feat: 引入流式生成器以优化内存使用，改进嵌入生成过程

Author: catlog22

codex-lens/src/codexlens/cli/embedding_manager.py

@@ -4,8 +4,9 @@
 import logging
 import sqlite3
 import time
+from itertools import islice
 from pathlib import Path
-from typing import Dict, List, Optional
+from typing import Dict, Generator, List, Optional, Tuple
 
 try:
     from codexlens.semantic import SEMANTIC_AVAILABLE
@@ -23,6 +24,61 @@
 EMBEDDING_BATCH_SIZE = 64  # Increased from 8 for better performance
 
 
+def _generate_chunks_from_cursor(
+    cursor,
+    chunker,
+    path_column: str,
+    file_batch_size: int,
+    failed_files: List[Tuple[str, str]],
+) -> Generator[Tuple, None, Tuple[int, int]]:
+    """Generator that yields chunks from database cursor in a streaming fashion.
+
+    This avoids loading all chunks into memory at once, significantly reducing
+    peak memory usage for large codebases.
+
+    Args:
+        cursor: SQLite cursor with file data
+        chunker: Chunker instance for splitting files
+        path_column: Column name for file path
+        file_batch_size: Number of files to fetch at a time
+        failed_files: List to append failed files to
+
+    Yields:
+        (chunk, file_path) tuples
+
+    Returns:
+        (total_files_processed, batch_count) after iteration completes
+    """
+    total_files = 0
+    batch_count = 0
+
+    while True:
+        file_batch = cursor.fetchmany(file_batch_size)
+        if not file_batch:
+            break
+
+        batch_count += 1
+
+        for file_row in file_batch:
+            file_path = file_row[path_column]
+            content = file_row["content"]
+            language = file_row["language"] or "python"
+
+            try:
+                chunks = chunker.chunk_sliding_window(
+                    content,
+                    file_path=file_path,
+                    language=language
+                )
+                if chunks:
+                    total_files += 1
+                    for chunk in chunks:
+                        yield (chunk, file_path)
+            except Exception as e:
+                logger.error(f"Failed to chunk {file_path}: {e}")
+                failed_files.append((file_path, str(e)))
+
+
 def _get_path_column(conn: sqlite3.Connection) -> str:
     """Detect whether files table uses 'path' or 'full_path' column.
 
@@ -199,7 +255,9 @@ def generate_embeddings(
     try:
         # Initialize embedder (singleton, reused throughout the function)
         embedder = get_embedder(profile=model_profile)
-        chunker = Chunker(config=ChunkConfig(max_chunk_size=chunk_size))
+        # skip_token_count=True: Use fast estimation (len/4) instead of expensive tiktoken
+        # This significantly reduces CPU usage with minimal impact on metadata accuracy
+        chunker = Chunker(config=ChunkConfig(max_chunk_size=chunk_size, skip_token_count=True))
 
         if progress_callback:
             progress_callback(f"Using model: {embedder.model_name} ({embedder.embedding_dim} dimensions)")
@@ -238,78 +296,50 @@ def generate_embeddings(
                         progress_callback(f"Processing {total_files} files for embeddings in batches of {FILE_BATCH_SIZE}...")
 
                     cursor = conn.execute(f"SELECT {path_column}, content, language FROM files")
+
+                    # --- STREAMING GENERATOR APPROACH ---
+                    # Instead of accumulating all chunks from 100 files, we use a generator
+                    # that yields chunks on-demand, keeping memory usage low and constant.
+                    chunk_generator = _generate_chunks_from_cursor(
+                        cursor, chunker, path_column, FILE_BATCH_SIZE, failed_files
+                    )
+
                     batch_number = 0
+                    files_seen = set()
 
                     while True:
-                        # Fetch a batch of files (streaming, not fetchall)
-                        file_batch = cursor.fetchmany(FILE_BATCH_SIZE)
-                        if not file_batch:
+                        # Get a small batch of chunks from the generator (EMBEDDING_BATCH_SIZE at a time)
+                        chunk_batch = list(islice(chunk_generator, EMBEDDING_BATCH_SIZE))
+                        if not chunk_batch:
                             break
 
                         batch_number += 1
-                        batch_chunks_with_paths = []
-                        files_in_batch_with_chunks = set()
-
-                        # Step 1: Chunking for the current file batch
-                        for file_row in file_batch:
-                            file_path = file_row[path_column]
-                            content = file_row["content"]
-                            language = file_row["language"] or "python"
-
-                            try:
-                                chunks = chunker.chunk_sliding_window(
-                                    content,
-                                    file_path=file_path,
-                                    language=language
-                                )
-                                if chunks:
-                                    for chunk in chunks:
-                                        batch_chunks_with_paths.append((chunk, file_path))
-                                    files_in_batch_with_chunks.add(file_path)
-                            except Exception as e:
-                                logger.error(f"Failed to chunk {file_path}: {e}")
-                                failed_files.append((file_path, str(e)))
-
-                        if not batch_chunks_with_paths:
-                            continue
 
-                        batch_chunk_count = len(batch_chunks_with_paths)
-                        if progress_callback:
-                            progress_callback(f"  Batch {batch_number}: {len(file_batch)} files, {batch_chunk_count} chunks")
+                        # Track unique files for progress
+                        for _, file_path in chunk_batch:
+                            files_seen.add(file_path)
 
-                        # Step 2: Generate embeddings for this batch (use memory-efficient numpy method)
-                        batch_embeddings = []
+                        # Generate embeddings directly to numpy (no tolist() conversion)
                         try:
-                            for i in range(0, batch_chunk_count, EMBEDDING_BATCH_SIZE):
-                                batch_end = min(i + EMBEDDING_BATCH_SIZE, batch_chunk_count)
-                                batch_contents = [chunk.content for chunk, _ in batch_chunks_with_paths[i:batch_end]]
-                                # Use embed_to_numpy() to avoid unnecessary list conversion
-                                embeddings_numpy = embedder.embed_to_numpy(batch_contents)
-                                # Convert to list only for storage (VectorStore expects list format)
-                                embeddings = [emb.tolist() for emb in embeddings_numpy]
-                                batch_embeddings.extend(embeddings)
-                                # Explicit cleanup of intermediate data
-                                del batch_contents, embeddings_numpy
-                        except Exception as e:
-                            logger.error(f"Failed to generate embeddings for batch {batch_number}: {str(e)}")
-                            failed_files.extend([(file_row[path_column], str(e)) for file_row in file_batch])
-                            continue
+                            batch_contents = [chunk.content for chunk, _ in chunk_batch]
+                            embeddings_numpy = embedder.embed_to_numpy(batch_contents)
 
-                        # Step 3: Assign embeddings to chunks
-                        for (chunk, _), embedding in zip(batch_chunks_with_paths, batch_embeddings):
-                            chunk.embedding = embedding
+                            # Use add_chunks_batch_numpy to avoid numpy->list->numpy roundtrip
+                            vector_store.add_chunks_batch_numpy(chunk_batch, embeddings_numpy)
 
-                        # Step 4: Store this batch to database (ANN update deferred in bulk_insert mode)
-                        try:
-                            vector_store.add_chunks_batch(batch_chunks_with_paths)
-                            total_chunks_created += batch_chunk_count
-                            total_files_processed += len(files_in_batch_with_chunks)
-                        except Exception as e:
-                            logger.error(f"Failed to store batch {batch_number}: {str(e)}")
-                            failed_files.extend([(file_row[path_column], str(e)) for file_row in file_batch])
+                            total_chunks_created += len(chunk_batch)
+                            total_files_processed = len(files_seen)
+
+                            if progress_callback and batch_number % 10 == 0:
+                                progress_callback(f"  Batch {batch_number}: {total_chunks_created} chunks, {total_files_processed} files")
 
-                        # Release batch references (let Python GC handle cleanup naturally)
-                        del batch_chunks_with_paths, batch_embeddings
+                            # Cleanup intermediate data
+                            del batch_contents, embeddings_numpy, chunk_batch
+
+                        except Exception as e:
+                            logger.error(f"Failed to process embedding batch {batch_number}: {str(e)}")
+                            # Continue to next batch instead of failing entirely
+                            continue
 
                 # Notify before ANN index finalization (happens when bulk_insert context exits)
                 if progress_callback: