53 lines
2.1 KiB
Python
53 lines
2.1 KiB
Python
from typing import Literal
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from pydantic import Field
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from letta.constants import DEFAULT_EMBEDDING_CHUNK_SIZE, LLM_MAX_CONTEXT_WINDOW
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from letta.log import get_logger
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from letta.schemas.embedding_config import EmbeddingConfig
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from letta.schemas.enums import ProviderCategory, ProviderType
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from letta.schemas.llm_config import LLMConfig
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from letta.schemas.providers.openai import OpenAIProvider
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logger = get_logger(__name__)
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# ALLOWED_PREFIXES = {"gpt-4", "gpt-5", "o1", "o3", "o4"}
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# DISALLOWED_KEYWORDS = {"transcribe", "search", "realtime", "tts", "audio", "computer", "o1-mini", "o1-preview", "o1-pro", "chat"}
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# DEFAULT_EMBEDDING_BATCH_SIZE = 1024
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class OpenRouterProvider(OpenAIProvider):
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provider_type: Literal[ProviderType.openai] = Field(ProviderType.openai, description="The type of the provider.")
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provider_category: ProviderCategory = Field(ProviderCategory.base, description="The category of the provider (base or byok)")
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api_key: str | None = Field(None, description="API key for the OpenRouter API.", deprecated=True)
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base_url: str = Field("https://openrouter.ai/api/v1", description="Base URL for the OpenRouter API.")
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def _list_llm_models(self, data: list[dict]) -> list[LLMConfig]:
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"""
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This handles filtering out LLM Models by provider that meet Letta's requirements.
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"""
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configs = []
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for model in data:
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check = self._do_model_checks_for_name_and_context_size(model)
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if check is None:
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continue
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model_name, context_window_size = check
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handle = self.get_handle(model_name)
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config = LLMConfig(
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model=model_name,
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model_endpoint_type="openai",
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model_endpoint=self.base_url,
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context_window=context_window_size,
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handle=handle,
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max_tokens=self.get_default_max_output_tokens(model_name),
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provider_name=self.name,
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provider_category=self.provider_category,
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)
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config = self._set_model_parameter_tuned_defaults(model_name, config)
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configs.append(config)
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return configs
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