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fix(as_embedding): support both agentscope 2.0.2 and 2.0.3 (#323)

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2.0.3 promoted `dimensions` to a required first-class constructor
argument while keeping a backfill from `parameters.dimensions`; 2.0.2
has no such argument and reads `dimensions` from `Parameters`. Keep
`dimensions` in `Parameters` for both versions and, when the model
constructor accepts `dimensions`, pass `dimensions=None` so 2.0.3's
backfill promotes it out of `parameters`.

Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
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Sen Huang 2026-07-06 19:03:32 +08:00 • committed by GitHub
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24
reme/components/as_embedding/__init__.py
View file

@ -1,5 +1,6 @@
"""AgentScope embedding model wrappers."""
import inspect
from typing import Any
from agentscope.credential import (
@ -50,16 +51,21 @@ class BaseAsEmbedding(BaseComponent):
if model_cls is None:
raise ValueError(f"{self.credential_cls.__name__} does not support embeddings.")
params_dict = dict(kwargs.pop("parameters", None) or {})
# agentscope >=2.0.2 takes `dimensions` as an explicit constructor
# argument rather than reading it from Parameters. Accept it from the
# component top level or (for backward-compatible configs) from
# parameters, and pass it through explicitly.
dimensions = kwargs.pop("dimensions", None)
if dimensions is None:
dimensions = params_dict.pop("dimensions", None)
params_dict = kwargs.pop("parameters", None)
parameters = model_cls.Parameters(**params_dict) if params_dict else None
self.model = model_cls(credential=credential, dimensions=dimensions, parameters=parameters, **kwargs)
# agentscope 2.0.3 made ``dimensions`` a required first-class
# constructor argument, while keeping a backward-compat backfill
# that promotes it from ``parameters.dimensions`` when the explicit
# value is ``None``. 2.0.2 has no such argument and reads
# ``dimensions`` straight from ``Parameters``. Keep ``dimensions``
# in ``Parameters`` for both, and on 2.0.3 pass ``dimensions=None``
# so its backfill picks it up.
extra: dict[str, Any] = {}
if "dimensions" in inspect.signature(model_cls.__init__).parameters:
extra["dimensions"] = None
self.model = model_cls(credential=credential, parameters=parameters, **extra, **kwargs)
@R.register("openai")