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@@ -455,7 +455,7 @@ def create(
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chat_completion_request=ChatCompletionRequest(
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chat_completion_request=ChatCompletionRequest(
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model="command-r-plus", # TODO
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model="command-r-plus", # TODO
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messages=[cast_message_to_subtype(m.to_openai_dict()) for m in messages],
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messages=[cast_message_to_subtype(m.to_openai_dict()) for m in messages],
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tools=[{"type": "function", "function": f} for f in functions] if functions else None,
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tools=tools,
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tool_choice=function_call,
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tool_choice=function_call,
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# user=str(user_id),
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# user=str(user_id),
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# NOTE: max_tokens is required for Anthropic API
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# NOTE: max_tokens is required for Anthropic API
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@@ -463,6 +463,10 @@ def create(
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),
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),
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)
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)
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elif llm_config.model_endpoint_type == "groq":
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if stream:
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raise NotImplementedError(f"Streaming not yet implemented for {llm_config.model_endpoint_type}")
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# local model
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# local model
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else:
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else:
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if stream:
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if stream:
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@@ -1,4 +1,4 @@
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from typing import Optional
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from typing import Literal, Optional
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from pydantic import BaseModel, ConfigDict, Field
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from pydantic import BaseModel, ConfigDict, Field
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@@ -16,7 +16,7 @@ class LLMConfig(BaseModel):
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"""
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"""
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# TODO: 🤮 don't default to a vendor! bug city!
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# TODO: 🤮 don't default to a vendor! bug city!
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model: str = Field(..., description="LLM model name. ")
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model: Literal["openai", "anthropic", "cohere", "google_ai", "azure", "groq"] = Field(..., description="LLM model name. ")
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model_endpoint_type: str = Field(..., description="The endpoint type for the model.")
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model_endpoint_type: str = Field(..., description="The endpoint type for the model.")
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model_endpoint: str = Field(..., description="The endpoint for the model.")
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model_endpoint: str = Field(..., description="The endpoint for the model.")
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model_wrapper: Optional[str] = Field(None, description="The wrapper for the model.")
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model_wrapper: Optional[str] = Field(None, description="The wrapper for the model.")
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