feat:合并工具调用模型和心流模型
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@@ -50,8 +50,8 @@ class ToolUser:
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prompt += message_txt
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# prompt += f"你注意到{sender_name}刚刚说:{message_txt}\n"
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prompt += f"注意你就是{bot_name},{bot_name}是你的名字。根据之前的聊天记录补充问题信息,搜索时避开你的名字。\n"
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prompt += "必须调用 'lpmm_get_knowledge' 工具来获取知识。\n"
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prompt += "你现在需要对群里的聊天内容进行回复,现在选择工具来对消息和你的回复进行处理,你是否需要额外的信息,比如回忆或者搜寻已有的知识,改变关系和情感,或者了解你现在正在做什么。"
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# prompt += "必须调用 'lpmm_get_knowledge' 工具来获取知识。\n"
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prompt += "你现在需要对群里的聊天内容进行回复,请你思考应该使用什么工具,然后选择工具来对消息和你的回复进行处理,你是否需要额外的信息,比如回忆或者搜寻已有的知识,改变关系和情感,或者了解你现在正在做什么。"
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prompt = await relationship_manager.convert_all_person_sign_to_person_name(prompt)
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prompt = parse_text_timestamps(prompt, mode="lite")
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@@ -68,7 +68,7 @@ class ToolUser:
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return get_all_tool_definitions()
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@staticmethod
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async def _execute_tool_call(tool_call, message_txt: str):
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async def _execute_tool_call(tool_call):
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"""执行特定的工具调用
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Args:
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@@ -89,7 +89,7 @@ class ToolUser:
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return None
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# 执行工具
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result = await tool_instance.execute(function_args, message_txt)
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result = await tool_instance.execute(function_args)
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if result:
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# 直接使用 function_name 作为 tool_type
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tool_type = function_name
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@@ -159,13 +159,13 @@ class ToolUser:
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tool_calls_str = ""
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for tool_call in tool_calls:
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tool_calls_str += f"{tool_call['function']['name']}\n"
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logger.info(f"根据:\n{prompt}\n模型请求调用{len(tool_calls)}个工具: {tool_calls_str}")
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logger.info(f"根据:\n{prompt}\n\n内容:{content}\n\n模型请求调用{len(tool_calls)}个工具: {tool_calls_str}")
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tool_results = []
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structured_info = {} # 动态生成键
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# 执行所有工具调用
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for tool_call in tool_calls:
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result = await self._execute_tool_call(tool_call, message_txt)
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result = await self._execute_tool_call(tool_call)
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if result:
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tool_results.append(result)
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# 使用工具名称作为键
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