fall back to original faiss as i merge the PR

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

packages/leann-backend-hnsw/CMakeLists.txt

@@ -29,12 +29,25 @@ if(APPLE)
     set(CMAKE_OSX_DEPLOYMENT_TARGET "11.0" CACHE STRING "Minimum macOS version")
 endif()
 
-# Use system ZeroMQ instead of building from source
+# Find ZMQ using pkg-config with IMPORTED_TARGET for automatic target creation
 find_package(PkgConfig REQUIRED)
-pkg_check_modules(ZMQ REQUIRED libzmq)
+
+# On ARM64 macOS, ensure pkg-config finds ARM64 Homebrew packages first
+if(APPLE AND CMAKE_SYSTEM_PROCESSOR MATCHES "aarch64|arm64")
+    set(ENV{PKG_CONFIG_PATH} "/opt/homebrew/lib/pkgconfig:/opt/homebrew/share/pkgconfig:$ENV{PKG_CONFIG_PATH}")
+endif()
+
+pkg_check_modules(ZMQ REQUIRED IMPORTED_TARGET libzmq)
+
+# This creates PkgConfig::ZMQ target automatically with correct properties
+if(TARGET PkgConfig::ZMQ)
+    message(STATUS "Found and configured ZMQ target: PkgConfig::ZMQ")
+else()
+    message(FATAL_ERROR "pkg_check_modules did not create IMPORTED target for ZMQ.")
+endif()
 
 # Add cppzmq headers
-include_directories(third_party/cppzmq)
+include_directories(SYSTEM third_party/cppzmq)
 
 # Configure msgpack-c - disable boost dependency
 set(MSGPACK_USE_BOOST OFF CACHE BOOL "" FORCE)

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

@@ -1236,6 +1236,17 @@ class LeannChat:
             "Please provide the best answer you can based on this context and your knowledge."
         )
 
+        print("The context provided to the LLM is:")
+        print(f"{'Relevance':<10} | {'Chunk id':<10} | {'Content':<60} | {'Source':<80}")
+        print("-" * 150)
+        for r in results:
+            chunk_relevance = f"{r.score:.3f}"
+            chunk_id = r.id
+            chunk_content = r.text[:60]
+            chunk_source = r.metadata.get("source", "")[:80]
+            print(
+                f"{chunk_relevance:<10} | {chunk_id:<10} | {chunk_content:<60} | {chunk_source:<80}"
+            )
         ask_time = time.time()
         ans = self.llm.ask(prompt, **llm_kwargs)
         ask_time = time.time() - ask_time

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

@@ -834,6 +834,11 @@ class OpenAIChat(LLMInterface):
 
         try:
             response = self.client.chat.completions.create(**params)
+            print(
+                f"Total tokens = {response.usage.total_tokens}, prompt tokens = {response.usage.prompt_tokens}, completion tokens = {response.usage.completion_tokens}"
+            )
+            if response.choices[0].finish_reason == "length":
+                print("The query is exceeding the maximum allowed number of tokens")
             return response.choices[0].message.content.strip()
         except Exception as e:
             logger.error(f"Error communicating with OpenAI: {e}")

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

@@ -1,5 +1,6 @@
 import argparse
 import asyncio
+import time
 from pathlib import Path
 from typing import Any, Optional, Union
 
@@ -106,7 +107,7 @@ Examples:
             help="Documents directories and/or files (default: current directory)",
         )
         build_parser.add_argument(
-            "--backend",
+            "--backend-name",
             type=str,
             default="hnsw",
             choices=["hnsw", "diskann"],
@@ -254,6 +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")
@@ -1186,6 +1192,7 @@ Examples:
                 for doc in other_docs:
                     file_path = doc.metadata.get("file_path", "")
                     if file_filter(file_path):
+                        doc.metadata["source"] = file_path
                         filtered_docs.append(doc)
 
                 documents.extend(filtered_docs)
@@ -1261,7 +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,
@@ -1272,6 +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"
@@ -1283,14 +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:
-                    all_texts.append(node.get_content())
+                    all_texts.append({"text": node.get_content(), "metadata": chunk_metadata})
 
         print(f"Loaded {len(documents)} documents, {len(all_texts)} chunks")
         return all_texts
@@ -1365,7 +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":
@@ -1377,7 +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,
@@ -1388,8 +1416,8 @@ Examples:
             num_threads=args.num_threads,
         )
 
-        for chunk_text in all_texts:
-            builder.add_text(chunk_text)
+        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}")
@@ -1510,7 +1538,25 @@ 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()
 
     async def ask_questions(self, args):
@@ -1542,6 +1588,7 @@ Examples:
             llm_kwargs["thinking_budget"] = args.thinking_budget
 
         def _ask_once(prompt: str) -> None:
+            query_start_time = time.time()
             response = chat.ask(
                 prompt,
                 top_k=args.top_k,
@@ -1552,7 +1599,9 @@ Examples:
                 pruning_strategy=args.pruning_strategy,
                 llm_kwargs=llm_kwargs,
             )
+            query_completion_time = time.time() - query_start_time
             print(f"LEANN: {response}")
+            print(f"The query took {query_completion_time:.3f} seconds to finish")
 
         initial_query = (args.query or "").strip()
 

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

@@ -574,9 +574,10 @@ def compute_embeddings_ollama(
     host: Optional[str] = None,
 ) -> np.ndarray:
     """
-    Compute embeddings using Ollama API with simplified batch processing.
+    Compute embeddings using Ollama API with true batch processing.
 
-    Uses batch size of 32 for MPS/CPU and 128 for CUDA to optimize performance.
+    Uses the /api/embed endpoint which supports batch inputs.
+    Batch size: 32 for MPS/CPU, 128 for CUDA to optimize performance.
 
     Args:
         texts: List of texts to compute embeddings for
@@ -681,11 +682,11 @@ def compute_embeddings_ollama(
             logger.info(f"Resolved model name '{model_name}' to '{resolved_model_name}'")
         model_name = resolved_model_name
 
-        # Verify the model supports embeddings by testing it
+        # Verify the model supports embeddings by testing it with /api/embed
         try:
             test_response = requests.post(
-                f"{resolved_host}/api/embeddings",
-                json={"model": model_name, "prompt": "test"},
+                f"{resolved_host}/api/embed",
+                json={"model": model_name, "input": "test"},
                 timeout=10,
             )
             if test_response.status_code != 200:
@@ -717,56 +718,55 @@ def compute_embeddings_ollama(
         # If torch is not available, use conservative batch size
         batch_size = 32
 
-    logger.info(f"Using batch size: {batch_size}")
+    logger.info(f"Using batch size: {batch_size} for true batch processing")
 
     def get_batch_embeddings(batch_texts):
-        """Get embeddings for a batch of texts."""
-        all_embeddings = []
-        failed_indices = []
+        """Get embeddings for a batch of texts using /api/embed endpoint."""
+        max_retries = 3
+        retry_count = 0
 
-        for i, text in enumerate(batch_texts):
-            max_retries = 3
-            retry_count = 0
+        # Truncate very long texts to avoid API issues
+        truncated_texts = [text[:8000] if len(text) > 8000 else text for text in batch_texts]
 
-            # Truncate very long texts to avoid API issues
-            truncated_text = text[:8000] if len(text) > 8000 else text
-            while retry_count < max_retries:
-                try:
-                    response = requests.post(
-                        f"{resolved_host}/api/embeddings",
-                        json={"model": model_name, "prompt": truncated_text},
-                        timeout=30,
+        while retry_count < max_retries:
+            try:
+                # Use /api/embed endpoint with "input" parameter for batch processing
+                response = requests.post(
+                    f"{resolved_host}/api/embed",
+                    json={"model": model_name, "input": truncated_texts},
+                    timeout=60,  # Increased timeout for batch processing
+                )
+                response.raise_for_status()
+
+                result = response.json()
+                batch_embeddings = result.get("embeddings")
+
+                if batch_embeddings is None:
+                    raise ValueError("No embeddings returned from API")
+
+                if not isinstance(batch_embeddings, list):
+                    raise ValueError(f"Invalid embeddings format: {type(batch_embeddings)}")
+
+                if len(batch_embeddings) != len(batch_texts):
+                    raise ValueError(
+                        f"Mismatch: requested {len(batch_texts)} embeddings, got {len(batch_embeddings)}"
                     )
-                    response.raise_for_status()
 
-                    result = response.json()
-                    embedding = result.get("embedding")
+                return batch_embeddings, []
 
-                    if embedding is None:
-                        raise ValueError(f"No embedding returned for text {i}")
+            except requests.exceptions.Timeout:
+                retry_count += 1
+                if retry_count >= max_retries:
+                    logger.warning(f"Timeout for batch after {max_retries} retries")
+                    return None, list(range(len(batch_texts)))
 
-                    if not isinstance(embedding, list) or len(embedding) == 0:
-                        raise ValueError(f"Invalid embedding format for text {i}")
+            except Exception as e:
+                retry_count += 1
+                if retry_count >= max_retries:
+                    logger.error(f"Failed to get embeddings for batch: {e}")
+                    return None, list(range(len(batch_texts)))
 
-                    all_embeddings.append(embedding)
-                    break
-
-                except requests.exceptions.Timeout:
-                    retry_count += 1
-                    if retry_count >= max_retries:
-                        logger.warning(f"Timeout for text {i} after {max_retries} retries")
-                        failed_indices.append(i)
-                        all_embeddings.append(None)
-                        break
-
-                except Exception as e:
-                    retry_count += 1
-                    if retry_count >= max_retries:
-                        logger.error(f"Failed to get embedding for text {i}: {e}")
-                        failed_indices.append(i)
-                        all_embeddings.append(None)
-                        break
-        return all_embeddings, failed_indices
+        return None, list(range(len(batch_texts)))
 
     # Process texts in batches
     all_embeddings = []
@@ -784,7 +784,7 @@ def compute_embeddings_ollama(
     num_batches = (len(texts) + batch_size - 1) // batch_size
 
     if show_progress:
-        batch_iterator = tqdm(range(num_batches), desc="Computing Ollama embeddings")
+        batch_iterator = tqdm(range(num_batches), desc="Computing Ollama embeddings (batched)")
     else:
         batch_iterator = range(num_batches)
 
@@ -795,10 +795,14 @@ def compute_embeddings_ollama(
 
         batch_embeddings, batch_failed = get_batch_embeddings(batch_texts)
 
-        # Adjust failed indices to global indices
-        global_failed = [start_idx + idx for idx in batch_failed]
-        all_failed_indices.extend(global_failed)
-        all_embeddings.extend(batch_embeddings)
+        if batch_embeddings is not None:
+            all_embeddings.extend(batch_embeddings)
+        else:
+            # Entire batch failed, add None placeholders
+            all_embeddings.extend([None] * len(batch_texts))
+            # Adjust failed indices to global indices
+            global_failed = [start_idx + idx for idx in batch_failed]
+            all_failed_indices.extend(global_failed)
 
     # Handle failed embeddings
     if all_failed_indices:

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

@@ -60,6 +60,11 @@ def handle_request(request):
                                     "maximum": 128,
                                     "description": "Search complexity level. Use 16-32 for fast searches (recommended), 64+ for higher precision when needed.",
                                 },
+                                "show_metadata": {
+                                    "type": "boolean",
+                                    "default": False,
+                                    "description": "Include file paths and metadata in search results. Useful for understanding which files contain the results.",
+                                },
                             },
                             "required": ["index_name", "query"],
                         },
@@ -104,6 +109,8 @@ def handle_request(request):
                     f"--complexity={args.get('complexity', 32)}",
                     "--non-interactive",
                 ]
+                if args.get("show_metadata", False):
+                    cmd.append("--show-metadata")
                 result = subprocess.run(cmd, capture_output=True, text=True)
 
             elif tool_name == "leann_list":