Teknium
224 lines
9.5 KiB
Python
224 lines
9.5 KiB
Python
"""Response intake for the conversation turn loop: normalize the raw provider response into
|
|
the assistant message, splice agent-as-provider projections, fire ``post_api_request``, relay
|
|
reasoning to the progress callback, and apply the incomplete-scratchpad / Codex-incomplete
|
|
continuation guards. Extracted from ``run_conversation``; nothing here imports
|
|
``agent.conversation_loop`` at module level (cycle).
|
|
"""
|
|
|
|
from __future__ import annotations
|
|
|
|
from dataclasses import dataclass
|
|
import json
|
|
import logging
|
|
import re
|
|
from typing import Any, Dict, Optional
|
|
|
|
from agent.provider_projection import splice_provider_projection
|
|
from agent.trajectory import has_incomplete_scratchpad
|
|
from agent.turn_truncation import continue_codex_incomplete
|
|
|
|
logger = logging.getLogger("agent.conversation_loop")
|
|
|
|
|
|
@dataclass
|
|
class ResponseIntakeVerdict:
|
|
"""``action``: ``"fallthrough"`` (process ``assistant_message``), ``"continue"`` (retry the
|
|
iteration: incomplete scratchpad / Codex continuation) or ``"return"`` (``result`` is the
|
|
turn's result dict). ``assistant_message``/``finish_reason`` are the normalized outputs."""
|
|
|
|
action: str
|
|
assistant_message: Any
|
|
finish_reason: Any
|
|
result: Optional[Dict[str, Any]] = None
|
|
|
|
|
|
def normalize_model_response(
|
|
agent: Any,
|
|
*,
|
|
response: Any,
|
|
messages: Any,
|
|
api_messages: Any,
|
|
conversation_history: Any,
|
|
api_call_count: Any,
|
|
api_duration: Any,
|
|
api_start_time: Any,
|
|
api_request_id: Any,
|
|
effective_task_id: Any,
|
|
turn_id: Any,
|
|
) -> ResponseIntakeVerdict:
|
|
"""Normalize ``response`` into ``assistant_message`` (str content, never dict/list) and run
|
|
the post-response hooks and continuation guards, in the original order."""
|
|
from agent.conversation_loop import (
|
|
_moa_reference_metrics_for_hook,
|
|
)
|
|
assistant_message = None
|
|
finish_reason = None
|
|
|
|
def _verdict(action: str, result: Optional[Dict[str, Any]] = None) -> ResponseIntakeVerdict:
|
|
return ResponseIntakeVerdict(
|
|
action=action,
|
|
assistant_message=assistant_message,
|
|
finish_reason=finish_reason,
|
|
result=result,
|
|
)
|
|
|
|
_transport = agent._get_transport()
|
|
_normalize_kwargs = {}
|
|
if agent.api_mode == "anthropic_messages":
|
|
_normalize_kwargs["strip_tool_prefix"] = agent._is_anthropic_oauth
|
|
normalized = _transport.normalize_response(response, **_normalize_kwargs)
|
|
assistant_message = normalized
|
|
finish_reason = normalized.finish_reason
|
|
|
|
# Some OpenAI-compatible servers (llama-server) return content as dict/list,
|
|
# which crashes downstream .strip(); normalize to str.
|
|
if assistant_message.content is not None and not isinstance(assistant_message.content, str):
|
|
raw = assistant_message.content
|
|
if isinstance(raw, dict):
|
|
assistant_message.content = raw.get("text", "") or raw.get("content", "") or json.dumps(raw)
|
|
elif isinstance(raw, list):
|
|
# Multimodal content list — extract text parts
|
|
parts = []
|
|
for part in raw:
|
|
if isinstance(part, str):
|
|
parts.append(part)
|
|
elif isinstance(part, dict) and part.get("type") == "text":
|
|
parts.append(part.get("text", ""))
|
|
elif isinstance(part, dict) and "text" in part:
|
|
parts.append(str(part["text"]))
|
|
assistant_message.content = "\n".join(parts)
|
|
else:
|
|
assistant_message.content = str(raw)
|
|
|
|
# ── Agent-as-provider projection ──────────────────────────────
|
|
# Splice the provider-agent's own tool work in as call/result rows before
|
|
# this turn's assistant message; no-op for ordinary providers.
|
|
splice_provider_projection(agent, response, messages)
|
|
|
|
try:
|
|
from hermes_cli.lifecycle import (
|
|
has_hook,
|
|
invoke_hook as _invoke_hook,
|
|
)
|
|
if has_hook("post_api_request"):
|
|
_assistant_tool_calls = (
|
|
getattr(assistant_message, "tool_calls", None) or []
|
|
)
|
|
_assistant_text = assistant_message.content or ""
|
|
_api_ended_at = api_start_time + api_duration
|
|
_invoke_hook(
|
|
"post_api_request",
|
|
task_id=effective_task_id,
|
|
turn_id=turn_id,
|
|
api_request_id=api_request_id,
|
|
session_id=agent.session_id or "",
|
|
platform=agent.platform or "",
|
|
model=agent.model,
|
|
provider=agent.provider,
|
|
base_url=agent.base_url,
|
|
api_mode=agent.api_mode,
|
|
api_call_count=api_call_count,
|
|
api_duration=api_duration,
|
|
started_at=api_start_time,
|
|
ended_at=_api_ended_at,
|
|
# First stream chunk time (epoch s) from
|
|
# interruptible_streaming_api_call; None if not streamed / no
|
|
# chunk. TTFB = first_chunk_at - started_at.
|
|
first_chunk_at=getattr(
|
|
agent, "_last_api_first_chunk_at", None
|
|
),
|
|
finish_reason=finish_reason,
|
|
message_count=len(api_messages),
|
|
response_model=getattr(response, "model", None),
|
|
response=agent._api_response_payload_for_hook(
|
|
response,
|
|
assistant_message,
|
|
finish_reason=finish_reason,
|
|
),
|
|
usage=agent._usage_summary_for_api_request_hook(response),
|
|
assistant_message=assistant_message,
|
|
assistant_content_chars=len(_assistant_text),
|
|
assistant_tool_call_count=len(_assistant_tool_calls),
|
|
moa_references=_moa_reference_metrics_for_hook(agent),
|
|
)
|
|
except Exception:
|
|
pass
|
|
|
|
# Handle assistant response
|
|
if assistant_message.content and not agent.quiet_mode:
|
|
if agent.verbose_logging:
|
|
agent._vprint(f"{agent.log_prefix}🤖 Assistant: {assistant_message.content}")
|
|
else:
|
|
agent._vprint(f"{agent.log_prefix}🤖 Assistant: {assistant_message.content[:100]}{'...' if len(assistant_message.content) > 100 else ''}")
|
|
|
|
# Notify progress callback of model's thinking (used by subagent
|
|
# delegation to relay the child's reasoning to the parent display).
|
|
if (assistant_message.content and agent.tool_progress_callback):
|
|
_think_text = assistant_message.content.strip()
|
|
# Strip reasoning XML tags that shouldn't leak to parent display
|
|
_think_text = re.sub(
|
|
r'</?(?:REASONING_SCRATCHPAD|think|reasoning)>', '', _think_text
|
|
).strip()
|
|
# For subagents: relay first line to parent display (existing behaviour).
|
|
# For all agents with a structured callback: emit reasoning.available event.
|
|
first_line = _think_text.split('\n')[0][:80] if _think_text else ""
|
|
if first_line and getattr(agent, '_delegate_depth', 0) > 0:
|
|
try:
|
|
agent.tool_progress_callback("_thinking", first_line)
|
|
except Exception:
|
|
pass
|
|
elif _think_text:
|
|
try:
|
|
agent.tool_progress_callback("reasoning.available", "_thinking", _think_text[:500], None)
|
|
except Exception:
|
|
pass
|
|
|
|
# Check for incomplete <REASONING_SCRATCHPAD> (opened but never closed)
|
|
# This means the model ran out of output tokens mid-reasoning — retry up to 2 times
|
|
if has_incomplete_scratchpad(assistant_message.content or ""):
|
|
agent._incomplete_scratchpad_retries += 1
|
|
|
|
agent._buffer_vprint("⚠️ Incomplete <REASONING_SCRATCHPAD> detected (opened but never closed)")
|
|
|
|
if agent._incomplete_scratchpad_retries <= 2:
|
|
agent._buffer_vprint(f"🔄 Retrying API call ({agent._incomplete_scratchpad_retries}/2)...")
|
|
# Don't add the broken message, just retry
|
|
return _verdict("continue")
|
|
else:
|
|
# Max retries - discard this turn and save as partial
|
|
agent._flush_status_buffer()
|
|
agent._vprint(f"{agent.log_prefix}❌ Max retries (2) for incomplete scratchpad. Saving as partial.", force=True)
|
|
agent._incomplete_scratchpad_retries = 0
|
|
|
|
rolled_back_messages = agent._get_messages_up_to_last_assistant(messages)
|
|
agent._cleanup_task_resources(effective_task_id)
|
|
agent._persist_session(messages, conversation_history)
|
|
|
|
return _verdict("return", {
|
|
"final_response": "Incomplete REASONING_SCRATCHPAD after 2 retries",
|
|
"messages": rolled_back_messages,
|
|
"api_calls": api_call_count,
|
|
"completed": False,
|
|
"partial": True,
|
|
"error": "Incomplete REASONING_SCRATCHPAD after 2 retries"
|
|
})
|
|
|
|
# Reset incomplete scratchpad counter on clean response
|
|
agent._incomplete_scratchpad_retries = 0
|
|
|
|
if agent.api_mode == "codex_responses" and finish_reason == "incomplete":
|
|
_codex_result = continue_codex_incomplete(
|
|
agent,
|
|
assistant_message,
|
|
finish_reason,
|
|
messages=messages,
|
|
conversation_history=conversation_history,
|
|
api_call_count=api_call_count,
|
|
)
|
|
if _codex_result is not None:
|
|
return _verdict("return", _codex_result)
|
|
return _verdict("continue")
|
|
elif hasattr(agent, "_codex_incomplete_retries"):
|
|
agent._codex_incomplete_retries = 0
|
|
return _verdict("fallthrough")
|