Cache lookup operation

# 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…

rate_limiter = RateLimiter(max_calls=100, period=60) # 100 calls per minute # Define a function to handle authentication async def authenticate(username, password): try: # Check cache first token = await caches.get(f"t…

# 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…

[Turn 6468] User: I'm trying to implement a caching strategy for my vector search results, and I've been experimenting with different approaches. Currently, I'm using Redis 7.0.12, and I've achieved 60ms access time for 3,000 hits. However,…

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…

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)) @app.post("/api/v1/hybrid-search", response_mo…

response = SearchResponse(results=combined_results, total_results=total_results) r.set(cache_key, response.json(), ex=60) # Cache for 60 seconds return response @app.get("/health") def health_check(): return {"status"…

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) …

# Simulate cache lookups start_time = time.time() latencies = [] for _ in range(14000): start_query_time = time.time() result = search_query("example") end_query_time = time.time() latencies.append(end_query_time - start_que…

client = redis.Redis(host='node3', port=6379, db=0) # Cache the data client.set(data, True) ``` Can you help me optimize my load balancing strategy and ensure that the cache load is distributed evenly across the nodes? ->-> …

# Calculate the hash of the data hash_value = hashlib.md5(data.encode()).hexdigest() # Convert the hash to an integer hash_int = int(hash_value, 16) # Determine which node to use based on the hash node_index = hash_i…

for result in results: print(result) # Run the main function asyncio.run(main()) ``` ### Explanation 1. **Tokenization and Segmentation**: - Tokenize the input text using the tokenizer. - Segment the input text into chu…

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…

3. **Map Roles to Permissions**: Programmatically map Keycloak roles to query permissions. 4. **Apply Access Control Logic**: Apply the access control logic in your application. 5. **Secure Endpoints**: Secure your endpoints using a framewo…

for word, synonyms in thesaurus.items(): word_embedding = get_contextual_embeddings(word) similarities = [np.dot(term_embedding, get_contextual_embeddings(syn)) for syn in synonyms] closest_synonyms.extend([synon…

futures = [executor.submit(reformulate_query, query) for query in queries] for future in as_completed(futures): results.append(future.result()) return results # Define a function to tokenize queries def toke…

print(f"Error retrieving cached tokens: {str(e)}") return None # Example usage tokens = [{"id": 1, "text": "This is an example token."}] # Cache the tokens cache_tokens(tokens, ttl=3600) # Retrieve the cached tokens cache…