chore(logging): surface recompute and batching params; enable INFO logging in benchmark

benchmarks/run_evaluation.py

@@ -6,6 +6,8 @@ results and the golden standard results, making the comparison robust to ID chan
 """
 
 import argparse
+import logging
+import os
 import json
 import sys
 import time
@@ -14,6 +16,11 @@ from pathlib import Path
 import numpy as np
 from leann.api import LeannBuilder, LeannChat, LeannSearcher
 
+# Configure logging level (default INFO; override with LEANN_LOG_LEVEL)
+_log_level_str = os.getenv("LEANN_LOG_LEVEL", "INFO").upper()
+_log_level = getattr(logging, _log_level_str, logging.INFO)
+logging.basicConfig(level=_log_level, format="%(asctime)s %(levelname)s %(name)s: %(message)s")
+
 
 def download_data_if_needed(data_root: Path, download_embeddings: bool = False):
     """Checks if the data directory exists, and if not, downloads it from HF Hub."""

packages/leann-backend-hnsw/leann_backend_hnsw/hnsw_backend.py

@@ -233,6 +233,18 @@ class HNSWSearcher(BaseSearcher):
         # HNSW-specific batch processing parameter
         params.batch_size = batch_size
 
+        # Log recompute mode and batching for visibility
+        logger.info(
+            "HNSW search: recompute=%s, zmq_port=%s, batch_size=%d, efSearch=%d, beam=%d, prune_ratio=%.3f, strategy=%s",
+            bool(recompute_embeddings),
+            str(zmq_port),
+            int(batch_size),
+            int(complexity),
+            int(beam_width),
+            float(prune_ratio),
+            pruning_strategy,
+        )
+
         batch_size_query = query.shape[0]
         distances = np.empty((batch_size_query, top_k), dtype=np.float32)
         labels = np.empty((batch_size_query, top_k), dtype=np.int64)