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refactor(utils.py): make it clearer how vertex ai params are handled '

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This commit is contained in:
Krrish Dholakia 2024-04-17 16:20:56 -07:00
parent 409bd5b4ab
commit 32d94feddd
4 changed files with 74 additions and 42 deletions
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50
litellm/utils.py
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@ -4878,37 +4878,11 @@ def get_optional_params(
)
_check_valid_arg(supported_params=supported_params)
if temperature is not None:
optional_params["temperature"] = temperature
if top_p is not None:
optional_params["top_p"] = top_p
if stream:
optional_params["stream"] = stream
if n is not None:
optional_params["candidate_count"] = n
if stop is not None:
if isinstance(stop, str):
optional_params["stop_sequences"] = [stop]
elif isinstance(stop, list):
optional_params["stop_sequences"] = stop
if max_tokens is not None:
optional_params["max_output_tokens"] = max_tokens
if response_format is not None and response_format["type"] == "json_object":
optional_params["response_mime_type"] = "application/json"
if tools is not None and isinstance(tools, list):
from vertexai.preview import generative_models
optional_params = litellm.VertexAIConfig().map_openai_params(
non_default_params=non_default_params,
optional_params=optional_params,
)
gtool_func_declarations = []
for tool in tools:
gtool_func_declaration = generative_models.FunctionDeclaration(
name=tool["function"]["name"],
description=tool["function"].get("description", ""),
parameters=tool["function"].get("parameters", {}),
)
gtool_func_declarations.append(gtool_func_declaration)
optional_params["tools"] = [
generative_models.Tool(function_declarations=gtool_func_declarations)
]
print_verbose(
f"(end) INSIDE THE VERTEX AI OPTIONAL PARAM BLOCK - optional_params: {optional_params}"
)
@ -5610,17 +5584,7 @@ def get_supported_openai_params(model: str, custom_llm_provider: str):
elif custom_llm_provider == "palm" or custom_llm_provider == "gemini":
return ["temperature", "top_p", "stream", "n", "stop", "max_tokens"]
elif custom_llm_provider == "vertex_ai":
return [
"temperature",
"top_p",
"max_tokens",
"stream",
"tools",
"tool_choice",
"response_format",
"n",
"stop",
]
return litellm.VertexAIConfig().get_supported_openai_params()
elif custom_llm_provider == "sagemaker":
return ["stream", "temperature", "max_tokens", "top_p", "stop", "n"]
elif custom_llm_provider == "aleph_alpha":
@ -10595,7 +10559,9 @@ def trim_messages(
if max_tokens is None:
# Check if model is valid
if model in litellm.model_cost:
max_tokens_for_model = litellm.model_cost[model].get("max_input_tokens", litellm.model_cost[model]["max_tokens"])
max_tokens_for_model = litellm.model_cost[model].get(
"max_input_tokens", litellm.model_cost[model]["max_tokens"]
)
max_tokens = int(max_tokens_for_model * trim_ratio)
else:
# if user did not specify max (input) tokens