mirror of
https://github.com/BerriAI/litellm.git
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Merge branch 'main' into explicit-args-acomplete
This commit is contained in:
Mateo Cámara
GitHub
commit
203089e6c7
10 changed files with 271 additions and 477 deletions
331
litellm/main.py
331
litellm/main.py
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@ -1173,7 +1173,7 @@ def completion(
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acompletion=acompletion,
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logging_obj=logging,
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custom_prompt_dict=custom_prompt_dict,
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timeout=timeout
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timeout=timeout,
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)
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if (
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"stream" in optional_params
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@ -2894,158 +2894,167 @@ def image_generation(
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Currently supports just Azure + OpenAI.
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"""
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aimg_generation = kwargs.get("aimg_generation", False)
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litellm_call_id = kwargs.get("litellm_call_id", None)
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logger_fn = kwargs.get("logger_fn", None)
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proxy_server_request = kwargs.get("proxy_server_request", None)
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model_info = kwargs.get("model_info", None)
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metadata = kwargs.get("metadata", {})
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try:
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aimg_generation = kwargs.get("aimg_generation", False)
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litellm_call_id = kwargs.get("litellm_call_id", None)
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logger_fn = kwargs.get("logger_fn", None)
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proxy_server_request = kwargs.get("proxy_server_request", None)
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model_info = kwargs.get("model_info", None)
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metadata = kwargs.get("metadata", {})
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model_response = litellm.utils.ImageResponse()
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if model is not None or custom_llm_provider is not None:
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model, custom_llm_provider, dynamic_api_key, api_base = get_llm_provider(model=model, custom_llm_provider=custom_llm_provider, api_base=api_base) # type: ignore
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else:
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model = "dall-e-2"
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custom_llm_provider = "openai" # default to dall-e-2 on openai
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openai_params = [
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"user",
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"request_timeout",
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"api_base",
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"api_version",
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"api_key",
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"deployment_id",
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"organization",
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"base_url",
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"default_headers",
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"timeout",
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"max_retries",
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"n",
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"quality",
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"size",
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"style",
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]
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litellm_params = [
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"metadata",
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"aimg_generation",
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"caching",
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"mock_response",
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"api_key",
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"api_version",
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"api_base",
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"force_timeout",
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"logger_fn",
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"verbose",
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"custom_llm_provider",
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"litellm_logging_obj",
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"litellm_call_id",
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"use_client",
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"id",
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"fallbacks",
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"azure",
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"headers",
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"model_list",
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"num_retries",
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"context_window_fallback_dict",
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"roles",
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"final_prompt_value",
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"bos_token",
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"eos_token",
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"request_timeout",
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"complete_response",
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"self",
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"client",
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"rpm",
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"tpm",
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"input_cost_per_token",
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"output_cost_per_token",
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"hf_model_name",
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"proxy_server_request",
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"model_info",
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"preset_cache_key",
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"caching_groups",
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"ttl",
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"cache",
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]
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default_params = openai_params + litellm_params
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non_default_params = {
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k: v for k, v in kwargs.items() if k not in default_params
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} # model-specific params - pass them straight to the model/provider
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optional_params = get_optional_params_image_gen(
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n=n,
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quality=quality,
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response_format=response_format,
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size=size,
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style=style,
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user=user,
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custom_llm_provider=custom_llm_provider,
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**non_default_params,
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)
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logging = litellm_logging_obj
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logging.update_environment_variables(
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model=model,
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user=user,
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optional_params=optional_params,
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litellm_params={
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"timeout": timeout,
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"azure": False,
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"litellm_call_id": litellm_call_id,
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"logger_fn": logger_fn,
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"proxy_server_request": proxy_server_request,
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"model_info": model_info,
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"metadata": metadata,
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"preset_cache_key": None,
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"stream_response": {},
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},
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)
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if custom_llm_provider == "azure":
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# azure configs
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api_type = get_secret("AZURE_API_TYPE") or "azure"
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api_base = api_base or litellm.api_base or get_secret("AZURE_API_BASE")
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api_version = (
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api_version or litellm.api_version or get_secret("AZURE_API_VERSION")
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model_response = litellm.utils.ImageResponse()
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if model is not None or custom_llm_provider is not None:
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model, custom_llm_provider, dynamic_api_key, api_base = get_llm_provider(model=model, custom_llm_provider=custom_llm_provider, api_base=api_base) # type: ignore
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else:
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model = "dall-e-2"
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custom_llm_provider = "openai" # default to dall-e-2 on openai
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openai_params = [
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"user",
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"request_timeout",
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"api_base",
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"api_version",
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"api_key",
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"deployment_id",
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"organization",
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"base_url",
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"default_headers",
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"timeout",
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"max_retries",
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"n",
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"quality",
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"size",
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"style",
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]
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litellm_params = [
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"metadata",
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"aimg_generation",
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"caching",
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"mock_response",
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"api_key",
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"api_version",
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"api_base",
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"force_timeout",
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"logger_fn",
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"verbose",
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"custom_llm_provider",
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"litellm_logging_obj",
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"litellm_call_id",
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"use_client",
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"id",
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"fallbacks",
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"azure",
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"headers",
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"model_list",
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"num_retries",
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"context_window_fallback_dict",
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"roles",
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"final_prompt_value",
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"bos_token",
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"eos_token",
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"request_timeout",
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"complete_response",
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"self",
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"client",
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"rpm",
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"tpm",
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"input_cost_per_token",
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"output_cost_per_token",
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"hf_model_name",
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"proxy_server_request",
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"model_info",
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"preset_cache_key",
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"caching_groups",
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"ttl",
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"cache",
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]
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default_params = openai_params + litellm_params
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non_default_params = {
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k: v for k, v in kwargs.items() if k not in default_params
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} # model-specific params - pass them straight to the model/provider
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optional_params = get_optional_params_image_gen(
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n=n,
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quality=quality,
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response_format=response_format,
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size=size,
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style=style,
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user=user,
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custom_llm_provider=custom_llm_provider,
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**non_default_params,
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)
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api_key = (
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api_key
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or litellm.api_key
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or litellm.azure_key
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or get_secret("AZURE_OPENAI_API_KEY")
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or get_secret("AZURE_API_KEY")
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)
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azure_ad_token = optional_params.pop("azure_ad_token", None) or get_secret(
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"AZURE_AD_TOKEN"
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)
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model_response = azure_chat_completions.image_generation(
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logging = litellm_logging_obj
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logging.update_environment_variables(
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model=model,
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prompt=prompt,
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timeout=timeout,
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api_key=api_key,
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api_base=api_base,
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logging_obj=litellm_logging_obj,
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user=user,
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optional_params=optional_params,
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model_response=model_response,
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api_version=api_version,
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aimg_generation=aimg_generation,
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)
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elif custom_llm_provider == "openai":
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model_response = openai_chat_completions.image_generation(
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model=model,
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prompt=prompt,
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timeout=timeout,
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api_key=api_key,
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api_base=api_base,
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logging_obj=litellm_logging_obj,
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optional_params=optional_params,
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model_response=model_response,
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aimg_generation=aimg_generation,
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litellm_params={
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"timeout": timeout,
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"azure": False,
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"litellm_call_id": litellm_call_id,
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"logger_fn": logger_fn,
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"proxy_server_request": proxy_server_request,
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"model_info": model_info,
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"metadata": metadata,
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"preset_cache_key": None,
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"stream_response": {},
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},
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)
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return model_response
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if custom_llm_provider == "azure":
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# azure configs
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api_type = get_secret("AZURE_API_TYPE") or "azure"
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api_base = api_base or litellm.api_base or get_secret("AZURE_API_BASE")
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api_version = (
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api_version or litellm.api_version or get_secret("AZURE_API_VERSION")
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)
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api_key = (
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api_key
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or litellm.api_key
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or litellm.azure_key
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or get_secret("AZURE_OPENAI_API_KEY")
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or get_secret("AZURE_API_KEY")
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)
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azure_ad_token = optional_params.pop("azure_ad_token", None) or get_secret(
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"AZURE_AD_TOKEN"
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)
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model_response = azure_chat_completions.image_generation(
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model=model,
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prompt=prompt,
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timeout=timeout,
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api_key=api_key,
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api_base=api_base,
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logging_obj=litellm_logging_obj,
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optional_params=optional_params,
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model_response=model_response,
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api_version=api_version,
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aimg_generation=aimg_generation,
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)
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elif custom_llm_provider == "openai":
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model_response = openai_chat_completions.image_generation(
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model=model,
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prompt=prompt,
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timeout=timeout,
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api_key=api_key,
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api_base=api_base,
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logging_obj=litellm_logging_obj,
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optional_params=optional_params,
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model_response=model_response,
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aimg_generation=aimg_generation,
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)
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return model_response
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except Exception as e:
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## Map to OpenAI Exception
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raise exception_type(
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model=model,
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custom_llm_provider=custom_llm_provider,
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original_exception=e,
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completion_kwargs=locals(),
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)
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##### Health Endpoints #######################
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@ -3170,7 +3179,8 @@ def config_completion(**kwargs):
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"No config path set, please set a config path using `litellm.config_path = 'path/to/config.json'`"
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)
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def stream_chunk_builder_text_completion(chunks: list, messages: Optional[List]=None):
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def stream_chunk_builder_text_completion(chunks: list, messages: Optional[List] = None):
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id = chunks[0]["id"]
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object = chunks[0]["object"]
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created = chunks[0]["created"]
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@ -3187,23 +3197,27 @@ def stream_chunk_builder_text_completion(chunks: list, messages: Optional[List]=
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"system_fingerprint": system_fingerprint,
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"choices": [
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{
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"text": None,
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"index": 0,
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"logprobs": logprobs,
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"finish_reason": finish_reason
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"text": None,
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"index": 0,
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"logprobs": logprobs,
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"finish_reason": finish_reason,
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}
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],
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"usage": {
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"prompt_tokens": None,
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"completion_tokens": None,
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"total_tokens": None
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}
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"total_tokens": None,
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},
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}
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content_list = []
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for chunk in chunks:
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choices = chunk["choices"]
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for choice in choices:
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if choice is not None and hasattr(choice, "text") and choice.get("text") is not None:
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if (
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choice is not None
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and hasattr(choice, "text")
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and choice.get("text") is not None
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):
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_choice = choice.get("text")
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content_list.append(_choice)
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@ -3235,13 +3249,18 @@ def stream_chunk_builder_text_completion(chunks: list, messages: Optional[List]=
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)
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return response
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def stream_chunk_builder(chunks: list, messages: Optional[list] = None):
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id = chunks[0]["id"]
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object = chunks[0]["object"]
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created = chunks[0]["created"]
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model = chunks[0]["model"]
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system_fingerprint = chunks[0].get("system_fingerprint", None)
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if isinstance(chunks[0]["choices"][0], litellm.utils.TextChoices): # route to the text completion logic
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if isinstance(
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chunks[0]["choices"][0], litellm.utils.TextChoices
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): # route to the text completion logic
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return stream_chunk_builder_text_completion(chunks=chunks, messages=messages)
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role = chunks[0]["choices"][0]["delta"]["role"]
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finish_reason = chunks[-1]["choices"][0]["finish_reason"]
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|
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