FastAPI import

@app.post("/retrieve", response_model=QueryResponse) def retrieve(query_request: QueryRequest): # Implement the retrieval logic here results = ["Result 1", "Result 2", "Result 3"] return {"results": results} ``` And here's an ex…

- The `model.generate` method is used to generate the answer based on the tokenized input. The `with torch.no_grad()` context manager disables gradient calculation, which is not needed during inference and helps save memory. 4. **Decodi…

5. **What are the challenges of using a microservices architecture, and how do you plan to address them?** - **Response**: "While a microservices architecture offers many benefits, it also comes with some challenges: - **Complexity*…

import logging app = FastAPI() # 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(RequestValidationE…

logging.info(f"Response status: {response.status_code}") logging.info(f"Total request processing took {time.time() - start_time:.4f} seconds") return response # Example endpoint @app.get("/items") async def read_items(): re…

- **Token Validation**: Replace the simulated user authentication logic with actual token validation logic. - **Role-Based Access Control**: You can extend the role-based access control logic to include more granular permissions if needed. …

### Step 3: Use Specific Exceptions Instead of catching a generic `Exception`, catch specific exceptions that might occur during parsing. This will help you pinpoint the exact issue. ### Step 4: Add Debugging Information Add debugging in…

from fastapi import FastAPI, Depends from pydantic import BaseModel from typing import List, Optional import redis from fastapi.middleware.cors import CORSMiddleware app = FastAPI() # Initialize Redis client r = redis.Redis(host='localhos…

class QueryResult(BaseModel): id: int title: str content: str class QueryResponse(BaseModel): results: List[QueryResult] total_results: int ``` ### Step 3: Initialize Redis Client Initialize the Redis client and confi…

- **Batch Processing**: If possible, batch process multiple requests together to reduce the overhead of individual validations. - **Caching**: Use caching to store and reuse the results of expensive operations, as previously discussed. - …

def health_check(): return {"status": "OK"} ``` #### Dense Retrieval Service ```python from fastapi import FastAPI, HTTPException from pydantic import BaseModel import requests app = FastAPI() class SearchQuery(BaseModel): query…

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…

Redis can be used to cache frequently accessed data, reducing the load on your backend services and minimizing memory usage. #### Step 1: Install Redis Ensure Redis is installed and running on your server. ```sh sudo apt-get update sudo …

Implement conditional requests using `ETag` or `Last-Modified` headers to serve cached responses when the data hasn't changed. ### 4. **Client-Side Caching** Encourage client-side caching by setting appropriate cache control headers in you…