Exa Performance Tuning

Exa Performance Tuning

Overview

Optimize Exa AI search API response times and throughput for production RAG pipelines and search integrations. Exa search latency varies by type: keyword search (200-500ms), neural search (500-2000ms), and auto mode (300-1500ms).

Prerequisites

• Exa API integration (exa-js SDK or REST API)
• Cache infrastructure (Redis or in-memory LRU)
• Understanding of search patterns in your application

Instructions

Step 1: Choose Search Type by Latency Requirement

import Exa from 'exa-js';

// Match search type to latency budget
function optimizedSearch(exa: Exa, query: string, latencyBudgetMs: number) {
  if (latencyBudgetMs < 500) {  # HTTP 500 Internal Server Error
    // Fast path: keyword search for structured/exact queries
    return exa.search(query, { type: 'keyword', numResults: 3 });
  } else if (latencyBudgetMs < 1500) {  # 1500 = configured value
    // Balanced: auto mode picks best approach
    return exa.search(query, { type: 'auto', numResults: 5 });
  } else {
    // Quality: neural search for semantic understanding
    return exa.search(query, { type: 'neural', numResults: 10 });
  }
}

Step 2: Cache Search Results

import { LRUCache } from 'lru-cache';

const searchCache = new LRUCache<string, any>({
  max: 10000,  # 10000: 10 seconds in ms
  ttl: 2 * 3600_000, // 2-hour TTL for most searches
});

async function cachedSearch(exa: Exa, query: string, options: any) {
  const key = `${query}:${options.type}:${options.numResults}`;
  const cached = searchCache.get(key);
  if (cached) return cached; // Cache hit: 0ms vs 500-2000ms  # HTTP 500 Internal Server Error

  const results = await exa.search(query, options);
  searchCache.set(key, results);
  return results;
}

Step 3: Minimize Result Count

// Each additional result adds latency (content retrieval)
const RESULT_CONFIGS: Record<string, number> = {
  'rag-context':     3,   // Only need top 3 for RAG
  'autocomplete':    5,   // Quick suggestions
  'deep-research':  10,   // Comprehensive coverage
};

// Don't default to numResults: 10 when 3 suffices
// Reducing from 10 to 3 results saves ~200-500ms per search  # HTTP 200 OK

Step 4: Parallelize Independent Searches

// When RAG needs multiple search contexts, run them in parallel
async function parallelContextSearch(exa: Exa, queries: string[]) {
  const searches = queries.map(q =>
    cachedSearch(exa, q, { type: 'auto', numResults: 3 })
  );
  return Promise.all(searches);
  // 3 parallel searches: ~600ms total
  // 3 sequential searches: ~1800ms total
}

Step 5: Use Content Retrieval Selectively

// The /get-contents endpoint is separate from search
// Only fetch full content for results you'll actually use
async function searchThenFetch(exa: Exa, query: string) {
  // Step 1: Fast search for URLs only
  const results = await exa.search(query, { type: 'auto', numResults: 5 });

  // Step 2: Only fetch content for top 2 results
  const topUrls = results.results.slice(0, 2).map(r => r.url);
  const contents = await exa.getContents(topUrls, { text: { maxCharacters: 2000 } });  # 2000: 2 seconds in ms

  return contents;
}
// Saves content retrieval time for 3 results you won't use

Error Handling

Examples

Basic usage: Apply exa performance tuning to a standard project setup with default configuration options.

Advanced scenario: Customize exa performance tuning for production environments with multiple constraints and team-specific requirements.

Output

• Configuration files or code changes applied to the project
• Validation report confirming correct implementation
• Summary of changes made and their rationale

Resources

• Official ORM documentation
• Community best practices and patterns
• Related skills in this plugin pack