feat: embeddings table + projector handlers (pure-Python cosine, T88)

tests/test_embeddings_state.py

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+from __future__ import annotations
+
+from chat.db.connection import open_db
+from chat.db.migrate import apply_migrations
+from chat.eventlog.log import append_event
+from chat.eventlog.projector import project
+import chat.state.memory  # registers memory_written handler
+import chat.state.embeddings  # registers embedding handlers
+from chat.state.embeddings import get_embedding, list_embeddings_for_owner
+
+
+def _base_memory(**overrides):
+    payload = {
+        "owner_id": "bot_a",
+        "chat_id": "chat_bot_a",
+        "scene_id": 1,
+        "pov_summary": "She laughed at his joke about owls.",
+        "witness_you": 1,
+        "witness_host": 1,
+        "witness_guest": 0,
+        "chat_clock_at": "2026-04-26T10:00:00",
+        "source": "direct",
+        "reliability": 1.0,
+        "significance": 1,
+        "pinned": 0,
+        "auto_pinned": 0,
+    }
+    payload.update(overrides)
+    return payload
+
+
+def _vec(n: int = 384, base: float = 0.1) -> list[float]:
+    """Return a length-n float vector with predictable values for assertions."""
+    return [round(base + i * 0.001, 6) for i in range(n)]
+
+
+def test_embedding_indexed_inserts_row(tmp_path):
+    db = tmp_path / "t.db"
+    apply_migrations(db)
+    with open_db(db) as conn:
+        append_event(conn, kind="memory_written", payload=_base_memory())
+        project(conn)
+        memory_id = conn.execute("SELECT id FROM memories").fetchone()[0]
+
+        vector = _vec(384, base=0.1)
+        append_event(
+            conn,
+            kind="embedding_indexed",
+            payload={
+                "memory_id": memory_id,
+                "vector": vector,
+                "model": "test-model",
+                "dim": 384,
+            },
+        )
+        project(conn)
+
+        emb = get_embedding(conn, memory_id)
+        assert emb is not None
+        assert emb["memory_id"] == memory_id
+        assert emb["vector"] == vector
+        assert emb["model"] == "test-model"
+        assert emb["dim"] == 384
+        assert emb["indexed_at"] is not None
+
+
+def test_embedding_deindexed_removes_row(tmp_path):
+    db = tmp_path / "t.db"
+    apply_migrations(db)
+    with open_db(db) as conn:
+        append_event(conn, kind="memory_written", payload=_base_memory())
+        project(conn)
+        memory_id = conn.execute("SELECT id FROM memories").fetchone()[0]
+
+        append_event(
+            conn,
+            kind="embedding_indexed",
+            payload={
+                "memory_id": memory_id,
+                "vector": _vec(),
+                "model": "test-model",
+                "dim": 384,
+            },
+        )
+        project(conn)
+        assert get_embedding(conn, memory_id) is not None
+
+        append_event(
+            conn,
+            kind="embedding_deindexed",
+            payload={"memory_id": memory_id},
+        )
+        project(conn)
+        assert get_embedding(conn, memory_id) is None
+
+
+def test_embedding_indexed_replaces_existing(tmp_path):
+    db = tmp_path / "t.db"
+    apply_migrations(db)
+    with open_db(db) as conn:
+        append_event(conn, kind="memory_written", payload=_base_memory())
+        project(conn)
+        memory_id = conn.execute("SELECT id FROM memories").fetchone()[0]
+
+        vec_a = _vec(384, base=0.1)
+        vec_b = _vec(384, base=0.5)
+        append_event(
+            conn,
+            kind="embedding_indexed",
+            payload={
+                "memory_id": memory_id,
+                "vector": vec_a,
+                "model": "test-model",
+                "dim": 384,
+            },
+        )
+        project(conn)
+        first = get_embedding(conn, memory_id)
+        assert first is not None
+        assert first["vector"] == vec_a
+
+        append_event(
+            conn,
+            kind="embedding_indexed",
+            payload={
+                "memory_id": memory_id,
+                "vector": vec_b,
+                "model": "test-model",
+                "dim": 384,
+            },
+        )
+        project(conn)
+        second = get_embedding(conn, memory_id)
+        assert second is not None
+        assert second["vector"] == vec_b
+        # Still exactly one row for this memory.
+        count = conn.execute(
+            "SELECT COUNT(*) FROM embeddings WHERE memory_id = ?", (memory_id,)
+        ).fetchone()[0]
+        assert count == 1
+
+
+def test_list_embeddings_for_owner_returns_joined_rows(tmp_path):
+    db = tmp_path / "t.db"
+    apply_migrations(db)
+    with open_db(db) as conn:
+        # Two memories for bot_a, one for bot_b.
+        append_event(
+            conn,
+            kind="memory_written",
+            payload=_base_memory(
+                owner_id="bot_a",
+                pov_summary="Alpha memory.",
+                significance=2,
+            ),
+        )
+        append_event(
+            conn,
+            kind="memory_written",
+            payload=_base_memory(
+                owner_id="bot_a",
+                pov_summary="Beta memory.",
+                significance=3,
+            ),
+        )
+        append_event(
+            conn,
+            kind="memory_written",
+            payload=_base_memory(
+                owner_id="bot_b",
+                pov_summary="Gamma memory.",
+                significance=1,
+            ),
+        )
+        project(conn)
+
+        rows = conn.execute(
+            "SELECT id, owner_id FROM memories ORDER BY id"
+        ).fetchall()
+        # Index every memory with a distinct vector so we can check ordering.
+        for i, (mid, _owner) in enumerate(rows):
+            append_event(
+                conn,
+                kind="embedding_indexed",
+                payload={
+                    "memory_id": mid,
+                    "vector": _vec(384, base=0.1 * (i + 1)),
+                    "model": "test-model",
+                    "dim": 384,
+                },
+            )
+        project(conn)
+
+        a_rows = list_embeddings_for_owner(conn, "bot_a")
+        assert len(a_rows) == 2
+        summaries = {r["pov_summary"] for r in a_rows}
+        assert summaries == {"Alpha memory.", "Beta memory."}
+        sigs = {r["significance"] for r in a_rows}
+        assert sigs == {2, 3}
+        for r in a_rows:
+            assert r["model"] == "test-model"
+            assert r["dim"] == 384
+            assert isinstance(r["vector"], list)
+            assert len(r["vector"]) == 384
+            assert r["witness_you"] == 1
+            assert r["witness_host"] == 1
+            assert r["witness_guest"] == 0
+
+        b_rows = list_embeddings_for_owner(conn, "bot_b")
+        assert len(b_rows) == 1
+        assert b_rows[0]["pov_summary"] == "Gamma memory."
+
+
+def test_get_embedding_returns_none_when_missing(tmp_path):
+    db = tmp_path / "t.db"
+    apply_migrations(db)
+    with open_db(db) as conn:
+        assert get_embedding(conn, 999) is None