exa-performance-tuning

•

1.9k2643 months ago

Optimize Exa API performance with caching, batching, and connection pooling. Use when experiencing slow API responses, implementing caching strategies, or optimizing request throughput for Exa integrations. Trigger with phrases like "exa performance", "optimize exa", "exa latency", "exa caching", "exa slow", "exa batch".

Quick Install

npxskills add jeremylongshore/claude-code-plugins-plus-skills--skill exa-performance-tuning

Instructions

Exa Performance Tuning

Overview

Optimize Exa search API response times for production workloads. Key levers: search type selection (instant < fast < auto < neural < deep), result count reduction, content scope control, result caching, and parallel query execution.

Latency by Search Type

Type	Typical Latency	Use Case
`instant`	< 150ms	Real-time autocomplete, typeahead
`fast`	p50 < 425ms	Speed-critical user-facing search
`auto`	300-1500ms	General purpose (default)
`neural`	500-2000ms	Best semantic quality

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Tags & Topics

aiautomationclaude-codedevopsmcpai-agents

deep

import Exa from "exa-js";

const exa = new Exa(process.env.EXA_API_KEY);

function selectSearchType(latencyBudgetMs: number) {
  if (latencyBudgetMs < 200) return "instant";
  if (latencyBudgetMs < 500) return "fast";
  if (latencyBudgetMs < 1500) return "auto";
  if (latencyBudgetMs < 3000) return "neural";
  return "deep";
}

async function optimizedSearch(query: string, latencyBudgetMs: number) {
  const type = selectSearchType(latencyBudgetMs);
  const numResults = latencyBudgetMs < 500 ? 3 : latencyBudgetMs < 2000 ? 5 : 10;

  return exa.search(query, { type, numResults });
}

// Each content option adds latency. Only request what you need.

// Fastest: metadata only (no content retrieval)
const metadataOnly = await exa.search("query", { numResults: 5 });

// Medium: highlights only (much smaller than full text)
const highlightsOnly = await exa.searchAndContents("query", {
  numResults: 5,
  highlights: { maxCharacters: 300 },
  // No text or summary — saves content retrieval time
});

// Slower: full text (use maxCharacters to limit)
const withText = await exa.searchAndContents("query", {
  numResults: 3,  // fewer results = faster
  text: { maxCharacters: 1000 },  // limit content size
});

import { LRUCache } from "lru-cache";

const searchCache = new LRUCache<string, any>({
  max: 5000,
  ttl: 2 * 3600 * 1000, // 2-hour TTL
});

async function cachedSearch(query: string, opts: any) {
  const key = `${query}:${opts.type || "auto"}:${opts.numResults || 10}`;
  const cached = searchCache.get(key);
  if (cached) return cached; // Cache hit: 0ms vs 500-2000ms

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

// Run independent queries concurrently instead of sequentially
async function parallelSearch(queries: string[]) {
  const searches = queries.map(q =>
    cachedSearch(q, { type: "auto", numResults: 3 })
  );
  return Promise.all(searches);
  // 3 parallel searches: ~600ms total (limited by slowest)
  // 3 sequential searches: ~1800ms total
}

// Phase 1: Fast search for URLs only
// Phase 2: Selective content retrieval for top results only
async function twoPhaseSearch(query: string) {
  // Phase 1: metadata only (fast)
  const results = await exa.search(query, { type: "auto", numResults: 10 });

  // Phase 2: get content only for top 3 results
  const topUrls = results.results.slice(0, 3).map(r => r.url);
  const contents = await exa.getContents(topUrls, {
    text: { maxCharacters: 2000 },
    highlights: { maxCharacters: 500, query },
  });

  return contents;
  // Saves content retrieval time for 7 results you won't use
}

function normalizeQuery(query: string): string {
  return query
    .toLowerCase()
    .trim()
    .replace(/\s+/g, " ")       // collapse whitespace
    .replace(/[?.!,;:]+$/, ""); // strip trailing punctuation
}

async function normalizedSearch(query: string, opts: any) {
  return cachedSearch(normalizeQuery(query), opts);
}
// Increases cache hit rate by 20-40% for user-generated queries

Strategy	Latency Savings	Implementation
`instant` type	5-10x faster than neural	One-line change
Reduce numResults (10 -> 3)	~200-500ms saved	One-line change
Highlights instead of text	~100-300ms saved	Replace `text` with `highlights`
LRU cache	100% for cache hits	~20 lines
Parallel queries	2-3x throughput	`Promise.all` wrapper
Two-phase search	~30-50% for large result sets	~15 lines

Issue	Cause	Solution
Search taking 3s+	Neural search on complex query	Switch to `fast` or `auto` type
Timeout on content	Large pages, slow sources	Set `maxCharacters` limit
Cache miss rate high	Unique queries each time	Normalize queries before caching
Rate limit (429)	Too many concurrent searches	Add request queue with concurrency limit

exa-performance-tuning

Exa Performance Tuning

Overview

Latency by Search Type

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Exa Performance Tuning

Overview

Latency by Search Type

Instructions

Step 1: Match Search Type to Latency Budget

Step 2: Minimize Content Retrieval

Step 3: Cache Search Results

Step 4: Parallelize Independent Searches

Step 5: Two-Phase Search Pattern

Step 6: Query Normalization for Cache Hits

Performance Comparison

Error Handling

Resources

Next Steps

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