456 lines
22 KiB
Python
456 lines
22 KiB
Python
from __future__ import annotations
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import asyncio
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import json
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import time
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from typing import Any, Dict, Optional
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from config.model_profiles import get_model_profile
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from .utils_common import debug_log, brief_log, log_backend_chunk
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from .chat_flow_runner_helpers import extract_intent_from_partial
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from .chat_flow_task_support import wait_retry_delay, cancel_pending_tools
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from .state import get_stop_flag, clear_stop_flag
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async def run_streaming_attempts(*, web_terminal, messages, tools, sender, client_sid: str, username: str, conversation_id: Optional[str], current_iteration: int, max_api_retries: int, retry_delay_seconds: int, detected_tool_intent: Dict[str, str], full_response: str, tool_calls: list, current_thinking: str, detected_tools: Dict[str, str], last_usage_payload, in_thinking: bool, thinking_started: bool, thinking_ended: bool, text_started: bool, text_has_content: bool, text_streaming: bool, text_chunk_index: int, last_text_chunk_time, chunk_count: int, reasoning_chunks: int, content_chunks: int, tool_chunks: int, last_finish_reason: Optional[str], accumulated_response: str) -> Dict[str, Any]:
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api_error = None
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for api_attempt in range(max_api_retries + 1):
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api_error = None
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if api_attempt > 0:
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full_response = ""
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tool_calls = []
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current_thinking = ""
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detected_tools = {}
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last_usage_payload = None
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in_thinking = False
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thinking_started = False
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thinking_ended = False
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text_started = False
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text_has_content = False
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text_streaming = False
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text_chunk_index = 0
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last_text_chunk_time = None
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chunk_count = 0
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reasoning_chunks = 0
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content_chunks = 0
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tool_chunks = 0
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last_finish_reason = None
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# 收集流式响应
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async for chunk in web_terminal.api_client.chat(messages, tools, stream=True):
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chunk_count += 1
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# 检查停止标志
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client_stop_info = get_stop_flag(client_sid, username)
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if client_stop_info:
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stop_requested = client_stop_info.get('stop', False) if isinstance(client_stop_info, dict) else client_stop_info
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if stop_requested:
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debug_log(f"检测到停止请求，中断流处理")
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cancel_pending_tools(tool_calls_list=tool_calls, sender=sender, messages=messages)
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sender('task_stopped', {
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'message': '命令执行被用户取消',
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'reason': 'user_stop'
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})
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clear_stop_flag(client_sid, username)
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return {
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"stopped": True,
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"full_response": full_response,
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"tool_calls": tool_calls,
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"current_thinking": current_thinking,
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"detected_tools": detected_tools,
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"last_usage_payload": last_usage_payload,
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"in_thinking": in_thinking,
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"thinking_started": thinking_started,
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"thinking_ended": thinking_ended,
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"text_started": text_started,
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"text_has_content": text_has_content,
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"text_streaming": text_streaming,
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"text_chunk_index": text_chunk_index,
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"last_text_chunk_time": last_text_chunk_time,
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"chunk_count": chunk_count,
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"reasoning_chunks": reasoning_chunks,
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"content_chunks": content_chunks,
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"tool_chunks": tool_chunks,
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"last_finish_reason": last_finish_reason,
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"accumulated_response": accumulated_response,
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}
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if isinstance(chunk, dict) and chunk.get("error"):
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api_error = chunk.get("error")
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break
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# 先尝试记录 usage（有些平台会在最后一个 chunk 里携带 usage 但 choices 为空）
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usage_info = chunk.get("usage")
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if usage_info:
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last_usage_payload = usage_info
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if "choices" not in chunk:
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debug_log(f"Chunk {chunk_count}: 无choices字段")
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continue
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if not chunk.get("choices"):
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debug_log(f"Chunk {chunk_count}: choices为空列表")
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continue
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choice = chunk["choices"][0]
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if not usage_info and isinstance(choice, dict) and choice.get("usage"):
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# 兼容部分供应商将 usage 放在 choice 内的格式（例如部分 Kimi/Qwen 返回）
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last_usage_payload = choice.get("usage")
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delta = choice.get("delta", {})
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finish_reason = choice.get("finish_reason")
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if finish_reason:
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last_finish_reason = finish_reason
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# 处理思考内容（兼容 reasoning_content / reasoning_details）
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reasoning_content = ""
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if "reasoning_content" in delta:
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reasoning_content = delta.get("reasoning_content") or ""
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elif "reasoning_details" in delta:
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details = delta.get("reasoning_details")
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if isinstance(details, list):
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parts = []
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for item in details:
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if isinstance(item, dict):
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text = item.get("text")
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if text:
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parts.append(text)
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if parts:
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reasoning_content = "".join(parts)
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if reasoning_content:
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reasoning_chunks += 1
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debug_log(f" 思考内容 #{reasoning_chunks}: {len(reasoning_content)} 字符")
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if not thinking_started:
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in_thinking = True
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thinking_started = True
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sender('thinking_start', {})
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await asyncio.sleep(0.05)
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current_thinking += reasoning_content
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sender('thinking_chunk', {'content': reasoning_content})
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# 处理正常内容
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if "content" in delta:
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content = delta["content"]
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if content:
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content_chunks += 1
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debug_log(f" 正式内容 #{content_chunks}: {repr(content[:100] if content else 'None')}")
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if in_thinking and not thinking_ended:
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in_thinking = False
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thinking_ended = True
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sender('thinking_end', {'full_content': current_thinking})
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await asyncio.sleep(0.1)
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if not text_started:
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text_started = True
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text_streaming = True
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sender('text_start', {})
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brief_log("模型输出了内容")
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await asyncio.sleep(0.05)
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full_response += content
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accumulated_response += content
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text_has_content = True
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emit_time = time.time()
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elapsed = 0.0 if last_text_chunk_time is None else emit_time - last_text_chunk_time
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last_text_chunk_time = emit_time
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text_chunk_index += 1
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log_backend_chunk(
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conversation_id,
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current_iteration,
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text_chunk_index,
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elapsed,
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len(content),
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content[:32]
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)
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sender('text_chunk', {
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'content': content,
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'index': text_chunk_index,
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'elapsed': elapsed
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})
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# 收集工具调用 - 实时发送准备状态
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if "tool_calls" in delta:
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tool_chunks += 1
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for tc in delta["tool_calls"]:
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found = False
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for existing in tool_calls:
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if existing.get("index") == tc.get("index"):
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if "function" in tc and "arguments" in tc["function"]:
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arg_chunk = tc["function"]["arguments"]
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existing_fn = existing.get("function", {})
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existing_args = existing_fn.get("arguments", "")
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existing_fn["arguments"] = (existing_args or "") + (arg_chunk or "")
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existing["function"] = existing_fn
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combined_args = existing_fn.get("arguments", "")
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tool_id = existing.get("id") or tc.get("id")
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tool_name = (
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existing_fn.get("name")
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or tc.get("function", {}).get("name", "")
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)
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intent_value = extract_intent_from_partial(combined_args)
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if (
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intent_value
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and tool_id
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and detected_tool_intent.get(tool_id) != intent_value
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):
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detected_tool_intent[tool_id] = intent_value
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brief_log(f"[intent] 增量提取 {tool_name}: {intent_value}")
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sender('tool_intent', {
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'id': tool_id,
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'name': tool_name,
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'intent': intent_value,
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'conversation_id': conversation_id
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})
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debug_log(f" 发送工具意图: {tool_name} -> {intent_value}")
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await asyncio.sleep(0.01)
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found = True
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break
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if not found and tc.get("id"):
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tool_id = tc["id"]
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tool_name = tc.get("function", {}).get("name", "")
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arguments_str = tc.get("function", {}).get("arguments", "") or ""
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# 新工具检测到，立即发送准备事件
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if tool_id not in detected_tools and tool_name:
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detected_tools[tool_id] = tool_name
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# 尝试提前提取 intent
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intent_value = None
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if arguments_str:
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intent_value = extract_intent_from_partial(arguments_str)
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if intent_value:
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detected_tool_intent[tool_id] = intent_value
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brief_log(f"[intent] 预提取 {tool_name}: {intent_value}")
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# 立即发送工具准备中事件
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brief_log(f"[tool] 准备调用 {tool_name} (id={tool_id}) intent={intent_value or '-'}")
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sender('tool_preparing', {
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'id': tool_id,
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'name': tool_name,
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'message': f'准备调用 {tool_name}...',
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'intent': intent_value,
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'conversation_id': conversation_id
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})
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debug_log(f" 发送工具准备事件: {tool_name}")
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await asyncio.sleep(0.1)
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tool_calls.append({
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"id": tool_id,
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"index": tc.get("index"),
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"type": "function",
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"function": {
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"name": tool_name,
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"arguments": arguments_str
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}
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})
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# 尝试从增量参数中抽取 intent，并单独推送
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if tool_id and arguments_str:
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intent_value = extract_intent_from_partial(arguments_str)
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if intent_value and detected_tool_intent.get(tool_id) != intent_value:
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detected_tool_intent[tool_id] = intent_value
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sender('tool_intent', {
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'id': tool_id,
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'name': tool_name,
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'intent': intent_value,
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'conversation_id': conversation_id
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})
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debug_log(f" 发送工具意图: {tool_name} -> {intent_value}")
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await asyncio.sleep(0.01)
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debug_log(f" 新工具: {tool_name}")
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# 检查是否被停止
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client_stop_info = get_stop_flag(client_sid, username)
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if client_stop_info:
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stop_requested = client_stop_info.get('stop', False) if isinstance(client_stop_info, dict) else client_stop_info
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if stop_requested:
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debug_log("任务在流处理完成后检测到停止状态")
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sender('task_stopped', {
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'message': '命令执行被用户取消',
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'reason': 'user_stop'
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})
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cancel_pending_tools(tool_calls_list=tool_calls, sender=sender, messages=messages)
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clear_stop_flag(client_sid, username)
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return {
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"stopped": True,
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"full_response": full_response,
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"tool_calls": tool_calls,
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"current_thinking": current_thinking,
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"detected_tools": detected_tools,
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"last_usage_payload": last_usage_payload,
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"in_thinking": in_thinking,
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"thinking_started": thinking_started,
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"thinking_ended": thinking_ended,
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"text_started": text_started,
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"text_has_content": text_has_content,
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"text_streaming": text_streaming,
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"text_chunk_index": text_chunk_index,
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"last_text_chunk_time": last_text_chunk_time,
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"chunk_count": chunk_count,
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"reasoning_chunks": reasoning_chunks,
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"content_chunks": content_chunks,
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"tool_chunks": tool_chunks,
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"last_finish_reason": last_finish_reason,
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"accumulated_response": accumulated_response,
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}
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# === API响应完成后只计算输出token ===
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if last_usage_payload:
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try:
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web_terminal.context_manager.apply_usage_statistics(last_usage_payload)
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debug_log(
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f"Usage统计: prompt={last_usage_payload.get('prompt_tokens', 0)}, "
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f"completion={last_usage_payload.get('completion_tokens', 0)}, "
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f"total={last_usage_payload.get('total_tokens', 0)}"
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)
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except Exception as e:
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debug_log(f"Usage统计更新失败: {e}")
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else:
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debug_log("未获取到usage字段，跳过token统计更新")
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if api_error:
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try:
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debug_log(f"API错误原始数据: {json.dumps(api_error, ensure_ascii=False)}")
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except Exception:
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debug_log(f"API错误原始数据(不可序列化): {repr(api_error)}")
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error_message = ""
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error_status = None
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error_type = None
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error_code = None
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error_text = ""
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request_dump = None
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error_base_url = None
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error_model_id = None
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if isinstance(api_error, dict):
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error_status = api_error.get("status_code")
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error_type = api_error.get("error_type") or api_error.get("type")
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error_code = api_error.get("error_code") or api_error.get("code")
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error_text = api_error.get("error_text") or ""
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error_message = (
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api_error.get("error_message")
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or api_error.get("message")
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or error_text
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or ""
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)
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request_dump = api_error.get("request_dump")
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error_base_url = api_error.get("base_url")
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error_model_id = api_error.get("model_id")
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elif isinstance(api_error, str):
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error_message = api_error
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if not error_message:
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if error_status:
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error_message = f"API 请求失败（HTTP {error_status}）"
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else:
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error_message = "API 请求失败"
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# 若命中阿里云配额错误，立即写入状态并切换到官方 API
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try:
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from utils.aliyun_fallback import compute_disabled_until, set_disabled_until
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disabled_until, reason = compute_disabled_until(error_message)
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if disabled_until and reason:
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set_disabled_until(getattr(web_terminal, "model_key", None) or "kimi-k2.5", disabled_until, reason)
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profile = get_model_profile(getattr(web_terminal, "model_key", None) or "kimi-k2.5")
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web_terminal.apply_model_profile(profile)
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except Exception as exc:
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debug_log(f"处理阿里云配额回退失败: {exc}")
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can_retry = (
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api_attempt < max_api_retries
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and not full_response
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and not tool_calls
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and not current_thinking
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)
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sender('error', {
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'message': error_message,
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'status_code': error_status,
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'error_type': error_type,
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'error_code': error_code,
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'error_text': error_text,
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'request_dump': request_dump,
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'base_url': error_base_url,
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'model_id': error_model_id,
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'retry': bool(can_retry),
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'retry_in': retry_delay_seconds if can_retry else None,
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'attempt': api_attempt + 1,
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'max_attempts': max_api_retries + 1
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})
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if can_retry:
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try:
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profile = get_model_profile(getattr(web_terminal, "model_key", None) or "kimi-k2.5")
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web_terminal.apply_model_profile(profile)
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except Exception as exc:
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debug_log(f"重试前更新模型配置失败: {exc}")
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cancelled = await wait_retry_delay(delay_seconds=retry_delay_seconds, client_sid=client_sid, username=username, sender=sender, get_stop_flag=get_stop_flag, clear_stop_flag=clear_stop_flag)
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if cancelled:
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return {
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"stopped": True,
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"full_response": full_response,
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"tool_calls": tool_calls,
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"current_thinking": current_thinking,
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"detected_tools": detected_tools,
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"last_usage_payload": last_usage_payload,
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"in_thinking": in_thinking,
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"thinking_started": thinking_started,
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"thinking_ended": thinking_ended,
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"text_started": text_started,
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"text_has_content": text_has_content,
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"text_streaming": text_streaming,
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"text_chunk_index": text_chunk_index,
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"last_text_chunk_time": last_text_chunk_time,
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"chunk_count": chunk_count,
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"reasoning_chunks": reasoning_chunks,
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"content_chunks": content_chunks,
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"tool_chunks": tool_chunks,
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"last_finish_reason": last_finish_reason,
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"accumulated_response": accumulated_response,
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}
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continue
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cancel_pending_tools(tool_calls_list=tool_calls, sender=sender, messages=messages)
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return {
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"stopped": True,
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"full_response": full_response,
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"tool_calls": tool_calls,
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"current_thinking": current_thinking,
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"detected_tools": detected_tools,
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"last_usage_payload": last_usage_payload,
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"in_thinking": in_thinking,
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"thinking_started": thinking_started,
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"thinking_ended": thinking_ended,
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"text_started": text_started,
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"text_has_content": text_has_content,
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"text_streaming": text_streaming,
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"text_chunk_index": text_chunk_index,
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"last_text_chunk_time": last_text_chunk_time,
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"chunk_count": chunk_count,
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"reasoning_chunks": reasoning_chunks,
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"content_chunks": content_chunks,
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"tool_chunks": tool_chunks,
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"last_finish_reason": last_finish_reason,
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"accumulated_response": accumulated_response,
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}
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break
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return {
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"stopped": False,
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"full_response": full_response,
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"tool_calls": tool_calls,
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"current_thinking": current_thinking,
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"detected_tools": detected_tools,
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"last_usage_payload": last_usage_payload,
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"in_thinking": in_thinking,
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"thinking_started": thinking_started,
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"thinking_ended": thinking_ended,
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"text_started": text_started,
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"text_has_content": text_has_content,
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"text_streaming": text_streaming,
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"text_chunk_index": text_chunk_index,
|
||
"last_text_chunk_time": last_text_chunk_time,
|
||
"chunk_count": chunk_count,
|
||
"reasoning_chunks": reasoning_chunks,
|
||
"content_chunks": content_chunks,
|
||
"tool_chunks": tool_chunks,
|
||
"last_finish_reason": last_finish_reason,
|
||
"accumulated_response": accumulated_response,
|
||
}
|