Integration: Exa
Search the web with Exa's AI-powered search, get content, answers, and conduct deep research
Authors
Exa
Overview
The Exa integration for Haystack provides components to search the web, fetch content, get AI-powered answers, and conduct deep research using Exa’s API.
Installation
pip install exa-haystack
Components
- ExaWebSearch: AI-powered web search with multiple speed/quality modes (
auto,instant,fast,deep,deep-reasoning,deep-max,neural) - ExaFindSimilar: Find pages similar to a URL
- ExaContents: Fetch full content for URLs with freshness control
- ExaAnswer: Get AI-powered answers with citations and optional structured output
- ExaStreamAnswer: Streaming answers with SSE and optional structured output
- ExaResearch: Deep research with automatic source gathering
Usage
Web Search
from haystack_integrations.components.websearch.exa import ExaWebSearch
search = ExaWebSearch(num_results=5, type="auto", text=True)
results = search.run(query="latest AI developments")
for doc in results["documents"]:
print(doc.meta["title"], doc.meta["url"])
Use type="instant" for sub-150ms searches, type="deep" or type="deep-reasoning" for higher-quality results, or type="auto" (default) to let Exa choose.
Category Filtering
search = ExaWebSearch(num_results=5, category="research paper")
results = search.run(query="transformer architectures")
Available categories: company, research paper, news, pdf, tweet, personal site, financial report, people.
Content Freshness
search = ExaWebSearch(num_results=5, max_age_hours=24, text=True)
results = search.run(query="breaking news today")
Use max_age_hours to control content freshness: 0 = always livecrawl, -1 = cache only, positive integer = max cache age in hours.
Structured Deep Search Output
search = ExaWebSearch(
type="deep",
output_schema={"type": "object", "properties": {"summary": {"type": "string"}}},
)
results = search.run(query="AI in healthcare")
print(results["deep_output"]) # Structured output from deep search
Find Similar Pages
from haystack_integrations.components.websearch.exa import ExaFindSimilar
similar = ExaFindSimilar(num_results=5)
results = similar.run(url="https://example.com/article")
Fetch Content from URLs
from haystack_integrations.components.websearch.exa import ExaContents
contents = ExaContents(text=True, max_age_hours=0)
results = contents.run(urls=["https://example.com/page1", "https://example.com/page2"])
for doc in results["documents"]:
print(doc.meta["title"], len(doc.content), "chars")
# Per-URL status info
for status in results["statuses"]:
print(status["id"], status["status"])
AI-Powered Answers
from haystack_integrations.components.websearch.exa import ExaAnswer
answer = ExaAnswer()
result = answer.run(query="What is retrieval augmented generation?")
print(result["answer"])
for citation in result["citations"]:
print(citation.meta["title"], citation.meta["url"])
Structured Answer Output
answer = ExaAnswer(
output_schema={
"type": "object",
"properties": {
"summary": {"type": "string"},
"key_points": {"type": "array", "items": {"type": "string"}},
},
}
)
result = answer.run(query="What is RAG?")
Streaming Answers
from haystack_integrations.components.websearch.exa import ExaStreamAnswer
stream = ExaStreamAnswer()
result = stream.run(query="Explain quantum computing")
for chunk in result["stream"]:
print(chunk, end="", flush=True)
Deep Research
from haystack_integrations.components.websearch.exa import ExaResearch
research = ExaResearch()
result = research.run(query="Impact of AI on healthcare")
print(result["report"])
Authentication
Set your Exa API key as an environment variable:
export EXA_API_KEY="your-api-key"
Or pass it directly:
from haystack.utils import Secret
search = ExaWebSearch(api_key=Secret.from_token("your-api-key"))