feat(ledger): classify_shapes + list_shapes MCP tools; get_snippet carries the consumer map (#2789, milestone 294 step 3)
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The judgment write path. classify_shapes applies a batch of classifications to a project's live ledger rows — all-or-nothing (#2709's lesson: the whole batch is validated, write-ACL'd, and every snippet target proven readable before any row is touched); rows match by exact (path, symbol), kind narrows, and shapes no live row matches come back as 'unmatched' rather than errors. variant/exempt REQUIRE the reason — the why is the record (note 2786) — and 'unclassified' deliberately withdraws a judgment back to the todo. The 'via' channel is caller-restricted to agent|audit|import; hook and mechanical stay server-internal so a caller can't launder judgment as machinery. list_shapes is the todo query (status=unclassified) with composable filters: path is exact-or-under like recorded locations, snippet_id reads a consumer map, include_vanished reads history; paged with the true total. get_snippet now attaches and — the structured consumer map, filtered to projects the CALLER can read so a shared snippet never side- channels another project's file layout; attached only when non-empty (#2483). Integration tests pin the batch atomicity, ACL gates, filter composition, the consumer map on the MCP pull, and the SET NULL companion: a judgment whose snippet was purged rejoins the todo on the next sync. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
This commit is contained in:
@@ -5,8 +5,8 @@ to a FastMCP instance. `register_all(mcp)` is the single entry point called
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from `mcp.server.build_mcp_server`.
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"""
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from scribe.mcp.tools import (
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design_systems, milestones, notes, processes, projects, recent, repos, rulebooks, search, snippets,
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systems, tags, tasks, trash,
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design_systems, milestones, notes, processes, projects, recent, repos, rulebooks, search, shapes,
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snippets, systems, tags, tasks, trash,
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)
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@@ -24,5 +24,6 @@ def register_all(mcp) -> None:
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repos.register(mcp)
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processes.register(mcp)
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snippets.register(mcp)
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shapes.register(mcp)
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rulebooks.register(mcp)
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trash.register(mcp)
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@@ -0,0 +1,95 @@
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"""Shape-ledger MCP tools — the classification write/read surface (#2789).
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The accounting model (note 2786): the snippet library records CANON (small);
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the ledger accounts for EVERY extracted shape (total). These tools are how
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agents move shapes out of `unclassified` — the todo state — and how they read
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what still needs judgment. The ledger rows themselves are fed by the coverage
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refresh; these tools only ever judge what the sync has seen.
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"""
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from __future__ import annotations
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from scribe.mcp._context import current_user_id
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from scribe.services import shape_ledger as shape_ledger_svc
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async def classify_shapes(
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project_id: int, classifications: list[dict], via: str = "agent"
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) -> dict:
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"""Record judgments for a project's code shapes — in batch, as rows.
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EVERY shape in a bound repo should end up classified (note 2786):
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- `instance` of snippet N — it conforms to recorded canon (family-level
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canon in another project counts; that fully accounts for the shape).
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- `variant` of snippet N — a deliberate, named departure. `reason`
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(the why) is REQUIRED; it is the record.
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- `exempt` — judged genuinely one-off. `reason` REQUIRED.
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- `canonical` of snippet N — this row IS the snippet's reference
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(rarely set by hand; the coverage sync stamps these mechanically).
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- `unclassified` — withdraw a judgment; the shape rejoins the todo.
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Consumer maps belong HERE, not in prose: when an audit enumerates call
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sites of a canonical helper, each call site's defining shape is an
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`instance` row — a sentence in a verification detail cannot be sorted,
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queried, or diffed.
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Args:
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project_id: The project whose ledger is being judged.
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classifications: Objects of {path, symbol, status, kind?, snippet_id?,
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reason?}. path+symbol name the shape exactly as list_shapes shows
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it; kind ("sym"/"css") narrows when one file defines both.
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snippet_id is required for canonical/instance/variant; reason is
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required for variant/exempt.
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via: Who is judging — "agent" (default), "audit" (a sweep), or
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"import" (carrying maps recorded elsewhere).
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All-or-nothing: a structural error, a missing snippet target, or no write
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access applies NOTHING. Returns {"classified": N, "unmatched": [...]} —
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unmatched names shapes no live ledger row matches (the tree may have
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moved since you listed; re-run the project's coverage refresh to re-sync).
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"""
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uid = current_user_id()
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return await shape_ledger_svc.classify_shapes(
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uid, project_id, classifications, via=via
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)
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async def list_shapes(
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project_id: int,
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status: str = "",
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path: str = "",
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snippet_id: int = 0,
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include_vanished: bool = False,
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limit: int = 100,
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offset: int = 0,
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) -> dict:
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"""Read a project's shape ledger — `status="unclassified"` IS the todo.
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Every extracted definition in the project's bound repos has a row here
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(fed by the coverage refresh). Filters compose:
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Args:
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status: canonical | instance | variant | exempt | unclassified.
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path: exact file, or a directory — matches everything beneath it
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(the coverage line's "largest" dirs go straight in here).
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snippet_id: rows classified against this snippet — a consumer map.
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include_vanished: include shapes no longer in the tree (history).
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limit/offset: page through big ledgers (limit caps at 500).
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Returns {"shapes": [...], "total": N} — total counts every match, not
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just this page. Classify what you can judge with classify_shapes; a
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repeating shape with NO recorded canon is a derive-one-first moment
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(consolidate onto a reference, create_snippet it, then classify the
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rest against it), never N loose classifications.
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"""
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uid = current_user_id()
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rows, total = await shape_ledger_svc.list_project_shapes(
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uid, project_id,
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status=status, path=path, snippet_id=snippet_id,
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include_vanished=include_vanished, limit=limit, offset=offset,
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)
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return {"shapes": [r.to_dict() for r in rows], "total": total}
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def register(mcp) -> None:
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for fn in (classify_shapes, list_shapes):
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mcp.tool(name=fn.__name__)(fn)
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@@ -207,6 +207,12 @@ async def get_snippet(snippet_id: int) -> dict:
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the source moved on — trust the location over the cached body and
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consider verify_snippet after you look.
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When the shape ledger has judgments against this snippet, the response
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carries `instances` (shapes classified as conforming to it — the
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structured consumer map) and/or `variants` (named departures, each with
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its why). Consult them before changing the snippet's contract: they are
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the call sites your change lands on (classify_shapes maintains them).
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If the record belongs to someone else it carries `shared: true` with the
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`owner` and your `permission`. Read that as ONE PERSON'S SUGGESTION, not as
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established practice here: judge it on its merits, say whose it is when you
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@@ -229,6 +235,16 @@ async def get_snippet(snippet_id: int) -> dict:
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await systems_tools.attach_systems(
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uid, note.user_id, data, note.id, note.project_id
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)
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# The structured consumer map (#2789): ledger rows judged against this
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# snippet. Attached only when non-empty (#2483) — and never for projects
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# the caller can't read.
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from scribe.services import shape_ledger as shape_ledger_svc
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consumers = await shape_ledger_svc.snippet_consumers(uid, int(note.id))
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if consumers["instances"]:
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data["instances"] = consumers["instances"]
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if consumers["variants"]:
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data["variants"] = consumers["variants"]
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return data
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@@ -174,3 +174,237 @@ async def live_rows(project_id: int) -> list[CodeShape]:
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)
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).scalars().all()
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)
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# --- classification (#2789): the judgment write path --------------------------
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# Statuses an explicit classification may set. All five: setting a row back to
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# `unclassified` is how a judgment is deliberately withdrawn.
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_SETTABLE = ("canonical", "instance", "variant", "exempt", "unclassified")
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# Who may appear as the classifier on this path. `hook` and `mechanical` are
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# server-internal feeds (steps 5-6) — a caller claiming them would launder a
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# judgment as machinery.
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_CALLER_VIAS = ("agent", "audit", "import")
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def validate_classifications(items: list[dict]) -> str | None:
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"""The structural error a classification batch would earn, or None.
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Pure and checked BEFORE anything is touched: a batch either applies or
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errors whole — the StrictArgs lesson (#2709), a caller must never learn
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later that half a batch silently happened.
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"""
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if not items:
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return "classifications is empty — nothing to apply"
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for i, item in enumerate(items):
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if not isinstance(item, dict):
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return f"classifications[{i}] is not an object"
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path = (item.get("path") or "").strip()
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symbol = (item.get("symbol") or "").strip()
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if not path or not symbol:
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return f"classifications[{i}] needs both path and symbol"
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status = item.get("status") or ""
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if status not in _SETTABLE:
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return (
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f"classifications[{i}] has unknown status {status!r} "
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f"(one of: {', '.join(_SETTABLE)})"
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)
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snippet_id = item.get("snippet_id") or 0
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if status in _NEEDS_TARGET and not snippet_id:
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return (
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f"classifications[{i}]: status {status!r} needs snippet_id — "
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"the snippet this shape is (or departs from)"
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)
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if status in ("variant", "exempt") and not (item.get("reason") or "").strip():
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return (
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f"classifications[{i}]: status {status!r} needs a reason — "
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"the WHY is the record (note 2786)"
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)
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return None
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async def classify_shapes(
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user_id: int,
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project_id: int,
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classifications: list[dict],
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*,
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via: str = "agent",
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) -> dict:
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"""Apply a batch of judgments to a project's live ledger rows.
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All-or-nothing on errors: the whole batch is validated (structure, write
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access, every snippet target readable by the caller) before any row is
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touched. Rows are matched by exact (path, symbol) — plus kind when the
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item carries one — and a target no live row matches is reported in
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``unmatched``, not an error: the tree may simply have moved since the
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caller listed. Idempotent by construction.
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"""
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from scribe.services import access
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from scribe.services import snippets as snippets_svc
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if via not in _CALLER_VIAS:
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raise ValueError(f"via must be one of: {', '.join(_CALLER_VIAS)}")
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error = validate_classifications(classifications)
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if error:
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raise ValueError(error)
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if not await access.can_write_project(user_id, project_id):
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raise ValueError(f"project {project_id} not found or no write access")
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# Snippet targets resolve through the caller's own read access — a
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# family-canon snippet in another project counts (note 2786), a snippet
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# the caller cannot read does not exist for them.
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target_ids = {
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int(item["snippet_id"])
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for item in classifications
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if item.get("status") in _NEEDS_TARGET
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}
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for sid in sorted(target_ids):
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if await snippets_svc.get_snippet(user_id, sid) is None:
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raise ValueError(f"snippet {sid} not found (or not readable)")
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now = datetime.now(timezone.utc)
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classified = 0
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unmatched: list[dict] = []
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async with async_session() as session:
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rows = (
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await session.execute(
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select(CodeShape).where(
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CodeShape.project_id == project_id,
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CodeShape.vanished_at.is_(None),
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)
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)
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).scalars().all()
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by_key: dict[tuple[str, str], list[CodeShape]] = {}
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for row in rows:
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by_key.setdefault((row.path, row.symbol), []).append(row)
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for item in classifications:
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matches = by_key.get(
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((item.get("path") or "").strip(), (item.get("symbol") or "").strip())
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) or []
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kind = (item.get("kind") or "").strip()
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if kind:
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matches = [r for r in matches if r.kind == kind]
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if not matches:
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unmatched.append({
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"path": item.get("path"), "symbol": item.get("symbol"),
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})
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continue
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status = item["status"]
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for row in matches:
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row.status = status
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if status == "unclassified":
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row.snippet_id = None
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row.reason = None
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row.classified_by = None
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row.classified_at = None
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else:
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row.snippet_id = (
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int(item["snippet_id"]) if status in _NEEDS_TARGET else None
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)
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row.reason = (item.get("reason") or "").strip() or None
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row.classified_by = via
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row.classified_at = now
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classified += 1
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await session.commit()
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return {"classified": classified, "unmatched": unmatched}
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async def list_project_shapes(
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user_id: int,
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project_id: int,
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*,
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status: str = "",
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path: str = "",
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snippet_id: int = 0,
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include_vanished: bool = False,
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limit: int = 100,
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offset: int = 0,
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) -> tuple[list[CodeShape], int]:
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"""A filtered page of a project's ledger, with the unfiltered-match total.
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([], 0) when the caller can't read the project — the same silence every
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other project list gives. ``path`` matches the exact file or anything
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beneath it, mirroring recorded-location semantics.
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"""
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from sqlalchemy import func, or_
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from scribe.services import access
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if not await access.can_read_project(user_id, project_id):
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return [], 0
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conds = [CodeShape.project_id == project_id]
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if not include_vanished:
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conds.append(CodeShape.vanished_at.is_(None))
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if status:
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conds.append(CodeShape.status == status)
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if path:
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clean = path.strip().strip("/")
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conds.append(or_(
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CodeShape.path == clean, CodeShape.path.like(clean + "/%")
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))
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if snippet_id:
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conds.append(CodeShape.snippet_id == snippet_id)
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async with async_session() as session:
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total = (
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await session.execute(
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select(func.count()).select_from(CodeShape).where(*conds)
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)
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).scalar_one()
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rows = (
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await session.execute(
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select(CodeShape).where(*conds)
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.order_by(CodeShape.path, CodeShape.symbol, CodeShape.kind)
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.limit(max(1, min(limit, 500))).offset(max(0, offset))
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)
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).scalars().all()
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return list(rows), int(total)
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def _consumer_dict(row: CodeShape) -> dict:
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"""The compact shape a snippet's consumer map carries — enough to open
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the file, none of the ledger bookkeeping."""
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out = {
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"project_id": row.project_id,
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"repo": row.repo_key,
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"path": row.path,
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"symbol": row.symbol,
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"kind": row.kind,
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"classified_by": row.classified_by,
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}
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if row.reason:
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out["reason"] = row.reason
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return out
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async def snippet_consumers(user_id: int, note_id: int) -> dict:
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"""The structured consumer map for one snippet (#2789): its `instances`
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(rows judged to conform) and `variants` (named departures, each carrying
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its why). Rows are filtered to projects the CALLER can read — a shared
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snippet must not become a side channel into someone else's project
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layout. Empty lists mean "attach nothing" (#2483)."""
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from scribe.services import access
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async with async_session() as session:
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rows = (
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await session.execute(
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select(CodeShape).where(
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CodeShape.snippet_id == note_id,
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CodeShape.status.in_(("instance", "variant")),
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CodeShape.vanished_at.is_(None),
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)
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)
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).scalars().all()
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readable: dict[int, bool] = {}
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out: dict[str, list[dict]] = {"instances": [], "variants": []}
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for row in rows:
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if row.project_id not in readable:
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readable[row.project_id] = await access.can_read_project(
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user_id, row.project_id
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)
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if not readable[row.project_id]:
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continue
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out["instances" if row.status == "instance" else "variants"].append(
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_consumer_dict(row)
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)
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return out
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Reference in New Issue
Block a user