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Litellm code qa common config (#7113)
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* feat(base_llm): initial commit for common base config class

Addresses code qa critique https://github.com/andrewyng/aisuite/issues/113#issuecomment-2512369132

* feat(base_llm/): add transform request/response abstract methods to base config class

* feat(cohere-+-clarifai): refactor integrations to use common base config class

* fix: fix linting errors

* refactor(anthropic/): move anthropic + vertex anthropic to use base config

* test: fix xai test

* test: fix tests

* fix: fix linting errors

* test: comment out WIP test

* fix(transformation.py): fix is pdf used check

* fix: fix linting error
This commit is contained in:
Krish Dholakia 2024-12-09 15:58:25 -08:00 • committed by GitHub
parent 51ead67b4f
commit 5bbf906c83
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41 changed files with 1877 additions and 1998 deletions
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95
litellm/utils.py
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@ -2821,9 +2821,14 @@ def get_optional_params( # noqa: PLR0915
)
_check_valid_arg(supported_params=supported_params)
optional_params = litellm.AnthropicConfig().map_openai_params(
model=model,
non_default_params=non_default_params,
optional_params=optional_params,
messages=messages,
drop_params=(
drop_params
if drop_params is not None and isinstance(drop_params, bool)
else False
),
)
elif custom_llm_provider == "cohere":
## check if unsupported param passed in
@ -2832,24 +2837,16 @@ def get_optional_params( # noqa: PLR0915
)
_check_valid_arg(supported_params=supported_params)
# handle cohere params
if stream:
optional_params["stream"] = stream
if temperature is not None:
optional_params["temperature"] = temperature
if max_tokens is not None:
optional_params["max_tokens"] = max_tokens
if n is not None:
optional_params["num_generations"] = n
if logit_bias is not None:
optional_params["logit_bias"] = logit_bias
if top_p is not None:
optional_params["p"] = top_p
if frequency_penalty is not None:
optional_params["frequency_penalty"] = frequency_penalty
if presence_penalty is not None:
optional_params["presence_penalty"] = presence_penalty
if stop is not None:
optional_params["stop_sequences"] = stop
optional_params = litellm.CohereConfig().map_openai_params(
non_default_params=non_default_params,
optional_params=optional_params,
model=model,
drop_params=(
drop_params
if drop_params is not None and isinstance(drop_params, bool)
else False
),
)
elif custom_llm_provider == "cohere_chat":
## check if unsupported param passed in
supported_params = get_supported_openai_params(
@ -2857,26 +2854,17 @@ def get_optional_params( # noqa: PLR0915
)
_check_valid_arg(supported_params=supported_params)
# handle cohere params
if stream:
optional_params["stream"] = stream
if temperature is not None:
optional_params["temperature"] = temperature
if max_tokens is not None:
optional_params["max_tokens"] = max_tokens
if n is not None:
optional_params["num_generations"] = n
if top_p is not None:
optional_params["p"] = top_p
if frequency_penalty is not None:
optional_params["frequency_penalty"] = frequency_penalty
if presence_penalty is not None:
optional_params["presence_penalty"] = presence_penalty
if stop is not None:
optional_params["stop_sequences"] = stop
if tools is not None:
optional_params["tools"] = tools
if seed is not None:
optional_params["seed"] = seed
optional_params = litellm.CohereChatConfig().map_openai_params(
non_default_params=non_default_params,
optional_params=optional_params,
model=model,
drop_params=(
drop_params
if drop_params is not None and isinstance(drop_params, bool)
else False
),
)
elif custom_llm_provider == "maritalk":
## check if unsupported param passed in
supported_params = get_supported_openai_params(
@ -3071,8 +3059,14 @@ def get_optional_params( # noqa: PLR0915
)
_check_valid_arg(supported_params=supported_params)
optional_params = litellm.VertexAIAnthropicConfig().map_openai_params(
model=model,
non_default_params=non_default_params,
optional_params=optional_params,
drop_params=(
drop_params
if drop_params is not None and isinstance(drop_params, bool)
else False
),
)
elif custom_llm_provider == "vertex_ai" and model in litellm.vertex_llama3_models:
supported_params = get_supported_openai_params(
@ -6220,14 +6214,14 @@ def validate_chat_completion_user_messages(messages: List[AllMessageValues]):
return messages
from litellm.llms.OpenAI.chat.gpt_transformation import OpenAIGPTConfig
from litellm.llms.base_llm.transformation import BaseConfig
class ProviderConfigManager:
@staticmethod
def get_provider_config(
def get_provider_chat_config(
model: str, provider: litellm.LlmProviders
) -> OpenAIGPTConfig:
) -> BaseConfig:
"""
Returns the provider config for a given provider.
"""
@ -6239,8 +6233,23 @@ class ProviderConfigManager:
return litellm.GroqChatConfig()
elif litellm.LlmProviders.DATABRICKS == provider:
return litellm.DatabricksConfig()
elif litellm.LlmProviders.XAI == provider:
return litellm.XAIChatConfig()
elif litellm.LlmProviders.TEXT_COMPLETION_OPENAI == provider:
return litellm.OpenAITextCompletionConfig()
elif litellm.LlmProviders.COHERE_CHAT == provider:
return litellm.CohereChatConfig()
elif litellm.LlmProviders.COHERE == provider:
return litellm.CohereConfig()
elif litellm.LlmProviders.CLARIFAI == provider:
return litellm.ClarifaiConfig()
elif litellm.LlmProviders.ANTHROPIC == provider:
return litellm.AnthropicConfig()
elif litellm.LlmProviders.VERTEX_AI == provider:
if "claude" in model:
return litellm.VertexAIAnthropicConfig()
return OpenAIGPTConfig()
return litellm.OpenAIGPTConfig()
def get_end_user_id_for_cost_tracking(