253 lines
7.9 KiB
Python
253 lines
7.9 KiB
Python
# built-in dependencies
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import os
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import json
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import hashlib
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import struct
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import math
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from typing import Any, Dict, Optional, List, Union
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# project dependencies
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from deepface.modules.database.types import Database
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from deepface.modules.modeling import build_model
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from deepface.commons.logger import Logger
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logger = Logger()
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class PineconeClient(Database):
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"""
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Pinecone client for storing and retrieving face embeddings and indices.
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"""
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def __init__(
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self,
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connection_details: Optional[Union[str, Dict[str, Any]]] = None,
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connection: Any = None,
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):
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try:
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from pinecone import Pinecone, ServerlessSpec
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except (ModuleNotFoundError, ImportError) as e:
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raise ValueError(
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"pinecone is an optional dependency. Install with 'pip install pinecone'"
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) from e
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self.pinecone = Pinecone
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self.serverless_spec = ServerlessSpec
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if connection is not None:
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self.client = connection
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else:
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self.conn_details = connection_details or os.environ.get("DEEPFACE_PINECONE_API_KEY")
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if not isinstance(self.conn_details, str):
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raise ValueError(
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"Pinecone api key must be provided as a string in connection_details "
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"or via DEEPFACE_PINECONE_API_KEY environment variable."
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)
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self.client = self.pinecone(api_key=self.conn_details)
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def initialize_database(self, **kwargs: Any) -> None:
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"""
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Ensure Pinecone index exists.
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"""
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model_name = kwargs.get("model_name", "VGG-Face")
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detector_backend = kwargs.get("detector_backend", "opencv")
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aligned = kwargs.get("aligned", True)
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l2_normalized = kwargs.get("l2_normalized", False)
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index_name = self.__generate_index_name(
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model_name, detector_backend, aligned, l2_normalized
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)
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if self.client.has_index(index_name):
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logger.debug(f"Pinecone index '{index_name}' already exists.")
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return
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model = build_model(task="facial_recognition", model_name=model_name)
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dimensions = model.output_shape
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similarity_function = "cosine" if l2_normalized else "euclidean"
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self.client.create_index(
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name=index_name,
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dimension=dimensions,
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metric=similarity_function,
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spec=self.serverless_spec(
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cloud=os.getenv("DEEPFACE_PINECONE_CLOUD", "aws"),
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region=os.getenv("DEEPFACE_PINECONE_REGION", "us-east-1"),
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),
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)
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logger.debug(f"Created Pinecone index '{index_name}' with dimension {dimensions}.")
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def insert_embeddings(self, embeddings: List[Dict[str, Any]], batch_size: int = 100) -> int:
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"""
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Insert embeddings into Pinecone database in batches.
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"""
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if not embeddings:
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raise ValueError("No embeddings to insert.")
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self.initialize_database(
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model_name=embeddings[0]["model_name"],
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detector_backend=embeddings[0]["detector_backend"],
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aligned=embeddings[0]["aligned"],
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l2_normalized=embeddings[0]["l2_normalized"],
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)
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index_name = self.__generate_index_name(
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embeddings[0]["model_name"],
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embeddings[0]["detector_backend"],
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embeddings[0]["aligned"],
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embeddings[0]["l2_normalized"],
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)
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# connect to the index
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index = self.client.Index(index_name)
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total = 0
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for i in range(0, len(embeddings), batch_size):
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batch = embeddings[i : i + batch_size]
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vectors = []
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for e in batch:
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face_json = json.dumps(e["face"].tolist())
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face_hash = hashlib.sha256(face_json.encode()).hexdigest()
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embedding_bytes = struct.pack(f'{len(e["embedding"])}d', *e["embedding"])
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embedding_hash = hashlib.sha256(embedding_bytes).hexdigest()
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vectors.append(
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{
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"id": f"{face_hash}:{embedding_hash}",
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"values": e["embedding"],
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"metadata": {
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"img_name": e["img_name"],
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# "face": e["face"].tolist(),
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# "face_shape": list(e["face"].shape),
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},
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}
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)
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index.upsert(vectors=vectors)
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total += len(vectors)
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return total
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def search_by_vector(
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self,
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vector: List[float],
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model_name: str = "VGG-Face",
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detector_backend: str = "opencv",
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aligned: bool = True,
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l2_normalized: bool = False,
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limit: int = 10,
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) -> List[Dict[str, Any]]:
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"""
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ANN search using the main vector (embedding).
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"""
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out: List[Dict[str, Any]] = []
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self.initialize_database(
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model_name=model_name,
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detector_backend=detector_backend,
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aligned=aligned,
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l2_normalized=l2_normalized,
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)
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index_name = self.__generate_index_name(
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model_name, detector_backend, aligned, l2_normalized
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)
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index = self.client.Index(index_name)
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results = index.query(
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vector=vector,
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top_k=limit,
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include_metadata=True,
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include_values=False,
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)
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if not results.matches:
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return out
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for res in results.matches:
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score = float(res.score)
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if l2_normalized:
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distance = 1 - score
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else:
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distance = math.sqrt(max(score, 0.0))
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out.append(
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{
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"id": res.id,
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"distance": distance,
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"img_name": res.metadata.get("img_name"),
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}
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)
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return out
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def fetch_all_embeddings(
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self,
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model_name: str,
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detector_backend: str,
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aligned: bool,
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l2_normalized: bool,
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batch_size: int = 1000,
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) -> List[Dict[str, Any]]:
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"""
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Fetch all embeddings from Pinecone database in batches.
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"""
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out: List[Dict[str, Any]] = []
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self.initialize_database(
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model_name=model_name,
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detector_backend=detector_backend,
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aligned=aligned,
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l2_normalized=l2_normalized,
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)
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index_name = self.__generate_index_name(
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model_name, detector_backend, aligned, l2_normalized
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)
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index = self.client.Index(index_name)
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# Fetch all IDs
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ids: List[str] = []
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for _id in index.list():
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ids.extend(_id)
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for i in range(0, len(ids), batch_size):
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batch_ids = ids[i : i + batch_size]
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fetched = index.fetch(ids=batch_ids)
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for _id, v in fetched.get("vectors", {}).items():
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md = v.get("metadata") or {}
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out.append(
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{
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"id": _id,
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"embedding": v.get("values"),
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"img_name": md.get("img_name"),
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"face_hash": md.get("face_hash"),
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"embedding_hash": md.get("embedding_hash"),
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}
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)
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return out
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def close(self) -> None:
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"""Pinecone client does not require explicit closure"""
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return
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@staticmethod
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def __generate_index_name(
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model_name: str,
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detector_backend: str,
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aligned: bool,
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l2_normalized: bool,
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) -> str:
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"""
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Generate Pinecone index name based on parameters.
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"""
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index_name_attributes = [
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"embeddings",
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model_name.replace("-", ""),
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detector_backend,
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"Aligned" if aligned else "Unaligned",
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"Norm" if l2_normalized else "Raw",
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]
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return "-".join(index_name_attributes).lower()
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