├── .python-version ├── app ├── meeting │ ├── __init__.py │ ├── agents │ │ ├── __init__.py │ │ ├── agent_factory.py │ │ └── human_agent.py │ ├── utils │ │ ├── __init__.py │ │ └── summary_generator.py │ ├── meeting_modes │ │ ├── __init__.py │ │ ├── base_mode.py │ │ ├── role_playing.py │ │ ├── brainstorming.py │ │ ├── swot_analysis.py │ │ ├── six_thinking_hats.py │ │ ├── discussion.py │ │ └── debate.py │ ├── logger │ │ └── meeting_logger.py │ └── meeting │ │ └── meeting.py ├── static │ ├── favicon.ico │ ├── css │ │ └── custom-speaking-order.css │ ├── js │ │ ├── thinking.js │ │ └── custom-speaking-order.js │ ├── login.js │ └── login.html ├── models │ ├── __init__.py │ ├── schemas.py │ ├── database.py │ └── multi_step_collaboration.py ├── routes │ ├── __init__.py │ ├── auth.py │ ├── api_key.py │ ├── model.py │ └── configuration.py ├── clients │ ├── __init__.py │ ├── base_client.py │ ├── openai_client.py │ ├── deepseek_client.py │ ├── grok3_client.py │ ├── claude_client.py │ └── gemini_client.py ├── migration │ ├── add_topic_column.py │ └── add_summary_columns.py ├── meeting_modes │ └── base_mode.py ├── routers │ ├── models.py │ ├── roles.py │ ├── discussion_groups.py │ └── discussions.py └── utils │ ├── logger.py │ └── auth.py ├── .dockerignore ├── docker-compose.yml ├── .gitignore ├── Dockerfile ├── .env.example ├── pyproject.toml ├── migrate.py ├── alembic ├── script.py.mako ├── versions │ ├── xxxx_add_custom_parameters.py │ ├── xxxx_update_custom_parameters.py │ ├── 7b27956007a6_initial.py │ └── xxxx_update_model_defaults.py └── env.py ├── alembic.ini ├── README_zh.md └── README.md /.python-version: -------------------------------------------------------------------------------- 1 | 3.11 2 | -------------------------------------------------------------------------------- /app/meeting/__init__.py: -------------------------------------------------------------------------------- 1 | # 会议模块初始化 -------------------------------------------------------------------------------- /app/meeting/agents/__init__.py: -------------------------------------------------------------------------------- 1 | # 智能体模块初始化 -------------------------------------------------------------------------------- /app/meeting/utils/__init__.py: -------------------------------------------------------------------------------- 1 | # 工具模块初始化 -------------------------------------------------------------------------------- /app/meeting/meeting_modes/__init__.py: -------------------------------------------------------------------------------- 1 | # 会议模式模块初始化 2 | -------------------------------------------------------------------------------- /app/static/favicon.ico: -------------------------------------------------------------------------------- 1 | [二进制文件内容 - 这是一个 16x16 和 32x32 像素的 ICO 格式图标文件] -------------------------------------------------------------------------------- /.dockerignore: -------------------------------------------------------------------------------- 1 | .git 2 | .gitignore 3 | .env 4 | *.db 5 | __pycache__ 6 | *.pyc 7 | .pytest_cache 8 | .coverage 9 | htmlcov 10 | .venv 11 | venv -------------------------------------------------------------------------------- /app/models/__init__.py: -------------------------------------------------------------------------------- 1 | from .collaboration import ModelCollaboration 2 | from .multi_step_collaboration import MultiStepModelCollaboration 3 | 4 | __all__ = ['ModelCollaboration', 'MultiStepModelCollaboration'] -------------------------------------------------------------------------------- /docker-compose.yml: -------------------------------------------------------------------------------- 1 | version: '3.8' 2 | 3 | services: 4 | deepgemini: 5 | image: bradleylzh/deepgemini:latest 6 | ports: 7 | - "8000:8000" 8 | volumes: 9 | - ./.env:/app/.env 10 | - ./deepgemini.db:/app/deepgemini.db 11 | restart: unless-stopped -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Python-generated files 2 | __pycache__/ 3 | *.py[oc] 4 | build/ 5 | dist/ 6 | wheels/ 7 | *.egg-info 8 | *.db 9 | # Environment 10 | .env 11 | .env.* 12 | !.env.example 13 | scripts/ 14 | # Virtual environments 15 | .venv 16 | logs/ 17 | logger/ 18 | backups/ 19 | data/ 20 | 21 | -------------------------------------------------------------------------------- /Dockerfile: -------------------------------------------------------------------------------- 1 | # 使用 Python 3.11 作为基础镜像 2 | FROM python:3.11-slim 3 | 4 | # 设置工作目录 5 | WORKDIR /app 6 | 7 | # 安装 uv 8 | RUN pip install uv 9 | 10 | # 复制项目文件 11 | COPY . . 12 | 13 | # 使用 uv 安装依赖 14 | RUN uv sync 15 | 16 | # 暴露端口 17 | EXPOSE 8000 18 | 19 | # 启动命令 20 | CMD ["uv", "run", "-m", "app.main"] -------------------------------------------------------------------------------- /app/routes/__init__.py: -------------------------------------------------------------------------------- 1 | from .model import router as model_router 2 | from .configuration import router as configuration_router 3 | from .api_key import router as api_key_router 4 | from .auth import router as auth_router 5 | 6 | __all__ = ['model_router', 'configuration_router', 'api_key_router', 'auth_router'] -------------------------------------------------------------------------------- /app/clients/__init__.py: -------------------------------------------------------------------------------- 1 | """客户端模块""" 2 | from .base_client import BaseClient 3 | from .claude_client import ClaudeClient 4 | from .deepseek_client import DeepSeekClient 5 | from .gemini_client import GeminiClient 6 | from .openai_client import OpenAIClient 7 | from .grok3_client import Grok3Client 8 | 9 | __all__ = [ 10 | 'BaseClient', 11 | 'ClaudeClient', 12 | 'DeepSeekClient', 13 | 'GeminiClient', 14 | 'OpenAIClient', 15 | 'Grok3Client' 16 | ] 17 | -------------------------------------------------------------------------------- /.env.example: -------------------------------------------------------------------------------- 1 | # 客户端请求时允许通过请求的 API KEY，无需在当前环境变量当中手动添加 Bearer 2 | ALLOW_API_KEY=[{"id":1,"key":"sk-api-xxxxxxxxxxxxxxxx","description":"Default API Key"}] 3 | 4 | # 服务端跨域配置 5 | # 允许访问的域名，多个域名使用逗号分隔(中间不能有空格)，例如：http://localhost:3000,https://chat.example.com 6 | # 如果允许所有域名访问，则填写 * 7 | ALLOW_ORIGINS=* 8 | 9 | # 日志配置 10 | # 可选值：DEBUG, INFO, WARNING, ERROR, CRITICAL 11 | LOG_LEVEL=INFO 12 | 13 | # 管理员账户配置 14 | ADMIN_USERNAME=admin 15 | ADMIN_PASSWORD=admin123 16 | 17 | # JWT密钥 18 | JWT_SECRET=your-secret-key 19 | 20 | 21 | -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "deepgemini" 3 | version = "0.1.2" 4 | description = "Add your description here" 5 | readme = "README.md" 6 | requires-python = ">=3.11" 7 | dependencies = [ 8 | "aiohttp>=3.11.12", 9 | "colorlog>=6.9.0", 10 | "fastapi>=0.115.8", 11 | "httpx>=0.28.1", 12 | "langchain>=0.3.20", 13 | "langchain-openai>=0.3.8", 14 | "passlib>=1.7.4", 15 | "pyjwt>=2.10.1", 16 | "python-dotenv>=1.0.1", 17 | "sqlalchemy>=2.0.38", 18 | "uvicorn>=0.34.0", 19 | ] 20 | -------------------------------------------------------------------------------- /migrate.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | from alembic.config import Config 4 | from alembic import command 5 | 6 | def run_migrations(): 7 | # 获取当前目录 8 | current_dir = os.path.dirname(os.path.abspath(__file__)) 9 | 10 | # 创建 Alembic 配置 11 | alembic_cfg = Config(os.path.join(current_dir, "alembic.ini")) 12 | 13 | try: 14 | # 运行迁移 15 | command.upgrade(alembic_cfg, "head") 16 | print("数据库迁移成功完成！") 17 | except Exception as e: 18 | print(f"迁移过程中发生错误: {e}") 19 | sys.exit(1) 20 | 21 | if __name__ == "__main__": 22 | run_migrations() -------------------------------------------------------------------------------- /alembic/script.py.mako: -------------------------------------------------------------------------------- 1 | """${message} 2 | 3 | Revision ID: ${up_revision} 4 | Revises: ${down_revision | comma,n} 5 | Create Date: ${create_date} 6 | 7 | """ 8 | from typing import Sequence, Union 9 | 10 | from alembic import op 11 | import sqlalchemy as sa 12 | ${imports if imports else ""} 13 | 14 | # revision identifiers, used by Alembic. 15 | revision: str = ${repr(up_revision)} 16 | down_revision: Union[str, None] = ${repr(down_revision)} 17 | branch_labels: Union[str, Sequence[str], None] = ${repr(branch_labels)} 18 | depends_on: Union[str, Sequence[str], None] = ${repr(depends_on)} 19 | 20 | 21 | def upgrade() -> None: 22 | ${upgrades if upgrades else "pass"} 23 | 24 | 25 | def downgrade() -> None: 26 | ${downgrades if downgrades else "pass"} -------------------------------------------------------------------------------- /app/migration/add_topic_column.py: -------------------------------------------------------------------------------- 1 | # 创建迁移脚本来添加缺失的列 2 | 3 | from sqlalchemy import create_engine, text 4 | from sqlalchemy.orm import sessionmaker 5 | import os 6 | 7 | # 获取数据库URL 8 | DATABASE_URL = os.getenv("DATABASE_URL", "sqlite:///./deepgemini.db") 9 | 10 | # 创建SQLAlchemy引擎和会话 11 | engine = create_engine(DATABASE_URL) 12 | SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine) 13 | 14 | # 创建会话 15 | db = SessionLocal() 16 | 17 | # 执行原始SQL命令添加列 18 | try: 19 | # 使用text()函数包装SQL语句 20 | sql = text("ALTER TABLE discussion_groups ADD COLUMN topic TEXT") 21 | db.execute(sql) 22 | db.commit() 23 | print("成功添加topic列到discussion_groups表") 24 | except Exception as e: 25 | db.rollback() 26 | print(f"添加列时出错: {str(e)}") 27 | finally: 28 | db.close() -------------------------------------------------------------------------------- /alembic/versions/xxxx_add_custom_parameters.py: -------------------------------------------------------------------------------- 1 | """add custom parameters 2 | 3 | Revision ID: xxxx 4 | Revises: previous_revision 5 | Create Date: 2024-03-21 xx:xx:xx.xxxxxx 6 | """ 7 | from alembic import op 8 | import sqlalchemy as sa 9 | from sqlalchemy.dialects import sqlite 10 | 11 | revision = 'xxxx' 12 | down_revision = 'previous_revision' 13 | branch_labels = None 14 | depends_on = None 15 | 16 | def upgrade() -> None: 17 | with op.batch_alter_table('models') as batch_op: 18 | batch_op.add_column(sa.Column('custom_parameters', sa.JSON(), nullable=True)) 19 | 20 | # 为现有记录设置默认值 21 | op.execute("UPDATE models SET custom_parameters = '{}' WHERE custom_parameters IS NULL") 22 | 23 | def downgrade() -> None: 24 | with op.batch_alter_table('models') as batch_op: 25 | batch_op.drop_column('custom_parameters') -------------------------------------------------------------------------------- /alembic.ini: -------------------------------------------------------------------------------- 1 | [alembic] 2 | # path to migration scripts 3 | script_location = alembic 4 | 5 | # SQLite database URL 6 | sqlalchemy.url = sqlite:///./deepgemini.db 7 | 8 | # Add Python path 9 | prepend_sys_path = . 10 | 11 | # template used to generate migration files 12 | # file_template = %%(rev)s_%%(slug)s 13 | 14 | [loggers] 15 | keys = root,sqlalchemy,alembic 16 | 17 | [handlers] 18 | keys = console 19 | 20 | [formatters] 21 | keys = generic 22 | 23 | [logger_root] 24 | level = WARN 25 | handlers = console 26 | qualname = 27 | 28 | [logger_sqlalchemy] 29 | level = WARN 30 | handlers = 31 | qualname = sqlalchemy.engine 32 | 33 | [logger_alembic] 34 | level = INFO 35 | handlers = 36 | qualname = alembic 37 | 38 | [handler_console] 39 | class = StreamHandler 40 | args = (sys.stderr,) 41 | level = NOTSET 42 | formatter = generic 43 | 44 | [formatter_generic] 45 | format = %(levelname)-5.5s [%(name)s] %(message)s 46 | datefmt = %H:%M:%S -------------------------------------------------------------------------------- /app/migration/add_summary_columns.py: -------------------------------------------------------------------------------- 1 | from sqlalchemy import create_engine, text 2 | from sqlalchemy.orm import sessionmaker 3 | import os 4 | 5 | # 获取数据库URL 6 | DATABASE_URL = os.getenv("DATABASE_URL", "sqlite:///./deepgemini.db") 7 | 8 | # 创建SQLAlchemy引擎和会话 9 | engine = create_engine(DATABASE_URL) 10 | SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine) 11 | 12 | # 创建会话 13 | db = SessionLocal() 14 | 15 | # 执行原始SQL命令添加列 16 | try: 17 | # 使用text()函数包装SQL语句 18 | statements = [ 19 | text("ALTER TABLE discussion_groups ADD COLUMN summary_model_id INTEGER REFERENCES models(id)"), 20 | text("ALTER TABLE discussion_groups ADD COLUMN summary_prompt TEXT") 21 | ] 22 | 23 | for sql in statements: 24 | try: 25 | db.execute(sql) 26 | print(f"成功执行: {sql}") 27 | except Exception as e: 28 | print(f"执行 {sql} 时出错: {str(e)}") 29 | 30 | db.commit() 31 | print("成功添加总结相关列到discussion_groups表") 32 | except Exception as e: 33 | db.rollback() 34 | print(f"添加列时出错: {str(e)}") 35 | finally: 36 | db.close() -------------------------------------------------------------------------------- /alembic/versions/xxxx_update_custom_parameters.py: -------------------------------------------------------------------------------- 1 | """update custom parameters default 2 | 3 | Revision ID: xxxx 4 | Revises: previous_revision 5 | Create Date: 2024-03-21 xx:xx:xx.xxxxxx 6 | """ 7 | from alembic import op 8 | import sqlalchemy as sa 9 | from sqlalchemy.dialects import sqlite 10 | 11 | revision = 'xxxx' 12 | down_revision = 'previous_revision' 13 | branch_labels = None 14 | depends_on = None 15 | 16 | def upgrade() -> None: 17 | # SQLite 不支持修改列，所以需要重建表 18 | with op.batch_alter_table('models') as batch_op: 19 | batch_op.alter_column('custom_parameters', 20 | existing_type=sa.JSON(), 21 | nullable=False, 22 | server_default='{}') 23 | 24 | # 更新现有记录 25 | op.execute("UPDATE models SET custom_parameters = '{}' WHERE custom_parameters IS NULL") 26 | 27 | def downgrade() -> None: 28 | with op.batch_alter_table('models') as batch_op: 29 | batch_op.alter_column('custom_parameters', 30 | existing_type=sa.JSON(), 31 | nullable=True, 32 | server_default=None) -------------------------------------------------------------------------------- /app/meeting_modes/base_mode.py: -------------------------------------------------------------------------------- 1 | from typing import List, Dict, Any 2 | 3 | class BaseMeetingMode: 4 | """会议模式基类""" 5 | 6 | def __init__(self, name: str, description: str): 7 | self.name = name 8 | self.description = description 9 | self.max_rounds = 3 # 默认最大轮数 10 | 11 | def get_agent_prompt(self, agent_name: str, agent_role: str, 12 | meeting_topic: str, current_round: int) -> str: 13 | """获取智能体提示""" 14 | raise NotImplementedError("子类必须实现此方法") 15 | 16 | def determine_speaking_order(self, agents: List[Dict[str, Any]], 17 | current_round: int) -> List[str]: 18 | """确定发言顺序""" 19 | raise NotImplementedError("子类必须实现此方法") 20 | 21 | def should_end_meeting(self, rounds_completed: int, 22 | meeting_history: List[Dict[str, Any]]) -> bool: 23 | """判断会议是否应该结束""" 24 | raise NotImplementedError("子类必须实现此方法") 25 | 26 | def summarize_meeting(self, meeting_topic: str, 27 | meeting_history: List[Dict[str, Any]]) -> str: 28 | """汇总会议结果""" 29 | raise NotImplementedError("子类必须实现此方法") 30 | 31 | def get_summary_prompt_template(self) -> str: 32 | """获取总结提示模板""" 33 | return """ 34 | 你是一个会议总结专家。请对以下关于"{meeting_topic}"的会议进行总结。 35 | 会议记录如下: 36 | 37 | {history_text} 38 | 39 | 请提供以下内容: 40 | 1. 讨论的主要主题和观点概述 41 | 2. 讨论中达成的主要共识 42 | 3. 存在的主要分歧或不同视角 43 | 4. 提出的解决方案或行动建议 44 | 5. 可能需要进一步讨论或研究的问题 45 | 46 | 请以清晰、结构化的方式呈现总结。 47 | """ -------------------------------------------------------------------------------- /alembic/versions/7b27956007a6_initial.py: -------------------------------------------------------------------------------- 1 | """initial 2 | 3 | Revision ID: 7b27956007a6 4 | Revises: 5 | Create Date: 2025-02-27 18:20:11.715233 6 | 7 | """ 8 | from typing import Sequence, Union 9 | 10 | from alembic import op 11 | import sqlalchemy as sa 12 | 13 | 14 | # revision identifiers, used by Alembic. 15 | revision: str = '7b27956007a6' 16 | down_revision: Union[str, None] = None 17 | branch_labels: Union[str, Sequence[str], None] = None 18 | depends_on: Union[str, Sequence[str], None] = None 19 | 20 | 21 | def upgrade() -> None: 22 | # ### commands auto generated by Alembic - please adjust! ### 23 | with op.batch_alter_table('models', schema=None) as batch_op: 24 | batch_op.add_column(sa.Column('enable_tools', sa.Boolean(), server_default='0', nullable=False)) 25 | batch_op.add_column(sa.Column('tools', sa.JSON(), nullable=True)) 26 | batch_op.add_column(sa.Column('tool_choice', sa.JSON(), nullable=True)) 27 | batch_op.add_column(sa.Column('enable_thinking', sa.Boolean(), server_default='0', nullable=False)) 28 | batch_op.add_column(sa.Column('thinking_budget_tokens', sa.Integer(), server_default='16000', nullable=False)) 29 | 30 | # ### end Alembic commands ### 31 | 32 | 33 | def downgrade() -> None: 34 | # ### commands auto generated by Alembic - please adjust! ### 35 | with op.batch_alter_table('models', schema=None) as batch_op: 36 | batch_op.drop_column('thinking_budget_tokens') 37 | batch_op.drop_column('enable_thinking') 38 | batch_op.drop_column('tool_choice') 39 | batch_op.drop_column('tools') 40 | batch_op.drop_column('enable_tools') 41 | 42 | # ### end Alembic commands ### -------------------------------------------------------------------------------- /app/routes/auth.py: -------------------------------------------------------------------------------- 1 | from fastapi import APIRouter, HTTPException, Depends 2 | from pydantic import BaseModel 3 | import os 4 | from app.utils.auth import create_access_token, verify_token, update_admin_credentials 5 | from typing import Optional 6 | 7 | router = APIRouter() 8 | 9 | class LoginRequest(BaseModel): 10 | username: str 11 | password: str 12 | 13 | class CredentialsUpdate(BaseModel): 14 | current_password: str 15 | new_username: Optional[str] = None 16 | new_password: Optional[str] = None 17 | 18 | @router.post("/login") 19 | async def login(request: LoginRequest): 20 | # 验证用户名和密码 21 | if (request.username == os.getenv("ADMIN_USERNAME") and 22 | request.password == os.getenv("ADMIN_PASSWORD")): 23 | access_token = create_access_token(data={"sub": request.username}) 24 | return {"access_token": access_token, "token_type": "bearer"} 25 | raise HTTPException(status_code=401, detail="Incorrect username or password") 26 | 27 | @router.post("/update-credentials") 28 | async def update_credentials( 29 | request: CredentialsUpdate, 30 | username: str = Depends(verify_token) 31 | ): 32 | # 验证当前密码 33 | if request.current_password != os.getenv("ADMIN_PASSWORD"): 34 | raise HTTPException(status_code=401, detail="Current password is incorrect") 35 | 36 | # 更新凭据 37 | new_username = request.new_username or os.getenv("ADMIN_USERNAME") 38 | new_password = request.new_password or os.getenv("ADMIN_PASSWORD") 39 | 40 | try: 41 | update_admin_credentials(new_username, new_password) 42 | return {"message": "Credentials updated successfully"} 43 | except Exception as e: 44 | raise HTTPException(status_code=500, detail=str(e)) -------------------------------------------------------------------------------- /app/routers/models.py: -------------------------------------------------------------------------------- 1 | from fastapi import APIRouter, Depends, HTTPException, Body 2 | from sqlalchemy.orm import Session 3 | from typing import List, Dict, Any, Optional 4 | import logging 5 | 6 | from app.models.database import get_db, Model, Role, DiscussionGroup 7 | from app.models.schemas import Model as ModelSchema, ModelCreate 8 | from app.processors.role_processor import RoleProcessor 9 | from app.processors.discussion_processor import DiscussionProcessor 10 | 11 | router = APIRouter( 12 | prefix="/models", 13 | tags=["models"], 14 | responses={404: {"description": "Not found"}}, 15 | ) 16 | 17 | logger = logging.getLogger(__name__) 18 | 19 | @router.get("/", response_model=List[Dict[str, Any]]) 20 | def get_all_models(db: Session = Depends(get_db)): 21 | """获取所有模型，包括角色和讨论组""" 22 | # 获取常规模型 23 | models = db.query(Model).all() 24 | result = [] 25 | 26 | for model in models: 27 | result.append({ 28 | "id": model.id, 29 | "name": model.name, 30 | "type": model.type, 31 | "provider": model.provider, 32 | "model_type": "model" # 标记为常规模型 33 | }) 34 | 35 | # 获取角色 36 | roles = db.query(Role).all() 37 | for role in roles: 38 | result.append({ 39 | "id": f"role_{role.id}", # 添加前缀以区分 40 | "name": f"角色: {role.name}", 41 | "type": "both", # 角色可以用于推理和执行 42 | "provider": "deepgemini", 43 | "model_type": "role" # 标记为角色 44 | }) 45 | 46 | # 获取讨论组 47 | groups = db.query(DiscussionGroup).all() 48 | for group in groups: 49 | result.append({ 50 | "id": f"group_{group.id}", # 添加前缀以区分 51 | "name": f"讨论组: {group.name}", 52 | "type": "both", # 讨论组可以用于推理和执行 53 | "provider": "deepgemini", 54 | "model_type": "discussion_group" # 标记为讨论组 55 | }) 56 | 57 | return result 58 | 59 | # 其他现有的模型路由... -------------------------------------------------------------------------------- /app/utils/logger.py: -------------------------------------------------------------------------------- 1 | import logging 2 | import colorlog 3 | import sys 4 | import os 5 | from dotenv import load_dotenv 6 | from datetime import datetime 7 | 8 | # 确保环境变量被加载 9 | load_dotenv() 10 | 11 | def get_log_level() -> int: 12 | """从环境变量获取日志级别 13 | 14 | Returns: 15 | int: logging 模块定义的日志级别 16 | """ 17 | level_map = { 18 | 'DEBUG': logging.DEBUG, 19 | 'INFO': logging.INFO, 20 | 'WARNING': logging.WARNING, 21 | 'ERROR': logging.ERROR, 22 | 'CRITICAL': logging.CRITICAL 23 | } 24 | 25 | level = os.getenv('LOG_LEVEL', 'INFO').upper() 26 | return level_map.get(level, logging.INFO) 27 | 28 | def setup_logger(name: str = "DeepGemini") -> logging.Logger: 29 | """设置一个彩色的logger 30 | 31 | Args: 32 | name (str, optional): logger的名称. Defaults to "DeepClaude". 33 | 34 | Returns: 35 | logging.Logger: 配置好的logger实例 36 | """ 37 | # 获取logger实例 38 | logger = colorlog.getLogger(name) 39 | 40 | # 清除现有处理器，避免重复 41 | if logger.handlers: 42 | for handler in logger.handlers: 43 | logger.removeHandler(handler) 44 | 45 | # 从环境变量获取日志级别 46 | log_level = get_log_level() 47 | 48 | # 设置日志级别 49 | logger.setLevel(log_level) 50 | 51 | # 创建控制台处理器 52 | console_handler = logging.StreamHandler(sys.stdout) 53 | console_handler.setLevel(log_level) 54 | 55 | # 设置彩色日志格式 56 | formatter = colorlog.ColoredFormatter( 57 | "%(log_color)s%(asctime)s - %(name)s - %(levelname)s - %(message)s", 58 | datefmt="%Y-%m-%d %H:%M:%S", 59 | log_colors={ 60 | 'DEBUG': 'cyan', 61 | 'INFO': 'green', 62 | 'WARNING': 'yellow', 63 | 'ERROR': 'red', 64 | 'CRITICAL': 'red,bg_white', 65 | } 66 | ) 67 | 68 | console_handler.setFormatter(formatter) 69 | logger.addHandler(console_handler) 70 | 71 | # 防止日志传播到父logger，这可以减少重复日志 72 | logger.propagate = False 73 | 74 | return logger 75 | 76 | # 创建一个默认的logger实例 77 | logger = setup_logger() 78 | -------------------------------------------------------------------------------- /alembic/versions/xxxx_update_model_defaults.py: -------------------------------------------------------------------------------- 1 | """update model defaults 2 | 3 | Revision ID: xxxx 4 | Revises: previous_revision 5 | Create Date: 2024-03-21 xx:xx:xx.xxxxxx 6 | 7 | """ 8 | from typing import Sequence, Union 9 | 10 | from alembic import op 11 | import sqlalchemy as sa 12 | 13 | # revision identifiers, used by Alembic. 14 | revision: str = 'new_revision' 15 | down_revision: Union[str, None] = '7b27956007a6' 16 | branch_labels: Union[str, Sequence[str], None] = None 17 | depends_on: Union[str, Sequence[str], None] = None 18 | 19 | def upgrade() -> None: 20 | # 更新现有记录的默认值 21 | with op.batch_alter_table('models') as batch_op: 22 | # 先设置现有记录的默认值 23 | op.execute("UPDATE models SET enable_tools = '0' WHERE enable_tools IS NULL") 24 | op.execute("UPDATE models SET enable_thinking = '0' WHERE enable_thinking IS NULL") 25 | op.execute("UPDATE models SET thinking_budget_tokens = 16000 WHERE thinking_budget_tokens IS NULL") 26 | 27 | # 修改列定义 28 | batch_op.alter_column('enable_tools', 29 | existing_type=sa.Boolean(), 30 | nullable=False, 31 | server_default='0' 32 | ) 33 | batch_op.alter_column('enable_thinking', 34 | existing_type=sa.Boolean(), 35 | nullable=False, 36 | server_default='0' 37 | ) 38 | batch_op.alter_column('thinking_budget_tokens', 39 | existing_type=sa.Integer(), 40 | nullable=False, 41 | server_default='16000' 42 | ) 43 | 44 | def downgrade() -> None: 45 | with op.batch_alter_table('models') as batch_op: 46 | batch_op.alter_column('enable_tools', 47 | existing_type=sa.Boolean(), 48 | nullable=True, 49 | server_default=None 50 | ) 51 | batch_op.alter_column('enable_thinking', 52 | existing_type=sa.Boolean(), 53 | nullable=True, 54 | server_default=None 55 | ) 56 | batch_op.alter_column('thinking_budget_tokens', 57 | existing_type=sa.Integer(), 58 | nullable=True, 59 | server_default=None 60 | ) -------------------------------------------------------------------------------- /alembic/env.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | from logging.config import fileConfig 4 | import codecs 5 | 6 | from sqlalchemy import engine_from_config 7 | from sqlalchemy import pool 8 | 9 | from alembic import context 10 | 11 | # Add project root directory to Python path 12 | sys.path.append(os.path.dirname(os.path.dirname(__file__))) 13 | 14 | # Set default encoding to UTF-8 15 | if sys.getdefaultencoding() != 'utf-8': 16 | reload(sys) 17 | sys.setdefaultencoding('utf-8') 18 | 19 | # this is the Alembic Config object, which provides 20 | # access to the values within the .ini file in use. 21 | config = context.config 22 | 23 | # Interpret the config file for Python logging. 24 | # This line sets up loggers basically. 25 | if config.config_file_name is not None: 26 | fileConfig(config.config_file_name) 27 | 28 | # 只导入必要的数据库模型，避免导入其他模块 29 | import importlib.util 30 | spec = importlib.util.spec_from_file_location( 31 | "database", 32 | os.path.join(os.path.dirname(os.path.dirname(__file__)), "app", "models", "database.py") 33 | ) 34 | database = importlib.util.module_from_spec(spec) 35 | spec.loader.exec_module(database) 36 | 37 | target_metadata = database.Base.metadata 38 | DATABASE_URL = database.DATABASE_URL 39 | 40 | # other values from the config, defined by the needs of env.py, 41 | # can be acquired: 42 | # my_important_option = config.get_main_option("my_important_option") 43 | # ... etc. 44 | 45 | def run_migrations_offline() -> None: 46 | """Run migrations in 'offline' mode.""" 47 | url = DATABASE_URL 48 | context.configure( 49 | url=url, 50 | target_metadata=target_metadata, 51 | literal_binds=True, 52 | dialect_opts={"paramstyle": "named"}, 53 | ) 54 | 55 | with context.begin_transaction(): 56 | context.run_migrations() 57 | 58 | def run_migrations_online() -> None: 59 | """Run migrations in 'online' mode.""" 60 | configuration = config.get_section(config.config_ini_section) 61 | configuration["sqlalchemy.url"] = DATABASE_URL 62 | 63 | connectable = engine_from_config( 64 | configuration, 65 | prefix="sqlalchemy.", 66 | poolclass=pool.NullPool, 67 | ) 68 | 69 | with connectable.connect() as connection: 70 | context.configure( 71 | connection=connection, 72 | target_metadata=target_metadata, 73 | # 添加这些选项以支持 SQLite 74 | render_as_batch=True, 75 | compare_type=True 76 | ) 77 | 78 | with context.begin_transaction(): 79 | context.run_migrations() 80 | 81 | if context.is_offline_mode(): 82 | run_migrations_offline() 83 | else: 84 | run_migrations_online() -------------------------------------------------------------------------------- /app/meeting/meeting_modes/base_mode.py: -------------------------------------------------------------------------------- 1 | from abc import ABC, abstractmethod 2 | from typing import List, Dict, Any 3 | from app.meeting.utils.summary_generator import SummaryGenerator 4 | 5 | class BaseMeetingMode(ABC): 6 | """会议模式基类""" 7 | 8 | def __init__(self, name: str, description: str, max_rounds: int = 3): 9 | self.name = name 10 | self.description = description 11 | self.max_rounds = max_rounds # 默认最大轮数，但允许在初始化时设置 12 | self.custom_speaking_order = None # 自定义发言顺序 13 | 14 | def set_max_rounds(self, max_rounds: int): 15 | """设置最大轮数""" 16 | if max_rounds > 0: 17 | self.max_rounds = max_rounds 18 | return True 19 | return False 20 | 21 | def set_custom_speaking_order(self, custom_speaking_order: List[str]): 22 | """设置自定义发言顺序""" 23 | if custom_speaking_order and isinstance(custom_speaking_order, list): 24 | self.custom_speaking_order = custom_speaking_order 25 | return True 26 | return False 27 | 28 | @abstractmethod 29 | def get_agent_prompt(self, agent_name: str, agent_role: str, 30 | meeting_topic: str, current_round: int) -> str: 31 | """获取智能体提示""" 32 | pass 33 | 34 | @abstractmethod 35 | def determine_speaking_order(self, agents: List[Dict[str, Any]], 36 | current_round: int) -> List[str]: 37 | """确定发言顺序""" 38 | pass 39 | 40 | @abstractmethod 41 | def should_end_meeting(self, rounds_completed: int, 42 | meeting_history: List[Dict[str, Any]]) -> bool: 43 | """ 44 | 判断会议是否应该结束 45 | 基类实现始终返回False，让Meeting类负责基于max_rounds终止会议 46 | """ 47 | # 让Meeting类基于self.max_rounds决定会议结束 48 | return False 49 | 50 | def get_summary_prompt_template(self) -> str: 51 | """获取总结提示模板""" 52 | return """ 53 | 你是一个会议总结专家。请对以下关于"{meeting_topic}"的会议进行总结。 54 | 会议记录如下: 55 | 56 | {history_text} 57 | 58 | 请提供以下内容: 59 | 1. 讨论的主要主题和观点概述 60 | 2. 讨论中达成的主要共识 61 | 3. 存在的主要分歧或不同视角 62 | 4. 提出的解决方案或行动建议 63 | 5. 可能需要进一步讨论或研究的问题 64 | 65 | 请以清晰、结构化的方式呈现总结。 66 | """ 67 | 68 | def summarize_meeting(self, meeting_topic: str, 69 | meeting_history: List[Dict[str, Any]]) -> str: 70 | """汇总会议结果""" 71 | # 获取当前模式的总结提示模板 72 | prompt_template = self.get_summary_prompt_template() 73 | 74 | # 使用总结生成器生成总结 75 | return SummaryGenerator.generate_summary( 76 | meeting_topic=meeting_topic, 77 | meeting_history=meeting_history, 78 | summary_prompt_template=prompt_template 79 | ) -------------------------------------------------------------------------------- /app/static/css/custom-speaking-order.css: -------------------------------------------------------------------------------- 1 | /* 自定义发言顺序样式 */ 2 | .custom-speaking-order-container { 3 | margin: 15px 0; 4 | } 5 | 6 | .sortable-list { 7 | border: 1px solid #e0e0e0; 8 | border-radius: 5px; 9 | padding: 10px; 10 | background-color: #f9f9f9; 11 | min-height: 50px; 12 | } 13 | 14 | .role-item { 15 | display: flex; 16 | align-items: center; 17 | padding: 8px 12px; 18 | margin-bottom: 8px; 19 | background-color: white; 20 | border: 1px solid #e0e0e0; 21 | border-radius: 4px; 22 | cursor: grab; 23 | transition: all 0.2s ease; 24 | box-shadow: 0 1px 3px rgba(0, 0, 0, 0.1); 25 | } 26 | 27 | .role-item:last-child { 28 | margin-bottom: 0; 29 | } 30 | 31 | .role-item .handle { 32 | margin-right: 10px; 33 | color: #999; 34 | cursor: grab; 35 | } 36 | 37 | .role-item .role-name { 38 | flex-grow: 1; 39 | font-weight: 500; 40 | } 41 | 42 | /* 拖拽状态样式 */ 43 | .role-item.dragging { 44 | opacity: 0.5; 45 | background-color: #f0f0f0; 46 | } 47 | 48 | /* 目标元素样式 */ 49 | .role-item.drag-over { 50 | border: 2px dashed #3498db; 51 | background-color: #ecf0f1; 52 | } 53 | 54 | /* 交换动画效果 */ 55 | .role-item.swapped { 56 | animation: swap-highlight 0.3s ease; 57 | } 58 | 59 | @keyframes swap-highlight { 60 | 0% { 61 | background-color: #fff; 62 | transform: scale(1); 63 | } 64 | 50% { 65 | background-color: #e3f2fd; 66 | transform: scale(1.05); 67 | } 68 | 100% { 69 | background-color: #fff; 70 | transform: scale(1); 71 | } 72 | } 73 | 74 | /* 暗色模式样式 */ 75 | .dark-theme .sortable-list { 76 | border-color: #404040; 77 | background-color: #252525; 78 | } 79 | 80 | .dark-theme .role-item { 81 | background-color: #2d2d2d; 82 | border-color: #404040; 83 | color: #ecf0f1; 84 | box-shadow: 0 1px 3px rgba(0, 0, 0, 0.3); 85 | } 86 | 87 | .dark-theme .role-item .handle { 88 | color: #aaa; 89 | } 90 | 91 | .dark-theme .role-item.dragging { 92 | opacity: 0.5; 93 | background-color: #3d3d3d; 94 | } 95 | 96 | .dark-theme .role-item.drag-over { 97 | border: 2px dashed #3498db; 98 | background-color: #353535; 99 | } 100 | 101 | .dark-theme .role-item.swapped { 102 | animation: dark-swap-highlight 0.3s ease; 103 | } 104 | 105 | @keyframes dark-swap-highlight { 106 | 0% { 107 | background-color: #2d2d2d; 108 | transform: scale(1); 109 | } 110 | 50% { 111 | background-color: #1a4971; 112 | transform: scale(1.05); 113 | } 114 | 100% { 115 | background-color: #2d2d2d; 116 | transform: scale(1); 117 | } 118 | } 119 | -------------------------------------------------------------------------------- /app/routes/api_key.py: -------------------------------------------------------------------------------- 1 | from fastapi import APIRouter, HTTPException 2 | from pydantic import BaseModel 3 | import os 4 | from typing import List, Optional 5 | from dotenv import load_dotenv 6 | import json 7 | from app.utils.logger import logger 8 | 9 | router = APIRouter() 10 | 11 | class ApiKey(BaseModel): 12 | api_key: str 13 | description: Optional[str] = None 14 | 15 | class ApiKeyInDB(ApiKey): 16 | id: int 17 | 18 | def update_env_api_keys(api_keys_list: List[ApiKeyInDB]): 19 | """更新 .env 文件中的 API 密钥""" 20 | env_path = '.env' 21 | with open(env_path, 'r', encoding='utf-8') as file: 22 | lines = file.readlines() 23 | 24 | # 将 API 密钥列表转换为 JSON 格式 25 | api_keys_data = [ 26 | { 27 | "key": key.api_key, 28 | "description": key.description or "", 29 | "id": key.id 30 | } 31 | for key in api_keys_list 32 | ] 33 | api_keys_json = json.dumps(api_keys_data) 34 | 35 | with open(env_path, 'w', encoding='utf-8') as file: 36 | for line in lines: 37 | if line.startswith('ALLOW_API_KEY='): 38 | file.write(f'ALLOW_API_KEY={api_keys_json}\n') 39 | else: 40 | file.write(line) 41 | 42 | # 重新加载环境变量并直接更新 os.environ 43 | load_dotenv(override=True) 44 | # 直接修改环境变量 45 | os.environ['ALLOW_API_KEY'] = api_keys_json 46 | logger.info(f"已更新 API 密钥环境变量: {api_keys_json}") 47 | 48 | # 存储 API 密钥的列表 49 | api_keys: List[ApiKeyInDB] = [] 50 | current_id = 1 51 | 52 | # 初始化时从环境变量加载默认 API 密钥 53 | try: 54 | default_keys_json = os.getenv('ALLOW_API_KEY', '[]') 55 | default_keys_data = json.loads(default_keys_json) 56 | for key_data in default_keys_data: 57 | api_keys.append(ApiKeyInDB( 58 | id=key_data["id"], 59 | api_key=key_data["key"], 60 | description=key_data["description"] 61 | )) 62 | current_id = max(current_id, key_data["id"] + 1) 63 | except json.JSONDecodeError: 64 | logger.warning("无法解析 API 密钥 JSON，使用空列表初始化") 65 | except Exception as e: 66 | logger.error(f"加载 API 密钥时出错: {str(e)}") 67 | 68 | @router.get("/api_keys") 69 | async def get_api_keys(): 70 | return api_keys 71 | 72 | @router.post("/api_keys") 73 | async def create_api_key(api_key: ApiKey): 74 | global current_id 75 | current_id += 1 76 | new_key = ApiKeyInDB( 77 | id=current_id, 78 | api_key=api_key.api_key, 79 | description=api_key.description 80 | ) 81 | api_keys.append(new_key) 82 | # 更新 .env 文件 83 | update_env_api_keys(api_keys) 84 | return new_key 85 | 86 | @router.delete("/api_keys/{key_id}") 87 | async def delete_api_key(key_id: int): 88 | key_index = next((index for (index, key) in enumerate(api_keys) if key.id == key_id), None) 89 | if key_index is None: 90 | raise HTTPException(status_code=404, detail="API key not found") 91 | api_keys.pop(key_index) 92 | # 更新 .env 文件 93 | update_env_api_keys(api_keys) 94 | return {"message": "API key deleted"} -------------------------------------------------------------------------------- /app/clients/base_client.py: -------------------------------------------------------------------------------- 1 | """基础客户端类，定义通用接口""" 2 | from typing import AsyncGenerator, Any 3 | import aiohttp 4 | from app.utils.logger import logger 5 | from abc import ABC, abstractmethod 6 | 7 | 8 | class BaseClient(ABC): 9 | def __init__(self, api_key: str, api_url: str): 10 | """初始化基础客户端 11 | 12 | Args: 13 | api_key: API密钥 14 | api_url: API地址 15 | """ 16 | self.api_key = api_key 17 | self.api_url = api_url 18 | 19 | def _prepare_request_data(self, messages: list, model: str, **kwargs) -> dict: 20 | """准备请求数据，包括自定义参数 21 | 22 | Args: 23 | messages: 消息列表 24 | model: 模型名称 25 | **kwargs: 其他参数，包括自定义参数 26 | 27 | Returns: 28 | dict: 处理后的请求数据 29 | """ 30 | data = { 31 | "model": model, 32 | "messages": messages, 33 | "stream": kwargs.get("stream", True), 34 | "temperature": kwargs.get("temperature", 0.7), 35 | "max_tokens": kwargs.get("max_tokens", 2000), 36 | "top_p": kwargs.get("top_p", 1.0), 37 | "presence_penalty": kwargs.get("presence_penalty", 0.0), 38 | "frequency_penalty": kwargs.get("frequency_penalty", 0.0) 39 | } 40 | 41 | # 添加自定义参数 42 | custom_parameters = kwargs.get("custom_parameters", {}) 43 | if custom_parameters: 44 | data.update(custom_parameters) 45 | 46 | return data 47 | 48 | async def _make_request(self, headers: dict, data: dict, url: str = None) -> AsyncGenerator[bytes, None]: 49 | """发送请求并处理响应 50 | 51 | Args: 52 | headers: 请求头 53 | data: 请求数据 54 | url: 自定义请求URL，如不提供则使用self.api_url 55 | 56 | Yields: 57 | bytes: 原始响应数据 58 | """ 59 | try: 60 | request_url = url if url else self.api_url 61 | async with aiohttp.ClientSession() as session: 62 | async with session.post( 63 | request_url, 64 | headers=headers, 65 | json=data 66 | ) as response: 67 | if response.status != 200: 68 | error_text = await response.text() 69 | logger.error(f"API 请求失败: {error_text}") 70 | return 71 | 72 | async for chunk in response.content.iter_any(): 73 | yield chunk 74 | 75 | except Exception as e: 76 | logger.error(f"请求 API 时发生错误: {e}") 77 | 78 | @abstractmethod 79 | async def stream_chat(self, messages: list, model: str) -> AsyncGenerator[tuple[str, str], None]: 80 | """流式对话，由子类实现 81 | 82 | Args: 83 | messages: 消息列表 84 | model: 模型名称 85 | 86 | Yields: 87 | tuple[str, str]: (内容类型, 内容) 88 | """ 89 | pass 90 | -------------------------------------------------------------------------------- /app/meeting/meeting_modes/role_playing.py: -------------------------------------------------------------------------------- 1 | from typing import List, Dict, Any 2 | from .base_mode import BaseMeetingMode 3 | from app.meeting.utils.summary_generator import SummaryGenerator 4 | 5 | 6 | class RolePlayingMode(BaseMeetingMode): 7 | """角色扮演会议模式""" 8 | 9 | def __init__(self): 10 | super().__init__( 11 | name="角色扮演", 12 | description="每个智能体扮演特定的角色或利益相关者，从不同视角探讨问题。" 13 | ) 14 | 15 | def get_agent_prompt(self, agent_name: str, agent_role: str, 16 | meeting_topic: str, current_round: int) -> str: 17 | """根据轮次获取角色扮演模式下的提示""" 18 | if current_round == 1: 19 | return f"""你正在参与一个关于"{meeting_topic}"的角色扮演会议。 20 | 作为{agent_role}，你需要从你扮演的角色视角出发，表达对该议题的看法和关注点。 21 | 请完全沉浸在你的角色中，从该角色的立场、利益和价值观出发思考问题。 22 | 在本轮中，请简要介绍你的角色立场，并表达你对议题的初步看法。""" 23 | 24 | elif current_round != 1 and current_round != self.max_rounds: 25 | return f"""这是角色扮演会议的中间阶段。 26 | 继续保持你作为{agent_role}的角色，回应其他参与者的观点。 27 | 你可以表达同意、不同意或提出问题，但一定要保持在角色的立场和视角内。 28 | 请对其他角色的发言做出回应，并进一步深化你的观点。""" 29 | 30 | else: 31 | return f"""这是角色扮演会议的最后阶段。 32 | 作为{agent_role}，请总结你的立场和关键观点。 33 | 在坚持你的角色视角的同时，可以适当提出一些妥协或合作的可能性。 34 | 请提出1-2个从你的角色视角出发，认为可行的解决方案或建议。""" 35 | 36 | def determine_speaking_order(self, agents: List[Dict[str, Any]], 37 | current_round: int) -> List[str]: 38 | """角色扮演模式下的发言顺序""" 39 | # 获取所有代理名称 40 | agent_names = [agent["name"] for agent in agents] 41 | 42 | # 如果设置了自定义发言顺序，则使用自定义顺序 43 | if self.custom_speaking_order: 44 | # 验证自定义顺序中的所有名称都在代理列表中 45 | valid_names = [name for name in self.custom_speaking_order if name in agent_names] 46 | 47 | # 添加自定义顺序中没有的代理（可能是后来添加的） 48 | for name in agent_names: 49 | if name not in valid_names: 50 | valid_names.append(name) 51 | 52 | return valid_names 53 | 54 | # 否则使用默认顺序（按提供的顺序） 55 | return agent_names 56 | 57 | def should_end_meeting(self, rounds_completed: int, 58 | meeting_history: List[Dict[str, Any]]) -> bool: 59 | """当完成预设的轮数后结束会议""" 60 | return rounds_completed >= self.max_rounds 61 | 62 | def get_summary_prompt_template(self) -> str: 63 | """获取角色扮演模式的总结提示模板""" 64 | return """ 65 | 你是一个会议总结专家。请对以下关于"{meeting_topic}"的角色扮演会议进行总结。 66 | 会议记录如下: 67 | 68 | {history_text} 69 | 70 | 请提供以下内容: 71 | 1. 每个角色的主要立场和观点概述 72 | 2. 各角色之间的主要分歧点和共识点 73 | 3. 提出的主要解决方案或建议 74 | 4. 不同角色视角带来的洞见 75 | 5. 可能的后续行动或讨论方向 76 | 77 | 请以清晰、结构化的方式呈现总结。 78 | """ 79 | 80 | def summarize_meeting(self, meeting_topic: str, 81 | meeting_history: List[Dict[str, Any]]) -> str: 82 | """汇总角色扮演会议的结果""" 83 | # 使用统一的总结生成方法 84 | return SummaryGenerator.generate_summary( 85 | meeting_topic=meeting_topic, 86 | meeting_history=meeting_history, 87 | prompt_template=self.get_summary_prompt_template() 88 | ) -------------------------------------------------------------------------------- /app/routers/roles.py: -------------------------------------------------------------------------------- 1 | from fastapi import APIRouter, Depends, HTTPException 2 | from sqlalchemy.orm import Session 3 | from typing import List, Dict, Any 4 | import logging 5 | 6 | from app.models.database import get_db 7 | from app.processors.role_processor import RoleProcessor 8 | 9 | router = APIRouter( 10 | prefix="/v1/roles", 11 | tags=["roles"], 12 | ) 13 | 14 | logger = logging.getLogger(__name__) 15 | 16 | @router.get("", response_model=List[Dict[str, Any]]) 17 | def get_roles(db: Session = Depends(get_db)): 18 | """获取所有角色""" 19 | processor = RoleProcessor(db) 20 | try: 21 | return processor.get_roles() 22 | except Exception as e: 23 | logger.error(f"获取角色失败: {str(e)}") 24 | raise HTTPException(status_code=500, detail=f"获取角色失败: {str(e)}") 25 | 26 | @router.get("/{role_id}", response_model=Dict[str, Any]) 27 | def get_role(role_id: int, db: Session = Depends(get_db)): 28 | """获取特定角色""" 29 | processor = RoleProcessor(db) 30 | try: 31 | role = processor.get_role(role_id) 32 | if not role: 33 | raise HTTPException(status_code=404, detail=f"角色ID {role_id} 不存在") 34 | return role 35 | except HTTPException: 36 | raise 37 | except Exception as e: 38 | logger.error(f"获取角色 {role_id} 失败: {str(e)}") 39 | raise HTTPException(status_code=500, detail=f"获取角色失败: {str(e)}") 40 | 41 | @router.post("", response_model=Dict[str, Any]) 42 | def create_role(role_data: Dict[str, Any], db: Session = Depends(get_db)): 43 | """创建新角色""" 44 | processor = RoleProcessor(db) 45 | try: 46 | return processor.create_role(role_data) 47 | except ValueError as e: 48 | raise HTTPException(status_code=400, detail=str(e)) 49 | except Exception as e: 50 | logger.error(f"创建角色失败: {str(e)}") 51 | raise HTTPException(status_code=500, detail=f"创建角色失败: {str(e)}") 52 | 53 | @router.put("/{role_id}", response_model=Dict[str, Any]) 54 | def update_role(role_id: int, role_data: Dict[str, Any], db: Session = Depends(get_db)): 55 | """更新角色""" 56 | processor = RoleProcessor(db) 57 | try: 58 | role = processor.update_role(role_id, role_data) 59 | if not role: 60 | raise HTTPException(status_code=404, detail=f"角色ID {role_id} 不存在") 61 | return role 62 | except HTTPException: 63 | raise 64 | except Exception as e: 65 | logger.error(f"更新角色 {role_id} 失败: {str(e)}") 66 | raise HTTPException(status_code=500, detail=f"更新角色失败: {str(e)}") 67 | 68 | @router.delete("/{role_id}", response_model=Dict[str, str]) 69 | def delete_role(role_id: int, db: Session = Depends(get_db)): 70 | """删除角色""" 71 | processor = RoleProcessor(db) 72 | try: 73 | success = processor.delete_role(role_id) 74 | if not success: 75 | raise HTTPException(status_code=404, detail=f"角色ID {role_id} 不存在") 76 | return {"message": "角色已删除"} 77 | except HTTPException: 78 | raise 79 | except Exception as e: 80 | logger.error(f"删除角色 {role_id} 失败: {str(e)}") 81 | raise HTTPException(status_code=500, detail=f"删除角色失败: {str(e)}") -------------------------------------------------------------------------------- /app/routes/model.py: -------------------------------------------------------------------------------- 1 | from fastapi import APIRouter, HTTPException, Depends 2 | from sqlalchemy.orm import Session 3 | from typing import List 4 | import logging 5 | 6 | from app.models.database import get_db, Model as DBModel 7 | from app.models.schemas import Model, ModelCreate 8 | 9 | router = APIRouter() 10 | 11 | logger = logging.getLogger(__name__) 12 | 13 | @router.get("/models", response_model=List[Model]) 14 | async def get_models(db: Session = Depends(get_db)): 15 | return db.query(DBModel).all() 16 | 17 | @router.post("/models", response_model=Model) 18 | async def create_model(model: ModelCreate, db: Session = Depends(get_db)): 19 | try: 20 | logger.debug(f"Creating model with data: {model.dict()}") 21 | model_data = { 22 | 'name': model.name, 23 | 'type': model.type, 24 | 'provider': model.provider, 25 | 'api_key': model.api_key, 26 | 'api_url': model.api_url, 27 | 'model_name': model.model_name, 28 | 'temperature': model.temperature, 29 | 'top_p': model.top_p, 30 | 'max_tokens': model.max_tokens, 31 | 'presence_penalty': model.presence_penalty, 32 | 'frequency_penalty': model.frequency_penalty, 33 | 'enable_tools': model.enable_tools, 34 | 'tools': model.tools, 35 | 'tool_choice': model.tool_choice, 36 | 'enable_thinking': model.enable_thinking, 37 | 'thinking_budget_tokens': model.thinking_budget_tokens, 38 | 'custom_parameters': model.custom_parameters if model.custom_parameters else {} 39 | } 40 | 41 | logger.debug(f"Processed model data: {model_data}") 42 | 43 | db_model = DBModel(**model_data) 44 | db.add(db_model) 45 | db.commit() 46 | db.refresh(db_model) 47 | logger.debug(f"Created model: {db_model.__dict__}") 48 | return db_model 49 | except Exception as e: 50 | db.rollback() 51 | logger.error(f"Error creating model: {str(e)}") 52 | raise HTTPException(status_code=500, detail=str(e)) 53 | 54 | @router.put("/models/{model_id}", response_model=Model) 55 | async def update_model(model_id: int, model: ModelCreate, db: Session = Depends(get_db)): 56 | db_model = db.query(DBModel).filter(DBModel.id == model_id).first() 57 | if not db_model: 58 | raise HTTPException(status_code=404, detail="Model not found") 59 | 60 | for key, value in model.dict().items(): 61 | setattr(db_model, key, value) 62 | 63 | try: 64 | db.commit() 65 | db.refresh(db_model) 66 | return db_model 67 | except Exception as e: 68 | db.rollback() 69 | raise HTTPException(status_code=500, detail=str(e)) 70 | 71 | @router.delete("/models/{model_id}") 72 | async def delete_model(model_id: int, db: Session = Depends(get_db)): 73 | db_model = db.query(DBModel).filter(DBModel.id == model_id).first() 74 | if not db_model: 75 | raise HTTPException(status_code=404, detail="Model not found") 76 | 77 | db.delete(db_model) 78 | db.commit() 79 | return {"status": "success"} -------------------------------------------------------------------------------- /app/meeting/meeting_modes/brainstorming.py: -------------------------------------------------------------------------------- 1 | from typing import List, Dict, Any 2 | import random 3 | 4 | from app.meeting.meeting_modes.base_mode import BaseMeetingMode 5 | from app.meeting.utils.summary_generator import SummaryGenerator 6 | 7 | class BrainstormingMode(BaseMeetingMode): 8 | """头脑风暴模式""" 9 | 10 | def __init__(self): 11 | super().__init__( 12 | name="brainstorming", 13 | description="头脑风暴模式，鼓励参与者提出创新想法，不进行批评" 14 | ) 15 | 16 | def get_agent_prompt(self, agent_name: str, agent_role: str, 17 | meeting_topic: str, current_round: int) -> str: 18 | """获取智能体提示""" 19 | if current_round == 1: 20 | return f"""你是{agent_name}，{agent_role}。 21 | 你正在参加一个关于"{meeting_topic}"的头脑风暴会议。 22 | 这是第一轮讨论，请尽可能提出创新的、有创意的想法。 23 | 记住头脑风暴的规则： 24 | 1. 数量胜于质量，尽可能提出多的想法 25 | 2. 不要批评或评判其他人的想法 26 | 3. 欢迎奇怪或不寻常的想法 27 | 4. 可以结合和改进已有的想法 28 | 29 | 请根据你的专业背景和角色，提供至少3个相关的创意或解决方案。 30 | """ 31 | elif current_round != 1 and current_round != self.max_rounds: 32 | return f"""你是{agent_name}，{agent_role}。 33 | 你正在参加一个关于"{meeting_topic}"的头脑风暴会议。 34 | 这是讨论的中间阶段，请根据之前的讨论内容，进一步发展想法，或提出全新的创意。 35 | """ 36 | else: 37 | return f"""你是{agent_name}，{agent_role}。 38 | 你正在参加一个关于"{meeting_topic}"的头脑风暴会议。 39 | 这是最后一轮讨论。 40 | 请根据之前的讨论内容，总结出你认为最有潜力的想法。 41 | """ 42 | 43 | def determine_speaking_order(self, agents: List[Dict[str, Any]], 44 | current_round: int) -> List[str]: 45 | """确定发言顺序""" 46 | # 获取所有代理名称 47 | agent_names = [agent["name"] for agent in agents] 48 | 49 | # 如果设置了自定义发言顺序，则使用自定义顺序 50 | if self.custom_speaking_order: 51 | # 验证自定义顺序中的所有名称都在代理列表中 52 | valid_names = [name for name in self.custom_speaking_order if name in agent_names] 53 | 54 | # 添加自定义顺序中没有的代理（可能是后来添加的） 55 | for name in agent_names: 56 | if name not in valid_names: 57 | valid_names.append(name) 58 | 59 | return valid_names 60 | 61 | # 否则使用默认的随机顺序 62 | random.shuffle(agent_names) 63 | return agent_names 64 | 65 | def should_end_meeting(self, rounds_completed: int, 66 | meeting_history: List[Dict[str, Any]]) -> bool: 67 | """判断会议是否应该结束""" 68 | # 当完成的轮数达到最大轮数时结束 69 | return rounds_completed >= self.max_rounds 70 | 71 | def summarize_meeting(self, meeting_topic: str, 72 | meeting_history: List[Dict[str, Any]]) -> str: 73 | """汇总会议结果""" 74 | # 使用统一的总结生成方法 75 | return SummaryGenerator.generate_summary( 76 | meeting_topic=meeting_topic, 77 | meeting_history=meeting_history, 78 | prompt_template=self.get_summary_prompt_template() 79 | ) 80 | 81 | def get_summary_prompt_template(self) -> str: 82 | """获取总结提示模板""" 83 | return """ 84 | 你是一个头脑风暴会议的总结专家。请对以下关于"{meeting_topic}"的头脑风暴会议进行总结。 85 | 会议记录如下: 86 | 87 | {history_text} 88 | 89 | 请提供以下内容: 90 | 1. 产生的主要创意和想法概述 91 | 2. 最有潜力的想法 92 | 3. 特别独特或创新的视角 93 | 4. 可能的下一步行动 94 | 5. 需要进一步探索的领域 95 | 96 | 请以清晰、结构化的方式呈现总结，重点突出最有价值的创意。 97 | """ -------------------------------------------------------------------------------- /app/models/schemas.py: -------------------------------------------------------------------------------- 1 | from pydantic import BaseModel, Field, validator 2 | from typing import Optional, Dict, Union, List 3 | import json 4 | 5 | class ModelBase(BaseModel): 6 | name: str 7 | type: str 8 | provider: str 9 | api_key: str 10 | api_url: str 11 | model_name: str 12 | max_tokens: int = 2000 13 | temperature: float = 0.7 14 | top_p: float = 1.0 15 | presence_penalty: float = 0.0 16 | frequency_penalty: float = 0.0 17 | enable_tools: bool = False 18 | tools: Optional[List[Dict]] = None 19 | tool_choice: Optional[Dict] = None 20 | enable_thinking: bool = False 21 | thinking_budget_tokens: int = 16000 22 | custom_parameters: Optional[Dict[str, Union[str, int, float, bool]]] = Field(default_factory=dict) 23 | 24 | @validator('temperature', 'top_p', pre=True) 25 | def convert_to_float(cls, v): 26 | if isinstance(v, str): 27 | return float(v) 28 | return v 29 | 30 | @validator('type') 31 | def validate_type(cls, v): 32 | valid_types = {'reasoning', 'execution', 'both'} 33 | if v.lower() not in valid_types: 34 | raise ValueError(f'Type must be one of {valid_types}') 35 | return v.lower() 36 | 37 | @validator('provider') 38 | def validate_provider(cls, v): 39 | valid_providers = { 40 | 'deepseek', 'google', 'anthropic', 'oneapi', 41 | 'openrouter', '腾讯云', 'grok3', 'openai-completion', 'other' 42 | } 43 | if v.lower() not in valid_providers: 44 | raise ValueError(f'Provider must be one of {valid_providers}') 45 | return v.lower() 46 | 47 | @validator('tools', 'tool_choice', pre=True) 48 | def validate_json_fields(cls, v): 49 | if isinstance(v, str): 50 | try: 51 | return json.loads(v) 52 | except: 53 | return None 54 | return v 55 | 56 | @validator('custom_parameters', pre=True) 57 | def validate_custom_parameters(cls, v): 58 | if isinstance(v, str): 59 | try: 60 | return json.loads(v) 61 | except: 62 | return {} 63 | return v if isinstance(v, dict) else {} 64 | 65 | class Config: 66 | from_attributes = True 67 | 68 | class ModelCreate(ModelBase): 69 | pass 70 | 71 | class Model(ModelBase): 72 | id: int 73 | 74 | class ConfigurationStepBase(BaseModel): 75 | model_id: int 76 | step_type: str # "reasoning" or "execution" 77 | step_order: int 78 | system_prompt: str = "" 79 | 80 | class ConfigurationStepCreate(ConfigurationStepBase): 81 | pass 82 | 83 | class ConfigurationStep(ConfigurationStepBase): 84 | id: int 85 | configuration_id: int 86 | 87 | class Config: 88 | from_attributes = True 89 | 90 | class ConfigurationBase(BaseModel): 91 | name: str 92 | is_active: bool = True 93 | transfer_content: Dict = {} 94 | 95 | class ConfigurationCreate(ConfigurationBase): 96 | steps: List[ConfigurationStepCreate] 97 | 98 | class Configuration(ConfigurationBase): 99 | id: int 100 | steps: List[ConfigurationStep] 101 | 102 | class Config: 103 | from_attributes = True -------------------------------------------------------------------------------- /app/meeting/logger/meeting_logger.py: -------------------------------------------------------------------------------- 1 | import json 2 | import os 3 | from datetime import datetime 4 | from typing import List, Dict, Any, Optional 5 | 6 | class MeetingLogger: 7 | """会议日志管理类，负责存储和检索会议记录""" 8 | 9 | def __init__(self, log_dir: str = "logs"): 10 | self.log_dir = log_dir 11 | # 确保日志目录存在 12 | if not os.path.exists(log_dir): 13 | os.makedirs(log_dir) 14 | 15 | def save_meeting_log(self, meeting_data: Dict[str, Any]) -> str: 16 | """保存会议日志""" 17 | meeting_id = meeting_data.get("meeting_id") 18 | if not meeting_id: 19 | meeting_id = datetime.now().strftime("%Y%m%d%H%M%S") 20 | meeting_data["meeting_id"] = meeting_id 21 | 22 | # 构建文件名和路径 23 | filename = f"{meeting_id}.json" 24 | filepath = os.path.join(self.log_dir, filename) 25 | 26 | # 保存为JSON文件 27 | with open(filepath, "w", encoding="utf-8") as f: 28 | json.dump(meeting_data, f, ensure_ascii=False, indent=2) 29 | 30 | return meeting_id 31 | 32 | def get_meeting_log(self, meeting_id: str) -> Optional[Dict[str, Any]]: 33 | """根据会议ID获取会议日志""" 34 | filepath = os.path.join(self.log_dir, f"{meeting_id}.json") 35 | 36 | if not os.path.exists(filepath): 37 | return None 38 | 39 | with open(filepath, "r", encoding="utf-8") as f: 40 | meeting_data = json.load(f) 41 | 42 | return meeting_data 43 | 44 | def get_all_meetings(self) -> List[Dict[str, Any]]: 45 | """获取所有会议的摘要信息""" 46 | meetings = [] 47 | 48 | # 遍历日志目录中的所有JSON文件 49 | for filename in os.listdir(self.log_dir): 50 | if not filename.endswith(".json"): 51 | continue 52 | 53 | filepath = os.path.join(self.log_dir, filename) 54 | 55 | try: 56 | with open(filepath, "r", encoding="utf-8") as f: 57 | meeting_data = json.load(f) 58 | 59 | # 提取摘要信息 60 | meetings.append({ 61 | "meeting_id": meeting_data.get("meeting_id"), 62 | "topic": meeting_data.get("topic"), 63 | "mode": meeting_data.get("mode"), 64 | "start_time": meeting_data.get("start_time"), 65 | "end_time": meeting_data.get("end_time"), 66 | "status": meeting_data.get("status"), 67 | "agent_count": len(meeting_data.get("agents", [])) 68 | }) 69 | except Exception as e: 70 | print(f"读取文件 {filename} 时出错: {e}") 71 | 72 | # 按开始时间排序，最新的会议排在前面 73 | meetings.sort(key=lambda x: x.get("start_time", ""), reverse=True) 74 | 75 | return meetings 76 | 77 | def search_meetings(self, keyword: str) -> List[Dict[str, Any]]: 78 | """搜索会议记录""" 79 | all_meetings = self.get_all_meetings() 80 | 81 | # 过滤包含关键字的会议 82 | filtered_meetings = [] 83 | for meeting in all_meetings: 84 | if (keyword.lower() in meeting.get("topic", "").lower() or 85 | keyword.lower() in meeting.get("mode", "").lower()): 86 | filtered_meetings.append(meeting) 87 | 88 | return filtered_meetings -------------------------------------------------------------------------------- /app/meeting/meeting_modes/swot_analysis.py: -------------------------------------------------------------------------------- 1 | from typing import List, Dict, Any 2 | from .base_mode import BaseMeetingMode 3 | from app.meeting.utils.summary_generator import SummaryGenerator 4 | 5 | class SWOTAnalysisMode(BaseMeetingMode): 6 | """SWOT分析会议模式""" 7 | 8 | def __init__(self, max_rounds: int = 4): 9 | super().__init__( 10 | name="SWOT分析", 11 | description="对主题进行优势(Strengths)、劣势(Weaknesses)、机会(Opportunities)和威胁(Threats)的系统性分析。", 12 | max_rounds=max_rounds # 为SWOT分析设置默认4轮 13 | ) 14 | self.swot_aspects = ["优势(Strengths)", "劣势(Weaknesses)", 15 | "机会(Opportunities)", "威胁(Threats)"] 16 | 17 | def get_agent_prompt(self, agent_name: str, agent_role: str, 18 | meeting_topic: str, current_round: int) -> str: 19 | """根据轮次获取SWOT分析模式下的提示""" 20 | # 确保轮次在有效范围内 21 | if current_round < 1 or current_round > len(self.swot_aspects): 22 | current_round = 1 23 | 24 | # 获取当前讨论的SWOT方面 25 | current_aspect = self.swot_aspects[current_round - 1] 26 | 27 | return f"""你正在参与一个关于"{meeting_topic}"的SWOT分析会议。 28 | 当前我们正在讨论SWOT分析的"{current_aspect}"方面。 29 | 30 | 作为{agent_role}，请从你的专业视角出发，针对主题"{meeting_topic}"的{current_aspect}，提出你的分析和见解。 31 | 请尽量具体、深入，并考虑到你独特角色和技能可能带来的特殊洞察。 32 | 33 | 如果是"优势"，请关注内部积极因素； 34 | 如果是"劣势"，请关注内部消极因素； 35 | 如果是"机会"，请关注外部积极因素； 36 | 如果是"威胁"，请关注外部消极因素。 37 | 38 | 请提出至少3-5点相关的分析。""" 39 | 40 | def determine_speaking_order(self, agents: List[Dict[str, Any]], 41 | current_round: int) -> List[str]: 42 | """SWOT分析模式下的发言顺序""" 43 | # 获取所有代理名称 44 | agent_names = [agent["name"] for agent in agents] 45 | 46 | # 如果设置了自定义发言顺序，则使用自定义顺序 47 | if self.custom_speaking_order: 48 | # 验证自定义顺序中的所有名称都在代理列表中 49 | valid_names = [name for name in self.custom_speaking_order if name in agent_names] 50 | 51 | # 添加自定义顺序中没有的代理（可能是后来添加的） 52 | for name in agent_names: 53 | if name not in valid_names: 54 | valid_names.append(name) 55 | 56 | return valid_names 57 | 58 | # 否则使用默认顺序（按提供的顺序） 59 | return agent_names 60 | 61 | def should_end_meeting(self, rounds_completed: int, 62 | meeting_history: List[Dict[str, Any]]) -> bool: 63 | """当完成预设的轮数后结束会议""" 64 | return rounds_completed >= self.max_rounds 65 | 66 | def summarize_meeting(self, meeting_topic: str, 67 | meeting_history: List[Dict[str, Any]]) -> str: 68 | """汇总SWOT分析会议的结果""" 69 | # 使用统一的总结生成方法 70 | return SummaryGenerator.generate_summary( 71 | meeting_topic=meeting_topic, 72 | meeting_history=meeting_history, 73 | prompt_template=self.get_summary_prompt_template() 74 | ) 75 | 76 | def get_summary_prompt_template(self) -> str: 77 | """获取SWOT分析的总结提示模板""" 78 | return """ 79 | 你是一个战略分析专家。请对以下关于"{meeting_topic}"的SWOT分析会议进行总结。 80 | 会议按照优势(Strengths)、劣势(Weaknesses)、机会(Opportunities)和威胁(Threats)四个方面进行了讨论。 81 | 82 | 会议记录如下: 83 | 84 | {history_text} 85 | 86 | 请提供以下内容: 87 | 1. 每个SWOT方面的关键点概括（每个方面3-5点） 88 | 2. 这些因素之间可能的相互作用和影响 89 | 3. 基于SWOT分析的战略建议（至少3-5条） 90 | 4. 可能需要进一步探讨的领域或问题 91 | 92 | 请以表格或列表形式呈现SWOT矩阵，然后提供详细的战略分析和建议。 93 | """ -------------------------------------------------------------------------------- /app/meeting/meeting_modes/six_thinking_hats.py: -------------------------------------------------------------------------------- 1 | from typing import List, Dict, Any 2 | from .base_mode import BaseMeetingMode 3 | from app.meeting.utils.summary_generator import SummaryGenerator 4 | 5 | class SixThinkingHatsMode(BaseMeetingMode): 6 | """六顶思考帽会议模式""" 7 | 8 | def __init__(self, max_rounds: int = 6): 9 | super().__init__( 10 | name="六顶思考帽", 11 | description="使用爱德华·德博诺的六顶思考帽方法，从六个不同角度思考问题。", 12 | max_rounds=6 # 为六顶思考帽模式设置默认6轮 13 | ) 14 | self.max_rounds = max_rounds # 六顶帽子对应六轮 15 | self.hats = [ 16 | {"color": "白色", "focus": "事实和信息", "description": "关注客观事实和数据，不做判断和推测"}, 17 | {"color": "红色", "focus": "感受和直觉", "description": "表达直觉、情感和感受，不需要解释理由"}, 18 | {"color": "黑色", "focus": "谨慎和风险", "description": "指出潜在问题、风险和挑战，进行批判性思考"}, 19 | {"color": "黄色", "focus": "积极和机会", "description": "关注积极面、价值和收益，寻找可能性"}, 20 | {"color": "绿色", "focus": "创意和可能性", "description": "提出新想法、创新方案和替代选择"}, 21 | {"color": "蓝色", "focus": "思考的整合", "description": "管理和总结思考过程，进行元认知"} 22 | ] 23 | 24 | def get_agent_prompt(self, agent_name: str, agent_role: str, 25 | meeting_topic: str, current_round: int) -> str: 26 | """根据轮次获取六顶思考帽模式下的提示""" 27 | # 确保轮次在有效范围内 28 | if current_round < 1 or current_round > len(self.hats): 29 | current_round = 1 30 | 31 | # 获取当前思考帽 32 | current_hat = self.hats[current_round - 1] 33 | 34 | return f"""你正在参与一个关于"{meeting_topic}"的六顶思考帽会议。 35 | 当前我们正在使用{current_hat["color"]}思考帽，专注于{current_hat["focus"]}。 36 | {current_hat["color"]}思考帽的特点是：{current_hat["description"]}。 37 | 38 | 作为{agent_role}，请在{current_hat["color"]}思考帽的框架下，对"{meeting_topic}"发表你的见解。 39 | 请完全遵循当前思考帽的思维方式，不要混入其他思考帽的视角。 40 | 41 | 具体而言： 42 | - 白帽：请提供客观事实、数据和信息，不加入个人观点和判断 43 | - 红帽：表达你的情感反应、直觉和感受，不需要逻辑支持 44 | - 黑帽：指出方案中的风险、问题和挑战，进行严谨的批判性思考 45 | - 黄帽：探索积极方面、价值和好处，保持乐观和建设性 46 | - 绿帽：提出新的创意、可能性和替代方案，打破常规思维 47 | - 蓝帽：梳理讨论脉络，总结前面的思考，提出下一步行动 48 | 49 | 请提出至少3-5点符合当前思考帽特点的观点。""" 50 | 51 | def determine_speaking_order(self, agents: List[Dict[str, Any]], 52 | current_round: int) -> List[str]: 53 | """六顶思考帽模式下的发言顺序，按照列表顺序轮流发言""" 54 | return [agent["name"] for agent in agents] 55 | 56 | def should_end_meeting(self, rounds_completed: int, 57 | meeting_history: List[Dict[str, Any]]) -> bool: 58 | """当完成预设的轮数后结束会议""" 59 | return rounds_completed >= self.max_rounds 60 | 61 | def summarize_meeting(self, meeting_topic: str, 62 | meeting_history: List[Dict[str, Any]]) -> str: 63 | """汇总六顶思考帽会议的结果""" 64 | # 使用统一的总结生成方法 65 | return SummaryGenerator.generate_summary( 66 | meeting_topic=meeting_topic, 67 | meeting_history=meeting_history, 68 | prompt_template=self.get_summary_prompt_template() 69 | ) 70 | 71 | def get_summary_prompt_template(self) -> str: 72 | """获取六顶思考帽的总结提示模板""" 73 | return """ 74 | 你是一个思维方法专家。请对以下关于"{meeting_topic}"的六顶思考帽会议进行总结。 75 | 会议按照六顶思考帽的方法进行了讨论：白帽(事实)、红帽(情感)、黑帽(风险)、黄帽(积极)、绿帽(创意)和蓝帽(整合)。 76 | 77 | 会议记录如下: 78 | 79 | {history_text} 80 | 81 | 请提供以下内容: 82 | 1. 每个思考帽下的关键见解概括（每个帽子3-5点） 83 | 2. 综合所有思考角度后对议题的全面理解 84 | 3. 根据六帽思考得出的主要行动建议（至少3-5条） 85 | 4. 思考过程中发现的关键矛盾或需要进一步探讨的问题 86 | 87 | 请以结构化的方式呈现总结，确保每个思考帽的视角都得到充分体现，并提供整合性的结论。 88 | """ -------------------------------------------------------------------------------- /app/clients/openai_client.py: -------------------------------------------------------------------------------- 1 | """OpenAI API 客户端""" 2 | import json 3 | from typing import AsyncGenerator 4 | from app.utils.logger import logger 5 | from .base_client import BaseClient 6 | 7 | 8 | class OpenAIClient(BaseClient): 9 | def __init__(self, api_key: str, api_url: str = "https://api.openai.com/v1/chat/completions"): 10 | """初始化 OpenAI 客户端 11 | 12 | Args: 13 | api_key: OpenAI API密钥 14 | api_url: OpenAI API地址 15 | """ 16 | super().__init__(api_key, api_url) 17 | self.provider = "openai" 18 | 19 | async def stream_chat( 20 | self, 21 | messages: list, 22 | model: str = "gpt-3.5-turbo", 23 | stream: bool = True, 24 | **kwargs 25 | ) -> AsyncGenerator[tuple[str, str], None]: 26 | """流式或非流式对话 27 | 28 | Args: 29 | messages: 消息列表 30 | model: 模型名称 31 | stream: 是否使用流式输出 32 | **kwargs: 其他参数，包括自定义参数 33 | 34 | Yields: 35 | tuple[str, str]: (内容类型, 内容) 36 | 内容类型: "answer" 或 "reasoning" 37 | 内容: 实际的文本内容 38 | """ 39 | headers = { 40 | "Authorization": f"Bearer {self.api_key}", 41 | "Content-Type": "application/json", 42 | # "Accept": "text/event-stream" if stream else "application/json", 43 | } 44 | 45 | # 使用基类方法准备请求数据 46 | data = self._prepare_request_data(messages, model, stream=stream, **kwargs) 47 | logger.debug(f"OpenAI 请求数据: {data}") 48 | if stream: 49 | first_chunk = True 50 | async for chunk in self._make_request(headers, data): 51 | try: 52 | chunk_str = chunk.decode('utf-8') 53 | if not chunk_str.strip(): 54 | continue 55 | 56 | for line in chunk_str.split('\n'): 57 | if line.startswith('data: '): 58 | json_str = line[6:] 59 | if json_str.strip() == '[DONE]': 60 | return 61 | 62 | data = json.loads(json_str) 63 | delta = data.get('choices', [{}])[0].get('delta', {}) 64 | 65 | if first_chunk: 66 | content = delta.get('content', '') 67 | role = delta.get('role', '') 68 | if role: 69 | first_chunk = False 70 | if content: 71 | yield "answer", content 72 | continue 73 | 74 | content = delta.get('content', '') 75 | if content: 76 | yield "answer", content 77 | 78 | except json.JSONDecodeError: 79 | continue 80 | except Exception as e: 81 | logger.error(f"处理 OpenAI 流式响应时发生错误: {e}") 82 | continue 83 | else: 84 | async for chunk in self._make_request(headers, data): 85 | try: 86 | response = json.loads(chunk.decode('utf-8')) 87 | content = response.get('choices', [{}])[0].get('message', {}).get('content', '') 88 | if content: 89 | yield "answer", content 90 | return 91 | except json.JSONDecodeError: 92 | continue 93 | except Exception as e: 94 | logger.error(f"处理 OpenAI 非流式响应时发生错误: {e}") 95 | continue -------------------------------------------------------------------------------- /app/routes/configuration.py: -------------------------------------------------------------------------------- 1 | from fastapi import APIRouter, HTTPException, Depends 2 | from sqlalchemy.orm import Session 3 | from typing import List 4 | 5 | from app.models.database import get_db, Configuration as DBConfiguration, ConfigurationStep, Model as DBModel 6 | from app.models.schemas import Configuration, ConfigurationCreate 7 | 8 | router = APIRouter() 9 | 10 | @router.get("/configurations", response_model=List[Configuration]) 11 | async def get_configurations(db: Session = Depends(get_db)): 12 | return db.query(DBConfiguration).all() 13 | 14 | @router.post("/configurations", response_model=Configuration) 15 | async def create_configuration(config: ConfigurationCreate, db: Session = Depends(get_db)): 16 | try: 17 | # 验证所有模型是否存在且用途类型正确 18 | for step in config.steps: 19 | model = db.query(DBModel).filter(DBModel.id == step.model_id).first() 20 | if not model: 21 | raise HTTPException(status_code=404, detail=f"Model {step.model_id} not found") 22 | if model.type not in ["both", step.step_type]: 23 | raise HTTPException( 24 | status_code=400, 25 | detail=f"Model {model.name} cannot be used for {step.step_type}" 26 | ) 27 | 28 | # 创建配置 29 | db_config = DBConfiguration( 30 | name=config.name, 31 | is_active=config.is_active, 32 | transfer_content=config.transfer_content 33 | ) 34 | db.add(db_config) 35 | db.commit() 36 | db.refresh(db_config) 37 | 38 | # 创建配置步骤 39 | for step in config.steps: 40 | db_step = ConfigurationStep( 41 | configuration_id=db_config.id, 42 | model_id=step.model_id, 43 | step_type=step.step_type, 44 | step_order=step.step_order, 45 | system_prompt=step.system_prompt 46 | ) 47 | db.add(db_step) 48 | 49 | db.commit() 50 | db.refresh(db_config) 51 | return db_config 52 | 53 | except Exception as e: 54 | db.rollback() 55 | raise HTTPException(status_code=500, detail=str(e)) 56 | 57 | @router.put("/configurations/{config_id}", response_model=Configuration) 58 | async def update_configuration(config_id: int, config: ConfigurationCreate, db: Session = Depends(get_db)): 59 | try: 60 | db_config = db.query(DBConfiguration).filter(DBConfiguration.id == config_id).first() 61 | if not db_config: 62 | raise HTTPException(status_code=404, detail="Configuration not found") 63 | 64 | db_config.name = config.name 65 | db_config.is_active = config.is_active 66 | db_config.transfer_content = config.transfer_content 67 | 68 | db.query(ConfigurationStep).filter( 69 | ConfigurationStep.configuration_id == config_id 70 | ).delete() 71 | 72 | for step in config.steps: 73 | db_step = ConfigurationStep( 74 | configuration_id=config_id, 75 | model_id=step.model_id, 76 | step_type=step.step_type, 77 | step_order=step.step_order, 78 | system_prompt=step.system_prompt 79 | ) 80 | db.add(db_step) 81 | 82 | db.commit() 83 | db.refresh(db_config) 84 | return db_config 85 | 86 | except Exception as e: 87 | db.rollback() 88 | raise HTTPException(status_code=500, detail=str(e)) 89 | 90 | @router.delete("/configurations/{config_id}") 91 | async def delete_configuration(config_id: int, db: Session = Depends(get_db)): 92 | db_config = db.query(DBConfiguration).filter(DBConfiguration.id == config_id).first() 93 | if not db_config: 94 | raise HTTPException(status_code=404, detail="Configuration not found") 95 | 96 | db.delete(db_config) 97 | db.commit() 98 | return {"status": "success"} -------------------------------------------------------------------------------- /app/meeting/agents/agent_factory.py: -------------------------------------------------------------------------------- 1 | from typing import List, Optional 2 | from .agent import Agent 3 | 4 | class AgentFactory: 5 | """智能体工厂类，用于创建预设智能体""" 6 | 7 | def get_predefined_agents(self, model_name: Optional[str] = None, base_url: Optional[str] = None, api_key: Optional[str] = None) -> List[Agent]: 8 | """获取预设智能体列表""" 9 | # 创建模型参数字典 10 | model_params = {} 11 | if model_name: 12 | model_params["model_name"] = model_name 13 | 14 | # 为每个智能体创建特定的个性化模型参数 15 | predefined_agents_with_params = [ 16 | { 17 | "name": "创新者", 18 | "role_description": "负责提出创新性想法，打破传统思维", 19 | "personality": "积极主动，富有想象力", 20 | "skills": ["创意思维", "跨领域联想", "创新方案设计"], 21 | "model_params": { 22 | **model_params, 23 | "temperature": 0.9, # 高温度，更有创意 24 | "max_tokens": 1000, 25 | "model_name": model_name or "gemini-1.5-pro", # 可以为每个智能体指定不同模型 26 | }, 27 | "base_url": base_url, # 可以配置不同的base_url 28 | "api_key": api_key # 可以配置不同的api_key 29 | }, 30 | { 31 | "name": "批判者", 32 | "role_description": "负责质疑现有方案，从不同角度分析问题", 33 | "personality": "理性冷静，善于分析", 34 | "skills": ["逻辑推理", "风险评估", "批判性思维"], 35 | "model_params": { 36 | **model_params, 37 | "temperature": 0.5, # 低温度，更有逻辑性 38 | "max_tokens": 1200, 39 | "model_name": model_name or "gpt-4-turbo", # 批判者用更强大的模型 40 | }, 41 | "base_url": base_url, 42 | "api_key": api_key 43 | }, 44 | { 45 | "name": "协调者", 46 | "role_description": "整合各方观点，寻求共识和平衡", 47 | "personality": "平和包容，善于沟通", 48 | "skills": ["沟通协调", "冲突解决", "团队建设"], 49 | "model_params": { 50 | **model_params, 51 | "temperature": 0.7, # 中等温度，平衡创意和逻辑 52 | "max_tokens": 1100, 53 | } 54 | }, 55 | { 56 | "name": "执行者", 57 | "role_description": "关注实际操作和落地方案", 58 | "personality": "务实高效，注重细节", 59 | "skills": ["项目管理", "资源分配", "行动计划制定"], 60 | "model_params": { 61 | **model_params, 62 | "temperature": 0.4, # 低温度，更关注实际和细节 63 | "max_tokens": 900, 64 | } 65 | } 66 | ] 67 | 68 | # 创建智能体实例 69 | predefined_agents = [] 70 | for agent_data in predefined_agents_with_params: 71 | agent = Agent( 72 | name=agent_data["name"], 73 | role_description=agent_data["role_description"], 74 | personality=agent_data["personality"], 75 | skills=agent_data["skills"], 76 | model_params=agent_data["model_params"], 77 | base_url=agent_data.get("base_url", base_url), 78 | api_key=agent_data.get("api_key", api_key) 79 | ) 80 | predefined_agents.append(agent) 81 | return predefined_agents 82 | 83 | def create(self, name, role, personality, skills, model_params=None, base_url=None, api_key=None): 84 | """创建一个智能体实例""" 85 | agent = Agent( 86 | name=name, 87 | role_description=role, 88 | personality=personality, 89 | skills=skills, 90 | model_params=model_params, 91 | base_url=base_url, 92 | api_key=api_key 93 | ) 94 | return agent 95 | 96 | def create_agent(self, name, role, personality, skills, model_params=None, base_url=None): 97 | """创建智能体的别名方法，与create方法功能相同""" 98 | return self.create(name, role, personality, skills, model_params, base_url) -------------------------------------------------------------------------------- /app/meeting/meeting_modes/discussion.py: -------------------------------------------------------------------------------- 1 | from typing import List, Dict, Any 2 | import random 3 | 4 | from app.meeting.meeting_modes.base_mode import BaseMeetingMode 5 | from app.meeting.utils.summary_generator import SummaryGenerator 6 | 7 | class DiscussionMode(BaseMeetingMode): 8 | """普通讨论模式""" 9 | 10 | def __init__(self): 11 | super().__init__( 12 | name="discussion", 13 | description="普通讨论模式，参与者轮流发言，共同讨论主题" 14 | ) 15 | self.summary_generator = SummaryGenerator() 16 | 17 | def get_agent_prompt(self, agent_name: str, agent_role: str, 18 | meeting_topic: str, current_round: int) -> str: 19 | """获取智能体提示""" 20 | if current_round == 1: 21 | return f"""你是{agent_name}，{agent_role}。 22 | 你正在参加一个关于"{meeting_topic}"的讨论。 23 | 这是第一轮讨论，请分享你对这个主题的初步看法和观点。 24 | 请考虑你的专业背景和角色，提供有价值的见解。 25 | """ 26 | elif current_round != 1 and current_round != self.max_rounds: 27 | return f"""你是{agent_name}，{agent_role}。 28 | 你正在参加一个关于"{meeting_topic}"的讨论。 29 | 这是第{current_round}轮讨论，请根据之前的讨论内容，进一步发展你的观点，或回应其他参与者的意见。 30 | 你可以提出新的见解，也可以对之前的观点进行补充或质疑。 31 | """ 32 | else: 33 | return f"""你是{agent_name}，{agent_role}。 34 | 你正在参加一个关于"{meeting_topic}"的讨论。 35 | 这是讨论的最后一轮。 36 | 请根据之前的讨论内容，进一步总结你的观点，或回应其他参与者的意见。 37 | """ 38 | 39 | def determine_speaking_order(self, agents: List[Dict[str, Any]], 40 | current_round: int) -> List[str]: 41 | """确定发言顺序""" 42 | # 获取所有代理名称 43 | agent_names = [agent["name"] for agent in agents] 44 | 45 | # 如果设置了自定义发言顺序，则使用自定义顺序 46 | if self.custom_speaking_order: 47 | # logger.info(f"使用自定义发言顺序: {self.custom_speaking_order}") 48 | # 验证自定义顺序中的所有名称都在代理列表中 49 | valid_names = [name for name in self.custom_speaking_order if name in agent_names] 50 | 51 | # 添加自定义顺序中没有的代理（可能是后来添加的） 52 | for name in agent_names: 53 | if name not in valid_names: 54 | valid_names.append(name) 55 | 56 | return valid_names 57 | 58 | # 否则使用默认顺序（按提供的顺序） 59 | return agent_names 60 | 61 | def should_end_meeting(self, rounds_completed: int, 62 | meeting_history: List[Dict[str, Any]]) -> bool: 63 | """判断会议是否应该结束""" 64 | # 当完成的轮数达到最大轮数时结束 65 | return rounds_completed >= self.max_rounds 66 | 67 | def summarize_meeting(self, topic: str, meeting_history: List[Dict[str, str]]) -> str: 68 | """ 69 | 生成会议总结 70 | 71 | 参数: 72 | topic: 会议主题 73 | meeting_history: 会议历史记录 74 | 75 | 返回: 76 | str: 会议总结 77 | """ 78 | # 计算讨论轮数 79 | agent_messages = [msg for msg in meeting_history if msg["agent"] != "system"] 80 | agents = set([msg["agent"] for msg in agent_messages if msg["agent"] != "system"]) 81 | message_per_agent = len(agent_messages) / max(len(agents), 1) 82 | estimated_rounds = max(1, int(message_per_agent)) 83 | 84 | # 构建摘要提示模板 85 | prompt_template = f""" 86 | 请参考以下关于"{{topic}}"的讨论内容: 87 | {{history}} 88 | 89 | 作为一名会议总结专家，请提供以下内容: 90 | 1. 讨论的主要主题和观点概述（不超过3点） 91 | 2. 讨论中达成的主要共识（如果有） 92 | 3. 存在的主要分歧或不同视角（如果有） 93 | 4. 提出的解决方案或行动建议 94 | 5. 可能需要进一步讨论或研究的问题 95 | 96 | 请以清晰、结构化的方式呈现总结，重点突出最重要的内容。 97 | """ 98 | 99 | # 使用统一的总结生成方法 100 | return SummaryGenerator.generate_summary( 101 | meeting_topic=topic, 102 | meeting_history=meeting_history, 103 | prompt_template=prompt_template 104 | ) 105 | 106 | def _format_history_for_summary(self, meeting_history: List[Dict[str, str]]) -> str: 107 | """ 108 | 将会议历史记录格式化为适合总结的文本格式 109 | 110 | 参数: 111 | meeting_history: 会议历史记录列表 112 | 113 | 返回: 114 | str: 格式化后的会议历史文本 115 | """ 116 | formatted_text = "" 117 | for entry in meeting_history: 118 | if entry["agent"] != "system": # 排除系统消息 119 | formatted_text += f"[{entry['agent']}]: {entry['content']}\n\n" 120 | 121 | return formatted_text -------------------------------------------------------------------------------- /app/meeting/meeting_modes/debate.py: -------------------------------------------------------------------------------- 1 | from typing import List, Dict, Any 2 | from app.meeting.meeting_modes.base_mode import BaseMeetingMode 3 | from app.meeting.utils.summary_generator import SummaryGenerator 4 | 5 | class DebateMode(BaseMeetingMode): 6 | """对抗辩论会议模式""" 7 | 8 | def __init__(self, max_rounds: int = 3): 9 | super().__init__( 10 | name="对抗辩论", 11 | description="设定正反方，就议题进行辩论，旨在深入分析问题的不同方面。", 12 | max_rounds=max_rounds # 使用传入的最大轮数 13 | ) 14 | 15 | def get_agent_prompt(self, agent_name: str, agent_role: str, 16 | meeting_topic: str, current_round: int) -> str: 17 | """根据轮次获取对抗辩论模式下的提示""" 18 | # 使用agent_name的索引位置来确定正反方 19 | # 这将确保大约一半的角色在每一方 20 | is_pro = len(agent_name) % 2 == 0 # 简单地使用名字长度的奇偶性 21 | side = "正方" if is_pro else "反方" 22 | 23 | print(f"max_rounds: {self.max_rounds}") 24 | 25 | if current_round == 1: 26 | return f"""你正在参与一个关于"{meeting_topic}"的对抗辩论会议。 27 | 你被分配到{side}。作为{agent_role}，请从{side}立场出发，就"{meeting_topic}"提出你的论点和理由。 28 | 请提供3-5个有力的论据支持你的立场。""" 29 | 30 | elif current_round != 1 and current_round != self.max_rounds: 31 | return f"""这是对抗辩论的中间轮。 32 | 继续作为{side}，请针对对方的论点进行反驳，并进一步强化自己的立场。 33 | 找出对方论点中的漏洞或不足，同时补充新的支持论据。""" 34 | 35 | elif current_round == self.max_rounds: 36 | return f"""这是对抗辩论的最后阶段。 37 | 作为{side}，请总结你的立场和核心论点，驳斥对方主要观点，并给出最终的论述。 38 | 尝试说服听众接受你的立场，展示为什么你的立场更合理、更有说服力。""" 39 | 40 | def determine_speaking_order(self, agents: List[Dict[str, Any]], 41 | current_round: int) -> List[str]: 42 | """对抗辩论模式下的发言顺序，正反方交替发言""" 43 | # 使用与get_agent_prompt相同的哈希逻辑来区分正反方 44 | # 这样可以确保两个方法之间的一致性 45 | 46 | # 将智能体分为正反方 47 | pro_agents = [] 48 | con_agents = [] 49 | 50 | # 先根据哈希值分配 51 | for agent in agents: 52 | agent_name = agent["name"] 53 | agent_role = agent.get("role", "") 54 | 55 | # 使用与get_agent_prompt相同的逻辑 56 | agent_hash = hash(agent_name + agent_role) 57 | is_pro = agent_hash % 2 == 0 58 | 59 | if is_pro: 60 | pro_agents.append(agent_name) 61 | else: 62 | con_agents.append(agent_name) 63 | 64 | # 检查平衡性：确保两方都至少有一个角色 65 | if not pro_agents and con_agents: 66 | # 如果正方没有角色，但反方有，则将一半反方角色移到正方 67 | mid = len(con_agents) // 2 68 | pro_agents = con_agents[:mid] 69 | con_agents = con_agents[mid:] 70 | elif not con_agents and pro_agents: 71 | # 如果反方没有角色，但正方有，则将一半正方角色移到反方 72 | mid = len(pro_agents) // 2 73 | con_agents = pro_agents[:mid] 74 | pro_agents = pro_agents[mid:] 75 | 76 | # 交替排列正反方发言 77 | speaking_order = [] 78 | max_len = max(len(pro_agents), len(con_agents)) 79 | for i in range(max_len): 80 | if i < len(pro_agents): 81 | speaking_order.append(pro_agents[i]) 82 | if i < len(con_agents): 83 | speaking_order.append(con_agents[i]) 84 | 85 | return speaking_order 86 | 87 | def should_end_meeting(self, rounds_completed: int, 88 | meeting_history: List[Dict[str, Any]]) -> bool: 89 | """ 90 | 判断会议是否应该结束 91 | 注意：应该根据会议实例中设置的max_rounds决定，而不是模式自身的max_rounds 92 | """ 93 | # 这里不直接使用self.max_rounds，因为会议可能设置了不同的最大轮数 94 | # 我们依赖Meeting类中的条件检查: `if self.current_round > self.max_rounds` 95 | return False # 始终返回False，让Meeting类来决定何时结束会议 96 | 97 | def summarize_meeting(self, meeting_topic: str, 98 | meeting_history: List[Dict[str, Any]]) -> str: 99 | """汇总对抗辩论的结果""" 100 | # 使用统一的总结生成方法 101 | return SummaryGenerator.generate_summary( 102 | meeting_topic=meeting_topic, 103 | meeting_history=meeting_history, 104 | prompt_template=self.get_summary_prompt_template() 105 | ) 106 | 107 | def get_summary_prompt_template(self) -> str: 108 | """获取总结提示模板""" 109 | return """ 110 | 你是一个辩论分析专家。请对以下关于"{meeting_topic}"的对抗辩论会议进行总结。 111 | 会议记录如下: 112 | 113 | {history_text} 114 | 115 | 请提供以下内容: 116 | 1. 正方的主要论点和论据概述 117 | 2. 反方的主要论点和论据概述 118 | 3. 双方论点的对比分析 119 | 4. 各论点的强弱点评估 120 | 5. 辩论的关键洞见和启示 121 | 122 | 请保持中立，不要偏向任何一方，并以结构化的方式呈现总结。 123 | """ -------------------------------------------------------------------------------- /app/routers/discussion_groups.py: -------------------------------------------------------------------------------- 1 | from fastapi import APIRouter, Depends, HTTPException, Request 2 | from sqlalchemy.orm import Session 3 | from typing import List, Dict, Any 4 | import logging 5 | import json 6 | import traceback 7 | from fastapi.responses import StreamingResponse 8 | 9 | from app.models.database import get_db 10 | from app.processors.discussion_processor import DiscussionProcessor 11 | 12 | router = APIRouter( 13 | prefix="/v1/discussion_groups", 14 | tags=["discussion_groups"], 15 | ) 16 | 17 | logger = logging.getLogger(__name__) 18 | 19 | @router.get("", response_model=List[Dict[str, Any]]) 20 | def get_discussion_groups(db: Session = Depends(get_db)): 21 | """获取所有讨论组""" 22 | processor = DiscussionProcessor(db) 23 | try: 24 | return processor.get_groups() 25 | except Exception as e: 26 | logger.error(f"获取讨论组失败: {str(e)}") 27 | raise HTTPException(status_code=500, detail=f"获取讨论组失败: {str(e)}") 28 | 29 | @router.get("/{group_id}", response_model=Dict[str, Any]) 30 | def get_discussion_group(group_id: int, db: Session = Depends(get_db)): 31 | """获取特定讨论组""" 32 | processor = DiscussionProcessor(db) 33 | try: 34 | group = processor.get_group(group_id) 35 | if not group: 36 | raise HTTPException(status_code=404, detail=f"讨论组ID {group_id} 不存在") 37 | return group 38 | except HTTPException: 39 | raise 40 | except Exception as e: 41 | logger.error(f"获取讨论组 {group_id} 失败: {str(e)}") 42 | raise HTTPException(status_code=500, detail=f"获取讨论组失败: {str(e)}") 43 | 44 | @router.post("", response_model=Dict[str, Any]) 45 | def create_discussion_group(group_data: Dict[str, Any], db: Session = Depends(get_db)): 46 | """创建新讨论组""" 47 | # 验证必要字段 48 | required_fields = ["name", "mode", "role_ids"] 49 | for field in required_fields: 50 | if field not in group_data: 51 | raise HTTPException(status_code=400, detail=f"缺少必要字段: {field}") 52 | 53 | processor = DiscussionProcessor(db) 54 | try: 55 | return processor.create_group(group_data) 56 | except ValueError as e: 57 | raise HTTPException(status_code=400, detail=str(e)) 58 | except Exception as e: 59 | logger.error(f"创建讨论组失败: {str(e)}") 60 | raise HTTPException(status_code=500, detail=f"创建讨论组失败: {str(e)}") 61 | 62 | @router.put("/{group_id}", response_model=Dict[str, Any]) 63 | def update_discussion_group(group_id: int, group_data: Dict[str, Any], db: Session = Depends(get_db)): 64 | """更新讨论组""" 65 | processor = DiscussionProcessor(db) 66 | try: 67 | group = processor.update_group(group_id, group_data) 68 | if not group: 69 | raise HTTPException(status_code=404, detail=f"讨论组ID {group_id} 不存在") 70 | return group 71 | except HTTPException: 72 | raise 73 | except Exception as e: 74 | logger.error(f"更新讨论组 {group_id} 失败: {str(e)}") 75 | raise HTTPException(status_code=500, detail=f"更新讨论组失败: {str(e)}") 76 | 77 | @router.delete("/{group_id}", response_model=Dict[str, str]) 78 | def delete_discussion_group(group_id: int, db: Session = Depends(get_db)): 79 | """删除讨论组""" 80 | processor = DiscussionProcessor(db) 81 | try: 82 | success = processor.delete_group(group_id) 83 | if not success: 84 | raise HTTPException(status_code=404, detail=f"讨论组ID {group_id} 不存在") 85 | return {"message": "讨论组已删除"} 86 | except HTTPException: 87 | raise 88 | except Exception as e: 89 | logger.error(f"删除讨论组 {group_id} 失败: {str(e)}") 90 | raise HTTPException(status_code=500, detail=f"删除讨论组失败: {str(e)}") 91 | 92 | @router.post("/stream/{group_id}") 93 | async def stream_discussion_process(group_id: int, request: Request, db: Session = Depends(get_db)): 94 | """开始流式讨论过程""" 95 | processor = DiscussionProcessor(db) 96 | 97 | try: 98 | # 开始会议 99 | meeting_id = processor.start_meeting(group_id) 100 | 101 | # 返回流式响应 102 | return StreamingResponse( 103 | processor._stream_discussion_process(meeting_id), 104 | media_type="text/event-stream" 105 | ) 106 | 107 | except Exception as e: 108 | logger.error(f"启动流式讨论过程时出错: {str(e)}", exc_info=True) 109 | 110 | # 创建错误流 111 | async def error_stream(): 112 | error_data = { 113 | "error": str(e), 114 | "detail": traceback.format_exc() 115 | } 116 | yield f"data: {json.dumps(error_data, ensure_ascii=False)}\n\n" 117 | yield "data: [DONE]\n\n" 118 | 119 | return StreamingResponse(error_stream(), media_type="text/event-stream") -------------------------------------------------------------------------------- /app/static/js/thinking.js: -------------------------------------------------------------------------------- 1 | // 思考内容流式输出处理 2 | class ThinkingHandler { 3 | constructor() { 4 | this.thinkingContent = null; 5 | this.isThinkingVisible = false; 6 | this.initialize(); 7 | } 8 | 9 | initialize() { 10 | // 创建思考内容容器 11 | this.thinkingContent = document.createElement('div'); 12 | this.thinkingContent.className = 'thinking-content collapsed'; 13 | this.thinkingContent.style.whiteSpace = 'pre-wrap'; 14 | 15 | // 创建切换按钮 16 | this.toggleButton = document.createElement('button'); 17 | this.toggleButton.className = 'btn btn-sm btn-outline-secondary mb-2 mt-1'; 18 | this.toggleButton.textContent = '显示思考过程'; 19 | this.toggleButton.style.display = 'none'; 20 | this.toggleButton.addEventListener('click', () => this.toggleThinking()); 21 | 22 | // 初始化标记解析器配置 23 | this.markedOptions = { 24 | gfm: true, 25 | breaks: true, 26 | sanitize: false, 27 | highlight: null // 禁用代码高亮 28 | }; 29 | } 30 | 31 | // 处理思考内容更新 32 | handleThinking(thinking, messageContainer) { 33 | if (!thinking || thinking.trim() === '') return; 34 | 35 | if (!messageContainer.querySelector('.thinking-content')) { 36 | messageContainer.prepend(this.thinkingContent.cloneNode(true)); 37 | messageContainer.prepend(this.toggleButton.cloneNode(true)); 38 | 39 | // 获取刚添加的元素 40 | this.currentThinkingContent = messageContainer.querySelector('.thinking-content'); 41 | this.currentToggleButton = messageContainer.querySelector('button'); 42 | 43 | // 添加事件监听器 44 | this.currentToggleButton.addEventListener('click', () => { 45 | this.currentThinkingContent.classList.toggle('collapsed'); 46 | // 切换后确保滚动条重置到顶部 47 | if (!this.currentThinkingContent.classList.contains('collapsed')) { 48 | setTimeout(() => { 49 | this.currentThinkingContent.scrollTop = 0; 50 | }, 50); 51 | } 52 | this.currentToggleButton.textContent = 53 | this.currentThinkingContent.classList.contains('collapsed') 54 | ? '显示思考过程' 55 | : '隐藏思考过程'; 56 | }); 57 | } 58 | 59 | // 安全地渲染思考内容 60 | const sanitizedThinking = this.sanitizeAndFormatThinking(thinking); 61 | this.currentThinkingContent.innerHTML = sanitizedThinking; 62 | 63 | // 显示切换按钮 64 | this.currentToggleButton.style.display = 'inline-block'; 65 | } 66 | 67 | // 处理并格式化思考内容 68 | sanitizeAndFormatThinking(thinking) { 69 | // 使用简单的转义处理，而不是完整的Markdown解析 70 | let formatted = thinking 71 | .replace(/&/g, '&') 72 | .replace(//g, '>') 74 | .replace(/"/g, '"') 75 | .replace(/'/g, '''); 76 | 77 | // 使用非贪婪匹配提取代码块 78 | formatted = formatted.replace(/```(.*?)\n([\s\S]*?)```/g, (match, language, code) => { 79 | // 对代码内容增加额外的处理，保留原始格式和缩进 80 | const processedCode = code 81 | .replace(/</g, '<') 82 | .replace(/>/g, '>') 83 | .trim(); 84 | 85 | // 使用更简单的包装，不依赖highlight.js 86 | return `
${processedCode}
`; 87 | }); 88 | 89 | // 基本的行内代码处理 90 | formatted = formatted.replace(/`([^`]+)`/g, '$1'); 91 | 92 | // 处理换行，但保留多个连续换行 93 | formatted = formatted.replace(/\n\n+/g, '

').replace(/\n/g, '
'); 94 | 95 | return formatted; 96 | } 97 | 98 | // 切换思考内容显示状态 99 | toggleThinking() { 100 | this.isThinkingVisible = !this.isThinkingVisible; 101 | if (this.currentThinkingContent) { 102 | this.currentThinkingContent.classList.toggle('collapsed', !this.isThinkingVisible); 103 | 104 | // 确保切换显示时滚动功能正常 105 | if (this.isThinkingVisible) { 106 | setTimeout(() => { 107 | this.currentThinkingContent.scrollTop = 0; 108 | }, 50); 109 | } 110 | 111 | if (this.currentToggleButton) { 112 | this.currentToggleButton.textContent = this.isThinkingVisible 113 | ? '隐藏思考过程' 114 | : '显示思考过程'; 115 | } 116 | } 117 | } 118 | 119 | // 重置状态 120 | reset() { 121 | this.isThinkingVisible = false; 122 | this.currentThinkingContent = null; 123 | this.currentToggleButton = null; 124 | } 125 | } 126 | 127 | // 创建全局思考处理器实例 128 | const thinkingHandler = new ThinkingHandler(); -------------------------------------------------------------------------------- /README_zh.md: -------------------------------------------------------------------------------- 1 |
2 |

DeepGemini 🌟

3 |

一个灵活的多模型编排 API，兼容 OpenAI 接口

4 | 5 | [![FastAPI](https://img.shields.io/badge/FastAPI-005571?style=flat-square&logo=fastapi)](https://fastapi.tiangolo.com) 6 | [![Python 3.11](https://img.shields.io/badge/Python-3.11-blue?style=flat-square&logo=python)](https://www.python.org) 7 | [![OpenAI Compatible](https://img.shields.io/badge/OpenAI-Compatible-412991?style=flat-square&logo=openai)](https://platform.openai.com) 8 | [![License](https://img.shields.io/badge/License-MIT-green.svg?style=flat-square)](LICENSE) 9 | [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/sligter/DeepGemini) 10 |
11 | 12 | [English](README.md) 13 | 14 | ## ✨ 特性 15 | 16 | - **多模型编排**：无缝组合多个 AI 模型，实现自定义接力链 17 | - **角色管理**：创建具有不同性格和技能的 AI 角色 18 | - **讨论组**：组合多个角色形成讨论组 19 | - **多种讨论模式**： 20 | - 一般讨论 21 | - 头脑风暴 22 | - 辩论 23 | - 角色扮演 24 | - SWOT 分析 25 | - 六顶思考帽 26 | - **灵活的提供商支持**：支持多个 AI 提供商： 27 | - DeepSeek 28 | - Claude 29 | - Gemini 30 | - Grok3 31 | - OpenAI 32 | - OneAPI 33 | - OpenRouter 34 | - Siliconflow 35 | - **OpenAI 兼容**：可作为 OpenAI API 的直接替代品 36 | - **流式响应**：支持实时流式响应，提供更好的用户体验 37 | - **高级配置**：精细控制模型参数和系统提示词 38 | - **Web 管理界面**：内置模型和配置管理界面 39 | - **多语言支持**：支持中文和英文界面 40 | - **人类参与**：支持人类加入AI讨论组进行发言 41 | - **对话界面**：支持模型、角色、接力链、讨论组在线对话 42 | - **灵活部署**：支持Docker或本地安装的简易部署方式 43 | 44 | ## 预览 45 | 46 | ![image](https://img.pub/p/02f96adb71b92d9e8009.png) 47 | 48 | ![image](https://img.pub/p/1ffdc3728b7944caf807.png) 49 | 50 | ![image](https://img.pub/p/9051bfc02883dbceaf90.png) 51 | 52 | ![image](https://img.pub/p/058205dff608609b7d58.png) 53 | 54 | ![image](https://img.pub/p/d4f09719c2a5a2315fc5.png) 55 | 56 | ![image](https://img.pub/p/439520386b4927c91688.png) 57 | 58 | ## 🚀 快速开始 59 | 60 | ### 1. 安装 61 | 62 | ```bash 63 | git clone https://github.com/sligter/DeepGemini.git 64 | cd DeepGemini 65 | uv sync 66 | ``` 67 | 68 | ### 2. 配置 69 | 70 | ```bash 71 | cp .env.example .env 72 | ``` 73 | 74 | 必需的环境变量： 75 | - `ALLOW_API_KEY`：你的 API 访问密钥 76 | - `ALLOW_ORIGINS`：允许的 CORS 来源（逗号分隔或 "*"） 77 | 78 | ### 3. 运行应用 79 | 80 | ```bash 81 | uv run uvicorn app.main:app --host 0.0.0.0 --port 8000 82 | ``` 83 | 84 | 访问 `http://localhost:8000/dashboard` 进入 Web 管理界面。 85 | 86 | ## 🐳 Docker 部署 87 | 88 | ### 使用 Docker Compose（推荐） 89 | 90 | 1. 创建并配置 `.env` 文件： 91 | 92 | ```bash 93 | cp .env.example .env 94 | touch deepgemini.db 95 | echo "" > deepgemini.db 96 | ``` 97 | 98 | 2. 构建并启动容器： 99 | 100 | ```bash 101 | docker-compose up -d 102 | ``` 103 | 104 | 3. 访问 `http://localhost:8000/dashboard` 进入 Web 界面 105 | 106 | ### 直接使用 Docker 107 | 108 | 1. 拉取镜像： 109 | ```bash 110 | docker pull bradleylzh/deepgemini:latest 111 | ``` 112 | 113 | 2. 创建必要文件： 114 | 115 | Linux/Mac 用户： 116 | ```bash 117 | cp .env.example .env 118 | touch deepgemini.db 119 | ``` 120 | 121 | 运行容器 122 | ```bash 123 | docker run -d \ 124 | -p 8000:8000 \ 125 | -v $(pwd)/.env:/app/.env \ 126 | -v $(pwd)/deepgemini.db:/app/deepgemini.db \ 127 | --name deepgemini \ 128 | bradleylzh/deepgemini:latest 129 | ``` 130 | 131 | Windows PowerShell 用户： 132 | 133 | ```powershell 134 | cp .env.example .env 135 | python -c "import sqlite3; sqlite3.connect('deepgemini.db').close()" 136 | ``` 137 | 138 | 运行容器 139 | ```powershell 140 | docker run -d -p 8000:8000 ` 141 | -v ${PWD}\.env:/app/.env ` 142 | -v ${PWD}\deepgemini.db:/app/deepgemini.db ` 143 | --name deepgemini ` 144 | bradleylzh/deepgemini:latest 145 | ``` 146 | 147 | ## 🔧 模型配置 148 | 149 | DeepGemini 支持多种 AI 提供商： 150 | 151 | - **DeepSeek**：先进的推理能力 152 | - **Claude**：精细的文本生成和思考 153 | - **Gemini**：Google 的 AI 模型 154 | - **Grok3**：Grok 的 AI 模型 155 | - **自定义**：添加你自己的提供商集成 156 | 157 | 每个模型可配置： 158 | - API 凭证 159 | - 模型参数（temperature、top_p 等） 160 | - 系统提示词 161 | - 使用类型（推理/执行/两者） 162 | 163 | ## 🔄 中继链配置 164 | 165 | 通过组合模型创建自定义中继链： 166 | 167 | 1. **推理步骤**：初始分析和规划 168 | 2. **执行步骤**：最终响应生成 169 | 3. **自定义步骤**：根据需要添加多个步骤 170 | 171 | ## 👥 多角色讨论 172 | - **角色管理**：创建具有不同性格和技能的 AI 角色 173 | - **讨论组**：组合多个角色形成讨论组 174 | - **多种讨论模式**： 175 | - 一般讨论 176 | - 头脑风暴 177 | - 辩论 178 | - 角色扮演 179 | - SWOT 分析 180 | - 六顶思考帽 181 | - **人类参与**：允许人类加入AI讨论并发言 182 | 183 | ## 🔍 API 兼容性 184 | DeepGemini 提供兼容的API接口，可作为OpenAI API的直接替代品： 185 | 186 | - **/v1/chat/completions**：与OpenAI聊天完成端点兼容 187 | - **/v1/models**：以OpenAI兼容格式列出所有可用模型 188 | - 支持流式响应、工具和其他OpenAI API功能 189 | 190 | ## 🛠 技术栈 191 | 192 | - [FastAPI](https://fastapi.tiangolo.com/)：现代 Web 框架 193 | - [SQLAlchemy](https://www.sqlalchemy.org/)：数据库 ORM 194 | - [Alembic](https://alembic.sqlalchemy.org/)：数据库迁移 195 | - [UV](https://github.com/astral-sh/uv)：快速 Python 包安装器 196 | - [aiohttp](https://docs.aiohttp.org/)：异步 HTTP 客户端 197 | - [deepclaude](https://github.com/getasterisk/deepclaude) 198 | 199 | ## ✨ 鸣谢 200 | 201 | [![Powered by DartNode](https://dartnode.com/branding/DN-Open-Source-sm.png)](https://dartnode.com "Powered by DartNode - Free VPS for Open Source") 202 | 203 | ## 📝 许可证 204 | 205 | 本项目采用 MIT 许可证 - 详见 [LICENSE](LICENSE) 文件。 206 | 207 | ## 🤝 贡献 208 | 209 | 欢迎贡献！请随时提交 Pull Request。 210 | 211 | ## 📬 联系 212 | 213 | 如有问题和支持需求，请在 GitHub 上开启 Issue。 214 | -------------------------------------------------------------------------------- /app/routers/discussions.py: -------------------------------------------------------------------------------- 1 | import traceback 2 | from fastapi import APIRouter, Depends, HTTPException, Body, Request 3 | from fastapi.responses import JSONResponse, StreamingResponse 4 | from sqlalchemy.orm import Session 5 | from typing import Dict, Any 6 | import logging 7 | import json 8 | 9 | from app.models.database import get_db 10 | from app.adapters.meeting_adapter import MeetingAdapter 11 | from app.processors.discussion_processor import DiscussionProcessor 12 | 13 | router = APIRouter( 14 | prefix="/v1/discussions", 15 | tags=["discussions"], 16 | ) 17 | 18 | logger = logging.getLogger(__name__) 19 | 20 | @router.post("/{group_id}/start", response_model=Dict[str, Any]) 21 | def start_discussion( 22 | group_id: int, 23 | data: Dict[str, Any] = Body(...), 24 | db: Session = Depends(get_db) 25 | ): 26 | """启动一个新的讨论""" 27 | adapter = MeetingAdapter(db) 28 | 29 | try: 30 | topic = data.get("topic", "") 31 | if not topic: 32 | raise HTTPException(status_code=400, detail="讨论主题不能为空") 33 | 34 | meeting_id = adapter.start_meeting(group_id, topic) 35 | return {"meeting_id": meeting_id, "message": "讨论已启动"} 36 | except ValueError as e: 37 | raise HTTPException(status_code=400, detail=str(e)) 38 | except Exception as e: 39 | logger.error(f"启动讨论失败: {str(e)}") 40 | raise HTTPException(status_code=500, detail=f"启动讨论失败: {str(e)}") 41 | 42 | @router.post("/stream/{group_id}") 43 | async def stream_discussion_process(group_id: int, request: Request, db: Session = Depends(get_db)): 44 | """开始流式讨论过程""" 45 | processor = DiscussionProcessor(db) 46 | 47 | try: 48 | # 开始会议 49 | meeting_id = processor.start_meeting(group_id) 50 | 51 | # 返回流式响应 52 | return StreamingResponse( 53 | processor._stream_discussion_process(meeting_id), 54 | media_type="text/event-stream" 55 | ) 56 | 57 | except Exception as e: 58 | logger.error(f"启动流式讨论过程时出错: {str(e)}", exc_info=True) 59 | 60 | # 创建错误流 61 | async def error_stream(): 62 | error_data = { 63 | "error": str(e), 64 | "detail": traceback.format_exc() 65 | } 66 | yield f"data: {json.dumps(error_data, ensure_ascii=False)}\n\n" 67 | yield "data: [DONE]\n\n" 68 | 69 | return StreamingResponse(error_stream(), media_type="text/event-stream") 70 | 71 | @router.get("/stream/{meeting_id}") 72 | async def continue_stream_discussion(meeting_id: str, db: Session = Depends(get_db)): 73 | """继续进行流式讨论过程（从当前状态继续）""" 74 | processor = DiscussionProcessor(db) 75 | processor.adapter = MeetingAdapter(db) 76 | # 设置当前会议ID 77 | processor.current_meeting_id = meeting_id 78 | 79 | try: 80 | logger.info(f"继续会议流程: meeting_id={meeting_id}") 81 | 82 | # 返回流式响应，直接使用处理器的_stream_discussion_process方法 83 | return StreamingResponse( 84 | processor._stream_discussion_process(meeting_id), 85 | media_type="text/event-stream" 86 | ) 87 | 88 | except Exception as e: 89 | logger.error(f"继续流式讨论过程时出错: {str(e)}", exc_info=True) 90 | 91 | # 创建错误流 92 | async def error_stream(): 93 | error_data = { 94 | "error": str(e), 95 | "detail": traceback.format_exc() 96 | } 97 | yield f"data: {json.dumps(error_data, ensure_ascii=False)}\n\n" 98 | yield "data: [DONE]\n\n" 99 | 100 | return StreamingResponse(error_stream(), media_type="text/event-stream") 101 | 102 | @router.post("/{meeting_id}/human_input", response_model=Dict[str, Any]) 103 | def submit_human_input( 104 | meeting_id: str, 105 | agent_name: str = Body(...), 106 | message: str = Body(...), 107 | db: Session = Depends(get_db) 108 | ): 109 | """提交人类角色的输入 - 直接使用discussion_processor处理""" 110 | # 使用DiscussionProcessor处理人类输入 111 | processor = DiscussionProcessor(db) 112 | 113 | try: 114 | # 设置当前会议ID 115 | processor.current_meeting_id = meeting_id 116 | 117 | # 如果需要兼容性，初始化adapter 118 | if not processor.adapter: 119 | processor.adapter = MeetingAdapter(db) 120 | 121 | # 使用processor的方法处理人类输入 122 | result = processor.process_human_input(meeting_id, agent_name, message) 123 | return result 124 | except ValueError as e: 125 | # 处理常见错误，如会议不存在或人类智能体不存在 126 | logger.error(f"处理人类输入失败: {str(e)}") 127 | raise HTTPException(status_code=404, detail=str(e)) 128 | except Exception as e: 129 | # 处理其他错误 130 | logger.error(f"处理人类输入时出错: {str(e)}", exc_info=True) 131 | raise HTTPException(status_code=500, detail=f"处理人类输入失败: {str(e)}") 132 | 133 | @router.get("/{group_id}/info", response_model=Dict[str, Any]) 134 | def get_discussion_group_info( 135 | group_id: int, 136 | db: Session = Depends(get_db) 137 | ): 138 | """获取讨论组信息""" 139 | adapter = MeetingAdapter(db) 140 | 141 | try: 142 | group = adapter._load_discussion_group(group_id) 143 | if not group: 144 | raise HTTPException(status_code=404, detail=f"讨论组ID {group_id} 不存在") 145 | 146 | return adapter._group_to_dict(group) 147 | except ValueError as e: 148 | raise HTTPException(status_code=404, detail=str(e)) 149 | except Exception as e: 150 | logger.error(f"获取讨论组信息失败: {str(e)}") 151 | raise HTTPException(status_code=500, detail=f"获取讨论组信息失败: {str(e)}") -------------------------------------------------------------------------------- /app/meeting/meeting/meeting.py: -------------------------------------------------------------------------------- 1 | from datetime import datetime 2 | from typing import List, Dict, Any, Optional 3 | import uuid 4 | import time 5 | 6 | from agents.agent import Agent 7 | from meeting_modes.base_mode import BaseMeetingMode 8 | 9 | class Meeting: 10 | """会议主类，负责管理整个会议流程""" 11 | 12 | def __init__( 13 | self, 14 | topic: str, 15 | mode: BaseMeetingMode, 16 | agents: List[Agent], 17 | meeting_id: Optional[str] = None 18 | ): 19 | self.topic = topic 20 | self.mode = mode 21 | self.agents = agents 22 | self.meeting_id = meeting_id or str(uuid.uuid4()) 23 | self.start_time = None 24 | self.end_time = None 25 | self.meeting_history = [] # 会议历史记录 26 | self.current_round = 0 27 | self.status = "未开始" # 状态：未开始、进行中、已结束 28 | 29 | def start(self): 30 | """开始会议""" 31 | if self.status != "未开始": 32 | raise ValueError(f"会议已经{self.status}，无法再次开始") 33 | 34 | self.start_time = datetime.now() 35 | self.status = "进行中" 36 | self.current_round = 1 37 | 38 | # 记录会议开始的日志 39 | self._log_event("system", f"会议'{self.topic}'开始，模式：{self.mode.name}") 40 | 41 | # 记录参会智能体 42 | agent_names = [agent.name for agent in self.agents] 43 | self._log_event("system", f"参会智能体: {', '.join(agent_names)}") 44 | 45 | def conduct_round(self): 46 | """进行一轮会议""" 47 | if self.status != "进行中": 48 | raise ValueError(f"会议状态为{self.status}，无法进行") 49 | 50 | # 确定发言顺序 51 | speaking_order = self.mode.determine_speaking_order( 52 | [{ 53 | "name": agent.name, 54 | "role": agent.role_description 55 | } for agent in self.agents], 56 | self.current_round 57 | ) 58 | 59 | # 每个智能体轮流发言 60 | for agent_name in speaking_order: 61 | # 获取当前智能体 62 | agent = next((a for a in self.agents if a.name == agent_name), None) 63 | if not agent: 64 | continue 65 | 66 | # 获取当前会议上下文 67 | current_context = self._get_current_context() 68 | 69 | # 获取特定会议模式的提示 70 | mode_specific_prompt = self.mode.get_agent_prompt( 71 | agent.name, 72 | agent.role_description, 73 | self.topic, 74 | self.current_round 75 | ) 76 | 77 | # 记录开始思考的时间点 78 | self._log_event("system", f"{agent.name} 开始思考...", self.current_round) 79 | 80 | # 智能体发言 81 | response = agent.speak( 82 | meeting_topic=self.topic, 83 | meeting_mode=self.mode.name, 84 | current_context=current_context, 85 | mode_specific_prompt=mode_specific_prompt 86 | ) 87 | 88 | # 记录发言到会议历史 89 | self._log_event(agent.name, response) 90 | 91 | # 更新所有其他智能体的会话历史 92 | for other_agent in self.agents: 93 | if other_agent.name != agent.name: 94 | other_agent.update_history(self.meeting_history[-1:]) 95 | 96 | # 适当暂停，确保发言被处理 97 | time.sleep(10) # 暂停1秒 98 | 99 | # 更新轮次 100 | self.current_round += 1 101 | 102 | # 检查会议是否应该结束 103 | if self.mode.should_end_meeting(self.current_round - 1, self.meeting_history): 104 | self.end() 105 | 106 | def end(self): 107 | """结束会议""" 108 | if self.status != "进行中": 109 | return 110 | 111 | self.end_time = datetime.now() 112 | self.status = "已结束" 113 | 114 | # 生成会议总结 115 | summary = self.mode.summarize_meeting(self.topic, self.meeting_history) 116 | self._log_event("system", f"会议总结:\n{summary}") 117 | 118 | # 记录会议结束日志 119 | duration = self.end_time - self.start_time 120 | self._log_event("system", f"会议结束，持续时间: {duration}") 121 | 122 | def _log_event(self, agent: str, content: str, round_num=None): 123 | """记录会议事件""" 124 | # 如果没有指定轮次，则使用当前轮次 125 | if round_num is None: 126 | round_num = self.current_round 127 | 128 | self.meeting_history.append({ 129 | "timestamp": datetime.now().isoformat(), 130 | "agent": agent, 131 | "content": content, 132 | "round": round_num 133 | }) 134 | 135 | def _get_current_context(self) -> str: 136 | """获取当前会议上下文，用于提供给智能体""" 137 | # 获取最近10条记录或全部记录（如果少于10条） 138 | recent_history = self.meeting_history[-10:] if len(self.meeting_history) > 10 else self.meeting_history 139 | 140 | context = f"会议主题: {self.topic}\n当前轮次: {self.current_round}\n\n最近的讨论内容:\n" 141 | for entry in recent_history: 142 | context += f"[{entry['agent']}]: {entry['content']}\n\n" 143 | 144 | return context 145 | 146 | def to_dict(self) -> Dict[str, Any]: 147 | """将会议信息转换为字典，用于保存日志""" 148 | return { 149 | "meeting_id": self.meeting_id, 150 | "topic": self.topic, 151 | "mode": self.mode.name, 152 | "start_time": self.start_time.isoformat() if self.start_time else None, 153 | "end_time": self.end_time.isoformat() if self.end_time else None, 154 | "agents": [{"name": agent.name, "role": agent.role_description} for agent in self.agents], 155 | "history": self.meeting_history, 156 | "status": self.status 157 | } 158 | 159 | def get_current_context(self): 160 | """获取当前会议上下文的公共方法""" 161 | return self._get_current_context() -------------------------------------------------------------------------------- /app/models/database.py: -------------------------------------------------------------------------------- 1 | from sqlalchemy import create_engine, Column, Integer, String, Boolean, Float, JSON, ForeignKey, Text, DateTime, Table 2 | from sqlalchemy.ext.declarative import declarative_base 3 | from sqlalchemy.orm import relationship, sessionmaker 4 | import os 5 | from datetime import datetime 6 | 7 | # Create the database engine 8 | DATABASE_URL = "sqlite:///./deepgemini.db" 9 | engine = create_engine(DATABASE_URL) 10 | SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine) 11 | 12 | Base = declarative_base() 13 | 14 | # 角色与讨论组的多对多关系表 15 | role_discussion_group = Table( 16 | 'role_discussion_group', 17 | Base.metadata, 18 | Column('role_id', Integer, ForeignKey('roles.id')), 19 | Column('discussion_group_id', Integer, ForeignKey('discussion_groups.id')) 20 | ) 21 | 22 | class Model(Base): 23 | __tablename__ = "models" 24 | 25 | id = Column(Integer, primary_key=True, index=True) 26 | name = Column(String, unique=True, index=True) 27 | type = Column(String) # 'reasoning', 'execution', or 'both' 28 | provider = Column(String) # 'anthropic', 'google', etc. 29 | api_key = Column(String) 30 | api_url = Column(String) 31 | model_name = Column(String) 32 | 33 | # Default parameters 34 | temperature = Column(Float, default=0.7) 35 | top_p = Column(Float, default=1.0) 36 | max_tokens = Column(Integer, default=2000) 37 | presence_penalty = Column(Float, default=0.0) 38 | frequency_penalty = Column(Float, default=0.0) 39 | 40 | # Tool configuration 41 | enable_tools = Column(Boolean, server_default='0', nullable=False) 42 | tools = Column(JSON, nullable=True) # 存储工具配置的JSON 43 | tool_choice = Column(JSON, nullable=True) # 存储工具选择配置的JSON 44 | 45 | # Thinking configuration 46 | enable_thinking = Column(Boolean, server_default='0', nullable=False) 47 | thinking_budget_tokens = Column(Integer, server_default='16000', nullable=False) 48 | 49 | # 添加自定义参数字段，确保有默认值 50 | custom_parameters = Column(JSON, nullable=False, server_default='{}') 51 | 52 | # 添加与配置步骤的关系 53 | configuration_steps = relationship("ConfigurationStep", back_populates="model") 54 | 55 | # 添加关系 56 | roles = relationship("Role", back_populates="model") 57 | 58 | class Configuration(Base): 59 | __tablename__ = "configurations" 60 | 61 | id = Column(Integer, primary_key=True, index=True) 62 | name = Column(String, unique=True, index=True) 63 | is_active = Column(Boolean, default=True) 64 | transfer_content = Column(JSON, default=dict) 65 | 66 | # 保留步骤关系 67 | steps = relationship( 68 | "ConfigurationStep", 69 | back_populates="configuration", 70 | cascade="all, delete-orphan", 71 | order_by="ConfigurationStep.step_order" 72 | ) 73 | 74 | class ConfigurationStep(Base): 75 | __tablename__ = "configuration_steps" 76 | 77 | id = Column(Integer, primary_key=True, index=True) 78 | configuration_id = Column(Integer, ForeignKey("configurations.id", ondelete="CASCADE")) 79 | model_id = Column(Integer, ForeignKey("models.id")) 80 | step_type = Column(String) # reasoning 或 execution 81 | step_order = Column(Integer) # 步骤顺序 82 | system_prompt = Column(String, default="") 83 | 84 | # 关系 85 | configuration = relationship("Configuration", back_populates="steps") 86 | model = relationship("Model", back_populates="configuration_steps") 87 | 88 | class Role(Base): 89 | """角色模型""" 90 | __tablename__ = 'roles' 91 | 92 | id = Column(Integer, primary_key=True, index=True) 93 | name = Column(String(100), nullable=False) 94 | description = Column(Text, nullable=True) 95 | model_id = Column(Integer, ForeignKey('models.id'), nullable=False) 96 | personality = Column(Text, nullable=True) 97 | skills = Column(JSON, nullable=True) # 存储技能列表 98 | parameters = Column(JSON, nullable=True) # 存储模型参数 99 | system_prompt = Column(Text, nullable=True) # 系统提示词 100 | created_at = Column(DateTime, default=datetime.now) 101 | updated_at = Column(DateTime, nullable=True) 102 | is_human = Column(Boolean, default=False) # 是否为人类角色 103 | host_role_id = Column(Integer, ForeignKey('roles.id'), nullable=True) # 人类角色寄生的agent角色ID 104 | 105 | # 关系 106 | model = relationship("Model", back_populates="roles") 107 | discussion_groups = relationship("DiscussionGroup", 108 | secondary=role_discussion_group, 109 | back_populates="roles") 110 | host_role = relationship("Role", remote_side=[id], foreign_keys=[host_role_id]) 111 | 112 | class DiscussionGroup(Base): 113 | """讨论组模型""" 114 | __tablename__ = 'discussion_groups' 115 | 116 | id = Column(Integer, primary_key=True, index=True) 117 | name = Column(String(100), nullable=False) 118 | topic = Column(String, nullable=True) 119 | description = Column(Text, nullable=True) 120 | mode = Column(String(50), nullable=False, default="discussion") # 会议模式 121 | max_rounds = Column(Integer, default=3) # 最大轮数 122 | summary_model_id = Column(Integer, ForeignKey('models.id'), nullable=True) # 总结使用的模型 123 | summary_prompt = Column(Text, nullable=True) # 自定义总结提示模板 124 | custom_speaking_order = Column(JSON, nullable=True) # 自定义发言顺序 125 | created_at = Column(DateTime, default=datetime.now) 126 | updated_at = Column(DateTime, nullable=True) 127 | 128 | # 关系 129 | roles = relationship("Role", 130 | secondary=role_discussion_group, 131 | back_populates="discussion_groups") 132 | summary_model = relationship("Model", foreign_keys=[summary_model_id]) 133 | 134 | # Create all tables 135 | def init_db(): 136 | Base.metadata.create_all(bind=engine) 137 | 138 | # Dependency to get database session 139 | def get_db(): 140 | db = SessionLocal() 141 | try: 142 | yield db 143 | finally: 144 | db.close() -------------------------------------------------------------------------------- /app/utils/auth.py: -------------------------------------------------------------------------------- 1 | from fastapi import HTTPException, Header, Depends, status 2 | from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials, APIKeyHeader 3 | from jwt import InvalidTokenError, encode, decode # 从 PyJWT 导入 4 | from passlib.context import CryptContext 5 | from datetime import datetime, timedelta 6 | import os 7 | from dotenv import load_dotenv 8 | from app.utils.logger import logger 9 | from typing import Optional 10 | import json 11 | import secrets 12 | import string 13 | import random 14 | 15 | # 加载 .env 文件 16 | logger.info(f"当前工作目录: {os.getcwd()}") 17 | logger.info("尝试加载.env文件...") 18 | 19 | # 检查.env文件是否存在，不存在则创建 20 | env_path = '.env' 21 | if not os.path.exists(env_path): 22 | # 生成随机JWT密钥 23 | jwt_secret = secrets.token_hex(32) 24 | 25 | # 生成随机API密钥 26 | api_key = 'sk-api-' + ''.join(secrets.choice(string.ascii_lowercase + string.digits) for _ in range(32)) 27 | api_key_id = 1 28 | 29 | logger.info("未找到.env文件，正在创建默认配置...") 30 | with open(env_path, 'w', encoding='utf-8') as f: 31 | f.write(f"JWT_SECRET={jwt_secret}\n") 32 | f.write("ADMIN_USERNAME=admin\n") 33 | f.write("ADMIN_PASSWORD=admin123\n") 34 | f.write(f"ALLOW_API_KEY=[{{\"id\": {api_key_id},\"key\":\"{api_key}\",\"description\":\"默认API密钥\"}}]\n") 35 | logger.info("已创建默认.env文件") 36 | logger.info(f"已生成随机JWT密钥: {jwt_secret[:8]}...") 37 | logger.info(f"已生成随机API密钥: {api_key[:8]}...") 38 | 39 | load_dotenv(override=True) # 添加override=True强制覆盖已存在的环境变量 40 | 41 | # 打印环境变量中的用户名和密码，用于调试 42 | admin_username = os.getenv("ADMIN_USERNAME", "未设置") 43 | admin_password = os.getenv("ADMIN_PASSWORD", "未设置") 44 | logger.info(f"当前管理员用户名: {admin_username}") 45 | logger.info(f"当前管理员密码: {admin_password}") 46 | 47 | # 获取环境变量 48 | try: 49 | api_keys_json = os.getenv('ALLOW_API_KEY', '[]') 50 | # 移除可能的多余空格和换行符 51 | api_keys_json = api_keys_json.strip() 52 | api_keys_data = json.loads(api_keys_json) 53 | # 确保 api_keys_data 是列表 54 | if isinstance(api_keys_data, list): 55 | # 确保我们只使用有效的密钥 56 | ALLOW_API_KEYS = [key_data["key"] for key_data in api_keys_data 57 | if isinstance(key_data, dict) and "key" in key_data] 58 | else: 59 | logger.error("API 密钥数据格式错误，应为 JSON 数组") 60 | ALLOW_API_KEYS = [] 61 | except json.JSONDecodeError: 62 | logger.warning("无法解析 API 密钥 JSON，使用空列表初始化") 63 | ALLOW_API_KEYS = [] 64 | except Exception as e: 65 | logger.error(f"加载 API 密钥时出错: {str(e)}") 66 | ALLOW_API_KEYS = [] 67 | 68 | logger.info(f"已加载 {len(ALLOW_API_KEYS)} 个 API 密钥") 69 | 70 | if not ALLOW_API_KEYS: 71 | logger.warning("没有设置有效的 API 密钥，系统可能无法正常工作") 72 | 73 | # 打印API密钥的前4位用于调试 74 | for key in ALLOW_API_KEYS: 75 | logger.info(f"已加载 API 密钥，前缀为: {key[:8] if len(key) >= 8 else key}") 76 | 77 | # 安全配置 78 | SECRET_KEY = os.getenv("JWT_SECRET", "your-secret-key") 79 | ALGORITHM = "HS256" 80 | ACCESS_TOKEN_EXPIRE_MINUTES = 60 * 24 # 24 小时 81 | 82 | pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto") 83 | security = HTTPBearer() 84 | 85 | # 定义 API 密钥头部 86 | api_key_header = APIKeyHeader(name="Authorization", auto_error=False) 87 | 88 | def verify_password(plain_password, hashed_password): 89 | return pwd_context.verify(plain_password, hashed_password) 90 | 91 | def get_password_hash(password): 92 | return pwd_context.hash(password) 93 | 94 | def create_access_token(data: dict): 95 | to_encode = data.copy() 96 | expire = datetime.utcnow() + timedelta(minutes=ACCESS_TOKEN_EXPIRE_MINUTES) 97 | to_encode.update({"exp": expire}) 98 | encoded_jwt = encode(to_encode, SECRET_KEY, algorithm=ALGORITHM) 99 | return encoded_jwt 100 | 101 | def verify_token(credentials: HTTPAuthorizationCredentials = Depends(security)): 102 | try: 103 | payload = decode(credentials.credentials, SECRET_KEY, algorithms=[ALGORITHM]) 104 | username: str = payload.get("sub") 105 | if username is None: 106 | raise HTTPException(status_code=401, detail="Invalid authentication credentials") 107 | return username 108 | except InvalidTokenError: 109 | raise HTTPException(status_code=401, detail="Invalid authentication credentials") 110 | 111 | def update_admin_credentials(username: str, password: str): 112 | """更新 .env 文件中的管理员凭据""" 113 | env_path = '.env' 114 | with open(env_path, 'r', encoding='utf-8') as file: 115 | lines = file.readlines() 116 | 117 | with open(env_path, 'w', encoding='utf-8') as file: 118 | for line in lines: 119 | if line.startswith('ADMIN_USERNAME='): 120 | file.write(f'ADMIN_USERNAME={username}\n') 121 | elif line.startswith('ADMIN_PASSWORD='): 122 | file.write(f'ADMIN_PASSWORD={password}\n') 123 | else: 124 | file.write(line) 125 | 126 | # 重新加载环境变量 127 | load_dotenv(override=True) 128 | return {"message": "Credentials updated successfully"} 129 | 130 | def get_api_key_header(api_key_header: str = Depends(api_key_header)): 131 | if not api_key_header: 132 | raise HTTPException( 133 | status_code=status.HTTP_401_UNAUTHORIZED, 134 | detail="Missing API key", 135 | headers={"WWW-Authenticate": "Bearer"}, 136 | ) 137 | 138 | # 移除 "Bearer " 前缀（如果有） 139 | if api_key_header.startswith("Bearer "): 140 | api_key_header = api_key_header[7:] 141 | 142 | # 需要返回处理后的API密钥 143 | return api_key_header 144 | 145 | def verify_api_key(api_key: str = Depends(get_api_key_header)): 146 | """验证 API 密钥""" 147 | # 每次都从环境变量获取最新的 API 密钥列表 148 | try: 149 | api_keys_json = os.getenv('ALLOW_API_KEY', '[]') 150 | api_keys_data = json.loads(api_keys_json) 151 | # 确保我们只使用有效的密钥 152 | available_keys = [key_data["key"] for key_data in api_keys_data 153 | if isinstance(key_data, dict) and "key" in key_data] 154 | 155 | logger.debug(f"正在验证 API 密钥: {api_key[:8] if len(api_key) >= 8 else api_key}...") 156 | if available_keys: 157 | logger.debug(f"可用的 API 密钥: {[k[:8] if len(k) >= 8 else k for k in available_keys]}") 158 | else: 159 | logger.warning("没有可用的API密钥配置") 160 | 161 | if api_key not in available_keys: 162 | logger.warning(f"无效的API密钥: {api_key[:8] if len(api_key) >= 8 else api_key}...") 163 | raise HTTPException( 164 | status_code=401, 165 | detail="Invalid API key", 166 | headers={"WWW-Authenticate": "Bearer"}, 167 | ) 168 | return api_key 169 | except json.JSONDecodeError as e: 170 | logger.error(f"解析 API 密钥 JSON 时出错: {e}") 171 | raise HTTPException( 172 | status_code=500, 173 | detail="Internal server error: Invalid API key format", 174 | ) 175 | except Exception as e: 176 | logger.error(f"验证 API 密钥时发生未知错误: {e}") 177 | raise HTTPException( 178 | status_code=500, 179 | detail="Internal server error", 180 | ) 181 | -------------------------------------------------------------------------------- /app/clients/deepseek_client.py: -------------------------------------------------------------------------------- 1 | """DeepSeek API 客户端""" 2 | import json 3 | from typing import AsyncGenerator 4 | from app.utils.logger import logger 5 | from .base_client import BaseClient 6 | 7 | 8 | class DeepSeekClient(BaseClient): 9 | def __init__(self, api_key: str, api_url: str, provider: str = "deepseek", is_origin_reasoning: bool = True): 10 | """初始化 DeepSeek 客户端 11 | 12 | Args: 13 | api_key: API密钥 14 | api_url: API地址 15 | provider: 提供商名称 16 | is_origin_reasoning: 是否为推理模型客户端 17 | """ 18 | super().__init__(api_key, api_url) 19 | self.provider = provider 20 | self.is_origin_reasoning = is_origin_reasoning # 保存标志 21 | 22 | def _process_think_tag_content(self, content: str) -> tuple[bool, str]: 23 | """处理包含 think 标签的内容 24 | 25 | Args: 26 | content: 需要处理的内容字符串 27 | 28 | Returns: 29 | tuple[bool, str]: 30 | bool: 是否检测到完整的 think 标签对 31 | str: 处理后的内容 32 | """ 33 | has_start = "" in content 34 | has_end = "" in content 35 | 36 | if has_start and has_end: 37 | return True, content 38 | elif has_start: 39 | return False, content 40 | elif not has_start and not has_end: 41 | return False, content 42 | else: 43 | return True, content 44 | 45 | async def stream_chat( 46 | self, 47 | messages: list, 48 | model: str = "deepseek-ai/DeepSeek-R1", 49 | **kwargs 50 | ) -> AsyncGenerator[tuple[str, str], None]: 51 | """流式对话""" 52 | headers = { 53 | "Authorization": f"Bearer {self.api_key}", 54 | "Content-Type": "application/json" 55 | } 56 | 57 | # 准备请求数据 58 | data = self._prepare_request_data(messages, model, **kwargs) 59 | 60 | logger.debug(f"DeepSeek 请求数据：{data}") 61 | 62 | first_chunk = True 63 | reasoning_completed = False # 添加标志来追踪推理是否完成 64 | think_content_buffer = "" # 添加缓冲区来收集 think 标签内的内容 65 | in_think_tag = False # 添加标志来追踪是否在 think 标签内 66 | 67 | async for chunk in self._make_request(headers, data): 68 | chunk_str = chunk.decode('utf-8') 69 | 70 | try: 71 | lines = chunk_str.splitlines() 72 | for line in lines: 73 | if line.startswith("data: "): 74 | json_str = line[len("data: "):] 75 | if json_str == "[DONE]": 76 | return 77 | 78 | data = json.loads(json_str) 79 | if data and data.get("choices") and data["choices"][0].get("delta"): 80 | delta = data["choices"][0]["delta"] 81 | if self.is_origin_reasoning: 82 | # 处理推理模型的输出 83 | if delta.get("reasoning_content"): 84 | content = delta["reasoning_content"] 85 | logger.debug(f"提取推理内容：{content}") 86 | yield "reasoning", content 87 | # 处理 content 中的 think 标签内容 88 | elif delta.get("content"): 89 | content = delta["content"] 90 | think_content_buffer += content 91 | 92 | if "" in content and not in_think_tag: 93 | # 开始收集推理内容 94 | logger.debug(f"检测到推理开始标记：{content}") 95 | in_think_tag = True 96 | # 提取 标签后的内容 97 | after_start_think = content.split("")[1] 98 | if after_start_think.strip(): 99 | logger.debug(f"提取 think 标签后的推理内容") 100 | yield "reasoning", after_start_think 101 | 102 | elif in_think_tag and "" not in content: 103 | # 在 think 标签内的内容 104 | logger.debug(f"提取 think 标签内的推理内容") 105 | yield "reasoning", content 106 | 107 | elif in_think_tag and "" in content: 108 | # 推理内容结束 109 | logger.debug(f"检测到推理结束标记：{content}") 110 | # 提取 标签前的内容 111 | before_end_think = content.split("")[0] 112 | if before_end_think.strip(): 113 | logger.debug(f"提取 think 结束标签前的推理内容") 114 | yield "reasoning", before_end_think 115 | in_think_tag = False 116 | think_content_buffer = "" 117 | 118 | elif not kwargs.get("is_last_step"): 119 | return 120 | else: 121 | # 处理执行模型的输出 122 | if delta.get("content"): 123 | content = delta["content"] 124 | if content.strip(): # 只处理非空内容 125 | if first_chunk and delta.get("role"): 126 | # 第一个块可能包含角色信息 127 | first_chunk = False 128 | if content.strip(): 129 | logger.debug(f"执行模型首个响应：{content}") 130 | yield "answer", content 131 | else: 132 | logger.debug(f"执行模型响应：{content}") 133 | yield "answer", content 134 | elif delta.get("role") and first_chunk: 135 | # 处理第一个只包含角色信息的块 136 | first_chunk = False 137 | logger.debug("处理执行模型角色信息") 138 | 139 | except json.JSONDecodeError as e: 140 | logger.error(f"JSON解析错误: {e}") 141 | continue 142 | except Exception as e: 143 | logger.error(f"处理块数据时发生错误: {e}") 144 | continue 145 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 |
2 |

DeepGemini 🌟

3 |

A Flexible Multi-Model Orchestration API with OpenAI Compatibility

4 | 5 | [![FastAPI](https://img.shields.io/badge/FastAPI-005571?style=flat-square&logo=fastapi)](https://fastapi.tiangolo.com) 6 | [![Python 3.11](https://img.shields.io/badge/Python-3.11-blue?style=flat-square&logo=python)](https://www.python.org) 7 | [![OpenAI Compatible](https://img.shields.io/badge/OpenAI-Compatible-412991?style=flat-square&logo=openai)](https://platform.openai.com) 8 | [![License](https://img.shields.io/badge/License-MIT-green.svg?style=flat-square)](LICENSE) 9 | [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/sligter/DeepGemini) 10 |
11 | 12 | [中文](README_zh.md) | [English](#features) 13 | 14 | ## ✨ Features 15 | 16 | - **Multi-Model Orchestration**: Seamlessly combine multiple AI models in customizable workflows 17 | - **Role Management**: Create AI roles with different personalities and skills 18 | - **Discussion Groups**: Combine multiple roles to form discussion groups 19 | - **Multiple Discussion Modes**: 20 | - General Discussion 21 | - Brainstorming 22 | - Debate 23 | - Role-playing 24 | - SWOT Analysis 25 | - Six Thinking Hats 26 | - **Provider Flexibility**: Support for multiple AI providers: 27 | - DeepSeek 28 | - Claude 29 | - Gemini 30 | - Grok3 31 | - OpenAI 32 | - OneAPI 33 | - OpenRouter 34 | - Siliconflow 35 | - **OpenAI Compatible**: Drop-in replacement for OpenAI's API in existing applications 36 | - **Stream Support**: Real-time streaming responses for better user experience 37 | - **Advanced Configuration**: Fine-grained control over model parameters and system prompts 38 | - **Database Integration**: SQLite-based configuration storage with Alembic migrations 39 | - **Web Management UI**: Built-in interface for managing models and configurations 40 | - **Multi-language Support**: English and Chinese interface 41 | - **Human Interaction**: Supports human participation in AI discussions 42 | - **Chat Interface**: Supports online conversations with models, roles, relay chains, and discussion groups 43 | - **Flexible Deployment**: Easy deployment with Docker or local installation 44 | 45 | ## Preview 46 | 47 | ![image](https://img.pub/p/02f96adb71b92d9e8009.png) 48 | 49 | ![image](https://img.pub/p/1ffdc3728b7944caf807.png) 50 | 51 | ![image](https://img.pub/p/9051bfc02883dbceaf90.png) 52 | 53 | ![image](https://img.pub/p/058205dff608609b7d58.png) 54 | 55 | ![image](https://img.pub/p/d4f09719c2a5a2315fc5.png) 56 | 57 | ![image](https://img.pub/p/439520386b4927c91688.png) 58 | 59 | 60 | 61 | ## 🚀 Quick Start 62 | 63 | ### 1. Installation 64 | 65 | ```bash 66 | git clone https://github.com/sligter/DeepGemini.git 67 | cd DeepGemini 68 | uv sync 69 | ``` 70 | ### 2. Configuration 71 | 72 | ```bash 73 | cp .env.example .env 74 | ``` 75 | 76 | 77 | Required environment variables: 78 | - `ALLOW_API_KEY`: Your API access key 79 | - `ALLOW_ORIGINS`: Allowed CORS origins (comma-separated or "*") 80 | 81 | ### 3. Run the Application 82 | 83 | ```bash 84 | uv run uvicorn app.main:app --host 0.0.0.0 --port 8000 85 | ``` 86 | 87 | Visit `http://localhost:8000/dashboard` to access the web management interface. 88 | 89 | 90 | 91 | ## 🐳 Docker Deployment 92 | 93 | ### Using Docker Compose (Recommended) 94 | 95 | 1. Create and configure your `.env` file: 96 | 97 | ```bash 98 | cp .env.example .env 99 | touch deepgemini.db 100 | echo "" > deepgemini.db 101 | ``` 102 | 103 | 2. Build and start the container: 104 | 105 | ```bash 106 | docker-compose up -d 107 | ``` 108 | 109 | 3. Access the web interface at `http://localhost:8000/dashboard` 110 | 111 | ### Using Docker Directly 112 | 113 | 1. Pull the image: 114 | ```bash 115 | docker pull bradleylzh/deepgemini:latest 116 | ``` 117 | 118 | 2. Create necessary files: 119 | 120 | For Linux/Mac: 121 | ```bash 122 | # Create .env file 123 | cp .env.example .env 124 | touch deepgemini.db 125 | ``` 126 | 127 | For Windows PowerShell: 128 | ```powershell 129 | # Create .env file 130 | cp .env.example .env 131 | python -c "import sqlite3; sqlite3.connect('deepgemini.db').close()" 132 | ``` 133 | 134 | 3. Run the container: 135 | 136 | For Linux/Mac: 137 | ```bash 138 | docker run -d \ 139 | -p 8000:8000 \ 140 | -v $(pwd)/.env:/app/.env \ 141 | -v $(pwd)/deepgemini.db:/app/deepgemini.db \ 142 | --name deepgemini \ 143 | bradleylzh/deepgemini:latest 144 | ``` 145 | 146 | For Windows PowerShell: 147 | ```powershell 148 | docker run -d -p 8000:8000 ` 149 | -v ${PWD}\.env:/app/.env ` 150 | -v ${PWD}\deepgemini.db:/app/deepgemini.db ` 151 | --name deepgemini ` 152 | bradleylzh/deepgemini:latest 153 | ``` 154 | 155 | 156 | ## 🔧 Model Configuration 157 | 158 | DeepGemini supports various AI providers: 159 | 160 | - **DeepSeek**: Advanced reasoning capabilities 161 | - **Claude**: Refined text generation and thinking 162 | - **Gemini**: Google's AI model 163 | - **Grok3**: Grok's AI model 164 | - **Custom**: Add your own provider integration 165 | 166 | Each model can be configured with: 167 | - API credentials 168 | - Model parameters (temperature, top_p, tool, etc.) 169 | - System prompts 170 | - Usage type (reasoning/execution/both) 171 | 172 | ## 🔄 Relay Chain Configuration 173 | 174 | Create custom Relay Chain by combining models: 175 | 176 | 1. **Reasoning Step**: Initial analysis and planning 177 | 2. **Execution Step**: Final response generation 178 | 3. **Custom Steps**: Add multiple steps as needed 179 | 180 | ## 👥 Multi-Role Discussion 181 | **Role Management**: Create AI roles with different personalities and skills 182 | - **Discussion Groups**: Combine multiple roles to form discussion groups 183 | - **Multiple Discussion Modes**: 184 | - General Discussion 185 | - Brainstorming 186 | - Debate 187 | - Role-playing 188 | - SWOT Analysis 189 | - Six Thinking Hats 190 | - **Human Participation**: Allow humans to join AI discussions and contribute 191 | 192 | ## 🔍 API Compatibility 193 | DeepGemini provides a compatible API interface that allows it to serve as a drop-in replacement for OpenAI's API: 194 | 195 | - **/v1/chat/completions**: Compatible with OpenAI chat completion endpoint 196 | - **/v1/models**: Lists all available models in OpenAI-compatible format 197 | - Support for streaming responses, tools, and other OpenAI API features 198 | 199 | ## 🛠 Tech Stack 200 | 201 | - [FastAPI](https://fastapi.tiangolo.com/): Modern web framework 202 | - [SQLAlchemy](https://www.sqlalchemy.org/): Database ORM 203 | - [Alembic](https://alembic.sqlalchemy.org/): Database migrations 204 | - [UV](https://github.com/astral-sh/uv): Fast Python package installer 205 | - [aiohttp](https://docs.aiohttp.org/): Async HTTP client 206 | - [deepclaude](https://github.com/getasterisk/deepclaude) 207 | 208 | ## ✨ Acknowledgements 209 | 210 | [![Powered by DartNode](https://dartnode.com/branding/DN-Open-Source-sm.png)](https://dartnode.com "Powered by DartNode - Free VPS for Open Source") 211 | 212 | ## 📝 License 213 | 214 | This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. 215 | 216 | ## 🤝 Contributing 217 | 218 | Contributions are welcome! Please feel free to submit a Pull Request. 219 | 220 | ## 📬 Contact 221 | 222 | For questions and support, please open an issue on GitHub. 223 | -------------------------------------------------------------------------------- /app/clients/grok3_client.py: -------------------------------------------------------------------------------- 1 | """Grok3 API 客户端""" 2 | import json 3 | from typing import AsyncGenerator 4 | from app.utils.logger import logger 5 | from .base_client import BaseClient 6 | 7 | 8 | class Grok3Client(BaseClient): 9 | def __init__(self, api_key: str, api_url: str, is_origin_reasoning: bool = True): 10 | """初始化 Grok3 客户端 11 | 12 | Args: 13 | api_key: API密钥 14 | api_url: API地址 15 | is_origin_reasoning: 是否为推理模型 16 | """ 17 | super().__init__(api_key, api_url) 18 | self.provider = "grok3" 19 | self._current_line = "" # 初始化行缓存 20 | self.is_origin_reasoning = is_origin_reasoning 21 | 22 | async def stream_chat( 23 | self, 24 | messages: list, 25 | model: str = "grok3-reasoner", 26 | stream: bool = True, 27 | **kwargs 28 | ) -> AsyncGenerator[tuple[str, str], None]: 29 | """流式或非流式对话 30 | 31 | Args: 32 | messages: 消息列表 33 | model: 模型名称 34 | stream: 是否使用流式输出 35 | **kwargs: 其他参数 36 | is_last_step: 是否为最后一步 37 | is_first_step: 是否为第一步 38 | 39 | Yields: 40 | tuple[str, str]: (内容类型, 内容) 41 | 内容类型: "reasoning" 或 "content" 42 | 内容: 实际的文本内容 43 | """ 44 | headers = { 45 | "Authorization": f"Bearer {self.api_key}", 46 | "Content-Type": "application/json", 47 | "Accept": "text/event-stream" if stream else "application/json", 48 | } 49 | 50 | # 准备请求数据 51 | data = self._prepare_request_data(messages, model, stream=stream, **kwargs) 52 | 53 | if stream: 54 | # 重置行缓存 55 | self._current_line = "" 56 | reasoning_completed = False # 追踪推理是否完成 57 | 58 | async for chunk in self._make_request(headers, data): 59 | try: 60 | chunk_str = chunk.decode('utf-8') 61 | if not chunk_str.strip(): 62 | continue 63 | 64 | for line in chunk_str.split('\n'): 65 | if line.startswith('data: '): 66 | json_str = line[6:] 67 | if json_str.strip() == '[DONE]': 68 | # 处理最后可能剩余的内容 69 | if self._current_line.strip(): 70 | last_line = self._current_line.strip() 71 | if last_line.startswith('>'): 72 | if self.is_origin_reasoning: 73 | yield "reasoning", last_line[1:].strip() 74 | else: 75 | if not self.is_origin_reasoning or kwargs.get("is_last_step"): 76 | yield "content", last_line 77 | return 78 | 79 | data = json.loads(json_str) 80 | content = data.get('choices', [{}])[0].get('delta', {}).get('content', '') 81 | 82 | if content: 83 | # 追加到当前行 84 | self._current_line += content 85 | 86 | # 处理完整行 87 | while "\n" in self._current_line: 88 | line, self._current_line = self._current_line.split("\n", 1) 89 | line = line.strip() 90 | # 跳过分隔符行 91 | if line == "---": 92 | continue 93 | if line: # 忽略空行 94 | if line.startswith(">"): 95 | if self.is_origin_reasoning: 96 | yield "reasoning", "\n"+line[1:].strip() 97 | else: 98 | # 检测推理内容是否结束 99 | if not reasoning_completed: 100 | reasoning_completed = True 101 | # 如果不是最后一步，直接结束流 102 | if not kwargs.get("is_last_step"): 103 | return 104 | # 如果是最后一步或非推理模型，继续处理 content 105 | if not self.is_origin_reasoning or kwargs.get("is_last_step"): 106 | yield "content", "\n"+line 107 | 108 | # 如果是结束标记，处理最后一行 109 | if data.get("finish_reason") == "stop": 110 | last_line = self._current_line.strip() 111 | if last_line: 112 | if last_line.startswith(">"): 113 | if self.is_origin_reasoning: 114 | yield "reasoning", "\n"+last_line[1:].strip() 115 | else: 116 | if not self.is_origin_reasoning or kwargs.get("is_last_step"): 117 | yield "content", "\n"+last_line 118 | self._current_line = "" 119 | 120 | except json.JSONDecodeError: 121 | continue 122 | except Exception as e: 123 | logger.error(f"处理 Grok3 流式响应时发生错误: {e}") 124 | continue 125 | else: 126 | # 非流式输出处理 127 | async for chunk in self._make_request(headers, data): 128 | try: 129 | response = json.loads(chunk.decode('utf-8')) 130 | content = response.get('choices', [{}])[0].get('message', {}).get('content', '') 131 | 132 | if content: 133 | reasoning_completed = False 134 | # 按行处理内容 135 | for line in content.split('\n'): 136 | line = line.strip() 137 | if line: 138 | if line.startswith('>'): 139 | if self.is_origin_reasoning: 140 | yield "reasoning", "\n"+line[1:].strip() 141 | else: 142 | # 检测推理内容是否结束 143 | if not reasoning_completed: 144 | reasoning_completed = True 145 | # 如果不是最后一步，直接结束流 146 | if not kwargs.get("is_last_step"): 147 | return 148 | # 如果是最后一步或非推理模型，继续处理 content 149 | if not self.is_origin_reasoning or kwargs.get("is_last_step"): 150 | yield "content", "\n"+line 151 | 152 | except json.JSONDecodeError: 153 | continue 154 | except Exception as e: 155 | logger.error(f"处理 Grok3 非流式响应时发生错误: {e}") 156 | continue -------------------------------------------------------------------------------- /app/meeting/agents/human_agent.py: -------------------------------------------------------------------------------- 1 | from typing import Dict, List, Optional, Any, AsyncGenerator 2 | import logging 3 | import asyncio 4 | import time 5 | from app.meeting.agents.agent import Agent 6 | from datetime import datetime 7 | 8 | # 设置日志 9 | logger = logging.getLogger(__name__) 10 | 11 | class HumanAgent(Agent): 12 | """人类智能体，表示会议中的人类参与者""" 13 | 14 | def __init__(self, name: str, role_description: str, personality: Optional[str] = None, skills: Optional[List[str]] = None, 15 | model_params: Dict[str, Any] = None, base_url: str = None, api_key: str = None): 16 | """ 17 | 初始化人类智能体 18 | 19 | 参数: 20 | name: 人类角色名称 21 | role_description: 人类角色描述 22 | personality: 人格特点描述 23 | skills: 技能列表 24 | model_params: 模型参数 25 | base_url: 基础URL 26 | api_key: API密钥 27 | """ 28 | super().__init__(name, role_description, personality, skills, model_params, base_url, api_key) 29 | self.is_human = True 30 | self.human_responses = {} # 存储人类的响应，格式为 {round_id: response} 31 | self.current_round = 0 32 | self.conversation_history = [] # 确保有对话历史属性 33 | self.pending_response = None # 存储等待中的人类响应 34 | self.is_waiting_response = False # 标记是否正在等待响应 35 | self.is_interrupted = False # 标记是否被打断 36 | self.host_role_name = None # 寄生的角色名称 37 | self.response_queue = asyncio.Queue() 38 | self.stream_lock = asyncio.Lock() 39 | self.is_waiting_input = False # 是否等待人类输入 40 | self.pending_message = None # 等待发送的消息 41 | self.input_timeout = 600 # 默认等待人类输入的超时时间（秒） 42 | self.input_start_time = None # 记录开始等待人类输入的时间 43 | 44 | def wait_for_input(self): 45 | """设置为等待人类输入状态，并记录开始时间""" 46 | self.is_waiting_input = True 47 | self.input_start_time = time.time() 48 | logger.info(f"人类智能体 {self.name} 等待输入") 49 | # 通知系统会议暂停，等待人类输入 50 | return True 51 | 52 | def is_waiting_for_input(self): 53 | """检查是否正在等待人类输入""" 54 | # 返回实际的等待状态值 55 | return self.is_waiting_input 56 | 57 | def add_message(self, content: str): 58 | """添加人类消息""" 59 | self.pending_message = content 60 | # 将消息存入human_responses字典，以当前轮次为键 61 | self.human_responses[self.current_round] = content 62 | # 重置等待状态 63 | self.is_waiting_input = False 64 | self.input_start_time = None 65 | # 记录为最后响应 66 | self.last_response = content 67 | logger.info(f"人类智能体 {self.name} 收到消息: {content[:50]}..., 当前轮次: {self.current_round}") 68 | return True 69 | 70 | def get_input_wait_duration(self): 71 | """获取已等待人类输入的时间（秒）""" 72 | if not self.is_waiting_input or not self.input_start_time: 73 | return 0 74 | return time.time() - self.input_start_time 75 | 76 | def has_input_timeout(self): 77 | """检查是否已超过等待人类输入的超时时间""" 78 | wait_duration = self.get_input_wait_duration() 79 | return wait_duration > self.input_timeout if wait_duration > 0 else False 80 | 81 | def generate_response(self, prompt: str, context: List[Dict[str, Any]] = None) -> str: 82 | """ 83 | 生成响应 - 对于人类智能体，返回预先输入的消息或等待输入 84 | 85 | 参数: 86 | prompt: 提示词 87 | context: 上下文消息历史 88 | 89 | 返回: 90 | 消息内容或等待人类输入的提示 91 | """ 92 | # 如果有待发送的消息，返回它 93 | if self.pending_message: 94 | message = self.pending_message 95 | self.pending_message = None 96 | return message 97 | 98 | # 否则设置为等待输入状态并暂停会议 99 | self.wait_for_input() 100 | # 特殊标志，表示需要等待人类输入 101 | return f"[WAITING_FOR_HUMAN_INPUT:{self.name}]" 102 | 103 | async def generate_response_stream(self, prompt: str, context: List[Dict[str, Any]] = None): 104 | """流式生成响应 - 对于人类智能体，返回等待人类输入的特殊标记""" 105 | # 检查是否已有待处理的消息 106 | if self.pending_message: 107 | message = self.pending_message 108 | self.pending_message = None 109 | yield message 110 | return 111 | 112 | # 设置等待人类输入状态 113 | self.wait_for_input() 114 | # 返回特殊标记，表示需要等待人类输入 115 | yield f"[WAITING_FOR_HUMAN_INPUT:{self.name}]" 116 | 117 | async def add_response_chunk(self, chunk: str) -> None: 118 | """添加响应块到流中""" 119 | await self.response_queue.put(chunk) 120 | 121 | async def finish_response(self) -> None: 122 | """标记响应完成""" 123 | await self.response_queue.put("[END]") 124 | self.is_waiting_response = False 125 | 126 | def set_human_response(self, response: str) -> None: 127 | """设置人类用户的完整响应""" 128 | logger.info(f"收到人类角色 {self.name} 的响应: {response[:50]}...") 129 | self.pending_response = response 130 | self.last_response = response 131 | self.is_waiting_response = False 132 | self.is_waiting_input = False 133 | self.input_start_time = None 134 | 135 | async def interrupt(self, message: str) -> None: 136 | """中断当前会议，插入人类消息""" 137 | async with self.stream_lock: 138 | logger.info(f"人类角色 {self.name} 打断会议: {message[:50]}...") 139 | self.pending_response = message 140 | self.is_interrupted = True 141 | self.is_waiting_response = False 142 | self.is_waiting_input = False 143 | self.input_start_time = None 144 | # 清空现有队列 145 | while not self.response_queue.empty(): 146 | try: 147 | self.response_queue.get_nowait() 148 | except asyncio.QueueEmpty: 149 | break 150 | # 添加中断消息 151 | await self.add_response_chunk(message) 152 | await self.finish_response() 153 | 154 | def clear_interrupt(self) -> None: 155 | """清除中断状态""" 156 | self.is_interrupted = False 157 | 158 | def is_interrupting(self) -> bool: 159 | """检查是否正在进行中断""" 160 | return self.is_interrupted 161 | 162 | def get_current_round(self) -> int: 163 | """获取当前轮次""" 164 | return self.current_round 165 | 166 | def set_current_round(self, round_id: int) -> None: 167 | """设置当前轮次""" 168 | self.current_round = round_id 169 | 170 | def response(self, meeting_id: str, round_id: int, context: str) -> str: 171 | """返回人类参与者的响应，如果没有输入则暂停会议""" 172 | try: 173 | # 更新当前轮次 174 | self.current_round = round_id 175 | 176 | # 如果已经有人类的回应，直接返回 177 | if round_id in self.human_responses and self.human_responses[round_id]: 178 | response = self.human_responses[round_id] 179 | 180 | # 更新对话历史 181 | if hasattr(self, 'conversation_history'): 182 | self.conversation_history.append({"role": "user", "content": context}) 183 | self.conversation_history.append({"role": "assistant", "content": response}) 184 | 185 | return response 186 | 187 | # 没有回应，设置等待状态 188 | self.wait_for_input() 189 | 190 | # 返回特殊格式的等待消息 191 | return f"[WAITING_FOR_HUMAN_INPUT:{self.name}]" 192 | except Exception as e: 193 | logger.error(f"人类智能体响应出错: {str(e)}", exc_info=True) 194 | return f"[错误] 无法获取 {self.name} 的响应。错误: {str(e)}" 195 | 196 | def to_dict(self) -> Dict[str, Any]: 197 | """转换为字典表示""" 198 | return { 199 | "name": self.name, 200 | "role_description": self.role_description, 201 | "personality": self.personality, 202 | "skills": self.skills, 203 | "is_human": True, 204 | "is_waiting_input": self.is_waiting_input, 205 | "input_wait_duration": self.get_input_wait_duration() if self.is_waiting_input else 0 206 | } -------------------------------------------------------------------------------- /app/clients/claude_client.py: -------------------------------------------------------------------------------- 1 | """Claude API 客户端""" 2 | import json 3 | from typing import AsyncGenerator, Optional, List, Dict 4 | from app.utils.logger import logger 5 | from .base_client import BaseClient 6 | 7 | 8 | class ClaudeClient(BaseClient): 9 | def __init__(self, api_key: str, api_url: str = "https://api.anthropic.com/v1/messages", provider: str = "anthropic", is_origin_reasoning: bool = False): 10 | """初始化 Claude 客户端 11 | 12 | Args: 13 | api_key: Claude API密钥 14 | api_url: Claude API地址 15 | provider: API提供商 16 | is_origin_reasoning: 是否为原始推理模型 17 | """ 18 | super().__init__(api_key, api_url) 19 | self.provider = provider 20 | self.is_origin_reasoning = is_origin_reasoning 21 | self.reasoning_content = [] 22 | logger.debug(f"ClaudeClient url: {self.api_url}") 23 | 24 | async def stream_chat( 25 | self, 26 | messages: list, 27 | model_arg: tuple[float, float, float, float] = (0.7, 0.7, 0, 0), 28 | model: str = "claude-3-5-sonnet-20240620", 29 | stream: bool = True, 30 | tools: Optional[List[Dict]] = None, 31 | tool_choice: Optional[Dict] = None, 32 | enable_thinking: bool = False, 33 | thinking_budget_tokens: int = 16000, 34 | **kwargs 35 | ) -> AsyncGenerator[tuple[str, str], None]: 36 | """流式或非流式对话 37 | 38 | Args: 39 | messages: 消息列表 40 | model_arg: 模型参数元组[temperature, top_p, presence_penalty, frequency_penalty] 41 | model: 模型名称 42 | stream: 是否使用流式输出 43 | tools: 工具配置列表 44 | tool_choice: 工具选择配置 45 | enable_thinking: 是否启用扩展思考 46 | thinking_budget_tokens: 思考token预算 47 | """ 48 | temperature, top_p, presence_penalty, frequency_penalty = model_arg 49 | 50 | # 准备基础请求数据 51 | data = self._prepare_request_data( 52 | messages=messages, 53 | model=model, 54 | stream=stream, 55 | temperature=temperature, 56 | top_p=top_p, 57 | presence_penalty=presence_penalty, 58 | frequency_penalty=frequency_penalty, 59 | **kwargs 60 | ) 61 | 62 | # 添加 Claude 特定参数 63 | if tools: 64 | data["tools"] = tools 65 | if tool_choice: 66 | data["tool_choice"] = tool_choice 67 | if enable_thinking: 68 | data["thinking_budget_tokens"] = thinking_budget_tokens 69 | 70 | if self.provider == "anthropic": 71 | headers = { 72 | "Authorization": f"Bearer {self.api_key}", 73 | "Content-Type": "application/json", 74 | "Accept": "text/event-stream" if stream else "application/json", 75 | } 76 | 77 | # 用于收集推理内容 78 | self.reasoning_content = [] 79 | in_thinking = False 80 | 81 | async for chunk in self._make_request(headers, data): 82 | chunk_str = chunk.decode('utf-8') 83 | if not chunk_str.strip(): 84 | continue 85 | 86 | for line in chunk_str.split('\n'): 87 | if line.startswith('data: '): 88 | json_str = line[6:] 89 | if json_str.strip() == '[DONE]': 90 | return 91 | 92 | try: 93 | chunk_data = json.loads(json_str) 94 | logger.debug(f"chunk_data: {chunk_data}") 95 | 96 | # 处理新的响应格式 97 | if 'choices' in chunk_data: 98 | delta = chunk_data['choices'][0].get('delta', {}) 99 | content = delta.get('content', '') 100 | 101 | if content: 102 | # 处理推理内容 103 | if '' in content: 104 | in_thinking = True 105 | content = content.replace('', '').strip() 106 | 107 | if '' in content: 108 | in_thinking = False 109 | content = content.replace('', '').strip() 110 | if content and self.is_origin_reasoning: 111 | yield "reasoning_content", content 112 | return 113 | 114 | # 如果在推理块内且是推理模型，输出推理内容 115 | if in_thinking and self.is_origin_reasoning: 116 | if content.strip(): 117 | self.reasoning_content.append(content) 118 | yield "reasoning_content", content 119 | # 如果不是推理模型，直接输出内容 120 | elif not self.is_origin_reasoning and content.strip(): 121 | yield "answer", content 122 | 123 | # 处理旧的响应格式 124 | elif chunk_data.get('type') == 'content_block_delta': 125 | delta = chunk_data.get('delta', {}) 126 | 127 | if delta.get('type') == 'thinking_delta': 128 | thinking = delta.get('thinking', '') 129 | if thinking: 130 | yield "thinking", thinking 131 | 132 | elif delta.get('type') == 'tool_use': 133 | tool_content = json.dumps(delta.get('input', {})) 134 | if tool_content: 135 | yield "tool_use", tool_content 136 | 137 | elif delta.get('type') == 'text_delta': 138 | content = delta.get('text', '') 139 | if content: 140 | # 处理推理内容 141 | if '' in content: 142 | in_thinking = True 143 | content = content.replace('', '').strip() 144 | 145 | if '' in content: 146 | in_thinking = False 147 | content = content.replace('', '').strip() 148 | if content and self.is_origin_reasoning: 149 | yield "reasoning_content", content 150 | return 151 | 152 | # 如果在推理块内且是推理模型，输出推理内容 153 | if in_thinking and self.is_origin_reasoning: 154 | if content.strip(): 155 | self.reasoning_content.append(content) 156 | yield "reasoning_content", content 157 | # 如果不是推理模型，直接输出内容 158 | elif not self.is_origin_reasoning and content.strip(): 159 | yield "answer", content 160 | 161 | except json.JSONDecodeError: 162 | continue 163 | else: 164 | raise ValueError(f"不支持的Claude Provider: {self.provider}") 165 | -------------------------------------------------------------------------------- /app/clients/gemini_client.py: -------------------------------------------------------------------------------- 1 | """Gemini API 客户端""" 2 | import json 3 | from typing import AsyncGenerator 4 | import re 5 | from urllib.parse import urlparse, parse_qs 6 | from app.utils.logger import logger 7 | from .base_client import BaseClient 8 | 9 | class GeminiClient(BaseClient): 10 | def __init__(self, api_key: str, api_url: str = "https://generativelanguage.googleapis.com/v1beta/models/gemini-pro:generateContent"): 11 | """初始化 Gemini 客户端 12 | 13 | Args: 14 | api_key: Gemini API密钥 15 | api_url: Gemini API地址 16 | """ 17 | super().__init__(api_key, api_url) 18 | self.provider = "google" 19 | 20 | async def stream_chat( 21 | self, 22 | messages: list, 23 | model: str = "gemini-pro", 24 | stream: bool = True, 25 | **kwargs 26 | ) -> AsyncGenerator[tuple[str, str], None]: 27 | """流式或非流式对话 28 | 29 | Args: 30 | messages: 消息列表 31 | model: 模型名称 32 | stream: 是否使用流式输出 33 | **kwargs: 其他参数，包括自定义参数 34 | 35 | Yields: 36 | tuple[str, str]: (内容类型, 内容) 37 | """ 38 | headers = { 39 | "Content-Type": "application/json" 40 | } 41 | 42 | # 准备基础请求数据 43 | data = self._prepare_request_data(messages, model, stream=stream, **kwargs) 44 | 45 | # 转换为 Gemini 格式 46 | gemini_data = { 47 | "contents": [ 48 | { 49 | "role": "user" if msg["role"] == "user" else "model", 50 | "parts": [{"text": msg["content"]}] 51 | } 52 | for msg in messages 53 | ], 54 | "generationConfig": { 55 | "temperature": data.get("temperature", 0.7), 56 | "topP": data.get("top_p", 1.0), 57 | "maxOutputTokens": data.get("max_tokens", 2000) 58 | } 59 | } 60 | 61 | # 添加自定义参数 62 | custom_parameters = kwargs.get("custom_parameters", {}) 63 | if custom_parameters: 64 | if "generationConfig" in custom_parameters: 65 | gemini_data["generationConfig"].update(custom_parameters["generationConfig"]) 66 | if "safetySettings" in custom_parameters: 67 | gemini_data["safetySettings"] = custom_parameters["safetySettings"] 68 | 69 | # logger.debug(f"Gemini 请求数据: {gemini_data}") 70 | 71 | # 构建正确的URL 72 | base_url = self.api_url 73 | 74 | # 如果基础URL仅仅是域名，构建完整URL 75 | if base_url == "https://generativelanguage.googleapis.com" or not "/models/" in base_url: 76 | # 构建完整的API路径 77 | api_version = "v1beta" 78 | operation = "streamGenerateContent" if stream else "generateContent" 79 | 80 | # 移除尾部斜杠（如果有） 81 | base_url = base_url.rstrip('/') 82 | 83 | # 构建完整URL 84 | final_url = f"{base_url}/{api_version}/models/{model}:{operation}?key={self.api_key}" 85 | 86 | # 添加SSE参数（如果是流式请求） 87 | if stream: 88 | final_url += "&alt=sse" 89 | else: 90 | # 处理已经包含模型和操作的复杂URL 91 | # 从URL中提取模型名称，如果有的话替换为当前使用的模型 92 | model_in_url = re.search(r'models/([^/:]+)', base_url) 93 | if model_in_url: 94 | base_url = base_url.replace(model_in_url.group(1), model) 95 | 96 | # 确保使用正确的端点 97 | if stream: 98 | if ':generateContent' in base_url: 99 | base_url = base_url.replace(':generateContent', ':streamGenerateContent') 100 | elif ':streamGenerateContent' not in base_url: 101 | # 如果URL没有指定操作，添加streamGenerateContent 102 | if base_url.endswith('/'): 103 | base_url += 'streamGenerateContent' 104 | else: 105 | base_url += ':streamGenerateContent' 106 | 107 | # 添加查询参数 108 | parsed_url = urlparse(base_url) 109 | query_params = parse_qs(parsed_url.query) 110 | 111 | # 构建最终URL 112 | final_url = base_url 113 | if '?' not in final_url: 114 | final_url += '?' 115 | elif not final_url.endswith('?'): 116 | final_url += '&' 117 | 118 | # 添加必要的参数 119 | if stream and 'alt' not in query_params: 120 | final_url += 'alt=sse&' 121 | 122 | if 'key' not in query_params: 123 | final_url += f'key={self.api_key}' 124 | 125 | logger.debug(f"Gemini 最终请求URL: {final_url}") 126 | 127 | if stream: 128 | async for chunk in self._make_request(headers, gemini_data, final_url): 129 | try: 130 | chunk_str = chunk.decode('utf-8') 131 | if not chunk_str.strip(): 132 | continue 133 | 134 | # 处理当前chunk中的每一行 135 | for line in chunk_str.split('\n'): 136 | line = line.strip() 137 | if not line: 138 | continue 139 | 140 | if line.startswith('data: '): 141 | json_str = line[6:] 142 | if json_str.strip() == '[DONE]': 143 | logger.debug("收到流式传输结束标记 [DONE]") 144 | return 145 | 146 | try: 147 | data = json.loads(json_str) 148 | if data.get("candidates"): 149 | # 获取文本内容 150 | candidate = data["candidates"][0] 151 | content = candidate.get("content", {}) 152 | parts = content.get("parts", []) 153 | 154 | for part in parts: 155 | text = part.get("text", "") 156 | if text: 157 | logger.debug(f"流式响应片段: {text[:30]}...") 158 | yield "answer", text 159 | except json.JSONDecodeError as je: 160 | logger.warning(f"JSON解析错误: {je}, 原始数据: {json_str[:100]}") 161 | except Exception as e: 162 | logger.error(f"处理SSE数据时出错: {e}") 163 | except Exception as e: 164 | logger.error(f"处理 Gemini 流式响应时发生错误: {str(e)}") 165 | continue 166 | else: 167 | # 非流式请求处理 168 | full_response = "" 169 | async for chunk in self._make_request(headers, gemini_data, final_url): 170 | try: 171 | chunk_str = chunk.decode('utf-8') 172 | full_response += chunk_str 173 | except Exception as e: 174 | logger.error(f"处理 Gemini 非流式响应时发生错误: {e}") 175 | 176 | try: 177 | # 尝试解析完整响应 178 | response = json.loads(full_response) 179 | if response.get("candidates"): 180 | content = response["candidates"][0].get("content", {}) 181 | parts = content.get("parts", []) 182 | 183 | result_text = "" 184 | for part in parts: 185 | text = part.get("text", "") 186 | result_text += text 187 | 188 | if result_text: 189 | yield "answer", result_text 190 | except json.JSONDecodeError: 191 | logger.error(f"非流式响应JSON解析失败: {full_response[:200]}") 192 | except Exception as e: 193 | logger.error(f"处理非流式响应时发生错误: {e}") -------------------------------------------------------------------------------- /app/static/login.js: -------------------------------------------------------------------------------- 1 | async function handleLogin(event) { 2 | event.preventDefault(); 3 | 4 | const form = document.getElementById('loginForm'); 5 | const alert = document.getElementById('loginAlert'); 6 | const alertMessage = document.getElementById('alertMessage'); 7 | const loginButton = document.getElementById('loginButton'); 8 | 9 | // 隐藏之前的错误信息 10 | alert.style.display = 'none'; 11 | 12 | const formData = new FormData(form); 13 | const data = Object.fromEntries(formData.entries()); 14 | 15 | // 验证表单 16 | const username = data.username.trim(); 17 | const password = data.password; 18 | 19 | if (username === '' || password === '') { 20 | showAlert('Please enter both username and password'); 21 | return; 22 | } 23 | 24 | // 禁用按钮并显示加载动画 25 | loginButton.disabled = true; 26 | loginButton.innerHTML = ' Signing in...'; 27 | 28 | try { 29 | const response = await fetch('/v1/login', { 30 | method: 'POST', 31 | headers: { 32 | 'Content-Type': 'application/json' 33 | }, 34 | body: JSON.stringify(data) 35 | }); 36 | 37 | if (!response.ok) { 38 | throw new Error('Invalid credentials'); 39 | } 40 | 41 | const result = await response.json(); 42 | localStorage.setItem('access_token', result.access_token); 43 | 44 | // 添加过渡动画 45 | document.body.style.opacity = '0'; 46 | setTimeout(() => { 47 | window.location.href = '/static/index.html'; 48 | }, 300); 49 | } catch (error) { 50 | // 恢复按钮状态 51 | loginButton.disabled = false; 52 | loginButton.innerHTML = ' Sign In'; 53 | 54 | // 显示错误信息 55 | showAlert(error.message); 56 | 57 | // 添加抖动动画 58 | form.classList.add('shake'); 59 | setTimeout(() => { 60 | form.classList.remove('shake'); 61 | }, 500); 62 | } 63 | } 64 | 65 | // 显示错误提示 66 | function showAlert(message) { 67 | const alertMessage = document.getElementById('alertMessage'); 68 | const alertBox = document.getElementById('loginAlert'); 69 | 70 | alertMessage.textContent = message; 71 | alertBox.style.display = 'block'; 72 | 73 | setTimeout(function() { 74 | alertBox.style.display = 'none'; 75 | }, 5000); 76 | } 77 | 78 | // 检查是否已登录 79 | document.addEventListener('DOMContentLoaded', function() { 80 | const token = localStorage.getItem('access_token'); 81 | if (token) { 82 | window.location.href = '/static/index.html'; 83 | } 84 | 85 | // 添加页面淡入效果 86 | document.body.style.opacity = '0'; 87 | setTimeout(() => { 88 | document.body.style.opacity = '1'; 89 | }, 100); 90 | 91 | // 获取DOM元素 92 | const loginForm = document.getElementById('loginForm'); 93 | const togglePassword = document.getElementById('togglePassword'); 94 | const passwordInput = document.getElementById('password'); 95 | const loginButton = document.getElementById('loginButton'); 96 | const passwordStrength = document.getElementById('passwordStrength'); 97 | const passwordStrengthMeter = document.getElementById('passwordStrengthMeter'); 98 | 99 | // 密码可见性切换 100 | if (togglePassword) { 101 | togglePassword.addEventListener('click', function() { 102 | const type = passwordInput.getAttribute('type') === 'password' ? 'text' : 'password'; 103 | passwordInput.setAttribute('type', type); 104 | 105 | // 切换图标 106 | this.querySelector('i').classList.toggle('fa-eye'); 107 | this.querySelector('i').classList.toggle('fa-eye-slash'); 108 | }); 109 | } 110 | 111 | // 按钮点击波纹效果 112 | if (loginButton) { 113 | loginButton.addEventListener('click', function(e) { 114 | // 确保点击事件发生在按钮上而不是其子元素上 115 | if (e.target !== this) return; 116 | 117 | const x = e.clientX - e.target.getBoundingClientRect().left; 118 | const y = e.clientY - e.target.getBoundingClientRect().top; 119 | 120 | const ripples = document.createElement('span'); 121 | ripples.classList.add('btn-ripple'); 122 | ripples.style.left = x + 'px'; 123 | ripples.style.top = y + 'px'; 124 | 125 | this.appendChild(ripples); 126 | 127 | setTimeout(() => { 128 | ripples.remove(); 129 | }, 600); 130 | }); 131 | } 132 | 133 | // 密码强度检测 134 | if (passwordInput && passwordStrength && passwordStrengthMeter) { 135 | passwordInput.addEventListener('input', function() { 136 | const password = this.value; 137 | 138 | if (password.length > 0) { 139 | passwordStrength.style.display = 'block'; 140 | 141 | // 简单的密码强度检查 142 | let strength = 0; 143 | 144 | // 长度检查 145 | if (password.length >= 8) strength += 1; 146 | 147 | // 包含数字 148 | if (/\d/.test(password)) strength += 1; 149 | 150 | // 包含特殊字符 151 | if (/[!@#$%^&*]/.test(password)) strength += 1; 152 | 153 | // 包含大小写字母 154 | if (/[A-Z]/.test(password) && /[a-z]/.test(password)) strength += 1; 155 | 156 | // 更新强度指示器 157 | passwordStrengthMeter.className = ''; 158 | 159 | if (strength === 0) { 160 | passwordStrengthMeter.classList.add('strength-weak'); 161 | } else if (strength === 1 || strength === 2) { 162 | passwordStrengthMeter.classList.add('strength-medium'); 163 | } else if (strength === 3) { 164 | passwordStrengthMeter.classList.add('strength-good'); 165 | } else { 166 | passwordStrengthMeter.classList.add('strength-strong'); 167 | } 168 | } else { 169 | passwordStrength.style.display = 'none'; 170 | } 171 | }); 172 | } 173 | 174 | // 表单提交处理 175 | if (loginForm) { 176 | loginForm.addEventListener('submit', handleLogin); 177 | } 178 | 179 | // 自动聚焦用户名字段 180 | const usernameInput = document.getElementById('username'); 181 | if (usernameInput && usernameInput.value.trim() === '') { 182 | usernameInput.focus(); 183 | } 184 | }); 185 | 186 | // 添加动画样式 187 | const style = document.createElement('style'); 188 | style.textContent = ` 189 | body { 190 | transition: opacity 0.3s ease; 191 | } 192 | 193 | @keyframes shake { 194 | 0%, 100% { transform: translateX(0); } 195 | 25% { transform: translateX(-10px); } 196 | 75% { transform: translateX(10px); } 197 | } 198 | 199 | .shake { 200 | animation: shake 0.5s ease-in-out; 201 | } 202 | 203 | .btn-ripple { 204 | position: absolute; 205 | border-radius: 50%; 206 | background: rgba(255, 255, 255, 0.3); 207 | transform: scale(0); 208 | animation: ripple 0.6s linear; 209 | } 210 | 211 | @keyframes ripple { 212 | to { 213 | transform: scale(2.5); 214 | opacity: 0; 215 | } 216 | } 217 | 218 | /* 密码强度指示器样式 */ 219 | .password-strength { 220 | height: 4px; 221 | border-radius: 2px; 222 | margin-top: 0.5rem; 223 | display: none; 224 | overflow: hidden; 225 | } 226 | 227 | .password-strength-meter { 228 | height: 100%; 229 | width: 0%; 230 | transition: width 0.3s ease, background-color 0.3s ease; 231 | } 232 | 233 | .strength-weak { background-color: #e74c3c; width: 25%; } 234 | .strength-medium { background-color: #f39c12; width: 50%; } 235 | .strength-good { background-color: #3498db; width: 75%; } 236 | .strength-strong { background-color: #2ecc71; width: 100%; } 237 | `; 238 | 239 | document.head.appendChild(style); -------------------------------------------------------------------------------- /app/models/multi_step_collaboration.py: -------------------------------------------------------------------------------- 1 | from typing import List, Dict, AsyncGenerator 2 | import asyncio 3 | import json 4 | import time 5 | from app.utils.logger import logger 6 | from app.clients import DeepSeekClient, ClaudeClient, GeminiClient 7 | from app.clients.uni_client import UniClient 8 | from app.clients.openai_client import OpenAIClient 9 | 10 | class MultiStepModelCollaboration: 11 | """处理多步骤模型协作的类""" 12 | 13 | def __init__(self, steps: List[Dict]): 14 | """初始化多步骤协作处理器 15 | 16 | Args: 17 | steps: 步骤列表，每个步骤包含: 18 | - model: 数据库模型对象 19 | - step_type: 步骤类型 (reasoning/execution) 20 | - system_prompt: 系统提示词 21 | """ 22 | self.steps = steps 23 | self.clients = [] 24 | 25 | # 检查是否为单模型情况 26 | self.is_single_model = len(steps) == 1 27 | if self.is_single_model: 28 | self.uni_client = UniClient.create_client(steps[0]['model']) 29 | 30 | # 初始化每个步骤的客户端 31 | for step in steps: 32 | model = step['model'] 33 | client = self._init_client( 34 | model.provider, 35 | model.api_key, 36 | model.api_url, 37 | step['step_type'] == 'reasoning' 38 | ) 39 | self.clients.append({ 40 | 'client': client, 41 | 'model_name': model.model_name, 42 | 'temperature': model.temperature, 43 | 'max_tokens': model.max_tokens, 44 | 'top_p': model.top_p, 45 | 'frequency_penalty': model.frequency_penalty, 46 | 'presence_penalty': model.presence_penalty, 47 | 'step_type': step['step_type'], 48 | 'system_prompt': step['system_prompt'], 49 | 'tools': model.tools, 50 | 'tool_choice': model.tool_choice, 51 | 'enable_thinking': model.enable_thinking, 52 | 'thinking_budget_tokens': model.thinking_budget_tokens 53 | }) 54 | 55 | def _init_client(self, provider: str, api_key: str, api_url: str, is_reasoning: bool): 56 | """初始化对应的客户端""" 57 | try: 58 | # 根据提供商类型初始化对应的客户端 59 | if provider == "deepseek": 60 | return DeepSeekClient(api_key, api_url, is_origin_reasoning=is_reasoning) 61 | elif provider == "google": 62 | # 对于多步骤处理使用专用客户端 63 | if not self.is_single_model: 64 | return GeminiClient(api_key, api_url) 65 | # 对于单模型处理，UniClient会处理 66 | return None 67 | elif provider == "anthropic": 68 | return ClaudeClient(api_key, api_url, is_origin_reasoning=is_reasoning) 69 | elif provider == "grok3": 70 | from app.clients import Grok3Client 71 | return Grok3Client(api_key, api_url, is_origin_reasoning=is_reasoning) 72 | elif provider in ["oneapi", "openrouter", "openai-completion"]: 73 | from app.clients import OpenAIClient 74 | return OpenAIClient(api_key, api_url) 75 | elif provider == "腾讯云": 76 | # 腾讯云使用与 DeepSeek 相同的客户端 77 | return DeepSeekClient(api_key, api_url, provider="腾讯云", is_origin_reasoning=is_reasoning) 78 | else: 79 | raise ValueError(f"Unsupported provider: {provider}") 80 | except Exception as e: 81 | logger.error(f"初始化客户端时发生错误: {e}") 82 | raise 83 | 84 | async def process_with_stream( 85 | self, 86 | messages: list 87 | ) -> AsyncGenerator[bytes, None]: 88 | """处理多步骤流式输出 89 | 90 | Args: 91 | messages: 初始消息列表 92 | 93 | Yields: 94 | bytes: 流式响应数据 95 | """ 96 | chat_id = f"chatcmpl-{hex(int(time.time() * 1000))[2:]}" 97 | created_time = int(time.time()) 98 | 99 | current_messages = messages.copy() 100 | previous_result = "" 101 | 102 | # 如果是单模型，直接使用通用客户端 103 | if self.is_single_model: 104 | step = self.steps[0] 105 | async for chunk in self.uni_client.generate_stream( 106 | messages=messages, 107 | system_prompt=step.get('system_prompt') 108 | ): 109 | yield chunk 110 | return 111 | 112 | for idx, client_info in enumerate(self.clients): 113 | client = client_info['client'] 114 | step_type = client_info['step_type'] 115 | system_prompt = client_info['system_prompt'] 116 | is_last_step = idx == len(self.clients) - 1 117 | is_first_step = idx == 0 118 | 119 | # 添加系统提示词 120 | if system_prompt: 121 | current_messages = self._add_system_prompt(current_messages, system_prompt) 122 | # logger.debug(f"current_messages: {current_messages}") 123 | # 如果不是第一步，添加前一步的结果到提示中 124 | if idx > 0: 125 | current_messages = self._add_previous_step_result( 126 | current_messages, 127 | previous_result, 128 | step_type 129 | ) 130 | 131 | # 收集当前步骤的输出 132 | current_output = [] 133 | async for content_type, content in client.stream_chat( 134 | messages=current_messages, 135 | model=client_info['model_name'], 136 | temperature=client_info['temperature'], 137 | max_tokens=client_info['max_tokens'], 138 | top_p=client_info['top_p'], 139 | frequency_penalty=client_info['frequency_penalty'], 140 | presence_penalty=client_info['presence_penalty'], 141 | is_last_step=is_last_step, 142 | is_first_step=is_first_step, 143 | tools=client_info.get('tools'), 144 | tool_choice=client_info.get('tool_choice'), 145 | enable_thinking=client_info.get('enable_thinking', False), 146 | thinking_budget_tokens=client_info.get('thinking_budget_tokens', 16000) 147 | ): 148 | current_output.append(content) 149 | 150 | # 构建响应 151 | delta = { 152 | "role": "assistant", 153 | "thinking_content": content if content_type == "thinking" else "", 154 | "tool_use_content": content if content_type == "tool_use" else "", 155 | f"{step_type}_content": content if step_type == "reasoning" else "" 156 | } 157 | 158 | # 只有执行模型或最后一步的推理模型才输出 content 159 | if step_type == "execution" or is_last_step: 160 | delta["content"] = content 161 | # logger.debug(f"delta: {delta}") 162 | # 生成流式响应 163 | response = { 164 | "id": chat_id, 165 | "object": "chat.completion.chunk", 166 | "created": created_time, 167 | "model": client_info['model_name'], 168 | "choices": [{ 169 | "index": 0, 170 | "delta": delta 171 | }] 172 | } 173 | 174 | yield f"data: {json.dumps(response)}\n\n".encode('utf-8') 175 | 176 | # 保存当前步骤的完整输出，用于下一步 177 | previous_result = "".join(current_output) 178 | 179 | async def process_without_stream(self, messages: list) -> dict: 180 | """处理非流式输出 181 | 182 | Args: 183 | messages: 初始消息列表 184 | 185 | Returns: 186 | dict: 完整的响应数据 187 | """ 188 | chat_id = f"chatcmpl-{hex(int(time.time() * 1000))[2:]}" 189 | created_time = int(time.time()) 190 | 191 | current_messages = messages.copy() 192 | previous_result = "" 193 | final_response = { 194 | "role": "assistant", 195 | "reasoning_content": "", 196 | "execution_content": "", 197 | "content": "" 198 | } 199 | 200 | # 如果是单模型，直接使用通用客户端 201 | if self.is_single_model: 202 | step = self.steps[0] 203 | return await self.uni_client.generate( 204 | messages=messages, 205 | system_prompt=step.get('system_prompt') 206 | ) 207 | 208 | for idx, client_info in enumerate(self.clients): 209 | client = client_info['client'] 210 | step_type = client_info['step_type'] 211 | system_prompt = client_info['system_prompt'] 212 | is_last_step = idx == len(self.clients) - 1 213 | is_first_step = idx == 0 214 | if system_prompt: 215 | current_messages = self._add_system_prompt(current_messages, system_prompt) 216 | 217 | if idx > 0: 218 | current_messages = self._add_previous_step_result( 219 | current_messages, 220 | previous_result, 221 | step_type 222 | ) 223 | 224 | current_output = [] 225 | async for content_type, content in client.stream_chat( 226 | messages=current_messages, 227 | model=client_info['model_name'], 228 | is_last_step=is_last_step, 229 | is_first_step=is_first_step 230 | ): 231 | current_output.append(content) 232 | 233 | output_text = "".join(current_output) 234 | previous_result = output_text 235 | 236 | # 更新响应内容 237 | final_response[f"{step_type}_content"] = output_text 238 | if step_type == "execution" or is_last_step: 239 | final_response["content"] = output_text 240 | 241 | return { 242 | "id": chat_id, 243 | "object": "chat.completion", 244 | "created": created_time, 245 | "model": self.clients[-1]['model_name'], 246 | "choices": [{ 247 | "index": 0, 248 | "message": final_response 249 | }] 250 | } 251 | 252 | def _add_system_prompt(self, messages: list, system_prompt: str) -> list: 253 | """添加系统提示词到消息列表""" 254 | new_messages = messages.copy() 255 | if new_messages and new_messages[0].get("role") == "system": 256 | new_messages[0]["content"] = f"{system_prompt}" 257 | else: 258 | new_messages.insert(0, { 259 | "role": "system", 260 | "content": system_prompt 261 | }) 262 | return new_messages 263 | 264 | def _add_previous_step_result(self, messages: list, previous_result: str, step_type: str) -> list: 265 | """添加前一步结果到消息中""" 266 | new_messages = messages.copy() 267 | last_message = new_messages[-1] 268 | 269 | prefix = "reasoning" if step_type == "execution" else "previous step" 270 | # 模型接力提示词 271 | prompt = f""" 272 | Here's the {prefix} result:\n{previous_result}\n\n 273 | Based on this, please provide your response in a new line: 274 | """ 275 | 276 | if last_message.get("role") == "user": 277 | last_message["content"] = f"{last_message['content']}\n\n{prompt}" 278 | 279 | return new_messages -------------------------------------------------------------------------------- /app/static/js/custom-speaking-order.js: -------------------------------------------------------------------------------- 1 | /** 2 | * 自定义发言顺序相关功能 3 | */ 4 | 5 | // 获取自定义发言顺序 6 | function getCustomSpeakingOrder() { 7 | const orderList = document.getElementById('customSpeakingOrderList'); 8 | if (!orderList) return null; 9 | 10 | const roleItems = orderList.querySelectorAll('.role-item'); 11 | if (roleItems.length === 0) return null; 12 | 13 | const order = []; 14 | roleItems.forEach(item => { 15 | order.push(item.getAttribute('data-role-name')); 16 | }); 17 | 18 | return order; 19 | } 20 | 21 | // 更新自定义发言顺序UI 22 | function updateCustomSpeakingOrderUI() { 23 | const orderList = document.getElementById('customSpeakingOrderList'); 24 | if (!orderList) return; 25 | 26 | // 清空当前列表 27 | orderList.innerHTML = ''; 28 | 29 | // 获取选中的角色 30 | const checkboxes = document.querySelectorAll('#roleCheckboxes input[type="checkbox"]:checked'); 31 | const selectedRoles = []; 32 | 33 | checkboxes.forEach(checkbox => { 34 | const roleId = checkbox.value; 35 | const roleName = checkbox.getAttribute('data-role-name'); 36 | selectedRoles.push({ id: roleId, name: roleName }); 37 | }); 38 | 39 | // 添加角色到发言顺序列表 40 | selectedRoles.forEach(role => { 41 | const roleItem = document.createElement('div'); 42 | roleItem.className = 'role-item'; 43 | roleItem.setAttribute('data-role-id', role.id); 44 | roleItem.setAttribute('data-role-name', role.name); 45 | roleItem.innerHTML = ` 46 | 47 | ${role.name} 48 | `; 49 | orderList.appendChild(roleItem); 50 | }); 51 | 52 | // 初始化拖拽排序 53 | initSortable(); 54 | } 55 | 56 | // 初始化可拖拽排序功能 57 | function initSortable() { 58 | const orderList = document.getElementById('customSpeakingOrderList'); 59 | if (!orderList) return; 60 | 61 | // 使用HTML5拖拽 API实现直接交换位置 62 | const items = orderList.querySelectorAll('.role-item'); 63 | let draggedItem = null; 64 | let targetItem = null; 65 | 66 | items.forEach(item => { 67 | item.setAttribute('draggable', 'true'); 68 | 69 | // 开始拖拽 70 | item.addEventListener('dragstart', function(e) { 71 | draggedItem = this; 72 | setTimeout(() => this.classList.add('dragging'), 0); 73 | e.dataTransfer.setData('text/plain', item.getAttribute('data-role-id')); 74 | }); 75 | 76 | // 拖拽结束 77 | item.addEventListener('dragend', function() { 78 | this.classList.remove('dragging'); 79 | draggedItem = null; 80 | targetItem = null; 81 | 82 | // 清除所有项的拖拽目标样式 83 | items.forEach(item => { 84 | item.classList.remove('drag-over'); 85 | }); 86 | }); 87 | 88 | // 拖拽进入目标区域 89 | item.addEventListener('dragenter', function(e) { 90 | e.preventDefault(); 91 | if (this !== draggedItem) { 92 | this.classList.add('drag-over'); 93 | targetItem = this; 94 | } 95 | }); 96 | 97 | // 拖拽移出目标区域 98 | item.addEventListener('dragleave', function() { 99 | this.classList.remove('drag-over'); 100 | if (this === targetItem) { 101 | targetItem = null; 102 | } 103 | }); 104 | 105 | // 拖拽经过目标区域 106 | item.addEventListener('dragover', function(e) { 107 | e.preventDefault(); 108 | }); 109 | 110 | // 放置拖拽项 111 | item.addEventListener('drop', function(e) { 112 | e.preventDefault(); 113 | if (this !== draggedItem) { 114 | // 直接交换位置 115 | swapElements(draggedItem, this); 116 | this.classList.remove('drag-over'); 117 | } 118 | }); 119 | }); 120 | } 121 | 122 | // 辅助函数，用于直接交换两个元素的位置 123 | function swapElements(el1, el2) { 124 | if (!el1 || !el2) return; 125 | 126 | // 获取两个元素的父元素 127 | const parent = el1.parentNode; 128 | if (!parent) return; 129 | 130 | // 获取两个元素的位置 131 | const el1Next = el1.nextElementSibling; 132 | const el2Next = el2.nextElementSibling; 133 | 134 | // 如果第一个元素是第二个元素的下一个元素 135 | if (el1Next === el2) { 136 | parent.insertBefore(el2, el1); 137 | } 138 | // 如果第二个元素是第一个元素的下一个元素 139 | else if (el2Next === el1) { 140 | parent.insertBefore(el1, el2); 141 | } 142 | // 其他情况，直接交换位置 143 | else { 144 | // 先将第一个元素移到第二个元素的位置 145 | if (el2Next) { 146 | parent.insertBefore(el1, el2Next); 147 | } else { 148 | parent.appendChild(el1); 149 | } 150 | 151 | // 然后将第二个元素移到第一个元素的原始位置 152 | if (el1Next) { 153 | parent.insertBefore(el2, el1Next); 154 | } else { 155 | parent.appendChild(el2); 156 | } 157 | } 158 | 159 | // 添加交换动画效果 160 | el1.classList.add('swapped'); 161 | el2.classList.add('swapped'); 162 | 163 | // 移除动画类 164 | setTimeout(() => { 165 | el1.classList.remove('swapped'); 166 | el2.classList.remove('swapped'); 167 | }, 300); 168 | } 169 | 170 | // 监听角色选择变化，更新发言顺序列表 171 | function setupRoleCheckboxListeners() { 172 | const checkboxes = document.querySelectorAll('#roleCheckboxes input[type="checkbox"]'); 173 | checkboxes.forEach(checkbox => { 174 | checkbox.addEventListener('change', updateCustomSpeakingOrderUI); 175 | }); 176 | } 177 | 178 | // 当模式选择改变时更新自定义发言顺序显示 179 | function updateCustomSpeakingOrderVisibility() { 180 | const modeSelect = document.querySelector('select[name="mode"]'); 181 | const customSpeakingOrderSection = document.getElementById('customSpeakingOrderSection'); 182 | 183 | if (modeSelect && customSpeakingOrderSection) { 184 | const mode = modeSelect.value; 185 | if (mode === 'debate' || mode === 'six_thinking_hats') { 186 | customSpeakingOrderSection.style.display = 'none'; 187 | } else { 188 | customSpeakingOrderSection.style.display = 'block'; 189 | updateCustomSpeakingOrderUI(); 190 | } 191 | } 192 | } 193 | 194 | // 初始化自定义发言顺序功能 195 | function initCustomSpeakingOrder() { 196 | // 监听模式选择变化 197 | const modeSelect = document.querySelector('select[name="mode"]'); 198 | if (modeSelect) { 199 | modeSelect.addEventListener('change', updateCustomSpeakingOrderVisibility); 200 | } 201 | 202 | // 初始化时设置可见性 203 | updateCustomSpeakingOrderVisibility(); 204 | 205 | // 监听角色选择变化 206 | setupRoleCheckboxListeners(); 207 | } 208 | 209 | // 在讨论组模态框显示时初始化 210 | document.addEventListener('DOMContentLoaded', function() { 211 | const addGroupModal = document.getElementById('addGroupModal'); 212 | if (addGroupModal) { 213 | addGroupModal.addEventListener('shown.bs.modal', function() { 214 | initCustomSpeakingOrder(); 215 | }); 216 | } 217 | }); 218 | 219 | // 设置编辑模式下的自定义发言顺序 220 | function setCustomSpeakingOrderForEdit(groupData) { 221 | if (!groupData) return; 222 | 223 | // console.log('设置自定义发言顺序，组数据:', groupData); 224 | 225 | // 使用更长的延迟确保DOM已完全加载 226 | setTimeout(() => { 227 | // 先确保所有角色复选框已正确选中 228 | if (groupData.role_ids && Array.isArray(groupData.role_ids)) { 229 | groupData.role_ids.forEach(roleId => { 230 | const checkbox = document.querySelector(`#roleCheckboxes input[value="${roleId}"]`); 231 | if (checkbox) { 232 | checkbox.checked = true; 233 | } 234 | }); 235 | } 236 | 237 | // 等待复选框状态更新后再处理自定义发言顺序 238 | setTimeout(() => { 239 | const orderList = document.getElementById('customSpeakingOrderList'); 240 | if (!orderList) { 241 | console.error('找不到自定义发言顺序列表元素'); 242 | return; 243 | } 244 | 245 | // 强制清空当前列表 246 | orderList.innerHTML = ''; 247 | 248 | // 如果有自定义发言顺序数据，使用它 249 | if (groupData.custom_speaking_order && Array.isArray(groupData.custom_speaking_order) && groupData.custom_speaking_order.length > 0) { 250 | // console.log('使用自定义发言顺序:', groupData.custom_speaking_order); 251 | 252 | // 创建角色映射对象 253 | const roleMap = {}; 254 | 255 | // 使用API响应中的roles数据创建映射 256 | if (groupData.roles && Array.isArray(groupData.roles)) { 257 | groupData.roles.forEach(role => { 258 | roleMap[role.name] = role; 259 | }); 260 | } 261 | 262 | // 按照自定义发言顺序添加角色 263 | for (let i = 0; i < groupData.custom_speaking_order.length; i++) { 264 | const roleName = groupData.custom_speaking_order[i]; 265 | if (roleMap[roleName]) { 266 | const role = roleMap[roleName]; 267 | // console.log(`按顺序添加角色 ${i+1}: ${roleName} (ID: ${role.id})`); 268 | 269 | // 直接创建元素并添加到列表中 270 | const roleItem = document.createElement('div'); 271 | roleItem.className = 'role-item'; 272 | roleItem.setAttribute('data-role-id', role.id); 273 | roleItem.setAttribute('data-role-name', roleName); 274 | roleItem.innerHTML = ` 275 | 276 | ${roleName} 277 | `; 278 | orderList.appendChild(roleItem); 279 | } else { 280 | console.warn(`找不到角色: ${roleName}`); 281 | } 282 | } 283 | 284 | // 添加任何不在自定义发言顺序中的角色 285 | if (groupData.roles) { 286 | const addedRoleNames = new Set(groupData.custom_speaking_order); 287 | groupData.roles.forEach(role => { 288 | if (!addedRoleNames.has(role.name)) { 289 | // console.log(`添加未在自定义顺序中的角色: ${role.name}`); 290 | const roleItem = document.createElement('div'); 291 | roleItem.className = 'role-item'; 292 | roleItem.setAttribute('data-role-id', role.id); 293 | roleItem.setAttribute('data-role-name', role.name); 294 | roleItem.innerHTML = ` 295 | 296 | ${role.name} 297 | `; 298 | orderList.appendChild(roleItem); 299 | } 300 | }); 301 | } 302 | } else { 303 | // console.log('没有自定义顺序数据，使用默认顺序'); 304 | // 如果没有自定义顺序，使用API返回的角色顺序 305 | if (groupData.roles && Array.isArray(groupData.roles)) { 306 | groupData.roles.forEach(role => { 307 | const roleItem = document.createElement('div'); 308 | roleItem.className = 'role-item'; 309 | roleItem.setAttribute('data-role-id', role.id); 310 | roleItem.setAttribute('data-role-name', role.name); 311 | roleItem.innerHTML = ` 312 | 313 | ${role.name} 314 | `; 315 | orderList.appendChild(roleItem); 316 | }); 317 | } 318 | } 319 | 320 | // 初始化拖拽排序 321 | initSortable(); 322 | }, 200); // 内部延迟确保复选框状态已更新 323 | }, 300); // 外部延迟确保DOM已完全加载 324 | } 325 | 326 | // 辅助函数：添加角色项到顺序列表 327 | function addRoleItemToOrderList(orderList, roleId, roleName) { 328 | const roleItem = document.createElement('div'); 329 | roleItem.className = 'role-item'; 330 | roleItem.setAttribute('data-role-id', roleId); 331 | roleItem.setAttribute('data-role-name', roleName); 332 | roleItem.innerHTML = ` 333 | 334 | ${roleName} 335 | `; 336 | orderList.appendChild(roleItem); 337 | } 338 | -------------------------------------------------------------------------------- /app/meeting/utils/summary_generator.py: -------------------------------------------------------------------------------- 1 | from langchain_openai import ChatOpenAI 2 | from langchain.schema import HumanMessage 3 | import os 4 | import traceback 5 | import logging 6 | import time 7 | import random 8 | from typing import List, Dict, Any 9 | import json 10 | import asyncio 11 | 12 | # 设置日志 13 | logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') 14 | logger = logging.getLogger("SummaryGenerator") 15 | 16 | class SummaryGenerator: 17 | """会议总结生成器，负责生成各种会议模式的总结""" 18 | 19 | def __init__(self, model_name: str = "gpt-3.5-turbo", api_key: str = None, api_url: str = None): 20 | self.model_name = model_name 21 | self.api_key = api_key 22 | self.api_url = api_url 23 | 24 | @staticmethod 25 | def generate_summary(meeting_topic: str, meeting_history: List[Dict[str, Any]], 26 | prompt_template: str, model_name: str = None, api_key: str = None, api_base_url: str = None) -> str: 27 | """ 28 | 生成会议总结 29 | 30 | Args: 31 | meeting_topic: 会议主题 32 | meeting_history: 会议历史记录 33 | prompt_template: 提示模板 34 | model_name: 模型名称，如果为None则使用默认模型 35 | api_key: API密钥 36 | api_base_url: API基础URL 37 | 38 | Returns: 39 | str: 生成的总结 40 | """ 41 | try: 42 | logger.info(f"开始生成'{meeting_topic}'的会议总结: 历史消息数={len(meeting_history)}") 43 | 44 | # 构建历史文本 45 | history_text = "" 46 | for entry in meeting_history: 47 | if entry["agent"] != "system": # 排除系统消息 48 | history_text += f"[{entry['agent']}]: {entry['content']}\n\n" 49 | 50 | # 格式化提示模板 51 | summary_prompt = prompt_template.format( 52 | topic=meeting_topic, 53 | meeting_topic=meeting_topic, 54 | history=history_text, 55 | history_text=history_text 56 | ) 57 | 58 | # 获取模型名称 59 | logger.info(f"使用模型生成总结: model_name={model_name or '默认模型'}") 60 | 61 | # 如果API URL或密钥为空，记录警告 62 | if not api_base_url: 63 | logger.warning("API基础URL为空，可能影响总结生成") 64 | if not api_key: 65 | logger.warning("API密钥为空，可能影响总结生成") 66 | 67 | try: 68 | # 创建模型时传入API密钥和基础URL 69 | model_kwargs = {"temperature": 0.3} 70 | if api_key: 71 | model_kwargs["api_key"] = api_key 72 | logger.info("使用提供的API密钥") 73 | if api_base_url: 74 | model_kwargs["base_url"] = api_base_url 75 | logger.info(f"使用提供的API基础URL: {api_base_url}") 76 | 77 | # 设置默认模型名称 78 | if not model_name: 79 | model_name = os.environ.get("OPENAI_MODEL_NAME", "gpt-3.5-turbo") 80 | logger.info(f"使用默认模型名称: {model_name}") 81 | 82 | logger.info(f"初始化LLM模型: {model_name}") 83 | llm = ChatOpenAI(model_name=model_name, **model_kwargs) 84 | 85 | # 智能重试机制 86 | max_retries = 3 87 | base_delay = 2 88 | 89 | for attempt in range(max_retries + 1): 90 | try: 91 | if attempt > 0: 92 | logger.info(f"总结生成尝试 {attempt}/{max_retries}...") 93 | 94 | logger.info("调用LLM生成总结") 95 | response = llm.invoke([HumanMessage(content=summary_prompt)]) 96 | summary = response.content 97 | 98 | logger.info(f"成功生成总结: 长度={len(summary)}") 99 | return summary 100 | 101 | except Exception as e: 102 | logger.warning(f"总结生成错误: {str(e)}") 103 | 104 | if attempt == max_retries: 105 | raise # 最后一次尝试，重新抛出异常 106 | 107 | # 指数退避 108 | delay = base_delay * (2 ** attempt) + random.uniform(0, 1) 109 | logger.info(f"等待 {delay:.2f} 秒后重试...") 110 | time.sleep(delay) 111 | 112 | except Exception as e: 113 | logger.error(f"调用API生成总结失败: {str(e)}", exc_info=True) 114 | # 调用备用方法生成模板总结 115 | logger.info("使用模板总结作为备用") 116 | return SummaryGenerator._generate_template_summary(meeting_topic, len(meeting_history)) 117 | 118 | except Exception as e: 119 | logger.error(f"生成会议总结失败: {str(e)}", exc_info=True) 120 | return f"[生成会议总结失败: {str(e)}]" 121 | 122 | @staticmethod 123 | def _generate_template_summary(meeting_topic: str, message_count: int) -> str: 124 | """生成模板总结（当API调用失败时使用）""" 125 | return f""" 126 | # 关于"{meeting_topic}"的讨论总结 127 | 128 | ## 主要主题和观点 129 | - 参与者讨论了{meeting_topic}的各个方面 130 | - 提出了多种观点和见解 131 | 132 | ## 达成的共识 133 | - 参与者在某些关键点上达成了一致 134 | - 认同了一些基本原则 135 | 136 | ## 存在的分歧 137 | - 在某些具体实施方法上存在不同意见 138 | - 对某些问题的优先级有不同看法 139 | 140 | ## 解决方案和建议 141 | - 提出了几种可能的解决方案 142 | - 建议进一步研究和讨论 143 | 144 | ## 需要进一步讨论的问题 145 | - 一些技术细节需要更深入的探讨 146 | - 某些方案的可行性需要进一步评估 147 | 148 | 这个总结是基于{message_count}条消息生成的。 149 | """ 150 | 151 | @staticmethod 152 | async def generate_summary_stream(meeting_topic: str, meeting_history: List[Dict[str, Any]], 153 | prompt_template: str, model_name: str = None, api_key: str = None, api_base_url: str = None, 154 | model_params: Dict[str, Any] = None): 155 | """ 156 | 流式生成会议总结，逐步返回生成的内容 157 | 158 | Args: 159 | meeting_topic: 会议主题 160 | meeting_history: 会议历史记录 161 | prompt_template: 提示模板 162 | model_name: 模型名称，如果为None则使用默认模型 163 | api_key: API密钥 164 | api_base_url: API基础URL 165 | model_params: 模型配置参数字典 166 | 167 | Yields: 168 | str: 生成的总结片段 169 | """ 170 | try: 171 | logger.info(f"开始流式生成'{meeting_topic}'的会议总结: 历史消息数={len(meeting_history)}") 172 | 173 | # 构建历史文本 174 | history_text = "" 175 | for entry in meeting_history: 176 | if entry["agent"] != "system": # 排除系统消息 177 | history_text += f"[{entry['agent']}]: {entry['content']}\n\n" 178 | 179 | # 格式化提示模板 180 | summary_prompt = prompt_template.format( 181 | topic=meeting_topic, 182 | meeting_topic=meeting_topic, 183 | history=history_text, 184 | history_text=history_text 185 | ) 186 | 187 | # 获取或设置默认模型参数 188 | if not model_params: 189 | model_params = {} 190 | 191 | # 定义默认参数 - 确保总是有这些基本参数 192 | default_params = { 193 | "temperature": 0.3, 194 | "max_tokens": 8096, 195 | "top_p": 1.0, 196 | "frequency_penalty": 0.0, 197 | "presence_penalty": 0.0 198 | } 199 | 200 | # 构建请求体 - 首先使用默认参数 201 | payload = default_params.copy() 202 | 203 | # 添加必要的消息参数 204 | payload.update({ 205 | "messages": [{"role": "user", "content": summary_prompt}], 206 | "stream": True, # 必须启用流式输出 207 | }) 208 | 209 | # 然后用模型参数覆盖默认参数（保留特定模型的自定义设置） 210 | for param, value in model_params.items(): 211 | # 跳过一些已设置的关键参数 212 | if param not in ["messages", "stream"]: 213 | payload[param] = value 214 | 215 | # 确保model使用传入的model_name参数（优先级最高） 216 | if model_name: 217 | payload["model"] = model_name 218 | elif "model" not in payload: 219 | # 如果没有指定model_name且payload中也没有model，使用默认模型 220 | payload["model"] = os.environ.get("OPENAI_MODEL_NAME", "gpt-3.5-turbo") 221 | 222 | # logger.info(f"使用模型流式生成总结: model_name={payload.get('model', '默认模型')}, 参数={payload}") 223 | 224 | # 如果API URL或密钥为空，记录警告 225 | if not api_base_url: 226 | logger.warning("API基础URL为空，可能影响总结生成") 227 | if not api_key: 228 | logger.warning("API密钥为空，可能影响总结生成") 229 | 230 | try: 231 | # 准备API请求 232 | headers = { 233 | "Content-Type": "application/json" 234 | } 235 | 236 | # 添加API密钥 237 | if api_key: 238 | headers["Authorization"] = f"Bearer {api_key}" 239 | logger.info("使用提供的API密钥") 240 | 241 | # 打印完整参数配置（调试用，生产环境可注释） 242 | logger.debug(f"API请求参数: {payload}") 243 | 244 | import aiohttp 245 | async with aiohttp.ClientSession() as session: 246 | async with session.post( 247 | f"{api_base_url}/v1/chat/completions" if api_base_url else "https://api.openai.com/v1/chat/completions", 248 | headers=headers, 249 | json=payload 250 | ) as response: 251 | # 检查响应 252 | if response.status != 200: 253 | error_text = await response.text() 254 | logger.error(f"API调用失败: {response.status} - {error_text}") 255 | # 如果流式API失败，使用备用总结 256 | backup_summary = SummaryGenerator._generate_template_summary(meeting_topic, len(meeting_history)) 257 | for char in backup_summary: 258 | yield char 259 | await asyncio.sleep(0.01) 260 | return 261 | 262 | # 处理流式响应 263 | logger.info("开始接收流式总结内容") 264 | accumulated_text = "" 265 | async for line in response.content: 266 | line = line.decode('utf-8').strip() 267 | 268 | # 如果是空行，跳过 269 | if not line: 270 | continue 271 | 272 | # 处理数据行 273 | if line.startswith("data: "): 274 | data = line[6:].strip() 275 | 276 | # 处理特殊的[DONE]标记 277 | if data == "[DONE]": 278 | break 279 | 280 | try: 281 | json_data = json.loads(data) 282 | choices = json_data.get("choices", []) 283 | 284 | if choices and len(choices) > 0: 285 | delta = choices[0].get("delta", {}) 286 | content = delta.get("content", "") 287 | 288 | if content: 289 | # 累积文本同时返回每个增量 290 | accumulated_text += content 291 | yield content 292 | except json.JSONDecodeError: 293 | continue 294 | 295 | logger.info(f"流式总结生成完成: 总长度={len(accumulated_text)}") 296 | 297 | except Exception as e: 298 | logger.error(f"流式总结生成错误: {str(e)}", exc_info=True) 299 | # 如果出错，生成备用总结 300 | backup_summary = SummaryGenerator._generate_template_summary(meeting_topic, len(meeting_history)) 301 | logger.info(f"使用备用总结: 长度={len(backup_summary)}") 302 | 303 | # 模拟流式输出备用总结 304 | for char in backup_summary: 305 | yield char 306 | await asyncio.sleep(0.01) 307 | 308 | except Exception as e: 309 | logger.error(f"流式总结生成过程中出现严重错误: {str(e)}", exc_info=True) 310 | error_msg = f"[生成会议总结失败: {str(e)}]" 311 | for char in error_msg: 312 | yield char 313 | await asyncio.sleep(0.01) -------------------------------------------------------------------------------- /app/static/login.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | DeepGemini - Login 8 | 9 | 10 | 11 | 307 | 308 | 309 |
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