From 4ebfca096ce5313c602dbe6a620e245a376cadc5 Mon Sep 17 00:00:00 2001 From: SengokuCola <1026294844@qq.com> Date: Wed, 4 Jun 2025 23:27:24 +0800 Subject: [PATCH] =?UTF-8?q?fix=EF=BC=9A=E7=BB=9F=E4=B8=80llm=E8=AF=B7?= =?UTF-8?q?=E6=B1=82=E5=87=BD=E6=95=B0=EF=BC=8C=E6=94=B9=E5=8A=A8=E6=A8=A1?= =?UTF-8?q?=E5=9E=8B=E5=90=8D=E7=A7=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- scripts/070configexe.py | 9 ++- .../expressors/default_expressor.py | 3 +- .../expressors/exprssion_learner.py | 2 +- .../info_processors/tool_processor.py | 24 +++---- src/chat/focus_chat/memory_activator.py | 9 +-- .../planners/actions/emoji_action.py | 15 +++-- .../focus_chat/replyer/default_replyer.py | 3 +- src/chat/memory_system/Hippocampus.py | 12 ++-- src/chat/normal_chat/normal_chat_generator.py | 10 +-- src/chat/normal_chat/normal_chat_planner.py | 4 +- src/config/official_configs.py | 7 +- src/individuality/expression_style.py | 2 +- src/llm_models/utils_model.py | 43 ++----------- src/person_info/person_info.py | 4 +- src/tools/not_used/get_memory.py | 64 ------------------- template/bot_config_template.toml | 48 ++++++-------- tests/test_utils_model.py | 44 +++++++++++++ 17 files changed, 116 insertions(+), 187 deletions(-) delete mode 100644 src/tools/not_used/get_memory.py create mode 100644 tests/test_utils_model.py diff --git a/scripts/070configexe.py b/scripts/070configexe.py index 7eaa6cefd..340e9fa3c 100644 --- a/scripts/070configexe.py +++ b/scripts/070configexe.py @@ -426,8 +426,8 @@ class ConfigEditor: # "model.memory_summary": "记忆概括模型", # "model.vlm": "图像识别模型", # "model.embedding": "嵌入模型", - # "model.normal_chat_1": "普通聊天：主要聊天模型", - # "model.normal_chat_2": "普通聊天：次要聊天模型", + # "model.replyer_1": "普通聊天：主要聊天模型", + # "model.replyer_2": "普通聊天：次要聊天模型", # "model.focus_working_memory": "专注模式：工作记忆模型", # "model.focus_tool_use": "专注模式：工具调用模型", # "model.focus_planner": "专注模式：决策模型", @@ -489,12 +489,11 @@ class ConfigEditor: "model.memory_summary": "记忆概括模型", "model.vlm": "图像识别模型", "model.embedding": "嵌入模型", - "model.normal_chat_1": "主要聊天模型", - "model.normal_chat_2": "次要聊天模型", + "model.replyer_1": "主要聊天模型", + "model.replyer_2": "次要聊天模型", "model.focus_working_memory": "工作记忆模型", "model.focus_tool_use": "工具调用模型", "model.focus_planner": "决策模型", - "model.focus_expressor": "表达器模型", } section_trans = self.translations.get("sections", {}).get(full_section_path, {}) section_name = section_trans.get("name") or section_translations.get(full_section_path) or section diff --git a/src/chat/focus_chat/expressors/default_expressor.py b/src/chat/focus_chat/expressors/default_expressor.py index 66caf1619..d82d98ae0 100644 --- a/src/chat/focus_chat/expressors/default_expressor.py +++ b/src/chat/focus_chat/expressors/default_expressor.py @@ -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", ) diff --git a/src/chat/focus_chat/expressors/exprssion_learner.py b/src/chat/focus_chat/expressors/exprssion_learner.py index 001103abc..d7da9af11 100644 --- a/src/chat/focus_chat/expressors/exprssion_learner.py +++ b/src/chat/focus_chat/expressors/exprssion_learner.py @@ -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", diff --git a/src/chat/focus_chat/info_processors/tool_processor.py b/src/chat/focus_chat/info_processors/tool_processor.py index 5e26829f3..55ab7d0eb 100644 --- a/src/chat/focus_chat/info_processors/tool_processor.py +++ b/src/chat/focus_chat/info_processors/tool_processor.py @@ -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 diff --git a/src/chat/focus_chat/memory_activator.py b/src/chat/focus_chat/memory_activator.py index 8ab20e010..590ba58f5 100644 --- a/src/chat/focus_chat/memory_activator.py +++ b/src/chat/focus_chat/memory_activator.py @@ -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: diff --git a/src/chat/focus_chat/planners/actions/emoji_action.py b/src/chat/focus_chat/planners/actions/emoji_action.py index edf306ac7..b085b2f07 100644 --- a/src/chat/focus_chat/planners/actions/emoji_action.py +++ b/src/chat/focus_chat/planners/actions/emoji_action.py @@ -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 diff --git a/src/chat/focus_chat/replyer/default_replyer.py b/src/chat/focus_chat/replyer/default_replyer.py index 8c9d02ee1..830df3b4e 100644 --- a/src/chat/focus_chat/replyer/default_replyer.py +++ b/src/chat/focus_chat/replyer/default_replyer.py @@ -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", ) diff --git a/src/chat/memory_system/Hippocampus.py b/src/chat/memory_system/Hippocampus.py index 463d477ab..4bcaa6f21 100644 --- a/src/chat/memory_system/Hippocampus.py +++ b/src/chat/memory_system/Hippocampus.py @@ -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"] diff --git a/src/chat/normal_chat/normal_chat_generator.py b/src/chat/normal_chat/normal_chat_generator.py index 50c69c191..e300cdfa9 100644 --- a/src/chat/normal_chat/normal_chat_generator.py +++ b/src/chat/normal_chat/normal_chat_generator.py @@ -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 diff --git a/src/chat/normal_chat/normal_chat_planner.py b/src/chat/normal_chat/normal_chat_planner.py index 1257f1909..114636be1 100644 --- a/src/chat/normal_chat/normal_chat_planner.py +++ b/src/chat/normal_chat/normal_chat_planner.py @@ -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: diff --git a/src/config/official_configs.py b/src/config/official_configs.py index ad2ef013d..423357f5c 100644 --- a/src/config/official_configs.py +++ b/src/config/official_configs.py @@ -424,10 +424,10 @@ class ModelConfig(ConfigBase): utils_small: dict[str, Any] = field(default_factory=lambda: {}) """组件小模型配置""" - normal_chat_1: dict[str, Any] = field(default_factory=lambda: {}) + replyer_1: dict[str, Any] = field(default_factory=lambda: {}) """normal_chat首要回复模型模型配置""" - normal_chat_2: dict[str, Any] = field(default_factory=lambda: {}) + replyer_2: dict[str, Any] = field(default_factory=lambda: {}) """normal_chat次要回复模型配置""" memory_summary: dict[str, Any] = field(default_factory=lambda: {}) @@ -449,9 +449,6 @@ class ModelConfig(ConfigBase): relation: dict[str, Any] = field(default_factory=lambda: {}) """关系模型配置""" - focus_expressor: dict[str, Any] = field(default_factory=lambda: {}) - """专注表达器模型配置""" - embedding: dict[str, Any] = field(default_factory=lambda: {}) """嵌入模型配置""" diff --git a/src/individuality/expression_style.py b/src/individuality/expression_style.py index 77438d330..fe81eac5b 100644 --- a/src/individuality/expression_style.py +++ b/src/individuality/expression_style.py @@ -34,7 +34,7 @@ def init_prompt() -> None: class PersonalityExpression: def __init__(self): self.express_learn_model: LLMRequest = LLMRequest( - model=global_config.model.focus_expressor, + model=global_config.model.replyer_1, max_tokens=512, request_type="expressor.learner", ) diff --git a/src/llm_models/utils_model.py b/src/llm_models/utils_model.py index f2781f22f..4f5cd3b0f 100644 --- a/src/llm_models/utils_model.py +++ b/src/llm_models/utils_model.py @@ -500,11 +500,11 @@ class LLMRequest: logger.warning(f"检测到403错误，模型从 {old_model_name} 降级为 {self.model_name}") # 对全局配置进行更新 - if global_config.model.normal_chat_2.get("name") == old_model_name: - global_config.model.normal_chat_2["name"] = self.model_name + if global_config.model.replyer_2.get("name") == old_model_name: + global_config.model.replyer_2["name"] = self.model_name logger.warning(f"将全局配置中的 llm_normal 模型临时降级至{self.model_name}") - if global_config.model.normal_chat_1.get("name") == old_model_name: - global_config.model.normal_chat_1["name"] = self.model_name + if global_config.model.replyer_1.get("name") == old_model_name: + global_config.model.replyer_1["name"] = self.model_name logger.warning(f"将全局配置中的 llm_reasoning 模型临时降级至{self.model_name}") if payload and "model" in payload: @@ -715,18 +715,6 @@ class LLMRequest: return {"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json"} # 防止小朋友们截图自己的key - async def generate_response(self, prompt: str) -> Tuple: - """根据输入的提示生成模型的异步响应""" - - response = await self._execute_request(endpoint="/chat/completions", prompt=prompt) - # 根据返回值的长度决定怎么处理 - if len(response) == 3: - content, reasoning_content, tool_calls = response - return content, reasoning_content, self.model_name, tool_calls - else: - content, reasoning_content = response - return content, reasoning_content, self.model_name - async def generate_response_for_image(self, prompt: str, image_base64: str, image_format: str) -> Tuple: """根据输入的提示和图片生成模型的异步响应""" @@ -761,29 +749,6 @@ class LLMRequest: content, reasoning_content = response return content, (reasoning_content, self.model_name) - async def generate_response_tool_async(self, prompt: str, tools: list, **kwargs) -> tuple[str, str, list]: - """异步方式根据输入的提示生成模型的响应""" - # 构建请求体，不硬编码max_tokens - data = { - "model": self.model_name, - "messages": [{"role": "user", "content": prompt}], - **self.params, - **kwargs, - "tools": tools, - } - - response = await self._execute_request(endpoint="/chat/completions", payload=data, prompt=prompt) - logger.debug(f"向模型 {self.model_name} 发送工具调用请求，包含 {len(tools)} 个工具，返回结果: {response}") - # 检查响应是否包含工具调用 - if len(response) == 3: - content, reasoning_content, tool_calls = response - 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: - content, reasoning_content = response - logger.debug("收到普通响应，无工具调用") - return content, reasoning_content, None - async def get_embedding(self, text: str) -> Union[list, None]: """异步方法：获取文本的embedding向量 diff --git a/src/person_info/person_info.py b/src/person_info/person_info.py index 2facda2ed..13dae0479 100644 --- a/src/person_info/person_info.py +++ b/src/person_info/person_info.py @@ -238,7 +238,7 @@ class PersonInfoManager: old_name = await self.get_value(person_id, "person_name") old_reason = await self.get_value(person_id, "name_reason") - max_retries = 5 + max_retries = 8 current_try = 0 existing_names_str = "" current_name_set = set(self.person_name_list.values()) @@ -269,7 +269,7 @@ class PersonInfoManager: "nickname": "昵称", "reason": "理由" }""" - response = await self.qv_name_llm.generate_response(qv_name_prompt) + response, (reasoning_content, model_name) = await self.qv_name_llm.generate_response_async(qv_name_prompt) logger.trace(f"取名提示词：{qv_name_prompt}\n取名回复：{response}") result = self._extract_json_from_text(response[0]) diff --git a/src/tools/not_used/get_memory.py b/src/tools/not_used/get_memory.py deleted file mode 100644 index 2f40d3813..000000000 --- a/src/tools/not_used/get_memory.py +++ /dev/null @@ -1,64 +0,0 @@ -from src.tools.tool_can_use.base_tool import BaseTool -from src.chat.memory_system.Hippocampus import HippocampusManager -from src.common.logger import get_module_logger -from typing import Dict, Any - -logger = get_module_logger("mid_chat_mem_tool") - - -class GetMemoryTool(BaseTool): - """从记忆系统中获取相关记忆的工具""" - - name = "get_memory" - description = "使用工具从记忆系统中获取相关记忆" - parameters = { - "type": "object", - "properties": { - "topic": {"type": "string", "description": "要查询的相关主题,用逗号隔开"}, - "max_memory_num": {"type": "integer", "description": "最大返回记忆数量"}, - }, - "required": ["topic"], - } - - async def execute(self, function_args: Dict[str, Any]) -> Dict[str, Any]: - """执行记忆获取 - - Args: - function_args: 工具参数 - - Returns: - Dict: 工具执行结果 - """ - try: - topic = function_args.get("topic") - max_memory_num = function_args.get("max_memory_num", 2) - - # 将主题字符串转换为列表 - topic_list = topic.split(",") - - # 调用记忆系统 - related_memory = await HippocampusManager.get_instance().get_memory_from_topic( - valid_keywords=topic_list, max_memory_num=max_memory_num, max_memory_length=2, max_depth=3 - ) - - memory_info = "" - if related_memory: - for memory in related_memory: - memory_info += memory[1] + "\n" - - if memory_info: - content = f"你记得这些事情: {memory_info}\n" - content += "以上是你的回忆，不一定是目前聊天里的人说的，也不一定是现在发生的事情，请记住。\n" - - else: - content = f"{topic}的记忆，你记不太清" - - return {"type": "memory", "id": topic_list, "content": content} - except Exception as e: - logger.error(f"记忆获取工具执行失败: {str(e)}") - # 在失败时也保持格式一致，但id可能不适用或设为None/Error - return {"type": "memory_error", "id": topic_list, "content": f"记忆获取失败: {str(e)}"} - - -# 注册工具 -# register_tool(GetMemoryTool) diff --git a/template/bot_config_template.toml b/template/bot_config_template.toml index 2762c15f3..413b9a915 100644 --- a/template/bot_config_template.toml +++ b/template/bot_config_template.toml @@ -1,5 +1,5 @@ [inner] -version = "2.12.2" +version = "2.13.0" #----以下是给开发人员阅读的，如果你只是部署了麦麦，不需要阅读---- #如果你想要修改配置文件，请在修改后将version的值进行变更 @@ -72,7 +72,7 @@ ban_msgs_regex = [ [normal_chat] #普通聊天 #一般回复参数 -normal_chat_first_probability = 0.3 # 麦麦回答时选择首要模型的概率（与之相对的，次要模型的概率为1 - normal_chat_first_probability） +normal_chat_first_probability = 0.5 # 麦麦回答时选择首要模型的概率（与之相对的，次要模型的概率为1 - normal_chat_first_probability） max_context_size = 15 #上下文长度 emoji_chance = 0.2 # 麦麦一般回复时使用表情包的概率，设置为1让麦麦自己决定发不发 thinking_timeout = 120 # 麦麦最长思考时间，超过这个时间的思考会放弃（往往是api反应太慢） @@ -200,6 +200,22 @@ pri_out = 0 temp = 0.7 enable_thinking = false # 是否启用思考 +[model.replyer_1] # 首要回复模型，还用于表达器和表达方式学习 +name = "Pro/deepseek-ai/DeepSeek-V3" +provider = "SILICONFLOW" +pri_in = 2 #模型的输入价格（非必填，可以记录消耗） +pri_out = 8 #模型的输出价格（非必填，可以记录消耗） +#默认temp 0.2 如果你使用的是老V3或者其他模型，请自己修改temp参数 +temp = 0.2 #模型的温度，新V3建议0.1-0.3 + +[model.replyer_2] # 一般聊天模式的次要回复模型 +name = "Pro/deepseek-ai/DeepSeek-R1" +provider = "SILICONFLOW" +pri_in = 4.0 #模型的输入价格（非必填，可以记录消耗） +pri_out = 16.0 #模型的输出价格（非必填，可以记录消耗） +temp = 0.7 + + [model.memory_summary] # 记忆的概括模型 name = "Qwen/Qwen3-30B-A3B" provider = "SILICONFLOW" @@ -236,23 +252,6 @@ provider = "SILICONFLOW" pri_in = 0 pri_out = 0 -#------------普通聊天必填模型------------ - -[model.normal_chat_1] # 一般聊天模式的首要回复模型，推荐使用 推理模型 -name = "Pro/deepseek-ai/DeepSeek-R1" -provider = "SILICONFLOW" -pri_in = 4.0 #模型的输入价格（非必填，可以记录消耗） -pri_out = 16.0 #模型的输出价格（非必填，可以记录消耗） -temp = 0.7 - -[model.normal_chat_2] # 一般聊天模式的次要回复模型，推荐使用 非推理模型 -name = "Pro/deepseek-ai/DeepSeek-V3" -provider = "SILICONFLOW" -pri_in = 2 #模型的输入价格（非必填，可以记录消耗） -pri_out = 8 #模型的输出价格（非必填，可以记录消耗） -#默认temp 0.2 如果你使用的是老V3或者其他模型，请自己修改temp参数 -temp = 0.2 #模型的温度，新V3建议0.1-0.3 - #------------专注聊天必填模型------------ [model.focus_working_memory] #工作记忆模型 @@ -272,16 +271,7 @@ pri_out = 2 temp = 0.7 enable_thinking = false # 是否启用思考（qwen3 only） -#表达器模型，用于表达麦麦的想法，生成最终回复，对语言风格影响极大 -#也用于表达方式学习 -[model.focus_expressor] -name = "Pro/deepseek-ai/DeepSeek-V3" -# name = "Qwen/Qwen3-30B-A3B" -provider = "SILICONFLOW" -# enable_thinking = false # 是否启用思考(qwen3 only) -pri_in = 2 -pri_out = 8 -temp = 0.3 + diff --git a/tests/test_utils_model.py b/tests/test_utils_model.py new file mode 100644 index 000000000..242c88ddb --- /dev/null +++ b/tests/test_utils_model.py @@ -0,0 +1,44 @@ +import asyncio +import pytest +from src.llm_models.utils_model import LLMRequest +import os +from dotenv import load_dotenv + +# 加载环境变量 +load_dotenv() + +@pytest.mark.asyncio +async def test_model_request(): + # 创建模型配置 + model_config = { + "name": "deepseek-v3", # 使用测试模型 + "provider": "CHATANY", # 使用测试提供商 + "temp": 0.3, + "enable_thinking": False + } + + # 创建LLMRequest实例 + llm = LLMRequest(model=model_config) + + # 测试提示词 + test_prompt = "你好，请做个自我介绍" + + try: + # 测试生成响应 + content, (reasoning_content, model_name) = await llm.generate_response_async(test_prompt) + + # 打印结果 + print(f"\n模型名称: {model_name}") + print(f"回复内容: {content}") + print(f"推理内容: {reasoning_content}") + + # 基本断言 + assert content is not None, "回复内容不应为空" + assert isinstance(content, str), "回复内容应为字符串" + + except Exception as e: + pytest.fail(f"测试失败: {str(e)}") + +if __name__ == "__main__": + # 直接运行测试 + asyncio.run(test_model_request()) \ No newline at end of file