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222 | class AlephAlphaEmbedding(BaseEmbedding):
"""AlephAlphaEmbedding使用Aleph Alpha API为文本生成嵌入。"""
model: str = Field(
default=DEFAULT_ALEPHALPHA_MODEL, description="The Aleph Alpha model to use."
)
token: str = Field(default=None, description="The Aleph Alpha API token.")
representation: Optional[str] = Field(
default=SemanticRepresentation.Query,
description="The representation type to use for generating embeddings.",
)
compress_to_size: Optional[int] = Field(
default=None,
description="The size to compress the embeddings to.",
gt=0,
)
base_url: Optional[str] = Field(
default=DEFAULT_ALEPHALPHA_HOST, description="The hostname of the API base_url."
)
timeout: Optional[float] = Field(
default=None, description="The timeout to use in seconds.", gte=0
)
max_retries: int = Field(
default=10, description="The maximum number of API retries.", gte=0
)
normalize: Optional[bool] = Field(
default=False, description="Return normalized embeddings."
)
hosting: Optional[str] = Field(default=None, description="The hosting to use.")
nice: bool = Field(default=False, description="Whether to be nice to the API.")
verify_ssl: bool = Field(default=True, description="Whether to verify SSL.")
additional_kwargs: Dict[str, Any] = Field(
default_factory=dict, description="Additional kwargs for the Aleph Alpha API."
)
# Instance variables initialized via Pydantic's mechanism
_client: Any = PrivateAttr()
_aclient: Any = PrivateAttr()
def __init__(
self,
model: str = DEFAULT_ALEPHALPHA_MODEL,
token: Optional[str] = None,
representation: Optional[str] = None,
base_url: Optional[str] = DEFAULT_ALEPHALPHA_HOST,
hosting: Optional[str] = None,
timeout: Optional[float] = None,
max_retries: int = 10,
nice: bool = False,
verify_ssl: bool = True,
additional_kwargs: Optional[Dict[str, Any]] = None,
):
"""用于使用AlephAlpha API生成嵌入的类表示。
Args:
token：用于AlephAlpha API的令牌。
model：用于生成嵌入的模型。
base_url：AlephAlpha API的基本URL。
nice：是否对AlephAlpha API使用“nice”模式。
additional_kwargs：AlephAlpha API的额外kwargs。
"""
additional_kwargs = additional_kwargs or {}
super().__init__(
model=model,
representation=representation,
base_url=base_url,
token=token,
nice=nice,
additional_kwargs=additional_kwargs,
)
self.token = get_from_param_or_env("aa_token", token, "AA_TOKEN", "")
if representation is not None and isinstance(representation, str):
try:
representation_enum = SemanticRepresentation[
representation.capitalize()
]
except KeyError:
raise ValueError(
f"{representation} is not a valid representation type. Available types are: {list(SemanticRepresentation.__members__.keys())}"
)
self.representation = representation_enum
else:
self.representation = representation
self._client = None
self._aclient = None
@classmethod
def class_name(cls) -> str:
return "AlephAlphaEmbedding"
def _get_credential_kwargs(self) -> Dict[str, Any]:
return {
"token": self.token,
"host": self.base_url,
"hosting": self.hosting,
"request_timeout_seconds": self.timeout,
"total_retries": self.max_retries,
"nice": self.nice,
"verify_ssl": self.verify_ssl,
}
def _get_client(self) -> Client:
if self._client is None:
self._client = Client(**self._get_credential_kwargs())
return self._client
def _get_aclient(self) -> AsyncClient:
if self._aclient is None:
self._aclient = AsyncClient(**self._get_credential_kwargs())
return self._aclient
def _get_embedding(self, text: str, representation: str) -> List[float]:
"""使用AlephAlpha嵌入句子。"""
client = self._get_client()
request = SemanticEmbeddingRequest(
prompt=Prompt.from_text(text),
representation=representation or self.representation,
compress_to_size=self.compress_to_size,
normalize=self.normalize,
)
result = client.semantic_embed(request=request, model=self.model)
return result.embedding
async def _aget_embedding(self, text: str, representation: str) -> List[float]:
"""异步获取嵌入。"""
aclient = self._get_aclient()
request = SemanticEmbeddingRequest(
prompt=Prompt.from_text(text),
representation=representation or self.representation,
compress_to_size=self.compress_to_size,
normalize=self.normalize,
)
result = await aclient.semantic_embed(request=request, model=self.model)
return result.embedding
def _get_embeddings(
self, texts: List[str], representation: str
) -> List[List[float]]:
"""使用AlephAlpha嵌入句子。"""
client = self._get_client()
request = BatchSemanticEmbeddingRequest(
prompts=[Prompt.from_text(text) for text in texts],
representation=representation or self.representation,
compress_to_size=self.compress_to_size,
normalize=self.normalize,
)
result: BatchSemanticEmbeddingResponse = client.batch_semantic_embed(
request=request, model=self.model
)
return result.embeddings
async def _aget_embeddings(
self, texts: List[str], representation: str
) -> List[List[float]]:
"""异步获取嵌入。"""
aclient = self._get_aclient()
request = BatchSemanticEmbeddingRequest(
prompts=[Prompt.from_text(text) for text in texts],
representation=representation or self.representation,
compress_to_size=self.compress_to_size,
normalize=self.normalize,
)
result: BatchSemanticEmbeddingResponse = await aclient.batch_semantic_embed(
request=request, model=self.model
)
return result.embeddings
def _get_query_embedding(self, query: str) -> List[float]:
"""获取查询嵌入。对于查询嵌入，representation='query'。"""
return self._get_embedding(query, SemanticRepresentation.Query)
async def _aget_query_embedding(self, query: str) -> List[float]:
"""异步获取查询嵌入。对于查询嵌入，representation='query'。"""
return self._aget_embedding(query, SemanticRepresentation.Query)
def _get_text_embedding(self, text: str) -> List[float]:
"""获取文本嵌入。对于文本嵌入，representation='document'。"""
return self._get_embedding(text, SemanticRepresentation.Document)
async def _aget_text_embedding(self, text: str) -> List[float]:
"""异步获取文本嵌入。"""
return self._aget_embedding(text, SemanticRepresentation.Document)
def _get_text_embeddings(self, texts: List[str]) -> List[List[float]]:
"""获取文本嵌入。"""
return self._get_embeddings(texts, SemanticRepresentation.Document)
async def _aget_text_embeddings(self, texts: List[str]) -> List[List[float]]:
"""异步获取文本嵌入。"""
return self._aget_embeddings(texts, SemanticRepresentation.Document)
|