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fix:FFUF

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This commit is contained in:
SengokuCola
2025-04-24 14:19:26 +08:00
parent f8450f705a
commit 3075664480
13 changed files with 224 additions and 225 deletions
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4
src/do_tool/tool_use.py
View File

@@ -159,7 +159,9 @@ class ToolUser:
tool_calls_str = ""
for tool_call in tool_calls:
tool_calls_str += f"{tool_call['function']['name']}\n"
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}")
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}"
)
tool_results = []
structured_info = {} # 动态生成键

8
src/heart_flow/observation.py
View File

@@ -92,7 +92,6 @@ class ChattingObservation(Observation):
self.last_observe_time = new_messages_list[-1]["time"]
self.talking_message.extend(new_messages_list)
if len(self.talking_message) > self.max_now_obs_len:
# 计算需要移除的消息数量，保留最新的 max_now_obs_len 条
messages_to_remove_count = len(self.talking_message) - self.max_now_obs_len
@@ -100,12 +99,9 @@ class ChattingObservation(Observation):
self.talking_message = self.talking_message[messages_to_remove_count:] # 保留后半部分，即最新的
oldest_messages_str = await build_readable_messages(
messages=oldest_messages,
timestamp_mode="normal",
read_mark=0
messages=oldest_messages, timestamp_mode="normal", read_mark=0
)
# 调用 LLM 总结主题
prompt = (
f"请总结以下聊天记录的主题：\n{oldest_messages_str}\n用一句话概括包括人物事件和主要信息，不要分点："
@@ -145,7 +141,7 @@ class ChattingObservation(Observation):
messages=self.talking_message,
timestamp_mode="normal",
read_mark=last_obs_time_mark,
)
)
logger.trace(
f"Chat {self.chat_id} - 压缩早期记忆：{self.mid_memory_info}\n现在聊天内容：{self.talking_message_str}"

28
src/heart_flow/sub_heartflow.py
View File

@@ -6,12 +6,10 @@ from src.config.config import global_config
import time
from typing import Optional, List, Dict, Callable
import traceback
from src.plugins.chat.utils import parse_text_timestamps
import enum
from src.common.logger import get_module_logger, LogConfig, SUB_HEARTFLOW_STYLE_CONFIG # noqa: E402
from src.individuality.individuality import Individuality
import random
from src.plugins.person_info.relationship_manager import relationship_manager
from ..plugins.utils.prompt_builder import Prompt, global_prompt_manager
from src.plugins.chat.message import MessageRecv
from src.plugins.chat.chat_stream import chat_manager
@@ -20,7 +18,7 @@ from src.plugins.heartFC_chat.heartFC_chat import HeartFChatting
from src.plugins.heartFC_chat.normal_chat import NormalChat
from src.do_tool.tool_use import ToolUser
from src.heart_flow.mai_state_manager import MaiStateInfo
from src.plugins.utils.json_utils import safe_json_dumps, process_llm_tool_response, normalize_llm_response, process_llm_tool_calls
from src.plugins.utils.json_utils import safe_json_dumps, normalize_llm_response, process_llm_tool_calls
# 定义常量 (从 interest.py 移动过来)
MAX_INTEREST = 15.0
@@ -115,8 +113,6 @@ class InterestChatting:
self.above_threshold = False
self.start_hfc_probability = 0.0
def add_interest_dict(self, message: MessageRecv, interest_value: float, is_mentioned: bool):
self.interest_dict[message.message_info.message_id] = (message, interest_value, is_mentioned)
self.last_interaction_time = time.time()
@@ -547,9 +543,7 @@ class SubHeartflow:
# 加权随机选择思考指导
hf_do_next = local_random.choices(
[option[0] for option in hf_options],
weights=[option[1] for option in hf_options],
k=1
[option[0] for option in hf_options], weights=[option[1] for option in hf_options], k=1
)[0]
# ---------- 4. 构建最终提示词 ----------
@@ -588,7 +582,7 @@ class SubHeartflow:
# 从标准化响应中提取内容
if len(normalized_response) >= 2:
content = normalized_response[0]
reasoning_content = normalized_response[1] if len(normalized_response) > 1 else ""
_reasoning_content = normalized_response[1] if len(normalized_response) > 1 else ""
# 处理可能的工具调用
if len(normalized_response) == 3:
@@ -599,11 +593,12 @@ class SubHeartflow:
if success and valid_tool_calls:
# 记录工具调用信息
tool_calls_str = ", ".join([
call.get("function", {}).get("name", "未知工具")
for call in valid_tool_calls
])
logger.info(f"[{self.subheartflow_id}] 模型请求调用{len(valid_tool_calls)}个工具: {tool_calls_str}")
tool_calls_str = ", ".join(
[call.get("function", {}).get("name", "未知工具") for call in valid_tool_calls]
)
logger.info(
f"[{self.subheartflow_id}] 模型请求调用{len(valid_tool_calls)}个工具: {tool_calls_str}"
)
# 收集工具执行结果
await self._execute_tool_calls(valid_tool_calls, tool_instance)
@@ -652,10 +647,7 @@ class SubHeartflow:
if tool_name not in structured_info:
structured_info[tool_name] = []
structured_info[tool_name].append({
"name": result["name"],
"content": result["content"]
})
structured_info[tool_name].append({"name": result["name"], "content": result["content"]})
except Exception as tool_e:
logger.error(f"[{self.subheartflow_id}] 工具执行失败: {tool_e}")

6
src/heart_flow/subheartflow_manager.py
View File

@@ -290,9 +290,9 @@ class SubHeartflowManager:
log_prefix_flow = f"[{stream_name}]"
# 只处理 CHAT 状态的子心流
# The code snippet is checking if the `chat_status` attribute of `sub_hf.chat_state` is not equal to
# `ChatState.CHAT`. If the condition is met, the code will continue to the next iteration of the loop
# or block of code where this snippet is located.
# The code snippet is checking if the `chat_status` attribute of `sub_hf.chat_state` is not equal to
# `ChatState.CHAT`. If the condition is met, the code will continue to the next iteration of the loop
# or block of code where this snippet is located.
# if sub_hf.chat_state.chat_status != ChatState.CHAT:
# continue

1
src/plugins/chat/bot.py
View File

@@ -78,7 +78,6 @@ class ChatBot:
groupinfo = message.message_info.group_info
userinfo = message.message_info.user_info
if userinfo.user_id in global_config.ban_user_id:
logger.debug(f"用户{userinfo.user_id}被禁止回复")
return

4
src/plugins/chat/utils.py
View File

@@ -328,7 +328,9 @@ def split_into_sentences_w_remove_punctuation(text: str) -> List[str]:
final_sentences = [content for content, sep in merged_segments if content] # 只保留有内容的段
# 清理可能引入的空字符串和仅包含空白的字符串
final_sentences = [s for s in final_sentences if s.strip()] # 过滤掉空字符串以及仅包含空白（如换行符、空格）的字符串
final_sentences = [
s for s in final_sentences if s.strip()
] # 过滤掉空字符串以及仅包含空白（如换行符、空格）的字符串
logger.debug(f"分割并合并后的句子: {final_sentences}")
return final_sentences

24
src/plugins/heartFC_chat/heartFC_chat.py
View File

@@ -2,6 +2,7 @@ import asyncio
import time
import traceback
from typing import List, Optional, Dict, Any, TYPE_CHECKING
# import json # 移除，因为使用了json_utils
from src.plugins.chat.message import MessageRecv, BaseMessageInfo, MessageThinking, MessageSending
from src.plugins.chat.message import MessageSet, Seg # Local import needed after move
@@ -17,7 +18,7 @@ from src.plugins.heartFC_chat.heartFC_generator import HeartFCGenerator
from src.do_tool.tool_use import ToolUser
from ..chat.message_sender import message_manager # <-- Import the global manager
from src.plugins.chat.emoji_manager import emoji_manager
from src.plugins.utils.json_utils import extract_tool_call_arguments, safe_json_dumps, process_llm_tool_response # 导入新的JSON工具
from src.plugins.utils.json_utils import process_llm_tool_response # 导入新的JSON工具
# --- End import ---
@@ -37,7 +38,7 @@ if TYPE_CHECKING:
# Keep this if HeartFCController methods are still needed elsewhere,
# but the instance variable will be removed from HeartFChatting
# from .heartFC_controler import HeartFCController
from src.heart_flow.heartflow import SubHeartflow, heartflow # <-- 同时导入 heartflow 实例用于类型检查
from src.heart_flow.heartflow import SubHeartflow # <-- 同时导入 heartflow 实例用于类型检查
PLANNER_TOOL_DEFINITION = [
{
@@ -327,7 +328,6 @@ class HeartFChatting:
with Timer("Wait New Msg", cycle_timers): # <--- Start Wait timer
wait_start_time = time.monotonic()
while True:
# 检查是否有新消息
has_new = await observation.has_new_messages_since(planner_start_db_time)
if has_new:
@@ -434,19 +434,17 @@ class HeartFChatting:
except Exception as e:
logger.error(f"{log_prefix}[Planner] 获取观察信息时出错: {e}")
try:
current_mind, _past_mind = await self.sub_hf.do_thinking_before_reply()
except Exception as e_subhf:
logger.error(f"{log_prefix}[Planner] SubHeartflow 思考失败: {e_subhf}")
current_mind = "[思考时出错]"
# --- 使用 LLM 进行决策 --- #
action = "no_reply" # 默认动作
emoji_query = "" # 默认表情查询
emoji_query = "" # 默认表情查询
reasoning = "默认决策或获取决策失败"
llm_error = False # LLM错误标志
llm_error = False # LLM错误标志
try:
prompt = await self._build_planner_prompt(observed_messages_str, current_mind, self.sub_hf.structured_info)
@@ -475,9 +473,7 @@ class HeartFChatting:
# 使用辅助函数处理工具调用响应
success, arguments, error_msg = process_llm_tool_response(
response,
expected_tool_name="decide_reply_action",
log_prefix=f"{log_prefix}[Planner] "
response, expected_tool_name="decide_reply_action", log_prefix=f"{log_prefix}[Planner] "
)
if success:
@@ -487,9 +483,7 @@ class HeartFChatting:
emoji_query = arguments.get("emoji_query", "")
# 记录决策结果
logger.debug(
f"{log_prefix}[Planner] 决策结果: {action}, 理由: {reasoning}, 表情查询: '{emoji_query}'"
)
logger.debug(f"{log_prefix}[Planner] 决策结果: {action}, 理由: {reasoning}, 表情查询: '{emoji_query}'")
else:
# 处理工具调用失败
logger.warning(f"{log_prefix}[Planner] {error_msg}")
@@ -608,7 +602,9 @@ class HeartFChatting:
logger.info(f"{log_prefix} HeartFChatting关闭完成")
async def _build_planner_prompt(self, observed_messages_str: str, current_mind: Optional[str], structured_info: Dict[str, Any]) -> str:
async def _build_planner_prompt(
self, observed_messages_str: str, current_mind: Optional[str], structured_info: Dict[str, Any]
) -> str:
"""构建 Planner LLM 的提示词"""
prompt = f"你的名字是 {global_config.BOT_NICKNAME}。是一个女大二学生，正在QQ聊天，正在决定是否以及如何回应当前的聊天。\n"

8
src/plugins/heartFC_chat/heartFC_generator.py
View File

@@ -72,7 +72,13 @@ class HeartFCGenerator:
return None
async def _generate_response_with_model(
self, structured_info: str, current_mind_info: str, reason: str, message: MessageRecv, model: LLMRequest, thinking_id: str
self,
structured_info: str,
current_mind_info: str,
reason: str,
message: MessageRecv,
model: LLMRequest,
thinking_id: str,
) -> str:
sender_name = ""

13
src/plugins/heartFC_chat/heartflow_prompt_builder.py
View File

@@ -81,13 +81,22 @@ class PromptBuilder:
self.activate_messages = ""
async def build_prompt(
self, build_mode, reason, current_mind_info, structured_info, message_txt: str, sender_name: str = "某人", chat_stream=None
self,
build_mode,
reason,
current_mind_info,
structured_info,
message_txt: str,
sender_name: str = "某人",
chat_stream=None,
) -> Optional[tuple[str, str]]:
if build_mode == "normal":
return await self._build_prompt_normal(chat_stream, message_txt, sender_name)
elif build_mode == "focus":
return await self._build_prompt_focus(reason, current_mind_info, structured_info, chat_stream, message_txt, sender_name)
return await self._build_prompt_focus(
reason, current_mind_info, structured_info, chat_stream, message_txt, sender_name
)
return None
async def _build_prompt_focus(

10
src/plugins/models/utils_model.py
View File

@@ -786,11 +786,7 @@ class LLMRequest:
message["tool_calls"] = tool_calls
result = {
"choices": [
{
"message": message
}
],
"choices": [{"message": message}],
"usage": usage,
}
return result
@@ -1139,7 +1135,7 @@ class LLMRequest:
"messages": [{"role": "user", "content": prompt}],
**self.params,
**kwargs,
"tools": tools
"tools": tools,
}
logger.debug(f"向模型 {self.model_name} 发送工具调用请求，包含 {len(tools)} 个工具")
@@ -1150,7 +1146,7 @@ class LLMRequest:
logger.debug(f"收到工具调用响应，包含 {len(tool_calls) if tool_calls else 0} 个工具调用")
return content, reasoning_content, tool_calls
else:
logger.debug(f"收到普通响应，无工具调用")
logger.debug("收到普通响应，无工具调用")
return response
async def get_embedding(self, text: str) -> Union[list, None]:

4
src/plugins/utils/chat_message_builder.py
View File

@@ -303,7 +303,9 @@ async def build_readable_messages(
)
readable_read_mark = translate_timestamp_to_human_readable(read_mark, mode=timestamp_mode)
read_mark_line = f"\n\n--- 以上消息已读 (标记时间: {readable_read_mark}) ---\n--- 请关注你上次思考之后以下的新消息---\n"
read_mark_line = (
f"\n\n--- 以上消息已读 (标记时间: {readable_read_mark}) ---\n--- 请关注你上次思考之后以下的新消息---\n"
)
# 组合结果，确保空部分不引入多余的标记或换行
if formatted_before and formatted_after:

28
src/plugins/utils/json_utils.py
View File

@@ -1,13 +1,14 @@
import json
import logging
from typing import Any, Dict, Optional, TypeVar, Generic, List, Union, Callable, Tuple
from typing import Any, Dict, TypeVar, List, Union, Callable, Tuple
# 定义类型变量用于泛型类型提示
T = TypeVar('T')
T = TypeVar("T")
# 获取logger
logger = logging.getLogger("json_utils")
def safe_json_loads(json_str: str, default_value: T = None) -> Union[Any, T]:
"""
安全地解析JSON字符串，出错时返回默认值
@@ -31,8 +32,8 @@ def safe_json_loads(json_str: str, default_value: T = None) -> Union[Any, T]:
logger.error(f"JSON解析过程中发生意外错误: {e}")
return default_value
def extract_tool_call_arguments(tool_call: Dict[str, Any],
default_value: Dict[str, Any] = None) -> Dict[str, Any]:
def extract_tool_call_arguments(tool_call: Dict[str, Any], default_value: Dict[str, Any] = None) -> Dict[str, Any]:
"""
从LLM工具调用对象中提取参数
@@ -68,9 +69,10 @@ def extract_tool_call_arguments(tool_call: Dict[str, Any],
logger.error(f"提取工具调用参数时出错: {e}")
return default_result
def get_json_value(json_obj: Dict[str, Any], key_path: str,
default_value: T = None,
transform_func: Callable[[Any], T] = None) -> Union[Any, T]:
def get_json_value(
json_obj: Dict[str, Any], key_path: str, default_value: T = None, transform_func: Callable[[Any], T] = None
) -> Union[Any, T]:
"""
从JSON对象中按照路径提取值，支持点表示法路径，如"data.items.0.name"
@@ -117,8 +119,8 @@ def get_json_value(json_obj: Dict[str, Any], key_path: str,
logger.error(f"从JSON获取值时出错: {e}, 路径: {key_path}")
return default_value
def safe_json_dumps(obj: Any, default_value: str = "{}", ensure_ascii: bool = False,
pretty: bool = False) -> str:
def safe_json_dumps(obj: Any, default_value: str = "{}", ensure_ascii: bool = False, pretty: bool = False) -> str:
"""
安全地将Python对象序列化为JSON字符串
@@ -141,6 +143,7 @@ def safe_json_dumps(obj: Any, default_value: str = "{}", ensure_ascii: bool = Fa
logger.error(f"JSON序列化过程中发生意外错误: {e}")
return default_value
def merge_json_objects(*objects: Dict[str, Any]) -> Dict[str, Any]:
"""
合并多个JSON对象(字典)
@@ -157,6 +160,7 @@ def merge_json_objects(*objects: Dict[str, Any]) -> Dict[str, Any]:
result.update(obj)
return result
def normalize_llm_response(response: Any, log_prefix: str = "") -> Tuple[bool, List[Any], str]:
"""
标准化LLM响应格式，将各种格式（如元组）转换为统一的列表格式
@@ -196,6 +200,7 @@ def normalize_llm_response(response: Any, log_prefix: str = "") -> Tuple[bool, L
return True, response, ""
def process_llm_tool_calls(response: List[Any], log_prefix: str = "") -> Tuple[bool, List[Dict[str, Any]], str]:
"""
处理并提取LLM响应中的工具调用列表
@@ -247,10 +252,9 @@ def process_llm_tool_calls(response: List[Any], log_prefix: str = "") -> Tuple[b
return True, valid_tool_calls, ""
def process_llm_tool_response(
response: Any,
expected_tool_name: str = None,
log_prefix: str = ""
response: Any, expected_tool_name: str = None, log_prefix: str = ""
) -> Tuple[bool, Dict[str, Any], str]:
"""
处理LLM返回的工具调用响应，进行常见错误检查并提取参数

69
tool_call_benchmark.py
View File

@@ -6,6 +6,7 @@ from src.do_tool.tool_use import ToolUser
import statistics
import json
async def run_test(test_name, test_function, iterations=5):
"""
运行指定次数的测试并计算平均响应时间
@@ -23,7 +24,7 @@ async def run_test(test_name, test_function, iterations=5):
responses = []
for i in range(iterations):
print(f" 运行第 {i+1}/{iterations} 次测试...")
print(f" 运行第 {i + 1}/{iterations} 次测试...")
start_time = time.time()
response = await test_function()
end_time = time.time()
@@ -38,11 +39,12 @@ async def run_test(test_name, test_function, iterations=5):
"最长耗时": max(times),
"标准差": statistics.stdev(times) if len(times) > 1 else 0,
"所有耗时": times,
"响应结果": responses
"响应结果": responses,
}
return results
async def test_with_tool_calls():
"""使用工具调用的LLM请求测试"""
# 创建LLM模型实例
@@ -60,7 +62,7 @@ async def test_with_tool_calls():
# 简单的测试提示词
prompt = "请分析当前天气情况，并查询今日历史上的重要事件。并且3.9和3.11谁比较大?请使用适当的工具来获取这些信息。"
prompt = '''
prompt = """
你的名字是麦麦，你包容开放，情绪敏感，有时候有些搞怪幽默, 是一个学习心理学和脑科学的女大学生，现在在读大二，你会刷贴吧，有时候会想瑟瑟，喜欢刷小红书
-----------------------------------
现在是2025-04-24 12:37:00，你正在上网，和qq群里的网友们聊天，群里正在聊的话题是：
@@ -89,7 +91,7 @@ async def test_with_tool_calls():
回复的要求是：平淡一些，简短一些，说中文，如果你要回复，最好只回复一个人的一个话题
请注意不要输出多余内容(包括前后缀，冒号和引号，括号， 表情，等)，不要带有括号和动作描写。不要回复自己的发言，尽量不要说你说过的话。
现在请你继续生成你在这个聊天中的想法，在原来想法的基础上继续思考，不要分点输出,生成内心想法，文字不要浮夸
在输出完想法后，请你思考应该使用什么工具，如果你需要做某件事，来对消息和你的回复进行处理，请使用工具。'''
在输出完想法后，请你思考应该使用什么工具，如果你需要做某件事，来对消息和你的回复进行处理，请使用工具。"""
# 发送带有工具调用的请求
response = await llm_model.generate_response_tool_async(prompt=prompt, tools=tools)
@@ -109,32 +111,27 @@ async def test_with_tool_calls():
if tool_calls:
print("\n 工具调用详情:")
for i, tool_call in enumerate(tool_calls):
tool_name = tool_call['function']['name']
tool_params = tool_call['function'].get('arguments', {})
print(f" - 工具 {i+1}: {tool_name}")
print(f" 参数: {json.dumps(tool_params, ensure_ascii=False)[:100]}..."
if len(json.dumps(tool_params, ensure_ascii=False)) > 100
else f" 参数: {json.dumps(tool_params, ensure_ascii=False)}")
tool_name = tool_call["function"]["name"]
tool_params = tool_call["function"].get("arguments", {})
print(f" - 工具 {i + 1}: {tool_name}")
print(
f" 参数: {json.dumps(tool_params, ensure_ascii=False)[:100]}..."
if len(json.dumps(tool_params, ensure_ascii=False)) > 100
else f" 参数: {json.dumps(tool_params, ensure_ascii=False)}"
)
result_info = {
"内容": content,
"推理内容": reasoning_content,
"工具调用": tool_calls
}
result_info = {"内容": content, "推理内容": reasoning_content, "工具调用": tool_calls}
else:
content, reasoning_content = response
print(" 工具调用请求未生成任何工具调用")
print("\n 生成的内容:")
print(f" {content[:200]}..." if len(content) > 200 else f" {content}")
result_info = {
"内容": content,
"推理内容": reasoning_content,
"工具调用": []
}
result_info = {"内容": content, "推理内容": reasoning_content, "工具调用": []}
return result_info
async def test_without_tool_calls():
"""不使用工具调用的LLM请求测试"""
# 创建LLM模型实例
@@ -146,7 +143,7 @@ async def test_without_tool_calls():
)
# 简单的测试提示词（与工具调用相同，以便公平比较）
prompt = '''
prompt = """
你的名字是麦麦，你包容开放，情绪敏感，有时候有些搞怪幽默, 是一个学习心理学和脑科学的女大学生，现在在读大二，你会刷贴吧，有时候会想瑟瑟，喜欢刷小红书
刚刚你的想法是：
我是麦麦，我想，('小千石问3.8和3.11谁大，已经简单回答了3.11大，现在可以继续聊猫猫头表情包，毕竟大家好像对版本问题兴趣不大，而且猫猫头的话题更轻松有趣。', '')
@@ -181,7 +178,7 @@ async def test_without_tool_calls():
回复的要求是：平淡一些，简短一些，说中文，如果你要回复，最好只回复一个人的一个话题
请注意不要输出多余内容(包括前后缀，冒号和引号，括号， 表情，等)，不要带有括号和动作描写。不要回复自己的发言，尽量不要说你说过的话。
现在请你继续生成你在这个聊天中的想法，在原来想法的基础上继续思考，不要分点输出,生成内心想法，文字不要浮夸
在输出完想法后，请你思考应该使用什么工具，如果你需要做某件事，来对消息和你的回复进行处理，请使用工具。'''
在输出完想法后，请你思考应该使用什么工具，如果你需要做某件事，来对消息和你的回复进行处理，请使用工具。"""
# 发送不带工具调用的请求
response, reasoning_content = await llm_model.generate_response_async(prompt)
@@ -190,14 +187,11 @@ async def test_without_tool_calls():
print("\n 生成的内容:")
print(f" {response[:200]}..." if len(response) > 200 else f" {response}")
result_info = {
"内容": response,
"推理内容": reasoning_content,
"工具调用": []
}
result_info = {"内容": response, "推理内容": reasoning_content, "工具调用": []}
return result_info
async def main():
"""主测试函数"""
print("=" * 50)
@@ -256,8 +250,9 @@ async def main():
"不使用工具调用_详细响应": [
{
"内容摘要": resp["内容"][:200] + "..." if len(resp["内容"]) > 200 else resp["内容"],
"推理内容摘要": resp["推理内容"][:200] + "..." if len(resp["推理内容"]) > 200 else resp["推理内容"]
} for resp in results_without_tools["响应结果"]
"推理内容摘要": resp["推理内容"][:200] + "..." if len(resp["推理内容"]) > 200 else resp["推理内容"],
}
for resp in results_without_tools["响应结果"]
],
"使用工具调用": {
k: (v if k != "所有耗时" else [float(f"{t:.2f}") for t in v])
@@ -270,20 +265,20 @@ async def main():
"推理内容摘要": resp["推理内容"][:200] + "..." if len(resp["推理内容"]) > 200 else resp["推理内容"],
"工具调用数量": len(resp["工具调用"]),
"工具调用详情": [
{
"工具名称": tool["function"]["name"],
"参数": tool["function"].get("arguments", {})
} for tool in resp["工具调用"]
]
} for resp in results_with_tools["响应结果"]
{"工具名称": tool["function"]["name"], "参数": tool["function"].get("arguments", {})}
for tool in resp["工具调用"]
],
}
for resp in results_with_tools["响应结果"]
],
"差异百分比": float(f"{diff_percent:.2f}")
"差异百分比": float(f"{diff_percent:.2f}"),
}
with open("llm_tool_benchmark_results.json", "w", encoding="utf-8") as f:
json.dump(results, f, ensure_ascii=False, indent=2)
print(f"\n测试结果已保存到 llm_tool_benchmark_results.json")
print("\n测试结果已保存到 llm_tool_benchmark_results.json")
if __name__ == "__main__":
asyncio.run(main())