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refactor(vertex): drop unused resolved_files from embed response parsing

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Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
This commit is contained in:
kerry 2026-09-15 00:45:08 +00:00
parent e26a4970dd
commit 0c91d9157c
3 changed files with 1 additions and 16 deletions
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2
litellm/llms/vertex_ai/gemini_embeddings/batch_embed_content_handler.py
View file

@ -268,7 +268,6 @@ class GoogleBatchEmbeddings(VertexLLM):
model_response=model_response,
model=model,
response_json=_json_response,
resolved_files=resolved_files,
)
else:
_predictions: Final = VertexAIBatchEmbeddingsResponseObject(**_json_response)
@ -372,7 +371,6 @@ class GoogleBatchEmbeddings(VertexLLM):
model_response=model_response,
model=model,
response_json=_json_response,
resolved_files=resolved_files,
)
else:
_predictions: Final = VertexAIBatchEmbeddingsResponseObject(**_json_response)

3
litellm/llms/vertex_ai/gemini_embeddings/batch_embed_content_transformation.py
View file

@ -4,7 +4,7 @@ Transformation logic from OpenAI /v1/embeddings format to Google AI Studio /batc
Why separate file? Make it easy to see how transformation works
"""
from collections.abc import Mapping, Sequence
from collections.abc import Sequence
from typing import Final
from pydantic import TypeAdapter, ValidationError
@ -356,7 +356,6 @@ def process_embed_content_response(
model_response: EmbeddingResponse,
model: str,
response_json: dict,
resolved_files: Mapping[str, Mapping[str, str]] | None = None,
) -> EmbeddingResponse:
"""
Process Gemini embedContent response (single embedding for multimodal input).

12
tests/test_litellm/llms/vertex_ai/gemini_embeddings/test_batch_embed_content_transformation.py
View file

@ -411,12 +411,6 @@ class TestProcessEmbedContentResponseUsage:
model_response=EmbeddingResponse(),
model=self.MODEL,
response_json=response_json,
resolved_files={
"files/img123": {
"mime_type": "image/png",
"uri": "https://example.com/img123",
}
},
)
assert result.usage.prompt_tokens_details.image_tokens == 258
assert result.usage.prompt_tokens_details.text_tokens == 0
@ -443,12 +437,6 @@ class TestProcessEmbedContentResponseUsage:
model_response=EmbeddingResponse(),
model=self.MODEL,
response_json=response_json,
resolved_files={
"files/clip1": {
"mime_type": "audio/mpeg",
"uri": "https://example.com/clip1",
}
},
)
assert result.usage.prompt_tokens_details.audio_tokens == 64
assert result.usage.prompt_tokens_details.image_tokens == 0