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chore: Add kwarg options to GCP Vectoriser transform method #233

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chore: Add kwarg options to GCP Vectoriser transform method #233

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5ade3f1
rework gcp transform method to include kwargs in embed content call
frayle-ons Sep 4, 2026
77493c5
Merge branch 'main' into 232-gcp-transform-kwargs
frayle-ons Sep 22, 2026
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17 changes: 13 additions & 4 deletions src/classifai/vectorisers/gcp.py
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Original file line number Diff line number Diff line change
Expand Up @@ -103,12 +103,13 @@ def __init__(
context={"vectoriser": "gcp", "cause": str(e), "cause_type": type(e).__name__},
) from e

def transform(self, texts: str | list[str]) -> np.ndarray:
def transform(self, texts: str | list[str], **kwargs) -> np.ndarray:
"""Transforms input text(s) into embeddings using the GenAI API.

Args:
texts (str | list[str]): The input text(s) to embed. Can be a
single string or a list of strings.
**kwargs: Additional parameters to pass to the `embed_content` method.

Returns:
numpy.ndarray: A 2D array of embeddings, where each row
Expand All @@ -119,15 +120,23 @@ def transform(self, texts: str | list[str]) -> np.ndarray:
`VectorisationError`: If the response format from the GenAI API is
unexpected.
"""
from google import genai # type: ignore

# If a single string is passed as arg to texts, convert to list
if isinstance(texts, str):
texts = [texts]

# Dynamically create the EmbedContentConfig, preserving the constructor's task_type
config = genai.types.EmbedContentConfig(
task_type=kwargs.pop(
"task_type", self.model_config.task_type
), # Use the constructor's task_type if not overridden
**kwargs, # Pass any additional configuration options
)

# The Vertex AI call to embed content
try:
embeddings = self.vectoriser.models.embed_content(
model=self.model_name, contents=texts, config=self.model_config
)
embeddings = self.vectoriser.models.embed_content(model=self.model_name, contents=texts, config=config)
except Exception as e:
raise ExternalServiceError(
"GCP embedding request failed.",
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