Source code for langchain.document_loaders.blackboard
"""Loader that loads all documents from a blackboard course."""
import contextlib
import re
from pathlib import Path
from typing import Any, List, Optional, Tuple
from urllib.parse import unquote
from langchain.docstore.document import Document
from langchain.document_loaders.directory import DirectoryLoader
from langchain.document_loaders.pdf import PyPDFLoader
from langchain.document_loaders.web_base import WebBaseLoader
[docs]class BlackboardLoader(WebBaseLoader):
    """Loader that loads all documents from a Blackboard course.
    This loader is not compatible with all Blackboard courses. It is only
    compatible with courses that use the new Blackboard interface.
    To use this loader, you must have the BbRouter cookie. You can get this
    cookie by logging into the course and then copying the value of the
    BbRouter cookie from the browser's developer tools.
    Example:
        .. code-block:: python
            from langchain.document_loaders import BlackboardLoader
            loader = BlackboardLoader(
                blackboard_course_url="https://blackboard.example.com/webapps/blackboard/execute/announcement?method=search&context=course_entry&course_id=_123456_1",
                bbrouter="expires:12345...",
            )
            documents = loader.load()
    """
    base_url: str
    folder_path: str
    load_all_recursively: bool
    def __init__(
        self,
        blackboard_course_url: str,
        bbrouter: str,
        load_all_recursively: bool = True,
        basic_auth: Optional[Tuple[str, str]] = None,
        cookies: Optional[dict] = None,
    ):
        """Initialize with blackboard course url.


):
        """Initialize with blackboard course url.
        The BbRouter cookie is required for most blackboard courses.
        Args:
            blackboard_course_url: Blackboard course url.
            bbrouter: BbRouter cookie.
            load_all_recursively: If True, load all documents recursively.
            basic_auth: Basic auth credentials.
            cookies: Cookies.
        Raises:
            ValueError: If blackboard course url is invalid.
        """
        super().__init__(blackboard_course_url)
        # Get base url
        try:
            self.base_url = blackboard_course_url.split("/webapps/blackboard")[0]
        except IndexError:
            raise ValueError(
                "Invalid blackboard course url. "
                "Please provide a url that starts with "
                "https://<blackboard_url>/webapps/blackboard"
            )
        if basic_auth is not None:
            self.session.auth = basic_auth
        # Combine cookies
        if cookies is None:
            cookies = {}
        cookies.update({"BbRouter": bbrouter})
        self.session.cookies.update(cookies)
        self.load_all_recursively = load_all_recursively
        self.check_bs4()
[docs]    def check_bs4(self) -> None:
        """Check if BeautifulSoup4 is installed.
        Raises:
            ImportError: If BeautifulSoup4 is not installed.
        """
        try:
            import bs4  # noqa: F401
        except ImportError:
            raise ImportError(
                "BeautifulSoup4 is required for BlackboardLoader. "
                "Please install it with `pip install beautifulsoup4`."
            )
[docs]    def load(self) -> List[Document]:
        """Load data into document objects.
        Returns:
            List of documents.


"""Load data into document objects.
        Returns:
            List of documents.
        """
        if self.load_all_recursively:
            soup_info = self.scrape()
            self.folder_path = self._get_folder_path(soup_info)
            relative_paths = self._get_paths(soup_info)
            documents = []
            for path in relative_paths:
                url = self.base_url + path
                print(f"Fetching documents from {url}")
                soup_info = self._scrape(url)
                with contextlib.suppress(ValueError):
                    documents.extend(self._get_documents(soup_info))
            return documents
        else:
            print(f"Fetching documents from {self.web_path}")
            soup_info = self.scrape()
            self.folder_path = self._get_folder_path(soup_info)
            return self._get_documents(soup_info)
    def _get_folder_path(self, soup: Any) -> str:
        """Get the folder path to save the documents in.
        Args:
            soup: BeautifulSoup4 soup object.
        Returns:
            Folder path.
        """
        # Get the course name
        course_name = soup.find("span", {"id": "crumb_1"})
        if course_name is None:
            raise ValueError("No course name found.")
        course_name = course_name.text.strip()
        # Prepare the folder path
        course_name_clean = (
            unquote(course_name)
            .replace(" ", "_")
            .replace("/", "_")
            .replace(":", "_")
            .replace(",", "_")
            .replace("?", "_")
            .replace("'", "_")
            .replace("!", "_")
            .replace('"', "_")
        )
        # Get the folder path
        folder_path = Path(".") / course_name_clean


# Get the folder path
        folder_path = Path(".") / course_name_clean
        return str(folder_path)
    def _get_documents(self, soup: Any) -> List[Document]:
        """Fetch content from page and return Documents.
        Args:
            soup: BeautifulSoup4 soup object.
        Returns:
            List of documents.
        """
        attachments = self._get_attachments(soup)
        self._download_attachments(attachments)
        documents = self._load_documents()
        return documents
    def _get_attachments(self, soup: Any) -> List[str]:
        """Get all attachments from a page.
        Args:
            soup: BeautifulSoup4 soup object.
        Returns:
            List of attachments.
        """
        from bs4 import BeautifulSoup, Tag
        # Get content list
        content_list = soup.find("ul", {"class": "contentList"})
        if content_list is None:
            raise ValueError("No content list found.")
        content_list: BeautifulSoup  # type: ignore
        # Get all attachments
        attachments = []
        for attachment in content_list.find_all("ul", {"class": "attachments"}):
            attachment: Tag  # type: ignore
            for link in attachment.find_all("a"):
                link: Tag  # type: ignore
                href = link.get("href")
                # Only add if href is not None and does not start with #
                if href is not None and not href.startswith("#"):
                    attachments.append(href)
        return attachments
    def _download_attachments(self, attachments: List[str]) -> None:
        """Download all attachments.
        Args:
            attachments: List of attachments.
        """
        # Make sure the folder exists
        Path(self.folder_path).mkdir(parents=True, exist_ok=True)


Path(self.folder_path).mkdir(parents=True, exist_ok=True)
        # Download all attachments
        for attachment in attachments:
            self.download(attachment)
    def _load_documents(self) -> List[Document]:
        """Load all documents in the folder.
        Returns:
            List of documents.
        """
        # Create the document loader
        loader = DirectoryLoader(
            path=self.folder_path, glob="*.pdf", loader_cls=PyPDFLoader  # type: ignore
        )
        # Load the documents
        documents = loader.load()
        # Return all documents
        return documents
    def _get_paths(self, soup: Any) -> List[str]:
        """Get all relative paths in the navbar."""
        relative_paths = []
        course_menu = soup.find("ul", {"class": "courseMenu"})
        if course_menu is None:
            raise ValueError("No course menu found.")
        for link in course_menu.find_all("a"):
            href = link.get("href")
            if href is not None and href.startswith("/"):
                relative_paths.append(href)
        return relative_paths
[docs]    def download(self, path: str) -> None:
        """Download a file from a url.
        Args:
            path: Path to the file.
        """
        # Get the file content
        response = self.session.get(self.base_url + path, allow_redirects=True)
        # Get the filename
        filename = self.parse_filename(response.url)
        # Write the file to disk
        with open(Path(self.folder_path) / filename, "wb") as f:
            f.write(response.content)
[docs]    def parse_filename(self, url: str) -> str:
        """Parse the filename from a url.
        Args:


"""Parse the filename from a url.
        Args:
            url: Url to parse the filename from.
        Returns:
            The filename.
        """
        if (url_path := Path(url)) and url_path.suffix == ".pdf":
            return url_path.name
        else:
            return self._parse_filename_from_url(url)
    def _parse_filename_from_url(self, url: str) -> str:
        """Parse the filename from a url.
        Args:
            url: Url to parse the filename from.
        Returns:
            The filename.
        Raises:
            ValueError: If the filename could not be parsed.
        """
        filename_matches = re.search(r"filename%2A%3DUTF-8%27%27(.+)", url)
        if filename_matches:
            filename = filename_matches.group(1)
        else:
            raise ValueError(f"Could not parse filename from {url}")
        if ".pdf" not in filename:
            raise ValueError(f"Incorrect file type: {filename}")
        filename = filename.split(".pdf")[0] + ".pdf"
        filename = unquote(filename)
        filename = filename.replace("%20", " ")
        return filename
if __name__ == "__main__":
    loader = BlackboardLoader(
        "https://<YOUR BLACKBOARD URL"
        " HERE>/webapps/blackboard/content/listContent.jsp?course_id=_<YOUR COURSE ID"
        " HERE>_1&content_id=_<YOUR CONTENT ID HERE>_1&mode=reset",
        "<YOUR BBROUTER COOKIE HERE>",
        load_all_recursively=True,
    )
    documents = loader.load()
    print(f"Loaded {len(documents)} pages of PDFs from {loader.web_path}")


Source code for langchain.document_loaders.ifixit
"""Loader that loads iFixit data."""
from typing import List, Optional
import requests
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
from langchain.document_loaders.web_base import WebBaseLoader
IFIXIT_BASE_URL = "https://www.ifixit.com/api/2.0"
[docs]class IFixitLoader(BaseLoader):
    """Load iFixit repair guides, device wikis and answers.
    iFixit is the largest, open repair community on the web. The site contains nearly
    100k repair manuals, 200k Questions & Answers on 42k devices, and all the data is
    licensed under CC-BY.
    This loader will allow you to download the text of a repair guide, text of Q&A's
    and wikis from devices on iFixit using their open APIs and web scraping.
    """
    def __init__(self, web_path: str):
        """Initialize with web path."""
        if not web_path.startswith("https://www.ifixit.com"):
            raise ValueError("web path must start with 'https://www.ifixit.com'")
        path = web_path.replace("https://www.ifixit.com", "")
        allowed_paths = ["/Device", "/Guide", "/Answers", "/Teardown"]
        """ TODO: Add /Wiki """
        if not any(path.startswith(allowed_path) for allowed_path in allowed_paths):
            raise ValueError(
                "web path must start with /Device, /Guide, /Teardown or /Answers"
            )
        pieces = [x for x in path.split("/") if x]
        """Teardowns are just guides by a different name"""


"""Teardowns are just guides by a different name"""
        self.page_type = pieces[0] if pieces[0] != "Teardown" else "Guide"
        if self.page_type == "Guide" or self.page_type == "Answers":
            self.id = pieces[2]
        else:
            self.id = pieces[1]
        self.web_path = web_path
[docs]    def load(self) -> List[Document]:
        if self.page_type == "Device":
            return self.load_device()
        elif self.page_type == "Guide" or self.page_type == "Teardown":
            return self.load_guide()
        elif self.page_type == "Answers":
            return self.load_questions_and_answers()
        else:
            raise ValueError("Unknown page type: " + self.page_type)
[docs]    @staticmethod
    def load_suggestions(query: str = "", doc_type: str = "all") -> List[Document]:
        res = requests.get(
            IFIXIT_BASE_URL + "/suggest/" + query + "?doctypes=" + doc_type
        )
        if res.status_code != 200:
            raise ValueError(
                'Could not load suggestions for "' + query + '"\n' + res.json()
            )
        data = res.json()
        results = data["results"]
        output = []
        for result in results:
            try:
                loader = IFixitLoader(result["url"])
                if loader.page_type == "Device":
                    output += loader.load_device(include_guides=False)
                else:
                    output += loader.load()
            except ValueError:
                continue
        return output
[docs]    def load_questions_and_answers(
        self, url_override: Optional[str] = None


self, url_override: Optional[str] = None
    ) -> List[Document]:
        loader = WebBaseLoader(self.web_path if url_override is None else url_override)
        soup = loader.scrape()
        output = []
        title = soup.find("h1", "post-title").text
        output.append("# " + title)
        output.append(soup.select_one(".post-content .post-text").text.strip())
        answersHeader = soup.find("div", "post-answers-header")
        if answersHeader:
            output.append("\n## " + answersHeader.text.strip())
        for answer in soup.select(".js-answers-list .post.post-answer"):
            if answer.has_attr("itemprop") and "acceptedAnswer" in answer["itemprop"]:
                output.append("\n### Accepted Answer")
            elif "post-helpful" in answer["class"]:
                output.append("\n### Most Helpful Answer")
            else:
                output.append("\n### Other Answer")
            output += [
                a.text.strip() for a in answer.select(".post-content .post-text")
            ]
            output.append("\n")
        text = "\n".join(output).strip()
        metadata = {"source": self.web_path, "title": title}
        return [Document(page_content=text, metadata=metadata)]
[docs]    def load_device(
        self, url_override: Optional[str] = None, include_guides: bool = True
    ) -> List[Document]:
        documents = []
        if url_override is None:
            url = IFIXIT_BASE_URL + "/wikis/CATEGORY/" + self.id
        else:
            url = url_override
        res = requests.get(url)
        data = res.json()
        text = "\n".join(
            [


data = res.json()
        text = "\n".join(
            [
                data[key]
                for key in ["title", "description", "contents_raw"]
                if key in data
            ]
        ).strip()
        metadata = {"source": self.web_path, "title": data["title"]}
        documents.append(Document(page_content=text, metadata=metadata))
        if include_guides:
            """Load and return documents for each guide linked to from the device"""
            guide_urls = [guide["url"] for guide in data["guides"]]
            for guide_url in guide_urls:
                documents.append(IFixitLoader(guide_url).load()[0])
        return documents
[docs]    def load_guide(self, url_override: Optional[str] = None) -> List[Document]:
        if url_override is None:
            url = IFIXIT_BASE_URL + "/guides/" + self.id
        else:
            url = url_override
        res = requests.get(url)
        if res.status_code != 200:
            raise ValueError(
                "Could not load guide: " + self.web_path + "\n" + res.json()
            )
        data = res.json()
        doc_parts = ["# " + data["title"], data["introduction_raw"]]
        doc_parts.append("\n\n###Tools Required:")
        if len(data["tools"]) == 0:
            doc_parts.append("\n - None")
        else:
            for tool in data["tools"]:
                doc_parts.append("\n - " + tool["text"])
        doc_parts.append("\n\n###Parts Required:")
        if len(data["parts"]) == 0:
            doc_parts.append("\n - None")
        else:
            for part in data["parts"]:


else:
            for part in data["parts"]:
                doc_parts.append("\n - " + part["text"])
        for row in data["steps"]:
            doc_parts.append(
                "\n\n## "
                + (
                    row["title"]
                    if row["title"] != ""
                    else "Step {}".format(row["orderby"])
                )
            )
            for line in row["lines"]:
                doc_parts.append(line["text_raw"])
        doc_parts.append(data["conclusion_raw"])
        text = "\n".join(doc_parts)
        metadata = {"source": self.web_path, "title": data["title"]}
        return [Document(page_content=text, metadata=metadata)]


Source code for langchain.document_loaders.url
"""Loader that uses unstructured to load HTML files."""
import logging
from typing import Any, List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
logger = logging.getLogger(__name__)
[docs]class UnstructuredURLLoader(BaseLoader):
    """Loader that uses unstructured to load HTML files."""
    def __init__(
        self,
        urls: List[str],
        continue_on_failure: bool = True,
        mode: str = "single",
        show_progress_bar: bool = False,
        **unstructured_kwargs: Any,
    ):
        """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`"
            )
        self._validate_mode(mode)
        self.mode = mode
        headers = unstructured_kwargs.pop("headers", {})
        if len(headers.keys()) != 0:
            warn_about_headers = False
            if self.__is_non_html_available():
                warn_about_headers = not self.__is_headers_available_for_non_html()
            else:
                warn_about_headers = not self.__is_headers_available_for_html()
            if warn_about_headers:
                logger.warning(
                    "You are using an old version of unstructured. "
                    "The headers parameter is ignored"
                )
        self.urls = urls
        self.continue_on_failure = continue_on_failure
        self.headers = headers
        self.unstructured_kwargs = unstructured_kwargs
        self.show_progress_bar = show_progress_bar


self.unstructured_kwargs = unstructured_kwargs
        self.show_progress_bar = show_progress_bar
    def _validate_mode(self, mode: str) -> None:
        _valid_modes = {"single", "elements"}
        if mode not in _valid_modes:
            raise ValueError(
                f"Got {mode} for `mode`, but should be one of `{_valid_modes}`"
            )
    def __is_headers_available_for_html(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 __is_headers_available_for_non_html(self) -> bool:
        _unstructured_version = self.__version.split("-")[0]
        unstructured_version = tuple([int(x) for x in _unstructured_version.split(".")])
        return unstructured_version >= (0, 5, 13)
    def __is_non_html_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, 12)
[docs]    def load(self) -> List[Document]:
        """Load file."""
        from unstructured.partition.auto import partition
        from unstructured.partition.html import partition_html
        docs: List[Document] = list()
        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 "


"Please install with 'pip install tqdm' or set "
                    "show_progress_bar=False."
                ) from e
            urls = tqdm(self.urls)
        else:
            urls = self.urls
        for url in urls:
            try:
                if self.__is_non_html_available():
                    if self.__is_headers_available_for_non_html():
                        elements = partition(
                            url=url, headers=self.headers, **self.unstructured_kwargs
                        )
                    else:
                        elements = partition(url=url, **self.unstructured_kwargs)
                else:
                    if self.__is_headers_available_for_html():
                        elements = partition_html(
                            url=url, headers=self.headers, **self.unstructured_kwargs
                        )
                    else:
                        elements = partition_html(url=url, **self.unstructured_kwargs)
            except Exception as e:
                if self.continue_on_failure:
                    logger.error(f"Error fetching or processing {url}, exeption: {e}")
                    continue
                else:
                    raise e
            if self.mode == "single":
                text = "\n\n".join([str(el) for el in elements])
                metadata = {"source": url}
                docs.append(Document(page_content=text, metadata=metadata))
            elif self.mode == "elements":
                for element in elements:
                    metadata = element.metadata.to_dict()
                    metadata["category"] = element.category
                    docs.append(Document(page_content=str(element), metadata=metadata))
        return docs


Source code for langchain.document_loaders.docugami
"""Loader that loads processed documents from Docugami."""
import io
import logging
import os
import re
from pathlib import Path
from typing import Any, Dict, List, Mapping, Optional, Sequence, Union
import requests
from pydantic import BaseModel, root_validator
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
TD_NAME = "{http://www.w3.org/1999/xhtml}td"
TABLE_NAME = "{http://www.w3.org/1999/xhtml}table"
XPATH_KEY = "xpath"
DOCUMENT_ID_KEY = "id"
DOCUMENT_NAME_KEY = "name"
STRUCTURE_KEY = "structure"
TAG_KEY = "tag"
PROJECTS_KEY = "projects"
DEFAULT_API_ENDPOINT = "https://api.docugami.com/v1preview1"
logger = logging.getLogger(__name__)
[docs]class DocugamiLoader(BaseLoader, BaseModel):
    """Loader that loads processed docs from Docugami.
    To use, you should have the ``lxml`` python package installed.
    """
    api: str = DEFAULT_API_ENDPOINT
    access_token: Optional[str] = os.environ.get("DOCUGAMI_API_KEY")
    docset_id: Optional[str]
    document_ids: Optional[Sequence[str]]
    file_paths: Optional[Sequence[Union[Path, str]]]
    min_chunk_size: int = 32  # appended to the next chunk to avoid over-chunking
[docs]    @root_validator
    def validate_local_or_remote(cls, values: Dict[str, Any]) -> Dict[str, Any]:
        """Validate that either local file paths are given, or remote API docset ID."""
        if values.get("file_paths") and values.get("docset_id"):


if values.get("file_paths") and values.get("docset_id"):
            raise ValueError("Cannot specify both file_paths and remote API docset_id")
        if not values.get("file_paths") and not values.get("docset_id"):
            raise ValueError("Must specify either file_paths or remote API docset_id")
        if values.get("docset_id") and not values.get("access_token"):
            raise ValueError("Must specify access token if using remote API docset_id")
        return values
    def _parse_dgml(
        self, document: Mapping, content: bytes, doc_metadata: Optional[Mapping] = None
    ) -> List[Document]:
        """Parse a single DGML document into a list of Documents."""
        try:
            from lxml import etree
        except ImportError:
            raise ImportError(
                "Could not import lxml python package. "
                "Please install it with `pip install lxml`."
            )
        # helpers
        def _xpath_qname_for_chunk(chunk: Any) -> str:
            """Get the xpath qname for a chunk."""
            qname = f"{chunk.prefix}:{chunk.tag.split('}')[-1]}"
            parent = chunk.getparent()
            if parent is not None:
                doppelgangers = [x for x in parent if x.tag == chunk.tag]
                if len(doppelgangers) > 1:
                    idx_of_self = doppelgangers.index(chunk)
                    qname = f"{qname}[{idx_of_self + 1}]"
            return qname
        def _xpath_for_chunk(chunk: Any) -> str:
            """Get the xpath for a chunk."""
            ancestor_chain = chunk.xpath("ancestor-or-self::*")


ancestor_chain = chunk.xpath("ancestor-or-self::*")
            return "/" + "/".join(_xpath_qname_for_chunk(x) for x in ancestor_chain)
        def _structure_value(node: Any) -> str:
            """Get the structure value for a node."""
            structure = (
                "table"
                if node.tag == TABLE_NAME
                else node.attrib["structure"]
                if "structure" in node.attrib
                else None
            )
            return structure
        def _is_structural(node: Any) -> bool:
            """Check if a node is structural."""
            return _structure_value(node) is not None
        def _is_heading(node: Any) -> bool:
            """Check if a node is a heading."""
            structure = _structure_value(node)
            return structure is not None and structure.lower().startswith("h")
        def _get_text(node: Any) -> str:
            """Get the text of a node."""
            return " ".join(node.itertext()).strip()
        def _has_structural_descendant(node: Any) -> bool:
            """Check if a node has a structural descendant."""
            for child in node:
                if _is_structural(child) or _has_structural_descendant(child):
                    return True
            return False
        def _leaf_structural_nodes(node: Any) -> List:
            """Get the leaf structural nodes of a node."""
            if _is_structural(node) and not _has_structural_descendant(node):
                return [node]
            else:
                leaf_nodes = []
                for child in node:
                    leaf_nodes.extend(_leaf_structural_nodes(child))
                return leaf_nodes
        def _create_doc(node: Any, text: str) -> Document:
            """Create a Document from a node and text."""


"""Create a Document from a node and text."""
            metadata = {
                XPATH_KEY: _xpath_for_chunk(node),
                DOCUMENT_ID_KEY: document["id"],
                DOCUMENT_NAME_KEY: document["name"],
                STRUCTURE_KEY: node.attrib.get("structure", ""),
                TAG_KEY: re.sub(r"\{.*\}", "", node.tag),
            }
            if doc_metadata:
                metadata.update(doc_metadata)
            return Document(
                page_content=text,
                metadata=metadata,
            )
        # parse the tree and return chunks
        tree = etree.parse(io.BytesIO(content))
        root = tree.getroot()
        chunks: List[Document] = []
        prev_small_chunk_text = None
        for node in _leaf_structural_nodes(root):
            text = _get_text(node)
            if prev_small_chunk_text:
                text = prev_small_chunk_text + " " + text
                prev_small_chunk_text = None
            if _is_heading(node) or len(text) < self.min_chunk_size:
                # Save headings or other small chunks to be appended to the next chunk
                prev_small_chunk_text = text
            else:
                chunks.append(_create_doc(node, text))
        if prev_small_chunk_text and len(chunks) > 0:
            # small chunk at the end left over, just append to last chunk
            chunks[-1].page_content += " " + prev_small_chunk_text
        return chunks
    def _document_details_for_docset_id(self, docset_id: str) -> List[Dict]:
        """Gets all document details for the given docset ID"""
        url = f"{self.api}/docsets/{docset_id}/documents"
        all_documents = []
        while url:
            response = requests.get(
                url,


while url:
            response = requests.get(
                url,
                headers={"Authorization": f"Bearer {self.access_token}"},
            )
            if response.ok:
                data = response.json()
                all_documents.extend(data["documents"])
                url = data.get("next", None)
            else:
                raise Exception(
                    f"Failed to download {url} (status: {response.status_code})"
                )
        return all_documents
    def _project_details_for_docset_id(self, docset_id: str) -> List[Dict]:
        """Gets all project details for the given docset ID"""
        url = f"{self.api}/projects?docset.id={docset_id}"
        all_projects = []
        while url:
            response = requests.request(
                "GET",
                url,
                headers={"Authorization": f"Bearer {self.access_token}"},
                data={},
            )
            if response.ok:
                data = response.json()
                all_projects.extend(data["projects"])
                url = data.get("next", None)
            else:
                raise Exception(
                    f"Failed to download {url} (status: {response.status_code})"
                )
        return all_projects
    def _metadata_for_project(self, project: Dict) -> Dict:
        """Gets project metadata for all files"""
        project_id = project.get("id")
        url = f"{self.api}/projects/{project_id}/artifacts/latest"
        all_artifacts = []
        while url:
            response = requests.request(
                "GET",
                url,
                headers={"Authorization": f"Bearer {self.access_token}"},
                data={},
            )
            if response.ok:
                data = response.json()


data={},
            )
            if response.ok:
                data = response.json()
                all_artifacts.extend(data["artifacts"])
                url = data.get("next", None)
            else:
                raise Exception(
                    f"Failed to download {url} (status: {response.status_code})"
                )
        per_file_metadata = {}
        for artifact in all_artifacts:
            artifact_name = artifact.get("name")
            artifact_url = artifact.get("url")
            artifact_doc = artifact.get("document")
            if artifact_name == "report-values.xml" and artifact_url and artifact_doc:
                doc_id = artifact_doc["id"]
                metadata: Dict = {}
                # the evaluated XML for each document is named after the project
                response = requests.request(
                    "GET",
                    f"{artifact_url}/content",
                    headers={"Authorization": f"Bearer {self.access_token}"},
                    data={},
                )
                if response.ok:
                    try:
                        from lxml import etree
                    except ImportError:
                        raise ImportError(
                            "Could not import lxml python package. "
                            "Please install it with `pip install lxml`."
                        )
                    artifact_tree = etree.parse(io.BytesIO(response.content))
                    artifact_root = artifact_tree.getroot()
                    ns = artifact_root.nsmap
                    entries = artifact_root.xpath("//pr:Entry", namespaces=ns)
                    for entry in entries:
                        heading = entry.xpath("./pr:Heading", namespaces=ns)[0].text
                        value = " ".join(
                            entry.xpath("./pr:Value", namespaces=ns)[0].itertext()
                        ).strip()
                        metadata[heading] = value
                    per_file_metadata[doc_id] = metadata
                else:
                    raise Exception(


per_file_metadata[doc_id] = metadata
                else:
                    raise Exception(
                        f"Failed to download {artifact_url}/content "
                        + "(status: {response.status_code})"
                    )
        return per_file_metadata
    def _load_chunks_for_document(
        self, docset_id: str, document: Dict, doc_metadata: Optional[Dict] = None
    ) -> List[Document]:
        """Load chunks for a document."""
        document_id = document["id"]
        url = f"{self.api}/docsets/{docset_id}/documents/{document_id}/dgml"
        response = requests.request(
            "GET",
            url,
            headers={"Authorization": f"Bearer {self.access_token}"},
            data={},
        )
        if response.ok:
            return self._parse_dgml(document, response.content, doc_metadata)
        else:
            raise Exception(
                f"Failed to download {url} (status: {response.status_code})"
            )
[docs]    def load(self) -> List[Document]:
        """Load documents."""
        chunks: List[Document] = []
        if self.access_token and self.docset_id:
            # remote mode
            _document_details = self._document_details_for_docset_id(self.docset_id)
            if self.document_ids:
                _document_details = [
                    d for d in _document_details if d["id"] in self.document_ids
                ]
            _project_details = self._project_details_for_docset_id(self.docset_id)
            combined_project_metadata = {}
            if _project_details:
                # if there are any projects for this docset, load project metadata
                for project in _project_details:
                    metadata = self._metadata_for_project(project)


for project in _project_details:
                    metadata = self._metadata_for_project(project)
                    combined_project_metadata.update(metadata)
            for doc in _document_details:
                doc_metadata = combined_project_metadata.get(doc["id"])
                chunks += self._load_chunks_for_document(
                    self.docset_id, doc, doc_metadata
                )
        elif self.file_paths:
            # local mode (for integration testing, or pre-downloaded XML)
            for path in self.file_paths:
                path = Path(path)
                with open(path, "rb") as file:
                    chunks += self._parse_dgml(
                        {
                            DOCUMENT_ID_KEY: path.name,
                            DOCUMENT_NAME_KEY: path.name,
                        },
                        file.read(),
                    )
        return chunks


Source code for langchain.document_loaders.gitbook
"""Loader that loads GitBook."""
from typing import Any, List, Optional
from urllib.parse import urljoin, urlparse
from langchain.docstore.document import Document
from langchain.document_loaders.web_base import WebBaseLoader
[docs]class GitbookLoader(WebBaseLoader):
    """Load GitBook data.
    1. load from either a single page, or
    2. load all (relative) paths in the navbar.
    """
    def __init__(
        self,
        web_page: str,
        load_all_paths: bool = False,
        base_url: Optional[str] = None,
        content_selector: str = "main",
    ):
        """Initialize with web page and whether to load all paths.
        Args:
            web_page: The web page to load or the starting point from where
                relative paths are discovered.
            load_all_paths: If set to True, all relative paths in the navbar
                are loaded instead of only `web_page`.
            base_url: If `load_all_paths` is True, the relative paths are
                appended to this base url. Defaults to `web_page` if not set.
        """
        self.base_url = base_url or web_page
        if self.base_url.endswith("/"):
            self.base_url = self.base_url[:-1]
        if load_all_paths:
            # set web_path to the sitemap if we want to crawl all paths
            web_paths = f"{self.base_url}/sitemap.xml"
        else:
            web_paths = web_page
        super().__init__(web_paths)
        self.load_all_paths = load_all_paths
        self.content_selector = content_selector
[docs]    def load(self) -> List[Document]:


[docs]    def load(self) -> List[Document]:
        """Fetch text from one single GitBook page."""
        if self.load_all_paths:
            soup_info = self.scrape()
            relative_paths = self._get_paths(soup_info)
            documents = []
            for path in relative_paths:
                url = urljoin(self.base_url, path)
                print(f"Fetching text from {url}")
                soup_info = self._scrape(url)
                documents.append(self._get_document(soup_info, url))
            return [d for d in documents if d]
        else:
            soup_info = self.scrape()
            documents = [self._get_document(soup_info, self.web_path)]
            return [d for d in documents if d]
    def _get_document(
        self, soup: Any, custom_url: Optional[str] = None
    ) -> Optional[Document]:
        """Fetch content from page and return Document."""
        page_content_raw = soup.find(self.content_selector)
        if not page_content_raw:
            return None
        content = page_content_raw.get_text(separator="\n").strip()
        title_if_exists = page_content_raw.find("h1")
        title = title_if_exists.text if title_if_exists else ""
        metadata = {"source": custom_url or self.web_path, "title": title}
        return Document(page_content=content, metadata=metadata)
    def _get_paths(self, soup: Any) -> List[str]:
        """Fetch all relative paths in the navbar."""
        return [urlparse(loc.text).path for loc in soup.find_all("loc")]


Source code for langchain.document_loaders.psychic
"""Loader that loads documents from Psychic.dev."""
from typing import List, Optional
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class PsychicLoader(BaseLoader):
    """Loader that loads documents from Psychic.dev."""
    def __init__(
        self, api_key: str, account_id: str, connector_id: Optional[str] = None
    ):
        """Initialize with API key, connector id, and account id."""
        try:
            from psychicapi import ConnectorId, Psychic  # noqa: F401
        except ImportError:
            raise ImportError(
                "`psychicapi` package not found, please run `pip install psychicapi`"
            )
        self.psychic = Psychic(secret_key=api_key)
        self.connector_id = ConnectorId(connector_id)
        self.account_id = account_id
[docs]    def load(self) -> List[Document]:
        """Load documents."""
        psychic_docs = self.psychic.get_documents(
            connector_id=self.connector_id, account_id=self.account_id
        )
        return [
            Document(
                page_content=doc["content"],
                metadata={"title": doc["title"], "source": doc["uri"]},
            )
            for doc in psychic_docs.documents
        ]


Source code for langchain.document_loaders.web_base
"""Web base loader class."""
import asyncio
import logging
import warnings
from typing import Any, Dict, Iterator, List, Optional, Union
import aiohttp
import requests
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
logger = logging.getLogger(__name__)
default_header_template = {
    "User-Agent": "",
    "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*"
    ";q=0.8",
    "Accept-Language": "en-US,en;q=0.5",
    "Referer": "https://www.google.com/",
    "DNT": "1",
    "Connection": "keep-alive",
    "Upgrade-Insecure-Requests": "1",
}
def _build_metadata(soup: Any, url: str) -> dict:
    """Build metadata from BeautifulSoup output."""
    metadata = {"source": url}
    if title := soup.find("title"):
        metadata["title"] = title.get_text()
    if description := soup.find("meta", attrs={"name": "description"}):
        metadata["description"] = description.get("content", None)
    if html := soup.find("html"):
        metadata["language"] = html.get("lang", None)
    return metadata
[docs]class WebBaseLoader(BaseLoader):
    """Loader that uses urllib and beautiful soup to load webpages."""
    web_paths: List[str]
    requests_per_second: int = 2
    """Max number of concurrent requests to make."""
    default_parser: str = "html.parser"
    """Default parser to use for BeautifulSoup."""
    requests_kwargs: Dict[str, Any] = {}
    """kwargs for requests"""


requests_kwargs: Dict[str, Any] = {}
    """kwargs for requests"""
    raise_for_status: bool = False
    """Raise an exception if http status code denotes an error."""
    bs_get_text_kwargs: Dict[str, Any] = {}
    """kwargs for beatifulsoup4 get_text"""
    def __init__(
        self,
        web_path: Union[str, List[str]],
        header_template: Optional[dict] = None,
        verify: Optional[bool] = True,
        proxies: Optional[dict] = None,
    ):
        """Initialize with webpage path."""
        # TODO: Deprecate web_path in favor of web_paths, and remove this
        # left like this because there are a number of loaders that expect single
        # urls
        if isinstance(web_path, str):
            self.web_paths = [web_path]
        elif isinstance(web_path, List):
            self.web_paths = web_path
        self.session = requests.Session()
        try:
            import bs4  # noqa:F401
        except ImportError:
            raise ValueError(
                "bs4 package not found, please install it with " "`pip install bs4`"
            )
        # Choose to verify
        self.verify = verify
        headers = header_template or default_header_template
        if not headers.get("User-Agent"):
            try:
                from fake_useragent import UserAgent
                headers["User-Agent"] = UserAgent().random
            except ImportError:
                logger.info(
                    "fake_useragent not found, using default user agent."
                    "To get a realistic header for requests, "
                    "`pip install fake_useragent`."
                )
        self.session.headers = dict(headers)
        if proxies:
            self.session.proxies.update(proxies)
    @property


if proxies:
            self.session.proxies.update(proxies)
    @property
    def web_path(self) -> str:
        if len(self.web_paths) > 1:
            raise ValueError("Multiple webpaths found.")
        return self.web_paths[0]
    async def _fetch(
        self, url: str, retries: int = 3, cooldown: int = 2, backoff: float = 1.5
    ) -> str:
        # For SiteMap SSL verification
        if not self.requests_kwargs.get("verify", True):
            connector = aiohttp.TCPConnector(ssl=False)
        else:
            connector = None
        async with aiohttp.ClientSession(connector=connector) as session:
            for i in range(retries):
                try:
                    async with session.get(
                        url, headers=self.session.headers, verify=self.verify
                    ) as response:
                        return await response.text()
                except aiohttp.ClientConnectionError as e:
                    if i == retries - 1:
                        raise
                    else:
                        logger.warning(
                            f"Error fetching {url} with attempt "
                            f"{i + 1}/{retries}: {e}. Retrying..."
                        )
                        await asyncio.sleep(cooldown * backoff**i)
        raise ValueError("retry count exceeded")
    async def _fetch_with_rate_limit(
        self, url: str, semaphore: asyncio.Semaphore
    ) -> str:
        async with semaphore:
            return await self._fetch(url)
[docs]    async def fetch_all(self, urls: List[str]) -> Any:
        """Fetch all urls concurrently with rate limiting."""
        semaphore = asyncio.Semaphore(self.requests_per_second)
        tasks = []
        for url in urls:


tasks = []
        for url in urls:
            task = asyncio.ensure_future(self._fetch_with_rate_limit(url, semaphore))
            tasks.append(task)
        try:
            from tqdm.asyncio import tqdm_asyncio
            return await tqdm_asyncio.gather(
                *tasks, desc="Fetching pages", ascii=True, mininterval=1
            )
        except ImportError:
            warnings.warn("For better logging of progress, `pip install tqdm`")
            return await asyncio.gather(*tasks)
    @staticmethod
    def _check_parser(parser: str) -> None:
        """Check that parser is valid for bs4."""
        valid_parsers = ["html.parser", "lxml", "xml", "lxml-xml", "html5lib"]
        if parser not in valid_parsers:
            raise ValueError(
                "`parser` must be one of " + ", ".join(valid_parsers) + "."
            )
[docs]    def scrape_all(self, urls: List[str], parser: Union[str, None] = None) -> List[Any]:
        """Fetch all urls, then return soups for all results."""
        from bs4 import BeautifulSoup
        results = asyncio.run(self.fetch_all(urls))
        final_results = []
        for i, result in enumerate(results):
            url = urls[i]
            if parser is None:
                if url.endswith(".xml"):
                    parser = "xml"
                else:
                    parser = self.default_parser
                self._check_parser(parser)
            final_results.append(BeautifulSoup(result, parser))
        return final_results
    def _scrape(self, url: str, parser: Union[str, None] = None) -> Any:
        from bs4 import BeautifulSoup
        if parser is None:
            if url.endswith(".xml"):


if parser is None:
            if url.endswith(".xml"):
                parser = "xml"
            else:
                parser = self.default_parser
        self._check_parser(parser)
        html_doc = self.session.get(url, verify=self.verify, **self.requests_kwargs)
        if self.raise_for_status:
            html_doc.raise_for_status()
        html_doc.encoding = html_doc.apparent_encoding
        return BeautifulSoup(html_doc.text, parser)
[docs]    def scrape(self, parser: Union[str, None] = None) -> Any:
        """Scrape data from webpage and return it in BeautifulSoup format."""
        if parser is None:
            parser = self.default_parser
        return self._scrape(self.web_path, parser)
[docs]    def lazy_load(self) -> Iterator[Document]:
        """Lazy load text from the url(s) in web_path."""
        for path in self.web_paths:
            soup = self._scrape(path)
            text = soup.get_text(**self.bs_get_text_kwargs)
            metadata = _build_metadata(soup, path)
            yield Document(page_content=text, metadata=metadata)
[docs]    def load(self) -> List[Document]:
        """Load text from the url(s) in web_path."""
        return list(self.lazy_load())
[docs]    def aload(self) -> List[Document]:
        """Load text from the urls in web_path async into Documents."""
        results = self.scrape_all(self.web_paths)
        docs = []
        for i in range(len(results)):
            soup = results[i]
            text = soup.get_text(**self.bs_get_text_kwargs)
            metadata = _build_metadata(soup, self.web_paths[i])
            docs.append(Document(page_content=text, metadata=metadata))
        return docs


Source code for langchain.document_loaders.conllu
"""Load CoNLL-U files."""
import csv
from typing import List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class CoNLLULoader(BaseLoader):
    """Load CoNLL-U files."""
    def __init__(self, file_path: str):
        """Initialize with file path."""
        self.file_path = file_path
[docs]    def load(self) -> List[Document]:
        """Load from file path."""
        with open(self.file_path, encoding="utf8") as f:
            tsv = list(csv.reader(f, delimiter="\t"))
            # If len(line) > 1, the line is not a comment
            lines = [line for line in tsv if len(line) > 1]
        text = ""
        for i, line in enumerate(lines):
            # Do not add a space after a punctuation mark or at the end of the sentence
            if line[9] == "SpaceAfter=No" or i == len(lines) - 1:
                text += line[1]
            else:
                text += line[1] + " "
        metadata = {"source": self.file_path}
        return [Document(page_content=text, metadata=metadata)]


Source code for langchain.document_loaders.slack_directory
"""Loader for documents from a Slack export."""
import json
import zipfile
from pathlib import Path
from typing import Dict, List, Optional
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class SlackDirectoryLoader(BaseLoader):
    """Loader for loading documents from a Slack directory dump."""
    def __init__(self, zip_path: str, workspace_url: Optional[str] = None):
        """Initialize the SlackDirectoryLoader.
        Args:
            zip_path (str): The path to the Slack directory dump zip file.
            workspace_url (Optional[str]): The Slack workspace URL.
              Including the URL will turn
              sources into links. Defaults to None.
        """
        self.zip_path = Path(zip_path)
        self.workspace_url = workspace_url
        self.channel_id_map = self._get_channel_id_map(self.zip_path)
    @staticmethod
    def _get_channel_id_map(zip_path: Path) -> Dict[str, str]:
        """Get a dictionary mapping channel names to their respective IDs."""
        with zipfile.ZipFile(zip_path, "r") as zip_file:
            try:
                with zip_file.open("channels.json", "r") as f:
                    channels = json.load(f)
                return {channel["name"]: channel["id"] for channel in channels}
            except KeyError:
                return {}
[docs]    def load(self) -> List[Document]:
        """Load and return documents from the Slack directory dump."""
        docs = []
        with zipfile.ZipFile(self.zip_path, "r") as zip_file:
            for channel_path in zip_file.namelist():
                channel_name = Path(channel_path).parent.name
                if not channel_name:
                    continue


if not channel_name:
                    continue
                if channel_path.endswith(".json"):
                    messages = self._read_json(zip_file, channel_path)
                    for message in messages:
                        document = self._convert_message_to_document(
                            message, channel_name
                        )
                        docs.append(document)
        return docs
    def _read_json(self, zip_file: zipfile.ZipFile, file_path: str) -> List[dict]:
        """Read JSON data from a zip subfile."""
        with zip_file.open(file_path, "r") as f:
            data = json.load(f)
        return data
    def _convert_message_to_document(
        self, message: dict, channel_name: str
    ) -> Document:
        """
        Convert a message to a Document object.
        Args:
            message (dict): A message in the form of a dictionary.
            channel_name (str): The name of the channel the message belongs to.
        Returns:
            Document: A Document object representing the message.
        """
        text = message.get("text", "")
        metadata = self._get_message_metadata(message, channel_name)
        return Document(
            page_content=text,
            metadata=metadata,
        )
    def _get_message_metadata(self, message: dict, channel_name: str) -> dict:
        """Create and return metadata for a given message and channel."""
        timestamp = message.get("ts", "")
        user = message.get("user", "")
        source = self._get_message_source(channel_name, user, timestamp)
        return {
            "source": source,
            "channel": channel_name,
            "timestamp": timestamp,
            "user": user,
        }


"timestamp": timestamp,
            "user": user,
        }
    def _get_message_source(self, channel_name: str, user: str, timestamp: str) -> str:
        """
        Get the message source as a string.
        Args:
            channel_name (str): The name of the channel the message belongs to.
            user (str): The user ID who sent the message.
            timestamp (str): The timestamp of the message.
        Returns:
            str: The message source.
        """
        if self.workspace_url:
            channel_id = self.channel_id_map.get(channel_name, "")
            return (
                f"{self.workspace_url}/archives/{channel_id}"
                + f"/p{timestamp.replace('.', '')}"
            )
        else:
            return f"{channel_name} - {user} - {timestamp}"


Source code for langchain.document_loaders.base
"""Abstract interface for document loader implementations."""
from abc import ABC, abstractmethod
from typing import Iterator, List, Optional
from langchain.document_loaders.blob_loaders import Blob
from langchain.schema import Document
from langchain.text_splitter import RecursiveCharacterTextSplitter, TextSplitter
[docs]class BaseLoader(ABC):
    """Interface for loading documents.
    Implementations should implement the lazy-loading method using generators
    to avoid loading all documents into memory at once.
    The `load` method will remain as is for backwards compatibility, but its
    implementation should be just `list(self.lazy_load())`.
    """
    # Sub-classes should implement this method
    # as return list(self.lazy_load()).
    # This method returns a List which is materialized in memory.
[docs]    @abstractmethod
    def load(self) -> List[Document]:
        """Load data into document objects."""
[docs]    def load_and_split(
        self, text_splitter: Optional[TextSplitter] = None
    ) -> List[Document]:
        """Load documents and split into chunks."""
        if text_splitter is None:
            _text_splitter: TextSplitter = RecursiveCharacterTextSplitter()
        else:
            _text_splitter = text_splitter
        docs = self.load()
        return _text_splitter.split_documents(docs)
    # Attention: This method will be upgraded into an abstractmethod once it's
    #            implemented in all the existing subclasses.
[docs]    def lazy_load(
        self,
    ) -> Iterator[Document]:
        """A lazy loader for document content."""
        raise NotImplementedError(
            f"{self.__class__.__name__} does not implement lazy_load()"
        )


f"{self.__class__.__name__} does not implement lazy_load()"
        )
[docs]class BaseBlobParser(ABC):
    """Abstract interface for blob parsers.
    A blob parser is provides a way to parse raw data stored in a blob into one
    or more documents.
    The parser can be composed with blob loaders, making it easy to re-use
    a parser independent of how the blob was originally loaded.
    """
[docs]    @abstractmethod
    def lazy_parse(self, blob: Blob) -> Iterator[Document]:
        """Lazy parsing interface.
        Subclasses are required to implement this method.
        Args:
            blob: Blob instance
        Returns:
            Generator of documents
        """
[docs]    def parse(self, blob: Blob) -> List[Document]:
        """Eagerly parse the blob into a document or documents.
        This is a convenience method for interactive development environment.
        Production applications should favor the lazy_parse method instead.
        Subclasses should generally not over-ride this parse method.
        Args:
            blob: Blob instance
        Returns:
            List of documents
        """
        return list(self.lazy_parse(blob))


Source code for langchain.document_loaders.whatsapp_chat
import re
from pathlib import Path
from typing import List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]def concatenate_rows(date: str, sender: str, text: str) -> str:
    """Combine message information in a readable format ready to be used."""
    return f"{sender} on {date}: {text}\n\n"
[docs]class WhatsAppChatLoader(BaseLoader):
    """Loader that loads WhatsApp messages text file."""
    def __init__(self, path: str):
        """Initialize with path."""
        self.file_path = path
[docs]    def load(self) -> List[Document]:
        """Load documents."""
        p = Path(self.file_path)
        text_content = ""
        with open(p, encoding="utf8") as f:
            lines = f.readlines()
        message_line_regex = r"""
            \[?
            (
                \d{1,4}
                [\/.]
                \d{1,2}
                [\/.]
                \d{1,4}
                ,\s
                \d{1,2}
                :\d{2}
                (?:
                    :\d{2}
                )?
                (?:[\s_](?:AM|PM))?
            )
            \]?
            [\s-]*
            ([~\w\s]+)
            [:]+
            \s
            (.+)
        """
        ignore_lines = ["This message was deleted", "<Media omitted>"]
        for line in lines:
            result = re.match(
                message_line_regex, line.strip(), flags=re.VERBOSE | re.IGNORECASE
            )
            if result:


)
            if result:
                date, sender, text = result.groups()
                if text not in ignore_lines:
                    text_content += concatenate_rows(date, sender, text)
        metadata = {"source": str(p)}
        return [Document(page_content=text_content, metadata=metadata)]


Source code for langchain.document_loaders.html_bs
"""Loader that uses bs4 to load HTML files, enriching metadata with page title."""
import logging
from typing import Dict, List, Union
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
logger = logging.getLogger(__name__)
[docs]class BSHTMLLoader(BaseLoader):
    """Loader that uses beautiful soup to parse HTML files."""
    def __init__(
        self,
        file_path: str,
        open_encoding: Union[str, None] = None,
        bs_kwargs: Union[dict, None] = None,
        get_text_separator: str = "",
    ) -> None:
        """Initialise with path, and optionally, file encoding to use, and any kwargs
        to pass to the BeautifulSoup object."""
        try:
            import bs4  # noqa:F401
        except ImportError:
            raise ValueError(
                "beautifulsoup4 package not found, please install it with "
                "`pip install beautifulsoup4`"
            )
        self.file_path = file_path
        self.open_encoding = open_encoding
        if bs_kwargs is None:
            bs_kwargs = {"features": "lxml"}
        self.bs_kwargs = bs_kwargs
        self.get_text_separator = get_text_separator
[docs]    def load(self) -> List[Document]:
        from bs4 import BeautifulSoup
        """Load HTML document into document objects."""
        with open(self.file_path, "r", encoding=self.open_encoding) as f:
            soup = BeautifulSoup(f, **self.bs_kwargs)
        text = soup.get_text(self.get_text_separator)
        if soup.title:
            title = str(soup.title.string)
        else:
            title = ""
        metadata: Dict[str, Union[str, None]] = {


title = ""
        metadata: Dict[str, Union[str, None]] = {
            "source": self.file_path,
            "title": title,
        }
        return [Document(page_content=text, metadata=metadata)]


Source code for langchain.document_loaders.word_document
"""Loader that loads word documents."""
import os
import tempfile
from abc import ABC
from typing import List
from urllib.parse import urlparse
import requests
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
from langchain.document_loaders.unstructured import UnstructuredFileLoader
[docs]class Docx2txtLoader(BaseLoader, ABC):
    """Loads a DOCX with docx2txt and chunks at character level.
    Defaults to check for local file, but if the file is a web path, it will download it
    to a temporary file, and use that, then clean up the temporary file after completion
    """
    def __init__(self, file_path: str):
        """Initialize with file path."""
        self.file_path = file_path
        if "~" in self.file_path:
            self.file_path = os.path.expanduser(self.file_path)
        # If the file is a web path, download it to a temporary file, and use that
        if not os.path.isfile(self.file_path) and self._is_valid_url(self.file_path):
            r = requests.get(self.file_path)
            if r.status_code != 200:
                raise ValueError(
                    "Check the url of your file; returned status code %s"
                    % r.status_code
                )
            self.web_path = self.file_path
            self.temp_file = tempfile.NamedTemporaryFile()
            self.temp_file.write(r.content)
            self.file_path = self.temp_file.name
        elif not os.path.isfile(self.file_path):
            raise ValueError("File path %s is not a valid file or url" % self.file_path)
    def __del__(self) -> None:
        if hasattr(self, "temp_file"):
            self.temp_file.close()


if hasattr(self, "temp_file"):
            self.temp_file.close()
[docs]    def load(self) -> List[Document]:
        """Load given path as single page."""
        import docx2txt
        return [
            Document(
                page_content=docx2txt.process(self.file_path),
                metadata={"source": self.file_path},
            )
        ]
    @staticmethod
    def _is_valid_url(url: str) -> bool:
        """Check if the url is valid."""
        parsed = urlparse(url)
        return bool(parsed.netloc) and bool(parsed.scheme)
[docs]class UnstructuredWordDocumentLoader(UnstructuredFileLoader):
    """Loader that uses unstructured to load word documents."""
    def _get_elements(self) -> List:
        from unstructured.__version__ import __version__ as __unstructured_version__
        from unstructured.file_utils.filetype import FileType, detect_filetype
        unstructured_version = tuple(
            [int(x) for x in __unstructured_version__.split(".")]
        )
        # NOTE(MthwRobinson) - magic will raise an import error if the libmagic
        # system dependency isn't installed. If it's not installed, we'll just
        # check the file extension
        try:
            import magic  # noqa: F401
            is_doc = detect_filetype(self.file_path) == FileType.DOC
        except ImportError:
            _, extension = os.path.splitext(str(self.file_path))
            is_doc = extension == ".doc"
        if is_doc and unstructured_version < (0, 4, 11):
            raise ValueError(
                f"You are on unstructured version {__unstructured_version__}. "


f"You are on unstructured version {__unstructured_version__}. "
                "Partitioning .doc files is only supported in unstructured>=0.4.11. "
                "Please upgrade the unstructured package and try again."
            )
        if is_doc:
            from unstructured.partition.doc import partition_doc
            return partition_doc(filename=self.file_path, **self.unstructured_kwargs)
        else:
            from unstructured.partition.docx import partition_docx
            return partition_docx(filename=self.file_path, **self.unstructured_kwargs)


Source code for langchain.document_loaders.recursive_url_loader
from typing import Iterator, List, Optional, Set
from urllib.parse import urlparse
import requests
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class RecursiveUrlLoader(BaseLoader):
    """Loader that loads all child links from a given url."""
    def __init__(self, url: str, exclude_dirs: Optional[str] = None) -> None:
        """Initialize with URL to crawl and any sub-directories to exclude."""
        self.url = url
        self.exclude_dirs = exclude_dirs
[docs]    def get_child_links_recursive(
        self, url: str, visited: Optional[Set[str]] = None
    ) -> Set[str]:
        """Recursively get all child links starting with the path of the input URL."""
        try:
            from bs4 import BeautifulSoup
        except ImportError:
            raise ImportError(
                "The BeautifulSoup package is required for the RecursiveUrlLoader."
            )
        # Construct the base and parent URLs
        parsed_url = urlparse(url)
        base_url = f"{parsed_url.scheme}://{parsed_url.netloc}"
        parent_url = "/".join(parsed_url.path.split("/")[:-1])
        current_path = parsed_url.path
        # Add a trailing slash if not present
        if not base_url.endswith("/"):
            base_url += "/"
        if not parent_url.endswith("/"):
            parent_url += "/"
        # Exclude the root and parent from list
        visited = set() if visited is None else visited
        # Exclude the links that start with any of the excluded directories
        if self.exclude_dirs and any(
            url.startswith(exclude_dir) for exclude_dir in self.exclude_dirs
        ):
            return visited


):
            return visited
        # Get all links that are relative to the root of the website
        response = requests.get(url)
        soup = BeautifulSoup(response.text, "html.parser")
        all_links = [link.get("href") for link in soup.find_all("a")]
        # Extract only the links that are children of the current URL
        child_links = list(
            {
                link
                for link in all_links
                if link and link.startswith(current_path) and link != current_path
            }
        )
        # Get absolute path for all root relative links listed
        absolute_paths = [
            f"{urlparse(base_url).scheme}://{urlparse(base_url).netloc}{link}"
            for link in child_links
        ]
        # Store the visited links and recursively visit the children
        for link in absolute_paths:
            # Check all unvisited links
            if link not in visited:
                visited.add(link)
                # If the link is a directory (w/ children) then visit it
                if link.endswith("/"):
                    visited.update(self.get_child_links_recursive(link, visited))
        return visited
[docs]    def lazy_load(self) -> Iterator[Document]:
        from langchain.document_loaders import WebBaseLoader
        """Lazy load web pages."""
        child_links = self.get_child_links_recursive(self.url)
        loader = WebBaseLoader(list(child_links))
        return loader.lazy_load()
[docs]    def load(self) -> List[Document]:
        """Load web pages."""
        return list(self.lazy_load())


Source code for langchain.document_loaders.onedrive_file
from __future__ import annotations
import tempfile
from typing import TYPE_CHECKING, List
from pydantic import BaseModel, Field
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
from langchain.document_loaders.unstructured import UnstructuredFileLoader
if TYPE_CHECKING:
    from O365.drive import File
CHUNK_SIZE = 1024 * 1024 * 5
[docs]class OneDriveFileLoader(BaseLoader, BaseModel):
    file: File = Field(...)
[docs]    class Config:
        arbitrary_types_allowed = True
[docs]    def load(self) -> List[Document]:
        """Load Documents"""
        with tempfile.TemporaryDirectory() as temp_dir:
            file_path = f"{temp_dir}/{self.file.name}"
            self.file.download(to_path=temp_dir, chunk_size=CHUNK_SIZE)
            loader = UnstructuredFileLoader(file_path)
            return loader.load()


Source code for langchain.document_loaders.mediawikidump
"""Load Data from a MediaWiki dump xml."""
from typing import List, Optional
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class MWDumpLoader(BaseLoader):
    """
    Load MediaWiki dump from XML file
    Example:
        .. code-block:: python
            from langchain.document_loaders import MWDumpLoader
            loader = MWDumpLoader(
                file_path="myWiki.xml",
                encoding="utf8"
            )
            docs = loader.load()
            from langchain.text_splitter import RecursiveCharacterTextSplitter
            text_splitter = RecursiveCharacterTextSplitter(
                chunk_size=1000, chunk_overlap=0
            )
            texts = text_splitter.split_documents(docs)
    :param file_path: XML local file path
    :type file_path: str
    :param encoding: Charset encoding, defaults to "utf8"
    :type encoding: str, optional
    """
    def __init__(self, file_path: str, encoding: Optional[str] = "utf8"):
        """Initialize with file path."""
        self.file_path = file_path
        self.encoding = encoding
[docs]    def load(self) -> List[Document]:
        """Load from file path."""
        import mwparserfromhell
        import mwxml
        dump = mwxml.Dump.from_file(open(self.file_path, encoding=self.encoding))
        docs = []
        for page in dump.pages:
            for revision in page:
                code = mwparserfromhell.parse(revision.text)
                text = code.strip_code(
                    normalize=True, collapse=True, keep_template_params=False
                )
                metadata = {"source": page.title}


)
                metadata = {"source": page.title}
                docs.append(Document(page_content=text, metadata=metadata))
        return docs


Source code for langchain.document_loaders.helpers
"""Document loader helpers."""
import concurrent.futures
from typing import List, NamedTuple, Optional, cast
[docs]class FileEncoding(NamedTuple):
    encoding: Optional[str]
    confidence: float
    language: Optional[str]
[docs]def detect_file_encodings(file_path: str, timeout: int = 5) -> List[FileEncoding]:
    """Try to detect the file encoding.
    Returns a list of `FileEncoding` tuples with the detected encodings ordered
    by confidence.
    """
    import chardet
    def read_and_detect(file_path: str) -> List[dict]:
        with open(file_path, "rb") as f:
            rawdata = f.read()
        return cast(List[dict], chardet.detect_all(rawdata))
    with concurrent.futures.ThreadPoolExecutor() as executor:
        future = executor.submit(read_and_detect, file_path)
        try:
            encodings = future.result(timeout=timeout)
        except concurrent.futures.TimeoutError:
            raise TimeoutError(
                f"Timeout reached while detecting encoding for {file_path}"
            )
    if all(encoding["encoding"] is None for encoding in encodings):
        raise RuntimeError(f"Could not detect encoding for {file_path}")
    return [FileEncoding(**enc) for enc in encodings if enc["encoding"] is not None]


Source code for langchain.document_loaders.bilibili
import json
import re
import warnings
from typing import List, Tuple
import requests
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class BiliBiliLoader(BaseLoader):
    """Loader that loads bilibili transcripts."""
    def __init__(self, video_urls: List[str]):
        """Initialize with bilibili url."""
        self.video_urls = video_urls
[docs]    def load(self) -> List[Document]:
        """Load from bilibili url."""
        results = []
        for url in self.video_urls:
            transcript, video_info = self._get_bilibili_subs_and_info(url)
            doc = Document(page_content=transcript, metadata=video_info)
            results.append(doc)
        return results
    def _get_bilibili_subs_and_info(self, url: str) -> Tuple[str, dict]:
        try:
            from bilibili_api import sync, video
        except ImportError:
            raise ValueError(
                "requests package not found, please install it with "
                "`pip install bilibili-api-python`"
            )
        bvid = re.search(r"BV\w+", url)
        if bvid is not None:
            v = video.Video(bvid=bvid.group())
        else:
            aid = re.search(r"av[0-9]+", url)
            if aid is not None:
                try:
                    v = video.Video(aid=int(aid.group()[2:]))
                except AttributeError:
                    raise ValueError(f"{url} is not bilibili url.")
            else:
                raise ValueError(f"{url} is not bilibili url.")
        video_info = sync(v.get_info())


video_info = sync(v.get_info())
        video_info.update({"url": url})
        # Get subtitle url
        subtitle = video_info.pop("subtitle")
        sub_list = subtitle["list"]
        if sub_list:
            sub_url = sub_list[0]["subtitle_url"]
            result = requests.get(sub_url)
            raw_sub_titles = json.loads(result.content)["body"]
            raw_transcript = " ".join([c["content"] for c in raw_sub_titles])
            raw_transcript_with_meta_info = (
                f"Video Title: {video_info['title']},"
                f"description: {video_info['desc']}\n\n"
                f"Transcript: {raw_transcript}"
            )
            return raw_transcript_with_meta_info, video_info
        else:
            raw_transcript = ""
            warnings.warn(
                f"""
                No subtitles found for video: {url}.
                Return Empty transcript.
                """
            )
            return raw_transcript, video_info


Source code for langchain.document_loaders.xml
"""Loader that loads Microsoft Excel files."""
from typing import Any, List
from langchain.document_loaders.unstructured import (
    UnstructuredFileLoader,
    validate_unstructured_version,
)
[docs]class UnstructuredXMLLoader(UnstructuredFileLoader):
    """Loader that uses unstructured to load XML files."""
    def __init__(
        self, file_path: str, mode: str = "single", **unstructured_kwargs: Any
    ):
        validate_unstructured_version(min_unstructured_version="0.6.7")
        super().__init__(file_path=file_path, mode=mode, **unstructured_kwargs)
    def _get_elements(self) -> List:
        from unstructured.partition.xml import partition_xml
        return partition_xml(filename=self.file_path, **self.unstructured_kwargs)


Source code for langchain.document_loaders.powerpoint
"""Loader that loads powerpoint files."""
import os
from typing import List
from langchain.document_loaders.unstructured import UnstructuredFileLoader
[docs]class UnstructuredPowerPointLoader(UnstructuredFileLoader):
    """Loader that uses unstructured to load powerpoint files."""
    def _get_elements(self) -> List:
        from unstructured.__version__ import __version__ as __unstructured_version__
        from unstructured.file_utils.filetype import FileType, detect_filetype
        unstructured_version = tuple(
            [int(x) for x in __unstructured_version__.split(".")]
        )
        # NOTE(MthwRobinson) - magic will raise an import error if the libmagic
        # system dependency isn't installed. If it's not installed, we'll just
        # check the file extension
        try:
            import magic  # noqa: F401
            is_ppt = detect_filetype(self.file_path) == FileType.PPT
        except ImportError:
            _, extension = os.path.splitext(str(self.file_path))
            is_ppt = extension == ".ppt"
        if is_ppt and unstructured_version < (0, 4, 11):
            raise ValueError(
                f"You are on unstructured version {__unstructured_version__}. "
                "Partitioning .ppt files is only supported in unstructured>=0.4.11. "
                "Please upgrade the unstructured package and try again."
            )
        if is_ppt:
            from unstructured.partition.ppt import partition_ppt
            return partition_ppt(filename=self.file_path, **self.unstructured_kwargs)
        else:
            from unstructured.partition.pptx import partition_pptx
            return partition_pptx(filename=self.file_path, **self.unstructured_kwargs)


Source code for langchain.document_loaders.chatgpt
"""Load conversations from ChatGPT data export"""
import datetime
import json
from typing import List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]def concatenate_rows(message: dict, title: str) -> str:
    """
    Combine message information in a readable format ready to be used.
    Args:
        message: Message to be concatenated
        title: Title of the conversation
    Returns:
        Concatenated message
    """
    if not message:
        return ""
    sender = message["author"]["role"] if message["author"] else "unknown"
    text = message["content"]["parts"][0]
    date = datetime.datetime.fromtimestamp(message["create_time"]).strftime(
        "%Y-%m-%d %H:%M:%S"
    )
    return f"{title} - {sender} on {date}: {text}\n\n"
[docs]class ChatGPTLoader(BaseLoader):
    """Loader that loads conversations from exported ChatGPT data."""
    def __init__(self, log_file: str, num_logs: int = -1):
        self.log_file = log_file
        self.num_logs = num_logs
[docs]    def load(self) -> List[Document]:
        with open(self.log_file, encoding="utf8") as f:
            data = json.load(f)[: self.num_logs] if self.num_logs else json.load(f)
        documents = []
        for d in data:
            title = d["title"]
            messages = d["mapping"]
            text = "".join(
                [
                    concatenate_rows(messages[key]["message"], title)
                    for idx, key in enumerate(messages)
                    if not (
                        idx == 0


if not (
                        idx == 0
                        and messages[key]["message"]["author"]["role"] == "system"
                    )
                ]
            )
            metadata = {"source": str(self.log_file)}
            documents.append(Document(page_content=text, metadata=metadata))
        return documents


Source code for langchain.document_loaders.odt
"""Loader that loads Open Office ODT files."""
from typing import Any, List
from langchain.document_loaders.unstructured import (
    UnstructuredFileLoader,
    validate_unstructured_version,
)
[docs]class UnstructuredODTLoader(UnstructuredFileLoader):
    """Loader that uses unstructured to load open office ODT files."""
    def __init__(
        self, file_path: str, mode: str = "single", **unstructured_kwargs: Any
    ):
        validate_unstructured_version(min_unstructured_version="0.6.3")
        super().__init__(file_path=file_path, mode=mode, **unstructured_kwargs)
    def _get_elements(self) -> List:
        from unstructured.partition.odt import partition_odt
        return partition_odt(filename=self.file_path, **self.unstructured_kwargs)


Source code for langchain.document_loaders.python
import tokenize
from langchain.document_loaders.text import TextLoader
[docs]class PythonLoader(TextLoader):
    """
    Load Python files, respecting any non-default encoding if specified.
    """
    def __init__(self, file_path: str):
        with open(file_path, "rb") as f:
            encoding, _ = tokenize.detect_encoding(f.readline)
        super().__init__(file_path=file_path, encoding=encoding)


Source code for langchain.document_loaders.telegram
"""Loader that loads Telegram chat json dump."""
from __future__ import annotations
import asyncio
import json
from pathlib import Path
from typing import TYPE_CHECKING, Dict, List, Optional, Union
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
if TYPE_CHECKING:
    import pandas as pd
    from telethon.hints import EntityLike
[docs]def concatenate_rows(row: dict) -> str:
    """Combine message information in a readable format ready to be used."""
    date = row["date"]
    sender = row["from"]
    text = row["text"]
    return f"{sender} on {date}: {text}\n\n"
[docs]class TelegramChatFileLoader(BaseLoader):
    """Loader that loads Telegram chat json directory dump."""
    def __init__(self, path: str):
        """Initialize with path."""
        self.file_path = path
[docs]    def load(self) -> List[Document]:
        """Load documents."""
        p = Path(self.file_path)
        with open(p, encoding="utf8") as f:
            d = json.load(f)
        text = "".join(
            concatenate_rows(message)
            for message in d["messages"]
            if message["type"] == "message" and isinstance(message["text"], str)
        )
        metadata = {"source": str(p)}
        return [Document(page_content=text, metadata=metadata)]
[docs]def text_to_docs(text: Union[str, List[str]]) -> List[Document]:
    """Converts a string or list of strings to a list of Documents with metadata."""
    if isinstance(text, str):


if isinstance(text, str):
        # Take a single string as one page
        text = [text]
    page_docs = [Document(page_content=page) for page in text]
    # Add page numbers as metadata
    for i, doc in enumerate(page_docs):
        doc.metadata["page"] = i + 1
    # Split pages into chunks
    doc_chunks = []
    for doc in page_docs:
        text_splitter = RecursiveCharacterTextSplitter(
            chunk_size=800,
            separators=["\n\n", "\n", ".", "!", "?", ",", " ", ""],
            chunk_overlap=20,
        )
        chunks = text_splitter.split_text(doc.page_content)
        for i, chunk in enumerate(chunks):
            doc = Document(
                page_content=chunk, metadata={"page": doc.metadata["page"], "chunk": i}
            )
            # Add sources a metadata
            doc.metadata["source"] = f"{doc.metadata['page']}-{doc.metadata['chunk']}"
            doc_chunks.append(doc)
    return doc_chunks
[docs]class TelegramChatApiLoader(BaseLoader):
    """Loader that loads Telegram chat json directory dump."""
    def __init__(
        self,
        chat_entity: Optional[EntityLike] = None,
        api_id: Optional[int] = None,
        api_hash: Optional[str] = None,
        username: Optional[str] = None,
        file_path: str = "telegram_data.json",
    ):
        """Initialize with API parameters."""
        self.chat_entity = chat_entity
        self.api_id = api_id
        self.api_hash = api_hash
        self.username = username
        self.file_path = file_path
[docs]    async def fetch_data_from_telegram(self) -> None:


[docs]    async def fetch_data_from_telegram(self) -> None:
        """Fetch data from Telegram API and save it as a JSON file."""
        from telethon.sync import TelegramClient
        data = []
        async with TelegramClient(self.username, self.api_id, self.api_hash) as client:
            async for message in client.iter_messages(self.chat_entity):
                is_reply = message.reply_to is not None
                reply_to_id = message.reply_to.reply_to_msg_id if is_reply else None
                data.append(
                    {
                        "sender_id": message.sender_id,
                        "text": message.text,
                        "date": message.date.isoformat(),
                        "message.id": message.id,
                        "is_reply": is_reply,
                        "reply_to_id": reply_to_id,
                    }
                )
        with open(self.file_path, "w", encoding="utf-8") as f:
            json.dump(data, f, ensure_ascii=False, indent=4)
    def _get_message_threads(self, data: pd.DataFrame) -> dict:
        """Create a dictionary of message threads from the given data.
        Args:
            data (pd.DataFrame): A DataFrame containing the conversation \
                data with columns:
                - message.sender_id
                - text
                - date
                - message.id
                - is_reply
                - reply_to_id
        Returns:
            dict: A dictionary where the key is the parent message ID and \
                the value is a list of message IDs in ascending order.
        """
        def find_replies(parent_id: int, reply_data: pd.DataFrame) -> List[int]:
            """
            Recursively find all replies to a given parent message ID.
            Args:
                parent_id (int): The parent message ID.


Args:
                parent_id (int): The parent message ID.
                reply_data (pd.DataFrame): A DataFrame containing reply messages.
            Returns:
                list: A list of message IDs that are replies to the parent message ID.
            """
            # Find direct replies to the parent message ID
            direct_replies = reply_data[reply_data["reply_to_id"] == parent_id][
                "message.id"
            ].tolist()
            # Recursively find replies to the direct replies
            all_replies = []
            for reply_id in direct_replies:
                all_replies += [reply_id] + find_replies(reply_id, reply_data)
            return all_replies
        # Filter out parent messages
        parent_messages = data[~data["is_reply"]]
        # Filter out reply messages and drop rows with NaN in 'reply_to_id'
        reply_messages = data[data["is_reply"]].dropna(subset=["reply_to_id"])
        # Convert 'reply_to_id' to integer
        reply_messages["reply_to_id"] = reply_messages["reply_to_id"].astype(int)
        # Create a dictionary of message threads with parent message IDs as keys and \
        # lists of reply message IDs as values
        message_threads = {
            parent_id: [parent_id] + find_replies(parent_id, reply_messages)
            for parent_id in parent_messages["message.id"]
        }
        return message_threads
    def _combine_message_texts(
        self, message_threads: Dict[int, List[int]], data: pd.DataFrame
    ) -> str:
        """
        Combine the message texts for each parent message ID based \
            on the list of message threads.
        Args:
            message_threads (dict): A dictionary where the key is the parent message \


message_threads (dict): A dictionary where the key is the parent message \
                ID and the value is a list of message IDs in ascending order.
            data (pd.DataFrame): A DataFrame containing the conversation data:
                - message.sender_id
                - text
                - date
                - message.id
                - is_reply
                - reply_to_id
        Returns:
            str: A combined string of message texts sorted by date.
        """
        combined_text = ""
        # Iterate through sorted parent message IDs
        for parent_id, message_ids in message_threads.items():
            # Get the message texts for the message IDs and sort them by date
            message_texts = (
                data[data["message.id"].isin(message_ids)]
                .sort_values(by="date")["text"]
                .tolist()
            )
            message_texts = [str(elem) for elem in message_texts]
            # Combine the message texts
            combined_text += " ".join(message_texts) + ".\n"
        return combined_text.strip()
[docs]    def load(self) -> List[Document]:
        """Load documents."""
        if self.chat_entity is not None:
            try:
                import nest_asyncio
                nest_asyncio.apply()
                asyncio.run(self.fetch_data_from_telegram())
            except ImportError:
                raise ImportError(
                    """`nest_asyncio` package not found.
                    please install with `pip install nest_asyncio`
                    """
                )
        p = Path(self.file_path)
        with open(p, encoding="utf8") as f:
            d = json.load(f)
        try:
            import pandas as pd
        except ImportError:
            raise ImportError(
                """`pandas` package not found.
                please install with `pip install pandas`
                """


please install with `pip install pandas`
                """
            )
        normalized_messages = pd.json_normalize(d)
        df = pd.DataFrame(normalized_messages)
        message_threads = self._get_message_threads(df)
        combined_texts = self._combine_message_texts(message_threads, df)
        return text_to_docs(combined_texts)


Source code for langchain.document_loaders.url_selenium
"""Loader that uses Selenium to load a page, then uses unstructured to load the html.
"""
import logging
from typing import TYPE_CHECKING, List, Literal, Optional, Union
if TYPE_CHECKING:
    from selenium.webdriver import Chrome, Firefox
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
logger = logging.getLogger(__name__)
[docs]class SeleniumURLLoader(BaseLoader):
    """Loader that uses Selenium and to load a page and unstructured to load the html.
    This is useful for loading pages that require javascript to render.
    Attributes:
        urls (List[str]): List of URLs to load.
        continue_on_failure (bool): If True, continue loading other URLs on failure.
        browser (str): The browser to use, either 'chrome' or 'firefox'.
        binary_location (Optional[str]): The location of the browser binary.
        executable_path (Optional[str]): The path to the browser executable.
        headless (bool): If True, the browser will run in headless mode.
        arguments [List[str]]: List of arguments to pass to the browser.
    """
    def __init__(
        self,
        urls: List[str],
        continue_on_failure: bool = True,
        browser: Literal["chrome", "firefox"] = "chrome",
        binary_location: Optional[str] = None,
        executable_path: Optional[str] = None,
        headless: bool = True,
        arguments: List[str] = [],
    ):
        """Load a list of URLs using Selenium and unstructured."""
        try:
            import selenium  # noqa:F401
        except ImportError:
            raise ImportError(
                "selenium package not found, please install it with "


raise ImportError(
                "selenium package not found, please install it with "
                "`pip install selenium`"
            )
        try:
            import unstructured  # noqa:F401
        except ImportError:
            raise ImportError(
                "unstructured package not found, please install it with "
                "`pip install unstructured`"
            )
        self.urls = urls
        self.continue_on_failure = continue_on_failure
        self.browser = browser
        self.binary_location = binary_location
        self.executable_path = executable_path
        self.headless = headless
        self.arguments = arguments
    def _get_driver(self) -> Union["Chrome", "Firefox"]:
        """Create and return a WebDriver instance based on the specified browser.
        Raises:
            ValueError: If an invalid browser is specified.
        Returns:
            Union[Chrome, Firefox]: A WebDriver instance for the specified browser.
        """
        if self.browser.lower() == "chrome":
            from selenium.webdriver import Chrome
            from selenium.webdriver.chrome.options import Options as ChromeOptions
            chrome_options = ChromeOptions()
            for arg in self.arguments:
                chrome_options.add_argument(arg)
            if self.headless:
                chrome_options.add_argument("--headless")
                chrome_options.add_argument("--no-sandbox")
            if self.binary_location is not None:
                chrome_options.binary_location = self.binary_location
            if self.executable_path is None:
                return Chrome(options=chrome_options)
            return Chrome(executable_path=self.executable_path, options=chrome_options)
        elif self.browser.lower() == "firefox":
            from selenium.webdriver import Firefox
            from selenium.webdriver.firefox.options import Options as FirefoxOptions
            firefox_options = FirefoxOptions()
            for arg in self.arguments:
                firefox_options.add_argument(arg)


for arg in self.arguments:
                firefox_options.add_argument(arg)
            if self.headless:
                firefox_options.add_argument("--headless")
            if self.binary_location is not None:
                firefox_options.binary_location = self.binary_location
            if self.executable_path is None:
                return Firefox(options=firefox_options)
            return Firefox(
                executable_path=self.executable_path, options=firefox_options
            )
        else:
            raise ValueError("Invalid browser specified. Use 'chrome' or 'firefox'.")
[docs]    def load(self) -> List[Document]:
        """Load the specified URLs using Selenium and create Document instances.
        Returns:
            List[Document]: A list of Document instances with loaded content.
        """
        from unstructured.partition.html import partition_html
        docs: List[Document] = list()
        driver = self._get_driver()
        for url in self.urls:
            try:
                driver.get(url)
                page_content = driver.page_source
                elements = partition_html(text=page_content)
                text = "\n\n".join([str(el) for el in elements])
                metadata = {"source": url}
                docs.append(Document(page_content=text, metadata=metadata))
            except Exception as e:
                if self.continue_on_failure:
                    logger.error(f"Error fetching or processing {url}, exception: {e}")
                else:
                    raise e
        driver.quit()
        return docs


Source code for langchain.document_loaders.s3_file
"""Loading logic for loading documents from an s3 file."""
import os
import tempfile
from typing import List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
from langchain.document_loaders.unstructured import UnstructuredFileLoader
[docs]class S3FileLoader(BaseLoader):
    """Loading logic for loading documents from s3."""
    def __init__(self, bucket: str, key: str):
        """Initialize with bucket and key name."""
        self.bucket = bucket
        self.key = key
[docs]    def load(self) -> List[Document]:
        """Load documents."""
        try:
            import boto3
        except ImportError:
            raise ImportError(
                "Could not import `boto3` python package. "
                "Please install it with `pip install boto3`."
            )
        s3 = boto3.client("s3")
        with tempfile.TemporaryDirectory() as temp_dir:
            file_path = f"{temp_dir}/{self.key}"
            os.makedirs(os.path.dirname(file_path), exist_ok=True)
            s3.download_file(self.bucket, self.key, file_path)
            loader = UnstructuredFileLoader(file_path)
            return loader.load()


Source code for langchain.document_loaders.gcs_file
"""Loading logic for loading documents from a GCS file."""
import os
import tempfile
from typing import List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
from langchain.document_loaders.unstructured import UnstructuredFileLoader
[docs]class GCSFileLoader(BaseLoader):
    """Loading logic for loading documents from GCS."""
    def __init__(self, project_name: str, bucket: str, blob: str):
        """Initialize with bucket and key name."""
        self.bucket = bucket
        self.blob = blob
        self.project_name = project_name
[docs]    def load(self) -> List[Document]:
        """Load documents."""
        try:
            from google.cloud import storage
        except ImportError:
            raise ValueError(
                "Could not import google-cloud-storage python package. "
                "Please install it with `pip install google-cloud-storage`."
            )
        # Initialise a client
        storage_client = storage.Client(self.project_name)
        # Create a bucket object for our bucket
        bucket = storage_client.get_bucket(self.bucket)
        # Create a blob object from the filepath
        blob = bucket.blob(self.blob)
        with tempfile.TemporaryDirectory() as temp_dir:
            file_path = f"{temp_dir}/{self.blob}"
            os.makedirs(os.path.dirname(file_path), exist_ok=True)
            # Download the file to a destination
            blob.download_to_filename(file_path)
            loader = UnstructuredFileLoader(file_path)
            return loader.load()


Source code for langchain.document_loaders.tomarkdown
"""Loader that loads HTML to markdown using 2markdown."""
from __future__ import annotations
from typing import Iterator, List
import requests
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
[docs]class ToMarkdownLoader(BaseLoader):
    """Loader that loads HTML to markdown using 2markdown."""
    def __init__(self, url: str, api_key: str):
        """Initialize with url and api key."""
        self.url = url
        self.api_key = api_key
[docs]    def lazy_load(
        self,
    ) -> Iterator[Document]:
        """Lazily load the file."""
        response = requests.post(
            "https://2markdown.com/api/2md",
            headers={"X-Api-Key": self.api_key},
            json={"url": self.url},
        )
        text = response.json()["article"]
        metadata = {"source": self.url}
        yield Document(page_content=text, metadata=metadata)
[docs]    def load(self) -> List[Document]:
        """Load file."""
        return list(self.lazy_load())