JOJO
faef998a9a
- 改用 psutil 实时 RSS 取代历史最大 ru_maxrss - 新增后台每秒采样线程，检测到 >100MB/s 增长立即记录 - WebTerminal 初始化时自动启动采样 - 增加子智能体恢复加载 conversation.json、httpx 流式/非流式响应、 工具返回结果、模型输出组装等埋点
709 lines
32 KiB
Python
709 lines
32 KiB
Python
# ========== api_client.py ==========
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# utils/api_client.py - OpenAI-compatible API 客户端（支持Web模式）
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import httpx
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import json
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import asyncio
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import base64
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import mimetypes
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import os
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from typing import List, Dict, Optional, AsyncGenerator, Any
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from pathlib import Path
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from datetime import datetime
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from pathlib import Path
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from typing import Tuple
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try:
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from config import (
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OUTPUT_FORMATS,
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DEFAULT_RESPONSE_MAX_TOKENS,
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LOGS_DIR,
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)
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except ImportError:
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import sys
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from pathlib import Path
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project_root = Path(__file__).resolve().parents[1]
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if str(project_root) not in sys.path:
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sys.path.insert(0, str(project_root))
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from config import (
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OUTPUT_FORMATS,
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DEFAULT_RESPONSE_MAX_TOKENS,
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LOGS_DIR,
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)
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from utils.log_rotation import append_line, prune_dir
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from utils.api_client.utils import _api_dump_enabled
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from modules.memory_debug import log_memory_event, estimate_messages_size
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class DeepSeekClientChatMixin:
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async def chat(
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self,
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messages: List[Dict],
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tools: Optional[List[Dict]] = None,
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stream: bool = True
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) -> AsyncGenerator[Dict, None]:
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"""
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异步调用 OpenAI-compatible API
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Args:
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messages: 消息列表
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tools: 工具定义列表
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stream: 是否流式输出
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Yields:
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响应内容块
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"""
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# 检查API密钥
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if not self.api_key or self.api_key.startswith("your-"):
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self._print(f"{OUTPUT_FORMATS['error']} API密钥未配置，请检查模型配置")
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return
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# 决定是否使用思考模式
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current_thinking_mode = self.get_current_thinking_mode()
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api_config = self._select_api_config(current_thinking_mode)
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headers = self._build_headers(api_config["api_key"])
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# 如果当前为快速模式但已有思考内容，提示沿用
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if self.thinking_mode and not current_thinking_mode and self.current_task_thinking:
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self._print(f"{OUTPUT_FORMATS['info']} [任务内快速模式] 使用本次任务的思考继续处理...")
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# 记录本次调用的模式
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self.last_call_used_thinking = current_thinking_mode
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if current_thinking_mode and self.force_thinking_next_call:
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self.force_thinking_next_call = False
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if not current_thinking_mode and self.skip_thinking_next_call:
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self.skip_thinking_next_call = False
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try:
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override_max = self.thinking_max_tokens if current_thinking_mode else self.fast_max_tokens
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if override_max is not None:
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max_tokens = int(override_max)
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else:
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max_tokens = int(DEFAULT_RESPONSE_MAX_TOKENS)
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if max_tokens <= 0:
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raise ValueError("max_tokens must be positive")
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except (TypeError, ValueError):
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max_tokens = 4096
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# 动态收缩 max_tokens，避免超过模型上下文窗口
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budget_max_context = self.max_context_tokens or self.default_context_window
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if budget_max_context and budget_max_context > 0:
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used_tokens = max(0, int(self.current_context_tokens or 0))
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available = budget_max_context - used_tokens
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if available <= 0:
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# 兜底：让上游错误处理，这里至少给1防止API报参数错误
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max_tokens = 1
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else:
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max_tokens = min(max_tokens, available)
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final_messages = self._merge_system_messages(messages)
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final_messages = self._sanitize_messages_for_model_capability(final_messages)
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final_messages = self._sanitize_message_fields_for_api(final_messages)
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log_memory_event(
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"api_client_chat_request_prepared",
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model_key=self.model_key,
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input_messages_size=estimate_messages_size(messages),
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final_messages_size=estimate_messages_size(final_messages),
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)
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payload = {
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"model": api_config["model_id"],
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"messages": final_messages,
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"stream": stream,
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}
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payload["max_tokens"] = max_tokens
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# 注入模型配置中的额外参数
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extra_params = self.thinking_extra_params if current_thinking_mode else self.fast_extra_params
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if extra_params:
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payload.update(extra_params)
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if tools:
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payload["tools"] = tools
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payload["tool_choice"] = "auto"
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# 将本次请求落盘，便于出错时快速定位
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try:
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self._debug_log({
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"event": "request_prepare",
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"model_key": self.model_key,
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"thinking_mode_flag": bool(self.thinking_mode),
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"deep_thinking_mode": bool(self.deep_thinking_mode),
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"deep_thinking_session": bool(self.deep_thinking_session),
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"current_call_use_thinking": bool(current_thinking_mode),
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"api_base_url": api_config.get("base_url"),
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"api_model_id": api_config.get("model_id"),
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"payload_model": payload.get("model"),
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"payload_has_thinking": "thinking" in payload,
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"payload_thinking": payload.get("thinking"),
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"payload_enable_thinking": payload.get("enable_thinking"),
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"payload_max_tokens": payload.get("max_tokens"),
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"payload_max_completion_tokens": payload.get("max_completion_tokens"),
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})
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except Exception:
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pass
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dump_path = self._dump_request_payload(payload, api_config, headers)
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log_memory_event(
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"api_client_chat_request_payload",
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model_key=self.model_key,
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payload_chars=len(json.dumps(payload, ensure_ascii=False)),
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)
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try:
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async with httpx.AsyncClient(http2=True, timeout=300) as client:
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if stream:
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async with client.stream(
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"POST",
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f"{api_config['base_url']}/chat/completions",
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json=payload,
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headers=headers
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) as response:
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# 检查响应状态
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if response.status_code != 200:
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error_bytes = await response.aread()
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error_text = error_bytes.decode('utf-8', errors='ignore') if hasattr(error_bytes, 'decode') else str(error_bytes)
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self.last_error_info = {
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"status_code": response.status_code,
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"error_text": error_text,
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"error_type": None,
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"error_message": None,
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"request_dump": (str(dump_path) if dump_path else None),
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"base_url": api_config.get("base_url"),
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"model_id": api_config.get("model_id"),
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"model_key": self.model_key
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}
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try:
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parsed = json.loads(error_text)
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err = parsed.get("error") if isinstance(parsed, dict) else {}
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if isinstance(err, dict):
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self.last_error_info["error_type"] = err.get("type")
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self.last_error_info["error_message"] = err.get("message")
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except Exception:
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pass
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self._debug_log({
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"event": "http_error_stream",
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"status_code": response.status_code,
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"error_text": error_text,
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"base_url": api_config.get("base_url"),
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"model_id": api_config.get("model_id"),
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"model_key": self.model_key,
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"request_dump": (str(dump_path) if dump_path else None)
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})
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self._print(
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f"{OUTPUT_FORMATS['error']} API请求失败 ({response.status_code}): {error_text} "
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f"(base_url={api_config.get('base_url')}, model_id={api_config.get('model_id')})"
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)
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self._mark_request_error(dump_path, response.status_code, error_text)
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yield {"error": self.last_error_info}
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return
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chunk_count = 0
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async for line in response.aiter_lines():
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chunk_count += 1
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if chunk_count % 100 == 0:
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log_memory_event(
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"api_client_stream_chunk",
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model_key=self.model_key,
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chunk_count=chunk_count,
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)
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if line.startswith("data:"):
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json_str = line[5:].strip()
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if json_str == "[DONE]":
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break
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try:
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data = json.loads(json_str)
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yield data
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except json.JSONDecodeError:
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continue
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else:
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response = await client.post(
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f"{api_config['base_url']}/chat/completions",
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json=payload,
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headers=headers
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)
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try:
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response_text = response.text
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except Exception:
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response_text = ""
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log_memory_event(
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"api_client_nonstream_response",
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model_key=self.model_key,
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status_code=response.status_code,
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response_chars=len(response_text),
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)
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if response.status_code != 200:
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error_text = response.text
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self.last_error_info = {
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"status_code": response.status_code,
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"error_text": error_text,
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"error_type": None,
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"error_message": None,
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"request_dump": (str(dump_path) if dump_path else None),
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"base_url": api_config.get("base_url"),
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"model_id": api_config.get("model_id"),
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"model_key": self.model_key
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}
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try:
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parsed = response.json()
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err = parsed.get("error") if isinstance(parsed, dict) else {}
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if isinstance(err, dict):
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self.last_error_info["error_type"] = err.get("type")
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self.last_error_info["error_message"] = err.get("message")
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except Exception:
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pass
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self._debug_log({
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"event": "http_error",
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"status_code": response.status_code,
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"error_text": error_text,
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"base_url": api_config.get("base_url"),
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"model_id": api_config.get("model_id"),
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"model_key": self.model_key,
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"request_dump": (str(dump_path) if dump_path else None)
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})
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self._print(
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f"{OUTPUT_FORMATS['error']} API请求失败 ({response.status_code}): {error_text} "
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f"(base_url={api_config.get('base_url')}, model_id={api_config.get('model_id')})"
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)
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self._mark_request_error(dump_path, response.status_code, error_text)
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yield {"error": self.last_error_info}
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return
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# 成功则清空错误状态
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self.last_error_info = None
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yield response.json()
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except httpx.ConnectError as e:
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connect_detail = str(e).strip() or repr(e)
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self._print(
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f"{OUTPUT_FORMATS['error']} 无法连接到API服务器，请检查网络连接"
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f"（{connect_detail}）"
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)
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self.last_error_info = {
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"status_code": None,
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"error_text": "connect_error",
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"error_type": "connection_error",
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"error_message": f"无法连接到API服务器: {connect_detail}",
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"error_detail": connect_detail,
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"request_dump": (str(dump_path) if dump_path else None),
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"base_url": api_config.get("base_url"),
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"model_id": api_config.get("model_id"),
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"model_key": self.model_key
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}
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self._debug_log({
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"event": "connect_error",
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"status_code": None,
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"error_text": "connect_error",
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"error_detail": connect_detail,
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"base_url": api_config.get("base_url"),
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"model_id": api_config.get("model_id"),
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"model_key": self.model_key,
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"request_dump": (str(dump_path) if dump_path else None)
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})
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self._mark_request_error(dump_path, error_text=f"connect_error: {connect_detail}")
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yield {"error": self.last_error_info}
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except httpx.TimeoutException:
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self._print(f"{OUTPUT_FORMATS['error']} API请求超时")
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self.last_error_info = {
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"status_code": None,
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"error_text": "timeout",
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"error_type": "timeout",
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"error_message": "API请求超时",
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"request_dump": (str(dump_path) if dump_path else None),
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"base_url": api_config.get("base_url"),
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"model_id": api_config.get("model_id"),
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"model_key": self.model_key
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}
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self._debug_log({
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"event": "timeout",
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"status_code": None,
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"error_text": "timeout",
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"base_url": api_config.get("base_url"),
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"model_id": api_config.get("model_id"),
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"model_key": self.model_key,
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"request_dump": (str(dump_path) if dump_path else None)
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})
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self._mark_request_error(dump_path, error_text="timeout")
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yield {"error": self.last_error_info}
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except httpx.RemoteProtocolError as e:
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disconnect_detail = str(e).strip() or repr(e)
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self._print(f"{OUTPUT_FORMATS['error']} API服务器连接断开（{disconnect_detail}）")
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self.last_error_info = {
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"status_code": None,
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"error_text": disconnect_detail,
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"error_type": "connection_error",
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"error_message": f"API服务器连接断开: {disconnect_detail}",
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"request_dump": (str(dump_path) if dump_path else None),
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"base_url": api_config.get("base_url"),
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"model_id": api_config.get("model_id"),
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"model_key": self.model_key
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}
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self._debug_log({
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"event": "server_disconnected",
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"status_code": None,
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"error_text": disconnect_detail,
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"base_url": api_config.get("base_url"),
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"model_id": api_config.get("model_id"),
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"model_key": self.model_key,
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"request_dump": (str(dump_path) if dump_path else None)
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})
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self._mark_request_error(dump_path, error_text=f"server_disconnected: {disconnect_detail}")
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yield {"error": self.last_error_info}
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except Exception as e:
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error_text = str(e).strip() or repr(e)
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self._print(f"{OUTPUT_FORMATS['error']} API调用异常: {error_text}")
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self.last_error_info = {
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"status_code": None,
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"error_text": error_text,
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"error_type": "exception",
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"error_message": error_text,
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"request_dump": (str(dump_path) if dump_path else None),
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"base_url": api_config.get("base_url"),
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"model_id": api_config.get("model_id"),
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"model_key": self.model_key
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}
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self._debug_log({
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"event": "exception",
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"status_code": None,
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"error_text": error_text,
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"base_url": api_config.get("base_url"),
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"model_id": api_config.get("model_id"),
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"model_key": self.model_key,
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"request_dump": (str(dump_path) if dump_path else None)
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})
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self._mark_request_error(dump_path, error_text=error_text)
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yield {"error": self.last_error_info}
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async def chat_with_tools(
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self,
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messages: List[Dict],
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tools: List[Dict],
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tool_handler: callable
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) -> str:
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"""
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带工具调用的对话（支持多轮）
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Args:
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messages: 消息列表
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tools: 工具定义
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tool_handler: 工具处理函数
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Returns:
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最终回答
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"""
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final_response = ""
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max_iterations = 200 # 最大迭代次数
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iteration = 0
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all_tool_results = [] # 记录所有工具调用结果
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while iteration < max_iterations:
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iteration += 1
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# 调用API（始终提供工具定义）
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full_response = ""
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tool_calls = []
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current_thinking = ""
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# 状态标志
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in_thinking = False
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thinking_printed = False
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async for chunk in self.chat(messages, tools, stream=True):
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if chunk.get("error"):
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# 直接返回错误，让上层处理
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err = chunk["error"]
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self.last_error_info = err
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err_msg = err.get("error_message") or err.get("error_text") or "API调用失败"
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status = err.get("status_code")
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self._print(f"{OUTPUT_FORMATS['error']} 模型API错误{f'({status})' if status is not None else ''}: {err_msg}")
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return ""
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if "choices" not in chunk:
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continue
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delta = chunk["choices"][0].get("delta", {})
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# 处理思考内容
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reasoning_content = self._extract_reasoning_delta(delta)
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if reasoning_content:
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if not in_thinking:
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self._print("💭 [正在思考]\n", end="", flush=True)
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in_thinking = True
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thinking_printed = True
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current_thinking += reasoning_content
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self._print(reasoning_content, end="", flush=True)
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# 处理正常内容 - 独立的if，不是elif
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if "content" in delta:
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content = delta["content"]
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if content: # 只处理非空内容
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# 如果之前在输出思考，先结束思考输出
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if in_thinking:
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self._print("\n\n💭 [思考结束]\n\n", end="", flush=True)
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in_thinking = False
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full_response += content
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self._print(content, end="", flush=True)
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# 收集工具调用 - 改进的拼接逻辑
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# 收集工具调用 - 修复JSON分片问题
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if "tool_calls" in delta:
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for tool_call in delta["tool_calls"]:
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tool_index = tool_call.get("index", 0)
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|
||
# 查找或创建对应索引的工具调用
|
||
existing_call = None
|
||
for existing in tool_calls:
|
||
if existing.get("index") == tool_index:
|
||
existing_call = existing
|
||
break
|
||
|
||
if not existing_call and tool_call.get("id"):
|
||
# 创建新的工具调用
|
||
new_call = {
|
||
"id": tool_call.get("id"),
|
||
"index": tool_index,
|
||
"type": tool_call.get("type", "function"),
|
||
"function": {
|
||
"name": tool_call.get("function", {}).get("name", ""),
|
||
"arguments": ""
|
||
}
|
||
}
|
||
tool_calls.append(new_call)
|
||
existing_call = new_call
|
||
|
||
# 安全地拼接arguments - 简单字符串拼接，不尝试JSON验证
|
||
if existing_call and "function" in tool_call and "arguments" in tool_call["function"]:
|
||
new_args = tool_call["function"]["arguments"]
|
||
if new_args: # 只拼接非空内容
|
||
existing_call["function"]["arguments"] += new_args
|
||
|
||
self._print("") # 最终换行
|
||
|
||
# 如果思考还没结束（只调用工具没有文本），手动结束
|
||
if in_thinking:
|
||
self._print("\n💭 [思考结束]\n")
|
||
|
||
# 记录思考内容并更新调用状态
|
||
if self.last_call_used_thinking and current_thinking:
|
||
self.current_task_thinking = current_thinking
|
||
if self.current_task_first_call:
|
||
self.current_task_first_call = False # 标记当前任务的第一次调用已完成
|
||
|
||
# 如果没有工具调用，说明完成了
|
||
if not tool_calls:
|
||
if full_response: # 有正常回复，任务完成
|
||
final_response = full_response
|
||
break
|
||
elif iteration == 1: # 第一次就没有工具调用也没有内容，可能有问题
|
||
self._print(f"{OUTPUT_FORMATS['warning']} 模型未返回内容")
|
||
break
|
||
|
||
# 构建助手消息 - 始终包含所有收集到的内容
|
||
assistant_content_parts = []
|
||
|
||
# 添加正式回复内容（如果有）
|
||
if full_response:
|
||
assistant_content_parts.append(full_response)
|
||
|
||
# 添加工具调用说明
|
||
if tool_calls:
|
||
tool_names = [tc['function']['name'] for tc in tool_calls]
|
||
assistant_content_parts.append(f"执行工具: {', '.join(tool_names)}")
|
||
|
||
# 合并所有内容
|
||
assistant_content = "\n".join(assistant_content_parts) if assistant_content_parts else "执行工具调用"
|
||
|
||
assistant_message = {
|
||
"role": "assistant",
|
||
"content": assistant_content,
|
||
"tool_calls": tool_calls
|
||
}
|
||
if current_thinking:
|
||
assistant_message["reasoning_content"] = current_thinking
|
||
messages.append(assistant_message)
|
||
|
||
# 执行所有工具调用 - 使用鲁棒的参数解析
|
||
for tool_call in tool_calls:
|
||
function_name = tool_call["function"]["name"]
|
||
arguments_str = tool_call["function"]["arguments"]
|
||
|
||
# 使用改进的参数解析方法，增强JSON修复能力
|
||
success, arguments, error_msg = self._safe_tool_arguments_parse(arguments_str, function_name)
|
||
|
||
if not success:
|
||
self._print(f"{OUTPUT_FORMATS['error']} 工具参数解析失败: {error_msg}")
|
||
self._print(f" 工具名称: {function_name}")
|
||
self._print(f" 参数长度: {len(arguments_str)} 字符")
|
||
|
||
# 返回详细的错误信息给模型
|
||
error_response = {
|
||
"success": False,
|
||
"error": error_msg,
|
||
"tool_name": function_name,
|
||
"arguments_length": len(arguments_str),
|
||
"suggestion": "请检查参数格式或减少参数长度后重试"
|
||
}
|
||
|
||
# 如果参数过长，提供分块建议
|
||
if len(arguments_str) > 10000:
|
||
error_response["suggestion"] = "参数过长，建议分块处理或使用更简洁的内容"
|
||
|
||
messages.append({
|
||
"role": "tool",
|
||
"tool_call_id": tool_call["id"],
|
||
"name": function_name,
|
||
"content": json.dumps(error_response, ensure_ascii=False)
|
||
})
|
||
|
||
# 记录失败的调用，防止死循环检测失效
|
||
all_tool_results.append({
|
||
"tool": function_name,
|
||
"args": {"parse_error": error_msg, "length": len(arguments_str)},
|
||
"result": f"参数解析失败: {error_msg}"
|
||
})
|
||
continue
|
||
|
||
self._print(f"\n{OUTPUT_FORMATS['action']} 调用工具: {function_name}")
|
||
|
||
tool_result = await tool_handler(function_name, arguments)
|
||
|
||
# 解析工具结果，提取关键信息
|
||
result_data = None
|
||
try:
|
||
result_data = json.loads(tool_result)
|
||
if function_name == "read_file":
|
||
tool_result_msg = self._format_read_file_result(result_data)
|
||
else:
|
||
tool_result_msg = tool_result
|
||
except Exception:
|
||
tool_result_msg = tool_result
|
||
|
||
tool_message_content = tool_result_msg
|
||
if (
|
||
isinstance(result_data, dict)
|
||
and result_data.get("success") is not False
|
||
):
|
||
if function_name == "view_image":
|
||
img_path = result_data.get("path")
|
||
if img_path:
|
||
text_part = tool_result_msg if isinstance(tool_result_msg, str) else ""
|
||
tool_message_content = self._build_content_with_images(text_part, [img_path])
|
||
elif function_name == "view_video":
|
||
video_path = result_data.get("path")
|
||
if video_path:
|
||
text_part = tool_result_msg if isinstance(tool_result_msg, str) else ""
|
||
tool_message_content = self._build_content_with_images(text_part, [], [video_path])
|
||
|
||
messages.append({
|
||
"role": "tool",
|
||
"tool_call_id": tool_call["id"],
|
||
"name": function_name,
|
||
"content": tool_message_content
|
||
})
|
||
|
||
# 记录工具结果
|
||
all_tool_results.append({
|
||
"tool": function_name,
|
||
"args": arguments,
|
||
"result": tool_result_msg
|
||
})
|
||
|
||
# 如果连续多次调用同样的工具，可能陷入循环
|
||
if len(all_tool_results) >= 8:
|
||
recent_tools = [r["tool"] for r in all_tool_results[-8:]]
|
||
if len(set(recent_tools)) == 1: # 最近8次都是同一个工具
|
||
self._print(f"\n{OUTPUT_FORMATS['warning']} 检测到重复操作，停止执行")
|
||
break
|
||
|
||
if iteration >= max_iterations:
|
||
self._print(f"\n{OUTPUT_FORMATS['warning']} 达到最大迭代次数限制")
|
||
|
||
return final_response
|
||
|
||
async def simple_chat(self, messages: List[Dict]) -> tuple:
|
||
"""
|
||
简单对话（无工具调用）
|
||
|
||
Args:
|
||
messages: 消息列表
|
||
|
||
Returns:
|
||
(模型回答, 思考内容)
|
||
"""
|
||
full_response = ""
|
||
thinking_content = ""
|
||
in_thinking = False
|
||
|
||
# 如果思考模式且已有本任务的思考内容，补充到上下文，确保多次调用时思考不割裂
|
||
if (
|
||
self.thinking_mode
|
||
and not self.current_task_first_call
|
||
and self.current_task_thinking
|
||
):
|
||
thinking_context = (
|
||
"\n=== 📋 本次任务的思考 ===\n"
|
||
f"{self.current_task_thinking}\n"
|
||
"=== 思考结束 ===\n"
|
||
"提示：以上是本轮任务先前的思考，请在此基础上继续。"
|
||
)
|
||
messages.append({
|
||
"role": "system",
|
||
"content": thinking_context
|
||
})
|
||
thinking_context_injected = True
|
||
|
||
try:
|
||
async for chunk in self.chat(messages, tools=None, stream=True):
|
||
if chunk.get("error"):
|
||
err = chunk["error"]
|
||
self.last_error_info = err
|
||
err_msg = err.get("error_message") or err.get("error_text") or "API调用失败"
|
||
status = err.get("status_code")
|
||
self._print(f"{OUTPUT_FORMATS['error']} 模型API错误{f'({status})' if status is not None else ''}: {err_msg}")
|
||
return "", ""
|
||
|
||
if "choices" not in chunk:
|
||
continue
|
||
|
||
delta = chunk["choices"][0].get("delta", {})
|
||
|
||
# 处理思考内容
|
||
reasoning_content = self._extract_reasoning_delta(delta)
|
||
if reasoning_content:
|
||
if not in_thinking:
|
||
self._print("💭 [正在思考]\n", end="", flush=True)
|
||
in_thinking = True
|
||
thinking_content += reasoning_content
|
||
self._print(reasoning_content, end="", flush=True)
|
||
|
||
# 处理正常内容 - 独立的if而不是elif
|
||
if "content" in delta:
|
||
content = delta["content"]
|
||
if content: # 只处理非空内容
|
||
if in_thinking:
|
||
self._print("\n\n💭 [思考结束]\n\n", end="", flush=True)
|
||
in_thinking = False
|
||
full_response += content
|
||
self._print(content, end="", flush=True)
|
||
|
||
self._print("") # 最终换行
|
||
|
||
# 如果思考还没结束（极少情况），手动结束
|
||
if in_thinking:
|
||
self._print("\n💭 [思考结束]\n")
|
||
|
||
if self.last_call_used_thinking and thinking_content:
|
||
self.current_task_thinking = thinking_content
|
||
if self.current_task_first_call:
|
||
self.current_task_first_call = False
|
||
|
||
# 如果没有收到任何响应
|
||
if not full_response and not thinking_content:
|
||
self._print(f"{OUTPUT_FORMATS['error']} API未返回任何内容，请检查API密钥和模型ID")
|
||
return "", ""
|
||
|
||
except Exception as e:
|
||
self._print(f"{OUTPUT_FORMATS['error']} API调用失败: {e}")
|
||
return "", ""
|
||
|
||
return full_response, thinking_content
|