fix: complete Python 3.9 type annotation compatibility fixes

Fix remaining Python 3.9 incompatible type annotations throughout the
leann-core package that were causing test failures in CI. The union operator
(|) syntax for type hints was introduced in Python 3.10 and causes
"TypeError: unsupported operand type(s) for |" errors in Python 3.9.

Changes:
- Convert dict[str, Any] | None to Optional[dict[str, Any]]
- Convert int | None to Optional[int]
- Convert subprocess.Popen | None to Optional[subprocess.Popen]
- Convert LeannBackendFactoryInterface | None to Optional[LeannBackendFactoryInterface]
- Add missing Optional imports to all affected files

This resolves all test failures related to type annotation syntax and ensures
compatibility with Python 3.9 as specified in pyproject.toml.

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

Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Andy Lee
2025-08-10 18:48:10 -07:00
parent 037aad0870
commit fe9381fc8b
4 changed files with 13 additions and 12 deletions

View File

@@ -10,7 +10,7 @@ import time
import warnings
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Literal
from typing import Any, Literal, Optional
import numpy as np
@@ -33,7 +33,7 @@ def compute_embeddings(
model_name: str,
mode: str = "sentence-transformers",
use_server: bool = True,
port: int | None = None,
port: Optional[int] = None,
is_build=False,
) -> np.ndarray:
"""
@@ -157,12 +157,12 @@ class LeannBuilder:
self,
backend_name: str,
embedding_model: str = "facebook/contriever",
dimensions: int | None = None,
dimensions: Optional[int] = None,
embedding_mode: str = "sentence-transformers",
**backend_kwargs,
):
self.backend_name = backend_name
backend_factory: LeannBackendFactoryInterface | None = BACKEND_REGISTRY.get(backend_name)
backend_factory: Optional[LeannBackendFactoryInterface] = BACKEND_REGISTRY.get(backend_name)
if backend_factory is None:
raise ValueError(f"Backend '{backend_name}' not found or not registered.")
self.backend_factory = backend_factory
@@ -242,7 +242,7 @@ class LeannBuilder:
self.backend_kwargs = backend_kwargs
self.chunks: list[dict[str, Any]] = []
def add_text(self, text: str, metadata: dict[str, Any] | None = None):
def add_text(self, text: str, metadata: Optional[dict[str, Any]] = None):
if metadata is None:
metadata = {}
passage_id = metadata.get("id", str(len(self.chunks)))
@@ -592,7 +592,7 @@ class LeannChat:
def __init__(
self,
index_path: str,
llm_config: dict[str, Any] | None = None,
llm_config: Optional[dict[str, Any]] = None,
enable_warmup: bool = False,
**kwargs,
):
@@ -608,7 +608,7 @@ class LeannChat:
prune_ratio: float = 0.0,
recompute_embeddings: bool = True,
pruning_strategy: Literal["global", "local", "proportional"] = "global",
llm_kwargs: dict[str, Any] | None = None,
llm_kwargs: Optional[dict[str, Any]] = None,
expected_zmq_port: int = 5557,
**search_kwargs,
):

View File

@@ -761,7 +761,7 @@ class SimulatedChat(LLMInterface):
return "This is a simulated answer from the LLM based on the retrieved context."
def get_llm(llm_config: dict[str, Any] | None = None) -> LLMInterface:
def get_llm(llm_config: Optional[dict[str, Any]] = None) -> LLMInterface:
"""
Factory function to get an LLM interface based on configuration.

View File

@@ -6,6 +6,7 @@ import subprocess
import sys
import time
from pathlib import Path
from typing import Optional
import psutil
@@ -182,8 +183,8 @@ class EmbeddingServerManager:
e.g., "leann_backend_diskann.embedding_server"
"""
self.backend_module_name = backend_module_name
self.server_process: subprocess.Popen | None = None
self.server_port: int | None = None
self.server_process: Optional[subprocess.Popen] = None
self.server_port: Optional[int] = None
self._atexit_registered = False
def start_server(

View File

@@ -1,7 +1,7 @@
import json
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Literal
from typing import Any, Literal, Optional
import numpy as np
@@ -169,7 +169,7 @@ class BaseSearcher(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: Optional[int] = None,
**kwargs,
) -> dict[str, Any]:
"""