exa-search

name	exa-search
description	Exa.ai search API integration for neural and keyword web search with content retrieval. Use when implementing web search features, integrating Exa SDK (exa_py, exa-js), or retrieving web content. Triggers on: Exa, exa_py, exa-js, neural search, web search API, search_and_contents, searchAndContents, find_similar, findSimilar, domain filtering, date filtering, text extraction, page summaries, highlights, search auto mode, fast search, search categories, livecrawl, excluding domains, include text, exclude text, EXA_API_KEY.
license	MIT
metadata	{"author":"ejirocodes","version":"1.0.0"}

Exa Search Integration

Quick Reference

Topic	When to Use	Reference
Search Modes	Choosing between auto, neural, and keyword search	search-modes.md
Filters	Domain, date, text, and category filtering	filters.md
Contents	Text extraction, highlights, summaries, livecrawl	contents.md
SDK Patterns	Python (exa_py) and TypeScript (exa-js) usage	sdk-patterns.md

Essential Patterns

Basic Search (Python)

from exa_py import Exa

exa = Exa(api_key="your-api-key")  # or set EXA_API_KEY env var

results = exa.search_and_contents(
    "latest developments in quantum computing",
    type="auto",
    num_results=10,
    text=True,
    highlights=True
)

for result in results.results:
    print(f"{result.title}: {result.url}")
    print(result.text[:500])

Basic Search (TypeScript)

import Exa from "exa-js";

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

const results = await exa.searchAndContents(
  "latest developments in quantum computing",
  {
    type: "auto",
    numResults: 10,
    text: true,
    highlights: true,
  }
);

results.results.forEach((result) => {
  console.log(`${result.title}: ${result.url}`);
});

Search with Filters

results = exa.search_and_contents(
    "AI startup funding rounds",
    type="neural",
    num_results=10,
    include_domains=["techcrunch.com", "venturebeat.com"],
    start_published_date="2024-01-01",
    text={"max_characters": 2000},
    summary=True
)

Find Similar Links

similar = exa.find_similar_and_contents(
    "https://example.com/interesting-article",
    num_results=10,
    exclude_source_domain=True,
    text=True
)

Search Mode Selection

Mode	When to Use	Notes
auto	Default for most queries	Exa optimizes between neural/keyword automatically
neural	Natural language, conceptual queries	Best for "what is...", "how to...", topic exploration
keyword	Exact matches, technical terms, names	Best for specific product names, error codes, proper nouns

Common Mistakes

1. Using keyword for conceptual queries - Neural search understands intent better; use auto or neural for natural language questions
2. Not setting text=True - Search returns URLs only by default; explicitly request content with text=True
3. Ignoring highlights - Use highlights=True for relevant snippets without downloading full page text
4. Missing API key - Set EXA_API_KEY environment variable or pass explicitly to constructor
5. Over-filtering initially - Start with broad searches, then add domain/date filters to refine
6. Not using summary - For RAG applications, summary=True provides concise context without full page text
7. Expecting scores in auto mode - Relevance scores are only returned with type="neural"; auto mode doesn't include them