app

elif current_user.has_role('staff'): return 'Staff access' return 'Unauthorized' if __name__ == '__main__': app.run(debug=True) ``` ### Step 3: Restrict Access to OCR Output To restrict access to OCR…

data = {'message': 'Data retrieved successfully'} return jsonify(data) except TimeoutException as e: return jsonify({'error': str(e)}), 504 finally: # Cancel the alarm signal.alarm(0) if __na…

return decorator ``` ### Step 5: Define Routes Define routes that require specific roles. ```python @app.route('/') def home(): return "Welcome to the Home Page" @app.route('/tech_evaluation') @role_required('TechEvaluator') def…

app.config["SQLALCHEMY_DATABASE_URI"] = "sqlite:///risk.db" db = SQLAlchemy(app) class RiskReport(db.Model): id = db.Column(db.Integer, primary_key=True) report_data = db.Column(db.String(1000), nullable=False) @app.route("/api/v1…

[Turn 1991] Assistant: Certainly! Let's complete the implementation of a microservices architecture using Flask and Docker. We'll define the services (`user-service`, `product-service`, and `order-service`) and provide a Docker setup to run…

def require_jwt(view_func): @wraps(view_func) def decorated_function(*args, **kwargs): token = request.headers.get('Authorization') if not token or not validate_jwt_token(token.split(' ')[1]): return json…

# Replace with your actual API key validation logic return api_key == os.environ.get('API_KEY') # Decorator for API key validation def require_api_key(view_func): @wraps(view_func) def decorated_function(*args, **kwargs): …

return jsonify({"service": "service2", "status": "healthy"}) if __name__ == "__main__": app.run(host="0.0.0.0", port=int(os.environ.get("PORT", 5000))) ``` ### Dockerfiles #### `service1/Dockerfile` ```dockerfile FROM python:3.…

[Turn 2181] Assistant: Absolutely! Linking Jira issues back to your local database allows you to maintain a consistent and integrated view of your project status. Here's how you can extend your Flask application to include this functionalit…

data = { "fields": { "project": {"key": "YOUR_PROJECT_KEY"}, "summary": name, "description": description, "issuetype": {"name": "Task"}, "priority": {"name": "High" if …

Containerize your microservice using Docker. Create a `Dockerfile`: ```Dockerfile FROM node:14 WORKDIR /usr/src/app COPY package*.json ./ RUN npm install COPY . . EXPOSE 3000 CMD ["node", "app.js"] ``` Build and run the Docker cont…

const PORT = process.env.PORT || 3000; ``` #### 2. Error Handling Add middleware to handle errors. ```javascript app.use((err, req, res, next) => { console.error(err.stack); res.status(500).json({ error: "Something went wrong." }…

circuitBreaker.executeSupplier(() => { // Call another service const response = callAnotherService(); return response; }).then(result => { res.json(result); }).catch(error => { res.status(…

args=training_args, train_dataset=tokenized_dataset["train"], eval_dataset=tokenized_dataset["validation"] ) # Train the model trainer.train() ``` #### 3. Self-Hosted Model Deployment ##### Environment Setup - **Hardware**: …

INFO:root:Function critical_assignment took 1.000123 seconds Latency: 1.000123 seconds ``` ### Next Steps 1. **Run the Code:** - Execute the code and observe the output and logs. 2. **Modify and Test:** - Adjust the `critical_assig…

# Configure logging logging.basicConfig(level=logging.INFO) class TeamTask(BaseModel): task_id: int = Field(..., gt=0) role: str = Field(..., min_length=1) @app.exception_handler(RequestValidationError) async def validation_except…

[Turn 3653] Assistant: Implementing caching mechanisms can significantly improve the performance of your application by reducing the load on your backend services and minimizing latency. Here are some specific caching mechanisms you can con…

- `-k uvicorn.workers.UvicornWorker`: Use Uvicorn as the worker class, which supports asynchronous applications. ### Additional Considerations 1. **Caching**: Use caching mechanisms like Redis to store frequently accessed data. 2. **Load …

if authenticate_user(username, password): response = jsonify({'token': 'example_token'}) response.headers['Cache-Control'] = 'public, max-age=60' # Cache for 60 seconds return response else: return j…

# Fetch sprint data from Jira def get_sprint_data(): jira = JIRA(server='https://your-jira-server.com', basic_auth=('username', 'password')) sprints = jira.sprints('YOUR_PROJECT_KEY') sprint_data = [] for sprint in sprints: …

if __name__ == '__main__': app.run(host='0.0.0.0', port=5000) ``` ### Integration with Monitoring Tools Integrate with monitoring tools like Prometheus to track metrics and set up alerts: ```yaml scrape_configs: - job_name: 'ingest…

response = await client.get("http://localhost:8000/api/v1/sparse-search") if response.status_code == 200: print(response.json()) else: raise HTTPException(status_code=response.status_code) # …

@app.route("/api/v1/endpoint", methods=["GET"]) @limiter.limit("10/second;30/minute", per_method=True, override_defaults=False) def handle_request(): # Handle the request response = jsonify({"message": "Request handled successfully"…

# Custom key function to identify user roles def get_user_role(): # Assume user role is stored in the request context return getattr(g, 'user_role', 'basic') # Configure Flask-Limiter with custom key function limiter = Limiter( …

return getattr(g, 'user_role', 'basic') # Configure Flask-Limiter with custom key function limiter = Limiter( app, key_func=get_user_role, default_limits=["20/minute"] ) # Decorator to set user role for the request def set…

[Turn 5305] Assistant: Certainly! Let's enhance your FastAPI example by adding validation, error handling, and integrating a simple SQLite database to make it more realistic. We'll use SQLAlchemy for database interactions and Pydantic for d…

[Turn 5312] User: As I continue to learn more about FastAPI and its capabilities, I'm interested in exploring how to implement authentication and authorization in my APIs to restrict access to certain endpoints. Here's a basic example using…

[Turn 5330] User: I'm trying to set up authorization for my API, defining access and setting 4 roles for API usage, but I'm having trouble figuring out how to restrict exposure to only 3% of sensitive endpoints. Can you help me with that? I…

- Integrate it with FastAPI using middleware. 3. **Implement Timeouts**: - Use FastAPI's `async` and `await` to handle asynchronous operations. - Use `asyncio.wait_for` to enforce timeouts. ### Example Implementation Here's how …

- **Custom Headers**: You can customize headers to provide more information about rate limits, such as `X-RateLimit-Limit`, `X-RateLimit-Remaining`, and `X-RateLimit-Reset`. - **Storage Backend**: For production environments, consider using…

reset = limiter.current_limit_reset response.headers["X-RateLimit-Limit"] = str(limiter.current_limit) response.headers["X-RateLimit-Remaining"] = str(remaining) response.headers["X-RateLimit-Reset"]…

[Turn 5446] User: I've been looking into using Uvicorn 0.22.0 as the server for its 99.9% uptime for 2K connections, and I was wondering if someone could help me configure it to work with my OAuth 2.0 flows and role-based access control, co…

uvicorn.run(config) ``` Any feedback on this would be great, and maybe some suggestions on how to improve it, considering I'm aiming for 99.9% uptime and handling 3,500 concurrent requests, so any advice on that would be great too ->-> …

def create_access_token(data: dict, expires_delta: Optional[timedelta] = None): to_encode = data.copy() if expires_delta: expire = datetime.utcnow() + expires_delta else: expire = datetime.utcnow() + timedelta(mi…

from fastapi import FastAPI, Depends, HTTPException from fastapi.security import OAuth2PasswordBearer, OAuth2PasswordRequestForm from pydantic import BaseModel import jwt from datetime import datetime, timedelta from typing import Optional,…

log_queue.put_nowait(log_entry) # Log login failures def log_login_failure(username, error_message): log_message('ERROR', f'Login failure for {username}', {'username': username, 'error': error_message}) # Example usage log_login_f…

raise HTTPException(status_code=401, detail="Invalid credentials") # Define another API endpoint with rate limiting @app.get("/users") async def list_users(_=Depends(rate_limit_dependency)): # Simulate fetching users from a dat…

expiry_time = datetime.fromtimestamp(token_info['expires_in'] + token_info['issued_at']) current_time = datetime.utcnow() time_to_expiry = (expiry_time - current_time).total_seconds() if time_to_expi…

try: # Get the log message from the request body message = await request.json() log_message = message.get("message") if not log_message: raise HTTPException(status_code=400, detail="Message is…

[Turn 5746] User: Can someone review my code for refining 20% of monitoring dashboards and provide feedback on how to improve it? I've set a review with 3 team members, but I want to make sure I'm on the right track ``` import dash import …

AUTH0_DOMAIN = 'your-domain.auth0.com' AUTH0_CALLBACK_URL = '/callback' auth0_bp = make_auth0_blueprint( client_id=AUTH0_CLIENT_ID, client_secret=AUTH0_CLIENT_SECRET, domain=AUTH0_DOMAIN, redirect_url=AUTH0_CALLBACK_URL ) a…

query = request.json['query'] results = es.search(index="documents", body={"query": {"match": {"text": query}}}) return jsonify(results) if __name__ == '__main__': app.run(host='0.0.0.0', port=5000) ``` - **Den…

app.run(host='0.0.0.0', port=5000) ``` #### Caching with Redis - **Redis Example**: ```python import redis r = redis.Redis(host='localhost', port=6379, db=0) def get_cached_result(query_vector): key = f"query:{quer…

app.run(debug=True) ``` ### Explanation: 1. **Keycloak Configuration**: - Configure Keycloak with the necessary realm, client, and roles. - Use the `KeycloakOpenID` client to interact with Keycloak. 2. **Authentication**: - …

SENSITIVE_SCORE_ACCESS_ROLE = KeycloakRole('sensitive-score-access') # Decorator to check for specific role def require_role(role): def decorator(f): def wrapper(*args, **kwargs): if not keycloak.has_role(role): …

``` Here, `-w 4` specifies 4 worker processes, and `-t 2.5` sets a 2.5-second timeout. ### Step 4: Implement Hybrid Ranking Logic Here's a complete example implementation: ```python from flask import Flask, request, jsonify from flask_l…

# Further validation logic if 'required_field' not in data: raise ValueError("Missing required field in request data") return data except ValueError as ve: logging.error(f"ValueError:…

# For demonstration, let's assume we have a function `perform_vector_search` results = perform_vector_search(query_vector, top_k) return jsonify(results) api.add_resource(VectorSearch, '/vector-search') ```…

By following these strategies and using the provided code example, you can effectively integrate vector search with approximate nearest neighbors to achieve better search results and performance. If you have any specific questions or need f…

# Configure caching cache_config = { 'CACHE_TYPE': 'RedisCache', 'CACHE_REDIS_URL': 'redis://localhost:6379/0' } cache = Cache(app, config=cache_config) async def expensive_operation(): # Simulate an expensive operation awa…

5. **Connection Pooling**: Use connection pooling to manage database connections more efficiently. 6. **Compression**: Compress data before sending it over the network to reduce transfer time. ### Example Code with Caching Your provided c…

Implement a circuit breaker to prevent cascading failures. A circuit breaker monitors the health of a service and temporarily stops requests to a failing service. ### 2. **Fallback Mechanism** Provide fallback mechanisms to return default …

```python import aiohttp import asyncio async def fetch(session, url): async with session.get(url) as response: return await response.json() async def main(): async with aiohttp.ClientSession() as session: tasks = …

FastAPI's dependency injection system can help manage dependencies efficiently, such as database sessions or external service clients. ```python from fastapi import Depends, FastAPI from sqlalchemy.orm import Session from fastapi_sqlalchem…

query_results = [QueryResult(**result) for result in results] # Store the result in the cache r.set(cache_key, QueryResponse(results=query_results, total_results=total_results).json(), ex=60) # Cache for 60 seconds …

# Middleware to handle CORS app.add_middleware( CORSMiddleware, allow_origins=["*"], allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) ``` ### Step 6: Run the Application Run your FastAPI application …

cache_key = f"search:{query.query}:{query.limit}" # Check if the result is already in the cache cached_result = r.get(cache_key) if cached_result: return SearchResponse.parse_raw(cached_result) # Simula…

dense_results = [DenseResult(**result) for result in results] return jsonify(DenseResponse(results=dense_results, total_results=_results).dict()) if __name__ == '__main__': app.run(port=5002) # hybrid_ranking_service.py f…

class QueryRequest(BaseModel): query: str limit: int class QueryResponse(BaseModel): results: List[HybridResult] total_results: int @app.route('/query', methods=['POST']) def query(): query = QueryRequest(**request.jso…

except requests.exceptions.RequestException as e: raise HTTPException(status_code=500, detail=str(e)) @app.get("/health") def health_check(): return {"status": "OK"} ``` ### Step 5: Handle Errors and Exceptions Handle pot…

Let's walk through an example implementation using FastAPI and Istio for service discovery and circuit breakers. #### Step 1: Define the Services Assume you have two services: `sparse-retrieval` and `dense-retrieval`. #### Step 2: Implem…

response = requests.post(f"http://dense-retrieval:80/search", json=query.dict()) response.raise_for_status() return response.json() @app.post("/search", response_model=SearchResponse) async def search(query: SearchQuery): t…

from prometheus_client import start_http_server, Summary, Counter app = FastAPI() # Prometheus metrics REQUEST_TIME = Summary('request_processing_seconds', 'Time spent processing request') TOTAL_REQUESTS = Counter('total_requests', 'Total…

logger = logging.getLogger(__name__) @app.post("/search", response_model=SearchResponse) async def search(query: SearchQuery): try: sparse_results = call_sparse_retrieval(query) except HTTPException as e: logger.err…

#### Example Setup 1. **Install Sentry SDK**: ```sh pip install sentry-sdk ``` 2. **Configure Sentry in Your Application**: ```python import sentry_sdk from fastapi import FastAPI, HTTPException from pydantic import B…

except requests.exceptions.Timeout as e: raise HTTPException(status_code=504, detail=str(e)) except Exception as e: raise HTTPException(status_code=500, detail=str(e)) @retry(stop=stop_after_attempt(3…

return JSONResponse(content={"error_code": e.status_code, "message": e.detail}, status_code=e.status_code) try: dense_results = call_dense_retrieval(query) except HTTPException as e: de…

except requests.exceptions.Timeout as e: client.put_log_events( logGroupName='your-log-group', logStreamName='your-log-stream', logEvents=[ { …

return JSONResponse(content={"error_code": e.status_code, "message": e.detail}, status_code=e.status_code) combined_results = sparse_results["results"] + dense_results["results"] total_results = len(combined_results) …

# Configure logging logging.basicConfig(level=logging.ERROR, format='%(asctime)s - %(levelname)s - %(message)s') # Load the SpaCy model try: nlp = spacy.load("en_core_web_sm") except OSError as e: logging.error(f"Failed to load Spa…

return 'Invalid credentials', 401 @app.route('/logout') @login_required def logout(): logout_user() return redirect(url_for('login')) @app.route('/') @login_required def home(): return f'Welcome, {current_user.username}!' …

return 'Unauthorized', 403 # Example training loop for epoch in range(10): # Number of epochs optimizer.zero_grad() inputs = torch.tensor([1, 2, 3]) # Example inputs targets = torch.tensor([0]) # …

def cache_tokenized_results(results, key='tokenized_results', expire_time=300): serialized_results = pickle.dumps(results) encrypted_results = cipher_suite.encrypt(serialized_results) redis_client.setex(key, expire_time, encrypt…

api.add_resource(DenseTuneEndpoint, '/api/v1/dense-tune') if __name__ == '__main__': app.run(debug=True) ``` ### Explanation 1. **Specific Exception Handling**: - `ValueError`: Raised for invalid input. - `TimeoutError`: Raised…

Let's define two services: `TuningService` and `RetrievalService`. We'll use Flask for creating RESTful APIs and RabbitMQ for message queuing. #### 1. Define the Services First, define the services with their respective responsibilities. …

self.rabbitmq_connection = self.connect_rabbitmq() def connect_rabbitmq(self): connection = pika.BlockingConnection(pika.ConnectionParameters('localhost')) channel = connection.channel() channel.queue_de…

feedback_data = json.loads(cached_data) print(f'Retrieved from cache. Response time: {time.time() - start_time} seconds') return JSONResponse(content=feedback_data) # Simulate some processing time await …

Offload long-running tasks to background workers to prevent blocking the main request-response cycle. This can be achieved using task queues like Celery. ### 6. Optimize Database Queries If your evaluation pipeline involves database querie…

@app.route("/api/v1/model-evaluate", methods=["GET"]) def evaluate_model(): try: # Simulate running the evaluation pipeline # ... (code omitted for brevity) result = {"results": [1, 2, 3]} return jsonify(…

return [{"id": i, "value": i * 10} for i in range(1000)] # Example data def fetch_limited_tuning_data(): # Logic to fetch 1% of tuning data all_data = fetch_all_tuning_data() limited_data = all_data[:len(all_data)//100] #…

full_access_role = keycloak_admin.create_role({"name": "full-tuning-data-access"}) limited_access_role = keycloak_admin.create_role({"name": "limited-tuning-data-access"}) ``` ### Step 2: Assign Roles to Users Next, assign the roles to th…

keycloak = Keycloak(app, server_url="https://my-keycloak-server.com", client_id="my-client-id", client_secret="my-client-secret", realm_name="my-realm") # Define API endpoint for …

print(f"Processing time: {end_time - start_time} seconds") return {"message": "Training documents retrieved successfully"} if __name__ == "__main__": import uvicorn uvicorn.run(app, host="0.0.0.0", port=8000)…

app.run(debug=True) ``` ### Running with Gunicorn ```sh gunicorn -w 4 -b 0.0.0.0:5000 main:app ``` ### Conclusion To achieve the best performance improvements, updating to FastAPI is recommended due to its built-in support for async…

app.config["CACHE_REDIS_URL"] = "redis://localhost:6379/0" cache = Cache(app) @app.route('/api/v1/training-docs', methods=['GET']) @cache.cached(timeout=60) # Cache the result for 60 seconds def get_training_docs(): start_time = time…

redis_client.set(f"synonym:{term}", json.dumps(expanded_synonyms), ex=3600) return expanded_synonyms else: return [] tasks = [expand_term(term) for term in ter…