25d54dd896
* auto fixes * auto fix pt2 and transitive deps and undefined var checking locals() * manual fixes (ignored or letta-code fixed) * fix circular import
77 lines
3.9 KiB
Python
77 lines
3.9 KiB
Python
from typing import Optional
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from pydantic import Field
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from letta.schemas.enums import PrimitiveType
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from letta.schemas.letta_base import OrmMetadataBase
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class ProviderModelBase(OrmMetadataBase):
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__id_prefix__ = PrimitiveType.PROVIDER_MODEL.value
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class ProviderModel(ProviderModelBase):
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"""
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Pydantic model for provider models.
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This represents individual models available from providers with a unique handle
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that decouples the user-facing API from provider-specific implementation details.
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"""
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id: str = ProviderModelBase.generate_id_field()
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# The unique handle used in the API (e.g., "openai/gpt-4o-mini", "anthropic/claude-3-5-sonnet")
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# Format: {provider_display_name}/{model_display_name}
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handle: str = Field(..., description="Unique handle for API reference (format: provider_display_name/model_display_name)")
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# Display name shown in the UI for the model
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name: str = Field(..., description="The actual model name used by the provider")
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display_name: str = Field(..., description="Display name for the model shown in UI")
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# Foreign key to the provider
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provider_id: str = Field(..., description="Provider ID reference")
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# Optional organization ID - NULL for global models, set for org-scoped models
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organization_id: Optional[str] = Field(None, description="Organization ID if org-scoped, NULL if global")
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# Model type: llm or embedding
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model_type: str = Field(..., description="Type of model (llm or embedding)")
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# Whether the model is enabled (default True)
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enabled: bool = Field(default=True, description="Whether the model is enabled")
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# Model endpoint type (e.g., "openai", "anthropic", etc.)
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model_endpoint_type: str = Field(..., description="The endpoint type for the model (e.g., 'openai', 'anthropic')")
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# Additional metadata fields
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max_context_window: Optional[int] = Field(None, description="Context window size for the model")
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supports_token_streaming: Optional[bool] = Field(None, description="Whether token streaming is supported")
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supports_tool_calling: Optional[bool] = Field(None, description="Whether tool calling is supported")
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embedding_dim: Optional[int] = Field(None, description="Embedding dimension for embedding models")
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class ProviderModelCreate(ProviderModelBase):
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"""Schema for creating a new provider model"""
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handle: str = Field(..., description="Unique handle for API reference (format: provider_display_name/model_display_name)")
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display_name: str = Field(..., description="Display name for the model shown in UI")
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model_name: str = Field(..., description="The actual model name used by the provider")
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model_display_name: str = Field(..., description="Model display name used in the handle")
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provider_display_name: str = Field(..., description="Display name for the provider")
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provider_id: str = Field(..., description="Provider ID reference")
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model_type: str = Field(..., description="Type of model (llm or embedding)")
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enabled: bool = Field(default=True, description="Whether the model is enabled")
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context_window: Optional[int] = Field(None, description="Context window size for the model")
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supports_streaming: Optional[bool] = Field(None, description="Whether streaming is supported")
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supports_function_calling: Optional[bool] = Field(None, description="Whether function calling is supported")
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class ProviderModelUpdate(ProviderModelBase):
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"""Schema for updating a provider model"""
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display_name: Optional[str] = Field(None, description="Display name for the model shown in UI")
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enabled: Optional[bool] = Field(None, description="Whether the model is enabled")
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context_window: Optional[int] = Field(None, description="Context window size for the model")
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supports_streaming: Optional[bool] = Field(None, description="Whether streaming is supported")
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supports_function_calling: Optional[bool] = Field(None, description="Whether function calling is supported")
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