重构仓库目录为统一的 runtime 分层并清理历史 openclaw 残留。

本次迁移将网关/通道/工具/技能/脚本与协议资源集中到新结构，统一路径常量与脚本转发机制，减少顶层噪音并保证运行与测试行为一致。

Made-with: Cursor

runtime/tools/experts/productivity/kb_tools.py

@@ -0,0 +1,96 @@
+from __future__ import annotations
+
+import hashlib
+from typing import Any
+
+from oclaw.platform.config.paths import db_path
+from oclaw.platform.embeddings.embedding_client import build_default_embedding_client
+from oclaw.platform.persistence.sqlite_store import SqliteStore
+from oclaw.runtime.tools.base import ToolSpec
+
+
+def _chunk_id(source: str, text: str) -> str:
+    raw = f"{source}\n{text}".encode("utf-8", errors="ignore")
+    return hashlib.sha1(raw).hexdigest()
+
+
+def kb_add_tool() -> ToolSpec:
+    def handler(args: dict[str, Any]) -> dict[str, Any]:
+        try:
+            tenant_id = str(args.get("tenant_id") or "").strip()
+            user_id = str(args.get("user_id") or "").strip()
+            text = str(args.get("text") or "").strip()
+            title = str(args.get("title") or "").strip()
+            if not tenant_id or not user_id or not text:
+                return {"ok": False, "error": "tenant_id, user_id, text are required"}
+            source = f"builtin:tenant:{tenant_id}:kb"
+            if title:
+                source = f"{source}:{title[:48]}"
+            cid = _chunk_id(source, text)
+            store = SqliteStore(db_path())
+            store.upsert_knowledge_chunk(
+                chunk_id=cid,
+                source=source,
+                content=text,
+                metadata={"tenant_id": tenant_id, "user_id": user_id, "title": title, "source": source},
+            )
+            client = build_default_embedding_client()
+            emb = client.embed(text[:8000])
+            store.upsert_knowledge_embedding(chunk_id=cid, model=emb.model, vector=emb.vector)
+            return {"ok": True, "chunk_id": cid, "source": source, "embedding_model": emb.model}
+        except Exception as e:
+            return {"ok": False, "error": f"{type(e).__name__}: {e}"}
+
+    return ToolSpec(
+        name="kb_add",
+        description="Add a knowledge snippet for a tenant into the vector knowledge base.",
+        parameters={
+            "type": "object",
+            "properties": {"tenant_id": {"type": "string"}, "user_id": {"type": "string"}, "title": {"type": "string", "description": "Optional title/label."}, "text": {"type": "string", "description": "Knowledge content to store."}},
+            "required": ["tenant_id", "user_id", "text"],
+            "additionalProperties": False,
+        },
+        handler=handler,
+        tags=frozenset({"productivity", "rag", "write"}),
+    )
+
+
+def kb_search_tool() -> ToolSpec:
+    def handler(args: dict[str, Any]) -> dict[str, Any]:
+        try:
+            tenant_id = str(args.get("tenant_id") or "").strip()
+            query = str(args.get("query") or "").strip()
+            limit = int(args.get("limit") or 3)
+            if not tenant_id or not query:
+                return {"ok": False, "error": "tenant_id and query are required"}
+            store = SqliteStore(db_path())
+            from oclaw.runtime.orchestration.memory import retrieve_context
+
+            rows = retrieve_context(store, query, limit=max(1, min(limit, 6)))
+            filtered = [r for r in rows if str(r.get("source") or "").startswith(f"builtin:tenant:{tenant_id}:")]
+            hits = filtered[: max(1, min(limit, 6))]
+            if not hits:
+                like_rows = store.search_knowledge(query=query, limit=max(1, min(limit, 6)))
+                hits = [r for r in like_rows if str(r.get("source") or "").startswith(f"builtin:tenant:{tenant_id}:")][: max(1, min(limit, 6))]
+            refs = []
+            for h in hits:
+                refs.append({"source": str(h.get("source") or ""), "snippet": str(h.get("content") or "")[:240]})
+            return {"ok": True, "hits": refs}
+        except Exception as e:
+            return {"ok": False, "error": f"{type(e).__name__}: {e}"}
+
+    return ToolSpec(
+        name="kb_search",
+        description="Search tenant knowledge base and return citations/snippets.",
+        parameters={
+            "type": "object",
+            "properties": {"tenant_id": {"type": "string"}, "query": {"type": "string"}, "limit": {"type": "integer", "default": 3}},
+            "required": ["tenant_id", "query"],
+            "additionalProperties": False,
+        },
+        handler=handler,
+        tags=frozenset({"productivity", "rag"}),
+    )
+
+
+__all__ = ["kb_add_tool", "kb_search_tool"]