File size: 2,289 Bytes
8ede856 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | from typing import Literal, TypedDict
import aiohttp
from astrbot.core import logger
from astrbot.core.utils.http_ssl import build_tls_connector
class LLMModalities(TypedDict):
input: list[Literal["text", "image", "audio", "video"]]
output: list[Literal["text", "image", "audio", "video"]]
class LLMLimit(TypedDict):
context: int
output: int
class LLMMetadata(TypedDict):
id: str
reasoning: bool
tool_call: bool
knowledge: str
release_date: str
modalities: LLMModalities
open_weights: bool
limit: LLMLimit
LLM_METADATAS: dict[str, LLMMetadata] = {}
async def update_llm_metadata() -> None:
url = "https://models.dev/api.json"
try:
async with aiohttp.ClientSession(
trust_env=True, connector=build_tls_connector()
) as session:
async with session.get(url) as response:
data = await response.json()
global LLM_METADATAS
models = {}
for info in data.values():
for model in info.get("models", {}).values():
model_id = model.get("id")
if not model_id:
continue
models[model_id] = LLMMetadata(
id=model_id,
reasoning=model.get("reasoning", False),
tool_call=model.get("tool_call", False),
knowledge=model.get("knowledge", "none"),
release_date=model.get("release_date", ""),
modalities=model.get(
"modalities", {"input": [], "output": []}
),
open_weights=model.get("open_weights", False),
limit=model.get("limit", {"context": 0, "output": 0}),
)
# Replace the global cache in-place so references remain valid
LLM_METADATAS.clear()
LLM_METADATAS.update(models)
logger.info(f"Successfully fetched metadata for {len(models)} LLMs.")
except Exception as e:
logger.error(f"Failed to fetch LLM metadata: {e}")
return
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