json.loads

```python from kafka import KafkaProducer, KafkaConsumer from kafka.errors import KafkaError, TimeoutError import json import time # Kafka producer configuration producer = KafkaProducer( bootstrap_servers='localhost:9092', value_s…

redis-server /path/to/redis.conf ``` ### Step 2: Implement Caching in Your Application Use the `redis-py` library to interact with Redis from your Python application. Here is an example of how to set up caching for log summaries: `…

self.channel = self.connection.channel() self.channel.queue_declare(queue=self.queue_name) def load_and_send_vectors(self): vectors = np.load(self.filepath) for vector in vectors: self.channe…

redis_client.set(f'document:{document_id}', document_json) def get_cached_document(document_id): """ Retrieve a cached document from Redis. :param document_id: Unique identifier for the document. :return: Cached documen…

3. **Integrate the Modules**: Ensure that the output of the synonym expansion module is correctly fed into the query rewriting pipeline. ### Example Implementation Let's assume the query rewriting pipeline expects a list of synonyms in a …

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…

### Example Implementation Here's an enhanced version of your API endpoint that handles these scenarios: ```python from flask import Flask, request, jsonify from flask_limiter import Limiter from flask_limiter.util import get_remote_addre…

### 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…

redis_client.setex(key, ttl, json.dumps(result)) def get_cached_query(query): """ Retrieve the cached query result. """ key = NAMESPACE + query cached_result = redis_client.get(key) if cached_result: ret…

except Exception as e: logging.error(f"Error caching query results: {str(e)}") return False def get_cached_query_results(query_id): try: # Create a Redis client redis_client = redis.Redis(host='local…

- Serialize the query results to JSON using `json.dumps`. - Store the serialized results in Redis with a key that includes the query ID. - Use `setex` to set the key with an expiration time to ensure the cache is refreshed periodic…

- Use `setex` to cache the tokens with an expiration time. - This ensures that the cache is refreshed periodically. 4. **Retrieve Cached Tokens**: - Retrieve the cached tokens using `get`. - Deserialize the tokens from JSON usi…

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…