Extract

response = client.search(
    query="AI healthcare applications",
    include_raw_content=True,
    max_results=5
)

# Step 1: Search
search_results = client.search(
    query="Python async best practices",
    max_results=10
)

# Step 2: Filter by relevance score
relevant_urls = [
    r["url"] for r in search_results["results"]
    if r["score"] > 0.5
]

# Step 3: Extract with targeting
extracted = client.extract(
    urls=relevant_urls[:20],
    query="async patterns and concurrency",  # Reranks chunks
    chunks_per_source=3  # Prevents context explosion
)

for item in extracted["results"]:
    print(f"URL: {item['url']}")
    print(f"Content: {item['raw_content'][:500]}...")

extracted = client.extract(
    urls=[
        "https://example.com/ml-healthcare",
        "https://example.com/ai-diagnostics",
        "https://example.com/medical-ai"
    ],
    query="AI diagnostic tools accuracy",
    chunks_per_source=2  # 2 most relevant chunks per URL
)

# For complex pages
extracted = client.extract(
    urls=["https://example.com/complex-page"],
    extract_depth="advanced"
)

result = client.extract(urls=[url], extract_depth="basic")
if url in [f["url"] for f in result.get("failed_results", [])]:
    result = client.extract(urls=[url], extract_depth="advanced")

import asyncio
from tavily import AsyncTavilyClient

client = AsyncTavilyClient()

async def content_pipeline(topic):
    # 1. Search with sub-queries for breadth
    queries = [
        f"{topic} overview",
        f"{topic} best practices",
        f"{topic} recent developments"
    ]
    responses = await asyncio.gather(
        *(client.search(q, search_depth="advanced", max_results=10) for q in queries)
    )

    # 2. Filter and aggregate by score
    urls = []
    for response in responses:
        urls.extend([
            r['url'] for r in response['results']
            if r['score'] > 0.5
        ])

    # 3. Deduplicate
    urls = list(set(urls))[:20]

    # 4. Extract with error handling
    extracted = await asyncio.gather(
        *(client.extract(urls=[url], query=topic, extract_depth="advanced")
          for url in urls),
        return_exceptions=True
    )

    # 5. Filter successful extractions
    return [e for e in extracted if not isinstance(e, Exception)]

asyncio.run(content_pipeline("machine learning in healthcare"))