# Memory Service

**Package:** `@nexusai/memory-service`  
**Location:** `packages/memory-service`  
**Deployed on:** Mini PC 1 (192.168.0.81)  
**Port:** 3002

## Purpose

Responsible for all reading and writing of long-term memory. Acts as the
sole interface to both SQLite and Qdrant — no other service accesses these
stores directly. On episode creation, automatically triggers entity and
relationship extraction and embeds results into Qdrant.

## Dependencies

- `express` — HTTP API
- `better-sqlite3` — SQLite driver
- `@qdrant/js-client-rest` — Qdrant vector store client
- `dotenv` — environment variable loading
- `@nexusai/shared` — shared utilities and constants

## Environment Variables

| Variable | Required | Default | Description |
|---|---|---|---|
| PORT | No | 3002 | Port to listen on |
| 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 |

## Internal Structure

```
src/
├── db/
│   ├── index.js       # SQLite connection + initialization + migrations
│   ├── schema.js      # Table definitions, indexes, FTS5, triggers
│   ├── projects.js    # Project CRUD functions
│   └── summaries.js   # Summary CRUD functions
├── episodic/
│   └── index.js       # Session + episode CRUD, FTS search, embedding write path
├── semantic/
│   └── index.js       # Qdrant collection management, upsert, search, delete
├── entities/
│   ├── index.js       # Entity + relationship CRUD (upsert, mention tracking)
│   └── extraction.js  # Automatic entity + relationship extraction via qwen2.5:3b
├── graph/
│   └── index.js       # Knowledge graph traversal (neighborhood queries, recursive CTE)
└── index.js           # Express app + all route definitions
```

## SQLite Schema

Eight core tables:

- **sessions** — top-level conversation containers. Fields: `external_id`, `name`, `project_id`, `metadata`
- **episodes** — individual exchanges (user message + AI response) tied to a session
- **entities** — named things the system learns about (people, places, concepts, etc.). Fields include `mention_count`, `confidence`, `source`, `last_seen_at`
- **relationships** — directional labeled links between entities (`from_id`, `to_id`, `label`). Fields include `mention_count`, `notes`
- **entity_episodes** — join table linking entities to the episodes where they were extracted. Used for provenance and orphan cleanup
- **summaries** — condensed episode groups for efficient context retrieval
- **projects** — named groupings of sessions with `name`, `description`, `colour`, `icon`, `isolated`, `notes`, `system_prompt`

### Migrations

Schema changes that cannot use `CREATE TABLE IF NOT EXISTS` are applied as
idempotent migrations in `db/index.js` at startup:

```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 {}
```

`entity_episodes` is defined in `schema.js` itself (not a migration) since it is a new table.

New migrations are always appended — never modify the schema file for existing tables since `ALTER TABLE` cannot use `IF NOT EXISTS`.

### 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.

### SQLite Configuration

- `journal_mode = WAL` — non-blocking reads during writes
- `foreign_keys = ON` — enforces referential integrity and cascade deletes
- PRAGMAs set via `db.pragma()`, not `db.exec()`

### Dynamic Updates

Both `updateSession` and `updateProject` build their `SET` clause dynamically
from only the fields passed — prevents partial updates from overwriting fields
that weren't touched.

`updateProject` allowlist:
```js
const allowed = ['name', 'description', 'colour', 'icon', 'isolated', 'notes', 'system_prompt'];
```

## Qdrant / Semantic Layer

Three Qdrant collections are initialized on service startup via `semantic.initCollections()`:

| Collection | Purpose |
|---|---|
| `episodes` | Embeddings for individual conversation exchanges |
| `entities` | Embeddings for named entities |
| `summaries` | Embeddings for condensed episode summaries |

All collections use **768-dimension vectors** with **Cosine similarity**,
matching `nomic-embed-text` via Ollama. Vector size and distance metric are
defined in `@nexusai/shared` — not hardcoded here.

`initCollections()` iterates `Object.values(COLLECTIONS)` and creates any
collection that doesn't already exist at startup — all three collections are
guaranteed to exist before any requests are handled.

Each collection exposes upsert, search (with optional Qdrant filter), and
delete operations. The `wait: true` flag is used on all writes.

## Embedding Write Path

When a new episode is created:

1. Episode saved to SQLite synchronously — response returned immediately
2. User message + AI response combined: `User: ...\nAssistant: ...`
3. Text sent to embedding service (`POST /embed`)
4. Vector upserted into `episodes` Qdrant collection with payload `{ sessionId, createdAt }`

This step is **fire-and-forget** — if embedding fails, the episode is still
saved and searchable via FTS. The error is logged but not surfaced.

> The Qdrant payload stores `sessionId` (the internal integer ID). See
> `memory-isolation.md` for how project-level filtering works.

## Entity Layer

Entities and relationships use upsert semantics with composite unique
constraints to prevent duplicates:

- `UNIQUE(name, type)` on entities — conflict increments `mention_count` and updates `last_seen_at`
- `UNIQUE(from_id, to_id, label)` on relationships — conflict increments `mention_count` and preserves existing `notes`
- `ON DELETE CASCADE` on relationship foreign keys

After each episode is saved, `extraction.js` automatically extracts named
entities **and relationships** from the conversation using `qwen2.5:3b` on
Ollama — fire-and-forget. Each saved entity is also linked to the episode
via the `entity_episodes` join table.

> For full details on the extraction pipeline and JSON format, see `entity-extraction.md`.  
> For the knowledge graph traversal layer, see `knowledge-graph.md`.

## Knowledge Graph Layer

`src/graph/index.js` provides SQLite-based graph traversal over the entities
and relationships tables. Two functions are exposed via HTTP:

- **`getNeighborhood(entityId, depth)`** — recursive CTE traversal, bidirectional, returns `{ nodes, edges }`
- **`getEntityNeighbors(entityIds[])`** — bulk 1-hop traversal for orchestration context assembly

> For design rationale, traversal queries, and integration with orchestration, see `knowledge-graph.md`.

## Summaries Layer

Session summaries are generated by `orchestration-service/src/services/summarization.js`
after each episode write and stored here via `POST /summaries`. The memory
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`.

## Delete Behaviour (SQLite + Qdrant consistency)

SQLite cascades handle relational cleanup, but Qdrant is a separate store and
must be cleaned explicitly. Each delete path that removes embedded rows also
removes the corresponding vectors:

| Delete | SQLite effect | Qdrant cleanup |
|---|---|---|
| `DELETE /episodes/:id` | Row removed | `semantic.deleteEpisode(id)` — vector by point ID |
| `DELETE /sessions/by-external/:id` | Session + episodes cascade-deleted | `semantic.deleteEpisodesBySession(id)` — **payload-filter** delete on `sessionId` |
| `DELETE /entities/:id` | Row removed, relationships cascade | `semantic.deleteEntity(id)` — vector by point ID |

All three Qdrant deletes are **fire-and-forget** with error logging, matching
the fire-and-forget write path — a Qdrant failure logs but does not fail the
delete.

The session path uses a **payload-filter** delete (matching on the `sessionId`
field in the vector payload) rather than enumerating episode point IDs. This
matters because the SQLite cascade has already removed the episode rows by the
time cleanup runs, so there are no IDs left to enumerate — the filter deletes
by payload regardless. It also cleans up any pre-existing orphans for that
session as a side effect.

> **Not cleaned on session delete:** entity vectors. Entities are shared across
> sessions and projects (`UNIQUE(name, type)` is global), so deleting one
> session must not remove entities that other sessions still reference. Entity
> vector lifecycle is tied to explicit entity deletion and the (planned) memory
> consolidation / orphan-cleanup pass.

> **Historical orphans:** vectors orphaned by session deletes *before* this
> cleanup existed are not removed retroactively. A one-time sweep (scroll the
> `episodes` collection, delete points whose `sessionId` no longer exists in
> SQLite) clears them.

## Project Delete Behaviour

Deleting a project runs as a transaction — it first nulls out `project_id`
on all assigned sessions, then deletes the project. This avoids a foreign
key constraint failure since `sessions.project_id` has no `ON DELETE` rule:

```js
const doDelete = db.transaction(() => {
  db.prepare(`UPDATE sessions SET project_id = NULL WHERE project_id = ?`).run(id);
  db.prepare(`DELETE FROM projects WHERE id = ?`).run(id);
});
```

For all HTTP endpoints, see `api-routes.md`.