diff --git a/docs/extras/integrations/providers/bageldb.mdx b/docs/extras/integrations/providers/bageldb.mdx new file mode 100644 index 00000000000..ec05493169d --- /dev/null +++ b/docs/extras/integrations/providers/bageldb.mdx @@ -0,0 +1,21 @@ +# BagelDB + +> [BagelDB](https://www.bageldb.ai/) (`Open Vector Database for AI`), is like GitHub for AI data. +It is a collaborative platform where users can create, +share, and manage vector datasets. It can support private projects for independent developers, +internal collaborations for enterprises, and public contributions for data DAOs. + +## Installation and Setup + +```bash +pip install betabageldb +``` + + +## VectorStore + +See a [usage example](/docs/integrations/vectorstores/bageldb). + +```python +from langchain.vectorstores import Bagel +``` diff --git a/docs/extras/integrations/vectorstores/bageldb.ipynb b/docs/extras/integrations/vectorstores/bageldb.ipynb new file mode 100644 index 00000000000..7f65486569f --- /dev/null +++ b/docs/extras/integrations/vectorstores/bageldb.ipynb @@ -0,0 +1,300 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# BagelDB\n", + "\n", + "> [BagelDB](https://www.bageldb.ai/) (`Open Vector Database for AI`), is like GitHub for AI data.\n", + "It is a collaborative platform where users can create,\n", + "share, and manage vector datasets. It can support private projects for independent developers,\n", + "internal collaborations for enterprises, and public contributions for data DAOs.\n", + "\n", + "### Installation and Setup\n", + "\n", + "```bash\n", + "pip install betabageldb\n", + "```\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create VectorStore from texts" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.vectorstores import Bagel\n", + "\n", + "texts = [\"hello bagel\", \"hello langchain\", \"I love salad\", \"my car\", \"a dog\"]\n", + "# create cluster and add texts\n", + "cluster = Bagel.from_texts(cluster_name=\"testing\", texts=texts)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Document(page_content='hello bagel', metadata={}),\n", + " Document(page_content='my car', metadata={}),\n", + " Document(page_content='I love salad', metadata={})]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# similarity search\n", + "cluster.similarity_search(\"bagel\", k=3)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(Document(page_content='hello bagel', metadata={}), 0.27392977476119995),\n", + " (Document(page_content='my car', metadata={}), 1.4783176183700562),\n", + " (Document(page_content='I love salad', metadata={}), 1.5342965126037598)]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# the score is a distance metric, so lower is better\n", + "cluster.similarity_search_with_score(\"bagel\", k=3)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# delete the cluster\n", + "cluster.delete_cluster()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create VectorStore from docs" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.document_loaders import TextLoader\n", + "from langchain.text_splitter import CharacterTextSplitter\n", + "\n", + "loader = TextLoader(\"../../../state_of_the_union.txt\")\n", + "documents = loader.load()\n", + "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n", + "docs = text_splitter.split_documents(documents)[:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "# create cluster with docs\n", + "cluster = Bagel.from_documents(cluster_name=\"testing_with_docs\", documents=docs)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Madam Speaker, Madam Vice President, our First Lady and Second Gentleman. Members of Congress and the \n" + ] + } + ], + "source": [ + "# similarity search\n", + "query = \"What did the president say about Ketanji Brown Jackson\"\n", + "docs = cluster.similarity_search(query)\n", + "print(docs[0].page_content[:102])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Get all text/doc from Cluster" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "texts = [\"hello bagel\", \"this is langchain\"]\n", + "cluster = Bagel.from_texts(cluster_name=\"testing\", texts=texts)\n", + "cluster_data = cluster.get()" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['ids', 'embeddings', 'metadatas', 'documents'])" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# all keys\n", + "cluster_data.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'ids': ['578c6d24-3763-11ee-a8ab-b7b7b34f99ba',\n", + " '578c6d25-3763-11ee-a8ab-b7b7b34f99ba',\n", + " 'fb2fc7d8-3762-11ee-a8ab-b7b7b34f99ba',\n", + " 'fb2fc7d9-3762-11ee-a8ab-b7b7b34f99ba',\n", + " '6b40881a-3762-11ee-a8ab-b7b7b34f99ba',\n", + " '6b40881b-3762-11ee-a8ab-b7b7b34f99ba',\n", + " '581e691e-3762-11ee-a8ab-b7b7b34f99ba',\n", + " '581e691f-3762-11ee-a8ab-b7b7b34f99ba'],\n", + " 'embeddings': None,\n", + " 'metadatas': [{}, {}, {}, {}, {}, {}, {}, {}],\n", + " 'documents': ['hello bagel',\n", + " 'this is langchain',\n", + " 'hello bagel',\n", + " 'this is langchain',\n", + " 'hello bagel',\n", + " 'this is langchain',\n", + " 'hello bagel',\n", + " 'this is langchain']}" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# all values and keys\n", + "cluster_data" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "cluster.delete_cluster()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Create cluster with metadata & filter using metadata" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(Document(page_content='hello bagel', metadata={'source': 'notion'}), 0.0)]" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "texts = [\"hello bagel\", \"this is langchain\"]\n", + "metadatas = [{\"source\": \"notion\"}, {\"source\": \"google\"}]\n", + "\n", + "cluster = Bagel.from_texts(cluster_name=\"testing\", texts=texts, metadatas=metadatas)\n", + "cluster.similarity_search_with_score(\"hello bagel\", where={\"source\": \"notion\"})" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "# delete the cluster\n", + "cluster.delete_cluster()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/libs/langchain/langchain/vectorstores/__init__.py b/libs/langchain/langchain/vectorstores/__init__.py index aefd5e69caa..4c254daede5 100644 --- a/libs/langchain/langchain/vectorstores/__init__.py +++ b/libs/langchain/langchain/vectorstores/__init__.py @@ -27,6 +27,7 @@ from langchain.vectorstores.annoy import Annoy from langchain.vectorstores.atlas import AtlasDB from langchain.vectorstores.awadb import AwaDB from langchain.vectorstores.azuresearch import AzureSearch +from langchain.vectorstores.bageldb import Bagel from langchain.vectorstores.base import VectorStore from langchain.vectorstores.cassandra import Cassandra from langchain.vectorstores.chroma import Chroma @@ -75,6 +76,7 @@ __all__ = [ "AtlasDB", "AwaDB", "AzureSearch", + "Bagel", "Cassandra", "Chroma", "Clickhouse", diff --git a/libs/langchain/langchain/vectorstores/bageldb.py b/libs/langchain/langchain/vectorstores/bageldb.py new file mode 100644 index 00000000000..ab0e7868caa --- /dev/null +++ b/libs/langchain/langchain/vectorstores/bageldb.py @@ -0,0 +1,432 @@ +"""BagelDB integration""" +from __future__ import annotations + +import uuid +from typing import ( + TYPE_CHECKING, + Any, + Callable, + Dict, + Iterable, + List, + Optional, + Tuple, + Type, +) + +if TYPE_CHECKING: + import bagel + import bagel.config + from bagel.api.types import ID, OneOrMany, Where, WhereDocument + +from langchain.docstore.document import Document +from langchain.embeddings.base import Embeddings +from langchain.utils import xor_args +from langchain.vectorstores.base import VectorStore + +DEFAULT_K = 5 + + +def _results_to_docs(results: Any) -> List[Document]: + return [doc for doc, _ in _results_to_docs_and_scores(results)] + + +def _results_to_docs_and_scores(results: Any) -> List[Tuple[Document, float]]: + return [ + (Document(page_content=result[0], metadata=result[1] or {}), result[2]) + for result in zip( + results["documents"][0], + results["metadatas"][0], + results["distances"][0], + ) + ] + + +class Bagel(VectorStore): + """Wrapper around BagelDB.ai vector store. + + To use, you should have the ``betabageldb`` python package installed. + + Example: + .. code-block:: python + + from langchain.vectorstores import Bagel + vectorstore = Bagel(cluster_name="langchain_store") + """ + + _LANGCHAIN_DEFAULT_CLUSTER_NAME = "langchain" + + def __init__( + self, + cluster_name: str = _LANGCHAIN_DEFAULT_CLUSTER_NAME, + client_settings: Optional[bagel.config.Settings] = None, + embedding_function: Optional[Embeddings] = None, + cluster_metadata: Optional[Dict] = None, + client: Optional[bagel.Client] = None, + relevance_score_fn: Optional[Callable[[float], float]] = None, + ) -> None: + """Initialize with bagel client""" + try: + import bagel + import bagel.config + except ImportError: + raise ValueError("Please install bagel `pip install betabageldb`.") + if client is not None: + self._client_settings = client_settings + self._client = client + else: + if client_settings: + _client_settings = client_settings + else: + _client_settings = bagel.config.Settings( + bagel_api_impl="rest", + bagel_server_host="api.bageldb.ai", + ) + self._client_settings = _client_settings + self._client = bagel.Client(_client_settings) + + self._cluster = self._client.get_or_create_cluster( + name=cluster_name, + metadata=cluster_metadata, + ) + self.override_relevance_score_fn = relevance_score_fn + self._embedding_function = embedding_function + + @property + def embeddings(self) -> Optional[Embeddings]: + return self._embedding_function + + @xor_args(("query_texts", "query_embeddings")) + def __query_cluster( + self, + query_texts: Optional[List[str]] = None, + query_embeddings: Optional[List[List[float]]] = None, + n_results: int = 4, + where: Optional[Dict[str, str]] = None, + **kwargs: Any, + ) -> List[Document]: + """Query the BagelDB cluster based on the provided parameters.""" + try: + import bagel # noqa: F401 + except ImportError: + raise ValueError("Please install bagel `pip install betabageldb`.") + return self._cluster.find( + query_texts=query_texts, + query_embeddings=query_embeddings, + n_results=n_results, + where=where, + **kwargs, + ) + + def add_texts( + self, + texts: Iterable[str], + metadatas: Optional[List[dict]] = None, + ids: Optional[List[str]] = None, + embeddings: Optional[List[List[float]]] = None, + **kwargs: Any, + ) -> List[str]: + """ + Add texts along with their corresponding embeddings and optional + metadata to the BagelDB cluster. + + Args: + texts (Iterable[str]): Texts to be added. + embeddings (Optional[List[float]]): List of embeddingvectors + metadatas (Optional[List[dict]]): Optional list of metadatas. + ids (Optional[List[str]]): List of unique ID for the texts. + + Returns: + List[str]: List of unique ID representing the added texts. + """ + # creating unique ids if None + if ids is None: + ids = [str(uuid.uuid1()) for _ in texts] + + texts = list(texts) + if self._embedding_function and embeddings is None and texts: + embeddings = self._embedding_function.embed_documents(texts) + if metadatas: + length_diff = len(texts) - len(metadatas) + if length_diff: + metadatas = metadatas + [{}] * length_diff + empty_ids = [] + non_empty_ids = [] + for idx, metadata in enumerate(metadatas): + if metadata: + non_empty_ids.append(idx) + else: + empty_ids.append(idx) + if non_empty_ids: + metadatas = [metadatas[idx] for idx in non_empty_ids] + texts_with_metadatas = [texts[idx] for idx in non_empty_ids] + embeddings_with_metadatas = ( + [embeddings[idx] for idx in non_empty_ids] if embeddings else None + ) + ids_with_metadata = [ids[idx] for idx in non_empty_ids] + self._cluster.upsert( + embeddings=embeddings_with_metadatas, + metadatas=metadatas, + documents=texts_with_metadatas, + ids=ids_with_metadata, + ) + if empty_ids: + texts_without_metadatas = [texts[j] for j in empty_ids] + embeddings_without_metadatas = ( + [embeddings[j] for j in empty_ids] if embeddings else None + ) + ids_without_metadatas = [ids[j] for j in empty_ids] + self._cluster.upsert( + embeddings=embeddings_without_metadatas, + documents=texts_without_metadatas, + ids=ids_without_metadatas, + ) + else: + metadatas = [{}] * len(texts) + self._cluster.upsert( + embeddings=embeddings, + documents=texts, + metadatas=metadatas, + ids=ids, + ) + return ids + + def similarity_search( + self, + query: str, + k: int = DEFAULT_K, + where: Optional[Dict[str, str]] = None, + **kwargs: Any, + ) -> List[Document]: + """ + Run a similarity search with BagelDB. + + Args: + query (str): The query text to search for similar documents/texts. + k (int): The number of results to return. + where (Optional[Dict[str, str]]): Metadata filters to narrow down. + + Returns: + List[Document]: List of documents objects representing + the documents most similar to the query text. + """ + docs_and_scores = self.similarity_search_with_score(query, k, where=where) + return [doc for doc, _ in docs_and_scores] + + def similarity_search_with_score( + self, + query: str, + k: int = DEFAULT_K, + where: Optional[Dict[str, str]] = None, + **kwargs: Any, + ) -> List[Tuple[Document, float]]: + """ + Run a similarity search with BagelDB and return documents with their + corresponding similarity scores. + + Args: + query (str): The query text to search for similar documents. + k (int): The number of results to return. + where (Optional[Dict[str, str]]): Filter using metadata. + + Returns: + List[Tuple[Document, float]]: List of tuples, each containing a + Document object representing a similar document and its + corresponding similarity score. + + """ + results = self.__query_cluster(query_texts=[query], n_results=k, where=where) + return _results_to_docs_and_scores(results) + + @classmethod + def from_texts( + cls: Type[Bagel], + texts: List[str], + embedding: Optional[Embeddings] = None, + metadatas: Optional[List[dict]] = None, + ids: Optional[List[str]] = None, + cluster_name: str = _LANGCHAIN_DEFAULT_CLUSTER_NAME, + client_settings: Optional[bagel.config.Settings] = None, + cluster_metadata: Optional[Dict] = None, + client: Optional[bagel.Client] = None, + text_embeddings: Optional[List[List[float]]] = None, + **kwargs: Any, + ) -> Bagel: + """ + Create and initialize a Bagel instance from list of texts. + + Args: + texts (List[str]): List of text content to be added. + cluster_name (str): The name of the BagelDB cluster. + client_settings (Optional[bagel.config.Settings]): Client settings. + cluster_metadata (Optional[Dict]): Metadata of the cluster. + embeddings (Optional[Embeddings]): List of embedding. + metadatas (Optional[List[dict]]): List of metadata. + ids (Optional[List[str]]): List of unique ID. Defaults to None. + client (Optional[bagel.Client]): Bagel client instance. + + Returns: + Bagel: Bagel vectorstore. + """ + bagel_cluster = cls( + cluster_name=cluster_name, + embedding_function=embedding, + client_settings=client_settings, + client=client, + cluster_metadata=cluster_metadata, + **kwargs, + ) + _ = bagel_cluster.add_texts( + texts=texts, embeddings=text_embeddings, metadatas=metadatas, ids=ids + ) + return bagel_cluster + + def delete_cluster(self) -> None: + """Delete the cluster.""" + self._client.delete_cluster(self._cluster.name) + + def similarity_search_by_vector_with_relevance_scores( + self, + query_embeddings: List[float], + k: int = DEFAULT_K, + where: Optional[Dict[str, str]] = None, + **kwargs: Any, + ) -> List[Tuple[Document, float]]: + """ + Return docs most similar to embedding vector and similarity score. + """ + results = self.__query_cluster( + query_embeddings=query_embeddings, n_results=k, where=where + ) + return _results_to_docs_and_scores(results) + + def similarity_search_by_vector( + self, + embedding: List[float], + k: int = DEFAULT_K, + where: Optional[Dict[str, str]] = None, + **kwargs: Any, + ) -> List[Document]: + """Return docs most similar to embedding vector.""" + results = self.__query_cluster( + query_embeddings=embedding, n_results=k, where=where + ) + return _results_to_docs(results) + + def _select_relevance_score_fn(self) -> Callable[[float], float]: + """ + Select and return the appropriate relevance score function based + on the distance metric used in the BagelDB cluster. + """ + if self.override_relevance_score_fn: + return self.override_relevance_score_fn + + distance = "l2" + distance_key = "hnsw:space" + metadata = self._cluster.metadata + + if metadata and distance_key in metadata: + distance = metadata[distance_key] + + if distance == "cosine": + return self._cosine_relevance_score_fn + elif distance == "l2": + return self._euclidean_relevance_score_fn + elif distance == "ip": + return self._max_inner_product_relevance_score_fn + else: + raise ValueError( + "No supported normalization function for distance" + f" metric of type: {distance}. Consider providing" + " relevance_score_fn to Bagel constructor." + ) + + @classmethod + def from_documents( + cls: Type[Bagel], + documents: List[Document], + embedding: Optional[Embeddings] = None, + ids: Optional[List[str]] = None, + cluster_name: str = _LANGCHAIN_DEFAULT_CLUSTER_NAME, + client_settings: Optional[bagel.config.Settings] = None, + client: Optional[bagel.Client] = None, + cluster_metadata: Optional[Dict] = None, + **kwargs: Any, + ) -> Bagel: + """ + Create a Bagel vectorstore from a list of documents. + + Args: + documents (List[Document]): List of Document objects to add to the + Bagel vectorstore. + embedding (Optional[List[float]]): List of embedding. + ids (Optional[List[str]]): List of IDs. Defaults to None. + cluster_name (str): The name of the BagelDB cluster. + client_settings (Optional[bagel.config.Settings]): Client settings. + client (Optional[bagel.Client]): Bagel client instance. + cluster_metadata (Optional[Dict]): Metadata associated with the + Bagel cluster. Defaults to None. + + Returns: + Bagel: Bagel vectorstore. + """ + texts = [doc.page_content for doc in documents] + metadatas = [doc.metadata for doc in documents] + return cls.from_texts( + texts=texts, + embedding=embedding, + metadatas=metadatas, + ids=ids, + cluster_name=cluster_name, + client_settings=client_settings, + client=client, + cluster_metadata=cluster_metadata, + **kwargs, + ) + + def update_document(self, document_id: str, document: Document) -> None: + """Update a document in the cluster. + + Args: + document_id (str): ID of the document to update. + document (Document): Document to update. + """ + text = document.page_content + metadata = document.metadata + self._cluster.update( + ids=[document_id], + documents=[text], + metadatas=[metadata], + ) + + def get( + self, + ids: Optional[OneOrMany[ID]] = None, + where: Optional[Where] = None, + limit: Optional[int] = None, + offset: Optional[int] = None, + where_document: Optional[WhereDocument] = None, + include: Optional[List[str]] = None, + ) -> Dict[str, Any]: + """Gets the collection.""" + kwargs = { + "ids": ids, + "where": where, + "limit": limit, + "offset": offset, + "where_document": where_document, + } + + if include is not None: + kwargs["include"] = include + + return self._cluster.get(**kwargs) + + def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> None: + """ + Delete by IDs. + + Args: + ids: List of ids to delete. + """ + self._cluster.delete(ids=ids) diff --git a/libs/langchain/poetry.lock b/libs/langchain/poetry.lock index 282136c1add..a17a0224f48 100644 --- a/libs/langchain/poetry.lock +++ b/libs/langchain/poetry.lock @@ -518,7 +518,7 @@ name = "arxiv" version = "1.4.7" description = "Python wrapper for the arXiv API: http://arxiv.org/help/api/" category = "main" -optional = true +optional = false python-versions = ">=3.7" files = [ {file = "arxiv-1.4.7-py3-none-any.whl", hash = "sha256:22b8f610957bb6859a25fac9dc205ab6ba76d521791119a5762ea52625e398a0"}, @@ -964,6 +964,35 @@ tracing-otlp = ["opentelemetry-exporter-otlp (==1.17.0)"] tracing-zipkin = ["opentelemetry-exporter-zipkin (==1.17.0)"] triton = ["tritonclient[all] (>=2.29.0)"] +[[package]] +name = "betabageldb" +version = "0.2.32" +description = "BagelDB is a Python library for interacting with the BagelDB API." +category = "main" +optional = false +python-versions = "*" +files = [ + {file = "betabageldb-0.2.32-py3-none-any.whl", hash = "sha256:1fc6fc6b1353bc8b8ca5f72ad0aa5d38069fd0d7236a6d4c96c12bc7bad8913e"}, + {file = "betabageldb-0.2.32.tar.gz", hash = "sha256:17ca10b8edf7b7689c92e904bbe90292c71bbf8d2fa11e468fdda3af7ab222bf"}, +] + +[package.dependencies] +certifi = ">=2023.5.7" +charset-normalizer = ">=3.2.0" +graphlib-backport = ">=1.0.3" +idna = ">=3.4" +numpy = ">=1.21.6" +overrides = ">=7.3.1" +pandas = ">=2.0.1" +pydantic = ">=1.10.10,<2.0" +python-dateutil = ">=2.8.2" +pytz = ">=2023.3" +requests = ">=2.28" +six = ">=1.16.0" +typing-extensions = ">=4.6.3" +tzdata = ">=2022.1" +urllib3 = ">=1.26.16" + [[package]] name = "bibtexparser" version = "1.4.0" @@ -1100,6 +1129,21 @@ files = [ [package.dependencies] numpy = ">=1.15.0" +[[package]] +name = "blurhash" +version = "1.1.4" +description = "Pure-Python implementation of the blurhash algorithm." +category = "dev" +optional = false +python-versions = "*" +files = [ + {file = "blurhash-1.1.4-py2.py3-none-any.whl", hash = "sha256:7611c1bc41383d2349b6129208587b5d61e8792ce953893cb49c38beeb400d1d"}, + {file = "blurhash-1.1.4.tar.gz", hash = "sha256:da56b163e5a816e4ad07172f5639287698e09d7f3dc38d18d9726d9c1dbc4cee"}, +] + +[package.extras] +test = ["Pillow", "numpy", "pytest"] + [[package]] name = "boto3" version = "1.26.76" @@ -1520,87 +1564,87 @@ files = [ [[package]] name = "charset-normalizer" -version = "3.1.0" +version = "3.2.0" description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet." category = "main" optional = false python-versions = ">=3.7.0" files = [ - {file = "charset-normalizer-3.1.0.tar.gz", hash = "sha256:34e0a2f9c370eb95597aae63bf85eb5e96826d81e3dcf88b8886012906f509b5"}, - {file = "charset_normalizer-3.1.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:e0ac8959c929593fee38da1c2b64ee9778733cdf03c482c9ff1d508b6b593b2b"}, - {file = "charset_normalizer-3.1.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:d7fc3fca01da18fbabe4625d64bb612b533533ed10045a2ac3dd194bfa656b60"}, - {file = "charset_normalizer-3.1.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:04eefcee095f58eaabe6dc3cc2262f3bcd776d2c67005880894f447b3f2cb9c1"}, - {file = "charset_normalizer-3.1.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:20064ead0717cf9a73a6d1e779b23d149b53daf971169289ed2ed43a71e8d3b0"}, - {file = "charset_normalizer-3.1.0-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1435ae15108b1cb6fffbcea2af3d468683b7afed0169ad718451f8db5d1aff6f"}, - {file = "charset_normalizer-3.1.0-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c84132a54c750fda57729d1e2599bb598f5fa0344085dbde5003ba429a4798c0"}, - {file = "charset_normalizer-3.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:75f2568b4189dda1c567339b48cba4ac7384accb9c2a7ed655cd86b04055c795"}, - {file = "charset_normalizer-3.1.0-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:11d3bcb7be35e7b1bba2c23beedac81ee893ac9871d0ba79effc7fc01167db6c"}, - {file = "charset_normalizer-3.1.0-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:891cf9b48776b5c61c700b55a598621fdb7b1e301a550365571e9624f270c203"}, - {file = "charset_normalizer-3.1.0-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:5f008525e02908b20e04707a4f704cd286d94718f48bb33edddc7d7b584dddc1"}, - {file = "charset_normalizer-3.1.0-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:b06f0d3bf045158d2fb8837c5785fe9ff9b8c93358be64461a1089f5da983137"}, - {file = "charset_normalizer-3.1.0-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:49919f8400b5e49e961f320c735388ee686a62327e773fa5b3ce6721f7e785ce"}, - {file = "charset_normalizer-3.1.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:22908891a380d50738e1f978667536f6c6b526a2064156203d418f4856d6e86a"}, - {file = "charset_normalizer-3.1.0-cp310-cp310-win32.whl", hash = "sha256:12d1a39aa6b8c6f6248bb54550efcc1c38ce0d8096a146638fd4738e42284448"}, - {file = "charset_normalizer-3.1.0-cp310-cp310-win_amd64.whl", hash = "sha256:65ed923f84a6844de5fd29726b888e58c62820e0769b76565480e1fdc3d062f8"}, - {file = "charset_normalizer-3.1.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:9a3267620866c9d17b959a84dd0bd2d45719b817245e49371ead79ed4f710d19"}, - {file = "charset_normalizer-3.1.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6734e606355834f13445b6adc38b53c0fd45f1a56a9ba06c2058f86893ae8017"}, - {file = "charset_normalizer-3.1.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:f8303414c7b03f794347ad062c0516cee0e15f7a612abd0ce1e25caf6ceb47df"}, - {file = "charset_normalizer-3.1.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:aaf53a6cebad0eae578f062c7d462155eada9c172bd8c4d250b8c1d8eb7f916a"}, - {file = "charset_normalizer-3.1.0-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:3dc5b6a8ecfdc5748a7e429782598e4f17ef378e3e272eeb1340ea57c9109f41"}, - {file = "charset_normalizer-3.1.0-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e1b25e3ad6c909f398df8921780d6a3d120d8c09466720226fc621605b6f92b1"}, - {file = "charset_normalizer-3.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0ca564606d2caafb0abe6d1b5311c2649e8071eb241b2d64e75a0d0065107e62"}, - {file = "charset_normalizer-3.1.0-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b82fab78e0b1329e183a65260581de4375f619167478dddab510c6c6fb04d9b6"}, - {file = "charset_normalizer-3.1.0-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:bd7163182133c0c7701b25e604cf1611c0d87712e56e88e7ee5d72deab3e76b5"}, - {file = "charset_normalizer-3.1.0-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:11d117e6c63e8f495412d37e7dc2e2fff09c34b2d09dbe2bee3c6229577818be"}, - {file = "charset_normalizer-3.1.0-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:cf6511efa4801b9b38dc5546d7547d5b5c6ef4b081c60b23e4d941d0eba9cbeb"}, - {file = "charset_normalizer-3.1.0-cp311-cp311-musllinux_1_1_s390x.whl", hash = "sha256:abc1185d79f47c0a7aaf7e2412a0eb2c03b724581139193d2d82b3ad8cbb00ac"}, - {file = "charset_normalizer-3.1.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:cb7b2ab0188829593b9de646545175547a70d9a6e2b63bf2cd87a0a391599324"}, - {file = "charset_normalizer-3.1.0-cp311-cp311-win32.whl", hash = "sha256:c36bcbc0d5174a80d6cccf43a0ecaca44e81d25be4b7f90f0ed7bcfbb5a00909"}, - {file = "charset_normalizer-3.1.0-cp311-cp311-win_amd64.whl", hash = "sha256:cca4def576f47a09a943666b8f829606bcb17e2bc2d5911a46c8f8da45f56755"}, - {file = "charset_normalizer-3.1.0-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:0c95f12b74681e9ae127728f7e5409cbbef9cd914d5896ef238cc779b8152373"}, - {file = "charset_normalizer-3.1.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:fca62a8301b605b954ad2e9c3666f9d97f63872aa4efcae5492baca2056b74ab"}, - {file = "charset_normalizer-3.1.0-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ac0aa6cd53ab9a31d397f8303f92c42f534693528fafbdb997c82bae6e477ad9"}, - {file = "charset_normalizer-3.1.0-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c3af8e0f07399d3176b179f2e2634c3ce9c1301379a6b8c9c9aeecd481da494f"}, - {file = "charset_normalizer-3.1.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3a5fc78f9e3f501a1614a98f7c54d3969f3ad9bba8ba3d9b438c3bc5d047dd28"}, - {file = "charset_normalizer-3.1.0-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:628c985afb2c7d27a4800bfb609e03985aaecb42f955049957814e0491d4006d"}, - {file = "charset_normalizer-3.1.0-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:74db0052d985cf37fa111828d0dd230776ac99c740e1a758ad99094be4f1803d"}, - {file = "charset_normalizer-3.1.0-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:1e8fcdd8f672a1c4fc8d0bd3a2b576b152d2a349782d1eb0f6b8e52e9954731d"}, - {file = "charset_normalizer-3.1.0-cp37-cp37m-musllinux_1_1_ppc64le.whl", hash = "sha256:04afa6387e2b282cf78ff3dbce20f0cc071c12dc8f685bd40960cc68644cfea6"}, - {file = "charset_normalizer-3.1.0-cp37-cp37m-musllinux_1_1_s390x.whl", hash = "sha256:dd5653e67b149503c68c4018bf07e42eeed6b4e956b24c00ccdf93ac79cdff84"}, - {file = "charset_normalizer-3.1.0-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:d2686f91611f9e17f4548dbf050e75b079bbc2a82be565832bc8ea9047b61c8c"}, - {file = "charset_normalizer-3.1.0-cp37-cp37m-win32.whl", hash = "sha256:4155b51ae05ed47199dc5b2a4e62abccb274cee6b01da5b895099b61b1982974"}, - {file = "charset_normalizer-3.1.0-cp37-cp37m-win_amd64.whl", hash = "sha256:322102cdf1ab682ecc7d9b1c5eed4ec59657a65e1c146a0da342b78f4112db23"}, - {file = "charset_normalizer-3.1.0-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:e633940f28c1e913615fd624fcdd72fdba807bf53ea6925d6a588e84e1151531"}, - {file = "charset_normalizer-3.1.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:3a06f32c9634a8705f4ca9946d667609f52cf130d5548881401f1eb2c39b1e2c"}, - {file = "charset_normalizer-3.1.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:7381c66e0561c5757ffe616af869b916c8b4e42b367ab29fedc98481d1e74e14"}, - {file = "charset_normalizer-3.1.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3573d376454d956553c356df45bb824262c397c6e26ce43e8203c4c540ee0acb"}, - {file = "charset_normalizer-3.1.0-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:e89df2958e5159b811af9ff0f92614dabf4ff617c03a4c1c6ff53bf1c399e0e1"}, - {file = "charset_normalizer-3.1.0-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:78cacd03e79d009d95635e7d6ff12c21eb89b894c354bd2b2ed0b4763373693b"}, - {file = "charset_normalizer-3.1.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:de5695a6f1d8340b12a5d6d4484290ee74d61e467c39ff03b39e30df62cf83a0"}, - {file = "charset_normalizer-3.1.0-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1c60b9c202d00052183c9be85e5eaf18a4ada0a47d188a83c8f5c5b23252f649"}, - {file = "charset_normalizer-3.1.0-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:f645caaf0008bacf349875a974220f1f1da349c5dbe7c4ec93048cdc785a3326"}, - {file = "charset_normalizer-3.1.0-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:ea9f9c6034ea2d93d9147818f17c2a0860d41b71c38b9ce4d55f21b6f9165a11"}, - {file = "charset_normalizer-3.1.0-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:80d1543d58bd3d6c271b66abf454d437a438dff01c3e62fdbcd68f2a11310d4b"}, - {file = "charset_normalizer-3.1.0-cp38-cp38-musllinux_1_1_s390x.whl", hash = "sha256:73dc03a6a7e30b7edc5b01b601e53e7fc924b04e1835e8e407c12c037e81adbd"}, - {file = "charset_normalizer-3.1.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:6f5c2e7bc8a4bf7c426599765b1bd33217ec84023033672c1e9a8b35eaeaaaf8"}, - {file = "charset_normalizer-3.1.0-cp38-cp38-win32.whl", hash = "sha256:12a2b561af122e3d94cdb97fe6fb2bb2b82cef0cdca131646fdb940a1eda04f0"}, - {file = "charset_normalizer-3.1.0-cp38-cp38-win_amd64.whl", hash = "sha256:3160a0fd9754aab7d47f95a6b63ab355388d890163eb03b2d2b87ab0a30cfa59"}, - {file = "charset_normalizer-3.1.0-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:38e812a197bf8e71a59fe55b757a84c1f946d0ac114acafaafaf21667a7e169e"}, - {file = "charset_normalizer-3.1.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:6baf0baf0d5d265fa7944feb9f7451cc316bfe30e8df1a61b1bb08577c554f31"}, - {file = "charset_normalizer-3.1.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:8f25e17ab3039b05f762b0a55ae0b3632b2e073d9c8fc88e89aca31a6198e88f"}, - {file = "charset_normalizer-3.1.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3747443b6a904001473370d7810aa19c3a180ccd52a7157aacc264a5ac79265e"}, - {file = "charset_normalizer-3.1.0-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:b116502087ce8a6b7a5f1814568ccbd0e9f6cfd99948aa59b0e241dc57cf739f"}, - {file = "charset_normalizer-3.1.0-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:d16fd5252f883eb074ca55cb622bc0bee49b979ae4e8639fff6ca3ff44f9f854"}, - {file = "charset_normalizer-3.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:21fa558996782fc226b529fdd2ed7866c2c6ec91cee82735c98a197fae39f706"}, - {file = "charset_normalizer-3.1.0-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:6f6c7a8a57e9405cad7485f4c9d3172ae486cfef1344b5ddd8e5239582d7355e"}, - {file = "charset_normalizer-3.1.0-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:ac3775e3311661d4adace3697a52ac0bab17edd166087d493b52d4f4f553f9f0"}, - {file = "charset_normalizer-3.1.0-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:10c93628d7497c81686e8e5e557aafa78f230cd9e77dd0c40032ef90c18f2230"}, - {file = "charset_normalizer-3.1.0-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:6f4f4668e1831850ebcc2fd0b1cd11721947b6dc7c00bf1c6bd3c929ae14f2c7"}, - {file = "charset_normalizer-3.1.0-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:0be65ccf618c1e7ac9b849c315cc2e8a8751d9cfdaa43027d4f6624bd587ab7e"}, - {file = "charset_normalizer-3.1.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:53d0a3fa5f8af98a1e261de6a3943ca631c526635eb5817a87a59d9a57ebf48f"}, - {file = "charset_normalizer-3.1.0-cp39-cp39-win32.whl", hash = "sha256:a04f86f41a8916fe45ac5024ec477f41f886b3c435da2d4e3d2709b22ab02af1"}, - {file = "charset_normalizer-3.1.0-cp39-cp39-win_amd64.whl", hash = "sha256:830d2948a5ec37c386d3170c483063798d7879037492540f10a475e3fd6f244b"}, - {file = "charset_normalizer-3.1.0-py3-none-any.whl", hash = "sha256:3d9098b479e78c85080c98e1e35ff40b4a31d8953102bb0fd7d1b6f8a2111a3d"}, + {file = "charset-normalizer-3.2.0.tar.gz", hash = "sha256:3bb3d25a8e6c0aedd251753a79ae98a093c7e7b471faa3aa9a93a81431987ace"}, + {file = "charset_normalizer-3.2.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:0b87549028f680ca955556e3bd57013ab47474c3124dc069faa0b6545b6c9710"}, + {file = "charset_normalizer-3.2.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:7c70087bfee18a42b4040bb9ec1ca15a08242cf5867c58726530bdf3945672ed"}, + {file = "charset_normalizer-3.2.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:a103b3a7069b62f5d4890ae1b8f0597618f628b286b03d4bc9195230b154bfa9"}, + {file = "charset_normalizer-3.2.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:94aea8eff76ee6d1cdacb07dd2123a68283cb5569e0250feab1240058f53b623"}, + {file = "charset_normalizer-3.2.0-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:db901e2ac34c931d73054d9797383d0f8009991e723dab15109740a63e7f902a"}, + {file = "charset_normalizer-3.2.0-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b0dac0ff919ba34d4df1b6131f59ce95b08b9065233446be7e459f95554c0dc8"}, + {file = "charset_normalizer-3.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:193cbc708ea3aca45e7221ae58f0fd63f933753a9bfb498a3b474878f12caaad"}, + {file = "charset_normalizer-3.2.0-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:09393e1b2a9461950b1c9a45d5fd251dc7c6f228acab64da1c9c0165d9c7765c"}, + {file = "charset_normalizer-3.2.0-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:baacc6aee0b2ef6f3d308e197b5d7a81c0e70b06beae1f1fcacffdbd124fe0e3"}, + {file = "charset_normalizer-3.2.0-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:bf420121d4c8dce6b889f0e8e4ec0ca34b7f40186203f06a946fa0276ba54029"}, + {file = "charset_normalizer-3.2.0-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:c04a46716adde8d927adb9457bbe39cf473e1e2c2f5d0a16ceb837e5d841ad4f"}, + {file = "charset_normalizer-3.2.0-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:aaf63899c94de41fe3cf934601b0f7ccb6b428c6e4eeb80da72c58eab077b19a"}, + {file = "charset_normalizer-3.2.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:d62e51710986674142526ab9f78663ca2b0726066ae26b78b22e0f5e571238dd"}, + {file = "charset_normalizer-3.2.0-cp310-cp310-win32.whl", hash = "sha256:04e57ab9fbf9607b77f7d057974694b4f6b142da9ed4a199859d9d4d5c63fe96"}, + {file = "charset_normalizer-3.2.0-cp310-cp310-win_amd64.whl", hash = "sha256:48021783bdf96e3d6de03a6e39a1171ed5bd7e8bb93fc84cc649d11490f87cea"}, + {file = "charset_normalizer-3.2.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:4957669ef390f0e6719db3613ab3a7631e68424604a7b448f079bee145da6e09"}, + {file = "charset_normalizer-3.2.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:46fb8c61d794b78ec7134a715a3e564aafc8f6b5e338417cb19fe9f57a5a9bf2"}, + {file = "charset_normalizer-3.2.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:f779d3ad205f108d14e99bb3859aa7dd8e9c68874617c72354d7ecaec2a054ac"}, + {file = "charset_normalizer-3.2.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f25c229a6ba38a35ae6e25ca1264621cc25d4d38dca2942a7fce0b67a4efe918"}, + {file = "charset_normalizer-3.2.0-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:2efb1bd13885392adfda4614c33d3b68dee4921fd0ac1d3988f8cbb7d589e72a"}, + {file = "charset_normalizer-3.2.0-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:1f30b48dd7fa1474554b0b0f3fdfdd4c13b5c737a3c6284d3cdc424ec0ffff3a"}, + {file = "charset_normalizer-3.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:246de67b99b6851627d945db38147d1b209a899311b1305dd84916f2b88526c6"}, + {file = "charset_normalizer-3.2.0-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:9bd9b3b31adcb054116447ea22caa61a285d92e94d710aa5ec97992ff5eb7cf3"}, + {file = "charset_normalizer-3.2.0-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:8c2f5e83493748286002f9369f3e6607c565a6a90425a3a1fef5ae32a36d749d"}, + {file = "charset_normalizer-3.2.0-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:3170c9399da12c9dc66366e9d14da8bf7147e1e9d9ea566067bbce7bb74bd9c2"}, + {file = "charset_normalizer-3.2.0-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:7a4826ad2bd6b07ca615c74ab91f32f6c96d08f6fcc3902ceeedaec8cdc3bcd6"}, + {file = "charset_normalizer-3.2.0-cp311-cp311-musllinux_1_1_s390x.whl", hash = "sha256:3b1613dd5aee995ec6d4c69f00378bbd07614702a315a2cf6c1d21461fe17c23"}, + {file = "charset_normalizer-3.2.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:9e608aafdb55eb9f255034709e20d5a83b6d60c054df0802fa9c9883d0a937aa"}, + {file = "charset_normalizer-3.2.0-cp311-cp311-win32.whl", hash = "sha256:f2a1d0fd4242bd8643ce6f98927cf9c04540af6efa92323e9d3124f57727bfc1"}, + {file = "charset_normalizer-3.2.0-cp311-cp311-win_amd64.whl", hash = "sha256:681eb3d7e02e3c3655d1b16059fbfb605ac464c834a0c629048a30fad2b27489"}, + {file = "charset_normalizer-3.2.0-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:c57921cda3a80d0f2b8aec7e25c8aa14479ea92b5b51b6876d975d925a2ea346"}, + {file = "charset_normalizer-3.2.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:41b25eaa7d15909cf3ac4c96088c1f266a9a93ec44f87f1d13d4a0e86c81b982"}, + {file = "charset_normalizer-3.2.0-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:f058f6963fd82eb143c692cecdc89e075fa0828db2e5b291070485390b2f1c9c"}, + {file = "charset_normalizer-3.2.0-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a7647ebdfb9682b7bb97e2a5e7cb6ae735b1c25008a70b906aecca294ee96cf4"}, + {file = "charset_normalizer-3.2.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:eef9df1eefada2c09a5e7a40991b9fc6ac6ef20b1372abd48d2794a316dc0449"}, + {file = "charset_normalizer-3.2.0-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e03b8895a6990c9ab2cdcd0f2fe44088ca1c65ae592b8f795c3294af00a461c3"}, + {file = "charset_normalizer-3.2.0-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:ee4006268ed33370957f55bf2e6f4d263eaf4dc3cfc473d1d90baff6ed36ce4a"}, + {file = "charset_normalizer-3.2.0-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:c4983bf937209c57240cff65906b18bb35e64ae872da6a0db937d7b4af845dd7"}, + {file = "charset_normalizer-3.2.0-cp37-cp37m-musllinux_1_1_ppc64le.whl", hash = "sha256:3bb7fda7260735efe66d5107fb7e6af6a7c04c7fce9b2514e04b7a74b06bf5dd"}, + {file = "charset_normalizer-3.2.0-cp37-cp37m-musllinux_1_1_s390x.whl", hash = "sha256:72814c01533f51d68702802d74f77ea026b5ec52793c791e2da806a3844a46c3"}, + {file = "charset_normalizer-3.2.0-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:70c610f6cbe4b9fce272c407dd9d07e33e6bf7b4aa1b7ffb6f6ded8e634e3592"}, + {file = "charset_normalizer-3.2.0-cp37-cp37m-win32.whl", hash = "sha256:a401b4598e5d3f4a9a811f3daf42ee2291790c7f9d74b18d75d6e21dda98a1a1"}, + {file = "charset_normalizer-3.2.0-cp37-cp37m-win_amd64.whl", hash = "sha256:c0b21078a4b56965e2b12f247467b234734491897e99c1d51cee628da9786959"}, + {file = "charset_normalizer-3.2.0-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:95eb302ff792e12aba9a8b8f8474ab229a83c103d74a750ec0bd1c1eea32e669"}, + {file = "charset_normalizer-3.2.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:1a100c6d595a7f316f1b6f01d20815d916e75ff98c27a01ae817439ea7726329"}, + {file = "charset_normalizer-3.2.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:6339d047dab2780cc6220f46306628e04d9750f02f983ddb37439ca47ced7149"}, + {file = "charset_normalizer-3.2.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e4b749b9cc6ee664a3300bb3a273c1ca8068c46be705b6c31cf5d276f8628a94"}, + {file = "charset_normalizer-3.2.0-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:a38856a971c602f98472050165cea2cdc97709240373041b69030be15047691f"}, + {file = "charset_normalizer-3.2.0-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:f87f746ee241d30d6ed93969de31e5ffd09a2961a051e60ae6bddde9ec3583aa"}, + {file = "charset_normalizer-3.2.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:89f1b185a01fe560bc8ae5f619e924407efca2191b56ce749ec84982fc59a32a"}, + {file = "charset_normalizer-3.2.0-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e1c8a2f4c69e08e89632defbfabec2feb8a8d99edc9f89ce33c4b9e36ab63037"}, + {file = "charset_normalizer-3.2.0-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:2f4ac36d8e2b4cc1aa71df3dd84ff8efbe3bfb97ac41242fbcfc053c67434f46"}, + {file = "charset_normalizer-3.2.0-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:a386ebe437176aab38c041de1260cd3ea459c6ce5263594399880bbc398225b2"}, + {file = "charset_normalizer-3.2.0-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:ccd16eb18a849fd8dcb23e23380e2f0a354e8daa0c984b8a732d9cfaba3a776d"}, + {file = "charset_normalizer-3.2.0-cp38-cp38-musllinux_1_1_s390x.whl", hash = "sha256:e6a5bf2cba5ae1bb80b154ed68a3cfa2fa00fde979a7f50d6598d3e17d9ac20c"}, + {file = "charset_normalizer-3.2.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:45de3f87179c1823e6d9e32156fb14c1927fcc9aba21433f088fdfb555b77c10"}, + {file = "charset_normalizer-3.2.0-cp38-cp38-win32.whl", hash = "sha256:1000fba1057b92a65daec275aec30586c3de2401ccdcd41f8a5c1e2c87078706"}, + {file = "charset_normalizer-3.2.0-cp38-cp38-win_amd64.whl", hash = "sha256:8b2c760cfc7042b27ebdb4a43a4453bd829a5742503599144d54a032c5dc7e9e"}, + {file = "charset_normalizer-3.2.0-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:855eafa5d5a2034b4621c74925d89c5efef61418570e5ef9b37717d9c796419c"}, + {file = "charset_normalizer-3.2.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:203f0c8871d5a7987be20c72442488a0b8cfd0f43b7973771640fc593f56321f"}, + {file = "charset_normalizer-3.2.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:e857a2232ba53ae940d3456f7533ce6ca98b81917d47adc3c7fd55dad8fab858"}, + {file = "charset_normalizer-3.2.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5e86d77b090dbddbe78867a0275cb4df08ea195e660f1f7f13435a4649e954e5"}, + {file = "charset_normalizer-3.2.0-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:c4fb39a81950ec280984b3a44f5bd12819953dc5fa3a7e6fa7a80db5ee853952"}, + {file = "charset_normalizer-3.2.0-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:2dee8e57f052ef5353cf608e0b4c871aee320dd1b87d351c28764fc0ca55f9f4"}, + {file = "charset_normalizer-3.2.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8700f06d0ce6f128de3ccdbc1acaea1ee264d2caa9ca05daaf492fde7c2a7200"}, + {file = "charset_normalizer-3.2.0-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1920d4ff15ce893210c1f0c0e9d19bfbecb7983c76b33f046c13a8ffbd570252"}, + {file = "charset_normalizer-3.2.0-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:c1c76a1743432b4b60ab3358c937a3fe1341c828ae6194108a94c69028247f22"}, + {file = "charset_normalizer-3.2.0-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:f7560358a6811e52e9c4d142d497f1a6e10103d3a6881f18d04dbce3729c0e2c"}, + {file = "charset_normalizer-3.2.0-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:c8063cf17b19661471ecbdb3df1c84f24ad2e389e326ccaf89e3fb2484d8dd7e"}, + {file = "charset_normalizer-3.2.0-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:cd6dbe0238f7743d0efe563ab46294f54f9bc8f4b9bcf57c3c666cc5bc9d1299"}, + {file = "charset_normalizer-3.2.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:1249cbbf3d3b04902ff081ffbb33ce3377fa6e4c7356f759f3cd076cc138d020"}, + {file = "charset_normalizer-3.2.0-cp39-cp39-win32.whl", hash = "sha256:6c409c0deba34f147f77efaa67b8e4bb83d2f11c8806405f76397ae5b8c0d1c9"}, + {file = "charset_normalizer-3.2.0-cp39-cp39-win_amd64.whl", hash = "sha256:7095f6fbfaa55defb6b733cfeb14efaae7a29f0b59d8cf213be4e7ca0b857b80"}, + {file = "charset_normalizer-3.2.0-py3-none-any.whl", hash = "sha256:8e098148dd37b4ce3baca71fb394c81dc5d9c7728c95df695d2dca218edf40e6"}, ] [[package]] @@ -2365,7 +2409,7 @@ name = "deprecated" version = "1.2.14" description = "Python @deprecated decorator to deprecate old python classes, functions or methods." category = "main" -optional = true +optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" files = [ {file = "Deprecated-1.2.14-py2.py3-none-any.whl", hash = "sha256:6fac8b097794a90302bdbb17b9b815e732d3c4720583ff1b198499d78470466c"}, @@ -2860,7 +2904,7 @@ name = "feedparser" version = "6.0.10" description = "Universal feed parser, handles RSS 0.9x, RSS 1.0, RSS 2.0, CDF, Atom 0.3, and Atom 1.0 feeds" category = "main" -optional = true +optional = false python-versions = ">=3.6" files = [ {file = "feedparser-6.0.10-py3-none-any.whl", hash = "sha256:79c257d526d13b944e965f6095700587f27388e50ea16fd245babe4dfae7024f"}, @@ -3568,6 +3612,18 @@ requests = ">=2.0,<3.0" typing-extensions = ">=4.0,<5.0" websockets = ">=10.0,<12.0" +[[package]] +name = "graphlib-backport" +version = "1.0.3" +description = "Backport of the Python 3.9 graphlib module for Python 3.6+" +category = "main" +optional = false +python-versions = ">=3.6,<4.0" +files = [ + {file = "graphlib_backport-1.0.3-py3-none-any.whl", hash = "sha256:24246967b9e7e6a91550bc770e6169585d35aa32790258579a8a3899a8c18fde"}, + {file = "graphlib_backport-1.0.3.tar.gz", hash = "sha256:7bb8fc7757b8ae4e6d8000a26cd49e9232aaa9a3aa57edb478474b8424bfaae2"}, +] + [[package]] name = "graphql-core" version = "3.2.3" @@ -3659,7 +3715,7 @@ name = "grpcio" version = "1.47.5" description = "HTTP/2-based RPC framework" category = "main" -optional = true +optional = false python-versions = ">=3.6" files = [ {file = "grpcio-1.47.5-cp310-cp310-linux_armv7l.whl", hash = "sha256:acc73289d0c44650aa1f21eccfa967f5623b01c3b5e2b4596fe5f9c5bf10956d"}, @@ -5583,6 +5639,32 @@ files = [ [package.dependencies] marshmallow = ">=2.0.0" +[[package]] +name = "mastodon-py" +version = "1.8.1" +description = "Python wrapper for the Mastodon API" +category = "dev" +optional = false +python-versions = "*" +files = [ + {file = "Mastodon.py-1.8.1-py2.py3-none-any.whl", hash = "sha256:22bc7e060518ef2eaa69d911cde6e4baf56bed5ea0dd407392c49051a7ac526a"}, + {file = "Mastodon.py-1.8.1.tar.gz", hash = "sha256:4a64cb94abadd6add73e4b8eafdb5c466048fa5f638284fd2189034104d4687e"}, +] + +[package.dependencies] +blurhash = ">=1.1.4" +decorator = ">=4.0.0" +python-dateutil = "*" +python-magic = {version = "*", markers = "platform_system != \"Windows\""} +python-magic-bin = {version = "*", markers = "platform_system == \"Windows\""} +requests = ">=2.4.2" +six = "*" + +[package.extras] +blurhash = ["blurhash (>=1.1.4)"] +test = ["blurhash (>=1.1.4)", "cryptography (>=1.6.0)", "http-ece (>=1.0.5)", "pytest", "pytest-cov", "pytest-mock", "pytest-runner", "pytest-vcr", "pytz", "requests-mock", "vcrpy"] +webpush = ["cryptography (>=1.6.0)", "http-ece (>=1.0.5)"] + [[package]] name = "matplotlib" version = "3.7.2" @@ -5755,7 +5837,7 @@ name = "momento" version = "1.6.0" description = "SDK for Momento" category = "main" -optional = true +optional = false python-versions = ">=3.7,<4.0" files = [ {file = "momento-1.6.0-py3-none-any.whl", hash = "sha256:a7f9a85a4372274bd5eafba95d6ab72bafa2d947abfbb16f8d0afb4b6501e4fb"}, @@ -5772,7 +5854,7 @@ name = "momento-wire-types" version = "0.64.1" description = "Momento Client Proto Generated Files" category = "main" -optional = true +optional = false python-versions = ">=3.7,<4.0" files = [ {file = "momento_wire_types-0.64.1-py3-none-any.whl", hash = "sha256:b6fb773831e7aaf95c60223e450e985606076a45df120814ecb9a97904948fe5"}, @@ -7374,7 +7456,7 @@ files = [ name = "overrides" version = "7.3.1" description = "A decorator to automatically detect mismatch when overriding a method." -category = "dev" +category = "main" optional = false python-versions = ">=3.6" files = [ @@ -8380,48 +8462,48 @@ files = [ [[package]] name = "pydantic" -version = "1.10.9" +version = "1.10.12" description = "Data validation and settings management using python type hints" category = "main" optional = false python-versions = ">=3.7" files = [ - {file = "pydantic-1.10.9-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e692dec4a40bfb40ca530e07805b1208c1de071a18d26af4a2a0d79015b352ca"}, - {file = "pydantic-1.10.9-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:3c52eb595db83e189419bf337b59154bdcca642ee4b2a09e5d7797e41ace783f"}, - {file = "pydantic-1.10.9-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:939328fd539b8d0edf244327398a667b6b140afd3bf7e347cf9813c736211896"}, - {file = "pydantic-1.10.9-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b48d3d634bca23b172f47f2335c617d3fcb4b3ba18481c96b7943a4c634f5c8d"}, - {file = "pydantic-1.10.9-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:f0b7628fb8efe60fe66fd4adadd7ad2304014770cdc1f4934db41fe46cc8825f"}, - {file = "pydantic-1.10.9-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:e1aa5c2410769ca28aa9a7841b80d9d9a1c5f223928ca8bec7e7c9a34d26b1d4"}, - {file = "pydantic-1.10.9-cp310-cp310-win_amd64.whl", hash = "sha256:eec39224b2b2e861259d6f3c8b6290d4e0fbdce147adb797484a42278a1a486f"}, - {file = "pydantic-1.10.9-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:d111a21bbbfd85c17248130deac02bbd9b5e20b303338e0dbe0faa78330e37e0"}, - {file = "pydantic-1.10.9-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:2e9aec8627a1a6823fc62fb96480abe3eb10168fd0d859ee3d3b395105ae19a7"}, - {file = "pydantic-1.10.9-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:07293ab08e7b4d3c9d7de4949a0ea571f11e4557d19ea24dd3ae0c524c0c334d"}, - {file = "pydantic-1.10.9-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7ee829b86ce984261d99ff2fd6e88f2230068d96c2a582f29583ed602ef3fc2c"}, - {file = "pydantic-1.10.9-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:4b466a23009ff5cdd7076eb56aca537c745ca491293cc38e72bf1e0e00de5b91"}, - {file = "pydantic-1.10.9-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:7847ca62e581e6088d9000f3c497267868ca2fa89432714e21a4fb33a04d52e8"}, - {file = "pydantic-1.10.9-cp311-cp311-win_amd64.whl", hash = "sha256:7845b31959468bc5b78d7b95ec52fe5be32b55d0d09983a877cca6aedc51068f"}, - {file = "pydantic-1.10.9-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:517a681919bf880ce1dac7e5bc0c3af1e58ba118fd774da2ffcd93c5f96eaece"}, - {file = "pydantic-1.10.9-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:67195274fd27780f15c4c372f4ba9a5c02dad6d50647b917b6a92bf00b3d301a"}, - {file = "pydantic-1.10.9-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2196c06484da2b3fded1ab6dbe182bdabeb09f6318b7fdc412609ee2b564c49a"}, - {file = "pydantic-1.10.9-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:6257bb45ad78abacda13f15bde5886efd6bf549dd71085e64b8dcf9919c38b60"}, - {file = "pydantic-1.10.9-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:3283b574b01e8dbc982080d8287c968489d25329a463b29a90d4157de4f2baaf"}, - {file = "pydantic-1.10.9-cp37-cp37m-win_amd64.whl", hash = "sha256:5f8bbaf4013b9a50e8100333cc4e3fa2f81214033e05ac5aa44fa24a98670a29"}, - {file = "pydantic-1.10.9-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:b9cd67fb763248cbe38f0593cd8611bfe4b8ad82acb3bdf2b0898c23415a1f82"}, - {file = "pydantic-1.10.9-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:f50e1764ce9353be67267e7fd0da08349397c7db17a562ad036aa7c8f4adfdb6"}, - {file = "pydantic-1.10.9-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:73ef93e5e1d3c8e83f1ff2e7fdd026d9e063c7e089394869a6e2985696693766"}, - {file = "pydantic-1.10.9-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:128d9453d92e6e81e881dd7e2484e08d8b164da5507f62d06ceecf84bf2e21d3"}, - {file = "pydantic-1.10.9-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:ad428e92ab68798d9326bb3e5515bc927444a3d71a93b4a2ca02a8a5d795c572"}, - {file = "pydantic-1.10.9-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:fab81a92f42d6d525dd47ced310b0c3e10c416bbfae5d59523e63ea22f82b31e"}, - {file = "pydantic-1.10.9-cp38-cp38-win_amd64.whl", hash = "sha256:963671eda0b6ba6926d8fc759e3e10335e1dc1b71ff2a43ed2efd6996634dafb"}, - {file = "pydantic-1.10.9-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:970b1bdc6243ef663ba5c7e36ac9ab1f2bfecb8ad297c9824b542d41a750b298"}, - {file = "pydantic-1.10.9-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:7e1d5290044f620f80cf1c969c542a5468f3656de47b41aa78100c5baa2b8276"}, - {file = "pydantic-1.10.9-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:83fcff3c7df7adff880622a98022626f4f6dbce6639a88a15a3ce0f96466cb60"}, - {file = "pydantic-1.10.9-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:0da48717dc9495d3a8f215e0d012599db6b8092db02acac5e0d58a65248ec5bc"}, - {file = "pydantic-1.10.9-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:0a2aabdc73c2a5960e87c3ffebca6ccde88665616d1fd6d3db3178ef427b267a"}, - {file = "pydantic-1.10.9-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:9863b9420d99dfa9c064042304868e8ba08e89081428a1c471858aa2af6f57c4"}, - {file = "pydantic-1.10.9-cp39-cp39-win_amd64.whl", hash = "sha256:e7c9900b43ac14110efa977be3da28931ffc74c27e96ee89fbcaaf0b0fe338e1"}, - {file = "pydantic-1.10.9-py3-none-any.whl", hash = "sha256:6cafde02f6699ce4ff643417d1a9223716ec25e228ddc3b436fe7e2d25a1f305"}, - {file = "pydantic-1.10.9.tar.gz", hash = "sha256:95c70da2cd3b6ddf3b9645ecaa8d98f3d80c606624b6d245558d202cd23ea3be"}, + {file = "pydantic-1.10.12-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:a1fcb59f2f355ec350073af41d927bf83a63b50e640f4dbaa01053a28b7a7718"}, + {file = "pydantic-1.10.12-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:b7ccf02d7eb340b216ec33e53a3a629856afe1c6e0ef91d84a4e6f2fb2ca70fe"}, + {file = "pydantic-1.10.12-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8fb2aa3ab3728d950bcc885a2e9eff6c8fc40bc0b7bb434e555c215491bcf48b"}, + {file = "pydantic-1.10.12-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:771735dc43cf8383959dc9b90aa281f0b6092321ca98677c5fb6125a6f56d58d"}, + {file = "pydantic-1.10.12-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:ca48477862372ac3770969b9d75f1bf66131d386dba79506c46d75e6b48c1e09"}, + {file = "pydantic-1.10.12-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:a5e7add47a5b5a40c49b3036d464e3c7802f8ae0d1e66035ea16aa5b7a3923ed"}, + {file = "pydantic-1.10.12-cp310-cp310-win_amd64.whl", hash = "sha256:e4129b528c6baa99a429f97ce733fff478ec955513630e61b49804b6cf9b224a"}, + {file = "pydantic-1.10.12-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:b0d191db0f92dfcb1dec210ca244fdae5cbe918c6050b342d619c09d31eea0cc"}, + {file = "pydantic-1.10.12-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:795e34e6cc065f8f498c89b894a3c6da294a936ee71e644e4bd44de048af1405"}, + {file = "pydantic-1.10.12-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:69328e15cfda2c392da4e713443c7dbffa1505bc9d566e71e55abe14c97ddc62"}, + {file = "pydantic-1.10.12-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2031de0967c279df0d8a1c72b4ffc411ecd06bac607a212892757db7462fc494"}, + {file = "pydantic-1.10.12-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:ba5b2e6fe6ca2b7e013398bc7d7b170e21cce322d266ffcd57cca313e54fb246"}, + {file = "pydantic-1.10.12-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:2a7bac939fa326db1ab741c9d7f44c565a1d1e80908b3797f7f81a4f86bc8d33"}, + {file = "pydantic-1.10.12-cp311-cp311-win_amd64.whl", hash = "sha256:87afda5539d5140cb8ba9e8b8c8865cb5b1463924d38490d73d3ccfd80896b3f"}, + {file = "pydantic-1.10.12-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:549a8e3d81df0a85226963611950b12d2d334f214436a19537b2efed61b7639a"}, + {file = "pydantic-1.10.12-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:598da88dfa127b666852bef6d0d796573a8cf5009ffd62104094a4fe39599565"}, + {file = "pydantic-1.10.12-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ba5c4a8552bff16c61882db58544116d021d0b31ee7c66958d14cf386a5b5350"}, + {file = "pydantic-1.10.12-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:c79e6a11a07da7374f46970410b41d5e266f7f38f6a17a9c4823db80dadf4303"}, + {file = "pydantic-1.10.12-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:ab26038b8375581dc832a63c948f261ae0aa21f1d34c1293469f135fa92972a5"}, + {file = "pydantic-1.10.12-cp37-cp37m-win_amd64.whl", hash = "sha256:e0a16d274b588767602b7646fa05af2782576a6cf1022f4ba74cbb4db66f6ca8"}, + {file = "pydantic-1.10.12-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:6a9dfa722316f4acf4460afdf5d41d5246a80e249c7ff475c43a3a1e9d75cf62"}, + {file = "pydantic-1.10.12-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:a73f489aebd0c2121ed974054cb2759af8a9f747de120acd2c3394cf84176ccb"}, + {file = "pydantic-1.10.12-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6b30bcb8cbfccfcf02acb8f1a261143fab622831d9c0989707e0e659f77a18e0"}, + {file = "pydantic-1.10.12-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2fcfb5296d7877af406ba1547dfde9943b1256d8928732267e2653c26938cd9c"}, + {file = "pydantic-1.10.12-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:2f9a6fab5f82ada41d56b0602606a5506aab165ca54e52bc4545028382ef1c5d"}, + {file = "pydantic-1.10.12-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:dea7adcc33d5d105896401a1f37d56b47d443a2b2605ff8a969a0ed5543f7e33"}, + {file = "pydantic-1.10.12-cp38-cp38-win_amd64.whl", hash = "sha256:1eb2085c13bce1612da8537b2d90f549c8cbb05c67e8f22854e201bde5d98a47"}, + {file = "pydantic-1.10.12-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:ef6c96b2baa2100ec91a4b428f80d8f28a3c9e53568219b6c298c1125572ebc6"}, + {file = "pydantic-1.10.12-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:6c076be61cd0177a8433c0adcb03475baf4ee91edf5a4e550161ad57fc90f523"}, + {file = "pydantic-1.10.12-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2d5a58feb9a39f481eda4d5ca220aa8b9d4f21a41274760b9bc66bfd72595b86"}, + {file = "pydantic-1.10.12-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e5f805d2d5d0a41633651a73fa4ecdd0b3d7a49de4ec3fadf062fe16501ddbf1"}, + {file = "pydantic-1.10.12-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:1289c180abd4bd4555bb927c42ee42abc3aee02b0fb2d1223fb7c6e5bef87dbe"}, + {file = "pydantic-1.10.12-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:5d1197e462e0364906cbc19681605cb7c036f2475c899b6f296104ad42b9f5fb"}, + {file = "pydantic-1.10.12-cp39-cp39-win_amd64.whl", hash = "sha256:fdbdd1d630195689f325c9ef1a12900524dceb503b00a987663ff4f58669b93d"}, + {file = "pydantic-1.10.12-py3-none-any.whl", hash = "sha256:b749a43aa51e32839c9d71dc67eb1e4221bb04af1033a32e3923d46f9effa942"}, + {file = "pydantic-1.10.12.tar.gz", hash = "sha256:0fe8a415cea8f340e7a9af9c54fc71a649b43e8ca3cc732986116b3cb135d303"}, ] [package.dependencies] @@ -8478,6 +8560,24 @@ files = [ [package.dependencies] typing-extensions = "*" +[[package]] +name = "pygithub" +version = "1.59.1" +description = "Use the full Github API v3" +category = "dev" +optional = false +python-versions = ">=3.7" +files = [ + {file = "PyGithub-1.59.1-py3-none-any.whl", hash = "sha256:3d87a822e6c868142f0c2c4bf16cce4696b5a7a4d142a7bd160e1bdf75bc54a9"}, + {file = "PyGithub-1.59.1.tar.gz", hash = "sha256:c44e3a121c15bf9d3a5cc98d94c9a047a5132a9b01d22264627f58ade9ddc217"}, +] + +[package.dependencies] +deprecated = "*" +pyjwt = {version = ">=2.4.0", extras = ["crypto"]} +pynacl = ">=1.4.0" +requests = ">=2.14.0" + [[package]] name = "pygments" version = "2.15.1" @@ -8498,7 +8598,7 @@ name = "pyjwt" version = "2.7.0" description = "JSON Web Token implementation in Python" category = "main" -optional = true +optional = false python-versions = ">=3.7" files = [ {file = "PyJWT-2.7.0-py3-none-any.whl", hash = "sha256:ba2b425b15ad5ef12f200dc67dd56af4e26de2331f965c5439994dad075876e1"}, @@ -8683,6 +8783,33 @@ files = [ {file = "PyMuPDF-1.22.3.tar.gz", hash = "sha256:5ecd928e96e63092571020973aa145b57b75707f3a3df97c742e563112615891"}, ] +[[package]] +name = "pynacl" +version = "1.5.0" +description = "Python binding to the Networking and Cryptography (NaCl) library" +category = "dev" +optional = false +python-versions = ">=3.6" +files = [ + {file = "PyNaCl-1.5.0-cp36-abi3-macosx_10_10_universal2.whl", hash = "sha256:401002a4aaa07c9414132aaed7f6836ff98f59277a234704ff66878c2ee4a0d1"}, + {file = "PyNaCl-1.5.0-cp36-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.manylinux_2_24_aarch64.whl", hash = "sha256:52cb72a79269189d4e0dc537556f4740f7f0a9ec41c1322598799b0bdad4ef92"}, + {file = "PyNaCl-1.5.0-cp36-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a36d4a9dda1f19ce6e03c9a784a2921a4b726b02e1c736600ca9c22029474394"}, + {file = "PyNaCl-1.5.0-cp36-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl", hash = "sha256:0c84947a22519e013607c9be43706dd42513f9e6ae5d39d3613ca1e142fba44d"}, + {file = "PyNaCl-1.5.0-cp36-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:06b8f6fa7f5de8d5d2f7573fe8c863c051225a27b61e6860fd047b1775807858"}, + {file = "PyNaCl-1.5.0-cp36-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:a422368fc821589c228f4c49438a368831cb5bbc0eab5ebe1d7fac9dded6567b"}, + {file = "PyNaCl-1.5.0-cp36-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:61f642bf2378713e2c2e1de73444a3778e5f0a38be6fee0fe532fe30060282ff"}, + {file = "PyNaCl-1.5.0-cp36-abi3-win32.whl", hash = "sha256:e46dae94e34b085175f8abb3b0aaa7da40767865ac82c928eeb9e57e1ea8a543"}, + {file = "PyNaCl-1.5.0-cp36-abi3-win_amd64.whl", hash = "sha256:20f42270d27e1b6a29f54032090b972d97f0a1b0948cc52392041ef7831fee93"}, + {file = "PyNaCl-1.5.0.tar.gz", hash = "sha256:8ac7448f09ab85811607bdd21ec2464495ac8b7c66d146bf545b0f08fb9220ba"}, +] + +[package.dependencies] +cffi = ">=1.4.1" + +[package.extras] +docs = ["sphinx (>=1.6.5)", "sphinx-rtd-theme"] +tests = ["hypothesis (>=3.27.0)", "pytest (>=3.2.1,!=3.3.0)"] + [[package]] name = "pynvml" version = "11.5.0" @@ -9181,6 +9308,31 @@ files = [ {file = "python_json_logger-2.0.7-py3-none-any.whl", hash = "sha256:f380b826a991ebbe3de4d897aeec42760035ac760345e57b812938dc8b35e2bd"}, ] +[[package]] +name = "python-magic" +version = "0.4.27" +description = "File type identification using libmagic" +category = "dev" +optional = false +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" +files = [ + {file = "python-magic-0.4.27.tar.gz", hash = "sha256:c1ba14b08e4a5f5c31a302b7721239695b2f0f058d125bd5ce1ee36b9d9d3c3b"}, + {file = "python_magic-0.4.27-py2.py3-none-any.whl", hash = "sha256:c212960ad306f700aa0d01e5d7a325d20548ff97eb9920dcd29513174f0294d3"}, +] + +[[package]] +name = "python-magic-bin" +version = "0.4.14" +description = "File type identification using libmagic binary package" +category = "dev" +optional = false +python-versions = "*" +files = [ + {file = "python_magic_bin-0.4.14-py2.py3-none-macosx_10_6_intel.whl", hash = "sha256:7b1743b3dbf16601d6eedf4e7c2c9a637901b0faaf24ad4df4d4527e7d8f66a4"}, + {file = "python_magic_bin-0.4.14-py2.py3-none-win32.whl", hash = "sha256:34a788c03adde7608028203e2dbb208f1f62225ad91518787ae26d603ae68892"}, + {file = "python_magic_bin-0.4.14-py2.py3-none-win_amd64.whl", hash = "sha256:90be6206ad31071a36065a2fc169c5afb5e0355cbe6030e87641c6c62edc2b69"}, +] + [[package]] name = "python-multipart" version = "0.0.6" @@ -9431,14 +9583,14 @@ cffi = {version = "*", markers = "implementation_name == \"pypy\""} [[package]] name = "qdrant-client" -version = "1.3.1" +version = "1.4.0" description = "Client library for the Qdrant vector search engine" category = "main" optional = true python-versions = ">=3.7,<3.12" files = [ - {file = "qdrant_client-1.3.1-py3-none-any.whl", hash = "sha256:9640855585d1f532094e342f07e0f2ef00652a60fc5d903c92ca3989a1e86318"}, - {file = "qdrant_client-1.3.1.tar.gz", hash = "sha256:a999358b10e611d71b4b04c6ded36a6cfc963e56b4c3f99d9c1a603ca524a82e"}, + {file = "qdrant_client-1.4.0-py3-none-any.whl", hash = "sha256:2f9e563955b5163da98016f2ed38d9aea5058576c7c5844e9aa205d28155f56d"}, + {file = "qdrant_client-1.4.0.tar.gz", hash = "sha256:2e54f5a80eb1e7e67f4603b76365af4817af15fb3d0c0f44de4fd93afbbe5537"}, ] [package.dependencies] @@ -9447,8 +9599,7 @@ grpcio-tools = ">=1.41.0" httpx = {version = ">=0.14.0", extras = ["http2"]} numpy = {version = ">=1.21", markers = "python_version >= \"3.8\""} portalocker = ">=2.7.0,<3.0.0" -pydantic = ">=1.8,<2.0" -typing-extensions = ">=4.0.0,<4.6.0" +pydantic = ">=1.10.8" urllib3 = ">=1.26.14,<2.0.0" [[package]] @@ -10325,7 +10476,7 @@ name = "sgmllib3k" version = "1.0.0" description = "Py3k port of sgmllib." category = "main" -optional = true +optional = false python-versions = "*" files = [ {file = "sgmllib3k-1.0.0.tar.gz", hash = "sha256:7868fb1c8bfa764c1ac563d3cf369c381d1325d36124933a726f29fcdaa812e9"}, @@ -11977,14 +12128,14 @@ files = [ [[package]] name = "typing-extensions" -version = "4.5.0" +version = "4.7.1" description = "Backported and Experimental Type Hints for Python 3.7+" category = "main" optional = false python-versions = ">=3.7" files = [ - {file = "typing_extensions-4.5.0-py3-none-any.whl", hash = "sha256:fb33085c39dd998ac16d1431ebc293a8b3eedd00fd4a32de0ff79002c19511b4"}, - {file = "typing_extensions-4.5.0.tar.gz", hash = "sha256:5cb5f4a79139d699607b3ef622a1dedafa84e115ab0024e0d9c044a9479ca7cb"}, + {file = "typing_extensions-4.7.1-py3-none-any.whl", hash = "sha256:440d5dd3af93b060174bf433bccd69b0babc3b15b1a8dca43789fd7f61514b36"}, + {file = "typing_extensions-4.7.1.tar.gz", hash = "sha256:b75ddc264f0ba5615db7ba217daeb99701ad295353c45f9e95963337ceeeffb2"}, ] [[package]] @@ -13271,7 +13422,7 @@ clarifai = ["clarifai"] cohere = ["cohere"] docarray = ["docarray"] embeddings = ["sentence-transformers"] -extended-testing = ["amazon-textract-caller", "anthropic", "atlassian-python-api", "beautifulsoup4", "bibtexparser", "cassio", "chardet", "esprima", "feedparser", "geopandas", "gitpython", "gql", "html2text", "jinja2", "jq", "lxml", "mwparserfromhell", "mwxml", "newspaper3k", "openai", "openai", "pandas", "pdfminer-six", "pgvector", "psychicapi", "py-trello", "pymupdf", "pypdf", "pypdfium2", "pyspark", "rank-bm25", "rapidfuzz", "requests-toolbelt", "scikit-learn", "streamlit", "sympy", "telethon", "tqdm", "xata", "xinference", "xmltodict", "zep-python"] +extended-testing = ["amazon-textract-caller", "anthropic", "atlassian-python-api", "beautifulsoup4", "betabageldb", "bibtexparser", "cassio", "chardet", "esprima", "feedparser", "geopandas", "gitpython", "gql", "html2text", "jinja2", "jq", "lxml", "mwparserfromhell", "mwxml", "newspaper3k", "openai", "openai", "pandas", "pdfminer-six", "pgvector", "psychicapi", "py-trello", "pymupdf", "pypdf", "pypdfium2", "pyspark", "rank-bm25", "rapidfuzz", "requests-toolbelt", "scikit-learn", "streamlit", "sympy", "telethon", "tqdm", "xata", "xinference", "xmltodict", "zep-python"] javascript = ["esprima"] llms = ["anthropic", "clarifai", "cohere", "huggingface_hub", "manifest-ml", "nlpcloud", "openai", "openllm", "openlm", "torch", "transformers", "xinference"] openai = ["openai", "tiktoken"] @@ -13281,4 +13432,4 @@ text-helpers = ["chardet"] [metadata] lock-version = "2.0" python-versions = ">=3.8.1,<4.0" -content-hash = "74907003b75271582d92396b8323021eb8e1596624536d7653b548828af4a40c" +content-hash = "b519c9ac1e3bfe6ff4d10bab2005d3571e9303561863a313a218e8534af56033" diff --git a/libs/langchain/pyproject.toml b/libs/langchain/pyproject.toml index 6131595b8c9..e098d7ec348 100644 --- a/libs/langchain/pyproject.toml +++ b/libs/langchain/pyproject.toml @@ -133,6 +133,8 @@ newspaper3k = {version = "^0.2.8", optional = true} amazon-textract-caller = {version = "<2", optional = true} xata = {version = "^1.0.0a7", optional = true} xmltodict = {version = "^0.13.0", optional = true} +betabageldb = {version = "0.2.32", optional = true, python = ">=3.7,<3.11"} + [tool.poetry.group.test.dependencies] # The only dependencies that should be added are @@ -179,6 +181,13 @@ wrapt = "^1.15.0" openai = "^0.27.4" python-dotenv = "^1.0.0" cassio = "^0.0.7" +arxiv = "^1.4" +mastodon-py = "^1.8.1" +momento = "^1.5.0" +# Please do not add any dependencies in the test_integration group +# See instructions above ^^ +pygithub = "^1.59.0" +betabageldb = "^0.2.32" [tool.poetry.group.lint.dependencies] ruff = "^0.0.249" @@ -349,6 +358,7 @@ extended_testing = [ "feedparser", "xata", "xmltodict", + "betabageldb", "anthropic", ] @@ -401,4 +411,4 @@ ignore-regex = '.*(Stati Uniti|Tense=Pres).*' # whats is a typo but used frequently in queries so kept as is # aapply - async apply # unsecure - typo but part of API, decided to not bother for now -ignore-words-list = 'momento,collison,ned,foor,reworkd,parth,whats,aapply,mysogyny,unsecure,damon' +ignore-words-list = 'momento,collison,ned,foor,reworkd,parth,whats,aapply,mysogyny,unsecure,damon' \ No newline at end of file diff --git a/libs/langchain/tests/integration_tests/vectorstores/test_bagel.py b/libs/langchain/tests/integration_tests/vectorstores/test_bagel.py new file mode 100644 index 00000000000..c04bb0f2518 --- /dev/null +++ b/libs/langchain/tests/integration_tests/vectorstores/test_bagel.py @@ -0,0 +1,169 @@ +from bagel.config import Settings + +from langchain.docstore.document import Document +from langchain.vectorstores import Bagel +from tests.integration_tests.vectorstores.fake_embeddings import ( + FakeEmbeddings, +) + + +def test_similarity_search() -> None: + """Test smiliarity search""" + setting = Settings( + bagel_api_impl="rest", + bagel_server_host="api.bageldb.ai", + ) + bagel = Bagel(client_settings=setting) + bagel.add_texts(texts=["hello bagel", "hello langchain"]) + result = bagel.similarity_search(query="bagel", k=1) + assert result == [Document(page_content="hello bagel")] + bagel.delete_cluster() + + +def test_bagel() -> None: + """Test from_texts""" + texts = ["hello bagel", "hello langchain"] + txt_search = Bagel.from_texts(cluster_name="testing", texts=texts) + output = txt_search.similarity_search("hello bagel", k=1) + assert output == [Document(page_content="hello bagel")] + txt_search.delete_cluster() + + +def test_with_metadatas() -> None: + """Test end to end construction and search.""" + texts = ["hello bagel", "hello langchain"] + metadatas = [{"metadata": str(i)} for i in range(len(texts))] + txt_search = Bagel.from_texts( + cluster_name="testing", + texts=texts, + metadatas=metadatas, + ) + output = txt_search.similarity_search("hello bagel", k=1) + assert output == [Document(page_content="hello bagel", metadata={"metadata": "0"})] + txt_search.delete_cluster() + + +def test_with_metadatas_with_scores() -> None: + """Test end to end construction and scored search.""" + texts = ["hello bagel", "hello langchain"] + metadatas = [{"page": str(i)} for i in range(len(texts))] + txt_search = Bagel.from_texts( + cluster_name="testing", texts=texts, metadatas=metadatas + ) + output = txt_search.similarity_search_with_score("hello bagel", k=1) + assert output == [ + (Document(page_content="hello bagel", metadata={"page": "0"}), 0.0) + ] + txt_search.delete_cluster() + + +def test_with_metadatas_with_scores_using_vector() -> None: + """Test end to end construction and scored search, using embedding vector.""" + texts = ["hello bagel", "hello langchain"] + metadatas = [{"page": str(i)} for i in range(len(texts))] + embeddings = [[1.1, 2.3, 3.2], [0.3, 0.3, 0.1]] + + vector_search = Bagel.from_texts( + cluster_name="testing_vector", + texts=texts, + metadatas=metadatas, + text_embeddings=embeddings, + ) + + embedded_query = [1.1, 2.3, 3.2] + output = vector_search.similarity_search_by_vector_with_relevance_scores( + query_embeddings=embedded_query, k=1 + ) + assert output == [ + (Document(page_content="hello bagel", metadata={"page": "0"}), 0.0) + ] + vector_search.delete_cluster() + + +def test_with_metadatas_with_scores_using_vector_embe() -> None: + """Test end to end construction and scored search, using embedding vector.""" + texts = ["hello bagel", "hello langchain"] + metadatas = [{"page": str(i)} for i in range(len(texts))] + embedding_function = FakeEmbeddings() + + vector_search = Bagel.from_texts( + cluster_name="testing_vector_embedding1", + texts=texts, + metadatas=metadatas, + embedding=embedding_function, + ) + embedded_query = embedding_function.embed_query("hello bagel") + output = vector_search.similarity_search_by_vector_with_relevance_scores( + query_embeddings=embedded_query, k=1 + ) + assert output == [ + (Document(page_content="hello bagel", metadata={"page": "0"}), 0.0) + ] + vector_search.delete_cluster() + + +def test_search_filter() -> None: + """Test end to end construction and search with metadata filtering.""" + texts = ["hello bagel", "hello langchain"] + metadatas = [{"first_letter": text[0]} for text in texts] + txt_search = Bagel.from_texts( + cluster_name="testing", + texts=texts, + metadatas=metadatas, + ) + output = txt_search.similarity_search("bagel", k=1, where={"first_letter": "h"}) + assert output == [ + Document(page_content="hello bagel", metadata={"first_letter": "h"}) + ] + output = txt_search.similarity_search("langchain", k=1, where={"first_letter": "h"}) + assert output == [ + Document(page_content="hello langchain", metadata={"first_letter": "h"}) + ] + txt_search.delete_cluster() + + +def test_search_filter_with_scores() -> None: + texts = ["hello bagel", "this is langchain"] + metadatas = [{"source": "notion"}, {"source": "google"}] + txt_search = Bagel.from_texts( + cluster_name="testing", + texts=texts, + metadatas=metadatas, + ) + output = txt_search.similarity_search_with_score( + "hello bagel", k=1, where={"source": "notion"} + ) + assert output == [ + (Document(page_content="hello bagel", metadata={"source": "notion"}), 0.0) + ] + txt_search.delete_cluster() + + +def test_with_include_parameter() -> None: + """Test end to end construction and include parameter.""" + texts = ["hello bagel", "this is langchain"] + docsearch = Bagel.from_texts(cluster_name="testing", texts=texts) + output = docsearch.get(include=["embeddings"]) + assert output["embeddings"] is not None + output = docsearch.get() + assert output["embeddings"] is None + docsearch.delete_cluster() + + +def test_bagel_update_document() -> None: + """Test the update_document function in the Bagel class.""" + initial_content = "bagel" + document_id = "doc1" + original_doc = Document(page_content=initial_content, metadata={"page": "0"}) + + docsearch = Bagel.from_documents( + cluster_name="testing_docs", + documents=[original_doc], + ids=[document_id], + ) + + updated_content = "updated bagel doc" + updated_doc = Document(page_content=updated_content, metadata={"page": "0"}) + docsearch.update_document(document_id=document_id, document=updated_doc) + output = docsearch.similarity_search(updated_content, k=1) + assert output == [Document(page_content=updated_content, metadata={"page": "0"})]