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