ab339886dd
- Add --distance-metric parameter to diskann_embedding_server.py for consistency with other backends - Remove pytest.skip and pytest.xfail markers for macOS C++ ABI issues as they have been fixed - Fix test assertions to handle SearchResult objects correctly - All tests now pass on macOS with the C++ ABI compatibility fixes
285 lines
10 KiB
Python
285 lines
10 KiB
Python
"""
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DiskANN-specific embedding server
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"""
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import argparse
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import json
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import logging
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import os
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import sys
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import threading
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import time
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from pathlib import Path
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import numpy as np
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import zmq
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# Set up logging based on environment variable
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LOG_LEVEL = os.getenv("LEANN_LOG_LEVEL", "WARNING").upper()
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logger = logging.getLogger(__name__)
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# Force set logger level (don't rely on basicConfig in subprocess)
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log_level = getattr(logging, LOG_LEVEL, logging.WARNING)
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logger.setLevel(log_level)
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# Ensure we have a handler if none exists
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if not logger.handlers:
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handler = logging.StreamHandler()
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formatter = logging.Formatter("%(asctime)s - %(levelname)s - %(message)s")
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handler.setFormatter(formatter)
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logger.addHandler(handler)
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logger.propagate = False
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def create_diskann_embedding_server(
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passages_file: str | None = None,
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zmq_port: int = 5555,
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model_name: str = "sentence-transformers/all-mpnet-base-v2",
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embedding_mode: str = "sentence-transformers",
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distance_metric: str = "l2",
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):
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"""
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Create and start a ZMQ-based embedding server for DiskANN backend.
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Uses ROUTER socket and protobuf communication as required by DiskANN C++ implementation.
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"""
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logger.info(f"Starting DiskANN server on port {zmq_port} with model {model_name}")
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logger.info(f"Using embedding mode: {embedding_mode}")
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# Add leann-core to path for unified embedding computation
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current_dir = Path(__file__).parent
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leann_core_path = current_dir.parent.parent / "leann-core" / "src"
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sys.path.insert(0, str(leann_core_path))
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try:
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from leann.api import PassageManager
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from leann.embedding_compute import compute_embeddings
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logger.info("Successfully imported unified embedding computation module")
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except ImportError as e:
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logger.error(f"Failed to import embedding computation module: {e}")
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return
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finally:
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sys.path.pop(0)
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# Check port availability
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import socket
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def check_port(port):
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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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if check_port(zmq_port):
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logger.error(f"Port {zmq_port} is already in use")
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return
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# Only support metadata file, fail fast for everything else
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if not passages_file or not passages_file.endswith(".meta.json"):
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raise ValueError("Only metadata files (.meta.json) are supported")
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# Load metadata to get passage sources
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with open(passages_file) as f:
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meta = json.load(f)
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passages = PassageManager(meta["passage_sources"])
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logger.info(
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f"Loaded PassageManager with {len(passages.global_offset_map)} passages from metadata"
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)
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# Import protobuf after ensuring the path is correct
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try:
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from . import embedding_pb2
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except ImportError as e:
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logger.error(f"Failed to import protobuf module: {e}")
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return
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def zmq_server_thread():
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"""ZMQ server thread using REP socket for universal compatibility"""
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context = zmq.Context()
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socket = context.socket(
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zmq.REP
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) # REP socket for both BaseSearcher and DiskANN C++ REQ clients
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socket.bind(f"tcp://*:{zmq_port}")
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logger.info(f"DiskANN ZMQ REP server listening on port {zmq_port}")
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socket.setsockopt(zmq.RCVTIMEO, 300000)
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socket.setsockopt(zmq.SNDTIMEO, 300000)
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while True:
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try:
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# REP socket receives single-part messages
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message = socket.recv()
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# Check for empty messages - REP socket requires response to every request
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if len(message) == 0:
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logger.debug("Received empty message, sending empty response")
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socket.send(b"") # REP socket must respond to every request
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continue
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logger.debug(f"Received ZMQ request of size {len(message)} bytes")
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logger.debug(f"Message preview: {message[:50]}") # Show first 50 bytes
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e2e_start = time.time()
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# Try protobuf first (for DiskANN C++ node_ids requests - primary use case)
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texts = []
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node_ids = []
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is_text_request = False
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try:
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req_proto = embedding_pb2.NodeEmbeddingRequest()
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req_proto.ParseFromString(message)
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node_ids = list(req_proto.node_ids)
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if not node_ids:
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raise RuntimeError(
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f"PROTOBUF: Received empty node_ids! Message size: {len(message)}"
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)
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logger.info(
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f"✅ PROTOBUF: Node ID request for {len(node_ids)} node embeddings: {node_ids[:10]}"
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)
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except Exception as protobuf_error:
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logger.debug(f"Protobuf parsing failed: {protobuf_error}")
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# Fallback to msgpack (for BaseSearcher direct text requests)
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try:
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import msgpack
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request = msgpack.unpackb(message)
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# For BaseSearcher compatibility, request is a list of texts directly
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if isinstance(request, list) and all(
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isinstance(item, str) for item in request
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):
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texts = request
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is_text_request = True
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logger.info(f"✅ MSGPACK: Direct text request for {len(texts)} texts")
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else:
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raise ValueError("Not a valid msgpack text request")
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except Exception as msgpack_error:
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raise RuntimeError(
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f"Both protobuf and msgpack parsing failed! Protobuf: {protobuf_error}, Msgpack: {msgpack_error}"
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)
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# Look up texts by node IDs (only if not direct text request)
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if not is_text_request:
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for nid in node_ids:
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try:
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passage_data = passages.get_passage(str(nid))
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txt = passage_data["text"]
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if not txt:
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raise RuntimeError(f"FATAL: Empty text for passage ID {nid}")
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texts.append(txt)
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except KeyError as e:
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logger.error(f"Passage ID {nid} not found: {e}")
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raise e
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except Exception as e:
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logger.error(f"Exception looking up passage ID {nid}: {e}")
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raise
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# Debug logging
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logger.debug(f"Processing {len(texts)} texts")
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logger.debug(f"Text lengths: {[len(t) for t in texts[:5]]}") # Show first 5
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# Process embeddings using unified computation
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embeddings = compute_embeddings(texts, model_name, mode=embedding_mode)
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logger.info(
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f"Computed embeddings for {len(texts)} texts, shape: {embeddings.shape}"
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)
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# Prepare response based on request type
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if is_text_request:
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# For BaseSearcher compatibility: return msgpack format
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import msgpack
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response_data = msgpack.packb(embeddings.tolist())
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else:
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# For DiskANN C++ compatibility: return protobuf format
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resp_proto = embedding_pb2.NodeEmbeddingResponse()
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hidden_contiguous = np.ascontiguousarray(embeddings, dtype=np.float32)
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# Serialize embeddings data
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resp_proto.embeddings_data = hidden_contiguous.tobytes()
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resp_proto.dimensions.append(hidden_contiguous.shape[0])
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resp_proto.dimensions.append(hidden_contiguous.shape[1])
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response_data = resp_proto.SerializeToString()
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# Send response back to the client
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socket.send(response_data)
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e2e_end = time.time()
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logger.info(f"⏱️ ZMQ E2E time: {e2e_end - e2e_start:.6f}s")
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except zmq.Again:
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logger.debug("ZMQ socket timeout, continuing to listen")
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continue
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except Exception as e:
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logger.error(f"Error in ZMQ server loop: {e}")
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import traceback
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traceback.print_exc()
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raise
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zmq_thread = threading.Thread(target=zmq_server_thread, daemon=True)
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zmq_thread.start()
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logger.info(f"Started DiskANN ZMQ server thread on port {zmq_port}")
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# Keep the main thread alive
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try:
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while True:
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time.sleep(1)
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except KeyboardInterrupt:
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logger.info("DiskANN Server shutting down...")
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return
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if __name__ == "__main__":
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import signal
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import sys
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def signal_handler(sig, frame):
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logger.info(f"Received signal {sig}, shutting down gracefully...")
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sys.exit(0)
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# Register signal handlers for graceful shutdown
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signal.signal(signal.SIGTERM, signal_handler)
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signal.signal(signal.SIGINT, signal_handler)
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parser = argparse.ArgumentParser(description="DiskANN Embedding service")
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parser.add_argument("--zmq-port", type=int, default=5555, help="ZMQ port to run on")
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parser.add_argument(
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"--passages-file",
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type=str,
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help="Metadata JSON file containing passage sources",
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)
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parser.add_argument(
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"--model-name",
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type=str,
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default="sentence-transformers/all-mpnet-base-v2",
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help="Embedding model name",
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)
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parser.add_argument(
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"--embedding-mode",
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type=str,
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default="sentence-transformers",
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choices=["sentence-transformers", "openai", "mlx"],
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help="Embedding backend mode",
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)
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parser.add_argument(
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"--distance-metric",
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type=str,
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default="l2",
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choices=["l2", "mips", "cosine"],
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help="Distance metric for similarity computation",
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)
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args = parser.parse_args()
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# Create and start the DiskANN embedding server
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create_diskann_embedding_server(
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passages_file=args.passages_file,
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zmq_port=args.zmq_port,
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model_name=args.model_name,
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embedding_mode=args.embedding_mode,
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distance_metric=args.distance_metric,
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)
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