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217 | class HuggingFaceInferenceAPIEmbedding(BaseEmbedding): # type: ignore[misc]
"""
Wrapper on the Hugging Face's Inference API for embeddings.
Overview of the design:
- Uses the feature extraction task: https://huggingface.co/tasks/feature-extraction
"""
pooling: Optional[Pooling] = Field(
default=Pooling.CLS,
description="Pooling strategy. If None, the model's default pooling is used.",
)
query_instruction: Optional[str] = Field(
default=None, description="Instruction to prepend during query embedding."
)
text_instruction: Optional[str] = Field(
default=None, description="Instruction to prepend during text embedding."
)
# Corresponds with huggingface_hub.InferenceClient
model_name: Optional[str] = Field(
default=None,
description="Hugging Face model name. If None, the task will be used.",
)
token: Union[str, bool, None] = Field(
default=None,
description=(
"Hugging Face token. Will default to the locally saved token. Pass "
"token=False if you don’t want to send your token to the server."
),
)
timeout: Optional[float] = Field(
default=None,
description=(
"The maximum number of seconds to wait for a response from the server."
" Loading a new model in Inference API can take up to several minutes."
" Defaults to None, meaning it will loop until the server is available."
),
)
headers: Optional[Dict[str, str]] = Field(
default=None,
description=(
"Additional headers to send to the server. By default only the"
" authorization and user-agent headers are sent. Values in this dictionary"
" will override the default values."
),
)
cookies: Optional[Dict[str, str]] = Field(
default=None, description="Additional cookies to send to the server."
)
task: Optional[str] = Field(
default=None,
description=(
"Optional task to pick Hugging Face's recommended model, used when"
" model_name is left as default of None."
),
)
_sync_client: "InferenceClient" = PrivateAttr()
_async_client: "AsyncInferenceClient" = PrivateAttr()
_get_model_info: "Callable[..., ModelInfo]" = PrivateAttr()
def _get_inference_client_kwargs(self) -> Dict[str, Any]:
"""Extract the Hugging Face InferenceClient construction parameters."""
return {
"model": self.model_name,
"token": self.token,
"timeout": self.timeout,
"headers": self.headers,
"cookies": self.cookies,
}
def __init__(self, **kwargs: Any) -> None:
"""Initialize.
Args:
kwargs: See the class-level Fields.
"""
if kwargs.get("model_name") is None:
task = kwargs.get("task", "")
# NOTE: task being None or empty string leads to ValueError,
# which ensures model is present
kwargs["model_name"] = InferenceClient.get_recommended_model(task=task)
logger.debug(
f"Using Hugging Face's recommended model {kwargs['model_name']}"
f" given task {task}."
)
print(kwargs["model_name"], flush=True)
super().__init__(**kwargs) # Populate pydantic Fields
self._sync_client = InferenceClient(**self._get_inference_client_kwargs())
self._async_client = AsyncInferenceClient(**self._get_inference_client_kwargs())
self._get_model_info = model_info
def validate_supported(self, task: str) -> None:
"""
Confirm the contained model_name is deployed on the Inference API service.
Args:
task: Hugging Face task to check within. A list of all tasks can be
found here: https://huggingface.co/tasks
"""
all_models = self._sync_client.list_deployed_models(frameworks="all")
try:
if self.model_name not in all_models[task]:
raise ValueError(
"The Inference API service doesn't have the model"
f" {self.model_name!r} deployed."
)
except KeyError as exc:
raise KeyError(
f"Input task {task!r} not in possible tasks {list(all_models.keys())}."
) from exc
def get_model_info(self, **kwargs: Any) -> "ModelInfo":
"""Get metadata on the current model from Hugging Face."""
return self._get_model_info(self.model_name, **kwargs)
@classmethod
def class_name(cls) -> str:
return "HuggingFaceInferenceAPIEmbedding"
async def _async_embed_single(self, text: str) -> Embedding:
embedding = await self._async_client.feature_extraction(text)
if len(embedding.shape) == 1:
return embedding.tolist()
embedding = embedding.squeeze(axis=0)
if len(embedding.shape) == 1: # Some models pool internally
return embedding.tolist()
try:
return self.pooling(embedding).tolist() # type: ignore[misc]
except TypeError as exc:
raise ValueError(
f"Pooling is required for {self.model_name} because it returned"
" a > 1-D value, please specify pooling as not None."
) from exc
async def _async_embed_bulk(self, texts: Sequence[str]) -> List[Embedding]:
"""
Embed a sequence of text, in parallel and asynchronously.
NOTE: this uses an externally created asyncio event loop.
"""
tasks = [self._async_embed_single(text) for text in texts]
return await asyncio.gather(*tasks)
def _get_query_embedding(self, query: str) -> Embedding:
"""
Embed the input query synchronously.
NOTE: a new asyncio event loop is created internally for this.
"""
return asyncio.run(self._aget_query_embedding(query))
def _get_text_embedding(self, text: str) -> Embedding:
"""
Embed the text query synchronously.
NOTE: a new asyncio event loop is created internally for this.
"""
return asyncio.run(self._aget_text_embedding(text))
def _get_text_embeddings(self, texts: List[str]) -> List[Embedding]:
"""
Embed the input sequence of text synchronously and in parallel.
NOTE: a new asyncio event loop is created internally for this.
"""
loop = asyncio.new_event_loop()
try:
tasks = [
loop.create_task(self._aget_text_embedding(text)) for text in texts
]
loop.run_until_complete(asyncio.wait(tasks))
finally:
loop.close()
return [task.result() for task in tasks]
async def _aget_query_embedding(self, query: str) -> Embedding:
return await self._async_embed_single(
text=format_query(query, self.model_name, self.query_instruction)
)
async def _aget_text_embedding(self, text: str) -> Embedding:
return await self._async_embed_single(
text=format_text(text, self.model_name, self.text_instruction)
)
async def _aget_text_embeddings(self, texts: List[str]) -> List[Embedding]:
return await self._async_embed_bulk(
texts=[
format_text(text, self.model_name, self.text_instruction)
for text in texts
]
)
|
