feat(knowledge): auto-maintain index.md, improve import UX, fix embedding provider

- Auto-rebuild knowledge/index.md from the real directory tree on
  create/import so it never drifts or loses documents (no longer relies
  on the agent hand-writing it).
- Auto-open the created/imported document in the tree after success.
- Add create_document status message, shorten EN action buttons, and
  localize the "insert template" content.
- Show filename for protected system files (index.md/log.md) in the tree
  instead of their H1 heading.
- Reuse a shared embedding-provider factory so knowledge index sync also
  gets vectors instead of degrading to keyword-only search.

agent/memory/embedding/factory.py

@@ -0,0 +1,209 @@
+"""
+Shared embedding provider factory.
+
+Resolves the embedding provider purely from config.json, so every caller
+(agent initialization, knowledge base sync, index rebuild, ...) selects the
+same provider instead of silently degrading to keyword-only search.
+
+Two paths:
+  A. Default (no `embedding_provider` in config.json):
+     Auto-init OpenAI -> LinkAI fallback.
+  B. Explicit (`embedding_provider` is set):
+     Initialize the requested vendor with unified dim (default per vendor).
+"""
+
+import os
+from typing import Optional
+
+from common.log import logger
+
+# Track whether the embedding model log has been printed in this process,
+# so we avoid spamming it once per session/caller.
+_embedding_logged: bool = False
+
+
+def create_default_embedding_provider():
+    """Build the embedding provider from config, or None for keyword-only mode."""
+    from config import conf
+
+    explicit_provider = (conf().get("embedding_provider") or "").strip().lower()
+    if not explicit_provider:
+        return _init_legacy_provider()
+    return _init_explicit_provider(explicit_provider)
+
+
+def _init_legacy_provider():
+    """Legacy auto-init path: OpenAI -> LinkAI."""
+    from agent.memory.embedding.provider import create_embedding_provider
+    from config import conf
+
+    embedding_provider = None
+    embedding_model = None
+
+    openai_api_key = conf().get("open_ai_api_key", "")
+    openai_api_base = conf().get("open_ai_api_base", "")
+    if openai_api_key and openai_api_key not in ["", "YOUR API KEY", "YOUR_API_KEY"]:
+        try:
+            model = "text-embedding-3-small"
+            embedding_provider = create_embedding_provider(
+                provider="openai",
+                model=model,
+                api_key=openai_api_key,
+                api_base=openai_api_base or "https://api.openai.com/v1",
+            )
+            embedding_model = f"openai/{model}"
+        except Exception as e:
+            logger.warning(f"[EmbeddingFactory] OpenAI embedding failed: {e}")
+
+    if embedding_provider is None:
+        linkai_api_key = conf().get("linkai_api_key", "") or os.environ.get("LINKAI_API_KEY", "")
+        linkai_api_base = conf().get("linkai_api_base", "https://api.link-ai.tech")
+        if linkai_api_key and linkai_api_key not in ["", "YOUR API KEY", "YOUR_API_KEY"]:
+            try:
+                model = "text-embedding-3-small"
+                embedding_provider = create_embedding_provider(
+                    provider="linkai",
+                    model=model,
+                    api_key=linkai_api_key,
+                    api_base=f"{linkai_api_base}/v1",
+                )
+                embedding_model = f"linkai/{model}"
+            except Exception as e:
+                logger.warning(f"[EmbeddingFactory] LinkAI embedding failed: {e}")
+
+    if embedding_provider is not None and embedding_model:
+        _log_provider_once(f"{embedding_model} (dim={embedding_provider.dimensions})")
+
+    return embedding_provider
+
+
+def _init_explicit_provider(provider_key: str):
+    """Explicit-provider path: build the configured vendor."""
+    from agent.memory.embedding.provider import EMBEDDING_VENDORS, create_embedding_provider
+    from config import conf
+
+    # Custom providers ("custom:<id>") resolve credentials from custom_providers.
+    resolved_provider_key = provider_key
+    if provider_key.startswith("custom:"):
+        resolved_provider_key = "custom"
+
+    meta = EMBEDDING_VENDORS.get(resolved_provider_key)
+    if meta is None:
+        logger.error(
+            f"[EmbeddingFactory] Unknown embedding_provider '{provider_key}'. "
+            f"Supported: {sorted(EMBEDDING_VENDORS.keys())}. "
+            f"Memory will run in keyword-only mode."
+        )
+        return None
+
+    api_key = _resolve_api_key(provider_key)
+    api_base = _resolve_api_base(provider_key, meta["default_base_url"])
+
+    if not api_key:
+        logger.error(
+            f"[EmbeddingFactory] embedding_provider='{provider_key}' is set but its "
+            f"API key is missing. Memory will run in keyword-only mode."
+        )
+        return None
+
+    model = (conf().get("embedding_model") or "").strip()
+    # Custom providers without a model fall back to the provider's default.
+    if not model and resolved_provider_key == "custom":
+        from models.custom_provider import parse_custom_bot_type, get_custom_providers, _find_provider_by_id
+        _, custom_id = parse_custom_bot_type(provider_key)
+        if custom_id:
+            entry = _find_provider_by_id(get_custom_providers(), custom_id)
+            if entry and entry.get("model"):
+                model = entry["model"]
+    if not model and resolved_provider_key != "custom":
+        model = meta["default_model"]
+
+    try:
+        cfg_dim = int(conf().get("embedding_dimensions") or 0)
+    except (TypeError, ValueError):
+        cfg_dim = 0
+    dim = cfg_dim if cfg_dim > 0 else meta["default_dimensions"]
+
+    try:
+        provider = create_embedding_provider(
+            provider=resolved_provider_key,
+            model=model,
+            api_key=api_key,
+            api_base=api_base,
+            dimensions=dim,
+        )
+    except Exception as e:
+        logger.error(
+            f"[EmbeddingFactory] Failed to init embedding provider "
+            f"'{provider_key}/{model}': {e}"
+        )
+        return None
+
+    _log_provider_once(f"{provider_key}/{model} (dim={provider.dimensions})")
+    return provider
+
+
+def _resolve_api_key(provider_key: str) -> str:
+    """Pick the API key for an explicit embedding provider from config."""
+    from config import conf
+
+    if provider_key.startswith("custom:"):
+        from models.custom_provider import parse_custom_bot_type, get_custom_providers, _find_provider_by_id
+        _, custom_id = parse_custom_bot_type(provider_key)
+        if custom_id:
+            entry = _find_provider_by_id(get_custom_providers(), custom_id)
+            if entry:
+                return entry.get("api_key", "")
+        return ""
+
+    key_map = {
+        "openai":    "open_ai_api_key",
+        "linkai":    "linkai_api_key",
+        "dashscope": "dashscope_api_key",
+        "doubao":    "ark_api_key",
+        "zhipu":     "zhipu_ai_api_key",
+    }
+    field = key_map.get(provider_key)
+    if not field:
+        return ""
+    value = conf().get(field, "") or ""
+    if value in ["", "YOUR API KEY", "YOUR_API_KEY"]:
+        return ""
+    return value
+
+
+def _resolve_api_base(provider_key: str, default_base: str) -> str:
+    """Pick the API base for an explicit embedding provider from config."""
+    from config import conf
+
+    if provider_key.startswith("custom:"):
+        from models.custom_provider import parse_custom_bot_type, get_custom_providers, _find_provider_by_id
+        _, custom_id = parse_custom_bot_type(provider_key)
+        if custom_id:
+            entry = _find_provider_by_id(get_custom_providers(), custom_id)
+            if entry and entry.get("api_base"):
+                return entry["api_base"]
+        return default_base
+
+    base_map = {
+        "openai":    "open_ai_api_base",
+        "linkai":    "linkai_api_base",
+        "doubao":    "ark_base_url",
+        "zhipu":     "zhipu_ai_api_base",
+    }
+    field = base_map.get(provider_key)
+    if not field:
+        return default_base
+    value = (conf().get(field) or "").strip()
+    if not value:
+        return default_base
+    if provider_key == "linkai" and not value.rstrip("/").endswith("/v1"):
+        return f"{value.rstrip('/')}/v1"
+    return value
+
+
+def _log_provider_once(detail: str):
+    global _embedding_logged
+    if not _embedding_logged:
+        logger.info(f"[EmbeddingFactory] Embedding model in use: {detail}")
+        _embedding_logged = True