63 lines
2.7 KiB
Python
63 lines
2.7 KiB
Python
"""
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SGLang provider for Letta.
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SGLang is a high-performance inference engine that exposes OpenAI-compatible API endpoints.
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"""
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from typing import Literal
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from pydantic import Field
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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.base import Provider
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class SGLangProvider(Provider):
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provider_type: Literal[ProviderType.sglang] = Field(ProviderType.sglang, 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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base_url: str = Field(..., description="Base URL for the SGLang API (e.g., http://localhost:30000).")
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api_key: str | None = Field(None, description="API key for the SGLang API (optional for local instances).")
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default_prompt_formatter: str | None = Field(default=None, description="Default prompt formatter (aka model wrapper).")
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handle_base: str | None = Field(None, description="Custom handle base name for model handles.")
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async def list_llm_models_async(self) -> list[LLMConfig]:
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from letta.llm_api.openai import openai_get_model_list_async
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# Ensure base_url ends with /v1 (SGLang uses same convention as vLLM)
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base_url = self.base_url.rstrip("/")
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if not base_url.endswith("/v1"):
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base_url = base_url + "/v1"
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# Decrypt API key before using (may be None for local instances)
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api_key = await self.api_key_enc.get_plaintext_async() if self.api_key_enc else None
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response = await openai_get_model_list_async(base_url, api_key=api_key)
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data = response.get("data", response)
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configs = []
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for model in data:
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model_name = model["id"]
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configs.append(
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LLMConfig(
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model=model_name,
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model_endpoint_type="openai", # SGLang is OpenAI-compatible
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model_endpoint=base_url,
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model_wrapper=self.default_prompt_formatter,
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context_window=model.get("max_model_len", 32768),
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handle=self.get_handle(model_name, base_name=self.handle_base) if self.handle_base else self.get_handle(model_name),
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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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)
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return configs
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async def list_embedding_models_async(self) -> list[EmbeddingConfig]:
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# SGLang embedding support not common for training use cases
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return []
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