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fix：统一llm请求函数，改动模型名称

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This commit is contained in:
SengokuCola
2025-06-04 23:27:24 +08:00
parent 077b67fa60
commit 4ebfca096c
17 changed files with 116 additions and 187 deletions
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3
src/chat/focus_chat/expressors/default_expressor.py
View File

@@ -76,8 +76,7 @@ class DefaultExpressor:
self.log_prefix = "expressor"
# TODO: API-Adapter修改标记
self.express_model = LLMRequest(
model=global_config.model.focus_expressor,
# temperature=global_config.model.focus_expressor["temp"],
model=global_config.model.replyer_1,
max_tokens=256,
request_type="focus.expressor",
)

2
src/chat/focus_chat/expressors/exprssion_learner.py
View File

@@ -65,7 +65,7 @@ class ExpressionLearner:
def __init__(self) -> None:
# TODO: API-Adapter修改标记
self.express_learn_model: LLMRequest = LLMRequest(
model=global_config.model.focus_expressor,
model=global_config.model.replyer_1,
temperature=0.1,
max_tokens=256,
request_type="expressor.learner",

24
src/chat/focus_chat/info_processors/tool_processor.py
View File

@@ -28,9 +28,7 @@ def init_prompt():
请仔细分析聊天内容，考虑以下几点：
1. 内容中是否包含需要查询信息的问题
2. 是否需要执行特定操作
3. 是否有明确的工具使用指令
4. 考虑用户与你的关系以及当前的对话氛围
2. 是否有明确的工具使用指令
If you need to use a tool, please directly call the corresponding tool function. If you do not need to use any tool, simply output "No tool needed".
"""
@@ -146,23 +144,27 @@ class ToolProcessor(BaseProcessor):
prompt = await global_prompt_manager.format_prompt(
"tool_executor_prompt",
memory_str=memory_str,
# extra_info="extra_structured_info",
chat_observe_info=chat_observe_info,
# chat_target_name=chat_target_name,
is_group_chat=is_group_chat,
# relation_prompt=relation_prompt,
# prompt_personality=prompt_personality,
# mood_info=mood_info,
bot_name=individuality.name,
time_now=time_now,
)
# 调用LLM，专注于工具使用
logger.debug(f"开始执行工具调用{prompt}")
response, _, tool_calls = await self.llm_model.generate_response_tool_async(prompt=prompt, tools=tools)
# logger.info(f"开始执行工具调用{prompt}")
response, other_info = await self.llm_model.generate_response_async(
prompt=prompt, tools=tools
)
if len(other_info) == 3:
reasoning_content, model_name, tool_calls = other_info
else:
reasoning_content, model_name = other_info
tool_calls = None
# print("tooltooltooltooltooltooltooltooltooltooltooltooltooltooltooltooltool")
if tool_calls:
logger.debug(f"获取到工具原始输出:\n{tool_calls}")
logger.info(f"获取到工具原始输出:\n{tool_calls}")
# 处理工具调用和结果收集，类似于SubMind中的逻辑
new_structured_items = []
used_tools = [] # 记录使用了哪些工具

9
src/chat/focus_chat/memory_activator.py
View File

@@ -112,14 +112,9 @@ class MemoryActivator:
# logger.debug(f"prompt: {prompt}")
response = await self.summary_model.generate_response(prompt)
response, (reasoning_content, model_name) = await self.summary_model.generate_response_async(prompt)
# logger.debug(f"response: {response}")
# 只取response的第一个元素（字符串）
response_str = response[0]
# print(f"response_str: {response_str[1]}")
keywords = list(get_keywords_from_json(response_str))
keywords = list(get_keywords_from_json(response))
# 更新关键词缓存
if keywords:

15
src/chat/focus_chat/planners/actions/emoji_action.py
View File

@@ -123,12 +123,13 @@ class EmojiAction(BaseAction):
)
reply_text = ""
for reply in reply_set:
type = reply[0]
data = reply[1]
if type == "text":
reply_text += data
elif type == "emoji":
reply_text += data
if reply_set:
for reply in reply_set:
type = reply[0]
data = reply[1]
if type == "text":
reply_text += data
elif type == "emoji":
reply_text += data
return success, reply_text

3
src/chat/focus_chat/replyer/default_replyer.py
View File

@@ -87,8 +87,7 @@ class DefaultReplyer:
self.log_prefix = "replyer"
# TODO: API-Adapter修改标记
self.express_model = LLMRequest(
model=global_config.model.focus_expressor,
# temperature=global_config.model.focus_expressor["temp"],
model=global_config.model.replyer_1,
max_tokens=256,
request_type="focus.expressor",
)

12
src/chat/memory_system/Hippocampus.py
View File

@@ -346,10 +346,10 @@ class Hippocampus:
# 使用LLM提取关键词
topic_num = min(5, max(1, int(len(text) * 0.1))) # 根据文本长度动态调整关键词数量
# logger.info(f"提取关键词数量: {topic_num}")
topics_response = await self.model_summary.generate_response(self.find_topic_llm(text, topic_num))
topics_response, (reasoning_content, model_name) = await self.model_summary.generate_response_async(self.find_topic_llm(text, topic_num))
# 提取关键词
keywords = re.findall(r"<([^>]+)>", topics_response[0])
keywords = re.findall(r"<([^>]+)>", topics_response)
if not keywords:
keywords = []
else:
@@ -701,10 +701,10 @@ class Hippocampus:
# 使用LLM提取关键词
topic_num = min(5, max(1, int(len(text) * 0.1))) # 根据文本长度动态调整关键词数量
# logger.info(f"提取关键词数量: {topic_num}")
topics_response = await self.model_summary.generate_response(self.find_topic_llm(text, topic_num))
topics_response, (reasoning_content, model_name) = await self.model_summary.generate_response_async(self.find_topic_llm(text, topic_num))
# 提取关键词
keywords = re.findall(r"<([^>]+)>", topics_response[0])
keywords = re.findall(r"<([^>]+)>", topics_response)
if not keywords:
keywords = []
else:
@@ -1248,12 +1248,12 @@ class ParahippocampalGyrus:
# 2. 使用LLM提取关键主题
topic_num = self.hippocampus.calculate_topic_num(input_text, compress_rate)
topics_response = await self.hippocampus.model_summary.generate_response(
topics_response, (reasoning_content, model_name) = await self.hippocampus.model_summary.generate_response_async(
self.hippocampus.find_topic_llm(input_text, topic_num)
)
# 提取<>中的内容
topics = re.findall(r"<([^>]+)>", topics_response[0])
topics = re.findall(r"<([^>]+)>", topics_response)
if not topics:
topics = ["none"]

10
src/chat/normal_chat/normal_chat_generator.py
View File

@@ -18,14 +18,14 @@ class NormalChatGenerator:
def __init__(self):
# TODO: API-Adapter修改标记
self.model_reasoning = LLMRequest(
model=global_config.model.normal_chat_1,
model=global_config.model.replyer_1,
# temperature=0.7,
max_tokens=3000,
request_type="normal.chat_1",
)
self.model_normal = LLMRequest(
model=global_config.model.normal_chat_2,
# temperature=global_config.model.normal_chat_2["temp"],
model=global_config.model.replyer_2,
# temperature=global_config.model.replyer_2["temp"],
max_tokens=256,
request_type="normal.chat_2",
)
@@ -103,7 +103,7 @@ class NormalChatGenerator:
logger.debug(f"构建prompt时间: {t_build_prompt.human_readable}")
try:
content, reasoning_content, self.current_model_name = await model.generate_response(prompt)
content, (reasoning_content, model_name) = await model.generate_response_async(prompt)
logger.debug(f"prompt:{prompt}\n生成回复：{content}")
@@ -147,7 +147,7 @@ class NormalChatGenerator:
"""
# 调用模型生成结果
result, _, _ = await self.model_sum.generate_response(prompt)
result, (reasoning_content, model_name) = await self.model_sum.generate_response_async(prompt)
result = result.strip()
# 解析模型输出的结果

4
src/chat/normal_chat/normal_chat_planner.py
View File

@@ -148,10 +148,12 @@ class NormalChatPlanner:
# 使用LLM生成动作决策
try:
content, reasoning_content, model_name = await self.planner_llm.generate_response(prompt)
content, (reasoning_content, model_name) = await self.planner_llm.generate_response_async(prompt)
logger.info(f"{self.log_prefix}规划器原始提示词: {prompt}")
logger.info(f"{self.log_prefix}规划器原始响应: {content}")
logger.info(f"{self.log_prefix}规划器推理: {reasoning_content}")
logger.info(f"{self.log_prefix}规划器模型: {model_name}")
# 解析JSON响应
try: