Cache check

The first loop will take longer because each query is unique and the function must simulate the delay. The second loop will be much faster because the repeated queries will be served from the cache. ### Example with External Caching (Redis…

import keycloak import asyncio from aiocache import caches, SimpleMemoryCache from aiocache.serializers import PickleSerializer from ratelimiter import RateLimiter # Initialize Keycloak keycloak_url = "https://my-keycloak-instance.com" rea…

# Cache the token await caches.set(f"token_{username}", token, ttl=3600) # Cache for 1 hour return token except keycloak.exceptions.KeycloakError as e: # Handle authentication errors print(f"Auth…

# Check cache first token = await caches.get(f"token_{username}") if token: return token # Enforce rate limiting with rate_limiter: token = await kc.token_async(userna…

{'class': 'aiocache.plugins.TimingPlugin'} ] } }) # Simulate a database query async def simulate_db_query(user_id, password): # Simulate a database query with a small delay await asyncio.sleep(0.01) retu…

precision = precision_score(true_labels.ravel(), predicted_labels.ravel()) print(f"Precision: {precision:.2f}") ``` ### Explanation 1. **Hybrid Search Function:** - Combines sparse and dense scores using adaptive weights. - Handles …

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…

# 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 …

return {"status": "OK"} # Middleware to handle CORS app.add_middleware( CORSMiddleware, allow_origins=["*"], allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) ``` ### Step 6: Run the Application …

# Check if the result is already in the cache cached_result = r.get(cache_key) if cached_result: return SearchResponse.parse_raw(cached_result) # Call the original…

A service mesh like Istio or Linkerd can help manage service-to-service communication, load balancing, and observability. #### Example with Istio 1. **Install Istio**: Follow the official documentation to install Istio in your Kubernetes …

except requests.exceptions.ConnectionError as e: raise HTTPException(status_code=503, detail=str(e)) except requests.exceptions.Timeout as e: raise HTTPException(status_code=504, detail=str(e)) except Exception a…

# Return the cached result cached_result = client.get(key) return jsonify({'cached_result': cached_result}) # Compute the result result = func(*args, **kwargs) …

Read-through caching involves checking the cache first and, if the data is not present, fetching it from the backend and then storing it in the cache for future requests. ### Combined Strategy Here's how you can combine sharding and read-…

But I'm getting an error: `redis.exceptions.ResponseError: unknown command 'SETEX'`. Can you help me fix this issue? ->-> 9,22 [Turn 7651] Assistant: It looks like you're encountering an error because the `SETEX` command is not recognized …

:return: List of processed segments. """ if len(input_sequence) > self.max_tokens: self.logger.info(f"Token overflow detected: {len(input_sequence)} tokens") segmented_inputs = self.segment_in…

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 …

# Implement result calculation logic here pass ``` ->-> 9,22 [Turn 9603] Assistant: Certainly! Implementing a caching strategy using Redis can significantly reduce the load on your security system by storing the results of frequent…

Below is an example implementation using Python's `concurrent.futures` for concurrency and `cachetools` for caching. This example also includes a basic load balancing mechanism using a round-robin strategy. #### Step 1: Install Required Pa…

Implement error handling to manage exceptions and return appropriate HTTP status codes. ### Example Implementation ```python from flask import Flask, request, jsonify from flask_limiter import Limiter from flask_limiter.util import get_re…

### Step 3: Initialize Redis for Caching Initialize Redis to cache the contextual embeddings and synonyms: ```python import redis redis_client = redis.Redis(host='localhost', port=6379, db=0) ``` ### Step 4: Generate Contextual Embeddin…

import torch from transformers import AutoModelForSeq2SeqLM, AutoTokenizer from concurrent.futures import ThreadPoolExecutor, as_completed import redis class ReformulationModel: def __init__(self): self.model = AutoModelForSeq2…

2. **Concurrency**: Use threading or multiprocessing to handle multiple queries concurrently. 3. **Caching**: Use Redis to cache frequent queries and their reformulated versions to reduce the load on the model. 4. **Efficient Tokenization**…

5. **Profiling**: We use `cProfile` to profile the `batch_reformulate_queries` function and identify bottlenecks. ### Next Steps 1. **Run the Code**: Execute the code to see the performance improvements and identify any bottlenecks. 2. **…

2. **Cache Functions**: - `cache_reformulated_query(query, reformulated_query, ttl=3600)`: Stores the reformulated query in Redis with an optional TTL (Time To Live). - `get_reformulated_query(query)`: Retrieves the reformulated query…