1e04c64ef3
Drop the duplicate chunk-reading helper, run_agent facade forward and _last_serving_provider agent state; the chat-completions loop already captures chunk.provider, so stamp it on the per-attempt diag there and read the hook's upstream_provider from the assembled response.provider.
195 lines
9.2 KiB
Python
195 lines
9.2 KiB
Python
"""Response intake for the conversation turn loop: normalize the raw provider response into
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the assistant message, splice agent-as-provider projections, fire ``post_api_request``, relay
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reasoning to the progress callback, and apply the incomplete-scratchpad / Codex-incomplete
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continuation guards. Nothing here imports ``agent.conversation_loop`` at module level (cycle).
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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import json
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import logging
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import re
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from typing import Any, Dict, Optional
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from agent.provider_projection import splice_provider_projection
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from agent.trajectory import has_incomplete_scratchpad
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from agent.turn_truncation import (
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CODEX_FALLBACK_ACTIVATED, continue_codex_incomplete, normalize_response_for_agent, partial_result,
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)
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logger = logging.getLogger("agent.conversation_loop")
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_REASONING_TAG_RE = re.compile(r'</?(?:REASONING_SCRATCHPAD|think|reasoning)>')
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@dataclass
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class ResponseIntakeVerdict:
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"""``action``: ``"fallthrough"`` (process ``assistant_message``), ``"continue"`` (retry the
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iteration: incomplete scratchpad / Codex continuation) or ``"return"`` (``result`` is the
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turn's result dict). ``assistant_message``/``finish_reason`` are the normalized outputs;
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``active_system_prompt`` is rebound after a Codex reasoning-only fallover (#67321)."""
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action: str
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assistant_message: Any
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finish_reason: Any
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result: Optional[Dict[str, Any]] = None
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active_system_prompt: Any = None
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def _coerce_content_text(raw: Any) -> str:
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"""Some OpenAI-compatible servers (llama-server) return content as dict/list, which
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crashes downstream ``.strip()``; normalize to str (multimodal lists → text parts)."""
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if isinstance(raw, dict):
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return raw.get("text", "") or raw.get("content", "") or json.dumps(raw)
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if isinstance(raw, list):
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parts = []
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for part in raw:
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if isinstance(part, str):
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parts.append(part)
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elif isinstance(part, dict) and part.get("type") == "text":
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parts.append(part.get("text", ""))
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elif isinstance(part, dict) and "text" in part:
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parts.append(str(part["text"]))
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return "\n".join(parts)
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return str(raw)
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def _fire_post_api_request_hook(
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agent: Any, response: Any, assistant_message: Any, finish_reason: Any, *, api_messages: Any,
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api_call_count: Any, api_duration: Any, api_start_time: Any, api_request_id: Any,
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effective_task_id: Any, turn_id: Any,
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) -> None:
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from agent.conversation_loop import _moa_reference_metrics_for_hook
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try:
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from hermes_cli.lifecycle import has_hook, invoke_hook as _invoke_hook
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if has_hook("post_api_request"):
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_invoke_hook(
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"post_api_request",
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task_id=effective_task_id,
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turn_id=turn_id,
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api_request_id=api_request_id,
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session_id=agent.session_id or "",
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platform=agent.platform or "",
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model=agent.model,
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provider=agent.provider,
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base_url=agent.base_url,
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api_mode=agent.api_mode,
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api_call_count=api_call_count,
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api_duration=api_duration,
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started_at=api_start_time,
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ended_at=api_start_time + api_duration,
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# First stream chunk time (epoch s); None if not streamed / no chunk.
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# TTFB = first_chunk_at - started_at.
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first_chunk_at=getattr(agent, "_last_api_first_chunk_at", None),
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finish_reason=finish_reason,
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message_count=len(api_messages),
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response_model=getattr(response, "model", None),
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response=agent._api_response_payload_for_hook(
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response, assistant_message, finish_reason=finish_reason
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),
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usage=agent._usage_summary_for_api_request_hook(response),
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assistant_message=assistant_message,
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assistant_content_chars=len(assistant_message.content or ""),
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assistant_tool_call_count=len(getattr(assistant_message, "tool_calls", None) or []),
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moa_references=_moa_reference_metrics_for_hook(agent),
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)
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except Exception:
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pass
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def _relay_thinking(agent: Any, content: str) -> None:
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"""Relay the model's text to the progress callback: subagents send the first line to
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the parent display; any agent with a structured callback gets ``reasoning.available``."""
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_think_text = _REASONING_TAG_RE.sub('', content.strip()).strip()
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first_line = _think_text.split('\n')[0][:80] if _think_text else ""
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if first_line and getattr(agent, '_delegate_depth', 0) > 0:
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try:
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agent.tool_progress_callback("_thinking", first_line)
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except Exception:
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pass
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elif _think_text:
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try:
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agent.tool_progress_callback("reasoning.available", "_thinking", _think_text[:500], None)
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except Exception:
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pass
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def normalize_model_response(
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agent: Any, *, response: Any, messages: Any, api_messages: Any, conversation_history: Any,
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api_call_count: Any, api_duration: Any, api_start_time: Any, api_request_id: Any,
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effective_task_id: Any, turn_id: Any, active_system_prompt: Any = None,
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) -> ResponseIntakeVerdict:
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"""Normalize ``response`` into ``assistant_message`` (str content, never dict/list) and run
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the post-response hooks and continuation guards, in the original order."""
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assistant_message = normalize_response_for_agent(agent, response)
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finish_reason = assistant_message.finish_reason
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def _verdict(action: str, result: Optional[Dict[str, Any]] = None) -> ResponseIntakeVerdict:
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return ResponseIntakeVerdict(
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action=action, assistant_message=assistant_message, finish_reason=finish_reason,
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result=result, active_system_prompt=active_system_prompt,
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)
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if assistant_message.content is not None and not isinstance(assistant_message.content, str):
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assistant_message.content = _coerce_content_text(assistant_message.content)
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# Agent-as-provider projection: splice the provider-agent's own tool work in as
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# call/result rows before this turn's assistant message; no-op for ordinary providers.
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splice_provider_projection(agent, response, messages)
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_fire_post_api_request_hook(
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agent, response, assistant_message, finish_reason, api_messages=api_messages,
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api_call_count=api_call_count, api_duration=api_duration, api_start_time=api_start_time,
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api_request_id=api_request_id, effective_task_id=effective_task_id, turn_id=turn_id,
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)
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content = assistant_message.content
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if content and not agent.quiet_mode:
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if agent.verbose_logging:
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agent._vprint(f"{agent.log_prefix}🤖 Assistant: {content}")
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else:
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agent._vprint(f"{agent.log_prefix}🤖 Assistant: {content[:100]}{'...' if len(content) > 100 else ''}")
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if content and agent.tool_progress_callback:
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_relay_thinking(agent, content)
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# Incomplete <REASONING_SCRATCHPAD> (opened, never closed): the model ran out of
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# output tokens mid-reasoning — retry up to 2 times, then save as partial.
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if has_incomplete_scratchpad(content or ""):
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agent._incomplete_scratchpad_retries += 1
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agent._buffer_vprint("⚠️ Incomplete <REASONING_SCRATCHPAD> detected (opened but never closed)")
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if agent._incomplete_scratchpad_retries <= 2:
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agent._buffer_vprint(f"🔄 Retrying API call ({agent._incomplete_scratchpad_retries}/2)...")
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return _verdict("continue") # don't add the broken message
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agent._flush_status_buffer()
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agent._vprint(f"{agent.log_prefix}❌ Max retries (2) for incomplete scratchpad. Saving as partial.", force=True, diagnostic=True)
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agent._incomplete_scratchpad_retries = 0
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rolled_back_messages = agent._get_messages_up_to_last_assistant(messages)
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agent._cleanup_task_resources(effective_task_id)
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agent._persist_session(messages, conversation_history)
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return _verdict("return", partial_result(
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rolled_back_messages, api_call_count, "Incomplete REASONING_SCRATCHPAD after 2 retries"
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))
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agent._incomplete_scratchpad_retries = 0
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if agent.api_mode == "codex_responses" and finish_reason == "incomplete":
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_codex_result = continue_codex_incomplete(
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agent, assistant_message, finish_reason, messages=messages,
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conversation_history=conversation_history, api_call_count=api_call_count,
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response=response,
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)
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if _codex_result is CODEX_FALLBACK_ACTIVATED:
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# The failover rewrote the Model:/Provider: identity on the cached system prompt;
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# rebind it so the next iteration's request is rebuilt with the new identity.
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from agent.conversation_loop import _sync_failover_system_message
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active_system_prompt = _sync_failover_system_message(agent, api_messages, active_system_prompt)
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return _verdict("continue")
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if _codex_result is not None:
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return _verdict("return", _codex_result)
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return _verdict("continue")
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if hasattr(agent, "_codex_incomplete_retries"):
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agent._codex_incomplete_retries = 0
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agent._codex_reasoning_only_streak = 0
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return _verdict("fallthrough")
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