headroom/tests/test_memory_query.py

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fix(proxy): MemoryDecision contract + 3 bypass bugs + drop 500-char query cap Three bug classes fixed plus three architectural extension points, together making the memory subsystem uniform across all five sites and ready for future Mem0/Letta/Cognee backend integration. ## Bug fixes * **3 sites silently ignored `x-headroom-bypass: true`** — ``anthropic.py:1303``, ``openai.py:1620`` (chat), ``gemini.py:382`` injected memory under bypass, mutating request bytes when the user explicitly asked for byte-faithful passthrough. Now gated on ``MemoryDecision.decide(...)`` which honours bypass uniformly. * **500-char query truncation** — ``memory_handler._extract_user_query`` capped at 500 chars, silently throwing away signal. None of Letta / Mem0 / Cognee / Supermemory truncate. Removed; the embedding model handles its own window. * **Gemini had no timeout** on ``search_and_format_context`` — the only chat handler without one. A slow backend could stall requests. Added ``asyncio.wait_for`` matching Anthropic + OpenAI Chat + Responses. * **WS injected into ``body["instructions"]``** — the system / cache-hot-zone field, violating invariant I2 (all other handlers inject at user-message tail). Switched to ``ws_response_body["input"]`` for string-shaped input; list-shaped input deferred to the Rust handler with a clear log. ## New value types (extension points) * ``MemoryDecision`` — frozen dataclass + factory. Five-way skip reason enum (``bypass_header`` / ``no_handler`` / ``no_user_id`` / ``mode_disabled`` / ``mode_tool``). ``apply_to_tags()`` surfaces the skip reason in ``RequestOutcome.tags["memory_skip_reason"]`` — dashboards can now slice memory-blind traffic by cause. * ``MemoryQuery`` — multi-source retrieval query. ``from_messages()`` walks the conversation and extracts latest user text + recent tool outputs + recent assistant turns at FULL fidelity (no truncation). Handles both OpenAI-shape ``role: tool`` and Anthropic-shape ``tool_result`` content blocks. ``to_embedding_input()`` produces a delimited concatenation the embedder sees as structured context. * ``MemoryInjectionBudget`` — uniform token / entry / similarity bound on the formatted injection block. Pre-this-PR no cap (~4000 tokens could land per request). Default 1024 tokens / 10 entries / 0.3 similarity floor. ``apply_to_text()`` truncates at line boundaries so dashboard renders intact bullet points. ## Migration scope — all 5 sites uniform at the GATE level | Site | Handler | Pre-PR gate | Post-PR gate | |---|---|---|---| | 1 | anthropic.py | `memory_handler and memory_user_id` | `memory_decision.inject` | | 2 | gemini.py | `memory_handler and memory_user_id` | `memory_decision.inject` | | 3 | openai.py chat | `memory_handler and memory_user_id` | `memory_decision.inject` | | 4 | openai.py Responses | `memory_handler and memory_user_id and not _bypass` | `responses_memory_decision.inject` | | 6 | openai.py WS | `memory_handler and body and not _ws_bypass` | `ws_memory_decision.inject` | Site 5 (Responses bypass-elif log-only branch) is preserved verbatim. ## Deliberately deferred (separate PRs) * **Memory injection order inversion** — sites 4 and 6 inject BEFORE compression; sites 1/2/3 inject AFTER. Moving 4 + 6 to post-compression needs its own focused cache-stability testing. * **Importance scoring** — recency × source × access-count. * **Per-memory atomize-and-split** — Mem0/Supermemory pattern. * **AST-aware code chunking for tool outputs** — Supermemory's code-chunk approach. The contracts shipped here (``MemoryQuery`` + ``MemoryInjectionBudget``) are the extension points those will plug into. ## Test coverage * 20 new tests on ``MemoryDecision`` * 14 new tests on ``MemoryQuery`` (full-fidelity, multi-source) * 10 new tests on ``MemoryInjectionBudget`` * 3 new AST contract tests (no raw gate; no system writes; every search call passes ``query=``) * All existing memory + cache-stability tests still pass (222 passed) ## Rust portability Every new value type ports cleanly to a frozen Rust struct. Pure functions, no I/O, no global state. Same Python ↔ Rust parity-test pattern that ``CompressionDecision`` already uses. ## Zero-regression contract Existing chat/completion harnesses (Claude Code, Codex, Cursor, Continue, Aider) see ZERO wire-byte changes when bypass is NOT set. When bypass IS set, the 3 chat handlers now correctly skip memory injection — that's the bug fix, not a regression.
2026-05-19 10:04:21 -05:00
"""Tests for :class:`headroom.proxy.memory_query.MemoryQuery`.
``MemoryQuery`` is the multi-source query value type that replaces
the pre-PR pattern of "use the latest user message, truncated to 500
chars". The truncation was a real bug — none of Letta/Mem0/Cognee/
Supermemory truncate the embedding input.
The query is built from three sources, all preserved at full fidelity:
* ``user_text`` latest user message, untruncated
* ``recent_tool_outputs`` last N tool results (often the most
relevant signal in coding sessions)
* ``recent_assistant_turns`` last K assistant turns for intent
Building the embedding input is a simple concatenation with delimiters
so the embedding model sees structured context, not a wall of text.
"""
from __future__ import annotations
from dataclasses import FrozenInstanceError
from headroom.proxy.memory_query import MemoryQuery
# ── Value-type contract ───────────────────────────────────────────────
def test_memory_query_is_frozen() -> None:
q = MemoryQuery(
user_text="hello",
recent_tool_outputs=(),
recent_assistant_turns=(),
conversation_id=None,
)
try:
q.user_text = "mutated" # type: ignore[misc]
except FrozenInstanceError:
pass
else:
raise AssertionError("MemoryQuery must be frozen")
def test_memory_query_value_equal() -> None:
a = MemoryQuery(
user_text="hi", recent_tool_outputs=(), recent_assistant_turns=(), conversation_id="c1"
)
b = MemoryQuery(
user_text="hi", recent_tool_outputs=(), recent_assistant_turns=(), conversation_id="c1"
)
assert a == b
# ── NO TRUNCATION — the entire point of this type ────────────────────
def test_full_user_message_is_preserved_no_500_char_cap() -> None:
"""Pre-PR: ``_extract_user_query`` capped at 500 chars. None of
the four memory systems we surveyed truncate. MemoryQuery must
preserve the full message embedding models handle their own
window (MiniLM 512 tok; BGE-small 8K tok)."""
long_msg = "a" * 8000 # 8KB user message
q = MemoryQuery(
user_text=long_msg,
recent_tool_outputs=(),
recent_assistant_turns=(),
conversation_id=None,
)
embedding_input = q.to_embedding_input()
# Original content fully present — count actual occurrences of "a" run.
assert "a" * 8000 in embedding_input
def test_tool_outputs_preserved_at_full_fidelity() -> None:
"""Tool results — often the strongest retrieval signal in coding
sessions must NOT be truncated."""
big_tool_output = "GREP RESULT\n" + "match line\n" * 1000 # large grep output
q = MemoryQuery(
user_text="how do I fix this?",
recent_tool_outputs=(big_tool_output,),
recent_assistant_turns=(),
conversation_id=None,
)
embedding_input = q.to_embedding_input()
assert "match line" * 1000 in embedding_input.replace("\n", "")
# ── Multi-source query construction ──────────────────────────────────
def test_embedding_input_includes_all_sources() -> None:
"""The query the embedder sees should include user msg + recent
tool outputs + recent assistant turns. Each source is delimited
so the embedder treats them as distinct context, not run-on text."""
q = MemoryQuery(
user_text="fix the auth bug",
recent_tool_outputs=("auth.py:42: KeyError",),
recent_assistant_turns=("I'll look at the auth flow",),
conversation_id=None,
)
txt = q.to_embedding_input()
assert "fix the auth bug" in txt
assert "auth.py:42: KeyError" in txt
assert "I'll look at the auth flow" in txt
def test_empty_sources_still_produce_valid_query() -> None:
"""A user-msg-only query (no tools, no prior assistant) is the
minimum viable case common on first turn."""
q = MemoryQuery(
user_text="hello",
recent_tool_outputs=(),
recent_assistant_turns=(),
conversation_id=None,
)
txt = q.to_embedding_input()
assert txt
assert "hello" in txt
def test_empty_user_text_is_valid_when_only_tool_signal() -> None:
"""Edge case: agent-driven request with no new user text (e.g. a
tool-call follow-up). Query is the tool output."""
q = MemoryQuery(
user_text="",
recent_tool_outputs=("ls -la /home/user/projects/headroom",),
recent_assistant_turns=(),
conversation_id=None,
)
txt = q.to_embedding_input()
assert "ls -la /home/user/projects/headroom" in txt
# ── from_messages constructor ────────────────────────────────────────
def test_from_messages_extracts_latest_user_text() -> None:
"""Construct from a chat-style messages list — picks the most
recent ``role: user`` content."""
messages = [
{"role": "user", "content": "first turn"},
{"role": "assistant", "content": "ack"},
{"role": "user", "content": "second turn"},
]
q = MemoryQuery.from_messages(messages, lookback_assistant=0, lookback_tools=0)
assert q.user_text == "second turn"
def test_from_messages_extracts_recent_assistant_turns_in_order() -> None:
"""Recent assistant turns are pulled in chronological order
(oldest of the lookback window first, latest last)."""
messages = [
{"role": "user", "content": "u1"},
{"role": "assistant", "content": "a1"},
{"role": "user", "content": "u2"},
{"role": "assistant", "content": "a2"},
{"role": "user", "content": "u3"},
]
q = MemoryQuery.from_messages(messages, lookback_assistant=2, lookback_tools=0)
assert q.recent_assistant_turns == ("a1", "a2")
assert q.user_text == "u3"
def test_from_messages_caps_assistant_lookback() -> None:
"""``lookback_assistant=K`` keeps only the K most recent assistant
turns. With lookback=1 and three assistant turns, only the latest."""
messages = [
{"role": "assistant", "content": "a1"},
{"role": "assistant", "content": "a2"},
{"role": "assistant", "content": "a3"},
{"role": "user", "content": "u"},
]
q = MemoryQuery.from_messages(messages, lookback_assistant=1, lookback_tools=0)
assert q.recent_assistant_turns == ("a3",)
def test_from_messages_extracts_tool_outputs() -> None:
"""Tool results are pulled from ``role: tool`` messages (OpenAI
shape) pre-PR these never participated in retrieval at all."""
messages = [
{"role": "user", "content": "list files"},
{"role": "assistant", "content": "I'll run ls"},
{"role": "tool", "content": "main.py\nREADME.md\n"},
{"role": "user", "content": "now read main.py"},
]
q = MemoryQuery.from_messages(messages, lookback_assistant=0, lookback_tools=2)
assert q.recent_tool_outputs == ("main.py\nREADME.md\n",)
def test_from_messages_handles_anthropic_tool_result_shape() -> None:
"""Anthropic shape: tool_result inside the user message as a
content block. The constructor should still extract it."""
messages = [
{"role": "user", "content": "go"},
{
"role": "user",
"content": [
{"type": "tool_result", "tool_use_id": "x", "content": "ANTHROPIC_TOOL_OUTPUT"}
],
},
{"role": "user", "content": "thanks"},
]
q = MemoryQuery.from_messages(messages, lookback_assistant=0, lookback_tools=2)
assert "ANTHROPIC_TOOL_OUTPUT" in q.recent_tool_outputs
def test_from_messages_empty_returns_empty_query() -> None:
"""No messages → empty query, no exception."""
q = MemoryQuery.from_messages([], lookback_assistant=2, lookback_tools=2)
assert q.user_text == ""
assert q.recent_assistant_turns == ()
assert q.recent_tool_outputs == ()
def test_from_messages_handles_assistant_only_messages() -> None:
"""Edge case: no user messages at all (rare; agent-driven). Should
still build a valid query."""
messages = [{"role": "assistant", "content": "assistant only"}]
q = MemoryQuery.from_messages(messages, lookback_assistant=2, lookback_tools=0)
assert q.user_text == ""
assert q.recent_assistant_turns == ("assistant only",)