Source code for langchain_community.document_loaders.news

"""Loader that uses unstructured to load HTML files."""

import logging
from typing import Any, Iterator, List

from langchain_core.documents import Document

from langchain_community.document_loaders.base import BaseLoader

logger = logging.getLogger(__name__)



[docs]
class NewsURLLoader(BaseLoader):
    """Load news articles from URLs using `Unstructured`.

    Args:
        urls: URLs to load. Each is loaded into its own document.
        text_mode: If True, extract text from URL and use that for page content.
            Otherwise, extract raw HTML.
        nlp: If True, perform NLP on the extracted contents, like providing a summary
            and extracting keywords.
        continue_on_failure: If True, continue loading documents even if
            loading fails for a particular URL.
        show_progress_bar: If True, use tqdm to show a loading progress bar. Requires
            tqdm to be installed, ``pip install tqdm``.
        **newspaper_kwargs: Any additional named arguments to pass to
            newspaper.Article().

    Example:
        .. code-block:: python

            from langchain_community.document_loaders import NewsURLLoader

            loader = NewsURLLoader(
                urls=["<url-1>", "<url-2>"],
            )
            docs = loader.load()

    Newspaper reference:
        https://newspaper.readthedocs.io/en/latest/
    """


[docs]
    def __init__(
        self,
        urls: List[str],
        text_mode: bool = True,
        nlp: bool = False,
        continue_on_failure: bool = True,
        show_progress_bar: bool = False,
        **newspaper_kwargs: Any,
    ) -> None:
        """Initialize with file path."""
        try:
            import newspaper

            self.__version = newspaper.__version__
        except ImportError:
            raise ImportError(
                "newspaper package not found, please install it with "
                "`pip install newspaper3k`"
            )

        self.urls = urls
        self.text_mode = text_mode
        self.nlp = nlp
        self.continue_on_failure = continue_on_failure
        self.newspaper_kwargs = newspaper_kwargs
        self.show_progress_bar = show_progress_bar



[docs]
    def load(self) -> List[Document]:
        iter = self.lazy_load()
        if self.show_progress_bar:
            try:
                from tqdm import tqdm
            except ImportError as e:
                raise ImportError(
                    "Package tqdm must be installed if show_progress_bar=True. "
                    "Please install with 'pip install tqdm' or set "
                    "show_progress_bar=False."
                ) from e
            iter = tqdm(iter)
        return list(iter)



[docs]
    def lazy_load(self) -> Iterator[Document]:
        try:
            from newspaper import Article
        except ImportError as e:
            raise ImportError(
                "Cannot import newspaper, please install with `pip install newspaper3k`"
            ) from e

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

                if self.nlp:
                    article.nlp()

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

            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.text_mode:
                content = article.text
            else:
                content = article.html

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

            yield Document(page_content=content, metadata=metadata)