diff --git a/bot.py b/bot.py index 72ea65d29..b8f154cd3 100644 --- a/bot.py +++ b/bot.py @@ -74,36 +74,6 @@ def easter_egg(): print(rainbow_text) -def scan_provider(env_config: dict): - provider = {} - - # 利用未初始化 env 时获取的 env_mask 来对新的环境变量集去重 - # 避免 GPG_KEY 这样的变量干扰检查 - env_config = dict(filter(lambda item: item[0] not in env_mask, env_config.items())) - - # 遍历 env_config 的所有键 - for key in env_config: - # 检查键是否符合 {provider}_BASE_URL 或 {provider}_KEY 的格式 - if key.endswith("_BASE_URL") or key.endswith("_KEY"): - # 提取 provider 名称 - provider_name = key.split("_", 1)[0] # 从左分割一次，取第一部分 - - # 初始化 provider 的字典（如果尚未初始化） - if provider_name not in provider: - provider[provider_name] = {"url": None, "key": None} - - # 根据键的类型填充 url 或 key - if key.endswith("_BASE_URL"): - provider[provider_name]["url"] = env_config[key] - elif key.endswith("_KEY"): - provider[provider_name]["key"] = env_config[key] - - # 检查每个 provider 是否同时存在 url 和 key - for provider_name, config in provider.items(): - if config["url"] is None or config["key"] is None: - logger.error(f"provider 内容：{config}\nenv_config 内容：{env_config}") - raise ValueError(f"请检查 '{provider_name}' 提供商配置是否丢失 BASE_URL 或 KEY 环境变量") - async def graceful_shutdown(): try: @@ -229,9 +199,6 @@ def raw_main(): easter_egg() - env_config = {key: os.getenv(key) for key in os.environ} - scan_provider(env_config) - # 返回MainSystem实例 return MainSystem() diff --git a/src/config/api_ada_configs.py b/src/config/api_ada_configs.py index ab41c72b7..348ad4a63 100644 --- a/src/config/api_ada_configs.py +++ b/src/config/api_ada_configs.py @@ -3,7 +3,7 @@ from typing import List, Dict from packaging.version import Version -NEWEST_VER = "0.1.0" # 当前支持的最新版本 +NEWEST_VER = "0.1.1" # 当前支持的最新版本 @dataclass class APIProvider: diff --git a/src/config/auto_update.py b/src/config/auto_update.py deleted file mode 100644 index 8d097ec49..000000000 --- a/src/config/auto_update.py +++ /dev/null @@ -1,162 +0,0 @@ -import shutil -import tomlkit -from tomlkit.items import Table, KeyType -from pathlib import Path -from datetime import datetime - - -def get_key_comment(toml_table, key): - # 获取key的注释（如果有） - if hasattr(toml_table, "trivia") and hasattr(toml_table.trivia, "comment"): - return toml_table.trivia.comment - if hasattr(toml_table, "value") and isinstance(toml_table.value, dict): - item = toml_table.value.get(key) - if item is not None and hasattr(item, "trivia"): - return item.trivia.comment - if hasattr(toml_table, "keys"): - for k in toml_table.keys(): - if isinstance(k, KeyType) and k.key == key: - return k.trivia.comment - return None - - -def compare_dicts(new, old, path=None, new_comments=None, old_comments=None, logs=None): - # 递归比较两个dict，找出新增和删减项，收集注释 - if path is None: - path = [] - if logs is None: - logs = [] - if new_comments is None: - new_comments = {} - if old_comments is None: - old_comments = {} - # 新增项 - for key in new: - if key == "version": - continue - if key not in old: - comment = get_key_comment(new, key) - logs.append(f"新增: {'.'.join(path + [str(key)])} 注释: {comment if comment else '无'}") - elif isinstance(new[key], (dict, Table)) and isinstance(old.get(key), (dict, Table)): - compare_dicts(new[key], old[key], path + [str(key)], new_comments, old_comments, logs) - # 删减项 - for key in old: - if key == "version": - continue - if key not in new: - comment = get_key_comment(old, key) - logs.append(f"删减: {'.'.join(path + [str(key)])} 注释: {comment if comment else '无'}") - return logs - - -def update_config(): - print("开始更新配置文件...") - # 获取根目录路径 - root_dir = Path(__file__).parent.parent.parent.parent - template_dir = root_dir / "template" - config_dir = root_dir / "config" - old_config_dir = config_dir / "old" - - # 创建old目录（如果不存在） - old_config_dir.mkdir(exist_ok=True) - - # 定义文件路径 - template_path = template_dir / "bot_config_template.toml" - old_config_path = config_dir / "bot_config.toml" - new_config_path = config_dir / "bot_config.toml" - - # 读取旧配置文件 - old_config = {} - if old_config_path.exists(): - print(f"发现旧配置文件: {old_config_path}") - with open(old_config_path, "r", encoding="utf-8") as f: - old_config = tomlkit.load(f) - - # 生成带时间戳的新文件名 - timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - old_backup_path = old_config_dir / f"bot_config_{timestamp}.toml" - - # 移动旧配置文件到old目录 - shutil.move(old_config_path, old_backup_path) - print(f"已备份旧配置文件到: {old_backup_path}") - - # 复制模板文件到配置目录 - print(f"从模板文件创建新配置: {template_path}") - shutil.copy2(template_path, new_config_path) - - # 读取新配置文件 - with open(new_config_path, "r", encoding="utf-8") as f: - new_config = tomlkit.load(f) - - # 检查version是否相同 - if old_config and "inner" in old_config and "inner" in new_config: - old_version = old_config["inner"].get("version") # type: ignore - new_version = new_config["inner"].get("version") # type: ignore - if old_version and new_version and old_version == new_version: - print(f"检测到版本号相同 (v{old_version})，跳过更新") - # 如果version相同，恢复旧配置文件并返回 - shutil.move(old_backup_path, old_config_path) # type: ignore - return - else: - print(f"检测到版本号不同: 旧版本 v{old_version} -> 新版本 v{new_version}") - - # 输出新增和删减项及注释 - if old_config: - print("配置项变动如下：") - logs = compare_dicts(new_config, old_config) - if logs: - for log in logs: - print(log) - else: - print("无新增或删减项") - - # 递归更新配置 - def update_dict(target, source): - for key, value in source.items(): - # 跳过version字段的更新 - if key == "version": - continue - if key in target: - if isinstance(value, dict) and isinstance(target[key], (dict, Table)): - update_dict(target[key], value) - else: - try: - # 对数组类型进行特殊处理 - if isinstance(value, list): - # 如果是空数组，确保它保持为空数组 - if not value: - target[key] = tomlkit.array() - else: - # 特殊处理正则表达式数组和包含正则表达式的结构 - if key == "ban_msgs_regex": - # 直接使用原始值，不进行额外处理 - target[key] = value - elif key == "regex_rules": - # 对于regex_rules，需要特殊处理其中的regex字段 - target[key] = value - else: - # 检查是否包含正则表达式相关的字典项 - contains_regex = False - if value and isinstance(value[0], dict) and "regex" in value[0]: - contains_regex = True - - target[key] = value if contains_regex else tomlkit.array(str(value)) - else: - # 其他类型使用item方法创建新值 - target[key] = tomlkit.item(value) - except (TypeError, ValueError): - # 如果转换失败，直接赋值 - target[key] = value - - # 将旧配置的值更新到新配置中 - print("开始合并新旧配置...") - update_dict(new_config, old_config) - - # 保存更新后的配置（保留注释和格式） - with open(new_config_path, "w", encoding="utf-8") as f: - f.write(tomlkit.dumps(new_config)) - print("配置文件更新完成") - - -if __name__ == "__main__": - update_config() diff --git a/template/bot_config_template.toml b/template/bot_config_template.toml index ff8a79e73..39f391fe8 100644 --- a/template/bot_config_template.toml +++ b/template/bot_config_template.toml @@ -229,120 +229,84 @@ show_prompt = false # 是否显示prompt [model] -model_max_output_length = 1024 # 模型单次返回的最大token数 +model_max_output_length = 800 # 模型单次返回的最大token数 -#------------必填：组件模型------------ +#------------模型任务配置------------ +# 所有模型名称需要对应 model_config.toml 中配置的模型名称 [model.utils] # 在麦麦的一些组件中使用的模型，例如表情包模块，取名模块，关系模块，是麦麦必须的模型 -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 +model_name = "siliconflow-deepseek-v3" # 对应 model_config.toml 中的模型名称 +temperature = 0.2 # 模型温度，新V3建议0.1-0.3 +max_tokens = 800 # 最大输出token数 [model.utils_small] # 在麦麦的一些组件中使用的小模型，消耗量较大，建议使用速度较快的小模型 -# 强烈建议使用免费的小模型 -name = "Qwen/Qwen3-8B" -provider = "SILICONFLOW" -pri_in = 0 -pri_out = 0 -temp = 0.7 +model_name = "qwen3-8b" # 对应 model_config.toml 中的模型名称 +temperature = 0.7 +max_tokens = 800 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_name = "siliconflow-deepseek-v3" # 对应 model_config.toml 中的模型名称 +temperature = 0.2 # 模型温度，新V3建议0.1-0.3 +max_tokens = 800 [model.replyer_2] # 次要回复模型 -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 +model_name = "siliconflow-deepseek-r1" # 对应 model_config.toml 中的模型名称 +temperature = 0.7 # 模型温度 +max_tokens = 800 [model.planner] #决策：负责决定麦麦该做什么的模型 -name = "Pro/deepseek-ai/DeepSeek-V3" -provider = "SILICONFLOW" -pri_in = 2 -pri_out = 8 -temp = 0.3 +model_name = "siliconflow-deepseek-v3" # 对应 model_config.toml 中的模型名称 +temperature = 0.3 +max_tokens = 800 [model.emotion] #负责麦麦的情绪变化 -name = "Pro/deepseek-ai/DeepSeek-V3" -provider = "SILICONFLOW" -pri_in = 2 -pri_out = 8 -temp = 0.3 - +model_name = "siliconflow-deepseek-v3" # 对应 model_config.toml 中的模型名称 +temperature = 0.3 +max_tokens = 800 [model.memory] # 记忆模型 -name = "Qwen/Qwen3-30B-A3B" -provider = "SILICONFLOW" -pri_in = 0.7 -pri_out = 2.8 -temp = 0.7 +model_name = "qwen3-30b" # 对应 model_config.toml 中的模型名称 +temperature = 0.7 +max_tokens = 800 enable_thinking = false # 是否启用思考 [model.vlm] # 图像识别模型 -name = "Pro/Qwen/Qwen2.5-VL-7B-Instruct" -provider = "SILICONFLOW" -pri_in = 0.35 -pri_out = 0.35 +model_name = "qwen2.5-vl-72b" # 对应 model_config.toml 中的模型名称 +max_tokens = 800 [model.voice] # 语音识别模型 -name = "FunAudioLLM/SenseVoiceSmall" -provider = "SILICONFLOW" -pri_in = 0 -pri_out = 0 +model_name = "sensevoice-small" # 对应 model_config.toml 中的模型名称 [model.tool_use] #工具调用模型，需要使用支持工具调用的模型 -name = "Qwen/Qwen3-14B" -provider = "SILICONFLOW" -pri_in = 0.5 -pri_out = 2 -temp = 0.7 +model_name = "qwen3-14b" # 对应 model_config.toml 中的模型名称 +temperature = 0.7 +max_tokens = 800 enable_thinking = false # 是否启用思考（qwen3 only） #嵌入模型 [model.embedding] -name = "BAAI/bge-m3" -provider = "SILICONFLOW" -pri_in = 0 -pri_out = 0 - +model_name = "bge-m3" # 对应 model_config.toml 中的模型名称 #------------LPMM知识库模型------------ [model.lpmm_entity_extract] # 实体提取模型 -name = "Pro/deepseek-ai/DeepSeek-V3" -provider = "SILICONFLOW" -pri_in = 2 -pri_out = 8 -temp = 0.2 - +model_name = "siliconflow-deepseek-v3" # 对应 model_config.toml 中的模型名称 +temperature = 0.2 +max_tokens = 800 [model.lpmm_rdf_build] # RDF构建模型 -name = "Pro/deepseek-ai/DeepSeek-V3" -provider = "SILICONFLOW" -pri_in = 2 -pri_out = 8 -temp = 0.2 - +model_name = "siliconflow-deepseek-v3" # 对应 model_config.toml 中的模型名称 +temperature = 0.2 +max_tokens = 800 [model.lpmm_qa] # 问答模型 -name = "Qwen/Qwen3-30B-A3B" -provider = "SILICONFLOW" -pri_in = 0.7 -pri_out = 2.8 -temp = 0.7 +model_name = "deepseek-r1-distill-qwen-32b" # 对应 model_config.toml 中的模型名称 +temperature = 0.7 +max_tokens = 800 enable_thinking = false # 是否启用思考 + [maim_message] auth_token = [] # 认证令牌，用于API验证，为空则不启用验证 # 以下项目若要使用需要打开use_custom，并单独配置maim_message的服务器 diff --git a/template/compare/model_config_template.toml b/template/compare/model_config_template.toml index f9055fcea..42633fa07 100644 --- a/template/compare/model_config_template.toml +++ b/template/compare/model_config_template.toml @@ -1,5 +1,5 @@ [inner] -version = "0.1.0" +version = "0.1.1" # 配置文件版本号迭代规则同bot_config.toml @@ -50,21 +50,79 @@ price_out = 8.0 #（可选，若无该字段，默认值为false） #force_stream_mode = true -#[[models]] -#model_identifier = "deepseek-reasoner" -#name = "deepseek-r1" -#api_provider = "DeepSeek" -#model_flags = ["text", "tool_calling", "reasoning"] -#price_in = 4.0 -#price_out = 16.0 -# -#[[models]] -#model_identifier = "BAAI/bge-m3" -#name = "siliconflow-bge-m3" -#api_provider = "SiliconFlow" -#model_flags = ["text", "embedding"] -#price_in = 0 -#price_out = 0 +[[models]] +model_identifier = "deepseek-reasoner" +name = "deepseek-r1" +api_provider = "DeepSeek" +model_flags = [ "text", "tool_calling", "reasoning",] +price_in = 4.0 +price_out = 16.0 + +[[models]] +model_identifier = "Pro/deepseek-ai/DeepSeek-V3" +name = "siliconflow-deepseek-v3" +api_provider = "SiliconFlow" +price_in = 2.0 +price_out = 8.0 + +[[models]] +model_identifier = "Pro/deepseek-ai/DeepSeek-R1" +name = "siliconflow-deepseek-r1" +api_provider = "SiliconFlow" +price_in = 4.0 +price_out = 16.0 + +[[models]] +model_identifier = "Pro/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B" +name = "deepseek-r1-distill-qwen-32b" +api_provider = "SiliconFlow" +price_in = 4.0 +price_out = 16.0 + +[[models]] +model_identifier = "Qwen/Qwen3-8B" +name = "qwen3-8b" +api_provider = "SiliconFlow" +price_in = 0 +price_out = 0 + +[[models]] +model_identifier = "Qwen/Qwen3-14B" +name = "qwen3-14b" +api_provider = "SiliconFlow" +price_in = 0.5 +price_out = 2.0 + +[[models]] +model_identifier = "Qwen/Qwen3-30B-A3B" +name = "qwen3-30b" +api_provider = "SiliconFlow" +price_in = 0.7 +price_out = 2.8 + +[[models]] +model_identifier = "Qwen/Qwen2.5-VL-72B-Instruct" +name = "qwen2.5-vl-72b" +api_provider = "SiliconFlow" +model_flags = [ "vision", "text",] +price_in = 4.13 +price_out = 4.13 + +[[models]] +model_identifier = "FunAudioLLM/SenseVoiceSmall" +name = "sensevoice-small" +api_provider = "SiliconFlow" +model_flags = [ "audio",] +price_in = 0 +price_out = 0 + +[[models]] +model_identifier = "BAAI/bge-m3" +name = "bge-m3" +api_provider = "SiliconFlow" +model_flags = [ "text", "embedding",] +price_in = 0 +price_out = 0 [task_model_usage] diff --git a/template/model_config_template.toml b/template/model_config_template.toml index f9055fcea..af3436929 100644 --- a/template/model_config_template.toml +++ b/template/model_config_template.toml @@ -1,5 +1,5 @@ [inner] -version = "0.1.0" +version = "0.1.1" # 配置文件版本号迭代规则同bot_config.toml @@ -50,27 +50,85 @@ price_out = 8.0 #（可选，若无该字段，默认值为false） #force_stream_mode = true -#[[models]] -#model_identifier = "deepseek-reasoner" -#name = "deepseek-r1" -#api_provider = "DeepSeek" -#model_flags = ["text", "tool_calling", "reasoning"] -#price_in = 4.0 -#price_out = 16.0 -# -#[[models]] -#model_identifier = "BAAI/bge-m3" -#name = "siliconflow-bge-m3" -#api_provider = "SiliconFlow" -#model_flags = ["text", "embedding"] -#price_in = 0 -#price_out = 0 +[[models]] +model_identifier = "deepseek-reasoner" +name = "deepseek-r1" +api_provider = "DeepSeek" +model_flags = [ "text", "tool_calling", "reasoning",] +price_in = 4.0 +price_out = 16.0 + +[[models]] +model_identifier = "Pro/deepseek-ai/DeepSeek-V3" +name = "siliconflow-deepseek-v3" +api_provider = "SiliconFlow" +price_in = 2.0 +price_out = 8.0 + +[[models]] +model_identifier = "Pro/deepseek-ai/DeepSeek-R1" +name = "siliconflow-deepseek-r1" +api_provider = "SiliconFlow" +price_in = 4.0 +price_out = 16.0 + +[[models]] +model_identifier = "Pro/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B" +name = "deepseek-r1-distill-qwen-32b" +api_provider = "SiliconFlow" +price_in = 4.0 +price_out = 16.0 + +[[models]] +model_identifier = "Qwen/Qwen3-8B" +name = "qwen3-8b" +api_provider = "SiliconFlow" +price_in = 0 +price_out = 0 + +[[models]] +model_identifier = "Qwen/Qwen3-14B" +name = "qwen3-14b" +api_provider = "SiliconFlow" +price_in = 0.5 +price_out = 2.0 + +[[models]] +model_identifier = "Qwen/Qwen3-30B-A3B" +name = "qwen3-30b" +api_provider = "SiliconFlow" +price_in = 0.7 +price_out = 2.8 + +[[models]] +model_identifier = "Qwen/Qwen2.5-VL-72B-Instruct" +name = "qwen2.5-vl-72b" +api_provider = "SiliconFlow" +model_flags = [ "vision", "text",] +price_in = 4.13 +price_out = 4.13 + +[[models]] +model_identifier = "FunAudioLLM/SenseVoiceSmall" +name = "sensevoice-small" +api_provider = "SiliconFlow" +model_flags = [ "audio",] +price_in = 0 +price_out = 0 + +[[models]] +model_identifier = "BAAI/bge-m3" +name = "bge-m3" +api_provider = "SiliconFlow" +model_flags = [ "text", "embedding",] +price_in = 0 +price_out = 0 [task_model_usage] -#llm_reasoning = {model="deepseek-r1", temperature=0.8, max_tokens=1024, max_retry=0} -#llm_normal = {model="deepseek-r1", max_tokens=1024, max_retry=0} -#embedding = "siliconflow-bge-m3" +llm_reasoning = {model="deepseek-r1", temperature=0.8, max_tokens=1024, max_retry=0} +llm_normal = {model="deepseek-r1", max_tokens=1024, max_retry=0} +embedding = "siliconflow-bge-m3" #schedule = [ # "deepseek-v3", # "deepseek-r1", diff --git a/template/template.env b/template/template.env index d86f23cd2..d9b6e2bd1 100644 --- a/template/template.env +++ b/template/template.env @@ -1,16 +1,2 @@ HOST=127.0.0.1 -PORT=8000 - -#key and url -SILICONFLOW_BASE_URL=https://api.siliconflow.cn/v1/ -DEEP_SEEK_BASE_URL=https://api.deepseek.com/v1 -CHAT_ANY_WHERE_BASE_URL=https://api.chatanywhere.tech/v1 -BAILIAN_BASE_URL = https://dashscope.aliyuncs.com/compatible-mode/v1 -xxxxxxx_BASE_URL=https://xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx - -# 定义你要用的api的key(需要去对应网站申请哦) -DEEP_SEEK_KEY= -CHAT_ANY_WHERE_KEY= -SILICONFLOW_KEY= -BAILIAN_KEY = -xxxxxxx_KEY= \ No newline at end of file +PORT=8000 \ No newline at end of file