---
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** | [SearchApi](/reference/integrations-searchapi) |
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/searchapi |
| **Package name** | `searchapi-haystack` |

</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

Install the `searchapi-haystack` package to use the `SearchApiWebSearch` component:

```shell
pip install searchapi-haystack
```

### 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_integrations.components.websearch.searchapi 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, `ChatPromptBuilder` and `OpenAIChatGenerator` 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_integrations.components.websearch.searchapi 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.prompt", "llm.messages")

query = "What is the most famous landmark in Berlin?"

pipe.run(data={"search": {"query": query}, "prompt_builder": {"query": query}})
```
