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## Description Extracts output-shaper turn classification into a pure `output_turn_policy` module. The shaper still owns request mutation and labels, while Anthropic-style and OpenAI Responses structural turn classification now live in a deterministic policy boundary. Closes # ## Type of Change - [ ] Bug fix (non-breaking change that fixes an issue) - [ ] New feature (non-breaking change that adds functionality) - [ ] Breaking change (fix or feature that would cause existing functionality to change) - [ ] Documentation update - [ ] Performance improvement - [x] Code refactoring (no functional changes) ## Changes Made - Added `headroom.proxy.output_turn_policy` with `TurnKind`, `classify_turn`, and `classify_openai_responses_input`. - Updated `output_shaper` to import and re-export the classifiers, preserving existing import behavior. - Added direct policy tests for Anthropic tool-result turns and OpenAI Responses input classification. - Included the current LiteLLM callback signature compatibility shim required for repo-wide mypy on main-based slices. ## Testing - [x] Unit tests pass (`pytest`) - [x] Linting passes (`ruff check .`) - [x] Type checking passes (`mypy headroom`) - [x] New tests added for new functionality - [ ] Manual testing performed ### Test Output ```text python -m pytest tests/test_output_turn_policy.py tests/test_output_shaper.py tests/test_litellm_callback.py -q 60 passed in 6.25s python -m ruff check . All checks passed! python -m ruff format --check . 1095 files already formatted python -m mypy headroom --ignore-missing-imports Success: no issues found in 409 source files gitleaks protect --staged --no-banner --redact no leaks found ``` ## Real Behavior Proof - Environment: Windows, Python 3.13.13, clean worktree based on `headroomlabs/main`. - Exact command / steps: targeted pytest, ruff, format check, repo-wide mypy, staged gitleaks scan. - Observed result: output turn policy/shaper/callback tests pass; static checks pass; no staged secrets detected. - Not tested: live provider calls; this slice only moves structural classification logic and preserves existing shaper behavior. ## Review Readiness - [x] I have performed a self-review - [x] This PR is ready for human review ## Checklist - [x] My code follows the project's style guidelines - [x] I have performed a self-review of my code - [x] I have commented my code, particularly in hard-to-understand areas - [ ] I have made corresponding changes to the documentation - [x] My changes generate no new warnings - [x] I have added tests that prove my fix is effective or that my feature works - [x] New and existing unit tests pass locally with my changes - [ ] I have updated the CHANGELOG.md if applicable ## Screenshots (if applicable) N/A ## Additional Notes Documentation and changelog updates are not applicable for this internal architecture slice. PR-specific GHAS checks will be monitored after opening.
102 lines
3.3 KiB
Python
102 lines
3.3 KiB
Python
"""Tests for pure output turn classification policy."""
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from __future__ import annotations
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from typing import Any
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from headroom.proxy.output_turn_policy import (
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TurnKind,
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classify_openai_responses_input,
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classify_turn,
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)
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def _tool_result(is_error: bool = False) -> dict[str, Any]:
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block: dict[str, Any] = {"type": "tool_result", "content": "ok"}
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if is_error:
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block["is_error"] = True
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return block
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def test_anthropic_text_user_message_is_new_ask() -> None:
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assert classify_turn([{"role": "user", "content": "explain this"}]) is TurnKind.NEW_USER_ASK
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def test_anthropic_clean_tool_results_are_mechanical() -> None:
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messages = [{"role": "user", "content": [_tool_result(), _tool_result()]}]
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assert classify_turn(messages) is TurnKind.MECHANICAL_CONTINUATION
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def test_anthropic_error_tool_result_is_error_continuation() -> None:
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messages = [{"role": "user", "content": [_tool_result(), _tool_result(is_error=True)]}]
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assert classify_turn(messages) is TurnKind.ERROR_CONTINUATION
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def test_anthropic_user_media_or_text_block_is_new_ask() -> None:
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assert (
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classify_turn([{"role": "user", "content": [{"type": "image", "source": {}}]}])
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is TurnKind.NEW_USER_ASK
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)
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assert (
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classify_turn(
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[{"role": "user", "content": [_tool_result(), {"type": "text", "text": "also"}]}]
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)
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is TurnKind.NEW_USER_ASK
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)
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def test_anthropic_unknown_shapes_are_unknown() -> None:
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assert classify_turn([]) is TurnKind.UNKNOWN
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assert classify_turn([{"role": "assistant", "content": "done"}]) is TurnKind.UNKNOWN
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assert classify_turn([{"role": "user", "content": []}]) is TurnKind.UNKNOWN
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assert classify_turn([{"role": "user", "content": [{}]}]) is TurnKind.UNKNOWN
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def test_openai_responses_string_input_is_new_ask() -> None:
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assert classify_openai_responses_input("explain this") is TurnKind.NEW_USER_ASK
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assert classify_openai_responses_input(" ") is TurnKind.UNKNOWN
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def test_openai_responses_tool_outputs_only_are_mechanical() -> None:
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assert (
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classify_openai_responses_input(
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[
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{"type": "function_call_output", "call_id": "call_1", "output": "ok"},
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{"type": "local_shell_call_output", "call_id": "call_2", "output": "ok"},
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]
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)
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is TurnKind.MECHANICAL_CONTINUATION
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)
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def test_openai_responses_user_message_or_input_media_is_new_ask() -> None:
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assert (
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classify_openai_responses_input(
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[
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{
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"type": "message",
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"role": "user",
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"content": [{"type": "input_text", "text": "also check foo.py"}],
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}
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]
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)
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is TurnKind.NEW_USER_ASK
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)
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assert (
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classify_openai_responses_input(
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[{"type": "message", "role": "user", "content": [{"type": "input_image"}]}]
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)
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is TurnKind.NEW_USER_ASK
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)
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def test_openai_responses_unknown_mixed_with_tool_output_is_unknown() -> None:
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assert (
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classify_openai_responses_input(
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[
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{"type": "function_call_output", "call_id": "call_1", "output": "ok"},
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{"type": "unrecognized_event"},
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]
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)
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is TurnKind.UNKNOWN
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)
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