49 lines
1.7 KiB
Python
49 lines
1.7 KiB
Python
from typing import Optional
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from letta.llm_api.llm_client_base import LLMClientBase
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from letta.schemas.llm_config import LLMConfig
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class LLMClient:
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"""Factory class for creating LLM clients based on the model endpoint type."""
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@staticmethod
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def create(
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agent_id: str,
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llm_config: LLMConfig,
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put_inner_thoughts_first: bool = True,
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actor_id: Optional[str] = None,
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) -> Optional[LLMClientBase]:
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"""
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Create an LLM client based on the model endpoint type.
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Args:
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agent_id: Unique identifier for the agent
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llm_config: Configuration for the LLM model
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put_inner_thoughts_first: Whether to put inner thoughts first in the response
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use_structured_output: Whether to use structured output
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use_tool_naming: Whether to use tool naming
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actor_id: Optional actor identifier
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Returns:
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An instance of LLMClientBase subclass
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Raises:
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ValueError: If the model endpoint type is not supported
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"""
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match llm_config.model_endpoint_type:
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case "google_ai":
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from letta.llm_api.google_ai_client import GoogleAIClient
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return GoogleAIClient(
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agent_id=agent_id, llm_config=llm_config, put_inner_thoughts_first=put_inner_thoughts_first, actor_id=actor_id
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)
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case "google_vertex":
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from letta.llm_api.google_vertex_client import GoogleVertexClient
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return GoogleVertexClient(
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agent_id=agent_id, llm_config=llm_config, put_inner_thoughts_first=put_inner_thoughts_first, actor_id=actor_id
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)
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case _:
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return None
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