178 lines
7.1 KiB
Python
178 lines
7.1 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, Any, List, Literal
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import contextlib
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import pickle
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from leann.searcher_base import BaseSearcher
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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_diskann_metrics():
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from . import _diskannpy as diskannpy
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return {
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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: Path):
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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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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, ids: List[str], 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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data_filename = f"{index_prefix}_data.bin"
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_write_vectors_to_bin(data, index_dir / data_filename)
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label_map = {i: str_id for i, str_id in enumerate(ids)}
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label_map_file = index_dir / "leann.labels.map"
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with open(label_map_file, 'wb') as f:
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pickle.dump(label_map, f)
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build_kwargs = {**self.build_params, **kwargs}
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metric_enum = _get_diskann_metrics().get(build_kwargs.get("distance_metric", "mips").lower())
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if metric_enum is None:
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raise ValueError(f"Unsupported distance_metric.")
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try:
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from . import _diskannpy as diskannpy
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with chdir(index_dir):
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diskannpy.build_disk_float_index(
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metric_enum, data_filename, index_prefix,
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build_kwargs.get("complexity", 64), build_kwargs.get("graph_degree", 32),
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build_kwargs.get("search_memory_maximum", 4.0), build_kwargs.get("build_memory_maximum", 8.0),
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build_kwargs.get("num_threads", 8), build_kwargs.get("pq_disk_bytes", 0), ""
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)
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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(BaseSearcher):
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def __init__(self, index_path: str, **kwargs):
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super().__init__(index_path, backend_module_name="leann_backend_diskann.embedding_server", **kwargs)
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from . import _diskannpy as diskannpy
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distance_metric = kwargs.get("distance_metric", "mips").lower()
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metric_enum = _get_diskann_metrics().get(distance_metric)
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if metric_enum is None:
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raise ValueError(f"Unsupported distance_metric '{distance_metric}'.")
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self.num_threads = kwargs.get("num_threads", 8)
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self.zmq_port = kwargs.get("zmq_port", 6666)
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full_index_prefix = str(self.index_dir / self.index_path.stem)
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self._index = diskannpy.StaticDiskFloatIndex(
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metric_enum, full_index_prefix, self.num_threads,
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kwargs.get("num_nodes_to_cache", 0), 1, self.zmq_port, "", ""
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)
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def search(self, query: np.ndarray, 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_recompute: bool = False,
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dedup_node_dis: bool = False,
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**kwargs) -> Dict[str, Any]:
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"""
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Search for nearest neighbors using DiskANN 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/candidate list size, higher = more accurate but slower
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beam_width: Number of parallel IO requests per iteration
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prune_ratio: Ratio of neighbors to prune via approximate distance (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 pruning strategy (default)
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- "local": Use local pruning strategy
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- "proportional": Not supported in DiskANN, falls back to global
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zmq_port: ZMQ port for embedding server
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batch_recompute: Whether to batch neighbor recomputation (DiskANN-specific)
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dedup_node_dis: Whether to cache and reuse distance computations (DiskANN-specific)
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**kwargs: Additional DiskANN-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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# DiskANN doesn't support "proportional" strategy
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if pruning_strategy == "proportional":
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raise NotImplementedError("DiskANN backend does not support 'proportional' pruning strategy. Use 'global' or 'local' instead.")
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# Use recompute_embeddings parameter
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use_recompute = recompute_embeddings
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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(f"FATAL: Recompute enabled but metadata file not found: {meta_file_path}")
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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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# Map pruning_strategy to DiskANN's global_pruning parameter
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if pruning_strategy == "local":
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use_global_pruning = False
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else: # "global"
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use_global_pruning = True
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labels, distances = self._index.batch_search(
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query, query.shape[0], top_k,
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complexity, beam_width, self.num_threads,
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kwargs.get("USE_DEFERRED_FETCH", False),
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kwargs.get("skip_search_reorder", False),
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use_recompute,
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dedup_node_dis,
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prune_ratio,
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batch_recompute,
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use_global_pruning
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)
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string_labels = [[self.label_map.get(int_label, f"unknown_{int_label}") for int_label in batch_labels] for batch_labels in labels]
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return {"labels": string_labels, "distances": distances} |