""" HNSW-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 msgpack 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_hnsw_embedding_server( passages_file: Optional[str] = None, zmq_port: int = 5555, model_name: str = "sentence-transformers/all-mpnet-base-v2", distance_metric: str = "mips", embedding_mode: str = "sentence-transformers", ): """ Create and start a ZMQ-based embedding server for HNSW backend. Simplified version using unified embedding computation module. """ logger.info(f"Starting HNSW 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) # Let PassageManager handle path resolution uniformly passages = PassageManager(meta["passage_sources"], metadata_file_path=passages_file) logger.info( f"Loaded PassageManager with {len(passages.global_offset_map)} passages from metadata" ) def zmq_server_thread(): """ZMQ server thread""" context = zmq.Context() socket = context.socket(zmq.REP) socket.bind(f"tcp://*:{zmq_port}") logger.info(f"HNSW ZMQ server listening on port {zmq_port}") socket.setsockopt(zmq.RCVTIMEO, 300000) socket.setsockopt(zmq.SNDTIMEO, 300000) while True: try: message_bytes = socket.recv() logger.debug(f"Received ZMQ request of size {len(message_bytes)} bytes") e2e_start = time.time() request_payload = msgpack.unpackb(message_bytes) # Handle direct text embedding request if isinstance(request_payload, list) and len(request_payload) > 0: # Check if this is a direct text request (list of strings) if all(isinstance(item, str) for item in request_payload): logger.info( f"Processing direct text embedding request for {len(request_payload)} texts in {embedding_mode} mode" ) # Use unified embedding computation (now with model caching) embeddings = compute_embeddings( request_payload, model_name, mode=embedding_mode ) response = embeddings.tolist() socket.send(msgpack.packb(response)) e2e_end = time.time() logger.info(f"⏱️ Text embedding E2E time: {e2e_end - e2e_start:.6f}s") continue # Handle distance calculation requests if ( isinstance(request_payload, list) and len(request_payload) == 2 and isinstance(request_payload[0], list) and isinstance(request_payload[1], list) ): node_ids = request_payload[0] query_vector = np.array(request_payload[1], dtype=np.float32) logger.debug("Distance calculation request received") logger.debug(f" Node IDs: {node_ids}") logger.debug(f" Query vector dim: {len(query_vector)}") # Get embeddings for node IDs texts = [] for nid in node_ids: try: passage_data = passages.get_passage(str(nid)) txt = passage_data["text"] texts.append(txt) except KeyError: logger.error(f"Passage ID {nid} not found") raise RuntimeError(f"FATAL: Passage with ID {nid} not found") except Exception as e: logger.error(f"Exception looking up passage ID {nid}: {e}") raise # Process embeddings embeddings = compute_embeddings(texts, model_name, mode=embedding_mode) logger.info( f"Computed embeddings for {len(texts)} texts, shape: {embeddings.shape}" ) # Calculate distances if distance_metric == "l2": distances = np.sum( np.square(embeddings - query_vector.reshape(1, -1)), axis=1 ) else: # mips or cosine distances = -np.dot(embeddings, query_vector) response_payload = distances.flatten().tolist() response_bytes = msgpack.packb([response_payload], use_single_float=True) logger.debug(f"Sending distance response with {len(distances)} distances") socket.send(response_bytes) e2e_end = time.time() logger.info(f"⏱️ Distance calculation E2E time: {e2e_end - e2e_start:.6f}s") continue # Standard embedding request (passage ID lookup) if ( not isinstance(request_payload, list) or len(request_payload) != 1 or not isinstance(request_payload[0], list) ): logger.error( f"Invalid MessagePack request format. Expected [[ids...]] or [texts...], got: {type(request_payload)}" ) socket.send(msgpack.packb([[], []])) continue node_ids = request_payload[0] logger.debug(f"Request for {len(node_ids)} node embeddings") # Look up texts by node IDs texts = [] 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: raise RuntimeError(f"FATAL: Passage with ID {nid} not found") except Exception as e: logger.error(f"Exception looking up passage ID {nid}: {e}") raise # Process embeddings embeddings = compute_embeddings(texts, model_name, mode=embedding_mode) logger.info( f"Computed embeddings for {len(texts)} texts, shape: {embeddings.shape}" ) # Serialization and response if np.isnan(embeddings).any() or np.isinf(embeddings).any(): logger.error( f"NaN or Inf detected in embeddings! Requested IDs: {node_ids[:5]}..." ) raise AssertionError() hidden_contiguous_f32 = np.ascontiguousarray(embeddings, dtype=np.float32) response_payload = [ list(hidden_contiguous_f32.shape), hidden_contiguous_f32.flatten().tolist(), ] response_bytes = msgpack.packb(response_payload, use_single_float=True) socket.send(response_bytes) 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() socket.send(msgpack.packb([[], []])) def zmq_server_thread_with_shutdown(shutdown_event): """ZMQ server thread that respects shutdown signal. Creates its own REP socket bound to zmq_port and polls with timeouts to allow graceful shutdown. """ logger.info("ZMQ server thread started with shutdown support") context = zmq.Context() rep_socket = context.socket(zmq.REP) rep_socket.bind(f"tcp://*:{zmq_port}") logger.info(f"HNSW ZMQ REP server listening on port {zmq_port}") rep_socket.setsockopt(zmq.RCVTIMEO, 1000) rep_socket.setsockopt(zmq.SNDTIMEO, 300000) try: while not shutdown_event.is_set(): try: e2e_start = time.time() logger.debug("🔍 Waiting for ZMQ message...") request_bytes = rep_socket.recv() # Rest of the processing logic (same as original) request = msgpack.unpackb(request_bytes) if len(request) == 1 and request[0] == "__QUERY_MODEL__": response_bytes = msgpack.packb([model_name]) rep_socket.send(response_bytes) continue # Handle direct text embedding request if ( isinstance(request, list) and request and all(isinstance(item, str) for item in request) ): embeddings = compute_embeddings(request, model_name, mode=embedding_mode) rep_socket.send(msgpack.packb(embeddings.tolist())) e2e_end = time.time() logger.info(f"⏱️ Text embedding E2E time: {e2e_end - e2e_start:.6f}s") continue node_ids = request if isinstance(request, list) else [] logger.info(f"ZMQ received {len(node_ids)} node IDs") texts = [] 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: raise RuntimeError(f"FATAL: Passage with ID {nid} not found") except Exception as e: logger.error(f"Exception looking up passage ID {nid}: {e}") raise embeddings = compute_embeddings(texts, model_name, mode=embedding_mode) logger.info( f"Computed embeddings for {len(texts)} texts, shape: {embeddings.shape}" ) if np.isnan(embeddings).any() or np.isinf(embeddings).any(): logger.error( f"NaN or Inf detected in embeddings! Requested IDs: {node_ids[:5]}..." ) raise AssertionError() hidden_contiguous_f32 = np.ascontiguousarray(embeddings, dtype=np.float32) response_payload = [ list(hidden_contiguous_f32.shape), hidden_contiguous_f32.flatten().tolist(), ] response_bytes = msgpack.packb(response_payload, use_single_float=True) rep_socket.send(response_bytes) e2e_end = time.time() logger.info(f"⏱️ ZMQ E2E time: {e2e_end - e2e_start:.6f}s") except zmq.Again: # Timeout - check shutdown_event and continue continue except Exception as e: if not shutdown_event.is_set(): logger.error(f"Error in ZMQ server loop: {e}") try: rep_socket.send(msgpack.packb([[], []])) except Exception: pass else: logger.info("Shutdown in progress, ignoring ZMQ error") break finally: try: rep_socket.close(0) except Exception: pass try: context.term() except Exception: pass logger.info("ZMQ server thread exiting gracefully") # Add shutdown coordination shutdown_event = threading.Event() def shutdown_zmq_server(): """Gracefully shutdown ZMQ server.""" logger.info("Initiating graceful shutdown...") shutdown_event.set() if zmq_thread.is_alive(): logger.info("Waiting for ZMQ thread to finish...") zmq_thread.join(timeout=5) if zmq_thread.is_alive(): logger.warning("ZMQ thread did not finish in time") # Clean up ZMQ resources try: # Note: socket and context are cleaned up by thread exit logger.info("ZMQ resources cleaned up") except Exception as e: logger.warning(f"Error cleaning ZMQ resources: {e}") # Clean up other resources try: import gc gc.collect() logger.info("Additional resources cleaned up") except Exception as e: logger.warning(f"Error cleaning additional resources: {e}") logger.info("Graceful shutdown completed") sys.exit(0) # Register signal handlers within this function scope import signal def signal_handler(sig, frame): logger.info(f"Received signal {sig}, shutting down gracefully...") shutdown_zmq_server() signal.signal(signal.SIGTERM, signal_handler) signal.signal(signal.SIGINT, signal_handler) # Pass shutdown_event to ZMQ thread zmq_thread = threading.Thread( target=lambda: zmq_server_thread_with_shutdown(shutdown_event), daemon=False, # Not daemon - we want to wait for it ) zmq_thread.start() logger.info(f"Started HNSW ZMQ server thread on port {zmq_port}") # Keep the main thread alive try: while not shutdown_event.is_set(): time.sleep(0.1) # Check shutdown more frequently except KeyboardInterrupt: logger.info("HNSW Server shutting down...") shutdown_zmq_server() return # If we reach here, shutdown was triggered by signal logger.info("Main loop exited, process should be shutting down") if __name__ == "__main__": import sys # Signal handlers are now registered within create_hnsw_embedding_server parser = argparse.ArgumentParser(description="HNSW 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="JSON file containing passage ID to text mapping", ) parser.add_argument( "--model-name", type=str, default="sentence-transformers/all-mpnet-base-v2", help="Embedding model name", ) parser.add_argument( "--distance-metric", type=str, default="mips", help="Distance metric to use" ) parser.add_argument( "--embedding-mode", type=str, default="sentence-transformers", choices=["sentence-transformers", "openai", "mlx", "ollama"], help="Embedding backend mode", ) args = parser.parse_args() # Create and start the HNSW embedding server create_hnsw_embedding_server( passages_file=args.passages_file, zmq_port=args.zmq_port, model_name=args.model_name, distance_metric=args.distance_metric, embedding_mode=args.embedding_mode, )