scrapeunblocker-haystack

Haystack integration for ScrapeUnblocker - fetch pages that block ordinary HTTP requests.

ScrapeUnblocker renders web pages in a real browser behind anti-bot protections such as Cloudflare, DataDome, PerimeterX and Akamai, so your pipeline gets the real content instead of a block page, a captcha, or an empty JavaScript shell.

Installation

pip install scrapeunblocker-haystack

Setup

Get an API key at scrapeunblocker.com and export it:

export SCRAPEUNBLOCKER_API_KEY=<your-api-key>

Both components read that variable by default, or accept a Secret explicitly.

Components

ScrapeUnblockerFetcher

Fetches URLs and returns one Document per page.

from scrapeunblocker_haystack import ScrapeUnblockerFetcher

fetcher = ScrapeUnblockerFetcher()
result = fetcher.run(urls=["https://example.com"])

print(result["documents"][0].content[:200])

By default a URL that cannot be fetched is logged and skipped, so one bad URL does not discard the rest of the batch.

Browser steps

Pass steps to drive the page in a real browser before its HTML is captured - wait for content to appear, click, type, scroll, and so on. Each step is a dict with an action key plus its parameters:

from scrapeunblocker_haystack import ScrapeUnblockerFetcher

fetcher = ScrapeUnblockerFetcher(
    steps=[
        {"action": "wait_for", "selector": "#results"},
        {"action": "type", "selector": "input#q", "value": "laptops"},
        {"action": "press_key", "value": "Enter"},
        {"action": "wait_for_text", "value": "in stock"},
        {"action": "click", "selector": "button.load-more"},
        {"action": "scroll", "value": "bottom"},
    ]
)
result = fetcher.run(urls=["https://example.com/search"])

Available actions:

Steps are non-idempotent. If a step fails, the fetch raises StepExecutionError (with step_index, action, selector and reason), which is logged and skipped by default, or re-raised when raise_on_failure=True.

List elements

Set list_elements=True to return the parsed elements JSON ({url, count, elements: [...]}) instead of the page HTML. The JSON is stored as the Document content, and the element count is available in meta["count"]:

fetcher = ScrapeUnblockerFetcher(list_elements=True)
result = fetcher.run(urls=["https://example.com"])

doc = result["documents"][0]
print(doc.meta["count"])          # number of elements
print(doc.content)                # {"url": ..., "count": ..., "elements": [...]}

ScrapeUnblockerWebSearch

Searches Google and returns the organic results as Document objects, with the snippet as content and title / link / position in the metadata.

from scrapeunblocker_haystack import ScrapeUnblockerWebSearch

search = ScrapeUnblockerWebSearch(top_k=5)
result = search.run(query="best web scraping api")

for doc in result["documents"]:
    print(doc.meta["title"], doc.meta["link"])

In a pipeline

Search the web, fetch the pages behind the results, and answer from them:

from haystack import Pipeline
from haystack.components.builders import ChatPromptBuilder
from haystack.components.converters import HTMLToDocument
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.dataclasses import ChatMessage

from scrapeunblocker_haystack import ScrapeUnblockerFetcher

prompt = [
    ChatMessage.from_user(
        "Answer the question using the pages below.\n\n"
        "{% for doc in documents %}{{ doc.content }}\n{% endfor %}\n"
        "Question: {{ question }}"
    )
]

pipe = Pipeline()
pipe.add_component("fetcher", ScrapeUnblockerFetcher())
pipe.add_component("converter", HTMLToDocument())
pipe.add_component("prompt_builder", ChatPromptBuilder(template=prompt, required_variables="*"))
pipe.add_component("llm", OpenAIChatGenerator())

pipe.connect("fetcher.documents", "converter.sources")
pipe.connect("converter.documents", "prompt_builder.documents")
pipe.connect("prompt_builder.prompt", "llm.messages")

result = pipe.run(
    {
        "fetcher": {"urls": ["https://example.com"]},
        "prompt_builder": {"question": "What is this page about?"},
    }
)
print(result["llm"]["replies"][0].text)

Serialization

Both components implement to_dict() / from_dict(), so pipelines using them can be saved and reloaded. The API key is serialized as a Haystack Secret reference, not as its value.

Links

• ScrapeUnblocker: www.scrapeunblocker.com
• API documentation: developers.scrapeunblocker.com
• Haystack: https://haystack.deepset.ai

License

Apache-2.0