chore: import upstream snapshot with attribution

docs-website/versioned_docs/version-2.26/pipeline-components/websearch/external-integrations-websearch.mdx

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+---
+title: "External Integrations"
+id: external-integrations-websearch
+slug: "/external-integrations-websearch"
+description: "External integrations that enable websearch with Haystack."
+---
+
+# External Integrations
+
+External integrations that enable websearch with Haystack.
+
+| Name | Description |
+| --- | --- |
+| [DuckDuckGo](https://haystack.deepset.ai/integrations/duckduckgo-api-websearch) | Use DuckDuckGo API for web searches. |
+| [Exa](https://haystack.deepset.ai/integrations/exa) | Search the web with Exa's AI-powered search, get content, answers, and conduct deep research. |
+| [Serpex](https://haystack.deepset.ai/integrations/serpex) | Multi-engine web search for Haystack — access Google, Bing, DuckDuckGo, Brave, Yahoo, and Yandex via Serpex API. |

docs-website/versioned_docs/version-2.26/pipeline-components/websearch/firecrawlwebsearch.mdx

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+---
+title: "FirecrawlWebSearch"
+id: firecrawlwebsearch
+slug: "/firecrawlwebsearch"
+description: "Search engine using the Firecrawl API."
+---
+
+# FirecrawlWebSearch
+
+Search the web and extract content using the Firecrawl API.
+
+<div className="key-value-table">
+
+|  |  |
+| --- | --- |
+| **Most common position in a pipeline** | Before a [`ChatPromptBuilder`](../builders/chatpromptbuilder.mdx) or right at the beginning of an indexing pipeline. |
+| **Mandatory init variables** | `api_key`: The Firecrawl API key. Can be set with the `FIRECRAWL_API_KEY` env var. |
+| **Mandatory run variables** | `query`: A string with your search query. |
+| **Output variables** | `documents`: A list of Haystack Documents containing the scraped content and metadata. <br /> <br />`links`: A list of strings of resulting URLs. |
+| **API reference** | [Firecrawl Search API](/reference/integrations-firecrawl) |
+| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/blob/main/integrations/firecrawl/src/haystack_integrations/components/websearch/firecrawl/firecrawl_websearch.py |
+
+</div>
+
+## Overview
+
+When you give `FirecrawlWebSearch` a query, it uses the Firecrawl Search API to search the web, crawl the resulting pages, and return the structured text as a list of Haystack `Document` objects. It also returns a list of the underlying URLs.
+
+Because Firecrawl actively scrapes and structures the content of the pages it finds into LLM-friendly formats, you generally don't need an additional component like `LinkContentFetcher` to read the web pages. `FirecrawlWebSearch` handles the retrieval and scraping all in one step.
+
+`FirecrawlWebSearch` requires a [Firecrawl](https://firecrawl.dev) API key to work. By default, it looks for a `FIRECRAWL_API_KEY` environment variable. Alternatively, you can pass an `api_key` directly during initialization.
+
+## Usage
+
+### On its own
+
+Here is a quick example of how `FirecrawlWebSearch` searches the web based on a query, scrapes the resulting web pages, and returns a list of Documents containing the page content.
+
+```python
+from haystack_integrations.components.websearch.firecrawl import FirecrawlWebSearch
+from haystack.utils import Secret
+
+web_search = FirecrawlWebSearch(
+    api_key=Secret.from_env_var("FIRECRAWL_API_KEY"),
+    top_k=5,
+    search_params={"scrape_options": {"formats": ["markdown"]}},
+)
+query = "What is Haystack by deepset?"
+
+response = web_search.run(query=query)
+
+for doc in response["documents"]:
+    print(doc.content)
+```
+
+### In a pipeline
+
+Here is an example of a Retrieval-Augmented Generation (RAG) pipeline where using `FirecrawlWebSearch` to look up an answer. Because Firecrawl returns the actual text of the scraped pages, you can pass its `documents` output directly into the `ChatPromptBuilder` to give the LLM the necessary context.
+
+```python
+from haystack import Pipeline
+from haystack.utils import Secret
+from haystack.components.builders.chat_prompt_builder import ChatPromptBuilder
+from haystack.components.generators.chat import OpenAIChatGenerator
+from haystack_integrations.components.websearch.firecrawl import FirecrawlWebSearch
+from haystack.dataclasses import ChatMessage
+
+web_search = FirecrawlWebSearch(
+    api_key=Secret.from_env_var("FIRECRAWL_API_KEY"),
+    top_k=2,
+    search_params={"scrape_options": {"formats": ["markdown"]}},
+)
+
+prompt_template = [
+    ChatMessage.from_system("You are a helpful assistant."),
+    ChatMessage.from_user(
+        "Given the information below:\n"
+        "{% for document in documents %}{{ document.content }}\n{% endfor %}\n"
+        "Answer the following question: {{ query }}.\nAnswer:",
+    ),
+]
+
+prompt_builder = ChatPromptBuilder(
+    template=prompt_template,
+    required_variables={"query", "documents"},
+)
+
+llm = OpenAIChatGenerator(
+    api_key=Secret.from_env_var("OPENAI_API_KEY"),
+    model="gpt-5-nano",
+)
+
+pipe = Pipeline()
+pipe.add_component("search", web_search)
+pipe.add_component("prompt_builder", prompt_builder)
+pipe.add_component("llm", llm)
+
+pipe.connect("search.documents", "prompt_builder.documents")
+pipe.connect("prompt_builder.prompt", "llm.messages")
+
+query = "What is Haystack by deepset?"
+
+result = pipe.run(data={"search": {"query": query}, "prompt_builder": {"query": query}})
+
+print(result["llm"]["replies"][0].text)
+```

docs-website/versioned_docs/version-2.26/pipeline-components/websearch/searchapiwebsearch.mdx

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+---
+title: "SearchApiWebSearch"
+id: searchapiwebsearch
+slug: "/searchapiwebsearch"
+description: "Search engine using Search API."
+---
+
+# SearchApiWebSearch
+
+Search engine using Search API.
+
+<div className="key-value-table">
+
+|  |  |
+| --- | --- |
+| **Most common position in a pipeline** | Before [`LinkContentFetcher`](../fetchers/linkcontentfetcher.mdx)  or [Converters](../converters.mdx) |
+| **Mandatory init variables** | `api_key`: The SearchAPI API key. Can be set with `SEARCHAPI_API_KEY` env var. |
+| **Mandatory run variables** | `query`: A string with your query |
+| **Output variables** | `documents`: A list of documents  <br /> <br />`links`: A list of strings of resulting links |
+| **API reference** | [Websearch](/reference/websearch-api) |
+| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/websearch/searchapi.py |
+
+</div>
+
+## Overview
+
+When you give `SearchApiWebSearch` a query, it returns a list of the URLs most relevant to your search. It uses page snippets (pieces of text displayed under the page title in search results) to find the answers, not the whole pages.
+
+To search the content of the web pages, use the [`LinkContentFetcher`](../fetchers/linkcontentfetcher.mdx) component.
+
+`SearchApiWebSearch` requires a [SearchApi](https://www.searchapi.io) key to work. It uses a `SEARCHAPI_API_KEY` environment variable by default. Otherwise, you can pass an `api_key` at initialization – see code examples below.
+
+:::info[Alternative search]
+
+To use [Serper Dev](https://serper.dev/?gclid=Cj0KCQiAgqGrBhDtARIsAM5s0_kPElllv3M59UPok1Ad-ZNudLaY21zDvbt5qw-b78OcUoqqvplVHRwaAgRgEALw_wcB) as an alternative, see its respective [documentation page](serperdevwebsearch.mdx).
+:::
+
+## Usage
+
+### On its own
+
+This is an example of how `SearchApiWebSearch` looks up answers to our query on the web and converts the results into a list of documents with content snippets of the results, as well as URLs as strings.
+
+```python
+from haystack.components.websearch import SearchApiWebSearch
+
+web_search = SearchApiWebSearch(api_key=Secret.from_token("<your-api-key>"))
+query = "What is the capital of Germany?"
+
+response = web_search.run(query)
+```
+
+### In a pipeline
+
+Here’s an example of a RAG pipeline where we use a `SearchApiWebSearch` to look up the answer to the query. The resulting documents are then passed to `LinkContentFetcher` to get the full text from the URLs. Finally, `PromptBuilder` and `OpenAIGenerator` work together to form the final answer.
+
+```python
+from haystack import Pipeline
+from haystack.utils import Secret
+from haystack.components.builders.chat_prompt_builder import ChatPromptBuilder
+from haystack.components.fetchers import LinkContentFetcher
+from haystack.components.converters import HTMLToDocument
+from haystack.components.generators.chat import OpenAIChatGenerator
+from haystack.components.websearch import SearchApiWebSearch
+from haystack.dataclasses import ChatMessage
+
+web_search = SearchApiWebSearch(api_key=Secret.from_token("<your-api-key>"), top_k=2)
+link_content = LinkContentFetcher()
+html_converter = HTMLToDocument()
+
+prompt_template = [
+    ChatMessage.from_system("You are a helpful assistant."),
+    ChatMessage.from_user(
+        "Given the information below:\n"
+        "{% for document in documents %}{{ document.content }}{% endfor %}\n"
+        "Answer question: {{ query }}.\nAnswer:",
+    ),
+]
+
+prompt_builder = ChatPromptBuilder(
+    template=prompt_template,
+    required_variables={"query", "documents"},
+)
+llm = OpenAIChatGenerator(
+    api_key=Secret.from_token("<your-api-key>"),
+)
+
+pipe = Pipeline()
+pipe.add_component("search", web_search)
+pipe.add_component("fetcher", link_content)
+pipe.add_component("converter", html_converter)
+pipe.add_component("prompt_builder", prompt_builder)
+pipe.add_component("llm", llm)
+
+pipe.connect("search.links", "fetcher.urls")
+pipe.connect("fetcher.streams", "converter.sources")
+pipe.connect("converter.documents", "prompt_builder.documents")
+pipe.connect("prompt_builder.messages", "llm.messages")
+
+query = "What is the most famous landmark in Berlin?"
+
+pipe.run(data={"search": {"query": query}, "prompt_builder": {"query": query}})
+```

docs-website/versioned_docs/version-2.26/pipeline-components/websearch/serperdevwebsearch.mdx

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+---
+title: "SerperDevWebSearch"
+id: serperdevwebsearch
+slug: "/serperdevwebsearch"
+description: "Search engine using SerperDev API."
+---
+
+# SerperDevWebSearch
+
+Search engine using SerperDev API.
+
+<div className="key-value-table">
+
+|  |  |
+| --- | --- |
+| **Most common position in a pipeline** | Before [`LinkContentFetcher`](../fetchers/linkcontentfetcher.mdx)  or [Converters](../converters.mdx) |
+| **Mandatory init variables** | `api_key`: The SearchAPI API key. Can be set with `SERPERDEV_API_KEY` env var. |
+| **Mandatory run variables** | `query`: A string with your query |
+| **Output variables** | `documents`: A list of documents  <br /> <br />`links`: A list of strings of resulting links |
+| **API reference** | [Websearch](/reference/websearch-api) |
+| **GitHub link** | https://github.com/deepset-ai/haystack/blob/main/haystack/components/websearch/serper_dev.py |
+
+</div>
+
+## Overview
+
+When you give `SerperDevWebSearch` a query, it returns a list of the URLs most relevant to your search. It uses page snippets (pieces of text displayed under the page title in search results) to find the answers, not the whole pages.
+
+To search the content of the web pages, use the [`LinkContentFetcher`](../fetchers/linkcontentfetcher.mdx) component.
+
+`SerperDevWebSearch` requires a [SerperDev](https://serper.dev/) key to work. It uses a `SERPERDEV_API_KEY` environment variable by default. Otherwise, you can pass an `api_key` at initialization – see code examples below.
+
+:::info[Alternative search]
+
+To use [Search API](https://www.searchapi.io/) as an alternative, see its respective [documentation page](searchapiwebsearch.mdx).
+:::
+
+## Usage
+
+### On its own
+
+This is an example of how `SerperDevWebSearch` looks up answers to our query on the web and converts the results into a list of documents with content snippets of the results, as well as URLs as strings.
+
+```python
+from haystack.components.websearch import SerperDevWebSearch
+from haystack.utils import Secret
+
+web_search = SerperDevWebSearch(api_key=Secret.from_token("<your-api-key>"))
+query = "What is the capital of Germany?"
+
+response = web_search.run(query)
+```
+
+### In a pipeline
+
+Here’s an example of a RAG pipeline where we use a `SerperDevWebSearch` to look up the answer to the query. The resulting documents are then passed to `LinkContentFetcher` to get the full text from the URLs. Finally, `PromptBuilder` and `OpenAIGenerator` work together to form the final answer.
+
+```python
+from haystack import Pipeline
+from haystack.utils import Secret
+from haystack.components.builders.chat_prompt_builder import ChatPromptBuilder
+from haystack.components.fetchers import LinkContentFetcher
+from haystack.components.converters import HTMLToDocument
+from haystack.components.generators.chat import OpenAIChatGenerator
+from haystack.components.websearch import SerperDevWebSearch
+from haystack.dataclasses import ChatMessage
+from haystack.utils import Secret
+
+web_search = SerperDevWebSearch(api_key=Secret.from_token("<your-api-key>"), top_k=2)
+link_content = LinkContentFetcher()
+html_converter = HTMLToDocument()
+
+prompt_template = [
+    ChatMessage.from_system("You are a helpful assistant."),
+    ChatMessage.from_user(
+        "Given the information below:\n"
+        "{% for document in documents %}{{ document.content }}{% endfor %}\n"
+        "Answer question: {{ query }}.\nAnswer:",
+    ),
+]
+
+prompt_builder = ChatPromptBuilder(
+    template=prompt_template,
+    required_variables={"query", "documents"},
+)
+llm = OpenAIChatGenerator(
+    api_key=Secret.from_token("<your-api-key>"),
+)
+
+pipe = Pipeline()
+pipe.add_component("search", web_search)
+pipe.add_component("fetcher", link_content)
+pipe.add_component("converter", html_converter)
+pipe.add_component("prompt_builder", prompt_builder)
+pipe.add_component("llm", llm)
+
+pipe.connect("search.links", "fetcher.urls")
+pipe.connect("fetcher.streams", "converter.sources")
+pipe.connect("converter.documents", "prompt_builder.documents")
+pipe.connect("prompt_builder.messages", "llm.messages")
+
+query = "What is the most famous landmark in Berlin?"
+
+pipe.run(data={"search": {"query": query}, "prompt_builder": {"query": query}})
+```
+
+## Additional References
+
+:notebook: Tutorial: [Building Fallbacks to Websearch with Conditional Routing](https://haystack.deepset.ai/tutorials/36_building_fallbacks_with_conditional_routing)

docs-website/versioned_docs/version-2.26/pipeline-components/websearch/tavilywebsearch.mdx

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+---
+title: "TavilyWebSearch"
+id: tavilywebsearch
+slug: "/tavilywebsearch"
+description: "Search engine using the Tavily AI-powered search API."
+---
+
+# TavilyWebSearch
+
+Search the web using the Tavily AI-powered search API, optimized for LLM applications.
+
+<div className="key-value-table">
+
+|  |  |
+| --- | --- |
+| **Most common position in a pipeline** | Before a [`ChatPromptBuilder`](../builders/chatpromptbuilder.mdx) or right at the beginning of an indexing pipeline |
+| **Mandatory init variables** | `api_key`: The Tavily API key. Can be set with the `TAVILY_API_KEY` env var. |
+| **Mandatory run variables** | `query`: A string with your search query. |
+| **Output variables** | `documents`: A list of Haystack Documents containing search result content and metadata. <br /> <br />`links`: A list of strings of resulting URLs. |
+| **API reference** | [Tavily Search API](/reference/integrations-tavily) |
+| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/blob/main/integrations/tavily/src/haystack_integrations/components/websearch/tavily/tavily_websearch.py |
+
+</div>
+
+## Overview
+
+When you give `TavilyWebSearch` a query, it uses the [Tavily](https://tavily.com) Search API to search the web and return relevant content as Haystack `Document` objects. It also returns a list of the source URLs.
+
+Tavily is an AI-powered search API built specifically for LLM applications. It returns clean, relevant snippets without the noise of traditional search engines, making it a great fit for RAG pipelines.
+
+`TavilyWebSearch` requires a Tavily API key to work. By default, it looks for a `TAVILY_API_KEY` environment variable. Alternatively, you can pass an `api_key` directly during initialization.
+
+## Usage
+
+### On its own
+
+Here is a quick example of how `TavilyWebSearch` searches the web based on a query and returns a list of Documents.
+
+```python
+from haystack_integrations.components.websearch.tavily import TavilyWebSearch
+from haystack.utils import Secret
+
+web_search = TavilyWebSearch(
+    api_key=Secret.from_env_var("TAVILY_API_KEY"),
+    top_k=5,
+)
+query = "What is Haystack by deepset?"
+
+response = web_search.run(query=query)
+
+for doc in response["documents"]:
+    print(doc.content)
+```
+
+### In a pipeline
+
+Here is an example of a Retrieval-Augmented Generation (RAG) pipeline that uses `TavilyWebSearch` to look up an answer on the web.
+
+```python
+from haystack import Pipeline
+from haystack.utils import Secret
+from haystack.components.builders.chat_prompt_builder import ChatPromptBuilder
+from haystack.components.generators.chat import OpenAIChatGenerator
+from haystack_integrations.components.websearch.tavily import TavilyWebSearch
+from haystack.dataclasses import ChatMessage
+
+web_search = TavilyWebSearch(
+    api_key=Secret.from_env_var("TAVILY_API_KEY"),
+    top_k=3,
+)
+
+prompt_template = [
+    ChatMessage.from_system("You are a helpful assistant."),
+    ChatMessage.from_user(
+        "Given the information below:\n"
+        "{% for document in documents %}{{ document.content }}\n{% endfor %}\n"
+        "Answer the following question: {{ query }}.\nAnswer:",
+    ),
+]
+
+prompt_builder = ChatPromptBuilder(
+    template=prompt_template,
+    required_variables={"query", "documents"},
+)
+
+llm = OpenAIChatGenerator(
+    api_key=Secret.from_env_var("OPENAI_API_KEY"),
+)
+
+pipe = Pipeline()
+pipe.add_component("search", web_search)
+pipe.add_component("prompt_builder", prompt_builder)
+pipe.add_component("llm", llm)
+
+pipe.connect("search.documents", "prompt_builder.documents")
+pipe.connect("prompt_builder.prompt", "llm.messages")
+
+query = "What is Haystack by deepset?"
+
+result = pipe.run(data={"search": {"query": query}, "prompt_builder": {"query": query}})
+
+print(result["llm"]["replies"][0].text)
+```