feat: sovereign memory store — zero-API durable memory (SQLite + FTS5 + HRR)

Implements the missing pieces of the MemPalace epic (#367):

- sovereign_store.py: Self-contained memory store replacing the third-party
  mempalace CLI and its ONNX dependency. Uses:
  * SQLite + FTS5 for keyword search (porter stemmer, unicode61)
  * HRR phase vectors (SHA-256 deterministic, numpy optional) for semantic similarity
  * Reciprocal Rank Fusion to merge keyword and semantic rankings
  * Trust scoring with boost/decay lifecycle
  * Room-based organization matching the existing PalaceRoom model

- promotion.py (MP-4, #371): Quality-gated scratchpad-to-palace promotion.
  Four heuristic gates, no LLM call:
  1. Length gate (min 5 words, max 500)
  2. Structure gate (rejects fragments and pure code)
  3. Duplicate gate (FTS5 + Jaccard overlap detection)
  4. Staleness gate (7-day threshold for old notes)
  Includes force override, batch promotion, and audit logging.

- 21 unit tests covering HRR vectors, store operations, search,
  trust lifecycle, and all promotion gates.

Zero external dependencies. Zero API calls. Zero cloud.

Refs: #367 #370 #371

hermes-sovereign/mempalace/__init__.py

@@ -5,10 +5,13 @@ Provides:
 - retrieval_enforcer.py: L0-L5 retrieval order enforcement
 - wakeup.py: Session wake-up protocol (~300-900 tokens)
 - scratchpad.py: JSON-based session scratchpad with palace promotion
+- sovereign_store.py: Zero-API durable memory (SQLite + FTS5 + HRR vectors)
+- promotion.py: Quality-gated scratchpad-to-palace promotion (MP-4)
 
 Epic: #367
 """
 
 from .mempalace import Mempalace, PalaceRoom, analyse_issues
+from .sovereign_store import SovereignStore
 
-__all__ = ["Mempalace", "PalaceRoom", "analyse_issues"]
+__all__ = ["Mempalace", "PalaceRoom", "analyse_issues", "SovereignStore"]

hermes-sovereign/mempalace/promotion.py

@@ -0,0 +1,188 @@
+"""Memory Promotion — quality-gated scratchpad-to-palace promotion.
+
+Implements MP-4 (#371): move session notes to durable memory only when
+they pass quality gates. No LLM calls — all heuristic-based.
+
+Quality gates:
+  1. Minimum content length (too short = noise)
+  2. Duplicate detection (FTS5 + HRR similarity check)
+  3. Structural quality (has subject-verb structure, not just a fragment)
+  4. Staleness check (don't promote stale notes from old sessions)
+
+Refs: Epic #367, Sub-issue #371
+"""
+
+from __future__ import annotations
+
+import re
+import time
+from typing import Optional
+
+try:
+    from .sovereign_store import SovereignStore
+except ImportError:
+    from sovereign_store import SovereignStore
+
+
+# ---------------------------------------------------------------------------
+# Quality gate thresholds
+# ---------------------------------------------------------------------------
+
+MIN_CONTENT_WORDS = 5
+MAX_CONTENT_WORDS = 500
+DUPLICATE_SIMILARITY = 0.85
+DUPLICATE_FTS_THRESHOLD = 3
+STALE_SECONDS = 86400 * 7
+MIN_TRUST_FOR_AUTO = 0.4
+
+
+# ---------------------------------------------------------------------------
+# Quality checks
+# ---------------------------------------------------------------------------
+
+def _check_length(content: str) -> tuple[bool, str]:
+    """Gate 1: Content length check."""
+    words = content.split()
+    if len(words) < MIN_CONTENT_WORDS:
+        return False, f"Too short ({len(words)} words, minimum {MIN_CONTENT_WORDS})"
+    if len(words) > MAX_CONTENT_WORDS:
+        return False, f"Too long ({len(words)} words, maximum {MAX_CONTENT_WORDS}). Summarize first."
+    return True, "OK"
+
+
+def _check_structure(content: str) -> tuple[bool, str]:
+    """Gate 2: Basic structural quality."""
+    if not re.search(r"[a-zA-Z]", content):
+        return False, "No alphabetic content — pure code/numbers are not memory-worthy"
+    if len(content.split()) < 3:
+        return False, "Fragment — needs at least subject + predicate"
+    return True, "OK"
+
+
+def _check_duplicate(content: str, store: SovereignStore, room: str) -> tuple[bool, str]:
+    """Gate 3: Duplicate detection via hybrid search."""
+    results = store.search(content, room=room, limit=5, min_trust=0.0)
+    for r in results:
+        if r["score"] > DUPLICATE_SIMILARITY:
+            return False, f"Duplicate detected: memory #{r['memory_id']} (score {r['score']:.3f})"
+        if _text_overlap(content, r["content"]) > 0.8:
+            return False, f"Near-duplicate text: memory #{r['memory_id']}"
+    return True, "OK"
+
+
+def _check_staleness(written_at: float) -> tuple[bool, str]:
+    """Gate 4: Staleness check."""
+    age = time.time() - written_at
+    if age > STALE_SECONDS:
+        days = int(age / 86400)
+        return False, f"Stale ({days} days old). Review manually before promoting."
+    return True, "OK"
+
+
+def _text_overlap(a: str, b: str) -> float:
+    """Jaccard similarity between two texts (word-level)."""
+    words_a = set(a.lower().split())
+    words_b = set(b.lower().split())
+    if not words_a or not words_b:
+        return 0.0
+    intersection = words_a & words_b
+    union = words_a | words_b
+    return len(intersection) / len(union)
+
+
+# ---------------------------------------------------------------------------
+# Public API
+# ---------------------------------------------------------------------------
+
+class PromotionResult:
+    """Result of a promotion attempt."""
+    def __init__(self, success: bool, memory_id: Optional[int], reason: str, gates: dict):
+        self.success = success
+        self.memory_id = memory_id
+        self.reason = reason
+        self.gates = gates
+
+    def __repr__(self):
+        status = "PROMOTED" if self.success else "REJECTED"
+        return f"PromotionResult({status}: {self.reason})"
+
+
+def evaluate_for_promotion(
+    content: str,
+    store: SovereignStore,
+    room: str = "general",
+    written_at: Optional[float] = None,
+) -> dict:
+    """Run all quality gates without actually promoting."""
+    if written_at is None:
+        written_at = time.time()
+    gates = {}
+    gates["length"] = _check_length(content)
+    gates["structure"] = _check_structure(content)
+    gates["duplicate"] = _check_duplicate(content, store, room)
+    gates["staleness"] = _check_staleness(written_at)
+    all_passed = all(passed for passed, _ in gates.values())
+    return {
+        "eligible": all_passed,
+        "gates": gates,
+        "content_preview": content[:100] + ("..." if len(content) > 100 else ""),
+    }
+
+
+def promote(
+    content: str,
+    store: SovereignStore,
+    session_id: str,
+    scratch_key: str,
+    room: str = "general",
+    category: str = "",
+    trust: float = 0.5,
+    written_at: Optional[float] = None,
+    force: bool = False,
+) -> PromotionResult:
+    """Promote a scratchpad note to durable palace memory."""
+    if written_at is None:
+        written_at = time.time()
+    gates = {}
+    if not force:
+        gates["length"] = _check_length(content)
+        gates["structure"] = _check_structure(content)
+        gates["duplicate"] = _check_duplicate(content, store, room)
+        gates["staleness"] = _check_staleness(written_at)
+        for gate_name, (passed, message) in gates.items():
+            if not passed:
+                return PromotionResult(
+                    success=False, memory_id=None,
+                    reason=f"Failed gate '{gate_name}': {message}", gates=gates,
+                )
+    memory_id = store.store(content, room=room, category=category, trust=trust)
+    store.log_promotion(session_id, scratch_key, memory_id, reason="auto" if not force else "forced")
+    return PromotionResult(success=True, memory_id=memory_id, reason="Promoted to durable memory", gates=gates)
+
+
+def promote_session_batch(
+    store: SovereignStore,
+    session_id: str,
+    notes: dict[str, dict],
+    room: str = "general",
+    force: bool = False,
+) -> list[PromotionResult]:
+    """Promote all notes from a session scratchpad."""
+    results = []
+    for key, entry in notes.items():
+        content = entry.get("value", str(entry)) if isinstance(entry, dict) else str(entry)
+        written_at = None
+        if isinstance(entry, dict) and "written_at" in entry:
+            try:
+                import datetime
+                written_at = datetime.datetime.strptime(
+                    entry["written_at"], "%Y-%m-%d %H:%M:%S"
+                ).timestamp()
+            except (ValueError, TypeError):
+                pass
+        result = promote(
+            content=str(content), store=store, session_id=session_id,
+            scratch_key=key, room=room, written_at=written_at, force=force,
+        )
+        results.append(result)
+    return results

hermes-sovereign/mempalace/sovereign_store.py

@@ -0,0 +1,474 @@
+"""Sovereign Memory Store — zero-API, zero-dependency durable memory.
+
+Replaces the third-party `mempalace` CLI and its ONNX requirement with a
+self-contained SQLite + FTS5 + HRR (Holographic Reduced Representation)
+store. Every operation is local: no network calls, no API keys, no cloud.
+
+Storage: ~/.hermes/palace/sovereign.db
+
+Capabilities:
+  - Durable fact storage with rooms, categories, and trust scores
+  - Hybrid retrieval: FTS5 keyword search + HRR cosine similarity
+  - Reciprocal Rank Fusion to merge keyword and semantic results
+  - Trust scoring: facts that get retrieved and confirmed gain trust
+  - Graceful numpy degradation: falls back to keyword-only if missing
+
+Refs: Epic #367, MP-3 #370, MP-4 #371
+"""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import math
+import sqlite3
+import struct
+import time
+from pathlib import Path
+from typing import Any, Optional
+
+# ---------------------------------------------------------------------------
+# HRR (Holographic Reduced Representations) — zero-dependency vectors
+# ---------------------------------------------------------------------------
+# Phase-encoded vectors via SHA-256. No ONNX, no embeddings API, no numpy
+# required (but uses numpy when available for speed).
+
+_TWO_PI = 2.0 * math.pi
+_DIM = 512  # Compact dimension — sufficient for memory retrieval
+
+try:
+    import numpy as np
+    _HAS_NUMPY = True
+except ImportError:
+    _HAS_NUMPY = False
+
+
+def _encode_atom_np(word: str, dim: int = _DIM) -> "np.ndarray":
+    """Deterministic phase vector via SHA-256 (numpy path)."""
+    values_per_block = 16
+    blocks_needed = math.ceil(dim / values_per_block)
+    uint16_values: list[int] = []
+    for i in range(blocks_needed):
+        digest = hashlib.sha256(f"{word}:{i}".encode()).digest()
+        uint16_values.extend(struct.unpack("<16H", digest))
+    return np.array(uint16_values[:dim], dtype=np.float64) * (_TWO_PI / 65536.0)
+
+
+def _encode_atom_pure(word: str, dim: int = _DIM) -> list[float]:
+    """Deterministic phase vector via SHA-256 (pure Python fallback)."""
+    values_per_block = 16
+    blocks_needed = math.ceil(dim / values_per_block)
+    uint16_values: list[int] = []
+    for i in range(blocks_needed):
+        digest = hashlib.sha256(f"{word}:{i}".encode()).digest()
+        for j in range(0, 32, 2):
+            uint16_values.append(int.from_bytes(digest[j:j+2], "little"))
+    return [v * (_TWO_PI / 65536.0) for v in uint16_values[:dim]]
+
+
+def encode_text(text: str, dim: int = _DIM):
+    """Encode a text string into an HRR phase vector by bundling word atoms.
+
+    Uses circular mean of per-word phase vectors — the standard HRR
+    superposition operation. Result is a fixed-width vector regardless
+    of input length.
+    """
+    words = text.lower().split()
+    if not words:
+        words = ["<empty>"]
+
+    if _HAS_NUMPY:
+        atoms = [_encode_atom_np(w, dim) for w in words]
+        # Circular mean: average the unit vectors, extract phase
+        unit_sum = sum(np.exp(1j * a) for a in atoms)
+        return np.angle(unit_sum) % _TWO_PI
+    else:
+        # Pure Python circular mean
+        real_sum = [0.0] * dim
+        imag_sum = [0.0] * dim
+        for w in words:
+            atom = _encode_atom_pure(w, dim)
+            for d in range(dim):
+                real_sum[d] += math.cos(atom[d])
+                imag_sum[d] += math.sin(atom[d])
+        return [math.atan2(imag_sum[d], real_sum[d]) % _TWO_PI for d in range(dim)]
+
+
+def cosine_similarity_phase(a, b) -> float:
+    """Cosine similarity between two phase vectors.
+
+    For phase vectors, similarity = mean(cos(a - b)).
+    """
+    if _HAS_NUMPY:
+        return float(np.mean(np.cos(np.array(a) - np.array(b))))
+    else:
+        n = len(a)
+        return sum(math.cos(a[i] - b[i]) for i in range(n)) / n
+
+
+def serialize_vector(vec) -> bytes:
+    """Serialize a vector to bytes for SQLite storage."""
+    if _HAS_NUMPY:
+        return vec.astype(np.float64).tobytes()
+    else:
+        return struct.pack(f"{len(vec)}d", *vec)
+
+
+def deserialize_vector(blob: bytes):
+    """Deserialize bytes back to a vector."""
+    n = len(blob) // 8  # float64 = 8 bytes
+    if _HAS_NUMPY:
+        return np.frombuffer(blob, dtype=np.float64)
+    else:
+        return list(struct.unpack(f"{n}d", blob))
+
+
+# ---------------------------------------------------------------------------
+# SQLite Schema
+# ---------------------------------------------------------------------------
+
+_SCHEMA = """
+CREATE TABLE IF NOT EXISTS memories (
+    memory_id       INTEGER PRIMARY KEY AUTOINCREMENT,
+    content         TEXT NOT NULL,
+    room            TEXT DEFAULT 'general',
+    category        TEXT DEFAULT '',
+    trust_score     REAL DEFAULT 0.5,
+    retrieval_count INTEGER DEFAULT 0,
+    created_at      REAL NOT NULL,
+    updated_at      REAL NOT NULL,
+    hrr_vector      BLOB
+);
+
+CREATE INDEX IF NOT EXISTS idx_memories_room ON memories(room);
+CREATE INDEX IF NOT EXISTS idx_memories_trust ON memories(trust_score DESC);
+
+-- FTS5 for fast keyword search
+CREATE VIRTUAL TABLE IF NOT EXISTS memories_fts USING fts5(
+    content, room, category,
+    content=memories, content_rowid=memory_id,
+    tokenize='porter unicode61'
+);
+
+-- Sync triggers
+CREATE TRIGGER IF NOT EXISTS memories_ai AFTER INSERT ON memories BEGIN
+    INSERT INTO memories_fts(rowid, content, room, category)
+        VALUES (new.memory_id, new.content, new.room, new.category);
+END;
+
+CREATE TRIGGER IF NOT EXISTS memories_ad AFTER DELETE ON memories BEGIN
+    INSERT INTO memories_fts(memories_fts, rowid, content, room, category)
+        VALUES ('delete', old.memory_id, old.content, old.room, old.category);
+END;
+
+CREATE TRIGGER IF NOT EXISTS memories_au AFTER UPDATE ON memories BEGIN
+    INSERT INTO memories_fts(memories_fts, rowid, content, room, category)
+        VALUES ('delete', old.memory_id, old.content, old.room, old.category);
+    INSERT INTO memories_fts(rowid, content, room, category)
+        VALUES (new.memory_id, new.content, new.room, new.category);
+END;
+
+-- Promotion log: tracks what moved from scratchpad to durable memory
+CREATE TABLE IF NOT EXISTS promotion_log (
+    log_id      INTEGER PRIMARY KEY AUTOINCREMENT,
+    session_id  TEXT NOT NULL,
+    scratch_key TEXT NOT NULL,
+    memory_id   INTEGER REFERENCES memories(memory_id),
+    promoted_at REAL NOT NULL,
+    reason      TEXT DEFAULT ''
+);
+"""
+
+
+# ---------------------------------------------------------------------------
+# SovereignStore
+# ---------------------------------------------------------------------------
+
+class SovereignStore:
+    """Zero-API durable memory store.
+
+    All operations are local SQLite. No network calls. No API keys.
+    HRR vectors provide semantic similarity without embedding models.
+    FTS5 provides fast keyword search. RRF merges both rankings.
+    """
+
+    def __init__(self, db_path: Optional[str] = None):
+        if db_path is None:
+            db_path = str(Path.home() / ".hermes" / "palace" / "sovereign.db")
+        self._db_path = db_path
+        Path(db_path).parent.mkdir(parents=True, exist_ok=True)
+        self._conn = sqlite3.connect(db_path)
+        self._conn.row_factory = sqlite3.Row
+        self._conn.executescript(_SCHEMA)
+
+    def close(self):
+        self._conn.close()
+
+    # ------------------------------------------------------------------
+    # Store
+    # ------------------------------------------------------------------
+
+    def store(
+        self,
+        content: str,
+        room: str = "general",
+        category: str = "",
+        trust: float = 0.5,
+    ) -> int:
+        """Store a fact in durable memory. Returns the memory_id."""
+        now = time.time()
+        vec = encode_text(content)
+        blob = serialize_vector(vec)
+        cur = self._conn.execute(
+            """INSERT INTO memories (content, room, category, trust_score,
+               created_at, updated_at, hrr_vector)
+               VALUES (?, ?, ?, ?, ?, ?, ?)""",
+            (content, room, category, trust, now, now, blob),
+        )
+        self._conn.commit()
+        return cur.lastrowid
+
+    def store_batch(self, items: list[dict]) -> list[int]:
+        """Store multiple facts. Each item: {content, room?, category?, trust?}."""
+        ids = []
+        now = time.time()
+        for item in items:
+            content = item["content"]
+            vec = encode_text(content)
+            blob = serialize_vector(vec)
+            cur = self._conn.execute(
+                """INSERT INTO memories (content, room, category, trust_score,
+                   created_at, updated_at, hrr_vector)
+                   VALUES (?, ?, ?, ?, ?, ?, ?)""",
+                (
+                    content,
+                    item.get("room", "general"),
+                    item.get("category", ""),
+                    item.get("trust", 0.5),
+                    now, now, blob,
+                ),
+            )
+            ids.append(cur.lastrowid)
+        self._conn.commit()
+        return ids
+
+    # ------------------------------------------------------------------
+    # Search — hybrid FTS5 + HRR with Reciprocal Rank Fusion
+    # ------------------------------------------------------------------
+
+    def search(
+        self,
+        query: str,
+        room: Optional[str] = None,
+        limit: int = 10,
+        min_trust: float = 0.0,
+        fts_weight: float = 0.5,
+        hrr_weight: float = 0.5,
+    ) -> list[dict]:
+        """Hybrid search: FTS5 keywords + HRR semantic similarity.
+
+        Uses Reciprocal Rank Fusion (RRF) to merge both rankings.
+        Returns list of dicts with content, room, score, trust_score.
+        """
+        k_rrf = 60  # Standard RRF constant
+
+        # Stage 1: FTS5 candidates
+        fts_results = self._fts_search(query, room, min_trust, limit * 3)
+
+        # Stage 2: HRR candidates (scan top N by trust)
+        hrr_results = self._hrr_search(query, room, min_trust, limit * 3)
+
+        # Stage 3: RRF fusion
+        scores: dict[int, float] = {}
+        meta: dict[int, dict] = {}
+
+        for rank, row in enumerate(fts_results):
+            mid = row["memory_id"]
+            scores[mid] = scores.get(mid, 0) + fts_weight / (k_rrf + rank + 1)
+            meta[mid] = dict(row)
+
+        for rank, row in enumerate(hrr_results):
+            mid = row["memory_id"]
+            scores[mid] = scores.get(mid, 0) + hrr_weight / (k_rrf + rank + 1)
+            if mid not in meta:
+                meta[mid] = dict(row)
+
+        # Sort by fused score
+        ranked = sorted(scores.items(), key=lambda x: x[1], reverse=True)[:limit]
+
+        results = []
+        for mid, score in ranked:
+            m = meta[mid]
+            # Bump retrieval count
+            self._conn.execute(
+                "UPDATE memories SET retrieval_count = retrieval_count + 1 WHERE memory_id = ?",
+                (mid,),
+            )
+            results.append({
+                "memory_id": mid,
+                "content": m["content"],
+                "room": m["room"],
+                "category": m.get("category", ""),
+                "trust_score": m["trust_score"],
+                "score": round(score, 6),
+            })
+
+        if results:
+            self._conn.commit()
+        return results
+
+    def _fts_search(
+        self, query: str, room: Optional[str], min_trust: float, limit: int
+    ) -> list[dict]:
+        """FTS5 full-text search."""
+        try:
+            if room:
+                rows = self._conn.execute(
+                    """SELECT m.memory_id, m.content, m.room, m.category,
+                              m.trust_score, m.retrieval_count
+                       FROM memories_fts f
+                       JOIN memories m ON f.rowid = m.memory_id
+                       WHERE memories_fts MATCH ? AND m.room = ?
+                             AND m.trust_score >= ?
+                       ORDER BY rank LIMIT ?""",
+                    (query, room, min_trust, limit),
+                ).fetchall()
+            else:
+                rows = self._conn.execute(
+                    """SELECT m.memory_id, m.content, m.room, m.category,
+                              m.trust_score, m.retrieval_count
+                       FROM memories_fts f
+                       JOIN memories m ON f.rowid = m.memory_id
+                       WHERE memories_fts MATCH ?
+                             AND m.trust_score >= ?
+                       ORDER BY rank LIMIT ?""",
+                    (query, min_trust, limit),
+                ).fetchall()
+            return [dict(r) for r in rows]
+        except sqlite3.OperationalError:
+            # Bad FTS query syntax — degrade gracefully
+            return []
+
+    def _hrr_search(
+        self, query: str, room: Optional[str], min_trust: float, limit: int
+    ) -> list[dict]:
+        """HRR cosine similarity search (brute-force scan, fast for <100K facts)."""
+        query_vec = encode_text(query)
+
+        if room:
+            rows = self._conn.execute(
+                """SELECT memory_id, content, room, category, trust_score,
+                          retrieval_count, hrr_vector
+                   FROM memories
+                   WHERE room = ? AND trust_score >= ? AND hrr_vector IS NOT NULL""",
+                (room, min_trust),
+            ).fetchall()
+        else:
+            rows = self._conn.execute(
+                """SELECT memory_id, content, room, category, trust_score,
+                          retrieval_count, hrr_vector
+                   FROM memories
+                   WHERE trust_score >= ? AND hrr_vector IS NOT NULL""",
+                (min_trust,),
+            ).fetchall()
+
+        scored = []
+        for r in rows:
+            stored_vec = deserialize_vector(r["hrr_vector"])
+            sim = cosine_similarity_phase(query_vec, stored_vec)
+            scored.append((sim, dict(r)))
+
+        scored.sort(key=lambda x: x[0], reverse=True)
+        return [item[1] for item in scored[:limit]]
+
+    # ------------------------------------------------------------------
+    # Trust management
+    # ------------------------------------------------------------------
+
+    def boost_trust(self, memory_id: int, delta: float = 0.05) -> None:
+        """Increase trust score when a memory proves useful."""
+        self._conn.execute(
+            """UPDATE memories SET trust_score = MIN(1.0, trust_score + ?),
+               updated_at = ? WHERE memory_id = ?""",
+            (delta, time.time(), memory_id),
+        )
+        self._conn.commit()
+
+    def decay_trust(self, memory_id: int, delta: float = 0.02) -> None:
+        """Decrease trust score when a memory is contradicted."""
+        self._conn.execute(
+            """UPDATE memories SET trust_score = MAX(0.0, trust_score - ?),
+               updated_at = ? WHERE memory_id = ?""",
+            (delta, time.time(), memory_id),
+        )
+        self._conn.commit()
+
+    # ------------------------------------------------------------------
+    # Room operations
+    # ------------------------------------------------------------------
+
+    def list_rooms(self) -> list[dict]:
+        """List all rooms with fact counts."""
+        rows = self._conn.execute(
+            """SELECT room, COUNT(*) as count,
+                      AVG(trust_score) as avg_trust
+               FROM memories GROUP BY room ORDER BY count DESC"""
+        ).fetchall()
+        return [dict(r) for r in rows]
+
+    def room_contents(self, room: str, limit: int = 50) -> list[dict]:
+        """Get all facts in a room, ordered by trust."""
+        rows = self._conn.execute(
+            """SELECT memory_id, content, category, trust_score,
+                      retrieval_count, created_at
+               FROM memories WHERE room = ?
+               ORDER BY trust_score DESC, created_at DESC LIMIT ?""",
+            (room, limit),
+        ).fetchall()
+        return [dict(r) for r in rows]
+
+    # ------------------------------------------------------------------
+    # Stats
+    # ------------------------------------------------------------------
+
+    def stats(self) -> dict:
+        """Return store statistics."""
+        row = self._conn.execute(
+            """SELECT COUNT(*) as total,
+                      AVG(trust_score) as avg_trust,
+                      SUM(retrieval_count) as total_retrievals,
+                      COUNT(DISTINCT room) as room_count
+               FROM memories"""
+        ).fetchone()
+        return dict(row)
+
+    # ------------------------------------------------------------------
+    # Promotion support (scratchpad → durable)
+    # ------------------------------------------------------------------
+
+    def log_promotion(
+        self,
+        session_id: str,
+        scratch_key: str,
+        memory_id: int,
+        reason: str = "",
+    ) -> None:
+        """Record a scratchpad-to-palace promotion in the audit log."""
+        self._conn.execute(
+            """INSERT INTO promotion_log
+               (session_id, scratch_key, memory_id, promoted_at, reason)
+               VALUES (?, ?, ?, ?, ?)""",
+            (session_id, scratch_key, memory_id, time.time(), reason),
+        )
+        self._conn.commit()
+
+    def recent_promotions(self, limit: int = 20) -> list[dict]:
+        """Get recent promotion log entries."""
+        rows = self._conn.execute(
+            """SELECT p.*, m.content, m.room
+               FROM promotion_log p
+               LEFT JOIN memories m ON p.memory_id = m.memory_id
+               ORDER BY p.promoted_at DESC LIMIT ?""",
+            (limit,),
+        ).fetchall()
+        return [dict(r) for r in rows]

hermes-sovereign/mempalace/tests/test_sovereign_store.py

@@ -0,0 +1,255 @@
+"""Tests for the Sovereign Memory Store and Promotion system.
+
+Zero-API, zero-network — everything runs against an in-memory SQLite DB.
+"""
+
+import os
+import sys
+import tempfile
+import time
+import unittest
+
+# Allow imports from parent package
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+
+from sovereign_store import (
+    SovereignStore,
+    encode_text,
+    cosine_similarity_phase,
+    serialize_vector,
+    deserialize_vector,
+)
+from promotion import (
+    evaluate_for_promotion,
+    promote,
+    promote_session_batch,
+)
+
+
+class TestHRRVectors(unittest.TestCase):
+    """Test the HRR encoding and similarity functions."""
+
+    def test_deterministic_encoding(self):
+        """Same text always produces the same vector."""
+        v1 = encode_text("hello world")
+        v2 = encode_text("hello world")
+        self.assertAlmostEqual(cosine_similarity_phase(v1, v2), 1.0, places=5)
+
+    def test_similar_texts_higher_similarity(self):
+        """Related texts should be more similar than unrelated ones."""
+        v_agent = encode_text("agent memory palace retrieval")
+        v_similar = encode_text("agent recall memory search")
+        v_unrelated = encode_text("banana strawberry fruit smoothie")
+        sim_related = cosine_similarity_phase(v_agent, v_similar)
+        sim_unrelated = cosine_similarity_phase(v_agent, v_unrelated)
+        self.assertGreater(sim_related, sim_unrelated)
+
+    def test_serialize_roundtrip(self):
+        """Vectors survive serialization to/from bytes."""
+        vec = encode_text("test serialization")
+        blob = serialize_vector(vec)
+        restored = deserialize_vector(blob)
+        sim = cosine_similarity_phase(vec, restored)
+        self.assertAlmostEqual(sim, 1.0, places=5)
+
+    def test_empty_text(self):
+        """Empty text gets a fallback encoding."""
+        vec = encode_text("")
+        self.assertEqual(len(vec) if hasattr(vec, '__len__') else len(list(vec)), 512)
+
+
+class TestSovereignStore(unittest.TestCase):
+    """Test the SQLite-backed sovereign store."""
+
+    def setUp(self):
+        self.db_path = os.path.join(tempfile.mkdtemp(), "test.db")
+        self.store = SovereignStore(db_path=self.db_path)
+
+    def tearDown(self):
+        self.store.close()
+        if os.path.exists(self.db_path):
+            os.remove(self.db_path)
+
+    def test_store_and_retrieve(self):
+        """Store a fact and find it via search."""
+        mid = self.store.store("Timmy is a sovereign AI agent on Hermes VPS", room="identity")
+        results = self.store.search("sovereign agent", room="identity")
+        self.assertTrue(any(r["memory_id"] == mid for r in results))
+
+    def test_fts_search(self):
+        """FTS5 keyword search works."""
+        self.store.store("The beacon game uses paperclips mechanics", room="projects")
+        self.store.store("Fleet agents handle delegation and dispatch", room="fleet")
+        results = self.store.search("paperclips")
+        self.assertTrue(len(results) > 0)
+        self.assertIn("paperclips", results[0]["content"].lower())
+
+    def test_hrr_search_semantic(self):
+        """HRR similarity finds related content even without exact keywords."""
+        self.store.store("Memory palace rooms organize facts spatially", room="memory")
+        self.store.store("Pizza delivery service runs on weekends", room="unrelated")
+        results = self.store.search("organize knowledge rooms", room="memory")
+        self.assertTrue(len(results) > 0)
+        self.assertIn("palace", results[0]["content"].lower())
+
+    def test_room_filtering(self):
+        """Room filter restricts search scope."""
+        self.store.store("Hermes harness manages tool calls", room="infrastructure")
+        self.store.store("Hermes mythology Greek god", room="lore")
+        results = self.store.search("Hermes", room="infrastructure")
+        self.assertTrue(all(r["room"] == "infrastructure" for r in results))
+
+    def test_trust_boost(self):
+        """Trust score increases when boosted."""
+        mid = self.store.store("fact", trust=0.5)
+        self.store.boost_trust(mid, delta=0.1)
+        results = self.store.room_contents("general")
+        fact = next(r for r in results if r["memory_id"] == mid)
+        self.assertAlmostEqual(fact["trust_score"], 0.6, places=2)
+
+    def test_trust_decay(self):
+        """Trust score decreases when decayed."""
+        mid = self.store.store("questionable fact", trust=0.5)
+        self.store.decay_trust(mid, delta=0.2)
+        results = self.store.room_contents("general")
+        fact = next(r for r in results if r["memory_id"] == mid)
+        self.assertAlmostEqual(fact["trust_score"], 0.3, places=2)
+
+    def test_batch_store(self):
+        """Batch store works."""
+        ids = self.store.store_batch([
+            {"content": "fact one", "room": "test"},
+            {"content": "fact two", "room": "test"},
+            {"content": "fact three", "room": "test"},
+        ])
+        self.assertEqual(len(ids), 3)
+        rooms = self.store.list_rooms()
+        test_room = next(r for r in rooms if r["room"] == "test")
+        self.assertEqual(test_room["count"], 3)
+
+    def test_stats(self):
+        """Stats returns correct counts."""
+        self.store.store("a fact", room="r1")
+        self.store.store("another fact", room="r2")
+        s = self.store.stats()
+        self.assertEqual(s["total"], 2)
+        self.assertEqual(s["room_count"], 2)
+
+    def test_retrieval_count_increments(self):
+        """Retrieval count goes up when a fact is found via search."""
+        self.store.store("unique searchable content xyz123", room="test")
+        self.store.search("xyz123")
+        results = self.store.room_contents("test")
+        self.assertTrue(any(r["retrieval_count"] > 0 for r in results))
+
+
+class TestPromotion(unittest.TestCase):
+    """Test the quality-gated promotion system."""
+
+    def setUp(self):
+        self.db_path = os.path.join(tempfile.mkdtemp(), "promo_test.db")
+        self.store = SovereignStore(db_path=self.db_path)
+
+    def tearDown(self):
+        self.store.close()
+
+    def test_successful_promotion(self):
+        """Good content passes all gates."""
+        result = promote(
+            content="Timmy runs on the Hermes VPS at 143.198.27.163 with local Ollama inference",
+            store=self.store,
+            session_id="test-session-001",
+            scratch_key="vps_info",
+            room="infrastructure",
+        )
+        self.assertTrue(result.success)
+        self.assertIsNotNone(result.memory_id)
+
+    def test_reject_too_short(self):
+        """Short fragments get rejected."""
+        result = promote(
+            content="yes",
+            store=self.store,
+            session_id="test",
+            scratch_key="short",
+        )
+        self.assertFalse(result.success)
+        self.assertIn("Too short", result.reason)
+
+    def test_reject_duplicate(self):
+        """Duplicate content gets rejected."""
+        self.store.store("SOUL.md is the canonical identity document for Timmy", room="identity")
+        result = promote(
+            content="SOUL.md is the canonical identity document for Timmy",
+            store=self.store,
+            session_id="test",
+            scratch_key="soul",
+            room="identity",
+        )
+        self.assertFalse(result.success)
+        self.assertIn("uplicate", result.reason)
+
+    def test_reject_stale(self):
+        """Old notes get flagged as stale."""
+        old_time = time.time() - (86400 * 10)
+        result = promote(
+            content="This is a note from long ago about something important",
+            store=self.store,
+            session_id="test",
+            scratch_key="old",
+            written_at=old_time,
+        )
+        self.assertFalse(result.success)
+        self.assertIn("Stale", result.reason)
+
+    def test_force_bypasses_gates(self):
+        """Force flag overrides quality gates."""
+        result = promote(
+            content="ok",
+            store=self.store,
+            session_id="test",
+            scratch_key="forced",
+            force=True,
+        )
+        self.assertTrue(result.success)
+
+    def test_evaluate_dry_run(self):
+        """Evaluate returns gate details without promoting."""
+        eval_result = evaluate_for_promotion(
+            content="The fleet uses kimi-k2.5 as the primary model for all agent operations",
+            store=self.store,
+            room="fleet",
+        )
+        self.assertTrue(eval_result["eligible"])
+        self.assertTrue(all(p for p, _ in eval_result["gates"].values()))
+
+    def test_batch_promotion(self):
+        """Batch promotion processes all notes."""
+        notes = {
+            "infra": {"value": "Hermes VPS runs Ubuntu 22.04 with 2 vCPUs and 4GB RAM", "written_at": time.strftime("%Y-%m-%d %H:%M:%S")},
+            "short": {"value": "no", "written_at": time.strftime("%Y-%m-%d %H:%M:%S")},
+            "model": {"value": "The primary local model is gemma4:latest running on Ollama", "written_at": time.strftime("%Y-%m-%d %H:%M:%S")},
+        }
+        results = promote_session_batch(self.store, "batch-session", notes, room="config")
+        promoted = [r for r in results if r.success]
+        rejected = [r for r in results if not r.success]
+        self.assertEqual(len(promoted), 2)
+        self.assertEqual(len(rejected), 1)
+
+    def test_promotion_logged(self):
+        """Successful promotions appear in the audit log."""
+        promote(
+            content="Forge is hosted at forge.alexanderwhitestone.com running Gitea",
+            store=self.store,
+            session_id="log-test",
+            scratch_key="forge",
+            room="infrastructure",
+        )
+        log = self.store.recent_promotions()
+        self.assertTrue(len(log) > 0)
+        self.assertEqual(log[0]["session_id"], "log-test")
+        self.assertEqual(log[0]["scratch_key"], "forge")
+
+
+if __name__ == "__main__":
+    unittest.main()