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# Source code for langchain_community.document_loaders.hugging_face_model

    from typing import Iterator, List, Optional

    import requests
    from langchain_core.documents import Document

    from langchain_community.document_loaders.base import BaseLoader

    class HuggingFaceModelLoader(BaseLoader):
        """
        Load model information from `Hugging Face Hub`, including README content.

        This loader interfaces with the Hugging Face Models API to fetch and load
        model metadata and README files.
        The API allows you to search and filter models based on specific criteria
        such as model tags, authors, and more.

        API URL: https://huggingface.co/api/models
        DOC URL: https://huggingface.co/docs/hub/en/api

        Examples:

            .. code-block:: python

                from langchain_community.document_loaders import HuggingFaceModelLoader

                # Initialize the loader with search criteria
                loader = HuggingFaceModelLoader(search="bert", limit=10)

                # Load models
                documents = loader.load()

                # Iterate through the fetched documents
                for doc in documents:
                    print(doc.page_content)  # README content of the model
                    print(doc.metadata)      # Metadata of the model
        """

        BASE_URL = "https://huggingface.co/api/models"
        README_BASE_URL = "https://huggingface.co/{model_id}/raw/main/README.md"

        def __init__(
            self,
            *,
            search: Optional[str] = None,
            author: Optional[str] = None,
            filter: Optional[str] = None,
            sort: Optional[str] = None,
            direction: Optional[str] = None,
            limit: Optional[int] = 3,
            full: Optional[bool] = None,
            config: Optional[bool] = None,
        ):
            """Initialize the HuggingFaceModelLoader.

            Args:
                search: Filter based on substrings for repos and their usernames.
                author: Filter models by an author or organization.
                filter: Filter based on tags.
                sort: Property to use when sorting.
                direction: Direction in which to sort.
                limit: Limit the number of models fetched.
                full: Whether to fetch most model data.
                config: Whether to also fetch the repo config.
            """

            self.params = {
                "search": search,
                "author": author,
                "filter": filter,
                "sort": sort,
                "direction": direction,
                "limit": limit,
                "full": full,
                "config": config,
            }

        def fetch_models(self) -> List[dict]:
            """Fetch model information from Hugging Face Hub."""
            response = requests.get(
                self.BASE_URL,
                params={k: v for k, v in self.params.items() if v is not None},
            )
            response.raise_for_status()
            return response.json()

        def fetch_readme_content(self, model_id: str) -> str:
            """Fetch the README content for a given model."""
            readme_url = self.README_BASE_URL.format(model_id=model_id)
            try:
                response = requests.get(readme_url)
                response.raise_for_status()
                return response.text
            except requests.RequestException:
                return "README not available for this model."

        def lazy_load(self) -> Iterator[Document]:
            """Load model information lazily, including README content."""
            models = self.fetch_models()

            for model in models:
                model_id = model.get("modelId", "")
                readme_content = self.fetch_readme_content(model_id)

                yield Document(
                    page_content=readme_content,
                    metadata=model,
                )