github/langchain
1
0
Fork 0
mirror of https://github.com/hwchase17/langchain.git synced 2025-05-12 10:37:32 +00:00
Code Issues Packages Projects Releases Wiki Activity

BagelDB (bageldb.ai), VectorStore integration. (#8971)

Browse Source 
- **Description**: [BagelDB](bageldb.ai) a collaborative vector
database. Integrated the bageldb PyPi package with langchain with
related tests and code.

  - **Issue**: Not applicable.
  - **Dependencies**: `betabageldb` PyPi package.
  - **Tag maintainer**: @rlancemartin, @eyurtsev, @baskaryan
  - **Twitter handle**: bageldb_ai (https://twitter.com/BagelDB_ai)
  
We ran `make format`, `make lint` and `make test` locally.

Followed the contribution guideline thoroughly
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md

---------

Co-authored-by: Towhid1 <nurulaktertowhid@gmail.com>
This commit is contained in:
Bidhan Roy 2023-08-10 18:48:36 -05:00 committed by GitHub
parent ee52482db8
commit 02430e25b6
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
7 changed files with 1218 additions and 133 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

21
docs/extras/integrations/providers/bageldb.mdx Normal file
View File

@ -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
```

300
docs/extras/integrations/vectorstores/bageldb.ipynb Normal file
View File

@ -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
}

2
libs/langchain/langchain/vectorstores/__init__.py
View File

@ -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",

432
libs/langchain/langchain/vectorstores/bageldb.py Normal file
View File

@ -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)

415
libs/langchain/poetry.lock generated
View File

@ -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"

10
libs/langchain/pyproject.toml
View File

@ -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",
]

169
libs/langchain/tests/integration_tests/vectorstores/test_bagel.py Normal file
View File

@ -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"})]