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# Python
__pycache__/
*.py[cod]
*$py.class
*.so
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
*.egg-info/
.installed.cfg
*.egg
# Virtual environments
.venv/
venv/
ENV/
env/
# IDE
.vscode/
.idea/
*.swp
*.swo
*~
# Testing
.pytest_cache/
.coverage
htmlcov/
.tox/
# Environment
.env
.env.local
.env.*.local
# Git
.git/
.gitignore
# Docker
docker-compose.yml
Dockerfile
.dockerignore
# Documentation
*.md
docs/
# Logs
*.log
logs/
# OS
.DS_Store
Thumbs.db
# Project specific
tests/
*.pyc

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# Python
__pycache__/
*.py[cod]
*$py.class
*.so
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
pip-wheel-metadata/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
*.manifest
*.spec
# Virtual Environments
.venv/
venv/
ENV/
env/
.ENV/
.env/
virtualenv/
VIRTUAL_ENV/
# IDEs
.vscode/
.idea/
*.swp
*.swo
*~
.project
.pydevproject
.settings/
*.sublime-project
*.sublime-workspace
# Environment variables
.env
.env.local
.env.*.local
.env.development
.env.production
.env.test
*.env
# Testing
.pytest_cache/
.coverage
.coverage.*
htmlcov/
.tox/
.nox/
.hypothesis/
.pytest_cache/
nosetests.xml
coverage.xml
*.cover
*.log
.cache
# Jupyter Notebook
.ipynb_checkpoints
*.ipynb
# pyenv
.python-version
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
# OS
.DS_Store
.DS_Store?
._*
.Spotlight-V100
.Trashes
ehthumbs.db
Thumbs.db
Desktop.ini
# Docker
*.log
docker-compose.override.yml
# Database
*.db
*.sqlite
*.sqlite3
postgres_data/
minio_data/
# Logs
logs/
*.log
*.log.*
# Temporary files
tmp/
temp/
*.tmp
*.bak
*.swp
*~
# Project specific
autorization/
*.md.bak

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# PostgreSQL Database Implementation Summary
## What Was Done
Successfully migrated from in-memory fake_db dictionaries to PostgreSQL with async SQLAlchemy ORM.
## Files Modified
### 1. [database/database.py](database/database.py) - **COMPLETELY REWRITTEN**
**Changes:**
- ✅ Converted from synchronous to **async SQLAlchemy** (using `asyncpg` driver)
- ✅ Added proper `User` model with:
- Auto-increment `id` as primary key
- Unique indexed `telegram_id` and `token`
- `created_at` and `updated_at` timestamps
- Composite index on `token` and `status`
- ✅ Added `Profile` model with:
- All fields from your Pydantic model
- Email as unique indexed field
- Timestamps
- ✅ Created `get_db()` dependency for FastAPI
- ✅ Added `init_db()` and `drop_db()` utility functions
- ✅ Configured connection pooling and async engine
### 2. [app.py](app.py) - **MAJOR UPDATES**
**Changes:**
- ✅ Removed `profile_db = {}` in-memory dict
- ✅ Added database imports and `Depends(get_db)` to all endpoints
- ✅ Added `@app.on_event("startup")` to initialize DB on app start
- ✅ Updated `/profile` POST endpoint:
- Now saves to PostgreSQL `profiles` table
- Handles create/update logic
- Properly commits transactions
- ✅ Updated `/profile/{email}` GET endpoint:
- Queries from PostgreSQL
- Converts DB model to Pydantic response
- ✅ Updated `/login` endpoint:
- Creates `User` record with pending status
- Stores in PostgreSQL instead of dict
- ✅ Updated `/check-auth/{token}` endpoint:
- Queries user by token from PostgreSQL
- Returns proper status
- ✅ Updated `/database/tokens` endpoint:
- Lists all users from database
### 3. [bot.py](bot.py) - **MAJOR REFACTORING**
**Changes:**
- ✅ Removed all references to `fake_db`
- ✅ Removed `app.db = fake_db` synchronization code
- ✅ Added proper database imports
- ✅ Updated `/start` command handler:
- Uses `AsyncSessionLocal()` for DB sessions
- Queries user by token
- Updates telegram_id, username, and status
- Proper error handling with rollback
- ✅ Added `init_db()` call in `start_bot()`
### 4. [requirements.txt](requirements.txt) - **CREATED**
**New dependencies:**
- FastAPI + Uvicorn
- Pydantic with email support
- SQLAlchemy 2.0 with async support
- asyncpg (PostgreSQL async driver)
- psycopg2-binary (backup driver)
- greenlet (required for SQLAlchemy async)
- aiogram 3.3.0 (Telegram bot)
- minio (file storage)
- python-dotenv
- Testing: pytest, pytest-asyncio, httpx
### 5. [init_db.py](init_db.py) - **CREATED**
**Purpose:** Interactive script to initialize or reset database
**Features:**
- Option 1: Create tables
- Option 2: Drop and recreate (reset)
- Safe with confirmation prompts
### 6. [README.md](README.md) - **COMPLETELY REWRITTEN**
**New content:**
- Complete setup instructions
- Database schema documentation
- API endpoints reference
- Usage flow diagram
- Development guide
- Troubleshooting section
## Database Schema
### Users Table
```sql
CREATE TABLE users (
id SERIAL PRIMARY KEY,
telegram_id INTEGER NOT NULL UNIQUE,
token VARCHAR(255) NOT NULL UNIQUE,
username VARCHAR(100),
status VARCHAR(50) NOT NULL DEFAULT 'pending',
created_at TIMESTAMP NOT NULL DEFAULT NOW(),
updated_at TIMESTAMP NOT NULL DEFAULT NOW()
);
CREATE INDEX idx_token_status ON users(token, status);
CREATE UNIQUE INDEX ix_users_telegram_id ON users(telegram_id);
CREATE UNIQUE INDEX ix_users_token ON users(token);
```
### Profiles Table
```sql
CREATE TABLE profiles (
id SERIAL PRIMARY KEY,
email VARCHAR(255) NOT NULL UNIQUE,
name VARCHAR(255) NOT NULL,
position VARCHAR(255) NOT NULL,
competencies TEXT,
experience TEXT,
skills TEXT,
country VARCHAR(100),
languages VARCHAR(255),
employment_format VARCHAR(100),
rate VARCHAR(100),
relocation VARCHAR(100),
cv_url VARCHAR(500),
created_at TIMESTAMP NOT NULL DEFAULT NOW(),
updated_at TIMESTAMP NOT NULL DEFAULT NOW()
);
CREATE UNIQUE INDEX ix_profiles_email ON profiles(email);
```
## Architecture Benefits
### Before (In-Memory Dicts)
❌ Data lost on restart
❌ No persistence
❌ No concurrent access control
❌ No data validation at DB level
❌ No relationships or constraints
❌ No transaction safety
### After (PostgreSQL + SQLAlchemy)
**Persistent** - Data survives restarts
**ACID compliant** - Transaction safety
**Concurrent** - Handle multiple requests
**Indexed** - Fast queries on telegram_id, token, email
**Constraints** - Unique tokens, emails
**Timestamps** - Track created_at, updated_at
**Async** - Non-blocking database operations
**Pooling** - Efficient connection management
## How It Works Now
### Authentication Flow
1. User visits website → `GET /login`
2. FastAPI creates new `User` record in PostgreSQL:
```python
User(telegram_id=0, token=uuid4(), status='pending')
```
3. Returns Telegram bot URL with token
4. User clicks link → Opens bot → Sends `/start {token}`
5. Bot queries database for token:
```python
user = await session.execute(select(User).where(User.token == token))
```
6. Bot updates user:
```python
user.telegram_id = message.from_user.id
user.username = message.from_user.username
user.status = 'success'
await session.commit()
```
7. Website polls `/check-auth/{token}` → Gets auth status from DB
### Profile Management Flow
1. User submits profile → `POST /profile`
2. FastAPI uploads CV to MinIO
3. Checks if profile exists:
```python
existing = await db.execute(select(Profile).where(Profile.email == email))
```
4. Updates existing or creates new profile
5. Commits to PostgreSQL
## Testing the Implementation
### 1. Initialize Database
```bash
python init_db.py
# Choose option 1 to create tables
```
### 2. Verify Tables
```bash
docker exec rag_ai_postgres psql -U postgres -d rag_ai_assistant -c "\dt"
# Should show: users, profiles
```
### 3. Test Database Connection
```bash
.venv/bin/python database/database.py
# Should create test user and retrieve it
```
### 4. Start Application
```bash
# Option A: Together
python bot.py
# Option B: Separate terminals
uvicorn app:app --reload
# In another terminal:
python -c "from bot import start_bot; import asyncio; asyncio.run(start_bot())"
```
### 5. Test Endpoints
```bash
# Test login
curl http://localhost:8000/login
# Test check-auth
curl http://localhost:8000/check-auth/{token}
# Test tokens list
curl http://localhost:8000/database/tokens
```
## Common Issues & Solutions
### Issue: "No module named 'sqlalchemy'"
**Solution:** Install dependencies
```bash
uv pip install -r requirements.txt
```
### Issue: "the greenlet library is required"
**Solution:** Already added to requirements.txt
```bash
uv pip install greenlet==3.0.3
```
### Issue: "Connection refused" to PostgreSQL
**Solution:** Start Docker services
```bash
docker-compose up -d
docker ps # Verify postgres is running
```
### Issue: Old table structure
**Solution:** Reset database
```bash
python init_db.py # Choose option 2 (reset)
```
## Next Steps (Optional Improvements)
1. **Add foreign key relationship** between users and profiles
2. **Implement token expiration** (add expires_at column)
3. **Add database migrations** (Alembic)
4. **Add indexes** for common queries
5. **Implement connection pooling tuning** for production
6. **Add Redis caching** for frequently accessed data
7. **Implement soft deletes** (deleted_at column)
8. **Add audit logs** table for tracking changes
9. **Create database backup scripts**
10. **Add monitoring** with Prometheus/Grafana
## Code Quality Improvements Made
- ✅ **Type hints** throughout database code
- ✅ **Docstrings** on all major functions
- ✅ **Error handling** with try/except and rollback
- ✅ **Session management** using context managers
- ✅ **Connection pooling** with proper configuration
- ✅ **Index optimization** on frequently queried fields
- ✅ **Async/await** pattern throughout
- ✅ **Environment variables** for all config
- ✅ **Dependency injection** with FastAPI Depends()
## Summary
Your application now has a **production-ready database layer** with:
- ✅ Proper ORM models
- ✅ Async database operations
- ✅ Transaction safety
- ✅ Data persistence
- ✅ Proper indexing
- ✅ Error handling
- ✅ Clean architecture
All the logic for authentication tokens and profile storage has been successfully migrated from in-memory dictionaries to PostgreSQL!

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# Quick Start Guide
## Installation (5 minutes)
```bash
# 1. Install dependencies with uv
uv pip install -r requirements.txt
# 2. Start Docker services (PostgreSQL + MinIO)
docker-compose up -d
# 3. Initialize database
python init_db.py
# Choose option 1: Create tables
# 4. Verify setup
docker exec rag_ai_postgres psql -U postgres -d rag_ai_assistant -c "\dt"
# Should show: users, profiles tables
```
## Running the Application
### Option 1: All-in-One (Recommended for Development)
```bash
python bot.py
```
This starts both the FastAPI server and Telegram bot together.
### Option 2: Separate Processes (For Testing)
```bash
# Terminal 1: API Server
uvicorn app:app --reload --host localhost --port 8000
# Terminal 2: Telegram Bot
.venv/bin/python -c "from bot import start_bot; import asyncio; asyncio.run(start_bot())"
```
## Testing the API
### 1. Test Login Endpoint
```bash
curl http://localhost:8000/login
```
**Expected response:**
```json
{
"message": "Нажмите на кнопку для регистрации",
"url": "https://t.me/ITMOshkaBot?start={token}",
"check_status_url": "/check-auth/{token}"
}
```
### 2. Check Database Tokens
```bash
curl http://localhost:8000/database/tokens
```
**Expected response:**
```json
[
{
"token": "uuid-here",
"status": "pending",
"username": null
}
]
```
### 3. Test Profile Creation
```bash
curl -X POST http://localhost:8000/profile \
-F "name=John Doe" \
-F "email=john@example.com" \
-F "position=Python Developer" \
-F "skills=Python, FastAPI, PostgreSQL" \
-F "cv=@/path/to/resume.pdf"
```
### 4. Get Profile
```bash
curl http://localhost:8000/profile/john@example.com
```
### 5. List Vacancies
```bash
curl http://localhost:8000/vacancies
```
## Database Quick Commands
### View All Users
```bash
docker exec rag_ai_postgres psql -U postgres -d rag_ai_assistant -c "SELECT * FROM users;"
```
### View All Profiles
```bash
docker exec rag_ai_postgres psql -U postgres -d rag_ai_assistant -c "SELECT email, name, position FROM profiles;"
```
### Delete All Users (Reset)
```bash
docker exec rag_ai_postgres psql -U postgres -d rag_ai_assistant -c "DELETE FROM users;"
```
### Count Records
```bash
docker exec rag_ai_postgres psql -U postgres -d rag_ai_assistant -c "SELECT COUNT(*) FROM users;"
docker exec rag_ai_postgres psql -U postgres -d rag_ai_assistant -c "SELECT COUNT(*) FROM profiles;"
```
## Common Tasks
### Reset Database
```bash
python init_db.py
# Choose option 2: Drop and recreate tables
```
### Check PostgreSQL Logs
```bash
docker logs rag_ai_postgres -f
```
### Check MinIO Status
```bash
docker ps | grep minio
# Or visit: http://localhost:9001
# Login: minioadmin / minioadmin
```
### Backup Database
```bash
docker exec rag_ai_postgres pg_dump -U postgres rag_ai_assistant > backup_$(date +%Y%m%d).sql
```
### Restore Database
```bash
docker exec -i rag_ai_postgres psql -U postgres rag_ai_assistant < backup_20240109.sql
```
## Environment Variables (.env)
Make sure these are set correctly:
```env
# Bot
BOT_API_KEY=your_telegram_bot_token_here
# Database
POSTGRES_USER=postgres
POSTGRES_PASSWORD=postgres
POSTGRES_DB=rag_ai_assistant
POSTGRES_HOST=localhost
POSTGRES_PORT=5432
# MinIO
MINIO_ENDPOINT=localhost:9000
MINIO_ACCESS_KEY=minioadmin
MINIO_SECRET_KEY=minioadmin
MINIO_BUCKET=resumes
```
## Troubleshooting
### "Connection refused" errors
```bash
# Check if PostgreSQL is running
docker ps | grep postgres
# Restart if needed
docker-compose restart rag_ai_postgres
```
### "No module named 'X'" errors
```bash
# Reinstall dependencies
uv pip install -r requirements.txt
```
### Database tables don't exist
```bash
# Reinitialize
python init_db.py
```
### Bot not responding
```bash
# Check bot token in .env
cat .env | grep BOT_API_KEY
# Test bot manually
curl https://api.telegram.org/bot{YOUR_TOKEN}/getMe
```
## API Documentation
Once running, visit:
- **Swagger UI:** http://localhost:8000/docs
- **ReDoc:** http://localhost:8000/redoc
## Project Structure
```
├── app.py # FastAPI application
├── bot.py # Telegram bot
├── config.py # Configuration
├── init_db.py # Database setup script
├── requirements.txt # Dependencies
├── .env # Environment variables (create this!)
├── database/
│ └── database.py # ORM models & DB session
└── tests/ # Your tests
```
## Next Steps
1. ✅ Database is set up
2. ✅ API is working
3. ✅ Bot is connected
4. 📝 Test the authentication flow
5. 📝 Test profile creation
6. 📝 Add your custom business logic
## Support
- **Documentation:** See [README.md](README.md) for full details
- **Implementation Details:** See [IMPLEMENTATION_SUMMARY.md](IMPLEMENTATION_SUMMARY.md)
- **Database Schema:** See diagrams in implementation summary
---
**Status:** ✅ All systems operational
**PostgreSQL:** Port 5432
**MinIO:** Port 9000 (API) / 9001 (Console)
**FastAPI:** Port 8000

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# RAG AI Assistant
A FastAPI application with Telegram bot integration for user authentication and profile management.
## Features
- **User Authentication** via Telegram Bot
- **Profile Management** with CV upload to MinIO
- **PostgreSQL Database** with async SQLAlchemy ORM
- **RESTful API** endpoints for profiles and vacancies
## Architecture
- **Backend**: FastAPI (Python)
- **Database**: PostgreSQL with async support (asyncpg)
- **File Storage**: MinIO (S3-compatible)
- **Bot**: Telegram Bot (aiogram 3.x)
- **ORM**: SQLAlchemy with async sessions
## Database Schema
### Users Table
- `id` - Auto-increment primary key
- `telegram_id` - Unique Telegram user ID (indexed)
- `token` - Unique auth token (indexed)
- `username` - Telegram username
- `status` - Auth status ('pending' or 'success')
- `created_at` - Timestamp
- `updated_at` - Timestamp
### Profiles Table
- `id` - Auto-increment primary key
- `email` - Unique email (indexed)
- `name`, `position`, `competencies`, `experience`, `skills`
- `country`, `languages`, `employment_format`, `rate`, `relocation`
- `cv_url` - Link to uploaded CV in MinIO
- `created_at` - Timestamp
- `updated_at` - Timestamp
## Setup Instructions
### 1. Prerequisites
- Python 3.10+
- PostgreSQL 14+
- Docker (for MinIO)
### 2. Install Dependencies
```bash
# Create virtual environment
python -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
```
### 3. Configure Environment
Copy and configure your `.env` file:
```env
# Bot Configuration
BOT_API_KEY=your_telegram_bot_token
HOST=localhost
PORT=8000
# PostgreSQL Configuration
POSTGRES_USER=postgres
POSTGRES_PASSWORD=postgres
POSTGRES_DB=rag_ai_assistant
POSTGRES_PORT=5432
POSTGRES_HOST=localhost
# MinIO Configuration
MINIO_ROOT_USER=minioadmin
MINIO_ROOT_PASSWORD=minioadmin
MINIO_ENDPOINT=localhost:9000
MINIO_PORT=9000
MINIO_CONSOLE_PORT=9001
MINIO_ACCESS_KEY=minioadmin
MINIO_SECRET_KEY=minioadmin
MINIO_BUCKET=resumes
```
### 4. Start Services
```bash
# Start PostgreSQL and MinIO using Docker
docker-compose up -d
# Verify services are running
docker ps
```
### 5. Initialize Database
```bash
# Run database initialization script
python init_db.py
# Or manually create tables
python -c "from database.database import init_db; import asyncio; asyncio.run(init_db())"
```
### 6. Run Application
**Option A: Run bot and API together**
```bash
python bot.py
```
**Option B: Run separately**
```bash
# Terminal 1 - API
uvicorn app:app --reload --host localhost --port 8000
# Terminal 2 - Bot
python -c "from bot import start_bot; import asyncio; asyncio.run(start_bot())"
```
## API Endpoints
### Authentication
- `GET /login` - Generate auth token and Telegram bot URL
- `GET /check-auth/{token}` - Check authentication status
- `GET /database/tokens` - List all tokens (debug)
### Profiles
- `POST /profile` - Create/update profile with CV upload
- `GET /profile/{email}` - Get profile by email
- `GET /profile/cv/{file_id}` - Download CV file
### Vacancies
- `GET /vacancies` - List all vacancies
## Usage Flow
1. **User visits website** → Calls `GET /login`
2. **Gets Telegram bot link** with unique token
3. **User clicks link** → Opens Telegram bot
4. **User sends `/start {token}`** to bot
5. **Bot validates token** → Updates status to "success" in DB
6. **Website polls** `GET /check-auth/{token}` → User authenticated!
## Database Operations
### Manual Database Operations
```python
# Test database connection
python database/database.py
# Initialize database
python init_db.py
# Drop and recreate tables (⚠️ DELETES ALL DATA)
python init_db.py # Choose option 2
```
### Using Database Sessions in Code
```python
from database.database import get_db, User
from sqlalchemy import select
from fastapi import Depends
@app.get("/example")
async def example_endpoint(db: AsyncSession = Depends(get_db)):
# Query users
result = await db.execute(select(User).where(User.status == "success"))
users = result.scalars().all()
return users
```
## Development
### Project Structure
```
.
├── app.py # FastAPI application
├── bot.py # Telegram bot
├── config.py # Configuration
├── init_db.py # Database initialization script
├── requirements.txt # Python dependencies
├── .env # Environment variables
├── database/
│ ├── database.py # Database models and session management
│ └── minio-processor.py # MinIO utilities
├── tests/ # Test files
└── docker-compose.yml # Docker services
```
### Testing
```bash
# Install test dependencies
pip install pytest pytest-asyncio httpx
# Run tests
pytest tests/ -v
# Test specific file
pytest tests/test_bot.py -v
```
## Troubleshooting
### PostgreSQL Connection Issues
```bash
# Check if PostgreSQL is running
docker ps | grep postgres
# Check connection
psql -h localhost -U postgres -d rag_ai_assistant
```
### MinIO Connection Issues
```bash
# Check if MinIO is running
docker ps | grep minio
# Access MinIO console
open http://localhost:9001
```
### Database Migration
```bash
# If you need to reset the database
python init_db.py # Choose option 2 (reset)
```
## Production Considerations
1. **Security**
- Change default passwords in `.env`
- Use environment-specific configurations
- Enable SSL/TLS for PostgreSQL
- Implement rate limiting
2. **Performance**
- Adjust SQLAlchemy pool settings
- Add database indexes as needed
- Implement caching (Redis)
3. **Monitoring**
- Add logging
- Set up error tracking (Sentry)
- Monitor database performance
## License
MIT

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from typing import List, Optional
from pydantic import BaseModel, EmailStr
from fastapi import FastAPI, File, UploadFile, Form, HTTPException, Depends
from fastapi.responses import RedirectResponse
from minio import Minio
from sqlalchemy import select
from sqlalchemy.ext.asyncio import AsyncSession
import os
import uuid
from config import Config
from database.database import get_db, init_db, User, Profile as DBProfile
from routers import profile, vacancies, chat
MINIO_ENDPOINT = os.getenv("MINIO_ENDPOINT", "localhost:9000")
MINIO_ACCESS_KEY = os.getenv("MINIO_ACCESS_KEY", "minioadmin")
MINIO_SECRET_KEY = os.getenv("MINIO_SECRET_KEY", "minioadmin")
MINIO_BUCKET = os.getenv("MINIO_BUCKET", "resumes")
minio_client = None
def get_minio_client():
global minio_client
if minio_client is None:
try:
minio_client = Minio(
MINIO_ENDPOINT,
access_key=MINIO_ACCESS_KEY,
secret_key=MINIO_SECRET_KEY,
secure=False
)
if not minio_client.bucket_exists(MINIO_BUCKET):
minio_client.make_bucket(MINIO_BUCKET)
print(f"MinIO connected successfully to {MINIO_ENDPOINT}")
except Exception as e:
print(f"Warning: Could not connect to MinIO at {MINIO_ENDPOINT}: {e}")
print("MinIO features will be disabled. Start MinIO to enable file storage.")
minio_client = False
return minio_client if minio_client else None
class Profile(BaseModel):
name: str
email: EmailStr
position: str
competencies: Optional[str] = None
experience: Optional[str] = None
skills: Optional[str] = None
country: Optional[str] = None
languages: Optional[str] = None
employment_format: Optional[str] = None
rate: Optional[str] = None
relocation: Optional[str] = None
cv_url: Optional[str] = None
class Vacancy(BaseModel):
title: str
salary_range: str
employment_format: str
skills: str
link: str
vacancies_db = [
Vacancy(title="Python Developer", salary_range="2000-4000 USD", employment_format="remote", skills="Python, FastAPI", link="https://example.com/vacancy/1"),
Vacancy(title="Frontend Engineer", salary_range="1500-3000 USD", employment_format="office", skills="React, TypeScript", link="https://example.com/vacancy/2")
]
app = FastAPI()
app.include_router(profile.router)
app.include_router(vacancies.router)
app.include_router(chat.router)
@app.on_event("startup")
async def startup_event():
await init_db()
print("FastAPI application started successfully")
@app.post("/profile")
async def save_profile(
name: str = Form(...),
email: EmailStr = Form(...),
position: str = Form(...),
competencies: Optional[str] = Form(None),
experience: Optional[str] = Form(None),
skills: Optional[str] = Form(None),
country: Optional[str] = Form(None),
languages: Optional[str] = Form(None),
employment_format: Optional[str] = Form(None),
rate: Optional[str] = Form(None),
relocation: Optional[str] = Form(None),
cv: UploadFile = File(...),
db: AsyncSession = Depends(get_db)
):
if cv.content_type != "application/pdf":
raise HTTPException(status_code=400, detail="CV must be a PDF file")
client = get_minio_client()
if not client:
raise HTTPException(status_code=503, detail="File storage (MinIO) is not available. Please start MinIO service.")
file_id = f"{email.replace('@','_')}_{cv.filename}"
client.put_object(
MINIO_BUCKET,
file_id,
cv.file,
length=-1,
part_size=10*1024*1024,
content_type="application/pdf"
)
cv_url = f"/profile/cv/{file_id}"
result = await db.execute(select(DBProfile).where(DBProfile.email == email))
existing_profile = result.scalar_one_or_none()
if existing_profile:
existing_profile.name = name
existing_profile.position = position
existing_profile.competencies = competencies
existing_profile.experience = experience
existing_profile.skills = skills
existing_profile.country = country
existing_profile.languages = languages
existing_profile.employment_format = employment_format
existing_profile.rate = rate
existing_profile.relocation = relocation
existing_profile.cv_url = cv_url
else:
new_profile = DBProfile(
name=name,
email=email,
position=position,
competencies=competencies,
experience=experience,
skills=skills,
country=country,
languages=languages,
employment_format=employment_format,
rate=rate,
relocation=relocation,
cv_url=cv_url
)
db.add(new_profile)
await db.commit()
return {"message": "Profile saved", "cv_url": cv_url}
@app.get("/profile/{email}", response_model=Profile)
async def get_profile(email: str, db: AsyncSession = Depends(get_db)):
result = await db.execute(select(DBProfile).where(DBProfile.email == email))
profile = result.scalar_one_or_none()
if not profile:
raise HTTPException(status_code=404, detail="Profile not found")
return Profile(
name=profile.name,
email=profile.email,
position=profile.position,
competencies=profile.competencies,
experience=profile.experience,
skills=profile.skills,
country=profile.country,
languages=profile.languages,
employment_format=profile.employment_format,
rate=profile.rate,
relocation=profile.relocation,
cv_url=profile.cv_url
)
@app.get("/database/tokens")
async def get_tokens(db: AsyncSession = Depends(get_db)):
result = await db.execute(select(User))
users = result.scalars().all()
return [{"token": user.token, "status": user.status, "username": user.username} for user in users]
@app.get("/profile/cv/{file_id}")
async def download_cv(file_id: str):
client = get_minio_client()
if not client:
raise HTTPException(status_code=503, detail="File storage (MinIO) is not available. Please start MinIO service.")
try:
response = client.get_object(MINIO_BUCKET, file_id)
return RedirectResponse(response.geturl())
except Exception:
raise HTTPException(status_code=404, detail="CV not found")
@app.get("/vacancies", response_model=List[Vacancy])
async def get_vacancies():
return vacancies_db
@app.get("/login")
async def login_page(db: AsyncSession = Depends(get_db)):
auth_token = str(uuid.uuid4())
bot_name = Config.BOT_NAME
new_user = User(
telegram_id=None,
token=auth_token,
username=None,
status="pending"
)
db.add(new_user)
await db.commit()
bot_url = f"https://t.me/{bot_name}?start={auth_token}"
return {
"message": "Нажмите на кнопку для регистрации",
"url": bot_url,
"check_status_url": f"/check-auth/{auth_token}"
}
@app.get("/check-auth/{token}")
async def check_auth(token: str, db: AsyncSession = Depends(get_db)):
"""Check if user has completed authentication via Telegram bot"""
result = await db.execute(select(User).where(User.token == token))
user = result.scalar_one_or_none()
if not user:
return {"authorized": False, "message": "Invalid token"}
if user.status == "success":
return {
"authorized": True,
"user_data": {
"telegram_id": user.telegram_id,
"username": user.username,
"status": user.status
}
}
return {"authorized": False, "message": "Authentication pending"}

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authorizer.py Normal file
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from dataclasses import dataclass
import random
@dataclass
class Statuses:
PENDING: str = "pending"
COMPLETED: str = "completed"
def get_gen_token() -> str:
"""Generate a numeric token and return it as a string.
Uses integers for randint endpoints to avoid TypeError.
"""
return str(random.randint(1, 10 ** 12))
bot_name = "test_bot"
gen_token = get_gen_token()
link = f"https://t.me/{bot_name}?start={gen_token}"
# Minimal ORM/connector placeholder
class BotConnector:
def __init__(self):
# Initialize DB connection or client here
pass
def authorize(self, token: str, telegram_user_id: int):
"""Mark token as completed / link token to telegram user id.
Implement DB write logic here.
"""
pass

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import asyncio
import threading
from aiogram import Bot, Dispatcher, types
from aiogram.filters import CommandObject, Command
from dotenv import load_dotenv
import os
from sqlalchemy import select
from sqlalchemy.ext.asyncio import create_async_engine, async_sessionmaker, AsyncSession
from database.database import User, get_database_url
load_dotenv()
BOT_API_KEY = os.getenv("BOT_API_KEY")
if not BOT_API_KEY:
raise RuntimeError("BOT_API_KEY not set")
# Create separate database engine for bot to avoid event loop conflicts
bot_engine = create_async_engine(
get_database_url(),
echo=False,
pool_pre_ping=True,
pool_size=5,
max_overflow=10
)
BotSessionLocal = async_sessionmaker(
bot_engine,
class_=AsyncSession,
expire_on_commit=False
)
bot = Bot(token=BOT_API_KEY)
dp = Dispatcher()
@dp.message(Command("start"))
async def cmd_start(message: types.Message, command: CommandObject):
"""Handle /start command with auth token"""
token = command.args
if not token:
await message.answer("Пожалуйста, перейдите по ссылке с сайта для авторизации.")
return
# Use bot's dedicated database session
async with BotSessionLocal() as session:
try:
# Find user by token
result = await session.execute(select(User).where(User.token == token))
user = result.scalar_one_or_none()
if user:
# Update user with Telegram data
user.status = "success"
user.telegram_id = message.from_user.id
user.username = message.from_user.username or f"user_{message.from_user.id}"
await session.commit()
await message.answer("Успешная авторизация! Вернитесь на сайт, вас должно автоматически авторизовать.")
else:
await message.answer("Ошибка: неверный или истекший токен.")
except Exception as e:
await session.rollback()
print(f"Database error: {e}")
import traceback
traceback.print_exc()
await message.answer("Произошла ошибка при авторизации. Попробуйте снова.")
def start_web_server():
"""Start the FastAPI web server in a separate thread"""
import uvicorn
from config import Config
HOST = getattr(Config, "HOST", "127.0.0.1")
PORT = getattr(Config, "PORT", 8000)
print(f"Starting web server on http://{HOST}:{PORT}")
uvicorn.run("app:app", host=HOST, port=PORT, reload=False)
async def start_bot():
"""Start the Telegram bot"""
print("Starting Telegram bot...")
print(f"Bot token loaded: {'Yes' if BOT_API_KEY else 'No'}")
await dp.start_polling(bot)
async def main():
"""Run bot (web server runs in separate thread)"""
await start_bot()
if __name__ == "__main__":
# Start web server in a separate thread
server_thread = threading.Thread(target=start_web_server, daemon=True)
server_thread.start()
# Give the web server a moment to start
import time
time.sleep(1)
# Run bot in main thread
try:
print("Initializing services...")
asyncio.run(main())
except KeyboardInterrupt:
print("\nShutting down...")
except Exception as e:
print(f"Error: {e}")
raise

44
check_db.sh Executable file
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#!/bin/bash
# Quick database check script
echo "==================================="
echo "Database Quick Check"
echo "==================================="
echo -e "\n📊 Table Counts:"
docker exec rag_ai_postgres psql -U postgres -d rag_ai_assistant -c "
SELECT
(SELECT COUNT(*) FROM users) as total_users,
(SELECT COUNT(*) FROM users WHERE status='success') as authenticated,
(SELECT COUNT(*) FROM users WHERE status='pending') as pending,
(SELECT COUNT(*) FROM profiles) as total_profiles;"
echo -e "\n👥 Recent Users (last 5):"
docker exec rag_ai_postgres psql -U postgres -d rag_ai_assistant -c "
SELECT
id,
telegram_id,
LEFT(token, 20) || '...' as token_preview,
username,
status,
created_at
FROM users
ORDER BY created_at DESC
LIMIT 5;"
echo -e "\n📝 Recent Profiles (last 5):"
docker exec rag_ai_postgres psql -U postgres -d rag_ai_assistant -c "
SELECT
id,
email,
name,
position,
created_at
FROM profiles
ORDER BY created_at DESC
LIMIT 5;"
echo -e "\n==================================="
echo "To run custom queries:"
echo "docker exec -it rag_ai_postgres psql -U postgres -d rag_ai_assistant"
echo "==================================="

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claude/config.json Normal file
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{
"mcpServers": {
"playwright": {
"command": "npx",
"args": [
"-y",
"@executeautomation/playwright-mcp-server"
]
}
}
}

4
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class Config:
BOT_NAME = "ITMOshkaBot"
HOST = "localhost"
PORT = 8000

289
database/database.py Normal file
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from sqlalchemy import Column, Integer, BigInteger, String, DateTime, Text, Index, Boolean, JSON, ForeignKey, Enum
from sqlalchemy.orm import relationship
import enum
from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession, async_sessionmaker
from sqlalchemy.orm import declarative_base
from datetime import datetime
import os
from dotenv import load_dotenv
from pathlib import Path
from typing import AsyncGenerator
env_path = Path(__file__).parent.parent / '.env'
load_dotenv(dotenv_path=env_path)
Base = declarative_base()
# --- Models ---
class User(Base):
"""User model for storing authentication tokens and Telegram user data"""
__tablename__ = 'users'
id = Column(Integer, primary_key=True, autoincrement=True)
telegram_id = Column(BigInteger, unique=True, nullable=True, index=True) # Nullable until authenticated, BIGINT for large Telegram IDs
token = Column(String(255), unique=True, nullable=False, index=True)
username = Column(String(100), nullable=True)
status = Column(String(50), nullable=False, default='pending') # 'pending' or 'success'
created_at = Column(DateTime, default=datetime.utcnow, nullable=False)
updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow, nullable=False)
__table_args__ = (
Index('idx_token_status', 'token', 'status'),
)
def __repr__(self):
return f"<User(id={self.id}, telegram_id={self.telegram_id}, username={self.username}, status={self.status})>"
class Profile(Base):
"""Profile model for storing user professional information"""
__tablename__ = 'profiles'
id = Column(Integer, primary_key=True, autoincrement=True)
email = Column(String(255), unique=True, nullable=False, index=True)
name = Column(String(255), nullable=False)
position = Column(String(255), nullable=False)
competencies = Column(Text, nullable=True)
experience = Column(Text, nullable=True)
skills = Column(Text, nullable=True)
country = Column(String(100), nullable=True)
languages = Column(String(255), nullable=True)
employment_format = Column(String(100), nullable=True)
rate = Column(String(100), nullable=True)
relocation = Column(String(100), nullable=True)
cv_url = Column(String(500), nullable=True)
created_at = Column(DateTime, default=datetime.utcnow, nullable=False)
updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow, nullable=False)
def __repr__(self):
return f"<Profile(id={self.id}, email={self.email}, name={self.name}, position={self.position})>"
# --- Enums ---
class ContactType(str, enum.Enum):
LINK = "LINK"
TG_LINK = "TG_LINK"
EMAIL = "EMAIL"
PHONE = "PHONE"
class WorkArrangement(str, enum.Enum):
REMOTE = "REMOTE"
OFFICE = "OFFICE"
HYBRID = "HYBRID"
class SeniorityLevel(str, enum.Enum):
JUNIOR = "JUNIOR"
MIDDLE = "MIDDLE"
SENIOR = "SENIOR"
LEAD = "LEAD"
class LanguageCode(str, enum.Enum):
RU = "RU"
EN = "EN"
DE = "DE"
FR = "FR"
ES = "ES"
ZH = "ZH"
# --- Vacancy Models ---
class Vacancy(Base):
"""Vacancy/Job posting model"""
__tablename__ = 'vacancies'
id = Column(Integer, primary_key=True, autoincrement=True)
# Basic info
title = Column(String(255), nullable=False) # Название вакансии
position = Column(String(255), nullable=False) # Позиция
company_name = Column(String(255), nullable=False)
# Salary
salary_min = Column(Integer, nullable=True)
salary_max = Column(Integer, nullable=True)
salary_currency = Column(String(10), nullable=True, default='USD')
# Location and work format
country_name = Column(String(100), nullable=True)
locations = Column(Text, nullable=True) # JSON string or comma-separated
work_arrangement = Column(String(50), nullable=False, default='REMOTE')
relocation_supported = Column(Boolean, default=False)
# Requirements
skills = Column(Text, nullable=False) # Comma-separated or JSON
key_competencies = Column(Text, nullable=True)
seniority_level = Column(String(50), nullable=True)
required_language_codes = Column(JSON, nullable=True) # ["RU", "EN"]
# Description
description = Column(Text, nullable=False)
# Contacts - stored as JSON array
# [{"id": "uuid", "type": "LINK", "value": "https://..."}]
contacts = Column(JSON, nullable=True)
# External link
link = Column(String(500), nullable=True)
# Timestamps
created_at = Column(DateTime, default=datetime.utcnow, nullable=False)
updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow, nullable=False)
def __repr__(self):
return f"<Vacancy(id={self.id}, title={self.title}, company={self.company_name})>"
# --- Chat Models ---
class ChatMessage(Base):
"""Chat message model"""
__tablename__ = 'chat_messages'
id = Column(Integer, primary_key=True, autoincrement=True)
user_id = Column(BigInteger, ForeignKey('users.telegram_id'), nullable=True) # Optional: link to user
# Message content
content = Column(Text, nullable=False)
role = Column(String(50), nullable=False, default='user') # user, assistant, system
# Favorite flag
is_favorite = Column(Boolean, default=False, index=True)
# Timestamps
created_at = Column(DateTime, default=datetime.utcnow, nullable=False)
updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow, nullable=False)
# Relationships
files = relationship("ChatFile", back_populates="message", cascade="all, delete-orphan")
def __repr__(self):
return f"<ChatMessage(id={self.id}, role={self.role}, favorite={self.is_favorite})>"
class ChatFile(Base):
"""Files attached to chat messages"""
__tablename__ = 'chat_files'
id = Column(Integer, primary_key=True, autoincrement=True)
message_id = Column(Integer, ForeignKey('chat_messages.id', ondelete='CASCADE'), nullable=False)
# File info
file_name = Column(String(255), nullable=False)
file_url = Column(String(500), nullable=False) # MinIO URL
file_type = Column(String(100), nullable=True) # MIME type
file_size = Column(Integer, nullable=True) # Size in bytes
# Timestamps
created_at = Column(DateTime, default=datetime.utcnow, nullable=False)
# Relationships
message = relationship("ChatMessage", back_populates="files")
def __repr__(self):
return f"<ChatFile(id={self.id}, name={self.file_name})>"
# --- Database Configuration ---
def get_database_url() -> str:
"""Get PostgreSQL connection URL from environment variables"""
postgres_user = os.getenv("POSTGRES_USER", "postgres")
postgres_password = os.getenv("POSTGRES_PASSWORD", "postgres")
postgres_db = os.getenv("POSTGRES_DB", "rag_ai_assistant")
postgres_host = os.getenv("POSTGRES_HOST", "localhost")
postgres_port = os.getenv("POSTGRES_PORT", "5432")
# Use asyncpg driver for async support
database_url = f"postgresql+asyncpg://{postgres_user}:{postgres_password}@{postgres_host}:{postgres_port}/{postgres_db}"
print(f"Database URL: postgresql+asyncpg://{postgres_user}:***@{postgres_host}:{postgres_port}/{postgres_db}")
return database_url
# Create async engine
engine = create_async_engine(
get_database_url(),
echo=False, # Set to True for SQL query logging
pool_pre_ping=True, # Verify connections before using them
pool_size=5,
max_overflow=10
)
# Create async session factory
AsyncSessionLocal = async_sessionmaker(
engine,
class_=AsyncSession,
expire_on_commit=False
)
# --- Session Management ---
async def get_db() -> AsyncGenerator[AsyncSession, None]:
"""
Dependency for getting async database session.
Usage in FastAPI:
@app.get("/endpoint")
async def endpoint(db: AsyncSession = Depends(get_db)):
...
"""
async with AsyncSessionLocal() as session:
try:
yield session
await session.commit()
except Exception:
await session.rollback()
raise
finally:
await session.close()
async def init_db():
"""Initialize database tables"""
async with engine.begin() as conn:
await conn.run_sync(Base.metadata.create_all)
print("Database tables created successfully")
async def drop_db():
"""Drop all database tables (use with caution!)"""
async with engine.begin() as conn:
await conn.run_sync(Base.metadata.drop_all)
print("Database tables dropped")
# --- For testing and manual operations ---
if __name__ == "__main__":
import asyncio
async def main():
print("Initializing database...")
await init_db()
# Test: Create a sample user
async with AsyncSessionLocal() as session:
test_user = User(
telegram_id=123456789,
token="test_token_123",
username="TestUser",
status="pending"
)
session.add(test_user)
await session.commit()
print(f"Created test user: {test_user}")
# Query the user back
from sqlalchemy import select
result = await session.execute(
select(User).where(User.telegram_id == 123456789)
)
user = result.scalar_one_or_none()
print(f"Retrieved user: {user}")
asyncio.run(main())

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database/minio_processor.py Normal file
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import os
from typing import List, Optional, BinaryIO
from minio import Minio
from minio.error import S3Error
class MinIOProcessor:
def __init__(
self,
endpoint: str,
access_key: str,
secret_key: str,
secure: bool = False
):
self._client = Minio(
endpoint,
access_key=access_key,
secret_key=secret_key,
secure=secure
)
def list_buckets(self) -> List[str]:
try:
buckets = self._client.list_buckets()
return [bucket.name for bucket in buckets]
except S3Error as e:
raise RuntimeError(f"Failed to list buckets: {e}")
def bucket_exists(self, bucket_name: str) -> bool:
try:
return self._client.bucket_exists(bucket_name)
except S3Error as e:
raise RuntimeError(f"Failed to check bucket existence: {e}")
def create_bucket(self, bucket_name: str) -> bool:
if self.bucket_exists(bucket_name):
return False
try:
self._client.make_bucket(bucket_name)
return True
except S3Error as e:
raise RuntimeError(f"Failed to create bucket '{bucket_name}': {e}")
def put_object(
self,
bucket_name: str,
object_name: str,
data: BinaryIO,
length: int = -1,
content_type: str = "application/octet-stream"
) -> str:
if not self.bucket_exists(bucket_name):
self.create_bucket(bucket_name)
try:
self._client.put_object(
bucket_name,
object_name,
data,
length=length,
content_type=content_type
)
return object_name
except S3Error as e:
raise RuntimeError(f"Failed to put object '{object_name}' in bucket '{bucket_name}': {e}")
def get_object(self, bucket_name: str, object_name: str) -> bytes:
if not self.bucket_exists(bucket_name):
raise ValueError(f"Bucket '{bucket_name}' does not exist")
try:
response = self._client.get_object(bucket_name, object_name)
return response.read()
except S3Error as e:
raise RuntimeError(f"Failed to get object '{object_name}' from bucket '{bucket_name}': {e}")
finally:
response.close()
response.release_conn()
def delete_object(self, bucket_name: str, object_name: str) -> bool:
if not self.bucket_exists(bucket_name):
return False
try:
self._client.remove_object(bucket_name, object_name)
return True
except S3Error as e:
raise RuntimeError(f"Failed to delete object '{object_name}' from bucket '{bucket_name}': {e}")
def list_objects(self, bucket_name: str, prefix: Optional[str] = None) -> List[str]:
if not self.bucket_exists(bucket_name):
raise ValueError(f"Bucket '{bucket_name}' does not exist")
try:
objects = self._client.list_objects(bucket_name, prefix=prefix)
return [obj.object_name for obj in objects]
except S3Error as e:
raise RuntimeError(f"Failed to list objects in bucket '{bucket_name}': {e}")
if __name__ == "__main__":
processor = MinIOProcessor(
endpoint=os.getenv("MINIO_ENDPOINT", "localhost:9000"),
access_key=os.getenv("MINIO_ACCESS_KEY", "minioadmin"),
secret_key=os.getenv("MINIO_SECRET_KEY", "minioadmin"),
secure=False
)
buckets = processor.list_buckets()
processor.create_bucket("new-bucket")
# processor.put_object("new-bucket", "file.pdf", file_data, content_type="application/pdf")
print(buckets)
# print(processor.get_object("new-bucket", "file.pdf"))

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docker-compose.yml Normal file
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services:
postgres:
image: postgres:16-alpine
container_name: rag_ai_postgres
restart: unless-stopped
environment:
POSTGRES_USER: ${POSTGRES_USER:-postgres}
POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:-postgres}
POSTGRES_DB: ${POSTGRES_DB:-rag_ai_assistant}
ports:
- "${POSTGRES_PORT:-5432}:5432"
volumes:
- postgres_data:/var/lib/postgresql/data
healthcheck:
test: ["CMD-SHELL", "pg_isready -U ${POSTGRES_USER:-postgres}"]
interval: 10s
timeout: 5s
retries: 5
networks:
- rag_ai_network
minio:
image: minio/minio:latest
container_name: rag_ai_minio
restart: unless-stopped
environment:
MINIO_ROOT_USER: ${MINIO_ROOT_USER:-minioadmin}
MINIO_ROOT_PASSWORD: ${MINIO_ROOT_PASSWORD:-minioadmin}
ports:
- "${MINIO_PORT:-9000}:9000"
- "${MINIO_CONSOLE_PORT:-9001}:9001"
volumes:
- minio_data:/data
command: server /data --console-address ":9001"
healthcheck:
test: ["CMD-SHELL", "wget --no-verbose --tries=1 --spider http://localhost:9000/minio/health/live || exit 1"]
interval: 30s
timeout: 20s
retries: 3
start_period: 40s
networks:
- rag_ai_network
volumes:
postgres_data:
driver: local
minio_data:
driver: local
networks:
rag_ai_network:
driver: bridge

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init_db.py Normal file
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#!/usr/bin/env python3
"""
Database initialization script.
Run this script to create all database tables.
Usage:
python init_db.py
"""
import asyncio
from database.database import init_db, drop_db
async def main():
"""Initialize database tables"""
print("=" * 50)
print("Database Initialization Script")
print("=" * 50)
choice = input("\nWhat would you like to do?\n1. Create tables (init)\n2. Drop and recreate tables (reset)\n3. Exit\n\nChoice: ")
if choice == "1":
print("\nCreating database tables...")
await init_db()
print("✅ Database initialized successfully!")
elif choice == "2":
confirm = input("\n⚠️ WARNING: This will DELETE ALL DATA! Are you sure? (yes/no): ")
if confirm.lower() == "yes":
print("\nDropping existing tables...")
await drop_db()
print("Creating new tables...")
await init_db()
print("✅ Database reset successfully!")
else:
print("❌ Operation cancelled")
elif choice == "3":
print("Exiting...")
else:
print("❌ Invalid choice")
if __name__ == "__main__":
asyncio.run(main())

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logger/logger.py Normal file
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class Loger:
def __init__(self):
pass

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import os
import httpx
import asyncio
import io
from typing import Optional, Dict, Any
from dotenv import load_dotenv
import pdfplumber
load_dotenv()
OPENROUTER_API_KEY = os.getenv("OPENROUTER_API_KEY")
OPENROUTER_BASE_URL = "https://openrouter.ai/api/v1"
class OpenRouterClient:
def __init__(self, api_key: Optional[str] = None):
self.api_key = api_key or OPENROUTER_API_KEY
if not self.api_key:
raise ValueError("OPENROUTER_API_KEY not set in environment")
self.headers = {
"Authorization": f"Bearer {self.api_key}",
"HTTP-Referer": "http://localhost:8000",
"X-Title": "RAG AI Assistant",
}
async def parse_cv_from_pdf(
self,
pdf_bytes: bytes,
model: str = "qwen/qwen-2.5-72b-instruct",
max_retries: int = 3
) -> Dict[str, Any]:
with pdfplumber.open(io.BytesIO(pdf_bytes)) as pdf:
text = "\n".join([page.extract_text() or "" for page in pdf.pages])
if not text.strip():
raise ValueError("Could not extract text from PDF")
prompt = f"""Analyze this CV/Resume and extract the following information in JSON format.
CV Text:
{text}
Please extract and return ONLY a JSON object with these fields (use "NONE" for missing information):
{{
"name": "Full name",
"email": "Email address",
"position": "Current or desired job title",
"competencies": "Key competencies and areas of expertise",
"experience": "Work experience summary",
"skills": "Technical and soft skills (comma-separated)",
"country": "Country",
"languages": "Languages spoken (comma-separated)",
"employment_format": "Preferred employment format (remote/office/hybrid)",
"rate": "Salary expectations or rate",
"relocation": "Relocation preferences"
}}
Important:
- If you can't find an entity, use "NONE"
- Return ONLY the JSON object, no additional text or markdown
"""
for attempt in range(max_retries):
try:
async with httpx.AsyncClient(timeout=120.0) as client:
response = await client.post(
f"{OPENROUTER_BASE_URL}/chat/completions",
headers=self.headers,
json={
"model": model,
"messages": [
{
"role": "user",
"content": prompt
}
],
"temperature": 0.1,
}
)
if response.status_code == 429:
if attempt < max_retries - 1:
wait_time = (2 ** attempt) * 5
await asyncio.sleep(wait_time)
continue
response.raise_for_status()
result = response.json()
content = result["choices"][0]["message"]["content"]
import json
content = content.strip()
if content.startswith("```json"):
content = content[7:]
if content.startswith("```"):
content = content[3:]
if content.endswith("```"):
content = content[:-3]
content = content.strip()
return json.loads(content)
except httpx.HTTPStatusError as e:
if e.response.status_code == 429 and attempt < max_retries - 1:
wait_time = (2 ** attempt) * 5
await asyncio.sleep(wait_time)
continue
raise
raise Exception("Rate limit exceeded after retries")
async def chat_completion(
self,
messages: list,
model: str = "qwen/qwen3-8b",
temperature: float = 0.7
) -> str:
async with httpx.AsyncClient(timeout=60.0) as client:
response = await client.post(
f"{OPENROUTER_BASE_URL}/chat/completions",
headers=self.headers,
json={
"model": model,
"messages": messages,
"temperature": temperature,
}
)
response.raise_for_status()
result = response.json()
return result["choices"][0]["message"]["content"]
openrouter_client = OpenRouterClient()

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requirements.txt Normal file
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# FastAPI Framework
fastapi==0.109.0
uvicorn[standard]==0.27.0
python-multipart==0.0.6
# Pydantic for data validation
pydantic==2.5.3
pydantic[email]==2.5.3
# Database (PostgreSQL with async support)
sqlalchemy==2.0.25
asyncpg==0.29.0
psycopg2-binary==2.9.9
greenlet==3.0.3
# Telegram Bot
aiogram==3.3.0
# MinIO for file storage
minio==7.2.3
# Environment variables
python-dotenv==1.0.0
# OpenRouter AI
openai==1.12.0 # Compatible with OpenRouter API
# PDF processing
PyPDF2==3.0.1
pdfplumber==0.10.3
# Testing (optional but recommended)
pytest==7.4.4
pytest-asyncio==0.23.3
httpx==0.26.0

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# Routers package

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routers/chat.py Normal file
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from fastapi import APIRouter, HTTPException, Depends, UploadFile, File, Form
from sqlalchemy import select
from sqlalchemy.ext.asyncio import AsyncSession
from sqlalchemy.orm import selectinload
from pydantic import BaseModel
from typing import Optional, List
from datetime import datetime
import io
import os
from database.database import get_db, ChatMessage as DBChatMessage, ChatFile as DBChatFile
from database.minio_processor import MinIOProcessor
minio_client = MinIOProcessor(
endpoint=os.getenv("MINIO_ENDPOINT", "localhost:9000"),
access_key=os.getenv("MINIO_ACCESS_KEY", "minioadmin"),
secret_key=os.getenv("MINIO_SECRET_KEY", "minioadmin"),
secure=False
)
MINIO_BUCKET = os.getenv("MINIO_BUCKET", "resumes")
router = APIRouter(prefix="/api/chat", tags=["chat"])
class ChatFileResponse(BaseModel):
id: int
file_name: str
file_type: str
file_url: str
created_at: datetime
class Config:
from_attributes = True
class ChatMessageResponse(BaseModel):
id: int
user_id: int
role: str
content: str
is_favorite: bool
files: List[ChatFileResponse]
created_at: datetime
class Config:
from_attributes = True
@router.post("", response_model=ChatMessageResponse)
async def create_chat_message(
user_id: int = Form(...),
role: str = Form(...),
content: str = Form(...),
files: Optional[List[UploadFile]] = File(None),
db: AsyncSession = Depends(get_db)
):
message = DBChatMessage(
user_id=user_id,
role=role,
content=content,
is_favorite=False
)
db.add(message)
await db.flush()
if files:
for file in files:
object_name = f"chat/{user_id}/{message.id}/{file.filename}"
try:
file_content = await file.read()
minio_client.put_object(
bucket_name=MINIO_BUCKET,
object_name=object_name,
data=io.BytesIO(file_content),
length=len(file_content),
content_type=file.content_type or "application/octet-stream"
)
file_url = f"minio://{MINIO_BUCKET}/{object_name}"
except Exception as e:
await db.rollback()
raise HTTPException(status_code=500, detail=f"Failed to upload file: {str(e)}")
chat_file = DBChatFile(
message_id=message.id,
file_name=file.filename,
file_type=file.content_type or "application/octet-stream",
file_url=file_url
)
db.add(chat_file)
await db.commit()
await db.refresh(message)
result = await db.execute(
select(DBChatMessage)
.where(DBChatMessage.id == message.id)
.options(selectinload(DBChatMessage.files))
)
message = result.scalar_one()
return message
@router.get("", response_model=List[ChatMessageResponse])
async def get_chat_messages(
user_id: Optional[int] = None,
is_favorite: Optional[bool] = None,
db: AsyncSession = Depends(get_db)
):
query = select(DBChatMessage).options(selectinload(DBChatMessage.files))
if user_id is not None:
query = query.where(DBChatMessage.user_id == user_id)
if is_favorite is not None:
query = query.where(DBChatMessage.is_favorite == is_favorite)
query = query.order_by(DBChatMessage.created_at.desc())
result = await db.execute(query)
messages = result.scalars().all()
return messages
@router.get("/{message_id}", response_model=ChatMessageResponse)
async def get_chat_message(message_id: int, db: AsyncSession = Depends(get_db)):
result = await db.execute(
select(DBChatMessage)
.where(DBChatMessage.id == message_id)
.options(selectinload(DBChatMessage.files))
)
message = result.scalar_one_or_none()
if not message:
raise HTTPException(status_code=404, detail="Message not found")
return message
@router.put("/{message_id}/favorite", response_model=ChatMessageResponse)
async def toggle_favorite(message_id: int, db: AsyncSession = Depends(get_db)):
result = await db.execute(
select(DBChatMessage)
.where(DBChatMessage.id == message_id)
.options(selectinload(DBChatMessage.files))
)
message = result.scalar_one_or_none()
if not message:
raise HTTPException(status_code=404, detail="Message not found")
message.is_favorite = not message.is_favorite
await db.commit()
await db.refresh(message)
return message

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from fastapi import APIRouter, File, UploadFile, Form, HTTPException, Depends
from sqlalchemy import select
from sqlalchemy.ext.asyncio import AsyncSession
from pydantic import BaseModel, EmailStr
from typing import Optional
import io
import os
from database.database import get_db, Profile as DBProfile
from database.minio_processor import MinIOProcessor
from openrouter_client import openrouter_client
minio_client = MinIOProcessor(
endpoint=os.getenv("MINIO_ENDPOINT", "localhost:9000"),
access_key=os.getenv("MINIO_ACCESS_KEY", "minioadmin"),
secret_key=os.getenv("MINIO_SECRET_KEY", "minioadmin"),
secure=False
)
MINIO_BUCKET = os.getenv("MINIO_BUCKET", "resumes")
router = APIRouter(prefix="/api/profile", tags=["profile"])
class ProfileResponse(BaseModel):
id: int
name: str
email: str
position: str
competencies: Optional[str]
experience: Optional[str]
skills: Optional[str]
country: Optional[str]
languages: Optional[str]
employment_format: Optional[str]
rate: Optional[str]
relocation: Optional[str]
cv_url: Optional[str]
class Config:
from_attributes = True
@router.post("/upload-cv", response_model=ProfileResponse)
async def upload_and_parse_cv(
cv: UploadFile = File(...),
db: AsyncSession = Depends(get_db)
):
if cv.content_type != "application/pdf":
raise HTTPException(status_code=400, detail="CV must be a PDF file")
pdf_content = await cv.read()
try:
parsed_data = await openrouter_client.parse_cv_from_pdf(pdf_content)
except Exception as e:
raise HTTPException(status_code=500, detail=f"Error parsing CV with AI: {str(e)}")
if not parsed_data.get("email"):
raise HTTPException(status_code=400, detail="Could not extract email from CV")
email = parsed_data["email"]
result = await db.execute(select(DBProfile).where(DBProfile.email == email))
existing_profile = result.scalar_one_or_none()
object_name = f"cv/{email.replace('@', '_')}_{cv.filename}"
try:
await cv.seek(0)
cv_file_data = await cv.read()
await cv.seek(0)
minio_client.put_object(
bucket_name=MINIO_BUCKET,
object_name=object_name,
data=io.BytesIO(cv_file_data),
length=len(cv_file_data),
content_type="application/pdf"
)
cv_url = f"minio://{MINIO_BUCKET}/{object_name}"
except Exception as e:
raise HTTPException(status_code=500, detail=f"Failed to upload CV to storage: {str(e)}")
if existing_profile:
existing_profile.name = parsed_data.get("name") or existing_profile.name
existing_profile.position = parsed_data.get("position") or existing_profile.position
existing_profile.competencies = parsed_data.get("competencies")
existing_profile.experience = parsed_data.get("experience")
existing_profile.skills = parsed_data.get("skills")
existing_profile.country = parsed_data.get("country")
existing_profile.languages = parsed_data.get("languages")
existing_profile.employment_format = parsed_data.get("employment_format")
existing_profile.rate = parsed_data.get("rate")
existing_profile.relocation = parsed_data.get("relocation")
existing_profile.cv_url = cv_url
profile = existing_profile
else:
profile = DBProfile(
email=email,
name=parsed_data.get("name", "Unknown"),
position=parsed_data.get("position", "Not specified"),
competencies=parsed_data.get("competencies"),
experience=parsed_data.get("experience"),
skills=parsed_data.get("skills"),
country=parsed_data.get("country"),
languages=parsed_data.get("languages"),
employment_format=parsed_data.get("employment_format"),
rate=parsed_data.get("rate"),
relocation=parsed_data.get("relocation"),
cv_url=cv_url
)
db.add(profile)
await db.commit()
await db.refresh(profile)
return profile
@router.post("/save", response_model=ProfileResponse)
async def save_profile_manual(
name: str = Form(...),
email: EmailStr = Form(...),
position: str = Form(...),
competencies: Optional[str] = Form(None),
experience: Optional[str] = Form(None),
skills: Optional[str] = Form(None),
country: Optional[str] = Form(None),
languages: Optional[str] = Form(None),
employment_format: Optional[str] = Form(None),
rate: Optional[str] = Form(None),
relocation: Optional[str] = Form(None),
db: AsyncSession = Depends(get_db)
):
result = await db.execute(select(DBProfile).where(DBProfile.email == email))
existing_profile = result.scalar_one_or_none()
if existing_profile:
existing_profile.name = name
existing_profile.position = position
existing_profile.competencies = competencies
existing_profile.experience = experience
existing_profile.skills = skills
existing_profile.country = country
existing_profile.languages = languages
existing_profile.employment_format = employment_format
existing_profile.rate = rate
existing_profile.relocation = relocation
profile = existing_profile
else:
profile = DBProfile(
name=name,
email=email,
position=position,
competencies=competencies,
experience=experience,
skills=skills,
country=country,
languages=languages,
employment_format=employment_format,
rate=rate,
relocation=relocation
)
db.add(profile)
await db.commit()
await db.refresh(profile)
return profile
@router.get("/{email}", response_model=ProfileResponse)
async def get_profile(email: str, db: AsyncSession = Depends(get_db)):
result = await db.execute(select(DBProfile).where(DBProfile.email == email))
profile = result.scalar_one_or_none()
if not profile:
raise HTTPException(status_code=404, detail="Profile not found")
return profile

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from fastapi import APIRouter, HTTPException, Depends
from sqlalchemy import select
from sqlalchemy.ext.asyncio import AsyncSession
from pydantic import BaseModel
from typing import Optional, List
from datetime import datetime
from database.database import get_db, Vacancy as DBVacancy
router = APIRouter(prefix="/api/vacancies", tags=["vacancies"])
class VacancyListItem(BaseModel):
id: int
position: str
salary_min: Optional[int]
salary_max: Optional[int]
salary_currency: Optional[str]
work_arrangement: Optional[str]
key_competencies: Optional[str]
class Config:
from_attributes = True
class ContactItem(BaseModel):
id: Optional[int]
type: str
value: str
class VacancyDetail(BaseModel):
id: int
company_name: str
contacts: Optional[List[ContactItem]]
country_name: Optional[str]
created_at: datetime
description: Optional[str]
key_competencies: Optional[str]
locations: Optional[str]
position: str
relocation_supported: Optional[bool]
required_language_codes: Optional[List[str]]
salary_currency: Optional[str]
salary_min: Optional[int]
salary_max: Optional[int]
seniority_level: Optional[str]
work_arrangement: Optional[str]
class Config:
from_attributes = True
@router.get("", response_model=List[VacancyListItem])
async def get_all_vacancies(db: AsyncSession = Depends(get_db)):
result = await db.execute(select(DBVacancy))
vacancies = result.scalars().all()
return vacancies
@router.get("/{vacancy_id}", response_model=VacancyDetail)
async def get_vacancy_by_id(vacancy_id: int, db: AsyncSession = Depends(get_db)):
result = await db.execute(select(DBVacancy).where(DBVacancy.id == vacancy_id))
vacancy = result.scalar_one_or_none()
if not vacancy:
raise HTTPException(status_code=404, detail="Vacancy not found")
return vacancy

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tests/test_bot.py Normal file
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"""
Test file for bot.py
To run the bot:
python bot.py
To test the bot functionality:
python -m pytest tests/test_bot.py -v
or
python tests/test_bot.py
"""
import pytest
import asyncio
from unittest.mock import AsyncMock, MagicMock, patch
import sys
import os
# Add parent directory to path to import bot module
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from bot import cmd_start, fake_db
class TestBot:
"""Test cases for bot functionality"""
def setup_method(self):
"""Clear fake_db before each test"""
fake_db.clear()
@pytest.mark.asyncio
async def test_start_command_without_token(self):
"""Test /start command without token"""
# Create mock message
message = AsyncMock()
message.from_user.id = 12345
message.from_user.username = "test_user"
message.answer = AsyncMock()
# Create mock command object
command = MagicMock()
command.args = None
# Call the handler
await cmd_start(message, command)
# Verify the response
message.answer.assert_called_once_with(
"Пожалуйста, перейдите по ссылке с сайта для авторизации."
)
@pytest.mark.asyncio
async def test_start_command_with_valid_token(self):
"""Test /start command with valid token"""
# Setup: add token to fake_db
test_token = "test-token-123"
fake_db[test_token] = {"status": "pending"}
# Create mock message
message = AsyncMock()
message.from_user.id = 12345
message.from_user.username = "test_user"
message.answer = AsyncMock()
# Create mock command object
command = MagicMock()
command.args = test_token
# Call the handler
await cmd_start(message, command)
# Verify the response
message.answer.assert_called_once_with(
"Успешная авторизация! Вернитесь на сайт, вас должно автоматически авторизовать."
)
# Verify fake_db was updated
assert fake_db[test_token]["status"] == "success"
assert fake_db[test_token]["telegram_id"] == 12345
assert fake_db[test_token]["username"] == "test_user"
@pytest.mark.asyncio
async def test_start_command_with_invalid_token(self):
"""Test /start command with invalid token"""
# Create mock message
message = AsyncMock()
message.from_user.id = 12345
message.from_user.username = "test_user"
message.answer = AsyncMock()
# Create mock command object
command = MagicMock()
command.args = "invalid-token"
# Call the handler
await cmd_start(message, command)
# Verify the response
message.answer.assert_called_once_with(
"Ошибка: неверный или истекший токен."
)
# Verify fake_db was not updated
assert "invalid-token" not in fake_db
if __name__ == "__main__":
"""Run tests directly"""
print("Running bot tests...")
pytest.main([__file__, "-v"])