mirror of
https://github.com/deepset-ai/haystack.git
synced 2025-07-29 11:50:34 +00:00
122 lines
4.4 KiB
Python
122 lines
4.4 KiB
Python
import json
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from pathlib import Path
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from re import search
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import pytest
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from haystack.nodes.connector import Crawler
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from haystack.schema import Document
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def test_crawler_url_none_exception(tmp_path):
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tmp_dir = tmp_path / "crawled_files"
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with pytest.raises(ValueError):
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Crawler(tmp_dir).crawl()
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def test_crawler_depth(tmp_path):
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tmp_dir = tmp_path / "crawled_files"
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_url = ["https://haystack.deepset.ai/overview/get-started"]
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crawler = Crawler(output_dir=tmp_dir)
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doc_path = crawler.crawl(urls=_url, crawler_depth=0)
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assert len(doc_path) == 1
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_urls = [
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"https://haystack.deepset.ai/overview/v1.2.0/get-started",
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"https://haystack.deepset.ai/overview/v1.1.0/get-started",
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"https://haystack.deepset.ai/overview/v1.0.0/get-started",
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]
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doc_path = crawler.crawl(urls=_urls, crawler_depth=0)
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assert len(doc_path) == 3
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doc_path = crawler.crawl(urls=_url, crawler_depth=1)
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assert len(doc_path) > 1
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for json_file in doc_path:
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assert isinstance(json_file, Path)
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with open(json_file.absolute(), "r") as read_file:
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data = json.load(read_file)
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assert "content" in data
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assert "meta" in data
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assert isinstance(data["content"], str)
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assert len(data["content"].split()) > 2
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def test_crawler_filter_urls(tmp_path):
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tmp_dir = tmp_path / "crawled_files"
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_url = ["https://haystack.deepset.ai/overview/v1.2.0/"]
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crawler = Crawler(output_dir=tmp_dir)
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doc_path = crawler.crawl(urls=_url, filter_urls=["haystack\.deepset\.ai\/overview\/v1\.3\.0\/"])
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assert len(doc_path) == 0
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doc_path = crawler.crawl(urls=_url, filter_urls=["haystack\.deepset\.ai\/overview\/v1\.2\.0\/"])
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assert len(doc_path) > 0
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doc_path = crawler.crawl(urls=_url, filter_urls=["google\.com"])
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assert len(doc_path) == 0
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def test_crawler_content(tmp_path):
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tmp_dir = tmp_path / "crawled_files"
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partial_content_match: list = [
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{
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"url": "https://haystack.deepset.ai/overview/v1.1.0/intro",
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"partial_content": [
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"Haystack is an open-source framework ",
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"for building search systems that work intelligently ",
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"over large document collections.",
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"Recent advances in NLP have enabled the application of ",
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"question answering, retrieval and summarization ",
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"to real world settings and Haystack is designed to be ",
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"the bridge between research and industry.",
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],
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},
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{
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"url": "https://haystack.deepset.ai/overview/v1.1.0/use-cases",
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"partial_content": [
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"Expect to see results that highlight",
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"the very sentence that contains the answer to your question.",
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"Thanks to the power of Transformer based language models,",
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"results are chosen based on compatibility in meaning",
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"rather than lexical overlap.",
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],
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},
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]
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crawler = Crawler(output_dir=tmp_dir)
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for _dict in partial_content_match:
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url: str = _dict["url"]
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partial_content: list = _dict["partial_content"]
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doc_path = crawler.crawl(urls=[url], crawler_depth=0)
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assert len(doc_path) == 1
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for json_file in doc_path:
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assert isinstance(json_file, Path)
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with open(json_file.absolute(), "r") as read_file:
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content = json.load(read_file)
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assert isinstance(content["content"], str)
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for partial_line in partial_content:
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assert search(partial_line, content["content"])
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assert partial_line in content["content"]
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def test_crawler_return_document(tmp_path):
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tmp_dir = tmp_path / "crawled_files"
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_url = ["https://haystack.deepset.ai/docs/v1.0.0/intromd"]
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crawler = Crawler(output_dir=tmp_dir)
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docs_path = crawler.crawl(urls=_url, crawler_depth=1)
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results, _ = crawler.run(urls=_url, crawler_depth=1, return_documents=True)
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documents = results["documents"]
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for json_file, document in zip(docs_path, documents):
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assert isinstance(json_file, Path)
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assert isinstance(document, Document)
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with open(json_file.absolute(), "r") as read_file:
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file_content = json.load(read_file)
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assert file_content["meta"] == document.meta
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assert file_content["content"] == document.content
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