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fix: silence repeated SiliconFlow embedding dimensions warning #9229

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fix: silence repeated SiliconFlow embedding dimensions warning #9229

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051eb4b
fix: silence repeated SiliconFlow embedding dimensions warning
Misyra Jul 12, 2026
227ff74
refactor: return copy of cached kwargs to protect internal state
Misyra Jul 12, 2026
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8 changes: 7 additions & 1 deletion astrbot/core/provider/sources/openai_embedding_source.py
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Original file line number Diff line number Diff line change
Expand Up @@ -44,6 +44,8 @@ def __init__(self, provider_config: dict, provider_settings: dict) -> None:
http_client=http_client,
)
self.model = provider_config.get("embedding_model", "text-embedding-3-small")
# 一次性构建并缓存 embedding 请求参数，避免每次调用都执行 SiliconFlow 检测并刷 WARN 日志
self._cached_kwargs = self._build_embedding_kwargs()

async def get_embedding(self, text: str) -> list[float]:
"""获取文本的嵌入"""
Expand All @@ -66,7 +68,11 @@ async def get_embeddings(self, text: list[str]) -> list[list[float]]:
return [item.embedding for item in embeddings.data]

def _embedding_kwargs(self) -> dict:
"""构建嵌入请求的可选参数"""
"""返回启动时构建并缓存的 embedding 请求参数"""
return self._cached_kwargs

@gemini-code-assist gemini-code-assist Bot Jul 12, 2026

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medium

Returning a direct reference to the internal mutable dictionary self._cached_kwargs can lead to unintended side effects if any caller or subclass modifies the returned dictionary. It is safer to return a copy of the dictionary to ensure the cached state remains immutable from the outside.

Suggested change
def _embedding_kwargs(self) -> dict:
"""构建嵌入请求的可选参数"""
"""返回启动时构建并缓存的 embedding 请求参数"""
return self._cached_kwargs
def _embedding_kwargs(self) -> dict:
"""返回启动时构建并缓存的 embedding 请求参数"""
return self._cached_kwargs.copy()

@Misyra Misyra Jul 12, 2026

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Good catch, thanks. Applied in commit 227ff74 — _embedding_kwargs() now returns self._cached_kwargs.copy() to prevent external mutation of the cached dict.


def _build_embedding_kwargs(self) -> dict:
"""构建嵌入请求的可选参数。仅在 __init__ 时调用一次，SiliconFlow 检测与 WARN 日志只触发一次。"""
kwargs = {}
if "embedding_dimensions" in self.provider_config:
try:
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