headroom/tests/test_proxy_pipeline_lifecycle.py
Focused Instability c2106cbdab
feat: probe-based retention scoring of recorded compression events (#862)
Closes #861

## What

First piece of compression quality measurement on **real proxied
sessions** (vs the existing public-benchmark evals): an opt-in recorder
captures (original, compressed) message pairs at each compression event,
and a deterministic offline prober scores what survived.

**Recorder** (`headroom/proxy/probe_recorder.py`)
- `CompressionEventRecorder` implements the existing `PipelineExtension`
protocol, subscribed to `INPUT_COMPRESSED`. Registered ONLY when
`HEADROOM_PROBE_RECORD_DIR` is set — off means not even constructed,
zero request-path overhead.
- One JSONL line per compression event that changed tokens: `{ts,
request_id, provider, model, tokens_before, tokens_after,
transforms_applied, original_messages, compressed_messages}`. One file
per PID (no interleaving), directory mode 0700.
- Fail-open everywhere: construction failure logs a warning and disables
recording; runtime exceptions are already swallowed by
`PipelineExtensionManager.emit`.
- Enabling handler change: the two `INPUT_COMPRESSED` emit sites
(anthropic + openai) add a read-only `original_messages` reference to
event metadata. No copies, no behavior change for other consumers.

**Probes** (`headroom/evals/session_probes.py` + `headroom evals probes`
CLI)
- Probe targets extracted from ORIGINAL tool-result content across three
dimensions: **exact numerics** (number + key context, incl. JSON-quoted
keys), **artifact trail** (paths, URLs, hex hashes, UUIDs), **error
evidence** (lines matching the existing `is_error_content` heuristic).
- Each target classified as **retained** (verbatim, or surviving a
legitimate format conversion — punctuation-normalized match; numerics
require key AND value to survive; error lines tolerate dropped JSON key
prefixes), **recoverable** (absent but a CCR retrieval marker is
present), or **lost** (gone with no retrieval path).
- Report: aggregate retention per dimension, bucketed by compression
ratio (the quality-per-ratio curve), and grouped per transform.
`--json-output` for machine-readable results.
- Fully offline: no LLM, no API key. The recording format is designed to
feed an LLM-judge pass later (out of scope per #861).

## Tests

33 new tests (red before, green after): `tests/test_probe_recorder.py`
(11 — event filtering, JSONL shape, env activation, fail-open on
unusable path, 0700 dir mode) and `tests/test_session_probes.py` (22 —
extraction per dimension incl. JSON-quoted numerics,
retained/recoverable/lost classification, format-change survival for
numerics and error lines, ratio bucketing, transform dedup,
malformed-line skipping, report rendering/serialization). Both proxy
lifecycle tests additionally assert the INPUT_COMPRESSED
`original_messages` metadata contract end-to-end through the real
anthropic/openai handlers, so a refactor cannot silently disable the
recorder.

Local runs: new tests + `tests/test_proxy_pipeline_lifecycle.py` +
`tests/test_canonical_pipeline.py` + `tests/test_pipeline.py` — 45
passed. `ruff check` + `uvx ruff format --check` clean.

### Self-review hardening (second commit)

- Hex artifact regex now requires at least one `a-f`, so bare decimal
runs (timestamps, counters) no longer inflate the artifact dimension.
- Inflation events (ratio > 1, the #847 territory) get an explicit
`1.00+ (inflated)` ratio bucket instead of silently dropping out of the
bucketed view.
- `run_probes` streams recording files line by line instead of slurping
them.
- Documented honestly: marker recoverability is event-scoped
(comparative metric, not absolute); recorder writes synchronously on the
request path (diagnostic sessions, not always-on).
- `headroom/evals/README.md` gained a Session Probes usage section.

## Real behavior proof

**Setup:** macOS (Darwin 25.5), Python 3.11.9, this branch, real proxy
(`python -m headroom.proxy.server --port 18994 --anthropic-api-url
http://127.0.0.1:18995`) with a local mock Anthropic upstream (no key),
`HEADROOM_PROBE_RECORD_DIR=/tmp/headroom-probe-proof/recordings`.

**Steps:** POSTed three Anthropic-format conversations whose tool
results carry large JSON arrays (220-row uniform logs, 3000-row logs,
800 heterogeneous events), each containing known numerics, paths, trace
hashes, and one error line.

**Observed** — recorder wrote `compression-events-<pid>.jsonl` (one line
per event); `headroom evals probes --recordings ...`:

```
Probed 3 compression events

Aggregate retention:
  numerics    97.7% retained,   2.3% recoverable,   0.0% lost (527 targets)
  artifacts  100.0% retained,   0.0% recoverable,   0.0% lost (6555 targets)
  errors     100.0% retained,   0.0% recoverable,   0.0% lost (3 targets)

By compression ratio (tokens_after / tokens_before):
  ratio 0.50-0.75:  numerics 100.0% retained  (CSV compaction — lossless, correctly recognized)
  ratio 0.75-1.00:  numerics  95.7% retained, 4.3% recoverable  (SmartCrusher sampling — dropped values carried a CCR marker)
```

All three classifications exercised: verbatim/format-change retention on
the CSV-compacted events, **recoverable** on the heterogeneous event
where SmartCrusher sampled rows out behind a `Retrieve more: hash=`
marker, and the injected error lines retained in every event (the
error-protection gate held). The `lost` path is covered by unit tests.
The first iteration of this proof exposed two real bugs — naive verbatim
matching misreported lossless JSON→CSV compaction as 100% lost, and
duplicated transform markers double-counted tallies — both fixed with
regression tests.

**Not tested live:** Gemini path (no `INPUT_COMPRESSED` emit parity —
pre-existing, same gap as #819); LLM-judge scoring (out of scope per
#861).

## Security

Recordings contain full conversation content in plaintext: opt-in env
var only, local disk only, dir mode 0700, documented in CLI help.

## Out of scope (per #861)

LLM-judge dimensions (decisions/intent, next steps), ACON-style
counterfactual replay, automatic rule revision.

---------

Co-authored-by: Ash Rhodes <ashley.rhodes@king.com>
2026-06-11 13:02:36 -05:00

237 lines
8 KiB
Python

from __future__ import annotations
import asyncio
from types import SimpleNamespace
from unittest.mock import AsyncMock, call, patch
import httpx
from fastapi.testclient import TestClient
from headroom.pipeline import PipelineStage
from headroom.proxy.server import ProxyConfig, create_app
class _RecordingExtension:
def __init__(self) -> None:
self.stages: list[PipelineStage] = []
self.events: list = []
def on_pipeline_event(self, event):
self.stages.append(event.stage)
self.events.append(event)
return None
class _DummyTokenizer:
def count_messages(self, messages):
return len(messages)
def _assert_compressed_event_carries_originals(events: list) -> None:
"""INPUT_COMPRESSED must expose the pre-compression messages to extensions.
The probe recorder (headroom.proxy.probe_recorder) depends on this
metadata contract; dropping it silently disables session recording.
"""
compressed = [event for event in events if event.stage is PipelineStage.INPUT_COMPRESSED]
assert compressed
original = compressed[0].metadata.get("original_messages")
assert isinstance(original, list)
assert any(
message.get("role") == "user" and "hello" in str(message.get("content"))
for message in original
if isinstance(message, dict)
)
def _assert_stage_order(stages: list[PipelineStage]) -> None:
expected = [
PipelineStage.SETUP,
PipelineStage.PRE_START,
PipelineStage.POST_START,
PipelineStage.INPUT_RECEIVED,
PipelineStage.INPUT_ROUTED,
PipelineStage.INPUT_COMPRESSED,
PipelineStage.INPUT_REMEMBERED,
PipelineStage.PRE_SEND,
PipelineStage.POST_SEND,
PipelineStage.RESPONSE_RECEIVED,
]
positions = [stages.index(stage) for stage in expected]
assert positions == sorted(positions)
def test_proxy_shutdown_unloads_image_models() -> None:
config = ProxyConfig(
optimize=False,
image_optimize=False,
cache_enabled=False,
rate_limit_enabled=False,
cost_tracking_enabled=False,
log_requests=False,
ccr_inject_tool=False,
ccr_handle_responses=False,
ccr_context_tracking=False,
)
app = create_app(config)
proxy = app.state.proxy
proxy.http_client = None
proxy.memory_handler = None
quota_registry = SimpleNamespace(stop_all=AsyncMock())
with (
patch("headroom.proxy.server.get_quota_registry", return_value=quota_registry),
patch("headroom.models.ml_models.MLModelRegistry.unload_prefix") as unload_prefix,
):
asyncio.run(proxy.shutdown())
assert unload_prefix.call_args_list == [
call("technique_router:"),
call("siglip:"),
]
quota_registry.stop_all.assert_awaited_once()
def test_openai_chat_pipeline_events_cover_proxy_lifecycle(monkeypatch) -> None:
recorder = _RecordingExtension()
config = ProxyConfig(
optimize=True,
image_optimize=False,
cache_enabled=False,
rate_limit_enabled=False,
cost_tracking_enabled=False,
log_requests=False,
ccr_inject_tool=False,
ccr_handle_responses=False,
ccr_context_tracking=False,
pipeline_extensions=[recorder],
discover_pipeline_extensions=False,
)
app = create_app(config)
with TestClient(app) as client:
proxy = client.app.state.proxy
proxy.openai_pipeline = SimpleNamespace(
apply=lambda messages, model, **kwargs: SimpleNamespace(
messages=[
{"role": "system", "content": "memory"},
{"role": "user", "content": "hello"},
],
transforms_applied=["router:text:kompress"],
tokens_before=10,
tokens_after=6,
)
)
proxy.memory_handler = SimpleNamespace(
config=SimpleNamespace(inject_context=True, inject_tools=False),
search_and_format_context=AsyncMock(return_value="memory"),
has_memory_tool_calls=lambda response, provider: False,
)
monkeypatch.setattr("headroom.tokenizers.get_tokenizer", lambda model: _DummyTokenizer())
async def _fake_retry(method, url, headers, body, stream=False, **kwargs): # noqa: ANN001
return httpx.Response(
200,
json={
"id": "chatcmpl_1",
"object": "chat.completion",
"choices": [{"message": {"role": "assistant", "content": "ok"}}],
"usage": {"prompt_tokens": 10, "completion_tokens": 3, "total_tokens": 13},
},
)
proxy._retry_request = _fake_retry
response = client.post(
"/v1/chat/completions",
headers={"Authorization": "Bearer sk-test", "x-headroom-user-id": "user-1"},
json={
"model": "gpt-5.4",
"messages": [{"role": "user", "content": "hello"}],
"tools": [{"type": "function", "function": {"name": "tool_a"}}],
},
)
assert response.status_code == 200
_assert_stage_order(recorder.stages)
_assert_compressed_event_carries_originals(recorder.events)
def test_anthropic_messages_pipeline_events_cover_proxy_lifecycle(monkeypatch) -> None:
recorder = _RecordingExtension()
config = ProxyConfig(
optimize=True,
cache_enabled=False,
rate_limit_enabled=False,
cost_tracking_enabled=False,
log_requests=False,
ccr_inject_tool=False,
ccr_handle_responses=False,
ccr_context_tracking=False,
image_optimize=False,
pipeline_extensions=[recorder],
discover_pipeline_extensions=False,
)
app = create_app(config)
with TestClient(app) as client:
proxy = client.app.state.proxy
proxy.anthropic_pipeline = SimpleNamespace(
apply=lambda messages, model, **kwargs: SimpleNamespace(
messages=[
{"role": "system", "content": "memory"},
{"role": "user", "content": "hello"},
],
transforms_applied=["router:text:kompress"],
tokens_before=10,
tokens_after=6,
)
)
proxy.memory_handler = SimpleNamespace(
config=SimpleNamespace(inject_context=True, inject_tools=False),
search_and_format_context=AsyncMock(return_value="memory"),
has_memory_tool_calls=lambda response, provider: False,
)
monkeypatch.setattr("headroom.tokenizers.get_tokenizer", lambda model: _DummyTokenizer())
async def _fake_retry(method, url, headers, body, stream=False, **kwargs): # noqa: ANN001
return httpx.Response(
200,
json={
"id": "msg_1",
"type": "message",
"role": "assistant",
"content": [{"type": "text", "text": "ok"}],
"usage": {
"input_tokens": 10,
"output_tokens": 3,
"cache_read_input_tokens": 0,
"cache_creation_input_tokens": 0,
},
},
)
proxy._retry_request = _fake_retry
response = client.post(
"/v1/messages",
headers={
"x-api-key": "test-key",
"anthropic-version": "2023-06-01",
"x-headroom-user-id": "user-1",
},
json={
"model": "claude-sonnet-4-6",
"max_tokens": 128,
"messages": [{"role": "user", "content": "hello"}],
"tools": [
{"name": "tool_a", "description": "a", "input_schema": {"type": "object"}}
],
},
)
assert response.status_code == 200
_assert_stage_order(recorder.stages)
_assert_compressed_event_carries_originals(recorder.events)