Soulter
64 lines
2.1 KiB
Python
64 lines
2.1 KiB
Python
from typing import Literal, TypedDict
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import aiohttp
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from astrbot.core import logger
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class LLMModalities(TypedDict):
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input: list[Literal["text", "image", "audio", "video"]]
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output: list[Literal["text", "image", "audio", "video"]]
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class LLMLimit(TypedDict):
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context: int
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output: int
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class LLMMetadata(TypedDict):
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id: str
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reasoning: bool
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tool_call: bool
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knowledge: str
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release_date: str
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modalities: LLMModalities
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open_weights: bool
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limit: LLMLimit
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LLM_METADATAS: dict[str, LLMMetadata] = {}
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async def update_llm_metadata():
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url = "https://models.dev/api.json"
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try:
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async with aiohttp.ClientSession() as session:
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async with session.get(url) as response:
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data = await response.json()
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global LLM_METADATAS
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models = {}
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for info in data.values():
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for model in info.get("models", {}).values():
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model_id = model.get("id")
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if not model_id:
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continue
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models[model_id] = LLMMetadata(
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id=model_id,
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reasoning=model.get("reasoning", False),
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tool_call=model.get("tool_call", False),
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knowledge=model.get("knowledge", "none"),
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release_date=model.get("release_date", ""),
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modalities=model.get(
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"modalities", {"input": [], "output": []}
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),
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open_weights=model.get("open_weights", False),
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limit=model.get("limit", {"context": 0, "output": 0}),
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)
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# Replace the global cache in-place so references remain valid
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LLM_METADATAS.clear()
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LLM_METADATAS.update(models)
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logger.info(f"Successfully fetched metadata for {len(models)} LLMs.")
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except Exception as e:
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logger.error(f"Failed to fetch LLM metadata: {e}")
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return
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