Exa Research & Answer API

Quick Reference

Topic When to Use Reference

Answer API Q&A with citations, grounded responses answer-api.md

Deep Search Smart query expansion, high-quality summaries deep-search.md

Citations Source attribution, verification citations.md

Essential Patterns

Answer API (Python)

from exa_py import Exa

exa = Exa()

response = exa.answer( "What are the key features of Python 3.12?", text=True )

print(response.answer) for citation in response.citations: print(f"Source: {citation.url}")

Streaming Answers

stream = exa.answer( "Explain the benefits of microservices architecture", stream=True )

for chunk in stream: print(chunk.text, end="", flush=True)

Access citations after streaming

print("\nSources:", stream.citations)

Deep Search with Summaries

results = exa.search_and_contents( "latest developments in quantum computing", type="neural", num_results=10, summary=True, use_autoprompt=True # Smart query expansion )

for result in results.results: print(f"{result.title}") print(f"Summary: {result.summary}")

When to Use

Feature Use Case Output

Answer API Direct Q&A needing citations Answer + source URLs

Deep Search Query expansion + summaries Enhanced search results

Exa Research Long-form async reports Structured JSON/Markdown

Common Mistakes

• Not using streaming for long answers - Use stream=True for better UX on complex questions

• Ignoring citations - Always include response.citations for verifiable responses

• Missing text=True

• Answer API needs content access; include text=True

• Over-complex queries - Answer API works best with clear, focused questions

• Not validating citations - Check citation.url exists before displaying to users