Maintained by deepset

Integration: Linkup

Search the web using the Linkup API, optimized for LLM and agent applications

Authors

deepset

GitHub Repo PyPI Package

Overview
Installation
Usage
- LinkupWebSearch
License

Overview

Linkup is a web search API built for LLM and agent applications, returning grounded results with source URLs.

This integration provides:

LinkupWebSearch: Searches the web using the Linkup API and returns results as Haystack Document objects along with source URLs.

You need a Linkup API key to use this integration. You can get one at linkup.so.

Installation

pip install linkup-haystack

Usage

LinkupWebSearch

LinkupWebSearch queries the Linkup Search API and returns results as Haystack Document objects containing the content snippets and metadata (title, URL). Source URLs are also returned separately.

Set your API key as the LINKUP_API_KEY environment variable.

Basic Example

from haystack_integrations.components.websearch.linkup import LinkupWebSearch

web_search = LinkupWebSearch(top_k=5)

result = web_search.run(query="What is Haystack by deepset?")
documents = result["documents"]
links = result["links"]

In a Pipeline

Here is an example of a RAG pipeline that uses LinkupWebSearch to retrieve web content and answer a question:

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.dataclasses import ChatMessage
from haystack_integrations.components.websearch.linkup import LinkupWebSearch

web_search = LinkupWebSearch(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"),
    model="gpt-4o-mini",
)

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)

Async Support

The component supports asynchronous execution via run_async:

import asyncio
from haystack_integrations.components.websearch.linkup import LinkupWebSearch

async def main():
    web_search = LinkupWebSearch(top_k=3)
    result = await web_search.run_async(query="What is Haystack by deepset?")
    print(f"Found {len(result['documents'])} documents")

asyncio.run(main())

Parameters

api_key: API key for Linkup. Defaults to the LINKUP_API_KEY environment variable.
top_k: Maximum number of results to return. Maps to the max_results parameter of the Linkup API. Defaults to 10.
depth: The depth of the search. Can be "fast" (beta, sub-second, keyword-based queries only), "standard" for a simple search, or "deep" for a more powerful agentic workflow. Defaults to "standard".
search_params: Additional parameters passed to the Linkup search API, such as include_images, from_date, to_date, include_domains, and exclude_domains.

License

linkup-haystack is distributed under the terms of the Apache-2.0 license.