query

from pydantic import BaseModel app = FastAPI() class QueryRequest(BaseModel): query: str class QueryResponse(BaseModel): results: list @app.post("/retrieve", response_model=QueryResponse) def retrieve(query_request: QueryRequest…

Here's an optimized version of your example code: ```python from elasticsearch import Elasticsearch # Initialize Elasticsearch with proper configuration es = Elasticsearch( hosts=["http://localhost:9200"], maxsize=25, # Increase …

[Turn 2506] User: I'm designing input structures for our LLM queries, and I'm proposing 6 context fields to improve answer relevance by 15%. I want to ensure that these fields are properly validated and sanitized to prevent any potential se…

Here's an example of how you might perform real-time analytics using Elasticsearch: ```python from elasticsearch import Elasticsearch es = Elasticsearch() def search_with_aggregation(es, index_name, query): # Create a new search quer…

Here is an example implementation that demonstrates how to integrate predictive pre-fetching into your current setup: #### Step 1: Historical Data Collection Collect historical query data and store it in a database or file. ```python imp…

from fastapi import FastAPI, Depends, HTTPException from pydantic import BaseModel from typing import List, Optional from sqlalchemy.orm import Session from fastapi_sqlalchemy import DBSessionMiddleware, db app = FastAPI() # Example in-me…

By following these steps, you can seamlessly integrate caching strategies with your existing FastAPI endpoints. This will help improve the performance and responsiveness of your hybrid search queries by leveraging in-memory caching with Red…

- **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. - …

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…

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…

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…

Here's an example implementation using FastAPI, Redis for caching, and a load balancer: ```python from fastapi import FastAPI, Depends, HTTPException, status from fastapi.security import OAuth2PasswordBearer from pydantic import BaseModel …

Here's an example implementation using FastAPI: ```python from fastapi import FastAPI, Depends, HTTPException, status from fastapi.security import OAuth2PasswordBearer from pydantic import BaseModel import requests from tenacity import ret…

def __init__(self, complexity_calculator: ComplexityCalculator, window_resizer: WindowResizer): self.complexity_calculator = complexity_calculator self.window_resizer = window_resizer self.uptime = 0.9985 de…

for epoch in range(100): for batch in data_loader: inputs = batch['query'].float().to(device) labels = batch['label'].long().to(device) optimizer.zero_grad() outputs = model(input…

optimizer = optim.Adam(model.parameters(), lr=0.001) # Using Adam optimizer scheduler = ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=5, verbose=True) scaler = GradScaler() try: for epoch in range(100): running…

'settings': { 'analysis': { 'analyzer': { 'synonym_analyzer': { 'type': 'custom', 'tokenizer': 'standard', 'filter': ['synonym_filter'] …

- **Cluster Size**: Aim for a minimum of 3-5 nodes for redundancy and load balancing. ### 2. **Index Settings** Optimize the index settings to reduce overhead and improve performance: - **Number of Shards**: Increase the number of shards …

def search_reformulated_query(query): return es.search(index="reformulated_queries", body={"query": {"match": {"query": query}}}) # Example usage: query = "This is a sample query" reformulated_query = "This is a reformulated query" ind…