fix: clean build system and Python 3.9 compatibility

Build system improvements:
- Simplify macOS environment detection using brew --prefix
- Remove complex hardcoded paths and CMAKE_ARGS
- Let CMake automatically find Homebrew packages via CMAKE_PREFIX_PATH
- Clean separation between Intel (/usr/local) and Apple Silicon (/opt/homebrew)

Python 3.9 compatibility:
- Set ruff target-version to py39 to match project requirements
- Replace str | None with Union[str, None] in type annotations
- Add Union imports where needed
- Fix core interface, CLI, chat, and embedding server files

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

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

@@ -10,6 +10,7 @@ import sys
 import threading
 import time
 from pathlib import Path
+from typing import Union
 
 import msgpack
 import numpy as np
@@ -33,7 +34,7 @@ if not logger.handlers:
 
 
 def create_hnsw_embedding_server(
-    passages_file: str | None = None,
+    passages_file: Union[str, None] = None,
     zmq_port: int = 5555,
     model_name: str = "sentence-transformers/all-mpnet-base-v2",
     distance_metric: str = "mips",

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

@@ -8,7 +8,7 @@ import difflib
 import logging
 import os
 from abc import ABC, abstractmethod
-from typing import Any
+from typing import Any, Union
 
 import torch
 
@@ -309,7 +309,7 @@ def search_hf_models(query: str, limit: int = 10) -> list[str]:
     return search_hf_models_fuzzy(query, limit)
 
 
-def validate_model_and_suggest(model_name: str, llm_type: str) -> str | None:
+def validate_model_and_suggest(model_name: str, llm_type: str) -> Union[str, None]:
     """Validate model name and provide suggestions if invalid"""
     if llm_type == "ollama":
         available_models = check_ollama_models()
@@ -683,7 +683,7 @@ class HFChat(LLMInterface):
 class OpenAIChat(LLMInterface):
     """LLM interface for OpenAI models."""
 
-    def __init__(self, model: str = "gpt-4o", api_key: str | None = None):
+    def __init__(self, model: str = "gpt-4o", api_key: Union[str, None] = None):
         self.model = model
         self.api_key = api_key or os.getenv("OPENAI_API_KEY")
 

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

@@ -1,6 +1,7 @@
 import argparse
 import asyncio
 from pathlib import Path
+from typing import Union
 
 from llama_index.core import SimpleDirectoryReader
 from llama_index.core.node_parser import SentenceSplitter
@@ -270,7 +271,7 @@ Examples:
                     print(f'  leann search {example_name} "your query"')
                     print(f"  leann ask {example_name} --interactive")
 
-    def load_documents(self, docs_dir: str, custom_file_types: str | None = None):
+    def load_documents(self, docs_dir: str, custom_file_types: Union[str, None] = None):
         print(f"Loading documents from {docs_dir}...")
         if custom_file_types:
             print(f"Using custom file types: {custom_file_types}")

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

@@ -1,5 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import Any, Literal
+from typing import Any, Literal, Union
 
 import numpy as np
 
@@ -34,7 +34,9 @@ class LeannBackendSearcherInterface(ABC):
         pass
 
     @abstractmethod
-    def _ensure_server_running(self, passages_source_file: str, port: int | None, **kwargs) -> int:
+    def _ensure_server_running(
+        self, passages_source_file: str, port: Union[int, None], **kwargs
+    ) -> int:
         """Ensure server is running"""
         pass
 
@@ -48,7 +50,7 @@ class LeannBackendSearcherInterface(ABC):
         prune_ratio: float = 0.0,
         recompute_embeddings: bool = False,
         pruning_strategy: Literal["global", "local", "proportional"] = "global",
-        zmq_port: int | None = None,
+        zmq_port: Union[int, None] = None,
         **kwargs,
     ) -> dict[str, Any]:
         """Search for nearest neighbors
@@ -74,7 +76,7 @@ class LeannBackendSearcherInterface(ABC):
         self,
         query: str,
         use_server_if_available: bool = True,
-        zmq_port: int | None = None,
+        zmq_port: Union[int, None] = None,
     ) -> np.ndarray:
         """Compute embedding for a query string