236 lines
8.5 KiB
Python
236 lines
8.5 KiB
Python
import numpy as np
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import os
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from pathlib import Path
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from typing import Dict, Any, List, Literal
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import pickle
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import shutil
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import time
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from leann.searcher_base import BaseSearcher
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from .convert_to_csr import convert_hnsw_graph_to_csr
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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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def get_metric_map():
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from . import faiss # type: ignore
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return {
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"mips": faiss.METRIC_INNER_PRODUCT,
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"l2": faiss.METRIC_L2,
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"cosine": faiss.METRIC_INNER_PRODUCT,
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}
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@register_backend("hnsw")
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class HNSWBackend(LeannBackendFactoryInterface):
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@staticmethod
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def builder(**kwargs) -> LeannBackendBuilderInterface:
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return HNSWBuilder(**kwargs)
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@staticmethod
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def searcher(index_path: str, **kwargs) -> LeannBackendSearcherInterface:
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return HNSWSearcher(index_path, **kwargs)
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class HNSWBuilder(LeannBackendBuilderInterface):
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def __init__(self, **kwargs):
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self.build_params = kwargs.copy()
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self.is_compact = self.build_params.setdefault("is_compact", True)
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self.is_recompute = self.build_params.setdefault("is_recompute", True)
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self.M = self.build_params.setdefault("M", 32)
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self.efConstruction = self.build_params.setdefault("efConstruction", 200)
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self.distance_metric = self.build_params.setdefault("distance_metric", "mips")
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self.dimensions = self.build_params.get("dimensions")
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def build(self, data: np.ndarray, ids: List[str], index_path: str, **kwargs):
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from . import faiss # type: ignore
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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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metric_enum = get_metric_map().get(self.distance_metric.lower())
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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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dim = self.dimensions or data.shape[1]
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index = faiss.IndexHNSWFlat(dim, self.M, metric_enum)
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index.hnsw.efConstruction = self.efConstruction
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if self.distance_metric.lower() == "cosine":
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faiss.normalize_L2(data)
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index.add(data.shape[0], faiss.swig_ptr(data))
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index_file = index_dir / f"{index_prefix}.index"
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faiss.write_index(index, str(index_file))
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if self.is_compact:
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self._convert_to_csr(index_file)
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def _convert_to_csr(self, index_file: Path):
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"""Convert built index to CSR format"""
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mode_str = "CSR-pruned" if self.is_recompute else "CSR-standard"
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print(f"INFO: Converting HNSW index to {mode_str} format...")
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csr_temp_file = index_file.with_suffix(".csr.tmp")
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success = convert_hnsw_graph_to_csr(
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str(index_file), str(csr_temp_file), prune_embeddings=self.is_recompute
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)
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if success:
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print("✅ CSR conversion successful.")
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index_file_old = index_file.with_suffix(".old")
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shutil.move(str(index_file), str(index_file_old))
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shutil.move(str(csr_temp_file), str(index_file))
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print(
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f"INFO: Replaced original index with {mode_str} version at '{index_file}'"
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)
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else:
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# Clean up and fail fast
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if csr_temp_file.exists():
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os.remove(csr_temp_file)
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raise RuntimeError(
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"CSR conversion failed - cannot proceed with compact format"
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)
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class HNSWSearcher(BaseSearcher):
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def __init__(self, index_path: str, **kwargs):
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super().__init__(
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index_path,
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backend_module_name="leann_backend_hnsw.hnsw_embedding_server",
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**kwargs,
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)
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from . import faiss # type: ignore
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self.distance_metric = self.meta.get("distance_metric", "mips").lower()
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metric_enum = get_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.is_compact, self.is_pruned = (
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self.meta.get("is_compact", True),
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self.meta.get("is_pruned", True),
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)
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index_file = self.index_dir / f"{self.index_path.stem}.index"
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if not index_file.exists():
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raise FileNotFoundError(f"HNSW index file not found at {index_file}")
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hnsw_config = faiss.HNSWIndexConfig()
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hnsw_config.is_compact = self.is_compact
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hnsw_config.is_recompute = self.is_pruned or kwargs.get("is_recompute", False)
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if self.is_pruned and not hnsw_config.is_recompute:
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raise RuntimeError("Index is pruned but recompute is disabled.")
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self._index = faiss.read_index(str(index_file), faiss.IO_FLAG_MMAP, hnsw_config)
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def search(
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self,
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query: np.ndarray,
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top_k: int,
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complexity: int = 64,
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beam_width: int = 1,
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prune_ratio: float = 0.0,
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recompute_embeddings: bool = False,
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pruning_strategy: Literal["global", "local", "proportional"] = "global",
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zmq_port: int = 5557,
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batch_size: int = 0,
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**kwargs,
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) -> Dict[str, Any]:
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"""
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Search for nearest neighbors using HNSW index.
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Args:
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query: Query vectors (B, D) where B is batch size, D is dimension
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top_k: Number of nearest neighbors to return
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complexity: Search complexity/efSearch, higher = more accurate but slower
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beam_width: Number of parallel search paths/beam_size
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prune_ratio: Ratio of neighbors to prune via PQ (0.0-1.0)
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recompute_embeddings: Whether to fetch fresh embeddings from server
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pruning_strategy: PQ candidate selection strategy:
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- "global": Use global PQ queue size for selection (default)
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- "local": Local pruning, sort and select best candidates
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- "proportional": Base selection on new neighbor count ratio
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zmq_port: ZMQ port for embedding server
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batch_size: Neighbor processing batch size, 0=disabled (HNSW-specific)
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**kwargs: Additional HNSW-specific parameters (for legacy compatibility)
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Returns:
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Dict with 'labels' (list of lists) and 'distances' (ndarray)
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"""
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from . import faiss # type: ignore
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# Use recompute_embeddings parameter
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use_recompute = recompute_embeddings or self.is_pruned
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if use_recompute:
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meta_file_path = self.index_dir / f"{self.index_path.name}.meta.json"
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if not meta_file_path.exists():
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raise RuntimeError(
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f"FATAL: Recompute enabled but metadata file not found: {meta_file_path}"
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)
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self._ensure_server_running(str(meta_file_path), port=zmq_port, **kwargs)
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if query.dtype != np.float32:
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query = query.astype(np.float32)
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if self.distance_metric == "cosine":
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faiss.normalize_L2(query)
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params = faiss.SearchParametersHNSW()
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params.zmq_port = zmq_port
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params.efSearch = complexity
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params.beam_size = beam_width
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# PQ pruning: direct mapping to HNSW's pq_pruning_ratio
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params.pq_pruning_ratio = prune_ratio
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# Map pruning_strategy to HNSW parameters
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if pruning_strategy == "local":
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params.local_prune = True
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params.send_neigh_times_ratio = 0.0
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elif pruning_strategy == "proportional":
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params.local_prune = False
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params.send_neigh_times_ratio = (
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1.0 # Any value > 1e-6 triggers proportional mode
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)
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else: # "global"
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params.local_prune = False
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params.send_neigh_times_ratio = 0.0
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# HNSW-specific batch processing parameter
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params.batch_size = batch_size
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batch_size_query = query.shape[0]
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distances = np.empty((batch_size_query, top_k), dtype=np.float32)
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labels = np.empty((batch_size_query, top_k), dtype=np.int64)
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self._index.search(
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query.shape[0],
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faiss.swig_ptr(query),
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top_k,
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faiss.swig_ptr(distances),
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faiss.swig_ptr(labels),
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params,
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)
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string_labels = [
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[str(int_label) for int_label in batch_labels]
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for batch_labels in labels
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]
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return {"labels": string_labels, "distances": distances}
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