Exa Core Workflow A — Neural Search
Overview
Primary workflow for Exa: semantic web search using search() and searchAndContents(). Exa's neural search understands query meaning rather than matching keywords, making it ideal for research, RAG pipelines, and content discovery. This skill covers search types, content extraction, filtering, and categories.
Prerequisites
exa-js installed and EXA_API_KEY configured- Understanding of neural vs keyword search tradeoffs
Search Types
| Type | Latency | Best For |
|---|
auto (default) | 300-1500ms | General queries; Exa picks best approach |
neural | 500-2000ms | Conceptual/semantic queries |
keyword | 200-500ms | Exact terms, names, URLs |
fast | p50 < 425ms | Speed-critical applications |
instant | < 150ms | Real-time autocomplete |
deep | 2-5s | Maximum quality, light deep search |
deep-reasoning | 5-15s | Complex research questions |
Instructions
Step 1: Basic Neural Search
import Exa from "exa-js";
const exa = new Exa(process.env.EXA_API_KEY);
const results = await exa.search(
"comprehensive guide to building production RAG systems",
{
type: "neural",
numResults: 10,
}
);
for (const r of results.results) {
console.log(`[${r.score.toFixed(2)}] ${r.title} — ${r.url}`);
console.log(` Published: ${r.publishedDate || "unknown"}`);
}
Step 2: Search with Content Extraction
const results = await exa.searchAndContents(
"best practices for vector database selection",
{
type: "auto",
numResults: 5,
text: { maxCharacters: 2000 },
highlights: {
maxCharacters: 500,
query: "comparison of vector databases",
},
summary: { query: "which vector database should I choose?" },
}
);
for (const r of results.results) {
console.log(`## ${r.title}`);
console.log(`Summary: ${r.summary}`);
console.log(`Highlights: ${r.highlights?.join(" ... ")}`);
console.log(`Full text: ${r.text?.substring(0, 300)}...`);
}
Step 3: Date and Domain Filtering
const results = await exa.searchAndContents(
"TypeScript 5.5 new features",
{
type: "auto",
numResults: 10,
startPublishedDate: "2024-06-01T00:00:00.000Z",
endPublishedDate: "2025-01-01T00:00:00.000Z",
includeDomains: ["devblogs.microsoft.com", "typescriptlang.org"],
includeText: ["TypeScript"],
text: true,
}
);
Step 4: Category-Scoped Search
const papers = await exa.searchAndContents(
"attention mechanism improvements for long context LLMs",
{
type: "neural",
numResults: 10,
category: "research paper",
text: { maxCharacters: 3000 },
highlights: true,
}
);
const companies = await exa.search(
"AI infrastructure startup founded 2024",
{
type: "auto",
numResults: 10,
category: "company",
}
);
Step 5: Content Freshness with LiveCrawl
const results = await exa.searchAndContents(
"latest AI model releases this week",
{
numResults: 5,
text: { maxCharacters: 1500 },
livecrawl: "preferred",
livecrawlTimeout: 10000,
}
);
Output
- Ranked search results with URLs, titles, scores, and published dates
- Optional text content, highlights, and summaries per result
- Results filtered by date range, domains, categories, and text content
Error Handling
| Error | HTTP Code | Cause | Solution |
|---|
INVALID_REQUEST_BODY | 400 | Invalid parameter types | Check query is string, numResults is integer |
INVALID_NUM_RESULTS | 400 | numResults > 100 with highlights | Reduce numResults or remove highlights |
| Empty results array | 200 | Date filter too narrow | Widen date range or remove filter |
| Low relevance scores | 200 | Keyword-style query | Rephrase as natural language statement |
FETCH_DOCUMENT_ERROR | 422 | URL content unretrievable | Use livecrawl: "fallback" or try without text |
Examples
RAG Context Retrieval
async function getRAGContext(question: string, maxResults = 5) {
const results = await exa.searchAndContents(question, {
type: "neural",
numResults: maxResults,
text: { maxCharacters: 2000 },
highlights: { maxCharacters: 500, query: question },
});
return results.results.map((r, i) => ({
source: `[${i + 1}] ${r.title} (${r.url})`,
content: r.text,
highlights: r.highlights,
}));
}
Resources
Next Steps
For similarity search and advanced retrieval, see exa-core-workflow-b.
