Datastore reproduce (#3)
* fix: diskann zmq port and passages * feat: auto discovery of packages and fix passage gen for diskann * docs: embedding pruning * refactor: passage structure * feat: reproducible research datas, rpj_wiki & dpr * refactor: chat and base searcher * feat: chat on mps
This commit is contained in:
Andy Lee
@@ -3,29 +3,25 @@ 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
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from typing import Dict, Any, List
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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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import pickle
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from leann.embedding_server_manager import EmbeddingServerManager
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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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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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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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@@ -51,210 +47,87 @@ class DiskannBackend(LeannBackendFactoryInterface):
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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 _generate_passages_file(self, index_dir: Path, index_prefix: str, **kwargs):
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"""Generate passages file for recompute mode, mirroring HNSW backend."""
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try:
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chunks = kwargs.get('chunks', [])
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if not chunks:
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print("INFO: No chunks data provided, skipping passages file generation for DiskANN.")
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return
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passages_data = {str(node_id): chunk["text"] for node_id, chunk in enumerate(chunks)}
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passages_file = index_dir / f"{index_prefix}.passages.json"
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with open(passages_file, 'w', encoding='utf-8') as f:
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json.dump(passages_data, f, ensure_ascii=False, indent=2)
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print(f"✅ Generated passages file for recompute mode at '{passages_file}' ({len(passages_data)} passages)")
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except Exception as e:
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print(f"💥 ERROR: Failed to generate passages file for DiskANN. Exception: {e}")
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pass
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def build(self, data: np.ndarray, index_path: str, **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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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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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_str = build_kwargs.get("distance_metric", "mips").lower()
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metric_enum = METRIC_MAP.get(metric_str)
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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 '{metric_str}'.")
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raise ValueError(f"Unsupported distance_metric.")
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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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is_recompute = build_kwargs.get("is_recompute", False)
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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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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,
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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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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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print(f"✅ DiskANN index built successfully at '{index_dir / index_prefix}'")
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if is_recompute:
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self._generate_passages_file(index_dir, index_prefix, **build_kwargs)
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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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class DiskannSearcher(BaseSearcher):
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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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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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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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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 '{self.distance_metric}'.")
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raise ValueError(f"Unsupported distance_metric '{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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self.index_dir = path.parent
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self.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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self.num_threads = kwargs.get("num_threads", 8)
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self.zmq_port = kwargs.get("zmq_port", 6666)
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try:
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full_index_prefix = str(self.index_dir / self.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, self.zmq_port, "", ""
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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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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, **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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port = kwargs.get("zmq_port", self.zmq_port)
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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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recompute = kwargs.get("recompute_beighbor_embeddings", False)
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if 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 mode enabled but metadata file not found: {meta_file_path}")
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zmq_port = kwargs.get("zmq_port", self.zmq_port)
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self._ensure_server_running(str(meta_file_path), port=zmq_port, **kwargs)
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passages_file = kwargs.get("passages_file")
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if not passages_file:
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potential_passages_file = self.index_dir / f"{self.index_prefix}.passages.json"
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if potential_passages_file.exists():
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passages_file = str(potential_passages_file)
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print(f"INFO: Automatically found passages file: {passages_file}")
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if not passages_file:
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raise RuntimeError(
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f"Recompute mode is enabled, but no passages file was found. "
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f"A '{self.index_prefix}.passages.json' file should exist in the index directory "
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f"'{self.index_dir}'. Ensure you build the index with 'recompute=True'."
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)
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server_started = self.embedding_server_manager.start_server(
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port=self.zmq_port,
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model_name=self.embedding_model,
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distance_metric=self.distance_metric,
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passages_file=passages_file
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)
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if not server_started:
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raise RuntimeError(f"Failed to start DiskANN embedding server on port {self.zmq_port}")
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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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labels, distances = self._index.batch_search(
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query, query.shape[0], top_k,
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kwargs.get("complexity", 256), kwargs.get("beam_width", 4), self.num_threads,
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kwargs.get("USE_DEFERRED_FETCH", False), kwargs.get("skip_search_reorder", False),
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recompute, kwargs.get("dedup_node_dis", False), kwargs.get("prune_ratio", 0.0),
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kwargs.get("batch_recompute", False), kwargs.get("global_pruning", False)
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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}
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