add gpu chunk embedd and add complexity in hnsw

examples/main_cli_example.py

@@ -29,7 +29,7 @@ node_parser = DoclingNodeParser(
 )
 print("Loading documents...")
 documents = SimpleDirectoryReader(
-    "examples/pangu", 
+    "examples/data", 
     recursive=True, 
     file_extractor=file_extractor,
     encoding="utf-8",
@@ -42,7 +42,7 @@ for doc in documents:
     for node in nodes:
         all_texts.append(node.get_content())
 
-INDEX_DIR = Path("./test_pdf_index_pangu")
+INDEX_DIR = Path("./test_pdf_index_pangu_hnsw")
 INDEX_PATH = str(INDEX_DIR / "pdf_documents.leann")
 
 if not INDEX_DIR.exists():
@@ -52,7 +52,7 @@ if not INDEX_DIR.exists():
 
     # CSR compact mode with recompute
     builder = LeannBuilder(
-        backend_name="hnsw",
+        backend_name="diskann",
         embedding_model="facebook/contriever",
         graph_degree=32, 
         complexity=64,

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

@@ -330,7 +330,7 @@ class HNSWSearcher(LeannBackendSearcherInterface):
         """Search using HNSW index with optional recompute functionality"""
         from . import faiss
         
-        ef = kwargs.get("ef", 200)
+        ef = kwargs.get("complexity", 200)
         
         if self.is_pruned:
             print(f"INFO: Index is pruned - ensuring embedding server is running for recompute.")

packages/leann-core/src/leann/api.py

@@ -1,3 +1,4 @@
+import torch
 from .registry import BACKEND_REGISTRY
 from .interface import LeannBackendFactoryInterface
 from typing import List, Dict, Any, Optional
@@ -34,6 +35,12 @@ def _compute_embeddings(chunks: List[str], model_name: str) -> np.ndarray:
         model = SentenceTransformer(model_name)
         model = model.half()
         print(f"INFO: Computing embeddings for {len(chunks)} chunks using SentenceTransformer model '{model_name}'...")
+        # use acclerater GPU or MAC GPU
+        import torch
+        if torch.cuda.is_available():
+            model = model.to("cuda")
+        elif torch.backends.mps.is_available():
+            model = model.to("mps")
         embeddings = model.encode(chunks, show_progress_bar=True)
     
     return np.asarray(embeddings, dtype=np.float32)