build_tool_preview -> _PREVIEW_BUILDERS per-tool table; git @refs -> _GIT_REFERENCE_ARGS; context_breakdown _skills_block/_append_overflow dedupe; prune_pre_checkpoint_items summary retention folded into one closure; build_skill_invocation_message reuses _render_skill_block; ruff SIM collapses; restored two compacted cache-policy invariant comments.
456 lines
18 KiB
Python
456 lines
18 KiB
Python
"""Native OpenAI Responses server-side compaction — gpt-5.6 on direct OpenAI routes only.
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Including ``context_management=[{"type": "compaction", "compact_threshold": N}]``
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in a ``/v1/responses`` request makes the server summarize older context into an
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opaque ``compaction`` item (``encrypted_content``, sealed to the issuing
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endpoint) once the input crosses N tokens; replaying that item stands in for
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the pruned history. Docs: https://developers.openai.com/api/docs/guides/compaction
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Support is deliberately narrow (live-verified):
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* gpt-5.6 family only — gpt-5.1/5.2 fail server-side (HTTP 500 blocking, a
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permanent stall streaming) with no structured "unsupported" rejection, so an
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explicit model-family check is the only safe gate.
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* Direct OpenAI routes only (api.openai.com or the ChatGPT Codex backend) —
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other Responses surfaces would 400 on the field and cannot mint/decrypt the blob.
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Hermes' local compressor stays armed as fallback owner: the native threshold is
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clamped below the local trigger so the server compacts first, and captured
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compaction items ride the existing ``codex_reasoning_items`` sidecar (persistence,
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replay, cross-issuer stamping, kill switch). This module stays free of
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transport/adapter imports so transport, adapter, and loop share the gate
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without cycles; ``context_compressor`` and ``message_content`` sit below it.
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"""
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from __future__ import annotations
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import logging
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from typing import Any, Dict, List, Optional
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from urllib.parse import urlsplit
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from agent.context_compressor import is_compaction_summary_message
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from agent.message_content import flatten_message_text
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logger = logging.getLogger(__name__)
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# Native compaction fires this many tokens below the local compressor's
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# trigger so the server always gets the first shot.
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LOCAL_TRIGGER_SAFETY_MARGIN = 8_192
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# Fallback when automatic mode has no local trigger to follow.
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DEFAULT_COMPACT_THRESHOLD = 200_000
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# Substring match so dated snapshots and variants (gpt-5.6-mini) stay eligible.
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_ELIGIBLE_MODEL_MARKER = "gpt-5.6"
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def is_native_compaction_model(model: Optional[str]) -> bool:
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"""True when the model is in the gpt-5.6 family."""
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return _ELIGIBLE_MODEL_MARKER in (model or "").lower()
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def resolve_native_compaction_capabilities(
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*,
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model: Optional[str],
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base_url: Optional[str],
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provider: Optional[str] = None,
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is_codex_backend: bool = False,
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) -> Dict[str, bool]:
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"""Resolve the native-compaction capability for a runtime destination.
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A resolved ``False`` is distinct from "unresolved" and must survive model
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switches unchanged.
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"""
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direct_default = (provider or "").strip().lower() == "openai" and not base_url
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eligible = is_native_compaction_model(model) and (
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direct_default
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or is_direct_openai_route(base_url, is_codex_backend=is_codex_backend)
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)
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return {"native_compaction": eligible}
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def is_direct_openai_route(
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base_url: Optional[str],
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*,
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is_codex_backend: bool = False,
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) -> bool:
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"""True for api.openai.com or the ChatGPT Codex backend — nothing else."""
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if is_codex_backend:
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return True
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try:
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hostname = (urlsplit(base_url or "").hostname or "").lower()
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except ValueError:
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return False
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return hostname == "api.openai.com"
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def resolve_compact_threshold(
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configured_threshold: Any,
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local_trigger_tokens: Any = None,
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) -> int:
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"""Resolve automatic mode or clamp an explicit native threshold.
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An omitted/invalid setting follows the local compressor trigger
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(``ContextCompressor.threshold_tokens``) minus the safety margin. An
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explicit positive integer is absolute unless it must be clamped so native
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compaction fires first. Booleans are never thresholds.
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"""
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local = None
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try:
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if local_trigger_tokens is not None and not isinstance(local_trigger_tokens, bool):
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local = int(local_trigger_tokens)
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except (TypeError, ValueError):
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local = None
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if local is not None and local <= 0:
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local = None
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upper = None
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if local is not None:
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if local > LOCAL_TRIGGER_SAFETY_MARGIN:
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upper = max(1_024, local - LOCAL_TRIGGER_SAFETY_MARGIN)
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else:
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upper = max(1_024, int(local * 0.8))
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try:
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configured = (
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None
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if isinstance(configured_threshold, (bool, float))
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else int(configured_threshold)
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)
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except (TypeError, ValueError):
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configured = None
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if configured is None or configured <= 0:
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return upper if upper is not None else DEFAULT_COMPACT_THRESHOLD
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if upper is None:
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return configured
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return max(1_024, min(configured, upper))
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_checkpoint_suppression_logged = False
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def _warn_native_compaction_suppressed_by_checkpoint_gate() -> None:
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"""Log once per process; the suppression itself is re-evaluated per request."""
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global _checkpoint_suppression_logged
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if _checkpoint_suppression_logged:
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return
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_checkpoint_suppression_logged = True
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logger.warning(
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"compression.checkpoint_required is enabled: server-side native "
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"compaction (context_management) is disabled for this agent so the "
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"checkpoint-aware Hermes compressor stays authoritative."
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)
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def native_compaction_context_management(
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agent: Any,
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*,
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is_codex_backend: bool,
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is_xai_responses: bool = False,
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is_github_responses: bool = False,
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) -> Optional[List[Dict[str, Any]]]:
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"""Return the ``context_management`` payload for this request, or None.
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None means "do not send the field" (request byte-identical to pre-feature).
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Every gate is re-checked per request so a mid-session model switch or the
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in-session kill switch (``agent.codex_responses_native_compaction = False``,
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set by rejection recovery) takes effect on the next call.
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"""
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capabilities = getattr(agent, "runtime_capabilities", None)
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if isinstance(capabilities, dict) and not capabilities.get("native_compaction", False):
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return None
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if not getattr(agent, "codex_responses_native_compaction", False):
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return None
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# compression.enabled: false disables ALL automatic compaction, native included.
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if not getattr(agent, "compression_enabled", True):
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return None
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# Server-side compaction is a lossy boundary the provider owns — no
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# pre-compress checkpoint can run first — so the checkpoint-aware Hermes
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# compressor stays authoritative. Explicit-True matches compress_context().
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if getattr(agent, "compression_checkpoint_required", False) is True:
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_warn_native_compaction_suppressed_by_checkpoint_gate()
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return None
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if is_xai_responses or is_github_responses:
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return None
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if not is_native_compaction_model(getattr(agent, "model", None)):
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return None
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trusted_proxy = bool(
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getattr(agent, "capabilities", {}).get("openai_native_compaction", False)
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)
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if not trusted_proxy and not is_direct_openai_route(
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getattr(agent, "base_url", None), is_codex_backend=is_codex_backend
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):
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return None
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compressor = getattr(agent, "context_compressor", None)
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threshold = resolve_compact_threshold(
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getattr(agent, "codex_responses_compact_threshold", None),
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getattr(compressor, "threshold_tokens", None) if compressor is not None else None,
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)
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return [{"type": "compaction", "compact_threshold": threshold}]
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# Retention budgets for plaintext user messages / local compression summaries
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# carried across a native compaction boundary (mirrors Codex CLI's
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# RETAINED_MESSAGE_TOKEN_BUDGET; the summary budget prevents summary inflation).
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RETAINED_USER_MESSAGE_TOKEN_BUDGET = 64_000
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RETAINED_SUMMARY_TOKEN_BUDGET = 32_000
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def _approx_tokens(text: str) -> int:
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"""Cheap chars//4 token estimate — same shape Codex uses for retention."""
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return max(1, len(text) // 4)
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def _extract_item_text(item: Any) -> Optional[str]:
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"""Measurable text from a Responses item (string/multipart/metadata), or None."""
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if not isinstance(item, dict):
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return None
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content = item.get("content")
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if content is None and "output_text" in item:
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content = item.get("output_text")
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if isinstance(content, str):
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return content if content.strip() else None
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if isinstance(content, list):
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parts = []
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for part in content:
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if isinstance(part, str):
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if part.strip():
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parts.append(part.strip())
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elif isinstance(part, dict):
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part_text = part.get("text") or part.get("input_text") or part.get("output_text")
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if isinstance(part_text, str) and part_text.strip():
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parts.append(part_text.strip())
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part_meta = part.get("metadata")
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if isinstance(part_meta, dict) and isinstance(part_meta.get("text"), str) and part_meta["text"].strip():
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parts.append(part_meta["text"].strip())
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text = " ".join(parts)
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return text if text.strip() else None
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return None
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def _has_retainable_image_content(item: Any) -> bool:
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"""True for a converted Responses message with a valid ``input_image`` part.
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Only the adapter-owned ``input_image`` shape counts: unknown or empty
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multipart placeholders must not become durable history for being non-empty.
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"""
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if not isinstance(item, dict):
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return False
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content = item.get("content")
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if not isinstance(content, list):
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return False
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for part in content:
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if not isinstance(part, dict):
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continue
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if str(part.get("type") or "").strip().lower() != "input_image":
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continue
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image_url = part.get("image_url")
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if isinstance(image_url, str) and image_url.strip():
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return True
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return False
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# Canonical provenance check (metadata marker, then canonical prefix classifier).
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# Deliberately NOT a second heuristic: no underscore-key scan, no matching on
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# ad-hoc headings — either could promote ordinary or adversarial content to
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# durable retained history.
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_is_summary_item = is_compaction_summary_message
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def prune_pre_checkpoint_items(
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items: List[Dict[str, Any]],
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retained_user_token_budget: int = RETAINED_USER_MESSAGE_TOKEN_BUDGET,
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retained_summary_token_budget: int = RETAINED_SUMMARY_TOKEN_BUDGET,
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enable_summary_retention: bool = True,
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item_sources: Optional[List[Any]] = None,
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) -> List[Dict[str, Any]]:
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"""Restructure Responses input around the newest compaction checkpoint.
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The server drops every input item preceding a replayed ``compaction`` item,
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which silently erases the user's plaintext asks and any local-compression
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summary (``role="assistant"``). With a checkpoint present, rebuild as::
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[checkpoint run] + [retained user & summary messages (newest-first budget)] + [post]
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- The NEWEST contiguous run of checkpoints wins.
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- User messages are kept verbatim within ``retained_user_token_budget``;
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the boundary message is head-truncated when it only partially fits
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(string content only — goals are stated up front). A recognized
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image-only user message is retained whole at one-token cost.
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- Summaries are retained whole within ``retained_summary_token_budget`` and
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never sliced (their structural framing would corrupt); one that doesn't
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fit is dropped. Identical summary text is never retained twice.
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- Relative order between user messages and summaries is preserved.
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- ``item_sources`` (parallel to ``items``) is the raw chat message each item
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was converted from. Conversion can be lossy for summaries (a
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merge-into-tail carrier becomes a typed ``function_call_output``, or an
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assistant carrier is shadowed by a stale exact replay), so when a source
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is itself a canonical summary carrier its content is read from the
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SOURCE and retained as a synthesized ``role="assistant"`` message.
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- ``enable_summary_retention`` is a function-level override for tests, not
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a config surface.
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"""
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if not isinstance(items, list) or not items:
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return items
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last_cp = None
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for i, item in enumerate(items):
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if isinstance(item, dict) and item.get("type") == "compaction":
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last_cp = i
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if last_cp is None:
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return items
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first_cp = last_cp
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while (
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first_cp > 0
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and isinstance(items[first_cp - 1], dict)
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and items[first_cp - 1].get("type") == "compaction"
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):
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first_cp -= 1
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pre = items[:first_cp]
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checkpoint_run = items[first_cp : last_cp + 1]
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post = items[last_cp + 1 :]
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if isinstance(item_sources, list) and len(item_sources) == len(items):
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pre_sources: List[Any] = item_sources[:first_cp]
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else:
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pre_sources = [None] * len(pre)
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retained_reversed: List[Dict[str, Any]] = []
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user_remaining = max(0, int(retained_user_token_budget))
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summary_remaining = max(0, int(retained_summary_token_budget))
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seen_summary_texts: set = set()
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def _retain_summary(text: Optional[str], retained_item: Dict[str, Any]) -> None:
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"""Retain a summary whole when it fits the budget and is not a duplicate."""
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nonlocal summary_remaining
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if not text or summary_remaining <= 0 or text in seen_summary_texts:
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return
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cost = _approx_tokens(text)
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if cost > summary_remaining:
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return # never slice a summary's structural framing
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seen_summary_texts.add(text)
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retained_reversed.append(retained_item)
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summary_remaining -= cost
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for item, source in zip(reversed(pre), reversed(pre_sources)):
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if not isinstance(item, dict):
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continue
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# Source-based detection sees past a lossy conversion; it only fires
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# when the source itself is a provenance-tagged summary carrier.
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if enable_summary_retention and isinstance(source, dict) and _is_summary_item(source):
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text = flatten_message_text(source.get("content"))
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_src_role = source.get("role")
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_retain_summary(text if text.strip() else None, {
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"role": _src_role if _src_role in ("user", "assistant") else "assistant",
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"content": text,
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})
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continue
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# Typed non-message items never carry role=user or a summary flag.
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if "type" in item and item.get("type") != "message":
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continue
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is_summary = enable_summary_retention and _is_summary_item(item)
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is_user = item.get("role") == "user"
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if not is_user and not is_summary:
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continue
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text = _extract_item_text(item)
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has_retainable_image = is_user and _has_retainable_image_content(item)
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if text is None and not has_retainable_image:
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continue
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if text is None:
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text = ""
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if is_summary:
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_retain_summary(text, item)
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elif is_user:
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if user_remaining <= 0:
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continue
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cost = _approx_tokens(text)
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if cost <= user_remaining:
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retained_reversed.append(item)
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user_remaining -= cost
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elif isinstance(item.get("content"), str):
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truncated = dict(item)
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truncated["content"] = item["content"][: user_remaining * 4]
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if truncated["content"].strip():
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retained_reversed.append(truncated)
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user_remaining = 0
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result = checkpoint_run + list(reversed(retained_reversed)) + post
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logger.debug(
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"Pruned pre-checkpoint items: %d input -> %d retained (user_rem=%d, summary_rem=%d)",
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len(items), len(result), user_remaining, summary_remaining,
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)
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return result
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_REJECTION_MARKERS = (
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"unknown", "unsupported", "invalid", "unexpected", "not permitted",
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"not allowed", "unrecognized", "extra field", "no such", "bad request",
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"not supported",
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)
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def is_native_compaction_rejection(error: Any, status_code: Any = None) -> bool:
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"""True when a provider error is a STRUCTURED rejection of ``context_management``.
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Drives the loop's one-shot recovery (strip the field, disable for the
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session, retry), so matching is narrow: a transient 5xx whose body merely
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ECHOES the request must not permanently downgrade native compaction. Requires
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``status_code`` 400 (or unknown — some transports surface only a message)
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AND the field name alongside rejection language.
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"""
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text = str(error or "").lower()
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if "context_management" not in text and "compact_threshold" not in text:
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return False
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if status_code is not None:
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try:
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if int(status_code) != 400:
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return False
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except (TypeError, ValueError):
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pass
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return any(marker in text for marker in _REJECTION_MARKERS)
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def has_compaction_checkpoint(items: Any) -> bool:
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"""Does this ``codex_reasoning_items`` sidecar carry a compaction checkpoint?
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A ``type: "compaction"`` item is cumulative context, not per-turn
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reasoning, and exists in exactly one place: anything that rewrites or
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discards the sidecar must ask this first or lose the compacted history.
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"""
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return any(
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isinstance(item, dict) and item.get("type") == "compaction"
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for item in (items if isinstance(items, list) else ())
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)
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def merge_interim_reasoning_items(
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prior_items: Any,
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new_items: Any,
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) -> List[Dict[str, Any]]:
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"""Merge ``codex_reasoning_items`` across Codex incomplete-continuation dedup.
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A checkpoint captured on the EARLIER response is not re-emitted by the
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continuation, so a blind overwrite drops the only copy. Rule: newer items
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win, but prior checkpoints are prepended unless the newer payload has its own.
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"""
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kept_checkpoints = [
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item
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for item in (prior_items if isinstance(prior_items, list) else [])
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if isinstance(item, dict) and item.get("type") == "compaction"
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]
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new_list = list(new_items) if isinstance(new_items, list) else []
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if has_compaction_checkpoint(new_list) or not kept_checkpoints:
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return new_list
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return kept_checkpoints + new_list
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