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

---
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)
```