documentation update

docs/reference/API-routes.md

@@ -205,6 +205,9 @@ Returns `503` if llama-server is unreachable.
 | `scoreThreshold` | float | 0–1 | Minimum similarity score for Qdrant results |
 | `semanticWeight` | float | 0–5 | RRF weight for Qdrant semantic results |
 | `keywordWeight` | float | 0–5 | RRF weight for FTS5 keyword results (`0` = disabled) |
+| `contextBudget` | integer | — | Token budget for context assembly (char/4 estimation) |
+| `entityWeight` | float | — | Scoring bonus for entity-linked episodes in the context pool |
+| `minRecentEpisodes` | integer | — | Guaranteed floor of recent episodes always included in context |
 | `modelsFolderPath` | string | — | Path to folder containing .gguf files |
 | `temperature` | float | 0–2 | Inference randomness |
 | `repeatPenalty` | float | 1–2 | Repeat token penalty |
@@ -253,6 +256,7 @@ orchestration.
 | GET | /sessions/by-external/:externalId | Get session by external ID |
 | PATCH | /sessions/by-external/:externalId | Update session fields |
 | DELETE | /sessions/by-external/:externalId | Delete session (cascades to episodes) |
+| GET | /sessions/:id/entity-ids | Entity IDs linked to this session's episodes (non-project memory isolation scoping) |
 
 > Route ordering: `by-external/:externalId` must be defined before `/:id`
 > to prevent `by-external` being captured as an ID param.
@@ -277,6 +281,10 @@ Both fields are optional. Only provided fields are updated.
 | GET | /episodes/search?q=&limit= | FTS keyword search across all episodes |
 | GET | /episodes/:id | Get episode by ID |
 | GET | /sessions/:id/episodes?limit=&offset= | Paginated episodes for a session |
+| GET | /sessions/:id/episode-stats | Aggregate: count, total tokens, max id (summarization threshold check) |
+| GET | /sessions/:id/episodes/since/:afterId | Episodes newer than :afterId, chronological (un-summarized tail) |
+| POST | /episodes/touch | Batch access-tracking bump — increments `access_count`, sets `last_accessed_at` |
+| GET | /sessions/:id/consolidation-candidates | Dry-run consolidation scoring (aging score, floors applied) |
 | DELETE | /episodes/:id | Delete episode (SQLite + Qdrant cleanup) |
 
 > Route ordering: `/episodes/search` must be defined before `/episodes/:id`.
@@ -291,6 +299,21 @@ Both fields are optional. Only provided fields are updated.
 }
 ```
 
+**POST /episodes/touch — body:**
+```json
+{ "ids": [54, 55, 56] }
+```
+Returns `{ "touched": 3 }`. Called fire-and-forget by orchestration after
+budget selection. Nonexistent IDs are silently skipped; `touched` echoes the
+request count, not rows matched.
+
+**GET /sessions/:id/consolidation-candidates** — returns
+`{ eligible, reason?, candidates: [...] }` where each candidate is
+`{ id, access_count, created_at, last_accessed_at, preview, aging_score }`,
+sorted by `aging_score` ascending (most eligible first). Sessions under
+`CONSOLIDATION.MIN_SESSION_EPISODES` return `eligible: false` with a `reason`.
+Observe-only — nothing is modified.
+
 ### Projects
 
 | Method | Path | Description |

docs/reference/testing.md

@@ -0,0 +1,54 @@
+# Testing
+
+NexusAI has a lightweight regression suite built on Node's **built-in** test
+runner (`node:test`) and assertions (`node:assert`) — no external test
+dependencies. It runs offline on any node in the homelab.
+
+```bash
+npm test          # = node --test  (discovers test/*.test.js at the repo root)
+```
+
+## What's covered
+
+| File | Subject | Imports the real… |
+|---|---|---|
+| `test/fusion.test.js` | RRF ranking math | `fuseEpisodeResults` (orchestration) |
+| `test/fts-query.test.js` | Keyword tokenizer + FTS5 matching | `buildFtsQuery` (memory-service) |
+| `test/llamacpp-stream.test.js` | SSE stream reassembly across chunks | `completeStream` (inference) |
+| `test/summarization.test.js` | Summarization decision logic | `maybeSummarize` (orchestration) |
+| `test/entity-extraction.test.js` | Greeting + regurgitation guards | `mentionedIn`, `isIgnoredName` (memory-service) |
+| `test/schema.test.js` | Fresh-DB schema completeness | `schema.js` string (memory-service) |
+| `test/trivial-turn.test.js` | Greeting/trivial-turn detection | `isTrivialTurn` (shared) |
+| `test/migrations.test.js` | Migration version-stepping | `migrate` (memory-service) |
+
+Tests import the **real** functions rather than reimplementing logic — the
+functions are exported for this purpose. External calls (Ollama, Qdrant, the
+memory service) are mocked via `global.fetch`; SQLite-backed tests use the
+built-in `node:sqlite` module against a throwaway in-memory database.
+
+## `node:sqlite` and skips
+
+`test/schema.test.js` and three cases in `test/fts-query.test.js` need
+`node:sqlite`, which requires **Node ≥ 22.5**. On older Node they `skip`
+themselves cleanly (guarded by `{ skip: !DatabaseSync }`) rather than failing,
+so the suite stays green everywhere — but those checks only *verify* anything on
+a node new enough to run them. A dev machine on current Node is the source of
+truth for the schema and FTS matching tests.
+
+`node:sqlite` is still marked experimental, so runs print a one-line
+`ExperimentalWarning`. It's harmless; `node --test --no-warnings` suppresses it.
+
+## Adding tests
+
+Keep the pattern: export the real function, import it, mock I/O at the boundary
+(`global.fetch`) or use `node:sqlite` for DB behaviour. Prefer testing pure
+logic (ranking, tokenizing, version-stepping, decision branches) over wiring.
+Several of these tests were written *after* a bug slipped through — each new
+class of mistake is worth a case so it can't recur silently.
+
+When a mocked service call changes shape (e.g. the `utilityInference` refactor
+moving summarization from Ollama's `/api/generate` to the inference service's
+`/utility/complete`), the mock URL router must move with it — an "unexpected
+fetch" throw in these tests usually means the code under test evolved, not
+broke. Runner-contract tests (migrations) inject stub no-op migration arrays
+rather than letting the real SQL-bearing migrations hit the minimal fake db.

docs/roadmap.md

@@ -68,12 +68,15 @@ The highest-leverage memory upgrade. Transforms NexusAI from "remembers conversa
 Multi-strategy retrieval merged into a single ranked result set.
 - [x] Reciprocal Rank Fusion (RRF) — merge semantic (Qdrant) + keyword (FTS5) results
 - [x] Configurable weights per retrieval strategy (`semanticWeight`, `keywordWeight` via `PATCH /settings`)
-- [x] Score threshold retained per-strategy; FTS scoped to session/project sessions; `keywordWeight: 0` default (disabled until tuned)
+- [x] Score threshold retained per-strategy; FTS scoped to session/project sessions
+- [x] FTS query tokenization (`buildFtsQuery`) — tokenize + stopword-filter + quoted `OR` terms; replaced whole-phrase matching that made keyword recall nil
+- [x] Fusion tuned and verified live (keyword path + project scoping confirmed via weight inversion); running `keywordWeight: 0.5` / `semanticWeight: 1.0`
 
 ### 3. Memory Consolidation Lifecycle
 Prevents long-term memory degradation and enables compression.
-- [ ] Episode aging — score/weight episodes by recency and access frequency
-- [ ] Consolidation pass — merge related low-weight episodes into summary nodes
+- [x] Episode aging — `access_count` + `last_accessed_at` columns (v2 migration), batch touch on retrieval selection (`POST /episodes/touch`), aging score `access_count / (1 + days since last access)`
+- [x] Dry-run candidates endpoint — `GET /sessions/:id/consolidation-candidates` with age + session-size floors (observe-only phase before destructive pass)
+- [ ] Consolidation pass — merge related low-weight episodes into summary nodes (incl. Qdrant vector + entity link cleanup)
 - [ ] Orphan cleanup — remove entities no longer referenced by active episodes
 
 ### 4. User Preference Model
@@ -88,11 +91,11 @@ Short-circuit simple requests before they reach the LLM.
 - [ ] Confidence bands — FAST PATH (memory lookup only) vs FULL (LLM + context)
 - [ ] Fast-path handlers — direct memory queries, session lookups, factual recalls
 
-### 6. Smarter Context Assembly *(inspired by acid2lake)*
+### 6. Smarter Context Assembly *(inspired by acid2lake)* ✅
 Budget-aware context selection instead of dumping all relevant memory into the prompt.
-- [ ] Token budget manager in orchestration
-- [ ] Priority scoring — recency × relevance × entity weight
-- [ ] Configurable context budget via env var
+- [x] Token budget manager in orchestration (`selectWithinBudget`, char/4 estimation on stored text)
+- [x] Priority scoring — RRF fusion + recency + entity boost (`buildScoredPool`)
+- [x] Configurable via settings (`contextBudget`, `entityWeight`, `minRecentEpisodes`) — live, no restart
 
 ### 7. Procedural Memory Store *(inspired by acid2lake)*
 Learns "how NexusAI has successfully handled this type of request before."
@@ -225,4 +228,4 @@ The JARVIS moment — NexusAI reasons, plans, and acts across multiple steps.
 
 ---
 
-*Last updated: April 2026*
+*Last updated: August 2026*

docs/services/entity-extraction.md

@@ -2,7 +2,7 @@
 
 **Location:** `packages/memory-service/src/entities/extraction.js`  
 **Triggered by:** Episode creation (`POST /episodes`)  
-**Model:** `qwen2.5:3b` via Ollama (configurable via `EXTRACTION_MODEL` env var)
+**Model:** the utility model served by the inference service (`/utility/complete`), configurable via `UTILITY_MODEL` on the inference service
 
 ## Purpose
 
@@ -28,7 +28,7 @@ swallowed.
 
 | Setting | Value | Notes |
 |---|---|---|
-| Model | `qwen2.5:3b` | Ollama, configurable via `EXTRACTION_MODEL` |
+| Model | utility model | Served by inference-service `/utility/complete`, set via `UTILITY_MODEL` |
 | Temperature | 0.1 | Low for consistent, deterministic output |
 | `num_predict` | 1500 | Higher ceiling to accommodate entity + relationship JSON |
 | `format` | `'json'` | Ollama constrained decoding — enforces valid JSON output |
@@ -131,6 +131,20 @@ check alone won't reject.
 > imperfectly — switch to `/[^\p{L}\p{N}\s]/gu` if the set gains non-ASCII
 > entries.
 
+**Regurgitation guard (`mentionedIn`):** the extraction prompt feeds the model
+a "known entities" hint block (the 20 most-recent entities) for spelling/type
+consistency. The small model (qwen2.5:3b) will sometimes echo that list back as
+if those entities appeared in the conversation — most visibly on contentless
+turns (a greeting produced fake extractions of unrelated authors, game titles,
+etc.). After parsing, each extracted name is checked against the actual
+`userMessage + aiResponse` text (case- and whitespace-normalized substring); any
+name not present is dropped before upsert. Since the prompt constrains names to
+short proper nouns, a genuinely-discussed entity appears verbatim while a
+regurgitated hint does not. Relationships referencing a dropped entity fall away
+automatically (they resolve against the surviving `entityMap`). A prompt line
+also tells the model the hint list is spelling-only — a backstop, with
+`mentionedIn` as the deterministic guarantee.
+
 ## Relationship Processing
 
 After all entities are saved, relationships are processed:

docs/services/memory-service.md

@@ -28,16 +28,16 @@ relationship extraction and embeds results into Qdrant.
 | SQLITE_PATH | Yes | — | Path to SQLite database file |
 | QDRANT_URL | No | http://localhost:6333 | Qdrant instance URL |
 | EMBEDDING_SERVICE_URL | No | http://localhost:3003 | Embedding service URL |
-| EXTRACTION_URL | No | http://localhost:11434 | Ollama URL for entity extraction |
-| EXTRACTION_MODEL | No | qwen2.5:3b | Ollama model used for entity extraction |
+| INFERENCE_SERVICE_URL | No | http://localhost:3001 | Inference service URL — entity extraction routes through its `/utility/complete` endpoint |
 
 ## Internal Structure
 
 ```
 src/
 ├── db/
-│   ├── index.js       # SQLite connection + initialization + migrations
-│   ├── schema.js      # Table definitions, indexes, FTS5, triggers
+│   ├── index.js       # SQLite connection + init + migrate() + one-time FTS backfill
+│   ├── migrations.js  # Forward-only versioned migration runner (PRAGMA user_version)
+│   ├── schema.js      # Complete current shape: tables, indexes, FTS5, triggers
 │   ├── projects.js    # Project CRUD functions
 │   └── summaries.js   # Summary CRUD functions
 ├── episodic/
@@ -64,36 +64,61 @@ Eight core tables:
 - **summaries** — condensed episode groups for efficient context retrieval
 - **projects** — named groupings of sessions with `name`, `description`, `colour`, `icon`, `isolated`, `notes`, `system_prompt`
 
-### Migrations
+### Schema & Migrations
 
-Schema changes that cannot use `CREATE TABLE IF NOT EXISTS` are applied as
-idempotent migrations in `db/index.js` at startup:
+`schema.js` holds the **complete current shape** — every table, column, index,
+the FTS5 virtual table, and its triggers — as the single source of truth for a
+fresh database. It uses `CREATE TABLE IF NOT EXISTS`, so on a fresh DB it builds
+everything; on an existing DB it skips tables that already exist (and therefore
+does **not** reconcile columns on old tables — that's what migrations are for).
+
+`db/migrations.js` is a forward-only versioned runner keyed on
+`PRAGMA user_version`:
 
 ```js
-try { db.exec(`ALTER TABLE sessions ADD COLUMN name TEXT`); } catch {}
-try { db.exec(`ALTER TABLE sessions ADD COLUMN project_id INTEGER REFERENCES projects(id)`); } catch {}
-try { db.exec(`CREATE INDEX IF NOT EXISTS idx_sessions_project ON sessions(project_id)`); } catch {}
-try { db.exec(`ALTER TABLE projects ADD COLUMN isolated INTEGER NOT NULL DEFAULT 0`); } catch {}
-try { db.exec(`ALTER TABLE projects ADD COLUMN notes TEXT`); } catch {}
-try { db.exec(`ALTER TABLE projects ADD COLUMN system_prompt TEXT`); } catch {}
-// Knowledge graph columns:
-try { db.exec(`ALTER TABLE entities ADD COLUMN mention_count INTEGER NOT NULL DEFAULT 1`) } catch {}
-try { db.exec(`ALTER TABLE entities ADD COLUMN confidence REAL NOT NULL DEFAULT 1.0`) } catch {}
-try { db.exec(`ALTER TABLE entities ADD COLUMN source TEXT NOT NULL DEFAULT 'extraction'`) } catch {}
-try { db.exec(`ALTER TABLE entities ADD COLUMN last_seen_at INTEGER`) } catch {}
-try { db.exec(`ALTER TABLE relationships ADD COLUMN mention_count INTEGER NOT NULL DEFAULT 1`) } catch {}
-try { db.exec(`ALTER TABLE relationships ADD COLUMN notes TEXT`) } catch {}
+const migrations = [
+  (_db) => {},   // v0 → v1: consolidated baseline (historical ALTERs folded into schema.js)
+  (db) => {      // v1 → v2: access tracking for consolidation lifecycle
+    db.exec(`ALTER TABLE episodes ADD COLUMN last_accessed_at INTEGER`);
+    db.exec(`ALTER TABLE episodes ADD COLUMN access_count INTEGER NOT NULL DEFAULT 0`);
+    db.exec(`UPDATE episodes SET last_accessed_at = created_at`);  // backfill
+  },
+];
+const LATEST_VERSION = migrations.length;  // derived, never hand-maintained
 ```
 
-`entity_episodes` is defined in `schema.js` itself (not a migration) since it is a new table.
+`migrate(db)` reads `user_version`, applies every entry newer than it (each in a
+transaction alongside its version bump), and stamps the result. A fresh DB is
+built whole by `schema.js` and simply stamped to `LATEST_VERSION`; the baseline
+entry is a no-op.
 
-New migrations are always appended — never modify the schema file for existing tables since `ALTER TABLE` cannot use `IF NOT EXISTS`.
+**Adding a schema change:** append a new function to the `migrations` array
+(which bumps `LATEST_VERSION` automatically). Never edit an already-shipped
+entry, and never edit a table in `schema.js` expecting existing DBs to pick it
+up — they won't. This replaces the previous pattern of stacking silent
+`try/catch ALTER TABLE` statements in `db/index.js` on every boot.
+
+> **Consolidation note:** the historical ALTERs were folded into `schema.js`
+> rather than preserved as replayable migrations, so this assumes a fresh
+> database (which is the case post-wipe). An older, pre-consolidation database
+> would **not** auto-upgrade — `schema.js` skips its existing tables and the
+> baseline migration is a no-op. To support upgrading old DBs, the v1 baseline
+> would instead perform guarded (`ADD COLUMN if missing`) catch-up.
 
 ### FTS5 Full-Text Search
 
-An `episodes_fts` virtual table enables keyword search across all episodes.
-Three triggers (`episodes_fts_insert`, `episodes_fts_update`, `episodes_fts_delete`)
-keep the FTS index automatically in sync with the episodes table.
+An `episodes_fts` external-content virtual table enables keyword search across
+episodes. Three triggers (`episodes_fts_insert`, `episodes_fts_update`,
+`episodes_fts_delete`) keep the index in sync with the `episodes` table
+automatically during normal operation.
+
+A one-time backfill in `db/index.js` handles the case where the FTS table is
+created on a DB that already holds episodes (e.g. episodes predating FTS). It is
+gated on "did `episodes_fts` not exist before this boot," checked via
+`sqlite_master` **before** running the schema — not on a row-count comparison,
+because `COUNT(*)` on an external-content FTS5 table proxies the content table
+and cannot detect a desync. This replaced an unconditional full FTS rebuild that
+previously ran on every startup.
 
 ### SQLite Configuration
 
@@ -101,6 +126,12 @@ keep the FTS index automatically in sync with the episodes table.
 - `foreign_keys = ON` — enforces referential integrity and cascade deletes
 - PRAGMAs set via `db.pragma()`, not `db.exec()`
 
+> **Copying a live WAL database:** `cp` on the `.db` file alone silently loses
+> everything in the un-checkpointed `-wal` file (recent writes, even the
+> migration version stamp). Always use
+> `sqlite3 nexusai.db "VACUUM INTO './copy.db'"` (or `.backup`) — safe while
+> the service is running, produces a complete single-file snapshot.
+
 ### Dynamic Updates
 
 Both `updateSession` and `updateProject` build their `SET` clause dynamically
@@ -184,6 +215,28 @@ service is responsible only for CRUD — generation logic lives in orchestration
 > For full details on trigger conditions, prompt format, cumulative updates,
 > and ChatML token stripping, see `summarization.md`.
 
+## Access Tracking & Consolidation (dry-run)
+
+Every episode selected into a chat context window (budget-selected, not the
+guaranteed-recency floor) gets an access bump via `POST /episodes/touch` —
+`access_count` incremented, `last_accessed_at` set to `Date.now()` (ms).
+Called fire-and-forget from orchestration; a failure loses one increment,
+nothing more.
+
+`GET /sessions/:id/consolidation-candidates` scores episodes by
+`access_count / (1 + days since last access)` — never-accessed episodes fall
+back to `created_at` for the recency term and score exactly 0 (most eligible).
+Two floors apply: episodes younger than `CONSOLIDATION.MIN_AGE_DAYS` are
+excluded in SQL; sessions under `CONSOLIDATION.MIN_SESSION_EPISODES` return
+`eligible: false` before scoring runs. The endpoint is observe-only — the
+destructive pass (merge → summarize → Qdrant cleanup → orphan sweep) is not
+yet built.
+
+> **Unit note:** `created_at` is unix **seconds** (`unixepoch()`);
+> `last_accessed_at` is unix **milliseconds** (`Date.now()`). The scoring
+> query normalizes with `created_at * 1000`. Keep this in mind for any new
+> queries touching both columns.
+
 ## Delete Behaviour (SQLite + Qdrant consistency)
 
 SQLite cascades handle relational cleanup, but Qdrant is a separate store and

docs/services/orchestration-service.md

@@ -30,8 +30,6 @@ or inference services — all traffic flows through orchestration.
 | LLAMA_SERVER_URL | No | http://localhost:8080 | Direct llama-server URL for /models/props |
 | QDRANT_URL | No | http://localhost:6333 | Qdrant URL for semantic search |
 | CORS_ORIGIN | No | http://localhost:5173 | Allowed origin for CORS requests |
-| EXTRACTION_URL | No | http://localhost:11434 | Ollama URL for summarisation |
-| EXTRACTION_MODEL | No | qwen2.5:3b | Ollama model used for summarisation |
 
 ## Internal Structure
 
@@ -75,6 +73,9 @@ via `appSettings.load()` — changes apply immediately without a service restart
 | `scoreThreshold` | 0.5 | Minimum similarity score for Qdrant semantic results |
 | `semanticWeight` | 1.0 | RRF weight for Qdrant semantic results |
 | `keywordWeight` | 0 | RRF weight for FTS5 keyword results (`0` = disabled) |
+| `contextBudget` | — | Token budget for context assembly (char/4 estimation on stored text) |
+| `entityWeight` | — | Scoring bonus for entity-linked episodes in the context pool |
+| `minRecentEpisodes` | — | Guaranteed floor of recent episodes always included in context |
 | `modelsFolderPath` | `/mnt/nexus-models` | Path to folder containing .gguf files |
 | `temperature` | 0.7 | Inference temperature |
 | `repeatPenalty` | 1.1 | Repeat token penalty |
@@ -103,35 +104,43 @@ difference is how the inference response is delivered to the client.
 
 4. **Recent episode retrieval** — fetch most recent episodes (`recentEpisodeLimit`).
 
-5. **Fused episode retrieval** — runs semantic (Qdrant) and keyword (FTS5)
+5. **Trivial-turn gate** — greetings/pleasantries (`isTrivialTurn`) skip all
+   retrieval (semantic, keyword, entity); recent history alone is the context.
+   Breaks the greeting → marginal-retrieval → confabulation loop.
+
+6. **Fused episode retrieval** — runs semantic (Qdrant) and keyword (FTS5)
    search in parallel, then merges results via Reciprocal Rank Fusion (RRF).
-   Both paths are filtered against `recentIds` before fusion. FTS is scoped
-   to the current session or all project sessions. If `keywordWeight` is `0`,
-   the FTS call is skipped entirely. Non-critical — failures fall back to
-   whichever strategy succeeded.
+   The query is embedded once and shared with entity search. Both paths are
+   filtered against `recentIds` before fusion. FTS is scoped to the current
+   session or all project sessions. If `keywordWeight` is `0`, the FTS call
+   is skipped entirely. Non-critical — failures fall back to whichever
+   strategy succeeded.
 
-6. **Entity search** — query `entities` Qdrant collection filtered by
-   `projectId`. Returns entity IDs alongside Qdrant payload data (the Qdrant
-   point ID equals the SQLite entity ID). Non-critical.
+7. **Entity search + graph expansion** — query `entities` Qdrant collection
+   (project-scoped, or session-scoped via `/sessions/:id/entity-ids` for
+   non-project chats). Entity IDs are expanded into a 1-hop subgraph via
+   `POST /graph/neighbors`; on failure, falls back to flat entity list.
+   Non-critical.
 
-7. **Graph neighborhood expansion** — call `POST /graph/neighbors` on
-   memory-service with the entity IDs from step 6. Returns a 1-hop subgraph
-   `{ nodes, edges }` — entity objects plus the relationships connecting them.
-   If no entities were found or the graph call fails, falls back to flat entity
-   list (no edges). Non-critical.
+8. **Scored pool + budget selection** — `buildScoredPool` combines RRF scores,
+   recency, and entity-linkage bonus; `selectWithinBudget` fills `contextBudget`
+   (char/4 token estimation on stored text) above a guaranteed floor of
+   `minRecentEpisodes` recent episodes. Selected episode IDs are then reported
+   to `POST /episodes/touch` fire-and-forget (access tracking for the
+   consolidation lifecycle).
 
-8. **Prompt assembly** — combine system prompt, graph context, fused episodes,
-   recent episodes, and user message.
+9. **Prompt assembly** — combine system prompt, graph context, selected
+   episodes, guaranteed recent episodes, and user message.
 
-9. **Inference** — send to inference service. `/chat` awaits full response;
-   `/chat/stream` pipes SSE chunks to the client.
+10. **Inference** — send to inference service. `/chat` awaits full response;
+    `/chat/stream` pipes SSE chunks to the client.
 
-10. **Episode write** — write exchange back to memory with `projectId`.
+11. **Episode write** — write exchange back to memory with `projectId`.
 
-11. **Summarisation trigger** — `triggerSummary(session, allEpisodes)` called
+12. **Summarisation trigger** — `triggerSummary(session)` called
     fire-and-forget. See `summarization.md` for full details.
 
-12. **Auto-naming** — on first message with no session name, fires a secondary
+13. **Auto-naming** — on first message with no session name, fires a secondary
     inference call (max 20 tokens, temperature 0.3) to generate a session name.
 
 ### Prompt Structure

docs/services/retrieval-fusion.md

@@ -37,9 +37,10 @@ Fusion lives in orchestration — the service already coordinates multiple data
 sources, and fusion is a retrieval strategy, not a storage concern.
 
 ```
-getFusedEpisodes()
-├── getSemanticEpisodes()     — Qdrant embed+search → fetch full rows by ID
-│   (existing path, unchanged)
+getFusedEpisodes(…, queryVector, …)
+├── getSemanticEpisodes(queryVector)  — Qdrant search → fetch full rows by ID
+│   (query embedded ONCE upstream in assembleContext and shared with entity
+│    search — no longer embedded separately here)
 └── getFTSResults()           — memory-service /episodes/search → full rows directly
     (skipped entirely if keywordWeight == 0)
          ↓
@@ -48,6 +49,10 @@ fuseEpisodeResults()          — pure RRF, no I/O
 fusedEpisodes[]               — top semanticLimit episodes by RRF score
 ```
 
+The query embedding is computed once per turn in `assembleContext` and passed
+into both fused retrieval and entity search; if embedding fails, both receive
+`null` and degrade to empty results rather than erroring.
+
 ### Data Shape Consistency
 
 Both sides must enter fusion as `Episode[]` — full SQLite row objects with
@@ -59,6 +64,38 @@ the same shape — and both must be filtered against `recentIds` first:
 FTS requests `semanticLimit * 2` results to provide headroom for the
 `recentIds` filter without under-serving the fusion.
 
+## Query Tokenization
+
+Before FTS5 sees the query, `buildFtsQuery(query)` (in
+`memory-service/src/episodic/index.js`) turns the raw message into a MATCH
+expression:
+
+1. Lowercase and split on any non-letter/number (`/[^\p{L}\p{N}]+/u`, unicode-aware)
+2. Drop stopwords (a small `FTS_STOPWORDS` set of common function words) and single-character tokens
+3. Wrap each surviving token in double quotes and join with ` OR `
+
+So `"How do I configure the Qdrant collection?"` becomes
+`"configure" OR "qdrant" OR "collection"`. If nothing survives (an
+all-stopword message like `"how do I do it?"`), it returns `null` and
+`searchEpisodes` returns `[]` — keyword search sits out that turn and
+semantic retrieval carries it.
+
+**Why this matters:** the earlier implementation quoted the *entire* message
+as one FTS5 phrase, which required the whole string to appear verbatim in an
+episode — so keyword recall was effectively nil for conversational queries.
+Tokenizing into OR-joined terms is what makes `keywordWeight > 0` actually
+contribute anything.
+
+**Injection safety:** quoting each token individually means any
+FTS5-significant token inside the user's message (a literal `OR`, `*`, `"`,
+etc.) is matched as a search term rather than parsed as an operator. This
+replaces the safety the old whole-phrase quoting provided.
+
+The stopword set is deliberately conservative and tuned iteratively — high
+frequency filler (`the`, `is`, `one`, `there`, …) is dropped, but borderline
+words that can carry signal (`time`, `good`, `way`) are kept. Add to the set
+when a common word is observed producing noisy matches.
+
 ## FTS Session Scoping
 
 Without scoping, FTS5 searches across all episodes in the database. For

docs/services/shared.md

@@ -203,6 +203,16 @@ SUMMARY_MAX_TOKENS=800
 SUMMARY_MIN_EPISODES=5
 ```
 
+#### `CONSOLIDATION`
+
+Controls the memory consolidation lifecycle (currently dry-run only).
+
+| Key | Value | Description |
+|---|---|---|
+| `MIN_AGE_DAYS` | `7` | Episodes younger than this are never consolidation candidates |
+| `MIN_SESSION_EPISODES` | `20` | Sessions with fewer episodes are skipped entirely |
+| `CANDIDATE_LIMIT` | `50` | Max candidates returned per scoring query |
+
 #### `SQLITE`
 
 | Key | Value | Description |

docs/services/summarization.md

@@ -6,13 +6,27 @@ the full context window with raw episodes.
 
 **Location:** `packages/orchestration-service/src/services/summarization.js`  
 **Triggered by:** `chat/index.js` after every episode write (fire-and-forget)  
-**Model:** `qwen2.5:3b` via Ollama on Mini PC 1 (192.168.0.81)
+**Model:** the utility model served by the inference service (`/utility/complete`), set via `UTILITY_MODEL` (backed by Ollama on Mini PC 1, 192.168.0.81)
 
 ---
 
 ## Trigger Conditions
 
-`triggerSummary(session, allEpisodes)` calls `maybeSummarize` fire-and-forget.
+`triggerSummary(session)` calls `maybeSummarize` fire-and-forget. It takes only
+the `session` — it no longer receives the full episode list. Instead
+`maybeSummarize` fetches exactly what it needs:
+
+1. `GET /sessions/:id/episode-stats` — a cheap aggregate (`COUNT`, `SUM(token_count)`,
+   `MAX(id)`) that gates the token threshold **without** pulling every episode row
+2. Only if over threshold: `GET /sessions/:id/episodes/since/:afterId` — the
+   un-summarized tail (episodes newer than the last summary's range), fetched in
+   full for the summary prompt
+
+This replaced an earlier approach where `chat/index.js` fetched the entire
+session (`getRecentEpisodes(session.id, 9999)`) on every message just to hand it
+over — an O(session length) cost per turn. The stats query now runs on every
+message; full episode text is fetched only when a summary actually fires.
+
 `maybeSummarize` proceeds only when both conditions are met:
 
 1. Total session token count exceeds `SUMMARIES.THRESHOLD_TOKENS` (default 200)
@@ -42,47 +56,50 @@ not all episodes in the session.
 
 ---
 
-## Ollama Request
+## Utility Inference Request
+
+Summaries are generated through the shared `utilityInference()` helper, which
+POSTs to the inference service's `/utility/complete` endpoint. `buildSummaryPrompt`
+returns a plain instruction string (no template tags) passed as the `user` message:
 
 ```js
-{
-    model: EXTRACTION_MODEL,   // qwen2.5:3b (set via EXTRACTION_MODEL env var)
-    prompt: buildSummaryPrompt(episodesToSummarize, existingSummary),
-    stream: false,
-    // No format: 'json' — free-text output required for summaries
-    options: {
-        temperature: 0.2,
-        num_predict: 500,
-    },
-}
+const content = await utilityInference({
+    user: buildSummaryPrompt(episodesToSummarize, existingSummary),
+    temperature: SUMMARIES.TEMPERATURE,            // 0.2
+    maxTokens:   SUMMARIES.SESSION_GEN_MAX_TOKENS,  // 500
+});
 ```
 
-`temperature: 0.2` is slightly higher than extraction (0.1) — summaries
-benefit from some fluency. `num_predict: 500` gives room for 5 thorough
-sentences without risk of runoff.
+`TEMPERATURE` (0.2) is slightly higher than extraction (0.1) — summaries benefit
+from some fluency. `SESSION_GEN_MAX_TOKENS` (500) gives room for ~5 thorough
+sentences without runoff. Both live in `@nexusai/shared` `SUMMARIES` constants.
+
+There is no `json: true` here — summaries are free-text, unlike entity extraction.
 
 ---
 
 ## Prompt Format
 
-ChatML format — native to qwen2.5:
+The prompt is plain text describing the task; the model's own prompt template
+(ChatML for qwen, etc.) is applied **server-side** by the inference service via
+Ollama's `/api/chat`. No `<|im_start|>` tags belong in this codebase, and the
+utility model can be swapped (via `UTILITY_MODEL` on the inference service) with
+no prompt changes here.
+
+Fresh summary instruction:
 
 ```
-<|im_start|>user
 Summarize the conversation below in 3-5 sentences.
 Write in third person. Do not quote directly — paraphrase only.
 Do not include greetings, sign-offs, or filler. Output only the summary text.
 
 Conversation:
 {context}
-<|im_end|>
-<|im_start|>assistant
 ```
 
-For cumulative updates, the instruction and context change:
+Cumulative update instruction:
 
 ```
-<|im_start|>user
 Update the summary below to incorporate the new exchanges.
 Write 3-5 sentences in third person. Do not quote directly — paraphrase only.
 Do not include greetings, sign-offs, or filler. Output only the updated summary text.
@@ -92,35 +109,22 @@ Previous summary:
 
 New exchanges:
 {context}
-<|im_end|>
-<|im_start|>assistant
 ```
 
 ### Input truncation
 
 Episode context is truncated to `MAX_CHARS = 3000` characters, keeping the
-most recent exchanges (sliced from the end). This keeps Qwen focused and
+most recent exchanges (sliced from the end). This keeps the model focused and
 prevents the prompt from exceeding its effective context window.
 
 ---
 
-## ChatML Token Stripping
+## Output Handling
 
-Qwen occasionally echoes ChatML tokens back into its response. The raw output
-is cleaned before saving:
-
-```js
-const raw = data.response?.trim() ?? '';
-const content = raw
-    .replace(/<\|im_start\|>.*?<\|im_end\|>/gs, '')
-    .replace(/<\|im_start\|>|<\|im_end\|>|<\|im_sep\|>/g, '')
-    .trim();
-return content;
-```
-
-Without this, leaked tokens get stored in the summary and then injected
-back into the next summarisation prompt — causing the model to append a new
-summary after the old one rather than replacing it.
+Because `/api/chat` applies and removes the prompt template server-side, the
+returned text is already clean — the previous ChatML token-stripping step (and
+the class of bug where leaked tokens got stored and re-injected into the next
+summarisation prompt) no longer applies.
 
 ---
 
@@ -193,8 +197,7 @@ Set in `packages/orchestration-service/src/.env`:
 
 | Variable | Default | Description |
 |---|---|---|
-| `EXTRACTION_URL` | `http://localhost:11434` | Ollama instance URL |
-| `EXTRACTION_MODEL` | `qwen2.5:3b` | Model for summarisation |
+| `INFERENCE_SERVICE_URL` | `http://localhost:3001` | Inference service — summaries route through its `/utility/complete` endpoint (model set via `UTILITY_MODEL` there) |
 | `MEMORY_SERVICE_URL` | `http://localhost:3002` | Memory service URL |
 | `SUMMARY_THRESHOLD_TOKENS` | `200` | Token threshold before summarisation triggers |
 | `SUMMARY_MAX_TOKENS` | `800` | Max summary length before a new row is created |

packages/inference-service/src/index.js

@@ -2,6 +2,7 @@ require ('dotenv').config();
 const express = require('express');
 const {getEnv, PORTS, OLLAMA, logger} = require('@nexusai/shared');
 const inferenceRouter = require('./routes/inference');
+const utilityRouter = require('./routes/utility')
 
 const app = express();
 app.use(express.json({ limit: '8mb' }));  // prompts include full context window
@@ -20,6 +21,7 @@ app.get('/health', (req, res) => {
     });
 });
 
+app.use('/', utilityRouter)
 app.use('/', inferenceRouter);
 
 // Start the server

packages/inference-service/src/routes/utility.js

@@ -0,0 +1,21 @@
+const { Router } = require('express');
+const { logger } = require('@nexusai/shared');
+const { utilityComplete } = require('../utility');
+
+const router = Router();
+
+router.post('/utility/complete', async (req, res) => {
+    const { system, user, json, temperature, maxTokens } = req.body;
+
+    if (!user) return res.status(400).json({ error: 'user message is required' });
+
+    try {
+        const result = await utilityComplete({ system, user, json, temperature, maxTokens });
+        res.json(result);
+    } catch (error) {
+        logger.error('[Utility] Completion error:', error.message);
+        res.status(500).json({ error: 'Utility inference failed', detail: error.message });
+    }
+});
+
+module.exports = router;

packages/inference-service/src/utility.js

@@ -0,0 +1,44 @@
+const { getEnv, UTILITY } = require ('@nexusai/shared')
+
+const UTILITY_URL = getEnv('UTILITY_URL', UTILITY.DEFAULT_URL);
+const UTILITY_MODEL = getEnv('UTILITY_MODEL', UTILITY.DEFAULT_MODEL);
+
+// Background task inference (extraction/summarization).  Uses ollama's /api/chat
+// so the model's own prompt template is server-side, no need for ChatML
+async function utilityComplete({
+    system,
+    user,
+    json = false,
+    temperature,
+    maxTokens
+}) {
+    const messages = [];
+    if(system) messages.push({ role: 'system', content: system});
+    messages.push ({role: 'user', content: user});
+    
+    const res = await fetch (`${UTILITY_URL}/api/chat`, {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json'},
+        body: JSON.stringify({
+            model: UTILITY_MODEL,
+            messages,
+            stream: false,
+            ...(json && {format: 'json' }),
+            options: {
+                temperature: temperature ?? UTILITY.TEMPERATURE,
+                num_predict: maxTokens ?? UTILITY.MAX_TOKENS,
+            },
+        }),
+        signal: AbortSignal.timeout(UTILITY.TIMEOUT_MS),
+    });
+
+    if (!res.ok) throw new Error(`Utility backend responded ${res.status}`);
+    const data = await res.json();
+
+    return {
+        text: (data.message?.content ?? '').trim(),
+        model: data.model,
+    }
+}
+
+module.exports = { utilityComplete};

packages/memory-service/src/db/migrations.js

@@ -16,6 +16,14 @@ const migrations = [
   // used to run (wrapped in try/catch) on every boot are folded into schema.js.
   // Nothing to do here — this entry exists to mark v1 as the consolidated baseline.
   (_db) => {},
+
+  (db) => {
+    db.exec(`
+      ALTER TABLE episodes ADD COLUMN last_accessed_at INTEGER;
+      ALTER TABLE episodes ADD COLUMN access_count INTEGER NOT NULL DEFAULT 0;
+      `)
+    db.exec(`UPDATE episodes SET last_accessed_at = created_at WHERE last_accessed_at IS NULL`)
+  }
 ];
 
 const LATEST_VERSION = migrations.length;

packages/memory-service/src/db/schema.js

@@ -15,13 +15,15 @@ const schema = `
   );
 
   CREATE TABLE IF NOT EXISTS episodes (
-    id            INTEGER PRIMARY KEY AUTOINCREMENT,
-    session_id    INTEGER NOT NULL REFERENCES sessions(id) ON DELETE CASCADE,
-    user_message  TEXT NOT NULL,
-    ai_response   TEXT NOT NULL,
-    created_at    INTEGER NOT NULL DEFAULT (unixepoch()),
-    token_count   INTEGER,
-    metadata      TEXT
+    id                INTEGER PRIMARY KEY AUTOINCREMENT,
+    session_id        INTEGER NOT NULL REFERENCES sessions(id) ON DELETE CASCADE,
+    user_message      TEXT NOT NULL,
+    ai_response       TEXT NOT NULL,
+    created_at        INTEGER NOT NULL DEFAULT (unixepoch()),
+    token_count       INTEGER,
+    metadata          TEXT,
+    last_accessed_at  INTEGER,
+    access_count      INTEGER NOT NULL DEFAULT 0
   );
 
   CREATE TABLE IF NOT EXISTS entities (

packages/memory-service/src/entities/extraction.js

@@ -1,9 +1,7 @@
 const semantic = require('../semantic')
-const { getEnv, SERVICES, formatEpisodeText, ENTITIES, logger } = require('@nexusai/shared');
+const { getEnv, SERVICES, formatEpisodeText, ENTITIES, logger, utilityInference } = require('@nexusai/shared');
 const { upsertEntity, upsertRelationship, linkEntityToEpisode } = require('./index');
 
-const EXTRACTION_URL = getEnv('EXTRACTION_URL', 'http://localhost:11434');
-const EXTRACTION_MODEL = getEnv('EXTRACTION_MODEL', 'qwen2.5:3b'); // ChatML format — see buildExtractionPrompt
 const EMBEDDING_SERVICE_URL = getEnv('EMBEDDING_SERVICE_URL', SERVICES.EMBEDDING_URL);
 
 const ENTITY_TYPES = ENTITIES.TYPES;
@@ -28,10 +26,9 @@ function mentionedIn(name, haystack) {
   return norm(haystack).includes(norm(name));
 }
 
-// NOTE: This prompt uses ChatML format (<|im_start|> / <|im_end|> tags), which is
-// specific to qwen-family models. If EXTRACTION_MODEL is changed to a Llama-family
-// or other model, this format will need to change — most alternatives use either
-// plain text or [INST] / <<SYS>> tags. Silent degradation is likely if mismatched.
+// Returns { system, user } for utilityInference. The model's prompt template
+// (ChatML for qwen, etc.) is applied by Ollama via the inference service's
+// /utility/complete route — no template tags belong in this file.
 function buildExtractionPrompt(userMessage, aiResponse, knownEntities = []) {
     const knownBlock = knownEntities.length > 0
         ? [
@@ -41,33 +38,30 @@ function buildExtractionPrompt(userMessage, aiResponse, knownEntities = []) {
           ].join('\n')
         : '';
 
-    return [
-        '<|im_start|>system',
-        'You are a named entity and relationship extractor. You output only valid JSON.',
-        '<|im_end|>',
-        '<|im_start|>user',
-        'Read the conversation below and extract all named entities and the relationships between them.',
-        `Entity types: ${ENTITY_TYPES.join(', ')}`,
-        'Use "character" for any fictional, game, or media characters (e.g. characters from anime, games, books, TV shows, movies)',
-        'Use "person" only for real people',
-        'For each entity provide:',
-        '  "name": short proper noun only (max 4 words)',
-        '  "type": one of the valid types',
-        '  "notes": one specific sentence about this entity based on the conversation',
-        'For relationships, use snake_case verb labels (e.g. works_on, manages, uses, knows, located_in, part_of, created_by).',
-        'Only include relationships between entities you have listed above.',
-        'The known-entities list below is ONLY for consistent spelling and types. Do NOT output an entity unless it actually appears in the conversation.',
-        'Return this exact JSON structure:',
-        '{ "entities": [{"name": "...", "type": "...", "notes": "..."}], "relationships": [{"from": "...", "fromType": "...", "to": "...", "toType": "...", "label": "...", "notes": "..."}] }',
-        '',
-        knownBlock,
-        '--- CONVERSATION ---',
-        `User: ${userMessage}`,
-        `Assistant: ${aiResponse}`,
-        '--- END CONVERSATION ---',
-        '<|im_end|>',
-        '<|im_start|>assistant',
-    ].join('\n');
+    return {
+        system: 'You are a named entity and relationship extractor. You output only valid JSON.',
+        user: [
+            'Read the conversation below and extract all named entities and the relationships between them.',
+            `Entity types: ${ENTITY_TYPES.join(', ')}`,
+            'Use "character" for any fictional, game, or media characters (e.g. characters from anime, games, books, TV shows, movies)',
+            'Use "person" only for real people',
+            'For each entity provide:',
+            '  "name": short proper noun only (max 4 words)',
+            '  "type": one of the valid types',
+            '  "notes": one specific sentence about this entity based on the conversation',
+            'For relationships, use snake_case verb labels (e.g. works_on, manages, uses, knows, located_in, part_of, created_by).',
+            'Only include relationships between entities you have listed above.',
+            'The known-entities list below is ONLY for consistent spelling and types. Do NOT output an entity unless it actually appears in the conversation.',
+            'Return this exact JSON structure:',
+            '{ "entities": [{"name": "...", "type": "...", "notes": "..."}], "relationships": [{"from": "...", "fromType": "...", "to": "...", "toType": "...", "label": "...", "notes": "..."}] }',
+            '',
+            knownBlock,
+            '--- CONVERSATION ---',
+            `User: ${userMessage}`,
+            `Assistant: ${aiResponse}`,
+            '--- END CONVERSATION ---',
+        ].join('\n'),
+    };
 }
 
 async function embedEntity(entity) {
@@ -91,30 +85,16 @@ async function extractAndStoreEntities(userMessage, aiResponse, episodeId=null,
         // Fetch existing entities to guide the model toward consistent name/type pairs
         const db = require('../db').getDB();
         const knownEntities = db.prepare(`SELECT name, type FROM entities ORDER BY rowid DESC LIMIT 20`).all();
-        const prompt = buildExtractionPrompt(userMessage, aiResponse, knownEntities);
+        const { system, user } = buildExtractionPrompt(userMessage, aiResponse, knownEntities);
 
-
-        const res = await fetch(`${EXTRACTION_URL}/api/generate`, {
-            method: 'POST',
-            headers: { 'Content-Type': 'application/json' },
-            body: JSON.stringify({
-                model: EXTRACTION_MODEL,
-                prompt: prompt,
-                stream: false,
-                format: 'json',
-                options: {
-                    temperature: ENTITIES.TEMPERATURE,
-                    num_predict: ENTITIES.NUM_PREDICT,
-                },
-            }),
-            signal: AbortSignal.timeout(60_000),
+        const raw = await utilityInference({
+            system,
+            user,
+            json: true,
+            temperature: ENTITIES.TEMPERATURE,
+            maxTokens: ENTITIES.NUM_PREDICT,
         });
 
-        if (!res.ok) throw new Error(`Ollama responded ${res.status}`);
-
-        const data = await res.json();
-        const raw = data.response?.trim() ?? '';
-
         const jsonMatch = raw.match(/\{[\s\S]*\}/);
         if (!jsonMatch) {
             logger.warn('[entities] No JSON object found in response');

packages/memory-service/src/episodic/index.js

@@ -1,5 +1,5 @@
 const {getDB} = require('../db');
-const { EPISODIC, getEnv, SERVICES, parseRow, formatEpisodeText, SUMMARIES, logger } = require('@nexusai/shared');
+const { EPISODIC, getEnv, SERVICES, parseRow, formatEpisodeText, SUMMARIES, logger, isTrivialTurn, CONSOLIDATION } = require('@nexusai/shared');
 const semantic = require('../semantic');
 const { extractAndStoreEntities } = require('../entities/extraction')
 
@@ -162,8 +162,16 @@ async function createEpisode(sessionId, userMessage, aiResponse, tokenCount = nu
     }))
     .catch(err => logger.error(`Failed to embed episode ${episode.id}:`, err.message));
 
-  extractAndStoreEntities(userMessage, aiResponse, episode.id, projectId)
-    .catch(err => logger.error(`Failed to extract entities for episode ${episode.id}:`, err.message));
+  // Skip entity extraction on contentless social turns (greetings, sign-offs).
+  // They carry nothing worth storing, and running extraction on them was a
+  // source of junk/confabulated entities. The informational content lives in
+  // substantive turns, which still extract normally.
+  if (isTrivialTurn(userMessage)) {
+    logger.debug(`[entities] Skipping extraction for episode ${episode.id} — trivial turn`);
+  } else {
+    extractAndStoreEntities(userMessage, aiResponse, episode.id, projectId)
+      .catch(err => logger.error(`Failed to extract entities for episode ${episode.id}:`, err.message));
+  }
 
 
   return episode;
@@ -258,6 +266,19 @@ function deleteEpisode(id) {
   db.prepare(`DELETE FROM episodes WHERE id = ?`).run(id);
 }
 
+function touchEpisodes(ids) {
+  if (!ids.length) return;
+
+  const db = getDB();   // <-- missing in your version
+  const placeholders = ids.map(() => '?').join(',');
+  db.prepare(`
+    UPDATE episodes
+    SET access_count = access_count + 1,
+        last_accessed_at = ?
+    WHERE id IN (${placeholders})
+    `).run(Date.now(), ...ids);
+}
+
 /******** Embedding Helper ********/
 async function getEpisodeEmbedding(userMessage, aiResponse){
   const url = getEnv('EMBEDDING_SERVICE_URL', SERVICES.EMBEDDING_URL);
@@ -290,6 +311,22 @@ function getEpisodesByProject(projectId, limit = SUMMARIES.MAX_PROJECT_EPISODE_L
     `).all(projectId, limit).map(parseRow);
 }
 
+function getConsolidationCandidates(sessionId, limit = CONSOLIDATION.CANDIDATE_LIMIT) {
+    const db = getDB();
+    const now = Date.now();
+    const cutoff = Math.floor(now / 1000) - CONSOLIDATION.MIN_AGE_DAYS *86400 // seconds, so it matches created_at
+
+    return db.prepare(`
+        SELECT id, access_count, created_at, last_accessed_at,
+              SUBSTR(user_message, 1, 80) AS preview,
+              CAST( access_count AS REAL) / (1+(?-COALESCE(last_accessed_at, created_at * 1000)) / 86400000.0) AS aging_score
+              FROM episodes
+              WHERE session_id = ? AND created_at < ?
+              ORDER BY aging_score ASC
+              LIMIT ?
+      `).all(now, sessionId, cutoff, limit);
+}
+
 module.exports = {
   createSession,
   getSession,
@@ -307,6 +344,8 @@ module.exports = {
   getEpisodesSince,
   searchEpisodes,
   deleteEpisode,
+  touchEpisodes,
   getEpisodesByProject,
   buildFtsQuery,
+  getConsolidationCandidates,
 };

packages/memory-service/src/graph/index.js

@@ -74,4 +74,17 @@ function getEpisodeIdsByEntities(entityIds) {
     ).all(...entityIds).map(r => r.episode_id);
 }
 
-module.exports = { getNeighborhood, getEntityNeighbors, getEpisodeIdsByEntities };
+//Entity IDs linked (via entity_episodes) to any episode in a given session
+//Scopes non-project entity search to the session's own entities under the "isolated chats" model.
+//Entities globally deduped
+function getEntityIdsBySession(sessionId){
+    const db = getDB();
+    return db.prepare(`
+        SELECT DISTINCT ee.entity_id
+        FROM entity_episodes ee
+        JOIN episodes e on e.id = ee.episode_id
+        WHERE e.session_id = ?
+    `).all(sessionId).map(r => r.entity_id);
+}
+
+module.exports = { getNeighborhood, getEntityNeighbors, getEpisodeIdsByEntities, getEntityIdsBySession };

packages/memory-service/src/index.js

@@ -1,6 +1,6 @@
 require ('dotenv').config();
 const express = require('express');
-const {getEnv, PORTS, EPISODIC, logger} = require('@nexusai/shared');
+const {getEnv, PORTS, EPISODIC, logger, CONSOLIDATION} = require('@nexusai/shared');
 const { getDB } = require('./db');
 const { createProject, getProjects, getProject, updateProject, deleteProject } = require('./db/projects');
 const { createSummary, getSummary, getSummariesBySession, getSummariesByProject, updateSummary, deleteSummary } = require('./db/summaries');
@@ -139,6 +139,13 @@ app.get('/episodes/search', (req, res) => {
   res.json(episodic.searchEpisodes(q, Number(limit), parsedSessionIds));
 });
 
+app.post('/episodes/touch', (req, res) => {
+  const { ids } = req.body;
+  if(!Array.isArray(ids)) return res.status(400).json({error: 'ids must be an array'});
+  episodic.touchEpisodes(ids);
+  res.json({touched: ids.length});
+})
+
 app.get('/episodes/:id', (req, res) => {
   const episode = episodic.getEpisode(req.params.id);
   if (!episode) return res.status(404).json({ error: 'Episode not found' });
@@ -162,12 +169,39 @@ app.get('/sessions/:id/episode-stats', (req, res) => {
   res.json(episodic.getSessionEpisodeStats(Number(req.params.id)));
 });
 
+//Entity IDs linked to this session's episodes: sesion-scoped entity search
+app.get('/sessions/:id/entity-ids', (req, res) => {
+  res.json({
+    entityIds: graph.getEntityIdsBySession(Number(req.params.id))
+  })
+})
+
 // Episodes newer than :afterId, chronological — the un-summarized tail.
 app.get('/sessions/:id/episodes/since/:afterId', (req, res) => {
   const episodes = episodic.getEpisodesSince(Number(req.params.id), Number(req.params.afterId));
   res.json(episodes);
 });
 
+app.get('/sessions/:id/consolidation-candidates', (req, res) => {
+  const sessionId = Number(req.params.id);
+  const stats = episodic.getSessionEpisodeStats(sessionId);
+
+  if(stats.count < CONSOLIDATION.MIN_SESSION_EPISODES) {
+    return res.json({
+      eligible: false,
+      reason: `session has ${stats.count} episodes, floor is ${CONSOLIDATION.MIN_SESSION_EPISODES}`,
+      candidates: [],
+    });
+  }
+
+  const candidates = episodic.getConsolidationCandidates(sessionId);
+  res.json({
+    eligible:true, 
+    sessionEpisodeCount: stats.count,
+    candidates
+  })
+})
+
 app.delete('/episodes/:id', (req, res) => {
   const id = Number(req.params.id);
   episodic.deleteEpisode(id);

packages/memory-service/src/summarization/project.js

@@ -1,4 +1,4 @@
-const { SERVICES, getEnv, SUMMARIES } = require('@nexusai/shared');
+const { SERVICES, getEnv, SUMMARIES, utilityInference } = require('@nexusai/shared');
 const { 
     getSessionSummariesForProject,
     getProjectOverviewSummary,
@@ -9,9 +9,6 @@ const {
  const { getEpisodesByProject } = require('../episodic');
  const { getProject } = require('../db/projects');
 
-const EXTRACTION_URL   = getEnv('EXTRACTION_URL', 'http://localhost:11434');
-const EXTRACTION_MODEL = getEnv('EXTRACTION_MODEL', 'qwen2.5:3b');
-
 const MAX_SUMMARY_CHARS = SUMMARIES.MAX_SUMMARY_CHARS; // generous ceiling before we truncate input
 
 function buildProjectSummaryPrompt(projectName, sessionSummaries) {
@@ -24,8 +21,9 @@ function buildProjectSummaryPrompt(projectName, sessionSummaries) {
         summaryBlock = summaryBlock.slice(-MAX_SUMMARY_CHARS);
     }
 
+    // No ChatML wrapper — the model's own prompt template is applied server-side
+    // by the inference service's /utility/complete route (Ollama /api/chat).
     return [
-        '<|im_start|>user',
         `The following are session summaries from a project called "${projectName}".`,
         'Write a project overview covering: goals, progress, key decisions, and current state.',
         'Scale the length to the material — use multiple paragraphs for complex projects, a few sentences for simple ones.',
@@ -33,8 +31,6 @@ function buildProjectSummaryPrompt(projectName, sessionSummaries) {
         'Write in third person. Output only the overview text, no headings or labels.',
         '',
         summaryBlock,
-        '<|im_end|>',
-        '<|im_start|>assistant',
     ].join('\n');
 }
 
@@ -49,8 +45,9 @@ function buildProjectSummaryFromEpisodesPrompt(projectName, episodes) {
         episodeBlock = episodeBlock.slice(-MAX_SUMMARY_CHARS);
     }
 
+    // No ChatML wrapper — the model's own prompt template is applied server-side
+    // by the inference service's /utility/complete route (Ollama /api/chat).
     return [
-        '<|im_start|>user',
         `The following are conversations from a project called "${projectName}".`,
         'Write a project overview covering: goals, progress, key decisions, and current state.',
         'Scale the length to the material — use multiple paragraphs for complex projects, a few sentences for simple ones.',
@@ -58,58 +55,25 @@ function buildProjectSummaryFromEpisodesPrompt(projectName, episodes) {
         'Write in third person. Output only the overview text, no headings or labels.',
         '',
         episodeBlock,
-        '<|im_end|>',
-        '<|im_start|>assistant',
     ].join('\n');
 }
 
 async function generateProjectSummaryFromEpisodes(projectName, episodes) {
-    const prompt = buildProjectSummaryFromEpisodesPrompt(projectName, episodes);
-
-    const res = await fetch(`${EXTRACTION_URL}/api/generate`, {
-        method: 'POST',
-        headers: { 'Content-Type': 'application/json' },
-        body: JSON.stringify({
-            model: EXTRACTION_MODEL,
-            prompt,
-            stream: false,
-            options: { temperature: 0.2, num_predict: 1200 },
-        }),
+    const user = buildProjectSummaryFromEpisodesPrompt(projectName, episodes);
+    return utilityInference({
+        user,
+        temperature: SUMMARIES.TEMPERATURE,
+        maxTokens: SUMMARIES.PROJECT_GEN_MAX_TOKENS,
     });
-
-    if (!res.ok) throw new Error(`Ollama responded ${res.status}`);
-    const data = await res.json();
-
-    const raw = data.response?.trim() ?? '';
-    return raw
-        .replace(/<\|im_start\|>.*?<\|im_end\|>/gs, '')
-        .replace(/<\|im_start\|>|<\|im_end\|>|<\|im_sep\|>/g, '')
-        .trim();
 }
 
 async function generateProjectSummary(projectName, sessionSummaries) {
-    const prompt = buildProjectSummaryPrompt(projectName, sessionSummaries);
-
-    const res = await fetch(`${EXTRACTION_URL}/api/generate`, {
-        method: 'POST',
-        headers: { 'Content-Type': 'application/json' },
-        body: JSON.stringify({
-            model: EXTRACTION_MODEL,
-            prompt,
-            stream: false,
-            // No format: 'json' — we want free-text narrative, same as session summarization
-            options: { temperature: 0.2, num_predict: 1200 },
-        }),
+    const user = buildProjectSummaryPrompt(projectName, sessionSummaries);
+    return utilityInference({
+        user,
+        temperature: SUMMARIES.TEMPERATURE,
+        maxTokens: SUMMARIES.PROJECT_GEN_MAX_TOKENS,
     });
-
-    if (!res.ok) throw new Error(`Ollama responded ${res.status}`);
-    const data = await res.json();
-
-    const raw = data.response?.trim() ?? '';
-    return raw
-        .replace(/<\|im_start\|>.*?<\|im_end\|>/gs, '')
-        .replace(/<\|im_start\|>|<\|im_end\|>|<\|im_sep\|>/g, '')
-        .trim();
 }
 
 // Main entry point — called by the route handler

packages/orchestration-service/src/chat/index.js

@@ -2,7 +2,7 @@ const memory = require("../services/memory");
 const inference = require("../services/inference");
 const embedding = require("../services/embedding");
 const qdrant = require("../services/qdrant");
-const { ORCHESTRATION, RETRIEVAL, logger } = require("@nexusai/shared");
+const { ORCHESTRATION, RETRIEVAL, logger, isTrivialTurn } = require("@nexusai/shared");
 const appSettings = require("../config/settings");
 const {triggerSummary} = require('../services/summarization')
 const graph = require('../services/graph');
@@ -127,16 +127,22 @@ async function getSemanticEpisodes(
   }
 }
 
-async function getRelevantEntities(vector, projectId = null) {
+async function getRelevantEntities(vector, { projectId = null, sessionId } = {}) {
     if (!vector) return [];
     try {
-        const results = await qdrant.searchEntities(vector, { projectId });
-        logger.info(
-            '[orchestration] Entity search results:',
-            results.map((r) => ({ name: r.payload?.name, score: r.score })),
-        );
-        // Include the Qdrant point ID (== SQLite entity ID) for graph traversal
-        return results.map((r) => r.payload ? { id: r.id, ...r.payload } : null).filter(Boolean);
+        let allowedIds;
+        if (projectId === null || projectId === undefined) {
+            // Non-project chat is its own island — scope to entities linked to
+            // THIS session. No links yet ⇒ nothing to retrieve, and we return
+            // early so searchEntities is never called unfiltered.
+            allowedIds = await memory.getEntityIdsBySession(sessionId);
+            logger.info(`[orchestration] Non-project chat, session ${sessionId}: ${allowedIds.length} linked entities`);
+            if (allowedIds.length === 0) return [];
+        }
+        const results = await qdrant.searchEntities(vector, { projectId, allowedIds });
+        logger.info('[orchestration] Entity search results:',
+            results.map(r => ({ name: r.payload?.name, score: r.score })));
+        return results.map(r => r.payload ? { id: r.id, ...r.payload } : null).filter(Boolean);
     } catch (err) {
         logger.debug('[orchestration] Entity search failed, continuing without:', err.message);
         return [];
@@ -177,8 +183,11 @@ function fuseEpisodeResults(semanticEps, keywordEps, { semanticWeight, keywordWe
 }
 
 function estimateTokens(episode) {
-    return episode.token_count
-        ?? Math.ceil((episode.user_message.length + episode.ai_response.length) / 4);
+    //NOTE: episode.token_count is not used here.  It stores the
+    //full inference cost of that turn(prompt + injected memory + response),
+    //this inflate the episodes apparent size and starving the budget.
+    //Therefore, char/4 on the actual stored text is an honest selectedl
+    return Math.ceil((episode.user_message.length + episode.ai_response.length) / 4);
 }
 
 function buildScoredPool(fusedWithScores, recentEpisodes, entityBoostedIds, { entityWeight }) {
@@ -252,6 +261,7 @@ async function getFusedEpisodes(userMessage, session, recentIds, projectSessionI
 }
 
 async function assembleContext(externalId, userMessage) {
+    logger.info(`[orchestration] assembleContext ENTERED — msg: "${userMessage}", trivial: ${isTrivialTurn(userMessage)}`);
     const settings = appSettings.load();
     const { recentEpisodeLimit, semanticLimit, scoreThreshold,
             temperature, repeatPenalty, topP, topK, systemPrompt,
@@ -283,20 +293,27 @@ async function assembleContext(externalId, userMessage) {
     const isFirstMessage = recentEpisodes.length === 0;
     const recentIds = new Set(recentEpisodes.map(e => e.id));
 
-    // 4. Embed the query once — the vector is shared by semantic episode search
-    //    and entity search, so embedding it twice was a wasted round-trip + Ollama call.
-    let queryVector = null;
-    try {
-        queryVector = await embedding.embed(userMessage);
-    } catch (err) {
-        logger.warn('[orchestration] Query embedding failed; semantic + entity search disabled this turn:', err.message);
-    }
+    // 4. Retrieval — skipped entirely on contentless social turns (greetings,
+    //    sign-offs). On those, recent history alone is the right context; running
+    //    semantic/keyword/entity retrieval only surfaces marginal noise the model
+    //    then confabulates around. Embed once (shared by episode + entity search).
+    let fusedWithScores = [];
+    let entityResults = [];
+    if (!isTrivialTurn(userMessage)) {
+        let queryVector = null;
+        try {
+            queryVector = await embedding.embed(userMessage);
+        } catch (err) {
+            logger.warn('[orchestration] Query embedding failed; semantic + entity search disabled this turn:', err.message);
+        }
 
-    // 4b. Fused retrieval + entity search in parallel (both are independent)
-    const [fusedWithScores, entityResults] = await Promise.all([
-        getFusedEpisodes(userMessage, session, recentIds, projectSessionIds, queryVector, { semanticLimit, scoreThreshold, semanticWeight, keywordWeight }),
-        getRelevantEntities(queryVector, session.project_id ?? null),
-    ]);
+        [fusedWithScores, entityResults] = await Promise.all([
+            getFusedEpisodes(userMessage, session, recentIds, projectSessionIds, queryVector, { semanticLimit, scoreThreshold, semanticWeight, keywordWeight }),
+            getRelevantEntities(queryVector, { projectId: session.project_id ?? null, sessionId: session.id }),
+        ]);
+    } else {
+        logger.debug('[orchestration] Trivial turn — skipping semantic/keyword/entity retrieval');
+    }
 
     // 5. Entity-linked episode IDs for scoring bonus
     const entityIds = entityResults.map(e => e.id);
@@ -314,6 +331,9 @@ async function assembleContext(externalId, userMessage) {
     const scoredPool = buildScoredPool(fusedWithScores, recentEpisodes, entityBoostedIds, { entityWeight });
     const { guaranteed, selected } = selectWithinBudget(scoredPool, contextBudget, minRecentEpisodes, recentEpisodes);
 
+    const selectedIds = selected.map(ep => ep.id);
+    memory.touchEpisodes(selectedIds);
+
     // 7. Graph neighborhood expansion
     let neighborhood = { nodes: [], edges: [] };
     if (entityIds.length > 0) {

packages/orchestration-service/src/services/memory.js

@@ -1,4 +1,4 @@
-const { getEnv, SERVICES, EPISODIC } = require('@nexusai/shared');
+const { getEnv, SERVICES, EPISODIC, logger } = require('@nexusai/shared');
 
 const BASE_URL = getEnv('MEMORY_SERVICE_URL', SERVICES.MEMORY_URL);
 
@@ -216,6 +216,23 @@ async function getEpisodesByEntities(entityIds) {
     return res.json(); // { episodeIds: [...] }
 }
 
+async function getEntityIdsBySession(sessionId){
+    const res = await fetch(`${BASE_URL}/sessions/${sessionId}/entity-ids`);
+    if (!res.ok) throw new Error(`Entity-ids-by-session error: ${res.status}`);
+    const { entityIds } = await res.json();
+    return entityIds;
+}
+
+// orchestration-service/src/services/memory.js
+async function touchEpisodes(ids) {
+    if (!ids.length) return;
+    fetch(`${BASE_URL}/episodes/touch`, {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify({ ids }),
+    }).catch(err => logger.warn(`[memory] touch failed: ${err.message}`));
+}
+
 module.exports = {
     getSessionByExternalId,
     createSession,
@@ -242,4 +259,6 @@ module.exports = {
     getProjectOverviewSummary,
     searchEpisodes,
     getEpisodesByEntities,
+    getEntityIdsBySession,
+    touchEpisodes,
 }

packages/orchestration-service/src/services/qdrant.js

@@ -29,30 +29,28 @@ async function searchEpisodes( vector, {limit = ORCHESTRATION.RECENT_EPISODE_LIM
     return data.result;
 }
 
-async function searchEntities(vector, { limit = ORCHESTRATION.ENTITIES_LIMIT, scoreThreshold = ORCHESTRATION.ENTITIES_THRESHOLD, projectId = undefined } = {}) {
+async function searchEntities(vector, { limit = ORCHESTRATION.ENTITIES_LIMIT, scoreThreshold = ORCHESTRATION.ENTITIES_THRESHOLD, projectId, allowedIds } = {}) {
     const body = { vector, limit, score_threshold: scoreThreshold, with_payload: true };
 
     if (projectId !== null && projectId !== undefined) {
-        body.filter = {
-            must: [{ key: 'projectId', match: { value: projectId } }]
-        };
+        // Project chat: entities shared across the project's sessions.
+        body.filter = { must: [{ key: 'projectId', match: { value: projectId } }] };
+    } else if (allowedIds && allowedIds.length > 0) {
+        // Non-project chat: restrict to this session's own entities (Model 2).
+        body.filter = { must: [{ has_id: allowedIds }] };
     }
+    // No else: the caller returns early when a non-project session has no linked
+    // entities, so an unfiltered (leaky) search is never reached.
+
     const res = await fetch(
         `${BASE_URL}/collections/${COLLECTIONS.ENTITIES}/points/search`,
-        {
-            method: 'POST',
-            headers: { 'Content-Type': 'application/json' },
-            body: JSON.stringify(body),
-        }
+        { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify(body) }
     );
-
     if (!res.ok) {
-        const body = await res.text();
-        throw new Error(`Qdrant error: ${res.status} - ${body}`);
+        const text = await res.text();
+        throw new Error(`Qdrant error: ${res.status} - ${text}`);
     }
-        
-    const data = await res.json();
-    return data.result;
+    return (await res.json()).result;
 }
 
 module.exports = { searchEpisodes, searchEntities };

packages/orchestration-service/src/services/summarization.js

@@ -1,7 +1,5 @@
-const { getEnv, SERVICES, SUMMARIES, logger } = require('@nexusai/shared');
+const { getEnv, SERVICES, SUMMARIES, logger, utilityInference } = require('@nexusai/shared');
 
-const EXTRACTION_URL  = getEnv('EXTRACTION_URL', 'http://localhost:11434');
-const EXTRACTION_MODEL = getEnv('EXTRACTION_MODEL', 'qwen2.5:3b');
 const MEMORY_URL      = getEnv('MEMORY_SERVICE_URL', SERVICES.MEMORY_URL);
 
 const THRESHOLD_TOKENS  = parseInt(getEnv('SUMMARY_THRESHOLD_TOKENS', SUMMARIES.THRESHOLD_TOKENS));
@@ -35,41 +33,20 @@ Do not include greetings, sign-offs, or filler. Output only the summary text.
 Conversation:
 ${context}`;
 
-    return [
-        '<|im_start|>user',   // ChatML for qwen2.5
-        instruction,
-        '<|im_end|>',
-        '<|im_start|>assistant',
-    ].join('\n');
+    // No ChatML wrapper — the model's own prompt template is applied server-side
+    // by the inference service's /utility/complete route (Ollama /api/chat).
+    return instruction;
 }
 
 async function generateSummary(episodes, existingSummary = null) {
-    const prompt = buildSummaryPrompt(episodes, existingSummary);
+    const user = buildSummaryPrompt(episodes, existingSummary);
 
-    const res = await fetch(`${EXTRACTION_URL}/api/generate`, {
-        method: 'POST',
-        headers: { 'Content-Type': 'application/json' },
-        body: JSON.stringify({
-            model: EXTRACTION_MODEL,
-            prompt,
-            stream: false,
-            options: {
-                temperature: 0.2,   // slightly higher than entities — summaries benefit from some fluency
-                num_predict: 500,   // generous but bounded — keeps summaries from running long
-            },
-        }),
+    const content = await utilityInference({
+        user,
+        temperature: SUMMARIES.TEMPERATURE,
+        maxTokens: SUMMARIES.SESSION_GEN_MAX_TOKENS,
     });
 
-    if (!res.ok) throw new Error(`Ollama responded ${res.status}`);
-    const data = await res.json();
-
-
-    const raw = data.response?.trim() ?? '';
-    // Strip any leaked ChatML tokens Qwen echoes back
-    const content = raw
-        .replace(/<\|im_start\|>.*?<\|im_end\|>/gs, '')
-        .replace(/<\|im_start\|>|<\|im_end\|>|<\|im_sep\|>/g, '')
-        .trim();
     return content;
 }
 

packages/shared/src/config/constants.js

@@ -78,6 +78,13 @@ const SUMMARIES = {
     MIN_EPISODES_SINCE: 5,      // don't resummarize until N new episodes since last summary
     MAX_SUMMARY_CHARS:  8000,   // max chars to include from recent episodes when generating summary (to control prompt size)
     MAX_PROJECT_EPISODE_LIMIT: 200, // max number of episodes to consider from the entire project when generating summary (to control prompt size)
+
+    // Generation params for the utility model (passed to utilityInference).
+    // Distinct from MAX_SUMMARY_TOKENS above, which gates STORED summary size;
+    // these two cap GENERATION length (num_predict) per summary type.
+    TEMPERATURE:            0.2,    // slightly higher than entities (0.1) — summaries benefit from some fluency
+    SESSION_GEN_MAX_TOKENS: 500,    // num_predict for a session summary (3-5 sentences)
+    PROJECT_GEN_MAX_TOKENS: 1200,   // num_predict for a project overview (multi-paragraph)
 }
 
 const ENTITIES = {
@@ -105,6 +112,20 @@ const RETRIEVAL = {
     KEYWORD_WEIGHT:     0.5,    // Weight applied to keyword (SQLite) results, 0 = disables, set >0 to enable and tune balance between semantic vs keyword matches
 }
 
+const UTILITY = {
+    DEFAULT_URL:   'http://localhost:11434', // Ollama host for background/utility tasks
+    DEFAULT_MODEL: 'qwen2.5:3b',
+    TEMPERATURE:   0.2,      // precise, low-creativity default for extraction/summaries
+    MAX_TOKENS:    1200,
+    TIMEOUT_MS:    120_000,  // extraction previously used 60s; summaries had none — standardized
+};
+
+const CONSOLIDATION = {
+    MIN_AGE_DAYS:           7,  // episodes younger than this are never consolidation candidates
+    MIN_SESSION_EPISODES:   20, // sessions smaller than this are left entirely alone
+    CANDIDATE_LIMIT: 50,    
+}
+
 module.exports = {
     QDRANT,
     COLLECTIONS,
@@ -119,4 +140,6 @@ module.exports = {
     SUMMARIES,
     ENTITIES,
     RETRIEVAL,
+    UTILITY,
+    CONSOLIDATION,
 };

packages/shared/src/index.js

@@ -1,7 +1,25 @@
 const {getEnv} = require('./config/env');
-const {QDRANT, COLLECTIONS, EPISODIC, SERVICES, OLLAMA, PORTS, LLAMACPP, INFERENCE_DEFAULTS, SQLITE, ORCHESTRATION, SUMMARIES, ENTITIES, RETRIEVAL } = require('./config/constants');
-const {parseRow, formatEpisodeText} = require('./utils')
+const { 
+        QDRANT, 
+        COLLECTIONS, 
+        EPISODIC, 
+        SERVICES, 
+        OLLAMA, 
+        PORTS, 
+        LLAMACPP, 
+        INFERENCE_DEFAULTS, 
+        SQLITE, 
+        ORCHESTRATION, 
+        SUMMARIES, 
+        ENTITIES, 
+        RETRIEVAL,
+        UTILITY,
+        CONSOLIDATION
+     } = require('./config/constants');
+const {parseRow, formatEpisodeText, isTrivialTurn} = require('./utils')
+
 const logger = require('./utils/logger');
+const {utilityInference} = require('./utils/utilityInference');
 
 module.exports = {
     getEnv, 
@@ -17,8 +35,12 @@ module.exports = {
     ORCHESTRATION,
     parseRow,
     formatEpisodeText,
+    isTrivialTurn,
     SUMMARIES,
     ENTITIES,
     logger,
     RETRIEVAL,
+    UTILITY,
+    CONSOLIDATION,
+    utilityInference,
 };

packages/shared/src/utils.js

@@ -10,4 +10,46 @@ function formatEpisodeText(userMessage, aiResponse) {
     return `User: ${userMessage}\nAssistant: ${aiResponse}`;
 }
 
-module.exports = { parseRow, formatEpisodeText };
+// Contentless "social" turns — greetings, sign-offs, acknowledgements — that
+// carry no information to store or recall. Used to skip entity extraction and
+// noisy retrieval on such turns.
+const TRIVIAL_TURNS = new Set([
+    'good morning', 'good night', 'good evening', 'good afternoon', 'morning', 'evening',
+    'hello', 'hi', 'hey', 'hey there', 'yo', 'sup', 'whats up', 'hiya', 'howdy',
+    'goodbye', 'bye', 'see you', 'see ya', 'see you later', 'talk later', 'talk soon',
+    'later', 'catch you later', 'gtg', 'gotta go', 'im off', 'heading out',
+    'thanks', 'thank you', 'thanks again', 'thank you so much', 'ty', 'thx', 'cheers',
+    'no worries', 'no problem', 'np', 'youre welcome', 'my pleasure',
+    'ok', 'okay', 'k', 'kk', 'alright', 'sure', 'sounds good', 'got it', 'gotcha',
+    'cool', 'nice', 'great', 'awesome', 'perfect', 'lol', 'haha',
+    'just saying hi', 'just saying hello', 'just checking in', 'just dropping in',
+    'stopping by', 'just stopping by', 'just wanted to say hi',
+]);
+
+// Trailing filler words that don't change a phrase's social nature, so
+// "good morning again" / "thanks everyone" collapse to a base phrase.
+const TRIVIAL_FILLER = new Set(['again', 'there', 'everyone', 'all', 'yall', 'folks', 'man', 'dude', 'friend', 'buddy']);
+
+// Conservative, HIGH-PRECISION check: is this a contentless social turn?
+// It exists to skip retrieval/extraction on turns with nothing to remember or
+// recall, and deliberately errs toward "substantive" — a real short query like
+// "capital of France" must NOT be treated as trivial. Genuine intent
+// classification is a separate, later concern (confidence-based routing), not
+// this heuristic. Extend TRIVIAL_TURNS as new pure-social phrases show up.
+function isTrivialTurn(message) {
+    if (!message) return true;
+    const norm = String(message)
+        .toLowerCase()
+        .replace(/[^\p{L}\p{N}\s]/gu, ' ')   // punctuation → space (unicode-aware)
+        .replace(/\s+/g, ' ')
+        .trim();
+    if (!norm) return true;                   // empty / punctuation-only
+    if (TRIVIAL_TURNS.has(norm)) return true;
+
+    // Drop trailing filler and re-check ("good morning again" → "good morning")
+    const words = norm.split(' ');
+    while (words.length > 1 && TRIVIAL_FILLER.has(words[words.length - 1])) words.pop();
+    return TRIVIAL_TURNS.has(words.join(' '));
+}
+
+module.exports = { parseRow, formatEpisodeText, isTrivialTurn };

packages/shared/src/utils/utilityInference.js

@@ -0,0 +1,20 @@
+const { getEnv } = require('../config/env');
+const { SERVICES } = require('../config/constants');
+
+// Client for the inference service's /utility/complete route.
+// Resolved at call time (not module load) so each service's .env applies.
+async function utilityInference({ system, user, json = false, temperature, maxTokens }) {
+    const base = getEnv('INFERENCE_SERVICE_URL', SERVICES.INFERENCE_URL);
+
+    const res = await fetch(`${base}/utility/complete`, {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify({ system, user, json, temperature, maxTokens }),
+    });
+
+    if (!res.ok) throw new Error(`Utility inference error: ${res.status}`);
+    const data = await res.json();
+    return data.text;
+}
+
+module.exports = { utilityInference };

test/migrations.test.js

@@ -25,14 +25,16 @@ function fakeDb(startVersion = 0) {
 
 test('a fresh DB (user_version 0) is stamped to LATEST_VERSION', () => {
   const db = fakeDb(0);
-  const result = migrate(db);
+  const stubs = Array.from({ length: LATEST_VERSION }, () => () => {});
+  const result = migrate(db, stubs);   // inject no-op migrations
   assert.strictEqual(result, LATEST_VERSION);
   assert.strictEqual(db.version, LATEST_VERSION);
 });
 
 test('an already-current DB is a no-op (no version writes)', () => {
   const db = fakeDb(LATEST_VERSION);
-  const result = migrate(db);
+  const stubs = Array.from({ length: LATEST_VERSION }, () => () => {});
+  const result = migrate(db, stubs);   // fixed: db is now the first arg
   assert.strictEqual(result, LATEST_VERSION);
   assert.deepStrictEqual(db.setCalls, [], 'should not touch user_version when current');
 });
@@ -63,3 +65,10 @@ test('only migrations newer than the current version run', () => {
   assert.deepStrictEqual(order, ['v2', 'v3'], 'v1 is not re-run');
   assert.deepStrictEqual(db.setCalls, [2, 3]);
 });
+
+test('LATEST_VERSION reflects the appended v1 → v2 migration', () => {
+  // Guards that adding the access-tracking migration bumped the target version.
+  // The real SQL is verified out-of-band (scratch run against a DB copy); here we
+  // only lock the runner-visible contract: the array length drives LATEST_VERSION.
+  assert.strictEqual(LATEST_VERSION, 2);
+});

test/schema.test.js

@@ -13,7 +13,7 @@ try { ({ DatabaseSync } = require('node:sqlite')); } catch { DatabaseSync = null
 // The complete intended shape = original base tables + every historical ALTER.
 const EXPECTED = {
   sessions:        ['id','external_id','created_at','updated_at','metadata','name','project_id'],
-  episodes:        ['id','session_id','user_message','ai_response','created_at','token_count','metadata'],
+  episodes:        ['access_count', 'ai_response', 'created_at', 'id', 'last_accessed_at', 'metadata', 'session_id', 'token_count', 'user_message'],
   entities:        ['id','name','type','notes','created_at','updated_at','metadata','mention_count','confidence','source','last_seen_at'],
   relationships:   ['id','from_id','to_id','label','created_at','metadata','mention_count','notes'],
   entity_episodes: ['entity_id','episode_id'],

test/summarization.test.js

@@ -19,7 +19,7 @@ function mockMemory({ stats, summaries, since }) {
       calls.sinceAfterId = Number(u.split('/since/').at(-1));
       return json(since.filter(ep => ep.id > calls.sinceAfterId));
     }
-    if (u.includes('/api/generate')) return json({ response: 'A concise third-person summary.' });
+    if (u.endsWith('/utility/complete')) return json({ text: 'A concise third-person summary.' });
     if (u.endsWith('/summaries') && opts.method === 'POST') { calls.posted = JSON.parse(opts.body); return json({ id: 99 }); }
     if (/\/summaries\/\d+$/.test(u) && opts.method === 'PATCH') { calls.patched = JSON.parse(opts.body); return json({ ok: true }); }
     throw new Error('unexpected fetch: ' + u);

test/trivial-turn.test.js

@@ -0,0 +1,42 @@
+// Trivial-turn guard — tests the REAL isTrivialTurn from @nexusai/shared, which
+// gates whether a turn skips retrieval + entity extraction. The critical property
+// is high precision: greetings/pleasantries are caught, but real short queries
+// must NOT be (a false positive would suppress retrieval on a genuine question).
+const { test } = require('node:test');
+const assert = require('node:assert');
+const { isTrivialTurn } = require('@nexusai/shared');
+
+test('plain greetings and pleasantries are trivial', () => {
+  for (const m of ['good morning', 'Good morning!', 'hello', 'hey there', 'hi',
+                    'thanks', 'thank you so much', 'bye', 'ok', 'sounds good',
+                    'just dropping in', 'just checking in', 'cheers']) {
+    assert.strictEqual(isTrivialTurn(m), true, `"${m}" should be trivial`);
+  }
+});
+
+test('trailing filler collapses to a base greeting', () => {
+  assert.strictEqual(isTrivialTurn('good morning again'), true);
+  assert.strictEqual(isTrivialTurn('thanks everyone'), true);
+  assert.strictEqual(isTrivialTurn('hello there'), true);
+});
+
+test('empty or punctuation-only input is trivial', () => {
+  assert.strictEqual(isTrivialTurn(''), true);
+  assert.strictEqual(isTrivialTurn('   '), true);
+  assert.strictEqual(isTrivialTurn('!!!'), true);
+  assert.strictEqual(isTrivialTurn(null), true);
+});
+
+test('real queries are NOT trivial — even short ones (the precision guarantee)', () => {
+  for (const m of ['what is the capital of France', 'France capital', 'capital of France?',
+                    'how do I configure Qdrant', 'One Piece', 'help me debug this',
+                    'what did we decide about the schema', 'morning routine ideas']) {
+    assert.strictEqual(isTrivialTurn(m), false, `"${m}" should be substantive`);
+  }
+});
+
+test('a greeting prefix does not make a substantive message trivial', () => {
+  // "good morning, can you help with X" carries a real request — must not be skipped
+  assert.strictEqual(isTrivialTurn('good morning, can you help me with the migration'), false);
+  assert.strictEqual(isTrivialTurn('hey, what is the One Piece'), false);
+});