fix: Restore embedding-mode parameter to all examples

- All examples now have --embedding-mode parameter (unified interface benefit)
- Default is 'sentence-transformers' (consistent with original behavior)
- Users can now use OpenAI or MLX embeddings with any data source
- Maintains functional equivalence with original scripts

examples/base_rag_example.py

@@ -23,12 +23,10 @@ class BaseRAGExample(ABC):
         name: str,
         description: str,
         default_index_name: str,
-        include_embedding_mode: bool = True,
     ):
         self.name = name
         self.description = description
         self.default_index_name = default_index_name
-        self.include_embedding_mode = include_embedding_mode
         self.parser = self._create_parser()
 
     def _create_parser(self) -> argparse.ArgumentParser:
@@ -73,14 +71,13 @@ class BaseRAGExample(ABC):
             default=embedding_model_default,
             help=f"Embedding model to use (default: {embedding_model_default})",
         )
-        if self.include_embedding_mode:
-            embedding_group.add_argument(
-                "--embedding-mode",
-                type=str,
-                default="sentence-transformers",
-                choices=["sentence-transformers", "openai", "mlx"],
-                help="Embedding backend mode (default: sentence-transformers)",
-            )
+        embedding_group.add_argument(
+            "--embedding-mode",
+            type=str,
+            default="sentence-transformers",
+            choices=["sentence-transformers", "openai", "mlx"],
+            help="Embedding backend mode (default: sentence-transformers)",
+        )
 
         # LLM parameters
         llm_group = parser.add_argument_group("LLM Parameters")
@@ -152,22 +149,16 @@ class BaseRAGExample(ABC):
         print(f"\n[Building Index] Creating {self.name} index...")
         print(f"Total text chunks: {len(texts)}")
 
-        # Build kwargs for LeannBuilder
-        builder_kwargs = {
-            "backend_name": "hnsw",
-            "embedding_model": args.embedding_model,
-            "graph_degree": 32,
-            "complexity": 64,
-            "is_compact": True,
-            "is_recompute": True,
-            "num_threads": 1,  # Force single-threaded mode
-        }
-
-        # Only add embedding_mode if it's not suppressed (for compatibility)
-        if hasattr(args, "embedding_mode") and args.embedding_mode is not None:
-            builder_kwargs["embedding_mode"] = args.embedding_mode
-
-        builder = LeannBuilder(**builder_kwargs)
+        builder = LeannBuilder(
+            backend_name="hnsw",
+            embedding_model=args.embedding_model,
+            embedding_mode=args.embedding_mode,
+            graph_degree=32,
+            complexity=64,
+            is_compact=True,
+            is_recompute=True,
+            num_threads=1,  # Force single-threaded mode
+        )
 
         # Add texts in batches for better progress tracking
         batch_size = 1000

examples/browser_rag.py

@@ -21,7 +21,7 @@ class BrowserRAG(BaseRAGExample):
         super().__init__(
             name="Browser History",
             description="Process and query Chrome browser history with LEANN",
-            default_index_name="google_history_index",  # Match original: "./google_history_index",
+            default_index_name="google_history_index",
         )
 
     def _add_specific_arguments(self, parser):

examples/document_rag.py

@@ -20,7 +20,7 @@ class DocumentRAG(BaseRAGExample):
         super().__init__(
             name="Document",
             description="Process and query documents (PDF, TXT, MD, etc.) with LEANN",
-            default_index_name="test_doc_files",  # Match original main_cli_example.py default
+            default_index_name="test_doc_files",
         )
 
     def _add_specific_arguments(self, parser):

examples/email_rag.py

@@ -20,8 +20,7 @@ class EmailRAG(BaseRAGExample):
         super().__init__(
             name="Email",
             description="Process and query Apple Mail emails with LEANN",
-            default_index_name="mail_index",  # Match original: "./mail_index"
-            include_embedding_mode=False,  # Original mail_reader_leann.py doesn't have embedding_mode
+            default_index_name="mail_index",
         )
 
     def _add_specific_arguments(self, parser):

examples/wechat_rag.py

@@ -25,8 +25,7 @@ class WeChatRAG(BaseRAGExample):
         super().__init__(
             name="WeChat History",
             description="Process and query WeChat chat history with LEANN",
-            default_index_name="wechat_history_magic_test_11Debug_new",  # Match original default
-            include_embedding_mode=False,  # Original wechat_history_reader_leann.py doesn't have embedding_mode
+            default_index_name="wechat_history_magic_test_11Debug_new",
         )
 
     def _add_specific_arguments(self, parser):

test/sanity_checks/debug_zmq_issue.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 """
-Debug script to test ZMQ communication with the exact same setup as main_cli_example.py
+Debug script to test ZMQ communication with embedding models.
 """
 
 import sys
@@ -13,9 +13,9 @@ from leann_backend_diskann import embedding_pb2
 
 
 def test_zmq_with_same_model():
-    print("=== Testing ZMQ with same model as main_cli_example.py ===")
+    print("=== Testing ZMQ with embedding model ===")
 
-    # Test the exact same model that main_cli_example.py uses
+    # Test with a common embedding model
     model_name = "sentence-transformers/all-mpnet-base-v2"
 
     # Start server with the same model

tests/test_document_rag.py

@@ -1,5 +1,5 @@
 """
-Test document_rag (formerly main_cli_example) functionality using pytest.
+Test document_rag functionality using pytest.
 """
 
 import os