Initial commit
This commit is contained in:
yichuan520030910320
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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.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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def _check_port(port: int) -> bool:
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with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
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return s.connect_ex(('localhost', port)) == 0
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class EmbeddingServerManager:
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def __init__(self):
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self.server_process = None
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self.server_port = None
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atexit.register(self.stop_server)
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def start_server(self, port=5555, model_name="sentence-transformers/all-mpnet-base-v2"):
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if self.server_process and self.server_process.poll() is None:
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print(f"INFO: Reusing existing server process for this session (PID {self.server_process.pid})")
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return True
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# 检查端口是否已被其他无关进程占用
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if _check_port(port):
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print(f"WARNING: Port {port} is already in use. Assuming an external server is running and connecting to it.")
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return True
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print(f"INFO: Starting session-level embedding server as a background process...")
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try:
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command = [
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sys.executable,
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"-m", "packages.leann-backend-diskann.leann_backend_diskann.embedding_server",
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"--zmq-port", str(port),
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"--model-name", model_name
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]
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project_root = Path(__file__).parent.parent.parent.parent
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print(f"INFO: Running command from project root: {project_root}")
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self.server_process = subprocess.Popen(
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command,
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cwd=project_root,
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stdout=subprocess.PIPE,
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stderr=subprocess.PIPE,
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text=True,
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encoding='utf-8'
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)
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self.server_port = port
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print(f"INFO: Server process started with PID: {self.server_process.pid}")
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max_wait, wait_interval = 30, 0.5
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for _ in range(int(max_wait / wait_interval)):
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if _check_port(port):
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print(f"✅ Embedding server is up and ready for this session.")
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log_thread = threading.Thread(target=self._log_monitor, daemon=True)
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log_thread.start()
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return True
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if self.server_process.poll() is not None:
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print("❌ ERROR: Server process terminated unexpectedly during startup.")
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self._log_monitor()
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return False
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time.sleep(wait_interval)
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print(f"❌ ERROR: Server process failed to start listening within {max_wait} seconds.")
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self.stop_server()
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return False
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except Exception as e:
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print(f"❌ ERROR: Failed to start embedding server process: {e}")
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return False
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def _log_monitor(self):
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if not self.server_process:
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return
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try:
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if self.server_process.stdout:
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for line in iter(self.server_process.stdout.readline, ''):
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print(f"[EmbeddingServer LOG]: {line.strip()}")
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self.server_process.stdout.close()
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if self.server_process.stderr:
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for line in iter(self.server_process.stderr.readline, ''):
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print(f"[EmbeddingServer ERROR]: {line.strip()}")
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self.server_process.stderr.close()
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except Exception as e:
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print(f"Log monitor error: {e}")
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def stop_server(self):
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if self.server_process and self.server_process.poll() is None:
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print(f"INFO: Terminating session server process (PID: {self.server_process.pid})...")
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self.server_process.terminate()
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try:
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self.server_process.wait(timeout=5)
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print("INFO: Server process terminated.")
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except subprocess.TimeoutExpired:
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print("WARNING: Server process did not terminate gracefully, killing it.")
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self.server_process.kill()
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self.server_process = None
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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}. Cannot infer vector dimension for searcher.")
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with open(meta_path, 'r') as f:
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meta = json.load(f)
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try:
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from sentence_transformers import SentenceTransformer
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model = SentenceTransformer(meta.get("embedding_model"))
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dimensions = model.get_sentence_embedding_dimension()
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kwargs['dimensions'] = dimensions
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except ImportError:
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raise ImportError("sentence-transformers is required to infer embedding dimensions. Please install it.")
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except Exception as e:
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raise RuntimeError(f"Could not load SentenceTransformer model to get dimension: {e}")
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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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path = Path(index_path)
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index_dir = path.parent
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index_prefix = path.stem
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metric_str = 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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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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dimensions = kwargs.get("dimensions")
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if not dimensions:
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raise ValueError("Vector dimension not provided to DiskannSearcher.")
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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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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", 100)
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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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zmq_port = kwargs.get("zmq_port", 5555)
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embedding_model = kwargs.get("embedding_model", "sentence-transformers/all-mpnet-base-v2")
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if not self.embedding_server_manager.start_server(zmq_port, embedding_model):
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print(f"WARNING: Failed to start embedding server, falling back to PQ computation")
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kwargs['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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