Merge branch 'pr-2915'

agent/knowledge/service.py

@@ -17,6 +17,7 @@ import shutil
 import threading
 from pathlib import Path
 from typing import Optional, Iterable
+from urllib.parse import quote
 
 from common.log import logger
 from config import conf
@@ -32,6 +33,10 @@ class KnowledgeService:
 
     PROTECTED_FILES = {"index.md", "log.md"}
     INVALID_NAME_RE = re.compile(r'[<>:"|?*\x00-\x1f]')
+    IMPORT_EXTENSIONS = {".md", ".txt"}
+    MAX_IMPORT_FILES = 100
+    MAX_IMPORT_FILE_SIZE = 10 * 1024 * 1024
+    MAX_IMPORT_TOTAL_SIZE = 200 * 1024 * 1024
 
     def __init__(self, workspace_root: str, memory_manager=None):
         self.workspace_root = os.path.abspath(workspace_root)
@@ -75,7 +80,14 @@ class KnowledgeService:
 
     def _manager(self):
         if self._memory_manager is None:
-            self._memory_manager = MemoryManager(MemoryConfig(workspace_root=self.workspace_root))
+            # Reuse the shared embedding provider selection so knowledge index
+            # sync gets vectors too, instead of degrading to keyword-only.
+            from agent.memory.embedding import create_default_embedding_provider
+            embedding_provider = create_default_embedding_provider()
+            self._memory_manager = MemoryManager(
+                MemoryConfig(workspace_root=self.workspace_root),
+                embedding_provider=embedding_provider,
+            )
         return self._memory_manager
 
     @staticmethod
@@ -100,9 +112,9 @@ class KnowledgeService:
             raise error[0]
         return result[0] if result else None
 
-    def _sync_index(self, old_paths: Iterable[str]):
+    def _sync_index(self, old_paths: Iterable[str], force: bool = False):
         old_paths = sorted(set(old_paths))
-        if not old_paths:
+        if not old_paths and not force:
             return
         manager = self._manager()
         for rel_path in old_paths:
@@ -110,6 +122,195 @@ class KnowledgeService:
         manager.mark_dirty()
         self._run_sync(manager.sync())
 
+    @staticmethod
+    def _extract_title(md_path: Path, fallback: str) -> str:
+        """Read a markdown file's H1 title, falling back to the file stem."""
+        try:
+            with open(md_path, "r", encoding="utf-8") as f:
+                for _ in range(20):
+                    line = f.readline()
+                    if not line:
+                        break
+                    stripped = line.strip()
+                    if stripped.startswith("# "):
+                        return stripped[2:].strip() or fallback
+        except Exception:
+            pass
+        return fallback
+
+    def rebuild_index_md(self) -> bool:
+        """Regenerate knowledge/index.md from the actual directory tree.
+
+        Keeps the index in sync with real files so it never drifts or loses
+        documents. Returns True when the file was (re)written.
+        """
+        root = Path(self.knowledge_dir)
+        if not root.is_dir():
+            return False
+
+        def collect(dir_path: Path) -> list:
+            # Return sorted (rel_path, title) tuples for *.md under dir_path,
+            # excluding protected files at the knowledge root and dot files.
+            entries = []
+            for md in sorted(dir_path.rglob("*.md")):
+                rel = md.relative_to(root).as_posix()
+                if any(part.startswith(".") for part in md.relative_to(root).parts):
+                    continue
+                if rel in self.PROTECTED_FILES:
+                    continue
+                entries.append((rel, self._extract_title(md, md.stem)))
+            return entries
+
+        all_entries = collect(root)
+
+        def link(rel: str) -> str:
+            # Encode each path segment so spaces / special chars stay valid in
+            # markdown links, while keeping the slashes between segments.
+            encoded = "/".join(quote(part) for part in rel.split("/"))
+            return f"./{encoded}"
+
+        lines = ["# 知识库目录", ""]
+        # Root-level documents first (no category dir).
+        root_docs = [(rel, title) for rel, title in all_entries if "/" not in rel]
+        for rel, title in root_docs:
+            lines.append(f"- [{title}]({link(rel)})")
+        if root_docs:
+            lines.append("")
+
+        # Group remaining documents by their top-level category.
+        categories = {}
+        for rel, title in all_entries:
+            if "/" not in rel:
+                continue
+            category = rel.split("/", 1)[0]
+            categories.setdefault(category, []).append((rel, title))
+
+        for category in sorted(categories.keys()):
+            lines.append(f"## {category}")
+            for rel, title in categories[category]:
+                lines.append(f"- [{title}]({link(rel)})")
+            lines.append("")
+
+        content = "\n".join(lines).rstrip() + "\n"
+        index_path = root / "index.md"
+        try:
+            index_path.write_text(content, encoding="utf-8")
+            return True
+        except Exception as exc:
+            logger.warning(f"[KnowledgeService] Failed to rebuild index.md: {exc}")
+            return False
+
+    def _sanitize_document_name(self, filename: str) -> str:
+        name = os.path.basename((filename or "").replace("\\", "/")).strip()
+        if not name:
+            raise ValueError("filename is required")
+        stem, ext = os.path.splitext(name)
+        if ext.lower() not in self.IMPORT_EXTENSIONS:
+            raise ValueError(f"unsupported file type: {ext or name}")
+        if not stem or stem in (".", "..") or self.INVALID_NAME_RE.search(stem):
+            raise ValueError("invalid filename")
+        safe_name = f"{stem}.md"
+        self._ensure_not_protected(safe_name)
+        return safe_name
+
+    @staticmethod
+    def _decode_document_content(content) -> str:
+        if isinstance(content, str):
+            return content
+        if not isinstance(content, (bytes, bytearray)):
+            raise ValueError("document content is required")
+        return bytes(content).decode("utf-8-sig", errors="replace")
+
+    def _resolve_import_destination(self, target_category: str, filename: str,
+                                    conflict_strategy: str) -> tuple:
+        target_rel, target_full = self._resolve_path(target_category, kind="category")
+        if not target_full.is_dir():
+            raise FileNotFoundError(f"category not found: {target_rel}")
+
+        safe_name = self._sanitize_document_name(filename)
+        destination = target_full / safe_name
+        rel_path = f"{target_rel}/{safe_name}"
+
+        if destination.exists():
+            if conflict_strategy == "skip":
+                return rel_path, destination, "skip"
+            if conflict_strategy == "rename":
+                stem = destination.stem
+                suffix = destination.suffix
+                for index in range(1, 1000):
+                    candidate = target_full / f"{stem}-{index}{suffix}"
+                    if not candidate.exists():
+                        candidate_rel = f"{target_rel}/{candidate.name}"
+                        return candidate_rel, candidate, "write"
+                raise FileExistsError(f"target already exists: {rel_path}")
+            if conflict_strategy != "overwrite":
+                raise ValueError("invalid conflict strategy")
+        return rel_path, destination, "write"
+
+    def create_document(self, path: str, content: str = "", overwrite: bool = False) -> dict:
+        rel_path, full_path = self._resolve_path(path, kind="document")
+        self._ensure_not_protected(rel_path)
+        if len((content or "").encode("utf-8")) > self.MAX_IMPORT_FILE_SIZE:
+            raise ValueError("file too large")
+        if full_path.exists() and not overwrite:
+            raise FileExistsError(f"target already exists: {rel_path}")
+        old_paths = [rel_path] if full_path.exists() else []
+        full_path.parent.mkdir(parents=True, exist_ok=True)
+        full_path.write_text(content or "", encoding="utf-8")
+        # Keep index.md in sync before reindexing so it is indexed too.
+        self.rebuild_index_md()
+        self._sync_index(old_paths, force=True)
+        return {"path": rel_path, "created": True, "overwritten": bool(old_paths)}
+
+    def import_documents(self, target_category: str, files: Iterable[dict],
+                         conflict_strategy: str = "skip") -> dict:
+        if not isinstance(files, list):
+            raise ValueError("files must be a list")
+        if len(files) > self.MAX_IMPORT_FILES:
+            raise ValueError(f"too many files: max {self.MAX_IMPORT_FILES}")
+        results = []
+        old_paths = []
+        imported = skipped = failed = 0
+        total_size = 0
+
+        for item in files:
+            filename = item.get("filename") if isinstance(item, dict) else None
+            try:
+                content_bytes = item.get("content") if isinstance(item, dict) else None
+                size = len(content_bytes.encode("utf-8")) if isinstance(content_bytes, str) else len(content_bytes or b"")
+                total_size += size
+                if total_size > self.MAX_IMPORT_TOTAL_SIZE:
+                    raise ValueError("import batch too large")
+                if size > self.MAX_IMPORT_FILE_SIZE:
+                    raise ValueError("file too large")
+                rel_path, destination, mode = self._resolve_import_destination(
+                    target_category, filename, conflict_strategy
+                )
+                if mode == "skip":
+                    skipped += 1
+                    results.append({"filename": filename, "path": rel_path, "status": "skipped",
+                                    "reason": "target_exists"})
+                    continue
+
+                old_exists = destination.exists()
+                content = self._decode_document_content(content_bytes)
+                destination.parent.mkdir(parents=True, exist_ok=True)
+                destination.write_text(content, encoding="utf-8")
+                if old_exists:
+                    old_paths.append(rel_path)
+                imported += 1
+                results.append({"filename": filename, "path": rel_path, "status": "imported",
+                                "overwritten": old_exists})
+            except Exception as exc:
+                failed += 1
+                results.append({"filename": filename or "", "status": "failed", "reason": str(exc)})
+
+        if imported:
+            # Keep index.md in sync before reindexing so it is indexed too.
+            self.rebuild_index_md()
+            self._sync_index(old_paths, force=True)
+        return {"results": results, "imported": imported, "skipped": skipped, "failed": failed}
+
     def create_category(self, path: str) -> dict:
         rel_path, full_path = self._resolve_path(path, kind="category")
         if full_path.exists():
@@ -283,14 +484,18 @@ class KnowledgeService:
                 if not is_root:
                     stats["pages"] += 1
                     stats["size"] += size
-                title = name.replace(".md", "")
-                try:
-                    with open(full, "r", encoding="utf-8") as f:
-                        first_line = f.readline().strip()
-                    if first_line.startswith("# "):
-                        title = first_line[2:].strip()
-                except Exception:
-                    pass
+                # Prefer the H1 heading as a readable title for normal docs.
+                # System files (index.md / log.md) keep their filename so the
+                # tree never hides what they actually are.
+                title = name[:-3]
+                if name not in self.PROTECTED_FILES:
+                    try:
+                        with open(full, "r", encoding="utf-8") as f:
+                            first_line = f.readline().strip()
+                        if first_line.startswith("# "):
+                            title = first_line[2:].strip() or title
+                    except Exception:
+                        pass
                 files.append({"name": name, "title": title, "size": size})
         return files, children
 
@@ -416,6 +621,15 @@ class KnowledgeService:
                 result = self.delete_documents(payload.get("paths") or [])
             elif action == "move_documents":
                 result = self.move_documents(payload.get("paths") or [], payload.get("target_category"))
+            elif action == "create_document":
+                result = self.create_document(payload.get("path"), payload.get("content", ""),
+                                              payload.get("overwrite", False))
+            elif action == "import_documents":
+                result = self.import_documents(
+                    payload.get("target_category"),
+                    payload.get("files") or [],
+                    payload.get("conflict_strategy", "skip"),
+                )
             else:
                 return {"action": action, "code": 400, "message": f"unknown action: {action}", "payload": None}
             return {"action": action, "code": 200, "message": "success", "payload": result}

agent/memory/__init__.py

@@ -7,7 +7,7 @@ conversation history persistence (SQLite).
 
 from agent.memory.manager import MemoryManager
 from agent.memory.config import MemoryConfig, get_default_memory_config, set_global_memory_config
-from agent.memory.embedding import create_embedding_provider
+from agent.memory.embedding import create_embedding_provider, create_default_embedding_provider
 from agent.memory.conversation_store import ConversationStore, get_conversation_store
 from agent.memory.summarizer import ensure_daily_memory_file
 
@@ -17,6 +17,7 @@ __all__ = [
     'get_default_memory_config',
     'set_global_memory_config',
     'create_embedding_provider',
+    'create_default_embedding_provider',
     'ConversationStore',
     'get_conversation_store',
     'ensure_daily_memory_file',

agent/memory/embedding/__init__.py

@@ -16,6 +16,7 @@ from agent.memory.embedding.provider import (
     OpenAIEmbeddingProvider,
     create_embedding_provider,
 )
+from agent.memory.embedding.factory import create_default_embedding_provider
 from agent.memory.embedding.rebuild import (
     RebuildResult,
     clear_index,
@@ -33,6 +34,7 @@ __all__ = [
     "EmbeddingProvider",
     "OpenAIEmbeddingProvider",
     "create_embedding_provider",
+    "create_default_embedding_provider",
     "RebuildResult",
     "clear_index",
     "rebuild_in_process",

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

agent/memory/embedding/rebuild.py

@@ -163,10 +163,9 @@ def main() -> int:
     logger.info(f"[RebuildIndex] Workspace: {workspace_root}")
     logger.info(f"[RebuildIndex] Index db:  {memory_config.get_db_path()}")
 
-    from bridge.agent_initializer import AgentInitializer
+    from agent.memory.embedding import create_default_embedding_provider
 
-    initializer = AgentInitializer(bridge=None, agent_bridge=None)
-    embedding_provider = initializer._init_embedding_provider(memory_config, session_id=None)
+    embedding_provider = create_default_embedding_provider()
     if embedding_provider is None:
         logger.error(
             "[RebuildIndex] No embedding provider could be initialized. "

bridge/agent_initializer.py

@@ -17,10 +17,6 @@ from common.utils import expand_path
 # Module-level lock to serialize scheduler init across concurrent sessions
 _scheduler_init_lock = threading.Lock()
 
-# Track whether the embedding model log has been printed in this process,
-# so we avoid spamming it once per session.
-_embedding_logged: bool = False
-
 
 class AgentInitializer:
     """
@@ -306,223 +302,15 @@ class AgentInitializer:
         """
         Initialize the embedding provider for memory.
 
-        Two paths:
+        Delegates to the shared factory so agent init, knowledge sync and
+        index rebuild all select the same provider:
           A. Default (no `embedding_provider` in config.json):
-             Auto-init OpenAI -> LinkAI fallback. Existing 1536-dim indices
-             keep working.
+             Auto-init OpenAI -> LinkAI fallback.
           B. Explicit (`embedding_provider` is set):
-             Initialize the requested vendor with unified dim (default 1024).
-             If the index was built with a different dim, vector search will
-             quietly return no results (cosine returns 0) and keyword search
-             takes over until the user runs /memory rebuild-index.
+             Initialize the requested vendor.
         """
-        from agent.memory import create_embedding_provider
-        from config import conf
-
-        explicit_provider = (conf().get("embedding_provider") or "").strip().lower()
-
-        if not explicit_provider:
-            return self._init_embedding_provider_legacy(session_id=session_id)
-
-        return self._init_embedding_provider_explicit(
-            memory_config, explicit_provider, session_id=session_id,
-        )
-
-    def _init_embedding_provider_legacy(self, session_id: Optional[str] = None):
-        """Legacy auto-init path: OpenAI -> LinkAI. Preserved verbatim for compat."""
-        from agent.memory 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"[AgentInitializer] 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"[AgentInitializer] LinkAI embedding failed: {e}")
-
-        if embedding_provider is not None and embedding_model:
-            global _embedding_logged
-            if not _embedding_logged:
-                logger.info(
-                    f"[AgentInitializer] Embedding model in use: {embedding_model} "
-                    f"(dim={embedding_provider.dimensions})"
-                )
-                _embedding_logged = True
-
-        return embedding_provider
-
-    def _init_embedding_provider_explicit(
-        self,
-        memory_config,
-        provider_key: str,
-        session_id: Optional[str] = None,
-    ):
-        """Explicit-provider path: build the configured vendor.
-
-        If the index was built with a different dim, vector search will
-        silently return no results (cosine returns 0 for mismatched dims)
-        and keyword search takes over. Users switch vendors by running
-        /memory rebuild-index — see docs.
-        """
-        from agent.memory import create_embedding_provider
-        from agent.memory.embedding import EMBEDDING_VENDORS
-        from config import conf
-
-        # Custom providers ("custom:<id>") resolve credentials
-        # from the custom_providers list.
-        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"[AgentInitializer] Unknown embedding_provider '{provider_key}'. "
-                f"Supported: {sorted(EMBEDDING_VENDORS.keys())}. "
-                f"Memory will run in keyword-only mode."
-            )
-            return None
-
-        api_key = self._resolve_embedding_api_key(provider_key)
-        api_base = self._resolve_embedding_api_base(provider_key, meta["default_base_url"])
-
-        if not api_key:
-            logger.error(
-                f"[AgentInitializer] 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"[AgentInitializer] Failed to init embedding provider "
-                f"'{provider_key}/{model}': {e}"
-            )
-            return None
-
-        global _embedding_logged
-        if not _embedding_logged:
-            logger.info(
-                f"[AgentInitializer] Embedding model in use: "
-                f"{provider_key}/{model} (dim={provider.dimensions})"
-            )
-            _embedding_logged = True
-        return provider
-
-    @staticmethod
-    def _resolve_embedding_api_key(provider_key: str) -> str:
-        """Pick the API key for an explicit embedding provider from config."""
-        from config import conf
-
-        # Custom providers ("custom:<id>") resolve from the custom_providers list.
-        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:
-                providers = get_custom_providers()
-                entry = _find_provider_by_id(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
-
-    @staticmethod
-    def _resolve_embedding_api_base(provider_key: str, default_base: str) -> str:
-        """Pick the API base for an explicit embedding provider from config."""
-        from config import conf
-
-        # Custom providers ("custom:<id>") resolve from the custom_providers list.
-        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:
-                providers = get_custom_providers()
-                entry = _find_provider_by_id(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
+        from agent.memory import create_default_embedding_provider
+        return create_default_embedding_provider()
 
     def _sync_memory(self, memory_manager, session_id: Optional[str] = None):
         """Sync memory database"""

channel/web/chat.html

@@ -845,6 +845,15 @@
                                             class="flex items-center gap-1.5 px-3 py-1.5 rounded-lg bg-primary-500 hover:bg-primary-600 text-white text-xs font-medium cursor-pointer transition-colors">
                                         <i class="fas fa-folder-plus"></i><span data-i18n="knowledge_new_category">新建分类</span>
                                     </button>
+                                    <button onclick="createKnowledgeDocument()"
+                                            class="flex items-center gap-1.5 px-3 py-1.5 rounded-lg border border-slate-200 dark:border-white/10 text-slate-600 dark:text-slate-300 hover:bg-slate-50 dark:hover:bg-white/5 text-xs font-medium cursor-pointer transition-colors">
+                                        <i class="fas fa-file-circle-plus"></i><span data-i18n="knowledge_new_document">新建文档</span>
+                                    </button>
+                                    <button onclick="selectKnowledgeImportFiles()"
+                                            class="flex items-center gap-1.5 px-3 py-1.5 rounded-lg border border-slate-200 dark:border-white/10 text-slate-600 dark:text-slate-300 hover:bg-slate-50 dark:hover:bg-white/5 text-xs font-medium cursor-pointer transition-colors">
+                                        <i class="fas fa-file-arrow-up"></i><span data-i18n="knowledge_import_documents">导入文档</span>
+                                    </button>
+                                    <input id="knowledge-import-input" type="file" class="hidden" multiple accept=".md,.txt,text/markdown,text/plain">
                                     <div class="flex items-center bg-slate-100 dark:bg-white/10 rounded-lg p-0.5">
                                         <button id="knowledge-tab-docs" onclick="switchKnowledgeTab('docs')"
                                                 class="knowledge-tab px-3 py-1.5 rounded-md text-xs font-medium cursor-pointer transition-colors duration-150 active">
@@ -1083,7 +1092,7 @@
 
     <!-- Knowledge Action Dialog -->
     <div id="knowledge-dialog-overlay" class="fixed inset-0 bg-black/50 z-[200] hidden flex items-center justify-center">
-        <div class="bg-white dark:bg-[#1A1A1A] rounded-2xl border border-slate-200 dark:border-white/10 shadow-xl w-full max-w-md mx-4 overflow-hidden">
+        <div id="knowledge-dialog-card" class="bg-white dark:bg-[#1A1A1A] rounded-2xl border border-slate-200 dark:border-white/10 shadow-xl w-full max-w-md mx-4 overflow-hidden">
             <div class="p-6">
                 <div class="flex items-center gap-3 mb-5">
                     <div class="w-10 h-10 rounded-xl bg-emerald-50 dark:bg-emerald-900/20 flex items-center justify-center">
@@ -1099,6 +1108,29 @@
                        class="w-full px-3 py-2 rounded-lg border border-slate-200 dark:border-slate-600 bg-slate-50 dark:bg-white/5 text-sm text-slate-800 dark:text-slate-100 focus:outline-none focus:border-primary-500">
                 <select id="knowledge-dialog-select"
                         class="hidden w-full px-3 py-2 rounded-lg border border-slate-200 dark:border-slate-600 bg-slate-50 dark:bg-[#222] text-sm text-slate-800 dark:text-slate-100 focus:outline-none focus:border-primary-500"></select>
+                <textarea id="knowledge-dialog-textarea" rows="8"
+                          class="hidden w-full px-3 py-2 rounded-lg border border-slate-200 dark:border-slate-600 bg-slate-50 dark:bg-white/5 text-sm text-slate-800 dark:text-slate-100 focus:outline-none focus:border-primary-500 font-mono resize-y"></textarea>
+                <div id="knowledge-document-form" class="hidden space-y-3">
+                    <div class="rounded-lg bg-emerald-50 dark:bg-emerald-900/15 border border-emerald-100 dark:border-emerald-800/40 px-3 py-2">
+                        <div id="knowledge-document-category-label" class="text-[11px] text-emerald-600 dark:text-emerald-400 mb-0.5"></div>
+                        <div id="knowledge-document-path-preview" class="text-xs font-mono text-emerald-700 dark:text-emerald-300 break-all"></div>
+                    </div>
+                    <div>
+                        <label id="knowledge-document-filename-label" class="block text-sm font-medium text-slate-600 dark:text-slate-300 mb-1.5"></label>
+                        <input id="knowledge-document-filename" type="text"
+                               class="w-full px-3 py-2 rounded-lg border border-slate-200 dark:border-slate-600 bg-slate-50 dark:bg-white/5 text-sm text-slate-800 dark:text-slate-100 focus:outline-none focus:border-primary-500"
+                               placeholder="note.md">
+                    </div>
+                    <div>
+                        <div class="flex items-center justify-between mb-1.5">
+                            <label id="knowledge-document-content-label" class="block text-sm font-medium text-slate-600 dark:text-slate-300"></label>
+                            <button id="knowledge-document-template" type="button" class="text-xs text-primary-500 hover:text-primary-600"></button>
+                        </div>
+                        <textarea id="knowledge-document-content" rows="14"
+                                  class="w-full px-3 py-2 rounded-lg border border-slate-200 dark:border-slate-600 bg-slate-50 dark:bg-white/5 text-sm text-slate-800 dark:text-slate-100 focus:outline-none focus:border-primary-500 font-mono resize-y"
+                                  placeholder="# Title&#10;&#10;Write your notes here..."></textarea>
+                    </div>
+                </div>
                 <p id="knowledge-dialog-hint" class="mt-2 text-xs text-slate-400 dark:text-slate-500"></p>
                 <p id="knowledge-dialog-error" class="mt-2 text-xs text-red-500 hidden"></p>
             </div>

channel/web/static/css/console.css

@@ -1293,6 +1293,18 @@
     background: rgba(74, 190, 110, 0.1);
     color: #4ABE6E;
 }
+.knowledge-import-drag-over {
+    outline: 2px dashed rgba(74, 190, 110, 0.55);
+    outline-offset: 4px;
+    border-radius: 14px;
+}
+#knowledge-dialog-card.knowledge-document-dialog {
+    max-width: 760px;
+}
+#knowledge-document-content {
+    min-height: 320px;
+    line-height: 1.55;
+}
 
 /* Graph legend */
 .knowledge-graph-legend {

channel/web/static/js/console.js

@@ -94,6 +94,8 @@ const I18N = {
         knowledge_empty_guide: '在对话中发送文档、链接或主题给 Agent，它会自动整理到你的知识库中。',
         knowledge_go_chat: '开始对话',
         knowledge_new_category: '新建分类',
+        knowledge_new_document: '新建文档',
+        knowledge_import_documents: '导入文档',
         welcome_subtitle: '我可以帮你解答问题、管理计算机、创造和执行技能，并通过<br>长期记忆和知识库不断成长',
         example_sys_title: '系统管理', example_sys_text: '查看工作空间里有哪些文件',
         example_task_title: '定时任务', example_task_text: '1分钟后提醒我检查服务器',
@@ -334,7 +336,9 @@ const I18N = {
         knowledge_select_hint: 'Select a document to view', knowledge_empty_hint: 'No knowledge pages yet',
         knowledge_empty_guide: 'Send documents, links or topics to the agent in chat, and it will automatically organize them into your knowledge base.',
         knowledge_go_chat: 'Start a conversation',
-        knowledge_new_category: 'New category',
+        knowledge_new_category: 'Category',
+        knowledge_new_document: 'Document',
+        knowledge_import_documents: 'Import',
         welcome_subtitle: 'I can help you answer questions, manage your computer, create and execute skills, and keep growing through <br> long-term memory and a personal knowledge base.',
         example_sys_title: 'System', example_sys_text: 'Show me the files in the workspace',
         example_task_title: 'Scheduler', example_task_text: 'Remind me to check the server in 5 minutes',
@@ -7763,8 +7767,11 @@ let _knowledgeTreeData = [];
 let _knowledgeRootFiles = [];
 let _knowledgeCurrentFile = null;
 let _knowledgeGraphLoaded = false;
+const KNOWLEDGE_IMPORT_MAX_FILES = 100;
+const KNOWLEDGE_IMPORT_MAX_FILE_SIZE = 10 * 1024 * 1024;
+const KNOWLEDGE_IMPORT_MAX_TOTAL_SIZE = 200 * 1024 * 1024;
 
-function loadKnowledgeView() {
+function loadKnowledgeView(targetPath) {
     // Reset to docs tab
     switchKnowledgeTab('docs');
     _knowledgeGraphLoaded = false;
@@ -7772,6 +7779,7 @@ function loadKnowledgeView() {
 
     fetch('/api/knowledge/list').then(r => r.json()).then(data => {
         if (data.status !== 'success') return;
+        initKnowledgeImportDropZone();
 
         const emptyEl = document.getElementById('knowledge-empty');
         const docsPanel = document.getElementById('knowledge-panel-docs');
@@ -7800,6 +7808,15 @@ function loadKnowledgeView() {
 
         renderKnowledgeTree(tree, rootFiles);
 
+        // Prefer opening the just created/imported file; ensure its group is
+        // expanded so the active item is visible in the tree.
+        const targetTitle = targetPath ? _findKnowledgeFileTitle(targetPath) : null;
+        if (targetTitle !== null) {
+            _expandKnowledgeGroupFor(targetPath);
+            openKnowledgeFile(targetPath, targetTitle);
+            return;
+        }
+
         // Auto-select the first file (desktop only)
         if (window.innerWidth >= 768) {
             const firstFile = rootFiles.length > 0 ? rootFiles[0] : null;
@@ -7817,6 +7834,36 @@ function loadKnowledgeView() {
     }).catch(() => {});
 }
 
+// Find a file's display title by its relative path within the knowledge tree.
+// Returns the title, or null when the path is not present.
+function _findKnowledgeFileTitle(path) {
+    if (!path) return null;
+    const rootHit = (_knowledgeRootFiles || []).find(f => f.name === path);
+    if (rootHit) return rootHit.title || rootHit.name;
+    const walk = (groups, parentPath) => {
+        for (const group of groups || []) {
+            const groupPath = parentPath ? `${parentPath}/${group.dir}` : group.dir;
+            const hit = (group.files || []).find(f => `${groupPath}/${f.name}` === path);
+            if (hit) return hit.title || hit.name;
+            const childHit = walk(group.children, groupPath);
+            if (childHit !== null) return childHit;
+        }
+        return null;
+    };
+    return walk(_knowledgeTreeData, '');
+}
+
+// Open every ancestor group of the given file path so it is visible.
+function _expandKnowledgeGroupFor(path) {
+    if (!path || !path.includes('/')) return;
+    const target = document.querySelector(`.knowledge-tree-file[data-path="${CSS.escape(path)}"]`);
+    let node = target ? target.closest('.knowledge-tree-group') : null;
+    while (node) {
+        node.classList.add('open');
+        node = node.parentElement ? node.parentElement.closest('.knowledge-tree-group') : null;
+    }
+}
+
 function renderKnowledgeTree(tree, rootFilesOrFilter, filter) {
     const container = document.getElementById('knowledge-tree');
     container.innerHTML = '';
@@ -7898,7 +7945,7 @@ function _knowledgeCategoryActions(path) {
     return `<span class="knowledge-actions">${_knowledgeActionButton('fa-pen', '重命名', `renameKnowledgeCategory(${value})`)}${_knowledgeActionButton('fa-trash', '删除', `deleteKnowledgeCategory(${value})`)}</span>`;
 }
 
-async function dispatchKnowledgeAction(action, payload) {
+async function dispatchKnowledgeAction(action, payload, openPathResolver) {
     _setKnowledgeStatus(currentLang === 'zh' ? '处理中...' : 'Working...', false, true);
     try {
         const response = await fetch('/api/knowledge/action', {
@@ -7913,7 +7960,9 @@ async function dispatchKnowledgeAction(action, payload) {
             return null;
         }
         _setKnowledgeStatus(_knowledgeResultMessage(action, result.payload), false);
-        loadKnowledgeView();
+        // Optionally auto-open the affected file after the tree refreshes.
+        const openPath = openPathResolver ? openPathResolver(result.payload) : null;
+        loadKnowledgeView(openPath || undefined);
         return result.payload;
     } catch (error) {
         _setKnowledgeStatus(currentLang === 'zh' ? '请求失败，请稍后重试' : 'Request failed, please try again', true);
@@ -7933,14 +7982,18 @@ function _setKnowledgeStatus(message, isError, persistent) {
 function _knowledgeResultMessage(action, payload) {
     if (currentLang !== 'zh') {
         return action === 'create_category' ? 'Category created' :
+            action === 'create_document' ? 'Document created' :
             action === 'rename_category' ? 'Category renamed' :
             action === 'delete_category' ? 'Category deleted' :
+            action === 'import_documents' ? `${payload?.imported || 0} imported · ${payload?.skipped || 0} skipped · ${payload?.failed || 0} failed` :
             action === 'move_documents' ? `${payload?.moved || 0} document moved` :
             `${payload?.deleted || 0} document deleted`;
     }
     return action === 'create_category' ? '分类已创建' :
+        action === 'create_document' ? '文档已创建' :
         action === 'rename_category' ? '分类已重命名' :
         action === 'delete_category' ? '分类已删除' :
+        action === 'import_documents' ? `导入 ${payload?.imported || 0} 个，跳过 ${payload?.skipped || 0} 个，失败 ${payload?.failed || 0} 个` :
         action === 'move_documents' ? `已移动 ${payload?.moved || 0} 个文档` :
         `已删除 ${payload?.deleted || 0} 个文档`;
 }
@@ -7956,8 +8009,15 @@ function _knowledgeCategoryPaths(groups, parent = '') {
 
 function openKnowledgeDialog(options) {
     const overlay = document.getElementById('knowledge-dialog-overlay');
+    const card = document.getElementById('knowledge-dialog-card');
     const input = document.getElementById('knowledge-dialog-input');
     const select = document.getElementById('knowledge-dialog-select');
+    const textarea = document.getElementById('knowledge-dialog-textarea');
+    const documentForm = document.getElementById('knowledge-document-form');
+    const documentFilename = document.getElementById('knowledge-document-filename');
+    const documentContent = document.getElementById('knowledge-document-content');
+    const templateBtn = document.getElementById('knowledge-document-template');
+    const documentPathPreview = document.getElementById('knowledge-document-path-preview');
     const submit = document.getElementById('knowledge-dialog-submit');
     const cancel = document.getElementById('knowledge-dialog-cancel');
     document.getElementById('knowledge-dialog-title').textContent = options.title;
@@ -7966,9 +8026,31 @@ function openKnowledgeDialog(options) {
     document.getElementById('knowledge-dialog-hint').textContent = options.hint || '';
     document.getElementById('knowledge-dialog-error').classList.add('hidden');
     document.getElementById('knowledge-dialog-icon').className = `fas ${options.icon || 'fa-folder'} text-emerald-500`;
-    input.classList.toggle('hidden', options.type === 'select');
+    card.classList.toggle('knowledge-document-dialog', options.type === 'document');
+    input.classList.toggle('hidden', options.type === 'select' || options.type === 'textarea' || options.type === 'document');
     select.classList.toggle('hidden', options.type !== 'select');
+    textarea.classList.toggle('hidden', options.type !== 'textarea');
+    documentForm.classList.toggle('hidden', options.type !== 'document');
     input.value = options.value || '';
+    textarea.value = options.value || '';
+    documentFilename.value = options.filename || '';
+    documentContent.value = options.content || '';
+    document.getElementById('knowledge-document-category-label').textContent = currentLang === 'zh' ? '目标分类' : 'Destination category';
+    documentPathPreview.textContent = options.category
+        ? `knowledge/${options.category}/`
+        : 'knowledge/';
+    documentFilename.oninput = null;
+    document.getElementById('knowledge-document-filename-label').textContent = currentLang === 'zh' ? '文件名' : 'Filename';
+    document.getElementById('knowledge-document-content-label').textContent = currentLang === 'zh' ? 'Markdown 内容' : 'Markdown content';
+    templateBtn.textContent = currentLang === 'zh' ? '插入模板' : 'Insert template';
+    templateBtn.onclick = () => {
+        if (documentContent.value.trim()) return;
+        const title = (documentFilename.value || 'untitled').replace(/\.md$/i, '');
+        documentContent.value = currentLang === 'zh'
+            ? `# ${title}\n\n## 摘要\n\n\n## 关键点\n\n- \n\n## 参考\n\n`
+            : `# ${title}\n\n## Summary\n\n\n## Key points\n\n- \n\n## References\n\n`;
+        documentContent.focus();
+    };
     if (options.type === 'select') {
         select.innerHTML = (options.choices || []).map(value => `<option value="${escapeHtml(value)}">${escapeHtml(value)}</option>`).join('');
     }
@@ -7978,7 +8060,13 @@ function openKnowledgeDialog(options) {
 
     const close = () => overlay.classList.add('hidden');
     const submitAction = async () => {
-        const value = (options.type === 'select' ? select.value : input.value).trim();
+        const rawValue = options.type === 'select' ? select.value :
+            (options.type === 'textarea' ? textarea.value :
+            (options.type === 'document' ? {
+                filename: documentFilename.value.trim(),
+                content: documentContent.value,
+            } : input.value));
+        const value = options.type === 'textarea' || options.type === 'document' ? rawValue : rawValue.trim();
         const error = options.validate ? options.validate(value) : (!value ? (currentLang === 'zh' ? '此项不能为空' : 'This field is required') : '');
         if (error) {
             const errorEl = document.getElementById('knowledge-dialog-error');
@@ -7996,7 +8084,7 @@ function openKnowledgeDialog(options) {
     overlay.onclick = event => { if (event.target === overlay) close(); };
     input.onkeydown = event => { if (event.key === 'Enter') submitAction(); };
     overlay.classList.remove('hidden');
-    setTimeout(() => (options.type === 'select' ? select : input).focus(), 0);
+    setTimeout(() => (options.type === 'select' ? select : (options.type === 'textarea' ? textarea : (options.type === 'document' ? documentFilename : input))).focus(), 0);
 }
 
 function createKnowledgeCategory() {
@@ -8010,6 +8098,166 @@ function createKnowledgeCategory() {
     });
 }
 
+function createKnowledgeDocument() {
+    const categories = _knowledgeCategoryPaths(_knowledgeTreeData);
+    if (!categories.length) {
+        _setKnowledgeStatus(currentLang === 'zh' ? '请先创建分类' : 'Create a category first', true);
+        return;
+    }
+    openKnowledgeDialog({
+        title: currentLang === 'zh' ? '新建文档' : 'New document',
+        subtitle: currentLang === 'zh' ? '先选择分类，然后输入文件名' : 'Choose a category, then enter a filename',
+        label: currentLang === 'zh' ? '目标分类' : 'Destination category',
+        type: 'select',
+        choices: categories,
+        icon: 'fa-file-circle-plus',
+        onSubmit: category => {
+            openKnowledgeDocumentEditor(category);
+            return null;
+        },
+    });
+}
+
+function openKnowledgeDocumentEditor(category) {
+    openKnowledgeDialog({
+        title: currentLang === 'zh' ? '新建文档' : 'New document',
+        subtitle: currentLang === 'zh' ? `保存到 ${category}` : `Save to ${category}`,
+        label: '',
+        hint: currentLang === 'zh' ? '文件名可省略 .md 后缀；保存后会自动同步索引。' : 'The .md suffix is optional. Index sync runs after saving.',
+        type: 'document',
+        category,
+        filename: '',
+        content: '',
+        icon: 'fa-file-circle-plus',
+        validate: value => {
+            if (!value.filename) return currentLang === 'zh' ? '文件名不能为空' : 'Filename is required';
+            if (/\.[^.]+$/i.test(value.filename) && !/\.md$/i.test(value.filename)) {
+                return currentLang === 'zh' ? '新建文档仅支持 .md 文件名' : 'New documents must be .md files';
+            }
+            if (!value.content.trim()) return currentLang === 'zh' ? '内容不能为空' : 'Content is required';
+            if (new Blob([value.content]).size > KNOWLEDGE_IMPORT_MAX_FILE_SIZE) {
+                return currentLang === 'zh' ? '内容不能超过 10MB' : 'Content cannot exceed 10MB';
+            }
+            return '';
+        },
+        onSubmit: value => {
+            const safeName = value.filename.endsWith('.md') ? value.filename : `${value.filename}.md`;
+            return dispatchKnowledgeAction('create_document', {
+                path: `${category}/${safeName}`,
+                content: value.content,
+                overwrite: false,
+            }, payload => payload?.path || `${category}/${safeName}`);
+        },
+    });
+}
+
+function selectKnowledgeImportFiles() {
+    const input = document.getElementById('knowledge-import-input');
+    input.value = '';
+    input.onchange = () => {
+        if (input.files && input.files.length) openKnowledgeImportDialog(Array.from(input.files));
+    };
+    input.click();
+}
+
+function openKnowledgeImportDialog(files) {
+    const validationError = validateKnowledgeImportFiles(files);
+    if (validationError) {
+        _setKnowledgeStatus(validationError, true);
+        return;
+    }
+    const choices = _knowledgeCategoryPaths(_knowledgeTreeData);
+    openKnowledgeDialog({
+        title: currentLang === 'zh' ? '导入文档' : 'Import documents',
+        subtitle: currentLang === 'zh' ? `已选择 ${files.length} 个文件` : `${files.length} file(s) selected`,
+        label: currentLang === 'zh' ? '目标分类' : 'Destination category',
+        hint: choices.length ? (currentLang === 'zh' ? '支持 Markdown 和 TXT，TXT 会转成 Markdown 文档' : 'Markdown and TXT are supported. TXT is converted to Markdown.') :
+            (currentLang === 'zh' ? '请先创建一个分类' : 'Create a category first'),
+        type: 'select',
+        choices,
+        icon: 'fa-file-arrow-up',
+        onSubmit: target => importKnowledgeDocuments(files, target),
+    });
+}
+
+async function importKnowledgeDocuments(files, targetCategory) {
+    const validationError = validateKnowledgeImportFiles(files);
+    if (validationError) {
+        _setKnowledgeStatus(validationError, true);
+        return null;
+    }
+    const supported = files.filter(file => /\.(md|txt)$/i.test(file.name || ''));
+    if (!supported.length) {
+        _setKnowledgeStatus(currentLang === 'zh' ? '请选择 .md 或 .txt 文件' : 'Choose .md or .txt files', true);
+        return null;
+    }
+    const formData = new FormData();
+    formData.append('target_category', targetCategory);
+    formData.append('conflict_strategy', 'rename');
+    supported.forEach(file => formData.append('files', file, file.name));
+    _setKnowledgeStatus(currentLang === 'zh' ? '正在导入...' : 'Importing...', false, true);
+    try {
+        const response = await fetch('/api/knowledge/import', { method: 'POST', body: formData });
+        const result = await response.json();
+        if (result.status !== 'success') {
+            _setKnowledgeStatus(result.message || (currentLang === 'zh' ? '导入失败' : 'Import failed'), true);
+            loadKnowledgeView();
+            return null;
+        }
+        _setKnowledgeStatus(_knowledgeResultMessage('import_documents', result.payload), false);
+        // Auto-open the first successfully imported document.
+        const firstImported = (result.payload?.results || []).find(item => item.status === 'imported');
+        loadKnowledgeView(firstImported ? firstImported.path : undefined);
+        return result.payload;
+    } catch (error) {
+        _setKnowledgeStatus(currentLang === 'zh' ? '导入请求失败' : 'Import request failed', true);
+        return null;
+    }
+}
+
+function validateKnowledgeImportFiles(files) {
+    if (!files || !files.length) return currentLang === 'zh' ? '请选择文件' : 'Choose files';
+    if (files.length > KNOWLEDGE_IMPORT_MAX_FILES) {
+        return currentLang === 'zh' ? `一次最多导入 ${KNOWLEDGE_IMPORT_MAX_FILES} 个文件` : `Import at most ${KNOWLEDGE_IMPORT_MAX_FILES} files at a time`;
+    }
+    let total = 0;
+    for (const file of files) {
+        total += file.size || 0;
+        if ((file.size || 0) > KNOWLEDGE_IMPORT_MAX_FILE_SIZE) {
+            return currentLang === 'zh' ? `${file.name} 超过 10MB` : `${file.name} exceeds 10MB`;
+        }
+    }
+    if (total > KNOWLEDGE_IMPORT_MAX_TOTAL_SIZE) {
+        return currentLang === 'zh' ? '单次导入总大小不能超过 200MB' : 'Total import size cannot exceed 200MB';
+    }
+    return '';
+}
+
+let _knowledgeImportDropReady = false;
+function initKnowledgeImportDropZone() {
+    if (_knowledgeImportDropReady) return;
+    const panel = document.getElementById('knowledge-panel-docs');
+    if (!panel) return;
+    _knowledgeImportDropReady = true;
+    ['dragenter', 'dragover'].forEach(name => {
+        panel.addEventListener(name, event => {
+            if (!event.dataTransfer || !event.dataTransfer.types.includes('Files')) return;
+            event.preventDefault();
+            panel.classList.add('knowledge-import-drag-over');
+        });
+    });
+    ['dragleave', 'drop'].forEach(name => {
+        panel.addEventListener(name, event => {
+            if (event.type === 'drop') {
+                event.preventDefault();
+                const files = Array.from(event.dataTransfer?.files || []);
+                if (files.length) openKnowledgeImportDialog(files);
+            }
+            panel.classList.remove('knowledge-import-drag-over');
+        });
+    });
+}
+
 function renameKnowledgeCategory(path) {
     openKnowledgeDialog({
         title: currentLang === 'zh' ? '重命名分类' : 'Rename category',

channel/web/web_channel.py

@@ -181,6 +181,29 @@ def _read_uploaded_file_bytes(file_obj) -> bytes:
     raise TypeError(f"Unsupported uploaded content type: {type(content).__name__}")
 
 
+def _read_uploaded_file_bytes_limited(file_obj, max_bytes: int) -> bytes:
+    """Read uploaded content and fail once it exceeds max_bytes."""
+    if isinstance(file_obj, bytes):
+        content = file_obj
+    elif isinstance(file_obj, str):
+        content = file_obj.encode("utf-8")
+    elif hasattr(file_obj, "file") and hasattr(file_obj.file, "read"):
+        content = file_obj.file.read(max_bytes + 1)
+    elif hasattr(file_obj, "read"):
+        content = file_obj.read(max_bytes + 1)
+    elif hasattr(file_obj, "value"):
+        content = file_obj.value
+    else:
+        raise ValueError("Unable to read uploaded file content")
+    if isinstance(content, str):
+        content = content.encode("utf-8")
+    if not isinstance(content, bytes):
+        raise TypeError(f"Unsupported uploaded content type: {type(content).__name__}")
+    if len(content) > max_bytes:
+        raise ValueError("file too large")
+    return content
+
+
 def _raw_web_input():
     """Return unprocessed multipart form data when web.py exposes rawinput."""
     rawinput = getattr(getattr(web, "webapi", None), "rawinput", None)
@@ -1162,6 +1185,7 @@ class WebChannel(ChatChannel):
             '/api/knowledge/read', 'KnowledgeReadHandler',
             '/api/knowledge/graph', 'KnowledgeGraphHandler',
             '/api/knowledge/action', 'KnowledgeActionHandler',
+            '/api/knowledge/import', 'KnowledgeImportHandler',
             '/api/scheduler', 'SchedulerHandler',
             '/api/scheduler/toggle', 'SchedulerToggleHandler',
             '/api/scheduler/update', 'SchedulerUpdateHandler',
@@ -4731,6 +4755,71 @@ class KnowledgeActionHandler:
             return json.dumps({"status": "error", "code": 500, "message": str(e), "payload": None})
 
 
+class KnowledgeImportHandler:
+    def POST(self):
+        _require_auth()
+        web.header('Content-Type', 'application/json; charset=utf-8')
+        try:
+            from agent.knowledge.service import KnowledgeService
+            content_length = int(getattr(web.ctx, "env", {}).get("CONTENT_LENGTH") or 0)
+            if content_length > KnowledgeService.MAX_IMPORT_TOTAL_SIZE:
+                return json.dumps({
+                    "status": "error",
+                    "code": 413,
+                    "message": "import batch too large",
+                    "payload": None,
+                })
+            params = _raw_web_input()
+            target_category = params.get("target_category", "")
+            conflict_strategy = params.get("conflict_strategy", "skip")
+            uploaded = _ensure_list(params.get("files"))
+            single = params.get("file")
+            if single is not None:
+                uploaded.append(single)
+            if not uploaded:
+                return json.dumps({"status": "error", "code": 400, "message": "No files uploaded", "payload": None})
+            if len(uploaded) > KnowledgeService.MAX_IMPORT_FILES:
+                return json.dumps({
+                    "status": "error",
+                    "code": 400,
+                    "message": f"too many files: max {KnowledgeService.MAX_IMPORT_FILES}",
+                    "payload": None,
+                })
+
+            files = []
+            total_size = 0
+            for file_obj in uploaded:
+                if file_obj is None:
+                    continue
+                filename = getattr(file_obj, "filename", "") or getattr(file_obj, "name", "")
+                content = _read_uploaded_file_bytes_limited(file_obj, KnowledgeService.MAX_IMPORT_FILE_SIZE)
+                total_size += len(content)
+                if total_size > KnowledgeService.MAX_IMPORT_TOTAL_SIZE:
+                    return json.dumps({
+                        "status": "error",
+                        "code": 413,
+                        "message": "import batch too large",
+                        "payload": None,
+                    })
+                files.append({
+                    "filename": filename,
+                    "content": content,
+                })
+
+            result = KnowledgeService(_get_workspace_root()).dispatch("import_documents", {
+                "target_category": target_category,
+                "conflict_strategy": conflict_strategy,
+                "files": files,
+            })
+            return json.dumps({
+                "status": "success" if result["code"] < 300 else "error",
+                **result,
+            }, ensure_ascii=False)
+        except Exception as e:
+            logger.error(f"[WebChannel] Knowledge import error: {e}", exc_info=True)
+            return json.dumps({"status": "error", "code": 500, "message": str(e), "payload": None})
+
+
 class VersionHandler:
     def GET(self):
         web.header('Content-Type', 'application/json; charset=utf-8')

tests/test_knowledge_service.py

@@ -209,3 +209,90 @@ def test_move_does_not_overwrite_target_created_concurrently(tmp_path):
     assert source.read_text(encoding="utf-8") == "source"
     assert target.read_text(encoding="utf-8") == "concurrent"
     assert manager.storage.deleted == []
+
+
+def test_create_document_writes_and_syncs(tmp_path):
+    svc, manager = service(tmp_path)
+    (tmp_path / "knowledge/notes").mkdir()
+
+    result = svc.dispatch("create_document", {
+        "path": "notes/new.md", "content": "# New\nBody",
+    })
+
+    assert result["code"] == 200
+    assert (tmp_path / "knowledge/notes/new.md").read_text(encoding="utf-8") == "# New\nBody"
+    assert manager.dirty == 1
+    assert manager.synced == 1
+
+
+def test_import_documents_supports_md_txt_and_rename_conflicts(tmp_path):
+    svc, manager = service(tmp_path)
+    (tmp_path / "knowledge/notes").mkdir()
+    (tmp_path / "knowledge/notes/a.md").write_text("existing", encoding="utf-8")
+
+    result = svc.dispatch("import_documents", {
+        "target_category": "notes",
+        "conflict_strategy": "rename",
+        "files": [
+            {"filename": "a.md", "content": b"# A"},
+            {"filename": "plain.txt", "content": "plain text"},
+        ],
+    })
+
+    assert result["code"] == 200
+    assert result["payload"]["imported"] == 2
+    assert (tmp_path / "knowledge/notes/a-1.md").read_text(encoding="utf-8") == "# A"
+    assert (tmp_path / "knowledge/notes/plain.md").read_text(encoding="utf-8") == "plain text"
+    assert manager.storage.deleted == []
+    assert manager.synced == 1
+
+
+def test_import_documents_skip_overwrite_and_failures(tmp_path):
+    svc, manager = service(tmp_path)
+    (tmp_path / "knowledge/notes").mkdir()
+    existing = tmp_path / "knowledge/notes/a.md"
+    existing.write_text("old", encoding="utf-8")
+
+    skipped = svc.dispatch("import_documents", {
+        "target_category": "notes",
+        "conflict_strategy": "skip",
+        "files": [{"filename": "a.md", "content": b"new"}],
+    })
+    assert skipped["payload"]["skipped"] == 1
+    assert existing.read_text(encoding="utf-8") == "old"
+    assert manager.synced == 0
+
+    overwritten = svc.dispatch("import_documents", {
+        "target_category": "notes",
+        "conflict_strategy": "overwrite",
+        "files": [
+            {"filename": "a.md", "content": b"new"},
+            {"filename": "bad.pdf", "content": b"%PDF"},
+        ],
+    })
+    assert overwritten["payload"]["imported"] == 1
+    assert overwritten["payload"]["failed"] == 1
+    assert existing.read_text(encoding="utf-8") == "new"
+    assert manager.storage.deleted == ["knowledge/notes/a.md"]
+    assert manager.synced == 1
+
+
+def test_import_documents_rejects_large_files_and_batches(tmp_path):
+    svc, manager = service(tmp_path)
+    (tmp_path / "knowledge/notes").mkdir()
+    assert svc.MAX_IMPORT_TOTAL_SIZE == 200 * 1024 * 1024
+
+    too_large = svc.dispatch("import_documents", {
+        "target_category": "notes",
+        "files": [{"filename": "big.md", "content": b"x" * (svc.MAX_IMPORT_FILE_SIZE + 1)}],
+    })
+    assert too_large["payload"]["failed"] == 1
+    assert too_large["payload"]["results"][0]["reason"] == "file too large"
+
+    too_many = svc.dispatch("import_documents", {
+        "target_category": "notes",
+        "files": [{"filename": f"{i}.md", "content": b"x"} for i in range(svc.MAX_IMPORT_FILES + 1)],
+    })
+    assert too_many["code"] == 403
+    assert "too many files" in too_many["message"]
+    assert manager.synced == 0

tests/test_knowledge_web.py

@@ -47,15 +47,81 @@ def test_knowledge_frontend_management_contract():
     js = (root / "channel/web/static/js/console.js").read_text(encoding="utf-8")
 
     assert 'id="knowledge-dialog-overlay"' in html
+    assert 'id="knowledge-dialog-textarea"' in html
+    assert 'id="knowledge-document-form"' in html
+    assert 'id="knowledge-document-path-preview"' in html
     assert "function openKnowledgeDialog(" in js
     assert "function _knowledgeCategoryPaths(" in js
     assert "dispatchKnowledgeAction('create_category'" in js
+    assert "dispatchKnowledgeAction('create_document'" in js
     assert "dispatchKnowledgeAction('rename_category'" in js
     assert "dispatchKnowledgeAction('delete_category'" in js
     assert "dispatchKnowledgeAction('delete_documents'" in js
     assert "dispatchKnowledgeAction('move_documents'" in js
+    assert 'id="knowledge-import-input"' in html
+    assert "function createKnowledgeDocument(" in js
+    assert "function openKnowledgeDocumentEditor(" in js
+    assert "documentPathPreview.textContent = options.category" in js
+    assert "options.type === 'document'" in js
+    assert "input.classList.toggle('hidden', options.type === 'select' || options.type === 'textarea' || options.type === 'document')" in js
+    assert "function selectKnowledgeImportFiles(" in js
+    assert "function importKnowledgeDocuments(" in js
+    assert "function validateKnowledgeImportFiles(" in js
+    assert "KNOWLEDGE_IMPORT_MAX_FILE_SIZE" in js
+    assert "fetch('/api/knowledge/import'" in js
+    assert "initKnowledgeImportDropZone()" in js
 
     knowledge_section = js[js.index("// Knowledge View"):js.index("function _hasFilterMatch")]
     assert "prompt(" not in knowledge_section
     assert "alert(" not in knowledge_section
     assert "if (path === 'index.md' || path === 'log.md') return '';" in knowledge_section
+
+
+class UploadedFile:
+    def __init__(self, filename, content):
+        self.filename = filename
+        self.value = content
+
+
+def test_knowledge_import_handler_delegates_to_dispatch(tmp_path):
+    from channel.web.web_channel import KnowledgeImportHandler
+
+    dispatched = {"action": "import_documents", "code": 200, "message": "success",
+                  "payload": {"imported": 2, "skipped": 0, "failed": 0}}
+    params = {
+        "target_category": "notes",
+        "conflict_strategy": "rename",
+        "files": [UploadedFile("a.md", b"# A"), UploadedFile("b.txt", b"B")],
+    }
+
+    with patch("channel.web.web_channel._require_auth"), \
+         patch("channel.web.web_channel.web.header"), \
+         patch("channel.web.web_channel._raw_web_input", return_value=params), \
+         patch("channel.web.web_channel._get_workspace_root", return_value=str(tmp_path)), \
+         patch("agent.knowledge.service.KnowledgeService.dispatch", return_value=dispatched) as dispatch:
+        response = json.loads(KnowledgeImportHandler().POST())
+
+    dispatch.assert_called_once()
+    action, payload = dispatch.call_args.args
+    assert action == "import_documents"
+    assert payload["target_category"] == "notes"
+    assert payload["conflict_strategy"] == "rename"
+    assert [f["filename"] for f in payload["files"]] == ["a.md", "b.txt"]
+    assert response["status"] == "success"
+    assert response["payload"]["imported"] == 2
+
+
+def test_knowledge_import_handler_rejects_large_content_length(tmp_path):
+    from channel.web.web_channel import KnowledgeImportHandler
+    from agent.knowledge.service import KnowledgeService
+    assert KnowledgeService.MAX_IMPORT_TOTAL_SIZE == 200 * 1024 * 1024
+
+    with patch("channel.web.web_channel._require_auth"), \
+         patch("channel.web.web_channel.web.header"), \
+         patch("channel.web.web_channel.web.ctx") as ctx:
+        ctx.env = {"CONTENT_LENGTH": str(KnowledgeService.MAX_IMPORT_TOTAL_SIZE + 1)}
+        response = json.loads(KnowledgeImportHandler().POST())
+
+    assert response["status"] == "error"
+    assert response["code"] == 413
+    assert response["message"] == "import batch too large"