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fix(vertex_ai.py): check if message length > 0 before merging
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df1a2ca5c2
commit
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2 changed files with 7 additions and 4 deletions
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@ -337,7 +337,7 @@ def _gemini_convert_messages_with_history(messages: list) -> List[ContentType]:
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_parts: List[PartType] = []
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for element in messages[msg_i]["content"]:
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if isinstance(element, dict):
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if element["type"] == "text":
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if element["type"] == "text" and len(element["text"]) > 0:
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_part = PartType(text=element["text"])
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_parts.append(_part)
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elif element["type"] == "image_url":
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@ -345,7 +345,10 @@ def _gemini_convert_messages_with_history(messages: list) -> List[ContentType]:
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_part = _process_gemini_image(image_url=image_url)
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_parts.append(_part) # type: ignore
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user_content.extend(_parts)
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else:
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elif (
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isinstance(messages[msg_i]["content"], str)
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and len(messages[msg_i]["content"]) > 0
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):
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_part = PartType(text=messages[msg_i]["content"])
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user_content.append(_part)
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@ -570,7 +570,6 @@ async def test_gemini_pro_vision(provider, sync_mode):
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# Google counts the prompt tokens for us, we should ensure we use the tokens from the orignal response
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assert prompt_tokens == 263 # the gemini api returns 263 to us
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assert False
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except litellm.RateLimitError as e:
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pass
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except Exception as e:
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@ -1164,6 +1163,7 @@ def test_gemini_pro_vision_async():
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resp = await litellm.acompletion(
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model="vertex_ai/gemini-pro-vision",
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messages=[
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{"role": "system", "content": ""},
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{
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"role": "user",
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"content": [
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@ -1175,7 +1175,7 @@ def test_gemini_pro_vision_async():
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},
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},
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],
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}
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},
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],
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)
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print("async response gemini pro vision")
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