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feat(memory): hot-swap embedding provider on rebuild-index

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Switching embedding provider in the web console no longer requires a
restart and no longer drops the running conversation
This commit is contained in:
zhayujie
2026-05-20 21:32:53 +08:00
parent c181e500bc
commit fff7326209
3 changed files with 156 additions and 76 deletions
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46
channel/web/web_channel.py
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@@ -1234,7 +1234,7 @@ class ModelsHandler:
# ids drawn from ConfigHandler.PROVIDER_MODELS where applicable.
_ASR_PROVIDERS = ["openai", "linkai", "baidu", "ali", "xunfei", "azure", "google"]
_TTS_PROVIDERS = ["openai", "linkai", "minimax", "baidu", "ali", "xunfei", "azure", "google", "elevenlabs", "edge", "pytts"]
_EMBEDDING_PROVIDERS = ["openai", "linkai", "dashscope", "doubao", "zhipu"]
_EMBEDDING_PROVIDERS = ["openai", "dashscope", "doubao", "zhipu", "linkai"]
# Capability-scoped model catalogs. The chat dropdown can reuse the
# provider's generic model list, but vision and image generation are
@@ -1522,29 +1522,24 @@ class ModelsHandler:
@classmethod
def _embedding_capability(cls, local_config: dict) -> dict:
# Embedding is "pick or empty" — runtime's legacy openai/linkai
# fallback is a safety net, not a UX-visible auto mode.
# `suggested_provider` is a UI-only hint (NOT persisted) that
# preselects the dropdown to whichever configured vendor we'd
# recommend, so users don't have to expand the menu to find it.
explicit = (local_config.get("embedding_provider") or "").strip().lower()
# When unset, the legacy auto path in agent_initializer.py picks
# openai -> linkai. We surface "auto" + an estimate of what it'd
# actually use, but don't probe the runtime here.
suggested = ""
if not explicit:
if cls._is_real_key(local_config.get("open_ai_api_key", "")):
effective = "openai"
elif cls._is_real_key(local_config.get("linkai_api_key", "")):
effective = "linkai"
else:
effective = ""
return {
"editable": True,
"strategy": "auto",
"current_provider": effective,
"current_model": local_config.get("embedding_model", "") or "",
"current_dim": int(local_config.get("embedding_dimensions") or 0) or None,
"providers": cls._EMBEDDING_PROVIDERS,
}
for pid in cls._EMBEDDING_PROVIDERS:
meta = ConfigHandler.PROVIDER_MODELS.get(pid) or {}
key_field = meta.get("api_key_field")
if key_field and cls._is_real_key(local_config.get(key_field, "")):
suggested = pid
break
return {
"editable": True,
"strategy": "specified",
"current_provider": explicit,
"suggested_provider": suggested,
"current_model": local_config.get("embedding_model", "") or "",
"current_dim": int(local_config.get("embedding_dimensions") or 0) or None,
"providers": cls._EMBEDDING_PROVIDERS,
@@ -1931,15 +1926,10 @@ class ModelsHandler:
file_cfg["embedding_model"] = ""
self._write_file_config(file_cfg)
logger.info(f"[ModelsHandler] embedding updated: provider={provider_id!r} model={model!r}")
# Embedding switches don't go through Bridge bots; the agent's
# MemoryManager rebuilds its provider on next process restart, but
# the index dim may now mismatch — frontend should warn the user.
return json.dumps({
"status": "success",
"provider": provider_id,
"model": model,
"warn_rebuild_index": True,
})
# The agent's MemoryManager picks the new provider on next process
# restart; the index dim may now mismatch so a rebuild is needed.
# The frontend surfaces this via a confirm + post-save dialog.
return json.dumps({"status": "success", "provider": provider_id, "model": model})
@staticmethod
def _reset_bridge() -> None: