261 lines
10 KiB
Python
261 lines
10 KiB
Python
"""主 agent loop: ReAct 风格,LLM ↔ Tool 反复直到无 tool_call。
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loop 不直接 print —— 进度通过 sink.emit(event) 上抛。Sink 决定怎么呈现
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(本地 console / SSE / 日志)。事件类型见 core/sinks.py 头部说明。
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LLM 调用走 `chat_stream`(流式),chunk 之间 poll cancel_check 实现快速中断。
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content delta 即时 emit `text` 事件让前端打字机渲染;chunks 攒齐后用
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`litellm.stream_chunk_builder` 拼回完整 response 给 tool_calls 解析 + usage 记账。
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"""
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from __future__ import annotations
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import json
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import time
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from pathlib import Path
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from typing import Any, Callable, List, Optional, Tuple
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from uuid import UUID
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import litellm
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from .capabilities import ModelCapabilities
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from .executor import ExecCtx, Executor
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from .llm import LLM
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from .session import Session
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from .storage import record_chat_usage
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_CANCELLED_TOOL_PLACEHOLDER = "[cancelled by user]"
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def _extract_delta_content(chunk: Any) -> Optional[str]:
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"""从 stream chunk 提 delta.content(文本片段)。chunk 形态 litellm ModelResponseStream:
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choices[0].delta.content。usage-only 收尾 chunk(没 choices / delta)返 None。
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"""
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try:
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choices = getattr(chunk, "choices", None)
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if not choices:
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return None
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delta = getattr(choices[0], "delta", None)
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if delta is None:
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return None
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content = getattr(delta, "content", None)
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return content if content else None
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except Exception:
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return None
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def _extract_usage(usage: Any) -> Tuple[int, int]:
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"""从 litellm response.usage 提 (prompt_tokens, completion_tokens)。"""
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if not usage:
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return 0, 0
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if hasattr(usage, "model_dump"):
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usage = usage.model_dump()
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elif hasattr(usage, "dict"):
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usage = usage.dict()
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if isinstance(usage, dict):
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return int(usage.get("prompt_tokens") or 0), int(usage.get("completion_tokens") or 0)
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return 0, 0
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class AgentLoop:
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def __init__(
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self,
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llm: LLM,
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executor: Executor,
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session: Session,
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capabilities: ModelCapabilities,
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user_id: UUID,
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working_dir: Path,
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sink: Optional[Any] = None,
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max_iterations: Optional[int] = None,
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cancel_check: Optional[Callable[[], bool]] = None,
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) -> None:
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self.llm = llm
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self.executor = executor
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self.session = session
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self.caps = capabilities
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self.user_id = user_id # usage_events 写入时按 user 维度聚合
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# ExecCtx 字段:user_id / task_id 已在,working_dir 单独传 —— 供 docker backend
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# (Step 3)拼 `--workdir /workspace/<wd_name>` 与临时文件命名空间使用。
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self.working_dir = working_dir
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self.max_iterations = max_iterations or capabilities.max_iterations
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self.sink = sink
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# 协作式 cancel:web 层注入 `lambda: broker.is_cancelled(task_id)`;
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# CLI 路径不设(None → 永不 cancel)。check 点在 ① 每轮 LLM 前 ② stream chunk 间
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# ③ tool_calls 之间。chunk 间 poll 让 cancel 延迟从「整轮 generation 时长」
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# (几十秒)降到「单 chunk 间隔」(~100ms)。
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self.cancel_check = cancel_check
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def _emit(self, event: dict) -> None:
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if self.sink is not None:
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self.sink.emit(event)
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def _is_cancelled(self) -> bool:
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return bool(self.cancel_check and self.cancel_check())
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def _fill_cancelled_tool_results(self, remaining: list) -> None:
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"""给未执行的 tool_call 补 cancelled tool result,保 LiteLLM 协议完整。
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每个 assistant tool_call 必须有对应的 tool message,否则 resume 时 LLM 报错。
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"""
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for tc in remaining:
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self.session.append({
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"role": "tool",
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"tool_call_id": tc.id,
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"content": _CANCELLED_TOOL_PLACEHOLDER,
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})
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def run(self, user_message: str) -> str:
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self.session.append({"role": "user", "content": user_message})
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for _ in range(self.max_iterations):
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if self._is_cancelled():
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self._emit({"type": "cancelled"})
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return "[cancelled]"
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self._emit({"type": "llm_start"})
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start = time.monotonic()
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response, cancelled_mid_stream = self._stream_llm()
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elapsed = time.monotonic() - start
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if cancelled_mid_stream:
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# 流中途收到 cancel:已接收的 chunk 丢弃,不入库不记账(部分 assistant
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# 内容也不持久化,下次 resume 上下文干净)。response 可能是 None。
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self._emit({"type": "cancelled"})
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return "[cancelled]"
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msg = response.choices[0].message
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asst_msg_id = self.session.append(msg)
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pt, ct = _extract_usage(getattr(response, "usage", None))
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# 记账(0006):一行 usage_event + 回填 messages.tokens_in/out + model_profile。
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# 任何失败都吞掉(litellm cost map miss / DB 异常),不阻塞主 loop;
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# message 仍在 session/DB 里,后续重启不影响。
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model_profile = f"{self.caps.family}.{self.caps.variant}"
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try:
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record_chat_usage(
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task_id=self.session.task_id,
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user_id=self.user_id,
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message_id=asst_msg_id,
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model_profile=model_profile,
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prompt_tokens=pt,
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completion_tokens=ct,
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response=response,
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)
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except Exception as e:
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self._emit({"type": "warn", "msg": f"record_usage failed: {type(e).__name__}: {e}"})
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self._emit({
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"type": "llm_end",
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"prompt_tokens": pt,
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"completion_tokens": ct,
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"elapsed": elapsed,
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})
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tool_calls = getattr(msg, "tool_calls", None) or []
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# content 已通过 stream 流式 emit 过 delta,这里不再 emit 整段 text 事件。
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if not tool_calls:
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self._emit({"type": "done"})
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return getattr(msg, "content", None) or ""
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for i, tc in enumerate(tool_calls):
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if self._is_cancelled():
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self._fill_cancelled_tool_results(tool_calls[i:])
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self._emit({"type": "cancelled"})
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return "[cancelled]"
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result = self._execute_tool_call(tc)
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self.session.append(
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{
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"role": "tool",
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"tool_call_id": tc.id,
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"name": tc.function.name,
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"content": result,
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}
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)
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self._emit({"type": "done"})
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return "[reached max iterations]"
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def _stream_llm(self) -> Tuple[Optional[Any], bool]:
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"""流式拉一轮 LLM,chunk 间 poll cancel,content delta 即时 emit。
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返回 (response, cancelled_mid_stream):
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- 正常完结 → (response, False);response 由 litellm.stream_chunk_builder 拼回,
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shape 与非流式 completion() 等价(choices[0].message + usage)
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- 中途 cancel → (None, True);已收 chunk 丢弃,内层 generator 在 finally 关闭底层连接
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"""
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chunks: List[Any] = []
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stream = self.llm.chat_stream(
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messages=self.session.messages,
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tools=self.executor.schemas(),
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reasoning_effort=self.caps.default_reasoning_effort or None,
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)
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cancelled = False
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try:
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for chunk in stream:
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if self._is_cancelled():
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cancelled = True
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break
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chunks.append(chunk)
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# delta.content 即时 emit 给前端打字机渲染;tool_call delta 不实时发
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# (拼接散在多 chunk 跨 frame 难看,等拼回后整条 tool_call 事件由
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# _execute_tool_call 时机发更直观)。
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delta_text = _extract_delta_content(chunk)
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if delta_text:
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self._emit({"type": "text", "delta": delta_text})
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finally:
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# generator 提前 break 时 GeneratorExit 触发 chat_stream finally → close 底层连接
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stream.close()
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if cancelled:
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return None, True
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# 用 litellm 官方 helper 拼回完整 response(包括 tool_calls 拼接 + usage)。
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# messages 参数仅用于失败时回填 prompt token 估算,正常路径 stream_options.include_usage
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# 已让最后一个 chunk 带准确 usage。
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response = litellm.stream_chunk_builder(chunks, messages=self.session.messages)
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return response, False
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def _execute_tool_call(self, tc: Any) -> str:
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name = tc.function.name
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raw_args = tc.function.arguments or "{}"
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try:
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args = json.loads(raw_args)
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except json.JSONDecodeError as e:
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return f"[Error] invalid JSON arguments for {name}: {e}"
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args_preview = json.dumps(args, ensure_ascii=False)
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if len(args_preview) > 200:
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args_preview = args_preview[:200] + "..."
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self._emit({
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"type": "tool_call",
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"name": name,
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"args": args,
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"args_preview": args_preview,
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})
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ctx = ExecCtx(
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user_id=self.user_id,
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task_id=self.session.task_id,
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working_dir=self.working_dir,
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cancel_check=self.cancel_check,
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)
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result = self.executor.call_tool(name, args, ctx).content
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# 控制返回给模型的 tool 结果体量,避免炸 context
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MAX_LEN = 16_000
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truncated = False
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if len(result) > MAX_LEN:
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result = result[:MAX_LEN] + f"\n[... truncated, {len(result) - MAX_LEN} chars ...]"
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truncated = True
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preview = result if len(result) < 400 else result[:400] + "..."
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self._emit({
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"type": "tool_result",
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"name": name,
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"result": result,
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"preview": preview,
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"truncated": truncated,
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})
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return result
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