Import AITURK IDE 1.0.0-beta.1 from Hermes 63279301; preserve MIT license

agent/learning_mutations.py

@@ -0,0 +1,206 @@
+"""User-initiated edit/delete for journey nodes (learned skills + memories).
+
+The journey graph (``agent.learning_graph``) gives every node a stable id:
+
+- **skills** → the skill name (e.g. ``"debugging-hermes-desktop"``)
+- **memories** → ``memory:<source>:<index>`` where ``source`` is ``memory``
+  (``MEMORY.md``) or ``profile`` (``USER.md``) and ``index`` is the node's
+  position in the combined card list (``MEMORY.md`` cards first, then
+  ``USER.md``).
+
+This module maps a node id back to its on-disk home and performs the mutation,
+shared by the CLI (``hermes journey delete|edit``), the TUI ``/journey`` overlay
+(gateway RPCs), and the desktop GUI (REST). Deleting a skill *archives* it
+(recoverable via ``hermes curator restore``); deleting a memory rewrites its
+file. Pure stdlib + existing skill/memory helpers.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+_MEMORY_FILES = {"memory": "MEMORY.md", "profile": "USER.md"}
+
+
+def parse_node_kind(node_id: str) -> str:
+    return "memory" if node_id.startswith("memory:") else "skill"
+
+
+def _memories_dir() -> Path:
+    from hermes_constants import get_hermes_home
+
+    return get_hermes_home() / "memories"
+
+
+def _parse_memory_id(node_id: str) -> tuple[str, int]:
+    """``memory:<source>:<index>`` → (source, global_index)."""
+    parts = node_id.split(":", 2)
+    if len(parts) != 3 or parts[0] != "memory" or parts[1] not in _MEMORY_FILES:
+        raise ValueError(f"bad memory node id: {node_id!r}")
+    try:
+        return parts[1], int(parts[2])
+    except ValueError as exc:
+        raise ValueError(f"bad memory node id: {node_id!r}") from exc
+
+
+def _memory_local_index(source: str, global_index: int) -> int:
+    """Global card index → position within the source's own file.
+
+    ``_memory_cards`` emits all ``MEMORY.md`` cards before ``USER.md`` cards, so
+    a profile card's local index is its global index minus the memory count.
+    """
+    from agent.learning_graph import _memory_cards
+
+    cards = _memory_cards()
+    if not 0 <= global_index < len(cards):
+        raise IndexError(f"memory index {global_index} out of range")
+    if cards[global_index].get("source") != source:
+        raise ValueError("memory node id is stale — refresh the graph")
+    if source == "memory":
+        return global_index
+    return global_index - sum(1 for c in cards if c.get("source") == "memory")
+
+
+def _locate_memory(source: str, gidx: int) -> tuple[Path, list[str], int]:
+    """Resolve a memory card to its file, all §-delimited entries, and local index.
+
+    Entries come from ``MemoryStore._read_file`` — the same parser the memory
+    tool uses — so journey indices stay aligned with what the graph renders.
+    """
+    from tools.memory_tool import MemoryStore
+
+    path = _memories_dir() / _MEMORY_FILES[source]
+    if not path.exists():
+        raise ValueError(f"{path.name} not found")
+    chunks = MemoryStore._read_file(path)
+    local = _memory_local_index(source, gidx)
+    if not 0 <= local < len(chunks):
+        raise ValueError("memory node id is stale — refresh the graph")
+    return path, chunks, local
+
+
+# ── Inspect (edit prefill) ──────────────────────────────────────────────────
+
+
+def node_detail(node_id: str) -> dict[str, Any]:
+    """Current content for an edit prefill. ``content`` is the full SKILL.md
+    (skills) or the raw memory chunk (memories)."""
+    try:
+        return _node_detail(node_id)
+    except (ValueError, IndexError) as exc:
+        return {"ok": False, "message": str(exc)}
+
+
+def _node_detail(node_id: str) -> dict[str, Any]:
+    if parse_node_kind(node_id) == "memory":
+        source, gidx = _parse_memory_id(node_id)
+        _, chunks, local = _locate_memory(source, gidx)
+        body = chunks[local].strip()
+
+        return {"ok": True, "kind": "memory", "id": node_id, "label": body.splitlines()[0][:80], "content": body}
+
+    from tools.skill_manager_tool import _find_skill
+
+    found = _find_skill(node_id)
+    if not found:
+        return {"ok": False, "message": f"skill '{node_id}' not found"}
+    skill_md = Path(found["path"]) / "SKILL.md"
+    if not skill_md.exists():
+        return {"ok": False, "message": f"SKILL.md missing for '{node_id}'"}
+
+    return {
+        "ok": True,
+        "kind": "skill",
+        "id": node_id,
+        "label": node_id,
+        "content": skill_md.read_text(encoding="utf-8"),
+    }
+
+
+# ── Delete ──────────────────────────────────────────────────────────────────
+
+
+def delete_node(node_id: str) -> dict[str, Any]:
+    try:
+        return _delete_memory(node_id) if parse_node_kind(node_id) == "memory" else _delete_skill(node_id)
+    except (ValueError, IndexError) as exc:
+        return {"ok": False, "message": str(exc)}
+
+
+def _delete_skill(name: str) -> dict[str, Any]:
+    from tools import skill_usage
+
+    if skill_usage.get_record(name).get("pinned"):
+        return {"ok": False, "message": f"'{name}' is pinned — unpin it first (hermes curator unpin {name})"}
+
+    ok, message = skill_usage.archive_skill(name)
+    if ok:
+        _clear_skill_cache()
+
+    return {"ok": ok, "message": f"archived '{name}' — restore with: hermes curator restore {name}" if ok else message}
+
+
+def _delete_memory(node_id: str) -> dict[str, Any]:
+    source, gidx = _parse_memory_id(node_id)
+    path, chunks, local = _locate_memory(source, gidx)
+
+    del chunks[local]
+    _write_memory(path, chunks)
+
+    return {"ok": True, "message": f"deleted memory from {path.name}"}
+
+
+# ── Edit ────────────────────────────────────────────────────────────────────
+
+
+def edit_node(node_id: str, content: str) -> dict[str, Any]:
+    try:
+        return _edit_memory(node_id, content) if parse_node_kind(node_id) == "memory" else _edit_skill(node_id, content)
+    except (ValueError, IndexError) as exc:
+        return {"ok": False, "message": str(exc)}
+
+
+def _edit_skill(name: str, content: str) -> dict[str, Any]:
+    from tools.skill_manager_tool import _edit_skill as _do_edit
+
+    result = _do_edit(name, content)
+    if result.get("success"):
+        _clear_skill_cache()
+
+        return {"ok": True, "message": f"updated '{name}'"}
+
+    return {"ok": False, "message": result.get("error", "edit failed")}
+
+
+def _edit_memory(node_id: str, content: str) -> dict[str, Any]:
+    source, gidx = _parse_memory_id(node_id)
+    body = content.strip()
+    if not body:
+        return {"ok": False, "message": "empty memory — use delete to remove it"}
+    path, chunks, local = _locate_memory(source, gidx)
+
+    chunks[local] = body
+    _write_memory(path, chunks)
+
+    return {"ok": True, "message": f"updated memory in {path.name}"}
+
+
+# ── Helpers ─────────────────────────────────────────────────────────────────
+
+
+def _write_memory(path: Path, chunks: list[str]) -> None:
+    """Atomic temp-file + rename via the memory tool, so a concurrent reader
+    never sees a half-written file (and the §-join stays single-sourced)."""
+    from tools.memory_tool import MemoryStore
+
+    MemoryStore._write_file(path, [c.strip() for c in chunks if c.strip()])
+
+
+def _clear_skill_cache() -> None:
+    try:
+        from agent.prompt_builder import clear_skills_system_prompt_cache
+
+        clear_skills_system_prompt_cache(clear_snapshot=True)
+    except Exception:
+        pass