docs: update Exa integration for v1.1.0 - new search types, parameters, and examples (#417)

Author: 10ishq

integrations/exa.md

@@ -28,11 +28,11 @@ pip install exa-haystack
 
 ### Components
 
-- **ExaWebSearch**: AI-powered web search (auto, fast, deep modes)
+- **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
-- **ExaAnswer**: Get AI-powered answers with citations
-- **ExaStreamAnswer**: Streaming answers with SSE
+- **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
@@ -42,8 +42,42 @@ pip install exa-haystack
 ```python
 from haystack_integrations.components.websearch.exa import ExaWebSearch
 
-search = ExaWebSearch(num_results=5)
+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
+
+```python
+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
+
+```python
+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
+
+```python
+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
@@ -60,8 +94,15 @@ results = similar.run(url="https://example.com/article")
 ```python
 from haystack_integrations.components.websearch.exa import ExaContents
 
-contents = 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
@@ -72,6 +113,24 @@ 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
+
+```python
+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
@@ -80,7 +139,8 @@ print(result["answer"])
 from haystack_integrations.components.websearch.exa import ExaStreamAnswer
 
 stream = ExaStreamAnswer()
-for chunk in stream.run(query="Explain quantum computing"):
+result = stream.run(query="Explain quantum computing")
+for chunk in result["stream"]:
     print(chunk, end="", flush=True)
 ```