diff --git a/README.md b/README.md index 7e3a740..7a6eec7 100644 --- a/README.md +++ b/README.md @@ -92,11 +92,11 @@ vicinity.save('my_vector_store') vicinity = Vicinity.load('my_vector_store') ``` -Pushing and loading a vector store from the Hugging Face Hub: +Pushing and loading a vector store from the Hugging Face Hub (note that you can optionally add the model used for generating embeddings to the metadata, e.g. `vicinity.metadata["model"] = "minishlab/potion-base-8M"`): ```python -vicinity.push_to_hub(model_name_or_path='my_vector_store', repo_id='my_vector_store') -vicinity = Vicinity.load_from_hub(repo_id='my_vector_store') +vicinity.push_to_hub(repo_id='minishlab/my-vicinity-repo') +vicinity = Vicinity.load_from_hub(repo_id='minishlab/my-vicinity-repo') ``` Evaluating a backend: @@ -118,6 +118,7 @@ Vicinity provides the following features: - Lightweight: Minimal dependencies and fast performance. - Flexible Backend Support: Use different backends for vector storage and search. - Serialization: Save and load vector stores for persistence. +- HuggingFace Hub Integration: Push and load vector stores directly to and from the HuggingFace Hub. - Evaluation: Easily evaluate the performance of different backends. - Easy to Use: Simple and intuitive API. diff --git a/tests/integrations/test_huggingface.py b/tests/integrations/test_huggingface.py deleted file mode 100644 index bf4fc43..0000000 --- a/tests/integrations/test_huggingface.py +++ /dev/null @@ -1,33 +0,0 @@ -from __future__ import annotations - -import io -import sys - -from vicinity import Vicinity -from vicinity.datatypes import Backend -from vicinity.integrations.huggingface import _MODEL_NAME_OR_PATH_PRINT_STATEMENT - -BackendType = tuple[Backend, str] - - -def test_load_from_hub(vicinity_instance: Vicinity) -> None: - """ - Test Vicinity.load_from_hub. - - :param vicinity_instance: A Vicinity instance. - """ - repo_id = "davidberenstein1957/my-vicinity-repo" - # get the first part of the print 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CommitInfo +import numpy as np - from vicinity.vicinity import Vicinity +from vicinity.backends import AbstractBackend, BasicVectorStore, get_backend_class +from vicinity.datatypes import Backend -_HUB_IMPORT_ERROR = ImportError( - "`datasets` and `huggingface_hub` are required to push to the Hugging Face Hub. Please install them with `pip install 'vicinity[huggingface]'`" -) -_MODEL_NAME_OR_PATH_PRINT_STATEMENT = ( - "Embeddings in Vicinity instance were created from model name or path: {model_name_or_path}" -) +try: + from datasets import Dataset, load_dataset + from huggingface_hub import CommitInfo, DatasetCard, snapshot_download, upload_file, upload_folder +except ImportError: + raise ImportError( + "ImportError: `datasets` and `huggingface_hub` are required to push to the Hugging Face Hub. " + "Please install them with `pip install 'vicinity[huggingface]'`" + ) logger = logging.getLogger(__name__) - -class HuggingFaceMixin: - def push_to_hub( - self, - model_name_or_path: str, - repo_id: str, - token: str | None = None, - private: bool = False, - **kwargs: Any, - ) -> "CommitInfo": - """ - Push the Vicinity instance to the Hugging Face Hub. - - :param model_name_or_path: The name of the model or the path to the local directory - that was used to create the embeddings in the Vicinity instance. - :param repo_id: The repository ID on the Hugging Face Hub - :param token: Optional authentication token for private repositories - :param private: Whether to create a private repository - :param **kwargs: Additional arguments passed to Dataset.push_to_hub() - :return: The commit info - """ - try: - from datasets import Dataset - from huggingface_hub import DatasetCard, upload_file, upload_folder - except ImportError: - raise _HUB_IMPORT_ERROR - - # Create and push dataset with items and vectors - if isinstance(self.items[0], dict): - dataset_dict = {k: [item[k] for item in self.items] for k in self.items[0].keys()} - else: - dataset_dict = {"items": self.items} - if self.vector_store is not None: - dataset_dict["vectors"] = self.vector_store.vectors - dataset = Dataset.from_dict(dataset_dict) - dataset.push_to_hub(repo_id, token=token, private=private, **kwargs) - - # Save backend and config files to temp directory and upload - with tempfile.TemporaryDirectory() as temp_dir: - temp_path = Path(temp_dir) - - # Save and upload backend - self.backend.save(temp_path) - upload_folder( - repo_id=repo_id, - folder_path=temp_path, - token=token, - repo_type="dataset", - path_in_repo="backend", - ) - - # Save and upload config - config = { - "metadata": self.metadata, - "backend_type": self.backend.backend_type.value, - "model_name_or_path": model_name_or_path, - } - config_path = temp_path / "config.json" - config_path.write_text(json.dumps(config)) - upload_file( - repo_id=repo_id, - path_or_fileobj=config_path, - token=token, - repo_type="dataset", - path_in_repo="config.json", - ) - - # Load the dataset card template from the related path - template_path = Path(__file__).parent / "dataset_card_template.md" - template = template_path.read_text() - content = template.format(repo_id=repo_id, num_items=len(self.items), config=json.dumps(config, indent=4)) - return DatasetCard(content=content).push_to_hub(repo_id=repo_id, token=token, repo_type="dataset") - - @classmethod - def load_from_hub(cls, repo_id: str, token: str | None = None, **kwargs: Any) -> "Vicinity": - """ - Load a Vicinity instance from the Hugging Face Hub. - - :param repo_id: The repository ID on the Hugging Face Hub. - :param token: Optional authentication token for private repositories. - :param **kwargs: Additional arguments passed to load_dataset. - :return: A Vicinity instance loaded from the Hub. - """ - try: - from datasets import load_dataset - from huggingface_hub import snapshot_download - except ImportError: - raise _HUB_IMPORT_ERROR - - # Load dataset and extract items and vectors - dataset = load_dataset(repo_id, token=token, split="train", **kwargs) - if "items" in dataset.column_names: - items = dataset["items"] - else: - # Create items from all columns except 'vectors' - items = [] - columns = [col for col in dataset.column_names if col != "vectors"] - for i in range(len(dataset)): - items.append({col: dataset[col][i] for col in columns}) - has_vectors = "vectors" in dataset.column_names - vector_store = BasicVectorStore(vectors=dataset["vectors"]) if has_vectors else None - - # Download and load config and backend - repo_path = Path(snapshot_download(repo_id=repo_id, token=token, repo_type="dataset")) - with open(repo_path / "config.json") as f: - config = json.load(f) - model_name_or_path = config.pop("model_name_or_path") - - print(_MODEL_NAME_OR_PATH_PRINT_STATEMENT.format(model_name_or_path=model_name_or_path)) - backend_type = Backend(config["backend_type"]) - backend = get_backend_class(backend_type).load(repo_path / "backend") - - return cls(items=items, backend=backend, metadata=config["metadata"], vector_store=vector_store) +_VICINITY_ITEM_COLUMN = "_vicinity_items" + + +def push_to_hub( + repo_id: str, + items: list[Any], + backend: AbstractBackend, + metadata: dict[str, str], + vector_store: BasicVectorStore | None = None, + token: str | None = None, + private: bool = False, + **kwargs: Any, +) -> "CommitInfo": + """ + Push the Vicinity instance to the Hugging Face Hub. + + :param repo_id: The repository ID on the Hugging Face Hub. + :param items: The items to push to the Hub. + :param backend: The backend used to create the embeddings in the Vicinity instance. + :param metadata: Metadata to include in the dataset card. + :param vector_store: The vector store used to create the embeddings in the Vicinity instance. + :param token: Optional authentication token for private repositories. + :param private: Whether to create a private repository. + :param **kwargs: Additional arguments passed to Dataset.push_to_hub(). + :return: The commit info. + """ + if isinstance(items[0], dict): + dataset_dict = {k: [item[k] for item in items] for k in items[0].keys()} + else: + dataset_dict = {_VICINITY_ITEM_COLUMN: items} + if vector_store is not None: + if isinstance(vector_store.vectors, np.ndarray): + vectors: list[list[float]] = vector_store.vectors.tolist() + dataset_dict["vectors"] = vectors + + dataset = Dataset.from_dict(dataset_dict) + dataset.push_to_hub(repo_id, token=token, private=private, **kwargs) + + # Save backend and config files to temp directory and upload + with tempfile.TemporaryDirectory() as temp_dir: + temp_path = Path(temp_dir) + + # Save and upload backend + backend.save(temp_path) + upload_folder( + repo_id=repo_id, + folder_path=temp_path, + token=token, + repo_type="dataset", + path_in_repo="backend", + ) + + # Save and upload config + config = { + "metadata": metadata, + "backend_type": backend.backend_type.value, + } + config_path = temp_path / "config.json" + config_path.write_text(json.dumps(config)) + upload_file( + repo_id=repo_id, + path_or_fileobj=config_path, + token=token, + repo_type="dataset", + path_in_repo="config.json", + ) + + # Load the dataset card template from the related path + template_path = Path(__file__).parent / "dataset_card_template.md" + template = template_path.read_text() + content = template.format(repo_id=repo_id, num_items=len(items), config=json.dumps(config, indent=4)) + return DatasetCard(content=content).push_to_hub(repo_id=repo_id, token=token, repo_type="dataset") + + +def load_from_hub( + repo_id: str, token: str | None = None, **kwargs: Any +) -> tuple[list[dict[str, Any]], BasicVectorStore | None, AbstractBackend, dict[str, Any]]: + """ + Load a Vicinity instance from the Hugging Face Hub. + + :param repo_id: The repository ID on the Hugging Face Hub. + :param token: Optional authentication token for private repositories. + :param **kwargs: Additional arguments passed to load_dataset. + :return: A Vicinity instance loaded from the Hub. + """ + # Load dataset and extract items and vectors + dataset = load_dataset(repo_id, token=token, split="train", **kwargs) + has_vectors = "vectors" in dataset.column_names + if has_vectors: + vectors = dataset["vectors"] + if _VICINITY_ITEM_COLUMN in dataset.column_names: + items = dataset[_VICINITY_ITEM_COLUMN] + else: + # Remove the vectors column (if it exists) and then convert to a list. + if has_vectors: + dataset = dataset.remove_columns("vectors") + items = dataset.to_list() + vector_store = BasicVectorStore(vectors=vectors) if has_vectors else None + + # Download and load config and backend + repo_path = Path(snapshot_download(repo_id=repo_id, token=token, repo_type="dataset")) + with open(repo_path / "config.json") as f: + config = json.load(f) + backend_type = Backend(config["backend_type"]) + backend = get_backend_class(backend_type).load(repo_path / "backend") + return items, vector_store, backend, config diff --git a/vicinity/vicinity.py b/vicinity/vicinity.py index 1b106b1..e9b7ff0 100644 --- a/vicinity/vicinity.py +++ b/vicinity/vicinity.py @@ -6,7 +6,7 @@ from io import open from pathlib import Path from time import perf_counter -from typing import Any, Iterable, Sequence, Union +from typing import Any, Iterable, Sequence, Type, Union import numpy as np import orjson @@ -19,10 +19,8 @@ logger = logging.getLogger(__name__) -from vicinity.integrations.huggingface import HuggingFaceMixin - -class Vicinity(HuggingFaceMixin): +class Vicinity: """ Work with vector representations of items. @@ -126,7 +124,7 @@ def query( :param vectors: The vectors to find the nearest neighbors to. :param k: The number of most similar items to retrieve. :return: For each item in the input, the num most similar items are returned in the form of - (NAME, SIMILARITY) tuples. + (NAME, DISTANCE) tuples. """ vectors = np.asarray(vectors) if np.ndim(vectors) == 1: @@ -153,7 +151,7 @@ def query_threshold( :param max_k: The maximum number of neighbors to consider for the threshold query. :return: For each item in the input, the items above the threshold are returned in the form of - (NAME, SIMILARITY) tuples. + (NAME, DISTANCE) tuples. """ vectors = np.asarray(vectors) if np.ndim(vectors) == 1: @@ -282,6 +280,49 @@ def delete(self, tokens: Sequence[Any]) -> None: for index in sorted(curr_indices, reverse=True): self.items.pop(index) + def push_to_hub( + self, + repo_id: str, + token: str | None = None, + private: bool = False, + **kwargs: Any, + ) -> None: + """ + Push the Vicinity instance to the Hugging Face Hub. + + :param repo_id: The repository ID on the Hugging Face Hub + :param token: Optional authentication token for private repositories + :param private: Whether to create a private repository + :param **kwargs: Additional arguments passed to Dataset.push_to_hub() + """ + from vicinity.integrations.huggingface import push_to_hub + + push_to_hub( + repo_id=repo_id, + token=token, + private=private, + items=self.items, + backend=self.backend, + metadata=self.metadata, + vector_store=self.vector_store, + **kwargs, + ) + + @classmethod + def load_from_hub(cls: Type[Vicinity], repo_id: str, token: str | None = None, **kwargs: Any) -> Vicinity: + """ + Load a Vicinity instance from the Hugging Face Hub. + + :param repo_id: The repository ID on the Hugging Face Hub. + :param token: Optional authentication token for private repositories. + :param **kwargs: Additional arguments passed to Dataset.load_from_hub(). + :return: A Vicinity instance. + """ + from vicinity.integrations.huggingface import load_from_hub + + items, vector_store, backend, config = load_from_hub(repo_id=repo_id, token=token) + return Vicinity(items, backend, metadata=config["metadata"], vector_store=vector_store) + def evaluate( self, full_vectors: npt.NDArray,