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## Description Fixes #2509. CCR marker resolution today depends entirely on the model calling `headroom_retrieve` back a tool-call round-trip. Callers with no such round-trip (e.g. Headroom running as a LiteLLM guardrail/proxy hop, per the issue's repro) never get an offered path to redeem a marker, so raw `<<ccr:HASH,type,size>>` text leaks straight to the agent. This adds an explicit, opt-in fallback: `--ccr-inline-resolve` / `HEADROOM_CCR_INLINE_RESOLVE`. When set, the proxy resolves markers directly from the compression store on the response path instead of waiting for a tool call. Off by default, guessing "this caller can't use tools" is fragile, so operators opt in explicitly for guardrail/proxy deployments. ## Type of Change - [x] Bug fix (non-breaking change which fixes an issue) - [ ] New feature (non-breaking change which adds functionality) - [ ] Breaking change - [ ] Documentation update ## Changes Made - `headroom/ccr/marker_resolution.py` (new): `resolve_markers_in_text` / `resolve_markers_in_response` regex-match `<<ccr:HASH,...>>`, look up the hash in `CompressionStore`, splice the original content back in. A miss (expired/evicted hash) leaves the marker in place with the miss reason appended, since there's no tool-call round-trip to report it back to the model. - `headroom/proxy/models.py`: `ProxyConfig.ccr_resolve_markers_inline: bool = False`. - `headroom/cli/proxy.py`: `--ccr-inline-resolve` flag / `HEADROOM_CCR_INLINE_RESOLVE` env, wired into `ProxyConfig`. - `headroom/proxy/handlers/anthropic.py`, `headroom/proxy/handlers/openai.py`: call `resolve_markers_in_response` on the finalized response JSON, right after existing CCR tool-call handling, at all three non-streaming response sites (Anthropic Messages, OpenAI Chat Completions backend path, OpenAI Responses API). Streaming responses are out of scope for this PR, tracked as follow-up, noted in the module docstring's scope. ## Testing - [x] Added new tests - [x] All tests pass locally ``` $ python -m pytest tests/test_ccr_marker_resolution.py -q ============================= test session starts ============================= collected 6 items tests\test_ccr_marker_resolution.py ...... [100%] ============================== 6 passed in 0.45s ============================== $ python -m pytest tests/test_ccr.py tests/test_ccr_response_handler.py tests/test_ccr_response_handler_extra.py tests/test_ccr_response_handler_openai_responses.py tests/test_proxy/test_openai_responses_ccr.py tests/test_proxy/test_anthropic_ccr_raise.py -q ======================= 83 passed, 1 warning in 34.50s ======================== ``` ## Real Behavior Proof - Environment: Windows 11, Python 3.13.11, local headroom repo (`G:\Programmi Aggiuntivi\headroom`) - Exact command / steps: `python -m pytest tests/test_ccr_marker_resolution.py tests/test_ccr.py tests/test_ccr_response_handler.py tests/test_ccr_response_handler_extra.py tests/test_ccr_response_handler_openai_responses.py tests/test_proxy/test_openai_responses_ccr.py tests/test_proxy/test_anthropic_ccr_raise.py -q` - Observed result: 89 passed, 0 failed (6 new + 83 existing CCR tests, no regressions). `ruff check`, `ruff format --check`, and `mypy --ignore-missing-imports` all clean on every changed/new file. - Not tested: the actual Docker Compose / LiteLLM guardrail deployment from the issue's repro steps (no such environment available here); streaming response paths (out of scope, see Changes Made). ## Review Readiness - [x] I have performed a self-review - [x] This PR is ready for human review --------- Co-authored-by: Claude Sonnet 5 <noreply@anthropic.com>
173 lines
5.2 KiB
Python
173 lines
5.2 KiB
Python
"""Handler-level wiring for ``--ccr-inline-resolve`` (issue #2509).
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The pure-module tests in ``test_ccr_marker_resolution.py`` prove the
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substitution logic. These tests prove the thing that actually broke: the
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resolve call has to run on the response path *when the model never emitted a
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``headroom_retrieve`` tool call at all*. That is the whole #2509 shape —
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Headroom behind a LiteLLM guardrail hop with no tool-call turn — so any wiring
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that sits behind a ``has_ccr_tool_calls`` gate is a no-op for its own use case.
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Every response fixture below therefore has zero tool calls.
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"""
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from __future__ import annotations
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import pytest
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fastapi = pytest.importorskip("fastapi")
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httpx = pytest.importorskip("httpx")
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from fastapi.testclient import TestClient # noqa: E402
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from headroom.cache.compression_store import ( # noqa: E402
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get_compression_store,
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reset_compression_store,
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)
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from headroom.proxy.server import ProxyConfig, create_app # noqa: E402
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ORIGINAL = "the original uncompressed content"
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@pytest.fixture(autouse=True)
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def reset_store():
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reset_compression_store()
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yield
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reset_compression_store()
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def _marker() -> str:
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hash_key = get_compression_store().store(
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original=ORIGINAL,
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compressed="[]",
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original_item_count=1,
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compressed_item_count=0,
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)
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return f"<<ccr:{hash_key},string,23.6KB>>"
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def _config(*, inline_resolve: bool) -> ProxyConfig:
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# No backend -> the "Direct OpenAI API (no backend configured)" path.
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return ProxyConfig(
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optimize=False,
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cache_enabled=False,
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rate_limit_enabled=False,
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ccr_resolve_markers_inline=inline_resolve,
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)
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def _chat_response(marker: str) -> dict:
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return {
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"id": "chatcmpl-1",
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"object": "chat.completion",
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"model": "gpt-4o",
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"choices": [
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{
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"index": 0,
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"message": {"role": "assistant", "content": f"here it is: {marker}"},
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"finish_reason": "stop",
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}
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],
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"usage": {"prompt_tokens": 10, "completion_tokens": 5, "total_tokens": 15},
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}
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def _responses_response(marker: str) -> dict:
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return {
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"id": "resp_1",
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"object": "response",
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"model": "gpt-4o",
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"status": "completed",
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"output": [
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{
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"type": "message",
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"role": "assistant",
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"content": [{"type": "output_text", "text": f"here it is: {marker}"}],
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}
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],
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"usage": {"input_tokens": 10, "output_tokens": 5, "total_tokens": 15},
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}
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def _anthropic_response(marker: str) -> dict:
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return {
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"id": "msg_1",
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"type": "message",
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"role": "assistant",
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"model": "claude-sonnet-4-20250514",
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"content": [{"type": "text", "text": f"here it is: {marker}"}],
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"stop_reason": "end_turn",
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"usage": {"input_tokens": 10, "output_tokens": 5},
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}
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def _run(config: ProxyConfig, path: str, body: dict, upstream: dict) -> httpx.Response:
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async def fake_retry(method, url, headers, req_body, *args, **kwargs):
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return httpx.Response(200, json=upstream, headers={"content-type": "application/json"})
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app = create_app(config)
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with TestClient(app) as client:
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client.app.state.proxy._retry_request = fake_retry
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return client.post(
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path,
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json=body,
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headers={"Authorization": "Bearer test-key", "x-api-key": "test-key"},
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)
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@pytest.mark.parametrize("inline_resolve", [True, False])
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def test_openai_chat_direct_path(inline_resolve):
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marker = _marker()
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resp = _run(
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_config(inline_resolve=inline_resolve),
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"/v1/chat/completions",
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{"model": "gpt-4o", "messages": [{"role": "user", "content": "hi"}], "stream": False},
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_chat_response(marker),
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)
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assert resp.status_code == 200, resp.text
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text = resp.json()["choices"][0]["message"]["content"]
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if inline_resolve:
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assert text == f"here it is: {ORIGINAL}"
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else:
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assert text == f"here it is: {marker}"
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@pytest.mark.parametrize("inline_resolve", [True, False])
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def test_openai_responses_path(inline_resolve):
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marker = _marker()
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resp = _run(
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_config(inline_resolve=inline_resolve),
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"/v1/responses",
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{"model": "gpt-4o", "input": "hi", "stream": False},
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_responses_response(marker),
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)
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assert resp.status_code == 200, resp.text
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text = resp.json()["output"][0]["content"][0]["text"]
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if inline_resolve:
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assert text == f"here it is: {ORIGINAL}"
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else:
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assert text == f"here it is: {marker}"
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@pytest.mark.parametrize("inline_resolve", [True, False])
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def test_anthropic_messages_path(inline_resolve):
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marker = _marker()
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resp = _run(
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_config(inline_resolve=inline_resolve),
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"/v1/messages",
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{
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"model": "claude-sonnet-4-20250514",
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"max_tokens": 64,
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"messages": [{"role": "user", "content": "hi"}],
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"stream": False,
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},
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_anthropic_response(marker),
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)
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assert resp.status_code == 200, resp.text
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text = resp.json()["content"][0]["text"]
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if inline_resolve:
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assert text == f"here it is: {ORIGINAL}"
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else:
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assert text == f"here it is: {marker}"
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