RAG error: {str(e)}") + return False + + +def test_redis(): + """Test Redis connection""" + print("\nTesting Redis connection...") + + try: + import redis + from rag.settings import settings + + r = redis.Redis( + host=settings.redis_cache_url, + port=settings.redis_cache_port, + db=settings.redis_cache_db + ) + + r.ping() + print("PASS - Redis connection successful") + return True + + except Exception as e: + print(f"FAIL - Redis error: {str(e)}") + print("TIP: Start Redis with: docker run -d -p 6379:6379 redis:alpine") + return False + + +async def test_interview_service(): + """Test interview service""" + print("\nTesting interview service...") + + try: + from app.services.interview_service import InterviewRoomService + from app.core.database import get_session as get_db + + async for db in get_db(): + service = InterviewRoomService(db) + + # Test token generation + token = service.generate_access_token("test_room", "test_user") + print(f"PASS - Token generated (length: {len(token)})") + + # Test fallback plan + plan = service._get_fallback_interview_plan() + print(f"PASS - Interview plan structure: {list(plan.keys())}") + + return True + + except Exception as e: + print(f"FAIL - Interview service error: {str(e)}") + return False + + +def test_ai_agent(): + """Test AI agent""" + print("\nTesting AI agent...") + + try: + from ai_interviewer_agent import InterviewAgent + + test_plan = { + "interview_structure": { + "duration_minutes": 15, + "greeting": "Hello! Test interview", + "sections": [ + { + "name": "Introduction", + "duration_minutes": 5, + "questions": ["Tell me about yourself"] + } + ] + }, + "candidate_info": { + "name": "Test Candidate", + "skills": ["Python"], + "total_years": 2 + } + } + + agent = InterviewAgent(test_plan) + + print(f"PASS - AI Agent initialized with {len(agent.sections)} sections") + print(f"Current section: {agent.get_current_section().get('name')}") + + return True + + except Exception as e: + print(f"FAIL - AI Agent error: {str(e)}") + return False + + +async def main(): + """Run all tests""" + print("=" * 50) + print("HR-AI SYSTEM TEST") + print("=" * 50) + + tests = [ + ("Database", test_database), + ("RAG System", test_rag), + ("Redis", lambda: test_redis()), + ("Interview Service", test_interview_service), + ("AI Agent", lambda: test_ai_agent()), + ] + + results = [] + + for test_name, test_func in tests: + try: + if asyncio.iscoroutinefunction(test_func): + result = await test_func() + else: + result = test_func() + results.append((test_name, result)) + except Exception as e: + print(f"CRITICAL ERROR in {test_name}: {str(e)}") + results.append((test_name, False)) + + # Summary + print("\n" + "=" * 50) + print("TEST RESULTS") + print("=" * 50) + + passed = 0 + for test_name, result in results: + status = "PASS" if result else "FAIL" + print(f"{test_name:20} {status}") + if result: + passed += 1 + + total = len(results) + print(f"\nRESULT: {passed}/{total} tests passed") + + if passed == total: + print("\nSYSTEM READY FOR TESTING!") + print("Next steps:") + print("1. Start FastAPI: uvicorn app.main:app --reload") + print("2. Start Celery: celery -A celery_worker.celery_app worker --loglevel=info") + print("3. Upload test resume via /resume/upload") + print("4. Check interview plan generation") + else: + print("\nSOME COMPONENTS NEED SETUP") + print("Check error messages above for troubleshooting") + + +if __name__ == "__main__": + asyncio.run(main()) \ No newline at end of file diff --git a/test_system.py b/test_system.py new file mode 100644 index 0000000..0ffd761 --- /dev/null +++ b/test_system.py @@ -0,0 +1,300 @@ +#!/usr/bin/env python3 +""" +Тестирование HR-AI системы по компонентам +""" + +import asyncio +import sys +import os +import requests +from pathlib import Path + +# Добавляем корневую директорию в PYTHONPATH +sys.path.append(str(Path(__file__).parent)) + +async def test_database_connection(): + """Тест подключения к PostgreSQL""" + print("Testing database connection...") + + try: + from app.core.database import get_db + from app.models.resume import Resume + from sqlalchemy import select + + # Получаем async сессию + async for db in get_db(): + # Пробуем выполнить простой запрос + result = await db.execute(select(Resume).limit(1)) + resumes = result.scalars().all() + + print("OK Database: connection successful") + print(f"Found resumes in database: {len(resumes)}") + return True + + except Exception as e: + print(f"FAIL Database: connection error - {str(e)}") + return False + + +async def test_rag_system(): + """Тест RAG системы (парсинг резюме)""" + print("\n🔍 Тестируем RAG систему...") + + try: + from rag.registry import registry + from rag.llm.model import ResumeParser + + # Инициализируем модели + chat_model = registry.get_chat_model() + embeddings_model = registry.get_embeddings_model() + + print("✅ RAG система: модели инициализированы") + + # Тестируем парсер резюме + parser = ResumeParser(chat_model) + + # Создаем тестовый текст резюме + test_resume_text = """ + Иван Петров + Python разработчик + Опыт работы: 3 года + Навыки: Python, Django, PostgreSQL, Docker + Образование: МГУ, факультет ВМК + """ + + parsed_resume = parser.parse_resume_text(test_resume_text) + + print("✅ RAG система: парсинг резюме работает") + print(f"📋 Распарсенные данные: {parsed_resume}") + + return True + + except Exception as e: + print(f"❌ RAG система: ошибка - {str(e)}") + return False + + +def test_redis_connection(): + """Тест подключения к Redis""" + print("\n🔍 Тестируем подключение к Redis...") + + try: + import redis + from rag.settings import settings + + r = redis.Redis( + host=settings.redis_cache_url, + port=settings.redis_cache_port, + db=settings.redis_cache_db + ) + + # Пробуем ping + r.ping() + + print("✅ Redis: подключение успешно") + return True + + except Exception as e: + print(f"❌ Redis: ошибка подключения - {str(e)}") + print("💡 Для запуска Redis используйте: docker run -d -p 6379:6379 redis:alpine") + return False + + +async def test_celery_tasks(): + """Тест Celery задач""" + print("\n🔍 Тестируем Celery задачи...") + + try: + from celery_worker.tasks import parse_resume_task + + print("✅ Celery: задачи импортируются") + print("💡 Для полного теста запустите: celery -A celery_worker.celery_app worker --loglevel=info") + + return True + + except Exception as e: + print(f"❌ Celery: ошибка - {str(e)}") + return False + + +async def test_interview_service(): + """Тест сервиса интервью (без LiveKit)""" + print("\n🔍 Тестируем сервис интервью...") + + try: + from app.services.interview_service import InterviewRoomService + from app.core.database import get_db + + async for db in get_db(): + service = InterviewRoomService(db) + + # Тестируем генерацию токена (должен работать даже без LiveKit сервера) + try: + token = service.generate_access_token("test_room", "test_user") + print("✅ Interview Service: генерация токенов работает") + print(f"🎫 Тестовый токен сгенерирован (длина: {len(token)})") + except Exception as e: + print(f"⚠️ Interview Service: ошибка токена - {str(e)}") + + # Тестируем fallback план интервью + fallback_plan = service._get_fallback_interview_plan() + print("✅ Interview Service: fallback план работает") + print(f"📋 Структура плана: {list(fallback_plan.keys())}") + + return True + + except Exception as e: + print(f"❌ Interview Service: ошибка - {str(e)}") + return False + + +def test_ai_agent_import(): + """Тест импорта AI агента""" + print("\n🔍 Тестируем AI агента...") + + try: + from ai_interviewer_agent import InterviewAgent + + # Тестовый план интервью + test_plan = { + "interview_structure": { + "duration_minutes": 15, + "greeting": "Привет! Тест интервью", + "sections": [ + { + "name": "Знакомство", + "duration_minutes": 5, + "questions": ["Расскажи о себе"] + }, + { + "name": "Опыт", + "duration_minutes": 10, + "questions": ["Какой у тебя опыт?"] + } + ] + }, + "candidate_info": { + "name": "Тестовый кандидат", + "skills": ["Python"], + "total_years": 2 + } + } + + agent = InterviewAgent(test_plan) + + print("✅ AI Agent: импорт и инициализация работают") + print(f"📊 Секций в плане: {len(agent.sections)}") + print(f"🎯 Текущая секция: {agent.get_current_section().get('name')}") + + # Тестируем извлечение системных инструкций + instructions = agent.get_system_instructions() + print(f"📝 Инструкции сгенерированы (длина: {len(instructions)})") + + return True + + except Exception as e: + print(f"❌ AI Agent: ошибка - {str(e)}") + return False + + +def check_external_services(): + """Проверка внешних сервисов""" + print("\n🔍 Проверяем внешние сервисы...") + + # Проверяем Milvus + try: + from rag.settings import settings + response = requests.get(f"{settings.milvus_uri}/health", timeout=5) + if response.status_code == 200: + print("✅ Milvus: сервер доступен") + else: + print("⚠️ Milvus: сервер недоступен") + except Exception: + print("❌ Milvus: сервер недоступен") + + # Проверяем LiveKit (если запущен) + try: + # LiveKit health check обычно на HTTP порту + livekit_http_url = settings.livekit_url.replace("ws://", "http://").replace(":7880", ":7880") + response = requests.get(livekit_http_url, timeout=2) + print("✅ LiveKit: сервер запущен") + except Exception: + print("❌ LiveKit: сервер не запущен") + print("💡 Для запуска LiveKit используйте Docker: docker run --rm -p 7880:7880 -p 7881:7881 livekit/livekit-server --dev") + + +async def run_all_tests(): + """Запуск всех тестов""" + print("=== HR-AI System Testing ===") + print("=" * 50) + + tests = [ + ("Database", test_database_connection), + ("RAG System", test_rag_system), + ("Redis", lambda: test_redis_connection()), + ("Celery", test_celery_tasks), + ("Interview Service", test_interview_service), + ("AI Agent", lambda: test_ai_agent_import()), + ] + + results = {} + + for test_name, test_func in tests: + try: + if asyncio.iscoroutinefunction(test_func): + result = await test_func() + else: + result = test_func() + results[test_name] = result + except Exception as e: + print(f"❌ {test_name}: критическая ошибка - {str(e)}") + results[test_name] = False + + # Проверяем внешние сервисы + check_external_services() + + # Итоговый отчет + print("\n" + "=" * 50) + print("📊 ИТОГОВЫЙ ОТЧЕТ") + print("=" * 50) + + passed = sum(1 for r in results.values() if r) + total = len(results) + + for test_name, result in results.items(): + status = "✅ PASS" if result else "❌ FAIL" + print(f"{test_name:20} {status}") + + print(f"\n🎯 Результат: {passed}/{total} тестов прошли успешно") + + if passed == total: + print("🎉 Система готова к тестированию!") + print_next_steps() + else: + print("⚠️ Некоторые компоненты требуют настройки") + print_troubleshooting() + + +def print_next_steps(): + """Следующие шаги для полного тестирования""" + print("\n📋 СЛЕДУЮЩИЕ ШАГИ:") + print("1. 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