chore: import upstream snapshot with attribution

docs-website/reference_versioned_docs/version-2.19/integrations-api/tavily.md

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+---
+title: "Tavily"
+id: integrations-tavily
+description: "Tavily integration for Haystack"
+slug: "/integrations-tavily"
+---
+
+
+## haystack_integrations.components.websearch.tavily.tavily_websearch
+
+### TavilyWebSearch
+
+A component that uses Tavily to search the web and return results as Haystack Documents.
+
+This component wraps the Tavily Search API, enabling web search queries that return
+structured documents with content and links.
+
+Tavily is an AI-powered search API optimized for LLM applications. You need a Tavily
+API key from [tavily.com](https://tavily.com).
+
+### Usage example
+
+```python
+from haystack_integrations.components.websearch.tavily import TavilyWebSearch
+from haystack.utils import Secret
+
+websearch = TavilyWebSearch(
+    api_key=Secret.from_env_var("TAVILY_API_KEY"),
+    top_k=5,
+)
+result = websearch.run(query="What is Haystack by deepset?")
+documents = result["documents"]
+links = result["links"]
+```
+
+#### __init__
+
+```python
+__init__(
+    api_key: Secret = Secret.from_env_var("TAVILY_API_KEY"),
+    top_k: int | None = 10,
+    search_params: dict[str, Any] | None = None,
+) -> None
+```
+
+Initialize the TavilyWebSearch component.
+
+**Parameters:**
+
+- **api_key** (<code>Secret</code>) – API key for Tavily. Defaults to the `TAVILY_API_KEY` environment variable.
+- **top_k** (<code>int | None</code>) – Maximum number of results to return.
+- **search_params** (<code>dict\[str, Any\] | None</code>) – Additional parameters passed to the Tavily search API.
+  See the [Tavily API reference](https://docs.tavily.com/docs/tavily-api/rest_api)
+  for available options. Supported keys include: `search_depth`, `include_answer`,
+  `include_raw_content`, `include_domains`, `exclude_domains`.
+
+#### warm_up
+
+```python
+warm_up() -> None
+```
+
+Initialize the Tavily sync and async clients.
+
+Called automatically on first use. Can be called explicitly to avoid cold-start latency.
+
+#### run
+
+```python
+run(query: str, search_params: dict[str, Any] | None = None) -> dict[str, Any]
+```
+
+Search the web using Tavily and return results as Documents.
+
+**Parameters:**
+
+- **query** (<code>str</code>) – Search query string.
+- **search_params** (<code>dict\[str, Any\] | None</code>) – Optional per-run override of search parameters.
+  If provided, fully replaces the init-time `search_params`.
+
+**Returns:**
+
+- <code>dict\[str, Any\]</code> – A dictionary with:
+- `documents`: List of Documents containing search result content.
+- `links`: List of URLs from the search results.
+
+#### run_async
+
+```python
+run_async(
+    query: str, search_params: dict[str, Any] | None = None
+) -> dict[str, Any]
+```
+
+Asynchronously search the web using Tavily and return results as Documents.
+
+**Parameters:**
+
+- **query** (<code>str</code>) – Search query string.
+- **search_params** (<code>dict\[str, Any\] | None</code>) – Optional per-run override of search parameters.
+  If provided, fully replaces the init-time `search_params`.
+
+**Returns:**
+
+- <code>dict\[str, Any\]</code> – A dictionary with:
+- `documents`: List of Documents containing search result content.
+- `links`: List of URLs from the search results.