dense-retrieval

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…

By implementing dynamic cache keys that incorporate both the language and query parameters, you can efficiently cache and retrieve results for multi-language queries. This approach ensures that the cache is tailored to the specific request,…

# Simulate a search operation return {"result": "Dense retrieval result"} # Create services sparse_service = SparseRetrievalService() dense_service = DenseRetrievalService() # Define an API endpoint for retrieval @app.rout…

def retrieval_endpoint(): query = request.args.get('query') # Call sparse retrieval service sparse_response = requests.get(f'http://sparse-service:5000/sparse-search?query={query}') sparse_result = sparse_response.json(…

result = sparse_service.search(query) return jsonify(result) if __name__ == '__main__': app.run(port=int(os.environ.get('PORT', 5000))) ``` #### Dense Retrieval Service ```python from flask import Flask, jsonify, request app…

[Turn 7231] Assistant: Transitioning to a microservices architecture can significantly enhance the scalability and maintainability of your hybrid search APIs, especially when handling high volumes of queries. Here's a step-by-step guide to …

consul services register -name query-aggregation -address localhost -port 5004 ``` #### Step 4: Use Consul DNS for Service Discovery Consul provides a DNS interface for service discovery. You can use the DNS interface to resolve service n…

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…

app: sparse-retrieval spec: containers: - name: sparse-retrieval image: your-image:sparse-retrieval ports: - containerPort: 80 --- apiVersion: v1 kind: Service metadata: name: sparse-retrie…

This service will aggregate results from both sparse and dense retrieval services. ```python from fastapi import FastAPI, HTTPException from pydantic import BaseModel import requests app = FastAPI() class SearchQuery(BaseModel): quer…

[Turn 7239] Assistant: Handling cases where one service is down, such as the sparse retrieval service, is crucial for maintaining the reliability and availability of your system. Here are some strategies to manage such scenarios effectively…

By implementing fallback mechanisms, using circuit breakers, and setting up monitoring and alerting, you can handle cases where one service is down, such as the sparse retrieval service, effectively. This ensures that your system remains re…

trafficPolicy: connectionPool: tcp: maxConnections: 100 connectTimeout: 5s outlierDetection: consecutiveErrors: 3 interval: 10s baseEjectionTime: 3m maxEjectionPercent: 100 ``` ### St…

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…

scrape_interval: 15s scrape_configs: - job_name: 'sparse-retrieval' static_configs: - targets: ['sparse-retrieval:8080'] - job_name: 'dense-retrieval' static_configs: - targets: ['dense-retrieval:8080'] ``` ### …

matchLabels: app: dense-retrieval template: metadata: labels: app: dense-retrieval spec: containers: - name: dense-retrieval image: your-image:dense-retrieval ports: - co…

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…