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

[docs]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: DOC URL: 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 = "" README_BASE_URL = "{model_id}/raw/main/"
[docs] 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, }
[docs] 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()
[docs] 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."
[docs] 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, )