From 21faa2f1632527e50a9e41dbf0e19de15ef63980 Mon Sep 17 00:00:00 2001 From: Toni Nowak Date: Thu, 16 Jul 2026 22:33:07 +0200 Subject: [PATCH 01/12] feat: reasoning_traces staging storage + schema v40 (U1) Brain-scoped reasoning_traces staging buffer mirroring the tool_events pattern, for the reasoning-training mining pipeline (run 007 Faza 1). - sqlite_schema: SCHEMA_VERSION 39->40, (39,40) migration + SCHEMA base block - surrealdb/schema: DEFINE TABLE reasoning_traces SCHEMAFULL + fields/indexes - SQLiteReasoningTracesMixin + SurrealDBReasoningTracesMixin (dedup by trace_hash: SQLite UNIQUE + INSERT OR IGNORE, SurrealDB pre-filter SELECT) - base.py graceful no-op defaults; InMemoryStorage functional impl; wired into SQLiteStorage / SurrealDBStorage - tests: real-DB SQLite + mock SurrealDB mixin (live-verified on SurrealDB v3.2.0) --- src/surreal_memory/storage/base.py | 63 ++++ src/surreal_memory/storage/memory_store.py | 128 ++++++++ .../storage/sqlite_reasoning_traces.py | 272 +++++++++++++++++ src/surreal_memory/storage/sqlite_schema.py | 50 ++- src/surreal_memory/storage/sqlite_store.py | 2 + .../storage/surrealdb/reasoning_traces.py | 288 ++++++++++++++++++ .../storage/surrealdb/schema.py | 21 ++ src/surreal_memory/storage/surrealdb/store.py | 2 + tests/unit/test_reasoning_traces_storage.py | 238 +++++++++++++++ tests/unit/test_surrealdb_reasoning_traces.py | 229 ++++++++++++++ 10 files changed, 1292 insertions(+), 1 deletion(-) create mode 100644 src/surreal_memory/storage/sqlite_reasoning_traces.py create mode 100644 src/surreal_memory/storage/surrealdb/reasoning_traces.py create mode 100644 tests/unit/test_reasoning_traces_storage.py create mode 100644 tests/unit/test_surrealdb_reasoning_traces.py diff --git a/src/surreal_memory/storage/base.py b/src/surreal_memory/storage/base.py index a963ac21..0c0727e8 100755 --- a/src/surreal_memory/storage/base.py +++ b/src/surreal_memory/storage/base.py @@ -1213,6 +1213,69 @@ async def get_tool_events_for_mining( """ return [] + # ---- Reasoning traces (staging buffer; graceful no-op defaults) ---- + + async def insert_reasoning_traces( + self, + brain_id: str, + traces: list[dict[str, Any]], + ) -> int: + """Insert reasoning traces into the staging buffer (dedup by trace_hash). + + Default: no reasoning-trace storage → 0 inserted. Backends that buffer + reasoning traces (SQLite, SurrealDB, in-memory) override this. + """ + return 0 + + async def get_unprocessed_reasoning_traces( + self, + brain_id: str, + limit: int = 200, + model: str | None = None, + ) -> list[dict[str, Any]]: + """Return unprocessed reasoning traces (oldest first). Default: empty.""" + return [] + + async def mark_reasoning_traces_processed( + self, + brain_id: str, + trace_ids: list[Any], + ) -> None: + """Mark reasoning traces as processed by id. Default: no-op.""" + return None + + async def set_trace_categories( + self, + brain_id: str, + categories: dict[Any, str], + ) -> None: + """Set category labels on reasoning traces. Default: no-op.""" + return None + + async def prune_reasoning_traces( + self, + brain_id: str, + keep_days: int = 90, + ) -> int: + """Delete processed reasoning traces older than keep_days. Default: 0.""" + return 0 + + async def cap_reasoning_traces( + self, + brain_id: str, + max_total: int = 20_000, + ) -> int: + """Enforce a per-brain cap on reasoning traces. Default: 0.""" + return 0 + + async def get_reasoning_stats(self, brain_id: str) -> dict[str, Any]: + """Aggregate reasoning-trace stats. Default: empty aggregates.""" + return {"by_model": {}, "by_category": {}, "total": 0, "unprocessed": 0} + + async def get_reasoning_trace_models(self, brain_id: str) -> list[str]: + """Return DISTINCT model names present in reasoning_traces. Default: empty.""" + return [] + async def add_retrieval_trace(self, trace: RetrievalTrace) -> str: """Persist a retrieval trace. Returns the trace ID.""" raise NotImplementedError diff --git a/src/surreal_memory/storage/memory_store.py b/src/surreal_memory/storage/memory_store.py index e7744c54..458d191f 100755 --- a/src/surreal_memory/storage/memory_store.py +++ b/src/surreal_memory/storage/memory_store.py @@ -49,6 +49,8 @@ def __init__(self) -> None: self._brains: dict[str, Brain] = {} self._co_activations: dict[str, list[dict[str, Any]]] = defaultdict(list) self._action_events: dict[str, list[dict[str, Any]]] = defaultdict(list) + self._reasoning_traces: dict[str, list[dict[str, Any]]] = defaultdict(list) + self._reasoning_trace_seq: dict[str, int] = defaultdict(int) self._retrieval_traces: dict[str, list[RetrievalTrace]] = defaultdict(list) self._versions: dict[str, dict[str, tuple[BrainVersion, str]]] = defaultdict(dict) self._review_schedules: dict[str, dict[str, Any]] = defaultdict(dict) @@ -826,6 +828,132 @@ async def get_tool_events_for_mining( async def mark_events_processed(self, brain_id: str, event_ids: list[int]) -> None: """Mark events as processed (no-op for in-memory storage).""" + # ========== Reasoning Traces (in-memory staging buffer) ========== + + async def insert_reasoning_traces(self, brain_id: str, traces: list[dict[str, Any]]) -> int: + """Insert reasoning traces into the in-memory buffer (dedup by trace_hash).""" + if not traces: + return 0 + existing = {r["trace_hash"] for r in self._reasoning_traces[brain_id]} + now = utcnow().isoformat() + inserted = 0 + for tr in traces: + trace_hash = str(tr.get("trace_hash", "")) + if not trace_hash or trace_hash in existing: + continue + existing.add(trace_hash) + self._reasoning_trace_seq[brain_id] += 1 + content = str(tr.get("content", "")) + raw_chars = tr.get("content_chars") + content_chars = int(raw_chars) if raw_chars is not None else len(content) + self._reasoning_traces[brain_id].append( + { + "id": self._reasoning_trace_seq[brain_id], + "trace_hash": trace_hash, + "model": tr.get("model", ""), + "session_id": tr.get("session_id", ""), + "project": tr.get("project", ""), + "task_context": str(tr.get("task_context", ""))[:500], + "content": content, + "content_chars": content_chars, + "category": tr.get("category", ""), + "processed": False, + "created_at": tr.get("created_at", now), + "ingested_at": now, + } + ) + inserted += 1 + return inserted + + async def get_unprocessed_reasoning_traces( + self, brain_id: str, limit: int = 200, model: str | None = None + ) -> list[dict[str, Any]]: + """Return unprocessed reasoning traces (oldest first), optional model filter.""" + rows = [ + r + for r in self._reasoning_traces[brain_id] + if not r["processed"] and (model is None or r["model"] == model) + ] + rows.sort(key=lambda r: str(r["created_at"])) + fields = ( + "id", + "trace_hash", + "model", + "session_id", + "project", + "task_context", + "content", + "content_chars", + "category", + "created_at", + ) + return [{k: r[k] for k in fields} for r in rows[: min(int(limit), 10000)]] + + async def mark_reasoning_traces_processed(self, brain_id: str, trace_ids: list[Any]) -> None: + """Mark reasoning traces as processed by row id.""" + ids = set(trace_ids) + for r in self._reasoning_traces[brain_id]: + if r["id"] in ids: + r["processed"] = True + + async def set_trace_categories(self, brain_id: str, categories: dict[Any, str]) -> None: + """Set category labels on reasoning traces (id -> category).""" + for r in self._reasoning_traces[brain_id]: + if r["id"] in categories: + r["category"] = categories[r["id"]] + + async def prune_reasoning_traces(self, brain_id: str, keep_days: int = 90) -> int: + """Delete processed reasoning traces older than keep_days.""" + cutoff = (utcnow() - timedelta(days=keep_days)).isoformat() + rows = self._reasoning_traces[brain_id] + kept = [r for r in rows if not (r["processed"] and str(r["created_at"]) < cutoff)] + deleted = len(rows) - len(kept) + self._reasoning_traces[brain_id] = kept + return deleted + + async def cap_reasoning_traces(self, brain_id: str, max_total: int = 20_000) -> int: + """Enforce a per-brain cap by deleting oldest processed traces.""" + rows = self._reasoning_traces[brain_id] + if len(rows) <= max_total: + return 0 + excess = len(rows) - max_total + processed_sorted = sorted( + (r for r in rows if r["processed"]), key=lambda r: str(r["created_at"]) + ) + victims = {r["id"] for r in processed_sorted[:excess]} + kept = [r for r in rows if r["id"] not in victims] + deleted = len(rows) - len(kept) + self._reasoning_traces[brain_id] = kept + return deleted + + async def get_reasoning_stats(self, brain_id: str) -> dict[str, Any]: + """Aggregate reasoning-trace stats: per model, per category, totals.""" + by_model: dict[str, dict[str, Any]] = {} + by_category: dict[str, int] = {} + for r in self._reasoning_traces[brain_id]: + model = r["model"] + entry = by_model.setdefault( + model, {"trace_count": 0, "unprocessed": 0, "last_trace_at": ""} + ) + entry["trace_count"] += 1 + if not r["processed"]: + entry["unprocessed"] += 1 + if str(r["created_at"]) > str(entry["last_trace_at"]): + entry["last_trace_at"] = r["created_at"] + by_category[r["category"]] = by_category.get(r["category"], 0) + 1 + total = sum(m["trace_count"] for m in by_model.values()) + unprocessed = sum(m["unprocessed"] for m in by_model.values()) + return { + "by_model": by_model, + "by_category": by_category, + "total": total, + "unprocessed": unprocessed, + } + + async def get_reasoning_trace_models(self, brain_id: str) -> list[str]: + """Return DISTINCT model names in reasoning_traces (sorted).""" + return sorted({r["model"] for r in self._reasoning_traces[brain_id] if r["model"]}) + # ========== Cleanup ========== async def clear(self, brain_id: str) -> None: diff --git a/src/surreal_memory/storage/sqlite_reasoning_traces.py b/src/surreal_memory/storage/sqlite_reasoning_traces.py new file mode 100644 index 00000000..d0135a03 --- /dev/null +++ b/src/surreal_memory/storage/sqlite_reasoning_traces.py @@ -0,0 +1,272 @@ +"""SQLite mixin for reasoning-trace storage (staging buffer for reasoning mining). + +Parity with the SQLite ``tool_events`` staging table, but for reasoning traces +mined from model ``thinking`` blocks. Rows are brain-scoped and deduplicated by +``trace_hash`` via UNIQUE(brain_id, trace_hash) + ``INSERT OR IGNORE``, so +re-ingesting an already-seen trace is a no-op. The consolidation +``PROCESS_REASONING_TRACES`` strategy ingests transcripts into this table; +``LEARN_REASONING`` later distills unprocessed rows into reasoning patterns. +""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +from surreal_memory.utils.timeutils import utcnow + +if TYPE_CHECKING: + import aiosqlite + +logger = logging.getLogger(__name__) + +# Cap per brain to prevent unbounded growth (config default max_traces_total). +_MAX_TRACES_PER_BRAIN = 20_000 + +# task_context is truncated to keep the staging table compact (plan: <=500). +_MAX_TASK_CONTEXT = 500 + + +class SQLiteReasoningTracesMixin: + """Mixin providing CRUD for the ``reasoning_traces`` table.""" + + def _ensure_conn(self) -> aiosqlite.Connection: + raise NotImplementedError + + def _ensure_read_conn(self) -> aiosqlite.Connection: + raise NotImplementedError + + def _get_brain_id(self) -> str: + raise NotImplementedError + + async def insert_reasoning_traces( + self, + brain_id: str, + traces: list[dict[str, Any]], + ) -> int: + """Insert reasoning traces into the staging table (dedup by trace_hash). + + Uses ``INSERT OR IGNORE`` against UNIQUE(brain_id, trace_hash), so + re-ingesting the same trace is a no-op. + + Args: + brain_id: Brain context. + traces: Dicts with keys: trace_hash, model, session_id, project, + task_context, content, content_chars (optional), category + (optional), created_at (optional ISO string). + + Returns: + Number of rows actually inserted (excludes ignored duplicates). + """ + if not traces: + return 0 + conn = self._ensure_conn() + now = utcnow().isoformat() + inserted = 0 + for tr in traces: + trace_hash = str(tr.get("trace_hash", "")) + if not trace_hash: + # A hashless trace cannot be deduped — skip (parity with the + # SurrealDB / in-memory backends, which also drop empty hashes). + continue + content = str(tr.get("content", "")) + raw_chars = tr.get("content_chars") + content_chars = int(raw_chars) if raw_chars is not None else len(content) + cursor = await conn.execute( + """INSERT OR IGNORE INTO reasoning_traces + (brain_id, trace_hash, model, session_id, project, + task_context, content, content_chars, category, + processed, created_at, ingested_at) + VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, 0, ?, ?)""", + ( + brain_id, + trace_hash, + tr.get("model", ""), + tr.get("session_id", ""), + tr.get("project", ""), + str(tr.get("task_context", ""))[:_MAX_TASK_CONTEXT], + content, + content_chars, + tr.get("category", ""), + tr.get("created_at", now), + now, + ), + ) + if cursor.rowcount and cursor.rowcount > 0: + inserted += 1 + await conn.commit() + return inserted + + async def get_unprocessed_reasoning_traces( + self, + brain_id: str, + limit: int = 200, + model: str | None = None, + ) -> list[dict[str, Any]]: + """Get unprocessed reasoning traces (oldest first) for distillation. + + Optionally filter by ``model`` so the distiller can batch per model. + """ + conn = self._ensure_read_conn() + safe_limit = min(int(limit), 10000) + query = ( + "SELECT id, trace_hash, model, session_id, project, task_context," + " content, content_chars, category, created_at" + " FROM reasoning_traces WHERE brain_id = ? AND processed = 0" + ) + params: list[Any] = [brain_id] + if model: + query += " AND model = ?" + params.append(model) + query += " ORDER BY created_at ASC LIMIT ?" + params.append(safe_limit) + results: list[dict[str, Any]] = [] + async with conn.execute(query, params) as cursor: + async for row in cursor: + results.append( + { + "id": row["id"], + "trace_hash": row["trace_hash"], + "model": row["model"], + "session_id": row["session_id"], + "project": row["project"], + "task_context": row["task_context"], + "content": row["content"], + "content_chars": row["content_chars"], + "category": row["category"], + "created_at": row["created_at"], + } + ) + return results + + async def mark_reasoning_traces_processed( + self, + brain_id: str, + trace_ids: list[Any], + ) -> None: + """Mark reasoning traces as processed by row id.""" + if not trace_ids: + return + conn = self._ensure_conn() + placeholders = ",".join("?" for _ in trace_ids) + # Table/column names are hardcoded — only placeholders are interpolated. + await conn.execute( + "UPDATE reasoning_traces SET processed = 1 " + f"WHERE brain_id = ? AND id IN ({placeholders})", + [brain_id, *trace_ids], + ) + await conn.commit() + + async def set_trace_categories( + self, + brain_id: str, + categories: dict[Any, str], + ) -> None: + """Set the category label on reasoning traces (id -> category).""" + if not categories: + return + conn = self._ensure_conn() + for trace_id, category in categories.items(): + await conn.execute( + "UPDATE reasoning_traces SET category = ? WHERE brain_id = ? AND id = ?", + (category, brain_id, trace_id), + ) + await conn.commit() + + async def prune_reasoning_traces( + self, + brain_id: str, + keep_days: int = 90, + ) -> int: + """Delete processed traces older than keep_days. Returns rows deleted.""" + from datetime import timedelta + + conn = self._ensure_conn() + cutoff = (utcnow() - timedelta(days=keep_days)).isoformat() + cursor = await conn.execute( + "DELETE FROM reasoning_traces WHERE brain_id = ? AND processed = 1 AND created_at < ?", + (brain_id, cutoff), + ) + deleted = cursor.rowcount + await conn.commit() + return deleted + + async def cap_reasoning_traces( + self, + brain_id: str, + max_total: int = _MAX_TRACES_PER_BRAIN, + ) -> int: + """Enforce max traces per brain by deleting oldest processed rows.""" + conn = self._ensure_conn() + async with conn.execute( + "SELECT COUNT(*) as cnt FROM reasoning_traces WHERE brain_id = ?", + (brain_id,), + ) as cursor: + row = await cursor.fetchone() + total = row["cnt"] if row else 0 + + if total <= max_total: + return 0 + + excess = total - max_total + cursor = await conn.execute( + """DELETE FROM reasoning_traces WHERE brain_id = ? AND id IN ( + SELECT id FROM reasoning_traces WHERE brain_id = ? AND processed = 1 + ORDER BY created_at ASC LIMIT ? + )""", + (brain_id, brain_id, excess), + ) + deleted = cursor.rowcount + await conn.commit() + return deleted + + async def get_reasoning_stats(self, brain_id: str) -> dict[str, Any]: + """Aggregate reasoning-trace stats: per model, per category, totals.""" + conn = self._ensure_read_conn() + + by_model: dict[str, dict[str, Any]] = {} + async with conn.execute( + """SELECT model, COUNT(*) as cnt, + SUM(CASE WHEN processed = 0 THEN 1 ELSE 0 END) as unproc, + MAX(created_at) as last_at + FROM reasoning_traces WHERE brain_id = ? + GROUP BY model""", + (brain_id,), + ) as cursor: + async for row in cursor: + by_model[row["model"]] = { + "trace_count": row["cnt"], + "unprocessed": row["unproc"], + "last_trace_at": row["last_at"], + } + + by_category: dict[str, int] = {} + async with conn.execute( + "SELECT category, COUNT(*) as cnt FROM reasoning_traces " + "WHERE brain_id = ? GROUP BY category", + (brain_id,), + ) as cursor: + async for row in cursor: + by_category[row["category"]] = row["cnt"] + + total = sum(m["trace_count"] for m in by_model.values()) + unprocessed = sum(m["unprocessed"] for m in by_model.values()) + return { + "by_model": by_model, + "by_category": by_category, + "total": total, + "unprocessed": unprocessed, + } + + async def get_reasoning_trace_models(self, brain_id: str) -> list[str]: + """Return DISTINCT model names present in reasoning_traces (sorted).""" + conn = self._ensure_read_conn() + results: list[str] = [] + async with conn.execute( + "SELECT DISTINCT model FROM reasoning_traces WHERE brain_id = ? ORDER BY model ASC", + (brain_id,), + ) as cursor: + async for row in cursor: + if row["model"]: + results.append(row["model"]) + return results diff --git a/src/surreal_memory/storage/sqlite_schema.py b/src/surreal_memory/storage/sqlite_schema.py index 47dc8427..33840cb5 100755 --- a/src/surreal_memory/storage/sqlite_schema.py +++ b/src/surreal_memory/storage/sqlite_schema.py @@ -11,7 +11,7 @@ logger = logging.getLogger(__name__) # Schema version for migrations -SCHEMA_VERSION = 39 +SCHEMA_VERSION = 40 # ── Migrations ────────────────────────────────────────────────────── # Each entry maps (from_version -> to_version) with a list of SQL statements. @@ -665,6 +665,32 @@ "CREATE INDEX IF NOT EXISTS idx_retrieval_traces_created " "ON retrieval_traces(brain_id, created_at)", ], + (39, 40): [ + # Reasoning traces: staging buffer for reasoning-trace mining (run 007). + "CREATE TABLE IF NOT EXISTS reasoning_traces (" + " id INTEGER PRIMARY KEY AUTOINCREMENT," + " brain_id TEXT NOT NULL," + " trace_hash TEXT NOT NULL," + " model TEXT NOT NULL DEFAULT ''," + " session_id TEXT NOT NULL DEFAULT ''," + " project TEXT NOT NULL DEFAULT ''," + " task_context TEXT NOT NULL DEFAULT ''," + " content TEXT NOT NULL DEFAULT ''," + " content_chars INTEGER NOT NULL DEFAULT 0," + " category TEXT NOT NULL DEFAULT ''," + " processed INTEGER NOT NULL DEFAULT 0," + " created_at TEXT NOT NULL," + " ingested_at TEXT NOT NULL," + " UNIQUE (brain_id, trace_hash)," + " FOREIGN KEY (brain_id) REFERENCES brains(id) ON DELETE CASCADE" + ")", + "CREATE INDEX IF NOT EXISTS idx_reasoning_traces_model " + "ON reasoning_traces(brain_id, model)", + "CREATE INDEX IF NOT EXISTS idx_reasoning_traces_processed " + "ON reasoning_traces(brain_id, processed)", + "CREATE INDEX IF NOT EXISTS idx_reasoning_traces_created " + "ON reasoning_traces(brain_id, created_at)", + ], } @@ -1339,4 +1365,26 @@ async def run_migrations(conn: aiosqlite.Connection, current_version: int) -> in PRIMARY KEY (brain_id, entity_type, prefix) ); CREATE INDEX IF NOT EXISTS idx_merkle_hashes_brain ON merkle_hashes(brain_id, entity_type); + +-- Reasoning traces (staging buffer for reasoning-trace mining) [schema v40] +CREATE TABLE IF NOT EXISTS reasoning_traces ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + brain_id TEXT NOT NULL, + trace_hash TEXT NOT NULL, + model TEXT NOT NULL DEFAULT '', + session_id TEXT NOT NULL DEFAULT '', + project TEXT NOT NULL DEFAULT '', + task_context TEXT NOT NULL DEFAULT '', + content TEXT NOT NULL DEFAULT '', + content_chars INTEGER NOT NULL DEFAULT 0, + category TEXT NOT NULL DEFAULT '', + processed INTEGER NOT NULL DEFAULT 0, + created_at TEXT NOT NULL, + ingested_at TEXT NOT NULL, + UNIQUE (brain_id, trace_hash), + FOREIGN KEY (brain_id) REFERENCES brains(id) ON DELETE CASCADE +); +CREATE INDEX IF NOT EXISTS idx_reasoning_traces_model ON reasoning_traces(brain_id, model); +CREATE INDEX IF NOT EXISTS idx_reasoning_traces_processed ON reasoning_traces(brain_id, processed); +CREATE INDEX IF NOT EXISTS idx_reasoning_traces_created ON reasoning_traces(brain_id, created_at); """ diff --git a/src/surreal_memory/storage/sqlite_store.py b/src/surreal_memory/storage/sqlite_store.py index ba7fba73..a033b168 100755 --- a/src/surreal_memory/storage/sqlite_store.py +++ b/src/surreal_memory/storage/sqlite_store.py @@ -36,6 +36,7 @@ from surreal_memory.storage.sqlite_merkle import SQLiteMerkleMixin from surreal_memory.storage.sqlite_neurons import SQLiteNeuronMixin from surreal_memory.storage.sqlite_projects import SQLiteProjectMixin +from surreal_memory.storage.sqlite_reasoning_traces import SQLiteReasoningTracesMixin from surreal_memory.storage.sqlite_retrieval_trace import SQLiteRetrievalTraceMixin from surreal_memory.storage.sqlite_reviews import SQLiteReviewsMixin from surreal_memory.storage.sqlite_schema import ( @@ -81,6 +82,7 @@ class SQLiteStorage( SQLiteDriftMixin, SQLiteEntityRefsMixin, SQLiteSourcesMixin, + SQLiteReasoningTracesMixin, SQLiteToolEventsMixin, SQLiteTrainingFilesMixin, SQLiteMerkleMixin, diff --git a/src/surreal_memory/storage/surrealdb/reasoning_traces.py b/src/surreal_memory/storage/surrealdb/reasoning_traces.py new file mode 100644 index 00000000..9bff5189 --- /dev/null +++ b/src/surreal_memory/storage/surrealdb/reasoning_traces.py @@ -0,0 +1,288 @@ +"""SurrealDB mixin for reasoning-trace storage. + +Parity with the SQLite ``reasoning_traces`` staging table. Rows are brain-scoped +and deduplicated by ``trace_hash`` (globally unique = sha256(session:uuid:index)). +SurrealDB has no cheap UNIQUE-on-insert story here, so dedup is done by a +pre-filter SELECT (consolidation is single-threaded per brain), mirroring the +SQLite ``INSERT OR IGNORE`` semantics. ``trace_hash`` doubles as the natural key +returned as ``id`` so ``mark_reasoning_traces_processed`` / ``set_trace_categories`` +filter by a plain string field instead of juggling SurrealDB record ids (mirrors +the ``event_id`` approach in the tool-events mixin). +""" + +from __future__ import annotations + +import uuid +from datetime import datetime, timedelta +from typing import Any + +from surreal_memory.utils.timeutils import utcnow + +# Cap per brain to prevent unbounded growth (config default max_traces_total). +_MAX_TRACES_PER_BRAIN = 20_000 + +# task_context is truncated to keep the staging table compact (plan: <=500). +_MAX_TASK_CONTEXT = 500 + + +def _iso(val: Any) -> str: + """Return an ISO-8601 string; callers parse created_at via fromisoformat.""" + if isinstance(val, datetime): + return val.isoformat() + return str(val) if val is not None else "" + + +def _as_datetime(val: Any) -> datetime: + if isinstance(val, datetime): + return val + if isinstance(val, str) and val: + try: + return datetime.fromisoformat(val.replace("Z", "+00:00")) + except ValueError: + return utcnow() + return utcnow() + + +class SurrealDBReasoningTracesMixin: + """Reasoning-trace CRUD backed by the ``reasoning_traces`` table.""" + + def _ensure_conn(self) -> Any: + raise NotImplementedError + + def _get_brain_id(self) -> str: + raise NotImplementedError + + async def _query(self, sql: str, **params: Any) -> list[dict[str, Any]]: + raise NotImplementedError + + async def insert_reasoning_traces( + self, + brain_id: str, + traces: list[dict[str, Any]], + ) -> int: + """Insert reasoning traces (dedup by trace_hash via pre-filter SELECT).""" + if not traces: + return 0 + # Deduplicate the incoming batch by trace_hash (keep first). + by_hash: dict[str, dict[str, Any]] = {} + for tr in traces: + h = str(tr.get("trace_hash", "")) + if h and h not in by_hash: + by_hash[h] = tr + if not by_hash: + return 0 + existing = await self._query( + "SELECT trace_hash FROM reasoning_traces " + "WHERE brain_id = $bid AND trace_hash IN $hashes", + bid=brain_id, + hashes=list(by_hash.keys()), + ) + seen = {r.get("trace_hash") for r in existing} + conn = self._ensure_conn() + now = utcnow() + inserted = 0 + for h, tr in by_hash.items(): + if h in seen: + continue + content = str(tr.get("content", "")) + raw_chars = tr.get("content_chars") + content_chars = int(raw_chars) if raw_chars is not None else len(content) + await conn.insert( + "reasoning_traces", + { + "id": str(uuid.uuid4()).replace("-", "_"), + "trace_hash": h, + "brain_id": brain_id, + "model": tr.get("model", ""), + "session_id": tr.get("session_id", ""), + "project": tr.get("project", ""), + "task_context": str(tr.get("task_context", ""))[:_MAX_TASK_CONTEXT], + "content": content, + "content_chars": content_chars, + "category": tr.get("category", ""), + "processed": False, + "created_at": _as_datetime(tr.get("created_at")), + "ingested_at": now, + }, + ) + inserted += 1 + return inserted + + async def get_unprocessed_reasoning_traces( + self, + brain_id: str, + limit: int = 200, + model: str | None = None, + ) -> list[dict[str, Any]]: + """Get unprocessed reasoning traces (oldest first), optional model filter.""" + safe_limit = min(int(limit), 10000) + sql = ( + "SELECT trace_hash, model, session_id, project, task_context," + " content, content_chars, category, created_at" + " FROM reasoning_traces WHERE brain_id = $bid AND processed = false" + ) + params: dict[str, Any] = {"bid": brain_id} + if model: + sql += " AND model = $model" + params["model"] = model + sql += f" ORDER BY created_at ASC LIMIT {safe_limit}" + rows = await self._query(sql, **params) + return [ + { + "id": r.get("trace_hash"), + "trace_hash": r.get("trace_hash"), + "model": r.get("model", ""), + "session_id": r.get("session_id", ""), + "project": r.get("project", ""), + "task_context": r.get("task_context", ""), + "content": r.get("content", ""), + "content_chars": r.get("content_chars", 0), + "category": r.get("category", ""), + "created_at": _iso(r.get("created_at")), + } + for r in rows + ] + + async def mark_reasoning_traces_processed( + self, + brain_id: str, + trace_ids: list[Any], + ) -> None: + """Mark reasoning traces as processed by their trace_hash.""" + if not trace_ids: + return + await self._query( + "UPDATE reasoning_traces SET processed = true " + "WHERE brain_id = $bid AND trace_hash IN $ids", + bid=brain_id, + ids=[str(t) for t in trace_ids], + ) + + async def set_trace_categories( + self, + brain_id: str, + categories: dict[Any, str], + ) -> None: + """Set the category label on reasoning traces (trace_hash -> category).""" + if not categories: + return + for trace_id, category in categories.items(): + await self._query( + "UPDATE reasoning_traces SET category = $cat " + "WHERE brain_id = $bid AND trace_hash = $id", + bid=brain_id, + cat=category, + id=str(trace_id), + ) + + async def prune_reasoning_traces( + self, + brain_id: str, + keep_days: int = 90, + ) -> int: + """Delete processed traces older than keep_days; return rows deleted.""" + cutoff = utcnow() - timedelta(days=keep_days) + rows = await self._query( + "SELECT count() AS c FROM reasoning_traces" + " WHERE brain_id = $bid AND processed = true AND created_at < $cutoff GROUP ALL", + bid=brain_id, + cutoff=cutoff, + ) + deleted = int(rows[0]["c"]) if rows else 0 + await self._query( + "DELETE reasoning_traces" + " WHERE brain_id = $bid AND processed = true AND created_at < $cutoff", + bid=brain_id, + cutoff=cutoff, + ) + return deleted + + async def cap_reasoning_traces( + self, + brain_id: str, + max_total: int = _MAX_TRACES_PER_BRAIN, + ) -> int: + """Enforce max traces per brain by deleting oldest processed rows.""" + rows = await self._query( + "SELECT count() AS c FROM reasoning_traces WHERE brain_id = $bid GROUP ALL", + bid=brain_id, + ) + total = int(rows[0]["c"]) if rows else 0 + if total <= max_total: + return 0 + excess = total - max_total + # NB: SurrealDB v3.2.0 requires every ORDER BY field to appear in the + # SELECT projection, so created_at is projected even though only + # trace_hash is consumed below. + victims = await self._query( + "SELECT trace_hash, created_at FROM reasoning_traces" + " WHERE brain_id = $bid AND processed = true" + f" ORDER BY created_at ASC LIMIT {int(excess)}", + bid=brain_id, + ) + ids = [r.get("trace_hash") for r in victims if r.get("trace_hash")] + if not ids: + return 0 + await self._query( + "DELETE reasoning_traces WHERE brain_id = $bid AND trace_hash IN $ids", + bid=brain_id, + ids=ids, + ) + return len(ids) + + async def get_reasoning_stats(self, brain_id: str) -> dict[str, Any]: + """Aggregate reasoning-trace stats: per model, per category, totals. + + ``last_trace_at`` is fetched per model with an ORDER BY ... LIMIT 1 + rather than a datetime aggregate, keeping the SurrealQL unambiguous. + """ + model_rows = await self._query( + "SELECT model, count() AS cnt FROM reasoning_traces" + " WHERE brain_id = $bid GROUP BY model", + bid=brain_id, + ) + unproc_rows = await self._query( + "SELECT model, count() AS cnt FROM reasoning_traces" + " WHERE brain_id = $bid AND processed = false GROUP BY model", + bid=brain_id, + ) + unproc_by_model = {r.get("model", ""): int(r.get("cnt", 0) or 0) for r in unproc_rows} + + by_model: dict[str, dict[str, Any]] = {} + for r in model_rows: + name = r.get("model", "") + latest = await self._query( + "SELECT created_at FROM reasoning_traces" + " WHERE brain_id = $bid AND model = $model" + " ORDER BY created_at DESC LIMIT 1", + bid=brain_id, + model=name, + ) + by_model[name] = { + "trace_count": int(r.get("cnt", 0) or 0), + "unprocessed": unproc_by_model.get(name, 0), + "last_trace_at": _iso(latest[0].get("created_at")) if latest else "", + } + + cat_rows = await self._query( + "SELECT category, count() AS cnt FROM reasoning_traces" + " WHERE brain_id = $bid GROUP BY category", + bid=brain_id, + ) + by_category = {r.get("category", ""): int(r.get("cnt", 0) or 0) for r in cat_rows} + total = sum(m["trace_count"] for m in by_model.values()) + unprocessed = sum(m["unprocessed"] for m in by_model.values()) + return { + "by_model": by_model, + "by_category": by_category, + "total": total, + "unprocessed": unprocessed, + } + + async def get_reasoning_trace_models(self, brain_id: str) -> list[str]: + """Return DISTINCT model names present in reasoning_traces (sorted).""" + rows = await self._query( + "SELECT model FROM reasoning_traces WHERE brain_id = $bid GROUP BY model", + bid=brain_id, + ) + return sorted({r.get("model", "") for r in rows if r.get("model")}) diff --git a/src/surreal_memory/storage/surrealdb/schema.py b/src/surreal_memory/storage/surrealdb/schema.py index 8464c3ec..52725527 100755 --- a/src/surreal_memory/storage/surrealdb/schema.py +++ b/src/surreal_memory/storage/surrealdb/schema.py @@ -430,6 +430,27 @@ DEFINE INDEX idx_tevt_unproc ON tool_events FIELDS brain_id, processed; DEFINE INDEX idx_tevt_eventid ON tool_events FIELDS brain_id, event_id; DEFINE INDEX idx_tevt_time ON tool_events FIELDS brain_id, created_at; + +-- Reasoning traces (staging buffer for reasoning-trace mining / distillation) +DEFINE TABLE reasoning_traces SCHEMAFULL; +DEFINE FIELD id ON reasoning_traces TYPE string; +DEFINE FIELD trace_hash ON reasoning_traces TYPE string; +DEFINE FIELD brain_id ON reasoning_traces TYPE string; +DEFINE FIELD model ON reasoning_traces TYPE string DEFAULT ''; +DEFINE FIELD session_id ON reasoning_traces TYPE string DEFAULT ''; +DEFINE FIELD project ON reasoning_traces TYPE string DEFAULT ''; +DEFINE FIELD task_context ON reasoning_traces TYPE string DEFAULT ''; +DEFINE FIELD content ON reasoning_traces TYPE string DEFAULT ''; +DEFINE FIELD content_chars ON reasoning_traces TYPE int DEFAULT 0; +DEFINE FIELD category ON reasoning_traces TYPE string DEFAULT ''; +DEFINE FIELD processed ON reasoning_traces TYPE bool DEFAULT false; +DEFINE FIELD created_at ON reasoning_traces TYPE datetime DEFAULT time::now(); +DEFINE FIELD ingested_at ON reasoning_traces TYPE datetime DEFAULT time::now(); +DEFINE INDEX idx_rtr_brain ON reasoning_traces FIELDS brain_id; +DEFINE INDEX idx_rtr_hash ON reasoning_traces FIELDS brain_id, trace_hash; +DEFINE INDEX idx_rtr_model ON reasoning_traces FIELDS brain_id, model; +DEFINE INDEX idx_rtr_unproc ON reasoning_traces FIELDS brain_id, processed; +DEFINE INDEX idx_rtr_time ON reasoning_traces FIELDS brain_id, created_at; """ diff --git a/src/surreal_memory/storage/surrealdb/store.py b/src/surreal_memory/storage/surrealdb/store.py index c5c14810..c3a9d9fb 100755 --- a/src/surreal_memory/storage/surrealdb/store.py +++ b/src/surreal_memory/storage/surrealdb/store.py @@ -30,6 +30,7 @@ from surreal_memory.storage.surrealdb.keyword_entity import SurrealDBKeywordEntityMixin from surreal_memory.storage.surrealdb.maturation import SurrealDBMaturationMixin from surreal_memory.storage.surrealdb.projects import SurrealDBProjectsMixin +from surreal_memory.storage.surrealdb.reasoning_traces import SurrealDBReasoningTracesMixin from surreal_memory.storage.surrealdb.retrieval_trace import SurrealDBRetrievalTraceMixin from surreal_memory.storage.surrealdb.review_schedules import SurrealDBReviewSchedulesMixin from surreal_memory.storage.surrealdb.schema import ensure_schema @@ -351,6 +352,7 @@ class SurrealDBStorage( SurrealDBCompressionMixin, SurrealDBActivityMixin, SurrealDBDepthPriorsMixin, + SurrealDBReasoningTracesMixin, SurrealDBToolEventsMixin, NeuralStorage, ): diff --git a/tests/unit/test_reasoning_traces_storage.py b/tests/unit/test_reasoning_traces_storage.py new file mode 100644 index 00000000..33c911d4 --- /dev/null +++ b/tests/unit/test_reasoning_traces_storage.py @@ -0,0 +1,238 @@ +"""Tests for the SQLite reasoning_traces staging storage (run 007, schema v40). + +Mirrors test_tool_memory.py: exercises a real in-process SQLiteStorage against a +temporary DB (no mocks), covering insert/dedup, unprocessed retrieval + model +filter, mark-processed, category tagging, prune/cap retention, and stats. +""" + +from __future__ import annotations + +from datetime import timedelta +from pathlib import Path + +import pytest + +from surreal_memory.storage.sqlite_store import SQLiteStorage +from surreal_memory.utils.timeutils import utcnow + +BRAIN = "test-brain" + + +@pytest.fixture +async def storage(tmp_path: Path) -> SQLiteStorage: + """Initialized SQLiteStorage (schema v40) with a test brain.""" + store = SQLiteStorage(tmp_path / "test.db") + await store.initialize() + await store._ensure_conn().execute( + "INSERT OR IGNORE INTO brains (id, name, config, created_at, updated_at) " + "VALUES (?, ?, ?, ?, ?)", + (BRAIN, "test", "{}", "2026-01-01T00:00:00", "2026-01-01T00:00:00"), + ) + await store._ensure_conn().commit() + store.set_brain(BRAIN) + return store + + +def _trace( + trace_hash: str = "h1", + model: str = "claude-fable-5", + *, + session_id: str = "s1", + project: str = "proj", + task_context: str = "ctx", + content: str = "restate goal then verify", + content_chars: int | None = None, + category: str = "", + created_at: str = "2026-03-01T10:00:00", +) -> dict: + d = { + "trace_hash": trace_hash, + "model": model, + "session_id": session_id, + "project": project, + "task_context": task_context, + "content": content, + "category": category, + "created_at": created_at, + } + if content_chars is not None: + d["content_chars"] = content_chars + return d + + +async def test_migration_created_reasoning_traces_table(storage: SQLiteStorage) -> None: + """The v40 migration/SCHEMA creates the reasoning_traces table.""" + async with storage._ensure_read_conn().execute( + "SELECT name FROM sqlite_master WHERE type='table' AND name='reasoning_traces'" + ) as cur: + row = await cur.fetchone() + assert row is not None + + +async def test_insert_returns_count_and_dedups_within_batch(storage: SQLiteStorage) -> None: + n = await storage.insert_reasoning_traces( + BRAIN, + [_trace("h1"), _trace("h2"), _trace("h1", content="dup")], + ) + assert n == 2 # h1 inserted once (OR IGNORE on the duplicate), h2 once + + +async def test_insert_is_idempotent_across_calls(storage: SQLiteStorage) -> None: + assert await storage.insert_reasoning_traces(BRAIN, [_trace("h1")]) == 1 + assert await storage.insert_reasoning_traces(BRAIN, [_trace("h1")]) == 0 + + +async def test_empty_insert_returns_zero(storage: SQLiteStorage) -> None: + assert await storage.insert_reasoning_traces(BRAIN, []) == 0 + + +async def test_get_unprocessed_orders_oldest_first_and_limits(storage: SQLiteStorage) -> None: + await storage.insert_reasoning_traces( + BRAIN, + [ + _trace("h3", created_at="2026-03-03T00:00:00"), + _trace("h1", created_at="2026-03-01T00:00:00"), + _trace("h2", created_at="2026-03-02T00:00:00"), + ], + ) + rows = await storage.get_unprocessed_reasoning_traces(BRAIN, limit=2) + assert [r["trace_hash"] for r in rows] == ["h1", "h2"] + + +async def test_get_unprocessed_model_filter(storage: SQLiteStorage) -> None: + await storage.insert_reasoning_traces( + BRAIN, + [ + _trace("h1", model="claude-fable-5"), + _trace("h2", model="claude-sonnet-5"), + _trace("h3", model="claude-fable-5"), + ], + ) + rows = await storage.get_unprocessed_reasoning_traces(BRAIN, model="claude-fable-5") + assert {r["trace_hash"] for r in rows} == {"h1", "h3"} + + +async def test_mark_processed_excludes_from_unprocessed(storage: SQLiteStorage) -> None: + await storage.insert_reasoning_traces(BRAIN, [_trace("h1"), _trace("h2")]) + rows = await storage.get_unprocessed_reasoning_traces(BRAIN) + ids = [r["id"] for r in rows] + await storage.mark_reasoning_traces_processed(BRAIN, [ids[0]]) + remaining = await storage.get_unprocessed_reasoning_traces(BRAIN) + assert len(remaining) == 1 + assert remaining[0]["id"] == ids[1] + + +async def test_set_trace_categories(storage: SQLiteStorage) -> None: + await storage.insert_reasoning_traces(BRAIN, [_trace("h1"), _trace("h2")]) + rows = await storage.get_unprocessed_reasoning_traces(BRAIN) + ids = {r["trace_hash"]: r["id"] for r in rows} + await storage.set_trace_categories(BRAIN, {ids["h1"]: "debugging", ids["h2"]: "planning"}) + stats = await storage.get_reasoning_stats(BRAIN) + assert stats["by_category"].get("debugging") == 1 + assert stats["by_category"].get("planning") == 1 + + +async def test_task_context_truncated_to_500(storage: SQLiteStorage) -> None: + await storage.insert_reasoning_traces(BRAIN, [_trace("h1", task_context="x" * 600)]) + rows = await storage.get_unprocessed_reasoning_traces(BRAIN) + assert len(rows[0]["task_context"]) == 500 + + +async def test_content_chars_defaults_to_len(storage: SQLiteStorage) -> None: + await storage.insert_reasoning_traces( + BRAIN, [_trace("h1", content="abcde", content_chars=None)] + ) + rows = await storage.get_unprocessed_reasoning_traces(BRAIN) + assert rows[0]["content_chars"] == 5 + + +async def test_prune_deletes_only_old_processed(storage: SQLiteStorage) -> None: + await storage.insert_reasoning_traces( + BRAIN, + [ + _trace("old_proc", created_at="2000-01-01T00:00:00"), + _trace("old_unproc", created_at="2000-01-01T00:00:00"), + _trace("recent_proc", created_at=(utcnow() - timedelta(days=1)).isoformat()), + ], + ) + rows = await storage.get_unprocessed_reasoning_traces(BRAIN) + by_hash = {r["trace_hash"]: r["id"] for r in rows} + await storage.mark_reasoning_traces_processed( + BRAIN, [by_hash["old_proc"], by_hash["recent_proc"]] + ) + deleted = await storage.prune_reasoning_traces(BRAIN, keep_days=90) + assert deleted == 1 # only old_proc (processed AND old) + stats = await storage.get_reasoning_stats(BRAIN) + assert stats["total"] == 2 + + +async def test_cap_deletes_oldest_processed_beyond_max(storage: SQLiteStorage) -> None: + traces = [_trace(f"h{i}", created_at=f"2026-03-{i:02d}T00:00:00") for i in range(1, 6)] + await storage.insert_reasoning_traces(BRAIN, traces) + rows = await storage.get_unprocessed_reasoning_traces(BRAIN) + await storage.mark_reasoning_traces_processed(BRAIN, [r["id"] for r in rows]) + deleted = await storage.cap_reasoning_traces(BRAIN, max_total=2) + assert deleted == 3 + stats = await storage.get_reasoning_stats(BRAIN) + assert stats["total"] == 2 # the 2 newest survive + + +async def test_get_reasoning_stats_shapes(storage: SQLiteStorage) -> None: + await storage.insert_reasoning_traces( + BRAIN, + [ + _trace("h1", model="claude-fable-5", created_at="2026-03-01T00:00:00"), + _trace("h2", model="claude-fable-5", created_at="2026-03-05T00:00:00"), + _trace("h3", model="claude-sonnet-5", created_at="2026-03-02T00:00:00"), + ], + ) + rows = await storage.get_unprocessed_reasoning_traces(BRAIN, model="claude-sonnet-5") + await storage.mark_reasoning_traces_processed(BRAIN, [rows[0]["id"]]) + stats = await storage.get_reasoning_stats(BRAIN) + assert stats["total"] == 3 + assert stats["unprocessed"] == 2 + assert stats["by_model"]["claude-fable-5"]["trace_count"] == 2 + assert stats["by_model"]["claude-fable-5"]["unprocessed"] == 2 + assert stats["by_model"]["claude-fable-5"]["last_trace_at"] == "2026-03-05T00:00:00" + assert stats["by_model"]["claude-sonnet-5"]["unprocessed"] == 0 + + +async def test_get_reasoning_trace_models_distinct_sorted(storage: SQLiteStorage) -> None: + await storage.insert_reasoning_traces( + BRAIN, + [ + _trace("h1", model="claude-sonnet-5"), + _trace("h2", model="claude-fable-5"), + _trace("h3", model="claude-fable-5"), + ], + ) + assert await storage.get_reasoning_trace_models(BRAIN) == [ + "claude-fable-5", + "claude-sonnet-5", + ] + + +async def test_traces_are_brain_scoped(storage: SQLiteStorage) -> None: + await storage._ensure_conn().execute( + "INSERT OR IGNORE INTO brains (id, name, config, created_at, updated_at) " + "VALUES (?, ?, ?, ?, ?)", + ("other", "other", "{}", "2026-01-01T00:00:00", "2026-01-01T00:00:00"), + ) + await storage._ensure_conn().commit() + await storage.insert_reasoning_traces(BRAIN, [_trace("h1")]) + await storage.insert_reasoning_traces("other", [_trace("h1")]) + assert (await storage.get_reasoning_stats(BRAIN))["total"] == 1 + assert (await storage.get_reasoning_stats("other"))["total"] == 1 + + +async def test_content_chars_explicit_zero_preserved(storage: SQLiteStorage) -> None: + await storage.insert_reasoning_traces(BRAIN, [_trace("h1", content="abcde", content_chars=0)]) + rows = await storage.get_unprocessed_reasoning_traces(BRAIN) + assert rows[0]["content_chars"] == 0 + + +async def test_empty_trace_hash_is_skipped(storage: SQLiteStorage) -> None: + n = await storage.insert_reasoning_traces(BRAIN, [_trace(""), _trace("h1")]) + assert n == 1 + rows = await storage.get_unprocessed_reasoning_traces(BRAIN) + assert [r["trace_hash"] for r in rows] == ["h1"] diff --git a/tests/unit/test_surrealdb_reasoning_traces.py b/tests/unit/test_surrealdb_reasoning_traces.py new file mode 100644 index 00000000..455b14a1 --- /dev/null +++ b/tests/unit/test_surrealdb_reasoning_traces.py @@ -0,0 +1,229 @@ +"""Unit tests for the SurrealDB reasoning-traces mixin (no live DB required). + +Mirrors test_surrealdb_tool_events.py: a fake store routes ``_query`` by SQL +fragment and records inserts/updates/deletes, so the mixin's SurrealQL and +mapping logic are verified without a running SurrealDB. Live-engine behaviour is +covered separately by the real-db test runner. +""" + +from __future__ import annotations + +from typing import Any + +from surreal_memory.storage.surrealdb.reasoning_traces import SurrealDBReasoningTracesMixin + + +class _RTStore(SurrealDBReasoningTracesMixin): + """Routes _query by SQL fragment; records inserts and mutating queries.""" + + def __init__(self, **cfg: Any) -> None: + self.cfg = cfg + self.inserts: list[tuple[str, dict[str, Any]]] = [] + self.queries: list[tuple[str, dict[str, Any]]] = [] + + def _get_brain_id(self) -> str: + return "default" + + def _ensure_conn(self) -> Any: + store = self + + class _Conn: + async def insert(self, table: str, data: dict[str, Any]) -> None: + store.inserts.append((table, data)) + + return _Conn() + + async def _query(self, sql: str, **params: Any) -> list[dict[str, Any]]: + self.queries.append((sql, params)) + c = self.cfg + if sql.startswith("UPDATE reasoning_traces SET processed = true"): + return [] + if sql.startswith("UPDATE reasoning_traces SET category"): + return [] + if sql.startswith("DELETE reasoning_traces"): + return [] + if ( + sql.startswith("SELECT trace_hash FROM reasoning_traces") + and "trace_hash IN $hashes" in sql + ): + return [{"trace_hash": h} for h in c.get("existing", [])] + if "processed = true" in sql and "ORDER BY created_at ASC" in sql: + return [{"trace_hash": h} for h in c.get("cap_victims", [])] + if "processed = false" in sql and "ORDER BY created_at ASC" in sql: + rows = list(c.get("unprocessed", [])) + if params.get("model"): + rows = [r for r in rows if r.get("model") == params["model"]] + return rows + if "processed = true AND created_at < $cutoff GROUP ALL" in sql: + return [{"c": c.get("prune_count", 0)}] + if "count() AS c FROM reasoning_traces WHERE brain_id = $bid GROUP ALL" in sql: + return [{"c": c.get("total", 0)}] + if "AND processed = false GROUP BY model" in sql: + return c.get("unproc_groups", []) + if "count() AS cnt FROM reasoning_traces WHERE brain_id = $bid GROUP BY model" in sql: + return c.get("model_groups", []) + if "ORDER BY created_at DESC LIMIT 1" in sql: + return c.get("latest", {}).get(params.get("model"), []) + if "GROUP BY category" in sql: + return c.get("cat_groups", []) + if sql.startswith( + "SELECT model FROM reasoning_traces WHERE brain_id = $bid GROUP BY model" + ): + return c.get("distinct_models", []) + return [] + + +def _tr(trace_hash: str, model: str = "claude-fable-5", **kw: Any) -> dict[str, Any]: + base = { + "trace_hash": trace_hash, + "model": model, + "session_id": "s1", + "project": "proj", + "task_context": "ctx", + "content": "restate goal then verify", + "created_at": "2026-03-01T10:00:00", + } + base.update(kw) + return base + + +async def test_insert_skips_existing_and_batch_dupes() -> None: + store = _RTStore(existing=["h2"]) + n = await store.insert_reasoning_traces( + "default", [_tr("h1"), _tr("h2"), _tr("h1", content="dup"), _tr("h3")] + ) + # h2 already exists; h1 duplicated in batch (inserted once); h3 new. + assert n == 2 + inserted_hashes = {data["trace_hash"] for _, data in store.inserts} + assert inserted_hashes == {"h1", "h3"} + for table, data in store.inserts: + assert table == "reasoning_traces" + assert data["processed"] is False + assert data["brain_id"] == "default" + + +async def test_insert_empty_returns_zero() -> None: + store = _RTStore() + assert await store.insert_reasoning_traces("default", []) == 0 + assert store.inserts == [] + + +async def test_insert_truncates_task_context_and_computes_chars() -> None: + store = _RTStore() + await store.insert_reasoning_traces( + "default", [_tr("h1", task_context="x" * 600, content="abcde")] + ) + data = store.inserts[0][1] + assert len(data["task_context"]) == 500 + assert data["content_chars"] == 5 + + +async def test_get_unprocessed_maps_rows_and_filters_model() -> None: + unprocessed = [ + {"trace_hash": "h1", "model": "claude-fable-5", "content": "c1", "created_at": "t1"}, + {"trace_hash": "h2", "model": "claude-sonnet-5", "content": "c2", "created_at": "t2"}, + ] + store = _RTStore(unprocessed=unprocessed) + rows = await store.get_unprocessed_reasoning_traces("default", model="claude-fable-5") + assert len(rows) == 1 + assert rows[0]["id"] == "h1" + assert rows[0]["trace_hash"] == "h1" + # limit is interpolated into the SQL + assert any("LIMIT" in sql for sql, _ in store.queries) + + +async def test_mark_processed_issues_update_by_trace_hash() -> None: + store = _RTStore() + await store.mark_reasoning_traces_processed("default", ["h1", "h2"]) + upd = [ + q for q in store.queries if q[0].startswith("UPDATE reasoning_traces SET processed = true") + ] + assert upd and upd[0][1]["ids"] == ["h1", "h2"] + + +async def test_mark_processed_empty_noop() -> None: + store = _RTStore() + await store.mark_reasoning_traces_processed("default", []) + assert store.queries == [] + + +async def test_set_categories_updates_per_hash() -> None: + store = _RTStore() + await store.set_trace_categories("default", {"h1": "debugging", "h2": "planning"}) + cat_updates = [ + q for q in store.queries if q[0].startswith("UPDATE reasoning_traces SET category") + ] + assert len(cat_updates) == 2 + seen = {(q[1]["id"], q[1]["cat"]) for q in cat_updates} + assert seen == {("h1", "debugging"), ("h2", "planning")} + + +async def test_prune_returns_count_and_deletes() -> None: + store = _RTStore(prune_count=3) + deleted = await store.prune_reasoning_traces("default", keep_days=90) + assert deleted == 3 + assert any(q[0].startswith("DELETE reasoning_traces") for q in store.queries) + + +async def test_cap_deletes_oldest_excess() -> None: + store = _RTStore(total=5, cap_victims=["h1", "h2", "h3"]) + deleted = await store.cap_reasoning_traces("default", max_total=2) + assert deleted == 3 + dels = [q for q in store.queries if q[0].startswith("DELETE reasoning_traces")] + assert dels and dels[0][1]["ids"] == ["h1", "h2", "h3"] + + +async def test_cap_noop_when_under_limit() -> None: + store = _RTStore(total=2) + assert await store.cap_reasoning_traces("default", max_total=5) == 0 + assert not any(q[0].startswith("DELETE reasoning_traces") for q in store.queries) + + +async def test_get_reasoning_stats_assembles_all_sections() -> None: + store = _RTStore( + model_groups=[ + {"model": "claude-fable-5", "cnt": 2}, + {"model": "claude-sonnet-5", "cnt": 1}, + ], + unproc_groups=[{"model": "claude-fable-5", "cnt": 2}], + latest={ + "claude-fable-5": [{"created_at": "2026-03-05T00:00:00"}], + "claude-sonnet-5": [{"created_at": "2026-03-02T00:00:00"}], + }, + cat_groups=[{"category": "debugging", "cnt": 1}, {"category": "planning", "cnt": 2}], + ) + stats = await store.get_reasoning_stats("default") + assert stats["total"] == 3 + assert stats["unprocessed"] == 2 + assert stats["by_model"]["claude-fable-5"]["trace_count"] == 2 + assert stats["by_model"]["claude-fable-5"]["unprocessed"] == 2 + assert stats["by_model"]["claude-fable-5"]["last_trace_at"] == "2026-03-05T00:00:00" + assert stats["by_model"]["claude-sonnet-5"]["unprocessed"] == 0 + assert stats["by_category"] == {"debugging": 1, "planning": 2} + + +async def test_get_reasoning_trace_models_distinct_sorted_nonempty() -> None: + store = _RTStore( + distinct_models=[ + {"model": "claude-sonnet-5"}, + {"model": "claude-fable-5"}, + {"model": ""}, + ] + ) + assert await store.get_reasoning_trace_models("default") == [ + "claude-fable-5", + "claude-sonnet-5", + ] + + +async def test_insert_preserves_explicit_content_chars_zero() -> None: + store = _RTStore() + await store.insert_reasoning_traces("default", [_tr("h1", content="abcde", content_chars=0)]) + assert store.inserts[0][1]["content_chars"] == 0 + + +async def test_insert_skips_empty_trace_hash() -> None: + store = _RTStore() + n = await store.insert_reasoning_traces("default", [_tr(""), _tr("h1")]) + assert n == 1 + assert {data["trace_hash"] for _, data in store.inserts} == {"h1"} From a330262c14b66d9455284c31b76edc29d96faa23 Mon Sep 17 00:00:00 2001 From: Toni Nowak Date: Thu, 16 Jul 2026 23:17:29 +0200 Subject: [PATCH 02/12] feat: ReasoningTrainingConfig + TOML/env wiring (U2) Add the frozen ReasoningTrainingConfig dataclass (mining + injection settings, opt-in/off by default) to UnifiedConfig, with config.toml + env support (run 007 Faza 2). - ReasoningTrainingConfig (all plan fields: mining/injection toggles, mining_models globs, injection_map target->source, categories, trace/scan/retention/cap limits, distiller + confidence knobs, redact_secrets) - load: [reasoning_training] TOML section + [reasoning_training.injection_map] subtable, env overrides SURREAL_MEMORY_REASONING_{MINING,INJECTION,MODELS,INJECTION_MAP} - save: emits the section incl. injection_map subtable; adds _sanitize_toml_glob so glob model names (claude-opus-*) survive serialization (the strict _sanitize_toml_str would drop the '*') - tests: defaults, from/to_dict roundtrip, min_confidence clamp, injection_map dict/list forms, TOML glob+injection_map roundtrip, env overrides, empty-env no-op --- src/surreal_memory/unified_config.py | 224 +++++++++++++++++++++++++++ tests/unit/test_unified_config.py | 107 +++++++++++++ 2 files changed, 331 insertions(+) diff --git a/src/surreal_memory/unified_config.py b/src/surreal_memory/unified_config.py index 56da25d1..1e5008e7 100755 --- a/src/surreal_memory/unified_config.py +++ b/src/surreal_memory/unified_config.py @@ -1063,6 +1063,189 @@ def from_dict(cls, data: dict[str, Any]) -> ToolMemoryConfig: ) +# Model names / injection globs may contain glob metacharacters (``*``/``?``/ +# ``[]``) that the stricter _TOML_SAFE_STRING rejects. This variant allows them +# while still blocking quotes/backslashes/control chars (TOML-injection safe). +_TOML_SAFE_GLOB = re.compile(r"^[a-zA-Z0-9_\-\./ *?\[\]]*$") + + +def _sanitize_toml_glob(value: str) -> str: + """Sanitize a model-name / glob string for safe TOML serialization.""" + if not isinstance(value, str): + return "" + cleaned = value.strip()[:_TOML_STR_MAX_LEN] + if not _TOML_SAFE_GLOB.match(cleaned): + return "" + return cleaned + + +_DEFAULT_REASONING_CATEGORIES: tuple[str, ...] = ( + "debugging", + "planning", + "implementation", + "refactoring", + "research", + "verification", + "architecture", + "data-analysis", +) + + +@dataclass(frozen=True) +class ReasoningTrainingConfig: + """Reasoning-training configuration (mining reasoning traces + injection). + + Opt-in and privacy-preserving: mining and injection are both OFF by default. + Mirrors the tool-memory pipeline but for model ``thinking`` blocks — mined + traces are distilled into ReasoningBank patterns and optionally injected into + other models' sessions per ``injection_map``. + """ + + mining_enabled: bool = False # opt-in (privacy): reads no transcripts until True + injection_enabled: bool = False # opt-in: inject learned strategies into sessions + # Glob patterns of source models to mine; () = all models with non-empty thinking. + mining_models: tuple[str, ...] = () + # target_glob -> source_model (one source per target in v1). + injection_map: tuple[tuple[str, str], ...] = () + categories: tuple[str, ...] = _DEFAULT_REASONING_CATEGORIES + min_trace_chars: int = 200 + max_trace_chars: int = 20_000 + max_traces_per_scan: int = 500 + scan_lookback_days: int = 30 # 0 = full backfill + retention_days: int = 90 + max_traces_total: int = 20_000 + min_cluster_support: int = 3 + max_patterns_per_run: int = 10 + min_confidence: float = 0.2 + min_patterns_per_category: int = 3 + injection_max_patterns: int = 5 + injection_max_chars: int = 4000 + distill_use_llm: bool = False + redact_secrets: bool = True + + def to_dict(self) -> dict[str, Any]: + return { + "mining_enabled": self.mining_enabled, + "injection_enabled": self.injection_enabled, + "mining_models": list(self.mining_models), + "injection_map": dict(self.injection_map), + "categories": list(self.categories), + "min_trace_chars": self.min_trace_chars, + "max_trace_chars": self.max_trace_chars, + "max_traces_per_scan": self.max_traces_per_scan, + "scan_lookback_days": self.scan_lookback_days, + "retention_days": self.retention_days, + "max_traces_total": self.max_traces_total, + "min_cluster_support": self.min_cluster_support, + "max_patterns_per_run": self.max_patterns_per_run, + "min_confidence": self.min_confidence, + "min_patterns_per_category": self.min_patterns_per_category, + "injection_max_patterns": self.injection_max_patterns, + "injection_max_chars": self.injection_max_chars, + "distill_use_llm": self.distill_use_llm, + "redact_secrets": self.redact_secrets, + } + + @classmethod + def from_dict(cls, data: dict[str, Any]) -> ReasoningTrainingConfig: + def _int(key: str, default: int, lo: int, hi: int) -> int: + try: + return max(lo, min(int(data.get(key, default)), hi)) + except (ValueError, TypeError): + return default + + models_raw = data.get("mining_models", []) + if isinstance(models_raw, (list, tuple)): + mining_models = tuple(str(m)[:128] for m in models_raw[:100] if str(m).strip()) + else: + mining_models = () + + map_raw = data.get("injection_map", {}) + injection_pairs: list[tuple[str, str]] = [] + if isinstance(map_raw, dict): + for target, source in map_raw.items(): + t, s = str(target).strip(), str(source).strip() + if t and s: + injection_pairs.append((t[:128], s[:128])) + elif isinstance(map_raw, (list, tuple)): + for item in map_raw: + if isinstance(item, (list, tuple)) and len(item) == 2: + t, s = str(item[0]).strip(), str(item[1]).strip() + if t and s: + injection_pairs.append((t[:128], s[:128])) + + cats_raw = data.get("categories") + if isinstance(cats_raw, (list, tuple)) and cats_raw: + categories = tuple(str(c)[:64] for c in cats_raw[:50] if str(c).strip()) + else: + categories = _DEFAULT_REASONING_CATEGORIES + + try: + min_confidence = max(0.0, min(float(data.get("min_confidence", 0.2)), 1.0)) + except (ValueError, TypeError): + min_confidence = 0.2 + + return cls( + mining_enabled=bool(data.get("mining_enabled", False)), + injection_enabled=bool(data.get("injection_enabled", False)), + mining_models=mining_models, + injection_map=tuple(injection_pairs), + categories=categories, + min_trace_chars=_int("min_trace_chars", 200, 0, 1_000_000), + max_trace_chars=_int("max_trace_chars", 20_000, 1, 10_000_000), + max_traces_per_scan=_int("max_traces_per_scan", 500, 1, 1_000_000), + scan_lookback_days=_int("scan_lookback_days", 30, 0, 100_000), + retention_days=_int("retention_days", 90, 1, 100_000), + max_traces_total=_int("max_traces_total", 20_000, 1, 100_000_000), + min_cluster_support=_int("min_cluster_support", 3, 1, 100_000), + max_patterns_per_run=_int("max_patterns_per_run", 10, 1, 100_000), + min_confidence=min_confidence, + min_patterns_per_category=_int("min_patterns_per_category", 3, 1, 100_000), + injection_max_patterns=_int("injection_max_patterns", 5, 1, 1000), + injection_max_chars=_int("injection_max_chars", 4000, 1, 1_000_000), + distill_use_llm=bool(data.get("distill_use_llm", False)), + redact_secrets=bool(data.get("redact_secrets", True)), + ) + + +def _load_reasoning_settings(data: dict[str, Any]) -> ReasoningTrainingConfig: + """Build ReasoningTrainingConfig from config.toml, letting env vars override. + + Env precedence (env wins over config.toml, config.toml wins over defaults): + SURREAL_MEMORY_REASONING_MINING -> mining_enabled (truthy parse) + SURREAL_MEMORY_REASONING_INJECTION -> injection_enabled (truthy parse) + SURREAL_MEMORY_REASONING_MODELS -> mining_models (comma-separated globs) + SURREAL_MEMORY_REASONING_INJECTION_MAP -> injection_map ("target=source,..." pairs) + """ + base = ReasoningTrainingConfig.from_dict(data) + overrides: dict[str, Any] = {} + + env_mining = _env_truthy(os.environ.get("SURREAL_MEMORY_REASONING_MINING")) + if env_mining is not None: + overrides["mining_enabled"] = env_mining + + env_injection = _env_truthy(os.environ.get("SURREAL_MEMORY_REASONING_INJECTION")) + if env_injection is not None: + overrides["injection_enabled"] = env_injection + + env_models = os.environ.get("SURREAL_MEMORY_REASONING_MODELS") + if env_models is not None and env_models.strip(): + overrides["mining_models"] = tuple(m.strip() for m in env_models.split(",") if m.strip()) + + env_map = os.environ.get("SURREAL_MEMORY_REASONING_INJECTION_MAP") + if env_map is not None and env_map.strip(): + pairs: list[tuple[str, str]] = [] + for chunk in env_map.split(","): + target, sep, source = chunk.partition("=") + if sep and target.strip() and source.strip(): + pairs.append((target.strip(), source.strip())) + overrides["injection_map"] = tuple(pairs) + + if overrides: + return dataclasses.replace(base, **overrides) + return base + + @dataclass(frozen=True) class TelegramConfig: """Telegram backup integration configuration. @@ -1469,6 +1652,9 @@ class UnifiedConfig: # Retrieval-trace telemetry (schema v9, opt-in; neutral default = off) trace: TraceConfig = field(default_factory=TraceConfig) + # Reasoning-training (mining reasoning traces + injection; opt-in, off by default) + reasoning_training: ReasoningTrainingConfig = field(default_factory=ReasoningTrainingConfig) + # CLI preferences json_output: bool = False default_depth: int | None = None @@ -1566,12 +1752,48 @@ def load(cls, config_path: Path | None = None) -> UnifiedConfig: response=ResponseConfig.from_dict(data.get("response", {})), budget=BudgetRetrievalConfig.from_dict(data.get("budget", {})), trace=TraceConfig.from_dict(data.get("trace", {})), + reasoning_training=_load_reasoning_settings(data.get("reasoning_training", {})), json_output=data.get("cli", {}).get("json_output", False), default_depth=data.get("cli", {}).get("default_depth"), default_max_tokens=data.get("cli", {}).get("default_max_tokens", 500), version=data.get("version", "1.0"), ) + def _reasoning_toml_lines(self) -> list[str]: + """Render the [reasoning_training] TOML section (+ injection_map subtable).""" + rt = self.reasoning_training + models = ", ".join(f'"{g}"' for m in rt.mining_models if (g := _sanitize_toml_glob(m))) + cats = ", ".join(f'"{g}"' for c in rt.categories if (g := _sanitize_toml_glob(c))) + lines = [ + "# Reasoning-training (mining reasoning traces + injection; opt-in, off by default)", + "[reasoning_training]", + f"mining_enabled = {'true' if rt.mining_enabled else 'false'}", + f"injection_enabled = {'true' if rt.injection_enabled else 'false'}", + f"mining_models = [{models}]", + f"categories = [{cats}]", + f"min_trace_chars = {rt.min_trace_chars}", + f"max_trace_chars = {rt.max_trace_chars}", + f"max_traces_per_scan = {rt.max_traces_per_scan}", + f"scan_lookback_days = {rt.scan_lookback_days}", + f"retention_days = {rt.retention_days}", + f"max_traces_total = {rt.max_traces_total}", + f"min_cluster_support = {rt.min_cluster_support}", + f"max_patterns_per_run = {rt.max_patterns_per_run}", + f"min_confidence = {rt.min_confidence}", + f"min_patterns_per_category = {rt.min_patterns_per_category}", + f"injection_max_patterns = {rt.injection_max_patterns}", + f"injection_max_chars = {rt.injection_max_chars}", + f"distill_use_llm = {'true' if rt.distill_use_llm else 'false'}", + f"redact_secrets = {'true' if rt.redact_secrets else 'false'}", + "[reasoning_training.injection_map]", + ] + for target, source in rt.injection_map: + gk = _sanitize_toml_glob(target) + gv = _sanitize_toml_glob(source) + if gk and gv: + lines.append(f'"{gk}" = "{gv}"') + return lines + def save(self) -> None: """Save configuration to TOML file (atomic write via temp+rename).""" import tempfile @@ -1785,6 +2007,8 @@ def save(self) -> None: f"retention_days = {self.trace.retention_days}", f"max_traces = {self.trace.max_traces}", "", + *self._reasoning_toml_lines(), + "", "# CLI preferences", "[cli]", f"json_output = {'true' if self.json_output else 'false'}", diff --git a/tests/unit/test_unified_config.py b/tests/unit/test_unified_config.py index 91589109..bdba714f 100755 --- a/tests/unit/test_unified_config.py +++ b/tests/unit/test_unified_config.py @@ -9,6 +9,7 @@ from surreal_memory.cli.config import _sync_brain_to_toml from surreal_memory.unified_config import ( _MIN_LEGACY_DB_BYTES, + ReasoningTrainingConfig, UnifiedConfig, _migrate_legacy_db, _read_current_brain_from_toml, @@ -527,3 +528,109 @@ def test_emits_only_once(self, monkeypatch, caplog): uc._warn_missing_surreal_pass() warnings = [r for r in caplog.records if "SURREALDB_PASS" in r.message] assert len(warnings) == 1 + + +class TestReasoningTrainingConfig: + """Tests for the [reasoning_training] config section (mining + injection).""" + + _ENV_VARS = ( + "SURREAL_MEMORY_REASONING_MINING", + "SURREAL_MEMORY_REASONING_INJECTION", + "SURREAL_MEMORY_REASONING_MODELS", + "SURREAL_MEMORY_REASONING_INJECTION_MAP", + ) + + def _clear_env(self, monkeypatch: pytest.MonkeyPatch) -> None: + for var in self._ENV_VARS: + monkeypatch.delenv(var, raising=False) + + def test_defaults_are_opt_in_off(self) -> None: + rt = ReasoningTrainingConfig() + assert rt.mining_enabled is False + assert rt.injection_enabled is False + assert rt.mining_models == () + assert rt.injection_map == () + assert "debugging" in rt.categories + assert "data-analysis" in rt.categories + assert rt.min_confidence == 0.2 + assert rt.redact_secrets is True + + def test_from_dict_to_dict_roundtrip(self) -> None: + rt = ReasoningTrainingConfig( + mining_enabled=True, + injection_enabled=True, + mining_models=("claude-fable-*",), + injection_map=(("claude-opus-*", "claude-fable-5"),), + min_confidence=0.5, + max_traces_total=1234, + ) + assert ReasoningTrainingConfig.from_dict(rt.to_dict()) == rt + + def test_from_dict_clamps_min_confidence(self) -> None: + assert ReasoningTrainingConfig.from_dict({"min_confidence": 5}).min_confidence == 1.0 + assert ReasoningTrainingConfig.from_dict({"min_confidence": -1}).min_confidence == 0.0 + + def test_from_dict_injection_map_dict_and_list_forms(self) -> None: + from_dict = ReasoningTrainingConfig.from_dict( + {"injection_map": {"claude-opus-*": "claude-fable-5"}} + ) + assert from_dict.injection_map == (("claude-opus-*", "claude-fable-5"),) + # Malformed list entries (wrong arity) are dropped; valid pairs kept. + from_list = ReasoningTrainingConfig.from_dict({"injection_map": [["a", "b"], ["bad"]]}) + assert from_list.injection_map == (("a", "b"),) + + def test_toml_roundtrip_preserves_globs_and_injection_map( + self, tmp_data_dir: Path, monkeypatch: pytest.MonkeyPatch + ) -> None: + self._clear_env(monkeypatch) + rt = ReasoningTrainingConfig( + mining_enabled=True, + injection_enabled=True, + mining_models=("claude-fable-*", "glm-5.2"), + injection_map=(("claude-opus-*", "claude-fable-5"),), + min_confidence=0.35, + scan_lookback_days=0, + ) + UnifiedConfig(data_dir=tmp_data_dir, current_brain="default", reasoning_training=rt).save() + reloaded = UnifiedConfig.load(config_path=tmp_data_dir / "config.toml") + + got = reloaded.reasoning_training + assert got.mining_enabled is True + assert got.injection_enabled is True + # Globs survive save/load (would be silently dropped by _sanitize_toml_str). + assert got.mining_models == ("claude-fable-*", "glm-5.2") + assert got.injection_map == (("claude-opus-*", "claude-fable-5"),) + assert got.min_confidence == 0.35 + assert got.scan_lookback_days == 0 + + def test_env_overrides_toml(self, tmp_data_dir: Path, monkeypatch: pytest.MonkeyPatch) -> None: + self._clear_env(monkeypatch) + UnifiedConfig(data_dir=tmp_data_dir, current_brain="default").save() # mining off + monkeypatch.setenv("SURREAL_MEMORY_REASONING_MINING", "true") + monkeypatch.setenv("SURREAL_MEMORY_REASONING_INJECTION", "1") + monkeypatch.setenv("SURREAL_MEMORY_REASONING_MODELS", "claude-fable-*, glm-5.2") + monkeypatch.setenv( + "SURREAL_MEMORY_REASONING_INJECTION_MAP", + "claude-opus-*=claude-fable-5, claude-haiku-*=glm-5.2", + ) + cfg = UnifiedConfig.load(config_path=tmp_data_dir / "config.toml") + + assert cfg.reasoning_training.mining_enabled is True + assert cfg.reasoning_training.injection_enabled is True + assert cfg.reasoning_training.mining_models == ("claude-fable-*", "glm-5.2") + assert cfg.reasoning_training.injection_map == ( + ("claude-opus-*", "claude-fable-5"), + ("claude-haiku-*", "glm-5.2"), + ) + + def test_empty_env_does_not_override( + self, tmp_data_dir: Path, monkeypatch: pytest.MonkeyPatch + ) -> None: + self._clear_env(monkeypatch) + rt = ReasoningTrainingConfig(mining_enabled=True, mining_models=("x-*",)) + UnifiedConfig(data_dir=tmp_data_dir, current_brain="default", reasoning_training=rt).save() + monkeypatch.setenv("SURREAL_MEMORY_REASONING_MODELS", "") # empty → ignored + cfg = UnifiedConfig.load(config_path=tmp_data_dir / "config.toml") + + assert cfg.reasoning_training.mining_enabled is True + assert cfg.reasoning_training.mining_models == ("x-*",) From 497a855d522d20afb357f6c4ec25820ad9a3c79b Mon Sep 17 00:00:00 2001 From: Toni Nowak Date: Fri, 17 Jul 2026 10:11:08 +0200 Subject: [PATCH 03/12] feat: reasoning_miner + PROCESS_REASONING_TRACES consolidation (U3) Mine model thinking blocks from ~/.claude transcripts into the reasoning_traces staging table, driven by consolidation (run 007 Faza 3). - engine/reasoning_miner.py: scan_transcripts (path-guarded to ~/.claude, incremental last_line resume, trace_hash dedup, model normalize/denylist/glob, /opus skip, min/max char gating) + ingest_reasoning_traces - privacy: redaction-before-stage of BOTH thinking content AND user task_context via a reasoning-tuned pattern set (min_severity=2 + Bearer/prose/vendor-key patterns); opt-in (mining_enabled) only; signatures never stored - consolidation: PROCESS_REASONING_TRACES strategy (tier 1, guarded by mining_enabled + dry_run, excluded from scheduled defaults like LEARN_HABITS) + ConsolidationReport.reasoning_traces_ingested - per-line size cap before json.loads (crafted-transcript DoS guard) - tests: 21 miner/consolidation cases; live-verified against SurrealDB v3.2.0 --- src/surreal_memory/engine/consolidation.py | 52 +++ src/surreal_memory/engine/reasoning_miner.py | 429 +++++++++++++++++++ tests/unit/test_reasoning_miner.py | 379 ++++++++++++++++ 3 files changed, 860 insertions(+) create mode 100644 src/surreal_memory/engine/reasoning_miner.py create mode 100644 tests/unit/test_reasoning_miner.py diff --git a/src/surreal_memory/engine/consolidation.py b/src/surreal_memory/engine/consolidation.py index b0623da9..59fae119 100755 --- a/src/surreal_memory/engine/consolidation.py +++ b/src/surreal_memory/engine/consolidation.py @@ -48,6 +48,7 @@ class ConsolidationStrategy(StrEnum): COMPRESS = "compress" LIFECYCLE = "lifecycle" PROCESS_TOOL_EVENTS = "process_tool_events" + PROCESS_REASONING_TRACES = "process_reasoning_traces" ESSENCE_BACKFILL = "essence_backfill" DETECT_DRIFT = "detect_drift" REPLAY = "replay" # Hippocampal replay: LTP/LTD on recent fibers @@ -113,6 +114,7 @@ class ConsolidationReport: tokens_saved: int = 0 neurons_reactivated: int = 0 essences_generated: int = 0 + reasoning_traces_ingested: int = 0 merge_details: list[MergeDetail] = field(default_factory=list) dry_run: bool = False extra: dict[str, Any] = field(default_factory=dict) @@ -212,6 +214,7 @@ class ConsolidationEngine: ConsolidationStrategy.LEARN_HABITS, ConsolidationStrategy.DEDUP, ConsolidationStrategy.PROCESS_TOOL_EVENTS, + ConsolidationStrategy.PROCESS_REASONING_TRACES, } ), frozenset( @@ -286,6 +289,9 @@ async def _run_strategy( ConsolidationStrategy.PROCESS_TOOL_EVENTS: lambda: self._process_tool_events( report, dry_run ), + ConsolidationStrategy.PROCESS_REASONING_TRACES: lambda: self._process_reasoning_traces( + report, dry_run + ), ConsolidationStrategy.DETECT_DRIFT: lambda: self._detect_drift(report, dry_run), ConsolidationStrategy.ESSENCE_BACKFILL: lambda: self._essence_backfill(report, dry_run), ConsolidationStrategy.REPLAY: lambda: self._replay(report, dry_run), @@ -1871,6 +1877,52 @@ async def _process_tool_events( result.synapses_created, ) + async def _process_reasoning_traces( + self, + report: ConsolidationReport, + dry_run: bool, + ) -> None: + """Mine reasoning traces from transcripts into the staging table. + + Scans ~/.claude transcripts for model thinking blocks and stages them + (redacted) via reasoning_miner. Only runs when + reasoning_training.mining_enabled is set (opt-in for privacy). Mirrors + PROCESS_TOOL_EVENTS and is likewise excluded from scheduled defaults. + """ + import logging as _logging + + from surreal_memory.unified_config import UnifiedConfig + + _logger = _logging.getLogger(__name__) + + brain_id = self._storage.current_brain_id + if not brain_id: + _logger.debug("PROCESS_REASONING_TRACES skipped: no brain context") + return + + try: + config = UnifiedConfig.load() + except Exception: + _logger.debug("PROCESS_REASONING_TRACES skipped: config load failed", exc_info=True) + return + + if not config.reasoning_training.mining_enabled: + return + + if dry_run: + _logger.debug("PROCESS_REASONING_TRACES skipped: dry_run mode") + return + + from surreal_memory.engine.reasoning_miner import ingest_reasoning_traces + + result = await ingest_reasoning_traces(self._storage, brain_id, config) + report.reasoning_traces_ingested = result.traces_ingested + if result.traces_ingested > 0: + _logger.debug( + "PROCESS_REASONING_TRACES: ingested %d reasoning traces", + result.traces_ingested, + ) + async def _detect_drift(self, report: ConsolidationReport, dry_run: bool) -> None: """Run semantic drift detection to find tag synonyms/aliases.""" _logger = logging.getLogger(__name__) diff --git a/src/surreal_memory/engine/reasoning_miner.py b/src/surreal_memory/engine/reasoning_miner.py new file mode 100644 index 00000000..e303fd54 --- /dev/null +++ b/src/surreal_memory/engine/reasoning_miner.py @@ -0,0 +1,429 @@ +"""Reasoning-trace miner: scan Claude Code transcripts for model ``thinking``. + +Mirrors the tool-events ingest pipeline, but reads reasoning (``thinking``) +blocks out of ``~/.claude/projects/*/*.jsonl`` transcripts into the +``reasoning_traces`` staging table. Runs inside consolidation (strategy +``PROCESS_REASONING_TRACES``), never on the hooks' hot path. + +Privacy: mining is opt-in (``reasoning_training.mining_enabled``); thinking text +is redacted via ``safety.sensitive.auto_redact_content`` BEFORE it is staged. +Deduplicated by ``trace_hash = sha256(sessionId:uuid:block_index)``; an +incremental scan-state file skips unchanged transcripts. +""" + +from __future__ import annotations + +import hashlib +import json +import logging +import re +import time +from dataclasses import dataclass +from datetime import UTC +from fnmatch import fnmatch +from functools import lru_cache +from pathlib import Path +from typing import TYPE_CHECKING, Any + +from surreal_memory.safety.sensitive import ( + SensitivePattern, + SensitiveType, + auto_redact_content, + get_default_patterns, +) +from surreal_memory.utils.timeutils import utcnow + +if TYPE_CHECKING: + from datetime import datetime + + from surreal_memory.storage.base import NeuralStorage + from surreal_memory.unified_config import ReasoningTrainingConfig, UnifiedConfig + +logger = logging.getLogger(__name__) + +# Models whose thinking blocks are empty (signature-only) — never a mining +# source. opus-4.8 is already excluded by the non-empty-thinking filter; this is +# a belt-and-suspenders prefix denylist (see run 007 BINDING CORRECTION #5). +_MODELS_WITHOUT_THINKING = ("claude-opus-4-8",) + +# The synthetic attribution used for non-model turns — never mine it. +_SYNTHETIC_MODEL = "" + +_TASK_CONTEXT_MAX = 400 +_DATE_SUFFIX = re.compile(r"-\d{8}$") + +# Skip absurdly large transcript lines before json.loads (crafted/corrupted +# transcript DoS guard). Normal entries (even with big tool_results) are well +# under this; a single reasoning trace is capped far lower by max_trace_chars. +_MAX_LINE_CHARS = 1_000_000 + + +@lru_cache(maxsize=1) +def _reasoning_redaction_patterns() -> tuple[SensitivePattern, ...]: + """Default redaction patterns plus prose / vendor-token patterns. + + Reasoning ``thinking`` is free-form narration (paraphrased configs, curl + commands, error output), so the ``key=value``-anchored defaults miss Bearer + headers and bare vendor keys. These extra severity-3 patterns close that gap. + """ + extra = ( + SensitivePattern( + name="Bearer Token (prose)", + pattern=r"(?i)bearer\s+[A-Za-z0-9._\-]{16,}", + type=SensitiveType.TOKEN, + description="Bearer token in narrative prose", + severity=3, + ), + SensitivePattern( + name="OpenAI Secret Key", + # \b anchor avoids matching "sk-" mid-word (task-, desk-, risk-, disk-…). + pattern=r"\bsk-(?:proj-)?[A-Za-z0-9_\-]{16,}", + type=SensitiveType.API_KEY, + description="OpenAI-style secret key", + severity=3, + ), + SensitivePattern( + name="GitHub Token", + pattern=r"gh[pousr]_[A-Za-z0-9]{20,}", + type=SensitiveType.TOKEN, + description="GitHub personal/OAuth token", + severity=3, + ), + SensitivePattern( + name="Slack Token", + pattern=r"xox[baprs]-[A-Za-z0-9\-]{10,}", + type=SensitiveType.TOKEN, + description="Slack token", + severity=3, + ), + SensitivePattern( + name="Google API Key", + pattern=r"AIza[0-9A-Za-z_\-]{16,}", + type=SensitiveType.API_KEY, + description="Google API key", + severity=3, + ), + SensitivePattern( + name="AWS Access Key ID (standalone)", + pattern=r"\bAKIA[0-9A-Z]{16}\b", + type=SensitiveType.AWS_KEY, + description="AWS access key id", + severity=3, + ), + ) + return get_default_patterns() + extra + + +def _redact(text: str, config: ReasoningTrainingConfig) -> str: + """Redact secrets from *text* when ``config.redact_secrets`` is set. + + Uses ``min_severity=2`` (so JWTs, which are severity 2, are caught) with the + reasoning-specific pattern set. Applied to BOTH thinking content and the + user-prompt ``task_context`` before anything is staged. + """ + if not config.redact_secrets: + return text + redacted, _matches, _orig_hash = auto_redact_content( + text, min_severity=2, patterns=list(_reasoning_redaction_patterns()) + ) + return redacted + + +def normalize_model(model: str) -> str: + """Canonicalize a model id by stripping a trailing ``-YYYYMMDD`` date suffix. + + ``claude-haiku-4-5-20251001`` -> ``claude-haiku-4-5``. Keeps mining, + distillation and injection agreeing on one canonical model name. + """ + return _DATE_SUFFIX.sub("", model.strip()) + + +def _is_denylisted(model: str) -> bool: + return any(model.startswith(prefix) for prefix in _MODELS_WITHOUT_THINKING) + + +def _model_matches(model: str, patterns: tuple[str, ...]) -> bool: + """Return True if *model* matches any glob in *patterns* (empty = all).""" + if not patterns: + return True + return any(fnmatch(model, pattern) for pattern in patterns) + + +def _extract_user_text(entry: dict[str, Any]) -> str: + """Extract plain user prompt text from a transcript entry (best-effort).""" + message = entry.get("message") + source = message if isinstance(message, dict) else entry + content = source.get("content") + if isinstance(content, str): + return content + if isinstance(content, list): + parts = [ + item.get("text", "") + for item in content + if isinstance(item, dict) and item.get("type") == "text" and item.get("text") + ] + return "\n".join(parts) + return "" + + +def _project_from_path(jsonl: Path) -> str: + """Derive a readable project name from the transcript's parent directory.""" + return jsonl.parent.name + + +def _now_ts(now: datetime | None) -> float: + if now is None: + return time.time() + aware = now if now.tzinfo is not None else now.replace(tzinfo=UTC) + return aware.timestamp() + + +def _load_scan_state(state_path: Path | None) -> dict[str, dict[str, Any]]: + if state_path is None or not state_path.exists(): + return {} + try: + data = json.loads(state_path.read_text(encoding="utf-8")) + except (OSError, json.JSONDecodeError): + return {} + return data if isinstance(data, dict) else {} + + +def _save_scan_state(state_path: Path | None, state: dict[str, dict[str, Any]]) -> None: + if state_path is None: + return + try: + state_path.parent.mkdir(parents=True, exist_ok=True) + state_path.write_text(json.dumps(state), encoding="utf-8") + except OSError: + logger.debug("reasoning scan-state write failed: %s", state_path, exc_info=True) + + +@dataclass +class ReasoningIngestResult: + """Outcome of a reasoning-trace ingest pass.""" + + traces_ingested: int = 0 + traces_scanned: int = 0 + + +def scan_transcripts( + config: ReasoningTrainingConfig, + *, + state_path: Path | None = None, + claude_dir: Path | None = None, + now: datetime | None = None, +) -> list[dict[str, Any]]: + """Scan ``~/.claude/projects/*/*.jsonl`` for mineable reasoning traces. + + Returns staging-ready dicts (trace_hash, model, session_id, project, + task_context, content, content_chars, created_at). Honors the config's + model globs, char limits, per-scan cap, lookback window and redaction flag, + and updates the incremental scan-state file. + + The per-scan cap is a soft target: a file is always scanned whole (so its + recorded state never hides un-returned traces), and scanning simply stops + after the running total reaches ``max_traces_per_scan``. + """ + claude_root = (claude_dir or (Path.home() / ".claude")).resolve() + projects_dir = claude_root / "projects" + if not projects_dir.is_dir(): + return [] + + now_ts = _now_ts(now) + cutoff_ts = ( + now_ts - config.scan_lookback_days * 86400 if config.scan_lookback_days > 0 else None + ) + fallback_created = (now or utcnow()).isoformat() + + state = _load_scan_state(state_path) + traces: list[dict[str, Any]] = [] + seen_hashes: set[str] = set() + + for jsonl in sorted(projects_dir.glob("*/*.jsonl")): + resolved = jsonl.resolve() + # Path-escape guard: never read outside ~/.claude (symlink / traversal). + if not resolved.is_relative_to(claude_root): + continue + try: + st = resolved.stat() + except OSError: + continue + # Lookback window: skip transcripts untouched within scan_lookback_days. + if cutoff_ts is not None and st.st_mtime < cutoff_ts: + continue + key = str(resolved) + prev = state.get(key, {}) + # Incremental: skip files whose size+mtime are unchanged since last scan. + if prev.get("size") == st.st_size and prev.get("mtime") == st.st_mtime: + continue + # Resume after the last processed line on append-only growth; otherwise + # (shrunk / rewritten / edited-in-place) rescan from the top — trace_hash + # dedup keeps any re-emitted traces harmless at insert time. + start_line = int(prev.get("last_line", 0)) if st.st_size > int(prev.get("size", 0)) else 0 + + file_traces, line_count = _scan_file( + resolved, config, seen_hashes, fallback_created, start_line=start_line + ) + traces.extend(file_traces) + state[key] = {"mtime": st.st_mtime, "size": st.st_size, "last_line": line_count} + if len(traces) >= config.max_traces_per_scan: + break + + _save_scan_state(state_path, state) + return traces + + +def _scan_file( + resolved: Path, + config: ReasoningTrainingConfig, + seen_hashes: set[str], + fallback_created: str, + *, + start_line: int = 0, +) -> tuple[list[dict[str, Any]], int]: + """Scan a single transcript file. Returns (traces, line_count). + + ``start_line`` resumes after an already-processed prefix (append-only + growth), so a resumed scan does not re-emit traces from earlier passes. + """ + project = _project_from_path(resolved) + out: list[dict[str, Any]] = [] + prev_user_text = "" + line_count = start_line + try: + with resolved.open(encoding="utf-8", errors="replace") as f: + for idx, raw in enumerate(f, start=1): + line_count = idx + if idx <= start_line: + continue + if len(raw) > _MAX_LINE_CHARS: + continue + raw = raw.strip() + if not raw: + continue + try: + entry = json.loads(raw) + except json.JSONDecodeError: + continue + if not isinstance(entry, dict): + continue + etype = entry.get("type") + if etype == "user": + # Redact the user prompt before it becomes task_context — it + # can contain pasted secrets just like thinking content. + prev_user_text = _redact(_extract_user_text(entry)[:_TASK_CONTEXT_MAX], config) + continue + if etype != "assistant": + continue + out.extend( + _traces_from_assistant( + entry, config, seen_hashes, project, prev_user_text, fallback_created + ) + ) + except OSError: + logger.debug("reasoning transcript read failed: %s", resolved, exc_info=True) + return out, line_count + + +def _traces_from_assistant( + entry: dict[str, Any], + config: ReasoningTrainingConfig, + seen_hashes: set[str], + project: str, + task_context: str, + fallback_created: str, +) -> list[dict[str, Any]]: + message = entry.get("message") + if not isinstance(message, dict): + return [] + model = normalize_model(str(message.get("model") or "")) + if not model or model == _SYNTHETIC_MODEL or _is_denylisted(model): + return [] + if not _model_matches(model, config.mining_models): + return [] + content = message.get("content") + if not isinstance(content, list): + return [] + session_id = str(entry.get("sessionId") or "") + uuid_ = str(entry.get("uuid") or "") + created_at = str(entry.get("timestamp") or fallback_created) + out: list[dict[str, Any]] = [] + for block_index, block in enumerate(content): + trace = _build_trace( + block, + block_index, + config, + model=model, + session_id=session_id, + uuid_=uuid_, + project=project, + task_context=task_context, + created_at=created_at, + seen_hashes=seen_hashes, + ) + if trace is not None: + out.append(trace) + return out + + +def _build_trace( + block: Any, + block_index: int, + config: ReasoningTrainingConfig, + *, + model: str, + session_id: str, + uuid_: str, + project: str, + task_context: str, + created_at: str, + seen_hashes: set[str], +) -> dict[str, Any] | None: + if not isinstance(block, dict) or block.get("type") != "thinking": + return None + thinking = block.get("thinking") + if not isinstance(thinking, str) or not thinking.strip(): + return None + if len(thinking) < config.min_trace_chars: + return None + trace_hash = hashlib.sha256(f"{session_id}:{uuid_}:{block_index}".encode()).hexdigest() + if trace_hash in seen_hashes: + return None + seen_hashes.add(trace_hash) + text = _redact(thinking[: config.max_trace_chars], config) + return { + "trace_hash": trace_hash, + "model": model, + "session_id": session_id, + "project": project, + "task_context": task_context, + "content": text, + "content_chars": len(text), + "created_at": created_at, + } + + +async def ingest_reasoning_traces( + storage: NeuralStorage, + brain_id: str, + config: UnifiedConfig, + *, + claude_dir: Path | None = None, + state_path: Path | None = None, + now: datetime | None = None, +) -> ReasoningIngestResult: + """Scan transcripts and insert new reasoning traces into staging. + + The scan-state file lives under ``config.data_dir`` unless overridden. + """ + resolved_state = state_path or (config.data_dir / "reasoning_scan_state.json") + traces = scan_transcripts( + config.reasoning_training, + state_path=resolved_state, + claude_dir=claude_dir, + now=now, + ) + if not traces: + return ReasoningIngestResult(traces_ingested=0, traces_scanned=0) + ingested = await storage.insert_reasoning_traces(brain_id, traces) + return ReasoningIngestResult(traces_ingested=ingested, traces_scanned=len(traces)) diff --git a/tests/unit/test_reasoning_miner.py b/tests/unit/test_reasoning_miner.py new file mode 100644 index 00000000..bd6451c4 --- /dev/null +++ b/tests/unit/test_reasoning_miner.py @@ -0,0 +1,379 @@ +"""Tests for engine/reasoning_miner.py — transcript scanning + ingest. + +Uses synthetic JSONL transcript fixtures under a temporary ``.claude`` dir (no +real thinking text). Covers dedup, model glob filter, truncation, ```` +and opus (empty/denylisted) skipping, min-length gating, secret redaction, +task_context capture, model normalization, incremental scan-state, and the +async ingest path against InMemoryStorage. +""" + +from __future__ import annotations + +import json +from datetime import UTC, datetime +from pathlib import Path + +from surreal_memory.engine.reasoning_miner import ( + ingest_reasoning_traces, + normalize_model, + scan_transcripts, +) +from surreal_memory.storage.memory_store import InMemoryStorage +from surreal_memory.unified_config import ReasoningTrainingConfig, UnifiedConfig + +_NOW = datetime(2026, 3, 1, 12, 0, 0, tzinfo=UTC) + + +def _cfg(**kw: object) -> ReasoningTrainingConfig: + base: dict[str, object] = { + "mining_enabled": True, + "min_trace_chars": 10, + "max_trace_chars": 10_000, + "scan_lookback_days": 0, + } + base.update(kw) + return ReasoningTrainingConfig(**base) # type: ignore[arg-type] + + +def _assistant( + thinking: str, + *, + model: str = "claude-fable-5", + session: str = "s1", + uuid: str = "u1", + timestamp: str = "2026-03-01T10:00:00Z", + extra_blocks: list | None = None, +) -> dict: + blocks: list = [{"type": "thinking", "thinking": thinking, "signature": "sig"}] + if extra_blocks: + blocks = extra_blocks + return { + "type": "assistant", + "sessionId": session, + "uuid": uuid, + "cwd": "/home/x/proj", + "timestamp": timestamp, + "message": {"model": model, "role": "assistant", "content": blocks}, + } + + +def _user(text: str, *, session: str = "s1", uuid: str = "u0") -> dict: + return { + "type": "user", + "sessionId": session, + "uuid": uuid, + "message": {"role": "user", "content": [{"type": "text", "text": text}]}, + } + + +def _write_transcript( + claude_dir: Path, entries: list[dict], *, slug: str = "proj-a", name: str = "t.jsonl" +) -> Path: + d = claude_dir / "projects" / slug + d.mkdir(parents=True, exist_ok=True) + p = d / name + p.write_text("\n".join(json.dumps(e) for e in entries) + "\n", encoding="utf-8") + return p + + +def _scan(claude_dir: Path, cfg: ReasoningTrainingConfig, state: Path) -> list[dict]: + return scan_transcripts(cfg, state_path=state, claude_dir=claude_dir, now=_NOW) + + +def test_normalize_model_strips_date_suffix() -> None: + assert normalize_model("claude-haiku-4-5-20251001") == "claude-haiku-4-5" + assert normalize_model("claude-fable-5") == "claude-fable-5" + assert normalize_model(" claude-sonnet-5 ") == "claude-sonnet-5" + + +def test_scan_extracts_thinking_with_task_context(tmp_path: Path) -> None: + claude = tmp_path / ".claude" + _write_transcript( + claude, + [ + _user("please fix the failing test in module X"), + _assistant("restate the goal, decompose, then verify the edge cases carefully"), + ], + ) + traces = _scan(claude, _cfg(), tmp_path / "state.json") + assert len(traces) == 1 + tr = traces[0] + assert tr["model"] == "claude-fable-5" + assert "verify the edge cases" in tr["content"] + assert tr["task_context"] == "please fix the failing test in module X" + assert tr["session_id"] == "s1" + assert len(tr["trace_hash"]) == 64 + assert tr["created_at"] == "2026-03-01T10:00:00Z" + + +def test_dedup_identical_entries(tmp_path: Path) -> None: + claude = tmp_path / ".claude" + entry = _assistant("a fairly long reasoning trace about verification", uuid="dup") + _write_transcript(claude, [entry, entry]) # same session:uuid:block_index + traces = _scan(claude, _cfg(), tmp_path / "state.json") + assert len(traces) == 1 + + +def test_incremental_state_skips_unchanged(tmp_path: Path) -> None: + claude = tmp_path / ".claude" + state = tmp_path / "state.json" + _write_transcript(claude, [_assistant("a long enough reasoning trace here")]) + assert len(_scan(claude, _cfg(), state)) == 1 + # Second scan, file unchanged → skipped entirely. + assert _scan(claude, _cfg(), state) == [] + + +def test_incremental_state_rescans_changed(tmp_path: Path) -> None: + claude = tmp_path / ".claude" + state = tmp_path / "state.json" + p = _write_transcript(claude, [_assistant("first reasoning trace long enough", uuid="a")]) + assert len(_scan(claude, _cfg(), state)) == 1 + # Append a new entry → file changes → rescan yields only the NEW trace (dedup). + with p.open("a", encoding="utf-8") as f: + f.write(json.dumps(_assistant("second reasoning trace long enough", uuid="b")) + "\n") + traces = _scan(claude, _cfg(), state) + assert len(traces) == 1 + assert "second reasoning" in traces[0]["content"] + + +def test_model_glob_filter(tmp_path: Path) -> None: + claude = tmp_path / ".claude" + _write_transcript( + claude, + [ + _assistant("fable reasoning trace long enough", model="claude-fable-5", uuid="a"), + _assistant("sonnet reasoning trace long enough", model="claude-sonnet-5", uuid="b"), + ], + ) + traces = _scan(claude, _cfg(mining_models=("claude-fable-*",)), tmp_path / "state.json") + assert [t["model"] for t in traces] == ["claude-fable-5"] + + +def test_synthetic_and_opus_are_skipped(tmp_path: Path) -> None: + claude = tmp_path / ".claude" + _write_transcript( + claude, + [ + _assistant("synthetic reasoning trace long enough", model="", uuid="a"), + _assistant("opus reasoning trace long enough", model="claude-opus-4-8", uuid="b"), + _assistant("fable reasoning trace long enough", model="claude-fable-5", uuid="c"), + ], + ) + traces = _scan(claude, _cfg(), tmp_path / "state.json") + assert [t["model"] for t in traces] == ["claude-fable-5"] + + +def test_empty_thinking_skipped(tmp_path: Path) -> None: + claude = tmp_path / ".claude" + _write_transcript(claude, [_assistant(" ", uuid="a")]) # opus-style empty thinking + assert _scan(claude, _cfg(), tmp_path / "state.json") == [] + + +def test_min_trace_chars_gate(tmp_path: Path) -> None: + claude = tmp_path / ".claude" + _write_transcript(claude, [_assistant("short", uuid="a")]) + assert _scan(claude, _cfg(min_trace_chars=50), tmp_path / "state.json") == [] + + +def test_truncation_to_max_chars(tmp_path: Path) -> None: + claude = tmp_path / ".claude" + _write_transcript(claude, [_assistant("x" * 500, uuid="a")]) + traces = _scan(claude, _cfg(max_trace_chars=100), tmp_path / "state.json") + assert len(traces[0]["content"]) == 100 + assert traces[0]["content_chars"] == 100 + + +def test_secret_redaction_before_staging(tmp_path: Path) -> None: + claude = tmp_path / ".claude" + secret = "password = hunter2supersecret" # noqa: S105 — synthetic test secret + thinking = f"first I reason about the config, then note that {secret}, then continue" + _write_transcript(claude, [_assistant(thinking, uuid="a")]) + + redacted = _scan(claude, _cfg(redact_secrets=True), tmp_path / "s1.json") + assert "hunter2supersecret" not in redacted[0]["content"] + assert "[REDACTED]" in redacted[0]["content"] + + kept = _scan(claude, _cfg(redact_secrets=False), tmp_path / "s2.json") + assert "hunter2supersecret" in kept[0]["content"] + + +def test_task_context_is_redacted(tmp_path: Path) -> None: + claude = tmp_path / ".claude" + _write_transcript( + claude, + [ + _user("my config has password = topsecret123 in it, help"), + _assistant("reasoning about the config problem in detail here"), + ], + ) + traces = _scan(claude, _cfg(redact_secrets=True), tmp_path / "state.json") + assert "topsecret123" not in traces[0]["task_context"] + assert "[REDACTED]" in traces[0]["task_context"] + + +def test_content_redacts_bearer_jwt_and_vendor_keys(tmp_path: Path) -> None: + claude = tmp_path / ".claude" + jwt = "eyJhbGciOiJIUzI1NiJ9.eyJzdWIiOiIxMjM0NTY3ODkwIn0.abc123def456ghi" + bearer_tok = "abcdefghijklmnop12345" + openai_key = "sk-proj-ABCDEFGHIJKLMNOP1234" + thinking = ( + f"I inspect the request; it sends Authorization: Bearer {bearer_tok} " + f"and a jwt {jwt} and an openai key {openai_key} to the API endpoint" + ) + _write_transcript(claude, [_assistant(thinking, uuid="a")]) + content = _scan(claude, _cfg(redact_secrets=True), tmp_path / "state.json")[0]["content"] + assert bearer_tok not in content + assert jwt not in content + assert openai_key not in content + assert "[REDACTED]" in content + + +def test_redaction_does_not_mangle_benign_hyphenated_prose(tmp_path: Path) -> None: + claude = tmp_path / ".claude" + # "task-"/"disk-" contain the substring "sk-" but must NOT be redacted. + thinking = "the task-queue-processor-v2-implementation refactors the disk-cache-layer module" + _write_transcript(claude, [_assistant(thinking, uuid="a")]) + content = _scan(claude, _cfg(redact_secrets=True), tmp_path / "state.json")[0]["content"] + assert "[REDACTED]" not in content + assert "task-queue-processor-v2-implementation" in content + + +def test_model_normalization_applied(tmp_path: Path) -> None: + claude = tmp_path / ".claude" + _write_transcript( + claude, + [_assistant("dated model reasoning trace", model="claude-haiku-4-5-20251001", uuid="a")], + ) + traces = _scan(claude, _cfg(), tmp_path / "state.json") + assert traces[0]["model"] == "claude-haiku-4-5" + + +def test_files_outside_projects_glob_ignored(tmp_path: Path) -> None: + claude = tmp_path / ".claude" + (claude / "projects").mkdir(parents=True, exist_ok=True) + # A jsonl directly under .claude (not projects/*/) must not be scanned. + (claude / "stray.jsonl").write_text( + json.dumps(_assistant("stray reasoning trace long enough", uuid="a")) + "\n", + encoding="utf-8", + ) + assert _scan(claude, _cfg(), tmp_path / "state.json") == [] + + +def test_max_traces_per_scan_soft_cap(tmp_path: Path) -> None: + claude = tmp_path / ".claude" + entries = [ + _assistant(f"reasoning trace number {i} long enough", uuid=f"u{i}") for i in range(5) + ] + _write_transcript(claude, entries) + traces = _scan(claude, _cfg(max_traces_per_scan=3), tmp_path / "state.json") + # Whole file is scanned (no mid-file loss); cap only stops scanning MORE files. + assert len(traces) == 5 + + +async def test_ingest_reasoning_traces_into_storage(tmp_path: Path) -> None: + claude = tmp_path / ".claude" + _write_transcript( + claude, + [ + _user("do the thing"), + _assistant("reasoning trace one long enough", model="claude-fable-5", uuid="a"), + _assistant("reasoning trace two long enough", model="claude-sonnet-5", uuid="b"), + ], + ) + storage = InMemoryStorage() + storage.set_brain("b1") + cfg = UnifiedConfig( + data_dir=tmp_path / ".surrealmemory", + current_brain="default", + reasoning_training=_cfg(), + ) + result = await ingest_reasoning_traces( + storage, "b1", cfg, claude_dir=claude, state_path=tmp_path / "state.json", now=_NOW + ) + assert result.traces_ingested == 2 + assert result.traces_scanned == 2 + stats = await storage.get_reasoning_stats("b1") + assert stats["total"] == 2 + assert set(stats["by_model"]) == {"claude-fable-5", "claude-sonnet-5"} + + +def _fake_unified_config(tmp_path: Path, *, mining_enabled: bool) -> UnifiedConfig: + return UnifiedConfig( + data_dir=tmp_path / ".surrealmemory", + current_brain="default", + reasoning_training=_cfg(mining_enabled=mining_enabled), + ) + + +async def test_consolidation_skips_when_mining_disabled(tmp_path, monkeypatch) -> None: + from surreal_memory.engine.consolidation import ConsolidationEngine, ConsolidationStrategy + + fake = _fake_unified_config(tmp_path, mining_enabled=False) + monkeypatch.setattr(UnifiedConfig, "load", staticmethod(lambda config_path=None: fake)) + calls = {"n": 0} + + async def _spy_ingest(*a, **k): # pragma: no cover - must NOT be reached + calls["n"] += 1 + from surreal_memory.engine.reasoning_miner import ReasoningIngestResult + + return ReasoningIngestResult(traces_ingested=9) + + monkeypatch.setattr( + "surreal_memory.engine.reasoning_miner.ingest_reasoning_traces", _spy_ingest + ) + + storage = InMemoryStorage() + storage.set_brain("b1") + report = await ConsolidationEngine(storage).run( + [ConsolidationStrategy.PROCESS_REASONING_TRACES] + ) + assert report.reasoning_traces_ingested == 0 + assert calls["n"] == 0 # guard short-circuits before invoking the miner + + +async def test_consolidation_ingests_when_enabled(tmp_path, monkeypatch) -> None: + from surreal_memory.engine.consolidation import ConsolidationEngine, ConsolidationStrategy + from surreal_memory.engine.reasoning_miner import ReasoningIngestResult + + fake = _fake_unified_config(tmp_path, mining_enabled=True) + monkeypatch.setattr(UnifiedConfig, "load", staticmethod(lambda config_path=None: fake)) + + async def _fake_ingest(storage, brain_id, config, **k): + return ReasoningIngestResult(traces_ingested=3, traces_scanned=3) + + monkeypatch.setattr( + "surreal_memory.engine.reasoning_miner.ingest_reasoning_traces", _fake_ingest + ) + + storage = InMemoryStorage() + storage.set_brain("b1") + report = await ConsolidationEngine(storage).run( + [ConsolidationStrategy.PROCESS_REASONING_TRACES] + ) + assert report.reasoning_traces_ingested == 3 + + +async def test_consolidation_dry_run_skips_ingest(tmp_path, monkeypatch) -> None: + from surreal_memory.engine.consolidation import ConsolidationEngine, ConsolidationStrategy + + fake = _fake_unified_config(tmp_path, mining_enabled=True) + monkeypatch.setattr(UnifiedConfig, "load", staticmethod(lambda config_path=None: fake)) + calls = {"n": 0} + + async def _spy_ingest(*a, **k): # pragma: no cover - must NOT be reached + calls["n"] += 1 + from surreal_memory.engine.reasoning_miner import ReasoningIngestResult + + return ReasoningIngestResult(traces_ingested=9) + + monkeypatch.setattr( + "surreal_memory.engine.reasoning_miner.ingest_reasoning_traces", _spy_ingest + ) + + storage = InMemoryStorage() + storage.set_brain("b1") + report = await ConsolidationEngine(storage).run( + [ConsolidationStrategy.PROCESS_REASONING_TRACES], dry_run=True + ) + assert report.reasoning_traces_ingested == 0 + assert calls["n"] == 0 From b261cea5c51312446aa6a955b6adcce11b26231e Mon Sep 17 00:00:00 2001 From: Toni Nowak Date: Fri, 17 Jul 2026 11:24:24 +0200 Subject: [PATCH 04/12] feat: reasoning_distiller + LEARN_REASONING consolidation (U4) Distill staged reasoning_traces into ReasoningBank pattern fibers (run 007 Faza 4), driven by consolidation's SUMMARIZE tier. - engine/reasoning_distiller.py: segment thinking into ~12 closed-vocabulary reasoning moves; classify category (bge-m3 seed-centroid cosine, keyword fallback, "other" excluded from coverage); cluster within (model, category) by cosine>=0.83 (embeddings) or move-set Jaccard>=0.6 (fallback) via UnionFind; build a ReasoningBank pattern (title/description=medoid/strategy=LCS moves) and materialize a fiber(_reasoning_pattern) + CONCEPT neuron + EFFECTIVE_FOR synapse, idempotent by a trace-set _reasoning_signature; reasoning_coverage(). - fully fail-soft: provider down -> keyword classification + move-set clustering. - consolidation: LEARN_REASONING strategy (SUMMARIZE tier, guarded by mining_enabled + dry_run, excluded from scheduled defaults) + handler wrapped in try/except + ConsolidationReport.reasoning_patterns_learned (+summary lines). - tests: 16 cases incl. cap-conservation, signature-collision, fail-soft embedder-raises; live-verified materialization on SurrealDB v3.2.0. --- src/surreal_memory/engine/consolidation.py | 62 +- .../engine/reasoning_distiller.py | 620 ++++++++++++++++++ tests/unit/test_reasoning_distiller.py | 325 +++++++++ 3 files changed, 1006 insertions(+), 1 deletion(-) create mode 100644 src/surreal_memory/engine/reasoning_distiller.py create mode 100644 tests/unit/test_reasoning_distiller.py diff --git a/src/surreal_memory/engine/consolidation.py b/src/surreal_memory/engine/consolidation.py index 59fae119..c2cfcaea 100755 --- a/src/surreal_memory/engine/consolidation.py +++ b/src/surreal_memory/engine/consolidation.py @@ -49,6 +49,7 @@ class ConsolidationStrategy(StrEnum): LIFECYCLE = "lifecycle" PROCESS_TOOL_EVENTS = "process_tool_events" PROCESS_REASONING_TRACES = "process_reasoning_traces" + LEARN_REASONING = "learn_reasoning" ESSENCE_BACKFILL = "essence_backfill" DETECT_DRIFT = "detect_drift" REPLAY = "replay" # Hippocampal replay: LTP/LTD on recent fibers @@ -115,6 +116,7 @@ class ConsolidationReport: neurons_reactivated: int = 0 essences_generated: int = 0 reasoning_traces_ingested: int = 0 + reasoning_patterns_learned: int = 0 merge_details: list[MergeDetail] = field(default_factory=list) dry_run: bool = False extra: dict[str, Any] = field(default_factory=dict) @@ -141,6 +143,8 @@ def summary(self) -> str: f" Memories promoted: {self.memories_promoted}", f" Fibers compressed: {self.fibers_compressed}", f" Tokens saved: {self.tokens_saved}", + f" Reasoning traces ingested: {self.reasoning_traces_ingested}", + f" Reasoning patterns learned: {self.reasoning_patterns_learned}", f" Duration: {self.duration_ms:.1f}ms", ] if self.merge_details: @@ -232,6 +236,7 @@ class ConsolidationEngine: ConsolidationStrategy.INFER, ConsolidationStrategy.SCHEMA, ConsolidationStrategy.ESSENCE_BACKFILL, + ConsolidationStrategy.LEARN_REASONING, } ), frozenset( @@ -292,6 +297,7 @@ async def _run_strategy( ConsolidationStrategy.PROCESS_REASONING_TRACES: lambda: self._process_reasoning_traces( report, dry_run ), + ConsolidationStrategy.LEARN_REASONING: lambda: self._learn_reasoning(report, dry_run), ConsolidationStrategy.DETECT_DRIFT: lambda: self._detect_drift(report, dry_run), ConsolidationStrategy.ESSENCE_BACKFILL: lambda: self._essence_backfill(report, dry_run), ConsolidationStrategy.REPLAY: lambda: self._replay(report, dry_run), @@ -1915,7 +1921,11 @@ async def _process_reasoning_traces( from surreal_memory.engine.reasoning_miner import ingest_reasoning_traces - result = await ingest_reasoning_traces(self._storage, brain_id, config) + try: + result = await ingest_reasoning_traces(self._storage, brain_id, config) + except Exception: + _logger.debug("PROCESS_REASONING_TRACES: ingest failed (non-critical)", exc_info=True) + return report.reasoning_traces_ingested = result.traces_ingested if result.traces_ingested > 0: _logger.debug( @@ -1923,6 +1933,56 @@ async def _process_reasoning_traces( result.traces_ingested, ) + async def _learn_reasoning( + self, + report: ConsolidationReport, + dry_run: bool, + ) -> None: + """Distill staged reasoning traces into ReasoningBank pattern fibers. + + Runs in the SUMMARIZE tier (after PROCESS_REASONING_TRACES ingest). Only + runs when reasoning_training.mining_enabled is set; excluded from + scheduled defaults (like PROCESS_REASONING_TRACES / LEARN_HABITS). + """ + import logging as _logging + + from surreal_memory.unified_config import UnifiedConfig + + _logger = _logging.getLogger(__name__) + + brain_id = self._storage.current_brain_id + if not brain_id: + _logger.debug("LEARN_REASONING skipped: no brain context") + return + + try: + config = UnifiedConfig.load() + except Exception: + _logger.debug("LEARN_REASONING skipped: config load failed", exc_info=True) + return + + if not config.reasoning_training.mining_enabled: + return + + if dry_run: + _logger.debug("LEARN_REASONING skipped: dry_run mode") + return + + from surreal_memory.engine.reasoning_distiller import distill_reasoning_patterns + + try: + result = await distill_reasoning_patterns(self._storage, brain_id, config) + except Exception: + _logger.debug("LEARN_REASONING: distillation failed (non-critical)", exc_info=True) + return + report.reasoning_patterns_learned = result.patterns_learned + if result.patterns_learned > 0: + _logger.debug( + "LEARN_REASONING: learned %d reasoning patterns from %d traces", + result.patterns_learned, + result.traces_processed, + ) + async def _detect_drift(self, report: ConsolidationReport, dry_run: bool) -> None: """Run semantic drift detection to find tag synonyms/aliases.""" _logger = logging.getLogger(__name__) diff --git a/src/surreal_memory/engine/reasoning_distiller.py b/src/surreal_memory/engine/reasoning_distiller.py new file mode 100644 index 00000000..7323cc48 --- /dev/null +++ b/src/surreal_memory/engine/reasoning_distiller.py @@ -0,0 +1,620 @@ +"""Reasoning distiller: staged reasoning_traces -> ReasoningBank patterns. + +Heuristic (no-LLM) distillation, run inside consolidation (strategy +``LEARN_REASONING``, SUMMARIZE tier, after ``PROCESS_REASONING_TRACES`` ingest): + + batch unprocessed traces per model + -> segment each thinking into ~12 closed-vocabulary reasoning "moves" + -> classify a category (bge-m3 embedding vs seed centroids; keyword fallback) + -> cluster within (model, category) by cosine (embeddings) or move-set Jaccard + -> for each cluster >= min_cluster_support: build a ReasoningBank pattern + (title / description / strategy / confidence / frequency) and materialize + it as a fiber (_reasoning_pattern) + CONCEPT neuron + EFFECTIVE_FOR synapse + -> mark traces processed; prune + cap the staging table. + +Fully fail-soft: with the embedding provider DOWN, classification falls back to +keywords and clustering to move-set Jaccard, so distillation still produces +patterns (never raises on a missing provider). +""" + +from __future__ import annotations + +import hashlib +import logging +import math +import os +import re +from collections import Counter +from dataclasses import dataclass +from typing import TYPE_CHECKING, Any + +from surreal_memory.core.fiber import Fiber +from surreal_memory.core.neuron import Neuron, NeuronType +from surreal_memory.core.synapse import Direction, Synapse, SynapseType +from surreal_memory.engine.clustering import UnionFind + +if TYPE_CHECKING: + from surreal_memory.engine.embedding.provider import EmbeddingProvider + from surreal_memory.storage.base import NeuralStorage + from surreal_memory.unified_config import ReasoningTrainingConfig, UnifiedConfig + +logger = logging.getLogger(__name__) + +_CLUSTER_COSINE = 0.83 +_CATEGORY_COS_THRESHOLD = 0.35 +_MOVE_JACCARD = 0.6 +_CLASSIFY_CHARS = 500 +_BATCH_PER_MODEL = 200 + +# ── Reasoning moves (closed vocabulary; regex discourse markers) ────────────── +_REASONING_MOVES: dict[str, re.Pattern[str]] = { + "restate-goal": re.compile( + r"(?i)\b(the goal is|the task is|i need to|we need to|objective is)\b" + ), + "decompose": re.compile( + r"(?i)\b(break (this|it) down|decompose|sub-?problem|break into|the steps are)\b" + ), + "hypothesize": re.compile( + r"(?i)\b(hypothes\w*|i suspect|maybe|might be|could be|perhaps|likely because)\b" + ), + "gather-evidence": re.compile( + r"(?i)\b(let me (check|look|read|grep)|looking at|the evidence|i see that|the code shows|confirmed that)\b" + ), + "verify": re.compile(r"(?i)\b(verify|confirm|double-?check|make sure|ensure that|validate)\b"), + "test-first": re.compile( + r"(?i)\b(write a test|test first|failing test|red test|tdd|add a test)\b" + ), + "check-edge-cases": re.compile( + r"(?i)\b(edge case|corner case|what if|boundary|off by one|empty (list|input)|null case|none case)\b" + ), + "backtrack": re.compile( + r"(?i)\b(actually|wait|let me reconsider|scratch that|on second thought|rethink|hold on)\b" + ), + "compare-alternatives": re.compile( + r"(?i)\b(option [a-z0-9]|alternative|versus|vs\.?|instead of|trade-?off|on the other hand|compared to)\b" + ), + "plan-steps": re.compile( + r"(?i)\b(my plan|the approach|step \d|next,? i|i'?ll (start|do|then)|let me first)\b" + ), + "self-correct": re.compile( + r"(?i)\b(i was wrong|correction|that'?s incorrect|my mistake|got it wrong|oops)\b" + ), + "summarize-decision": re.compile( + r"(?i)\b(in summary|to summarize|conclusion|so i'?ll|decided to|the decision|therefore)\b" + ), +} + +# ── Category classification: seed descriptions (embeddings) + keyword fallback ─ +_CATEGORY_SEEDS: dict[str, str] = { + "debugging": "debugging errors, root cause analysis, stack traces, fixing bugs and failures", + "planning": "planning steps, breaking down a task, deciding the approach and order of actions", + "implementation": "implementing code, writing functions, adding a feature, coding the solution", + "refactoring": "refactoring, cleaning up and restructuring code, improving readability, no behavior change", + "research": "researching, reading documentation, exploring the codebase, understanding how something works", + "verification": "verifying and testing, confirming correctness, checking outputs and edge cases", + "architecture": "architecture and system design, module boundaries, data flow, design decisions", + "data-analysis": "analyzing data, computing statistics, aggregating metrics, interpreting results", +} +_CATEGORY_KEYWORDS: dict[str, tuple[str, ...]] = { + "debugging": ( + "bug", + "error", + "exception", + "stack trace", + "root cause", + "traceback", + "crash", + "failing", + "debug", + ), + "refactoring": ( + "refactor", + "clean up", + "rename", + "restructure", + "simplify", + "extract", + "dead code", + "tidy up", + ), + "verification": ( + "verify", + "test", + "confirm", + "validate", + "assert", + "make sure", + "edge case", + "double-check", + ), + "architecture": ( + "architecture", + "design", + "module", + "boundary", + "data flow", + "interface", + "layer", + "component", + ), + "data-analysis": ( + "analyze", + "statistics", + "metric", + "aggregate", + "distribution", + "compute", + "measure", + ), + "research": ( + "read", + "documentation", + "docs", + "explore", + "investigate", + "grep", + "find out", + "look up", + ), + "planning": ("plan", "approach", "steps", "strategy", "break down", "sequence", "outline"), + "implementation": ( + "implement", + "write the", + "add a", + "function", + "build", + "create the", + "feature", + "method", + ), +} +# Keyword-fallback precedence (specific -> generic). +_CATEGORY_ORDER: tuple[str, ...] = ( + "debugging", + "refactoring", + "verification", + "architecture", + "data-analysis", + "research", + "planning", + "implementation", +) + +_OTHER = "other" + + +def _has_keyword(content_lower: str, keyword: str) -> bool: + """Whole-word (single token) / substring (phrase) keyword match.""" + if " " in keyword or "-" in keyword: + return keyword in content_lower + return re.search(rf"\b{re.escape(keyword)}\b", content_lower) is not None + + +def _cosine(a: list[float], b: list[float]) -> float: + dot = sum(x * y for x, y in zip(a, b, strict=False)) + norm_a = math.sqrt(sum(x * x for x in a)) + norm_b = math.sqrt(sum(x * x for x in b)) + if norm_a == 0.0 or norm_b == 0.0: + return 0.0 + return dot / (norm_a * norm_b) + + +def segment_moves(text: str) -> list[str]: + """Return the reasoning moves present in *text*, in closed-vocabulary order.""" + if not text: + return [] + return [move for move, pattern in _REASONING_MOVES.items() if pattern.search(text)] + + +def _classify_by_vector(vec: list[float], seeds: dict[str, list[float]]) -> str: + best_cat, best_sim = _OTHER, _CATEGORY_COS_THRESHOLD + for cat, cvec in seeds.items(): + sim = _cosine(vec, cvec) + if sim >= best_sim: + best_sim, best_cat = sim, cat + return best_cat + + +def _classify_by_keywords(text: str, categories: tuple[str, ...]) -> str: + low = text.lower() + for cat in _CATEGORY_ORDER: + if cat not in categories: + continue + if any(_has_keyword(low, kw) for kw in _CATEGORY_KEYWORDS.get(cat, ())): + return cat + return _OTHER + + +def _lcs_two(a: list[str], b: list[str]) -> list[str]: + m, n = len(a), len(b) + dp = [[0] * (n + 1) for _ in range(m + 1)] + for i in range(m - 1, -1, -1): + for j in range(n - 1, -1, -1): + dp[i][j] = dp[i + 1][j + 1] + 1 if a[i] == b[j] else max(dp[i + 1][j], dp[i][j + 1]) + out: list[str] = [] + i = j = 0 + while i < m and j < n: + if a[i] == b[j]: + out.append(a[i]) + i += 1 + j += 1 + elif dp[i + 1][j] >= dp[i][j + 1]: + i += 1 + else: + j += 1 + return out + + +def _lcs_all(seqs: list[list[str]]) -> list[str]: + seqs = [s for s in seqs if s] + if not seqs: + return [] + acc = seqs[0] + for s in seqs[1:]: + acc = _lcs_two(acc, s) + if not acc: + break + return acc + + +def _medoid_index(vectors: list[list[float]]) -> int: + best_i, best_score = 0, -2.0 + for i in range(len(vectors)): + score = sum(_cosine(vectors[i], vectors[j]) for j in range(len(vectors)) if j != i) + if score > best_score: + best_score, best_i = score, i + return best_i + + +def _cluster( + vectors: list[list[float]] | None, + moves: list[list[str]], +) -> list[list[int]]: + """Cluster items by cosine (vectors) or move-set Jaccard (fallback).""" + n = len(moves) + uf = UnionFind(n) + for i in range(n): + for j in range(i + 1, n): + if vectors is not None: + if _cosine(vectors[i], vectors[j]) >= _CLUSTER_COSINE: + uf.union(i, j) + else: + a, b = set(moves[i]), set(moves[j]) + union = a | b + if union and len(a & b) / len(union) >= _MOVE_JACCARD: + uf.union(i, j) + return list(uf.groups().values()) + + +def _build_pattern( + cluster_traces: list[dict[str, Any]], + cluster_vectors: list[list[float]] | None, + cluster_moves: list[list[str]], + model: str, + category: str, + traces_in_category: int, +) -> dict[str, Any]: + size = len(cluster_traces) + if cluster_vectors: + mi = _medoid_index(cluster_vectors) + else: + mi = max(range(size), key=lambda i: len(str(cluster_traces[i].get("content", "")))) + medoid_content = str(cluster_traces[mi].get("content", "")) + + move_counts = Counter(m for moves in cluster_moves for m in moves) + top_moves = [m for m, _ in move_counts.most_common(3)] + title = f"{category}: " + ", ".join(top_moves) if top_moves else category + + lcs = _lcs_all(cluster_moves) + strategy_moves = " -> ".join(lcs) if lcs else " -> ".join(top_moves) + strategy = f"Moves: {strategy_moves}\n{medoid_content[:400]}"[:600] + + confidence = min(1.0, size / traces_in_category) if traces_in_category else 0.0 + # Signature keys on the cluster's exact trace set (not the display title) so + # two distinct clusters that share the same top-moves title never collide. + trace_key = ",".join(sorted(str(t.get("trace_hash", "")) for t in cluster_traces)) + signature = hashlib.sha256(f"{model}:{category}:{trace_key}".encode()).hexdigest() + return { + "model": model, + "category": category, + "title": title, + "description": medoid_content[:200], + "strategy": strategy, + "confidence": round(confidence, 4), + "frequency": size, + "signature": signature, + } + + +async def _find_or_create_concept( + storage: NeuralStorage, content: str, metadata: dict[str, Any] | None = None +) -> str: + existing = await storage.find_neurons(content_exact=content, limit=1) + if existing: + return existing[0].id + neuron = Neuron.create(type=NeuronType.CONCEPT, content=content, metadata=metadata or {}) + await storage.add_neuron(neuron) + return neuron.id + + +async def _materialize_pattern( + storage: NeuralStorage, + pattern: dict[str, Any], + existing_sigs: set[str], +) -> bool: + """Create a fiber + CONCEPT neuron + EFFECTIVE_FOR synapse for *pattern*. + + Idempotent by ``_reasoning_signature``: returns False (no-op) if the pattern + was already materialized. + """ + sig = pattern["signature"] + if sig in existing_sigs: + return False + + category_nid = await _find_or_create_concept( + storage, f"reasoning_category:{pattern['category']}", {"_reasoning_category_concept": True} + ) + pattern_nid = await _find_or_create_concept( + storage, pattern["title"], {"_reasoning_pattern_concept": True} + ) + + existing_syn = await storage.get_synapses( + source_id=pattern_nid, target_id=category_nid, type=SynapseType.EFFECTIVE_FOR + ) + if existing_syn: + synapse_id = existing_syn[0].id + else: + synapse = Synapse.create( + source_id=pattern_nid, + target_id=category_nid, + type=SynapseType.EFFECTIVE_FOR, + weight=min(1.0, float(pattern["confidence"])), + direction=Direction.UNIDIRECTIONAL, + ) + await storage.add_synapse(synapse) + synapse_id = synapse.id + + fiber = Fiber.create( + neuron_ids={pattern_nid, category_nid}, + synapse_ids={synapse_id}, + anchor_neuron_id=pattern_nid, + pathway=[pattern_nid, category_nid], + summary=pattern["title"], + tags=set(), + metadata={ + "_reasoning_pattern": True, + "_source_model": pattern["model"], + "_reasoning_category": pattern["category"], + "_reasoning_title": pattern["title"], + "_reasoning_description": pattern["description"], + "_reasoning_strategy": pattern["strategy"], + "_reasoning_frequency": pattern["frequency"], + "_reasoning_confidence": pattern["confidence"], + "_reasoning_signature": sig, + }, + ) + await storage.add_fiber(fiber) + return True + + +def _get_embedder() -> EmbeddingProvider | None: + """Best-effort LOCAL embedding provider; None if unavailable (fail-soft). + + Mirrors the stop-hook pattern: only a local Ollama or a loopback + OpenAI-compatible endpoint (llamastash bge-m3) is used, so distillation stays + local + fast and never blocks on a remote/heavy provider. Any failure -> None + and the caller falls back to keyword classification + move-set clustering. + """ + try: + from surreal_memory.engine.semantic_discovery import _auto_detect_provider + + provider_name, model_name = _auto_detect_provider() + except Exception: + logger.debug("reasoning distiller: no embedding provider detected", exc_info=True) + return None + + try: + endpoint = os.environ.get("SURREAL_MEMORY_EMBEDDING_ENDPOINT", "") + is_local = any(host in endpoint for host in ("127.0.0.1", "localhost", "::1")) + if provider_name == "ollama": + from surreal_memory.engine.embedding.ollama_embedding import OllamaEmbedding + + return OllamaEmbedding(model=model_name) + if provider_name in ("openai", "openrouter") and is_local: + from surreal_memory.engine.embedding.openai_embedding import OpenAIEmbedding + + return OpenAIEmbedding(model=model_name) + except Exception: + logger.debug("reasoning distiller: embedding provider construction failed", exc_info=True) + return None + + +async def _seed_centroids( + embedder: EmbeddingProvider, categories: tuple[str, ...] +) -> dict[str, list[float]] | None: + try: + descriptions = [_CATEGORY_SEEDS.get(c, c) for c in categories] + vectors = await embedder.embed_batch(descriptions) + return {c: list(v) for c, v in zip(categories, vectors, strict=False)} + except Exception: + logger.debug("reasoning distiller: seed embedding failed", exc_info=True) + return None + + +async def _embed_texts( + embedder: EmbeddingProvider | None, texts: list[str] +) -> list[list[float]] | None: + if embedder is None or not texts: + return None + try: + return [list(v) for v in await embedder.embed_batch(texts)] + except Exception: + logger.debug("reasoning distiller: trace embedding failed", exc_info=True) + return None + + +@dataclass +class DistillResult: + """Outcome of a distillation pass.""" + + patterns_learned: int = 0 + traces_processed: int = 0 + models_seen: int = 0 + + +async def _process_model_batch( + storage: NeuralStorage, + brain_id: str, + rt: ReasoningTrainingConfig, + model: str, + traces: list[dict[str, Any]], + embedder: EmbeddingProvider | None, + seeds: dict[str, list[float]] | None, + existing_sigs: set[str], + budget: int, +) -> tuple[int, list[Any]]: + """Distill one model's trace batch. Returns (patterns_created, consumed_ids). + + ``consumed_ids`` are the traces safe to mark processed: ``other`` traces, + under-support categories, and every category fully clustered before the + ``budget`` (remaining max_patterns_per_run) ran out. A category left + unreached — or cut off mid-cluster — by the cap is NOT consumed, so the next + run revisits it (already-materialized patterns are skipped by signature). + """ + clf_texts = [ + f"{t.get('task_context', '')} {str(t.get('content', ''))[:_CLASSIFY_CHARS]}".strip() + for t in traces + ] + moves_list = [segment_moves(str(t.get("content", ""))) for t in traces] + vectors = await _embed_texts(embedder, clf_texts) + categories = [ + _classify_by_vector(vectors[i], seeds) + if (vectors is not None and seeds) + else _classify_by_keywords(clf_texts[i], rt.categories) + for i in range(len(traces)) + ] + await storage.set_trace_categories( + brain_id, {t["id"]: categories[i] for i, t in enumerate(traces)} + ) + + consumed: list[Any] = [traces[i]["id"] for i, c in enumerate(categories) if c == _OTHER] + by_category: dict[str, list[int]] = {} + for i, cat in enumerate(categories): + if cat != _OTHER: + by_category.setdefault(cat, []).append(i) + + created = 0 + for category, idxs in by_category.items(): + if created >= budget: + break # cap reached before this category → leave its traces unprocessed + if len(idxs) < rt.min_cluster_support: + consumed.extend(traces[i]["id"] for i in idxs) # too few to cluster; done + continue + sub_vectors = [vectors[i] for i in idxs] if vectors is not None else None + sub_moves = [moves_list[i] for i in idxs] + sub_traces = [traces[i] for i in idxs] + capped_mid_category = False + for local_cluster in _cluster(sub_vectors, sub_moves): + if len(local_cluster) < rt.min_cluster_support: + continue + if created >= budget: + capped_mid_category = True + break + cluster_traces = [sub_traces[k] for k in local_cluster] + cluster_vecs = [sub_vectors[k] for k in local_cluster] if sub_vectors else None + cluster_moves = [sub_moves[k] for k in local_cluster] + pattern = _build_pattern( + cluster_traces, cluster_vecs, cluster_moves, model, category, len(idxs) + ) + if await _materialize_pattern(storage, pattern, existing_sigs): + existing_sigs.add(pattern["signature"]) + created += 1 + if capped_mid_category: + break # do NOT consume this category's traces — revisit next run + consumed.extend(traces[i]["id"] for i in idxs) + return created, consumed + + +async def distill_reasoning_patterns( + storage: NeuralStorage, + brain_id: str, + config: UnifiedConfig, + *, + embedder: EmbeddingProvider | None = None, +) -> DistillResult: + """Distill unprocessed reasoning traces into ReasoningBank pattern fibers. + + ``storage`` must already be on ``brain_id`` (graph writes use the current + brain). Marks only the consumed traces processed, then prunes/caps staging. + """ + rt = config.reasoning_training + embedder = embedder or _get_embedder() + seeds = await _seed_centroids(embedder, rt.categories) if embedder is not None else None + + existing = await storage.find_fibers(metadata_key="_reasoning_pattern", limit=5000) + existing_sigs = { + str(f.metadata.get("_reasoning_signature")) + for f in existing + if f.metadata.get("_reasoning_signature") + } + + patterns_created = 0 + processed_ids: list[Any] = [] + models = await storage.get_reasoning_trace_models(brain_id) + + for model in models: + budget = rt.max_patterns_per_run - patterns_created + if budget <= 0: + break + traces = await storage.get_unprocessed_reasoning_traces( + brain_id, limit=rt.max_traces_per_scan, model=model + ) + traces = traces[:_BATCH_PER_MODEL] # one bounded batch per model per run + if not traces: + continue + created, consumed = await _process_model_batch( + storage, brain_id, rt, model, traces, embedder, seeds, existing_sigs, budget + ) + patterns_created += created + processed_ids.extend(consumed) + + if processed_ids: + await storage.mark_reasoning_traces_processed(brain_id, processed_ids) + await storage.prune_reasoning_traces(brain_id, rt.retention_days) + await storage.cap_reasoning_traces(brain_id, rt.max_traces_total) + + return DistillResult( + patterns_learned=patterns_created, + traces_processed=len(processed_ids), + models_seen=len(models), + ) + + +async def reasoning_coverage( + storage: NeuralStorage, + model: str, + config: UnifiedConfig, +) -> dict[str, Any]: + """Per-category coverage for *model*. + + A category is covered iff it has >= ``min_patterns_per_category`` pattern + fibers with ``_source_model == model`` and confidence >= ``min_confidence``. + ``coverage_percent`` = covered / len(categories) * 100 (``other`` excluded — + it is never in ``categories``). ``storage`` must be on the target brain. + """ + rt = config.reasoning_training + fibers = await storage.find_fibers(metadata_key="_reasoning_pattern", limit=5000) + counts: dict[str, int] = dict.fromkeys(rt.categories, 0) + for f in fibers: + md = f.metadata + if md.get("_source_model") != model: + continue + if float(md.get("_reasoning_confidence", 0.0)) < rt.min_confidence: + continue + cat = md.get("_reasoning_category") + if cat in counts: + counts[cat] += 1 + covered = {c: counts[c] >= rt.min_patterns_per_category for c in rt.categories} + n_covered = sum(1 for v in covered.values() if v) + percent = (n_covered / len(rt.categories) * 100.0) if rt.categories else 0.0 + return {"by_category": counts, "covered": covered, "coverage_percent": round(percent, 1)} diff --git a/tests/unit/test_reasoning_distiller.py b/tests/unit/test_reasoning_distiller.py new file mode 100644 index 00000000..13f0d651 --- /dev/null +++ b/tests/unit/test_reasoning_distiller.py @@ -0,0 +1,325 @@ +"""Tests for engine/reasoning_distiller.py — heuristic distillation. + +Runs against InMemoryStorage with the embedding provider forced OFF (the +`no_embedder` fixture), exercising the fail-soft keyword-classification + +move-set-Jaccard-clustering path (D5). Covers move segmentation, keyword +classification, clustering/pattern materialization, idempotency, mark-processed, +coverage math, and the LEARN_REASONING consolidation handler. +""" + +from __future__ import annotations + +from pathlib import Path + +import pytest + +from surreal_memory.engine.reasoning_distiller import ( + DistillResult, + _classify_by_keywords, + distill_reasoning_patterns, + reasoning_coverage, + segment_moves, +) +from surreal_memory.storage.memory_store import InMemoryStorage +from surreal_memory.unified_config import ReasoningTrainingConfig, UnifiedConfig + +BRAIN = "b1" + + +@pytest.fixture +def no_embedder(monkeypatch: pytest.MonkeyPatch) -> None: + """Force the fail-soft (no embedding provider) path — keyword + Jaccard.""" + monkeypatch.setattr("surreal_memory.engine.reasoning_distiller._get_embedder", lambda: None) + + +def _ucfg(tmp_path: Path, **rt_kw: object) -> UnifiedConfig: + base: dict[str, object] = { + "mining_enabled": True, + "min_cluster_support": 2, + "min_patterns_per_category": 1, + "min_confidence": 0.2, + "max_patterns_per_run": 10, + } + base.update(rt_kw) + return UnifiedConfig( + data_dir=tmp_path / ".surrealmemory", + current_brain="default", + reasoning_training=ReasoningTrainingConfig(**base), # type: ignore[arg-type] + ) + + +async def _seed(storage: InMemoryStorage, model: str, contents: list[str]) -> None: + traces = [ + { + "trace_hash": f"{model}-{i}", + "model": model, + "session_id": "s", + "project": "p", + "task_context": "", + "content": c, + "content_chars": len(c), + "created_at": "2026-03-01T00:00:00", + } + for i, c in enumerate(contents) + ] + await storage.insert_reasoning_traces(BRAIN, traces) + + +# 3 debugging traces sharing the move-set {restate-goal, gather-evidence, verify}. +_DEBUG_TRACES = [ + "I need to fix this. Let me check the error traceback. I verify the bug is gone.", + "I need to look at this. Let me check the exception. Verify the crash is fixed.", + "I need to resolve it. Let me check the failing traceback. I verify the error.", +] + + +def test_segment_moves_detects_closed_vocab() -> None: + moves = segment_moves("I need to fix it. Let me check the logs. Then I verify the result.") + assert "restate-goal" in moves + assert "gather-evidence" in moves + assert "verify" in moves + assert segment_moves("") == [] + + +def test_classify_by_keywords_and_other() -> None: + cats = ReasoningTrainingConfig().categories + assert _classify_by_keywords("hit an error with a traceback", cats) == "debugging" + assert _classify_by_keywords("let me refactor and clean up this module", cats) == "refactoring" + assert _classify_by_keywords("zzz qwerty plugh foobar nothing here", cats) == "other" + + +async def test_distill_creates_pattern_fiber(tmp_path: Path, no_embedder: None) -> None: + storage = InMemoryStorage() + storage.set_brain(BRAIN) + await _seed(storage, "claude-fable-5", _DEBUG_TRACES) + + result = await distill_reasoning_patterns(storage, BRAIN, _ucfg(tmp_path)) + assert isinstance(result, DistillResult) + assert result.patterns_learned == 1 + assert result.traces_processed == 3 + + fibers = await storage.find_fibers(metadata_key="_reasoning_pattern", limit=100) + assert len(fibers) == 1 + md = fibers[0].metadata + assert md["_reasoning_pattern"] is True + assert md["_source_model"] == "claude-fable-5" + assert md["_reasoning_category"] == "debugging" + assert md["_reasoning_frequency"] == 3 + assert md["_reasoning_confidence"] == 1.0 + assert md["_reasoning_signature"] + # CONCEPT neuron for the category + EFFECTIVE_FOR synapse exist. + cat_neuron = await storage.find_neurons(content_exact="reasoning_category:debugging", limit=1) + assert cat_neuron + + +async def test_distill_is_idempotent(tmp_path: Path, no_embedder: None) -> None: + storage = InMemoryStorage() + storage.set_brain(BRAIN) + await _seed(storage, "claude-fable-5", _DEBUG_TRACES) + first = await distill_reasoning_patterns(storage, BRAIN, _ucfg(tmp_path)) + assert first.patterns_learned == 1 + # Second pass: traces already processed → nothing new. + second = await distill_reasoning_patterns(storage, BRAIN, _ucfg(tmp_path)) + assert second.patterns_learned == 0 + assert second.traces_processed == 0 + fibers = await storage.find_fibers(metadata_key="_reasoning_pattern", limit=100) + assert len(fibers) == 1 + + +async def test_distill_marks_traces_processed(tmp_path: Path, no_embedder: None) -> None: + storage = InMemoryStorage() + storage.set_brain(BRAIN) + await _seed(storage, "claude-fable-5", _DEBUG_TRACES) + await distill_reasoning_patterns(storage, BRAIN, _ucfg(tmp_path)) + remaining = await storage.get_unprocessed_reasoning_traces(BRAIN, model="claude-fable-5") + assert remaining == [] + + +async def test_below_min_cluster_support_no_pattern(tmp_path: Path, no_embedder: None) -> None: + storage = InMemoryStorage() + storage.set_brain(BRAIN) + await _seed(storage, "claude-fable-5", _DEBUG_TRACES[:1]) # only 1 trace, support=2 + result = await distill_reasoning_patterns(storage, BRAIN, _ucfg(tmp_path)) + assert result.patterns_learned == 0 + # The trace is still marked processed (considered, just not clustered). + assert result.traces_processed == 1 + + +async def test_other_category_not_distilled(tmp_path: Path, no_embedder: None) -> None: + storage = InMemoryStorage() + storage.set_brain(BRAIN) + await _seed(storage, "claude-fable-5", ["qwerty plugh foobar", "zzz nothing", "blah blah"]) + result = await distill_reasoning_patterns(storage, BRAIN, _ucfg(tmp_path)) + assert result.patterns_learned == 0 + + +async def test_reasoning_coverage_math(tmp_path: Path, no_embedder: None) -> None: + storage = InMemoryStorage() + storage.set_brain(BRAIN) + await _seed(storage, "claude-fable-5", _DEBUG_TRACES) + cfg = _ucfg(tmp_path) + await distill_reasoning_patterns(storage, BRAIN, cfg) + + cov = await reasoning_coverage(storage, "claude-fable-5", cfg) + assert cov["by_category"]["debugging"] == 1 + assert cov["covered"]["debugging"] is True + assert cov["coverage_percent"] == round(1 / len(cfg.reasoning_training.categories) * 100, 1) + # A different model has no coverage. + cov_other = await reasoning_coverage(storage, "claude-sonnet-5", cfg) + assert cov_other["coverage_percent"] == 0.0 + + +async def test_coverage_respects_min_patterns_bar(tmp_path: Path, no_embedder: None) -> None: + storage = InMemoryStorage() + storage.set_brain(BRAIN) + await _seed(storage, "claude-fable-5", _DEBUG_TRACES) + await distill_reasoning_patterns(storage, BRAIN, _ucfg(tmp_path)) + # Require 5 patterns/category — the single pattern is below the bar. + strict = _ucfg(tmp_path, min_patterns_per_category=5) + cov = await reasoning_coverage(storage, "claude-fable-5", strict) + assert cov["by_category"]["debugging"] == 1 # still counted + assert cov["covered"]["debugging"] is False # but below the coverage bar + assert cov["coverage_percent"] == 0.0 + + +class _FakeEmbedder: + """Deterministic one-hot-by-category embedder for the embedding code path.""" + + async def embed(self, text: str) -> list[float]: + return _fake_vec(text) + + async def embed_batch(self, texts: list[str]) -> list[list[float]]: + return [_fake_vec(t) for t in texts] + + +def _fake_vec(text: str) -> list[float]: + cats = ReasoningTrainingConfig().categories + cat = _classify_by_keywords(text, cats) + vec = [0.0] * (len(cats) + 1) + vec[cats.index(cat) if cat in cats else len(cats)] = 1.0 + return vec + + +async def test_distill_uses_embedding_path(tmp_path: Path) -> None: + storage = InMemoryStorage() + storage.set_brain(BRAIN) + await _seed(storage, "claude-fable-5", _DEBUG_TRACES) + # Explicit embedder -> exercises _seed_centroids / _embed_texts / + # _classify_by_vector / cosine clustering / medoid (no live provider needed). + result = await distill_reasoning_patterns( + storage, BRAIN, _ucfg(tmp_path), embedder=_FakeEmbedder() + ) + assert result.patterns_learned == 1 + fibers = await storage.find_fibers(metadata_key="_reasoning_pattern", limit=100) + assert fibers[0].metadata["_reasoning_category"] == "debugging" + + +def test_backtrack_and_hypothesize_moves_match() -> None: + moves = segment_moves("Actually, I was wrong. My hypothesis is that the bug is here.") + assert "backtrack" in moves + assert "hypothesize" in moves + + +class _RaisingEmbedder: + async def embed(self, text: str) -> list[float]: + raise RuntimeError("provider down") + + async def embed_batch(self, texts: list[str]) -> list[list[float]]: + raise RuntimeError("provider down") + + +async def test_fail_soft_when_embedder_raises(tmp_path: Path) -> None: + storage = InMemoryStorage() + storage.set_brain(BRAIN) + await _seed(storage, "claude-fable-5", _DEBUG_TRACES) + # An embedder that raises must not break distillation — it falls back to + # keyword classification + move-set clustering (D5). + result = await distill_reasoning_patterns( + storage, BRAIN, _ucfg(tmp_path), embedder=_RaisingEmbedder() + ) + assert result.patterns_learned == 1 + + +async def test_cap_marks_only_consumed_traces(tmp_path: Path, no_embedder: None) -> None: + storage = InMemoryStorage() + storage.set_brain(BRAIN) + contents = ( + ["I need to fix the error bug. Let me check the traceback."] * 2 + + ["I need to refactor and clean up. Let me check the code."] * 2 + + ["I need to design the module architecture boundary. Let me check it."] * 2 + + ["I need to read the documentation docs. Let me check them."] * 2 + ) + await _seed(storage, "claude-fable-5", contents) + result = await distill_reasoning_patterns( + storage, BRAIN, _ucfg(tmp_path, max_patterns_per_run=2) + ) + assert result.patterns_learned == 2 + # Only the 2 fully-clustered categories are marked processed; the 2 categories + # the cap never reached keep their traces for the next run (no silent loss). + assert result.traces_processed == 4 + remaining = await storage.get_unprocessed_reasoning_traces( + BRAIN, model="claude-fable-5", limit=100 + ) + assert len(remaining) == 4 + + +async def test_distinct_clusters_same_top_moves_not_dropped( + tmp_path: Path, no_embedder: None +) -> None: + storage = InMemoryStorage() + storage.set_brain(BRAIN) + a = "I need to fix the bug. Let me check the error. I verify it." + b = ( + "I need to fix the bug. Let me check the error. I verify. " + "What if the edge case? Actually, wait. Instead of option 1." + ) + await _seed(storage, "claude-fable-5", [a, a, b, b]) + result = await distill_reasoning_patterns(storage, BRAIN, _ucfg(tmp_path)) + # Two distinct clusters (Jaccard 0.5) sharing the same top-3-moves title must + # NOT collide on signature — both materialize. + assert result.patterns_learned == 2 + fibers = await storage.find_fibers(metadata_key="_reasoning_pattern", limit=100) + assert len(fibers) == 2 + assert len({f.metadata["_reasoning_signature"] for f in fibers}) == 2 + + +# ── Consolidation LEARN_REASONING handler ──────────────────────────────────── + + +async def test_consolidation_learn_reasoning_runs(tmp_path: Path, monkeypatch) -> None: + from surreal_memory.engine.consolidation import ConsolidationEngine, ConsolidationStrategy + + fake = _ucfg(tmp_path, mining_enabled=True) + monkeypatch.setattr(UnifiedConfig, "load", staticmethod(lambda config_path=None: fake)) + + async def _fake_distill(storage, brain_id, config, **k): + return DistillResult(patterns_learned=4, traces_processed=12) + + monkeypatch.setattr( + "surreal_memory.engine.reasoning_distiller.distill_reasoning_patterns", _fake_distill + ) + storage = InMemoryStorage() + storage.set_brain(BRAIN) + report = await ConsolidationEngine(storage).run([ConsolidationStrategy.LEARN_REASONING]) + assert report.reasoning_patterns_learned == 4 + + +async def test_consolidation_learn_reasoning_guard_disabled(tmp_path: Path, monkeypatch) -> None: + from surreal_memory.engine.consolidation import ConsolidationEngine, ConsolidationStrategy + + fake = _ucfg(tmp_path, mining_enabled=False) + monkeypatch.setattr(UnifiedConfig, "load", staticmethod(lambda config_path=None: fake)) + calls = {"n": 0} + + async def _spy(*a, **k): # pragma: no cover - must NOT run + calls["n"] += 1 + return DistillResult(patterns_learned=9) + + monkeypatch.setattr( + "surreal_memory.engine.reasoning_distiller.distill_reasoning_patterns", _spy + ) + storage = InMemoryStorage() + storage.set_brain(BRAIN) + report = await ConsolidationEngine(storage).run([ConsolidationStrategy.LEARN_REASONING]) + assert report.reasoning_patterns_learned == 0 + assert calls["n"] == 0 From 48229687a44a13ff5cf83788c8b7eb0275762f7d Mon Sep 17 00:00:00 2001 From: Toni Nowak Date: Fri, 17 Jul 2026 12:43:53 +0200 Subject: [PATCH 05/12] feat: reasoning_injection + session_start (U5) engine/reasoning_injection.py: resolve_active_model fallback chain (payload -> transcript tail -> env -> ~/.claude/settings.json -> default, alias-expanded, date-normalized) and build_injection_context (injection_map glob/default match, source-model pattern fibers ranked by confidence*frequency, <=2/category + injection_max_patterns + char budget, markdown block) plus session-scoped idempotency markers. hooks/session_start.py: append the reasoning block after the memories block, opt-in via reasoning_training.injection_enabled, exception-isolated per block. unified_config._get_surrealdb_storage: reuse the cached instance only when its _conn is live, else reinitialize (mirrors the SQLite cache) so the memories block closing shared storage no longer leaves the reasoning block a dead handle. Security: transcript_path (untrusted hook stdin) is allowlisted to ~/.claude via resolve()+is_relative_to; stat/read faults degrade to the fallback chain. Truncation of the bounded pattern-fiber fetch is warned, not silent. --- .../engine/reasoning_injection.py | 277 +++++++++++++++ src/surreal_memory/hooks/session_start.py | 80 +++-- src/surreal_memory/unified_config.py | 6 +- tests/unit/test_hook_session_start.py | 82 +++++ tests/unit/test_reasoning_injection.py | 316 ++++++++++++++++++ tests/unit/test_surrealdb_brain_lookup.py | 51 +++ 6 files changed, 788 insertions(+), 24 deletions(-) create mode 100644 src/surreal_memory/engine/reasoning_injection.py create mode 100644 tests/unit/test_reasoning_injection.py diff --git a/src/surreal_memory/engine/reasoning_injection.py b/src/surreal_memory/engine/reasoning_injection.py new file mode 100644 index 00000000..1b2845ab --- /dev/null +++ b/src/surreal_memory/engine/reasoning_injection.py @@ -0,0 +1,277 @@ +"""Reasoning-strategy injection: pick the target model, build the prompt block. + +Pure, testable logic used by the SessionStart hook (and, from run 007 Faza 5b, +the UserPromptSubmit hook): + +- ``resolve_active_model(hook_input)`` — SessionStart payloads carry no ``model`` + field, so resolve it via a fallback chain (payload → transcript tail → + env → ~/.claude/settings.json → "default"). +- ``build_injection_context(storage, model, config)`` — map the active model to a + source model via ``injection_map`` (glob, first-match, "default" fallback), + pull that source's ReasoningBank pattern fibers, and render a compact markdown + block ("## Reasoning strategies (learned from )"). +- session-scoped idempotency markers so SessionStart + UserPromptSubmit inject at + most once per session. +""" + +from __future__ import annotations + +import json +import logging +import os +import re +import time +from fnmatch import fnmatch +from pathlib import Path +from typing import TYPE_CHECKING, Any + +from surreal_memory.engine.reasoning_miner import normalize_model + +if TYPE_CHECKING: + from surreal_memory.storage.base import NeuralStorage + from surreal_memory.unified_config import UnifiedConfig + +logger = logging.getLogger(__name__) + +_SYNTHETIC_MODEL = "" +_MAX_PER_CATEGORY = 2 +_MARKER_MAX_AGE_S = 7 * 86400 # prune injection markers older than 7 days +_TRANSCRIPT_TAIL_LINES = 300 +# Ceiling for the pattern-fiber fetch. Patterns are idempotent by +# _reasoning_signature (reasoning_distiller._materialize_pattern), so the +# population is bounded by distinct patterns and stays well under this; matches +# the distiller's own fetch limit. If it is ever hit, we warn rather than let a +# source model's patterns silently fall outside the window (the post-LIMIT +# metadata-filter failure mode documented in storage.find_fibers). +_PATTERN_FETCH_LIMIT = 5000 + +# Claude Code short model aliases -> canonical ids used across the reasoning +# pipeline. Full ids pass through ``normalize_model`` unchanged. +_MODEL_ALIASES: dict[str, str] = { + "sonnet": "claude-sonnet-5", + "opus": "claude-opus-4-8", + "opusplan": "claude-opus-4-8", + "haiku": "claude-haiku-4-5", + "fable": "claude-fable-5", +} + + +# ── Model resolution ───────────────────────────────────────────────────────── + + +def _model_from_transcript_tail(transcript_path: str) -> str: + """Return the last assistant turn's model from a JSONL transcript tail. + + ``transcript_path`` comes from untrusted hook stdin, so only transcripts + under ~/.claude are read (mirrors the pre_compact / stop path-allowlist + guard). Any stat/read error degrades to "" so the fallback chain continues. + """ + if not transcript_path: + return "" + try: + resolved = Path(transcript_path).resolve() + if not resolved.is_relative_to((Path.home() / ".claude").resolve()): + return "" + if not resolved.is_file(): + return "" + with resolved.open(encoding="utf-8", errors="replace") as f: + lines = f.readlines() + except OSError: + return "" + for raw in reversed(lines[-_TRANSCRIPT_TAIL_LINES:]): + raw = raw.strip() + if not raw: + continue + try: + entry = json.loads(raw) + except json.JSONDecodeError: + continue + if not isinstance(entry, dict) or entry.get("type") != "assistant": + continue + message = entry.get("message") + if not isinstance(message, dict): + continue + model = str(message.get("model") or "") + if model and model != _SYNTHETIC_MODEL: + return normalize_model(model) + return "" + + +def _model_from_settings() -> str: + """Return the model from ~/.claude/settings.json (alias-expanded).""" + try: + path = Path.home() / ".claude" / "settings.json" + if not path.exists(): + return "" + data = json.loads(path.read_text(encoding="utf-8") or "{}") + except (OSError, json.JSONDecodeError): + return "" + model = data.get("model") if isinstance(data, dict) else None + if not isinstance(model, str) or not model.strip(): + return "" + model = model.strip() + return _MODEL_ALIASES.get(model.lower(), normalize_model(model)) + + +def resolve_active_model(hook_input: dict[str, Any]) -> str: + """Resolve the active model for injection via a fallback chain. + + Order: (1) ``hook_input["model"]`` (future-proof), (2) last assistant + ``message.model`` from the transcript tail, (3) env + ``SMEM_REASONING_TARGET_MODEL`` / ``ANTHROPIC_MODEL``, (4) ~/.claude/ + settings.json ``model`` (alias-expanded), (5) ``"default"``. Results are + date-suffix-normalized so they agree with mining/distillation model names. + """ + payload_model = hook_input.get("model") + if isinstance(payload_model, str) and payload_model.strip(): + return _MODEL_ALIASES.get(payload_model.strip().lower(), normalize_model(payload_model)) + + from_transcript = _model_from_transcript_tail(str(hook_input.get("transcript_path") or "")) + if from_transcript: + return from_transcript + + for env_var in ("SMEM_REASONING_TARGET_MODEL", "ANTHROPIC_MODEL"): + value = os.environ.get(env_var) + if value and value.strip(): + return _MODEL_ALIASES.get(value.strip().lower(), normalize_model(value)) + + from_settings = _model_from_settings() + if from_settings: + return from_settings + + return "default" + + +# ── Injection context ──────────────────────────────────────────────────────── + + +def _resolve_source_model(model: str, injection_map: tuple[tuple[str, str], ...]) -> str | None: + """Map the active model to a source model via injection_map (glob first-match, + then the literal ``default`` key).""" + for target, source in injection_map: + if target != "default" and fnmatch(model, target): + return source + for target, source in injection_map: + if target == "default": + return source + return None + + +async def build_injection_context( + storage: NeuralStorage, + model: str, + config: UnifiedConfig, +) -> str: + """Render the reasoning-strategies markdown block for *model*, or "". + + Empty when injection is disabled, no injection_map entry matches, or the + mapped source model has no pattern fibers. ``storage`` must be on the target + brain (find_fibers uses the current brain). + """ + rt = config.reasoning_training + if not rt.injection_enabled: + return "" + source = _resolve_source_model(model, rt.injection_map) + if not source: + return "" + + fibers = await storage.find_fibers( + metadata_key="_reasoning_pattern", limit=_PATTERN_FETCH_LIMIT + ) + if len(fibers) >= _PATTERN_FETCH_LIMIT: + # Truncation would silently drop a model's patterns; surface it instead. + logger.warning( + "reasoning pattern-fiber fetch hit the %d-row ceiling; some '%s' " + "patterns may be missing from injection", + _PATTERN_FETCH_LIMIT, + source, + ) + candidates = [f for f in fibers if f.metadata.get("_source_model") == source] + if not candidates: + return "" + + def _rank(f: Any) -> float: + md = f.metadata + return float(md.get("_reasoning_confidence", 0.0)) * float( + md.get("_reasoning_frequency", 0.0) + ) + + candidates.sort(key=_rank, reverse=True) + + per_category: dict[str, int] = {} + chosen: list[Any] = [] + for f in candidates: + category = str(f.metadata.get("_reasoning_category", "")) + if per_category.get(category, 0) >= _MAX_PER_CATEGORY: + continue + per_category[category] = per_category.get(category, 0) + 1 + chosen.append(f) + if len(chosen) >= rt.injection_max_patterns: + break + if not chosen: + return "" + + header = f"## Reasoning strategies (learned from {source})" + parts = [header, ""] + total = len(header) + 1 + for i, f in enumerate(chosen, start=1): + md = f.metadata + title = str(md.get("_reasoning_title", "")).strip() + body = str(md.get("_reasoning_strategy") or md.get("_reasoning_description", "")).strip() + body = " ".join(body.split()) # collapse whitespace/newlines to one line + entry = f"{i}. **{title}** — {body}" if body else f"{i}. **{title}**" + # Always include the first entry; later ones respect the char budget. + if i > 1 and total + len(entry) + 1 > rt.injection_max_chars: + break + parts.append(entry) + total += len(entry) + 1 + return "\n".join(parts) + + +# ── Session idempotency markers ────────────────────────────────────────────── + + +def _marker_dir() -> Path: + # Honor SURREAL_MEMORY_DIR (matches hooks/post_tool_use._get_data_dir) so + # markers sit alongside the rest of the data dir and tests can redirect them. + custom = os.environ.get("SURREAL_MEMORY_DIR", "") + base = Path(custom) if custom else (Path.home() / ".surrealmemory") + return base / "reasoning_injected" + + +def _safe_session(session_id: str) -> str: + return re.sub(r"[^A-Za-z0-9_-]", "_", session_id)[:128] + + +def already_injected(session_id: str) -> bool: + """True if this session was already injected (marker present).""" + if not session_id: + return False + return (_marker_dir() / _safe_session(session_id)).exists() + + +def mark_injected(session_id: str) -> None: + """Record that this session has been injected; prune stale markers.""" + if not session_id: + return + directory = _marker_dir() + try: + directory.mkdir(parents=True, exist_ok=True) + (directory / _safe_session(session_id)).write_text("", encoding="utf-8") + except OSError: + logger.debug("reasoning injection marker write failed", exc_info=True) + return + _cleanup_markers(directory) + + +def _cleanup_markers(directory: Path) -> None: + cutoff = time.time() - _MARKER_MAX_AGE_S + try: + for marker in directory.iterdir(): + try: + if marker.is_file() and marker.stat().st_mtime < cutoff: + marker.unlink(missing_ok=True) + except OSError: + continue + except OSError: + logger.debug("reasoning injection marker cleanup failed", exc_info=True) diff --git a/src/surreal_memory/hooks/session_start.py b/src/surreal_memory/hooks/session_start.py index ba34a57d..62a5807e 100644 --- a/src/surreal_memory/hooks/session_start.py +++ b/src/surreal_memory/hooks/session_start.py @@ -81,6 +81,42 @@ def read_hook_input() -> dict[str, Any]: return {} +async def get_reasoning_injection(hook_input: dict[str, Any]) -> str: + """Build the reasoning-strategies context block for this session (or ""). + + Opt-in via reasoning_training.injection_enabled. Injects at most once per + session via a marker shared with the UserPromptSubmit hook. + """ + from surreal_memory.engine.reasoning_injection import ( + already_injected, + build_injection_context, + mark_injected, + resolve_active_model, + ) + from surreal_memory.unified_config import get_config, get_shared_storage + + config = get_config() + if not config.reasoning_training.injection_enabled: + return "" + session_id = str(hook_input.get("session_id") or "") + if already_injected(session_id): + return "" + + model = resolve_active_model(hook_input) + storage = await get_shared_storage(config.current_brain) + try: + block = await build_injection_context(storage, model, config) + finally: + try: + await storage.close() + except Exception: + logger.debug("storage.close() failed (non-fatal)", exc_info=True) + + if block: + mark_injected(session_id) + return block + + def main() -> None: """Entry point for SessionStart hook.""" logging.basicConfig(level=logging.WARNING, stream=sys.stderr) @@ -91,34 +127,32 @@ def main() -> None: from surreal_memory.hooks.project_context import derive_project_name project_name = derive_project_name(hook_input) - if not project_name: - print("[Surreal-Memory] No project resolved at session start", file=sys.stderr) # noqa: T201 - sys.exit(0) + sections: list[str] = [] + # Recent-memories block (scoped to the resolved project). + if project_name: + try: + memories = asyncio.run(get_recent_memories(project_name)) + except Exception: + memories = "" + print("[Surreal-Memory] Session start context load failed", file=sys.stderr) # noqa: T201 + if memories: + sections.append(f"## Recent Memories — project: {project_name}\n\n{memories}") + + # Reasoning-strategies block (opt-in; model-based, not project-scoped). try: - context_text = asyncio.run(get_recent_memories(project_name)) + reasoning = asyncio.run(get_reasoning_injection(hook_input)) except Exception: - print("[Surreal-Memory] Session start context load failed", file=sys.stderr) # noqa: T201 - sys.exit(0) # Never block Claude Code - - if not context_text: - print( # noqa: T201 - f"[Surreal-Memory] No memories to inject for project '{project_name}'", - file=sys.stderr, - ) - sys.exit(0) + reasoning = "" + print("[Surreal-Memory] Session start reasoning injection failed", file=sys.stderr) # noqa: T201 + if reasoning: + sections.append(reasoning) - count = context_text.count("\n") + 1 - print( # noqa: T201 - f"[Surreal-Memory] Injecting {count} memories for project '{project_name}'", - file=sys.stderr, - ) + if not sections: + print("[Surreal-Memory] No session-start context to inject", file=sys.stderr) # noqa: T201 + sys.exit(0) - response = { - "type": "context", - "content": f"## Recent Memories — project: {project_name}\n\n{context_text}", - } - print(json.dumps(response)) # noqa: T201 + print(json.dumps({"type": "context", "content": "\n\n".join(sections)})) # noqa: T201 if __name__ == "__main__": diff --git a/src/surreal_memory/unified_config.py b/src/surreal_memory/unified_config.py index 1e5008e7..64cbc438 100755 --- a/src/surreal_memory/unified_config.py +++ b/src/surreal_memory/unified_config.py @@ -2498,7 +2498,11 @@ async def _get_surrealdb_storage(config: UnifiedConfig, name: str) -> NeuralStor from surreal_memory.storage.surrealdb import SurrealDBStorage from surreal_memory.storage.surrealdb.connection import SurrealSettings - if _surrealdb_storage is not None: + # Reuse the cached instance only if its connection is still open. A prior + # caller may have close()d it (which nulls _conn but leaves this module-level + # reference set); returning it as-is would raise "not initialized" on first + # use. Mirror the SQLite cache's liveness check and reinitialize otherwise. + if _surrealdb_storage is not None and getattr(_surrealdb_storage, "_conn", None) is not None: _surrealdb_storage.set_brain(name) return _surrealdb_storage diff --git a/tests/unit/test_hook_session_start.py b/tests/unit/test_hook_session_start.py index 2b8521e0..655ecb09 100644 --- a/tests/unit/test_hook_session_start.py +++ b/tests/unit/test_hook_session_start.py @@ -143,3 +143,85 @@ def test_main_exits_silently_when_no_memories(capsys: pytest.CaptureFixture[str] assert exc_info.value.code == 0 captured = capsys.readouterr() assert captured.out.strip() == "" + + +_REASONING_BLOCK = "## Reasoning strategies (learned from claude-fable-5)\n\n1. **plan**" + + +def test_main_combines_memories_and_reasoning_blocks( + capsys: pytest.CaptureFixture[str], +) -> None: + """Both blocks present → main() emits one context with the memories block + followed by the reasoning block.""" + with ( + patch("sys.stdin", io.StringIO("{}")), + patch("surreal_memory.hooks.project_context.derive_project_name", return_value="proj"), + patch( + "surreal_memory.hooks.session_start.get_recent_memories", + new=AsyncMock(return_value="- mem bullet"), + ), + patch( + "surreal_memory.hooks.session_start.get_reasoning_injection", + new=AsyncMock(return_value=_REASONING_BLOCK), + ), + ): + main() + + response = json.loads(capsys.readouterr().out.strip()) + assert response["type"] == "context" + content = response["content"] + assert "## Recent Memories — project: proj" in content + assert "- mem bullet" in content + assert "## Reasoning strategies (learned from claude-fable-5)" in content + # Memories block comes before the reasoning block. + assert content.index("Recent Memories") < content.index("Reasoning strategies") + + +def test_main_reasoning_failure_isolated_from_memories( + capsys: pytest.CaptureFixture[str], +) -> None: + """A reasoning-block exception must not drop the memories block.""" + with ( + patch("sys.stdin", io.StringIO("{}")), + patch("surreal_memory.hooks.project_context.derive_project_name", return_value="proj"), + patch( + "surreal_memory.hooks.session_start.get_recent_memories", + new=AsyncMock(return_value="- mem bullet"), + ), + patch( + "surreal_memory.hooks.session_start.get_reasoning_injection", + new=AsyncMock(side_effect=RuntimeError("boom")), + ), + ): + main() + + captured = capsys.readouterr() + response = json.loads(captured.out.strip()) + assert "- mem bullet" in response["content"] + assert "Reasoning strategies" not in response["content"] + assert "reasoning injection failed" in captured.err.lower() + + +def test_main_memories_failure_isolated_from_reasoning( + capsys: pytest.CaptureFixture[str], +) -> None: + """A memories-block exception must not drop the reasoning block.""" + with ( + patch("sys.stdin", io.StringIO("{}")), + patch("surreal_memory.hooks.project_context.derive_project_name", return_value="proj"), + patch( + "surreal_memory.hooks.session_start.get_recent_memories", + new=AsyncMock(side_effect=RuntimeError("boom")), + ), + patch( + "surreal_memory.hooks.session_start.get_reasoning_injection", + new=AsyncMock(return_value=_REASONING_BLOCK), + ), + ): + main() + + captured = capsys.readouterr() + response = json.loads(captured.out.strip()) + assert "Reasoning strategies" in response["content"] + assert "Recent Memories" not in response["content"] + assert "context load failed" in captured.err.lower() diff --git a/tests/unit/test_reasoning_injection.py b/tests/unit/test_reasoning_injection.py new file mode 100644 index 00000000..1c2fd797 --- /dev/null +++ b/tests/unit/test_reasoning_injection.py @@ -0,0 +1,316 @@ +"""Tests for engine/reasoning_injection.py — model resolution + prompt block. + +Uses synthetic transcripts / settings.json (HOME redirected to tmp) and +InMemoryStorage seeded with pattern fibers. Covers the resolve_active_model +fallback chain, injection_map glob/default matching, per-category + max-patterns ++ char-budget selection, markdown format, and the session idempotency markers. +""" + +from __future__ import annotations + +import json +import logging +from pathlib import Path + +import pytest + +from surreal_memory.core.fiber import Fiber +from surreal_memory.core.neuron import Neuron, NeuronType +from surreal_memory.engine.reasoning_injection import ( + already_injected, + build_injection_context, + mark_injected, + resolve_active_model, +) +from surreal_memory.storage.memory_store import InMemoryStorage +from surreal_memory.unified_config import ReasoningTrainingConfig, UnifiedConfig + +BRAIN = "b1" +_ENV_MODEL_VARS = ("SMEM_REASONING_TARGET_MODEL", "ANTHROPIC_MODEL") + + +@pytest.fixture +def clean_env(monkeypatch: pytest.MonkeyPatch, tmp_path: Path) -> Path: + """Redirect HOME to tmp (no settings.json) and clear model env vars.""" + monkeypatch.setenv("HOME", str(tmp_path)) + for var in _ENV_MODEL_VARS: + monkeypatch.delenv(var, raising=False) + return tmp_path + + +def _ucfg(tmp_path: Path, **rt_kw: object) -> UnifiedConfig: + base: dict[str, object] = { + "injection_enabled": True, + "injection_max_patterns": 5, + "injection_max_chars": 4000, + } + base.update(rt_kw) + return UnifiedConfig( + data_dir=tmp_path / ".surrealmemory", + current_brain="default", + reasoning_training=ReasoningTrainingConfig(**base), # type: ignore[arg-type] + ) + + +async def _add_pattern( + storage: InMemoryStorage, + model: str, + category: str, + title: str, + strategy: str, + confidence: float, + frequency: int, +) -> None: + neuron = Neuron.create(type=NeuronType.CONCEPT, content=title) + await storage.add_neuron(neuron) + fiber = Fiber.create( + neuron_ids={neuron.id}, + synapse_ids=set(), + anchor_neuron_id=neuron.id, + summary=title, + tags=set(), + metadata={ + "_reasoning_pattern": True, + "_source_model": model, + "_reasoning_category": category, + "_reasoning_title": title, + "_reasoning_strategy": strategy, + "_reasoning_confidence": confidence, + "_reasoning_frequency": frequency, + "_reasoning_signature": title, + }, + ) + await storage.add_fiber(fiber) + + +# ── resolve_active_model chain ─────────────────────────────────────────────── + + +def test_resolve_from_payload_model(clean_env: Path) -> None: + assert resolve_active_model({"model": "claude-sonnet-5-20250101"}) == "claude-sonnet-5" + + +def test_resolve_from_transcript_tail(clean_env: Path) -> None: + # Transcripts are only read from under ~/.claude (spoof guard). HOME is + # redirected to clean_env, so write the transcript there. + tp = clean_env / ".claude" / "projects" / "x" / "t.jsonl" + tp.parent.mkdir(parents=True, exist_ok=True) + tp.write_text( + json.dumps({"type": "user", "message": {"content": "hi"}}) + + "\n" + + json.dumps({"type": "assistant", "message": {"model": "claude-haiku-4-5-20251001"}}) + + "\n", + encoding="utf-8", + ) + assert resolve_active_model({"transcript_path": str(tp)}) == "claude-haiku-4-5" + + +def test_resolve_transcript_outside_claude_rejected(clean_env: Path) -> None: + # A transcript_path outside ~/.claude is untrusted (hook stdin is attacker- + # controllable) and ignored; resolution falls through to "default". + outside = clean_env / "outside.jsonl" + outside.write_text( + json.dumps({"type": "assistant", "message": {"model": "claude-opus-4-8"}}) + "\n", + encoding="utf-8", + ) + assert resolve_active_model({"transcript_path": str(outside)}) == "default" + + +def test_resolve_transcript_symlink_escape_rejected(clean_env: Path) -> None: + # A symlink UNDER ~/.claude that points outside it must also be rejected: + # resolve() canonicalizes through the symlink before the containment check, + # so this can't be bypassed with a symlink (only a literal path prefix check + # would be fooled). Guards against a future refactor to os.path.abspath. + outside = clean_env / "real.jsonl" + outside.write_text( + json.dumps({"type": "assistant", "message": {"model": "claude-opus-4-8"}}) + "\n", + encoding="utf-8", + ) + claude_dir = clean_env / ".claude" + claude_dir.mkdir(parents=True, exist_ok=True) + link = claude_dir / "sneaky.jsonl" + link.symlink_to(outside) + assert resolve_active_model({"transcript_path": str(link)}) == "default" + + +def test_resolve_from_env(clean_env: Path, monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.setenv("SMEM_REASONING_TARGET_MODEL", "claude-opus-4-8") + assert resolve_active_model({}) == "claude-opus-4-8" + + +def test_resolve_from_env_alias(clean_env: Path, monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.setenv("ANTHROPIC_MODEL", "sonnet") + assert resolve_active_model({}) == "claude-sonnet-5" + + +def test_resolve_from_settings_alias(clean_env: Path) -> None: + claude_dir = clean_env / ".claude" + claude_dir.mkdir(parents=True, exist_ok=True) + (claude_dir / "settings.json").write_text(json.dumps({"model": "opusplan"}), encoding="utf-8") + assert resolve_active_model({}) == "claude-opus-4-8" + + +def test_resolve_default_when_nothing_available(clean_env: Path) -> None: + assert resolve_active_model({}) == "default" + + +def test_resolve_precedence_payload_over_env( + clean_env: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + monkeypatch.setenv("SMEM_REASONING_TARGET_MODEL", "claude-haiku-4-5") + assert resolve_active_model({"model": "claude-opus-4-8"}) == "claude-opus-4-8" + + +# ── build_injection_context ────────────────────────────────────────────────── + + +async def test_build_block_renders_ranked(tmp_path: Path) -> None: + storage = InMemoryStorage() + storage.set_brain(BRAIN) + await _add_pattern( + storage, "claude-fable-5", "planning", "planning: plan", "plan -> steps", 0.8, 2 + ) + await _add_pattern( + storage, "claude-fable-5", "debugging", "debugging: verify", "verify -> check", 1.0, 3 + ) + cfg = _ucfg(tmp_path, injection_map=(("claude-opus-*", "claude-fable-5"),)) + + block = await build_injection_context(storage, "claude-opus-4-8", cfg) + assert block.startswith("## Reasoning strategies (learned from claude-fable-5)") + assert "debugging: verify" in block + assert "planning: plan" in block + # Higher confidence*frequency ranks first. + assert block.index("debugging: verify") < block.index("planning: plan") + + +async def test_injection_disabled_returns_empty(tmp_path: Path) -> None: + storage = InMemoryStorage() + storage.set_brain(BRAIN) + await _add_pattern(storage, "claude-fable-5", "debugging", "t", "s", 1.0, 3) + cfg = _ucfg( + tmp_path, injection_enabled=False, injection_map=(("claude-opus-*", "claude-fable-5"),) + ) + assert await build_injection_context(storage, "claude-opus-4-8", cfg) == "" + + +async def test_no_map_match_returns_empty(tmp_path: Path) -> None: + storage = InMemoryStorage() + storage.set_brain(BRAIN) + await _add_pattern(storage, "claude-fable-5", "debugging", "t", "s", 1.0, 3) + cfg = _ucfg(tmp_path, injection_map=(("claude-haiku-*", "claude-fable-5"),)) + assert await build_injection_context(storage, "claude-opus-4-8", cfg) == "" + + +async def test_injection_map_default_key(tmp_path: Path) -> None: + storage = InMemoryStorage() + storage.set_brain(BRAIN) + await _add_pattern(storage, "claude-fable-5", "debugging", "debugging: x", "s", 1.0, 3) + cfg = _ucfg( + tmp_path, + injection_map=(("claude-haiku-*", "nope"), ("default", "claude-fable-5")), + ) + block = await build_injection_context(storage, "claude-opus-4-8", cfg) + assert "learned from claude-fable-5" in block + + +async def test_source_without_patterns_returns_empty(tmp_path: Path) -> None: + storage = InMemoryStorage() + storage.set_brain(BRAIN) + await _add_pattern(storage, "claude-fable-5", "debugging", "t", "s", 1.0, 3) + cfg = _ucfg(tmp_path, injection_map=(("claude-opus-*", "claude-sonnet-5"),)) + assert await build_injection_context(storage, "claude-opus-4-8", cfg) == "" + + +async def test_max_two_per_category(tmp_path: Path) -> None: + storage = InMemoryStorage() + storage.set_brain(BRAIN) + for i in range(4): + await _add_pattern( + storage, "claude-fable-5", "debugging", f"debugging: {i}", "s", 1.0, 3 - i + ) + cfg = _ucfg(tmp_path, injection_map=(("claude-opus-*", "claude-fable-5"),)) + block = await build_injection_context(storage, "claude-opus-4-8", cfg) + # Only 2 of the 4 debugging patterns are injected. + assert block.count("debugging:") == 2 + + +async def test_injection_max_patterns_cap(tmp_path: Path) -> None: + storage = InMemoryStorage() + storage.set_brain(BRAIN) + await _add_pattern(storage, "claude-fable-5", "debugging", "debugging: a", "s", 1.0, 3) + await _add_pattern(storage, "claude-fable-5", "planning", "planning: b", "s", 0.9, 3) + cfg = _ucfg( + tmp_path, injection_max_patterns=1, injection_map=(("claude-opus-*", "claude-fable-5"),) + ) + block = await build_injection_context(storage, "claude-opus-4-8", cfg) + assert "1. **" in block + assert "2. **" not in block + + +async def test_char_budget_limits_entries(tmp_path: Path) -> None: + storage = InMemoryStorage() + storage.set_brain(BRAIN) + long_strategy = "x" * 500 + await _add_pattern( + storage, "claude-fable-5", "debugging", "debugging: a", long_strategy, 1.0, 3 + ) + await _add_pattern(storage, "claude-fable-5", "planning", "planning: b", long_strategy, 0.9, 3) + cfg = _ucfg( + tmp_path, injection_max_chars=120, injection_map=(("claude-opus-*", "claude-fable-5"),) + ) + block = await build_injection_context(storage, "claude-opus-4-8", cfg) + # First entry always included; the second exceeds the tiny budget. + assert "1. **" in block + assert "2. **" not in block + + +async def test_build_block_warns_on_fetch_ceiling( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch, caplog: pytest.LogCaptureFixture +) -> None: + # When the pattern-fiber fetch hits its ceiling, a model's patterns could be + # truncated out; that must be a visible warning, not a silent empty injection. + from surreal_memory.engine import reasoning_injection as ri + + monkeypatch.setattr(ri, "_PATTERN_FETCH_LIMIT", 2) + storage = InMemoryStorage() + storage.set_brain(BRAIN) + await _add_pattern(storage, "claude-fable-5", "debugging", "debugging: a", "s", 1.0, 3) + await _add_pattern(storage, "claude-fable-5", "planning", "planning: b", "s", 0.9, 2) + cfg = _ucfg(tmp_path, injection_map=(("claude-opus-*", "claude-fable-5"),)) + + with caplog.at_level(logging.WARNING, logger="surreal_memory.engine.reasoning_injection"): + block = await build_injection_context(storage, "claude-opus-4-8", cfg) + + assert "ceiling" in caplog.text + assert block # still renders from whatever was fetched + + +# ── session idempotency markers ────────────────────────────────────────────── + + +def test_marker_roundtrip(clean_env: Path) -> None: + assert already_injected("sess-1") is False + mark_injected("sess-1") + assert already_injected("sess-1") is True + # Empty session id is a no-op and never "already injected". + assert already_injected("") is False + mark_injected("") + + +def test_marker_sanitizes_session_id(clean_env: Path) -> None: + mark_injected("../evil/../id") + marker_root = clean_env / ".surrealmemory" / "reasoning_injected" + assert marker_root.is_dir() + # No traversal escape — every marker stays directly under the marker root. + assert all(p.parent == marker_root for p in marker_root.iterdir()) + assert already_injected("../evil/../id") is True + + +def test_marker_dir_honors_env_override(monkeypatch: pytest.MonkeyPatch, tmp_path: Path) -> None: + # SURREAL_MEMORY_DIR relocates the whole data dir; markers must follow it + # (matches hooks/post_tool_use._get_data_dir) rather than pinning ~/.surrealmemory. + data_dir = tmp_path / "custom-data" + monkeypatch.setenv("SURREAL_MEMORY_DIR", str(data_dir)) + mark_injected("sess-env") + assert (data_dir / "reasoning_injected" / "sess-env").exists() + assert already_injected("sess-env") is True diff --git a/tests/unit/test_surrealdb_brain_lookup.py b/tests/unit/test_surrealdb_brain_lookup.py index 6bffdf44..20092cba 100644 --- a/tests/unit/test_surrealdb_brain_lookup.py +++ b/tests/unit/test_surrealdb_brain_lookup.py @@ -154,3 +154,54 @@ async def test_surrealdb_bootstrap_creates_brain_with_deterministic_id( assert len(storage.saved) == 1 assert storage.saved[0].id == "my-brain.v2" # deterministic, not a UUID assert storage.brain_context == "my-brain.v2" + + +async def test_surrealdb_bootstrap_reinitializes_after_close(monkeypatch: Any) -> None: + # A previously close()d cached instance nulls _conn. The bootstrap must NOT + # return that dead handle (find_fibers would raise "not initialized") — it must + # reinitialize a fresh store. Regression for the session_start memories→reasoning + # double-open sequence: the memories block closes shared storage, so the reasoning + # block's get_shared_storage() must reconnect rather than reuse the closed handle. + class _ClosedStore: + _conn = None + + def set_brain(self, _name: str) -> None: # pragma: no cover - must not run + raise AssertionError("closed instance must not be reused") + + closed = _ClosedStore() + existing = Brain.create("my-brain.v2", brain_id="my-brain.v2") + fake = _make_fake_store(existing=None, found_by_name=existing) + monkeypatch.setattr(_surrealdb_pkg, "SurrealDBStorage", fake) + monkeypatch.setattr(unified_config, "_surrealdb_storage", closed) + + storage = await unified_config._get_surrealdb_storage(_fake_config(), "my-brain.v2") + + assert storage is not closed # reinitialized, not the dead handle + assert storage.brain_context == "my-brain.v2" + + +async def test_surrealdb_bootstrap_reuses_live_connection(monkeypatch: Any) -> None: + # A cached instance with a live _conn is returned as-is (no reinitialize); only + # set_brain runs to switch the brain context. Constructing SurrealDBStorage here + # would mean the liveness check wrongly fell through — so make that path fail loud. + class _LiveStore: + _conn = object() + + def __init__(self) -> None: + self.brain_context: str | None = None + + def set_brain(self, name: str) -> None: + self.brain_context = name + + live = _LiveStore() + + def _boom(**_kwargs: Any) -> Any: # pragma: no cover - must not run + raise AssertionError("must not reinitialize a cached store with a live _conn") + + monkeypatch.setattr(_surrealdb_pkg, "SurrealDBStorage", _boom) + monkeypatch.setattr(unified_config, "_surrealdb_storage", live) + + storage = await unified_config._get_surrealdb_storage(_fake_config(), "brain-3") + + assert storage is live # same instance, reused + assert live.brain_context == "brain-3" From 028ad9f5b1ac9e1ed488091526c645b6eb389e51 Mon Sep 17 00:00:00 2001 From: Toni Nowak Date: Fri, 17 Jul 2026 13:24:00 +0200 Subject: [PATCH 06/12] feat: user_prompt_submit reasoning hook + registrations (U6) hooks/user_prompt_submit.py: new UserPromptSubmit hook. From the 2nd prompt on, the active model is resolvable from the transcript tail, so this injects the model's learned reasoning strategies (opt-in via injection_enabled). Shares the once-per-session marker with SessionStart, so the two never double-inject; always exits 0 so it can never block the prompt. engine/reasoning_injection.get_reasoning_context: shared orchestrator (config guard -> already_injected short-circuit -> resolve_active_model -> open storage -> build_injection_context -> always close -> mark_injected), used by both hooks. session_start.get_reasoning_injection is now a thin delegate to it. Registration mirrors SessionStart: pyproject entry point smem-hook-user-prompt- submit + .claude-plugin/hooks/hooks.json UserPromptSubmit entry. (setup.py/ doctor.py wire only the original PreCompact/Stop/PostToolUse into settings.json; SessionStart-class hooks are plugin-manifest only, so U6 matches that.) Output contract fix (verified against the installed claude-code binary): context reaches the model ONLY via hookSpecificOutput.additionalContext. Both hooks now emit that envelope; the prior session_start {"type":"context","content"} form was JSON-parsed with no recognized field, so U5's SessionStart injection (and the memories block) were silently invisible to the model. Now fixed for both. --- .claude-plugin/hooks/hooks.json | 6 ++ pyproject.toml | 1 + .../engine/reasoning_injection.py | 37 ++++++++ src/surreal_memory/hooks/session_start.py | 50 ++++------- .../hooks/user_prompt_submit.py | 83 +++++++++++++++++ tests/unit/test_hook_session_start.py | 24 ++--- tests/unit/test_hook_user_prompt_submit.py | 88 +++++++++++++++++++ tests/unit/test_reasoning_injection.py | 52 +++++++++++ 8 files changed, 299 insertions(+), 42 deletions(-) create mode 100644 src/surreal_memory/hooks/user_prompt_submit.py create mode 100644 tests/unit/test_hook_user_prompt_submit.py diff --git a/.claude-plugin/hooks/hooks.json b/.claude-plugin/hooks/hooks.json index e75971d7..0fb4b5d4 100755 --- a/.claude-plugin/hooks/hooks.json +++ b/.claude-plugin/hooks/hooks.json @@ -23,6 +23,12 @@ "command": "smem-hook-session-start", "description": "Inject recent memories as context at the start of a new session", "timeout": 10 + }, + { + "event": "UserPromptSubmit", + "command": "smem-hook-user-prompt-submit", + "description": "Inject learned reasoning strategies for the active model (opt-in)", + "timeout": 10 } ] } diff --git a/pyproject.toml b/pyproject.toml index c59093c1..e32e8f53 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -146,6 +146,7 @@ smem-hook-pre-compact = "surreal_memory.hooks.pre_compact:main" smem-hook-stop = "surreal_memory.hooks.stop:main" smem-hook-post-tool-use = "surreal_memory.hooks.post_tool_use:main" smem-hook-session-start = "surreal_memory.hooks.session_start:main" +smem-hook-user-prompt-submit = "surreal_memory.hooks.user_prompt_submit:main" smem-hook-task-context = "surreal_memory.hooks.task_context:main" [tool.hatch.build.targets.sdist] diff --git a/src/surreal_memory/engine/reasoning_injection.py b/src/surreal_memory/engine/reasoning_injection.py index 1b2845ab..531c2d1b 100644 --- a/src/surreal_memory/engine/reasoning_injection.py +++ b/src/surreal_memory/engine/reasoning_injection.py @@ -228,6 +228,43 @@ def _rank(f: Any) -> float: return "\n".join(parts) +# ── Hook orchestration (shared by SessionStart + UserPromptSubmit) ──────────── + + +async def get_reasoning_context(hook_input: dict[str, Any]) -> str: + """Resolve the active model, build its reasoning block, mark the session. + + Shared by the SessionStart and UserPromptSubmit hooks. Opt-in via + reasoning_training.injection_enabled; injects at most once per session via the + marker (already_injected/mark_injected), so whichever hook fires first wins and + the other is a no-op. Storage is opened on the current brain and always closed. + Returns "" when injection is disabled, already done this session, or nothing + matched. + """ + from surreal_memory.unified_config import get_config, get_shared_storage + + config = get_config() + if not config.reasoning_training.injection_enabled: + return "" + session_id = str(hook_input.get("session_id") or "") + if already_injected(session_id): + return "" + + model = resolve_active_model(hook_input) + storage = await get_shared_storage(config.current_brain) + try: + block = await build_injection_context(storage, model, config) + finally: + try: + await storage.close() + except Exception: + logger.debug("reasoning storage.close() failed (non-fatal)", exc_info=True) + + if block: + mark_injected(session_id) + return block + + # ── Session idempotency markers ────────────────────────────────────────────── diff --git a/src/surreal_memory/hooks/session_start.py b/src/surreal_memory/hooks/session_start.py index 62a5807e..60102924 100644 --- a/src/surreal_memory/hooks/session_start.py +++ b/src/surreal_memory/hooks/session_start.py @@ -6,7 +6,7 @@ Usage as Claude Code hook: Reads JSON from stdin (may be empty for SessionStart). - Outputs {"type": "context", "content": ""} to stdout. + Outputs hookSpecificOutput JSON (hookEventName + additionalContext) to stdout. Status messages go to stderr. Usage standalone: @@ -84,37 +84,13 @@ def read_hook_input() -> dict[str, Any]: async def get_reasoning_injection(hook_input: dict[str, Any]) -> str: """Build the reasoning-strategies context block for this session (or ""). - Opt-in via reasoning_training.injection_enabled. Injects at most once per - session via a marker shared with the UserPromptSubmit hook. + Thin delegate to the shared engine orchestrator, which SessionStart and the + UserPromptSubmit hook both call. Opt-in via reasoning_training.injection_enabled; + injects at most once per session via a marker shared between the two hooks. """ - from surreal_memory.engine.reasoning_injection import ( - already_injected, - build_injection_context, - mark_injected, - resolve_active_model, - ) - from surreal_memory.unified_config import get_config, get_shared_storage + from surreal_memory.engine.reasoning_injection import get_reasoning_context - config = get_config() - if not config.reasoning_training.injection_enabled: - return "" - session_id = str(hook_input.get("session_id") or "") - if already_injected(session_id): - return "" - - model = resolve_active_model(hook_input) - storage = await get_shared_storage(config.current_brain) - try: - block = await build_injection_context(storage, model, config) - finally: - try: - await storage.close() - except Exception: - logger.debug("storage.close() failed (non-fatal)", exc_info=True) - - if block: - mark_injected(session_id) - return block + return await get_reasoning_context(hook_input) def main() -> None: @@ -152,7 +128,19 @@ def main() -> None: print("[Surreal-Memory] No session-start context to inject", file=sys.stderr) # noqa: T201 sys.exit(0) - print(json.dumps({"type": "context", "content": "\n\n".join(sections)})) # noqa: T201 + # Claude Code adds a SessionStart hook's context to the model ONLY via the + # hookSpecificOutput.additionalContext JSON field (a top-level {"type":"context"} + # is parsed as JSON but carries no recognized field, so nothing is injected). + print( # noqa: T201 + json.dumps( + { + "hookSpecificOutput": { + "hookEventName": "SessionStart", + "additionalContext": "\n\n".join(sections), + } + } + ) + ) if __name__ == "__main__": diff --git a/src/surreal_memory/hooks/user_prompt_submit.py b/src/surreal_memory/hooks/user_prompt_submit.py new file mode 100644 index 00000000..e2b83aa4 --- /dev/null +++ b/src/surreal_memory/hooks/user_prompt_submit.py @@ -0,0 +1,83 @@ +"""UserPromptSubmit hook: inject learned reasoning strategies into the prompt. + +SessionStart runs before any assistant turn exists, so the active model often +can't be resolved yet. From the second prompt on, the model is resolvable from +the transcript tail, so this hook injects model-appropriate reasoning strategies +that SessionStart may have missed. It shares the once-per-session marker with +SessionStart (whichever fires first wins), so the two never double-inject. + +Opt-in via reasoning_training.injection_enabled. + +Claude Code injects a UserPromptSubmit hook's context ONLY via the +``hookSpecificOutput.additionalContext`` JSON field on stdout (exit 0). Plain +stdout is echoed to the transcript but is NOT added to the model's context, so +the block is emitted inside that JSON envelope. + +Usage as Claude Code hook: + Reads JSON from stdin (session_id, transcript_path, cwd, prompt). + Emits the reasoning block as hookSpecificOutput JSON on stdout (or nothing). + Status messages go to stderr. Always exits 0 — never blocks the prompt. + +Usage standalone: + echo '{}' | python -m surreal_memory.hooks.user_prompt_submit +""" + +from __future__ import annotations + +import asyncio +import json +import logging +import sys +from typing import Any + +logger = logging.getLogger(__name__) + + +def read_hook_input() -> dict[str, Any]: + """Read Claude Code hook JSON from stdin (empty/malformed -> {}).""" + try: + raw = sys.stdin.read() + if not raw.strip(): + return {} + result: dict[str, Any] = json.loads(raw) + return result + except (json.JSONDecodeError, OSError): + return {} + + +def main() -> None: + """Entry point for the UserPromptSubmit hook.""" + logging.basicConfig(level=logging.WARNING, stream=sys.stderr) + + hook_input = read_hook_input() + + from surreal_memory.engine.reasoning_injection import get_reasoning_context + + try: + block = asyncio.run(get_reasoning_context(hook_input)) + except Exception: + block = "" + # Never block the prompt — degrade to no injection. + print("[Surreal-Memory] UserPromptSubmit reasoning injection failed", file=sys.stderr) # noqa: T201 + + if block: + # Context reaches the model ONLY through hookSpecificOutput.additionalContext + # (plain stdout is transcript-only for this event). + print( # noqa: T201 + json.dumps( + { + "hookSpecificOutput": { + "hookEventName": "UserPromptSubmit", + "additionalContext": block, + } + } + ) + ) + else: + print("[Surreal-Memory] No reasoning strategies to inject", file=sys.stderr) # noqa: T201 + + sys.exit(0) + + +if __name__ == "__main__": + main() diff --git a/tests/unit/test_hook_session_start.py b/tests/unit/test_hook_session_start.py index 655ecb09..301c0bac 100644 --- a/tests/unit/test_hook_session_start.py +++ b/tests/unit/test_hook_session_start.py @@ -122,15 +122,16 @@ def test_read_hook_input_valid_json() -> None: def test_main_outputs_context_json_on_success(capsys: pytest.CaptureFixture[str]) -> None: - """When memories exist, main() writes a JSON context response to stdout.""" + """When memories exist, main() writes a hookSpecificOutput JSON response.""" with patch("sys.stdin", io.StringIO("{}")): with patch("asyncio.run", return_value="- Fixed auth bug\n- Added retry logic"): main() # happy path — no sys.exit, just returns captured = capsys.readouterr() response = json.loads(captured.out.strip()) - assert response["type"] == "context" - assert "Fixed auth bug" in response["content"] + hook_out = response["hookSpecificOutput"] + assert hook_out["hookEventName"] == "SessionStart" + assert "Fixed auth bug" in hook_out["additionalContext"] def test_main_exits_silently_when_no_memories(capsys: pytest.CaptureFixture[str]) -> None: @@ -168,8 +169,9 @@ def test_main_combines_memories_and_reasoning_blocks( main() response = json.loads(capsys.readouterr().out.strip()) - assert response["type"] == "context" - content = response["content"] + hook_out = response["hookSpecificOutput"] + assert hook_out["hookEventName"] == "SessionStart" + content = hook_out["additionalContext"] assert "## Recent Memories — project: proj" in content assert "- mem bullet" in content assert "## Reasoning strategies (learned from claude-fable-5)" in content @@ -196,9 +198,9 @@ def test_main_reasoning_failure_isolated_from_memories( main() captured = capsys.readouterr() - response = json.loads(captured.out.strip()) - assert "- mem bullet" in response["content"] - assert "Reasoning strategies" not in response["content"] + content = json.loads(captured.out.strip())["hookSpecificOutput"]["additionalContext"] + assert "- mem bullet" in content + assert "Reasoning strategies" not in content assert "reasoning injection failed" in captured.err.lower() @@ -221,7 +223,7 @@ def test_main_memories_failure_isolated_from_reasoning( main() captured = capsys.readouterr() - response = json.loads(captured.out.strip()) - assert "Reasoning strategies" in response["content"] - assert "Recent Memories" not in response["content"] + content = json.loads(captured.out.strip())["hookSpecificOutput"]["additionalContext"] + assert "Reasoning strategies" in content + assert "Recent Memories" not in content assert "context load failed" in captured.err.lower() diff --git a/tests/unit/test_hook_user_prompt_submit.py b/tests/unit/test_hook_user_prompt_submit.py new file mode 100644 index 00000000..7617e470 --- /dev/null +++ b/tests/unit/test_hook_user_prompt_submit.py @@ -0,0 +1,88 @@ +"""Tests for the UserPromptSubmit Claude Code hook. + +The hook emits the reasoning-strategies block inside a hookSpecificOutput JSON +envelope (additionalContext) — the only channel Claude Code adds to the model's +context for this event — and always exits 0 so it can never block the prompt. +The block itself is produced by the shared +engine.reasoning_injection.get_reasoning_context orchestrator, which is patched +here — its own behavior (resolve/build/marker) is covered in +test_reasoning_injection.py. +""" + +from __future__ import annotations + +import io +import json +from unittest.mock import AsyncMock, patch + +import pytest + +from surreal_memory.hooks.user_prompt_submit import main, read_hook_input + +_ORCHESTRATOR = "surreal_memory.engine.reasoning_injection.get_reasoning_context" +_REASONING_BLOCK = "## Reasoning strategies (learned from claude-fable-5)\n\n1. **plan**" + + +def test_read_hook_input_empty_stdin() -> None: + with patch("sys.stdin", io.StringIO("")): + assert read_hook_input() == {} + + +def test_read_hook_input_valid_json() -> None: + payload = {"session_id": "s1", "transcript_path": "/x/t.jsonl", "prompt": "hi"} + with patch("sys.stdin", io.StringIO(json.dumps(payload))): + assert read_hook_input() == payload + + +def test_read_hook_input_malformed_json() -> None: + with patch("sys.stdin", io.StringIO("not json")): + assert read_hook_input() == {} + + +def test_main_emits_hook_specific_output_json(capsys: pytest.CaptureFixture[str]) -> None: + # Context reaches the model ONLY via hookSpecificOutput.additionalContext — + # the hook must emit that JSON envelope, not raw stdout. + with patch("sys.stdin", io.StringIO("{}")): + with patch(_ORCHESTRATOR, new=AsyncMock(return_value=_REASONING_BLOCK)): + with pytest.raises(SystemExit) as exc: + main() + + assert exc.value.code == 0 + payload = json.loads(capsys.readouterr().out.strip()) + hook_out = payload["hookSpecificOutput"] + assert hook_out["hookEventName"] == "UserPromptSubmit" + assert hook_out["additionalContext"] == _REASONING_BLOCK + + +def test_main_no_block_prints_nothing_to_stdout(capsys: pytest.CaptureFixture[str]) -> None: + with patch("sys.stdin", io.StringIO("{}")): + with patch(_ORCHESTRATOR, new=AsyncMock(return_value="")): + with pytest.raises(SystemExit) as exc: + main() + + assert exc.value.code == 0 + captured = capsys.readouterr() + assert captured.out.strip() == "" # nothing injected into the prompt + assert "No reasoning strategies" in captured.err + + +def test_main_injection_failure_exits_zero(capsys: pytest.CaptureFixture[str]) -> None: + # An orchestrator exception must never block the prompt — exit 0, no stdout. + with patch("sys.stdin", io.StringIO("{}")): + with patch(_ORCHESTRATOR, new=AsyncMock(side_effect=RuntimeError("boom"))): + with pytest.raises(SystemExit) as exc: + main() + + assert exc.value.code == 0 + captured = capsys.readouterr() + assert captured.out.strip() == "" + assert "failed" in captured.err.lower() + + +def test_main_malformed_stdin_does_not_crash(capsys: pytest.CaptureFixture[str]) -> None: + with patch("sys.stdin", io.StringIO("not valid json")): + with patch(_ORCHESTRATOR, new=AsyncMock(return_value="")): + with pytest.raises(SystemExit) as exc: + main() + + assert exc.value.code == 0 diff --git a/tests/unit/test_reasoning_injection.py b/tests/unit/test_reasoning_injection.py index 1c2fd797..ab443657 100644 --- a/tests/unit/test_reasoning_injection.py +++ b/tests/unit/test_reasoning_injection.py @@ -11,6 +11,7 @@ import json import logging from pathlib import Path +from unittest.mock import AsyncMock, patch import pytest @@ -19,6 +20,7 @@ from surreal_memory.engine.reasoning_injection import ( already_injected, build_injection_context, + get_reasoning_context, mark_injected, resolve_active_model, ) @@ -285,6 +287,56 @@ async def test_build_block_warns_on_fetch_ceiling( assert block # still renders from whatever was fetched +# ── get_reasoning_context (shared hook orchestrator) ───────────────────────── + + +async def test_get_reasoning_context_disabled_returns_empty( + clean_env: Path, tmp_path: Path +) -> None: + cfg = _ucfg(tmp_path, injection_enabled=False) + with patch("surreal_memory.unified_config.get_config", return_value=cfg): + assert await get_reasoning_context({"session_id": "s-disabled"}) == "" + + +async def test_get_reasoning_context_builds_and_marks_session( + clean_env: Path, tmp_path: Path +) -> None: + storage = InMemoryStorage() + storage.set_brain("default") + await _add_pattern(storage, "claude-fable-5", "debugging", "debugging: verify", "s", 1.0, 3) + cfg = _ucfg(tmp_path, injection_map=(("claude-opus-*", "claude-fable-5"),)) + with ( + patch("surreal_memory.unified_config.get_config", return_value=cfg), + patch( + "surreal_memory.unified_config.get_shared_storage", + new=AsyncMock(return_value=storage), + ), + ): + block = await get_reasoning_context({"session_id": "s-happy", "model": "claude-opus-4-8"}) + + assert "learned from claude-fable-5" in block + # Session marker set → the sibling UserPromptSubmit hook won't re-inject. + assert already_injected("s-happy") is True + + +async def test_get_reasoning_context_skips_when_already_injected( + clean_env: Path, tmp_path: Path +) -> None: + mark_injected("s-dup") # e.g. SessionStart already injected this session + cfg = _ucfg(tmp_path, injection_map=(("claude-opus-*", "claude-fable-5"),)) + + async def _must_not_open(_name: str) -> object: + raise AssertionError("storage must not open when the session is already injected") + + with ( + patch("surreal_memory.unified_config.get_config", return_value=cfg), + patch("surreal_memory.unified_config.get_shared_storage", new=_must_not_open), + ): + result = await get_reasoning_context({"session_id": "s-dup", "model": "claude-opus-4-8"}) + + assert result == "" + + # ── session idempotency markers ────────────────────────────────────────────── From e442e942b5e6103b2bdf8b1864a5605e72001e77 Mon Sep 17 00:00:00 2001 From: Toni Nowak Date: Fri, 17 Jul 2026 14:38:54 +0200 Subject: [PATCH 07/12] feat: reasoning dashboard API route (U7) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit server/routes/reasoning_training.py: new local-only dashboard router (/api/dashboard/reasoning) — GET /status (config + per-model trace/pattern stats + per-model category coverage + mining-job state), PUT /config (partial update with 422 validation: no-thinking models, glob/literal injection sources, category charset, ranges), POST /mine (background job, 400 if mining disabled, per-brain 409), GET /patterns[+/{id}], DELETE /patterns[/{id}|?model=], DELETE /traces?model= (privacy wipe). Registered in routes/__init__ + app.py. storage: new delete_reasoning_traces_by_model across base/sqlite/surrealdb/memory (parameterized, empty-model no-op; SurrealDB live-verified on v3.2.0). unified_config.create_isolated_storage: a fresh, non-cached SurrealDB instance so the background mining job holds its own brain context — a concurrent request's set_brain() on the shared singleton can no longer cross-write its graph writes into the wrong brain. Mining job state is keyed per brain (no cross-brain block or status leak). reasoning_distiller now honors mining_models in distillation, not just ingestion, so POST /mine {models} restricts the whole run. Mining runs ingest+distill directly (not ConsolidationEngine) so backfill/models overrides are honored; gated on mining_enabled (privacy). Reviewed by real-db-test-runner (live), security-reviewer, python-reviewer (all APPROVE). --- .../engine/reasoning_distiller.py | 6 + src/surreal_memory/server/app.py | 2 + src/surreal_memory/server/routes/__init__.py | 2 + .../server/routes/reasoning_training.py | 614 ++++++++++++++++++ src/surreal_memory/storage/base.py | 8 + src/surreal_memory/storage/memory_store.py | 10 + .../storage/sqlite_reasoning_traces.py | 13 + .../storage/surrealdb/reasoning_traces.py | 18 + src/surreal_memory/unified_config.py | 73 ++- tests/unit/test_reasoning_distiller.py | 19 + tests/unit/test_reasoning_traces_storage.py | 31 + tests/unit/test_route_reasoning_training.py | 427 ++++++++++++ tests/unit/test_surrealdb_reasoning_traces.py | 19 + 13 files changed, 1231 insertions(+), 11 deletions(-) create mode 100644 src/surreal_memory/server/routes/reasoning_training.py create mode 100644 tests/unit/test_route_reasoning_training.py diff --git a/src/surreal_memory/engine/reasoning_distiller.py b/src/surreal_memory/engine/reasoning_distiller.py index 7323cc48..d90aa31b 100644 --- a/src/surreal_memory/engine/reasoning_distiller.py +++ b/src/surreal_memory/engine/reasoning_distiller.py @@ -26,6 +26,7 @@ import re from collections import Counter from dataclasses import dataclass +from fnmatch import fnmatch from typing import TYPE_CHECKING, Any from surreal_memory.core.fiber import Fiber @@ -561,6 +562,11 @@ async def distill_reasoning_patterns( patterns_created = 0 processed_ids: list[Any] = [] models = await storage.get_reasoning_trace_models(brain_id) + if rt.mining_models: + # Honor the configured source-model globs so distillation is restricted to + # the same models as ingestion (and to POST /mine's models= override). An + # empty mining_models means "all models" (unchanged default behavior). + models = [m for m in models if any(fnmatch(m, pat) for pat in rt.mining_models)] for model in models: budget = rt.max_patterns_per_run - patterns_created diff --git a/src/surreal_memory/server/app.py b/src/surreal_memory/server/app.py index b4cf4e29..cb78575f 100755 --- a/src/surreal_memory/server/app.py +++ b/src/surreal_memory/server/app.py @@ -25,6 +25,7 @@ memory_router, oauth_router, openclaw_router, + reasoning_training_router, sync_router, ) from surreal_memory.storage.base import NeuralStorage @@ -532,6 +533,7 @@ async def get_storage( app.include_router(integration_status_router) app.include_router(oauth_router) app.include_router(openclaw_router) + app.include_router(reasoning_training_router) # Health check endpoint @app.get("/health", response_model=HealthResponse, tags=["health"]) diff --git a/src/surreal_memory/server/routes/__init__.py b/src/surreal_memory/server/routes/__init__.py index 70715613..3a2b2d7c 100755 --- a/src/surreal_memory/server/routes/__init__.py +++ b/src/surreal_memory/server/routes/__init__.py @@ -10,6 +10,7 @@ from surreal_memory.server.routes.memory import router as memory_router from surreal_memory.server.routes.oauth import router as oauth_router from surreal_memory.server.routes.openclaw_api import router as openclaw_router +from surreal_memory.server.routes.reasoning_training import router as reasoning_training_router from surreal_memory.server.routes.sync import router as sync_router __all__ = [ @@ -21,5 +22,6 @@ "memory_router", "oauth_router", "openclaw_router", + "reasoning_training_router", "sync_router", ] diff --git a/src/surreal_memory/server/routes/reasoning_training.py b/src/surreal_memory/server/routes/reasoning_training.py new file mode 100644 index 00000000..892976e9 --- /dev/null +++ b/src/surreal_memory/server/routes/reasoning_training.py @@ -0,0 +1,614 @@ +"""Reasoning-training dashboard API routes. + +Exposes the reasoning-training pipeline to the dashboard, kept in its own router +(dashboard_api.py is already ~1800 lines). Local-only via require_local_request. + +Endpoints (prefix ``/api/dashboard/reasoning``): +- ``GET /status`` config + per-model trace/pattern stats + coverage + mining-job state +- ``PUT /config`` partial config update (toggles, mining_models, injection_map, limits) +- ``POST /mine`` trigger a one-off mining run in the background (409 if already running) +- ``GET /patterns`` list learned pattern fibers (filter by model/category, paginated) +- ``GET /patterns/{id}`` one pattern's full detail +- ``DELETE /patterns/{id}`` delete one pattern +- ``DELETE /patterns`` delete all patterns for a model +- ``DELETE /traces`` privacy wipe: delete all staged traces for a model +""" + +from __future__ import annotations + +import asyncio +import logging +import re +from dataclasses import replace as dc_replace +from typing import Annotated, Any + +from fastapi import APIRouter, Depends, HTTPException, Query +from pydantic import BaseModel, Field + +from surreal_memory.core.brain import Brain +from surreal_memory.engine.reasoning_miner import _MODELS_WITHOUT_THINKING, normalize_model +from surreal_memory.server.dependencies import get_brain, get_storage, require_local_request +from surreal_memory.server.models import ErrorResponse +from surreal_memory.storage.base import NeuralStorage +from surreal_memory.utils.timeutils import utcnow + +logger = logging.getLogger(__name__) + +router = APIRouter( + prefix="/api/dashboard/reasoning", + tags=["reasoning"], + dependencies=[Depends(require_local_request)], +) + +# Pattern fibers are idempotent by _reasoning_signature (bounded population); +# matches the distiller / injection fetch ceiling. +_PATTERN_FETCH_LIMIT = 5000 +_MAX_PAGE = 100 +# Model names / injection globs: letters, digits, dot, underscore, dash, glob chars. +_MODEL_NAME_RE = re.compile(r"^[A-Za-z0-9._*?-]{1,128}$") + +# Background mining jobs, keyed by brain_id so one brain's run neither blocks nor +# leaks status into another's. Assumes a single uvicorn worker (the dashboard is +# launched without workers=N — cli/commands/tools.py); a multi-worker deployment +# would need shared state (e.g. a DB row) instead of these process globals. +_mining_tasks: dict[str, asyncio.Task[None]] = {} +_mining_states: dict[str, dict[str, Any]] = {} + + +def _idle_mining_state() -> dict[str, Any]: + return { + "running": False, + "started_at": None, + "finished_at": None, + "traces_ingested": 0, + "patterns_learned": 0, + "dry_run": False, + "error": None, + } + + +# ── Response / request models ───────────────────────────────────────────────── + + +class ModelTraceStats(BaseModel): + """Per-model trace + pattern counts and coverage.""" + + model: str + trace_count: int = 0 + unprocessed: int = 0 + pattern_count: int = 0 + has_thinking_text: bool = True + last_trace_at: str | None = None + coverage_percent: float = 0.0 + + +class CategoryCoverage(BaseModel): + """Coverage of one category for one source model.""" + + category: str + pattern_count: int + covered: bool + + +class MiningJobState(BaseModel): + """State of the (single) background mining job.""" + + running: bool + started_at: str | None = None + finished_at: str | None = None + traces_ingested: int = 0 + patterns_learned: int = 0 + dry_run: bool = False + error: str | None = None + + +class ReasoningStatusResponse(BaseModel): + """Full reasoning-training status for the dashboard.""" + + config: dict[str, Any] + detected_models: list[str] + per_model: list[ModelTraceStats] + coverage_by_model: dict[str, list[CategoryCoverage]] + total_traces: int + unprocessed_traces: int + total_patterns: int + mining: MiningJobState + + +class ReasoningConfigUpdate(BaseModel): + """Partial reasoning-training config update (all fields optional).""" + + mining_enabled: bool | None = None + injection_enabled: bool | None = None + mining_models: list[str] | None = None + injection_map: dict[str, str] | None = None + categories: list[str] | None = None + scan_lookback_days: int | None = Field(None, ge=0, le=100_000) + retention_days: int | None = Field(None, ge=1, le=100_000) + max_traces_total: int | None = Field(None, ge=1, le=10_000_000) + min_cluster_support: int | None = Field(None, ge=1, le=100_000) + max_patterns_per_run: int | None = Field(None, ge=1, le=100_000) + min_confidence: float | None = Field(None, ge=0.0, le=1.0) + min_patterns_per_category: int | None = Field(None, ge=1, le=100_000) + injection_max_patterns: int | None = Field(None, ge=1, le=1000) + injection_max_chars: int | None = Field(None, ge=1, le=1_000_000) + + +class MineRequest(BaseModel): + """Trigger a one-off mining run.""" + + backfill: bool = Field(False, description="Scan the full history (scan_lookback_days=0)") + dry_run: bool = Field( + False, description="Don't write — report as a no-op (parity with consolidation)" + ) + models: list[str] | None = Field(None, description="Restrict mining to these source models") + + +class MineResponse(BaseModel): + """Response to a mining trigger.""" + + status: str + mining: MiningJobState + + +class PatternSummary(BaseModel): + """One learned reasoning pattern (list view).""" + + id: str + source_model: str + category: str + title: str + confidence: float + frequency: int + signature: str + + +class PatternDetail(PatternSummary): + """One learned reasoning pattern with its full strategy/description.""" + + strategy: str + description: str + summary: str + + +class PatternsListResponse(BaseModel): + """Paginated learned-pattern list.""" + + patterns: list[PatternSummary] + total: int + limit: int + offset: int + + +class DeleteResponse(BaseModel): + """Result of a delete operation.""" + + deleted: int + + +# ── Helpers ─────────────────────────────────────────────────────────────────── + + +def _has_thinking(model: str) -> bool: + """A model has minable thinking text unless it's on the denylist (prefix match).""" + norm = normalize_model(model) + return not any(norm.startswith(prefix) for prefix in _MODELS_WITHOUT_THINKING) + + +def _validate_model_name(name: str, *, field: str) -> str: + """Validate a model name / injection glob; 422 on a bad shape.""" + cleaned = name.strip() + if not cleaned or not _MODEL_NAME_RE.match(cleaned): + raise HTTPException(status_code=422, detail=f"Invalid {field} name: {name!r}") + return cleaned + + +def _pattern_meta(fiber: Any) -> dict[str, Any]: + return fiber.metadata if isinstance(fiber.metadata, dict) else {} + + +def _to_summary(fiber: Any) -> PatternSummary: + md = _pattern_meta(fiber) + return PatternSummary( + id=str(fiber.id), + source_model=str(md.get("_source_model", "")), + category=str(md.get("_reasoning_category", "")), + title=str(md.get("_reasoning_title", "")), + confidence=float(md.get("_reasoning_confidence", 0.0) or 0.0), + frequency=int(md.get("_reasoning_frequency", 0) or 0), + signature=str(md.get("_reasoning_signature", "")), + ) + + +async def _fetch_pattern_fibers(storage: NeuralStorage) -> list[Any]: + """All learned pattern fibers for the current brain (bounded population).""" + return await storage.find_fibers(metadata_key="_reasoning_pattern", limit=_PATTERN_FETCH_LIMIT) + + +# ── GET /status ─────────────────────────────────────────────────────────────── + + +@router.get("/status", response_model=ReasoningStatusResponse, summary="Reasoning-training status") +async def get_status( + storage: Annotated[NeuralStorage, Depends(get_storage)], + brain: Annotated[Brain, Depends(get_brain)], +) -> ReasoningStatusResponse: + """Config + per-model trace/pattern stats + per-model category coverage.""" + from surreal_memory.unified_config import get_config + + config = get_config() + rt = config.reasoning_training + brain_id = brain.id + + stats = await storage.get_reasoning_stats(brain_id) + by_model_traces: dict[str, Any] = stats.get("by_model", {}) + fibers = await _fetch_pattern_fibers(storage) + + # Per-model pattern counts and per-category coverage from one fiber fetch. + pattern_counts: dict[str, int] = {} + cat_counts: dict[str, dict[str, int]] = {} + for f in fibers: + md = _pattern_meta(f) + source = str(md.get("_source_model", "")) + if not source: + continue + pattern_counts[source] = pattern_counts.get(source, 0) + 1 + if float(md.get("_reasoning_confidence", 0.0) or 0.0) < rt.min_confidence: + continue + cat = str(md.get("_reasoning_category", "")) + if cat in rt.categories: + model_cats = cat_counts.setdefault(source, {}) + model_cats[cat] = model_cats.get(cat, 0) + 1 + + # detected_models = union of DISTINCT trace models, config mining_models, + # pattern source models, and injection sources. + detected = sorted( + set(by_model_traces) + | set(rt.mining_models) + | set(pattern_counts) + | {s for _, s in rt.injection_map} + ) + + per_model: list[ModelTraceStats] = [] + coverage_by_model: dict[str, list[CategoryCoverage]] = {} + for model in detected: + tstats = by_model_traces.get(model, {}) + counts = cat_counts.get(model, {}) + n_covered = sum( + 1 for c in rt.categories if counts.get(c, 0) >= rt.min_patterns_per_category + ) + cov_pct = round(n_covered / len(rt.categories) * 100.0, 1) if rt.categories else 0.0 + trace_count = int(tstats.get("trace_count", 0) or 0) + per_model.append( + ModelTraceStats( + model=model, + trace_count=trace_count, + unprocessed=int(tstats.get("unprocessed", 0) or 0), + pattern_count=pattern_counts.get(model, 0), + has_thinking_text=_has_thinking(model) or trace_count > 0, + last_trace_at=tstats.get("last_trace_at") or None, + coverage_percent=cov_pct, + ) + ) + coverage_by_model[model] = [ + CategoryCoverage( + category=c, + pattern_count=counts.get(c, 0), + covered=counts.get(c, 0) >= rt.min_patterns_per_category, + ) + for c in rt.categories + ] + + return ReasoningStatusResponse( + config=rt.to_dict(), + detected_models=detected, + per_model=per_model, + coverage_by_model=coverage_by_model, + total_traces=int(stats.get("total", 0) or 0), + unprocessed_traces=int(stats.get("unprocessed", 0) or 0), + total_patterns=len(fibers), + mining=MiningJobState(**_mining_states.get(brain_id, _idle_mining_state())), + ) + + +# ── PUT /config ─────────────────────────────────────────────────────────────── + + +@router.put( + "/config", + responses={422: {"model": ErrorResponse}}, + summary="Update reasoning-training config", +) +async def update_config(body: ReasoningConfigUpdate) -> dict[str, Any]: + """Partial config update. 422 on a model without thinking text or a bad name.""" + from surreal_memory.unified_config import get_config, set_config + + config = get_config() + rt = config.reasoning_training + changes: dict[str, Any] = {} + + if body.mining_enabled is not None: + changes["mining_enabled"] = body.mining_enabled + if body.injection_enabled is not None: + changes["injection_enabled"] = body.injection_enabled + + if body.mining_models is not None: + models: list[str] = [] + for m in body.mining_models: + name = _validate_model_name(m, field="mining_models") + # A source model must actually produce thinking text to be minable. + if not _has_thinking(name): + raise HTTPException( + status_code=422, + detail=f"Model {name!r} has no thinking text and cannot be mined", + ) + models.append(name) + changes["mining_models"] = tuple(dict.fromkeys(models)) + + if body.injection_map is not None: + pairs: list[tuple[str, str]] = [] + for target, source in body.injection_map.items(): + t = _validate_model_name(target, field="injection_map target") + s = _validate_model_name(source, field="injection_map source") + # The source is matched literally (==) against a pattern's _source_model + # (reasoning_injection), so a glob would silently never match — reject it. + if "*" in s or "?" in s: + raise HTTPException( + status_code=422, + detail=f"Injection source {source!r} must be a literal model, not a glob", + ) + # The source (whose patterns get injected) must have thinking text; + # the target (recipient) may be a thinking-less model like opus. + if not _has_thinking(s): + raise HTTPException( + status_code=422, + detail=f"Injection source {s!r} has no thinking text", + ) + pairs.append((t, s)) + changes["injection_map"] = tuple(pairs) + + if body.categories is not None: + cats: list[str] = [] + for c in body.categories: + name = c.strip() + if not name: + continue + if not _MODEL_NAME_RE.match(name): + raise HTTPException(status_code=422, detail=f"Invalid category name: {c!r}") + cats.append(name[:64]) + deduped = tuple(dict.fromkeys(cats)) + if not deduped: + # 422 rather than a silent no-op (categories drive coverage math). + raise HTTPException(status_code=422, detail="categories cannot be empty") + changes["categories"] = deduped + + for field_name in ( + "scan_lookback_days", + "retention_days", + "max_traces_total", + "min_cluster_support", + "max_patterns_per_run", + "min_confidence", + "min_patterns_per_category", + "injection_max_patterns", + "injection_max_chars", + ): + value = getattr(body, field_name) + if value is not None: + changes[field_name] = value + + new_rt = dc_replace(rt, **changes) + updated = dc_replace(config, reasoning_training=new_rt) + updated.save() + set_config(updated) + return {"status": "updated", "config": new_rt.to_dict()} + + +# ── POST /mine ──────────────────────────────────────────────────────────────── + + +async def _run_mining( + brain_id: str, backfill: bool, dry_run: bool, models: list[str] | None +) -> None: + """Background mining job: ingest reasoning traces then distill patterns. + + Uses direct ingest+distill (not ConsolidationEngine) so the request's + backfill / models overrides are honored — the engine's reasoning handlers + reload config from disk and would ignore them. Runs on an ISOLATED storage + (create_isolated_storage) so a concurrently-served request's set_brain() on + the shared SurrealDB singleton can't redirect this job's graph writes into + the wrong brain. + """ + from surreal_memory.engine.reasoning_distiller import distill_reasoning_patterns + from surreal_memory.engine.reasoning_miner import ingest_reasoning_traces + from surreal_memory.unified_config import create_isolated_storage, get_config + + state = _mining_states.setdefault(brain_id, _idle_mining_state()) + try: + if dry_run: + # Parity with consolidation dry_run: no scanning, no writes. + state.update(traces_ingested=0, patterns_learned=0) + return + + config = get_config() + rt = config.reasoning_training + overrides: dict[str, Any] = {} + if backfill: + overrides["scan_lookback_days"] = 0 + if models: + overrides["mining_models"] = tuple(models) + run_config = dc_replace(config, reasoning_training=dc_replace(rt, **overrides)) + + storage = await create_isolated_storage(brain_id) + # We own the isolated SurrealDB instance and must close it; SQLite / in-memory + # return the shared instance, which must NOT be closed out from under others. + owns_storage = config.storage_backend == "surrealdb" + try: + ingest = await ingest_reasoning_traces(storage, brain_id, run_config) + distill = await distill_reasoning_patterns(storage, brain_id, run_config) + state.update( + traces_ingested=ingest.traces_ingested, + patterns_learned=distill.patterns_learned, + ) + finally: + if owns_storage: + await storage.close() + except Exception: # never crash; surface a generic error via job state + logger.exception("reasoning mining job failed for brain %s", brain_id) + state["error"] = "mining failed; see server logs" + finally: + state["running"] = False + state["finished_at"] = utcnow().isoformat() + + +@router.post( + "/mine", + response_model=MineResponse, + status_code=202, + responses={400: {"model": ErrorResponse}, 409: {"model": ErrorResponse}}, + summary="Trigger a one-off mining run", +) +async def trigger_mining( + body: MineRequest, + brain: Annotated[Brain, Depends(get_brain)], +) -> MineResponse: + """Start a background mining run. 400 if mining disabled, 409 if already running. + + Job state and the concurrency guard are per-brain, so a run on one brain never + blocks or leaks status into another. + """ + from surreal_memory.unified_config import get_config + + config = get_config() + if not config.reasoning_training.mining_enabled: + # Privacy: never scan ~/.claude transcripts unless mining is enabled. + raise HTTPException(status_code=400, detail="Mining is disabled; enable it first") + + existing = _mining_tasks.get(brain.id) + if existing is not None and not existing.done(): + raise HTTPException(status_code=409, detail="A mining run is already in progress") + + models = None + if body.models: + models = [_validate_model_name(m, field="models") for m in body.models] + + state = _idle_mining_state() + state.update(running=True, started_at=utcnow().isoformat(), dry_run=body.dry_run) + _mining_states[brain.id] = state + _mining_tasks[brain.id] = asyncio.create_task( + _run_mining(brain.id, body.backfill, body.dry_run, models) + ) + return MineResponse(status="started", mining=MiningJobState(**state)) + + +# ── GET /patterns ───────────────────────────────────────────────────────────── + + +@router.get("/patterns", response_model=PatternsListResponse, summary="List learned patterns") +async def list_patterns( + storage: Annotated[NeuralStorage, Depends(get_storage)], + brain: Annotated[Brain, Depends(get_brain)], + model: str | None = Query(None, description="Filter by source model"), + category: str | None = Query(None, description="Filter by category"), + limit: int = Query(50, ge=1, le=_MAX_PAGE), + offset: int = Query(0, ge=0), +) -> PatternsListResponse: + """List learned reasoning patterns (filter by source model / category).""" + fibers = await _fetch_pattern_fibers(storage) + summaries = [_to_summary(f) for f in fibers] + if model: + summaries = [s for s in summaries if s.source_model == model] + if category: + summaries = [s for s in summaries if s.category == category] + summaries.sort(key=lambda s: s.confidence * s.frequency, reverse=True) + total = len(summaries) + page = summaries[offset : offset + limit] + return PatternsListResponse(patterns=page, total=total, limit=limit, offset=offset) + + +@router.get( + "/patterns/{pattern_id}", + response_model=PatternDetail, + responses={404: {"model": ErrorResponse}}, + summary="Get one pattern's detail", +) +async def get_pattern( + pattern_id: str, + storage: Annotated[NeuralStorage, Depends(get_storage)], + brain: Annotated[Brain, Depends(get_brain)], +) -> PatternDetail: + """Return one learned pattern with its full strategy/description.""" + fiber = await storage.get_fiber(pattern_id) + if fiber is None or not _pattern_meta(fiber).get("_reasoning_pattern"): + raise HTTPException(status_code=404, detail="Pattern not found") + md = _pattern_meta(fiber) + base = _to_summary(fiber) + return PatternDetail( + **base.model_dump(), + strategy=str(md.get("_reasoning_strategy", "")), + description=str(md.get("_reasoning_description", "")), + summary=str(fiber.summary or ""), + ) + + +@router.delete( + "/patterns/{pattern_id}", + response_model=DeleteResponse, + responses={404: {"model": ErrorResponse}}, + summary="Delete one learned pattern", +) +async def delete_pattern( + pattern_id: str, + storage: Annotated[NeuralStorage, Depends(get_storage)], + brain: Annotated[Brain, Depends(get_brain)], +) -> DeleteResponse: + """Delete one learned pattern fiber. + + The pattern's private title-neuron is currently left as a harmless graph orphan + (follow-up ticket); the shared reasoning_category neuron is kept by design. + """ + fiber = await storage.get_fiber(pattern_id) + if fiber is None or not _pattern_meta(fiber).get("_reasoning_pattern"): + raise HTTPException(status_code=404, detail="Pattern not found") + deleted = await storage.delete_fiber(pattern_id) + return DeleteResponse(deleted=1 if deleted else 0) + + +@router.delete( + "/patterns", response_model=DeleteResponse, summary="Delete all patterns for a model" +) +async def delete_patterns_by_model( + storage: Annotated[NeuralStorage, Depends(get_storage)], + brain: Annotated[Brain, Depends(get_brain)], + model: str = Query(..., description="Delete all patterns whose source model matches"), +) -> DeleteResponse: + """Delete every learned pattern for a source model. + + Only the pattern fibers are removed; the shared reasoning_category concept-neuron + is intentionally kept (other models' patterns reference it), and the pattern's + private title-neuron is currently left as a harmless graph orphan (follow-up + ticket). Raw mined thinking lives in traces — DELETE /traces is the full wipe. + """ + if not model.strip(): + return DeleteResponse(deleted=0) # never a blanket wipe + fibers = await _fetch_pattern_fibers(storage) + victims = [f for f in fibers if _pattern_meta(f).get("_source_model") == model] + deleted = 0 + for f in victims: + if await storage.delete_fiber(str(f.id)): + deleted += 1 + return DeleteResponse(deleted=deleted) + + +# ── DELETE /traces (privacy wipe) ───────────────────────────────────────────── + + +@router.delete("/traces", response_model=DeleteResponse, summary="Wipe staged traces for a model") +async def delete_traces_by_model( + storage: Annotated[NeuralStorage, Depends(get_storage)], + brain: Annotated[Brain, Depends(get_brain)], + model: str = Query(..., description="Delete all staged reasoning traces for this model"), +) -> DeleteResponse: + """Privacy wipe: delete all staged reasoning traces for a model.""" + deleted = await storage.delete_reasoning_traces_by_model(brain.id, model) + return DeleteResponse(deleted=deleted) diff --git a/src/surreal_memory/storage/base.py b/src/surreal_memory/storage/base.py index 0c0727e8..2a3d6e48 100755 --- a/src/surreal_memory/storage/base.py +++ b/src/surreal_memory/storage/base.py @@ -1276,6 +1276,14 @@ async def get_reasoning_trace_models(self, brain_id: str) -> list[str]: """Return DISTINCT model names present in reasoning_traces. Default: empty.""" return [] + async def delete_reasoning_traces_by_model(self, brain_id: str, model: str) -> int: + """Delete all reasoning traces for a model (privacy wipe). Default: 0. + + Backends that buffer reasoning traces (SQLite, SurrealDB, in-memory) + override this. + """ + return 0 + async def add_retrieval_trace(self, trace: RetrievalTrace) -> str: """Persist a retrieval trace. Returns the trace ID.""" raise NotImplementedError diff --git a/src/surreal_memory/storage/memory_store.py b/src/surreal_memory/storage/memory_store.py index 458d191f..a132803d 100755 --- a/src/surreal_memory/storage/memory_store.py +++ b/src/surreal_memory/storage/memory_store.py @@ -954,6 +954,16 @@ async def get_reasoning_trace_models(self, brain_id: str) -> list[str]: """Return DISTINCT model names in reasoning_traces (sorted).""" return sorted({r["model"] for r in self._reasoning_traces[brain_id] if r["model"]}) + async def delete_reasoning_traces_by_model(self, brain_id: str, model: str) -> int: + """Delete all reasoning traces for a model (privacy wipe).""" + if not model: + return 0 + rows = self._reasoning_traces[brain_id] + kept = [r for r in rows if r["model"] != model] + deleted = len(rows) - len(kept) + self._reasoning_traces[brain_id] = kept + return deleted + # ========== Cleanup ========== async def clear(self, brain_id: str) -> None: diff --git a/src/surreal_memory/storage/sqlite_reasoning_traces.py b/src/surreal_memory/storage/sqlite_reasoning_traces.py index d0135a03..b8b9e455 100644 --- a/src/surreal_memory/storage/sqlite_reasoning_traces.py +++ b/src/surreal_memory/storage/sqlite_reasoning_traces.py @@ -270,3 +270,16 @@ async def get_reasoning_trace_models(self, brain_id: str) -> list[str]: if row["model"]: results.append(row["model"]) return results + + async def delete_reasoning_traces_by_model(self, brain_id: str, model: str) -> int: + """Delete all reasoning traces for a model (privacy wipe). Returns rows deleted.""" + if not model: + return 0 + conn = self._ensure_conn() + cursor = await conn.execute( + "DELETE FROM reasoning_traces WHERE brain_id = ? AND model = ?", + (brain_id, model), + ) + deleted = cursor.rowcount + await conn.commit() + return deleted diff --git a/src/surreal_memory/storage/surrealdb/reasoning_traces.py b/src/surreal_memory/storage/surrealdb/reasoning_traces.py index 9bff5189..eb3876ef 100644 --- a/src/surreal_memory/storage/surrealdb/reasoning_traces.py +++ b/src/surreal_memory/storage/surrealdb/reasoning_traces.py @@ -286,3 +286,21 @@ async def get_reasoning_trace_models(self, brain_id: str) -> list[str]: bid=brain_id, ) return sorted({r.get("model", "") for r in rows if r.get("model")}) + + async def delete_reasoning_traces_by_model(self, brain_id: str, model: str) -> int: + """Delete all reasoning traces for a model (privacy wipe); return count.""" + if not model: + return 0 + rows = await self._query( + "SELECT count() AS c FROM reasoning_traces" + " WHERE brain_id = $bid AND model = $model GROUP ALL", + bid=brain_id, + model=model, + ) + deleted = int(rows[0]["c"]) if rows else 0 + await self._query( + "DELETE reasoning_traces WHERE brain_id = $bid AND model = $model", + bid=brain_id, + model=model, + ) + return deleted diff --git a/src/surreal_memory/unified_config.py b/src/surreal_memory/unified_config.py index 64cbc438..7f6be427 100755 --- a/src/surreal_memory/unified_config.py +++ b/src/surreal_memory/unified_config.py @@ -2490,6 +2490,22 @@ async def _get_sqlite_storage( _surrealdb_storage: NeuralStorage | None = None +def _resolve_embedding_dim(config: UnifiedConfig) -> int: + """Resolve the embedding vector dimension the SurrealDB HNSW index must match. + + Priority: explicit config.embedding.dimension (e.g. a local bge-m3 server → + 1024) > known Gemini model dim > 3072 default. + """ + if config.embedding.enabled: + if config.embedding.dimension and config.embedding.dimension > 0: + return int(config.embedding.dimension) + if config.embedding.model: + from surreal_memory.engine.embedding.gemini_embedding import _MODEL_DIMENSIONS + + return _MODEL_DIMENSIONS.get(config.embedding.model, 3072) + return 3072 # Gemini 2.0 default + + async def _get_surrealdb_storage(config: UnifiedConfig, name: str) -> NeuralStorage: """Create or return cached SurrealDBStorage.""" global _surrealdb_storage @@ -2506,17 +2522,8 @@ async def _get_surrealdb_storage(config: UnifiedConfig, name: str) -> NeuralStor _surrealdb_storage.set_brain(name) return _surrealdb_storage - # Resolve the embedding vector dimension that the SurrealDB HNSW index must - # match. Priority: explicit config.embedding.dimension (e.g. a local - # OpenAI-compatible bge-m3 server → 1024) > known Gemini model dim > 3072. - emb_dim = 3072 # Gemini 2.0 default - if config.embedding.enabled: - if config.embedding.dimension and config.embedding.dimension > 0: - emb_dim = int(config.embedding.dimension) - elif config.embedding.model: - from surreal_memory.engine.embedding.gemini_embedding import _MODEL_DIMENSIONS - - emb_dim = _MODEL_DIMENSIONS.get(config.embedding.model, 3072) + # Resolve the embedding vector dimension that the SurrealDB HNSW index must match. + emb_dim = _resolve_embedding_dim(config) _warn_missing_surreal_pass() @@ -2557,3 +2564,47 @@ async def _get_surrealdb_storage(config: UnifiedConfig, name: str) -> NeuralStor _surrealdb_storage = storage logger.info("SurrealDB storage initialized for brain '%s'", name) return storage + + +async def create_isolated_storage(brain_name: str | None = None) -> NeuralStorage: + """Create a FRESH, non-cached storage bound to *brain_name*. + + Unlike get_shared_storage, this never returns the module-global SurrealDB + singleton, so a long-running background job (e.g. reasoning mining) can hold + its own brain context without a concurrently-served request's set_brain() + racing it and cross-writing into the wrong brain. The caller MUST close() the + returned storage. SQLite already caches per brain-file (each brain a separate + instance) and the in-memory backend is test-only, so those return the shared + instance — only the SurrealDB global singleton needs true isolation. + """ + config = get_config() + name = brain_name or config.current_brain + + if config.storage_backend == "surrealdb": + from surreal_memory.core.brain import Brain + from surreal_memory.storage.surrealdb import SurrealDBStorage + from surreal_memory.storage.surrealdb.connection import SurrealSettings + + _warn_missing_surreal_pass() + settings = SurrealSettings.from_env() + storage = SurrealDBStorage( + url=settings.url, + namespace=settings.namespace, + database=settings.database, + user=settings.user, + password=settings.password, + embedding_dim=_resolve_embedding_dim(config), + ) + await storage.initialize() + brain = await storage.get_brain(name) + if brain is None: + brain = await storage.find_brain_by_name(name) + if brain is None: + brain = Brain.create(name, brain_id=name) + await storage.save_brain(brain) + storage.set_brain(name) + return storage + + # SQLite (per-brain cached) and in-memory (test-only) are not shared across + # brains in a racy way; reuse the shared instance. + return await get_shared_storage(name) diff --git a/tests/unit/test_reasoning_distiller.py b/tests/unit/test_reasoning_distiller.py index 13f0d651..fd3a1adc 100644 --- a/tests/unit/test_reasoning_distiller.py +++ b/tests/unit/test_reasoning_distiller.py @@ -112,6 +112,25 @@ async def test_distill_creates_pattern_fiber(tmp_path: Path, no_embedder: None) assert cat_neuron +async def test_distill_honors_mining_models_filter(tmp_path: Path, no_embedder: None) -> None: + # Pre-existing unprocessed traces for two models; a mining_models glob must + # restrict distillation to the matching model only (HIGH-2 regression: the + # POST /mine models= override must actually gate distillation, not just ingest). + storage = InMemoryStorage() + storage.set_brain(BRAIN) + await _seed(storage, "claude-fable-5", _DEBUG_TRACES) + await _seed(storage, "claude-sonnet-5", _DEBUG_TRACES) + + cfg = _ucfg(tmp_path, mining_models=("claude-fable-5",)) + await distill_reasoning_patterns(storage, BRAIN, cfg) + + fibers = await storage.find_fibers(metadata_key="_reasoning_pattern", limit=100) + assert {f.metadata["_source_model"] for f in fibers} == {"claude-fable-5"} + # sonnet's traces are untouched (not consumed by the restricted run). + remaining = await storage.get_unprocessed_reasoning_traces(BRAIN, limit=100) + assert {t["model"] for t in remaining} == {"claude-sonnet-5"} + + async def test_distill_is_idempotent(tmp_path: Path, no_embedder: None) -> None: storage = InMemoryStorage() storage.set_brain(BRAIN) diff --git a/tests/unit/test_reasoning_traces_storage.py b/tests/unit/test_reasoning_traces_storage.py index 33c911d4..7bcf4447 100644 --- a/tests/unit/test_reasoning_traces_storage.py +++ b/tests/unit/test_reasoning_traces_storage.py @@ -212,6 +212,37 @@ async def test_get_reasoning_trace_models_distinct_sorted(storage: SQLiteStorage ] +async def test_delete_reasoning_traces_by_model(storage: SQLiteStorage) -> None: + await storage.insert_reasoning_traces( + BRAIN, + [ + _trace("h1", model="claude-fable-5"), + _trace("h2", model="claude-fable-5"), + _trace("h3", model="claude-sonnet-5"), + ], + ) + deleted = await storage.delete_reasoning_traces_by_model(BRAIN, "claude-fable-5") + assert deleted == 2 + assert await storage.get_reasoning_trace_models(BRAIN) == ["claude-sonnet-5"] + # Empty model is a no-op (never a blanket wipe). + assert await storage.delete_reasoning_traces_by_model(BRAIN, "") == 0 + + +async def test_delete_reasoning_traces_by_model_in_memory() -> None: + # The in-memory backend is a real, selectable production backend — cover it too. + from surreal_memory.storage.memory_store import InMemoryStorage + + store = InMemoryStorage() + store.set_brain(BRAIN) + await store.insert_reasoning_traces( + BRAIN, + [_trace("h1", model="claude-fable-5"), _trace("h2", model="claude-sonnet-5")], + ) + assert await store.delete_reasoning_traces_by_model(BRAIN, "claude-fable-5") == 1 + assert await store.get_reasoning_trace_models(BRAIN) == ["claude-sonnet-5"] + assert await store.delete_reasoning_traces_by_model(BRAIN, "") == 0 + + async def test_traces_are_brain_scoped(storage: SQLiteStorage) -> None: await storage._ensure_conn().execute( "INSERT OR IGNORE INTO brains (id, name, config, created_at, updated_at) " diff --git a/tests/unit/test_route_reasoning_training.py b/tests/unit/test_route_reasoning_training.py new file mode 100644 index 00000000..7db40e5a --- /dev/null +++ b/tests/unit/test_route_reasoning_training.py @@ -0,0 +1,427 @@ +"""Tests for the reasoning-training dashboard router. + +Mirrors test_dashboard_api.py: a bare FastAPI app with just this router, storage +and brain injected via dependency_overrides. The router-level require_local_request +passes because TestClient's host is "testclient". Config is patched at +surreal_memory.unified_config.* (the handlers import get_config/set_config locally). +""" + +from __future__ import annotations + +from pathlib import Path +from types import SimpleNamespace +from typing import Any +from unittest.mock import AsyncMock + +import pytest +from fastapi import FastAPI +from fastapi.testclient import TestClient + +import surreal_memory.server.routes.reasoning_training as rt_module +from surreal_memory.core.brain import Brain +from surreal_memory.server.dependencies import get_brain, get_storage +from surreal_memory.server.routes.reasoning_training import router +from surreal_memory.unified_config import ReasoningTrainingConfig, UnifiedConfig + +BRAIN_ID = "default" + + +def _cfg(tmp_path: Path, **rt_kw: Any) -> UnifiedConfig: + return UnifiedConfig( + data_dir=tmp_path / ".surrealmemory", + current_brain=BRAIN_ID, + reasoning_training=ReasoningTrainingConfig(**rt_kw), + ) + + +def _fiber( + fid: str, + model: str, + category: str, + title: str = "t", + confidence: float = 1.0, + frequency: int = 3, + *, + pattern: bool = True, +) -> SimpleNamespace: + meta: dict[str, Any] = { + "_source_model": model, + "_reasoning_category": category, + "_reasoning_title": title, + "_reasoning_confidence": confidence, + "_reasoning_frequency": frequency, + "_reasoning_signature": f"sig-{fid}", + "_reasoning_strategy": "verify -> check", + "_reasoning_description": "medoid description", + } + if pattern: + meta["_reasoning_pattern"] = True + return SimpleNamespace(id=fid, summary=f"summary-{fid}", metadata=meta) + + +@pytest.fixture(autouse=True) +def _reset_mining_state() -> Any: + """Isolate the per-brain mining job state/tasks between tests.""" + rt_module._mining_tasks = {} + rt_module._mining_states = {} + yield + rt_module._mining_tasks = {} + rt_module._mining_states = {} + + +@pytest.fixture +def mock_storage() -> AsyncMock: + storage = AsyncMock() + storage.get_reasoning_stats = AsyncMock( + return_value={"by_model": {}, "by_category": {}, "total": 0, "unprocessed": 0} + ) + storage.find_fibers = AsyncMock(return_value=[]) + storage.get_fiber = AsyncMock(return_value=None) + storage.delete_fiber = AsyncMock(return_value=True) + storage.delete_reasoning_traces_by_model = AsyncMock(return_value=0) + return storage + + +@pytest.fixture +def client(mock_storage: AsyncMock) -> TestClient: + app = FastAPI() + app.include_router(router) + brain = Brain.create(BRAIN_ID, brain_id=BRAIN_ID) + app.dependency_overrides[get_storage] = lambda: mock_storage + app.dependency_overrides[get_brain] = lambda: brain + return TestClient(app) + + +# ── GET /status ─────────────────────────────────────────────────────────────── + + +def test_status_empty(client: TestClient, tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> None: + monkeypatch.setattr("surreal_memory.unified_config.get_config", lambda: _cfg(tmp_path)) + resp = client.get("/api/dashboard/reasoning/status") + assert resp.status_code == 200 + data = resp.json() + assert data["total_traces"] == 0 + assert data["total_patterns"] == 0 + assert data["detected_models"] == [] + assert data["mining"]["running"] is False + assert data["config"]["mining_enabled"] is False + + +def test_status_with_traces_and_patterns( + client: TestClient, mock_storage: AsyncMock, tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + monkeypatch.setattr("surreal_memory.unified_config.get_config", lambda: _cfg(tmp_path)) + mock_storage.get_reasoning_stats.return_value = { + "by_model": { + "claude-fable-5": { + "trace_count": 5, + "unprocessed": 2, + "last_trace_at": "2026-07-17T10:00:00", + } + }, + "by_category": {"debugging": 5}, + "total": 5, + "unprocessed": 2, + } + # 3 debugging patterns (>= min_patterns_per_category=3) → debugging covered. + mock_storage.find_fibers.return_value = [ + _fiber("p1", "claude-fable-5", "debugging"), + _fiber("p2", "claude-fable-5", "debugging"), + _fiber("p3", "claude-fable-5", "debugging"), + _fiber("p4", "claude-fable-5", "planning"), + ] + resp = client.get("/api/dashboard/reasoning/status") + assert resp.status_code == 200 + data = resp.json() + assert data["total_traces"] == 5 + assert data["total_patterns"] == 4 + assert "claude-fable-5" in data["detected_models"] + fable = next(m for m in data["per_model"] if m["model"] == "claude-fable-5") + assert fable["trace_count"] == 5 + assert fable["pattern_count"] == 4 + assert fable["has_thinking_text"] is True + assert fable["coverage_percent"] == pytest.approx(12.5) # 1 of 8 categories + cov = {c["category"]: c["covered"] for c in data["coverage_by_model"]["claude-fable-5"]} + assert cov["debugging"] is True + assert cov["planning"] is False + + +def test_status_denylisted_model_has_no_thinking( + client: TestClient, mock_storage: AsyncMock, tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + monkeypatch.setattr("surreal_memory.unified_config.get_config", lambda: _cfg(tmp_path)) + mock_storage.get_reasoning_stats.return_value = { + "by_model": {"claude-opus-4-8": {"trace_count": 0, "unprocessed": 0, "last_trace_at": ""}}, + "by_category": {}, + "total": 0, + "unprocessed": 0, + } + resp = client.get("/api/dashboard/reasoning/status") + data = resp.json() + opus = next(m for m in data["per_model"] if m["model"] == "claude-opus-4-8") + assert opus["has_thinking_text"] is False + + +# ── PUT /config ─────────────────────────────────────────────────────────────── + + +@pytest.fixture +def config_capture(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> dict[str, Any]: + """Patch get_config/set_config and stub save() so PUT /config never hits disk.""" + holder: dict[str, Any] = {"cfg": _cfg(tmp_path), "saved": None} + monkeypatch.setattr("surreal_memory.unified_config.get_config", lambda *_a, **_k: holder["cfg"]) + monkeypatch.setattr("surreal_memory.unified_config.set_config", lambda c: holder.update(cfg=c)) + monkeypatch.setattr(UnifiedConfig, "save", lambda self: holder.update(saved=self)) + return holder + + +def test_config_toggle_mining(client: TestClient, config_capture: dict[str, Any]) -> None: + resp = client.put("/api/dashboard/reasoning/config", json={"mining_enabled": True}) + assert resp.status_code == 200 + assert resp.json()["config"]["mining_enabled"] is True + assert config_capture["cfg"].reasoning_training.mining_enabled is True + assert config_capture["saved"] is not None # persisted + + +def test_config_rejects_model_without_thinking( + client: TestClient, config_capture: dict[str, Any] +) -> None: + resp = client.put( + "/api/dashboard/reasoning/config", json={"mining_models": ["claude-opus-4-8"]} + ) + assert resp.status_code == 422 + assert "thinking" in resp.json()["detail"].lower() + + +def test_config_rejects_injection_source_without_thinking( + client: TestClient, config_capture: dict[str, Any] +) -> None: + resp = client.put( + "/api/dashboard/reasoning/config", + json={"injection_map": {"claude-fable-5": "claude-opus-4-8"}}, + ) + assert resp.status_code == 422 + + +def test_config_allows_denylisted_target( + client: TestClient, config_capture: dict[str, Any] +) -> None: + # opus is a valid injection TARGET (recipient); fable is a thinking source. + resp = client.put( + "/api/dashboard/reasoning/config", + json={"injection_map": {"claude-opus-4-8": "claude-fable-5"}}, + ) + assert resp.status_code == 200 + assert config_capture["cfg"].reasoning_training.injection_map == ( + ("claude-opus-4-8", "claude-fable-5"), + ) + + +def test_config_allows_self_mapping(client: TestClient, config_capture: dict[str, Any]) -> None: + resp = client.put( + "/api/dashboard/reasoning/config", + json={"injection_map": {"claude-fable-5": "claude-fable-5"}}, + ) + assert resp.status_code == 200 + + +def test_config_min_confidence_out_of_range( + client: TestClient, config_capture: dict[str, Any] +) -> None: + resp = client.put("/api/dashboard/reasoning/config", json={"min_confidence": 1.5}) + assert resp.status_code == 422 + + +def test_config_rejects_bad_model_name(client: TestClient, config_capture: dict[str, Any]) -> None: + resp = client.put("/api/dashboard/reasoning/config", json={"mining_models": ["bad name!;drop"]}) + assert resp.status_code == 422 + + +def test_config_rejects_glob_injection_source( + client: TestClient, config_capture: dict[str, Any] +) -> None: + # Source is matched literally, so a glob source would silently never match. + resp = client.put( + "/api/dashboard/reasoning/config", + json={"injection_map": {"claude-opus-4-8": "claude-*"}}, + ) + assert resp.status_code == 422 + assert "literal" in resp.json()["detail"].lower() + + +def test_config_rejects_empty_categories( + client: TestClient, config_capture: dict[str, Any] +) -> None: + resp = client.put("/api/dashboard/reasoning/config", json={"categories": []}) + assert resp.status_code == 422 + + +def test_config_rejects_bad_category_name( + client: TestClient, config_capture: dict[str, Any] +) -> None: + resp = client.put("/api/dashboard/reasoning/config", json={"categories": ['bad"; drop']}) + assert resp.status_code == 422 + + +# ── POST /mine ──────────────────────────────────────────────────────────────── + + +def test_mine_disabled_returns_400( + client: TestClient, tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + monkeypatch.setattr("surreal_memory.unified_config.get_config", lambda: _cfg(tmp_path)) + resp = client.post("/api/dashboard/reasoning/mine", json={}) + assert resp.status_code == 400 + + +def test_mine_starts_when_enabled( + client: TestClient, tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + monkeypatch.setattr( + "surreal_memory.unified_config.get_config", + lambda: _cfg(tmp_path, mining_enabled=True), + ) + + async def _noop(*_a: Any, **_k: Any) -> None: + return None + + monkeypatch.setattr(rt_module, "_run_mining", _noop) + resp = client.post("/api/dashboard/reasoning/mine", json={"backfill": True}) + assert resp.status_code == 202 + body = resp.json() + assert body["status"] == "started" + assert body["mining"]["running"] is True + + +def test_mine_conflict_when_already_running( + client: TestClient, tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + monkeypatch.setattr( + "surreal_memory.unified_config.get_config", + lambda: _cfg(tmp_path, mining_enabled=True), + ) + monkeypatch.setattr(rt_module, "_mining_tasks", {BRAIN_ID: SimpleNamespace(done=lambda: False)}) + resp = client.post("/api/dashboard/reasoning/mine", json={}) + assert resp.status_code == 409 + + +def test_mine_isolated_per_brain( + client: TestClient, tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + # A running job on a DIFFERENT brain must neither block nor leak status into + # the requested brain (this client is scoped to BRAIN_ID="default"). + other_running = rt_module._idle_mining_state() + other_running.update(running=True, traces_ingested=99) + monkeypatch.setattr( + rt_module, "_mining_tasks", {"other-brain": SimpleNamespace(done=lambda: False)} + ) + monkeypatch.setattr(rt_module, "_mining_states", {"other-brain": other_running}) + + # GET /status for `default` does not see other-brain's job. + monkeypatch.setattr("surreal_memory.unified_config.get_config", lambda: _cfg(tmp_path)) + status = client.get("/api/dashboard/reasoning/status").json() + assert status["mining"]["running"] is False + assert status["mining"]["traces_ingested"] == 0 + + # POST /mine for `default` is not blocked by other-brain's running job. + monkeypatch.setattr( + "surreal_memory.unified_config.get_config", + lambda: _cfg(tmp_path, mining_enabled=True), + ) + + async def _noop(*_a: Any, **_k: Any) -> None: + return None + + monkeypatch.setattr(rt_module, "_run_mining", _noop) + assert client.post("/api/dashboard/reasoning/mine", json={}).status_code == 202 + + +# ── GET/DELETE patterns ─────────────────────────────────────────────────────── + + +def test_list_patterns_filters_and_ranks(client: TestClient, mock_storage: AsyncMock) -> None: + mock_storage.find_fibers.return_value = [ + _fiber("p1", "claude-fable-5", "debugging", confidence=0.5, frequency=2), + _fiber("p2", "claude-fable-5", "planning", confidence=1.0, frequency=3), + _fiber("p3", "claude-sonnet-5", "debugging", confidence=1.0, frequency=1), + ] + resp = client.get("/api/dashboard/reasoning/patterns", params={"model": "claude-fable-5"}) + assert resp.status_code == 200 + data = resp.json() + assert data["total"] == 2 + # Higher confidence*frequency ranks first (p2: 3.0 > p1: 1.0). + assert data["patterns"][0]["id"] == "p2" + + +def test_list_patterns_pagination(client: TestClient, mock_storage: AsyncMock) -> None: + mock_storage.find_fibers.return_value = [ + _fiber(f"p{i}", "claude-fable-5", "debugging", frequency=i) for i in range(5) + ] + resp = client.get("/api/dashboard/reasoning/patterns", params={"limit": 2, "offset": 0}) + data = resp.json() + assert data["total"] == 5 + assert len(data["patterns"]) == 2 + assert data["limit"] == 2 + + +def test_get_pattern_detail(client: TestClient, mock_storage: AsyncMock) -> None: + mock_storage.get_fiber.return_value = _fiber( + "p1", "claude-fable-5", "debugging", title="verify" + ) + resp = client.get("/api/dashboard/reasoning/patterns/p1") + assert resp.status_code == 200 + data = resp.json() + assert data["title"] == "verify" + assert data["strategy"] == "verify -> check" + assert data["description"] == "medoid description" + + +def test_get_pattern_404_for_non_pattern(client: TestClient, mock_storage: AsyncMock) -> None: + mock_storage.get_fiber.return_value = _fiber("x", "m", "c", pattern=False) + resp = client.get("/api/dashboard/reasoning/patterns/x") + assert resp.status_code == 404 + + +def test_delete_pattern(client: TestClient, mock_storage: AsyncMock) -> None: + mock_storage.get_fiber.return_value = _fiber("p1", "claude-fable-5", "debugging") + resp = client.request("DELETE", "/api/dashboard/reasoning/patterns/p1") + assert resp.status_code == 200 + assert resp.json()["deleted"] == 1 + mock_storage.delete_fiber.assert_awaited_once_with("p1") + + +def test_delete_pattern_404(client: TestClient, mock_storage: AsyncMock) -> None: + mock_storage.get_fiber.return_value = None + resp = client.request("DELETE", "/api/dashboard/reasoning/patterns/nope") + assert resp.status_code == 404 + + +def test_delete_patterns_by_model(client: TestClient, mock_storage: AsyncMock) -> None: + mock_storage.find_fibers.return_value = [ + _fiber("p1", "claude-fable-5", "debugging"), + _fiber("p2", "claude-fable-5", "planning"), + _fiber("p3", "claude-sonnet-5", "debugging"), + ] + resp = client.request( + "DELETE", "/api/dashboard/reasoning/patterns", params={"model": "claude-fable-5"} + ) + assert resp.status_code == 200 + assert resp.json()["deleted"] == 2 + assert mock_storage.delete_fiber.await_count == 2 + + +def test_delete_traces_by_model(client: TestClient, mock_storage: AsyncMock) -> None: + mock_storage.delete_reasoning_traces_by_model.return_value = 7 + resp = client.request( + "DELETE", "/api/dashboard/reasoning/traces", params={"model": "claude-fable-5"} + ) + assert resp.status_code == 200 + assert resp.json()["deleted"] == 7 + mock_storage.delete_reasoning_traces_by_model.assert_awaited_once_with( + BRAIN_ID, "claude-fable-5" + ) + + +def test_delete_traces_requires_model(client: TestClient) -> None: + resp = client.request("DELETE", "/api/dashboard/reasoning/traces") + assert resp.status_code == 422 # missing required query param diff --git a/tests/unit/test_surrealdb_reasoning_traces.py b/tests/unit/test_surrealdb_reasoning_traces.py index 455b14a1..6f96e150 100644 --- a/tests/unit/test_surrealdb_reasoning_traces.py +++ b/tests/unit/test_surrealdb_reasoning_traces.py @@ -54,6 +54,8 @@ async def _query(self, sql: str, **params: Any) -> list[dict[str, Any]]: if params.get("model"): rows = [r for r in rows if r.get("model") == params["model"]] return rows + if "AND model = $model GROUP ALL" in sql: + return [{"c": c.get("del_count", 0)}] if "processed = true AND created_at < $cutoff GROUP ALL" in sql: return [{"c": c.get("prune_count", 0)}] if "count() AS c FROM reasoning_traces WHERE brain_id = $bid GROUP ALL" in sql: @@ -108,6 +110,23 @@ async def test_insert_empty_returns_zero() -> None: assert store.inserts == [] +async def test_delete_by_model_counts_and_deletes() -> None: + store = _RTStore(del_count=3) + n = await store.delete_reasoning_traces_by_model("default", "claude-fable-5") + assert n == 3 + # A parameterized DELETE was issued for that model (no string interpolation). + assert any( + sql.startswith("DELETE reasoning_traces") and p.get("model") == "claude-fable-5" + for sql, p in store.queries + ) + + +async def test_delete_by_model_empty_is_noop() -> None: + store = _RTStore(del_count=99) + assert await store.delete_reasoning_traces_by_model("default", "") == 0 + assert store.queries == [] # short-circuits before issuing any query + + async def test_insert_truncates_task_context_and_computes_chars() -> None: store = _RTStore() await store.insert_reasoning_traces( From 398a74decebadd6d3a8677cf56b023cd8189a7aa Mon Sep 17 00:00:00 2001 From: Toni Nowak Date: Fri, 17 Jul 2026 15:26:57 +0200 Subject: [PATCH 08/12] feat: reasoning training dashboard page (U8) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit dashboard/src/features/reasoning/: ReasoningPage (KPI cards, per-model coverage bars + per-category badges, empty/loading/error states) composing MiningConfigCard (mining toggle, per-model source checkboxes disabled for no-thinking models, Run mining with backfill + 409/400 handling, per-model trace wipe), InjectionMappingCard (injection toggle, source→target rows with stable-id keys, duplicate-target guard), and PatternsTable (model/category filters, pagination with shrink-clamp, per-pattern and per-model delete via ConfirmDialog). api/hooks/useReasoning.ts: status (polls 3s while mining runs), optimistic config update (no toggle flicker), trigger mining, patterns list, and pattern/traces deletes with correct cache invalidation. api/client.ts: apiErrorMessage() parses FastAPI {detail} so toasts show the message, not raw JSON. api/types.ts: interfaces mirroring the U7 Pydantic models. Wired route (App.tsx), nav (Sidebar.tsx, GraduationCap), and i18n (en.json). Rebuilt static/dist. Patterns table refreshes when a mining run finishes. e2e (dashboard/e2e/reasoning.spec.ts, 2 tests) passes; tsc + eslint clean. react-reviewer APPROVE (2 HIGH + 5 MED fixed). --- dashboard/.gitignore | 5 + dashboard/e2e/reasoning.spec.ts | 180 ++++++++++++++ dashboard/src/App.tsx | 9 + dashboard/src/api/client.ts | 18 ++ dashboard/src/api/hooks/useReasoning.ts | 114 +++++++++ dashboard/src/api/types.ts | 127 ++++++++++ dashboard/src/components/layout/Sidebar.tsx | 2 + .../reasoning/InjectionMappingCard.tsx | 180 ++++++++++++++ .../features/reasoning/MiningConfigCard.tsx | 185 ++++++++++++++ .../src/features/reasoning/PatternsTable.tsx | 231 ++++++++++++++++++ .../src/features/reasoning/ReasoningPage.tsx | 140 +++++++++++ dashboard/src/i18n/en.json | 68 +++++- ...e-7MxRvurw.js => DiagramsPage-ClJ9QIg8.js} | 2 +- ...-_rzMyZka.js => EvolutionPage-C6VI1EZi.js} | 2 +- ...Page-DnkZu_wH.js => GraphPage-B5RqF55O.js} | 2 +- ...age-WgJz4uYi.js => HealthPage-DoDlPq43.js} | 2 +- .../static/dist/assets/OraclePage-BOi3rQAz.js | 1 + .../static/dist/assets/OraclePage-zAsy8lB5.js | 1 - .../dist/assets/OverviewPage-Bd8oQbjM.js | 1 - .../dist/assets/OverviewPage-BixPEj84.js | 1 + ...roGate-wWiPatWA.js => ProGate-DjphnyL3.js} | 2 +- .../dist/assets/ReasoningPage-BjC1wtHj.js | 1 + ...e-BD10gAE1.js => SettingsPage-CcFz-Brq.js} | 2 +- ...ge-B_CEarTZ.js => StoragePage-BsRzooql.js} | 2 +- ...cPage-XGbJa_ff.js => SyncPage-BRERoUGJ.js} | 2 +- ...e-ByrSgpuo.js => TimelinePage-2mV1L6iZ.js} | 2 +- ...-C3TV9sEV.js => ToolStatsPage-yrXaz2Sj.js} | 2 +- .../dist/assets/UncertaintyPage-B1-YQ66j.js | 1 - .../dist/assets/UncertaintyPage-CdFbDPD5.js | 1 + .../dist/assets/VisualizePage-6HaaRoFH.js | 2 - .../dist/assets/VisualizePage-CayiR_fG.js | 2 + .../{card-DAwbJLqc.js => card-Duzfr-hx.js} | 2 +- .../dist/assets/confirm-dialog-DE0Sgk6_.js | 1 + .../static/dist/assets/index-BgBv2qcr.css | 1 + .../static/dist/assets/index-CSMoVcy_.js | 54 ---- .../static/dist/assets/index-Cc1YGEOX.css | 1 - .../static/dist/assets/index-DXL5wCpD.js | 54 ++++ .../static/dist/assets/skeleton-CAu64_35.js | 1 - .../static/dist/assets/skeleton-CBW-y0cP.js | 1 + .../dist/assets/vendor-icons-C6dhS1UE.js | 1 + .../dist/assets/vendor-icons-IAbuZhWE.js | 1 - .../server/static/dist/index.html | 6 +- 42 files changed, 1336 insertions(+), 77 deletions(-) create mode 100644 dashboard/e2e/reasoning.spec.ts create mode 100644 dashboard/src/api/hooks/useReasoning.ts create mode 100644 dashboard/src/features/reasoning/InjectionMappingCard.tsx create mode 100644 dashboard/src/features/reasoning/MiningConfigCard.tsx create mode 100644 dashboard/src/features/reasoning/PatternsTable.tsx create mode 100644 dashboard/src/features/reasoning/ReasoningPage.tsx rename src/surreal_memory/server/static/dist/assets/{DiagramsPage-7MxRvurw.js => DiagramsPage-ClJ9QIg8.js} (99%) rename src/surreal_memory/server/static/dist/assets/{EvolutionPage-_rzMyZka.js => EvolutionPage-C6VI1EZi.js} (83%) rename src/surreal_memory/server/static/dist/assets/{GraphPage-DnkZu_wH.js => GraphPage-B5RqF55O.js} (99%) rename src/surreal_memory/server/static/dist/assets/{HealthPage-WgJz4uYi.js => HealthPage-DoDlPq43.js} (82%) create mode 100644 src/surreal_memory/server/static/dist/assets/OraclePage-BOi3rQAz.js delete mode 100644 src/surreal_memory/server/static/dist/assets/OraclePage-zAsy8lB5.js delete mode 100644 src/surreal_memory/server/static/dist/assets/OverviewPage-Bd8oQbjM.js create mode 100644 src/surreal_memory/server/static/dist/assets/OverviewPage-BixPEj84.js rename src/surreal_memory/server/static/dist/assets/{ProGate-wWiPatWA.js => ProGate-DjphnyL3.js} (71%) create mode 100644 src/surreal_memory/server/static/dist/assets/ReasoningPage-BjC1wtHj.js rename src/surreal_memory/server/static/dist/assets/{SettingsPage-BD10gAE1.js => SettingsPage-CcFz-Brq.js} (93%) rename src/surreal_memory/server/static/dist/assets/{StoragePage-B_CEarTZ.js => StoragePage-BsRzooql.js} (88%) rename src/surreal_memory/server/static/dist/assets/{SyncPage-XGbJa_ff.js => SyncPage-BRERoUGJ.js} (92%) rename src/surreal_memory/server/static/dist/assets/{TimelinePage-ByrSgpuo.js => TimelinePage-2mV1L6iZ.js} (87%) rename src/surreal_memory/server/static/dist/assets/{ToolStatsPage-C3TV9sEV.js => ToolStatsPage-yrXaz2Sj.js} (95%) delete mode 100644 src/surreal_memory/server/static/dist/assets/UncertaintyPage-B1-YQ66j.js create mode 100644 src/surreal_memory/server/static/dist/assets/UncertaintyPage-CdFbDPD5.js delete mode 100644 src/surreal_memory/server/static/dist/assets/VisualizePage-6HaaRoFH.js create mode 100644 src/surreal_memory/server/static/dist/assets/VisualizePage-CayiR_fG.js rename src/surreal_memory/server/static/dist/assets/{card-DAwbJLqc.js => card-Duzfr-hx.js} (86%) create mode 100644 src/surreal_memory/server/static/dist/assets/confirm-dialog-DE0Sgk6_.js create mode 100644 src/surreal_memory/server/static/dist/assets/index-BgBv2qcr.css delete mode 100644 src/surreal_memory/server/static/dist/assets/index-CSMoVcy_.js delete mode 100644 src/surreal_memory/server/static/dist/assets/index-Cc1YGEOX.css create mode 100644 src/surreal_memory/server/static/dist/assets/index-DXL5wCpD.js delete mode 100644 src/surreal_memory/server/static/dist/assets/skeleton-CAu64_35.js create mode 100644 src/surreal_memory/server/static/dist/assets/skeleton-CBW-y0cP.js create mode 100644 src/surreal_memory/server/static/dist/assets/vendor-icons-C6dhS1UE.js delete mode 100644 src/surreal_memory/server/static/dist/assets/vendor-icons-IAbuZhWE.js diff --git a/dashboard/.gitignore b/dashboard/.gitignore index a547bf36..8d354d3d 100755 --- a/dashboard/.gitignore +++ b/dashboard/.gitignore @@ -22,3 +22,8 @@ dist-ssr *.njsproj *.sln *.sw? + +# Playwright transient outputs (not baselines) +test-results +playwright-report +e2e/__screenshots__ diff --git a/dashboard/e2e/reasoning.spec.ts b/dashboard/e2e/reasoning.spec.ts new file mode 100644 index 00000000..91070de8 --- /dev/null +++ b/dashboard/e2e/reasoning.spec.ts @@ -0,0 +1,180 @@ +import { test, expect, type Page } from "@playwright/test" + +/** + * Browser QA for the U8 "Reasoning Training" dashboard page + * (features/reasoning/ReasoningPage.tsx, route /reasoning, nav key `nav.reasoningTraining`). + * + * No backend runs on :8000 here, so every API call is intercepted and fulfilled with static + * JSON. main.tsx mounts , so navigations target /ui/. + */ + +const CATEGORIES = [ + "debugging", + "planning", + "implementation", + "refactoring", + "research", + "verification", + "architecture", + "data-analysis", +] + +const STATUS_BODY = { + config: { + mining_enabled: true, + injection_enabled: false, + mining_models: [], + injection_map: {}, + categories: CATEGORIES, + min_trace_chars: 200, + max_trace_chars: 20000, + max_traces_per_scan: 500, + scan_lookback_days: 30, + retention_days: 90, + max_traces_total: 20000, + min_cluster_support: 3, + max_patterns_per_run: 10, + min_confidence: 0.2, + min_patterns_per_category: 3, + injection_max_patterns: 5, + injection_max_chars: 4000, + distill_use_llm: false, + redact_secrets: true, + }, + detected_models: ["claude-fable-5"], + per_model: [ + { + model: "claude-fable-5", + trace_count: 5, + unprocessed: 2, + pattern_count: 3, + has_thinking_text: true, + last_trace_at: "2026-07-17T10:00:00", + coverage_percent: 12.5, + }, + ], + coverage_by_model: { + "claude-fable-5": CATEGORIES.map((c) => ({ + category: c, + pattern_count: c === "debugging" ? 3 : 0, + covered: c === "debugging", + })), + }, + total_traces: 5, + unprocessed_traces: 2, + total_patterns: 3, + mining: { + running: false, + started_at: null, + finished_at: null, + traces_ingested: 0, + patterns_learned: 0, + dry_run: false, + error: null, + }, +} + +const PATTERNS_BODY = { + patterns: [ + { + id: "p1", + source_model: "claude-fable-5", + category: "debugging", + title: "restate then verify", + confidence: 1.0, + frequency: 3, + signature: "sig1", + }, + ], + total: 1, + limit: 20, + offset: 0, +} + +const HEALTH_REPORT = { + grade: "A", + purity_score: 0.9, + connectivity: 0.8, + diversity: 0.7, + freshness: 0.85, + consolidation_ratio: 0.5, + orphan_rate: 0.1, + activation_efficiency: 0.6, + recall_confidence: 0.75, + neuron_count: 100, + synapse_count: 200, + fiber_count: 50, + contradiction_count: 3, + conflict_rate: 0.12, + warnings: [], + recommendations: [], + top_penalties: [], +} + +function json(body: unknown) { + return { status: 200, contentType: "application/json", body: JSON.stringify(body) } +} + +async function installMocks(page: Page) { + await page.route("**/api/dashboard/**", (route) => route.fulfill(json({}))) + await page.route("**/api/dashboard/stats", (route) => + route.fulfill(json({ active_brain: "test-brain" })), + ) + await page.route("**/api/dashboard/brains", (route) => route.fulfill(json([]))) + await page.route("**/api/dashboard/fibers", (route) => route.fulfill(json({ fibers: [] }))) + await page.route("**/api/dashboard/health", (route) => route.fulfill(json(HEALTH_REPORT))) + await page.route( + (url) => url.pathname === "/health", + (route) => route.fulfill(json({ status: "ok", version: "test" })), + ) + // Patterns must be registered before status so the more specific glob wins + // (Playwright checks handlers last-registered-first). + await page.route("**/api/dashboard/reasoning/patterns**", (route) => + route.fulfill(json(PATTERNS_BODY)), + ) + await page.route("**/api/dashboard/reasoning/status", (route) => + route.fulfill(json(STATUS_BODY)), + ) +} + +test.describe("U8 Reasoning Training page", () => { + test("renders populated reasoning content", async ({ page }) => { + await installMocks(page) + await page.goto("/ui/reasoning") + + await expect( + page.getByRole("heading", { level: 1, name: "Reasoning Training" }), + ).toBeVisible({ timeout: 20_000 }) + + // KPI cards. + await expect(page.getByText("Total traces")).toBeVisible() + await expect(page.getByText("Learned patterns")).toBeVisible() + + // Coverage + config cards show the detected model. + await expect(page.getByText("claude-fable-5").first()).toBeVisible() + await expect(page.getByText("12.5%")).toBeVisible() + + // Patterns table row. + await expect(page.getByText("restate then verify")).toBeVisible() + + // No raw untranslated i18n keys should leak into the DOM. + await expect(page.getByText(/reasoning\.[a-zA-Z]/)).toHaveCount(0) + + await page.screenshot({ path: "e2e/__screenshots__/reasoning.png", fullPage: true }) + }) + + test("sidebar Reasoning Training nav link navigates from another route", async ({ page }) => { + await installMocks(page) + await page.goto("/ui/health") + await expect(page.getByRole("navigation")).toBeVisible({ timeout: 20_000 }) + + const link = page.getByRole("navigation").getByRole("link", { name: "Reasoning Training" }) + await expect(link).toBeVisible() + await link.click() + + await expect(page).toHaveURL(/\/ui\/reasoning$/) + await expect( + page.getByRole("heading", { level: 1, name: "Reasoning Training" }), + ).toBeVisible() + }) +}) diff --git a/dashboard/src/App.tsx b/dashboard/src/App.tsx index d1962367..a7d198f6 100755 --- a/dashboard/src/App.tsx +++ b/dashboard/src/App.tsx @@ -16,6 +16,7 @@ const OraclePage = lazy(() => import("@/features/oracle/OraclePage")) const ToolStatsPage = lazy(() => import("@/features/tool-stats/ToolStatsPage")) const VisualizePage = lazy(() => import("@/features/visualize/VisualizePage")) const StoragePage = lazy(() => import("@/features/storage/StoragePage")) +const ReasoningPage = lazy(() => import("@/features/reasoning/ReasoningPage")) export default function App() { return ( @@ -125,6 +126,14 @@ export default function App() { } /> + }> + + + } + /> } /> diff --git a/dashboard/src/api/client.ts b/dashboard/src/api/client.ts index 4723b685..b5d47734 100755 --- a/dashboard/src/api/client.ts +++ b/dashboard/src/api/client.ts @@ -60,4 +60,22 @@ export const api = { request(path, { ...options, method: "DELETE" }), } +/** + * Extract a human-readable message from an ApiError. FastAPI returns + * `{"detail": "..."}` bodies, which `request()` stores verbatim as the error + * message — parse it out so toasts show the message, not raw JSON. + */ +export function apiErrorMessage(err: unknown, fallback: string): string { + if (err instanceof ApiError) { + try { + const parsed = JSON.parse(err.message) + if (parsed && typeof parsed.detail === "string") return parsed.detail + } catch { + /* body was not JSON — fall through to the raw text */ + } + return err.message || fallback + } + return fallback +} + export { ApiError } diff --git a/dashboard/src/api/hooks/useReasoning.ts b/dashboard/src/api/hooks/useReasoning.ts new file mode 100644 index 00000000..1aa0b7b7 --- /dev/null +++ b/dashboard/src/api/hooks/useReasoning.ts @@ -0,0 +1,114 @@ +import { useMutation, useQuery, useQueryClient } from "@tanstack/react-query" +import { api } from "@/api/client" +import type { + MineRequest, + MineResponse, + PatternsListResponse, + ReasoningConfig, + ReasoningConfigUpdate, + ReasoningConfigUpdateResponse, + ReasoningDeleteResponse, + ReasoningStatusResponse, +} from "@/api/types" + +const keys = { + status: ["reasoning", "status"] as const, + patterns: ["reasoning", "patterns"] as const, + patternList: (model: string, category: string, limit: number, offset: number) => + ["reasoning", "patterns", model, category, limit, offset] as const, +} + +export function useReasoningStatus() { + return useQuery({ + queryKey: keys.status, + queryFn: () => api.get("/api/dashboard/reasoning/status"), + // Poll while a mining job is running so counts/coverage update live. + refetchInterval: (query) => (query.state.data?.mining.running ? 3000 : false), + }) +} + +export function useUpdateReasoningConfig() { + const queryClient = useQueryClient() + return useMutation({ + mutationFn: (body: ReasoningConfigUpdate) => + api.put("/api/dashboard/reasoning/config", body), + // Optimistically patch the cached status so toggles/checkboxes don't briefly + // revert to the old value in the window before the refetch lands. + onMutate: async (body) => { + await queryClient.cancelQueries({ queryKey: keys.status }) + const prev = queryClient.getQueryData(keys.status) + if (prev) { + queryClient.setQueryData(keys.status, { + ...prev, + config: { ...prev.config, ...body } as ReasoningConfig, + }) + } + return { prev } + }, + onError: (_err, _body, context) => { + if (context?.prev) queryClient.setQueryData(keys.status, context.prev) + }, + onSettled: () => queryClient.invalidateQueries({ queryKey: keys.status }), + }) +} + +export function useTriggerMining() { + const queryClient = useQueryClient() + return useMutation({ + mutationFn: (body: MineRequest) => + api.post("/api/dashboard/reasoning/mine", body), + onSuccess: () => queryClient.invalidateQueries({ queryKey: keys.status }), + }) +} + +export function useReasoningPatterns(model = "", category = "", limit = 50, offset = 0) { + const params = new URLSearchParams() + if (model) params.set("model", model) + if (category) params.set("category", category) + params.set("limit", String(limit)) + params.set("offset", String(offset)) + return useQuery({ + queryKey: keys.patternList(model, category, limit, offset), + queryFn: () => + api.get(`/api/dashboard/reasoning/patterns?${params.toString()}`), + }) +} + +export function useDeleteReasoningPattern() { + const queryClient = useQueryClient() + return useMutation({ + mutationFn: (id: string) => + api.delete( + `/api/dashboard/reasoning/patterns/${encodeURIComponent(id)}`, + ), + onSuccess: () => { + queryClient.invalidateQueries({ queryKey: keys.status }) + queryClient.invalidateQueries({ queryKey: keys.patterns }) + }, + }) +} + +export function useDeletePatternsByModel() { + const queryClient = useQueryClient() + return useMutation({ + mutationFn: (model: string) => + api.delete( + `/api/dashboard/reasoning/patterns?model=${encodeURIComponent(model)}`, + ), + onSuccess: () => { + queryClient.invalidateQueries({ queryKey: keys.status }) + queryClient.invalidateQueries({ queryKey: keys.patterns }) + }, + }) +} + +export function useWipeTraces() { + const queryClient = useQueryClient() + return useMutation({ + mutationFn: (model: string) => + api.delete( + `/api/dashboard/reasoning/traces?model=${encodeURIComponent(model)}`, + ), + onSuccess: () => queryClient.invalidateQueries({ queryKey: keys.status }), + }) +} diff --git a/dashboard/src/api/types.ts b/dashboard/src/api/types.ts index bf29d003..3700a72f 100755 --- a/dashboard/src/api/types.ts +++ b/dashboard/src/api/types.ts @@ -474,3 +474,130 @@ export interface UncertaintyOverview { scan: UncertaintyScan samples: UncertaintySamples } + +/* ------------------------------------------------------------------ */ +/* Reasoning training (matches server/routes/reasoning_training.py) */ +/* ------------------------------------------------------------------ */ + +export interface ReasoningConfig { + mining_enabled: boolean + injection_enabled: boolean + mining_models: string[] + injection_map: Record + categories: string[] + min_trace_chars: number + max_trace_chars: number + max_traces_per_scan: number + scan_lookback_days: number + retention_days: number + max_traces_total: number + min_cluster_support: number + max_patterns_per_run: number + min_confidence: number + min_patterns_per_category: number + injection_max_patterns: number + injection_max_chars: number + distill_use_llm: boolean + redact_secrets: boolean +} + +export interface ModelTraceStats { + model: string + trace_count: number + unprocessed: number + pattern_count: number + has_thinking_text: boolean + last_trace_at: string | null + coverage_percent: number +} + +export interface CategoryCoverage { + category: string + pattern_count: number + covered: boolean +} + +export interface MiningJobState { + running: boolean + started_at: string | null + finished_at: string | null + traces_ingested: number + patterns_learned: number + dry_run: boolean + error: string | null +} + +// GET /api/dashboard/reasoning/status +export interface ReasoningStatusResponse { + config: ReasoningConfig + detected_models: string[] + per_model: ModelTraceStats[] + coverage_by_model: Record + total_traces: number + unprocessed_traces: number + total_patterns: number + mining: MiningJobState +} + +// PUT /api/dashboard/reasoning/config (all fields optional) +export interface ReasoningConfigUpdate { + mining_enabled?: boolean + injection_enabled?: boolean + mining_models?: string[] + injection_map?: Record + categories?: string[] + scan_lookback_days?: number + retention_days?: number + max_traces_total?: number + min_cluster_support?: number + max_patterns_per_run?: number + min_confidence?: number + min_patterns_per_category?: number + injection_max_patterns?: number + injection_max_chars?: number +} + +export interface ReasoningConfigUpdateResponse { + status: string + config: ReasoningConfig +} + +// POST /api/dashboard/reasoning/mine +export interface MineRequest { + backfill?: boolean + dry_run?: boolean + models?: string[] +} + +export interface MineResponse { + status: string + mining: MiningJobState +} + +export interface PatternSummary { + id: string + source_model: string + category: string + title: string + confidence: number + frequency: number + signature: string +} + +export interface PatternDetail extends PatternSummary { + strategy: string + description: string + summary: string +} + +// GET /api/dashboard/reasoning/patterns +export interface PatternsListResponse { + patterns: PatternSummary[] + total: number + limit: number + offset: number +} + +export interface ReasoningDeleteResponse { + deleted: number +} diff --git a/dashboard/src/components/layout/Sidebar.tsx b/dashboard/src/components/layout/Sidebar.tsx index 2320719b..8be03780 100755 --- a/dashboard/src/components/layout/Sidebar.tsx +++ b/dashboard/src/components/layout/Sidebar.tsx @@ -14,6 +14,7 @@ import { Sparkle, ChartBar, ChartLine, + GraduationCap, } from "@phosphor-icons/react" import { cn } from "@/lib/utils" import { useLayoutStore } from "@/stores/useLayoutStore" @@ -30,6 +31,7 @@ const navItems = [ { to: "/sync", icon: Cloud, labelKey: "nav.sync" }, { to: "/oracle", icon: Sparkle, labelKey: "nav.oracle" }, { to: "/tool-stats", icon: ChartBar, labelKey: "nav.toolStats" }, + { to: "/reasoning", icon: GraduationCap, labelKey: "nav.reasoningTraining" }, { to: "/visualize", icon: ChartLine, labelKey: "nav.visualize" }, { to: "/storage", icon: HardDrive, labelKey: "nav.storage" }, { to: "/settings", icon: Gear, labelKey: "nav.settings" }, diff --git a/dashboard/src/features/reasoning/InjectionMappingCard.tsx b/dashboard/src/features/reasoning/InjectionMappingCard.tsx new file mode 100644 index 00000000..3dde6e1a --- /dev/null +++ b/dashboard/src/features/reasoning/InjectionMappingCard.tsx @@ -0,0 +1,180 @@ +import { useState } from "react" +import { toast } from "sonner" +import { useTranslation } from "react-i18next" +import { Plus, Trash } from "@phosphor-icons/react" +import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card" +import { Button } from "@/components/ui/button" +import { apiErrorMessage } from "@/api/client" +import { useUpdateReasoningConfig } from "@/api/hooks/useReasoning" +import type { ReasoningStatusResponse } from "@/api/types" + +interface Props { + status: ReasoningStatusResponse +} + +interface Pair { + id: string + target: string + source: string +} + +// Monotonic row-id source. Stable per-row ids keep React from reusing one row's +// DOM node for another after a delete (which would strand focus mid-edit). A +// module counter avoids a ref (which eslint bans from render) and only advances +// when rows are (re)created, not on every render. +let pairSeq = 0 +const nextPairId = () => `pair-${pairSeq++}` + +const makePairs = (map: Record): Pair[] => + Object.entries(map).map(([target, source]) => ({ id: nextPairId(), target, source })) + +export function InjectionMappingCard({ status }: Props) { + const { t } = useTranslation() + const updateConfig = useUpdateReasoningConfig() + + const injectionEnabled = status.config.injection_enabled + // Source models must have thinking text (the backend rejects others with 422). + const sourceModels = status.per_model.filter((m) => m.has_thinking_text).map((m) => m.model) + + const [pairs, setPairs] = useState(() => makePairs(status.config.injection_map)) + const [dirty, setDirty] = useState(false) + + // Reset the local editable list when the server map changes (after a save → + // refetch gives a new object reference). Adjusting state during render is the + // React-blessed alternative to a syncing effect. + const [prevMap, setPrevMap] = useState(status.config.injection_map) + if (status.config.injection_map !== prevMap) { + setPrevMap(status.config.injection_map) + setPairs(makePairs(status.config.injection_map)) + setDirty(false) + } + + const toggleInjection = (enabled: boolean) => { + updateConfig.mutate( + { injection_enabled: enabled }, + { + onSuccess: () => toast.success(t("reasoning.configSaved")), + onError: (err) => toast.error(apiErrorMessage(err, t("reasoning.configSaveFailed"))), + }, + ) + } + + const setPair = (id: string, patch: Partial) => { + setPairs((prev) => prev.map((p) => (p.id === id ? { ...p, ...patch } : p))) + setDirty(true) + } + const addPair = () => { + setPairs((prev) => [...prev, { id: nextPairId(), target: "", source: sourceModels[0] ?? "" }]) + setDirty(true) + } + const removePair = (id: string) => { + setPairs((prev) => prev.filter((p) => p.id !== id)) + setDirty(true) + } + + const save = () => { + const seen = new Set() + const map: Record = {} + for (const p of pairs) { + const target = p.target.trim() + const source = p.source.trim() + if (!target || !source) continue + if (seen.has(target)) { + // Two rows with the same target would silently collapse — surface it. + toast.error(t("reasoning.duplicateTarget", { target })) + return + } + seen.add(target) + map[target] = source + } + updateConfig.mutate( + { injection_map: map }, + { + onSuccess: () => { + toast.success(t("reasoning.configSaved")) + setDirty(false) + }, + onError: (err) => toast.error(apiErrorMessage(err, t("reasoning.configSaveFailed"))), + }, + ) + } + + return ( + + + {t("reasoning.injectionTitle")} + + + + +
+

+ {t("reasoning.injectionMap")} +

+ {pairs.length === 0 && ( +

{t("reasoning.noMappings")}

+ )} + {pairs.map((p) => ( +
+ + → + setPair(p.id, { target: e.target.value })} + placeholder={t("reasoning.targetPlaceholder")} + className="w-40 rounded-md border border-border bg-background px-2 py-1.5 font-mono text-xs" + aria-label={t("reasoning.target")} + /> + +
+ ))} +
+ + +
+

{t("reasoning.injectionMapHint")}

+
+ + + ) +} diff --git a/dashboard/src/features/reasoning/MiningConfigCard.tsx b/dashboard/src/features/reasoning/MiningConfigCard.tsx new file mode 100644 index 00000000..c87eeeda --- /dev/null +++ b/dashboard/src/features/reasoning/MiningConfigCard.tsx @@ -0,0 +1,185 @@ +import { useState } from "react" +import { toast } from "sonner" +import { useTranslation } from "react-i18next" +import { Trash } from "@phosphor-icons/react" +import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card" +import { Button } from "@/components/ui/button" +import { ConfirmDialog } from "@/components/ui/confirm-dialog" +import { ApiError, apiErrorMessage } from "@/api/client" +import { + useTriggerMining, + useUpdateReasoningConfig, + useWipeTraces, +} from "@/api/hooks/useReasoning" +import type { ReasoningStatusResponse } from "@/api/types" + +interface Props { + status: ReasoningStatusResponse +} + +export function MiningConfigCard({ status }: Props) { + const { t } = useTranslation() + const updateConfig = useUpdateReasoningConfig() + const triggerMining = useTriggerMining() + const wipeTraces = useWipeTraces() + + const miningEnabled = status.config.mining_enabled + const selected = new Set(status.config.mining_models) + const running = status.mining.running + + const [backfill, setBackfill] = useState(false) + const [wipeModel, setWipeModel] = useState(null) + + const confirmWipe = () => { + if (!wipeModel) return + const m = wipeModel + setWipeModel(null) + wipeTraces.mutate(m, { + onSuccess: (res) => toast.success(t("reasoning.tracesWiped", { count: res.deleted })), + onError: (err) => toast.error(apiErrorMessage(err, t("reasoning.configSaveFailed"))), + }) + } + + const toggleMining = (enabled: boolean) => { + updateConfig.mutate( + { mining_enabled: enabled }, + { + onSuccess: () => toast.success(t("reasoning.configSaved")), + onError: (err) => toast.error(apiErrorMessage(err, t("reasoning.configSaveFailed"))), + }, + ) + } + + const toggleModel = (model: string, on: boolean) => { + const next = new Set(selected) + if (on) next.add(model) + else next.delete(model) + updateConfig.mutate( + { mining_models: [...next] }, + { + onSuccess: () => toast.success(t("reasoning.configSaved")), + onError: (err) => toast.error(apiErrorMessage(err, t("reasoning.configSaveFailed"))), + }, + ) + } + + const runMining = () => { + triggerMining.mutate( + { backfill }, + { + onSuccess: () => toast.success(t("reasoning.miningStarted")), + onError: (err) => { + if (err instanceof ApiError && err.status === 409) { + toast.error(t("reasoning.miningInProgress")) + } else if (err instanceof ApiError && err.status === 400) { + toast.error(t("reasoning.miningDisabled")) + } else { + toast.error(t("reasoning.miningFailed")) + } + }, + }, + ) + } + + return ( + + + {t("reasoning.miningTitle")} + + + + +
+

+ {t("reasoning.miningModels")} +

+ {status.detected_models.length === 0 ? ( +

{t("reasoning.noModels")}

+ ) : ( +
+ {status.per_model.map((m) => ( +
+ + {m.trace_count > 0 && ( + + )} +
+ ))} +
+ )} +

{t("reasoning.miningModelsHint")}

+
+ +
+ + +
+ + + setWipeModel(null)} + /> + + ) +} diff --git a/dashboard/src/features/reasoning/PatternsTable.tsx b/dashboard/src/features/reasoning/PatternsTable.tsx new file mode 100644 index 00000000..2101ce4d --- /dev/null +++ b/dashboard/src/features/reasoning/PatternsTable.tsx @@ -0,0 +1,231 @@ +import { useState } from "react" +import { toast } from "sonner" +import { useTranslation } from "react-i18next" +import { Trash } from "@phosphor-icons/react" +import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card" +import { Button } from "@/components/ui/button" +import { Badge } from "@/components/ui/badge" +import { Skeleton } from "@/components/ui/skeleton" +import { ConfirmDialog } from "@/components/ui/confirm-dialog" +import { apiErrorMessage } from "@/api/client" +import { + useDeletePatternsByModel, + useDeleteReasoningPattern, + useReasoningPatterns, +} from "@/api/hooks/useReasoning" +import type { PatternSummary } from "@/api/types" + +interface Props { + detectedModels: string[] + categories: string[] +} + +const PAGE_SIZE = 20 + +export function PatternsTable({ detectedModels, categories }: Props) { + const { t } = useTranslation() + const [model, setModel] = useState("") + const [category, setCategory] = useState("") + const [offset, setOffset] = useState(0) + const [toDelete, setToDelete] = useState(null) + const [bulkModel, setBulkModel] = useState(null) + + const { data, isLoading, isError, refetch } = useReasoningPatterns( + model, + category, + PAGE_SIZE, + offset, + ) + const deletePattern = useDeleteReasoningPattern() + const deleteByModel = useDeletePatternsByModel() + + const total = data?.total ?? 0 + const patterns = data?.patterns ?? [] + + // If the data shrank below the current page (e.g. after a delete), step back so + // the user isn't stranded on an empty page with hidden pagination controls. + if (data && offset > 0 && offset >= total) { + setOffset(Math.max(0, total - PAGE_SIZE)) + } + + const resetPaging = () => setOffset(0) + + const confirmDelete = () => { + if (!toDelete) return + const id = toDelete.id + setToDelete(null) + deletePattern.mutate(id, { + onSuccess: () => toast.success(t("reasoning.patternDeleted")), + onError: () => toast.error(t("reasoning.patternDeleteFailed")), + }) + } + + const confirmBulkDelete = () => { + if (!bulkModel) return + const m = bulkModel + setBulkModel(null) + deleteByModel.mutate(m, { + onSuccess: (res) => + toast.success(t("reasoning.patternsDeletedCount", { count: res.deleted })), + onError: (err) => toast.error(apiErrorMessage(err, t("reasoning.patternDeleteFailed"))), + }) + } + + return ( + + + {t("reasoning.patternsTitle")} + + +
+ + + {model && ( + + )} +
+ + {isLoading ? ( + + ) : isError ? ( +
+

{t("reasoning.patternsError")}

+ +
+ ) : patterns.length === 0 ? ( +

{t("reasoning.noPatterns")}

+ ) : ( +
+ + + + + + + + + + + + {patterns.map((p) => ( + + + + + + + + + ))} + +
{t("reasoning.colTitle")}{t("reasoning.colModel")}{t("reasoning.colCategory")} + {t("reasoning.colConfidence")} + + {t("reasoning.colFrequency")} + +
{p.title}{p.source_model} + {p.category} + + {Math.round(p.confidence * 100)}% + {p.frequency} + +
+
+ )} + + {total > PAGE_SIZE && ( +
+ + {offset + 1}–{Math.min(offset + PAGE_SIZE, total)} / {total} + +
+ + +
+
+ )} + + + setToDelete(null)} + /> + + setBulkModel(null)} + /> + + ) +} diff --git a/dashboard/src/features/reasoning/ReasoningPage.tsx b/dashboard/src/features/reasoning/ReasoningPage.tsx new file mode 100644 index 00000000..fbd201da --- /dev/null +++ b/dashboard/src/features/reasoning/ReasoningPage.tsx @@ -0,0 +1,140 @@ +import { useEffect, useRef } from "react" +import { useTranslation } from "react-i18next" +import { useQueryClient } from "@tanstack/react-query" +import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card" +import { Button } from "@/components/ui/button" +import { Badge } from "@/components/ui/badge" +import { Skeleton } from "@/components/ui/skeleton" +import { useReasoningStatus } from "@/api/hooks/useReasoning" +import type { ReasoningStatusResponse } from "@/api/types" +import { MiningConfigCard } from "./MiningConfigCard" +import { InjectionMappingCard } from "./InjectionMappingCard" +import { PatternsTable } from "./PatternsTable" + +function Kpi({ label, value }: { label: string; value: number }) { + return ( + + + {label} + + +

{value.toLocaleString()}

+ + + ) +} + +function CoverageCard({ status }: { status: ReasoningStatusResponse }) { + const { t } = useTranslation() + const models = status.per_model.filter((m) => m.pattern_count > 0) + if (models.length === 0) return null + return ( + + + {t("reasoning.coverageTitle")} + + + {models.map((m) => { + const cats = status.coverage_by_model[m.model] ?? [] + return ( +
+
+ {m.model} + + {m.coverage_percent}% + +
+
+
+
+ {cats.length > 0 && ( +
+ {cats.map((c) => ( + + {c.category} ({c.pattern_count}) + + ))} +
+ )} +
+ ) + })} + + + ) +} + +export default function ReasoningPage() { + const { t } = useTranslation() + const queryClient = useQueryClient() + const { data: status, isLoading, isError, refetch } = useReasoningStatus() + + // A mining run creates new pattern fibers; when it finishes (running true→false), + // refresh the patterns table (the status poll already refreshes KPIs/coverage). + const running = status?.mining.running ?? false + const prevRunning = useRef(running) + useEffect(() => { + if (prevRunning.current && !running) { + queryClient.invalidateQueries({ queryKey: ["reasoning", "patterns"] }) + } + prevRunning.current = running + }, [running, queryClient]) + + return ( +
+
+

{t("reasoning.pageTitle")}

+

{t("reasoning.pageSubtitle")}

+
+ + {isLoading ? ( +
+ {[0, 1, 2, 3].map((i) => ( + + ))} +
+ ) : isError || !status ? ( + + +

{t("reasoning.statusError")}

+ + + + ) : ( + <> +
+ + + + +
+ + {status.total_traces === 0 && !status.mining.running && ( + + + {t("reasoning.emptyState")} + + + )} + + + +
+ + +
+ + + + )} +
+ ) +} diff --git a/dashboard/src/i18n/en.json b/dashboard/src/i18n/en.json index 827bd34b..a52a8a84 100755 --- a/dashboard/src/i18n/en.json +++ b/dashboard/src/i18n/en.json @@ -10,6 +10,7 @@ "sync": "Cloud Sync", "oracle": "Oracle", "toolStats": "Tool Stats", + "reasoningTraining": "Reasoning Training", "visualize": "Visualize", "storage": "Storage", "settings": "Settings" @@ -30,7 +31,72 @@ "mainNavigation": "Main navigation", "lightMode": "Light mode", "darkMode": "Dark mode", - "systemTheme": "System theme" + "systemTheme": "System theme", + "retry": "Retry", + "prev": "Previous", + "next": "Next" + }, + "reasoning": { + "pageTitle": "Reasoning Training", + "pageSubtitle": "Mine model thinking into reasoning strategies and inject them into other models.", + "statusError": "Failed to load reasoning status.", + "emptyState": "No reasoning traces yet. Enable mining below, then run a backfill to scan past sessions.", + "kpiTraces": "Total traces", + "kpiUnprocessed": "Unprocessed", + "kpiPatterns": "Learned patterns", + "kpiModels": "Detected models", + "coverageTitle": "Category coverage", + "miningTitle": "Mining", + "miningEnabled": "Enable mining (reads model thinking from ~/.claude transcripts)", + "miningModels": "Source models", + "miningModelsHint": "None selected = mine all models with thinking text.", + "noModels": "No models detected yet.", + "noThinkingText": "This model produces no thinking text and cannot be mined.", + "noThinkingShort": "no thinking", + "backfill": "Backfill (full history)", + "runMining": "Run mining", + "miningRunning": "Mining…", + "miningStarted": "Mining started.", + "miningInProgress": "A mining run is already in progress.", + "miningDisabled": "Enable mining first.", + "miningFailed": "Failed to start mining.", + "injectionTitle": "Injection", + "injectionEnabled": "Enable injection (add learned strategies to other models' sessions)", + "injectionMap": "Source → target mappings", + "injectionMapHint": "Inject a source model's learned strategies into the target model's sessions.", + "noMappings": "No mappings configured.", + "source": "Source model", + "target": "Target model", + "targetPlaceholder": "target model / glob", + "addMapping": "Add mapping", + "save": "Save", + "configSaved": "Configuration saved.", + "configSaveFailed": "Failed to save configuration.", + "patternsTitle": "Pattern library", + "filterModel": "Filter by model", + "filterCategory": "Filter by category", + "allModels": "All models", + "allCategories": "All categories", + "patternsError": "Failed to load patterns.", + "noPatterns": "No learned patterns yet.", + "colTitle": "Pattern", + "colModel": "Model", + "colCategory": "Category", + "colConfidence": "Confidence", + "colFrequency": "Frequency", + "patternDeleted": "Pattern deleted.", + "patternDeleteFailed": "Failed to delete pattern.", + "deletePatternTitle": "Delete pattern?", + "deletePatternDesc": "Delete the learned pattern \"{{title}}\"? This cannot be undone.", + "deleteAllForModel": "Delete all for model", + "deleteAllTitle": "Delete all patterns?", + "deleteAllDesc": "Delete every learned pattern for \"{{model}}\"? This cannot be undone.", + "patternsDeletedCount": "Deleted {{count}} pattern(s).", + "wipeTraces": "Wipe traces", + "wipeTracesTitle": "Wipe traces?", + "wipeTracesDesc": "Permanently delete all staged reasoning traces for \"{{model}}\"? This cannot be undone.", + "tracesWiped": "Wiped {{count}} trace(s).", + "duplicateTarget": "Duplicate target \"{{target}}\" — each target maps to only one source." }, "overview": { "title": "Overview", diff --git a/src/surreal_memory/server/static/dist/assets/DiagramsPage-7MxRvurw.js b/src/surreal_memory/server/static/dist/assets/DiagramsPage-ClJ9QIg8.js similarity index 99% rename from src/surreal_memory/server/static/dist/assets/DiagramsPage-7MxRvurw.js rename to src/surreal_memory/server/static/dist/assets/DiagramsPage-ClJ9QIg8.js index 6fbf6ca4..a4bfb40d 100644 --- a/src/surreal_memory/server/static/dist/assets/DiagramsPage-7MxRvurw.js +++ b/src/surreal_memory/server/static/dist/assets/DiagramsPage-ClJ9QIg8.js @@ -1,4 +1,4 @@ -import{j as R}from"./vendor-query-CqA1cBNl.js";import{d as hs,r as Z,b as gs}from"./vendor-react-BfuodpLv.js";import{m as ps,n as ms,u as ys,b as xs}from"./index-CSMoVcy_.js";import{C as Ht,a as Dn,b as Ln,c as Vt}from"./card-DAwbJLqc.js";import{S as 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${h.length}).`);if(typeof h[h.length-1]=="function"&&typeof h[h.length-2]!="function")throw new x(`Graph.${u}: missing initial value. 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l>"u")throw new k(`Graph.${a}: could not find the "${h}" node in the graph.`);Lt(!1,e==="mixed"?this.type:e,r,l,d)};const o="map"+t[0].toUpperCase()+t.slice(1);n.prototype[o]=function(h,d){const l=[];return this[a](h,(c,g)=>{l.push(d(c,g))}),l};const s="filter"+t[0].toUpperCase()+t.slice(1);n.prototype[s]=function(h,d){const l=[];return this[a](h,(c,g)=>{d(c,g)&&l.push(c)}),l};const u="reduce"+t[0].toUpperCase()+t.slice(1);n.prototype[u]=function(h,d,l){if(arguments.length<3)throw new x(`Graph.${u}: missing initial value. 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A serialized node should be a plain object with at least a "key" property.');if(!("key"in n))throw new x("Graph.import: serialized node is missing its key.");if("attributes"in n&&(!J(n.attributes)||n.attributes===null))throw new x("Graph.import: invalid attributes. Attributes should be a plain object, null or omitted.")}function sn(n){if(!J(n))throw new x('Graph.import: invalid serialized edge. A serialized edge should be a plain object with at least a "source" & "target" property.');if(!("source"in n))throw new x("Graph.import: serialized edge is missing its source.");if(!("target"in n))throw new x("Graph.import: serialized edge is missing its target.");if("attributes"in n&&(!J(n.attributes)||n.attributes===null))throw new x("Graph.import: invalid attributes. Attributes should be a plain object, null or omitted.");if("undirected"in n&&typeof n.undirected!="boolean")throw new x("Graph.import: invalid undirectedness information. Undirected should be boolean or omitted.")}const un=hr(),hn=new Set(["directed","undirected","mixed"]),zt=new Set(["domain","_events","_eventsCount","_maxListeners"]),dn=[{name:n=>`${n}Edge`,generateKey:!0},{name:n=>`${n}DirectedEdge`,generateKey:!0,type:"directed"},{name:n=>`${n}UndirectedEdge`,generateKey:!0,type:"undirected"},{name:n=>`${n}EdgeWithKey`},{name:n=>`${n}DirectedEdgeWithKey`,type:"directed"},{name:n=>`${n}UndirectedEdgeWithKey`,type:"undirected"}],ln={allowSelfLoops:!0,multi:!1,type:"mixed"};function cn(n,i,t){if(t&&!J(t))throw new x(`Graph.addNode: invalid attributes. Expecting an object but got "${t}"`);if(i=""+i,t=t||{},n._nodes.has(i))throw new I(`Graph.addNode: the "${i}" node already exist in the graph.`);const e=new n.NodeDataClass(i,t);return n._nodes.set(i,e),n.emit("nodeAdded",{key:i,attributes:t}),e}function $t(n,i,t){const e=new n.NodeDataClass(i,t);return n._nodes.set(i,e),n.emit("nodeAdded",{key:i,attributes:t}),e}function Ri(n,i,t,e,r,a,o,s){if(!e&&n.type==="undirected")throw new I(`Graph.${i}: you cannot add a directed edge to an undirected graph. Use the #.addEdge or #.addUndirectedEdge instead.`);if(e&&n.type==="directed")throw new I(`Graph.${i}: you cannot add an undirected edge to a directed graph. Use the #.addEdge or #.addDirectedEdge instead.`);if(s&&!J(s))throw new x(`Graph.${i}: invalid attributes. Expecting an object but got "${s}"`);if(a=""+a,o=""+o,s=s||{},!n.allowSelfLoops&&a===o)throw new I(`Graph.${i}: source & target are the same ("${a}"), thus creating a loop explicitly forbidden by this graph 'allowSelfLoops' option set to false.`);const u=n._nodes.get(a),h=n._nodes.get(o);if(!u)throw new k(`Graph.${i}: source node "${a}" not found.`);if(!h)throw new k(`Graph.${i}: target node "${o}" not found.`);const d={key:null,undirected:e,source:a,target:o,attributes:s};if(t)r=n._edgeKeyGenerator();else if(r=""+r,n._edges.has(r))throw new I(`Graph.${i}: the "${r}" edge already exists in the graph.`);if(!n.multi&&(e?typeof u.undirected[o]<"u":typeof u.out[o]<"u"))throw new I(`Graph.${i}: an edge linking "${a}" to "${o}" already exists. If you really want to add multiple edges linking those nodes, you should create a multi graph by using the 'multi' option.`);const l=new Ge(e,r,u,h,s);n._edges.set(r,l);const c=a===o;return e?(u.undirectedDegree++,h.undirectedDegree++,c&&(u.undirectedLoops++,n._undirectedSelfLoopCount++)):(u.outDegree++,h.inDegree++,c&&(u.directedLoops++,n._directedSelfLoopCount++)),n.multi?l.attachMulti():l.attach(),e?n._undirectedSize++:n._directedSize++,d.key=r,n.emit("edgeAdded",d),r}function fn(n,i,t,e,r,a,o,s,u){if(!e&&n.type==="undirected")throw new I(`Graph.${i}: you cannot merge/update a directed edge to an undirected graph. Use the #.mergeEdge/#.updateEdge or #.addUndirectedEdge instead.`);if(e&&n.type==="directed")throw new I(`Graph.${i}: you cannot merge/update an undirected edge to a directed graph. Use the #.mergeEdge/#.updateEdge or #.addDirectedEdge instead.`);if(s){if(u){if(typeof s!="function")throw new x(`Graph.${i}: invalid updater function. Expecting a function but got "${s}"`)}else if(!J(s))throw new x(`Graph.${i}: invalid attributes. Expecting an object but got "${s}"`)}a=""+a,o=""+o;let h;if(u&&(h=s,s=void 0),!n.allowSelfLoops&&a===o)throw new I(`Graph.${i}: source & target are the same ("${a}"), thus creating a loop explicitly forbidden by this graph 'allowSelfLoops' option set to false.`);let d=n._nodes.get(a),l=n._nodes.get(o),c,g;if(!t&&(c=n._edges.get(r),c)){if((c.source.key!==a||c.target.key!==o)&&(!e||c.source.key!==o||c.target.key!==a))throw new I(`Graph.${i}: inconsistency detected when attempting to merge the "${r}" edge with "${a}" source & "${o}" target vs. ("${c.source.key}", "${c.target.key}").`);g=c}if(!g&&!n.multi&&d&&(g=e?d.undirected[o]:d.out[o]),g){const T=[g.key,!1,!1,!1];if(u?!h:!s)return T;if(u){const A=g.attributes;g.attributes=h(A),n.emit("edgeAttributesUpdated",{type:"replace",key:g.key,attributes:g.attributes})}else Z(g.attributes,s),n.emit("edgeAttributesUpdated",{type:"merge",key:g.key,attributes:g.attributes,data:s});return T}s=s||{},u&&h&&(s=h(s));const b={key:null,undirected:e,source:a,target:o,attributes:s};if(t)r=n._edgeKeyGenerator();else if(r=""+r,n._edges.has(r))throw new I(`Graph.${i}: the "${r}" edge already exists in the graph.`);let w=!1,C=!1;d||(d=$t(n,a,{}),w=!0,a===o&&(l=d,C=!0)),l||(l=$t(n,o,{}),C=!0),c=new Ge(e,r,d,l,s),n._edges.set(r,c);const D=a===o;return e?(d.undirectedDegree++,l.undirectedDegree++,D&&(d.undirectedLoops++,n._undirectedSelfLoopCount++)):(d.outDegree++,l.inDegree++,D&&(d.directedLoops++,n._directedSelfLoopCount++)),n.multi?c.attachMulti():c.attach(),e?n._undirectedSize++:n._directedSize++,b.key=r,n.emit("edgeAdded",b),[r,!0,w,C]}function Ae(n,i){n._edges.delete(i.key);const{source:t,target:e,attributes:r}=i,a=i.undirected,o=t===e;a?(t.undirectedDegree--,e.undirectedDegree--,o&&(t.undirectedLoops--,n._undirectedSelfLoopCount--)):(t.outDegree--,e.inDegree--,o&&(t.directedLoops--,n._directedSelfLoopCount--)),n.multi?i.detachMulti():i.detach(),a?n._undirectedSize--:n._directedSize--,n.emit("edgeDropped",{key:i.key,attributes:r,source:t.key,target:e.key,undirected:a})}class W extends vi.EventEmitter{constructor(i){if(super(),i=Z({},ln,i),typeof i.multi!="boolean")throw new x(`Graph.constructor: invalid 'multi' option. Expecting a boolean but got "${i.multi}".`);if(!hn.has(i.type))throw new x(`Graph.constructor: invalid 'type' option. Should be one of "mixed", "directed" or "undirected" but got "${i.type}".`);if(typeof i.allowSelfLoops!="boolean")throw new x(`Graph.constructor: invalid 'allowSelfLoops' option. Expecting a boolean but got "${i.allowSelfLoops}".`);const t=i.type==="mixed"?bi:i.type==="directed"?wi:Ei;ae(this,"NodeDataClass",t);const e="geid_"+un()+"_";let r=0;const a=()=>{let o;do o=e+r++;while(this._edges.has(o));return o};ae(this,"_attributes",{}),ae(this,"_nodes",new Map),ae(this,"_edges",new Map),ae(this,"_directedSize",0),ae(this,"_undirectedSize",0),ae(this,"_directedSelfLoopCount",0),ae(this,"_undirectedSelfLoopCount",0),ae(this,"_edgeKeyGenerator",a),ae(this,"_options",i),zt.forEach(o=>ae(this,o,this[o])),he(this,"order",()=>this._nodes.size),he(this,"size",()=>this._edges.size),he(this,"directedSize",()=>this._directedSize),he(this,"undirectedSize",()=>this._undirectedSize),he(this,"selfLoopCount",()=>this._directedSelfLoopCount+this._undirectedSelfLoopCount),he(this,"directedSelfLoopCount",()=>this._directedSelfLoopCount),he(this,"undirectedSelfLoopCount",()=>this._undirectedSelfLoopCount),he(this,"multi",this._options.multi),he(this,"type",this._options.type),he(this,"allowSelfLoops",this._options.allowSelfLoops),he(this,"implementation",()=>"graphology")}_resetInstanceCounters(){this._directedSize=0,this._undirectedSize=0,this._directedSelfLoopCount=0,this._undirectedSelfLoopCount=0}hasNode(i){return this._nodes.has(""+i)}hasDirectedEdge(i,t){if(this.type==="undirected")return!1;if(arguments.length===1){const e=""+i,r=this._edges.get(e);return!!r&&!r.undirected}else if(arguments.length===2){i=""+i,t=""+t;const e=this._nodes.get(i);return e?e.out.hasOwnProperty(t):!1}throw new x(`Graph.hasDirectedEdge: invalid arity (${arguments.length}, instead of 1 or 2). You can either ask for an edge id or for the existence of an edge between a source & a target.`)}hasUndirectedEdge(i,t){if(this.type==="directed")return!1;if(arguments.length===1){const e=""+i,r=this._edges.get(e);return!!r&&r.undirected}else if(arguments.length===2){i=""+i,t=""+t;const e=this._nodes.get(i);return e?e.undirected.hasOwnProperty(t):!1}throw new x(`Graph.hasDirectedEdge: invalid arity (${arguments.length}, instead of 1 or 2). You can either ask for an edge id or for the existence of an edge between a source & a target.`)}hasEdge(i,t){if(arguments.length===1){const e=""+i;return this._edges.has(e)}else if(arguments.length===2){i=""+i,t=""+t;const e=this._nodes.get(i);return e?typeof e.out<"u"&&e.out.hasOwnProperty(t)||typeof e.undirected<"u"&&e.undirected.hasOwnProperty(t):!1}throw new x(`Graph.hasEdge: invalid arity (${arguments.length}, instead of 1 or 2). You can either ask for an edge id or for the existence of an edge between a source & a target.`)}directedEdge(i,t){if(this.type==="undirected")return;if(i=""+i,t=""+t,this.multi)throw new I("Graph.directedEdge: this method is irrelevant with multigraphs since there might be multiple edges between source & target. See #.directedEdges instead.");const e=this._nodes.get(i);if(!e)throw new k(`Graph.directedEdge: could not find the "${i}" source node in the graph.`);if(!this._nodes.has(t))throw new k(`Graph.directedEdge: could not find the "${t}" target node in the graph.`);const r=e.out&&e.out[t]||void 0;if(r)return r.key}undirectedEdge(i,t){if(this.type==="directed")return;if(i=""+i,t=""+t,this.multi)throw new I("Graph.undirectedEdge: this method is irrelevant with multigraphs since there might be multiple edges between source & target. See #.undirectedEdges instead.");const e=this._nodes.get(i);if(!e)throw new k(`Graph.undirectedEdge: could not find the "${i}" source node in the graph.`);if(!this._nodes.has(t))throw new k(`Graph.undirectedEdge: could not find the "${t}" target node in the graph.`);const r=e.undirected&&e.undirected[t]||void 0;if(r)return r.key}edge(i,t){if(this.multi)throw new I("Graph.edge: this method is irrelevant with multigraphs since there might be multiple edges between source & target. See #.edges instead.");i=""+i,t=""+t;const e=this._nodes.get(i);if(!e)throw new k(`Graph.edge: could not find the "${i}" source node in the graph.`);if(!this._nodes.has(t))throw new k(`Graph.edge: could not find the "${t}" target node in the graph.`);const r=e.out&&e.out[t]||e.undirected&&e.undirected[t]||void 0;if(r)return r.key}areDirectedNeighbors(i,t){i=""+i,t=""+t;const e=this._nodes.get(i);if(!e)throw new k(`Graph.areDirectedNeighbors: could not find the "${i}" node in the graph.`);return this.type==="undirected"?!1:t in e.in||t in e.out}areOutNeighbors(i,t){i=""+i,t=""+t;const e=this._nodes.get(i);if(!e)throw new k(`Graph.areOutNeighbors: could not find the "${i}" node in the graph.`);return this.type==="undirected"?!1:t in e.out}areInNeighbors(i,t){i=""+i,t=""+t;const e=this._nodes.get(i);if(!e)throw new k(`Graph.areInNeighbors: could not find the "${i}" node in the graph.`);return this.type==="undirected"?!1:t in e.in}areUndirectedNeighbors(i,t){i=""+i,t=""+t;const e=this._nodes.get(i);if(!e)throw new k(`Graph.areUndirectedNeighbors: could not find the "${i}" node in the graph.`);return this.type==="directed"?!1:t in e.undirected}areNeighbors(i,t){i=""+i,t=""+t;const e=this._nodes.get(i);if(!e)throw new k(`Graph.areNeighbors: could not find the "${i}" node in the graph.`);return this.type!=="undirected"&&(t in e.in||t in e.out)||this.type!=="directed"&&t in e.undirected}areInboundNeighbors(i,t){i=""+i,t=""+t;const e=this._nodes.get(i);if(!e)throw new k(`Graph.areInboundNeighbors: could not find the "${i}" node in the graph.`);return this.type!=="undirected"&&t in e.in||this.type!=="directed"&&t in e.undirected}areOutboundNeighbors(i,t){i=""+i,t=""+t;const e=this._nodes.get(i);if(!e)throw new k(`Graph.areOutboundNeighbors: could not find the "${i}" node in the graph.`);return this.type!=="undirected"&&t in e.out||this.type!=="directed"&&t in e.undirected}inDegree(i){i=""+i;const t=this._nodes.get(i);if(!t)throw new k(`Graph.inDegree: could not find the "${i}" node in the graph.`);return this.type==="undirected"?0:t.inDegree}outDegree(i){i=""+i;const t=this._nodes.get(i);if(!t)throw new k(`Graph.outDegree: could not find the "${i}" node in the graph.`);return this.type==="undirected"?0:t.outDegree}directedDegree(i){i=""+i;const t=this._nodes.get(i);if(!t)throw new k(`Graph.directedDegree: could not find the "${i}" node in the graph.`);return this.type==="undirected"?0:t.inDegree+t.outDegree}undirectedDegree(i){i=""+i;const t=this._nodes.get(i);if(!t)throw new k(`Graph.undirectedDegree: could not find the "${i}" node in the graph.`);return this.type==="directed"?0:t.undirectedDegree}inboundDegree(i){i=""+i;const t=this._nodes.get(i);if(!t)throw new k(`Graph.inboundDegree: could not find the "${i}" node in the graph.`);let e=0;return this.type!=="directed"&&(e+=t.undirectedDegree),this.type!=="undirected"&&(e+=t.inDegree),e}outboundDegree(i){i=""+i;const t=this._nodes.get(i);if(!t)throw new k(`Graph.outboundDegree: could not find the "${i}" node in the graph.`);let e=0;return this.type!=="directed"&&(e+=t.undirectedDegree),this.type!=="undirected"&&(e+=t.outDegree),e}degree(i){i=""+i;const t=this._nodes.get(i);if(!t)throw new k(`Graph.degree: could not find the "${i}" node in the graph.`);let e=0;return this.type!=="directed"&&(e+=t.undirectedDegree),this.type!=="undirected"&&(e+=t.inDegree+t.outDegree),e}inDegreeWithoutSelfLoops(i){i=""+i;const t=this._nodes.get(i);if(!t)throw new k(`Graph.inDegreeWithoutSelfLoops: could not find the "${i}" node in the graph.`);return this.type==="undirected"?0:t.inDegree-t.directedLoops}outDegreeWithoutSelfLoops(i){i=""+i;const t=this._nodes.get(i);if(!t)throw new k(`Graph.outDegreeWithoutSelfLoops: could not find the "${i}" node in the graph.`);return this.type==="undirected"?0:t.outDegree-t.directedLoops}directedDegreeWithoutSelfLoops(i){i=""+i;const t=this._nodes.get(i);if(!t)throw new k(`Graph.directedDegreeWithoutSelfLoops: could not find the "${i}" node in the graph.`);return this.type==="undirected"?0:t.inDegree+t.outDegree-t.directedLoops*2}undirectedDegreeWithoutSelfLoops(i){i=""+i;const t=this._nodes.get(i);if(!t)throw new k(`Graph.undirectedDegreeWithoutSelfLoops: could not find the "${i}" node in the graph.`);return this.type==="directed"?0:t.undirectedDegree-t.undirectedLoops*2}inboundDegreeWithoutSelfLoops(i){i=""+i;const t=this._nodes.get(i);if(!t)throw new k(`Graph.inboundDegreeWithoutSelfLoops: could not find the "${i}" node in the graph.`);let e=0,r=0;return this.type!=="directed"&&(e+=t.undirectedDegree,r+=t.undirectedLoops*2),this.type!=="undirected"&&(e+=t.inDegree,r+=t.directedLoops),e-r}outboundDegreeWithoutSelfLoops(i){i=""+i;const t=this._nodes.get(i);if(!t)throw new k(`Graph.outboundDegreeWithoutSelfLoops: could not find the "${i}" node in the graph.`);let e=0,r=0;return this.type!=="directed"&&(e+=t.undirectedDegree,r+=t.undirectedLoops*2),this.type!=="undirected"&&(e+=t.outDegree,r+=t.directedLoops),e-r}degreeWithoutSelfLoops(i){i=""+i;const t=this._nodes.get(i);if(!t)throw new k(`Graph.degreeWithoutSelfLoops: could not find the "${i}" node in the graph.`);let e=0,r=0;return this.type!=="directed"&&(e+=t.undirectedDegree,r+=t.undirectedLoops*2),this.type!=="undirected"&&(e+=t.inDegree+t.outDegree,r+=t.directedLoops*2),e-r}source(i){i=""+i;const t=this._edges.get(i);if(!t)throw new k(`Graph.source: could not find the "${i}" edge in the graph.`);return t.source.key}target(i){i=""+i;const t=this._edges.get(i);if(!t)throw new k(`Graph.target: could not find the "${i}" edge in the graph.`);return t.target.key}extremities(i){i=""+i;const t=this._edges.get(i);if(!t)throw new k(`Graph.extremities: could not find the "${i}" edge in the graph.`);return[t.source.key,t.target.key]}opposite(i,t){i=""+i,t=""+t;const e=this._edges.get(t);if(!e)throw new k(`Graph.opposite: could not find the "${t}" edge in the graph.`);const r=e.source.key,a=e.target.key;if(i===r)return a;if(i===a)return r;throw new k(`Graph.opposite: the "${i}" node is not attached to the "${t}" edge (${r}, ${a}).`)}hasExtremity(i,t){i=""+i,t=""+t;const e=this._edges.get(i);if(!e)throw new k(`Graph.hasExtremity: could not find the "${i}" edge in the graph.`);return e.source.key===t||e.target.key===t}isUndirected(i){i=""+i;const t=this._edges.get(i);if(!t)throw new k(`Graph.isUndirected: could not find the "${i}" edge in the graph.`);return t.undirected}isDirected(i){i=""+i;const t=this._edges.get(i);if(!t)throw new k(`Graph.isDirected: could not find the "${i}" edge in the graph.`);return!t.undirected}isSelfLoop(i){i=""+i;const t=this._edges.get(i);if(!t)throw new k(`Graph.isSelfLoop: could not find the "${i}" edge in the graph.`);return t.source===t.target}addNode(i,t){return cn(this,i,t).key}mergeNode(i,t){if(t&&!J(t))throw new x(`Graph.mergeNode: invalid attributes. Expecting an object but got "${t}"`);i=""+i,t=t||{};let e=this._nodes.get(i);return e?(t&&(Z(e.attributes,t),this.emit("nodeAttributesUpdated",{type:"merge",key:i,attributes:e.attributes,data:t})),[i,!1]):(e=new this.NodeDataClass(i,t),this._nodes.set(i,e),this.emit("nodeAdded",{key:i,attributes:t}),[i,!0])}updateNode(i,t){if(t&&typeof t!="function")throw new x(`Graph.updateNode: invalid updater function. Expecting a function but got "${t}"`);i=""+i;let e=this._nodes.get(i);if(e){if(t){const a=e.attributes;e.attributes=t(a),this.emit("nodeAttributesUpdated",{type:"replace",key:i,attributes:e.attributes})}return[i,!1]}const r=t?t({}):{};return e=new this.NodeDataClass(i,r),this._nodes.set(i,e),this.emit("nodeAdded",{key:i,attributes:r}),[i,!0]}dropNode(i){i=""+i;const t=this._nodes.get(i);if(!t)throw new k(`Graph.dropNode: could not find the "${i}" node in the graph.`);let e;if(this.type!=="undirected"){for(const r in t.out){e=t.out[r];do Ae(this,e),e=e.next;while(e)}for(const r in t.in){e=t.in[r];do Ae(this,e),e=e.next;while(e)}}if(this.type!=="directed")for(const r in t.undirected){e=t.undirected[r];do Ae(this,e),e=e.next;while(e)}this._nodes.delete(i),this.emit("nodeDropped",{key:i,attributes:t.attributes})}dropEdge(i){let t;if(arguments.length>1){const e=""+arguments[0],r=""+arguments[1];if(t=oe(this,e,r,this.type),!t)throw new k(`Graph.dropEdge: could not find the "${e}" -> "${r}" edge in the graph.`)}else if(i=""+i,t=this._edges.get(i),!t)throw new k(`Graph.dropEdge: could not find the "${i}" edge in the graph.`);return Ae(this,t),this}dropDirectedEdge(i,t){if(arguments.length<2)throw new I("Graph.dropDirectedEdge: it does not make sense to try and drop a directed edge by key. What if the edge with this key is undirected? Use #.dropEdge for this purpose instead.");if(this.multi)throw new I("Graph.dropDirectedEdge: cannot use a {source,target} combo when dropping an edge in a MultiGraph since we cannot infer the one you want to delete as there could be multiple ones.");i=""+i,t=""+t;const e=oe(this,i,t,"directed");if(!e)throw new k(`Graph.dropDirectedEdge: could not find a "${i}" -> "${t}" edge in the graph.`);return Ae(this,e),this}dropUndirectedEdge(i,t){if(arguments.length<2)throw new I("Graph.dropUndirectedEdge: it does not make sense to drop a directed edge by key. What if the edge with this key is undirected? Use #.dropEdge for this purpose instead.");if(this.multi)throw new I("Graph.dropUndirectedEdge: cannot use a {source,target} combo when dropping an edge in a MultiGraph since we cannot infer the one you want to delete as there could be multiple ones.");const e=oe(this,i,t,"undirected");if(!e)throw new k(`Graph.dropUndirectedEdge: could not find a "${i}" -> "${t}" edge in the graph.`);return Ae(this,e),this}clear(){this._edges.clear(),this._nodes.clear(),this._resetInstanceCounters(),this.emit("cleared")}clearEdges(){const i=this._nodes.values();let t;for(;t=i.next(),t.done!==!0;)t.value.clear();this._edges.clear(),this._resetInstanceCounters(),this.emit("edgesCleared")}getAttribute(i){return this._attributes[i]}getAttributes(){return this._attributes}hasAttribute(i){return this._attributes.hasOwnProperty(i)}setAttribute(i,t){return this._attributes[i]=t,this.emit("attributesUpdated",{type:"set",attributes:this._attributes,name:i}),this}updateAttribute(i,t){if(typeof t!="function")throw new x("Graph.updateAttribute: updater should be a function.");const e=this._attributes[i];return this._attributes[i]=t(e),this.emit("attributesUpdated",{type:"set",attributes:this._attributes,name:i}),this}removeAttribute(i){return delete this._attributes[i],this.emit("attributesUpdated",{type:"remove",attributes:this._attributes,name:i}),this}replaceAttributes(i){if(!J(i))throw new x("Graph.replaceAttributes: provided attributes are not a plain object.");return this._attributes=i,this.emit("attributesUpdated",{type:"replace",attributes:this._attributes}),this}mergeAttributes(i){if(!J(i))throw new x("Graph.mergeAttributes: provided attributes are not a plain object.");return Z(this._attributes,i),this.emit("attributesUpdated",{type:"merge",attributes:this._attributes,data:i}),this}updateAttributes(i){if(typeof i!="function")throw new x("Graph.updateAttributes: provided updater is not a function.");return this._attributes=i(this._attributes),this.emit("attributesUpdated",{type:"update",attributes:this._attributes}),this}updateEachNodeAttributes(i,t){if(typeof i!="function")throw new x("Graph.updateEachNodeAttributes: expecting an updater function.");if(t&&!Ut(t))throw new x("Graph.updateEachNodeAttributes: invalid hints. Expecting an object having the following shape: {attributes?: [string]}");const e=this._nodes.values();let r,a;for(;r=e.next(),r.done!==!0;)a=r.value,a.attributes=i(a.key,a.attributes);this.emit("eachNodeAttributesUpdated",{hints:t||null})}updateEachEdgeAttributes(i,t){if(typeof i!="function")throw new x("Graph.updateEachEdgeAttributes: expecting an updater function.");if(t&&!Ut(t))throw new x("Graph.updateEachEdgeAttributes: invalid hints. Expecting an object having the following shape: {attributes?: [string]}");const e=this._edges.values();let r,a,o,s;for(;r=e.next(),r.done!==!0;)a=r.value,o=a.source,s=a.target,a.attributes=i(a.key,a.attributes,o.key,s.key,o.attributes,s.attributes,a.undirected);this.emit("eachEdgeAttributesUpdated",{hints:t||null})}forEachAdjacencyEntry(i){if(typeof i!="function")throw new x("Graph.forEachAdjacencyEntry: expecting a callback.");et(!1,!1,!1,this,i)}forEachAdjacencyEntryWithOrphans(i){if(typeof i!="function")throw new x("Graph.forEachAdjacencyEntryWithOrphans: expecting a callback.");et(!1,!1,!0,this,i)}forEachAssymetricAdjacencyEntry(i){if(typeof i!="function")throw new x("Graph.forEachAssymetricAdjacencyEntry: expecting a callback.");et(!1,!0,!1,this,i)}forEachAssymetricAdjacencyEntryWithOrphans(i){if(typeof i!="function")throw new x("Graph.forEachAssymetricAdjacencyEntryWithOrphans: expecting a callback.");et(!1,!0,!0,this,i)}nodes(){return Array.from(this._nodes.keys())}forEachNode(i){if(typeof i!="function")throw new x("Graph.forEachNode: expecting a callback.");const t=this._nodes.values();let e,r;for(;e=t.next(),e.done!==!0;)r=e.value,i(r.key,r.attributes)}findNode(i){if(typeof i!="function")throw new x("Graph.findNode: expecting a callback.");const t=this._nodes.values();let e,r;for(;e=t.next(),e.done!==!0;)if(r=e.value,i(r.key,r.attributes))return r.key}mapNodes(i){if(typeof i!="function")throw new x("Graph.mapNode: expecting a callback.");const t=this._nodes.values();let e,r;const a=new Array(this.order);let o=0;for(;e=t.next(),e.done!==!0;)r=e.value,a[o++]=i(r.key,r.attributes);return a}someNode(i){if(typeof i!="function")throw new x("Graph.someNode: expecting a callback.");const t=this._nodes.values();let e,r;for(;e=t.next(),e.done!==!0;)if(r=e.value,i(r.key,r.attributes))return!0;return!1}everyNode(i){if(typeof i!="function")throw new x("Graph.everyNode: expecting a callback.");const t=this._nodes.values();let e,r;for(;e=t.next(),e.done!==!0;)if(r=e.value,!i(r.key,r.attributes))return!1;return!0}filterNodes(i){if(typeof i!="function")throw new x("Graph.filterNodes: expecting a callback.");const t=this._nodes.values();let e,r;const a=[];for(;e=t.next(),e.done!==!0;)r=e.value,i(r.key,r.attributes)&&a.push(r.key);return a}reduceNodes(i,t){if(typeof i!="function")throw new x("Graph.reduceNodes: expecting a callback.");if(arguments.length<2)throw new x("Graph.reduceNodes: missing initial value. You must provide it because the callback takes more than one argument and we cannot infer the initial value from the first iteration, as you could with a simple array.");let e=t;const r=this._nodes.values();let a,o;for(;a=r.next(),a.done!==!0;)o=a.value,e=i(e,o.key,o.attributes);return e}nodeEntries(){const i=this._nodes.values();return{[Symbol.iterator](){return this},next(){const t=i.next();if(t.done)return t;const e=t.value;return{value:{node:e.key,attributes:e.attributes},done:!1}}}}export(){const i=new Array(this._nodes.size);let t=0;this._nodes.forEach((r,a)=>{i[t++]=nn(a,r)});const e=new Array(this._edges.size);return t=0,this._edges.forEach((r,a)=>{e[t++]=an(this.type,a,r)}),{options:{type:this.type,multi:this.multi,allowSelfLoops:this.allowSelfLoops},attributes:this.getAttributes(),nodes:i,edges:e}}import(i,t=!1){if(i instanceof W)return i.forEachNode((u,h)=>{t?this.mergeNode(u,h):this.addNode(u,h)}),i.forEachEdge((u,h,d,l,c,g,b)=>{t?b?this.mergeUndirectedEdgeWithKey(u,d,l,h):this.mergeDirectedEdgeWithKey(u,d,l,h):b?this.addUndirectedEdgeWithKey(u,d,l,h):this.addDirectedEdgeWithKey(u,d,l,h)}),this;if(!J(i))throw new x("Graph.import: invalid argument. Expecting a serialized graph or, alternatively, a Graph instance.");if(i.attributes){if(!J(i.attributes))throw new x("Graph.import: invalid attributes. Expecting a plain object.");t?this.mergeAttributes(i.attributes):this.replaceAttributes(i.attributes)}let e,r,a,o,s;if(i.nodes){if(a=i.nodes,!Array.isArray(a))throw new x("Graph.import: invalid nodes. Expecting an array.");for(e=0,r=a.length;e{const a=Z({},e.attributes);e=new t.NodeDataClass(r,a),t._nodes.set(r,e)}),t}copy(i){if(i=i||{},typeof i.type=="string"&&i.type!==this.type&&i.type!=="mixed")throw new I(`Graph.copy: cannot create an incompatible copy from "${this.type}" type to "${i.type}" because this would mean losing information about the current graph.`);if(typeof i.multi=="boolean"&&i.multi!==this.multi&&i.multi!==!0)throw new I("Graph.copy: cannot create an incompatible copy by downgrading a multi graph to a simple one because this would mean losing information about the current graph.");if(typeof i.allowSelfLoops=="boolean"&&i.allowSelfLoops!==this.allowSelfLoops&&i.allowSelfLoops!==!0)throw new I("Graph.copy: cannot create an incompatible copy from a graph allowing self loops to one that does not because this would mean losing information about the current graph.");const t=this.emptyCopy(i),e=this._edges.values();let r,a;for(;r=e.next(),r.done!==!0;)a=r.value,Ri(t,"copy",!1,a.undirected,a.key,a.source.key,a.target.key,Z({},a.attributes));return t}toJSON(){return this.export()}toString(){return"[object Graph]"}inspect(){const i={};this._nodes.forEach((a,o)=>{i[o]=a.attributes});const t={},e={};this._edges.forEach((a,o)=>{const s=a.undirected?"--":"->";let u="",h=a.source.key,d=a.target.key,l;a.undirected&&h>d&&(l=h,h=d,d=l);const c=`(${h})${s}(${d})`;o.startsWith("geid_")?this.multi&&(typeof e[c]>"u"?e[c]=0:e[c]++,u+=`${e[c]}. `):u+=`[${o}]: `,u+=c,t[u]=a.attributes});const r={};for(const a in this)this.hasOwnProperty(a)&&!zt.has(a)&&typeof this[a]!="function"&&typeof a!="symbol"&&(r[a]=this[a]);return r.attributes=this._attributes,r.nodes=i,r.edges=t,ae(r,"constructor",this.constructor),r}}typeof Symbol<"u"&&(W.prototype[Symbol.for("nodejs.util.inspect.custom")]=W.prototype.inspect);dn.forEach(n=>{["add","merge","update"].forEach(i=>{const t=n.name(i),e=i==="add"?Ri:fn;n.generateKey?W.prototype[t]=function(r,a,o){return e(this,t,!0,(n.type||this.type)==="undirected",null,r,a,o,i==="update")}:W.prototype[t]=function(r,a,o,s){return e(this,t,!1,(n.type||this.type)==="undirected",r,a,o,s,i==="update")}})});Er(W);Gr(W);Kr(W);rn(W);class Ai extends W{constructor(i){const t=Z({type:"directed"},i);if("multi"in t&&t.multi!==!1)throw new x("DirectedGraph.from: inconsistent indication that the graph should be multi in given options!");if(t.type!=="directed")throw new x('DirectedGraph.from: inconsistent "'+t.type+'" type in given options!');super(t)}}class ki extends W{constructor(i){const t=Z({type:"undirected"},i);if("multi"in t&&t.multi!==!1)throw new x("UndirectedGraph.from: inconsistent indication that the graph should be multi in given options!");if(t.type!=="undirected")throw new x('UndirectedGraph.from: inconsistent "'+t.type+'" type in given options!');super(t)}}class xi extends W{constructor(i){const t=Z({multi:!0},i);if("multi"in t&&t.multi!==!0)throw new x("MultiGraph.from: inconsistent indication that the graph should be simple in given options!");super(t)}}class Di extends W{constructor(i){const t=Z({type:"directed",multi:!0},i);if("multi"in t&&t.multi!==!0)throw new x("MultiDirectedGraph.from: inconsistent indication that the graph should be simple in given options!");if(t.type!=="directed")throw new x('MultiDirectedGraph.from: inconsistent "'+t.type+'" type in given options!');super(t)}}class Li extends W{constructor(i){const t=Z({type:"undirected",multi:!0},i);if("multi"in t&&t.multi!==!0)throw new x("MultiUndirectedGraph.from: inconsistent indication that the graph should be simple in given options!");if(t.type!=="undirected")throw new x('MultiUndirectedGraph.from: inconsistent "'+t.type+'" type in given options!');super(t)}}function Fe(n){n.from=function(i,t){const e=Z({},i.options,t),r=new n(e);return r.import(i),r}}Fe(W);Fe(Ai);Fe(ki);Fe(xi);Fe(Di);Fe(Li);W.Graph=W;W.DirectedGraph=Ai;W.UndirectedGraph=ki;W.MultiGraph=xi;W.MultiDirectedGraph=Di;W.MultiUndirectedGraph=Li;W.InvalidArgumentsGraphError=x;W.NotFoundGraphError=k;W.UsageGraphError=I;function gn(n,i){if(typeof n!="object"||!n)return n;var t=n[Symbol.toPrimitive];if(t!==void 0){var e=t.call(n,i);if(typeof e!="object")return e;throw new TypeError("@@toPrimitive must return a primitive value.")}return String(n)}function We(n){var i=gn(n,"string");return typeof i=="symbol"?i:i+""}function Q(n,i){if(!(n instanceof i))throw new TypeError("Cannot call a class as a function")}function Bt(n,i){for(var t=0;tn.length)&&(i=n.length);for(var t=0,e=Array(i);t0&&(P=_[0]),P instanceof Error)throw P;var j=new Error("Unhandled error."+(P?" 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If you really want to add multiple edges linking those nodes, you should create a multi graph by using the 'multi' option.`);const l=new Ge(e,r,u,h,s);n._edges.set(r,l);const c=a===o;return e?(u.undirectedDegree++,h.undirectedDegree++,c&&(u.undirectedLoops++,n._undirectedSelfLoopCount++)):(u.outDegree++,h.inDegree++,c&&(u.directedLoops++,n._directedSelfLoopCount++)),n.multi?l.attachMulti():l.attach(),e?n._undirectedSize++:n._directedSize++,d.key=r,n.emit("edgeAdded",d),r}function fn(n,i,t,e,r,a,o,s,u){if(!e&&n.type==="undirected")throw new I(`Graph.${i}: you cannot merge/update a directed edge to an undirected graph. Use the #.mergeEdge/#.updateEdge or #.addUndirectedEdge instead.`);if(e&&n.type==="directed")throw new I(`Graph.${i}: you cannot merge/update an undirected edge to a directed graph. Use the #.mergeEdge/#.updateEdge or #.addDirectedEdge instead.`);if(s){if(u){if(typeof s!="function")throw new x(`Graph.${i}: invalid updater function. Expecting a function but got "${s}"`)}else if(!J(s))throw new x(`Graph.${i}: invalid attributes. Expecting an object but got "${s}"`)}a=""+a,o=""+o;let h;if(u&&(h=s,s=void 0),!n.allowSelfLoops&&a===o)throw new I(`Graph.${i}: source & target are the same ("${a}"), thus creating a loop explicitly forbidden by this graph 'allowSelfLoops' option set to false.`);let d=n._nodes.get(a),l=n._nodes.get(o),c,g;if(!t&&(c=n._edges.get(r),c)){if((c.source.key!==a||c.target.key!==o)&&(!e||c.source.key!==o||c.target.key!==a))throw new I(`Graph.${i}: inconsistency detected when attempting to merge the "${r}" edge with "${a}" source & "${o}" target vs. ("${c.source.key}", "${c.target.key}").`);g=c}if(!g&&!n.multi&&d&&(g=e?d.undirected[o]:d.out[o]),g){const T=[g.key,!1,!1,!1];if(u?!h:!s)return T;if(u){const A=g.attributes;g.attributes=h(A),n.emit("edgeAttributesUpdated",{type:"replace",key:g.key,attributes:g.attributes})}else Z(g.attributes,s),n.emit("edgeAttributesUpdated",{type:"merge",key:g.key,attributes:g.attributes,data:s});return T}s=s||{},u&&h&&(s=h(s));const b={key:null,undirected:e,source:a,target:o,attributes:s};if(t)r=n._edgeKeyGenerator();else if(r=""+r,n._edges.has(r))throw new I(`Graph.${i}: the "${r}" edge already exists in the graph.`);let w=!1,C=!1;d||(d=$t(n,a,{}),w=!0,a===o&&(l=d,C=!0)),l||(l=$t(n,o,{}),C=!0),c=new Ge(e,r,d,l,s),n._edges.set(r,c);const D=a===o;return e?(d.undirectedDegree++,l.undirectedDegree++,D&&(d.undirectedLoops++,n._undirectedSelfLoopCount++)):(d.outDegree++,l.inDegree++,D&&(d.directedLoops++,n._directedSelfLoopCount++)),n.multi?c.attachMulti():c.attach(),e?n._undirectedSize++:n._directedSize++,b.key=r,n.emit("edgeAdded",b),[r,!0,w,C]}function Ae(n,i){n._edges.delete(i.key);const{source:t,target:e,attributes:r}=i,a=i.undirected,o=t===e;a?(t.undirectedDegree--,e.undirectedDegree--,o&&(t.undirectedLoops--,n._undirectedSelfLoopCount--)):(t.outDegree--,e.inDegree--,o&&(t.directedLoops--,n._directedSelfLoopCount--)),n.multi?i.detachMulti():i.detach(),a?n._undirectedSize--:n._directedSize--,n.emit("edgeDropped",{key:i.key,attributes:r,source:t.key,target:e.key,undirected:a})}class W extends vi.EventEmitter{constructor(i){if(super(),i=Z({},ln,i),typeof i.multi!="boolean")throw new x(`Graph.constructor: invalid 'multi' option. Expecting a boolean but got "${i.multi}".`);if(!hn.has(i.type))throw new x(`Graph.constructor: invalid 'type' option. Should be one of "mixed", "directed" or "undirected" but got "${i.type}".`);if(typeof i.allowSelfLoops!="boolean")throw new x(`Graph.constructor: invalid 'allowSelfLoops' option. Expecting a boolean but got "${i.allowSelfLoops}".`);const t=i.type==="mixed"?bi:i.type==="directed"?wi:Ei;ae(this,"NodeDataClass",t);const e="geid_"+un()+"_";let r=0;const a=()=>{let o;do o=e+r++;while(this._edges.has(o));return o};ae(this,"_attributes",{}),ae(this,"_nodes",new Map),ae(this,"_edges",new Map),ae(this,"_directedSize",0),ae(this,"_undirectedSize",0),ae(this,"_directedSelfLoopCount",0),ae(this,"_undirectedSelfLoopCount",0),ae(this,"_edgeKeyGenerator",a),ae(this,"_options",i),zt.forEach(o=>ae(this,o,this[o])),he(this,"order",()=>this._nodes.size),he(this,"size",()=>this._edges.size),he(this,"directedSize",()=>this._directedSize),he(this,"undirectedSize",()=>this._undirectedSize),he(this,"selfLoopCount",()=>this._directedSelfLoopCount+this._undirectedSelfLoopCount),he(this,"directedSelfLoopCount",()=>this._directedSelfLoopCount),he(this,"undirectedSelfLoopCount",()=>this._undirectedSelfLoopCount),he(this,"multi",this._options.multi),he(this,"type",this._options.type),he(this,"allowSelfLoops",this._options.allowSelfLoops),he(this,"implementation",()=>"graphology")}_resetInstanceCounters(){this._directedSize=0,this._undirectedSize=0,this._directedSelfLoopCount=0,this._undirectedSelfLoopCount=0}hasNode(i){return this._nodes.has(""+i)}hasDirectedEdge(i,t){if(this.type==="undirected")return!1;if(arguments.length===1){const e=""+i,r=this._edges.get(e);return!!r&&!r.undirected}else if(arguments.length===2){i=""+i,t=""+t;const e=this._nodes.get(i);return e?e.out.hasOwnProperty(t):!1}throw new x(`Graph.hasDirectedEdge: invalid arity (${arguments.length}, instead of 1 or 2). You can either ask for an edge id or for the existence of an edge between a source & a target.`)}hasUndirectedEdge(i,t){if(this.type==="directed")return!1;if(arguments.length===1){const e=""+i,r=this._edges.get(e);return!!r&&r.undirected}else if(arguments.length===2){i=""+i,t=""+t;const e=this._nodes.get(i);return e?e.undirected.hasOwnProperty(t):!1}throw new x(`Graph.hasDirectedEdge: invalid arity (${arguments.length}, instead of 1 or 2). You can either ask for an edge id or for the existence of an edge between a source & a target.`)}hasEdge(i,t){if(arguments.length===1){const e=""+i;return this._edges.has(e)}else if(arguments.length===2){i=""+i,t=""+t;const e=this._nodes.get(i);return e?typeof e.out<"u"&&e.out.hasOwnProperty(t)||typeof e.undirected<"u"&&e.undirected.hasOwnProperty(t):!1}throw new x(`Graph.hasEdge: invalid arity (${arguments.length}, instead of 1 or 2). You can either ask for an edge id or for the existence of an edge between a source & a target.`)}directedEdge(i,t){if(this.type==="undirected")return;if(i=""+i,t=""+t,this.multi)throw new I("Graph.directedEdge: this method is irrelevant with multigraphs since there might be multiple edges between source & target. See #.directedEdges instead.");const e=this._nodes.get(i);if(!e)throw new k(`Graph.directedEdge: could not find the "${i}" source node in the graph.`);if(!this._nodes.has(t))throw new k(`Graph.directedEdge: could not find the "${t}" target node in the graph.`);const r=e.out&&e.out[t]||void 0;if(r)return r.key}undirectedEdge(i,t){if(this.type==="directed")return;if(i=""+i,t=""+t,this.multi)throw new I("Graph.undirectedEdge: this method is irrelevant with multigraphs since there might be multiple edges between source & target. See #.undirectedEdges instead.");const e=this._nodes.get(i);if(!e)throw new k(`Graph.undirectedEdge: could not find the "${i}" source node in the graph.`);if(!this._nodes.has(t))throw new k(`Graph.undirectedEdge: could not find the "${t}" target node in the graph.`);const r=e.undirected&&e.undirected[t]||void 0;if(r)return r.key}edge(i,t){if(this.multi)throw new I("Graph.edge: this method is irrelevant with multigraphs since there might be multiple edges between source & target. See #.edges instead.");i=""+i,t=""+t;const e=this._nodes.get(i);if(!e)throw new k(`Graph.edge: could not find the "${i}" source node in the graph.`);if(!this._nodes.has(t))throw new k(`Graph.edge: could not find the "${t}" target node in the graph.`);const r=e.out&&e.out[t]||e.undirected&&e.undirected[t]||void 0;if(r)return r.key}areDirectedNeighbors(i,t){i=""+i,t=""+t;const e=this._nodes.get(i);if(!e)throw new k(`Graph.areDirectedNeighbors: could not find the "${i}" node in the graph.`);return this.type==="undirected"?!1:t in e.in||t in e.out}areOutNeighbors(i,t){i=""+i,t=""+t;const e=this._nodes.get(i);if(!e)throw new k(`Graph.areOutNeighbors: could not find the "${i}" node in the graph.`);return this.type==="undirected"?!1:t in e.out}areInNeighbors(i,t){i=""+i,t=""+t;const e=this._nodes.get(i);if(!e)throw new k(`Graph.areInNeighbors: could not find the "${i}" node in the graph.`);return this.type==="undirected"?!1:t in e.in}areUndirectedNeighbors(i,t){i=""+i,t=""+t;const e=this._nodes.get(i);if(!e)throw new k(`Graph.areUndirectedNeighbors: could not find the "${i}" node in the graph.`);return this.type==="directed"?!1:t in e.undirected}areNeighbors(i,t){i=""+i,t=""+t;const e=this._nodes.get(i);if(!e)throw new k(`Graph.areNeighbors: could not find the "${i}" node in the graph.`);return this.type!=="undirected"&&(t in e.in||t in e.out)||this.type!=="directed"&&t in e.undirected}areInboundNeighbors(i,t){i=""+i,t=""+t;const e=this._nodes.get(i);if(!e)throw new k(`Graph.areInboundNeighbors: could not find the "${i}" node in the graph.`);return this.type!=="undirected"&&t in e.in||this.type!=="directed"&&t in e.undirected}areOutboundNeighbors(i,t){i=""+i,t=""+t;const e=this._nodes.get(i);if(!e)throw new k(`Graph.areOutboundNeighbors: could not find the "${i}" node in the graph.`);return this.type!=="undirected"&&t in e.out||this.type!=="directed"&&t in e.undirected}inDegree(i){i=""+i;const t=this._nodes.get(i);if(!t)throw new k(`Graph.inDegree: could not find the "${i}" node in the graph.`);return this.type==="undirected"?0:t.inDegree}outDegree(i){i=""+i;const t=this._nodes.get(i);if(!t)throw new k(`Graph.outDegree: could not find the "${i}" node in the graph.`);return this.type==="undirected"?0:t.outDegree}directedDegree(i){i=""+i;const t=this._nodes.get(i);if(!t)throw new k(`Graph.directedDegree: could not find the "${i}" node in the graph.`);return this.type==="undirected"?0:t.inDegree+t.outDegree}undirectedDegree(i){i=""+i;const t=this._nodes.get(i);if(!t)throw new k(`Graph.undirectedDegree: could not find the "${i}" node in the graph.`);return this.type==="directed"?0:t.undirectedDegree}inboundDegree(i){i=""+i;const t=this._nodes.get(i);if(!t)throw new k(`Graph.inboundDegree: could not find the "${i}" node in the graph.`);let e=0;return this.type!=="directed"&&(e+=t.undirectedDegree),this.type!=="undirected"&&(e+=t.inDegree),e}outboundDegree(i){i=""+i;const t=this._nodes.get(i);if(!t)throw new k(`Graph.outboundDegree: could not find the "${i}" node in the graph.`);let e=0;return this.type!=="directed"&&(e+=t.undirectedDegree),this.type!=="undirected"&&(e+=t.outDegree),e}degree(i){i=""+i;const t=this._nodes.get(i);if(!t)throw new k(`Graph.degree: could not find the "${i}" node in the graph.`);let e=0;return this.type!=="directed"&&(e+=t.undirectedDegree),this.type!=="undirected"&&(e+=t.inDegree+t.outDegree),e}inDegreeWithoutSelfLoops(i){i=""+i;const t=this._nodes.get(i);if(!t)throw new k(`Graph.inDegreeWithoutSelfLoops: could not find the "${i}" node in the graph.`);return this.type==="undirected"?0:t.inDegree-t.directedLoops}outDegreeWithoutSelfLoops(i){i=""+i;const t=this._nodes.get(i);if(!t)throw new k(`Graph.outDegreeWithoutSelfLoops: could not find the "${i}" node in the graph.`);return this.type==="undirected"?0:t.outDegree-t.directedLoops}directedDegreeWithoutSelfLoops(i){i=""+i;const t=this._nodes.get(i);if(!t)throw new k(`Graph.directedDegreeWithoutSelfLoops: could not find the "${i}" node in the graph.`);return this.type==="undirected"?0:t.inDegree+t.outDegree-t.directedLoops*2}undirectedDegreeWithoutSelfLoops(i){i=""+i;const t=this._nodes.get(i);if(!t)throw new k(`Graph.undirectedDegreeWithoutSelfLoops: could not find the "${i}" node in the graph.`);return this.type==="directed"?0:t.undirectedDegree-t.undirectedLoops*2}inboundDegreeWithoutSelfLoops(i){i=""+i;const t=this._nodes.get(i);if(!t)throw new k(`Graph.inboundDegreeWithoutSelfLoops: could not find the "${i}" node in the graph.`);let e=0,r=0;return this.type!=="directed"&&(e+=t.undirectedDegree,r+=t.undirectedLoops*2),this.type!=="undirected"&&(e+=t.inDegree,r+=t.directedLoops),e-r}outboundDegreeWithoutSelfLoops(i){i=""+i;const t=this._nodes.get(i);if(!t)throw new k(`Graph.outboundDegreeWithoutSelfLoops: could not find the "${i}" node in the graph.`);let e=0,r=0;return this.type!=="directed"&&(e+=t.undirectedDegree,r+=t.undirectedLoops*2),this.type!=="undirected"&&(e+=t.outDegree,r+=t.directedLoops),e-r}degreeWithoutSelfLoops(i){i=""+i;const t=this._nodes.get(i);if(!t)throw new k(`Graph.degreeWithoutSelfLoops: could not find the "${i}" node in the graph.`);let e=0,r=0;return this.type!=="directed"&&(e+=t.undirectedDegree,r+=t.undirectedLoops*2),this.type!=="undirected"&&(e+=t.inDegree+t.outDegree,r+=t.directedLoops*2),e-r}source(i){i=""+i;const t=this._edges.get(i);if(!t)throw new k(`Graph.source: could not find the "${i}" edge in the graph.`);return t.source.key}target(i){i=""+i;const t=this._edges.get(i);if(!t)throw new k(`Graph.target: could not find the "${i}" edge in the graph.`);return t.target.key}extremities(i){i=""+i;const t=this._edges.get(i);if(!t)throw new k(`Graph.extremities: could not find the "${i}" edge in the graph.`);return[t.source.key,t.target.key]}opposite(i,t){i=""+i,t=""+t;const e=this._edges.get(t);if(!e)throw new k(`Graph.opposite: could not find the "${t}" edge in the graph.`);const r=e.source.key,a=e.target.key;if(i===r)return a;if(i===a)return r;throw new k(`Graph.opposite: the "${i}" node is not attached to the "${t}" edge (${r}, ${a}).`)}hasExtremity(i,t){i=""+i,t=""+t;const e=this._edges.get(i);if(!e)throw new k(`Graph.hasExtremity: could not find the "${i}" edge in the graph.`);return e.source.key===t||e.target.key===t}isUndirected(i){i=""+i;const t=this._edges.get(i);if(!t)throw new k(`Graph.isUndirected: could not find the "${i}" edge in the graph.`);return t.undirected}isDirected(i){i=""+i;const t=this._edges.get(i);if(!t)throw new k(`Graph.isDirected: could not find the "${i}" edge in the graph.`);return!t.undirected}isSelfLoop(i){i=""+i;const t=this._edges.get(i);if(!t)throw new k(`Graph.isSelfLoop: could not find the "${i}" edge in the graph.`);return t.source===t.target}addNode(i,t){return cn(this,i,t).key}mergeNode(i,t){if(t&&!J(t))throw new x(`Graph.mergeNode: invalid attributes. Expecting an object but got "${t}"`);i=""+i,t=t||{};let e=this._nodes.get(i);return e?(t&&(Z(e.attributes,t),this.emit("nodeAttributesUpdated",{type:"merge",key:i,attributes:e.attributes,data:t})),[i,!1]):(e=new this.NodeDataClass(i,t),this._nodes.set(i,e),this.emit("nodeAdded",{key:i,attributes:t}),[i,!0])}updateNode(i,t){if(t&&typeof t!="function")throw new x(`Graph.updateNode: invalid updater function. Expecting a function but got "${t}"`);i=""+i;let e=this._nodes.get(i);if(e){if(t){const a=e.attributes;e.attributes=t(a),this.emit("nodeAttributesUpdated",{type:"replace",key:i,attributes:e.attributes})}return[i,!1]}const r=t?t({}):{};return e=new this.NodeDataClass(i,r),this._nodes.set(i,e),this.emit("nodeAdded",{key:i,attributes:r}),[i,!0]}dropNode(i){i=""+i;const t=this._nodes.get(i);if(!t)throw new k(`Graph.dropNode: could not find the "${i}" node in the graph.`);let e;if(this.type!=="undirected"){for(const r in t.out){e=t.out[r];do Ae(this,e),e=e.next;while(e)}for(const r in t.in){e=t.in[r];do Ae(this,e),e=e.next;while(e)}}if(this.type!=="directed")for(const r in t.undirected){e=t.undirected[r];do Ae(this,e),e=e.next;while(e)}this._nodes.delete(i),this.emit("nodeDropped",{key:i,attributes:t.attributes})}dropEdge(i){let t;if(arguments.length>1){const e=""+arguments[0],r=""+arguments[1];if(t=oe(this,e,r,this.type),!t)throw new k(`Graph.dropEdge: could not find the "${e}" -> "${r}" edge in the graph.`)}else if(i=""+i,t=this._edges.get(i),!t)throw new k(`Graph.dropEdge: could not find the "${i}" edge in the graph.`);return Ae(this,t),this}dropDirectedEdge(i,t){if(arguments.length<2)throw new I("Graph.dropDirectedEdge: it does not make sense to try and drop a directed edge by key. What if the edge with this key is undirected? Use #.dropEdge for this purpose instead.");if(this.multi)throw new I("Graph.dropDirectedEdge: cannot use a {source,target} combo when dropping an edge in a MultiGraph since we cannot infer the one you want to delete as there could be multiple ones.");i=""+i,t=""+t;const e=oe(this,i,t,"directed");if(!e)throw new k(`Graph.dropDirectedEdge: could not find a "${i}" -> "${t}" edge in the graph.`);return Ae(this,e),this}dropUndirectedEdge(i,t){if(arguments.length<2)throw new I("Graph.dropUndirectedEdge: it does not make sense to drop a directed edge by key. What if the edge with this key is undirected? Use #.dropEdge for this purpose instead.");if(this.multi)throw new I("Graph.dropUndirectedEdge: cannot use a {source,target} combo when dropping an edge in a MultiGraph since we cannot infer the one you want to delete as there could be multiple ones.");const e=oe(this,i,t,"undirected");if(!e)throw new k(`Graph.dropUndirectedEdge: could not find a "${i}" -> "${t}" edge in the graph.`);return Ae(this,e),this}clear(){this._edges.clear(),this._nodes.clear(),this._resetInstanceCounters(),this.emit("cleared")}clearEdges(){const i=this._nodes.values();let t;for(;t=i.next(),t.done!==!0;)t.value.clear();this._edges.clear(),this._resetInstanceCounters(),this.emit("edgesCleared")}getAttribute(i){return this._attributes[i]}getAttributes(){return this._attributes}hasAttribute(i){return this._attributes.hasOwnProperty(i)}setAttribute(i,t){return this._attributes[i]=t,this.emit("attributesUpdated",{type:"set",attributes:this._attributes,name:i}),this}updateAttribute(i,t){if(typeof t!="function")throw new x("Graph.updateAttribute: updater should be a function.");const e=this._attributes[i];return this._attributes[i]=t(e),this.emit("attributesUpdated",{type:"set",attributes:this._attributes,name:i}),this}removeAttribute(i){return delete this._attributes[i],this.emit("attributesUpdated",{type:"remove",attributes:this._attributes,name:i}),this}replaceAttributes(i){if(!J(i))throw new x("Graph.replaceAttributes: provided attributes are not a plain object.");return this._attributes=i,this.emit("attributesUpdated",{type:"replace",attributes:this._attributes}),this}mergeAttributes(i){if(!J(i))throw new x("Graph.mergeAttributes: provided attributes are not a plain object.");return Z(this._attributes,i),this.emit("attributesUpdated",{type:"merge",attributes:this._attributes,data:i}),this}updateAttributes(i){if(typeof i!="function")throw new x("Graph.updateAttributes: provided updater is not a function.");return this._attributes=i(this._attributes),this.emit("attributesUpdated",{type:"update",attributes:this._attributes}),this}updateEachNodeAttributes(i,t){if(typeof i!="function")throw new x("Graph.updateEachNodeAttributes: expecting an updater function.");if(t&&!Ut(t))throw new x("Graph.updateEachNodeAttributes: invalid hints. Expecting an object having the following shape: {attributes?: [string]}");const e=this._nodes.values();let r,a;for(;r=e.next(),r.done!==!0;)a=r.value,a.attributes=i(a.key,a.attributes);this.emit("eachNodeAttributesUpdated",{hints:t||null})}updateEachEdgeAttributes(i,t){if(typeof i!="function")throw new x("Graph.updateEachEdgeAttributes: expecting an updater function.");if(t&&!Ut(t))throw new x("Graph.updateEachEdgeAttributes: invalid hints. 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le({status:a}){const{t:n}=Q(),s=a.per_model.filter(i=>i.pattern_count>0);return s.length===0?null:e.jsxs(k,{children:[e.jsx(D,{children:e.jsx(F,{children:n("reasoning.coverageTitle")})}),e.jsx(_,{className:"space-y-4 text-sm",children:s.map(i=>{const l=a.coverage_by_model[i.model]??[];return e.jsxs("div",{className:"space-y-1.5",children:[e.jsxs("div",{className:"flex items-center justify-between",children:[e.jsx("span",{className:"font-mono text-xs",children:i.model}),e.jsxs("span",{className:"font-mono text-xs text-muted-foreground",children:[i.coverage_percent,"%"]})]}),e.jsx("div",{className:"h-2 overflow-hidden rounded-full bg-muted",children:e.jsx("div",{className:"h-full rounded-full bg-primary transition-all duration-500",style:{width:`${i.coverage_percent}%`}})}),l.length>0&&e.jsx("div",{className:"flex flex-wrap gap-1 pt-1",children:l.map(g=>e.jsxs(W,{variant:g.covered?"success":"outline",children:[g.category," 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--git a/src/surreal_memory/server/static/dist/assets/StoragePage-B_CEarTZ.js b/src/surreal_memory/server/static/dist/assets/StoragePage-BsRzooql.js similarity index 88% rename from src/surreal_memory/server/static/dist/assets/StoragePage-B_CEarTZ.js rename to src/surreal_memory/server/static/dist/assets/StoragePage-BsRzooql.js index 68bad672..3ce2286b 100644 --- a/src/surreal_memory/server/static/dist/assets/StoragePage-B_CEarTZ.js +++ b/src/surreal_memory/server/static/dist/assets/StoragePage-BsRzooql.js @@ -1 +1 @@ -import{u as c,j as e}from"./vendor-query-CqA1cBNl.js";import{o as x,u as i,b as n}from"./index-CSMoVcy_.js";import{C as m,a as u,b as h,c as g}from"./card-DAwbJLqc.js";import{a0 as d,x as f,z as N,p as b}from"./vendor-icons-IAbuZhWE.js";import{S as o}from"./skeleton-CAu64_35.js";import"./vendor-react-BfuodpLv.js";import"./vendor-ui-Qm4_4bAc.js";import"./vendor-recharts-BkwZfCWA.js";const j={status:["storage","status"],tierStats:["storage","tier-stats"]};function 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e.jsxs(m,{children:[e.jsx(u,{className:"pb-3",children:e.jsxs(h,{className:"flex items-center gap-2 text-base",children:[e.jsx(d,{className:"size-5","aria-hidden":"true"}),a("storage.currentBackend","Storage Backend")]})}),e.jsxs(g,{className:"space-y-4",children:[e.jsxs("div",{className:"flex flex-wrap items-center gap-3",children:[e.jsx(n,{variant:"default",className:"text-sm px-3 py-1",children:e.jsxs("span",{className:"flex items-center gap-1.5",children:[e.jsx(d,{className:"size-3.5","aria-hidden":"true"}),"SurrealDB"]})}),e.jsx(n,{variant:s.healthy?"outline":"destructive",className:"text-xs",children:s.healthy?a("storage.connected","Connected"):a("storage.disconnected","Disconnected")}),s.health_grade&&e.jsxs(n,{variant:"secondary",className:"text-xs",children:[a("storage.grade","Grade"),": ",s.health_grade]})]}),e.jsxs("div",{className:"flex items-center gap-2 text-sm",children:[s.healthy?e.jsx(p,{className:"size-4 shrink-0 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b/src/surreal_memory/server/static/dist/assets/SyncPage-BRERoUGJ.js @@ -1 +1 @@ -import{u as z,a as L,b as E,j as e}from"./vendor-query-CqA1cBNl.js";import{r as y}from"./vendor-react-BfuodpLv.js";import{o as N,u as P,B as i,b as p,t as a}from"./index-CSMoVcy_.js";import{C as o,a as d,b as u,c as m}from"./card-DAwbJLqc.js";import{T as q,g as D,U as F,u as R,V as T,W as B}from"./vendor-icons-IAbuZhWE.js";import"./vendor-ui-Qm4_4bAc.js";import"./vendor-recharts-BkwZfCWA.js";const b={status:["sync","status"]};function H(){return z({queryKey:b.status,queryFn:()=>N.get("/api/dashboard/sync-status"),refetchInterval:3e4})}function K(){const t=L();return E({mutationFn:x=>N.post("/api/dashboard/sync-config",x),onSuccess:()=>{t.invalidateQueries({queryKey:b.status})}})}const A="",I="https://your-worker.your-subdomain.workers.dev",Q={prefer_recent:"Prefer Recent",prefer_local:"Prefer Local",prefer_remote:"Prefer Remote",prefer_stronger:"Prefer Stronger"};function 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this.i18nFormat?.getResource?this.i18nFormat.getResource(l,r,u,c):this.resourceStore.getResource(l,r,u,c)}getUsedParamsDetails(l={}){const r=["defaultValue","ordinal","context","replace","lng","lngs","fallbackLng","ns","keySeparator","nsSeparator","returnObjects","returnDetails","joinArrays","postProcess","interpolation"],u=l.replace&&!se(l.replace);let c=u?l.replace:l;if(u&&typeof l.count<"u"&&(c.count=l.count),this.options.interpolation.defaultVariables&&(c={...this.options.interpolation.defaultVariables,...c}),!u){c={...c};for(const f of r)delete c[f]}return c}static hasDefaultValue(l){const r="defaultValue";for(const u in l)if(Object.prototype.hasOwnProperty.call(l,u)&&r===u.substring(0,r.length)&&l[u]!==void 0)return!0;return!1}}class Am{constructor(l){this.options=l,this.supportedLngs=this.options.supportedLngs||!1,this.logger=fn.create("languageUtils")}getScriptPartFromCode(l){if(l=Li(l),!l||l.indexOf("-")<0)return null;const r=l.split("-");return r.length===2||(r.pop(),r[r.length-1].toLowerCase()==="x")?null:this.formatLanguageCode(r.join("-"))}getLanguagePartFromCode(l){if(l=Li(l),!l||l.indexOf("-")<0)return l;const r=l.split("-");return this.formatLanguageCode(r[0])}formatLanguageCode(l){if(se(l)&&l.indexOf("-")>-1){let r;try{r=Intl.getCanonicalLocales(l)[0]}catch{}return r&&this.options.lowerCaseLng&&(r=r.toLowerCase()),r||(this.options.lowerCaseLng?l.toLowerCase():l)}return this.options.cleanCode||this.options.lowerCaseLng?l.toLowerCase():l}isSupportedCode(l){return(this.options.load==="languageOnly"||this.options.nonExplicitSupportedLngs)&&(l=this.getLanguagePartFromCode(l)),!this.supportedLngs||!this.supportedLngs.length||this.supportedLngs.indexOf(l)>-1}getBestMatchFromCodes(l){if(!l)return null;let r;return l.forEach(u=>{if(r)return;const c=this.formatLanguageCode(u);(!this.options.supportedLngs||this.isSupportedCode(c))&&(r=c)}),!r&&this.options.supportedLngs&&l.forEach(u=>{if(r)return;const c=this.getScriptPartFromCode(u);if(this.isSupportedCode(c))return r=c;const f=this.getLanguagePartFromCode(u);if(this.isSupportedCode(f))return r=f;r=this.options.supportedLngs.find(m=>{if(m===f)return m;if(!(m.indexOf("-")<0&&f.indexOf("-")<0)&&(m.indexOf("-")>0&&f.indexOf("-")<0&&m.substring(0,m.indexOf("-"))===f||m.indexOf(f)===0&&f.length>1))return m})}),r||(r=this.getFallbackCodes(this.options.fallbackLng)[0]),r}getFallbackCodes(l,r){if(!l)return[];if(typeof l=="function"&&(l=l(r)),se(l)&&(l=[l]),Array.isArray(l))return l;if(!r)return l.default||[];let u=l[r];return u||(u=l[this.getScriptPartFromCode(r)]),u||(u=l[this.formatLanguageCode(r)]),u||(u=l[this.getLanguagePartFromCode(r)]),u||(u=l.default),u||[]}toResolveHierarchy(l,r){const u=this.getFallbackCodes((r===!1?[]:r)||this.options.fallbackLng||[],l),c=[],f=m=>{m&&(this.isSupportedCode(m)?c.push(m):this.logger.warn(`rejecting language code not found in supportedLngs: ${m}`))};return se(l)&&(l.indexOf("-")>-1||l.indexOf("_")>-1)?(this.options.load!=="languageOnly"&&f(this.formatLanguageCode(l)),this.options.load!=="languageOnly"&&this.options.load!=="currentOnly"&&f(this.getScriptPartFromCode(l)),this.options.load!=="currentOnly"&&f(this.getLanguagePartFromCode(l))):se(l)&&f(this.formatLanguageCode(l)),u.forEach(m=>{c.indexOf(m)<0&&f(this.formatLanguageCode(m))}),c}}const Dm={zero:0,one:1,two:2,few:3,many:4,other:5},wm={select:i=>i===1?"one":"other",resolvedOptions:()=>({pluralCategories:["one","other"]})};class G0{constructor(l,r={}){this.languageUtils=l,this.options=r,this.logger=fn.create("pluralResolver"),this.pluralRulesCache={}}clearCache(){this.pluralRulesCache={}}getRule(l,r={}){const u=Li(l==="dev"?"en":l),c=r.ordinal?"ordinal":"cardinal",f=JSON.stringify({cleanedCode:u,type:c});if(f in this.pluralRulesCache)return this.pluralRulesCache[f];let m;try{m=new Intl.PluralRules(u,{type:c})}catch{if(typeof Intl>"u")return this.logger.error("No Intl support, please use an Intl polyfill!"),wm;if(!l.match(/-|_/))return wm;const b=this.languageUtils.getLanguagePartFromCode(l);m=this.getRule(b,r)}return this.pluralRulesCache[f]=m,m}needsPlural(l,r={}){let u=this.getRule(l,r);return u||(u=this.getRule("dev",r)),u?.resolvedOptions().pluralCategories.length>1}getPluralFormsOfKey(l,r,u={}){return this.getSuffixes(l,u).map(c=>`${r}${c}`)}getSuffixes(l,r={}){let u=this.getRule(l,r);return u||(u=this.getRule("dev",r)),u?u.resolvedOptions().pluralCategories.sort((c,f)=>Dm[c]-Dm[f]).map(c=>`${this.options.prepend}${r.ordinal?`ordinal${this.options.prepend}`:""}${c}`):[]}getSuffix(l,r,u={}){const c=this.getRule(l,u);return c?`${this.options.prepend}${u.ordinal?`ordinal${this.options.prepend}`:""}${c.select(r)}`:(this.logger.warn(`no plural rule found for: ${l}`),this.getSuffix("dev",r,u))}}const zm=(i,l,r,u=".",c=!0)=>{let f=L0(i,l,r);return!f&&c&&se(r)&&(f=nc(i,r,u),f===void 0&&(f=nc(l,r,u))),f},ko=i=>i.replace(/\$/g,"$$$$");class Rm{constructor(l={}){this.logger=fn.create("interpolator"),this.options=l,this.format=l?.interpolation?.format||(r=>r),this.init(l)}init(l={}){l.interpolation||(l.interpolation={escapeValue:!0});const{escape:r,escapeValue:u,useRawValueToEscape:c,prefix:f,prefixEscaped:m,suffix:h,suffixEscaped:b,formatSeparator:v,unescapeSuffix:x,unescapePrefix:g,nestingPrefix:D,nestingPrefixEscaped:R,nestingSuffix:j,nestingSuffixEscaped:B,nestingOptionsSeparator:k,maxReplaces:U,alwaysFormat:Q}=l.interpolation;this.escape=r!==void 0?r:B0,this.escapeValue=u!==void 0?u:!0,this.useRawValueToEscape=c!==void 0?c:!1,this.prefix=f?_a(f):m||"{{",this.suffix=h?_a(h):b||"}}",this.formatSeparator=v||",",this.unescapePrefix=x?"":g||"-",this.unescapeSuffix=this.unescapePrefix?"":x||"",this.nestingPrefix=D?_a(D):R||_a("$t("),this.nestingSuffix=j?_a(j):B||_a(")"),this.nestingOptionsSeparator=k||",",this.maxReplaces=U||1e3,this.alwaysFormat=Q!==void 0?Q:!1,this.resetRegExp()}reset(){this.options&&this.init(this.options)}resetRegExp(){const l=(r,u)=>r?.source===u?(r.lastIndex=0,r):new RegExp(u,"g");this.regexp=l(this.regexp,`${this.prefix}(.+?)${this.suffix}`),this.regexpUnescape=l(this.regexpUnescape,`${this.prefix}${this.unescapePrefix}(.+?)${this.unescapeSuffix}${this.suffix}`),this.nestingRegexp=l(this.nestingRegexp,`${this.nestingPrefix}((?:[^()"']+|"[^"]*"|'[^']*'|\\((?:[^()]|"[^"]*"|'[^']*')*\\))*?)${this.nestingSuffix}`)}interpolate(l,r,u,c){let f,m,h;const b=this.options&&this.options.interpolation&&this.options.interpolation.defaultVariables||{},v=R=>{if(R.indexOf(this.formatSeparator)<0){const U=zm(r,b,R,this.options.keySeparator,this.options.ignoreJSONStructure);return this.alwaysFormat?this.format(U,void 0,u,{...c,...r,interpolationkey:R}):U}const j=R.split(this.formatSeparator),B=j.shift().trim(),k=j.join(this.formatSeparator).trim();return this.format(zm(r,b,B,this.options.keySeparator,this.options.ignoreJSONStructure),k,u,{...c,...r,interpolationkey:B})};this.resetRegExp();const x=c?.missingInterpolationHandler||this.options.missingInterpolationHandler,g=c?.interpolation?.skipOnVariables!==void 0?c.interpolation.skipOnVariables:this.options.interpolation.skipOnVariables;return[{regex:this.regexpUnescape,safeValue:R=>ko(R)},{regex:this.regexp,safeValue:R=>this.escapeValue?ko(this.escape(R)):ko(R)}].forEach(R=>{for(h=0;f=R.regex.exec(l);){const j=f[1].trim();if(m=v(j),m===void 0)if(typeof x=="function"){const k=x(l,f,c);m=se(k)?k:""}else if(c&&Object.prototype.hasOwnProperty.call(c,j))m="";else if(g){m=f[0];continue}else this.logger.warn(`missed to pass in variable ${j} for interpolating ${l}`),m="";else!se(m)&&!this.useRawValueToEscape&&(m=xm(m));const B=R.safeValue(m);if(l=l.replace(f[0],B),g?(R.regex.lastIndex+=m.length,R.regex.lastIndex-=f[0].length):R.regex.lastIndex=0,h++,h>=this.maxReplaces)break}}),l}nest(l,r,u={}){let c,f,m;const h=(b,v)=>{const x=this.nestingOptionsSeparator;if(b.indexOf(x)<0)return b;const g=b.split(new RegExp(`${_a(x)}[ ]*{`));let D=`{${g[1]}`;b=g[0],D=this.interpolate(D,m);const R=D.match(/'/g),j=D.match(/"/g);((R?.length??0)%2===0&&!j||(j?.length??0)%2!==0)&&(D=D.replace(/'/g,'"'));try{m=JSON.parse(D),v&&(m={...v,...m})}catch(B){return this.logger.warn(`failed parsing options string in nesting for key ${b}`,B),`${b}${x}${D}`}return m.defaultValue&&m.defaultValue.indexOf(this.prefix)>-1&&delete m.defaultValue,b};for(;c=this.nestingRegexp.exec(l);){let b=[];m={...u},m=m.replace&&!se(m.replace)?m.replace:m,m.applyPostProcessor=!1,delete m.defaultValue;const v=/{.*}/.test(c[1])?c[1].lastIndexOf("}")+1:c[1].indexOf(this.formatSeparator);if(v!==-1&&(b=c[1].slice(v).split(this.formatSeparator).map(x=>x.trim()).filter(Boolean),c[1]=c[1].slice(0,v)),f=r(h.call(this,c[1].trim(),m),m),f&&c[0]===l&&!se(f))return f;se(f)||(f=xm(f)),f||(this.logger.warn(`missed to resolve ${c[1]} for nesting ${l}`),f=""),b.length&&(f=b.reduce((x,g)=>this.format(x,g,u.lng,{...u,interpolationkey:c[1].trim()}),f.trim())),l=l.replace(c[0],f),this.regexp.lastIndex=0}return l}}const Q0=i=>{let l=i.toLowerCase().trim();const r={};if(i.indexOf("(")>-1){const u=i.split("(");l=u[0].toLowerCase().trim();const c=u[1].substring(0,u[1].length-1);l==="currency"&&c.indexOf(":")<0?r.currency||(r.currency=c.trim()):l==="relativetime"&&c.indexOf(":")<0?r.range||(r.range=c.trim()):c.split(";").forEach(m=>{if(m){const[h,...b]=m.split(":"),v=b.join(":").trim().replace(/^'+|'+$/g,""),x=h.trim();r[x]||(r[x]=v),v==="false"&&(r[x]=!1),v==="true"&&(r[x]=!0),isNaN(v)||(r[x]=parseInt(v,10))}})}return{formatName:l,formatOptions:r}},Mm=i=>{const l={};return(r,u,c)=>{let f=c;c&&c.interpolationkey&&c.formatParams&&c.formatParams[c.interpolationkey]&&c[c.interpolationkey]&&(f={...f,[c.interpolationkey]:void 0});const m=u+JSON.stringify(f);let h=l[m];return h||(h=i(Li(u),c),l[m]=h),h(r)}},X0=i=>(l,r,u)=>i(Li(r),u)(l);class Z0{constructor(l={}){this.logger=fn.create("formatter"),this.options=l,this.init(l)}init(l,r={interpolation:{}}){this.formatSeparator=r.interpolation.formatSeparator||",";const u=r.cacheInBuiltFormats?Mm:X0;this.formats={number:u((c,f)=>{const m=new Intl.NumberFormat(c,{...f});return h=>m.format(h)}),currency:u((c,f)=>{const m=new Intl.NumberFormat(c,{...f,style:"currency"});return h=>m.format(h)}),datetime:u((c,f)=>{const m=new Intl.DateTimeFormat(c,{...f});return h=>m.format(h)}),relativetime:u((c,f)=>{const m=new Intl.RelativeTimeFormat(c,{...f});return h=>m.format(h,f.range||"day")}),list:u((c,f)=>{const m=new Intl.ListFormat(c,{...f});return h=>m.format(h)})}}add(l,r){this.formats[l.toLowerCase().trim()]=r}addCached(l,r){this.formats[l.toLowerCase().trim()]=Mm(r)}format(l,r,u,c={}){const f=r.split(this.formatSeparator);if(f.length>1&&f[0].indexOf("(")>1&&f[0].indexOf(")")<0&&f.find(h=>h.indexOf(")")>-1)){const h=f.findIndex(b=>b.indexOf(")")>-1);f[0]=[f[0],...f.splice(1,h)].join(this.formatSeparator)}return f.reduce((h,b)=>{const{formatName:v,formatOptions:x}=Q0(b);if(this.formats[v]){let g=h;try{const D=c?.formatParams?.[c.interpolationkey]||{},R=D.locale||D.lng||c.locale||c.lng||u;g=this.formats[v](h,R,{...x,...c,...D})}catch(D){this.logger.warn(D)}return g}else this.logger.warn(`there was no format function for ${v}`);return h},l)}}const F0=(i,l)=>{i.pending[l]!==void 0&&(delete i.pending[l],i.pendingCount--)};class J0 extends Sr{constructor(l,r,u,c={}){super(),this.backend=l,this.store=r,this.services=u,this.languageUtils=u.languageUtils,this.options=c,this.logger=fn.create("backendConnector"),this.waitingReads=[],this.maxParallelReads=c.maxParallelReads||10,this.readingCalls=0,this.maxRetries=c.maxRetries>=0?c.maxRetries:5,this.retryTimeout=c.retryTimeout>=1?c.retryTimeout:350,this.state={},this.queue=[],this.backend?.init?.(u,c.backend,c)}queueLoad(l,r,u,c){const f={},m={},h={},b={};return l.forEach(v=>{let x=!0;r.forEach(g=>{const D=`${v}|${g}`;!u.reload&&this.store.hasResourceBundle(v,g)?this.state[D]=2:this.state[D]<0||(this.state[D]===1?m[D]===void 0&&(m[D]=!0):(this.state[D]=1,x=!1,m[D]===void 0&&(m[D]=!0),f[D]===void 0&&(f[D]=!0),b[g]===void 0&&(b[g]=!0)))}),x||(h[v]=!0)}),(Object.keys(f).length||Object.keys(m).length)&&this.queue.push({pending:m,pendingCount:Object.keys(m).length,loaded:{},errors:[],callback:c}),{toLoad:Object.keys(f),pending:Object.keys(m),toLoadLanguages:Object.keys(h),toLoadNamespaces:Object.keys(b)}}loaded(l,r,u){const c=l.split("|"),f=c[0],m=c[1];r&&this.emit("failedLoading",f,m,r),!r&&u&&this.store.addResourceBundle(f,m,u,void 0,void 0,{skipCopy:!0}),this.state[l]=r?-1:2,r&&u&&(this.state[l]=0);const h={};this.queue.forEach(b=>{j0(b.loaded,[f],m),F0(b,l),r&&b.errors.push(r),b.pendingCount===0&&!b.done&&(Object.keys(b.loaded).forEach(v=>{h[v]||(h[v]={});const x=b.loaded[v];x.length&&x.forEach(g=>{h[v][g]===void 0&&(h[v][g]=!0)})}),b.done=!0,b.errors.length?b.callback(b.errors):b.callback())}),this.emit("loaded",h),this.queue=this.queue.filter(b=>!b.done)}read(l,r,u,c=0,f=this.retryTimeout,m){if(!l.length)return m(null,{});if(this.readingCalls>=this.maxParallelReads){this.waitingReads.push({lng:l,ns:r,fcName:u,tried:c,wait:f,callback:m});return}this.readingCalls++;const h=(v,x)=>{if(this.readingCalls--,this.waitingReads.length>0){const g=this.waitingReads.shift();this.read(g.lng,g.ns,g.fcName,g.tried,g.wait,g.callback)}if(v&&x&&c{this.read.call(this,l,r,u,c+1,f*2,m)},f);return}m(v,x)},b=this.backend[u].bind(this.backend);if(b.length===2){try{const v=b(l,r);v&&typeof v.then=="function"?v.then(x=>h(null,x)).catch(h):h(null,v)}catch(v){h(v)}return}return b(l,r,h)}prepareLoading(l,r,u={},c){if(!this.backend)return this.logger.warn("No backend was added via i18next.use. Will not load resources."),c&&c();se(l)&&(l=this.languageUtils.toResolveHierarchy(l)),se(r)&&(r=[r]);const f=this.queueLoad(l,r,u,c);if(!f.toLoad.length)return f.pending.length||c(),null;f.toLoad.forEach(m=>{this.loadOne(m)})}load(l,r,u){this.prepareLoading(l,r,{},u)}reload(l,r,u){this.prepareLoading(l,r,{reload:!0},u)}loadOne(l,r=""){const u=l.split("|"),c=u[0],f=u[1];this.read(c,f,"read",void 0,void 0,(m,h)=>{m&&this.logger.warn(`${r}loading namespace ${f} for language ${c} failed`,m),!m&&h&&this.logger.log(`${r}loaded namespace ${f} for language ${c}`,h),this.loaded(l,m,h)})}saveMissing(l,r,u,c,f,m={},h=()=>{}){if(this.services?.utils?.hasLoadedNamespace&&!this.services?.utils?.hasLoadedNamespace(r)){this.logger.warn(`did not save key "${u}" as the namespace "${r}" was not yet loaded`,"This means something IS WRONG in your setup. You access the t function before i18next.init / i18next.loadNamespace / i18next.changeLanguage was done. Wait for the callback or Promise to resolve before accessing it!!!");return}if(!(u==null||u==="")){if(this.backend?.create){const b={...m,isUpdate:f},v=this.backend.create.bind(this.backend);if(v.length<6)try{let x;v.length===5?x=v(l,r,u,c,b):x=v(l,r,u,c),x&&typeof x.then=="function"?x.then(g=>h(null,g)).catch(h):h(null,x)}catch(x){h(x)}else v(l,r,u,c,h,b)}!l||!l[0]||this.store.addResource(l[0],r,u,c)}}}const Go=()=>({debug:!1,initAsync:!0,ns:["translation"],defaultNS:["translation"],fallbackLng:["dev"],fallbackNS:!1,supportedLngs:!1,nonExplicitSupportedLngs:!1,load:"all",preload:!1,simplifyPluralSuffix:!0,keySeparator:".",nsSeparator:":",pluralSeparator:"_",contextSeparator:"_",partialBundledLanguages:!1,saveMissing:!1,updateMissing:!1,saveMissingTo:"fallback",saveMissingPlurals:!0,missingKeyHandler:!1,missingInterpolationHandler:!1,postProcess:!1,postProcessPassResolved:!1,returnNull:!1,returnEmptyString:!0,returnObjects:!1,joinArrays:!1,returnedObjectHandler:!1,parseMissingKeyHandler:!1,appendNamespaceToMissingKey:!1,appendNamespaceToCIMode:!1,overloadTranslationOptionHandler:i=>{let l={};if(typeof i[1]=="object"&&(l=i[1]),se(i[1])&&(l.defaultValue=i[1]),se(i[2])&&(l.tDescription=i[2]),typeof i[2]=="object"||typeof i[3]=="object"){const r=i[3]||i[2];Object.keys(r).forEach(u=>{l[u]=r[u]})}return l},interpolation:{escapeValue:!0,format:i=>i,prefix:"{{",suffix:"}}",formatSeparator:",",unescapePrefix:"-",nestingPrefix:"$t(",nestingSuffix:")",nestingOptionsSeparator:",",maxReplaces:1e3,skipOnVariables:!0},cacheInBuiltFormats:!0}),_m=i=>(se(i.ns)&&(i.ns=[i.ns]),se(i.fallbackLng)&&(i.fallbackLng=[i.fallbackLng]),se(i.fallbackNS)&&(i.fallbackNS=[i.fallbackNS]),i.supportedLngs?.indexOf?.("cimode")<0&&(i.supportedLngs=i.supportedLngs.concat(["cimode"])),typeof i.initImmediate=="boolean"&&(i.initAsync=i.initImmediate),i),ar=()=>{},$0=i=>{Object.getOwnPropertyNames(Object.getPrototypeOf(i)).forEach(r=>{typeof i[r]=="function"&&(i[r]=i[r].bind(i))})},Tg="__i18next_supportNoticeShown",W0=()=>typeof globalThis<"u"&&!!globalThis[Tg],I0=()=>{typeof globalThis<"u"&&(globalThis[Tg]=!0)},P0=i=>!!(i?.modules?.backend?.name?.indexOf("Locize")>0||i?.modules?.backend?.constructor?.name?.indexOf("Locize")>0||i?.options?.backend?.backends&&i.options.backend.backends.some(l=>l?.name?.indexOf("Locize")>0||l?.constructor?.name?.indexOf("Locize")>0)||i?.options?.backend?.projectId||i?.options?.backend?.backendOptions&&i.options.backend.backendOptions.some(l=>l?.projectId));class _i extends Sr{constructor(l={},r){if(super(),this.options=_m(l),this.services={},this.logger=fn,this.modules={external:[]},$0(this),r&&!this.isInitialized&&!l.isClone){if(!this.options.initAsync)return this.init(l,r),this;setTimeout(()=>{this.init(l,r)},0)}}init(l={},r){this.isInitializing=!0,typeof l=="function"&&(r=l,l={}),l.defaultNS==null&&l.ns&&(se(l.ns)?l.defaultNS=l.ns:l.ns.indexOf("translation")<0&&(l.defaultNS=l.ns[0]));const u=Go();this.options={...u,...this.options,..._m(l)},this.options.interpolation={...u.interpolation,...this.options.interpolation},l.keySeparator!==void 0&&(this.options.userDefinedKeySeparator=l.keySeparator),l.nsSeparator!==void 0&&(this.options.userDefinedNsSeparator=l.nsSeparator),typeof this.options.overloadTranslationOptionHandler!="function"&&(this.options.overloadTranslationOptionHandler=u.overloadTranslationOptionHandler),this.options.showSupportNotice!==!1&&!P0(this)&&!W0()&&(typeof console<"u"&&typeof console.info<"u"&&console.info("🌐 i18next is maintained with support from Locize — consider powering your project with managed localization (AI, CDN, integrations): https://locize.com 💙"),I0());const c=v=>v?typeof v=="function"?new v:v:null;if(!this.options.isClone){this.modules.logger?fn.init(c(this.modules.logger),this.options):fn.init(null,this.options);let v;this.modules.formatter?v=this.modules.formatter:v=Z0;const x=new Am(this.options);this.store=new Cm(this.options.resources,this.options);const g=this.services;g.logger=fn,g.resourceStore=this.store,g.languageUtils=x,g.pluralResolver=new G0(x,{prepend:this.options.pluralSeparator,simplifyPluralSuffix:this.options.simplifyPluralSuffix}),this.options.interpolation.format&&this.options.interpolation.format!==u.interpolation.format&&this.logger.deprecate("init: you are still using the legacy format function, please use the new approach: https://www.i18next.com/translation-function/formatting"),v&&(!this.options.interpolation.format||this.options.interpolation.format===u.interpolation.format)&&(g.formatter=c(v),g.formatter.init&&g.formatter.init(g,this.options),this.options.interpolation.format=g.formatter.format.bind(g.formatter)),g.interpolator=new Rm(this.options),g.utils={hasLoadedNamespace:this.hasLoadedNamespace.bind(this)},g.backendConnector=new J0(c(this.modules.backend),g.resourceStore,g,this.options),g.backendConnector.on("*",(R,...j)=>{this.emit(R,...j)}),this.modules.languageDetector&&(g.languageDetector=c(this.modules.languageDetector),g.languageDetector.init&&g.languageDetector.init(g,this.options.detection,this.options)),this.modules.i18nFormat&&(g.i18nFormat=c(this.modules.i18nFormat),g.i18nFormat.init&&g.i18nFormat.init(this)),this.translator=new pr(this.services,this.options),this.translator.on("*",(R,...j)=>{this.emit(R,...j)}),this.modules.external.forEach(R=>{R.init&&R.init(this)})}if(this.format=this.options.interpolation.format,r||(r=ar),this.options.fallbackLng&&!this.services.languageDetector&&!this.options.lng){const v=this.services.languageUtils.getFallbackCodes(this.options.fallbackLng);v.length>0&&v[0]!=="dev"&&(this.options.lng=v[0])}!this.services.languageDetector&&!this.options.lng&&this.logger.warn("init: no languageDetector is used and no lng is 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Please check the object you are passing to i18next.use()");return l.type==="backend"&&(this.modules.backend=l),(l.type==="logger"||l.log&&l.warn&&l.error)&&(this.modules.logger=l),l.type==="languageDetector"&&(this.modules.languageDetector=l),l.type==="i18nFormat"&&(this.modules.i18nFormat=l),l.type==="postProcessor"&&xg.addPostProcessor(l),l.type==="formatter"&&(this.modules.formatter=l),l.type==="3rdParty"&&this.modules.external.push(l),this}setResolvedLanguage(l){if(!(!l||!this.languages)&&!(["cimode","dev"].indexOf(l)>-1)){for(let r=0;r-1)&&this.store.hasLanguageSomeTranslations(u)){this.resolvedLanguage=u;break}}!this.resolvedLanguage&&this.languages.indexOf(l)<0&&this.store.hasLanguageSomeTranslations(l)&&(this.resolvedLanguage=l,this.languages.unshift(l))}}changeLanguage(l,r){this.isLanguageChangingTo=l;const u=zi();this.emit("languageChanging",l);const c=h=>{this.language=h,this.languages=this.services.languageUtils.toResolveHierarchy(h),this.resolvedLanguage=void 0,this.setResolvedLanguage(h)},f=(h,b)=>{b?this.isLanguageChangingTo===l&&(c(b),this.translator.changeLanguage(b),this.isLanguageChangingTo=void 0,this.emit("languageChanged",b),this.logger.log("languageChanged",b)):this.isLanguageChangingTo=void 0,u.resolve((...v)=>this.t(...v)),r&&r(h,(...v)=>this.t(...v))},m=h=>{!l&&!h&&this.services.languageDetector&&(h=[]);const b=se(h)?h:h&&h[0],v=this.store.hasLanguageSomeTranslations(b)?b:this.services.languageUtils.getBestMatchFromCodes(se(h)?[h]:h);v&&(this.language||c(v),this.translator.language||this.translator.changeLanguage(v),this.services.languageDetector?.cacheUserLanguage?.(v)),this.loadResources(v,x=>{f(x,v)})};return!l&&this.services.languageDetector&&!this.services.languageDetector.async?m(this.services.languageDetector.detect()):!l&&this.services.languageDetector&&this.services.languageDetector.async?this.services.languageDetector.detect.length===0?this.services.languageDetector.detect().then(m):this.services.languageDetector.detect(m):m(l),u}getFixedT(l,r,u){const c=(f,m,...h)=>{let b;typeof m!="object"?b=this.options.overloadTranslationOptionHandler([f,m].concat(h)):b={...m},b.lng=b.lng||c.lng,b.lngs=b.lngs||c.lngs,b.ns=b.ns||c.ns,b.keyPrefix!==""&&(b.keyPrefix=b.keyPrefix||u||c.keyPrefix);const v=this.options.keySeparator||".";let x;return b.keyPrefix&&Array.isArray(f)?x=f.map(g=>(typeof g=="function"&&(g=ac(g,{...this.options,...m})),`${b.keyPrefix}${v}${g}`)):(typeof f=="function"&&(f=ac(f,{...this.options,...m})),x=b.keyPrefix?`${b.keyPrefix}${v}${f}`:f),this.t(x,b)};return se(l)?c.lng=l:c.lngs=l,c.ns=r,c.keyPrefix=u,c}t(...l){return this.translator?.translate(...l)}exists(...l){return this.translator?.exists(...l)}setDefaultNamespace(l){this.options.defaultNS=l}hasLoadedNamespace(l,r={}){if(!this.isInitialized)return this.logger.warn("hasLoadedNamespace: i18next was not initialized",this.languages),!1;if(!this.languages||!this.languages.length)return this.logger.warn("hasLoadedNamespace: i18n.languages were undefined or empty",this.languages),!1;const u=r.lng||this.resolvedLanguage||this.languages[0],c=this.options?this.options.fallbackLng:!1,f=this.languages[this.languages.length-1];if(u.toLowerCase()==="cimode")return!0;const m=(h,b)=>{const v=this.services.backendConnector.state[`${h}|${b}`];return v===-1||v===0||v===2};if(r.precheck){const h=r.precheck(this,m);if(h!==void 0)return h}return!!(this.hasResourceBundle(u,l)||!this.services.backendConnector.backend||this.options.resources&&!this.options.partialBundledLanguages||m(u,l)&&(!c||m(f,l)))}loadNamespaces(l,r){const u=zi();return this.options.ns?(se(l)&&(l=[l]),l.forEach(c=>{this.options.ns.indexOf(c)<0&&this.options.ns.push(c)}),this.loadResources(c=>{u.resolve(),r&&r(c)}),u):(r&&r(),Promise.resolve())}loadLanguages(l,r){const u=zi();se(l)&&(l=[l]);const c=this.options.preload||[],f=l.filter(m=>c.indexOf(m)<0&&this.services.languageUtils.isSupportedCode(m));return f.length?(this.options.preload=c.concat(f),this.loadResources(m=>{u.resolve(),r&&r(m)}),u):(r&&r(),Promise.resolve())}dir(l){if(l||(l=this.resolvedLanguage||(this.languages?.length>0?this.languages[0]:this.language)),!l)return"rtl";try{const c=new Intl.Locale(l);if(c&&c.getTextInfo){const f=c.getTextInfo();if(f&&f.direction)return f.direction}}catch{}const r=["ar","shu","sqr","ssh","xaa","yhd","yud","aao","abh","abv","acm","acq","acw","acx","acy","adf","ads","aeb","aec","afb","ajp","apc","apd","arb","arq","ars","ary","arz","auz","avl","ayh","ayl","ayn","ayp","bbz","pga","he","iw","ps","pbt","pbu","pst","prp","prd","ug","ur","ydd","yds","yih","ji","yi","hbo","men","xmn","fa","jpr","peo","pes","prs","dv","sam","ckb"],u=this.services?.languageUtils||new Am(Go());return l.toLowerCase().indexOf("-latn")>1?"ltr":r.indexOf(u.getLanguagePartFromCode(l))>-1||l.toLowerCase().indexOf("-arab")>1?"rtl":"ltr"}static createInstance(l={},r){const u=new _i(l,r);return u.createInstance=_i.createInstance,u}cloneInstance(l={},r=ar){const u=l.forkResourceStore;u&&delete l.forkResourceStore;const c={...this.options,...l,isClone:!0},f=new _i(c);if((l.debug!==void 0||l.prefix!==void 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ht=_i.createInstance();ht.createInstance;ht.dir;ht.init;ht.loadResources;ht.reloadResources;ht.use;ht.changeLanguage;ht.getFixedT;ht.t;ht.exists;ht.setDefaultNamespace;ht.hasLoadedNamespace;ht.loadNamespaces;ht.loadLanguages;const eb=(i,l,r,u)=>{const c=[r,{code:l,...u||{}}];if(i?.services?.logger?.forward)return i.services.logger.forward(c,"warn","react-i18next::",!0);ja(c[0])&&(c[0]=`react-i18next:: ${c[0]}`),i?.services?.logger?.warn?i.services.logger.warn(...c):console?.warn&&console.warn(...c)},jm={},Og=(i,l,r,u)=>{ja(r)&&jm[r]||(ja(r)&&(jm[r]=new Date),eb(i,l,r,u))},Cg=(i,l)=>()=>{if(i.isInitialized)l();else{const r=()=>{setTimeout(()=>{i.off("initialized",r)},0),l()};i.on("initialized",r)}},lc=(i,l,r)=>{i.loadNamespaces(l,Cg(i,r))},Lm=(i,l,r,u)=>{if(ja(r)&&(r=[r]),i.options.preload&&i.options.preload.indexOf(l)>-1)return 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Either set keySeparator: false on init or make sure your translations are published in nested format.")}let $=[];const de=this.languageUtils.getFallbackCodes(this.options.fallbackLng,c.lng||this.language);if(this.options.saveMissingTo==="fallback"&&de&&de[0])for(let me=0;me{const G=ee&&q!==j?q:A;this.options.missingKeyHandler?this.options.missingKeyHandler(me,v,_,G,V,c):this.backendConnector?.saveMissing&&this.backendConnector.saveMissing(me,v,_,G,V,c),this.emit("missingKey",me,v,_,j)};this.options.saveMissing&&(this.options.saveMissingPlurals&&I?$.forEach(me=>{const _=this.pluralResolver.getSuffixes(me,c);ve&&c[`defaultValue${this.options.pluralSeparator}zero`]&&_.indexOf(`${this.options.pluralSeparator}zero`)<0&&_.push(`${this.options.pluralSeparator}zero`),_.forEach(q=>{oe([me],h+q,c[`defaultValue${q}`]||ae)})}):oe($,h,ae))}j=this.extendTranslation(j,l,c,z,o),Te&&j===h&&this.options.appendNamespaceToMissingKey&&(j=`${v}${x}${h}`),(Te||Ee)&&this.options.parseMissingKeyHandler&&(j=this.options.parseMissingKeyHandler(this.options.appendNamespaceToMissingKey?`${v}${x}${h}`:h,Ee?j:void 0,c))}return f?(z.res=j,z.usedParams=this.getUsedParamsDetails(c),z):j}extendTranslation(l,r,o,c,f){if(this.i18nFormat?.parse)l=this.i18nFormat.parse(l,{...this.options.interpolation.defaultVariables,...o},o.lng||this.language||c.usedLng,c.usedNS,c.usedKey,{resolved:c});else if(!o.skipInterpolation){o.interpolation&&this.interpolator.init({...o,interpolation:{...this.options.interpolation,...o.interpolation}});const b=se(l)&&(o?.interpolation?.skipOnVariables!==void 0?o.interpolation.skipOnVariables:this.options.interpolation.skipOnVariables);let v;if(b){const g=l.match(this.interpolator.nestingRegexp);v=g&&g.length}let x=o.replace&&!se(o.replace)?o.replace:o;if(this.options.interpolation.defaultVariables&&(x={...this.options.interpolation.defaultVariables,...x}),l=this.interpolator.interpolate(l,x,o.lng||this.language||c.usedLng,o),b){const g=l.match(this.interpolator.nestingRegexp),D=g&&g.length;vf?.[0]===g[0]&&!o.context?(this.logger.warn(`It seems you are nesting recursively key: ${g[0]} in key: ${r[0]}`),null):this.translate(...g,r),o)),o.interpolation&&this.interpolator.reset()}const m=o.postProcess||this.options.postProcess,h=se(m)?[m]:m;return l!=null&&h?.length&&o.applyPostProcessor!==!1&&(l=xg.handle(h,l,r,this.options&&this.options.postProcessPassResolved?{i18nResolved:{...c,usedParams:this.getUsedParamsDetails(o)},...o}:o,this)),l}resolve(l,r={}){let o,c,f,m,h;return se(l)&&(l=[l]),l.forEach(b=>{if(this.isValidLookup(o))return;const v=this.extractFromKey(b,r),x=v.key;c=x;let g=v.namespaces;this.options.fallbackNS&&(g=g.concat(this.options.fallbackNS));const D=r.count!==void 0&&!se(r.count),z=D&&!r.ordinal&&r.count===0,j=r.context!==void 0&&(se(r.context)||typeof r.context=="number")&&r.context!=="",B=r.lngs?r.lngs:this.languageUtils.toResolveHierarchy(r.lng||this.language,r.fallbackLng);g.forEach(K=>{this.isValidLookup(o)||(h=K,!Nm[`${B[0]}-${K}`]&&this.utils?.hasLoadedNamespace&&!this.utils?.hasLoadedNamespace(h)&&(Nm[`${B[0]}-${K}`]=!0,this.logger.warn(`key "${c}" for languages "${B.join(", ")}" won't get resolved as namespace "${h}" was not yet loaded`,"This means something IS WRONG in your setup. You access the t function before i18next.init / i18next.loadNamespace / i18next.changeLanguage was done. Wait for the callback or Promise to resolve before accessing it!!!")),B.forEach(U=>{if(this.isValidLookup(o))return;m=U;const Q=[x];if(this.i18nFormat?.addLookupKeys)this.i18nFormat.addLookupKeys(Q,x,U,K,r);else{let I;D&&(I=this.pluralResolver.getSuffix(U,r.count,r));const ee=`${this.options.pluralSeparator}zero`,J=`${this.options.pluralSeparator}ordinal${this.options.pluralSeparator}`;if(D&&(r.ordinal&&I.indexOf(J)===0&&Q.push(x+I.replace(J,this.options.pluralSeparator)),Q.push(x+I),z&&Q.push(x+ee)),j){const X=`${x}${this.options.contextSeparator||"_"}${r.context}`;Q.push(X),D&&(r.ordinal&&I.indexOf(J)===0&&Q.push(X+I.replace(J,this.options.pluralSeparator)),Q.push(X+I),z&&Q.push(X+ee))}}let Z;for(;Z=Q.pop();)this.isValidLookup(o)||(f=Z,o=this.getResource(U,K,Z,r))}))})}),{res:o,usedKey:c,exactUsedKey:f,usedLng:m,usedNS:h}}isValidLookup(l){return l!==void 0&&!(!this.options.returnNull&&l===null)&&!(!this.options.returnEmptyString&&l==="")}getResource(l,r,o,c={}){return this.i18nFormat?.getResource?this.i18nFormat.getResource(l,r,o,c):this.resourceStore.getResource(l,r,o,c)}getUsedParamsDetails(l={}){const r=["defaultValue","ordinal","context","replace","lng","lngs","fallbackLng","ns","keySeparator","nsSeparator","returnObjects","returnDetails","joinArrays","postProcess","interpolation"],o=l.replace&&!se(l.replace);let c=o?l.replace:l;if(o&&typeof l.count<"u"&&(c.count=l.count),this.options.interpolation.defaultVariables&&(c={...this.options.interpolation.defaultVariables,...c}),!o){c={...c};for(const f of r)delete c[f]}return c}static hasDefaultValue(l){const r="defaultValue";for(const o in l)if(Object.prototype.hasOwnProperty.call(l,o)&&r===o.substring(0,r.length)&&l[o]!==void 0)return!0;return!1}}class Am{constructor(l){this.options=l,this.supportedLngs=this.options.supportedLngs||!1,this.logger=fn.create("languageUtils")}getScriptPartFromCode(l){if(l=Li(l),!l||l.indexOf("-")<0)return null;const r=l.split("-");return r.length===2||(r.pop(),r[r.length-1].toLowerCase()==="x")?null:this.formatLanguageCode(r.join("-"))}getLanguagePartFromCode(l){if(l=Li(l),!l||l.indexOf("-")<0)return l;const r=l.split("-");return this.formatLanguageCode(r[0])}formatLanguageCode(l){if(se(l)&&l.indexOf("-")>-1){let r;try{r=Intl.getCanonicalLocales(l)[0]}catch{}return r&&this.options.lowerCaseLng&&(r=r.toLowerCase()),r||(this.options.lowerCaseLng?l.toLowerCase():l)}return this.options.cleanCode||this.options.lowerCaseLng?l.toLowerCase():l}isSupportedCode(l){return(this.options.load==="languageOnly"||this.options.nonExplicitSupportedLngs)&&(l=this.getLanguagePartFromCode(l)),!this.supportedLngs||!this.supportedLngs.length||this.supportedLngs.indexOf(l)>-1}getBestMatchFromCodes(l){if(!l)return null;let r;return l.forEach(o=>{if(r)return;const c=this.formatLanguageCode(o);(!this.options.supportedLngs||this.isSupportedCode(c))&&(r=c)}),!r&&this.options.supportedLngs&&l.forEach(o=>{if(r)return;const c=this.getScriptPartFromCode(o);if(this.isSupportedCode(c))return r=c;const f=this.getLanguagePartFromCode(o);if(this.isSupportedCode(f))return r=f;r=this.options.supportedLngs.find(m=>{if(m===f)return m;if(!(m.indexOf("-")<0&&f.indexOf("-")<0)&&(m.indexOf("-")>0&&f.indexOf("-")<0&&m.substring(0,m.indexOf("-"))===f||m.indexOf(f)===0&&f.length>1))return m})}),r||(r=this.getFallbackCodes(this.options.fallbackLng)[0]),r}getFallbackCodes(l,r){if(!l)return[];if(typeof l=="function"&&(l=l(r)),se(l)&&(l=[l]),Array.isArray(l))return l;if(!r)return l.default||[];let o=l[r];return o||(o=l[this.getScriptPartFromCode(r)]),o||(o=l[this.formatLanguageCode(r)]),o||(o=l[this.getLanguagePartFromCode(r)]),o||(o=l.default),o||[]}toResolveHierarchy(l,r){const o=this.getFallbackCodes((r===!1?[]:r)||this.options.fallbackLng||[],l),c=[],f=m=>{m&&(this.isSupportedCode(m)?c.push(m):this.logger.warn(`rejecting language code not found in supportedLngs: ${m}`))};return se(l)&&(l.indexOf("-")>-1||l.indexOf("_")>-1)?(this.options.load!=="languageOnly"&&f(this.formatLanguageCode(l)),this.options.load!=="languageOnly"&&this.options.load!=="currentOnly"&&f(this.getScriptPartFromCode(l)),this.options.load!=="currentOnly"&&f(this.getLanguagePartFromCode(l))):se(l)&&f(this.formatLanguageCode(l)),o.forEach(m=>{c.indexOf(m)<0&&f(this.formatLanguageCode(m))}),c}}const Dm={zero:0,one:1,two:2,few:3,many:4,other:5},wm={select:i=>i===1?"one":"other",resolvedOptions:()=>({pluralCategories:["one","other"]})};class X0{constructor(l,r={}){this.languageUtils=l,this.options=r,this.logger=fn.create("pluralResolver"),this.pluralRulesCache={}}clearCache(){this.pluralRulesCache={}}getRule(l,r={}){const o=Li(l==="dev"?"en":l),c=r.ordinal?"ordinal":"cardinal",f=JSON.stringify({cleanedCode:o,type:c});if(f in this.pluralRulesCache)return this.pluralRulesCache[f];let m;try{m=new Intl.PluralRules(o,{type:c})}catch{if(typeof Intl>"u")return this.logger.error("No Intl support, please use an Intl polyfill!"),wm;if(!l.match(/-|_/))return wm;const b=this.languageUtils.getLanguagePartFromCode(l);m=this.getRule(b,r)}return this.pluralRulesCache[f]=m,m}needsPlural(l,r={}){let o=this.getRule(l,r);return o||(o=this.getRule("dev",r)),o?.resolvedOptions().pluralCategories.length>1}getPluralFormsOfKey(l,r,o={}){return this.getSuffixes(l,o).map(c=>`${r}${c}`)}getSuffixes(l,r={}){let o=this.getRule(l,r);return o||(o=this.getRule("dev",r)),o?o.resolvedOptions().pluralCategories.sort((c,f)=>Dm[c]-Dm[f]).map(c=>`${this.options.prepend}${r.ordinal?`ordinal${this.options.prepend}`:""}${c}`):[]}getSuffix(l,r,o={}){const c=this.getRule(l,o);return c?`${this.options.prepend}${o.ordinal?`ordinal${this.options.prepend}`:""}${c.select(r)}`:(this.logger.warn(`no plural rule found for: ${l}`),this.getSuffix("dev",r,o))}}const Rm=(i,l,r,o=".",c=!0)=>{let f=B0(i,l,r);return!f&&c&&se(r)&&(f=nc(i,r,o),f===void 0&&(f=nc(l,r,o))),f},Ku=i=>i.replace(/\$/g,"$$$$");class zm{constructor(l={}){this.logger=fn.create("interpolator"),this.options=l,this.format=l?.interpolation?.format||(r=>r),this.init(l)}init(l={}){l.interpolation||(l.interpolation={escapeValue:!0});const{escape:r,escapeValue:o,useRawValueToEscape:c,prefix:f,prefixEscaped:m,suffix:h,suffixEscaped:b,formatSeparator:v,unescapeSuffix:x,unescapePrefix:g,nestingPrefix:D,nestingPrefixEscaped:z,nestingSuffix:j,nestingSuffixEscaped:B,nestingOptionsSeparator:K,maxReplaces:U,alwaysFormat:Q}=l.interpolation;this.escape=r!==void 0?r:q0,this.escapeValue=o!==void 0?o:!0,this.useRawValueToEscape=c!==void 0?c:!1,this.prefix=f?_a(f):m||"{{",this.suffix=h?_a(h):b||"}}",this.formatSeparator=v||",",this.unescapePrefix=x?"":g||"-",this.unescapeSuffix=this.unescapePrefix?"":x||"",this.nestingPrefix=D?_a(D):z||_a("$t("),this.nestingSuffix=j?_a(j):B||_a(")"),this.nestingOptionsSeparator=K||",",this.maxReplaces=U||1e3,this.alwaysFormat=Q!==void 0?Q:!1,this.resetRegExp()}reset(){this.options&&this.init(this.options)}resetRegExp(){const l=(r,o)=>r?.source===o?(r.lastIndex=0,r):new RegExp(o,"g");this.regexp=l(this.regexp,`${this.prefix}(.+?)${this.suffix}`),this.regexpUnescape=l(this.regexpUnescape,`${this.prefix}${this.unescapePrefix}(.+?)${this.unescapeSuffix}${this.suffix}`),this.nestingRegexp=l(this.nestingRegexp,`${this.nestingPrefix}((?:[^()"']+|"[^"]*"|'[^']*'|\\((?:[^()]|"[^"]*"|'[^']*')*\\))*?)${this.nestingSuffix}`)}interpolate(l,r,o,c){let f,m,h;const b=this.options&&this.options.interpolation&&this.options.interpolation.defaultVariables||{},v=z=>{if(z.indexOf(this.formatSeparator)<0){const U=Rm(r,b,z,this.options.keySeparator,this.options.ignoreJSONStructure);return this.alwaysFormat?this.format(U,void 0,o,{...c,...r,interpolationkey:z}):U}const j=z.split(this.formatSeparator),B=j.shift().trim(),K=j.join(this.formatSeparator).trim();return this.format(Rm(r,b,B,this.options.keySeparator,this.options.ignoreJSONStructure),K,o,{...c,...r,interpolationkey:B})};this.resetRegExp();const x=c?.missingInterpolationHandler||this.options.missingInterpolationHandler,g=c?.interpolation?.skipOnVariables!==void 0?c.interpolation.skipOnVariables:this.options.interpolation.skipOnVariables;return[{regex:this.regexpUnescape,safeValue:z=>Ku(z)},{regex:this.regexp,safeValue:z=>this.escapeValue?Ku(this.escape(z)):Ku(z)}].forEach(z=>{for(h=0;f=z.regex.exec(l);){const j=f[1].trim();if(m=v(j),m===void 0)if(typeof x=="function"){const K=x(l,f,c);m=se(K)?K:""}else if(c&&Object.prototype.hasOwnProperty.call(c,j))m="";else if(g){m=f[0];continue}else this.logger.warn(`missed to pass in variable ${j} for interpolating ${l}`),m="";else!se(m)&&!this.useRawValueToEscape&&(m=xm(m));const B=z.safeValue(m);if(l=l.replace(f[0],B),g?(z.regex.lastIndex+=m.length,z.regex.lastIndex-=f[0].length):z.regex.lastIndex=0,h++,h>=this.maxReplaces)break}}),l}nest(l,r,o={}){let c,f,m;const h=(b,v)=>{const x=this.nestingOptionsSeparator;if(b.indexOf(x)<0)return b;const g=b.split(new RegExp(`${_a(x)}[ ]*{`));let D=`{${g[1]}`;b=g[0],D=this.interpolate(D,m);const z=D.match(/'/g),j=D.match(/"/g);((z?.length??0)%2===0&&!j||(j?.length??0)%2!==0)&&(D=D.replace(/'/g,'"'));try{m=JSON.parse(D),v&&(m={...v,...m})}catch(B){return this.logger.warn(`failed parsing options string in nesting for key ${b}`,B),`${b}${x}${D}`}return m.defaultValue&&m.defaultValue.indexOf(this.prefix)>-1&&delete m.defaultValue,b};for(;c=this.nestingRegexp.exec(l);){let b=[];m={...o},m=m.replace&&!se(m.replace)?m.replace:m,m.applyPostProcessor=!1,delete m.defaultValue;const v=/{.*}/.test(c[1])?c[1].lastIndexOf("}")+1:c[1].indexOf(this.formatSeparator);if(v!==-1&&(b=c[1].slice(v).split(this.formatSeparator).map(x=>x.trim()).filter(Boolean),c[1]=c[1].slice(0,v)),f=r(h.call(this,c[1].trim(),m),m),f&&c[0]===l&&!se(f))return f;se(f)||(f=xm(f)),f||(this.logger.warn(`missed to resolve ${c[1]} for nesting ${l}`),f=""),b.length&&(f=b.reduce((x,g)=>this.format(x,g,o.lng,{...o,interpolationkey:c[1].trim()}),f.trim())),l=l.replace(c[0],f),this.regexp.lastIndex=0}return l}}const Z0=i=>{let l=i.toLowerCase().trim();const r={};if(i.indexOf("(")>-1){const o=i.split("(");l=o[0].toLowerCase().trim();const c=o[1].substring(0,o[1].length-1);l==="currency"&&c.indexOf(":")<0?r.currency||(r.currency=c.trim()):l==="relativetime"&&c.indexOf(":")<0?r.range||(r.range=c.trim()):c.split(";").forEach(m=>{if(m){const[h,...b]=m.split(":"),v=b.join(":").trim().replace(/^'+|'+$/g,""),x=h.trim();r[x]||(r[x]=v),v==="false"&&(r[x]=!1),v==="true"&&(r[x]=!0),isNaN(v)||(r[x]=parseInt(v,10))}})}return{formatName:l,formatOptions:r}},Mm=i=>{const l={};return(r,o,c)=>{let f=c;c&&c.interpolationkey&&c.formatParams&&c.formatParams[c.interpolationkey]&&c[c.interpolationkey]&&(f={...f,[c.interpolationkey]:void 0});const m=o+JSON.stringify(f);let h=l[m];return h||(h=i(Li(o),c),l[m]=h),h(r)}},F0=i=>(l,r,o)=>i(Li(r),o)(l);class J0{constructor(l={}){this.logger=fn.create("formatter"),this.options=l,this.init(l)}init(l,r={interpolation:{}}){this.formatSeparator=r.interpolation.formatSeparator||",";const o=r.cacheInBuiltFormats?Mm:F0;this.formats={number:o((c,f)=>{const m=new Intl.NumberFormat(c,{...f});return h=>m.format(h)}),currency:o((c,f)=>{const m=new Intl.NumberFormat(c,{...f,style:"currency"});return h=>m.format(h)}),datetime:o((c,f)=>{const m=new Intl.DateTimeFormat(c,{...f});return h=>m.format(h)}),relativetime:o((c,f)=>{const m=new Intl.RelativeTimeFormat(c,{...f});return h=>m.format(h,f.range||"day")}),list:o((c,f)=>{const m=new Intl.ListFormat(c,{...f});return h=>m.format(h)})}}add(l,r){this.formats[l.toLowerCase().trim()]=r}addCached(l,r){this.formats[l.toLowerCase().trim()]=Mm(r)}format(l,r,o,c={}){const f=r.split(this.formatSeparator);if(f.length>1&&f[0].indexOf("(")>1&&f[0].indexOf(")")<0&&f.find(h=>h.indexOf(")")>-1)){const h=f.findIndex(b=>b.indexOf(")")>-1);f[0]=[f[0],...f.splice(1,h)].join(this.formatSeparator)}return f.reduce((h,b)=>{const{formatName:v,formatOptions:x}=Z0(b);if(this.formats[v]){let g=h;try{const D=c?.formatParams?.[c.interpolationkey]||{},z=D.locale||D.lng||c.locale||c.lng||o;g=this.formats[v](h,z,{...x,...c,...D})}catch(D){this.logger.warn(D)}return g}else this.logger.warn(`there was no format function for ${v}`);return h},l)}}const $0=(i,l)=>{i.pending[l]!==void 0&&(delete i.pending[l],i.pendingCount--)};class W0 extends Sr{constructor(l,r,o,c={}){super(),this.backend=l,this.store=r,this.services=o,this.languageUtils=o.languageUtils,this.options=c,this.logger=fn.create("backendConnector"),this.waitingReads=[],this.maxParallelReads=c.maxParallelReads||10,this.readingCalls=0,this.maxRetries=c.maxRetries>=0?c.maxRetries:5,this.retryTimeout=c.retryTimeout>=1?c.retryTimeout:350,this.state={},this.queue=[],this.backend?.init?.(o,c.backend,c)}queueLoad(l,r,o,c){const f={},m={},h={},b={};return l.forEach(v=>{let x=!0;r.forEach(g=>{const D=`${v}|${g}`;!o.reload&&this.store.hasResourceBundle(v,g)?this.state[D]=2:this.state[D]<0||(this.state[D]===1?m[D]===void 0&&(m[D]=!0):(this.state[D]=1,x=!1,m[D]===void 0&&(m[D]=!0),f[D]===void 0&&(f[D]=!0),b[g]===void 0&&(b[g]=!0)))}),x||(h[v]=!0)}),(Object.keys(f).length||Object.keys(m).length)&&this.queue.push({pending:m,pendingCount:Object.keys(m).length,loaded:{},errors:[],callback:c}),{toLoad:Object.keys(f),pending:Object.keys(m),toLoadLanguages:Object.keys(h),toLoadNamespaces:Object.keys(b)}}loaded(l,r,o){const c=l.split("|"),f=c[0],m=c[1];r&&this.emit("failedLoading",f,m,r),!r&&o&&this.store.addResourceBundle(f,m,o,void 0,void 0,{skipCopy:!0}),this.state[l]=r?-1:2,r&&o&&(this.state[l]=0);const h={};this.queue.forEach(b=>{U0(b.loaded,[f],m),$0(b,l),r&&b.errors.push(r),b.pendingCount===0&&!b.done&&(Object.keys(b.loaded).forEach(v=>{h[v]||(h[v]={});const x=b.loaded[v];x.length&&x.forEach(g=>{h[v][g]===void 0&&(h[v][g]=!0)})}),b.done=!0,b.errors.length?b.callback(b.errors):b.callback())}),this.emit("loaded",h),this.queue=this.queue.filter(b=>!b.done)}read(l,r,o,c=0,f=this.retryTimeout,m){if(!l.length)return m(null,{});if(this.readingCalls>=this.maxParallelReads){this.waitingReads.push({lng:l,ns:r,fcName:o,tried:c,wait:f,callback:m});return}this.readingCalls++;const h=(v,x)=>{if(this.readingCalls--,this.waitingReads.length>0){const g=this.waitingReads.shift();this.read(g.lng,g.ns,g.fcName,g.tried,g.wait,g.callback)}if(v&&x&&c{this.read.call(this,l,r,o,c+1,f*2,m)},f);return}m(v,x)},b=this.backend[o].bind(this.backend);if(b.length===2){try{const v=b(l,r);v&&typeof v.then=="function"?v.then(x=>h(null,x)).catch(h):h(null,v)}catch(v){h(v)}return}return b(l,r,h)}prepareLoading(l,r,o={},c){if(!this.backend)return this.logger.warn("No backend was added via i18next.use. Will not load resources."),c&&c();se(l)&&(l=this.languageUtils.toResolveHierarchy(l)),se(r)&&(r=[r]);const f=this.queueLoad(l,r,o,c);if(!f.toLoad.length)return f.pending.length||c(),null;f.toLoad.forEach(m=>{this.loadOne(m)})}load(l,r,o){this.prepareLoading(l,r,{},o)}reload(l,r,o){this.prepareLoading(l,r,{reload:!0},o)}loadOne(l,r=""){const o=l.split("|"),c=o[0],f=o[1];this.read(c,f,"read",void 0,void 0,(m,h)=>{m&&this.logger.warn(`${r}loading namespace ${f} for language ${c} failed`,m),!m&&h&&this.logger.log(`${r}loaded namespace ${f} for language ${c}`,h),this.loaded(l,m,h)})}saveMissing(l,r,o,c,f,m={},h=()=>{}){if(this.services?.utils?.hasLoadedNamespace&&!this.services?.utils?.hasLoadedNamespace(r)){this.logger.warn(`did not save key "${o}" as the namespace "${r}" was not yet loaded`,"This means something IS WRONG in your setup. You access the t function before i18next.init / i18next.loadNamespace / i18next.changeLanguage was done. Wait for the callback or Promise to resolve before accessing it!!!");return}if(!(o==null||o==="")){if(this.backend?.create){const b={...m,isUpdate:f},v=this.backend.create.bind(this.backend);if(v.length<6)try{let x;v.length===5?x=v(l,r,o,c,b):x=v(l,r,o,c),x&&typeof x.then=="function"?x.then(g=>h(null,g)).catch(h):h(null,x)}catch(x){h(x)}else v(l,r,o,c,h,b)}!l||!l[0]||this.store.addResource(l[0],r,o,c)}}}const Gu=()=>({debug:!1,initAsync:!0,ns:["translation"],defaultNS:["translation"],fallbackLng:["dev"],fallbackNS:!1,supportedLngs:!1,nonExplicitSupportedLngs:!1,load:"all",preload:!1,simplifyPluralSuffix:!0,keySeparator:".",nsSeparator:":",pluralSeparator:"_",contextSeparator:"_",partialBundledLanguages:!1,saveMissing:!1,updateMissing:!1,saveMissingTo:"fallback",saveMissingPlurals:!0,missingKeyHandler:!1,missingInterpolationHandler:!1,postProcess:!1,postProcessPassResolved:!1,returnNull:!1,returnEmptyString:!0,returnObjects:!1,joinArrays:!1,returnedObjectHandler:!1,parseMissingKeyHandler:!1,appendNamespaceToMissingKey:!1,appendNamespaceToCIMode:!1,overloadTranslationOptionHandler:i=>{let l={};if(typeof i[1]=="object"&&(l=i[1]),se(i[1])&&(l.defaultValue=i[1]),se(i[2])&&(l.tDescription=i[2]),typeof i[2]=="object"||typeof i[3]=="object"){const r=i[3]||i[2];Object.keys(r).forEach(o=>{l[o]=r[o]})}return l},interpolation:{escapeValue:!0,format:i=>i,prefix:"{{",suffix:"}}",formatSeparator:",",unescapePrefix:"-",nestingPrefix:"$t(",nestingSuffix:")",nestingOptionsSeparator:",",maxReplaces:1e3,skipOnVariables:!0},cacheInBuiltFormats:!0}),_m=i=>(se(i.ns)&&(i.ns=[i.ns]),se(i.fallbackLng)&&(i.fallbackLng=[i.fallbackLng]),se(i.fallbackNS)&&(i.fallbackNS=[i.fallbackNS]),i.supportedLngs?.indexOf?.("cimode")<0&&(i.supportedLngs=i.supportedLngs.concat(["cimode"])),typeof i.initImmediate=="boolean"&&(i.initAsync=i.initImmediate),i),ar=()=>{},I0=i=>{Object.getOwnPropertyNames(Object.getPrototypeOf(i)).forEach(r=>{typeof i[r]=="function"&&(i[r]=i[r].bind(i))})},Tg="__i18next_supportNoticeShown",P0=()=>typeof globalThis<"u"&&!!globalThis[Tg],eb=()=>{typeof globalThis<"u"&&(globalThis[Tg]=!0)},tb=i=>!!(i?.modules?.backend?.name?.indexOf("Locize")>0||i?.modules?.backend?.constructor?.name?.indexOf("Locize")>0||i?.options?.backend?.backends&&i.options.backend.backends.some(l=>l?.name?.indexOf("Locize")>0||l?.constructor?.name?.indexOf("Locize")>0)||i?.options?.backend?.projectId||i?.options?.backend?.backendOptions&&i.options.backend.backendOptions.some(l=>l?.projectId));class _i extends Sr{constructor(l={},r){if(super(),this.options=_m(l),this.services={},this.logger=fn,this.modules={external:[]},I0(this),r&&!this.isInitialized&&!l.isClone){if(!this.options.initAsync)return this.init(l,r),this;setTimeout(()=>{this.init(l,r)},0)}}init(l={},r){this.isInitializing=!0,typeof l=="function"&&(r=l,l={}),l.defaultNS==null&&l.ns&&(se(l.ns)?l.defaultNS=l.ns:l.ns.indexOf("translation")<0&&(l.defaultNS=l.ns[0]));const o=Gu();this.options={...o,...this.options,..._m(l)},this.options.interpolation={...o.interpolation,...this.options.interpolation},l.keySeparator!==void 0&&(this.options.userDefinedKeySeparator=l.keySeparator),l.nsSeparator!==void 0&&(this.options.userDefinedNsSeparator=l.nsSeparator),typeof this.options.overloadTranslationOptionHandler!="function"&&(this.options.overloadTranslationOptionHandler=o.overloadTranslationOptionHandler),this.options.showSupportNotice!==!1&&!tb(this)&&!P0()&&(typeof console<"u"&&typeof console.info<"u"&&console.info("🌐 i18next is maintained with support from Locize — consider powering your project with managed localization (AI, CDN, integrations): https://locize.com 💙"),eb());const c=v=>v?typeof v=="function"?new v:v:null;if(!this.options.isClone){this.modules.logger?fn.init(c(this.modules.logger),this.options):fn.init(null,this.options);let v;this.modules.formatter?v=this.modules.formatter:v=J0;const x=new Am(this.options);this.store=new Cm(this.options.resources,this.options);const g=this.services;g.logger=fn,g.resourceStore=this.store,g.languageUtils=x,g.pluralResolver=new X0(x,{prepend:this.options.pluralSeparator,simplifyPluralSuffix:this.options.simplifyPluralSuffix}),this.options.interpolation.format&&this.options.interpolation.format!==o.interpolation.format&&this.logger.deprecate("init: you are still using the legacy format function, please use the new approach: https://www.i18next.com/translation-function/formatting"),v&&(!this.options.interpolation.format||this.options.interpolation.format===o.interpolation.format)&&(g.formatter=c(v),g.formatter.init&&g.formatter.init(g,this.options),this.options.interpolation.format=g.formatter.format.bind(g.formatter)),g.interpolator=new zm(this.options),g.utils={hasLoadedNamespace:this.hasLoadedNamespace.bind(this)},g.backendConnector=new W0(c(this.modules.backend),g.resourceStore,g,this.options),g.backendConnector.on("*",(z,...j)=>{this.emit(z,...j)}),this.modules.languageDetector&&(g.languageDetector=c(this.modules.languageDetector),g.languageDetector.init&&g.languageDetector.init(g,this.options.detection,this.options)),this.modules.i18nFormat&&(g.i18nFormat=c(this.modules.i18nFormat),g.i18nFormat.init&&g.i18nFormat.init(this)),this.translator=new pr(this.services,this.options),this.translator.on("*",(z,...j)=>{this.emit(z,...j)}),this.modules.external.forEach(z=>{z.init&&z.init(this)})}if(this.format=this.options.interpolation.format,r||(r=ar),this.options.fallbackLng&&!this.services.languageDetector&&!this.options.lng){const v=this.services.languageUtils.getFallbackCodes(this.options.fallbackLng);v.length>0&&v[0]!=="dev"&&(this.options.lng=v[0])}!this.services.languageDetector&&!this.options.lng&&this.logger.warn("init: no languageDetector is used and no lng is defined"),["getResource","hasResourceBundle","getResourceBundle","getDataByLanguage"].forEach(v=>{this[v]=(...x)=>this.store[v](...x)}),["addResource","addResources","addResourceBundle","removeResourceBundle"].forEach(v=>{this[v]=(...x)=>(this.store[v](...x),this)});const h=Ri(),b=()=>{const v=(x,g)=>{this.isInitializing=!1,this.isInitialized&&!this.initializedStoreOnce&&this.logger.warn("init: i18next is already initialized. You should call init just once!"),this.isInitialized=!0,this.options.isClone||this.logger.log("initialized",this.options),this.emit("initialized",this.options),h.resolve(g),r(x,g)};if(this.languages&&!this.isInitialized)return v(null,this.t.bind(this));this.changeLanguage(this.options.lng,v)};return this.options.resources||!this.options.initAsync?b():setTimeout(b,0),h}loadResources(l,r=ar){let o=r;const c=se(l)?l:this.language;if(typeof l=="function"&&(o=l),!this.options.resources||this.options.partialBundledLanguages){if(c?.toLowerCase()==="cimode"&&(!this.options.preload||this.options.preload.length===0))return o();const f=[],m=h=>{if(!h||h==="cimode")return;this.services.languageUtils.toResolveHierarchy(h).forEach(v=>{v!=="cimode"&&f.indexOf(v)<0&&f.push(v)})};c?m(c):this.services.languageUtils.getFallbackCodes(this.options.fallbackLng).forEach(b=>m(b)),this.options.preload?.forEach?.(h=>m(h)),this.services.backendConnector.load(f,this.options.ns,h=>{!h&&!this.resolvedLanguage&&this.language&&this.setResolvedLanguage(this.language),o(h)})}else o(null)}reloadResources(l,r,o){const c=Ri();return typeof l=="function"&&(o=l,l=void 0),typeof r=="function"&&(o=r,r=void 0),l||(l=this.languages),r||(r=this.options.ns),o||(o=ar),this.services.backendConnector.reload(l,r,f=>{c.resolve(),o(f)}),c}use(l){if(!l)throw new Error("You are passing an undefined module! Please check the object you are passing to i18next.use()");if(!l.type)throw new Error("You are passing a wrong module! Please check the object you are passing to i18next.use()");return l.type==="backend"&&(this.modules.backend=l),(l.type==="logger"||l.log&&l.warn&&l.error)&&(this.modules.logger=l),l.type==="languageDetector"&&(this.modules.languageDetector=l),l.type==="i18nFormat"&&(this.modules.i18nFormat=l),l.type==="postProcessor"&&xg.addPostProcessor(l),l.type==="formatter"&&(this.modules.formatter=l),l.type==="3rdParty"&&this.modules.external.push(l),this}setResolvedLanguage(l){if(!(!l||!this.languages)&&!(["cimode","dev"].indexOf(l)>-1)){for(let r=0;r-1)&&this.store.hasLanguageSomeTranslations(o)){this.resolvedLanguage=o;break}}!this.resolvedLanguage&&this.languages.indexOf(l)<0&&this.store.hasLanguageSomeTranslations(l)&&(this.resolvedLanguage=l,this.languages.unshift(l))}}changeLanguage(l,r){this.isLanguageChangingTo=l;const o=Ri();this.emit("languageChanging",l);const c=h=>{this.language=h,this.languages=this.services.languageUtils.toResolveHierarchy(h),this.resolvedLanguage=void 0,this.setResolvedLanguage(h)},f=(h,b)=>{b?this.isLanguageChangingTo===l&&(c(b),this.translator.changeLanguage(b),this.isLanguageChangingTo=void 0,this.emit("languageChanged",b),this.logger.log("languageChanged",b)):this.isLanguageChangingTo=void 0,o.resolve((...v)=>this.t(...v)),r&&r(h,(...v)=>this.t(...v))},m=h=>{!l&&!h&&this.services.languageDetector&&(h=[]);const b=se(h)?h:h&&h[0],v=this.store.hasLanguageSomeTranslations(b)?b:this.services.languageUtils.getBestMatchFromCodes(se(h)?[h]:h);v&&(this.language||c(v),this.translator.language||this.translator.changeLanguage(v),this.services.languageDetector?.cacheUserLanguage?.(v)),this.loadResources(v,x=>{f(x,v)})};return!l&&this.services.languageDetector&&!this.services.languageDetector.async?m(this.services.languageDetector.detect()):!l&&this.services.languageDetector&&this.services.languageDetector.async?this.services.languageDetector.detect.length===0?this.services.languageDetector.detect().then(m):this.services.languageDetector.detect(m):m(l),o}getFixedT(l,r,o){const c=(f,m,...h)=>{let b;typeof m!="object"?b=this.options.overloadTranslationOptionHandler([f,m].concat(h)):b={...m},b.lng=b.lng||c.lng,b.lngs=b.lngs||c.lngs,b.ns=b.ns||c.ns,b.keyPrefix!==""&&(b.keyPrefix=b.keyPrefix||o||c.keyPrefix);const v=this.options.keySeparator||".";let x;return b.keyPrefix&&Array.isArray(f)?x=f.map(g=>(typeof g=="function"&&(g=ac(g,{...this.options,...m})),`${b.keyPrefix}${v}${g}`)):(typeof f=="function"&&(f=ac(f,{...this.options,...m})),x=b.keyPrefix?`${b.keyPrefix}${v}${f}`:f),this.t(x,b)};return se(l)?c.lng=l:c.lngs=l,c.ns=r,c.keyPrefix=o,c}t(...l){return this.translator?.translate(...l)}exists(...l){return this.translator?.exists(...l)}setDefaultNamespace(l){this.options.defaultNS=l}hasLoadedNamespace(l,r={}){if(!this.isInitialized)return this.logger.warn("hasLoadedNamespace: i18next was not initialized",this.languages),!1;if(!this.languages||!this.languages.length)return this.logger.warn("hasLoadedNamespace: i18n.languages were undefined or empty",this.languages),!1;const o=r.lng||this.resolvedLanguage||this.languages[0],c=this.options?this.options.fallbackLng:!1,f=this.languages[this.languages.length-1];if(o.toLowerCase()==="cimode")return!0;const m=(h,b)=>{const v=this.services.backendConnector.state[`${h}|${b}`];return v===-1||v===0||v===2};if(r.precheck){const h=r.precheck(this,m);if(h!==void 0)return h}return!!(this.hasResourceBundle(o,l)||!this.services.backendConnector.backend||this.options.resources&&!this.options.partialBundledLanguages||m(o,l)&&(!c||m(f,l)))}loadNamespaces(l,r){const o=Ri();return this.options.ns?(se(l)&&(l=[l]),l.forEach(c=>{this.options.ns.indexOf(c)<0&&this.options.ns.push(c)}),this.loadResources(c=>{o.resolve(),r&&r(c)}),o):(r&&r(),Promise.resolve())}loadLanguages(l,r){const o=Ri();se(l)&&(l=[l]);const c=this.options.preload||[],f=l.filter(m=>c.indexOf(m)<0&&this.services.languageUtils.isSupportedCode(m));return f.length?(this.options.preload=c.concat(f),this.loadResources(m=>{o.resolve(),r&&r(m)}),o):(r&&r(),Promise.resolve())}dir(l){if(l||(l=this.resolvedLanguage||(this.languages?.length>0?this.languages[0]:this.language)),!l)return"rtl";try{const c=new Intl.Locale(l);if(c&&c.getTextInfo){const f=c.getTextInfo();if(f&&f.direction)return f.direction}}catch{}const r=["ar","shu","sqr","ssh","xaa","yhd","yud","aao","abh","abv","acm","acq","acw","acx","acy","adf","ads","aeb","aec","afb","ajp","apc","apd","arb","arq","ars","ary","arz","auz","avl","ayh","ayl","ayn","ayp","bbz","pga","he","iw","ps","pbt","pbu","pst","prp","prd","ug","ur","ydd","yds","yih","ji","yi","hbo","men","xmn","fa","jpr","peo","pes","prs","dv","sam","ckb"],o=this.services?.languageUtils||new Am(Gu());return l.toLowerCase().indexOf("-latn")>1?"ltr":r.indexOf(o.getLanguagePartFromCode(l))>-1||l.toLowerCase().indexOf("-arab")>1?"rtl":"ltr"}static createInstance(l={},r){const o=new _i(l,r);return o.createInstance=_i.createInstance,o}cloneInstance(l={},r=ar){const o=l.forkResourceStore;o&&delete l.forkResourceStore;const c={...this.options,...l,isClone:!0},f=new _i(c);if((l.debug!==void 0||l.prefix!==void 0)&&(f.logger=f.logger.clone(l)),["store","services","language"].forEach(h=>{f[h]=this[h]}),f.services={...this.services},f.services.utils={hasLoadedNamespace:f.hasLoadedNamespace.bind(f)},o){const h=Object.keys(this.store.data).reduce((b,v)=>(b[v]={...this.store.data[v]},b[v]=Object.keys(b[v]).reduce((x,g)=>(x[g]={...b[v][g]},x),b[v]),b),{});f.store=new Cm(h,c),f.services.resourceStore=f.store}if(l.interpolation){const b={...Gu().interpolation,...this.options.interpolation,...l.interpolation},v={...c,interpolation:b};f.services.interpolator=new zm(v)}return f.translator=new pr(f.services,c),f.translator.on("*",(h,...b)=>{f.emit(h,...b)}),f.init(c,r),f.translator.options=c,f.translator.backendConnector.services.utils={hasLoadedNamespace:f.hasLoadedNamespace.bind(f)},f}toJSON(){return{options:this.options,store:this.store,language:this.language,languages:this.languages,resolvedLanguage:this.resolvedLanguage}}}const ht=_i.createInstance();ht.createInstance;ht.dir;ht.init;ht.loadResources;ht.reloadResources;ht.use;ht.changeLanguage;ht.getFixedT;ht.t;ht.exists;ht.setDefaultNamespace;ht.hasLoadedNamespace;ht.loadNamespaces;ht.loadLanguages;const nb=(i,l,r,o)=>{const c=[r,{code:l,...o||{}}];if(i?.services?.logger?.forward)return i.services.logger.forward(c,"warn","react-i18next::",!0);ja(c[0])&&(c[0]=`react-i18next:: ${c[0]}`),i?.services?.logger?.warn?i.services.logger.warn(...c):console?.warn&&console.warn(...c)},jm={},Og=(i,l,r,o)=>{ja(r)&&jm[r]||(ja(r)&&(jm[r]=new Date),nb(i,l,r,o))},Cg=(i,l)=>()=>{if(i.isInitialized)l();else{const r=()=>{setTimeout(()=>{i.off("initialized",r)},0),l()};i.on("initialized",r)}},lc=(i,l,r)=>{i.loadNamespaces(l,Cg(i,r))},Lm=(i,l,r,o)=>{if(ja(r)&&(r=[r]),i.options.preload&&i.options.preload.indexOf(l)>-1)return 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index 083564bf..30b72795 100644 --- a/src/surreal_memory/server/static/dist/index.html +++ b/src/surreal_memory/server/static/dist/index.html @@ -5,13 +5,13 @@ dashboard - + - + - +
From ca8295d543adb917736c6396791561fea97b2ad8 Mon Sep 17 00:00:00 2001 From: Toni Nowak Date: Fri, 17 Jul 2026 16:12:28 +0200 Subject: [PATCH 09/12] feat: smem reasoning CLI + smem_reasoning MCP tool (U9) cli/commands/reasoning.py: new Typer sub-app `smem reasoning` with status (trace/pattern stats + per-model coverage), mine (--backfill/--dry-run/--models, gated on mining_enabled unless --force), patterns (--model/--category), and clear (--model privacy wipe). Registered in cli/main.py. Mirrors commands/habits.py. mcp/reasoning_handler.py: new ReasoningHandler mixin exposing smem_reasoning (actions status/mine/patterns/config), registered in mcp/server.py + schema in tool_schemas.py (58 tools now). mine runs on create_isolated_storage so a concurrent MCP call (HTTP transport is not serialized) can't redirect its graph writes into the wrong brain; gated on mining_enabled. Both surfaces are thin wrappers over the U3/U4/U7 engine + storage layer (get_reasoning_stats, reasoning_coverage, ingest+distill honoring mining_models, delete_reasoning_traces_by_model). Tests: test_cli_reasoning (8) + test_mcp_reasoning (7) + test_mcp count bump. python-reviewer APPROVE (1 HIGH + 2 MED fixed). --- src/surreal_memory/cli/commands/reasoning.py | 297 +++++++++++++++++++ src/surreal_memory/cli/main.py | 2 + src/surreal_memory/mcp/reasoning_handler.py | 162 ++++++++++ src/surreal_memory/mcp/server.py | 3 + src/surreal_memory/mcp/tool_schemas.py | 44 +++ tests/unit/test_cli_reasoning.py | 239 +++++++++++++++ tests/unit/test_mcp.py | 5 +- tests/unit/test_mcp_reasoning.py | 190 ++++++++++++ 8 files changed, 940 insertions(+), 2 deletions(-) create mode 100644 src/surreal_memory/cli/commands/reasoning.py create mode 100644 src/surreal_memory/mcp/reasoning_handler.py create mode 100644 tests/unit/test_cli_reasoning.py create mode 100644 tests/unit/test_mcp_reasoning.py diff --git a/src/surreal_memory/cli/commands/reasoning.py b/src/surreal_memory/cli/commands/reasoning.py new file mode 100644 index 00000000..6826b022 --- /dev/null +++ b/src/surreal_memory/cli/commands/reasoning.py @@ -0,0 +1,297 @@ +"""Reasoning-training CLI commands (status / mine / patterns / clear). + +Thin wrappers over the reasoning engine + storage (NOT the HTTP dashboard router): +mine model ``thinking`` into ReasoningBank patterns and inspect/clear them. +""" + +from __future__ import annotations + +from typing import Annotated, Any + +import typer + +from surreal_memory.cli._helpers import get_config, get_storage, output_result, run_async + +reasoning_app = typer.Typer( + help="Reasoning-training commands (mine model thinking into strategies)" +) + +_PATTERN_FETCH_LIMIT = 5000 + + +def _split_models(models: str | None) -> tuple[str, ...]: + """Parse a comma-separated --models value into a tuple of model names.""" + if not models: + return () + return tuple(m.strip() for m in models.split(",") if m.strip()) + + +@reasoning_app.command("status") +def reasoning_status( + json_output: Annotated[bool, typer.Option("--json", "-j", help="Output as JSON")] = False, +) -> None: + """Show reasoning-training status: trace counts, learned patterns, per-model coverage. + + Examples: + smem reasoning status + smem reasoning status --json + """ + + async def _status() -> None: + from surreal_memory.engine.reasoning_distiller import reasoning_coverage + from surreal_memory.unified_config import get_config as get_unified_config + + config = get_config() + storage = await get_storage(config) + try: + brain_id = storage.brain_id or "" + if not brain_id: + typer.secho("No brain configured.", fg=typer.colors.RED, err=True) + raise typer.Exit(1) + + ucfg = get_unified_config() + stats = await storage.get_reasoning_stats(brain_id) + fibers = await storage.find_fibers( + metadata_key="_reasoning_pattern", limit=_PATTERN_FETCH_LIMIT + ) + pattern_counts: dict[str, int] = {} + for f in fibers: + m = str(f.metadata.get("_source_model", "")) + if m: + pattern_counts[m] = pattern_counts.get(m, 0) + 1 + + models = sorted(set(stats.get("by_model", {})) | set(pattern_counts)) + per_model: list[dict[str, Any]] = [] + for m in models: + cov = await reasoning_coverage(storage, m, ucfg) + tstats = stats.get("by_model", {}).get(m, {}) + per_model.append( + { + "model": m, + "trace_count": tstats.get("trace_count", 0), + "unprocessed": tstats.get("unprocessed", 0), + "pattern_count": pattern_counts.get(m, 0), + "coverage_percent": cov["coverage_percent"], + } + ) + + result = { + "total_traces": stats.get("total", 0), + "unprocessed_traces": stats.get("unprocessed", 0), + "total_patterns": len(fibers), + "mining_enabled": ucfg.reasoning_training.mining_enabled, + "injection_enabled": ucfg.reasoning_training.injection_enabled, + "models": per_model, + } + + if json_output: + output_result(result, True) + else: + typer.echo( + f"Reasoning traces: {result['total_traces']} " + f"({result['unprocessed_traces']} unprocessed)" + ) + typer.echo(f"Learned patterns: {result['total_patterns']}") + typer.echo( + f"Mining: {'on' if result['mining_enabled'] else 'off'} | " + f"Injection: {'on' if result['injection_enabled'] else 'off'}" + ) + if per_model: + typer.echo("\nPer model:") + for pm in per_model: + typer.echo( + f" {pm['model']}: {pm['trace_count']} traces, " + f"{pm['pattern_count']} patterns, {pm['coverage_percent']}% coverage" + ) + finally: + await storage.close() + + run_async(_status()) + + +@reasoning_app.command("mine") +def reasoning_mine( + backfill: Annotated[ + bool, typer.Option("--backfill", help="Scan the full history (scan_lookback_days=0)") + ] = False, + dry_run: Annotated[ + bool, typer.Option("--dry-run", "-n", help="Don't write — report a no-op") + ] = False, + models: Annotated[ + str | None, typer.Option("--models", help="Comma-separated source models to restrict to") + ] = None, + force: Annotated[ + bool, typer.Option("--force", "-f", help="Run even if mining is disabled in config") + ] = False, + json_output: Annotated[bool, typer.Option("--json", "-j", help="Output as JSON")] = False, +) -> None: + """Mine reasoning traces from ~/.claude transcripts and distill patterns. + + Examples: + smem reasoning mine + smem reasoning mine --backfill --models claude-fable-5,claude-sonnet-5 + """ + + async def _mine() -> None: + from dataclasses import replace as dc_replace + + from surreal_memory.engine.reasoning_distiller import distill_reasoning_patterns + from surreal_memory.engine.reasoning_miner import ingest_reasoning_traces + from surreal_memory.unified_config import get_config as get_unified_config + + config = get_config() + storage = await get_storage(config) + try: + brain_id = storage.brain_id or "" + if not brain_id: + typer.secho("No brain configured.", fg=typer.colors.RED, err=True) + raise typer.Exit(1) + + ucfg = get_unified_config() + if not ucfg.reasoning_training.mining_enabled and not force: + # Privacy: don't scan ~/.claude transcripts unless mining is enabled. + typer.secho( + "Mining is disabled. Enable it in config or pass --force.", + fg=typer.colors.YELLOW, + err=True, + ) + raise typer.Exit(1) + + if dry_run: + result = {"traces_ingested": 0, "patterns_learned": 0, "dry_run": True} + if json_output: + output_result(result, True) + else: + typer.echo("Dry run: no changes.") + return + + rt = ucfg.reasoning_training + overrides: dict[str, Any] = {} + if backfill: + overrides["scan_lookback_days"] = 0 + model_list = _split_models(models) + if model_list: + overrides["mining_models"] = model_list + run_cfg = dc_replace(ucfg, reasoning_training=dc_replace(rt, **overrides)) + + ingest = await ingest_reasoning_traces(storage, brain_id, run_cfg) + distill = await distill_reasoning_patterns(storage, brain_id, run_cfg) + result = { + "traces_ingested": ingest.traces_ingested, + "patterns_learned": distill.patterns_learned, + } + if json_output: + output_result(result, True) + else: + typer.echo( + f"Mined {result['traces_ingested']} traces, " + f"learned {result['patterns_learned']} patterns." + ) + finally: + await storage.close() + + run_async(_mine()) + + +@reasoning_app.command("patterns") +def reasoning_patterns( + model: Annotated[ + str | None, typer.Option("--model", "-m", help="Filter by source model") + ] = None, + category: Annotated[ + str | None, typer.Option("--category", "-c", help="Filter by category") + ] = None, + json_output: Annotated[bool, typer.Option("--json", "-j", help="Output as JSON")] = False, +) -> None: + """List learned reasoning patterns. + + Examples: + smem reasoning patterns + smem reasoning patterns --model claude-fable-5 --category debugging + """ + + async def _patterns() -> None: + config = get_config() + storage = await get_storage(config) + try: + if not (storage.brain_id or ""): + typer.secho("No brain configured.", fg=typer.colors.RED, err=True) + raise typer.Exit(1) + fibers = await storage.find_fibers( + metadata_key="_reasoning_pattern", limit=_PATTERN_FETCH_LIMIT + ) + rows: list[dict[str, Any]] = [] + for f in fibers: + md = f.metadata + if model and md.get("_source_model") != model: + continue + if category and md.get("_reasoning_category") != category: + continue + rows.append( + { + "id": f.id, + "source_model": md.get("_source_model", ""), + "category": md.get("_reasoning_category", ""), + "title": md.get("_reasoning_title", ""), + "confidence": md.get("_reasoning_confidence", 0.0), + "frequency": md.get("_reasoning_frequency", 0), + } + ) + rows.sort( + key=lambda r: float(r["confidence"] or 0.0) * float(r["frequency"] or 0), + reverse=True, + ) + + if json_output: + output_result({"patterns": rows, "count": len(rows)}, True) + else: + if not rows: + typer.echo("No learned patterns.") + return + typer.echo(f"Learned patterns ({len(rows)}):") + for r in rows: + typer.echo( + f" [{r['category']}] {r['title']} — {r['source_model']} " + f"(conf {float(r['confidence'] or 0.0):.2f}, freq {r['frequency']})" + ) + finally: + await storage.close() + + run_async(_patterns()) + + +@reasoning_app.command("clear") +def reasoning_clear( + model: Annotated[str, typer.Option("--model", "-m", help="Model whose staged traces to wipe")], + force: Annotated[bool, typer.Option("--force", "-f", help="Skip confirmation")] = False, + json_output: Annotated[bool, typer.Option("--json", "-j", help="Output as JSON")] = False, +) -> None: + """Wipe staged reasoning traces for a model (privacy). + + Examples: + smem reasoning clear --model claude-fable-5 + smem reasoning clear --model claude-fable-5 --force + """ + + async def _clear() -> None: + config = get_config() + storage = await get_storage(config) + try: + brain_id = storage.brain_id or "" + if not brain_id: + typer.secho("No brain configured.", fg=typer.colors.RED, err=True) + raise typer.Exit(1) + + if not force and not typer.confirm(f"Wipe all staged reasoning traces for {model!r}?"): + typer.echo("Cancelled.") + return + + deleted = await storage.delete_reasoning_traces_by_model(brain_id, model) + if json_output: + output_result({"deleted": deleted}, True) + else: + typer.echo(f"Wiped {deleted} reasoning trace(s) for {model}.") + finally: + await storage.close() + + run_async(_clear()) diff --git a/src/surreal_memory/cli/main.py b/src/surreal_memory/cli/main.py index 12fdbfc5..400fb3cc 100755 --- a/src/surreal_memory/cli/main.py +++ b/src/surreal_memory/cli/main.py @@ -99,6 +99,7 @@ def _app_callback( from surreal_memory.cli.commands.config_cmd import config_app # noqa: E402 from surreal_memory.cli.commands.habits import habits_app # noqa: E402 from surreal_memory.cli.commands.project import project_app # noqa: E402 +from surreal_memory.cli.commands.reasoning import reasoning_app # noqa: E402 from surreal_memory.cli.commands.shared import shared_app # noqa: E402 from surreal_memory.cli.commands.storage import storage_app # noqa: E402 from surreal_memory.cli.commands.telegram import app as telegram_app # noqa: E402 @@ -110,6 +111,7 @@ def _app_callback( app.add_typer(shared_app, name="shared") app.add_typer(storage_app, name="storage") app.add_typer(habits_app, name="habits") +app.add_typer(reasoning_app, name="reasoning") app.add_typer(version_app, name="version") app.add_typer(telegram_app, name="telegram") diff --git a/src/surreal_memory/mcp/reasoning_handler.py b/src/surreal_memory/mcp/reasoning_handler.py new file mode 100644 index 00000000..eb806c56 --- /dev/null +++ b/src/surreal_memory/mcp/reasoning_handler.py @@ -0,0 +1,162 @@ +"""MCP handler mixin for reasoning-training (status / mine / patterns / config). + +Thin wrapper over the reasoning engine + storage, mirroring the smem_reasoning +dashboard router. Handlers return plain dicts — the server wraps them as MCP +TextContent. +""" + +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING, Any + +from surreal_memory.mcp.tool_handler_utils import _get_brain_or_error + +if TYPE_CHECKING: + from surreal_memory.storage.base import NeuralStorage + from surreal_memory.unified_config import UnifiedConfig + +logger = logging.getLogger(__name__) + +_PATTERN_FETCH_LIMIT = 5000 + + +class ReasoningHandler: + """Mixin providing the ``smem_reasoning`` tool (status/mine/patterns/config).""" + + if TYPE_CHECKING: + config: UnifiedConfig + + async def get_storage(self) -> NeuralStorage: + raise NotImplementedError + + async def _reasoning(self, args: dict[str, Any]) -> dict[str, Any]: + """Dispatch the reasoning tool by ``action``.""" + action = str(args.get("action", "status")) + + # config is a pure config read — no storage/brain needed. + if action == "config": + return {"config": self.config.reasoning_training.to_dict()} + + storage = await self.get_storage() + brain, err = await _get_brain_or_error(storage) + if err: + return err + brain_id = brain.id + + if action == "status": + return await self._reasoning_status(storage, brain_id) + if action == "patterns": + return await self._reasoning_patterns(storage, args) + if action == "mine": + return await self._reasoning_mine(brain_id, args) + return {"error": f"Unknown action: {action}"} + + async def _reasoning_status(self, storage: NeuralStorage, brain_id: str) -> dict[str, Any]: + from surreal_memory.engine.reasoning_distiller import reasoning_coverage + + stats = await storage.get_reasoning_stats(brain_id) + fibers = await storage.find_fibers( + metadata_key="_reasoning_pattern", limit=_PATTERN_FETCH_LIMIT + ) + pattern_counts: dict[str, int] = {} + for f in fibers: + m = str(f.metadata.get("_source_model", "")) + if m: + pattern_counts[m] = pattern_counts.get(m, 0) + 1 + + models = sorted(set(stats.get("by_model", {})) | set(pattern_counts)) + per_model: list[dict[str, Any]] = [] + for m in models: + cov = await reasoning_coverage(storage, m, self.config) + tstats = stats.get("by_model", {}).get(m, {}) + per_model.append( + { + "model": m, + "trace_count": tstats.get("trace_count", 0), + "unprocessed": tstats.get("unprocessed", 0), + "pattern_count": pattern_counts.get(m, 0), + "coverage_percent": cov["coverage_percent"], + } + ) + + return { + "total_traces": stats.get("total", 0), + "unprocessed_traces": stats.get("unprocessed", 0), + "total_patterns": len(fibers), + "mining_enabled": self.config.reasoning_training.mining_enabled, + "injection_enabled": self.config.reasoning_training.injection_enabled, + "models": per_model, + } + + async def _reasoning_patterns( + self, storage: NeuralStorage, args: dict[str, Any] + ) -> dict[str, Any]: + model = args.get("model") + category = args.get("category") + limit = max(1, min(int(args.get("limit", 50) or 50), 100)) + + fibers = await storage.find_fibers( + metadata_key="_reasoning_pattern", limit=_PATTERN_FETCH_LIMIT + ) + rows: list[dict[str, Any]] = [] + for f in fibers: + md = f.metadata + if model and md.get("_source_model") != model: + continue + if category and md.get("_reasoning_category") != category: + continue + rows.append( + { + "id": f.id, + "source_model": md.get("_source_model", ""), + "category": md.get("_reasoning_category", ""), + "title": md.get("_reasoning_title", ""), + "confidence": md.get("_reasoning_confidence", 0.0), + "frequency": md.get("_reasoning_frequency", 0), + } + ) + rows.sort( + key=lambda r: float(r["confidence"] or 0.0) * float(r["frequency"] or 0), reverse=True + ) + return {"patterns": rows[:limit], "count": len(rows)} + + async def _reasoning_mine(self, brain_id: str, args: dict[str, Any]) -> dict[str, Any]: + from dataclasses import replace as dc_replace + + from surreal_memory.engine.reasoning_distiller import distill_reasoning_patterns + from surreal_memory.engine.reasoning_miner import ingest_reasoning_traces + from surreal_memory.unified_config import create_isolated_storage + + rt = self.config.reasoning_training + if not rt.mining_enabled: + # Privacy: don't scan ~/.claude transcripts unless mining is enabled. + return {"error": "Mining is disabled; enable reasoning_training.mining_enabled first"} + if args.get("dry_run"): + return {"traces_ingested": 0, "patterns_learned": 0, "dry_run": True} + + overrides: dict[str, Any] = {} + if args.get("backfill"): + overrides["scan_lookback_days"] = 0 + models = args.get("models") + if isinstance(models, list) and models: + overrides["mining_models"] = tuple(str(m) for m in models) + elif isinstance(models, str) and models.strip(): + overrides["mining_models"] = tuple(m.strip() for m in models.split(",") if m.strip()) + run_cfg = dc_replace(self.config, reasoning_training=dc_replace(rt, **overrides)) + + # Isolated (non-cached) storage so a concurrently-served MCP call's + # set_brain() on the shared SurrealDB singleton can't redirect this job's + # graph writes into the wrong brain (the MCP HTTP transport is not serialized). + storage = await create_isolated_storage(brain_id) + owns_storage = self.config.storage_backend == "surrealdb" + try: + ingest = await ingest_reasoning_traces(storage, brain_id, run_cfg) + distill = await distill_reasoning_patterns(storage, brain_id, run_cfg) + return { + "traces_ingested": ingest.traces_ingested, + "patterns_learned": distill.patterns_learned, + } + finally: + if owns_storage: + await storage.close() diff --git a/src/surreal_memory/mcp/server.py b/src/surreal_memory/mcp/server.py index 85b3c968..e7ceb0d5 100755 --- a/src/surreal_memory/mcp/server.py +++ b/src/surreal_memory/mcp/server.py @@ -44,6 +44,7 @@ from surreal_memory.mcp.narrative_handler import NarrativeHandler from surreal_memory.mcp.onboarding_handler import OnboardingHandler from surreal_memory.mcp.prompt import get_mcp_instructions, get_system_prompt +from surreal_memory.mcp.reasoning_handler import ReasoningHandler from surreal_memory.mcp.review_handler import ReviewHandler from surreal_memory.mcp.scheduled_consolidation_handler import ScheduledConsolidationHandler from surreal_memory.mcp.session_handler import SessionHandler @@ -109,6 +110,7 @@ class MCPServer( SyncToolHandler, TelegramHandler, DriftHandler, + ReasoningHandler, ): """MCP server that exposes Surreal-Memory tools. @@ -245,6 +247,7 @@ async def call_tool(self, name: str, arguments: dict[str, Any]) -> dict[str, Any "smem_health": self._health, "smem_evolution": self._evolution, "smem_habits": self._habits, + "smem_reasoning": self._reasoning, "smem_version": self._version, "smem_transplant": self._transplant, "smem_conflicts": self._conflicts, diff --git a/src/surreal_memory/mcp/tool_schemas.py b/src/surreal_memory/mcp/tool_schemas.py index 6216a3a9..9e083116 100755 --- a/src/surreal_memory/mcp/tool_schemas.py +++ b/src/surreal_memory/mcp/tool_schemas.py @@ -829,6 +829,50 @@ def get_tool_schemas_for_tier(tier: str) -> list[dict[str, Any]]: "required": ["action"], }, }, + { + "name": "smem_reasoning", + "description": "Reasoning training: status, mine, patterns, or config.", + "inputSchema": { + "type": "object", + "properties": { + "action": { + "type": "string", + "enum": ["status", "mine", "patterns", "config"], + "description": ( + "status=trace/pattern stats + per-model coverage, " + "mine=scan ~/.claude thinking and distill patterns (needs mining_enabled), " + "patterns=list learned patterns, config=show reasoning_training config" + ), + }, + "model": { + "type": "string", + "description": "Filter patterns by source model (patterns action)", + }, + "category": { + "type": "string", + "description": "Filter patterns by category (patterns action)", + }, + "limit": { + "type": "integer", + "description": "Max patterns to return, 1-100 (patterns action, default 50)", + }, + "models": { + "type": "array", + "items": {"type": "string"}, + "description": "Restrict mining to these source models (mine action)", + }, + "backfill": { + "type": "boolean", + "description": "Scan the full history for mining (mine action)", + }, + "dry_run": { + "type": "boolean", + "description": "Mine as a no-op without writing (mine action)", + }, + }, + "required": ["action"], + }, + }, { "name": "smem_version", "description": "Brain version control: snapshot, list, rollback, diff.", diff --git a/tests/unit/test_cli_reasoning.py b/tests/unit/test_cli_reasoning.py new file mode 100644 index 00000000..bc3b4f97 --- /dev/null +++ b/tests/unit/test_cli_reasoning.py @@ -0,0 +1,239 @@ +"""Tests for the `smem reasoning` CLI (status / mine / patterns / clear). + +Drives the real Typer app via CliRunner with the reasoning command's storage + +config patched (no live DB). The command imports get_config/get_storage from its +own module, and get_unified_config / the engine fns lazily inside handlers. +""" + +from __future__ import annotations + +import json +from pathlib import Path +from types import SimpleNamespace +from unittest.mock import AsyncMock, MagicMock, patch + +from typer.testing import CliRunner + +from surreal_memory.cli.main import app +from surreal_memory.unified_config import ReasoningTrainingConfig, UnifiedConfig + +runner = CliRunner() + +CLI = "surreal_memory.cli.commands.reasoning" + + +def _fiber( + fid: str, model: str, category: str, title: str = "t", conf: float = 1.0, freq: int = 3 +) -> SimpleNamespace: + return SimpleNamespace( + id=fid, + summary=title, + metadata={ + "_reasoning_pattern": True, + "_source_model": model, + "_reasoning_category": category, + "_reasoning_title": title, + "_reasoning_confidence": conf, + "_reasoning_frequency": freq, + }, + ) + + +def _storage(stats: dict | None = None, fibers: list | None = None, deleted: int = 0) -> AsyncMock: + s = AsyncMock() + s.brain_id = "default" + s.get_reasoning_stats = AsyncMock( + return_value=stats or {"by_model": {}, "by_category": {}, "total": 0, "unprocessed": 0} + ) + s.find_fibers = AsyncMock(return_value=fibers or []) + s.delete_reasoning_traces_by_model = AsyncMock(return_value=deleted) + s.close = AsyncMock() + return s + + +def _ucfg(tmp_path: Path, **rt: object) -> UnifiedConfig: + return UnifiedConfig( + data_dir=tmp_path / ".surrealmemory", + current_brain="default", + reasoning_training=ReasoningTrainingConfig(**rt), # type: ignore[arg-type] + ) + + +def test_status_json(tmp_path: Path) -> None: + storage = _storage( + stats={ + "by_model": { + "claude-fable-5": {"trace_count": 5, "unprocessed": 2, "last_trace_at": "x"} + }, + "by_category": {}, + "total": 5, + "unprocessed": 2, + }, + fibers=[_fiber("p1", "claude-fable-5", "debugging")], + ) + with ( + patch(f"{CLI}.get_config", MagicMock()), + patch(f"{CLI}.get_storage", new=AsyncMock(return_value=storage)), + patch("surreal_memory.unified_config.get_config", return_value=_ucfg(tmp_path)), + patch( + "surreal_memory.engine.reasoning_distiller.reasoning_coverage", + new=AsyncMock( + return_value={"by_category": {}, "covered": {}, "coverage_percent": 12.5} + ), + ), + ): + result = runner.invoke(app, ["reasoning", "status", "--json"]) + + assert result.exit_code == 0, result.output + data = json.loads(result.output) + assert data["total_traces"] == 5 + assert data["total_patterns"] == 1 + assert data["models"][0]["model"] == "claude-fable-5" + assert data["models"][0]["coverage_percent"] == 12.5 + + +def test_patterns_filter_by_model(tmp_path: Path) -> None: + storage = _storage( + fibers=[ + _fiber("p1", "claude-fable-5", "debugging", "verify"), + _fiber("p2", "claude-sonnet-5", "planning", "plan"), + ] + ) + with ( + patch(f"{CLI}.get_config", MagicMock()), + patch(f"{CLI}.get_storage", new=AsyncMock(return_value=storage)), + ): + result = runner.invoke( + app, ["reasoning", "patterns", "--model", "claude-fable-5", "--json"] + ) + + assert result.exit_code == 0, result.output + data = json.loads(result.output) + assert data["count"] == 1 + assert data["patterns"][0]["source_model"] == "claude-fable-5" + + +def test_clear_force(tmp_path: Path) -> None: + storage = _storage(deleted=3) + with ( + patch(f"{CLI}.get_config", MagicMock()), + patch(f"{CLI}.get_storage", new=AsyncMock(return_value=storage)), + ): + result = runner.invoke( + app, ["reasoning", "clear", "--model", "claude-fable-5", "--force", "--json"] + ) + + assert result.exit_code == 0, result.output + assert json.loads(result.output)["deleted"] == 3 + storage.delete_reasoning_traces_by_model.assert_awaited_once_with("default", "claude-fable-5") + + +def test_mine_disabled_exits_nonzero(tmp_path: Path) -> None: + storage = _storage() + with ( + patch(f"{CLI}.get_config", MagicMock()), + patch(f"{CLI}.get_storage", new=AsyncMock(return_value=storage)), + patch("surreal_memory.unified_config.get_config", return_value=_ucfg(tmp_path)), + ): + result = runner.invoke(app, ["reasoning", "mine"]) + + assert result.exit_code == 1 # mining disabled, no --force + + +def test_mine_runs_when_enabled(tmp_path: Path) -> None: + storage = _storage() + ingest = SimpleNamespace(traces_ingested=4, traces_scanned=10) + distill = SimpleNamespace(patterns_learned=2, traces_processed=4, models_seen=1) + with ( + patch(f"{CLI}.get_config", MagicMock()), + patch(f"{CLI}.get_storage", new=AsyncMock(return_value=storage)), + patch( + "surreal_memory.unified_config.get_config", + return_value=_ucfg(tmp_path, mining_enabled=True), + ), + patch( + "surreal_memory.engine.reasoning_miner.ingest_reasoning_traces", + new=AsyncMock(return_value=ingest), + ), + patch( + "surreal_memory.engine.reasoning_distiller.distill_reasoning_patterns", + new=AsyncMock(return_value=distill), + ), + ): + result = runner.invoke(app, ["reasoning", "mine", "--json"]) + + assert result.exit_code == 0, result.output + data = json.loads(result.output) + assert data["traces_ingested"] == 4 + assert data["patterns_learned"] == 2 + + +def test_mine_dry_run(tmp_path: Path) -> None: + storage = _storage() + with ( + patch(f"{CLI}.get_config", MagicMock()), + patch(f"{CLI}.get_storage", new=AsyncMock(return_value=storage)), + patch( + "surreal_memory.unified_config.get_config", + return_value=_ucfg(tmp_path, mining_enabled=True), + ), + ): + result = runner.invoke(app, ["reasoning", "mine", "--dry-run", "--json"]) + + assert result.exit_code == 0, result.output + data = json.loads(result.output) + assert data["dry_run"] is True + assert data["traces_ingested"] == 0 + + +def test_mine_force_bypasses_disabled(tmp_path: Path) -> None: + # --force runs mining even when mining_enabled is False (the privacy escape hatch). + storage = _storage() + ingest = SimpleNamespace(traces_ingested=1, traces_scanned=1) + distill = SimpleNamespace(patterns_learned=0, traces_processed=1, models_seen=1) + with ( + patch(f"{CLI}.get_config", MagicMock()), + patch(f"{CLI}.get_storage", new=AsyncMock(return_value=storage)), + patch("surreal_memory.unified_config.get_config", return_value=_ucfg(tmp_path)), + patch( + "surreal_memory.engine.reasoning_miner.ingest_reasoning_traces", + new=AsyncMock(return_value=ingest), + ), + patch( + "surreal_memory.engine.reasoning_distiller.distill_reasoning_patterns", + new=AsyncMock(return_value=distill), + ), + ): + result = runner.invoke(app, ["reasoning", "mine", "--force", "--json"]) + + assert result.exit_code == 0, result.output + assert json.loads(result.output)["traces_ingested"] == 1 + + +def test_mine_applies_overrides(tmp_path: Path) -> None: + # --backfill + --models must flow into the run config passed to the engine. + storage = _storage() + ingest_mock = AsyncMock(return_value=SimpleNamespace(traces_ingested=1, traces_scanned=1)) + distill_mock = AsyncMock( + return_value=SimpleNamespace(patterns_learned=0, traces_processed=1, models_seen=1) + ) + with ( + patch(f"{CLI}.get_config", MagicMock()), + patch(f"{CLI}.get_storage", new=AsyncMock(return_value=storage)), + patch( + "surreal_memory.unified_config.get_config", + return_value=_ucfg(tmp_path, mining_enabled=True), + ), + patch("surreal_memory.engine.reasoning_miner.ingest_reasoning_traces", new=ingest_mock), + patch( + "surreal_memory.engine.reasoning_distiller.distill_reasoning_patterns", new=distill_mock + ), + ): + result = runner.invoke( + app, ["reasoning", "mine", "--backfill", "--models", "a,b", "--json"] + ) + + assert result.exit_code == 0, result.output + run_cfg = ingest_mock.await_args.args[2] # (storage, brain_id, config) + assert run_cfg.reasoning_training.scan_lookback_days == 0 + assert run_cfg.reasoning_training.mining_models == ("a", "b") diff --git a/tests/unit/test_mcp.py b/tests/unit/test_mcp.py index 8264fad2..4d9f2316 100755 --- a/tests/unit/test_mcp.py +++ b/tests/unit/test_mcp.py @@ -44,7 +44,7 @@ def test_get_tools(self, server: MCPServer) -> None: with patch("surreal_memory.plugins.get_plugin_tools", return_value=[]): tools = server.get_tools() - assert len(tools) == 57 + assert len(tools) == 58 tool_names = {tool["name"] for tool in tools} assert tool_names == { "smem_remember", @@ -63,6 +63,7 @@ def test_get_tools(self, server: MCPServer) -> None: "smem_health", "smem_evolution", "smem_habits", + "smem_reasoning", "smem_version", "smem_transplant", "smem_conflicts", @@ -1057,7 +1058,7 @@ async def test_tools_list_message(self, server: MCPServer) -> None: assert response["id"] == 2 assert "result" in response assert "tools" in response["result"] - assert len(response["result"]["tools"]) == 57 + assert len(response["result"]["tools"]) == 58 @pytest.mark.asyncio async def test_tools_call_message(self, server: MCPServer) -> None: diff --git a/tests/unit/test_mcp_reasoning.py b/tests/unit/test_mcp_reasoning.py new file mode 100644 index 00000000..fd6c2951 --- /dev/null +++ b/tests/unit/test_mcp_reasoning.py @@ -0,0 +1,190 @@ +"""Tests for the smem_reasoning MCP tool (status / mine / patterns / config). + +Builds an MCPServer with a real UnifiedConfig (the mine action does dc_replace on +config, which needs a real dataclass) and a mocked storage, then drives +server.call_tool directly (mirrors test_mcp.py's TestMCPToolCalls). +""" + +from __future__ import annotations + +from pathlib import Path +from types import SimpleNamespace +from unittest.mock import AsyncMock, MagicMock, patch + +import pytest + +from surreal_memory.mcp.server import MCPServer +from surreal_memory.unified_config import ReasoningTrainingConfig, UnifiedConfig + + +def _fiber( + fid: str, model: str, category: str, title: str = "t", conf: float = 1.0, freq: int = 3 +) -> SimpleNamespace: + return SimpleNamespace( + id=fid, + summary=title, + metadata={ + "_reasoning_pattern": True, + "_source_model": model, + "_reasoning_category": category, + "_reasoning_title": title, + "_reasoning_confidence": conf, + "_reasoning_frequency": freq, + }, + ) + + +def _make_server(tmp_path: Path, **rt: object) -> MCPServer: + cfg = UnifiedConfig( + data_dir=tmp_path / ".surrealmemory", + current_brain="test-brain", + reasoning_training=ReasoningTrainingConfig(**rt), # type: ignore[arg-type] + ) + with patch("surreal_memory.mcp.server.get_config", return_value=cfg): + return MCPServer() + + +def _storage(stats: dict | None = None, fibers: list | None = None) -> AsyncMock: + s = AsyncMock() + s.brain_id = "test-brain" + s.get_brain = AsyncMock(return_value=MagicMock(id="test-brain")) + s.get_reasoning_stats = AsyncMock( + return_value=stats or {"by_model": {}, "by_category": {}, "total": 0, "unprocessed": 0} + ) + s.find_fibers = AsyncMock(return_value=fibers or []) + return s + + +@pytest.mark.asyncio +async def test_reasoning_config_action(tmp_path: Path) -> None: + server = _make_server(tmp_path, mining_enabled=True) + result = await server.call_tool("smem_reasoning", {"action": "config"}) + assert result["config"]["mining_enabled"] is True + + +@pytest.mark.asyncio +async def test_reasoning_status(tmp_path: Path) -> None: + server = _make_server(tmp_path) + storage = _storage( + stats={ + "by_model": { + "claude-fable-5": {"trace_count": 5, "unprocessed": 2, "last_trace_at": "x"} + }, + "by_category": {}, + "total": 5, + "unprocessed": 2, + }, + fibers=[_fiber("p1", "claude-fable-5", "debugging")], + ) + with ( + patch.object(server, "get_storage", return_value=storage), + patch( + "surreal_memory.engine.reasoning_distiller.reasoning_coverage", + new=AsyncMock( + return_value={"by_category": {}, "covered": {}, "coverage_percent": 12.5} + ), + ), + ): + result = await server.call_tool("smem_reasoning", {"action": "status"}) + + assert result["total_traces"] == 5 + assert result["total_patterns"] == 1 + assert result["models"][0]["model"] == "claude-fable-5" + assert result["models"][0]["coverage_percent"] == 12.5 + + +@pytest.mark.asyncio +async def test_reasoning_patterns_filter(tmp_path: Path) -> None: + server = _make_server(tmp_path) + storage = _storage( + fibers=[ + _fiber("p1", "claude-fable-5", "debugging", "verify"), + _fiber("p2", "claude-sonnet-5", "planning", "plan"), + ] + ) + with patch.object(server, "get_storage", return_value=storage): + result = await server.call_tool( + "smem_reasoning", {"action": "patterns", "model": "claude-fable-5"} + ) + + assert result["count"] == 1 + assert result["patterns"][0]["source_model"] == "claude-fable-5" + + +@pytest.mark.asyncio +async def test_reasoning_mine_disabled(tmp_path: Path) -> None: + server = _make_server(tmp_path) # mining_enabled defaults False + storage = _storage() + with patch.object(server, "get_storage", return_value=storage): + result = await server.call_tool("smem_reasoning", {"action": "mine"}) + + assert "error" in result + assert "disabled" in result["error"].lower() + + +@pytest.mark.asyncio +async def test_reasoning_mine_runs(tmp_path: Path) -> None: + server = _make_server(tmp_path, mining_enabled=True) + storage = _storage() + ingest = SimpleNamespace(traces_ingested=4, traces_scanned=10) + distill = SimpleNamespace(patterns_learned=2, traces_processed=4, models_seen=1) + with ( + patch.object(server, "get_storage", return_value=storage), + # mine runs on an isolated (non-cached) storage to avoid the shared-singleton race. + patch( + "surreal_memory.unified_config.create_isolated_storage", + new=AsyncMock(return_value=storage), + ), + patch( + "surreal_memory.engine.reasoning_miner.ingest_reasoning_traces", + new=AsyncMock(return_value=ingest), + ), + patch( + "surreal_memory.engine.reasoning_distiller.distill_reasoning_patterns", + new=AsyncMock(return_value=distill), + ), + ): + result = await server.call_tool("smem_reasoning", {"action": "mine"}) + + assert result["traces_ingested"] == 4 + assert result["patterns_learned"] == 2 + + +@pytest.mark.asyncio +async def test_reasoning_mine_applies_overrides(tmp_path: Path) -> None: + # backfill + models must flow into the run config passed to the engine. + server = _make_server(tmp_path, mining_enabled=True) + storage = _storage() + ingest_mock = AsyncMock(return_value=SimpleNamespace(traces_ingested=1, traces_scanned=1)) + distill_mock = AsyncMock( + return_value=SimpleNamespace(patterns_learned=0, traces_processed=1, models_seen=1) + ) + with ( + patch.object(server, "get_storage", return_value=storage), + patch( + "surreal_memory.unified_config.create_isolated_storage", + new=AsyncMock(return_value=storage), + ), + patch("surreal_memory.engine.reasoning_miner.ingest_reasoning_traces", new=ingest_mock), + patch( + "surreal_memory.engine.reasoning_distiller.distill_reasoning_patterns", new=distill_mock + ), + ): + await server.call_tool( + "smem_reasoning", {"action": "mine", "backfill": True, "models": ["a", "b"]} + ) + + run_cfg = ingest_mock.await_args.args[2] # (storage, brain_id, config) + assert run_cfg.reasoning_training.scan_lookback_days == 0 + assert run_cfg.reasoning_training.mining_models == ("a", "b") + + +@pytest.mark.asyncio +async def test_reasoning_unknown_action(tmp_path: Path) -> None: + server = _make_server(tmp_path) + storage = _storage() + with patch.object(server, "get_storage", return_value=storage): + result = await server.call_tool("smem_reasoning", {"action": "bogus"}) + + assert "error" in result + assert "Unknown action" in result["error"] From fb08965050d42c19de1153b01299a35854439c6a Mon Sep 17 00:00:00 2001 From: Toni Nowak Date: Fri, 17 Jul 2026 17:00:47 +0200 Subject: [PATCH 10/12] test: unblock full-suite verify for reasoning-training (U10) - bump cross-cutting assertions the feature legitimately shifted: SCHEMA_VERSION 39->40 (U1 reasoning_traces) in 5 tests; MCP full-tier tool count 57->58 (U9 smem_reasoning) in test_tool_tiers - conftest: import the real surrealdb SDK early so a unit-test module's `sys.modules["surrealdb"]=MagicMock()` stub can't leak into the live SurrealDB tests in the same serial session (no-op when SDK absent) - integration/test_surrealdb_synapse_migration: update stale v8 stamp assertions to v9 (SCHEMA_VERSION/TARGET_VERSION became 9 in v2.9.0, after this test file's last edit) and tolerate a retryable SurrealDB write-conflict in the two-parallel-migration race (exactly-once is still asserted independently via the count checks) --- tests/conftest.py | 9 +++++ .../test_surrealdb_synapse_migration.py | 33 +++++++++++++------ tests/unit/test_baby_mi_features.py | 2 +- tests/unit/test_cascading_retrieval.py | 2 +- tests/unit/test_ephemeral.py | 2 +- tests/unit/test_source_registry.py | 2 +- tests/unit/test_surrealdb_store.py | 4 ++- tests/unit/test_tiered_memory_phase1.py | 2 +- tests/unit/test_tool_tiers.py | 8 ++--- 9 files changed, 44 insertions(+), 20 deletions(-) diff --git a/tests/conftest.py b/tests/conftest.py index 902f5496..c692b894 100755 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -15,6 +15,15 @@ from surreal_memory.core.synapse import Synapse, SynapseType from surreal_memory.storage.memory_store import InMemoryStorage +# Import the real surrealdb SDK early (when installed) so it is present in +# sys.modules before any unit test module runs its +# `if "surrealdb" not in sys.modules: sys.modules["surrealdb"] = MagicMock()` +# stub. Otherwise that MagicMock leaks into the live SurrealDB tests in the same +# serial session, breaking `from surrealdb import AsyncSurreal` and awaits on the +# fake connection. No-op when surrealdb isn't installed (the stubs then apply). +with contextlib.suppress(ImportError): + import surrealdb # noqa: F401 + @pytest.fixture def brain_config() -> BrainConfig: diff --git a/tests/integration/test_surrealdb_synapse_migration.py b/tests/integration/test_surrealdb_synapse_migration.py index e357676e..af1ec405 100644 --- a/tests/integration/test_surrealdb_synapse_migration.py +++ b/tests/integration/test_surrealdb_synapse_migration.py @@ -198,7 +198,7 @@ async def test_v7_upgrade_preserves_count_ids_endpoints_export_and_merkle() -> N assert post_merkle == pre_merkle # schema_meta stamped + backup retained - assert await M._read_stamped_version(conn) == 8 + assert await M._read_stamped_version(conn) == 9 assert await M._count(conn, M.BACKUP_TABLE) == len(expected) # get_neighbors + get_path parity via native edges @@ -238,7 +238,7 @@ async def test_second_initialize_is_noop() -> None: store2 = _store(db) await store2.initialize() store2.set_brain(brain.id) - assert await M._read_stamped_version(conn) == 8 + assert await M._read_stamped_version(conn) == 9 assert len(await store2.get_all_synapses()) == len(expected) @@ -253,7 +253,7 @@ async def test_two_parallel_apply_migrations_run_exactly_once() -> None: await seeder.save_brain(brain) expected = await _seed_v7(conn, seeder, brain.id) - # ensure the v8 schema exists (as first connect would) before the race + # ensure the current schema exists (as first connect would) before the race from surreal_memory.storage.surrealdb.schema import ensure_schema await ensure_schema(conn) @@ -264,30 +264,43 @@ async def test_two_parallel_apply_migrations_run_exactly_once() -> None: M.apply_migrations(conn2), return_exceptions=True, ) - # both calls return 8 (one migrates, the other observes the completed migration); - # any MigrationLockError is acceptable, but no duplication may occur. + + # Acceptable per-call outcomes under the race: 9 = TARGET_VERSION (this call drove the + # 7->8->9 migration or observed it already complete), a MigrationError/MigrationLockError + # (lost the lock), or a *retryable* SurrealDB write-conflict — two live connections + # stamping/DDL-ing concurrently can collide, and the server explicitly flags these + # "This transaction can be retried". None of these may break the exactly-once guarantee, + # which the state assertions below verify independently (they fail if any transient left + # the migration incomplete), so a retryable transient is only tolerated when the end + # state is still correct. + def _acceptable(r: object) -> bool: + if r == 9 or isinstance(r, M.MigrationError): + return True + return isinstance(r, Exception) and "can be retried" in str(r).lower() + for r in results: - assert r == 8 or isinstance(r, M.MigrationError), f"unexpected result {r!r}" + assert _acceptable(r), f"unexpected result {r!r}" - assert await M._read_stamped_version(conn) == 8 + # Regardless of which call won the race, the migration must have completed exactly once. + assert await M._read_stamped_version(conn) == 9 assert await M._count(conn, "synapse") == len(expected) # migrated exactly once assert await M._count(conn, M.BACKUP_TABLE) == len(expected) @pytest.mark.asyncio -async def test_fresh_db_is_v8_directly_no_backup() -> None: +async def test_fresh_db_is_target_version_directly_no_backup() -> None: db = _fresh_db() conn = await _raw_conn(db) brain = Brain.create(name="fresh-brain") store = _store(db) - await store.initialize() # fresh DB -> v8 directly, no migration + await store.initialize() # fresh DB -> v9 (target) directly, no migration store.set_brain(brain.id) await store.save_brain(brain) info = await conn.query("INFO FOR DB") sdef = (info.get("tables", {}) or {}).get("synapse", "") assert "TYPE RELATION" in str(sdef) - assert await M._read_stamped_version(conn) == 8 + assert await M._read_stamped_version(conn) == 9 # no migration ran -> no backup table rows assert await M._count(conn, M.BACKUP_TABLE) == 0 diff --git a/tests/unit/test_baby_mi_features.py b/tests/unit/test_baby_mi_features.py index a99cc512..83e30b5d 100755 --- a/tests/unit/test_baby_mi_features.py +++ b/tests/unit/test_baby_mi_features.py @@ -359,7 +359,7 @@ class TestTrustScore: """Trust score field on TypedMemory.""" def test_schema_version_25(self): - assert SCHEMA_VERSION == 39 + assert SCHEMA_VERSION == 40 def test_trust_score_field_on_typed_memory(self): tm = TypedMemory.create( diff --git a/tests/unit/test_cascading_retrieval.py b/tests/unit/test_cascading_retrieval.py index d3f17860..53c4a1fd 100755 --- a/tests/unit/test_cascading_retrieval.py +++ b/tests/unit/test_cascading_retrieval.py @@ -214,7 +214,7 @@ async def test_schema_version_is_27(self) -> None: """Schema version bumped to 27.""" from surreal_memory.storage.sqlite_schema import SCHEMA_VERSION - assert SCHEMA_VERSION == 39 + assert SCHEMA_VERSION == 40 @pytest.mark.asyncio async def test_fiber_fts_table_exists(self, storage: SQLiteStorage) -> None: diff --git a/tests/unit/test_ephemeral.py b/tests/unit/test_ephemeral.py index 6a1b643c..fab6646d 100755 --- a/tests/unit/test_ephemeral.py +++ b/tests/unit/test_ephemeral.py @@ -22,7 +22,7 @@ class TestSchemaVersion: def test_schema_version_is_33(self) -> None: - assert SCHEMA_VERSION == 39 + assert SCHEMA_VERSION == 40 # ── Neuron dataclass ──────────────────────────────────────────────── diff --git a/tests/unit/test_source_registry.py b/tests/unit/test_source_registry.py index 36ee0dbd..7584cd0d 100755 --- a/tests/unit/test_source_registry.py +++ b/tests/unit/test_source_registry.py @@ -102,7 +102,7 @@ class TestSchemaMigrationV23: def test_schema_version_is_25(self) -> None: from surreal_memory.storage.sqlite_schema import SCHEMA_VERSION - assert SCHEMA_VERSION == 39 + assert SCHEMA_VERSION == 40 def test_migration_22_23_exists(self) -> None: from surreal_memory.storage.sqlite_schema import MIGRATIONS diff --git a/tests/unit/test_surrealdb_store.py b/tests/unit/test_surrealdb_store.py index 17be2b99..ae28b91d 100644 --- a/tests/unit/test_surrealdb_store.py +++ b/tests/unit/test_surrealdb_store.py @@ -9,7 +9,9 @@ # surrealdb is an optional dependency not installed in the base test environment. # Inject a stub so that the lazy `from surrealdb import AsyncSurreal` inside -# store.py succeeds and the mock can override it. +# store.py succeeds and the mock can override it. (tests/conftest.py imports the +# real SDK early when installed, so this stub only fires in the base env — it no +# longer leaks a MagicMock into the live SurrealDB tests.) if "surrealdb" not in sys.modules: _fake_surrealdb = MagicMock() sys.modules["surrealdb"] = _fake_surrealdb diff --git a/tests/unit/test_tiered_memory_phase1.py b/tests/unit/test_tiered_memory_phase1.py index 50b77eb4..00edcb6e 100755 --- a/tests/unit/test_tiered_memory_phase1.py +++ b/tests/unit/test_tiered_memory_phase1.py @@ -164,7 +164,7 @@ class TestSchemaMigration: def test_migration_exists(self) -> None: from surreal_memory.storage.sqlite_schema import MIGRATIONS, SCHEMA_VERSION - assert SCHEMA_VERSION == 39 + assert SCHEMA_VERSION == 40 assert (36, 37) in MIGRATIONS assert (37, 38) in MIGRATIONS stmts = MIGRATIONS[(36, 37)] diff --git a/tests/unit/test_tool_tiers.py b/tests/unit/test_tool_tiers.py index 0785904f..6ec1fd23 100755 --- a/tests/unit/test_tool_tiers.py +++ b/tests/unit/test_tool_tiers.py @@ -65,7 +65,7 @@ class TestToolTiers: def test_full_tier_returns_all(self) -> None: tools = get_tool_schemas_for_tier("full") - assert len(tools) == 57 + assert len(tools) == 58 def test_full_tier_matches_get_tool_schemas(self) -> None: full = get_tool_schemas_for_tier("full") @@ -109,7 +109,7 @@ def test_minimal_tier_correct_names(self) -> None: def test_invalid_tier_defaults_to_full(self) -> None: tools = get_tool_schemas_for_tier("bogus") - assert len(tools) == 57 + assert len(tools) == 58 def test_tier_hierarchy_minimal_subset_of_standard(self) -> None: assert TOOL_TIERS["minimal"] < TOOL_TIERS["standard"] @@ -134,7 +134,7 @@ def test_get_tool_schemas_returns_copy(self) -> None: a = get_tool_schemas() b = get_tool_schemas() a.pop() - assert len(b) == 57 + assert len(b) == 58 def test_get_tool_schemas_for_tier_returns_copy(self) -> None: a = get_tool_schemas_for_tier("standard") @@ -160,7 +160,7 @@ def _make_server(self, tier: str) -> MCPServer: # noqa: F821 def test_server_full_tier(self) -> None: server = self._make_server("full") with patch("surreal_memory.plugins.get_plugin_tools", return_value=[]): - assert len(server.get_tools()) == 57 + assert len(server.get_tools()) == 58 def test_server_standard_tier(self) -> None: server = self._make_server("standard") From 60c90a1240f3d18b471d0c4f300929cfb168846f Mon Sep 17 00:00:00 2001 From: Toni Nowak Date: Fri, 17 Jul 2026 20:18:00 +0200 Subject: [PATCH 11/12] =?UTF-8?q?chore(release):=20bump=202.12.0=20?= =?UTF-8?q?=E2=80=94=20reasoning-training=20pipeline?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- pyproject.toml | 2 +- src/surreal_memory/__init__.py | 2 +- tests/unit/test_health_fixes.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 3c12cc1c..54ecb99a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "hatchling.build" [project] name = "surreal-memory" -version = "2.11.0" +version = "2.12.0" description = "Reflex-based memory system for AI agents with SurrealDB backend — retrieval through activation, not search" readme = "README.md" license = "MIT" diff --git a/src/surreal_memory/__init__.py b/src/surreal_memory/__init__.py index 9ea7bea4..52188bb0 100755 --- a/src/surreal_memory/__init__.py +++ b/src/surreal_memory/__init__.py @@ -16,7 +16,7 @@ from surreal_memory.engine.reflex_activation import CoActivation, ReflexActivation from surreal_memory.engine.retrieval import DepthLevel, ReflexPipeline, RetrievalResult -__version__ = "2.11.0" +__version__ = "2.12.0" __all__ = [ "__version__", diff --git a/tests/unit/test_health_fixes.py b/tests/unit/test_health_fixes.py index 7d0a613b..398d95ec 100755 --- a/tests/unit/test_health_fixes.py +++ b/tests/unit/test_health_fixes.py @@ -485,7 +485,7 @@ class TestVersionBump: def test_version_is_current(self) -> None: import surreal_memory - assert surreal_memory.__version__ == "2.11.0" + assert surreal_memory.__version__ == "2.12.0" class TestPackageIntegrity: From 7a33ded2935fbefff8a1e99f2b5f3d4669a49c0c Mon Sep 17 00:00:00 2001 From: Toni Nowak Date: Fri, 17 Jul 2026 20:21:56 +0200 Subject: [PATCH 12/12] docs: regenerate MCP + CLI reference for reasoning surface (smem_reasoning / smem reasoning) --- docs/api/mcp-tools.md | 21 ++++++++- docs/getting-started/cli-reference.md | 64 ++++++++++++++++++++++++++- 2 files changed, 81 insertions(+), 4 deletions(-) diff --git a/docs/api/mcp-tools.md b/docs/api/mcp-tools.md index 0aa0e339..e18c8f76 100755 --- a/docs/api/mcp-tools.md +++ b/docs/api/mcp-tools.md @@ -1,7 +1,7 @@ # MCP Tools Reference Complete reference for all Surreal-Memory MCP tools. -**57 tools** available via MCP stdio transport. +**58 tools** available via MCP stdio transport. !!! tip Tools are called as MCP tool calls, not CLI commands. In Claude Code, call `smem_recall` directly — do not run `smem recall` in terminal. @@ -62,6 +62,7 @@ Complete reference for all Surreal-Memory MCP tools. - [`smem_transplant`](#smem_transplant) - [`smem_conflicts`](#smem_conflicts) - [Other](#other) + - [`smem_reasoning`](#smem_reasoning) - [`smem_uncertainty`](#smem_uncertainty) - [`smem_visualize`](#smem_visualize) - [`smem_watch`](#smem_watch) @@ -708,6 +709,22 @@ Memory conflicts: list, resolve, or pre-check. ## Other {#other} +### `smem_reasoning` + +Reasoning training: status, mine, patterns, or config. + +| Parameter | Type | Required | Default | Description | +|-----------|------|----------|---------|-------------| +| `action` | string (`status`, `mine`, `patterns`, `config`) | Yes | — | status=trace/pattern stats + per-model coverage, mine=scan ~/.claude thinking and distill patterns (needs mining_enab... | +| `model` | string | No | — | Filter patterns by source model (patterns action) | +| `category` | string | No | — | Filter patterns by category (patterns action) | +| `limit` | integer | No | — | Max patterns to return, 1-100 (patterns action, default 50) | +| `models` | array[string] | No | — | Restrict mining to these source models (mine action) | +| `backfill` | boolean | No | — | Scan the full history for mining (mine action) | +| `dry_run` | boolean | No | — | Mine as a no-op without writing (mine action) | +| `compact` | boolean | No | — | Return compact response (strip metadata hints, truncate lists). Saves 60-80% tokens. | +| `token_budget` | integer | No | — | Max tokens for response. Progressively strips content to fit budget. | + ### `smem_uncertainty` How much can you trust this brain's memories? Uncertainty diagnostics — contradictions, low-evidence (low trust) facts, superseded facts, soon-expiring memories, and drift. Separate from smem_conflicts (which is CRUD). Read-only. Note: low_evidence/superseded sample the most-recent ~200 typed memories (see response 'scan.typed_scan_truncated'); contradiction count is capped. Drift is available on SQLite backends only. @@ -866,4 +883,4 @@ One-shot snapshot of the current working situation: active session task, top 3 r --- -*Auto-generated by `scripts/gen_mcp_docs.py` from `tool_schemas.py` — 57 tools.* +*Auto-generated by `scripts/gen_mcp_docs.py` from `tool_schemas.py` — 58 tools.* diff --git a/docs/getting-started/cli-reference.md b/docs/getting-started/cli-reference.md index 91a6afa9..7057d2a4 100755 --- a/docs/getting-started/cli-reference.md +++ b/docs/getting-started/cli-reference.md @@ -1,7 +1,7 @@ # CLI Reference Complete reference for the `smem` command-line interface. -**76 commands** available. +**80 commands** available. !!! tip Run `smem --help` or `smem --help` for the latest usage info. @@ -84,6 +84,10 @@ Complete reference for the `smem` command-line interface. - [`smem update`](#smem-update) - [Other](#other) - [`smem lifecycle`](#smem-lifecycle) + - [`smem reasoning clear`](#smem-reasoning-clear) + - [`smem reasoning mine`](#smem-reasoning-mine) + - [`smem reasoning patterns`](#smem-reasoning-patterns) + - [`smem reasoning status`](#smem-reasoning-status) - [`smem reindex`](#smem-reindex) - [`smem shared activate`](#smem-shared-activate) - [`smem storage status`](#smem-storage-status) @@ -1006,6 +1010,62 @@ smem lifecycle [OPTIONS] | `neuron_id` | text | No | — | (positional argument) | | `--brain / -b` | text | No | — | Brain name | +### `smem reasoning clear` + +Wipe staged reasoning traces for a model (privacy). + +``` +smem reasoning clear [OPTIONS] +``` + +| Option | Type | Required | Default | Description | +|--------|------|----------|---------|-------------| +| `--model / -m` | text | Yes | — | Model whose staged traces to wipe | +| `--force / -f` | boolean | No | `False` | Skip confirmation | +| `--json / -j` | boolean | No | `False` | Output as JSON | + +### `smem reasoning mine` + +Mine reasoning traces from ~/.claude transcripts and distill patterns. + +``` +smem reasoning mine [OPTIONS] +``` + +| Option | Type | Required | Default | Description | +|--------|------|----------|---------|-------------| +| `--backfill` | boolean | No | `False` | Scan the full history (scan_lookback_days=0) | +| `--dry-run / -n` | boolean | No | `False` | Don't write — report a no-op | +| `--models` | text | No | — | Comma-separated source models to restrict to | +| `--force / -f` | boolean | No | `False` | Run even if mining is disabled in config | +| `--json / -j` | boolean | No | `False` | Output as JSON | + +### `smem reasoning patterns` + +List learned reasoning patterns. + +``` +smem reasoning patterns [OPTIONS] +``` + +| Option | Type | Required | Default | Description | +|--------|------|----------|---------|-------------| +| `--model / -m` | text | No | — | Filter by source model | +| `--category / -c` | text | No | — | Filter by category | +| `--json / -j` | boolean | No | `False` | Output as JSON | + +### `smem reasoning status` + +Show reasoning-training status: trace counts, learned patterns, per-model coverage. + +``` +smem reasoning status [OPTIONS] +``` + +| Option | Type | Required | Default | Description | +|--------|------|----------|---------|-------------| +| `--json / -j` | boolean | No | `False` | Output as JSON | + ### `smem reindex` Re-embed neurons for the current brain using the effective provider. @@ -1125,4 +1185,4 @@ smem watch [OPTIONS] --- -*Auto-generated by `scripts/gen_cli_docs.py` from Typer app introspection — 76 commands.* +*Auto-generated by `scripts/gen_cli_docs.py` from Typer app introspection — 80 commands.*