merge: T92 pure-Python cosine vector search service
This commit is contained in:
Joseph Doherty
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"""Vector search service (T92, Phase 4).
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Pure-Python cosine similarity over the embeddings table. Phase 4 ships
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this without sqlite-vec because the host Python build doesn't support
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loadable extensions. For single-user scale (< few thousand memories
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per owner), iterating in Python is sub-millisecond.
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Phase 4.5+ may swap to sqlite-vec when the host Python supports
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enable_load_extension; the public API stays stable.
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"""
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from __future__ import annotations
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import math
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from sqlite3 import Connection
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from chat.state.embeddings import list_embeddings_for_owner
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_VALID_WITNESS_ROLES = {"you", "host", "guest"}
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def _cosine_similarity(a: list[float], b: list[float]) -> float:
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"""Cosine similarity. Assumes both vectors are non-zero."""
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if len(a) != len(b):
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return 0.0
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dot = sum(x * y for x, y in zip(a, b))
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norm_a = math.sqrt(sum(x * x for x in a)) or 1.0
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norm_b = math.sqrt(sum(x * x for x in b)) or 1.0
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return dot / (norm_a * norm_b)
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def vector_search(
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conn: Connection,
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*,
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owner_id: str,
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witness_role: str, # "you" | "host" | "guest"
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query_vector: list[float],
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k: int = 4,
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) -> list[dict]:
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"""Return top-K memories by cosine similarity to query_vector,
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witness-filtered for the viewer's POV. Returns rows with
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{memory_id, pov_summary, significance, score} sorted by score
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DESC. Empty list if no embeddings indexed for this owner.
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"""
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if witness_role not in _VALID_WITNESS_ROLES:
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raise ValueError(
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f"witness_role must be one of {_VALID_WITNESS_ROLES}, got {witness_role!r}"
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)
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rows = list_embeddings_for_owner(conn, owner_id)
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if not rows:
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return []
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# Witness-filter by the requesting role.
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witness_key = f"witness_{witness_role}"
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filtered = [r for r in rows if r.get(witness_key) == 1]
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if not filtered:
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return []
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scored: list[tuple[float, dict]] = []
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for row in filtered:
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score = _cosine_similarity(query_vector, row["vector"])
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scored.append(
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(
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score,
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{
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"memory_id": row["memory_id"],
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"pov_summary": row["pov_summary"],
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"significance": row["significance"],
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"score": score,
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},
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)
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)
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scored.sort(key=lambda t: t[0], reverse=True)
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return [item for _, item in scored[:k]]
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__all__ = ["vector_search"]
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@@ -0,0 +1,242 @@
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from __future__ import annotations
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import pytest
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from chat.db.connection import open_db
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from chat.db.migrate import apply_migrations
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from chat.eventlog.log import append_event
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from chat.eventlog.projector import project
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import chat.state.memory # registers memory_written handler
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import chat.state.embeddings # registers embedding handlers
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from chat.services.vector_search import vector_search
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def _base_memory(**overrides):
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payload = {
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"owner_id": "bot_a",
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"chat_id": "chat_bot_a",
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"scene_id": 1,
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"pov_summary": "She laughed at his joke about owls.",
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"witness_you": 1,
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"witness_host": 1,
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"witness_guest": 0,
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"chat_clock_at": "2026-04-26T10:00:00",
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"source": "direct",
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"reliability": 1.0,
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"significance": 1,
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"pinned": 0,
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"auto_pinned": 0,
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}
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payload.update(overrides)
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return payload
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def _one_hot(dim: int, idx: int) -> list[float]:
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"""Return a one-hot vector of length ``dim`` with 1.0 at ``idx``."""
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v = [0.0] * dim
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v[idx] = 1.0
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return v
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def _seed_memory_with_embedding(
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conn,
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*,
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owner_id: str,
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pov_summary: str,
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vector: list[float],
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significance: int = 1,
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witness_you: int = 1,
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witness_host: int = 1,
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witness_guest: int = 0,
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model: str = "test-model",
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) -> int:
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append_event(
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conn,
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kind="memory_written",
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payload=_base_memory(
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owner_id=owner_id,
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pov_summary=pov_summary,
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significance=significance,
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witness_you=witness_you,
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witness_host=witness_host,
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witness_guest=witness_guest,
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),
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)
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project(conn)
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memory_id = conn.execute(
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"SELECT id FROM memories WHERE pov_summary = ?", (pov_summary,)
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).fetchone()[0]
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append_event(
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conn,
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kind="embedding_indexed",
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payload={
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"memory_id": memory_id,
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"vector": vector,
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"model": model,
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"dim": len(vector),
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},
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)
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project(conn)
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return memory_id
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def test_vector_search_returns_nearest_neighbors(tmp_path):
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db = tmp_path / "t.db"
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apply_migrations(db)
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with open_db(db) as conn:
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dim = 8
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ids = []
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for i in range(5):
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mid = _seed_memory_with_embedding(
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conn,
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owner_id="bot_a",
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pov_summary=f"Memory {i}.",
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vector=_one_hot(dim, i),
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)
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ids.append(mid)
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# Query close to memory index 3 (one-hot at position 3, plus tiny noise).
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query = _one_hot(dim, 3)
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query[2] = 0.01
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results = vector_search(
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conn,
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owner_id="bot_a",
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witness_role="you",
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query_vector=query,
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k=3,
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)
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assert len(results) == 3
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# Top-1 must be memory at index 3.
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assert results[0]["memory_id"] == ids[3]
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assert results[0]["pov_summary"] == "Memory 3."
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# Score for the near-perfect match should be very close to 1.0.
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assert results[0]["score"] > 0.99
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# Results sorted by score DESC.
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scores = [r["score"] for r in results]
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assert scores == sorted(scores, reverse=True)
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# Second place should be memory index 2 (the small noise component).
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assert results[1]["memory_id"] == ids[2]
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def test_vector_search_respects_witness_filter(tmp_path):
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db = tmp_path / "t.db"
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apply_migrations(db)
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with open_db(db) as conn:
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dim = 4
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# Memory visible to you=1, host=1, guest=0.
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_seed_memory_with_embedding(
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conn,
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owner_id="bot_a",
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pov_summary="Restricted.",
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vector=_one_hot(dim, 0),
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witness_you=1,
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witness_host=1,
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witness_guest=0,
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)
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# Guest sees nothing.
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guest_results = vector_search(
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conn,
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owner_id="bot_a",
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witness_role="guest",
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query_vector=_one_hot(dim, 0),
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k=4,
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)
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assert guest_results == []
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# Host sees the memory.
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host_results = vector_search(
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conn,
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owner_id="bot_a",
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witness_role="host",
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query_vector=_one_hot(dim, 0),
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k=4,
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)
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assert len(host_results) == 1
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assert host_results[0]["pov_summary"] == "Restricted."
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# You also see it.
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you_results = vector_search(
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conn,
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owner_id="bot_a",
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witness_role="you",
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query_vector=_one_hot(dim, 0),
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k=4,
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)
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assert len(you_results) == 1
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def test_vector_search_respects_owner_filter(tmp_path):
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db = tmp_path / "t.db"
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apply_migrations(db)
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with open_db(db) as conn:
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dim = 4
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_seed_memory_with_embedding(
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conn,
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owner_id="bot_a",
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pov_summary="Owner A memory.",
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vector=_one_hot(dim, 0),
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)
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_seed_memory_with_embedding(
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conn,
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owner_id="bot_b",
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pov_summary="Owner B memory.",
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vector=_one_hot(dim, 0),
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)
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a_results = vector_search(
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conn,
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owner_id="bot_a",
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witness_role="you",
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query_vector=_one_hot(dim, 0),
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k=10,
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)
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assert len(a_results) == 1
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assert a_results[0]["pov_summary"] == "Owner A memory."
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b_results = vector_search(
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conn,
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owner_id="bot_b",
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witness_role="you",
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query_vector=_one_hot(dim, 0),
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k=10,
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)
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assert len(b_results) == 1
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assert b_results[0]["pov_summary"] == "Owner B memory."
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def test_vector_search_invalid_witness_role_raises(tmp_path):
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db = tmp_path / "t.db"
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apply_migrations(db)
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with open_db(db) as conn:
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with pytest.raises(ValueError, match="witness_role"):
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vector_search(
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conn,
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owner_id="bot_a",
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witness_role="invalid",
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query_vector=[1.0, 0.0, 0.0],
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k=4,
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)
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def test_vector_search_empty_when_no_embeddings_indexed(tmp_path):
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db = tmp_path / "t.db"
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apply_migrations(db)
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with open_db(db) as conn:
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# Seed a memory but don't index an embedding for it.
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append_event(
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conn,
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kind="memory_written",
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payload=_base_memory(owner_id="bot_a", pov_summary="No embedding here."),
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)
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project(conn)
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results = vector_search(
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conn,
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owner_id="bot_a",
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witness_role="you",
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query_vector=[1.0, 0.0, 0.0, 0.0],
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k=4,
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)
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assert results == []
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