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feat(vertex_ai_partner.py): initial working commit for calling vertex ai mistral

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Closes https://github.com/BerriAI/litellm/issues/4874
This commit is contained in:
Krrish Dholakia 2024-07-27 12:54:14 -07:00
parent 1a8f45e8da
commit 5b71421a7b
10 changed files with 343 additions and 140 deletions
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28
litellm/utils.py
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@ -3104,6 +3104,15 @@ def get_optional_params(
non_default_params=non_default_params,
optional_params=optional_params,
)
elif custom_llm_provider == "vertex_ai" and model in litellm.vertex_mistral_models:
supported_params = get_supported_openai_params(
model=model, custom_llm_provider=custom_llm_provider
)
_check_valid_arg(supported_params=supported_params)
optional_params = litellm.MistralConfig().map_openai_params(
non_default_params=non_default_params,
optional_params=optional_params,
)
elif custom_llm_provider == "sagemaker":
## check if unsupported param passed in
supported_params = get_supported_openai_params(
@ -4210,7 +4219,8 @@ def get_supported_openai_params(
if request_type == "chat_completion":
if model.startswith("meta/"):
return litellm.VertexAILlama3Config().get_supported_openai_params()
if model.startswith("mistral"):
return litellm.MistralConfig().get_supported_openai_params()
return litellm.VertexAIConfig().get_supported_openai_params()
elif request_type == "embeddings":
return litellm.VertexAITextEmbeddingConfig().get_supported_openai_params()
@ -9631,22 +9641,6 @@ class CustomStreamWrapper:
completion_tokens=response_obj["usage"].completion_tokens,
total_tokens=response_obj["usage"].total_tokens,
)
elif self.custom_llm_provider == "databricks":
response_obj = litellm.DatabricksConfig()._chunk_parser(chunk)
completion_obj["content"] = response_obj["text"]
print_verbose(f"completion obj content: {completion_obj['content']}")
if response_obj["is_finished"]:
self.received_finish_reason = response_obj["finish_reason"]
if (
self.stream_options
and self.stream_options.get("include_usage", False) == True
and response_obj["usage"] is not None
):
model_response.usage = litellm.Usage(
prompt_tokens=response_obj["usage"].prompt_tokens,
completion_tokens=response_obj["usage"].completion_tokens,
total_tokens=response_obj["usage"].total_tokens,
)
elif self.custom_llm_provider == "azure_text":
response_obj = self.handle_azure_text_completion_chunk(chunk)
completion_obj["content"] = response_obj["text"]