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>
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@@ -10,7 +10,7 @@ import time
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import warnings
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import warnings
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from dataclasses import dataclass, field
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from dataclasses import dataclass, field
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from pathlib import Path
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from pathlib import Path
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from typing import Any, Literal
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from typing import Any, Literal, Optional
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import numpy as np
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import numpy as np
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@@ -33,7 +33,7 @@ def compute_embeddings(
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model_name: str,
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model_name: str,
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mode: str = "sentence-transformers",
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mode: str = "sentence-transformers",
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use_server: bool = True,
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use_server: bool = True,
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port: int | None = None,
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port: Optional[int] = None,
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is_build=False,
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is_build=False,
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) -> np.ndarray:
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) -> np.ndarray:
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"""
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"""
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@@ -157,12 +157,12 @@ class LeannBuilder:
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self,
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self,
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backend_name: str,
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backend_name: str,
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embedding_model: str = "facebook/contriever",
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embedding_model: str = "facebook/contriever",
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dimensions: int | None = None,
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dimensions: Optional[int] = None,
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embedding_mode: str = "sentence-transformers",
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embedding_mode: str = "sentence-transformers",
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**backend_kwargs,
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**backend_kwargs,
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):
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):
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self.backend_name = backend_name
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self.backend_name = backend_name
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backend_factory: LeannBackendFactoryInterface | None = BACKEND_REGISTRY.get(backend_name)
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backend_factory: Optional[LeannBackendFactoryInterface] = BACKEND_REGISTRY.get(backend_name)
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if backend_factory is None:
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if backend_factory is None:
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raise ValueError(f"Backend '{backend_name}' not found or not registered.")
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raise ValueError(f"Backend '{backend_name}' not found or not registered.")
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self.backend_factory = backend_factory
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self.backend_factory = backend_factory
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@@ -242,7 +242,7 @@ class LeannBuilder:
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self.backend_kwargs = backend_kwargs
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self.backend_kwargs = backend_kwargs
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self.chunks: list[dict[str, Any]] = []
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self.chunks: list[dict[str, Any]] = []
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def add_text(self, text: str, metadata: dict[str, Any] | None = None):
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def add_text(self, text: str, metadata: Optional[dict[str, Any]] = None):
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if metadata is None:
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if metadata is None:
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metadata = {}
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metadata = {}
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passage_id = metadata.get("id", str(len(self.chunks)))
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passage_id = metadata.get("id", str(len(self.chunks)))
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@@ -592,7 +592,7 @@ class LeannChat:
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def __init__(
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def __init__(
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self,
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self,
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index_path: str,
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index_path: str,
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llm_config: dict[str, Any] | None = None,
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llm_config: Optional[dict[str, Any]] = None,
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enable_warmup: bool = False,
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enable_warmup: bool = False,
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**kwargs,
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**kwargs,
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):
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):
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@@ -608,7 +608,7 @@ class LeannChat:
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prune_ratio: float = 0.0,
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prune_ratio: float = 0.0,
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recompute_embeddings: bool = True,
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recompute_embeddings: bool = True,
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pruning_strategy: Literal["global", "local", "proportional"] = "global",
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pruning_strategy: Literal["global", "local", "proportional"] = "global",
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llm_kwargs: dict[str, Any] | None = None,
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llm_kwargs: Optional[dict[str, Any]] = None,
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expected_zmq_port: int = 5557,
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expected_zmq_port: int = 5557,
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**search_kwargs,
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**search_kwargs,
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):
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):
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@@ -761,7 +761,7 @@ class SimulatedChat(LLMInterface):
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return "This is a simulated answer from the LLM based on the retrieved context."
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return "This is a simulated answer from the LLM based on the retrieved context."
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def get_llm(llm_config: dict[str, Any] | None = None) -> LLMInterface:
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def get_llm(llm_config: Optional[dict[str, Any]] = None) -> LLMInterface:
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"""
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"""
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Factory function to get an LLM interface based on configuration.
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Factory function to get an LLM interface based on configuration.
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@@ -6,6 +6,7 @@ import subprocess
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import sys
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import sys
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import time
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import time
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from pathlib import Path
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from pathlib import Path
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from typing import Optional
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import psutil
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import psutil
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@@ -182,8 +183,8 @@ class EmbeddingServerManager:
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e.g., "leann_backend_diskann.embedding_server"
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e.g., "leann_backend_diskann.embedding_server"
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"""
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"""
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self.backend_module_name = backend_module_name
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self.backend_module_name = backend_module_name
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self.server_process: subprocess.Popen | None = None
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self.server_process: Optional[subprocess.Popen] = None
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self.server_port: int | None = None
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self.server_port: Optional[int] = None
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self._atexit_registered = False
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self._atexit_registered = False
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def start_server(
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def start_server(
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@@ -1,7 +1,7 @@
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import json
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import json
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from abc import ABC, abstractmethod
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from abc import ABC, abstractmethod
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from pathlib import Path
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from pathlib import Path
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from typing import Any, Literal
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from typing import Any, Literal, Optional
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import numpy as np
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import numpy as np
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@@ -169,7 +169,7 @@ class BaseSearcher(LeannBackendSearcherInterface, ABC):
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prune_ratio: float = 0.0,
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prune_ratio: float = 0.0,
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recompute_embeddings: bool = False,
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recompute_embeddings: bool = False,
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pruning_strategy: Literal["global", "local", "proportional"] = "global",
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pruning_strategy: Literal["global", "local", "proportional"] = "global",
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zmq_port: int | None = None,
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zmq_port: Optional[int] = None,
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**kwargs,
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**kwargs,
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) -> dict[str, Any]:
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) -> dict[str, Any]:
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"""
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"""
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