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Init file.

AsyncWebPageReader #

Bases: BaseReader

Asynchronous web page reader.

Reads pages from the web asynchronously.

Parameters:

Name Type Description Default
html_to_text bool

Whether to convert HTML to text. Requires html2text package.

 False
limit int

Maximum number of concurrent requests.

 10
dedupe bool

to deduplicate urls if there is exact-match within given list

 True
fail_on_error bool

if requested url does not return status code 200 the routine will raise an ValueError

 False
Source code in llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/async_web/base.py 
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class AsyncWebPageReader(BaseReader):
    """Asynchronous web page reader.

    Reads pages from the web asynchronously.

    Args:
        html_to_text (bool): Whether to convert HTML to text.
            Requires `html2text` package.
        limit (int): Maximum number of concurrent requests.
        dedupe (bool): to deduplicate urls if there is exact-match within given list
        fail_on_error (bool): if requested url does not return status code 200 the routine will raise an ValueError
    """

    def __init__(
        self,
        html_to_text: bool = False,
        limit: int = 10,
        dedupe: bool = True,
        fail_on_error: bool = False,
    ) -> None:
        """Initialize with parameters."""
        try:
            import html2text  # noqa: F401
        except ImportError:
            raise ImportError(
                "`html2text` package not found, please run `pip install html2text`"
            )
        try:
            import aiohttp  # noqa: F401
        except ImportError:
            raise ImportError(
                "`aiohttp` package not found, please run `pip install aiohttp`"
            )
        self._limit = limit
        self._html_to_text = html_to_text
        self._dedupe = dedupe
        self._fail_on_error = fail_on_error

    def load_data(self, urls: List[str]) -> List[Document]:
        """Load data from the input urls.

        Args:
            urls (List[str]): List of URLs to scrape.

        Returns:
            List[Document]: List of documents.

        """
        if self._dedupe:
            urls = list(dict.fromkeys(urls))

        import aiohttp

        def chunked_http_client(limit: int):
            semaphore = asyncio.Semaphore(limit)

            async def http_get(url: str, session: aiohttp.ClientSession):
                async with semaphore:
                    async with session.get(url) as response:
                        return response, await response.text()

            return http_get

        async def fetch_urls(urls: List[str]):
            http_client = chunked_http_client(self._limit)
            async with aiohttp.ClientSession() as session:
                tasks = [http_client(url, session) for url in urls]
                return await asyncio.gather(*tasks, return_exceptions=True)

        if not isinstance(urls, list):
            raise ValueError("urls must be a list of strings.")

        documents = []
        responses = asyncio.run(fetch_urls(urls))

        for i, response_tuple in enumerate(responses):
            if not isinstance(response_tuple, tuple):
                raise ValueError(f"One of the inputs is not a valid url: {urls[i]}")

            response, raw_page = response_tuple

            if response.status != 200:
                logger.warning(f"error fetching page from {urls[i]}")
                logger.info(response)

                if self._fail_on_error:
                    raise ValueError(
                        f"error fetching page from {urls[i]}. server returned status:"
                        f" {response.status} and response {raw_page}"
                    )

                continue

            if self._html_to_text:
                import html2text

                response_text = html2text.html2text(raw_page)
            else:
                response_text = raw_page

            documents.append(
                Document(text=response_text, extra_info={"Source": str(response.url)})
            )

        return documents

load_data #

load_data(urls: List[str]) -> List[Document]

Load data from the input urls.

Parameters:

Name Type Description Default
urls List[str]

List of URLs to scrape.

 required

Returns:

Type Description
List[Document]

List[Document]: List of documents.
Source code in llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/async_web/base.py 
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def load_data(self, urls: List[str]) -> List[Document]:
    """Load data from the input urls.

    Args:
        urls (List[str]): List of URLs to scrape.

    Returns:
        List[Document]: List of documents.

    """
    if self._dedupe:
        urls = list(dict.fromkeys(urls))

    import aiohttp

    def chunked_http_client(limit: int):
        semaphore = asyncio.Semaphore(limit)

        async def http_get(url: str, session: aiohttp.ClientSession):
            async with semaphore:
                async with session.get(url) as response:
                    return response, await response.text()

        return http_get

    async def fetch_urls(urls: List[str]):
        http_client = chunked_http_client(self._limit)
        async with aiohttp.ClientSession() as session:
            tasks = [http_client(url, session) for url in urls]
            return await asyncio.gather(*tasks, return_exceptions=True)

    if not isinstance(urls, list):
        raise ValueError("urls must be a list of strings.")

    documents = []
    responses = asyncio.run(fetch_urls(urls))

    for i, response_tuple in enumerate(responses):
        if not isinstance(response_tuple, tuple):
            raise ValueError(f"One of the inputs is not a valid url: {urls[i]}")

        response, raw_page = response_tuple

        if response.status != 200:
            logger.warning(f"error fetching page from {urls[i]}")
            logger.info(response)

            if self._fail_on_error:
                raise ValueError(
                    f"error fetching page from {urls[i]}. server returned status:"
                    f" {response.status} and response {raw_page}"
                )

            continue

        if self._html_to_text:
            import html2text

            response_text = html2text.html2text(raw_page)
        else:
            response_text = raw_page

        documents.append(
            Document(text=response_text, extra_info={"Source": str(response.url)})
        )

    return documents

BeautifulSoupWebReader #

Bases: BasePydanticReader

BeautifulSoup web page reader.

Reads pages from the web. Requires the bs4 and urllib packages.

Parameters:

Name Type Description Default
website_extractor Optional[Dict[str, Callable]]

A mapping of website hostname (e.g. google.com) to a function that specifies how to extract text from the BeautifulSoup obj. See DEFAULT_WEBSITE_EXTRACTOR.

 None
Source code in llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/beautiful_soup_web/base.py 
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class BeautifulSoupWebReader(BasePydanticReader):
    """BeautifulSoup web page reader.

    Reads pages from the web.
    Requires the `bs4` and `urllib` packages.

    Args:
        website_extractor (Optional[Dict[str, Callable]]): A mapping of website
            hostname (e.g. google.com) to a function that specifies how to
            extract text from the BeautifulSoup obj. See DEFAULT_WEBSITE_EXTRACTOR.
    """

    is_remote: bool = True
    _website_extractor: Dict[str, Callable] = PrivateAttr()

    def __init__(self, website_extractor: Optional[Dict[str, Callable]] = None) -> None:
        self._website_extractor = website_extractor or DEFAULT_WEBSITE_EXTRACTOR
        super().__init__()

    @classmethod
    def class_name(cls) -> str:
        """Get the name identifier of the class."""
        return "BeautifulSoupWebReader"

    def load_data(
        self,
        urls: List[str],
        custom_hostname: Optional[str] = None,
        include_url_in_text: Optional[bool] = True,
    ) -> List[Document]:
        """Load data from the urls.

        Args:
            urls (List[str]): List of URLs to scrape.
            custom_hostname (Optional[str]): Force a certain hostname in the case
                a website is displayed under custom URLs (e.g. Substack blogs)
            include_url_in_text (Optional[bool]): Include the reference url in the text of the document

        Returns:
            List[Document]: List of documents.

        """
        from urllib.parse import urlparse

        import requests
        from bs4 import BeautifulSoup

        documents = []
        for url in urls:
            try:
                page = requests.get(url)
            except Exception:
                raise ValueError(f"One of the inputs is not a valid url: {url}")

            hostname = custom_hostname or urlparse(url).hostname or ""

            soup = BeautifulSoup(page.content, "html.parser")

            data = ""
            extra_info = {"URL": url}
            if hostname in self._website_extractor:
                data, metadata = self._website_extractor[hostname](
                    soup=soup, url=url, include_url_in_text=include_url_in_text
                )
                extra_info.update(metadata)

            else:
                data = soup.getText()

            documents.append(Document(text=data, id_=url, extra_info=extra_info))

        return documents

class_name classmethod #

class_name() -> str

Get the name identifier of the class.

Source code in llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/beautiful_soup_web/base.py 
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@classmethod
def class_name(cls) -> str:
    """Get the name identifier of the class."""
    return "BeautifulSoupWebReader"

load_data #

load_data(urls: List[str], custom_hostname: Optional[str] = None, include_url_in_text: Optional[bool] = True) -> List[Document]

Load data from the urls.

Parameters:

Name Type Description Default
urls List[str]

List of URLs to scrape.

 required
custom_hostname Optional[str]

Force a certain hostname in the case a website is displayed under custom URLs (e.g. Substack blogs)

 None
include_url_in_text Optional[bool]

Include the reference url in the text of the document

 True

Returns:

Type Description
List[Document]

List[Document]: List of documents.
Source code in llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/beautiful_soup_web/base.py 
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def load_data(
    self,
    urls: List[str],
    custom_hostname: Optional[str] = None,
    include_url_in_text: Optional[bool] = True,
) -> List[Document]:
    """Load data from the urls.

    Args:
        urls (List[str]): List of URLs to scrape.
        custom_hostname (Optional[str]): Force a certain hostname in the case
            a website is displayed under custom URLs (e.g. Substack blogs)
        include_url_in_text (Optional[bool]): Include the reference url in the text of the document

    Returns:
        List[Document]: List of documents.

    """
    from urllib.parse import urlparse

    import requests
    from bs4 import BeautifulSoup

    documents = []
    for url in urls:
        try:
            page = requests.get(url)
        except Exception:
            raise ValueError(f"One of the inputs is not a valid url: {url}")

        hostname = custom_hostname or urlparse(url).hostname or ""

        soup = BeautifulSoup(page.content, "html.parser")

        data = ""
        extra_info = {"URL": url}
        if hostname in self._website_extractor:
            data, metadata = self._website_extractor[hostname](
                soup=soup, url=url, include_url_in_text=include_url_in_text
            )
            extra_info.update(metadata)

        else:
            data = soup.getText()

        documents.append(Document(text=data, id_=url, extra_info=extra_info))

    return documents

KnowledgeBaseWebReader #

Bases: BaseReader

Knowledge base reader.

Crawls and reads articles from a knowledge base/help center with Playwright. Tested on Zendesk and Intercom CMS, may work on others. Can be run in headless mode but it may be blocked by Cloudflare. Run it headed to be safe. Times out occasionally, just increase the default time out if it does. Requires the playwright package.

Parameters:

Name Type Description Default
root_url str

the base url of the knowledge base, with no trailing slash e.g. 'https://support.intercom.com'

 required
link_selectors List[str]

list of css selectors to find links to articles while crawling e.g. ['.article-list a', '.article-list a']

 required
article_path str

the url path of articles on this domain so the crawler knows when to stop e.g. '/articles'

 required
title_selector Optional[str]

css selector to find the title of the article e.g. '.article-title'

 None
subtitle_selector Optional[str]

css selector to find the subtitle/description of the article e.g. '.article-subtitle'

 None
body_selector Optional[str]

css selector to find the body of the article e.g. '.article-body'

 None
Source code in llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/knowledge_base/base.py 
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class KnowledgeBaseWebReader(BaseReader):
    """Knowledge base reader.

    Crawls and reads articles from a knowledge base/help center with Playwright.
    Tested on Zendesk and Intercom CMS, may work on others.
    Can be run in headless mode but it may be blocked by Cloudflare. Run it headed to be safe.
    Times out occasionally, just increase the default time out if it does.
    Requires the `playwright` package.

    Args:
        root_url (str): the base url of the knowledge base, with no trailing slash
            e.g. 'https://support.intercom.com'
        link_selectors (List[str]): list of css selectors to find links to articles while crawling
            e.g. ['.article-list a', '.article-list a']
        article_path (str): the url path of articles on this domain so the crawler knows when to stop
            e.g. '/articles'
        title_selector (Optional[str]): css selector to find the title of the article
            e.g. '.article-title'
        subtitle_selector (Optional[str]): css selector to find the subtitle/description of the article
            e.g. '.article-subtitle'
        body_selector (Optional[str]): css selector to find the body of the article
            e.g. '.article-body'
    """

    def __init__(
        self,
        root_url: str,
        link_selectors: List[str],
        article_path: str,
        title_selector: Optional[str] = None,
        subtitle_selector: Optional[str] = None,
        body_selector: Optional[str] = None,
    ) -> None:
        """Initialize with parameters."""
        self.root_url = root_url
        self.link_selectors = link_selectors
        self.article_path = article_path
        self.title_selector = title_selector
        self.subtitle_selector = subtitle_selector
        self.body_selector = body_selector

    def load_data(self) -> List[Document]:
        """Load data from the knowledge base."""
        from playwright.sync_api import sync_playwright

        with sync_playwright() as p:
            browser = p.chromium.launch(headless=False)

            # Crawl
            article_urls = self.get_article_urls(
                browser,
                self.root_url,
                self.root_url,
            )

            # Scrape
            documents = []
            for url in article_urls:
                article = self.scrape_article(
                    browser,
                    url,
                )
                extra_info = {
                    "title": article["title"],
                    "subtitle": article["subtitle"],
                    "url": article["url"],
                }
                documents.append(Document(text=article["body"], extra_info=extra_info))

            browser.close()

            return documents

    def scrape_article(
        self,
        browser: Any,
        url: str,
    ) -> Dict[str, str]:
        """Scrape a single article url.

        Args:
            browser (Any): a Playwright Chromium browser.
            url (str): URL of the article to scrape.

        Returns:
            Dict[str, str]: a mapping of article attributes to their values.

        """
        page = browser.new_page(ignore_https_errors=True)
        page.set_default_timeout(60000)
        page.goto(url, wait_until="domcontentloaded")

        title = (
            (
                page.query_selector(self.title_selector).evaluate(
                    "node => node.innerText"
                )
            )
            if self.title_selector
            else ""
        )
        subtitle = (
            (
                page.query_selector(self.subtitle_selector).evaluate(
                    "node => node.innerText"
                )
            )
            if self.subtitle_selector
            else ""
        )
        body = (
            (page.query_selector(self.body_selector).evaluate("node => node.innerText"))
            if self.body_selector
            else ""
        )

        page.close()
        print("scraped:", url)
        return {"title": title, "subtitle": subtitle, "body": body, "url": url}

    def get_article_urls(
        self, browser: Any, root_url: str, current_url: str
    ) -> List[str]:
        """Recursively crawl through the knowledge base to find a list of articles.

        Args:
            browser (Any): a Playwright Chromium browser.
            root_url (str): root URL of the knowledge base.
            current_url (str): current URL that is being crawled.

        Returns:
            List[str]: a list of URLs of found articles.

        """
        page = browser.new_page(ignore_https_errors=True)
        page.set_default_timeout(60000)
        page.goto(current_url, wait_until="domcontentloaded")

        # If this is a leaf node aka article page, return itself
        if self.article_path in current_url:
            print("Found an article: ", current_url)
            page.close()
            return [current_url]

        # Otherwise crawl this page and find all the articles linked from it
        article_urls = []
        links = []

        for link_selector in self.link_selectors:
            ahrefs = page.query_selector_all(link_selector)
            links.extend(ahrefs)

        for link in links:
            url = root_url + page.evaluate("(node) => node.getAttribute('href')", link)
            article_urls.extend(self.get_article_urls(browser, root_url, url))

        page.close()

        return article_urls

load_data #

load_data() -> List[Document]

Load data from the knowledge base.

Source code in llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/knowledge_base/base.py 
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def load_data(self) -> List[Document]:
    """Load data from the knowledge base."""
    from playwright.sync_api import sync_playwright

    with sync_playwright() as p:
        browser = p.chromium.launch(headless=False)

        # Crawl
        article_urls = self.get_article_urls(
            browser,
            self.root_url,
            self.root_url,
        )

        # Scrape
        documents = []
        for url in article_urls:
            article = self.scrape_article(
                browser,
                url,
            )
            extra_info = {
                "title": article["title"],
                "subtitle": article["subtitle"],
                "url": article["url"],
            }
            documents.append(Document(text=article["body"], extra_info=extra_info))

        browser.close()

        return documents

scrape_article #

scrape_article(browser: Any, url: str) -> Dict[str, str]

Scrape a single article url.

Parameters:

Name Type Description Default
browser Any

a Playwright Chromium browser.

 required
url str

URL of the article to scrape.

 required

Returns:

Type Description
Dict[str, str]

Dict[str, str]: a mapping of article attributes to their values.
Source code in llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/knowledge_base/base.py 
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def scrape_article(
    self,
    browser: Any,
    url: str,
) -> Dict[str, str]:
    """Scrape a single article url.

    Args:
        browser (Any): a Playwright Chromium browser.
        url (str): URL of the article to scrape.

    Returns:
        Dict[str, str]: a mapping of article attributes to their values.

    """
    page = browser.new_page(ignore_https_errors=True)
    page.set_default_timeout(60000)
    page.goto(url, wait_until="domcontentloaded")

    title = (
        (
            page.query_selector(self.title_selector).evaluate(
                "node => node.innerText"
            )
        )
        if self.title_selector
        else ""
    )
    subtitle = (
        (
            page.query_selector(self.subtitle_selector).evaluate(
                "node => node.innerText"
            )
        )
        if self.subtitle_selector
        else ""
    )
    body = (
        (page.query_selector(self.body_selector).evaluate("node => node.innerText"))
        if self.body_selector
        else ""
    )

    page.close()
    print("scraped:", url)
    return {"title": title, "subtitle": subtitle, "body": body, "url": url}

get_article_urls #

get_article_urls(browser: Any, root_url: str, current_url: str) -> List[str]

Recursively crawl through the knowledge base to find a list of articles.

Parameters:

Name Type Description Default
browser Any

a Playwright Chromium browser.

 required
root_url str

root URL of the knowledge base.

 required
current_url str

current URL that is being crawled.

 required

Returns:

Type Description
List[str]

List[str]: a list of URLs of found articles.
Source code in llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/knowledge_base/base.py 
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def get_article_urls(
    self, browser: Any, root_url: str, current_url: str
) -> List[str]:
    """Recursively crawl through the knowledge base to find a list of articles.

    Args:
        browser (Any): a Playwright Chromium browser.
        root_url (str): root URL of the knowledge base.
        current_url (str): current URL that is being crawled.

    Returns:
        List[str]: a list of URLs of found articles.

    """
    page = browser.new_page(ignore_https_errors=True)
    page.set_default_timeout(60000)
    page.goto(current_url, wait_until="domcontentloaded")

    # If this is a leaf node aka article page, return itself
    if self.article_path in current_url:
        print("Found an article: ", current_url)
        page.close()
        return [current_url]

    # Otherwise crawl this page and find all the articles linked from it
    article_urls = []
    links = []

    for link_selector in self.link_selectors:
        ahrefs = page.query_selector_all(link_selector)
        links.extend(ahrefs)

    for link in links:
        url = root_url + page.evaluate("(node) => node.getAttribute('href')", link)
        article_urls.extend(self.get_article_urls(browser, root_url, url))

    page.close()

    return article_urls

MainContentExtractorReader #

Bases: BaseReader

MainContentExtractor web page reader.

Reads pages from the web.

Parameters:

Name Type Description Default
text_format str

The format of the text. Defaults to "markdown". Requires MainContentExtractor package.

 'markdown'
Source code in llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/main_content_extractor/base.py 
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class MainContentExtractorReader(BaseReader):
    """MainContentExtractor web page reader.

    Reads pages from the web.

    Args:
        text_format (str, optional): The format of the text. Defaults to "markdown".
            Requires `MainContentExtractor` package.

    """

    def __init__(self, text_format: str = "markdown") -> None:
        """Initialize with parameters."""
        self.text_format = text_format

    def load_data(self, urls: List[str]) -> List[Document]:
        """Load data from the input directory.

        Args:
            urls (List[str]): List of URLs to scrape.

        Returns:
            List[Document]: List of documents.

        """
        if not isinstance(urls, list):
            raise ValueError("urls must be a list of strings.")

        from main_content_extractor import MainContentExtractor

        documents = []
        for url in urls:
            response = requests.get(url).text
            response = MainContentExtractor.extract(
                response, output_format=self.text_format, include_links=False
            )

            documents.append(Document(text=response))

        return documents

load_data #

load_data(urls: List[str]) -> List[Document]

Load data from the input directory.

Parameters:

Name Type Description Default
urls List[str]

List of URLs to scrape.

 required

Returns:

Type Description
List[Document]

List[Document]: List of documents.
Source code in llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/main_content_extractor/base.py 
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def load_data(self, urls: List[str]) -> List[Document]:
    """Load data from the input directory.

    Args:
        urls (List[str]): List of URLs to scrape.

    Returns:
        List[Document]: List of documents.

    """
    if not isinstance(urls, list):
        raise ValueError("urls must be a list of strings.")

    from main_content_extractor import MainContentExtractor

    documents = []
    for url in urls:
        response = requests.get(url).text
        response = MainContentExtractor.extract(
            response, output_format=self.text_format, include_links=False
        )

        documents.append(Document(text=response))

    return documents

NewsArticleReader #

Bases: BaseReader

Simple news article reader.

Reads news articles from the web and parses them using the newspaper library.

Parameters:

Name Type Description Default
text_mode bool

Whether to load a text version or HTML version of the content (default=True).

 True
use_nlp bool

Whether to use NLP to extract additional summary and keywords (default=True).

 True
newspaper_kwargs Any

Additional keyword arguments to pass to newspaper.Article. See https://newspaper.readthedocs.io/en/latest/user_guide/quickstart.html#article

 {}
Source code in llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/news/base.py 
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class NewsArticleReader(BaseReader):
    """Simple news article reader.

    Reads news articles from the web and parses them using the `newspaper` library.

    Args:
        text_mode (bool): Whether to load a text version or HTML version of the content (default=True).
        use_nlp (bool): Whether to use NLP to extract additional summary and keywords (default=True).
        newspaper_kwargs: Additional keyword arguments to pass to newspaper.Article. See
            https://newspaper.readthedocs.io/en/latest/user_guide/quickstart.html#article
    """

    def __init__(
        self, text_mode: bool = True, use_nlp: bool = True, **newspaper_kwargs: Any
    ) -> None:
        """Initialize with parameters."""
        if find_spec("newspaper") is None:
            raise ImportError(
                "`newspaper` package not found, please run `pip install newspaper3k`"
            )
        self.load_text = text_mode
        self.use_nlp = use_nlp
        self.newspaper_kwargs = newspaper_kwargs

    def load_data(self, urls: List[str]) -> List[Document]:
        """Load data from the list of news article urls.

        Args:
            urls (List[str]): List of URLs to load news articles.

        Returns:
            List[Document]: List of documents.

        """
        if not isinstance(urls, list) and not isinstance(urls, Generator):
            raise ValueError("urls must be a list or generator.")
        documents = []
        for url in urls:
            from newspaper import Article

            try:
                article = Article(url, **self.newspaper_kwargs)
                article.download()
                article.parse()

                if self.use_nlp:
                    article.nlp()

            except Exception as e:
                logger.error(f"Error fetching or processing {url}, exception: {e}")
                continue

            metadata = {
                "title": getattr(article, "title", ""),
                "link": getattr(article, "url", getattr(article, "canonical_link", "")),
                "authors": getattr(article, "authors", []),
                "language": getattr(article, "meta_lang", ""),
                "description": getattr(article, "meta_description", ""),
                "publish_date": getattr(article, "publish_date", ""),
            }

            if self.load_text:
                content = article.text
            else:
                content = article.html

            if self.use_nlp:
                metadata["keywords"] = getattr(article, "keywords", [])
                metadata["summary"] = getattr(article, "summary", "")

            documents.append(Document(text=content, metadata=metadata))

        return documents

load_data #

load_data(urls: List[str]) -> List[Document]

Load data from the list of news article urls.

Parameters:

Name Type Description Default
urls List[str]

List of URLs to load news articles.

 required

Returns:

Type Description
List[Document]

List[Document]: List of documents.
Source code in llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/news/base.py 
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def load_data(self, urls: List[str]) -> List[Document]:
    """Load data from the list of news article urls.

    Args:
        urls (List[str]): List of URLs to load news articles.

    Returns:
        List[Document]: List of documents.

    """
    if not isinstance(urls, list) and not isinstance(urls, Generator):
        raise ValueError("urls must be a list or generator.")
    documents = []
    for url in urls:
        from newspaper import Article

        try:
            article = Article(url, **self.newspaper_kwargs)
            article.download()
            article.parse()

            if self.use_nlp:
                article.nlp()

        except Exception as e:
            logger.error(f"Error fetching or processing {url}, exception: {e}")
            continue

        metadata = {
            "title": getattr(article, "title", ""),
            "link": getattr(article, "url", getattr(article, "canonical_link", "")),
            "authors": getattr(article, "authors", []),
            "language": getattr(article, "meta_lang", ""),
            "description": getattr(article, "meta_description", ""),
            "publish_date": getattr(article, "publish_date", ""),
        }

        if self.load_text:
            content = article.text
        else:
            content = article.html

        if self.use_nlp:
            metadata["keywords"] = getattr(article, "keywords", [])
            metadata["summary"] = getattr(article, "summary", "")

        documents.append(Document(text=content, metadata=metadata))

    return documents

ReadabilityWebPageReader #

Bases: BaseReader

Readability Webpage Loader.

Extracting relevant information from a fully rendered web page. During the processing, it is always assumed that web pages used as data sources contain textual content.

  1. Load the page and wait for it rendered. (playwright)
  2. Inject Readability.js to extract the main content.

Parameters:

Name Type Description Default
proxy Optional[str]

Proxy server. Defaults to None.

 None
wait_until Optional[Literal['commit', 'domcontentloaded', 'load', 'networkidle']]

Wait until the page is loaded. Defaults to "domcontentloaded".

 'domcontentloaded'
text_splitter TextSplitter

Text splitter. Defaults to None.

 None
normalizer Optional[Callable[[str], str]]

Text normalizer. Defaults to nfkc_normalize.

 required
Source code in llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/readability_web/base.py 
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class ReadabilityWebPageReader(BaseReader):
    """Readability Webpage Loader.

    Extracting relevant information from a fully rendered web page.
    During the processing, it is always assumed that web pages used as data sources contain textual content.

    1. Load the page and wait for it rendered. (playwright)
    2. Inject Readability.js to extract the main content.

    Args:
        proxy (Optional[str], optional): Proxy server. Defaults to None.
        wait_until (Optional[Literal["commit", "domcontentloaded", "load", "networkidle"]], optional): Wait until the page is loaded. Defaults to "domcontentloaded".
        text_splitter (TextSplitter, optional): Text splitter. Defaults to None.
        normalizer (Optional[Callable[[str], str]], optional): Text normalizer. Defaults to nfkc_normalize.
    """

    def __init__(
        self,
        proxy: Optional[str] = None,
        wait_until: Optional[
            Literal["commit", "domcontentloaded", "load", "networkidle"]
        ] = "domcontentloaded",
        text_splitter: Optional[TextSplitter] = None,
        normalize: Optional[Callable[[str], str]] = nfkc_normalize,
    ) -> None:
        self._launch_options = {
            "headless": True,
        }
        self._wait_until = wait_until
        if proxy:
            self._launch_options["proxy"] = {
                "server": proxy,
            }
        self._text_splitter = text_splitter
        self._normalize = normalize
        self._readability_js = None

    async def async_load_data(self, url: str) -> List[Document]:
        """Render and load data content from url.

        Args:
            url (str): URL to scrape.

        Returns:
            List[Document]: List of documents.

        """
        from playwright.async_api import async_playwright

        async with async_playwright() as async_playwright:
            browser = await async_playwright.chromium.launch(**self._launch_options)

            article = await self.scrape_page(
                browser,
                url,
            )
            extra_info = {
                key: article[key]
                for key in [
                    "title",
                    "length",
                    "excerpt",
                    "byline",
                    "dir",
                    "lang",
                    "siteName",
                ]
            }

            if self._normalize is not None:
                article["textContent"] = self._normalize(article["textContent"])
            texts = []
            if self._text_splitter is not None:
                texts = self._text_splitter.split_text(article["textContent"])
            else:
                texts = [article["textContent"]]

            await browser.close()

            return [Document(text=x, extra_info=extra_info) for x in texts]

    def load_data(self, url: str) -> List[Document]:
        return async_to_sync(self.async_load_data(url))

    async def scrape_page(
        self,
        browser: Browser,
        url: str,
    ) -> Dict[str, str]:
        """Scrape a single article url.

        Args:
            browser (Any): a Playwright Chromium browser.
            url (str): URL of the article to scrape.

        Returns:
            Ref: https://github.com/mozilla/readability
            title: article title;
            content: HTML string of processed article content;
            textContent: text content of the article, with all the HTML tags removed;
            length: length of an article, in characters;
            excerpt: article description, or short excerpt from the content;
            byline: author metadata;
            dir: content direction;
            siteName: name of the site.
            lang: content language

        """
        if self._readability_js is None:
            with open(path) as f:
                self._readability_js = f.read()

        inject_readability = f"""
            (function(){{
            {self._readability_js}
            function executor() {{
                return new Readability({{}}, document).parse();
            }}
            return executor();
            }}())
        """

        # browser = cast(Browser, browser)
        page = await browser.new_page(ignore_https_errors=True)
        page.set_default_timeout(60000)
        await page.goto(url, wait_until=self._wait_until)

        r = await page.evaluate(inject_readability)

        await page.close()
        print("scraped:", url)

        return r

async_load_data async #

async_load_data(url: str) -> List[Document]

Render and load data content from url.

Parameters:

Name Type Description Default
url str

URL to scrape.

 required

Returns:

Type Description
List[Document]

List[Document]: List of documents.
Source code in llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/readability_web/base.py 
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async def async_load_data(self, url: str) -> List[Document]:
    """Render and load data content from url.

    Args:
        url (str): URL to scrape.

    Returns:
        List[Document]: List of documents.

    """
    from playwright.async_api import async_playwright

    async with async_playwright() as async_playwright:
        browser = await async_playwright.chromium.launch(**self._launch_options)

        article = await self.scrape_page(
            browser,
            url,
        )
        extra_info = {
            key: article[key]
            for key in [
                "title",
                "length",
                "excerpt",
                "byline",
                "dir",
                "lang",
                "siteName",
            ]
        }

        if self._normalize is not None:
            article["textContent"] = self._normalize(article["textContent"])
        texts = []
        if self._text_splitter is not None:
            texts = self._text_splitter.split_text(article["textContent"])
        else:
            texts = [article["textContent"]]

        await browser.close()

        return [Document(text=x, extra_info=extra_info) for x in texts]

scrape_page async #

scrape_page(browser: Browser, url: str) -> Dict[str, str]

Scrape a single article url.

Parameters:

Name Type Description Default
browser Any

a Playwright Chromium browser.

 required
url str

URL of the article to scrape.

 required

Returns:

Name Type Description
Ref Dict[str, str]

https://github.com/mozilla/readability
title Dict[str, str]

article title;
content Dict[str, str]

HTML string of processed article content;
textContent Dict[str, str]

text content of the article, with all the HTML tags removed;
length Dict[str, str]

length of an article, in characters;
excerpt Dict[str, str]

article description, or short excerpt from the content;
byline Dict[str, str]

author metadata;
dir Dict[str, str]

content direction;
siteName Dict[str, str]

name of the site.
lang Dict[str, str]

content language
Source code in llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/readability_web/base.py 
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async def scrape_page(
    self,
    browser: Browser,
    url: str,
) -> Dict[str, str]:
    """Scrape a single article url.

    Args:
        browser (Any): a Playwright Chromium browser.
        url (str): URL of the article to scrape.

    Returns:
        Ref: https://github.com/mozilla/readability
        title: article title;
        content: HTML string of processed article content;
        textContent: text content of the article, with all the HTML tags removed;
        length: length of an article, in characters;
        excerpt: article description, or short excerpt from the content;
        byline: author metadata;
        dir: content direction;
        siteName: name of the site.
        lang: content language

    """
    if self._readability_js is None:
        with open(path) as f:
            self._readability_js = f.read()

    inject_readability = f"""
        (function(){{
        {self._readability_js}
        function executor() {{
            return new Readability({{}}, document).parse();
        }}
        return executor();
        }}())
    """

    # browser = cast(Browser, browser)
    page = await browser.new_page(ignore_https_errors=True)
    page.set_default_timeout(60000)
    await page.goto(url, wait_until=self._wait_until)

    r = await page.evaluate(inject_readability)

    await page.close()
    print("scraped:", url)

    return r

RssNewsReader #

Bases: BaseReader

RSS news reader.

Reads news content from RSS feeds and parses with NewsArticleReader.

Source code in llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/rss_news/base.py 
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class RssNewsReader(BaseReader):
    """RSS news reader.

    Reads news content from RSS feeds and parses with NewsArticleReader.

    """

    def __init__(self, **reader_kwargs: Any) -> None:
        """Initialize with parameters.

        Args:
            html_to_text (bool): Whether to convert HTML to text.
                Requires `html2text` package.

        """
        try:
            import feedparser  # noqa: F401
        except ImportError:
            raise ImportError(
                "`feedparser` package not found, please run `pip install feedparser`"
            )

        try:
            import listparser  # noqa: F401
        except ImportError:
            raise ImportError(
                "`listparser` package not found, please run `pip install listparser`"
            )

        self.reader_kwargs = reader_kwargs

    def load_data(self, urls: List[str] = None, opml: str = None) -> List[Document]:
        """Load data from either RSS feeds or OPML.

        Args:
            urls (List[str]): List of RSS URLs to load.
            opml (str): URL to OPML file or string or byte OPML content.

        Returns:
            List[Document]: List of documents.

        """
        if (urls is None) == (
            opml is None
        ):  # This is True if both are None or neither is None
            raise ValueError(
                "Provide either the urls or the opml argument, but not both."
            )

        import feedparser

        if urls and not isinstance(urls, list):
            raise ValueError("urls must be a list of strings.")

        documents = []

        if not urls and opml:
            try:
                import listparser
            except ImportError as e:
                raise ImportError(
                    "Package listparser must be installed if the opml arg is used. "
                    "Please install with 'pip install listparser' or use the "
                    "urls arg instead."
                ) from e
            rss = listparser.parse(opml)
            urls = [feed.url for feed in rss.feeds]

        for url in urls:
            try:
                feed = feedparser.parse(url)
                for i, entry in enumerate(feed.entries):
                    article = NewsArticleReader(**self.reader_kwargs).load_data(
                        urls=[entry.link],
                    )[0]
                    article.metadata["feed"] = url

                    documents.append(
                        Document(text=article.text, metadata=article.metadata)
                    )

            except Exception as e:
                logger.error(f"Error fetching or processing {url}, exception: {e}")
                continue

        return documents

load_data #

load_data(urls: List[str] = None, opml: str = None) -> List[Document]

Load data from either RSS feeds or OPML.

Parameters:

Name Type Description Default
urls List[str]

List of RSS URLs to load.

 None
opml str

URL to OPML file or string or byte OPML content.

 None

Returns:

Type Description
List[Document]

List[Document]: List of documents.
Source code in llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/rss_news/base.py 
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def load_data(self, urls: List[str] = None, opml: str = None) -> List[Document]:
    """Load data from either RSS feeds or OPML.

    Args:
        urls (List[str]): List of RSS URLs to load.
        opml (str): URL to OPML file or string or byte OPML content.

    Returns:
        List[Document]: List of documents.

    """
    if (urls is None) == (
        opml is None
    ):  # This is True if both are None or neither is None
        raise ValueError(
            "Provide either the urls or the opml argument, but not both."
        )

    import feedparser

    if urls and not isinstance(urls, list):
        raise ValueError("urls must be a list of strings.")

    documents = []

    if not urls and opml:
        try:
            import listparser
        except ImportError as e:
            raise ImportError(
                "Package listparser must be installed if the opml arg is used. "
                "Please install with 'pip install listparser' or use the "
                "urls arg instead."
            ) from e
        rss = listparser.parse(opml)
        urls = [feed.url for feed in rss.feeds]

    for url in urls:
        try:
            feed = feedparser.parse(url)
            for i, entry in enumerate(feed.entries):
                article = NewsArticleReader(**self.reader_kwargs).load_data(
                    urls=[entry.link],
                )[0]
                article.metadata["feed"] = url

                documents.append(
                    Document(text=article.text, metadata=article.metadata)
                )

        except Exception as e:
            logger.error(f"Error fetching or processing {url}, exception: {e}")
            continue

    return documents

RssReader #

Bases: BasePydanticReader

RSS reader.

Reads content from an RSS feed.

Source code in llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/rss/base.py 
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class RssReader(BasePydanticReader):
    """RSS reader.

    Reads content from an RSS feed.

    """

    is_remote: bool = True
    html_to_text: bool = False

    @classmethod
    def class_name(cls) -> str:
        return "RssReader"

    def load_data(self, urls: List[str]) -> List[Document]:
        """Load data from RSS feeds.

        Args:
            urls (List[str]): List of RSS URLs to load.

        Returns:
            List[Document]: List of documents.

        """
        import feedparser

        if not isinstance(urls, list):
            raise ValueError("urls must be a list of strings.")

        documents = []

        for url in urls:
            parsed = feedparser.parse(url)
            for entry in parsed.entries:
                doc_id = entry.id or entry.link
                if "content" in entry:
                    data = entry.content[0].value
                else:
                    data = entry.description or entry.summary

                if self.html_to_text:
                    import html2text

                    data = html2text.html2text(data)

                extra_info = {"title": entry.title, "link": entry.link}
                documents.append(Document(text=data, id_=doc_id, extra_info=extra_info))

        return documents

load_data #

load_data(urls: List[str]) -> List[Document]

Load data from RSS feeds.

Parameters:

Name Type Description Default
urls List[str]

List of RSS URLs to load.

 required

Returns:

Type Description
List[Document]

List[Document]: List of documents.
Source code in llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/rss/base.py 
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def load_data(self, urls: List[str]) -> List[Document]:
    """Load data from RSS feeds.

    Args:
        urls (List[str]): List of RSS URLs to load.

    Returns:
        List[Document]: List of documents.

    """
    import feedparser

    if not isinstance(urls, list):
        raise ValueError("urls must be a list of strings.")

    documents = []

    for url in urls:
        parsed = feedparser.parse(url)
        for entry in parsed.entries:
            doc_id = entry.id or entry.link
            if "content" in entry:
                data = entry.content[0].value
            else:
                data = entry.description or entry.summary

            if self.html_to_text:
                import html2text

                data = html2text.html2text(data)

            extra_info = {"title": entry.title, "link": entry.link}
            documents.append(Document(text=data, id_=doc_id, extra_info=extra_info))

    return documents

SimpleWebPageReader #

Bases: BasePydanticReader

Simple web page reader.

Reads pages from the web.

Parameters:

Name Type Description Default
html_to_text bool

Whether to convert HTML to text. Requires html2text package.

 False
metadata_fn Optional[Callable[[str], Dict]]

A function that takes in a URL and returns a dictionary of metadata. Default is None.

 None
Source code in llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/simple_web/base.py 
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class SimpleWebPageReader(BasePydanticReader):
    """Simple web page reader.

    Reads pages from the web.

    Args:
        html_to_text (bool): Whether to convert HTML to text.
            Requires `html2text` package.
        metadata_fn (Optional[Callable[[str], Dict]]): A function that takes in
            a URL and returns a dictionary of metadata.
            Default is None.
    """

    is_remote: bool = True
    html_to_text: bool

    _metadata_fn: Optional[Callable[[str], Dict]] = PrivateAttr()

    def __init__(
        self,
        html_to_text: bool = False,
        metadata_fn: Optional[Callable[[str], Dict]] = None,
    ) -> None:
        """Initialize with parameters."""
        try:
            import html2text  # noqa
        except ImportError:
            raise ImportError(
                "`html2text` package not found, please run `pip install html2text`"
            )
        self._metadata_fn = metadata_fn
        super().__init__(html_to_text=html_to_text)

    @classmethod
    def class_name(cls) -> str:
        return "SimpleWebPageReader"

    def load_data(self, urls: List[str]) -> List[Document]:
        """Load data from the input directory.

        Args:
            urls (List[str]): List of URLs to scrape.

        Returns:
            List[Document]: List of documents.

        """
        if not isinstance(urls, list):
            raise ValueError("urls must be a list of strings.")
        documents = []
        for url in urls:
            response = requests.get(url, headers=None).text
            if self.html_to_text:
                import html2text

                response = html2text.html2text(response)

            metadata: Optional[Dict] = None
            if self._metadata_fn is not None:
                metadata = self._metadata_fn(url)

            documents.append(Document(text=response, id_=url, metadata=metadata or {}))

        return documents

load_data #

load_data(urls: List[str]) -> List[Document]

Load data from the input directory.

Parameters:

Name Type Description Default
urls List[str]

List of URLs to scrape.

 required

Returns:

Type Description
List[Document]

List[Document]: List of documents.
Source code in llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/simple_web/base.py 
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def load_data(self, urls: List[str]) -> List[Document]:
    """Load data from the input directory.

    Args:
        urls (List[str]): List of URLs to scrape.

    Returns:
        List[Document]: List of documents.

    """
    if not isinstance(urls, list):
        raise ValueError("urls must be a list of strings.")
    documents = []
    for url in urls:
        response = requests.get(url, headers=None).text
        if self.html_to_text:
            import html2text

            response = html2text.html2text(response)

        metadata: Optional[Dict] = None
        if self._metadata_fn is not None:
            metadata = self._metadata_fn(url)

        documents.append(Document(text=response, id_=url, metadata=metadata or {}))

    return documents

SitemapReader #

Bases: BaseReader

Asynchronous sitemap reader for web.

Reads pages from the web based on their sitemap.xml.

Parameters:

Name Type Description Default
sitemap_url string

Path to the sitemap.xml. e.g. https://gpt-index.readthedocs.io/sitemap.xml

 required
html_to_text bool

Whether to convert HTML to text. Requires html2text package.

 False
limit int

Maximum number of concurrent requests.

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Source code in llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/sitemap/base.py 
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class SitemapReader(BaseReader):
    """Asynchronous sitemap reader for web.

    Reads pages from the web based on their sitemap.xml.

    Args:
        sitemap_url (string): Path to the sitemap.xml. e.g. https://gpt-index.readthedocs.io/sitemap.xml
        html_to_text (bool): Whether to convert HTML to text.
            Requires `html2text` package.
        limit (int): Maximum number of concurrent requests.

    """

    xml_schema_sitemap = "http://www.sitemaps.org/schemas/sitemap/0.9"

    def __init__(self, html_to_text: bool = False, limit: int = 10) -> None:
        """Initialize with parameters."""
        self._async_loader = AsyncWebPageReader(html_to_text=html_to_text, limit=limit)
        self._html_to_text = html_to_text
        self._limit = limit

    def _load_sitemap(self, sitemap_url: str) -> str:
        sitemap_url_request = urllib.request.urlopen(sitemap_url)

        return sitemap_url_request.read()

    def _parse_sitemap(self, raw_sitemap: str, filter_locs: str = None) -> list:
        sitemap = ET.fromstring(raw_sitemap)
        sitemap_urls = []

        for url in sitemap.findall(f"{{{self.xml_schema_sitemap}}}url"):
            location = url.find(f"{{{self.xml_schema_sitemap}}}loc").text

            if filter_locs is None or filter_locs in location:
                sitemap_urls.append(location)

        return sitemap_urls

    def load_data(self, sitemap_url: str, filter: str = None) -> List[Document]:
        sitemap = self._load_sitemap(sitemap_url=sitemap_url)
        sitemap_urls = self._parse_sitemap(sitemap, filter)

        return self._async_loader.load_data(urls=sitemap_urls)

TrafilaturaWebReader #

Bases: BasePydanticReader

Trafilatura web page reader.

Reads pages from the web. Requires the trafilatura package.

Source code in llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/trafilatura_web/base.py 
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class TrafilaturaWebReader(BasePydanticReader):
    """Trafilatura web page reader.

    Reads pages from the web.
    Requires the `trafilatura` package.

    """

    is_remote: bool = True

    @classmethod
    def class_name(cls) -> str:
        """Get the name identifier of the class."""
        return "TrafilaturaWebReader"

    def load_data(
        self,
        urls: List[str],
        include_comments=True,
        output_format="txt",
        include_tables=True,
        include_images=False,
        include_formatting=False,
        include_links=False,
    ) -> List[Document]:
        """Load data from the urls.

        Args:
            urls (List[str]): List of URLs to scrape.
            include_comments (bool, optional): Include comments in the output. Defaults to True.
            output_format (str, optional): Output format. Defaults to 'txt'.
            include_tables (bool, optional): Include tables in the output. Defaults to True.
            include_images (bool, optional): Include images in the output. Defaults to False.
            include_formatting (bool, optional): Include formatting in the output. Defaults to False.
            include_links (bool, optional): Include links in the output. Defaults to False.

        Returns:
            List[Document]: List of documents.

        """
        import trafilatura

        if not isinstance(urls, list):
            raise ValueError("urls must be a list of strings.")
        documents = []
        for url in urls:
            downloaded = trafilatura.fetch_url(url)
            response = trafilatura.extract(
                downloaded,
                include_comments=include_comments,
                output_format=output_format,
                include_tables=include_tables,
                include_images=include_images,
                include_formatting=include_formatting,
                include_links=include_links,
            )
            documents.append(Document(text=response, id_=url))

        return documents

class_name classmethod #

class_name() -> str

Get the name identifier of the class.

Source code in llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/trafilatura_web/base.py 
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@classmethod
def class_name(cls) -> str:
    """Get the name identifier of the class."""
    return "TrafilaturaWebReader"

load_data #

load_data(urls: List[str], include_comments=True, output_format='txt', include_tables=True, include_images=False, include_formatting=False, include_links=False) -> List[Document]

Load data from the urls.

Parameters:

Name Type Description Default
urls List[str]

List of URLs to scrape.

 required
include_comments bool

Include comments in the output. Defaults to True.

 True
output_format str

Output format. Defaults to 'txt'.

 'txt'
include_tables bool

Include tables in the output. Defaults to True.

 True
include_images bool

Include images in the output. Defaults to False.

 False
include_formatting bool

Include formatting in the output. Defaults to False.

 False
include_links bool

Include links in the output. Defaults to False.

 False

Returns:

Type Description
List[Document]

List[Document]: List of documents.
Source code in llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/trafilatura_web/base.py 
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def load_data(
    self,
    urls: List[str],
    include_comments=True,
    output_format="txt",
    include_tables=True,
    include_images=False,
    include_formatting=False,
    include_links=False,
) -> List[Document]:
    """Load data from the urls.

    Args:
        urls (List[str]): List of URLs to scrape.
        include_comments (bool, optional): Include comments in the output. Defaults to True.
        output_format (str, optional): Output format. Defaults to 'txt'.
        include_tables (bool, optional): Include tables in the output. Defaults to True.
        include_images (bool, optional): Include images in the output. Defaults to False.
        include_formatting (bool, optional): Include formatting in the output. Defaults to False.
        include_links (bool, optional): Include links in the output. Defaults to False.

    Returns:
        List[Document]: List of documents.

    """
    import trafilatura

    if not isinstance(urls, list):
        raise ValueError("urls must be a list of strings.")
    documents = []
    for url in urls:
        downloaded = trafilatura.fetch_url(url)
        response = trafilatura.extract(
            downloaded,
            include_comments=include_comments,
            output_format=output_format,
            include_tables=include_tables,
            include_images=include_images,
            include_formatting=include_formatting,
            include_links=include_links,
        )
        documents.append(Document(text=response, id_=url))

    return documents

UnstructuredURLLoader #

Bases: BaseReader

Loader that uses unstructured to load HTML files.

Source code in llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/unstructured_web/base.py 
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class UnstructuredURLLoader(BaseReader):
    """Loader that uses unstructured to load HTML files."""

    def __init__(
        self, urls: List[str], continue_on_failure: bool = True, headers: dict = {}
    ):
        """Initialize with file path."""
        try:
            import unstructured  # noqa:F401
            from unstructured.__version__ import __version__ as __unstructured_version__

            self.__version = __unstructured_version__
        except ImportError:
            raise ValueError(
                "unstructured package not found, please install it with "
                "`pip install unstructured`"
            )

        if not self.__is_headers_available() and len(headers.keys()) != 0:
            logger.warning(
                "You are using old version of unstructured. "
                "The headers parameter is ignored"
            )

        self.urls = urls
        self.continue_on_failure = continue_on_failure
        self.headers = headers

    def __is_headers_available(self) -> bool:
        _unstructured_version = self.__version.split("-")[0]
        unstructured_version = tuple([int(x) for x in _unstructured_version.split(".")])

        return unstructured_version >= (0, 5, 7)

    def load_data(self) -> List[Document]:
        """Load file."""
        from unstructured.partition.html import partition_html

        docs: List[Document] = []
        for url in self.urls:
            try:
                if self.__is_headers_available():
                    elements = partition_html(url=url, headers=self.headers)
                else:
                    elements = partition_html(url=url)
                text = "\n\n".join([str(el) for el in elements])
                metadata = {"source": url}
                docs.append(Document(text=text, extra_info=metadata))
            except Exception as e:
                if self.continue_on_failure:
                    logger.error(f"Error fetching or processing {url}, exception: {e}")
                else:
                    raise e  # noqa: TRY201
        return docs

load_data #

load_data() -> List[Document]

Load file.

Source code in llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/unstructured_web/base.py 
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def load_data(self) -> List[Document]:
    """Load file."""
    from unstructured.partition.html import partition_html

    docs: List[Document] = []
    for url in self.urls:
        try:
            if self.__is_headers_available():
                elements = partition_html(url=url, headers=self.headers)
            else:
                elements = partition_html(url=url)
            text = "\n\n".join([str(el) for el in elements])
            metadata = {"source": url}
            docs.append(Document(text=text, extra_info=metadata))
        except Exception as e:
            if self.continue_on_failure:
                logger.error(f"Error fetching or processing {url}, exception: {e}")
            else:
                raise e  # noqa: TRY201
    return docs

WholeSiteReader #

Bases: BaseReader

BFS Web Scraper for websites.

This class provides functionality to scrape entire websites using a breadth-first search algorithm. It navigates web pages from a given base URL, following links that match a specified prefix.

Attributes:

Name Type Description
prefix str

URL prefix to focus the scraping.
max_depth int

Maximum depth for BFS algorithm.

Parameters:

Name Type Description Default
prefix str

URL prefix for scraping.

 required
max_depth int

Maximum depth for BFS. Defaults to 10.

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Source code in llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/whole_site/base.py 
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class WholeSiteReader(BaseReader):
    """
    BFS Web Scraper for websites.

    This class provides functionality to scrape entire websites using a breadth-first search algorithm.
    It navigates web pages from a given base URL, following links that match a specified prefix.

    Attributes:
        prefix (str): URL prefix to focus the scraping.
        max_depth (int): Maximum depth for BFS algorithm.

    Args:
        prefix (str): URL prefix for scraping.
        max_depth (int, optional): Maximum depth for BFS. Defaults to 10.
    """

    def __init__(
        self,
        prefix: str,
        max_depth: int = 10,
        driver: Optional[webdriver.Chrome] = None,
    ) -> None:
        """
        Initialize the WholeSiteReader with the provided prefix and maximum depth.
        """
        self.prefix = prefix
        self.max_depth = max_depth
        self.driver = driver if driver else self.setup_driver()

    def setup_driver(self):
        """
        Sets up the Selenium WebDriver for Chrome.

        Returns:
            WebDriver: An instance of Chrome WebDriver.
        """
        try:
            import chromedriver_autoinstaller
        except ImportError:
            raise ImportError("Please install chromedriver_autoinstaller")

        opt = webdriver.ChromeOptions()
        opt.add_argument("--start-maximized")
        chromedriver_autoinstaller.install()
        return webdriver.Chrome(options=opt)

    def clean_url(self, url):
        return url.split("#")[0]

    def restart_driver(self):
        self.driver.quit()
        self.driver = self.setup_driver()

    def extract_content(self):
        WebDriverWait(self.driver, 10).until(
            EC.presence_of_element_located((By.TAG_NAME, "body"))
        )
        body_element = self.driver.find_element(By.TAG_NAME, "body")
        return body_element.text.strip()

    def extract_links(self):
        js_script = """
            var links = [];
            var elements = document.getElementsByTagName('a');
            for (var i = 0; i < elements.length; i++) {
                var href = elements[i].href;
                if (href) {
                    links.push(href);
                }
            }
            return links;
            """
        return self.driver.execute_script(js_script)

    def load_data(self, base_url: str) -> List[Document]:
        """Load data from the base URL using BFS algorithm.

        Args:
            base_url (str): Base URL to start scraping.


        Returns:
            List[Document]: List of scraped documents.
        """
        added_urls = set()
        urls_to_visit = [(base_url, 0)]
        documents = []

        while urls_to_visit:
            current_url, depth = urls_to_visit.pop(0)
            print(f"Visiting: {current_url}, {len(urls_to_visit)} left")

            try:
                self.driver.get(current_url)
                page_content = self.extract_content()
                added_urls.add(current_url)

                next_depth = depth + 1
                if next_depth <= self.max_depth:
                    # links = self.driver.find_elements(By.TAG_NAME, 'a')
                    links = self.extract_links()
                    # clean all urls
                    links = [self.clean_url(link) for link in links]
                    # extract new links
                    links = [link for link in links if link not in added_urls]
                    print(f"Found {len(links)} new potential links")

                    for href in links:
                        try:
                            if href.startswith(self.prefix) and href not in added_urls:
                                urls_to_visit.append((href, next_depth))
                                added_urls.add(href)
                        except Exception:
                            continue

                documents.append(
                    Document(text=page_content, extra_info={"URL": current_url})
                )
                time.sleep(1)

            except WebDriverException:
                print("WebDriverException encountered, restarting driver...")
                self.restart_driver()
            except Exception as e:
                print(f"An unexpected exception occurred: {e}, skipping URL...")
                continue

        self.driver.quit()
        return documents

setup_driver #

setup_driver()

Sets up the Selenium WebDriver for Chrome.

Returns:

Name Type Description
WebDriver

An instance of Chrome WebDriver.
Source code in llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/whole_site/base.py 
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def setup_driver(self):
    """
    Sets up the Selenium WebDriver for Chrome.

    Returns:
        WebDriver: An instance of Chrome WebDriver.
    """
    try:
        import chromedriver_autoinstaller
    except ImportError:
        raise ImportError("Please install chromedriver_autoinstaller")

    opt = webdriver.ChromeOptions()
    opt.add_argument("--start-maximized")
    chromedriver_autoinstaller.install()
    return webdriver.Chrome(options=opt)

load_data #

load_data(base_url: str) -> List[Document]

Load data from the base URL using BFS algorithm.

Parameters:

Name Type Description Default
base_url str

Base URL to start scraping.

 required

Returns:

Type Description
List[Document]

List[Document]: List of scraped documents.
Source code in llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/whole_site/base.py 
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def load_data(self, base_url: str) -> List[Document]:
    """Load data from the base URL using BFS algorithm.

    Args:
        base_url (str): Base URL to start scraping.


    Returns:
        List[Document]: List of scraped documents.
    """
    added_urls = set()
    urls_to_visit = [(base_url, 0)]
    documents = []

    while urls_to_visit:
        current_url, depth = urls_to_visit.pop(0)
        print(f"Visiting: {current_url}, {len(urls_to_visit)} left")

        try:
            self.driver.get(current_url)
            page_content = self.extract_content()
            added_urls.add(current_url)

            next_depth = depth + 1
            if next_depth <= self.max_depth:
                # links = self.driver.find_elements(By.TAG_NAME, 'a')
                links = self.extract_links()
                # clean all urls
                links = [self.clean_url(link) for link in links]
                # extract new links
                links = [link for link in links if link not in added_urls]
                print(f"Found {len(links)} new potential links")

                for href in links:
                    try:
                        if href.startswith(self.prefix) and href not in added_urls:
                            urls_to_visit.append((href, next_depth))
                            added_urls.add(href)
                    except Exception:
                        continue

            documents.append(
                Document(text=page_content, extra_info={"URL": current_url})
            )
            time.sleep(1)

        except WebDriverException:
            print("WebDriverException encountered, restarting driver...")
            self.restart_driver()
        except Exception as e:
            print(f"An unexpected exception occurred: {e}, skipping URL...")
            continue

    self.driver.quit()
    return documents