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chore: import upstream snapshot with attribution
2026-07-13 12:03:20 +08:00

131 行
5.0 KiB
Python

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"""Tests for :class:`headroom.proxy.memory_injection.MemoryInjectionBudget`.
Pre-PR Headroom had **no token cap on injected memory**: top_k=10
candidates × ~400 tokens each = up to ~4000 tokens injected per
request. None of Letta/Mem0/Cognee/Supermemory ship a token-uncapped
injection path on the hot wire.
``MemoryInjectionBudget`` is the single configurable cap applied to
every injection site so all 5 sites are uniformly bounded — set the
budget once, apply at every handler, dashboards see the same shape.
"""
from __future__ import annotations
from dataclasses import FrozenInstanceError
from headroom.proxy.memory_injection import MemoryInjectionBudget
# ── Value-type contract ───────────────────────────────────────────────
def test_budget_is_frozen() -> None:
b = MemoryInjectionBudget()
try:
b.max_tokens = 99 # type: ignore[misc]
except FrozenInstanceError:
pass
else:
raise AssertionError("MemoryInjectionBudget must be frozen")
def test_budget_defaults() -> None:
"""Default budget is conservative — 1024 tokens, 10 entries, 0.3
similarity floor. Operators can override via config; the default
is hard-set so a misconfiguration can't accidentally unbound
injection."""
b = MemoryInjectionBudget()
assert b.max_tokens == 1024
assert b.max_entries == 10
assert b.min_similarity == 0.3
def test_budget_value_equal() -> None:
a = MemoryInjectionBudget(max_tokens=512, max_entries=5, min_similarity=0.5)
b = MemoryInjectionBudget(max_tokens=512, max_entries=5, min_similarity=0.5)
assert a == b
# ── apply_to_text — bounding the formatted context block ─────────────
def test_apply_to_text_returns_input_when_under_budget() -> None:
"""Short context passes through unchanged — no spurious mutation."""
b = MemoryInjectionBudget(max_tokens=1024)
text = "## Relevant Memories\n1. small fact\n"
out = b.apply_to_text(text)
assert out == text
def test_apply_to_text_truncates_when_over_budget() -> None:
"""Large context is bounded — truncated at the budget. The
truncation here is on the OUTPUT (the formatted injection block),
NOT on the INPUT (which keeps full fidelity per MemoryQuery
contract)."""
# 4 tokens/char heuristic in our cap — make the input clearly
# over even the most generous budget.
b = MemoryInjectionBudget(max_tokens=128) # ~512 chars at 4 char/token
huge = "x" * 100000
out = b.apply_to_text(huge)
# Output should be substantially smaller than input.
assert len(out) < len(huge)
def test_apply_to_text_preserves_full_lines() -> None:
"""When truncating, prefer cutting at line boundaries so the
dashboard renders intact memory entries (no half-truncated bullet
point)."""
b = MemoryInjectionBudget(max_tokens=64) # very tight
text = "## Relevant Memories\n" + "".join(f"{i}. fact {i}\n" for i in range(100))
out = b.apply_to_text(text)
# No partial last line — every retained line ends in newline or is
# the final line.
if out and not out.endswith("\n"):
# The last char is the closing of the final line; it must not
# be in the middle of "fact " — easy heuristic: must not end
# mid-word with a hanging digit-then-period.
assert ". fact" not in out[-15:] or out.rstrip().endswith(("fact 0", "fact 1", "fact 2"))
def test_apply_to_text_handles_empty_input() -> None:
"""Empty input → empty output."""
assert MemoryInjectionBudget().apply_to_text("") == ""
# ── apply_to_entries — bounding the list before formatting ───────────
def test_apply_to_entries_caps_entry_count() -> None:
"""Even if the backend returns 100 candidates, the budget caps
entry count to ``max_entries``."""
b = MemoryInjectionBudget(max_entries=3)
entries = [{"content": f"entry {i}", "score": 0.9 - i * 0.01} for i in range(20)]
out = b.apply_to_entries(entries)
assert len(out) == 3
def test_apply_to_entries_preserves_order_of_input() -> None:
"""Budget doesn't re-rank — the backend's order is preserved. (The
backend should already have ranked by score; budget just caps.)"""
b = MemoryInjectionBudget(max_entries=2)
entries = [
{"content": "alpha", "score": 0.9},
{"content": "beta", "score": 0.8},
{"content": "gamma", "score": 0.7},
]
out = b.apply_to_entries(entries)
assert [e["content"] for e in out] == ["alpha", "beta"]
def test_apply_to_entries_filters_below_min_similarity() -> None:
"""Entries below ``min_similarity`` are dropped, regardless of
entry-count budget remaining."""
b = MemoryInjectionBudget(max_entries=10, min_similarity=0.5)
entries = [
{"content": "kept", "score": 0.9},
{"content": "dropped", "score": 0.3},
{"content": "kept2", "score": 0.55},
]
out = b.apply_to_entries(entries)
assert {e["content"] for e in out} == {"kept", "kept2"}