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fix:The handling of the streaming tool calls has been fixed, but there are still bugs in the model's reply messages with thoughtfulness.
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@@ -148,13 +148,13 @@ class LocalAgentRunner(runner.RequestRunner):
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if tool_call.function and tool_call.function.arguments:
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# 流式处理中,工具调用参数可能分多个chunk返回,需要追加而不是覆盖
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tool_calls_map[tool_call.id].function.arguments += tool_call.function.arguments
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print(list(tool_calls_map.values()) if (tool_calls_map and msg.is_final) else None)
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# continue
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# 每8个chunk或最后一个chunk时,输出所有累积的内容
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if msg_idx % 8 == 0 or msg.is_final:
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yield llm_entities.MessageChunk(
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role=last_role,
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content=accumulated_content, # 输出所有累积内容
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tool_calls=list(tool_calls_map.values()) if (tool_calls_map and msg.is_final) else None,
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is_final=msg.is_final,
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)
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@@ -178,12 +178,18 @@ class LocalAgentRunner(runner.RequestRunner):
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parameters = json.loads(func.arguments)
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func_ret = await self.ap.tool_mgr.execute_func_call(query, func.name, parameters)
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msg = llm_entities.Message(
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role='tool',
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content=json.dumps(func_ret, ensure_ascii=False),
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tool_call_id=tool_call.id,
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)
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if is_stream:
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msg = llm_entities.MessageChunk(
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role='tool',
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content=json.dumps(func_ret, ensure_ascii=False),
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tool_call_id=tool_call.id,
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)
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else:
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msg = llm_entities.Message(
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role='tool',
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content=json.dumps(func_ret, ensure_ascii=False),
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tool_call_id=tool_call.id,
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)
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yield msg
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