Feature/optimize ollama batching (#152)

* feat: add metadata output to search results

- Add --show-metadata flag to display file paths in search results
- Preserve document metadata (file_path, file_name, timestamps) during chunking
- Update MCP tool schema to support show_metadata parameter
- Enhance CLI search output to display metadata when requested
- Fix pre-existing bug: args.backend -> args.backend_name

Resolves yichuan-w/LEANN#144

* fix: resolve ZMQ linking issues in Python extension

- Use pkg_check_modules IMPORTED_TARGET to create PkgConfig::ZMQ
- Set PKG_CONFIG_PATH to prioritize ARM64 Homebrew on Apple Silicon
- Override macOS -undefined dynamic_lookup to force proper symbol resolution
- Use PUBLIC linkage for ZMQ in faiss library for transitive linking
- Mark cppzmq includes as SYSTEM to suppress warnings

Fixes editable install ZMQ symbol errors while maintaining compatibility
across Linux, macOS Intel, and macOS ARM64 platforms.

* style: apply ruff formatting

* chore: update faiss submodule to use ww2283 fork

Use ww2283/faiss fork with fix/zmq-linking branch to resolve CI checkout
failures. The ZMQ linking fixes are not yet merged upstream.

* feat: implement true batch processing for Ollama embeddings

Migrate from deprecated /api/embeddings to modern /api/embed endpoint
which supports batch inputs. This reduces HTTP overhead by sending
32 texts per request instead of making individual API calls.

Changes:
- Update endpoint from /api/embeddings to /api/embed
- Change parameter from 'prompt' (single) to 'input' (array)
- Update response parsing for batch embeddings array
- Increase timeout to 60s for batch processing
- Improve error handling for batch requests

Performance:
- Reduces API calls by 32x (batch size)
- Eliminates HTTP connection overhead per text
- Note: Ollama still processes batch items sequentially internally

Related: #151

* fall back to original faiss as i merge the PR

---------

Co-authored-by: yichuan520030910320 <yichuan_wang@berkeley.edu>

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

@@ -255,6 +255,11 @@ Examples:
             action="store_true",
             help="Non-interactive mode: automatically select index without prompting",
         )
+        search_parser.add_argument(
+            "--show-metadata",
+            action="store_true",
+            help="Display file paths and metadata in search results",
+        )
 
         # Ask command
         ask_parser = subparsers.add_parser("ask", help="Ask questions")
@@ -1263,7 +1268,7 @@ Examples:
                 from .chunking_utils import create_text_chunks
 
                 # Use enhanced chunking with AST support
-                all_texts = create_text_chunks(
+                chunk_texts = create_text_chunks(
                     documents,
                     chunk_size=self.node_parser.chunk_size,
                     chunk_overlap=self.node_parser.chunk_overlap,
@@ -1274,6 +1279,14 @@ Examples:
                     ast_fallback_traditional=getattr(args, "ast_fallback_traditional", True),
                 )
 
+                # Note: AST chunking currently returns plain text chunks without metadata
+                # We preserve basic file info by associating chunks with their source documents
+                # For better metadata preservation, documents list order should be maintained
+                for chunk_text in chunk_texts:
+                    # TODO: Enhance create_text_chunks to return metadata alongside text
+                    # For now, we store chunks with empty metadata
+                    all_texts.append({"text": chunk_text, "metadata": {}})
+
             except ImportError as e:
                 print(
                     f"⚠️  AST chunking utilities not available in package ({e}), falling back to traditional chunking"
@@ -1285,17 +1298,27 @@ Examples:
             for doc in tqdm(documents, desc="Chunking documents", unit="doc"):
                 # Check if this is a code file based on source path
                 source_path = doc.metadata.get("source", "")
+                file_path = doc.metadata.get("file_path", "")
                 is_code_file = any(source_path.endswith(ext) for ext in code_file_exts)
 
+                # Extract metadata to preserve with chunks
+                chunk_metadata = {
+                    "file_path": file_path or source_path,
+                    "file_name": doc.metadata.get("file_name", ""),
+                }
+
+                # Add optional metadata if available
+                if "creation_date" in doc.metadata:
+                    chunk_metadata["creation_date"] = doc.metadata["creation_date"]
+                if "last_modified_date" in doc.metadata:
+                    chunk_metadata["last_modified_date"] = doc.metadata["last_modified_date"]
+
                 # Use appropriate parser based on file type
                 parser = self.code_parser if is_code_file else self.node_parser
                 nodes = parser.get_nodes_from_documents([doc])
 
                 for node in nodes:
-                    text_with_source = (
-                        "Chunk source:" + source_path + "\n" + node.get_content().replace("\n", " ")
-                    )
-                    all_texts.append(text_with_source)
+                    all_texts.append({"text": node.get_content(), "metadata": chunk_metadata})
 
         print(f"Loaded {len(documents)} documents, {len(all_texts)} chunks")
         return all_texts
@@ -1370,7 +1393,7 @@ Examples:
 
         index_dir.mkdir(parents=True, exist_ok=True)
 
-        print(f"Building index '{index_name}' with {args.backend} backend...")
+        print(f"Building index '{index_name}' with {args.backend_name} backend...")
 
         embedding_options: dict[str, Any] = {}
         if args.embedding_mode == "ollama":
@@ -1382,7 +1405,7 @@ Examples:
                 embedding_options["api_key"] = resolved_embedding_key
 
         builder = LeannBuilder(
-            backend_name=args.backend,
+            backend_name=args.backend_name,
             embedding_model=args.embedding_model,
             embedding_mode=args.embedding_mode,
             embedding_options=embedding_options or None,
@@ -1393,10 +1416,8 @@ Examples:
             num_threads=args.num_threads,
         )
 
-        for chunk_text_with_source in all_texts:
-            chunk_source = chunk_text_with_source.split("\n")[0].split(":")[1]
-            chunk_text = chunk_text_with_source.split("\n")[1]
-            builder.add_text(chunk_text, {"source": chunk_source})
+        for chunk in all_texts:
+            builder.add_text(chunk["text"], metadata=chunk["metadata"])
 
         builder.build_index(index_path)
         print(f"Index built at {index_path}")
@@ -1517,6 +1538,23 @@ Examples:
         print(f"Search results for '{query}' (top {len(results)}):")
         for i, result in enumerate(results, 1):
             print(f"{i}. Score: {result.score:.3f}")
+
+            # Display metadata if flag is set
+            if args.show_metadata and result.metadata:
+                file_path = result.metadata.get("file_path", "")
+                if file_path:
+                    print(f"   📄 File: {file_path}")
+
+                file_name = result.metadata.get("file_name", "")
+                if file_name and file_name != file_path:
+                    print(f"   📝 Name: {file_name}")
+
+                # Show timestamps if available
+                if "creation_date" in result.metadata:
+                    print(f"   🕐 Created: {result.metadata['creation_date']}")
+                if "last_modified_date" in result.metadata:
+                    print(f"   🕑 Modified: {result.metadata['last_modified_date']}")
+
             print(f"   {result.text[:200]}...")
             print(f"   Source: {result.metadata.get('source', '')}")
             print()