224 lines
8.3 KiB
Python
224 lines
8.3 KiB
Python
import numpy as np
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import os
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import json
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import struct
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from pathlib import Path
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from typing import Dict
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import contextlib
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import threading
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import time
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import atexit
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import socket
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import subprocess
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import sys
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from leann.embedding_server_manager import EmbeddingServerManager
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from leann.registry import register_backend
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from leann.interface import (
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LeannBackendFactoryInterface,
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LeannBackendBuilderInterface,
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LeannBackendSearcherInterface
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)
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from . import _diskannpy as diskannpy
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METRIC_MAP = {
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"mips": diskannpy.Metric.INNER_PRODUCT,
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"l2": diskannpy.Metric.L2,
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"cosine": diskannpy.Metric.COSINE,
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}
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@contextlib.contextmanager
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def chdir(path):
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original_dir = os.getcwd()
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os.chdir(path)
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try:
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yield
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finally:
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os.chdir(original_dir)
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def _write_vectors_to_bin(data: np.ndarray, file_path: str):
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num_vectors, dim = data.shape
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with open(file_path, 'wb') as f:
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f.write(struct.pack('I', num_vectors))
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f.write(struct.pack('I', dim))
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f.write(data.tobytes())
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@register_backend("diskann")
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class DiskannBackend(LeannBackendFactoryInterface):
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@staticmethod
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def builder(**kwargs) -> LeannBackendBuilderInterface:
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return DiskannBuilder(**kwargs)
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@staticmethod
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def searcher(index_path: str, **kwargs) -> LeannBackendSearcherInterface:
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path = Path(index_path)
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meta_path = path.parent / f"{path.name}.meta.json"
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if not meta_path.exists():
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raise FileNotFoundError(f"Leann metadata file not found at {meta_path}.")
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with open(meta_path, 'r') as f:
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meta = json.load(f)
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# Pass essential metadata to the searcher
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kwargs['meta'] = meta
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return DiskannSearcher(index_path, **kwargs)
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class DiskannBuilder(LeannBackendBuilderInterface):
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def __init__(self, **kwargs):
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self.build_params = kwargs
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def build(self, data: np.ndarray, index_path: str, **kwargs):
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path = Path(index_path)
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index_dir = path.parent
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index_prefix = path.stem
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index_dir.mkdir(parents=True, exist_ok=True)
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if data.dtype != np.float32:
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data = data.astype(np.float32)
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if not data.flags['C_CONTIGUOUS']:
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data = np.ascontiguousarray(data)
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data_filename = f"{index_prefix}_data.bin"
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_write_vectors_to_bin(data, index_dir / data_filename)
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build_kwargs = {**self.build_params, **kwargs}
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metric_str = build_kwargs.get("distance_metric", "mips").lower()
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metric_enum = METRIC_MAP.get(metric_str)
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if metric_enum is None:
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raise ValueError(f"Unsupported distance_metric '{metric_str}'.")
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complexity = build_kwargs.get("complexity", 64)
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graph_degree = build_kwargs.get("graph_degree", 32)
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final_index_ram_limit = build_kwargs.get("search_memory_maximum", 4.0)
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indexing_ram_budget = build_kwargs.get("build_memory_maximum", 8.0)
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num_threads = build_kwargs.get("num_threads", 8)
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pq_disk_bytes = build_kwargs.get("pq_disk_bytes", 0)
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codebook_prefix = ""
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print(f"INFO: Building DiskANN index for {data.shape[0]} vectors with metric {metric_enum}...")
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try:
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with chdir(index_dir):
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diskannpy.build_disk_float_index(
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metric_enum,
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data_filename,
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index_prefix,
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complexity,
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graph_degree,
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final_index_ram_limit,
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indexing_ram_budget,
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num_threads,
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pq_disk_bytes,
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codebook_prefix
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)
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print(f"✅ DiskANN index built successfully at '{index_dir / index_prefix}'")
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except Exception as e:
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print(f"💥 ERROR: DiskANN index build failed. Exception: {e}")
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raise
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finally:
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temp_data_file = index_dir / data_filename
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if temp_data_file.exists():
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os.remove(temp_data_file)
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class DiskannSearcher(LeannBackendSearcherInterface):
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def __init__(self, index_path: str, **kwargs):
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self.meta = kwargs.get("meta", {})
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if not self.meta:
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raise ValueError("DiskannSearcher requires metadata from .meta.json.")
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dimensions = self.meta.get("dimensions")
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if not dimensions:
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raise ValueError("Dimensions not found in Leann metadata.")
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self.distance_metric = self.meta.get("distance_metric", "mips").lower()
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metric_enum = METRIC_MAP.get(self.distance_metric)
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if metric_enum is None:
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raise ValueError(f"Unsupported distance_metric '{self.distance_metric}'.")
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self.embedding_model = self.meta.get("embedding_model")
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if not self.embedding_model:
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print("WARNING: embedding_model not found in meta.json. Recompute will fail if attempted.")
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path = Path(index_path)
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index_dir = path.parent
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index_prefix = path.stem
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num_threads = kwargs.get("num_threads", 8)
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num_nodes_to_cache = kwargs.get("num_nodes_to_cache", 0)
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try:
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full_index_prefix = str(index_dir / index_prefix)
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self._index = diskannpy.StaticDiskFloatIndex(
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metric_enum, full_index_prefix, num_threads, num_nodes_to_cache, 1, "", ""
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)
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self.num_threads = num_threads
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self.embedding_server_manager = EmbeddingServerManager(
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backend_module_name="leann_backend_diskann.embedding_server"
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)
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print("✅ DiskANN index loaded successfully.")
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except Exception as e:
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print(f"💥 ERROR: Failed to load DiskANN index. Exception: {e}")
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raise
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def search(self, query: np.ndarray, top_k: int, **kwargs) -> Dict[str, any]:
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complexity = kwargs.get("complexity", 256)
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beam_width = kwargs.get("beam_width", 4)
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USE_DEFERRED_FETCH = kwargs.get("USE_DEFERRED_FETCH", False)
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skip_search_reorder = kwargs.get("skip_search_reorder", False)
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recompute_beighbor_embeddings = kwargs.get("recompute_beighbor_embeddings", False)
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dedup_node_dis = kwargs.get("dedup_node_dis", False)
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prune_ratio = kwargs.get("prune_ratio", 0.0)
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batch_recompute = kwargs.get("batch_recompute", False)
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global_pruning = kwargs.get("global_pruning", False)
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if recompute_beighbor_embeddings:
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print(f"INFO: DiskANN ZMQ mode enabled - ensuring embedding server is running")
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if not self.embedding_model:
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raise ValueError("Cannot use recompute_beighbor_embeddings without 'embedding_model' in meta.json.")
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zmq_port = kwargs.get("zmq_port", 6666)
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server_started = self.embedding_server_manager.start_server(
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port=zmq_port,
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model_name=self.embedding_model,
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distance_metric=self.distance_metric
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)
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if not server_started:
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print(f"WARNING: Failed to start embedding server, falling back to PQ computation")
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recompute_beighbor_embeddings = False
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if query.dtype != np.float32:
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query = query.astype(np.float32)
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if query.ndim == 1:
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query = np.expand_dims(query, axis=0)
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try:
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labels, distances = self._index.batch_search(
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query,
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query.shape[0],
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top_k,
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complexity,
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beam_width,
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self.num_threads,
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USE_DEFERRED_FETCH,
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skip_search_reorder,
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recompute_beighbor_embeddings,
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dedup_node_dis,
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prune_ratio,
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batch_recompute,
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global_pruning
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)
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return {"labels": labels, "distances": distances}
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except Exception as e:
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print(f"💥 ERROR: DiskANN search failed. Exception: {e}")
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batch_size = query.shape[0]
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return {"labels": np.full((batch_size, top_k), -1, dtype=np.int64),
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"distances": np.full((batch_size, top_k), float('inf'), dtype=np.float32)}
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def __del__(self):
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if hasattr(self, 'embedding_server_manager'):
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self.embedding_server_manager.stop_server()
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