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mempalace/tests/test_closet_llm.py
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"""Unit tests for the optional LLM-based closet regeneration.
These tests don't hit the network. They mock urllib to verify:
- LLMConfig correctly reads env vars and CLI overrides
- missing config is reported cleanly
- the OpenAI-compatible request shape is correct
- response parsing handles the standard chat-completions payload
"""
import json
import tempfile
from unittest.mock import patch
from mempalace.closet_llm import (
LLMConfig,
_call_llm,
_parsed_to_closet_lines,
regenerate_closets,
)
# ── LLMConfig ─────────────────────────────────────────────────────────────
class TestLLMConfig:
def test_reads_env_vars(self, monkeypatch):
monkeypatch.setenv("LLM_ENDPOINT", "http://localhost:11434/v1")
monkeypatch.setenv("LLM_KEY", "sk-abc")
monkeypatch.setenv("LLM_MODEL", "llama3:8b")
c = LLMConfig()
assert c.endpoint == "http://localhost:11434/v1"
assert c.key == "sk-abc"
assert c.model == "llama3:8b"
def test_cli_flags_override_env(self, monkeypatch):
monkeypatch.setenv("LLM_ENDPOINT", "http://env-endpoint/v1")
monkeypatch.setenv("LLM_MODEL", "env-model")
c = LLMConfig(endpoint="http://flag-endpoint/v1", model="flag-model")
assert c.endpoint == "http://flag-endpoint/v1"
assert c.model == "flag-model"
def test_trailing_slash_stripped(self):
c = LLMConfig(endpoint="http://foo/v1/", model="m")
assert c.endpoint == "http://foo/v1"
def test_missing_reports_required(self, monkeypatch):
monkeypatch.delenv("LLM_ENDPOINT", raising=False)
monkeypatch.delenv("LLM_KEY", raising=False)
monkeypatch.delenv("LLM_MODEL", raising=False)
c = LLMConfig()
missing = c.missing()
assert any("ENDPOINT" in m for m in missing)
assert any("MODEL" in m for m in missing)
# key is optional
assert not any("KEY" in m for m in missing)
def test_key_is_optional(self, monkeypatch):
monkeypatch.delenv("LLM_KEY", raising=False)
c = LLMConfig(endpoint="http://local/v1", model="m")
assert c.missing() == []
# ── _parsed_to_closet_lines ──────────────────────────────────────────────
class TestParsedToLines:
def test_topics_become_pointers(self):
parsed = {"topics": ["authentication", "jwt tokens"], "quotes": [], "summary": ""}
lines = _parsed_to_closet_lines(parsed, ["d1", "d2"], "Alice;Bob")
assert len(lines) == 2
assert "authentication|Alice;Bob|→d1,d2" in lines
assert "jwt tokens|Alice;Bob|→d1,d2" in lines
def test_quotes_and_summary_included(self):
parsed = {
"topics": ["t1"],
"quotes": ["[Igor] we ship Friday"],
"summary": "Release planning discussion",
}
lines = _parsed_to_closet_lines(parsed, ["d1"], "")
joined = "\n".join(lines)
assert "we ship Friday" in joined
assert "Release planning discussion" in joined
def test_caps_topics_at_15(self):
parsed = {"topics": [f"t{i}" for i in range(20)], "quotes": [], "summary": ""}
lines = _parsed_to_closet_lines(parsed, ["d1"], "")
assert len(lines) == 15
# ── _call_llm (HTTP mocked) ──────────────────────────────────────────────
class _FakeResp:
"""Mimics urlopen's context-manager response."""
def __init__(self, payload: dict, status: int = 200):
self._body = json.dumps(payload).encode("utf-8")
self.status = status
def __enter__(self):
return self
def __exit__(self, *a):
return False
def read(self):
return self._body
class TestCallLLM:
def _make_cfg(self):
return LLMConfig(endpoint="http://localhost:11434/v1", key="sk-test", model="llama3:8b")
def test_request_shape_and_parsing(self):
cfg = self._make_cfg()
captured = {}
def fake_urlopen(req, timeout=None):
captured["url"] = req.full_url
captured["headers"] = dict(req.header_items())
captured["body"] = json.loads(req.data.decode("utf-8"))
return _FakeResp(
{
"choices": [
{
"message": {
"content": json.dumps(
{
"topics": ["postgres"],
"quotes": ["[Igor] migrate now"],
"summary": "db migration",
}
)
}
}
],
"usage": {"prompt_tokens": 42, "completion_tokens": 17},
}
)
with patch("urllib.request.urlopen", side_effect=fake_urlopen):
parsed, usage = _call_llm(cfg, "/tmp/test.md", "w", "r", "content body")
assert parsed["topics"] == ["postgres"]
assert usage["prompt_tokens"] == 42
assert captured["url"] == "http://localhost:11434/v1/chat/completions"
# Authorization header is stored capitalized-then-lowercase depending on urllib version
auth_vals = {v for k, v in captured["headers"].items() if k.lower() == "authorization"}
assert "Bearer sk-test" in auth_vals
assert captured["body"]["model"] == "llama3:8b"
assert captured["body"]["messages"][0]["role"] == "user"
def test_omits_auth_header_when_no_key(self):
cfg = LLMConfig(endpoint="http://localhost:11434/v1", model="llama3:8b")
captured_headers = {}
def fake_urlopen(req, timeout=None):
captured_headers.update({k.lower(): v for k, v in req.header_items()})
return _FakeResp(
{
"choices": [{"message": {"content": '{"topics":[],"quotes":[],"summary":""}'}}],
"usage": {"prompt_tokens": 0, "completion_tokens": 0},
}
)
with patch("urllib.request.urlopen", side_effect=fake_urlopen):
_call_llm(cfg, "/tmp/x", "w", "r", "c")
assert "authorization" not in captured_headers
def test_strips_code_fences(self):
cfg = self._make_cfg()
fenced = '```json\n{"topics":["t1"],"quotes":[],"summary":""}\n```'
def fake_urlopen(req, timeout=None):
return _FakeResp(
{
"choices": [{"message": {"content": fenced}}],
"usage": {"prompt_tokens": 1, "completion_tokens": 1},
}
)
with patch("urllib.request.urlopen", side_effect=fake_urlopen):
parsed, _ = _call_llm(cfg, "/tmp/x", "w", "r", "c")
assert parsed == {"topics": ["t1"], "quotes": [], "summary": ""}
def test_returns_none_on_invalid_json(self):
cfg = self._make_cfg()
def fake_urlopen(req, timeout=None):
return _FakeResp(
{
"choices": [{"message": {"content": "not json at all"}}],
"usage": {"prompt_tokens": 1, "completion_tokens": 1},
}
)
with (
patch("urllib.request.urlopen", side_effect=fake_urlopen),
patch("mempalace.closet_llm.time.sleep"),
):
parsed, _ = _call_llm(cfg, "/tmp/x", "w", "r", "c")
assert parsed is None
def test_retries_on_json_decode_error(self):
cfg = self._make_cfg()
call_count = {"n": 0}
def fake_urlopen(req, timeout=None):
call_count["n"] += 1
return _FakeResp(
{
"choices": [{"message": {"content": "not json at all"}}],
"usage": {"prompt_tokens": 1, "completion_tokens": 1},
}
)
with (
patch("urllib.request.urlopen", side_effect=fake_urlopen),
patch("mempalace.closet_llm.time.sleep"),
):
parsed, _ = _call_llm(cfg, "/tmp/x", "w", "r", "c")
assert parsed is None
assert call_count["n"] == 3
# ── regenerate_closets error paths ───────────────────────────────────────
class TestRegenerateClosets:
def test_missing_config_returns_error(self, monkeypatch):
monkeypatch.delenv("LLM_ENDPOINT", raising=False)
monkeypatch.delenv("LLM_MODEL", raising=False)
with tempfile.TemporaryDirectory() as palace:
result = regenerate_closets(palace)
assert result["error"] == "missing-config"
assert any("ENDPOINT" in m for m in result["missing"])
def test_regen_purges_regex_closets_and_stamps_normalize_version(self, tmp_path):
"""Regression: before the hardening, regex closets for the same
source survived alongside fresh LLM closets (the old path used a
bare ``closets_col.delete(ids=...)`` with a swallowed exception).
Now we go through ``purge_file_closets`` + ``mine_lock`` + stamp
``NORMALIZE_VERSION`` so the next mine's stale-version gate doesn't
treat the LLM closets as leftovers to rebuild over."""
from mempalace.palace import (
NORMALIZE_VERSION,
get_closets_collection,
get_collection,
upsert_closet_lines,
)
palace = str(tmp_path / "palace")
# Seed one drawer and a pre-existing regex closet for the same source.
source = "/proj/story.md"
drawers = get_collection(palace, create=True)
drawers.upsert(
ids=["drawer_01"],
documents=["Content about JWT authentication."],
metadatas=[
{
"wing": "project",
"room": "auth",
"source_file": source,
"entities": "",
}
],
)
closets = get_closets_collection(palace)
upsert_closet_lines(
closets,
closet_id_base="closet_old_regex",
lines=["STALE_REGEX_TOPIC|;|→drawer_01"],
metadata={
"wing": "project",
"room": "auth",
"source_file": source,
"generated_by": "regex",
},
)
cfg = LLMConfig(endpoint="http://local/v1", model="llama3:8b")
def fake_urlopen(req, timeout=None):
return _FakeResp(
{
"choices": [
{
"message": {
"content": json.dumps(
{
"topics": ["jwt auth", "session expiry"],
"quotes": [],
"summary": "auth refactor",
}
)
}
}
],
"usage": {"prompt_tokens": 10, "completion_tokens": 5},
}
)
with patch("urllib.request.urlopen", side_effect=fake_urlopen):
result = regenerate_closets(palace, cfg=cfg)
assert result["processed"] == 1 and result["failed"] == 0
# Every surviving closet for this source must be LLM-generated and
# must carry the current NORMALIZE_VERSION.
survivors = closets.get(where={"source_file": source}, include=["documents", "metadatas"])
assert survivors["ids"], "LLM closets should have been written"
joined = "\n".join(survivors["documents"])
assert (
"STALE_REGEX_TOPIC" not in joined
), "pre-existing regex closet was not purged before LLM write"
assert "jwt auth" in joined
for meta in survivors["metadatas"]:
assert meta.get("generated_by", "").startswith("llm:")
assert meta.get("normalize_version") == NORMALIZE_VERSION
2026-04-29 19:01:54 -04:00
def test_regen_paginates_drawer_fetch(self, tmp_path):
"""Regression for #1073: drawers_col.get must be paginated at
batch_size=5000. A single get(limit=total, ...) on a palace with
more than SQLite's SQLITE_MAX_VARIABLE_NUMBER (32766) drawers
blows up inside chromadb. Matches the miner.status pattern
introduced in #851 (see #802, #850, #1073)."""
from mempalace import closet_llm as closet_llm_mod
palace = str(tmp_path / "palace")
# Build a fake collection: 12_000 drawers across 3 source files,
# enough to force 3 batches of batch_size=5000 (5000 + 5000 + 2000).
n_drawers = 12_000
ids = [f"d{i:05d}" for i in range(n_drawers)]
docs = [f"doc body {i}" for i in range(n_drawers)]
metas = [
{
"wing": "w",
"room": "r",
"source_file": f"/src/file_{i % 3}.md",
"entities": "",
}
for i in range(n_drawers)
]
get_calls: list = []
class FakeDrawersCol:
def count(self):
return n_drawers
def get(self, limit=None, offset=0, include=None, **kwargs):
get_calls.append({"limit": limit, "offset": offset, "include": include})
end = min(offset + (limit or n_drawers), n_drawers)
return {
"ids": ids[offset:end],
"documents": docs[offset:end],
"metadatas": metas[offset:end],
}
class FakeClosetsCol:
"""Accept the purge + upsert calls the success path makes."""
def get(self, *a, **kw):
return {"ids": [], "documents": [], "metadatas": []}
def delete(self, *a, **kw):
return None
def upsert(self, *a, **kw):
return None
fake_drawers = FakeDrawersCol()
fake_closets = FakeClosetsCol()
def fake_urlopen(req, timeout=None):
return _FakeResp(
{
"choices": [
{"message": {"content": '{"topics":["t1"],"quotes":[],"summary":""}'}}
],
"usage": {"prompt_tokens": 1, "completion_tokens": 1},
}
)
cfg = LLMConfig(endpoint="http://local/v1", model="m")
with (
patch.object(closet_llm_mod, "get_collection", return_value=fake_drawers),
patch.object(closet_llm_mod, "get_closets_collection", return_value=fake_closets),
patch.object(closet_llm_mod, "purge_file_closets", return_value=None),
patch.object(closet_llm_mod, "upsert_closet_lines", return_value=None),
patch("urllib.request.urlopen", side_effect=fake_urlopen),
):
result = regenerate_closets(palace, cfg=cfg, dry_run=True)
# Three paginated calls: (limit=5000, offset=0), (5000, 5000), (5000, 10000).
assert len(get_calls) == 3, f"expected 3 batched fetches, got {len(get_calls)}"
for call in get_calls:
assert (
call["limit"] == 5000
), f"batch must be 5000 — got {call['limit']} (would risk SQLITE_MAX_VARIABLE_NUMBER)"
# include must still request both documents and metadatas
assert "documents" in call["include"]
assert "metadatas" in call["include"]
assert [c["offset"] for c in get_calls] == [0, 5000, 10_000]
# by_source aggregation must be preserved exactly across batches:
# 12_000 drawers, 3 source files → 4_000 drawers each.
# dry_run=True short-circuits LLM calls but still walks by_source.
assert result.get("processed", 0) == 0 # dry_run
# Verify no single call tried to pull more than batch_size.
assert max(c["limit"] for c in get_calls) <= 5000
def test_regen_by_source_aggregates_across_batches(self, tmp_path):
"""Pagination must not change the by_source grouping — drawers for
the same source_file split across batches still land in one group."""
from mempalace import closet_llm as closet_llm_mod
palace = str(tmp_path / "palace")
# 7_500 drawers, alternating between two source files → forces
# splits across the 5000/2500 boundary. Each source ends up with
# 3_750 drawers after regrouping.
n_drawers = 7_500
ids = [f"d{i:05d}" for i in range(n_drawers)]
docs = [f"body-{i}" for i in range(n_drawers)]
metas = [
{
"wing": "w",
"room": "r",
"source_file": f"/src/file_{i % 2}.md",
"entities": "",
}
for i in range(n_drawers)
]
captured_sources: dict = {}
class FakeDrawersCol:
def count(self):
return n_drawers
def get(self, limit=None, offset=0, include=None, **kwargs):
end = min(offset + (limit or n_drawers), n_drawers)
return {
"ids": ids[offset:end],
"documents": docs[offset:end],
"metadatas": metas[offset:end],
}
class FakeClosetsCol:
def get(self, *a, **kw):
return {"ids": [], "documents": [], "metadatas": []}
def delete(self, *a, **kw):
return None
def upsert(self, *a, **kw):
return None
# Hook _call_llm to inspect what regenerate_closets aggregated
# per source before the HTTP boundary.
real_call_llm = closet_llm_mod._call_llm
def spying_call_llm(cfg, source_file, wing, room, content):
captured_sources[source_file] = content
return (
{"topics": ["t"], "quotes": [], "summary": ""},
{"prompt_tokens": 1, "completion_tokens": 1},
)
cfg = LLMConfig(endpoint="http://local/v1", model="m")
with (
patch.object(closet_llm_mod, "get_collection", return_value=FakeDrawersCol()),
patch.object(closet_llm_mod, "get_closets_collection", return_value=FakeClosetsCol()),
patch.object(closet_llm_mod, "purge_file_closets", return_value=None),
patch.object(closet_llm_mod, "upsert_closet_lines", return_value=None),
patch.object(closet_llm_mod, "_call_llm", side_effect=spying_call_llm),
):
regenerate_closets(palace, cfg=cfg)
# Both sources survived the pagination boundary.
assert set(captured_sources.keys()) == {"/src/file_0.md", "/src/file_1.md"}
# Each source accumulated exactly 3_750 drawer bodies, concatenated
# with the "\n\n" separator the regenerate path uses.
for source, content in captured_sources.items():
assert content.count("\n\n") == 3_749, (
f"{source}: expected 3_750 chunks joined (3_749 separators), "
f"got {content.count(chr(10) + chr(10)) + 1}"
)
# Silence unused-var lint.
assert real_call_llm is not None
def test_regen_uses_basename_not_split_slash(self, tmp_path, monkeypatch):
"""Regression: the old closet_id base used ``source.split('/')[-1]``
which silently degrades on Windows paths (``C:\\proj\\a.md`` →
the whole string). ``os.path.basename`` handles both separators."""
from mempalace.palace import get_collection, get_closets_collection
palace = str(tmp_path / "palace")
# Use a path whose basename differs between '/' split and
# os.path.basename only on a platform-aware function, but verify
# at minimum that IDs encode just the filename, not the full path.
source = "/deep/nested/project/dir/mydoc.md"
drawers = get_collection(palace, create=True)
drawers.upsert(
ids=["d1"],
documents=["body"],
metadatas=[{"wing": "w", "room": "r", "source_file": source, "entities": ""}],
)
cfg = LLMConfig(endpoint="http://local/v1", model="m")
def fake_urlopen(req, timeout=None):
return _FakeResp(
{
"choices": [
{"message": {"content": '{"topics":["t1"],"quotes":[],"summary":""}'}}
],
"usage": {"prompt_tokens": 1, "completion_tokens": 1},
}
)
with patch("urllib.request.urlopen", side_effect=fake_urlopen):
regenerate_closets(palace, cfg=cfg)
closets = get_closets_collection(palace)
ids = closets.get(where={"source_file": source}).get("ids", [])
assert ids
# IDs must not leak the full path (would happen if we used
# source.split('/')[-1] on Windows, or forgot to strip entirely).
for cid in ids:
assert "/" not in cid
assert "mydoc.md" in cid