fix(stream): prevent tool-call argument leakage into text chunks
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@ -15,6 +15,7 @@ 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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tool_call_stream_active = False
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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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@ -36,6 +37,7 @@ async def run_streaming_attempts(*, web_terminal, messages, tools, sender, clien
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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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tool_call_stream_active = False
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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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@ -128,51 +130,12 @@ async def run_streaming_attempts(*, web_terminal, messages, tools, sender, clien
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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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delta_tool_calls = delta.get("tool_calls")
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if isinstance(delta_tool_calls, list) and delta_tool_calls:
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tool_call_stream_active = True
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tool_chunks += 1
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for tc in delta["tool_calls"]:
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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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@ -261,6 +224,56 @@ async def run_streaming_attempts(*, web_terminal, messages, tools, sender, clien
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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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# 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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# 某些供应商在 tool_calls 流式阶段会把参数碎片误放进 content。
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# 一旦进入工具调用流，抑制正文 chunk，避免 write_file 参数泄露到前端。
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if tool_call_stream_active:
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debug_log(
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" 抑制可疑正文chunk（tool_call阶段）: "
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f"{repr((content or '')[:100])}"
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)
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continue
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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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# 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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