Identify and avoid Exa anti-patterns and common integration mistakes. Use when reviewing Exa code, onboarding new developers, or auditing existing Exa integrations for correctness. Trigger with phrases like "exa mistakes", "exa anti-patterns", "exa pitfalls", "exa what not to do", "exa code review".
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Real gotchas when integrating Exa's neural search API. Exa uses embeddings-based search rather than keyword matching, which creates a different class of failure modes than traditional search APIs. This skill covers the top pitfalls with wrong/right examples.
Exa's neural search interprets natural language semantically. Boolean operators and keyword syntax degrade results.
import Exa from "exa-js";
const exa = new Exa(process.env.EXA_API_KEY);
// BAD: keyword/boolean style — Exa ignores AND/OR
const bad = await exa.search(
"python AND machine learning OR deep learning 2024"
);
// GOOD: natural language statement
const good = await exa.search(
"recent tutorials on building ML models with Python",
{ type: "neural", numResults: 10 }
);Using neural search for exact lookups (URLs, names) or keyword search for conceptual queries silently degrades quality.
// BAD: neural search for a specific URL/identifier
const bad = await exa.search("arxiv.org/abs/2301.00001", { type: "neural" });
// GOOD: keyword for exact terms, neural for concepts
const exactMatch = await exa.search("arxiv.org/abs/2301.00001", {
type: "keyword",
});
const conceptual = await exa.search(
"transformer architecture improvements for long context",
{ type: "neural" }
);search() returns metadata only (URL, title, score). Content requires searchAndContents() or getContents().
// BAD: accessing .text from search() — it's undefined
const results = await exa.search("AI safety research");
const text = results.results[0].text; // undefined!
// GOOD: use searchAndContents for text/highlights
const withContent = await exa.searchAndContents("AI safety research", {
numResults: 5,
text: { maxCharacters: 2000 },
highlights: { maxCharacters: 500 },
});
console.log(withContent.results[0].text); // actual content
console.log(withContent.results[0].highlights); // key excerptsDate filters silently exclude results. A single-day window often returns nothing without error.
// BAD: too narrow, likely returns empty array
const bad = await exa.search("AI news", {
startPublishedDate: "2025-03-15T00:00:00.000Z",
endPublishedDate: "2025-03-15T23:59:59.000Z",
});
// GOOD: reasonable window with fallback
let results = await exa.search("AI news", {
startPublishedDate: "2025-03-01T00:00:00.000Z",
endPublishedDate: "2025-03-31T23:59:59.000Z",
numResults: 10,
});
// Fallback if no results
if (results.results.length === 0) {
results = await exa.search("AI news", { numResults: 10 });
}findSimilar expects a URL as its first argument. Passing a query string gives meaningless results.
// BAD: passing a query string to findSimilar
const bad = await exa.findSimilar("machine learning research papers");
// GOOD: pass a URL — findSimilar finds pages semantically similar to it
const good = await exa.findSimilar("https://arxiv.org/abs/2301.00001", {
numResults: 10,
excludeSourceDomain: true,
});The company and people categories do NOT support date filters. Using them returns a 400 error.
// BAD: date filter with company category → 400 error
const bad = await exa.search("AI startups", {
category: "company",
startPublishedDate: "2024-01-01T00:00:00.000Z", // not supported!
});
// GOOD: company search without date filters
const good = await exa.search("AI startups", {
category: "company",
numResults: 10,
});Requesting full text without maxCharacters can return massive payloads, increasing latency and cost.
// BAD: unlimited text retrieval
const bad = await exa.searchAndContents("topic", {
numResults: 20,
text: true, // could return megabytes of content
});
// GOOD: limit content size
const good = await exa.searchAndContents("topic", {
numResults: 10,
text: { maxCharacters: 2000 }, // cap at 2000 chars per result
highlights: { maxCharacters: 500 },
});Each new Exa() call creates a new HTTP client. Reuse a singleton for connection pooling.
// BAD: new client every request (in a route handler)
app.get("/search", async (req, res) => {
const exa = new Exa(process.env.EXA_API_KEY); // wasteful!
const results = await exa.search(req.query.q);
res.json(results);
});
// GOOD: singleton client
const exa = new Exa(process.env.EXA_API_KEY);
app.get("/search", async (req, res) => {
const results = await exa.search(req.query.q);
res.json(results);
});Exa error responses include requestId for support debugging. Always log it.
// BAD: generic error handling
try {
await exa.search("query");
} catch (err) {
console.error("Search failed"); // loses diagnostic info
}
// GOOD: capture requestId
try {
await exa.search("query");
} catch (err: any) {
console.error("Search failed:", {
status: err.status,
message: err.message,
requestId: err.requestId, // include when contacting support
tag: err.error_tag,
});
}searchAndContents when page content is neededfindSimilar receives URLs, not query stringscompany or people categoriesmaxCharacters set on text and highlightsrequestIdFor SDK patterns, see exa-sdk-patterns. For common errors, see exa-common-errors.
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