エイカク
d35771f97d
* fix: patch pip distlib finder for frozen electron runtime * fix: use certifi CA bundle for runtime SSL requests * fix: configure certifi CA before core imports * fix: improve mac font fallback for dashboard text * fix: harden frozen pip patch and unify TLS connector * refactor: centralize dashboard CJK font fallback stacks * perf: reuse TLS context and avoid repeated frozen pip patch * refactor: bootstrap TLS setup before core imports * fix: use async confirm dialog for provider deletions * fix: replace native confirm dialogs in dashboard - Add shared confirm helper in dashboard/src/utils/confirmDialog.ts for async dialog usage with safe fallback. - Migrate provider, chat, config, session, platform, persona, MCP, backup, and knowledge-base delete/close confirmations to use the shared helper. - Remove scattered inline confirm handling to keep behavior consistent and avoid native blocking dialog focus/caret issues in Electron. * fix: capture runtime bootstrap logs after logger init - Add bootstrap record buffer in runtime_bootstrap for early TLS patch logs before logger is ready. - Flush buffered bootstrap logs to astrbot logger at process startup in main.py. - Include concrete exception details for TLS bootstrap failures to improve diagnosis. * fix: harden runtime bootstrap and unify confirm handling - Simplify bootstrap log buffering and add a public initialize hook for non-main startup paths. - Guard aiohttp TLS patching with feature/type checks and keep graceful fallback when internals are unavailable. - Standardize dashboard confirmation flow via shared confirm helpers across composition and options API components. * refactor: simplify runtime tls bootstrap and tighten confirm typing * refactor: align ssl helper namespace and confirm usage
67 lines
2.2 KiB
Python
67 lines
2.2 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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from astrbot.core.utils.http_ssl import build_tls_connector
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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() -> None:
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url = "https://models.dev/api.json"
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try:
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async with aiohttp.ClientSession(
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trust_env=True, connector=build_tls_connector()
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) 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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