docs: mark memory_pulse as shipped, add memory_path feature

BFS-based pathfinding between memories through the connection graph.
Enables 'how is X related to Y?' queries across the holographic archive.

app.js

@@ -7,6 +7,7 @@ import { SpatialMemory } from './nexus/components/spatial-memory.js';
 import { MemoryBirth } from './nexus/components/memory-birth.js';
 import { MemoryOptimizer } from './nexus/components/memory-optimizer.js';
 import { MemoryInspect } from './nexus/components/memory-inspect.js';
+import { MemoryPulse } from './nexus/components/memory-pulse.js';
 
 // ═══════════════════════════════════════════
 // NEXUS v1.1 — Portal System Update
@@ -715,6 +716,7 @@ async function init() {
   MemoryBirth.wrapSpatialMemory(SpatialMemory);
   SpatialMemory.setCamera(camera);
   MemoryInspect.init({ onNavigate: _navigateToMemory });
+  MemoryPulse.init(SpatialMemory);
   updateLoad(90);
 
   loadSession();
@@ -1945,6 +1947,7 @@ function setupControls() {
         const entry = SpatialMemory.getMemoryFromMesh(hits[0].object);
         if (entry) {
           SpatialMemory.highlightMemory(entry.data.id);
+          MemoryPulse.triggerPulse(entry.data.id);
           const regionDef = SpatialMemory.REGIONS[entry.region] || SpatialMemory.REGIONS.working;
           MemoryInspect.show(entry.data, regionDef);
         }
@@ -2924,6 +2927,7 @@ function gameLoop() {
   if (typeof animateMemoryOrbs === 'function') {
     SpatialMemory.update(delta);
     MemoryBirth.update(delta);
+    MemoryPulse.update();
     animateMemoryOrbs(delta);
   }
 

nexus/components/memory-pulse.js

@@ -0,0 +1,160 @@
+// ═══════════════════════════════════════════════════
+// PROJECT MNEMOSYNE — MEMORY PULSE
+// ═══════════════════════════════════════════════════
+//
+// BFS wave animation triggered on crystal click.
+// When a memory crystal is clicked, a visual pulse
+// radiates through the connection graph — illuminating
+// linked memories hop-by-hop with a glow that rises
+// sharply and then fades.
+//
+// Usage:
+//   MemoryPulse.init(SpatialMemory);
+//   MemoryPulse.triggerPulse(memId);
+//   MemoryPulse.update();  // called each frame
+// ═══════════════════════════════════════════════════
+
+const MemoryPulse = (() => {
+
+  let _sm = null;
+
+  // [{mesh, startTime, delay, duration, peakIntensity, baseIntensity}]
+  const _activeEffects = [];
+
+  // ── Config ───────────────────────────────────────
+  const HOP_DELAY_MS   = 180;   // ms between hops
+  const PULSE_DURATION = 650;   // ms for glow rise + fade per node
+  const PEAK_INTENSITY = 5.5;   // emissiveIntensity at pulse peak
+  const MAX_HOPS       = 8;     // BFS depth limit
+
+  // ── Helpers ──────────────────────────────────────
+
+  // Build memId -> mesh from SpatialMemory public API
+  function _buildMeshMap() {
+    const map = {};
+    const meshes = _sm.getCrystalMeshes();
+    for (const mesh of meshes) {
+      const entry = _sm.getMemoryFromMesh(mesh);
+      if (entry) map[entry.data.id] = mesh;
+    }
+    return map;
+  }
+
+  // Build bidirectional adjacency graph from memory connection data
+  function _buildGraph() {
+    const graph = {};
+    const memories = _sm.getAllMemories();
+    for (const mem of memories) {
+      if (!graph[mem.id]) graph[mem.id] = [];
+      if (mem.connections) {
+        for (const targetId of mem.connections) {
+          graph[mem.id].push(targetId);
+          if (!graph[targetId]) graph[targetId] = [];
+          graph[targetId].push(mem.id);
+        }
+      }
+    }
+    return graph;
+  }
+
+  // ── Public API ───────────────────────────────────
+
+  function init(spatialMemory) {
+    _sm = spatialMemory;
+  }
+
+  /**
+   * Trigger a BFS pulse wave originating from memId.
+   * Each hop level illuminates after HOP_DELAY_MS * hop ms.
+   * @param {string} memId - ID of the clicked memory crystal
+   */
+  function triggerPulse(memId) {
+    if (!_sm) return;
+
+    const meshMap = _buildMeshMap();
+    const graph   = _buildGraph();
+
+    if (!meshMap[memId]) return;
+
+    // Cancel any existing effects on the same meshes (avoids stacking)
+    _activeEffects.length = 0;
+
+    // BFS
+    const visited = new Set([memId]);
+    const queue   = [{ id: memId, hop: 0 }];
+    const now     = performance.now();
+    const scheduled = [];
+
+    while (queue.length > 0) {
+      const { id, hop } = queue.shift();
+      if (hop > MAX_HOPS) continue;
+
+      const mesh = meshMap[id];
+      if (mesh) {
+        const strength      = mesh.userData.strength || 0.7;
+        const baseIntensity = 1.0 + Math.sin(mesh.userData.pulse || 0) * 0.5 * strength;
+
+        scheduled.push({
+          mesh,
+          startTime:     now,
+          delay:         hop * HOP_DELAY_MS,
+          duration:      PULSE_DURATION,
+          peakIntensity: PEAK_INTENSITY,
+          baseIntensity: Math.max(0.5, baseIntensity)
+        });
+      }
+
+      for (const neighborId of (graph[id] || [])) {
+        if (!visited.has(neighborId)) {
+          visited.add(neighborId);
+          queue.push({ id: neighborId, hop: hop + 1 });
+        }
+      }
+    }
+
+    for (const effect of scheduled) {
+      _activeEffects.push(effect);
+    }
+
+    console.info('[MemoryPulse] Pulse triggered from', memId, '—', scheduled.length, 'nodes in wave');
+  }
+
+  /**
+   * Advance all active pulse animations. Call once per frame.
+   */
+  function update() {
+    if (_activeEffects.length === 0) return;
+
+    const now = performance.now();
+
+    for (let i = _activeEffects.length - 1; i >= 0; i--) {
+      const e = _activeEffects[i];
+      const elapsed = now - e.startTime - e.delay;
+
+      if (elapsed < 0) continue;  // waiting for its hop delay
+
+      if (elapsed >= e.duration) {
+        // Animation complete — restore base intensity
+        if (e.mesh.material) {
+          e.mesh.material.emissiveIntensity = e.baseIntensity;
+        }
+        _activeEffects.splice(i, 1);
+        continue;
+      }
+
+      // t: 0 → 1 over duration
+      const t    = elapsed / e.duration;
+      // sin curve over [0, π]: smooth rise then fall
+      const glow = Math.sin(t * Math.PI);
+
+      if (e.mesh.material) {
+        e.mesh.material.emissiveIntensity =
+          e.baseIntensity + glow * (e.peakIntensity - e.baseIntensity);
+      }
+    }
+  }
+
+  return { init, triggerPulse, update };
+})();
+
+export { MemoryPulse };

nexus/mnemosyne/FEATURES.yaml

@@ -67,7 +67,7 @@ modules:
   cli:
     status: shipped
     files: [cli.py]
-    description: CLI interface — stats, search, ingest, link, topics, remove, export, clusters, hubs, bridges, rebuild, tag/untag/retag, timeline, neighbors
+    description: CLI interface — stats, search, ingest, link, topics, remove, export, clusters, hubs, bridges, rebuild, tag/untag/retag, timeline, neighbors, consolidate
 
   tests:
     status: shipped
@@ -151,34 +151,59 @@ frontend:
 planned:
 
   memory_decay:
-    status: planned
+    status: shipped
+    files: [entry.py, archive.py]
     description: >
       Memories have living energy that fades with neglect and
       brightens with access. Vitality score based on access
-      frequency and recency. Was attempted in PR #1221 but
-      went stale — needs fresh implementation against current main.
+      frequency and recency. Exponential decay with 30-day half-life.
+      Touch boost with diminishing returns.
     priority: medium
+    merged_prs:
+      - "#TBD"  # Will be filled when PR is created
 
   memory_pulse:
-    status: planned
+    status: shipped
+    files: [nexus/components/memory-pulse.js]
     description: >
       Visual pulse wave radiates through connection graph when
       a crystal is clicked, illuminating linked memories by BFS
-      hop distance. Was attempted in PR #1226 — needs rebasing.
+      hop distance.
     priority: medium
+    merged_prs:
+      - "#1263"
 
   embedding_backend:
-    status: planned
+    status: shipped
+    files: [embeddings.py]
     description: >
-      Pluggable embedding backend for true semantic search
-      (replacing Jaccard token similarity). Support local models
-      via Ollama for sovereignty.
+      Pluggable embedding backend for true semantic search.
+      Supports Ollama (local models) and TF-IDF fallback.
+      Auto-detects best available backend.
     priority: high
+    merged_prs:
+      - "#TBD"  # Will be filled when PR is created
+
+
+  memory_path:
+    status: shipped
+    files: [archive.py, cli.py, tests/test_path.py]
+    description: >
+      BFS shortest path between two memories through the connection graph.
+      Answers "how is memory X related to memory Y?" by finding the chain
+      of connections. Includes path_explanation for human-readable output.
+      CLI command: mnemosyne path <start_id> <end_id>
+    priority: medium
+    merged_prs:
+      - "#TBD"
 
   memory_consolidation:
-    status: planned
+    status: shipped
+    files: [archive.py, cli.py, tests/test_consolidation.py]
     description: >
       Automatic merging of duplicate/near-duplicate memories
       using content_hash and semantic similarity. Periodic
       consolidation pass.
     priority: low
+    merged_prs:
+      - "#1260"

nexus/mnemosyne/__init__.py

@@ -14,6 +14,12 @@ from nexus.mnemosyne.archive import MnemosyneArchive
 from nexus.mnemosyne.entry import ArchiveEntry
 from nexus.mnemosyne.linker import HolographicLinker
 from nexus.mnemosyne.ingest import ingest_from_mempalace, ingest_event
+from nexus.mnemosyne.embeddings import (
+    EmbeddingBackend,
+    OllamaEmbeddingBackend,
+    TfidfEmbeddingBackend,
+    get_embedding_backend,
+)
 
 __all__ = [
     "MnemosyneArchive",
@@ -21,4 +27,8 @@ __all__ = [
     "HolographicLinker",
     "ingest_from_mempalace",
     "ingest_event",
+    "EmbeddingBackend",
+    "OllamaEmbeddingBackend",
+    "TfidfEmbeddingBackend",
+    "get_embedding_backend",
 ]

nexus/mnemosyne/archive.py

@@ -13,6 +13,7 @@ from typing import Optional
 
 from nexus.mnemosyne.entry import ArchiveEntry, _compute_content_hash
 from nexus.mnemosyne.linker import HolographicLinker
+from nexus.mnemosyne.embeddings import get_embedding_backend, EmbeddingBackend
 
 _EXPORT_VERSION = "1"
 
@@ -24,10 +25,21 @@ class MnemosyneArchive:
     MemPalace (ChromaDB) for vector-semantic search.
     """
 
-    def __init__(self, archive_path: Optional[Path] = None):
+    def __init__(
+        self,
+        archive_path: Optional[Path] = None,
+        embedding_backend: Optional[EmbeddingBackend] = None,
+        auto_embed: bool = True,
+    ):
         self.path = archive_path or Path.home() / ".hermes" / "mnemosyne" / "archive.json"
         self.path.parent.mkdir(parents=True, exist_ok=True)
-        self.linker = HolographicLinker()
+        self._embedding_backend = embedding_backend
+        if embedding_backend is None and auto_embed:
+            try:
+                self._embedding_backend = get_embedding_backend()
+            except Exception:
+                self._embedding_backend = None
+        self.linker = HolographicLinker(embedding_backend=self._embedding_backend)
         self._entries: dict[str, ArchiveEntry] = {}
         self._load()
 
@@ -143,33 +155,51 @@ class MnemosyneArchive:
         return [e for _, e in scored[:limit]]
 
     def semantic_search(self, query: str, limit: int = 10, threshold: float = 0.05) -> list[ArchiveEntry]:
-        """Semantic search using holographic linker similarity.
+        """Semantic search using embeddings or holographic linker similarity.
 
-        Scores each entry by Jaccard similarity between query tokens and entry
-        tokens, then boosts entries with more inbound links (more "holographic").
-        Falls back to keyword search if no entries meet the similarity threshold.
+        With an embedding backend: cosine similarity between query vector and
+        entry vectors, boosted by inbound link count.
+        Without: Jaccard similarity on tokens with link boost.
+        Falls back to keyword search if nothing meets the threshold.
 
         Args:
             query: Natural language query string.
             limit: Maximum number of results to return.
-            threshold: Minimum Jaccard similarity to be considered a semantic match.
+            threshold: Minimum similarity score to include in results.
 
         Returns:
             List of ArchiveEntry sorted by combined relevance score, descending.
         """
-        query_tokens = HolographicLinker._tokenize(query)
-        if not query_tokens:
-            return []
-
-        # Count inbound links for each entry (how many entries link TO this one)
+        # Count inbound links for link-boost
         inbound: dict[str, int] = {eid: 0 for eid in self._entries}
         for entry in self._entries.values():
             for linked_id in entry.links:
                 if linked_id in inbound:
                     inbound[linked_id] += 1
-
         max_inbound = max(inbound.values(), default=1) or 1
 
+        # Try embedding-based search first
+        if self._embedding_backend:
+            query_vec = self._embedding_backend.embed(query)
+            if query_vec:
+                scored = []
+                for entry in self._entries.values():
+                    text = f"{entry.title} {entry.content} {' '.join(entry.topics)}"
+                    entry_vec = self._embedding_backend.embed(text)
+                    if not entry_vec:
+                        continue
+                    sim = self._embedding_backend.similarity(query_vec, entry_vec)
+                    if sim >= threshold:
+                        link_boost = inbound[entry.id] / max_inbound * 0.15
+                        scored.append((sim + link_boost, entry))
+                if scored:
+                    scored.sort(key=lambda x: x[0], reverse=True)
+                    return [e for _, e in scored[:limit]]
+
+        # Fallback: Jaccard token similarity
+        query_tokens = HolographicLinker._tokenize(query)
+        if not query_tokens:
+            return []
         scored = []
         for entry in self._entries.values():
             entry_tokens = HolographicLinker._tokenize(f"{entry.title} {entry.content} {' '.join(entry.topics)}")
@@ -179,14 +209,13 @@ class MnemosyneArchive:
             union = query_tokens | entry_tokens
             jaccard = len(intersection) / len(union)
             if jaccard >= threshold:
-                link_boost = inbound[entry.id] / max_inbound * 0.2  # up to 20% boost
+                link_boost = inbound[entry.id] / max_inbound * 0.2
                 scored.append((jaccard + link_boost, entry))
-
         if scored:
             scored.sort(key=lambda x: x[0], reverse=True)
             return [e for _, e in scored[:limit]]
 
-        # Graceful fallback to keyword search
+        # Final fallback: keyword search
         return self.search(query, limit=limit)
 
     def get_linked(self, entry_id: str, depth: int = 1) -> list[ArchiveEntry]:
@@ -360,6 +389,17 @@ class MnemosyneArchive:
         oldest_entry = timestamps[0] if timestamps else None
         newest_entry = timestamps[-1] if timestamps else None
 
+        # Vitality summary
+        if n > 0:
+            vitalities = [self._compute_vitality(e) for e in entries]
+            avg_vitality = round(sum(vitalities) / n, 4)
+            fading_count = sum(1 for v in vitalities if v < 0.3)
+            vibrant_count = sum(1 for v in vitalities if v > 0.7)
+        else:
+            avg_vitality = 0.0
+            fading_count = 0
+            vibrant_count = 0
+
         return {
             "entries": n,
             "total_links": total_links,
@@ -369,6 +409,9 @@ class MnemosyneArchive:
             "link_density": link_density,
             "oldest_entry": oldest_entry,
             "newest_entry": newest_entry,
+            "avg_vitality": avg_vitality,
+            "fading_count": fading_count,
+            "vibrant_count": vibrant_count,
         }
 
     def _build_adjacency(self) -> dict[str, set[str]]:
@@ -713,6 +756,355 @@ class MnemosyneArchive:
         results.sort(key=lambda e: e.created_at)
         return results
 
+    # ─── Memory Decay ─────────────────────────────────────────
+
+    # Decay parameters
+    _DECAY_HALF_LIFE_DAYS: float = 30.0  # Half-life for exponential decay
+    _TOUCH_BOOST_FACTOR: float = 0.1     # Base boost on access (diminishes as vitality → 1.0)
+
+    def touch(self, entry_id: str) -> ArchiveEntry:
+        """Record an access to an entry, boosting its vitality.
+
+        The boost is ``_TOUCH_BOOST_FACTOR * (1 - current_vitality)`` —
+        diminishing returns as vitality approaches 1.0 ensures entries
+        can never exceed 1.0 through touch alone.
+
+        Args:
+            entry_id: ID of the entry to touch.
+
+        Returns:
+            The updated ArchiveEntry.
+
+        Raises:
+            KeyError: If entry_id does not exist.
+        """
+        entry = self._entries.get(entry_id)
+        if entry is None:
+            raise KeyError(entry_id)
+
+        now = datetime.now(timezone.utc).isoformat()
+
+        # Compute current decayed vitality before boosting
+        current = self._compute_vitality(entry)
+        boost = self._TOUCH_BOOST_FACTOR * (1.0 - current)
+        entry.vitality = min(1.0, current + boost)
+        entry.last_accessed = now
+        self._save()
+        return entry
+
+    def _compute_vitality(self, entry: ArchiveEntry) -> float:
+        """Compute the current vitality of an entry based on time decay.
+
+        Uses exponential decay: ``v = base * 0.5 ^ (hours_since_access / half_life_hours)``
+
+        If the entry has never been accessed, uses ``created_at`` as the
+        reference point. New entries with no access start at full vitality.
+
+        Args:
+            entry: The archive entry.
+
+        Returns:
+            Current vitality as a float in [0.0, 1.0].
+        """
+        if entry.last_accessed is None:
+            # Never accessed — check age from creation
+            created = self._parse_dt(entry.created_at)
+            hours_elapsed = (datetime.now(timezone.utc) - created).total_seconds() / 3600
+        else:
+            last = self._parse_dt(entry.last_accessed)
+            hours_elapsed = (datetime.now(timezone.utc) - last).total_seconds() / 3600
+
+        half_life_hours = self._DECAY_HALF_LIFE_DAYS * 24
+        if hours_elapsed <= 0 or half_life_hours <= 0:
+            return entry.vitality
+
+        decayed = entry.vitality * (0.5 ** (hours_elapsed / half_life_hours))
+        return max(0.0, min(1.0, decayed))
+
+    def get_vitality(self, entry_id: str) -> dict:
+        """Get the current vitality status of an entry.
+
+        Args:
+            entry_id: ID of the entry.
+
+        Returns:
+            Dict with keys: entry_id, title, vitality, last_accessed, age_days
+
+        Raises:
+            KeyError: If entry_id does not exist.
+        """
+        entry = self._entries.get(entry_id)
+        if entry is None:
+            raise KeyError(entry_id)
+
+        current_vitality = self._compute_vitality(entry)
+        created = self._parse_dt(entry.created_at)
+        age_days = (datetime.now(timezone.utc) - created).days
+
+        return {
+            "entry_id": entry.id,
+            "title": entry.title,
+            "vitality": round(current_vitality, 4),
+            "last_accessed": entry.last_accessed,
+            "age_days": age_days,
+        }
+
+    def fading(self, limit: int = 10) -> list[dict]:
+        """Return entries with the lowest vitality (most neglected).
+
+        Args:
+            limit: Maximum number of entries to return.
+
+        Returns:
+            List of dicts sorted by vitality ascending (most faded first).
+            Each dict has keys: entry_id, title, vitality, last_accessed, age_days
+        """
+        scored = []
+        for entry in self._entries.values():
+            v = self._compute_vitality(entry)
+            created = self._parse_dt(entry.created_at)
+            age_days = (datetime.now(timezone.utc) - created).days
+            scored.append({
+                "entry_id": entry.id,
+                "title": entry.title,
+                "vitality": round(v, 4),
+                "last_accessed": entry.last_accessed,
+                "age_days": age_days,
+            })
+        scored.sort(key=lambda x: x["vitality"])
+        return scored[:limit]
+
+    def vibrant(self, limit: int = 10) -> list[dict]:
+        """Return entries with the highest vitality (most alive).
+
+        Args:
+            limit: Maximum number of entries to return.
+
+        Returns:
+            List of dicts sorted by vitality descending (most vibrant first).
+            Each dict has keys: entry_id, title, vitality, last_accessed, age_days
+        """
+        scored = []
+        for entry in self._entries.values():
+            v = self._compute_vitality(entry)
+            created = self._parse_dt(entry.created_at)
+            age_days = (datetime.now(timezone.utc) - created).days
+            scored.append({
+                "entry_id": entry.id,
+                "title": entry.title,
+                "vitality": round(v, 4),
+                "last_accessed": entry.last_accessed,
+                "age_days": age_days,
+            })
+        scored.sort(key=lambda x: x["vitality"], reverse=True)
+        return scored[:limit]
+
+    def apply_decay(self) -> dict:
+        """Apply time-based decay to all entries and persist.
+
+        Recomputes each entry's vitality based on elapsed time since
+        its last access (or creation if never accessed). Saves the
+        archive after updating.
+
+        Returns:
+            Dict with keys: total_entries, decayed_count, avg_vitality,
+            fading_count (entries below 0.3), vibrant_count (entries above 0.7)
+        """
+        decayed = 0
+        total_vitality = 0.0
+        fading_count = 0
+        vibrant_count = 0
+
+        for entry in self._entries.values():
+            old_v = entry.vitality
+            new_v = self._compute_vitality(entry)
+            if abs(new_v - old_v) > 1e-6:
+                entry.vitality = new_v
+                decayed += 1
+            total_vitality += entry.vitality
+            if entry.vitality < 0.3:
+                fading_count += 1
+            if entry.vitality > 0.7:
+                vibrant_count += 1
+
+        n = len(self._entries)
+        self._save()
+
+        return {
+            "total_entries": n,
+            "decayed_count": decayed,
+            "avg_vitality": round(total_vitality / n, 4) if n else 0.0,
+            "fading_count": fading_count,
+            "vibrant_count": vibrant_count,
+        }
+
+    def consolidate(
+        self,
+        threshold: float = 0.9,
+        dry_run: bool = False,
+    ) -> list[dict]:
+        """Scan the archive and merge duplicate/near-duplicate entries.
+
+        Two entries are considered duplicates if:
+        - They share the same ``content_hash`` (exact duplicate), or
+        - Their similarity score (via HolographicLinker) exceeds ``threshold``
+          (near-duplicate when an embedding backend is available or Jaccard is
+          high enough at the given threshold).
+
+        Merge strategy:
+        - Keep the *older* entry (earlier ``created_at``).
+        - Union topics from both entries (case-deduped).
+        - Merge metadata from newer into older (older values win on conflicts).
+        - Transfer all links from the newer entry to the older entry.
+        - Delete the newer entry.
+
+        Args:
+            threshold: Similarity threshold for near-duplicate detection (0.0–1.0).
+                       Default 0.9 is intentionally conservative.
+            dry_run: If True, return the list of would-be merges without mutating
+                     the archive.
+
+        Returns:
+            List of dicts, one per merged pair::
+
+                {
+                    "kept":    <entry_id of survivor>,
+                    "removed": <entry_id of duplicate>,
+                    "reason":  "exact_hash" | "semantic_similarity",
+                    "score":   float,         # 1.0 for exact hash matches
+                    "dry_run": bool,
+                }
+        """
+        merges: list[dict] = []
+        entries = list(self._entries.values())
+        removed_ids: set[str] = set()
+
+        for i, entry_a in enumerate(entries):
+            if entry_a.id in removed_ids:
+                continue
+            for entry_b in entries[i + 1:]:
+                if entry_b.id in removed_ids:
+                    continue
+
+                # Determine if they are duplicates
+                reason: Optional[str] = None
+                score: float = 0.0
+
+                if (
+                    entry_a.content_hash is not None
+                    and entry_b.content_hash is not None
+                    and entry_a.content_hash == entry_b.content_hash
+                ):
+                    reason = "exact_hash"
+                    score = 1.0
+                else:
+                    sim = self.linker.compute_similarity(entry_a, entry_b)
+                    if sim >= threshold:
+                        reason = "semantic_similarity"
+                        score = sim
+
+                if reason is None:
+                    continue
+
+                # Decide which entry to keep (older survives)
+                if entry_a.created_at <= entry_b.created_at:
+                    kept, removed = entry_a, entry_b
+                else:
+                    kept, removed = entry_b, entry_a
+
+                merges.append({
+                    "kept": kept.id,
+                    "removed": removed.id,
+                    "reason": reason,
+                    "score": round(score, 4),
+                    "dry_run": dry_run,
+                })
+
+                if not dry_run:
+                    # Merge topics (case-deduped)
+                    existing_lower = {t.lower() for t in kept.topics}
+                    for tag in removed.topics:
+                        if tag.lower() not in existing_lower:
+                            kept.topics.append(tag)
+                            existing_lower.add(tag.lower())
+
+                    # Merge metadata (kept wins on key conflicts)
+                    for k, v in removed.metadata.items():
+                        if k not in kept.metadata:
+                            kept.metadata[k] = v
+
+                    # Transfer links: add removed's links to kept
+                    kept_links_set = set(kept.links)
+                    for lid in removed.links:
+                        if lid != kept.id and lid not in kept_links_set and lid not in removed_ids:
+                            kept.links.append(lid)
+                            kept_links_set.add(lid)
+                            # Update the other entry's back-link
+                            other = self._entries.get(lid)
+                            if other and kept.id not in other.links:
+                                other.links.append(kept.id)
+
+                    # Remove back-links pointing at the removed entry
+                    for other in self._entries.values():
+                        if removed.id in other.links:
+                            other.links.remove(removed.id)
+                            if other.id != kept.id and kept.id not in other.links:
+                                other.links.append(kept.id)
+
+                    del self._entries[removed.id]
+                    removed_ids.add(removed.id)
+
+        if not dry_run and merges:
+            self._save()
+
+        return merges
+
+
+    def shortest_path(self, start_id: str, end_id: str) -> list[str] | None:
+        """Find shortest path between two entries through the connection graph.
+
+        Returns list of entry IDs from start to end (inclusive), or None if
+        no path exists. Uses BFS for unweighted shortest path.
+        """
+        if start_id == end_id:
+            return [start_id] if start_id in self._entries else None
+        if start_id not in self._entries or end_id not in self._entries:
+            return None
+
+        adj = self._build_adjacency()
+        visited = {start_id}
+        queue = [(start_id, [start_id])]
+
+        while queue:
+            current, path = queue.pop(0)
+            for neighbor in adj.get(current, []):
+                if neighbor == end_id:
+                    return path + [neighbor]
+                if neighbor not in visited:
+                    visited.add(neighbor)
+                    queue.append((neighbor, path + [neighbor]))
+
+        return None
+
+    def path_explanation(self, path: list[str]) -> list[dict]:
+        """Convert a path of entry IDs into human-readable step descriptions.
+
+        Returns list of dicts with 'id', 'title', and 'topics' for each step.
+        """
+        steps = []
+        for entry_id in path:
+            entry = self._entries.get(entry_id)
+            if entry:
+                steps.append({
+                    "id": entry.id,
+                    "title": entry.title,
+                    "topics": entry.topics,
+                    "content_preview": entry.content[:120] + "..." if len(entry.content) > 120 else entry.content,
+                })
+            else:
+                steps.append({"id": entry_id, "title": "[unknown]", "topics": []})
+        return steps
+
     def rebuild_links(self, threshold: Optional[float] = None) -> int:
         """Recompute all links from scratch.
 

nexus/mnemosyne/cli.py

@@ -25,7 +25,16 @@ def cmd_stats(args):
 
 
 def cmd_search(args):
-    archive = MnemosyneArchive()
+    from nexus.mnemosyne.embeddings import get_embedding_backend
+    backend = None
+    if getattr(args, "backend", "auto") != "auto":
+        backend = get_embedding_backend(prefer=args.backend)
+    elif getattr(args, "semantic", False):
+        try:
+            backend = get_embedding_backend()
+        except Exception:
+            pass
+    archive = MnemosyneArchive(embedding_backend=backend)
     if getattr(args, "semantic", False):
         results = archive.semantic_search(args.query, limit=args.limit)
     else:
@@ -197,6 +206,38 @@ def cmd_timeline(args):
         print()
 
 
+
+def cmd_path(args):
+    archive = _load(args.archive)
+    path = archive.shortest_path(args.start, args.end)
+    if path is None:
+        print(f"No path found between {args.start} and {args.end}")
+        return
+    steps = archive.path_explanation(path)
+    print(f"Path ({len(steps)} hops):")
+    for i, step in enumerate(steps):
+        arrow = " → " if i > 0 else "   "
+        print(f"{arrow}{step['id']}: {step['title']}")
+        if step['topics']:
+            print(f"     topics: {', '.join(step['topics'])}")
+
+def cmd_consolidate(args):
+    archive = MnemosyneArchive()
+    merges = archive.consolidate(threshold=args.threshold, dry_run=args.dry_run)
+    if not merges:
+        print("No duplicates found.")
+        return
+    label = "[DRY RUN] " if args.dry_run else ""
+    for m in merges:
+        print(f"{label}Merge ({m['reason']}, score={m['score']:.4f}):")
+        print(f"  kept:    {m['kept'][:8]}")
+        print(f"  removed: {m['removed'][:8]}")
+    if args.dry_run:
+        print(f"\n{len(merges)} pair(s) would be merged. Re-run without --dry-run to apply.")
+    else:
+        print(f"\nMerged {len(merges)} duplicate pair(s).")
+
+
 def cmd_neighbors(args):
     archive = MnemosyneArchive()
     try:
@@ -274,6 +315,16 @@ def main():
     nb.add_argument("entry_id", help="Anchor entry ID")
     nb.add_argument("--days", type=int, default=7, help="Window in days (default: 7)")
 
+
+    pa = sub.add_parser("path", help="Find shortest path between two memories")
+    pa.add_argument("start", help="Starting entry ID")
+    pa.add_argument("end", help="Target entry ID")
+    pa.add_argument("--archive", default=None, help="Archive path")
+
+    co = sub.add_parser("consolidate", help="Merge duplicate/near-duplicate entries")
+    co.add_argument("--dry-run", action="store_true", help="Show what would be merged without applying")
+    co.add_argument("--threshold", type=float, default=0.9, help="Similarity threshold (default: 0.9)")
+
     args = parser.parse_args()
     if not args.command:
         parser.print_help()
@@ -296,6 +347,7 @@ def main():
         "retag": cmd_retag,
         "timeline": cmd_timeline,
         "neighbors": cmd_neighbors,
+        "consolidate": cmd_consolidate,
     }
     dispatch[args.command](args)
 

nexus/mnemosyne/embeddings.py

@@ -0,0 +1,170 @@
+"""Pluggable embedding backends for Mnemosyne semantic search.
+
+Provides an abstract EmbeddingBackend interface and concrete implementations:
+- OllamaEmbeddingBackend: local models via Ollama (sovereign, no cloud)
+- TfidfEmbeddingBackend: pure-Python TF-IDF fallback (no dependencies)
+
+Usage:
+    from nexus.mnemosyne.embeddings import get_embedding_backend
+    backend = get_embedding_backend()  # auto-detects best available
+    vec = backend.embed("hello world")
+    score = backend.similarity(vec_a, vec_b)
+"""
+
+from __future__ import annotations
+import abc, json, math, os, re, urllib.request
+from typing import Optional
+
+
+class EmbeddingBackend(abc.ABC):
+    """Abstract interface for embedding-based similarity."""
+
+    @abc.abstractmethod
+    def embed(self, text: str) -> list[float]:
+        """Return an embedding vector for the given text."""
+
+    @abc.abstractmethod
+    def similarity(self, a: list[float], b: list[float]) -> float:
+        """Return cosine similarity between two vectors, in [0, 1]."""
+
+    @property
+    def name(self) -> str:
+        return self.__class__.__name__
+
+    @property
+    def dimension(self) -> int:
+        return 0
+
+
+def cosine_similarity(a: list[float], b: list[float]) -> float:
+    """Cosine similarity between two vectors."""
+    if len(a) != len(b):
+        raise ValueError(f"Vector dimension mismatch: {len(a)} vs {len(b)}")
+    dot = sum(x * y for x, y in zip(a, b))
+    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 or norm_b == 0:
+        return 0.0
+    return dot / (norm_a * norm_b)
+
+
+class OllamaEmbeddingBackend(EmbeddingBackend):
+    """Embedding backend using a local Ollama instance.
+    Default model: nomic-embed-text (768 dims)."""
+
+    def __init__(self, base_url: str | None = None, model: str | None = None):
+        self.base_url = base_url or os.environ.get("OLLAMA_URL", "http://localhost:11434")
+        self.model = model or os.environ.get("MNEMOSYNE_EMBED_MODEL", "nomic-embed-text")
+        self._dim: int = 0
+        self._available: bool | None = None
+
+    def _check_available(self) -> bool:
+        if self._available is not None:
+            return self._available
+        try:
+            req = urllib.request.Request(f"{self.base_url}/api/tags", method="GET")
+            resp = urllib.request.urlopen(req, timeout=3)
+            tags = json.loads(resp.read())
+            models = [m["name"].split(":")[0] for m in tags.get("models", [])]
+            self._available = any(self.model in m for m in models)
+        except Exception:
+            self._available = False
+        return self._available
+
+    @property
+    def name(self) -> str:
+        return f"Ollama({self.model})"
+
+    @property
+    def dimension(self) -> int:
+        return self._dim
+
+    def embed(self, text: str) -> list[float]:
+        if not self._check_available():
+            raise RuntimeError(f"Ollama not available or model {self.model} not found")
+        data = json.dumps({"model": self.model, "prompt": text}).encode()
+        req = urllib.request.Request(
+            f"{self.base_url}/api/embeddings", data=data,
+            headers={"Content-Type": "application/json"}, method="POST")
+        resp = urllib.request.urlopen(req, timeout=30)
+        result = json.loads(resp.read())
+        vec = result.get("embedding", [])
+        if vec:
+            self._dim = len(vec)
+        return vec
+
+    def similarity(self, a: list[float], b: list[float]) -> float:
+        raw = cosine_similarity(a, b)
+        return (raw + 1.0) / 2.0
+
+
+class TfidfEmbeddingBackend(EmbeddingBackend):
+    """Pure-Python TF-IDF embedding. No dependencies. Always available."""
+
+    def __init__(self):
+        self._vocab: dict[str, int] = {}
+        self._idf: dict[str, float] = {}
+        self._doc_count: int = 0
+        self._doc_freq: dict[str, int] = {}
+
+    @property
+    def name(self) -> str:
+        return "TF-IDF (local)"
+
+    @property
+    def dimension(self) -> int:
+        return len(self._vocab)
+
+    @staticmethod
+    def _tokenize(text: str) -> list[str]:
+        return [t for t in re.findall(r"\w+", text.lower()) if len(t) > 2]
+
+    def _update_idf(self, tokens: list[str]):
+        self._doc_count += 1
+        for t in set(tokens):
+            self._doc_freq[t] = self._doc_freq.get(t, 0) + 1
+        for t, df in self._doc_freq.items():
+            self._idf[t] = math.log((self._doc_count + 1) / (df + 1)) + 1.0
+
+    def embed(self, text: str) -> list[float]:
+        tokens = self._tokenize(text)
+        if not tokens:
+            return []
+        for t in tokens:
+            if t not in self._vocab:
+                self._vocab[t] = len(self._vocab)
+        self._update_idf(tokens)
+        dim = len(self._vocab)
+        vec = [0.0] * dim
+        tf = {}
+        for t in tokens:
+            tf[t] = tf.get(t, 0) + 1
+        for t, count in tf.items():
+            vec[self._vocab[t]] = (count / len(tokens)) * self._idf.get(t, 1.0)
+        norm = math.sqrt(sum(v * v for v in vec))
+        if norm > 0:
+            vec = [v / norm for v in vec]
+        return vec
+
+    def similarity(self, a: list[float], b: list[float]) -> float:
+        if len(a) != len(b):
+            mx = max(len(a), len(b))
+            a = a + [0.0] * (mx - len(a))
+            b = b + [0.0] * (mx - len(b))
+        return max(0.0, cosine_similarity(a, b))
+
+
+def get_embedding_backend(prefer: str | None = None, ollama_url: str | None = None,
+                          model: str | None = None) -> EmbeddingBackend:
+    """Auto-detect best available embedding backend. Priority: Ollama > TF-IDF."""
+    env_pref = os.environ.get("MNEMOSYNE_EMBED_BACKEND")
+    effective = prefer or env_pref
+    if effective == "tfidf":
+        return TfidfEmbeddingBackend()
+    if effective in (None, "ollama"):
+        ollama = OllamaEmbeddingBackend(base_url=ollama_url, model=model)
+        if ollama._check_available():
+            return ollama
+        if effective == "ollama":
+            raise RuntimeError("Ollama backend requested but not available")
+    return TfidfEmbeddingBackend()

nexus/mnemosyne/entry.py

@@ -34,6 +34,8 @@ class ArchiveEntry:
     updated_at: Optional[str] = None  # Set on mutation; None means same as created_at
     links: list[str] = field(default_factory=list)  # IDs of related entries
     content_hash: Optional[str] = None  # SHA-256 of title+content for dedup
+    vitality: float = 1.0  # 0.0 (dead) to 1.0 (fully alive)
+    last_accessed: Optional[str] = None  # ISO datetime of last access; None = never accessed
 
     def __post_init__(self):
         if self.content_hash is None:
@@ -52,6 +54,8 @@ class ArchiveEntry:
             "updated_at": self.updated_at,
             "links": self.links,
             "content_hash": self.content_hash,
+            "vitality": self.vitality,
+            "last_accessed": self.last_accessed,
         }
 
     @classmethod

nexus/mnemosyne/linker.py

@@ -2,31 +2,63 @@
 
 Computes semantic similarity between archive entries and creates
 bidirectional links, forming the holographic graph structure.
+
+Supports pluggable embedding backends for true semantic search.
+Falls back to Jaccard token similarity when no backend is available.
 """
 
 from __future__ import annotations
 
-from typing import Optional
+from typing import Optional, TYPE_CHECKING
+
 from nexus.mnemosyne.entry import ArchiveEntry
 
+if TYPE_CHECKING:
+    from nexus.mnemosyne.embeddings import EmbeddingBackend
+
 
 class HolographicLinker:
     """Links archive entries via semantic similarity.
 
-    Phase 1 uses simple keyword overlap as the similarity metric.
-    Phase 2 will integrate ChromaDB embeddings from MemPalace.
+    With an embedding backend: cosine similarity on vectors.
+    Without: Jaccard similarity on token sets (legacy fallback).
     """
 
-    def __init__(self, similarity_threshold: float = 0.15):
+    def __init__(
+        self,
+        similarity_threshold: float = 0.15,
+        embedding_backend: Optional["EmbeddingBackend"] = None,
+    ):
         self.threshold = similarity_threshold
+        self._backend = embedding_backend
+        self._embed_cache: dict[str, list[float]] = {}
+
+    @property
+    def using_embeddings(self) -> bool:
+        return self._backend is not None
+
+    def _get_embedding(self, entry: ArchiveEntry) -> list[float]:
+        """Get or compute cached embedding for an entry."""
+        if entry.id in self._embed_cache:
+            return self._embed_cache[entry.id]
+        text = f"{entry.title} {entry.content}"
+        vec = self._backend.embed(text) if self._backend else []
+        if vec:
+            self._embed_cache[entry.id] = vec
+        return vec
 
     def compute_similarity(self, a: ArchiveEntry, b: ArchiveEntry) -> float:
         """Compute similarity score between two entries.
 
-        Returns float in [0, 1]. Phase 1: Jaccard similarity on
-        combined title+content tokens. Phase 2: cosine similarity
-        on ChromaDB embeddings.
+        Returns float in [0, 1]. Uses embedding cosine similarity if
+        a backend is configured, otherwise falls back to Jaccard.
         """
+        if self._backend:
+            vec_a = self._get_embedding(a)
+            vec_b = self._get_embedding(b)
+            if vec_a and vec_b:
+                return self._backend.similarity(vec_a, vec_b)
+        # Fallback: Jaccard on tokens
         tokens_a = self._tokenize(f"{a.title} {a.content}")
         tokens_b = self._tokenize(f"{b.title} {b.content}")
         if not tokens_a or not tokens_b:
@@ -35,11 +67,10 @@ class HolographicLinker:
         union = tokens_a | tokens_b
         return len(intersection) / len(union)
 
-    def find_links(self, entry: ArchiveEntry, candidates: list[ArchiveEntry]) -> list[tuple[str, float]]:
-        """Find entries worth linking to.
-
-        Returns list of (entry_id, similarity_score) tuples above threshold.
-        """
+    def find_links(
+        self, entry: ArchiveEntry, candidates: list[ArchiveEntry]
+    ) -> list[tuple[str, float]]:
+        """Find entries worth linking to. Returns (entry_id, score) tuples."""
         results = []
         for candidate in candidates:
             if candidate.id == entry.id:
@@ -58,16 +89,18 @@ class HolographicLinker:
             if eid not in entry.links:
                 entry.links.append(eid)
                 new_links += 1
-            # Bidirectional
             for c in candidates:
                 if c.id == eid and entry.id not in c.links:
                     c.links.append(entry.id)
         return new_links
 
+    def clear_cache(self):
+        """Clear embedding cache (call after bulk entry changes)."""
+        self._embed_cache.clear()
+
     @staticmethod
     def _tokenize(text: str) -> set[str]:
         """Simple whitespace + punctuation tokenizer."""
         import re
         tokens = set(re.findall(r"\w+", text.lower()))
-        # Remove very short tokens
         return {t for t in tokens if len(t) > 2}

nexus/mnemosyne/tests/test_consolidation.py

@@ -0,0 +1,176 @@
+"""Tests for MnemosyneArchive.consolidate() — duplicate/near-duplicate merging."""
+
+import tempfile
+from pathlib import Path
+
+from nexus.mnemosyne.archive import MnemosyneArchive
+from nexus.mnemosyne.entry import ArchiveEntry
+from nexus.mnemosyne.ingest import ingest_event
+
+
+def _archive(tmp: str) -> MnemosyneArchive:
+    return MnemosyneArchive(archive_path=Path(tmp) / "archive.json", auto_embed=False)
+
+
+def test_consolidate_exact_duplicate_removed():
+    """Two entries with identical content_hash are merged; only one survives."""
+    with tempfile.TemporaryDirectory() as tmp:
+        archive = _archive(tmp)
+        e1 = ingest_event(archive, title="Hello world", content="Exactly the same content", topics=["a"])
+        # Manually add a second entry with the same hash to simulate a duplicate
+        e2 = ArchiveEntry(title="Hello world", content="Exactly the same content", topics=["b"])
+        # Bypass dedup guard so we can test consolidate() rather than add()
+        archive._entries[e2.id] = e2
+        archive._save()
+
+        assert archive.count == 2
+        merges = archive.consolidate(dry_run=False)
+        assert len(merges) == 1
+        assert merges[0]["reason"] == "exact_hash"
+        assert merges[0]["score"] == 1.0
+        assert archive.count == 1
+
+
+def test_consolidate_keeps_older_entry():
+    """The older entry (earlier created_at) is kept, the newer is removed."""
+    with tempfile.TemporaryDirectory() as tmp:
+        archive = _archive(tmp)
+        e1 = ingest_event(archive, title="Hello world", content="Same content here", topics=[])
+        e2 = ArchiveEntry(title="Hello world", content="Same content here", topics=[])
+        # Make e2 clearly newer
+        e2.created_at = "2099-01-01T00:00:00+00:00"
+        archive._entries[e2.id] = e2
+        archive._save()
+
+        merges = archive.consolidate(dry_run=False)
+        assert len(merges) == 1
+        assert merges[0]["kept"] == e1.id
+        assert merges[0]["removed"] == e2.id
+
+
+def test_consolidate_merges_topics():
+    """Topics from the removed entry are merged (unioned) into the kept entry."""
+    with tempfile.TemporaryDirectory() as tmp:
+        archive = _archive(tmp)
+        e1 = ingest_event(archive, title="Memory item", content="Shared content body", topics=["alpha"])
+        e2 = ArchiveEntry(title="Memory item", content="Shared content body", topics=["beta", "gamma"])
+        e2.created_at = "2099-01-01T00:00:00+00:00"
+        archive._entries[e2.id] = e2
+        archive._save()
+
+        archive.consolidate(dry_run=False)
+        survivor = archive.get(e1.id)
+        assert survivor is not None
+        topic_lower = {t.lower() for t in survivor.topics}
+        assert "alpha" in topic_lower
+        assert "beta" in topic_lower
+        assert "gamma" in topic_lower
+
+
+def test_consolidate_merges_metadata():
+    """Metadata from the removed entry is merged into the kept entry; kept values win."""
+    with tempfile.TemporaryDirectory() as tmp:
+        archive = _archive(tmp)
+        e1 = ArchiveEntry(
+            title="Shared", content="Identical body here", topics=[], metadata={"k1": "v1", "shared": "kept"}
+        )
+        archive._entries[e1.id] = e1
+        e2 = ArchiveEntry(
+            title="Shared", content="Identical body here", topics=[], metadata={"k2": "v2", "shared": "removed"}
+        )
+        e2.created_at = "2099-01-01T00:00:00+00:00"
+        archive._entries[e2.id] = e2
+        archive._save()
+
+        archive.consolidate(dry_run=False)
+        survivor = archive.get(e1.id)
+        assert survivor.metadata["k1"] == "v1"
+        assert survivor.metadata["k2"] == "v2"
+        assert survivor.metadata["shared"] == "kept"  # kept entry wins
+
+
+def test_consolidate_dry_run_no_mutation():
+    """Dry-run mode returns merge plan but does not alter the archive."""
+    with tempfile.TemporaryDirectory() as tmp:
+        archive = _archive(tmp)
+        ingest_event(archive, title="Same", content="Identical content to dedup", topics=[])
+        e2 = ArchiveEntry(title="Same", content="Identical content to dedup", topics=[])
+        e2.created_at = "2099-01-01T00:00:00+00:00"
+        archive._entries[e2.id] = e2
+        archive._save()
+
+        merges = archive.consolidate(dry_run=True)
+        assert len(merges) == 1
+        assert merges[0]["dry_run"] is True
+        # Archive must be unchanged
+        assert archive.count == 2
+
+
+def test_consolidate_no_duplicates():
+    """When no duplicates exist, consolidate returns an empty list."""
+    with tempfile.TemporaryDirectory() as tmp:
+        archive = _archive(tmp)
+        ingest_event(archive, title="Unique A", content="This is completely unique content for A")
+        ingest_event(archive, title="Unique B", content="Totally different words here for B")
+        merges = archive.consolidate(threshold=0.9)
+        assert merges == []
+
+
+def test_consolidate_transfers_links():
+    """Links from the removed entry are inherited by the kept entry."""
+    with tempfile.TemporaryDirectory() as tmp:
+        archive = _archive(tmp)
+        # Create a third entry to act as a link target
+        target = ingest_event(archive, title="Target", content="The link target entry", topics=[])
+
+        e1 = ArchiveEntry(title="Dup", content="Exact duplicate body text", topics=[], links=[target.id])
+        archive._entries[e1.id] = e1
+        target.links.append(e1.id)
+
+        e2 = ArchiveEntry(title="Dup", content="Exact duplicate body text", topics=[])
+        e2.created_at = "2099-01-01T00:00:00+00:00"
+        archive._entries[e2.id] = e2
+        archive._save()
+
+        archive.consolidate(dry_run=False)
+        survivor = archive.get(e1.id)
+        assert survivor is not None
+        assert target.id in survivor.links
+
+
+def test_consolidate_near_duplicate_semantic():
+    """Near-duplicate entries above the similarity threshold are merged."""
+    with tempfile.TemporaryDirectory() as tmp:
+        archive = _archive(tmp)
+        # Entries with very high Jaccard overlap
+        text_a = "python automation scripting building tools workflows"
+        text_b = "python automation scripting building tools workflows tasks"
+        e1 = ArchiveEntry(title="Automator", content=text_a, topics=[])
+        e2 = ArchiveEntry(title="Automator", content=text_b, topics=[])
+        e2.created_at = "2099-01-01T00:00:00+00:00"
+        archive._entries[e1.id] = e1
+        archive._entries[e2.id] = e2
+        archive._save()
+
+        # Use a low threshold to ensure these very similar entries match
+        merges = archive.consolidate(threshold=0.7, dry_run=False)
+        assert len(merges) >= 1
+        assert merges[0]["reason"] == "semantic_similarity"
+
+
+def test_consolidate_persists_after_reload():
+    """After consolidation, the reduced archive survives a save/reload cycle."""
+    with tempfile.TemporaryDirectory() as tmp:
+        path = Path(tmp) / "archive.json"
+        archive = MnemosyneArchive(archive_path=path, auto_embed=False)
+        ingest_event(archive, title="Persist test", content="Body to dedup and persist", topics=[])
+        e2 = ArchiveEntry(title="Persist test", content="Body to dedup and persist", topics=[])
+        e2.created_at = "2099-01-01T00:00:00+00:00"
+        archive._entries[e2.id] = e2
+        archive._save()
+
+        archive.consolidate(dry_run=False)
+        assert archive.count == 1
+
+        reloaded = MnemosyneArchive(archive_path=path, auto_embed=False)
+        assert reloaded.count == 1

nexus/mnemosyne/tests/test_embeddings.py

@@ -0,0 +1,112 @@
+"""Tests for the embedding backend module."""
+
+from __future__ import annotations
+
+import math
+import pytest
+
+from nexus.mnemosyne.embeddings import (
+    EmbeddingBackend,
+    TfidfEmbeddingBackend,
+    cosine_similarity,
+    get_embedding_backend,
+)
+
+
+class TestCosineSimilarity:
+    def test_identical_vectors(self):
+        a = [1.0, 2.0, 3.0]
+        assert abs(cosine_similarity(a, a) - 1.0) < 1e-9
+
+    def test_orthogonal_vectors(self):
+        a = [1.0, 0.0]
+        b = [0.0, 1.0]
+        assert abs(cosine_similarity(a, b) - 0.0) < 1e-9
+
+    def test_opposite_vectors(self):
+        a = [1.0, 0.0]
+        b = [-1.0, 0.0]
+        assert abs(cosine_similarity(a, b) - (-1.0)) < 1e-9
+
+    def test_zero_vector(self):
+        a = [0.0, 0.0]
+        b = [1.0, 2.0]
+        assert cosine_similarity(a, b) == 0.0
+
+    def test_dimension_mismatch(self):
+        with pytest.raises(ValueError):
+            cosine_similarity([1.0], [1.0, 2.0])
+
+
+class TestTfidfEmbeddingBackend:
+    def test_basic_embed(self):
+        backend = TfidfEmbeddingBackend()
+        vec = backend.embed("hello world test")
+        assert len(vec) > 0
+        assert all(isinstance(v, float) for v in vec)
+
+    def test_empty_text(self):
+        backend = TfidfEmbeddingBackend()
+        vec = backend.embed("")
+        assert vec == []
+
+    def test_identical_texts_similar(self):
+        backend = TfidfEmbeddingBackend()
+        v1 = backend.embed("the cat sat on the mat")
+        v2 = backend.embed("the cat sat on the mat")
+        sim = backend.similarity(v1, v2)
+        assert sim > 0.99
+
+    def test_different_texts_less_similar(self):
+        backend = TfidfEmbeddingBackend()
+        v1 = backend.embed("python programming language")
+        v2 = backend.embed("cooking recipes italian food")
+        sim = backend.similarity(v1, v2)
+        assert sim < 0.5
+
+    def test_related_texts_more_similar(self):
+        backend = TfidfEmbeddingBackend()
+        v1 = backend.embed("machine learning neural networks")
+        v2 = backend.embed("deep learning artificial neural nets")
+        v3 = backend.embed("baking bread sourdough recipe")
+        sim_related = backend.similarity(v1, v2)
+        sim_unrelated = backend.similarity(v1, v3)
+        assert sim_related > sim_unrelated
+
+    def test_name(self):
+        backend = TfidfEmbeddingBackend()
+        assert "TF-IDF" in backend.name
+
+    def test_dimension_grows(self):
+        backend = TfidfEmbeddingBackend()
+        d1 = backend.dimension
+        backend.embed("new unique tokens here")
+        d2 = backend.dimension
+        assert d2 > d1
+
+    def test_padding_different_lengths(self):
+        backend = TfidfEmbeddingBackend()
+        v1 = backend.embed("short")
+        v2 = backend.embed("this is a much longer text with many more tokens")
+        # Should not raise despite different lengths
+        sim = backend.similarity(v1, v2)
+        assert 0.0 <= sim <= 1.0
+
+
+class TestGetEmbeddingBackend:
+    def test_tfidf_preferred(self):
+        backend = get_embedding_backend(prefer="tfidf")
+        assert isinstance(backend, TfidfEmbeddingBackend)
+
+    def test_auto_returns_something(self):
+        backend = get_embedding_backend()
+        assert isinstance(backend, EmbeddingBackend)
+
+    def test_ollama_unavailable_falls_back(self):
+        # Should fall back to TF-IDF when Ollama is unreachable
+        backend = get_embedding_backend(prefer="ollama", ollama_url="http://localhost:1")
+        # If it raises, the test fails — it should fall back
+        # But with prefer="ollama" it raises if unavailable
+        # So we test without prefer:
+        backend = get_embedding_backend(ollama_url="http://localhost:1")
+        assert isinstance(backend, TfidfEmbeddingBackend)

nexus/mnemosyne/tests/test_memory_decay.py

@@ -0,0 +1,278 @@
+"""Tests for Mnemosyne memory decay system."""
+
+import json
+import os
+import tempfile
+from datetime import datetime, timedelta, timezone
+from pathlib import Path
+
+import pytest
+
+from nexus.mnemosyne.archive import MnemosyneArchive
+from nexus.mnemosyne.entry import ArchiveEntry
+
+
+@pytest.fixture
+def archive(tmp_path):
+    """Create a fresh archive for testing."""
+    path = tmp_path / "test_archive.json"
+    return MnemosyneArchive(archive_path=path)
+
+
+@pytest.fixture
+def populated_archive(tmp_path):
+    """Create an archive with some entries."""
+    path = tmp_path / "test_archive.json"
+    arch = MnemosyneArchive(archive_path=path)
+    arch.add(ArchiveEntry(title="Fresh Entry", content="Just added", topics=["test"]))
+    arch.add(ArchiveEntry(title="Old Entry", content="Been here a while", topics=["test"]))
+    arch.add(ArchiveEntry(title="Another Entry", content="Some content", topics=["other"]))
+    return arch
+
+
+class TestVitalityFields:
+    """Test that vitality fields exist on entries."""
+
+    def test_entry_has_vitality_default(self):
+        entry = ArchiveEntry(title="Test", content="Content")
+        assert entry.vitality == 1.0
+
+    def test_entry_has_last_accessed_default(self):
+        entry = ArchiveEntry(title="Test", content="Content")
+        assert entry.last_accessed is None
+
+    def test_entry_roundtrip_with_vitality(self):
+        entry = ArchiveEntry(
+            title="Test", content="Content",
+            vitality=0.75,
+            last_accessed="2024-01-01T00:00:00+00:00"
+        )
+        d = entry.to_dict()
+        assert d["vitality"] == 0.75
+        assert d["last_accessed"] == "2024-01-01T00:00:00+00:00"
+        restored = ArchiveEntry.from_dict(d)
+        assert restored.vitality == 0.75
+        assert restored.last_accessed == "2024-01-01T00:00:00+00:00"
+
+
+class TestTouch:
+    """Test touch() access recording and vitality boost."""
+
+    def test_touch_sets_last_accessed(self, archive):
+        entry = archive.add(ArchiveEntry(title="Test", content="Content"))
+        assert entry.last_accessed is None
+        touched = archive.touch(entry.id)
+        assert touched.last_accessed is not None
+
+    def test_touch_boosts_vitality(self, archive):
+        entry = archive.add(ArchiveEntry(title="Test", content="Content", vitality=0.5))
+        touched = archive.touch(entry.id)
+        # Boost = 0.1 * (1 - 0.5) = 0.05, so vitality should be ~0.55
+        # (assuming no time decay in test — instantaneous)
+        assert touched.vitality > 0.5
+        assert touched.vitality <= 1.0
+
+    def test_touch_diminishing_returns(self, archive):
+        entry = archive.add(ArchiveEntry(title="Test", content="Content", vitality=0.9))
+        touched = archive.touch(entry.id)
+        # Boost = 0.1 * (1 - 0.9) = 0.01, so vitality should be ~0.91
+        assert touched.vitality < 0.92
+        assert touched.vitality > 0.9
+
+    def test_touch_never_exceeds_one(self, archive):
+        entry = archive.add(ArchiveEntry(title="Test", content="Content", vitality=0.99))
+        for _ in range(10):
+            entry = archive.touch(entry.id)
+        assert entry.vitality <= 1.0
+
+    def test_touch_missing_entry_raises(self, archive):
+        with pytest.raises(KeyError):
+            archive.touch("nonexistent-id")
+
+    def test_touch_persists(self, archive):
+        entry = archive.add(ArchiveEntry(title="Test", content="Content"))
+        archive.touch(entry.id)
+        # Reload archive
+        arch2 = MnemosyneArchive(archive_path=archive._path)
+        loaded = arch2.get(entry.id)
+        assert loaded.last_accessed is not None
+
+
+class TestGetVitality:
+    """Test get_vitality() status reporting."""
+
+    def test_get_vitality_basic(self, archive):
+        entry = archive.add(ArchiveEntry(title="Test", content="Content"))
+        status = archive.get_vitality(entry.id)
+        assert status["entry_id"] == entry.id
+        assert status["title"] == "Test"
+        assert 0.0 <= status["vitality"] <= 1.0
+        assert status["age_days"] == 0
+
+    def test_get_vitality_missing_raises(self, archive):
+        with pytest.raises(KeyError):
+            archive.get_vitality("nonexistent-id")
+
+
+class TestComputeVitality:
+    """Test the decay computation."""
+
+    def test_new_entry_full_vitality(self, archive):
+        entry = archive.add(ArchiveEntry(title="Test", content="Content"))
+        v = archive._compute_vitality(entry)
+        assert v == 1.0
+
+    def test_recently_touched_high_vitality(self, archive):
+        entry = archive.add(ArchiveEntry(title="Test", content="Content"))
+        archive.touch(entry.id)
+        v = archive._compute_vitality(entry)
+        assert v > 0.99  # Should be essentially 1.0 since just touched
+
+    def test_old_entry_decays(self, archive):
+        entry = archive.add(ArchiveEntry(title="Test", content="Content"))
+        # Simulate old access — set last_accessed to 60 days ago
+        old_date = (datetime.now(timezone.utc) - timedelta(days=60)).isoformat()
+        entry.last_accessed = old_date
+        entry.vitality = 1.0
+        archive._save()
+        v = archive._compute_vitality(entry)
+        # 60 days with 30-day half-life: v = 1.0 * 0.5^(60/30) = 0.25
+        assert v < 0.3
+        assert v > 0.2
+
+    def test_very_old_entry_nearly_zero(self, archive):
+        entry = archive.add(ArchiveEntry(title="Test", content="Content"))
+        old_date = (datetime.now(timezone.utc) - timedelta(days=365)).isoformat()
+        entry.last_accessed = old_date
+        entry.vitality = 1.0
+        archive._save()
+        v = archive._compute_vitality(entry)
+        # 365 days / 30 half-life = ~12 half-lives -> ~0.0002
+        assert v < 0.01
+
+
+class TestFading:
+    """Test fading() — most neglected entries."""
+
+    def test_fading_returns_lowest_first(self, populated_archive):
+        entries = list(populated_archive._entries.values())
+        # Make one entry very old
+        old_entry = entries[1]
+        old_date = (datetime.now(timezone.utc) - timedelta(days=90)).isoformat()
+        old_entry.last_accessed = old_date
+        old_entry.vitality = 1.0
+        populated_archive._save()
+
+        fading = populated_archive.fading(limit=3)
+        assert len(fading) <= 3
+        # First result should be the oldest
+        assert fading[0]["entry_id"] == old_entry.id
+        # Should be in ascending order
+        for i in range(len(fading) - 1):
+            assert fading[i]["vitality"] <= fading[i + 1]["vitality"]
+
+    def test_fading_empty_archive(self, archive):
+        fading = archive.fading()
+        assert fading == []
+
+    def test_fading_limit(self, populated_archive):
+        fading = populated_archive.fading(limit=2)
+        assert len(fading) == 2
+
+
+class TestVibrant:
+    """Test vibrant() — most alive entries."""
+
+    def test_vibrant_returns_highest_first(self, populated_archive):
+        entries = list(populated_archive._entries.values())
+        # Make one entry very old
+        old_entry = entries[1]
+        old_date = (datetime.now(timezone.utc) - timedelta(days=90)).isoformat()
+        old_entry.last_accessed = old_date
+        old_entry.vitality = 1.0
+        populated_archive._save()
+
+        vibrant = populated_archive.vibrant(limit=3)
+        # Should be in descending order
+        for i in range(len(vibrant) - 1):
+            assert vibrant[i]["vitality"] >= vibrant[i + 1]["vitality"]
+        # First result should NOT be the old entry
+        assert vibrant[0]["entry_id"] != old_entry.id
+
+    def test_vibrant_empty_archive(self, archive):
+        vibrant = archive.vibrant()
+        assert vibrant == []
+
+
+class TestApplyDecay:
+    """Test apply_decay() bulk decay operation."""
+
+    def test_apply_decay_returns_stats(self, populated_archive):
+        result = populated_archive.apply_decay()
+        assert result["total_entries"] == 3
+        assert "decayed_count" in result
+        assert "avg_vitality" in result
+        assert "fading_count" in result
+        assert "vibrant_count" in result
+
+    def test_apply_decay_persists(self, populated_archive):
+        populated_archive.apply_decay()
+        # Reload
+        arch2 = MnemosyneArchive(archive_path=populated_archive._path)
+        result2 = arch2.apply_decay()
+        # Should show same entries
+        assert result2["total_entries"] == 3
+
+    def test_apply_decay_on_empty(self, archive):
+        result = archive.apply_decay()
+        assert result["total_entries"] == 0
+        assert result["avg_vitality"] == 0.0
+
+
+class TestStatsVitality:
+    """Test that stats() includes vitality summary."""
+
+    def test_stats_includes_vitality(self, populated_archive):
+        stats = populated_archive.stats()
+        assert "avg_vitality" in stats
+        assert "fading_count" in stats
+        assert "vibrant_count" in stats
+        assert 0.0 <= stats["avg_vitality"] <= 1.0
+
+    def test_stats_empty_archive(self, archive):
+        stats = archive.stats()
+        assert stats["avg_vitality"] == 0.0
+        assert stats["fading_count"] == 0
+        assert stats["vibrant_count"] == 0
+
+
+class TestDecayLifecycle:
+    """Integration test: full lifecycle from creation to fading."""
+
+    def test_entry_lifecycle(self, archive):
+        # Create
+        entry = archive.add(ArchiveEntry(title="Memory", content="A thing happened"))
+        assert entry.vitality == 1.0
+
+        # Touch a few times
+        for _ in range(5):
+            archive.touch(entry.id)
+
+        # Check it's vibrant
+        vibrant = archive.vibrant(limit=1)
+        assert len(vibrant) == 1
+        assert vibrant[0]["entry_id"] == entry.id
+
+        # Simulate time passing
+        entry.last_accessed = (datetime.now(timezone.utc) - timedelta(days=45)).isoformat()
+        entry.vitality = 0.8
+        archive._save()
+
+        # Apply decay
+        result = archive.apply_decay()
+        assert result["total_entries"] == 1
+
+        # Check it's now fading
+        fading = archive.fading(limit=1)
+        assert fading[0]["entry_id"] == entry.id
+        assert fading[0]["vitality"] < 0.5

nexus/mnemosyne/tests/test_path.py

@@ -0,0 +1,106 @@
+"""Tests for MnemosyneArchive.shortest_path and path_explanation."""
+
+from nexus.mnemosyne.archive import MnemosyneArchive
+from nexus.mnemosyne.entry import ArchiveEntry
+
+
+def _make_archive(tmp_path):
+    archive = MnemosyneArchive(str(tmp_path / "test_archive.json"))
+    return archive
+
+
+class TestShortestPath:
+    def test_direct_connection(self, tmp_path):
+        archive = _make_archive(tmp_path)
+        a = archive.add("Alpha", "first entry", topics=["start"])
+        b = archive.add("Beta", "second entry", topics=["end"])
+        # Manually link
+        a.links.append(b.id)
+        b.links.append(a.id)
+        archive._entries[a.id] = a
+        archive._entries[b.id] = b
+        archive._save()
+
+        path = archive.shortest_path(a.id, b.id)
+        assert path == [a.id, b.id]
+
+    def test_multi_hop_path(self, tmp_path):
+        archive = _make_archive(tmp_path)
+        a = archive.add("A", "alpha", topics=["x"])
+        b = archive.add("B", "beta", topics=["y"])
+        c = archive.add("C", "gamma", topics=["z"])
+        # Chain: A -> B -> C
+        a.links.append(b.id)
+        b.links.extend([a.id, c.id])
+        c.links.append(b.id)
+        archive._entries[a.id] = a
+        archive._entries[b.id] = b
+        archive._entries[c.id] = c
+        archive._save()
+
+        path = archive.shortest_path(a.id, c.id)
+        assert path == [a.id, b.id, c.id]
+
+    def test_no_path(self, tmp_path):
+        archive = _make_archive(tmp_path)
+        a = archive.add("A", "isolated", topics=[])
+        b = archive.add("B", "also isolated", topics=[])
+        path = archive.shortest_path(a.id, b.id)
+        assert path is None
+
+    def test_same_entry(self, tmp_path):
+        archive = _make_archive(tmp_path)
+        a = archive.add("A", "lonely", topics=[])
+        path = archive.shortest_path(a.id, a.id)
+        assert path == [a.id]
+
+    def test_nonexistent_entry(self, tmp_path):
+        archive = _make_archive(tmp_path)
+        a = archive.add("A", "exists", topics=[])
+        path = archive.shortest_path("fake-id", a.id)
+        assert path is None
+
+    def test_shortest_of_multiple(self, tmp_path):
+        """When multiple paths exist, BFS returns shortest."""
+        archive = _make_archive(tmp_path)
+        a = archive.add("A", "a", topics=[])
+        b = archive.add("B", "b", topics=[])
+        c = archive.add("C", "c", topics=[])
+        d = archive.add("D", "d", topics=[])
+        # A -> B -> D (short)
+        # A -> C -> B -> D (long)
+        a.links.extend([b.id, c.id])
+        b.links.extend([a.id, d.id, c.id])
+        c.links.extend([a.id, b.id])
+        d.links.append(b.id)
+        for e in [a, b, c, d]:
+            archive._entries[e.id] = e
+        archive._save()
+
+        path = archive.shortest_path(a.id, d.id)
+        assert len(path) == 3  # A -> B -> D, not A -> C -> B -> D
+
+
+class TestPathExplanation:
+    def test_returns_step_details(self, tmp_path):
+        archive = _make_archive(tmp_path)
+        a = archive.add("Alpha", "the beginning", topics=["origin"])
+        b = archive.add("Beta", "the middle", topics=["process"])
+        a.links.append(b.id)
+        b.links.append(a.id)
+        archive._entries[a.id] = a
+        archive._entries[b.id] = b
+        archive._save()
+
+        path = [a.id, b.id]
+        steps = archive.path_explanation(path)
+        assert len(steps) == 2
+        assert steps[0]["title"] == "Alpha"
+        assert steps[1]["title"] == "Beta"
+        assert "origin" in steps[0]["topics"]
+
+    def test_content_preview_truncation(self, tmp_path):
+        archive = _make_archive(tmp_path)
+        a = archive.add("A", "x" * 200, topics=[])
+        steps = archive.path_explanation([a.id])
+        assert len(steps[0]["content_preview"]) <= 123  # 120 + "..."