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144 | @deprecated.deprecated(
reason=(
"Should use `llama-index-embeddings-google-genai` instead, using Google's latest unified SDK. "
"See: https://docs.llamaindex.org.cn/en/stable/examples/embeddings/google_genai/"
)
)
class GeminiEmbedding(BaseEmbedding):
"""
Google Gemini embeddings.
Args:
model_name (str): Model for embedding.
Defaults to "models/embedding-001".
api_key (Optional[str]): API key to access the model. Defaults to None.
api_base (Optional[str]): API base to access the model. Defaults to Official Base.
transport (Optional[str]): Transport to access the model.
"""
_model: gemini = PrivateAttr()
_request_options: Optional[gemini.types.RequestOptions] = PrivateAttr()
title: Optional[str] = Field(
default="",
description="Title is only applicable for retrieval_document tasks, and is used to represent a document title. For other tasks, title is invalid.",
)
task_type: Optional[str] = Field(
default="retrieval_document",
description="The task for embedding model.",
)
api_key: Optional[str] = Field(
default=None,
description="API key to access the model. Defaults to None.",
)
def __init__(
self,
model_name: str = "models/embedding-001",
task_type: Optional[str] = "retrieval_document",
api_key: Optional[str] = None,
api_base: Optional[str] = None,
transport: Optional[str] = None,
title: Optional[str] = None,
embed_batch_size: int = DEFAULT_EMBED_BATCH_SIZE,
callback_manager: Optional[CallbackManager] = None,
request_options: Optional[gemini.types.RequestOptions] = None,
**kwargs: Any,
):
# API keys are optional. The API can be authorised via OAuth (detected
# environmentally) or by the GOOGLE_API_KEY environment variable.
config_params: Dict[str, Any] = {
"api_key": api_key or os.getenv("GOOGLE_API_KEY"),
}
if api_base:
config_params["client_options"] = {"api_endpoint": api_base}
if transport:
config_params["transport"] = transport
# transport: A string, one of: [`rest`, `grpc`, `grpc_asyncio`].
super().__init__(
api_key=api_key,
model_name=model_name,
embed_batch_size=embed_batch_size,
callback_manager=callback_manager,
title=title,
task_type=task_type,
**kwargs,
)
gemini.configure(**config_params)
self._model = gemini
self._request_options = request_options
@classmethod
def class_name(cls) -> str:
return "GeminiEmbedding"
def _get_query_embedding(self, query: str) -> List[float]:
"""Get query embedding."""
return self._model.embed_content(
model=self.model_name,
content=query,
title=self.title,
task_type=self.task_type,
request_options=self._request_options,
)["embedding"]
def _get_text_embedding(self, text: str) -> List[float]:
"""Get text embedding."""
return self._model.embed_content(
model=self.model_name,
content=text,
title=self.title,
task_type=self.task_type,
request_options=self._request_options,
)["embedding"]
def _get_text_embeddings(self, texts: List[str]) -> List[List[float]]:
"""Get text embeddings."""
return [
self._model.embed_content(
model=self.model_name,
content=text,
title=self.title,
task_type=self.task_type,
request_options=self._request_options,
)["embedding"]
for text in texts
]
async def _aget_query_embedding(self, query: str) -> List[float]:
"""The asynchronous version of _get_query_embedding."""
return (await self._aget_text_embeddings([query]))[0]
async def _aget_text_embedding(self, text: str) -> List[float]:
"""Asynchronously get text embedding."""
return (await self._aget_text_embeddings([text]))[0]
async def _aget_text_embeddings(self, texts: List[str]) -> List[List[float]]:
"""Asynchronously get text embeddings."""
response = await self._model.embed_content_async(
model=self.model_name,
content=texts,
title=self.title,
task_type=self.task_type,
request_options=self._request_options,
)
return response["embedding"]
|
