mirror of
https://github.com/BerriAI/litellm.git
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Merge branch 'main' into litellm_track_imagen_spend_logs
This commit is contained in:
Ishaan Jaff
GitHub
commit
c1adb0b7f2
32 changed files with 1384 additions and 226 deletions
139
litellm/utils.py
139
litellm/utils.py
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@ -2550,7 +2550,7 @@ def get_optional_params_image_gen(
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def get_optional_params_embeddings(
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# 2 optional params
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model=None,
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model: str,
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user=None,
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encoding_format=None,
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dimensions=None,
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@ -2606,7 +2606,7 @@ def get_optional_params_embeddings(
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):
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raise UnsupportedParamsError(
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status_code=500,
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message=f"Setting dimensions is not supported for OpenAI `text-embedding-3` and later models. To drop it from the call, set `litellm.drop_params = True`.",
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message="Setting dimensions is not supported for OpenAI `text-embedding-3` and later models. To drop it from the call, set `litellm.drop_params = True`.",
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)
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if custom_llm_provider == "triton":
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keys = list(non_default_params.keys())
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@ -2641,39 +2641,57 @@ def get_optional_params_embeddings(
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)
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final_params = {**optional_params, **kwargs}
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return final_params
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if custom_llm_provider == "vertex_ai":
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if len(non_default_params.keys()) > 0:
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if litellm.drop_params is True: # drop the unsupported non-default values
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keys = list(non_default_params.keys())
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for k in keys:
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non_default_params.pop(k, None)
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final_params = {**non_default_params, **kwargs}
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return final_params
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raise UnsupportedParamsError(
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status_code=500,
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message=f"Setting user/encoding format is not supported by {custom_llm_provider}. To drop it from the call, set `litellm.drop_params = True`.",
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)
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if custom_llm_provider == "bedrock":
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# if dimensions is in non_default_params -> pass it for model=bedrock/amazon.titan-embed-text-v2
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if (
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"dimensions" in non_default_params.keys()
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and "amazon.titan-embed-text-v2" in model
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):
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kwargs["dimensions"] = non_default_params["dimensions"]
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non_default_params.pop("dimensions", None)
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if "amazon.titan-embed-text-v1" in model:
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object: Any = litellm.AmazonTitanG1Config()
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elif "amazon.titan-embed-image-v1" in model:
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object = litellm.AmazonTitanMultimodalEmbeddingG1Config()
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elif "amazon.titan-embed-text-v2:0" in model:
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object = litellm.AmazonTitanV2Config()
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elif "cohere.embed-multilingual-v3" in model:
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object = litellm.BedrockCohereEmbeddingConfig()
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else: # unmapped model
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supported_params = []
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_check_valid_arg(supported_params=supported_params)
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final_params = {**kwargs}
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return final_params
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if len(non_default_params.keys()) > 0:
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if litellm.drop_params is True: # drop the unsupported non-default values
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keys = list(non_default_params.keys())
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for k in keys:
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non_default_params.pop(k, None)
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final_params = {**non_default_params, **kwargs}
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return final_params
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raise UnsupportedParamsError(
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status_code=500,
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message=f"Setting user/encoding format is not supported by {custom_llm_provider}. To drop it from the call, set `litellm.drop_params = True`.",
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)
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return {**non_default_params, **kwargs}
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supported_params = object.get_supported_openai_params()
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_check_valid_arg(supported_params=supported_params)
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optional_params = object.map_openai_params(
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non_default_params=non_default_params, optional_params={}
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)
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final_params = {**optional_params, **kwargs}
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return final_params
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# elif model == "amazon.titan-embed-image-v1":
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# supported_params = litellm.AmazonTitanG1Config().get_supported_openai_params()
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# _check_valid_arg(supported_params=supported_params)
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# optional_params = litellm.AmazonTitanG1Config().map_openai_params(
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# non_default_params=non_default_params, optional_params={}
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# )
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# final_params = {**optional_params, **kwargs}
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# return final_params
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# if (
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# "dimensions" in non_default_params.keys()
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# and "amazon.titan-embed-text-v2" in model
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# ):
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# kwargs["dimensions"] = non_default_params["dimensions"]
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# non_default_params.pop("dimensions", None)
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# if len(non_default_params.keys()) > 0:
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# if litellm.drop_params is True: # drop the unsupported non-default values
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# keys = list(non_default_params.keys())
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# for k in keys:
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# non_default_params.pop(k, None)
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# final_params = {**non_default_params, **kwargs}
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# return final_params
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# raise UnsupportedParamsError(
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# status_code=500,
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# message=f"Setting user/encoding format is not supported by {custom_llm_provider}. To drop it from the call, set `litellm.drop_params = True`.",
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# )
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# return {**non_default_params, **kwargs}
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if custom_llm_provider == "mistral":
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supported_params = get_supported_openai_params(
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model=model,
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@ -2869,6 +2887,7 @@ def get_optional_params(
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and custom_llm_provider != "groq"
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and custom_llm_provider != "nvidia_nim"
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and custom_llm_provider != "cerebras"
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and custom_llm_provider != "ai21_chat"
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and custom_llm_provider != "volcengine"
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and custom_llm_provider != "deepseek"
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and custom_llm_provider != "codestral"
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@ -3638,6 +3657,16 @@ def get_optional_params(
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optional_params=optional_params,
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model=model,
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)
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elif custom_llm_provider == "ai21_chat":
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supported_params = get_supported_openai_params(
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model=model, custom_llm_provider=custom_llm_provider
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)
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_check_valid_arg(supported_params=supported_params)
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optional_params = litellm.AI21ChatConfig().map_openai_params(
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non_default_params=non_default_params,
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optional_params=optional_params,
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model=model,
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)
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elif custom_llm_provider == "fireworks_ai":
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supported_params = get_supported_openai_params(
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model=model, custom_llm_provider=custom_llm_provider
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@ -4265,6 +4294,8 @@ def get_supported_openai_params(
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return litellm.NvidiaNimConfig().get_supported_openai_params(model=model)
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elif custom_llm_provider == "cerebras":
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return litellm.CerebrasConfig().get_supported_openai_params(model=model)
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elif custom_llm_provider == "ai21_chat":
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return litellm.AI21ChatConfig().get_supported_openai_params(model=model)
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elif custom_llm_provider == "volcengine":
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return litellm.VolcEngineConfig().get_supported_openai_params(model=model)
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elif custom_llm_provider == "groq":
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@ -4653,6 +4684,7 @@ def get_llm_provider(
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):
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custom_llm_provider = model.split("/", 1)[0]
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model = model.split("/", 1)[1]
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if custom_llm_provider == "perplexity":
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# perplexity is openai compatible, we just need to set this to custom_openai and have the api_base be https://api.perplexity.ai
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api_base = api_base or get_secret("PERPLEXITY_API_BASE") or "https://api.perplexity.ai" # type: ignore
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@ -4699,6 +4731,16 @@ def get_llm_provider(
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or "https://api.cerebras.ai/v1"
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) # type: ignore
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dynamic_api_key = api_key or get_secret("CEREBRAS_API_KEY")
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elif (custom_llm_provider == "ai21_chat") or (
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custom_llm_provider == "ai21" and model in litellm.ai21_chat_models
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):
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api_base = (
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api_base
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or get_secret("AI21_API_BASE")
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or "https://api.ai21.com/studio/v1"
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) # type: ignore
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dynamic_api_key = api_key or get_secret("AI21_API_KEY")
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custom_llm_provider = "ai21_chat"
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elif custom_llm_provider == "volcengine":
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# volcengine is openai compatible, we just need to set this to custom_openai and have the api_base be https://api.endpoints.anyscale.com/v1
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api_base = (
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@ -4852,6 +4894,9 @@ def get_llm_provider(
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elif endpoint == "https://api.cerebras.ai/v1":
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custom_llm_provider = "cerebras"
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dynamic_api_key = get_secret("CEREBRAS_API_KEY")
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elif endpoint == "https://api.ai21.com/studio/v1":
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custom_llm_provider = "ai21_chat"
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dynamic_api_key = get_secret("AI21_API_KEY")
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elif endpoint == "https://codestral.mistral.ai/v1":
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custom_llm_provider = "codestral"
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dynamic_api_key = get_secret("CODESTRAL_API_KEY")
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@ -4936,6 +4981,14 @@ def get_llm_provider(
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## ai21
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elif model in litellm.ai21_models:
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custom_llm_provider = "ai21"
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elif model in litellm.ai21_chat_models:
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custom_llm_provider = "ai21_chat"
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api_base = (
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api_base
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or get_secret("AI21_API_BASE")
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or "https://api.ai21.com/studio/v1"
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) # type: ignore
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dynamic_api_key = api_key or get_secret("AI21_API_KEY")
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## aleph_alpha
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elif model in litellm.aleph_alpha_models:
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custom_llm_provider = "aleph_alpha"
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@ -5783,6 +5836,11 @@ def validate_environment(
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keys_in_environment = True
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else:
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missing_keys.append("CEREBRAS_API_KEY")
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elif custom_llm_provider == "ai21_chat":
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if "AI21_API_KEY" in os.environ:
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keys_in_environment = True
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else:
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missing_keys.append("AI21_API_KEY")
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elif custom_llm_provider == "volcengine":
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if "VOLCENGINE_API_KEY" in os.environ:
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keys_in_environment = True
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@ -6194,7 +6252,10 @@ def convert_to_model_response_object(
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if "model" in response_object:
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if model_response_object.model is None:
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model_response_object.model = response_object["model"]
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elif "/" in model_response_object.model:
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elif (
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"/" in model_response_object.model
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and response_object["model"] is not None
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):
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openai_compatible_provider = model_response_object.model.split("/")[
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0
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]
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@ -9889,11 +9950,7 @@ class CustomStreamWrapper:
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if anthropic_response_obj["usage"] is not None:
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model_response.usage = litellm.Usage(
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prompt_tokens=anthropic_response_obj["usage"]["prompt_tokens"],
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completion_tokens=anthropic_response_obj["usage"][
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"completion_tokens"
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],
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total_tokens=anthropic_response_obj["usage"]["total_tokens"],
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**anthropic_response_obj["usage"]
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)
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if (
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@ -10508,10 +10565,10 @@ class CustomStreamWrapper:
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original_chunk.system_fingerprint
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)
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print_verbose(f"self.sent_first_chunk: {self.sent_first_chunk}")
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if self.sent_first_chunk == False:
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if self.sent_first_chunk is False:
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model_response.choices[0].delta["role"] = "assistant"
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self.sent_first_chunk = True
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elif self.sent_first_chunk == True and hasattr(
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elif self.sent_first_chunk is True and hasattr(
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model_response.choices[0].delta, "role"
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):
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_initial_delta = model_response.choices[
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@ -10576,7 +10633,7 @@ class CustomStreamWrapper:
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model_response.choices[0].delta.tool_calls is not None
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or model_response.choices[0].delta.function_call is not None
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):
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if self.sent_first_chunk == False:
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if self.sent_first_chunk is False:
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model_response.choices[0].delta["role"] = "assistant"
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self.sent_first_chunk = True
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return model_response
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