feat: add Gemini embedding provider and update OpenAI provider to support timeout configuration

astrbot/core/config/default.py

@@ -873,6 +873,17 @@ CONFIG_METADATA_2 = {
                         "embedding_dimensions": 1536,
                         "timeout": 20,
                     },
+                    "Gemini Embedding": {
+                        "id": "gemini_embedding",
+                        "type": "gemini_embedding",
+                        "provider_type": "embedding",
+                        "enable": True,
+                        "embedding_api_key": "",
+                        "embedding_api_base": "",
+                        "embedding_model": "gemini-embedding-exp-03-07",
+                        "embedding_dimensions": 768,
+                        "timeout": 20,
+                    },
                 },
                 "items": {
                     "embedding_dimensions": {
@@ -888,7 +899,10 @@ CONFIG_METADATA_2 = {
                     "embedding_api_key": {
                         "description": "API Key",
                         "type": "string",
-                        "hint": "API Key",
+                    },
+                    "embedding_api_base": {
+                        "description": "API Base URL",
+                        "type": "string",
                     },
                     "volcengine_cluster": {
                         "type": "string",

astrbot/core/provider/sources/gemini_embedding_source.py

@@ -0,0 +1,63 @@
+from google import genai
+from google.genai import types
+from google.genai.errors import APIError
+from ..provider import EmbeddingProvider
+from ..register import register_provider_adapter
+from ..entities import ProviderType
+
+
+@register_provider_adapter(
+    "gemini_embedding",
+    "Google Gemini Embedding 提供商适配器",
+    provider_type=ProviderType.EMBEDDING,
+)
+class GeminiEmbeddingProvider(EmbeddingProvider):
+    def __init__(self, provider_config: dict, provider_settings: dict) -> None:
+        super().__init__(provider_config, provider_settings)
+        self.provider_config = provider_config
+        self.provider_settings = provider_settings
+
+        api_key: str = provider_config.get("embedding_api_key")
+        api_base: str = provider_config.get("embedding_api_base", None)
+        timeout: int = int(provider_config.get("timeout", 20))
+
+        http_options = types.HttpOptions(timeout=timeout * 1000)
+        if api_base:
+            if api_base.endswith("/"):
+                api_base = api_base[:-1]
+            http_options.base_url = api_base
+
+        self.client = genai.Client(api_key=api_key, http_options=http_options).aio
+
+        self.model = provider_config.get(
+            "embedding_model", "gemini-embedding-exp-03-07"
+        )
+        self.dimension = provider_config.get("embedding_dimensions", 768)
+
+    async def get_embedding(self, text: str) -> list[float]:
+        """
+        获取文本的嵌入
+        """
+        try:
+            result = await self.client.models.embed_content(
+                model=self.model, contents=text
+            )
+            return result.embeddings[0].values
+        except APIError as e:
+            raise Exception(f"Gemini Embedding API请求失败: {e.message}")
+
+    async def get_embeddings(self, texts: list[str]) -> list[list[float]]:
+        """
+        批量获取文本的嵌入
+        """
+        try:
+            result = await self.client.models.embed_content(
+                model=self.model, contents=texts
+            )
+            return [embedding.values for embedding in result.embeddings]
+        except APIError as e:
+            raise Exception(f"Gemini Embedding API批量请求失败: {e.message}")
+
+    def get_dim(self) -> int:
+        """获取向量的维度"""
+        return self.dimension

astrbot/core/provider/sources/openai_embedding_source.py

@@ -19,6 +19,7 @@ class OpenAIEmbeddingProvider(EmbeddingProvider):
             base_url=provider_config.get(
                 "embedding_api_base", "https://api.openai.com/v1"
             ),
+            timeout=int(provider_config.get("timeout", 20)),
         )
         self.model = provider_config.get("embedding_model", "text-embedding-3-small")
         self.dimension = provider_config.get("embedding_dimensions", 1536)