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218 | class UpstageEmbedding(OpenAIEmbedding):
"""
Class for Upstage embeddings.
"""
additional_kwargs: Dict[str, Any] = Field(
default_factory=dict, description="Additional kwargs for the Upstage API."
)
api_key: str = Field(description="The Upstage API key.")
api_base: Optional[str] = Field(
default=DEFAULT_UPSTAGE_API_BASE, description="The base URL for Upstage API."
)
dimensions: Optional[int] = Field(
None,
description="Not supported yet. The number of dimensions the resulting output embeddings should have.",
)
def __init__(
self,
model: str = "solar-embedding-1-large",
embed_batch_size: int = 100,
dimensions: Optional[int] = None,
additional_kwargs: Dict[str, Any] = None,
api_key: Optional[str] = None,
api_base: Optional[str] = None,
max_retries: int = 10,
timeout: float = 60.0,
reuse_client: bool = True,
callback_manager: Optional[CallbackManager] = None,
default_headers: Optional[Dict[str, str]] = None,
http_client: Optional[httpx.Client] = None,
**kwargs: Any,
) -> None:
additional_kwargs = additional_kwargs or {}
if dimensions is not None:
warnings.warn("Received dimensions argument. This is not supported yet.")
additional_kwargs["dimensions"] = dimensions
if embed_batch_size > MAX_EMBED_BATCH_SIZE:
raise ValueError(
f"embed_batch_size should be less than or equal to {MAX_EMBED_BATCH_SIZE}."
)
if "upstage_api_key" in kwargs:
api_key = kwargs.pop("upstage_api_key")
api_key, api_base = resolve_upstage_credentials(
api_key=api_key, api_base=api_base
)
if "model_name" in kwargs:
model = kwargs.pop("model_name")
# if model endswith with "-query" or "-passage", remove the suffix and print a warning
if model.endswith(("-query", "-passage")):
model = model.rsplit("-", 1)[0]
logger.warning(
f"Model name should not end with '-query' or '-passage'. The suffix has been removed. "
f"Model name: {model}"
)
super().__init__(
embed_batch_size=embed_batch_size,
dimensions=dimensions,
callback_manager=callback_manager,
model_name=model,
additional_kwargs=additional_kwargs,
api_key=api_key,
api_base=api_base,
max_retries=max_retries,
reuse_client=reuse_client,
timeout=timeout,
default_headers=default_headers,
**kwargs,
)
self._client = None
self._aclient = None
self._http_client = http_client
self._query_engine, self._text_engine = get_engine(model)
def class_name(cls) -> str:
return "UpstageEmbedding"
def _get_credential_kwargs(self, is_async: bool = False) -> Dict[str, Any]:
return {
"api_key": self.api_key,
"base_url": self.api_base,
"max_retries": self.max_retries,
"timeout": self.timeout,
"default_headers": self.default_headers,
"http_client": self._async_http_client if is_async else self._http_client,
}
def _get_query_embedding(self, query: str) -> List[float]:
"""Get query embedding."""
client = self._get_client()
text = query.replace("\n", " ")
return (
client.embeddings.create(
input=text, model=self._query_engine, **self.additional_kwargs
)
.data[0]
.embedding
)
async def _aget_query_embedding(self, query: str) -> List[float]:
"""The asynchronous version of _get_query_embedding."""
client = self._get_aclient()
text = query.replace("\n", " ")
return (
(
await client.embeddings.create(
input=text, model=self._query_engine, **self.additional_kwargs
)
)
.data[0]
.embedding
)
def _get_text_embedding(self, text: str) -> List[float]:
"""Get text embedding."""
client = self._get_client()
return (
client.embeddings.create(
input=text, model=self._text_engine, **self.additional_kwargs
)
.data[0]
.embedding
)
async def _aget_text_embedding(self, text: str) -> List[float]:
"""Asynchronously get text embedding."""
client = self._get_aclient()
return (
(
await client.embeddings.create(
input=text, model=self._text_engine, **self.additional_kwargs
)
)
.data[0]
.embedding
)
def _get_text_embeddings(self, texts: List[str]) -> List[List[float]]:
"""Get text embeddings."""
client = self._get_client()
batch_size = min(self.embed_batch_size, len(texts))
texts = [text.replace("\n", " ") for text in texts]
embeddings = []
for i in range(0, len(texts), batch_size):
batch = texts[i : i + batch_size]
response = client.embeddings.create(
input=batch, model=self._text_engine, **self.additional_kwargs
)
embeddings.extend([r.embedding for r in response.data])
return embeddings
async def _aget_text_embeddings(self, texts: List[str]) -> List[List[float]]:
"""Asynchronously get text embeddings."""
client = self._get_aclient()
batch_size = min(self.embed_batch_size, len(texts))
texts = [text.replace("\n", " ") for text in texts]
embeddings = []
for i in range(0, len(texts), batch_size):
batch = texts[i : i + batch_size]
response = await client.embeddings.create(
input=batch, model=self._text_engine, **self.additional_kwargs
)
embeddings.extend([r.embedding for r in response.data])
return embeddings
|
