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/workspace_indexer.py

@@ -0,0 +1,101 @@
+from __future__ import annotations
+
+import hashlib
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+from oclaw.platform.embeddings.embedding_client import build_default_embedding_client
+from oclaw.platform.persistence.sqlite_store import SqliteStore
+from oclaw.platform.config.paths import PROJECT_ROOT
+
+
+def _default_workspace_root() -> Path:
+    import os
+
+    override = (os.getenv("AIA_WORKSPACE_ROOT") or "").strip()
+    if override:
+        return Path(override).expanduser().resolve()
+    return Path(PROJECT_ROOT).resolve()
+
+
+@dataclass(frozen=True)
+class IndexStats:
+    files_seen: int
+    chunks_upserted: int
+    embeddings_upserted: int
+
+
+def _chunk_id(source: str, text: str) -> str:
+    raw = f"{source}\n{text}".encode("utf-8", errors="ignore")
+    return hashlib.sha1(raw).hexdigest()
+
+
+def _iter_files(root: Path, patterns: list[str], *, max_files: int) -> list[Path]:
+    files: list[Path] = []
+    for pat in patterns:
+        for p in root.glob(pat):
+            if p.is_dir():
+                continue
+            files.append(p)
+            if len(files) >= max_files:
+                return files
+    # de-dup by path
+    uniq = []
+    seen = set()
+    for p in files:
+        rp = str(p)
+        if rp in seen:
+            continue
+        seen.add(rp)
+        uniq.append(p)
+    return uniq[:max_files]
+
+
+def index_workspace(
+    store: SqliteStore,
+    *,
+    root: str | None = None,
+    patterns: list[str] | None = None,
+    max_files: int = 200,
+    max_chars_per_chunk: int = 1600,
+) -> IndexStats:
+    r = Path(root).resolve() if root else _default_workspace_root()
+    pats = patterns or ["oclaw/**/*.py", "README*.md", "**/*.md"]
+    files = _iter_files(r, pats, max_files=max(1, int(max_files)))
+    client = build_default_embedding_client()
+    chunks_upserted = 0
+    embeds_upserted = 0
+
+    for p in files:
+        try:
+            text = p.read_text(encoding="utf-8", errors="replace")
+        except Exception:
+            continue
+        rel = str(p.relative_to(r))
+        # naive chunking by chars (good enough for MVP)
+        for idx in range(0, len(text), max(200, int(max_chars_per_chunk))):
+            chunk = text[idx : idx + int(max_chars_per_chunk)].strip()
+            if not chunk:
+                continue
+            source = f"workspace:{rel}#c{idx//max_chars_per_chunk:04d}"
+            cid = _chunk_id(source, chunk)
+            store.upsert_knowledge_chunk(
+                chunk_id=cid,
+                source=source,
+                content=chunk,
+                metadata={"source": source, "path": rel, "offset": idx},
+            )
+            chunks_upserted += 1
+            try:
+                emb = client.embed(chunk[:8000])
+                store.upsert_knowledge_embedding(chunk_id=cid, model=emb.model, vector=emb.vector)
+                embeds_upserted += 1
+            except Exception:
+                continue
+
+    return IndexStats(files_seen=len(files), chunks_upserted=chunks_upserted, embeddings_upserted=embeds_upserted)
+
+
+__all__ = ["IndexStats", "index_workspace"]
+