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Harrison/pinecone hybrid update (#2742)

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Co-authored-by: acatav <39461369+acatav@users.noreply.github.com>
Co-authored-by: Amnon Catav <catav.amnon1@gmail.com>
fix_agent_callbacks
Harrison Chase 1 year ago committed by GitHub
parent 744c25cd0a
commit 507cee5ee5
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4 changed files with 311 additions and 455 deletions
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156
docs/modules/indexes/retrievers/examples/pinecone_hybrid_search.ipynb
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@ -1,6 +1,7 @@
{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"id": "ab66dd43",
"metadata": {},
@ -9,12 +10,12 @@
"\n",
"This notebook goes over how to use a retriever that under the hood uses Pinecone and Hybrid Search.\n",
"\n",
"The logic of this retriever is largely taken from [this blog post](https://www.pinecone.io/learn/hybrid-search-intro/)"
"The logic of this retriever is taken from [this documentaion](https://docs.pinecone.io/docs/hybrid-search)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 75,
"id": "393ac030",
"metadata": {},
"outputs": [],
@ -31,43 +32,61 @@
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "15390796",
"attachments": {},
"cell_type": "markdown",
"id": "95d5d7f9",
"metadata": {},
"outputs": [],
"source": [
"import pinecone # !pip install pinecone-client\n",
"You should only have to do this part once.\n",
"\n",
"pinecone.init(\n",
" api_key=\"...\", # API key here\n",
" environment=\"...\" # find next to api key in console\n",
")\n",
"# choose a name for your index\n",
"index_name = \"...\""
"Note: it's important to make sure that the \"context\" field that holds the document text in the metadata is not indexed. Currently you need to specify explicitly the fields you do want to index. For more information checkout Pinecone's [docs](https://docs.pinecone.io/docs/manage-indexes#selective-metadata-indexing)."
]
},
{
"cell_type": "markdown",
"id": "95d5d7f9",
"cell_type": "code",
"execution_count": 76,
"id": "3b8f7697",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"WhoAmIResponse(username='load', user_label='label', projectname='load-test')"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"You should only have to do this part once."
"import os\n",
"import pinecone\n",
"\n",
"api_key = os.getenv(\"PINECONE_API_KEY\") or \"PINECONE_API_KEY\"\n",
"# find environment next to your API key in the Pinecone console\n",
"env = os.getenv(\"PINECONE_ENVIRONMENT\") or \"PINECONE_ENVIRONMENT\"\n",
"\n",
"index_name = \"langchain-pinecone-hybrid-search\"\n",
"\n",
"pinecone.init(api_key=api_key, enviroment=env)\n",
"pinecone.whoami()"
]
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 77,
"id": "cfa3a8d8",
"metadata": {},
"outputs": [],
"source": [
"# create the index\n",
" # create the index\n",
"pinecone.create_index(\n",
" name = index_name,\n",
" dimension = 1536, # dimensionality of dense model\n",
" metric = \"dotproduct\",\n",
" pod_type = \"s1\"\n",
" metric = \"dotproduct\", # sparse values supported only for dotproduct\n",
" pod_type = \"s1\",\n",
" metadata_config={\"indexed\": []} # see explaination above\n",
")"
]
},
@ -81,7 +100,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 78,
"id": "bcb3c8c2",
"metadata": {},
"outputs": [],
@ -90,18 +109,19 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "dbc025d6",
"metadata": {},
"source": [
"## Get embeddings and tokenizers\n",
"## Get embeddings and sparse encoders\n",
"\n",
"Embeddings are used for the dense vectors, tokenizer is used for the sparse vector"
]
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 79,
"id": "2f63c911",
"metadata": {},
"outputs": [],
@ -110,19 +130,51 @@
"embeddings = OpenAIEmbeddings()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "96bf8879",
"metadata": {},
"source": [
"To encode the text to sparse values you can either choose SPLADE or BM25. For out of domain tasks we recommend using BM25.\n",
"\n",
"For more information about the sparse encoders you can checkout pinecone-text library [docs](https://pinecone-io.github.io/pinecone-text/pinecone_text.html)."
]
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 80,
"id": "c3f030e5",
"metadata": {},
"outputs": [],
"source": [
"from transformers import BertTokenizerFast # !pip install transformers\n",
"from pinecone_text.sparse import BM25Encoder\n",
"# or from pinecone_text.sparse import SpladeEncoder if you wish to work with SPLADE\n",
"\n",
"# load bert tokenizer from huggingface\n",
"tokenizer = BertTokenizerFast.from_pretrained(\n",
" 'bert-base-uncased'\n",
")"
"# use default tf-idf values\n",
"bm25_encoder = BM25Encoder().default()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "23601ddb",
"metadata": {},
"source": [
"The above code is using default tfids values. It's highly recommended to fit the tf-idf values to your own corpus. You can do it as follow:\n",
"\n",
"```python\n",
"corpus = [\"foo\", \"bar\", \"world\", \"hello\"]\n",
"\n",
"# fit tf-idf values on your corpus\n",
"bm25_encoder.fit(corpus)\n",
"\n",
"# store the values to a json file\n",
"bm25_encoder.dump(\"bm25_values.json\")\n",
"\n",
"# load to your BM25Encoder object\n",
"bm25_encoder = BM25Encoder().load(\"bm25_values.json\")\n",
"```"
]
},
{
@ -137,12 +189,12 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 81,
"id": "ac77d835",
"metadata": {},
"outputs": [],
"source": [
"retriever = PineconeHybridSearchRetriever(embeddings=embeddings, index=index, tokenizer=tokenizer)"
"retriever = PineconeHybridSearchRetriever(embeddings=embeddings, sparse_encoder=bm25_encoder, index=index)"
]
},
{
@ -157,23 +209,16 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 82,
"id": "98b1c017",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "4d6f3ee7ca754d07a1a18d100d99e0cd",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/1 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 1/1 [00:02<00:00, 2.27s/it]\n"
]
}
],
"source": [
@ -192,7 +237,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 83,
"id": "c0455218",
"metadata": {},
"outputs": [],
@ -202,7 +247,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 84,
"id": "7dfa5c29",
"metadata": {},
"outputs": [
@ -212,7 +257,7 @@
"Document(page_content='foo', metadata={})"
]
},
"execution_count": 10,
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
@ -220,19 +265,11 @@
"source": [
"result[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "74bd9256",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"display_name": ".venv",
"language": "python",
"name": "python3"
},
@ -246,7 +283,12 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
"version": "3.9.13"
},
"vscode": {
"interpreter": {
"hash": "7ec0d8babd8cabf695a1d94b1e586d626e046c9df609f6bad065d15d49f67f54"
}
}
},
"nbformat": 4,

101
langchain/retrievers/pinecone_hybrid_search.py
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@ -1,32 +1,23 @@
"""Taken from: https://www.pinecone.io/learn/hybrid-search-intro/"""
from collections import Counter
from typing import Any, Dict, List, Tuple
"""Taken from: https://docs.pinecone.io/docs/hybrid-search"""
import hashlib
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
from langchain.schema import BaseRetriever, Document
def build_dict(input_batch: List[List[int]]) -> List[Dict]:
# store a batch of sparse embeddings
sparse_emb = []
# iterate through input batch
for token_ids in input_batch:
indices = []
values = []
# convert the input_ids list to a dictionary of key to frequency values
d = dict(Counter(token_ids))
for idx in d:
indices.append(idx)
values.append(d[idx])
sparse_emb.append({"indices": indices, "values": values})
# return sparse_emb list
return sparse_emb
def hash_text(text: str) -> str:
return str(hashlib.sha256(text.encode("utf-8")).hexdigest())
def create_index(
contexts: List[str], index: Any, embeddings: Embeddings, tokenizer: Any
contexts: List[str],
index: Any,
embeddings: Embeddings,
sparse_encoder: Any,
ids: Optional[List[str]] = None,
) -> None:
batch_size = 32
_iterator = range(0, len(contexts), batch_size)
@ -37,28 +28,33 @@ def create_index(
except ImportError:
pass
if ids is None:
# create unique ids using hash of the text
ids = [hash_text(context) for context in contexts]
for i in _iterator:
# find end of batch
i_end = min(i + batch_size, len(contexts))
# extract batch
context_batch = contexts[i:i_end]
# create unique IDs
ids = [str(x) for x in range(i, i_end)]
batch_ids = ids[i:i_end]
# add context passages as metadata
meta = [{"context": context} for context in context_batch]
# create dense vectors
dense_embeds = embeddings.embed_documents(context_batch)
# create sparse vectors
sparse_embeds = generate_sparse_vectors(context_batch, tokenizer)
sparse_embeds = sparse_encoder.encode_documents(context_batch)
for s in sparse_embeds:
s["values"] = [float(s1) for s1 in s["values"]]
vectors = []
# loop through the data and create dictionaries for upserts
for _id, sparse, dense, metadata in zip(ids, sparse_embeds, dense_embeds, meta):
for doc_id, sparse, dense, metadata in zip(
batch_ids, sparse_embeds, dense_embeds, meta
):
vectors.append(
{
"id": _id,
"id": doc_id,
"sparse_values": sparse,
"values": dense,
"metadata": metadata,
@ -69,38 +65,10 @@ def create_index(
index.upsert(vectors)
def generate_sparse_vectors(context_batch: List[str], tokenizer: Any) -> List[Dict]:
# create batch of input_ids
inputs = tokenizer(
context_batch,
padding=True,
truncation=True,
max_length=512, # special_tokens=False
)["input_ids"]
# create sparse dictionaries
sparse_embeds = build_dict(inputs)
return sparse_embeds
def hybrid_scale(
dense: List[float], sparse: Dict, alpha: float
) -> Tuple[List[float], Dict]:
# check alpha value is in range
if alpha < 0 or alpha > 1:
raise ValueError("Alpha must be between 0 and 1")
# scale sparse and dense vectors to create hybrid search vecs
hsparse = {
"indices": sparse["indices"],
"values": [v * (1 - alpha) for v in sparse["values"]],
}
hdense = [v * alpha for v in dense]
return hdense, hsparse
class PineconeHybridSearchRetriever(BaseRetriever, BaseModel):
embeddings: Embeddings
sparse_encoder: Any
index: Any
tokenizer: Any
top_k: int = 4
alpha: float = 0.5
@ -110,15 +78,32 @@ class PineconeHybridSearchRetriever(BaseRetriever, BaseModel):
extra = Extra.forbid
arbitrary_types_allowed = True
def add_texts(self, texts: List[str]) -> None:
create_index(texts, self.index, self.embeddings, self.tokenizer)
def add_texts(self, texts: List[str], ids: Optional[List[str]] = None) -> None:
create_index(texts, self.index, self.embeddings, self.sparse_encoder, ids=ids)
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
"""Validate that api key and python package exists in environment."""
try:
from pinecone_text.hybrid import hybrid_convex_scale # noqa:F401
from pinecone_text.sparse.base_sparse_encoder import (
BaseSparseEncoder, # noqa:F401
)
except ImportError:
raise ValueError(
"Could not import pinecone_text python package. "
"Please install it with `pip install pinecone_text`."
)
return values
def get_relevant_documents(self, query: str) -> List[Document]:
sparse_vec = generate_sparse_vectors([query], self.tokenizer)[0]
from pinecone_text.hybrid import hybrid_convex_scale
sparse_vec = self.sparse_encoder.encode_queries(query)
# convert the question into a dense vector
dense_vec = self.embeddings.embed_query(query)
# scale alpha with hybrid_scale
dense_vec, sparse_vec = hybrid_scale(dense_vec, sparse_vec, self.alpha)
dense_vec, sparse_vec = hybrid_convex_scale(dense_vec, sparse_vec, self.alpha)
sparse_vec["values"] = [float(s1) for s1 in sparse_vec["values"]]
# query pinecone with the query parameters
result = self.index.query(

501
poetry.lock generated
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@ -1,4 +1,4 @@
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# This file is automatically @generated by Poetry and should not be changed by hand.
[[package]]
name = "absl-py"
@ -1066,36 +1066,6 @@ pandas = ["pandas"]
sqlalchemy = ["sqlalchemy (>1.3.21,<1.4)"]
superset = ["apache-superset (>=1.4.1)"]
[[package]]
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[package.extras]
test = ["codecov (>=2.0.5)", "coverage (>=4.2)", "flake8 (>=3.0.4)", "path.py (>=11.5.0)", "pytest (>=3.0.3)", "pytest-cov (>=2.4.0)", "pytest-runner (>=2.9)", "pytest-virtualenv (>=1.7.0)", "scikit-build (>=0.10.0)", "setuptools (>=28.0.0)", "virtualenv (>=15.0.3)", "wheel"]
[[package]]
name = "cohere"
version = "3.10.0"
@ -2415,14 +2385,14 @@ files = [
[[package]]
name = "httpcore"
version = "0.16.3"
version = "0.17.0"
description = "A minimal low-level HTTP client."
category = "main"
optional = true
python-versions = ">=3.7"
files = [
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{file = "httpcore-0.17.0.tar.gz", hash = "sha256:cc045a3241afbf60ce056202301b4d8b6af08845e3294055eb26b09913ef903c"},
]
[package.dependencies]
@ -2506,26 +2476,26 @@ test = ["Cython (>=0.29.24,<0.30.0)"]
[[package]]
name = "httpx"
version = "0.23.3"
version = "0.24.0"
description = "The next generation HTTP client."
category = "main"
optional = true
python-versions = ">=3.7"
files = [
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{file = "httpx-0.23.3.tar.gz", hash = "sha256:9818458eb565bb54898ccb9b8b251a28785dd4a55afbc23d0eb410754fe7d0f9"},
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]
[package.dependencies]
certifi = "*"
h2 = {version = ">=3,<5", optional = true, markers = "extra == \"http2\""}
httpcore = ">=0.15.0,<0.17.0"
rfc3986 = {version = ">=1.3,<2", extras = ["idna2008"]}
httpcore = ">=0.15.0,<0.18.0"
idna = "*"
sniffio = "*"
[package.extras]
brotli = ["brotli", "brotlicffi"]
cli = ["click (>=8.0.0,<9.0.0)", "pygments (>=2.0.0,<3.0.0)", "rich (>=10,<13)"]
cli = ["click (>=8.0.0,<9.0.0)", "pygments (>=2.0.0,<3.0.0)", "rich (>=10,<14)"]
http2 = ["h2 (>=3,<5)"]
socks = ["socksio (>=1.0.0,<2.0.0)"]
@ -3375,17 +3345,6 @@ beautifulsoup4 = ">=4.8.1"
dnspython = ">=2.0"
requests = ">=2.20"
[[package]]
name = "lit"
version = "16.0.0"
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python-versions = "*"
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[[package]]
name = "livereload"
version = "2.6.3"
@ -3404,14 +3363,14 @@ tornado = {version = "*", markers = "python_version > \"2.7\""}
[[package]]
name = "loguru"
version = "0.6.0"
version = "0.7.0"
description = "Python logging made (stupidly) simple"
category = "main"
optional = false
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[package.dependencies]
numpy = "*"
pillow = ">=5.3.0,<8.3.0 || >=8.4.0"
requests = "*"
torch = "2.0.0"
torch = "1.13.1"
typing-extensions = "*"
[package.extras]
scipy = ["scipy"]
@ -8202,44 +8056,6 @@ torchhub = ["filelock", "huggingface-hub (>=0.11.0,<1.0)", "importlib-metadata",
video = ["av (==9.2.0)", "decord (==0.6.0)"]
vision = ["Pillow"]
[[package]]
name = "triton"
version = "2.0.0"
description = "A language and compiler for custom Deep Learning operations"
category = "main"
optional = false
python-versions = "*"
files = [
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]
[package.dependencies]
cmake = "*"
filelock = "*"
lit = "*"
torch = "*"
[package.extras]
tests = ["autopep8", "flake8", "isort", "numpy", "pytest", "scipy (>=1.7.1)"]
tutorials = ["matplotlib", "pandas", "tabulate"]
[[package]]
name = "typer"
version = "0.7.0"
@ -8500,13 +8316,13 @@ test = ["Cython (>=0.29.32,<0.30.0)", "aiohttp", "flake8 (>=3.9.2,<3.10.0)", "my
[[package]]
name = "validators"
version = "0.19.0"
version = "0.20.0"
description = "Python Data Validation for Humans™."
category = "main"
optional = true
python-versions = ">=3.4"
files = [
{file = "validators-0.19.0.tar.gz", hash = "sha256:dec45f4381f042f1e705cfa74949505b77f1e27e8b05409096fee8152c839cbe"},
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]
[package.dependencies]
@ -8637,21 +8453,21 @@ files = [
[[package]]
name = "weaviate-client"
version = "3.15.4"
version = "3.15.5"
description = "A python native weaviate client"
category = "main"
optional = true
python-versions = ">=3.7"
files = [
{file = "weaviate-client-3.15.4.tar.gz", hash = "sha256:5e61ebffefbedf62b0751d7de562ffd5384717c8ee6adfca4ea6eb150d012e1c"},
{file = "weaviate_client-3.15.4-py3-none-any.whl", hash = "sha256:e765b2f434d2a4301ad8d63052833ab7708d0ef430033496e3e7020ef72c9da0"},
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]
[package.dependencies]
authlib = ">=1.1.0"
requests = ">=2.28.0,<2.29.0"
tqdm = ">=4.59.0,<5.0.0"
validators = ">=0.18.2,<0.20.0"
validators = ">=0.18.2,<=0.21.0"
[[package]]
name = "webcolors"
@ -8796,6 +8612,17 @@ MarkupSafe = ">=2.1.1"
[package.extras]
watchdog = ["watchdog"]
[[package]]
name = "wget"
version = "3.2"
description = "pure python download utility"
category = "main"
optional = false
python-versions = "*"
files = [
{file = "wget-3.2.zip", hash = "sha256:35e630eca2aa50ce998b9b1a127bb26b30dfee573702782aa982f875e3f16061"},
]
[[package]]
name = "wheel"
version = "0.40.0"
@ -9158,13 +8985,13 @@ cffi = {version = ">=1.11", markers = "platform_python_implementation == \"PyPy\
cffi = ["cffi (>=1.11)"]
[extras]
all = ["aleph-alpha-client", "anthropic", "beautifulsoup4", "cohere", "deeplake", "elasticsearch", "faiss-cpu", "google-api-python-client", "google-search-results", "huggingface_hub", "jina", "jinja2", "manifest-ml", "networkx", "nlpcloud", "nltk", "nomic", "openai", "opensearch-py", "pgvector", "pinecone-client", "psycopg2-binary", "pyowm", "pypdf", "qdrant-client", "redis", "sentence-transformers", "spacy", "tensorflow-text", "tiktoken", "torch", "transformers", "weaviate-client", "wikipedia", "wolframalpha"]
all = ["anthropic", "cohere", "openai", "nlpcloud", "huggingface_hub", "jina", "manifest-ml", "elasticsearch", "opensearch-py", "google-search-results", "faiss-cpu", "sentence-transformers", "transformers", "spacy", "nltk", "wikipedia", "beautifulsoup4", "tiktoken", "torch", "jinja2", "pinecone-client", "pinecone-text", "weaviate-client", "redis", "google-api-python-client", "wolframalpha", "qdrant-client", "tensorflow-text", "pypdf", "networkx", "nomic", "aleph-alpha-client", "deeplake", "pgvector", "psycopg2-binary", "pyowm"]
cohere = ["cohere"]
llms = ["anthropic", "cohere", "huggingface_hub", "manifest-ml", "nlpcloud", "openai", "torch", "transformers"]
llms = ["anthropic", "cohere", "openai", "nlpcloud", "huggingface_hub", "manifest-ml", "torch", "transformers"]
openai = ["openai"]
qdrant = ["qdrant-client"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.8.1,<4.0"
content-hash = "a8fde2558f92b4c5ec1dce45f830adc6158dd2cf8c425a34a06523ee8e74487d"
content-hash = "11bbe0042c3c1e56a5d1abbaab33185efad5dcdacb095fc91e91c382f2c9ebb7"

8
pyproject.toml
Unescape Escape View File

@ -28,10 +28,11 @@ spacy = {version = "^3", optional = true}
nltk = {version = "^3", optional = true}
transformers = {version = "^4", optional = true}
beautifulsoup4 = {version = "^4", optional = true}
torch = {version = "^2", optional = true}
torch = {version = "^1", optional = true}
jinja2 = {version = "^3", optional = true}
tiktoken = {version = "^0.3.2", optional = true, python="^3.9"}
pinecone-client = {version = "^2", optional = true}
pinecone-text = {version = "^0.4.2", optional = true}
weaviate-client = {version = "^3", optional = true}
google-api-python-client = {version = "2.70.0", optional = true}
wolframalpha = {version = "5.0.0", optional = true}
@ -94,11 +95,12 @@ openai = "^0.27.4"
elasticsearch = {extras = ["async"], version = "^8.6.2"}
redis = "^4.5.4"
pinecone-client = "^2.2.1"
pinecone-text = "^0.4.2"
pgvector = "^0.1.6"
transformers = "^4.27.4"
pandas = "^2.0.0"
deeplake = "^3.2.21"
torch = "^2.0.0"
torch = "^1.0.0"
chromadb = "^0.3.21"
tiktoken = "^0.3.3"
@ -126,7 +128,7 @@ llms = ["anthropic", "cohere", "openai", "nlpcloud", "huggingface_hub", "manifes
qdrant = ["qdrant-client"]
openai = ["openai"]
cohere = ["cohere"]
all = ["anthropic", "cohere", "openai", "nlpcloud", "huggingface_hub", "jina", "manifest-ml", "elasticsearch", "opensearch-py", "google-search-results", "faiss-cpu", "sentence_transformers", "transformers", "spacy", "nltk", "wikipedia", "beautifulsoup4", "tiktoken", "torch", "jinja2", "pinecone-client", "weaviate-client", "redis", "google-api-python-client", "wolframalpha", "qdrant-client", "tensorflow-text", "pypdf", "networkx", "nomic", "aleph-alpha-client", "deeplake", "pgvector", "psycopg2-binary", "boto3", "pyowm"]
all = ["anthropic", "cohere", "openai", "nlpcloud", "huggingface_hub", "jina", "manifest-ml", "elasticsearch", "opensearch-py", "google-search-results", "faiss-cpu", "sentence_transformers", "transformers", "spacy", "nltk", "wikipedia", "beautifulsoup4", "tiktoken", "torch", "jinja2", "pinecone-client", "pinecone-text", "weaviate-client", "redis", "google-api-python-client", "wolframalpha", "qdrant-client", "tensorflow-text", "pypdf", "networkx", "nomic", "aleph-alpha-client", "deeplake", "pgvector", "psycopg2-binary", "boto3", "pyowm"]
[tool.ruff]
select = [

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