完成世界书、骰子、apiconfig页面处理
This commit is contained in:
389
backend/api/routes/apiConfigRoute.py
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389
backend/api/routes/apiConfigRoute.py
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@@ -0,0 +1,389 @@
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from fastapi import APIRouter, HTTPException, UploadFile, File
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from pydantic import BaseModel, Field
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from typing import Dict, Optional, List, Any
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import json
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import os
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from pathlib import Path
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from core.config import settings
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from cryptography.fernet import Fernet
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import base64
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from services.comfyui_workflow_manager import workflow_manager
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from services.llm_model_service import LLMModelService
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router = APIRouter(prefix="/api-config", tags=["API Configuration"])
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# 加密密钥（实际项目中应该从环境变量读取）
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ENCRYPTION_KEY = os.getenv('API_ENCRYPTION_KEY', Fernet.generate_key().decode())
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fernet = Fernet(ENCRYPTION_KEY.encode() if isinstance(ENCRYPTION_KEY, str) else ENCRYPTION_KEY)
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# 配置文件路径
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CONFIG_DIR = Path(settings.DATA_PATH) / "apiconfig"
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CONFIG_DIR.mkdir(parents=True, exist_ok=True)
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class ApiConfigItem(BaseModel):
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"""单个 API 配置项"""
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id: Optional[str] = None
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name: Optional[str] = ""
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category: Optional[str] = None # mainLLM, imageModel, secondaryLLM, ragEmbedding
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apiUrl: Optional[str] = ""
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apiKey: Optional[str] = None # 前端传入的可能是明文或空
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model: Optional[str] = ""
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# 生图模型的特殊字段
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mode: Optional[str] = None # 'local' | 'cloud'
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local: Optional[dict] = None
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cloud: Optional[dict] = None
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class ProfileSaveRequest(BaseModel):
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"""保存配置文件的请求"""
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profileId: str
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name: Optional[str] = None
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apis: Dict[str, ApiConfigItem] # key 是 category，value 是配置
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class ProfileResponse(BaseModel):
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"""配置文件响应（不包含明文 API Key）"""
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id: str
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name: str
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apis: Dict[str, dict] # apiKey 字段会被移除或脱敏
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def encrypt_api_key(api_key: str) -> str:
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"""加密 API Key"""
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if not api_key:
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return ""
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encrypted = fernet.encrypt(api_key.encode())
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return base64.urlsafe_b64encode(encrypted).decode()
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def decrypt_api_key(encrypted_key: str) -> str:
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"""解密 API Key（仅在后端内部使用）"""
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if not encrypted_key:
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return ""
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try:
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decoded = base64.urlsafe_b64decode(encrypted_key.encode())
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decrypted = fernet.decrypt(decoded)
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return decrypted.decode()
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except Exception:
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return ""
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def mask_api_key(api_key: str) -> str:
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"""脱敏 API Key（返回给前端）"""
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if not api_key or len(api_key) < 8:
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return "****"
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return api_key[:4] + "****" + api_key[-4:]
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def load_profile(profile_id: str) -> Optional[dict]:
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"""加载配置文件"""
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config_file = CONFIG_DIR / f"{profile_id}.json"
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if not config_file.exists():
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return None
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with open(config_file, 'r', encoding='utf-8') as f:
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return json.load(f)
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def save_profile(profile_id: str, profile_data: dict):
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"""保存配置文件"""
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config_file = CONFIG_DIR / f"{profile_id}.json"
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with open(config_file, 'w', encoding='utf-8') as f:
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json.dump(profile_data, f, ensure_ascii=False, indent=2)
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def list_profiles() -> List[dict]:
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"""列出所有配置文件"""
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profiles = []
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for config_file in CONFIG_DIR.glob("*.json"):
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try:
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with open(config_file, 'r', encoding='utf-8') as f:
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profile = json.load(f)
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profiles.append({
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"id": profile.get("id", config_file.stem),
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"name": profile.get("name", config_file.stem),
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"createdAt": profile.get("createdAt", "")
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})
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except Exception:
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continue
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return profiles
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@router.get("/profiles", response_model=List[dict])
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def get_all_profiles():
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"""获取所有配置文件列表"""
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return list_profiles()
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@router.get("/profiles/{profile_id}", response_model=ProfileResponse)
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def get_profile(profile_id: str):
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"""获取单个配置文件（API Key 已脱敏）"""
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profile = load_profile(profile_id)
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if not profile:
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raise HTTPException(status_code=404, detail="配置文件不存在")
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# 脱敏所有 API Key
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masked_apis = {}
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for category, api_config in profile.get("apis", {}).items():
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masked_config = api_config.copy()
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if "apiKey" in masked_config and masked_config["apiKey"]:
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masked_config["apiKey"] = mask_api_key(masked_config["apiKey"])
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masked_apis[category] = masked_config
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return {
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"id": profile.get("id", profile_id),
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"name": profile.get("name", profile_id),
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"apis": masked_apis
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}
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@router.post("/profiles", response_model=ProfileResponse)
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def create_or_update_profile(request: ProfileSaveRequest):
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"""创建或更新配置文件（增量更新）"""
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# 加载现有配置
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existing_profile = load_profile(request.profileId)
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if existing_profile:
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# 更新现有配置：只更新提供的 API 配置
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for category, api_config in request.apis.items():
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api_config_dict = api_config.dict(exclude_none=True)
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# 处理 API Key 加密
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if api_config.apiKey and api_config.apiKey != "****":
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# 如果是新的明文 key，加密它
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api_config_dict["apiKey"] = encrypt_api_key(api_config.apiKey)
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elif api_config.apiKey == "****":
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# 如果是脱敏的 key，保留原有的加密 key
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if category in existing_profile.get("apis", {}):
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api_config_dict["apiKey"] = existing_profile["apis"][category].get("apiKey", "")
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else:
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api_config_dict.pop("apiKey", None)
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# 更新配置
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if "apis" not in existing_profile:
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existing_profile["apis"] = {}
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existing_profile["apis"][category] = api_config_dict
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profile_data = existing_profile
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else:
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# 新建配置文件
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from datetime import datetime
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profile_data = {
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"id": request.profileId,
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"name": request.name or request.profileId,
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"createdAt": datetime.now().isoformat(),
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"apis": {}
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}
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# 添加所有 API 配置
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for category, api_config in request.apis.items():
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api_config_dict = api_config.dict(exclude_none=True)
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if api_config_dict.get("apiKey"):
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api_config_dict["apiKey"] = encrypt_api_key(api_config_dict["apiKey"])
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profile_data["apis"][category] = api_config_dict
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# 保存配置文件
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save_profile(request.profileId, profile_data)
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# 返回脱敏后的数据
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masked_apis = {}
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for category, api_config in profile_data.get("apis", {}).items():
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masked_config = api_config.copy()
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if "apiKey" in masked_config and masked_config["apiKey"]:
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masked_config["apiKey"] = mask_api_key(masked_config["apiKey"])
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masked_apis[category] = masked_config
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return {
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"id": profile_data.get("id", request.profileId),
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"name": profile_data.get("name", request.profileId),
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"apis": masked_apis
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}
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@router.delete("/profiles/{profile_id}")
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def delete_profile(profile_id: str):
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"""删除配置文件"""
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config_file = CONFIG_DIR / f"{profile_id}.json"
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if not config_file.exists():
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raise HTTPException(status_code=404, detail="配置文件不存在")
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config_file.unlink()
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return {"message": "配置文件已删除"}
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@router.post("/test-connection")
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def test_connection(api_config: ApiConfigItem):
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"""测试 API 连接并获取模型列表"""
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try:
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# 检测提供商类型
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provider = LLMModelService.detect_provider(api_config.apiUrl)
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# 获取模型列表
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models = LLMModelService.get_models_by_provider(
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provider=provider,
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api_key=api_config.apiKey or "",
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api_url=api_config.apiUrl
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)
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return {
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"success": True,
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"models": models,
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"provider": provider,
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"message": f"成功获取 {len(models)} 个模型"
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}
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except Exception as e:
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raise HTTPException(
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status_code=500,
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detail=f"获取模型列表失败: {str(e)}"
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)
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# ==================== ComfyUI Workflow Management ====================
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@router.get("/comfyui/workflows", response_model=List[Dict[str, Any]])
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def get_comfyui_workflows():
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"""获取所有可用的 ComfyUI 工作流列表"""
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return workflow_manager.list_workflows()
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@router.post("/comfyui/workflows/upload")
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async def upload_comfyui_workflow(file: UploadFile = File(...)):
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"""上传 ComfyUI 工作流 JSON 文件"""
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return await workflow_manager.upload_workflow(file)
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@router.delete("/comfyui/workflows/{filename}")
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def delete_comfyui_workflow(filename: str):
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"""删除 ComfyUI 工作流文件"""
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return workflow_manager.delete_workflow(filename)
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@router.get("/comfyui/workflows/{filename}")
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def get_comfyui_workflow(filename: str):
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"""获取指定工作流的详细内容"""
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return workflow_manager.load_workflow(filename)
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# ==================== Connection Testing ====================
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@router.post("/test-comfyui-connection")
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def test_comfyui_connection(request: dict):
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"""测试 ComfyUI 连接"""
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import requests as req
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api_url = request.get("apiUrl", "http://comfyui:8188")
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try:
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# 测试基本连通性
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response = req.get(f"{api_url}/system_stats", timeout=5)
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if response.status_code != 200:
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return {
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"success": False,
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"message": f"HTTP {response.status_code}"
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}
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stats = response.json()
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return {
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"success": True,
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"message": "连接成功",
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"stats": {
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"vram_total": stats.get("vram_total", 0),
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"vram_free": stats.get("vram_free", 0),
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"torch_version": stats.get("torch_version", ""),
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"device": stats.get("device", "")
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}
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}
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except req.exceptions.ConnectionError:
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return {
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"success": False,
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"message": "无法连接到 ComfyUI，请检查地址和端口"
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}
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except req.exceptions.Timeout:
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return {
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"success": False,
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"message": "连接超时，请检查 ComfyUI 是否正常运行"
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}
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except Exception as e:
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return {
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"success": False,
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"message": f"错误: {str(e)}"
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}
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@router.post("/test-cloud-connection")
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def test_cloud_connection(request: dict):
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"""测试云端 API 连接"""
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import openai
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provider = request.get("provider", "dall-e")
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api_key = request.get("apiKey", "")
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model = request.get("model", "dall-e-3")
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if not api_key:
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return {
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"success": False,
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"message": "API Key 不能为空"
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}
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try:
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if provider == "dall-e":
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# 测试 DALL-E
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client = openai.OpenAI(api_key=api_key)
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# 尝试获取模型列表（轻量级测试）
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models = client.models.list()
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# 检查指定的模型是否存在
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model_exists = any(m.id == model for m in models.data)
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if model_exists:
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return {
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"success": True,
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"message": f"连接成功，模型 {model} 可用"
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}
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else:
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return {
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"success": False,
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"message": f"模型 {model} 不可用"
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}
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elif provider == "stability":
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# 测试 Stability AI
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import requests as req
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response = req.get(
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"https://api.stability.ai/v1/engines/list",
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headers={
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"Authorization": f"Bearer {api_key}"
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},
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timeout=5
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)
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if response.status_code == 200:
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return {
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"success": True,
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"message": "连接成功"
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}
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else:
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return {
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"success": False,
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"message": f"HTTP {response.status_code}: {response.text}"
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}
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else:
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return {
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"success": False,
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"message": f"不支持的提供商: {provider}"
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}
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except Exception as e:
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return {
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"success": False,
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"message": f"连接失败: {str(e)}"
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}
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@@ -1,5 +1,6 @@
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3# 标准库导入
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# 标准库导入
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import os
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import json
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import shutil
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import logging
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from pathlib import Path
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@@ -12,6 +13,7 @@ from fastapi.responses import JSONResponse, FileResponse
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# 本地模块导入
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from models.internal import WorldInfo, WorldInfoEntry
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from core.config import settings
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from services.worldbook_service import worldbook_service
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# 配置日志
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logger = logging.getLogger(__name__)
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@@ -30,88 +32,213 @@ async def list_worldbooks():
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Returns:
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List[Dict[str, Any]]: 世界书列表
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"""
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# TODO: 实现 WorldBookService
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return []
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try:
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return worldbook_service.list_worldbooks()
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except Exception as e:
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logger.error(f"Failed to list worldbooks: {str(e)}")
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raise HTTPException(status_code=500, detail=str(e))
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@router.get("/{name}", response_model=Dict[str, Any])
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async def get_worldbook(name: str):
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"""
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获取指定名称的世界书
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"""
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raise HTTPException(status_code=501, detail="Not Implemented")
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try:
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return worldbook_service.get_worldbook(name)
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except FileNotFoundError as e:
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raise HTTPException(status_code=404, detail=str(e))
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except Exception as e:
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logger.error(f"Failed to get worldbook '{name}': {str(e)}")
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raise HTTPException(status_code=500, detail=str(e))
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@router.post("/", response_model=Dict[str, Any])
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async def create_worldbook(
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name: str = Form(...),
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description: str = Form(""),
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file: Optional[UploadFile] = File(None)
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):
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"""
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创建新世界书
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创建新世界书（可选择导入文件）
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"""
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raise HTTPException(status_code=501, detail="Not Implemented")
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try:
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# 如果提供了文件，从 SillyTavern 格式导入
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if file:
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content = await file.read()
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st_data = json.loads(content.decode('utf-8'))
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return worldbook_service.import_from_sillytavern(name, st_data)
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else:
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# 创建空世界书
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return worldbook_service.create_worldbook(name, description)
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except ValueError as e:
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raise HTTPException(status_code=400, detail=str(e))
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except Exception as e:
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logger.error(f"Failed to create worldbook '{name}': {str(e)}")
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raise HTTPException(status_code=500, detail=str(e))
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@router.put("/{name}", response_model=Dict[str, Any])
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async def update_worldbook(
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name: str,
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file: Optional[UploadFile] = File(None)
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description: Optional[str] = Form(None)
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):
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"""
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更新世界书
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更新世界书基本信息
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"""
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raise HTTPException(status_code=501, detail="Not Implemented")
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try:
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return worldbook_service.update_worldbook(name, description)
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except FileNotFoundError as e:
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raise HTTPException(status_code=404, detail=str(e))
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except Exception as e:
|
||||
logger.error(f"Failed to update worldbook '{name}': {str(e)}")
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@router.delete("/{name}")
|
||||
async def delete_worldbook(name: str):
|
||||
"""
|
||||
删除世界书
|
||||
"""
|
||||
raise HTTPException(status_code=501, detail="Not Implemented")
|
||||
try:
|
||||
worldbook_service.delete_worldbook(name)
|
||||
return {"message": f"Worldbook '{name}' deleted successfully"}
|
||||
except FileNotFoundError as e:
|
||||
raise HTTPException(status_code=404, detail=str(e))
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to delete worldbook '{name}': {str(e)}")
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@router.get("/{name}/entries", response_model=List[Dict[str, Any]])
|
||||
async def list_worldbook_entries(name: str):
|
||||
"""
|
||||
获取世界书的所有条目
|
||||
"""
|
||||
raise HTTPException(status_code=501, detail="Not Implemented")
|
||||
try:
|
||||
return worldbook_service.list_entries(name)
|
||||
except FileNotFoundError as e:
|
||||
raise HTTPException(status_code=404, detail=str(e))
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to list entries for worldbook '{name}': {str(e)}")
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@router.get("/{name}/entries/{uid}", response_model=Dict[str, Any])
|
||||
async def get_worldbook_entry(name: str, uid: int):
|
||||
async def get_worldbook_entry(name: str, uid: str):
|
||||
"""
|
||||
获取世界书的指定条目
|
||||
"""
|
||||
raise HTTPException(status_code=501, detail="Not Implemented")
|
||||
try:
|
||||
return worldbook_service.get_entry(name, uid)
|
||||
except FileNotFoundError as e:
|
||||
raise HTTPException(status_code=404, detail=str(e))
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to get entry '{uid}' from worldbook '{name}': {str(e)}")
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@router.post("/{name}/entries", response_model=Dict[str, Any])
|
||||
async def create_worldbook_entry(name: str, entry_data: Dict[str, Any]):
|
||||
"""
|
||||
在世界书中创建新条目
|
||||
"""
|
||||
raise HTTPException(status_code=501, detail="Not Implemented")
|
||||
try:
|
||||
return worldbook_service.create_entry(name, entry_data)
|
||||
except FileNotFoundError as e:
|
||||
raise HTTPException(status_code=404, detail=str(e))
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to create entry in worldbook '{name}': {str(e)}")
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@router.put("/{name}/entries/{uid}", response_model=Dict[str, Any])
|
||||
async def update_worldbook_entry(name: str, uid: int, entry_data: Dict[str, Any]):
|
||||
async def update_worldbook_entry(name: str, uid: str, entry_data: Dict[str, Any]):
|
||||
"""
|
||||
更新世界书的指定条目
|
||||
"""
|
||||
raise HTTPException(status_code=501, detail="Not Implemented")
|
||||
try:
|
||||
return worldbook_service.update_entry(name, uid, entry_data)
|
||||
except FileNotFoundError as e:
|
||||
raise HTTPException(status_code=404, detail=str(e))
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to update entry '{uid}' in worldbook '{name}': {str(e)}")
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@router.delete("/{name}/entries/{uid}")
|
||||
async def delete_worldbook_entry(name: str, uid: int):
|
||||
async def delete_worldbook_entry(name: str, uid: str):
|
||||
"""
|
||||
删除世界书的指定条目
|
||||
"""
|
||||
raise HTTPException(status_code=501, detail="Not Implemented")
|
||||
try:
|
||||
worldbook_service.delete_entry(name, uid)
|
||||
return {"message": f"Entry '{uid}' deleted successfully"}
|
||||
except FileNotFoundError as e:
|
||||
raise HTTPException(status_code=404, detail=str(e))
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to delete entry '{uid}' from worldbook '{name}': {str(e)}")
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@router.post("/{name}/import", response_model=Dict[str, Any])
|
||||
async def import_worldbook(name: str, file: UploadFile = File(...)):
|
||||
"""
|
||||
从文件导入世界书
|
||||
从文件导入世界书(自动检测 SillyTavern 或内部格式)
|
||||
"""
|
||||
raise HTTPException(status_code=501, detail="Not Implemented")
|
||||
try:
|
||||
content = await file.read()
|
||||
data = json.loads(content.decode('utf-8'))
|
||||
|
||||
# 智能检测格式
|
||||
from models.converters import WorldBookConverter
|
||||
format_type = WorldBookConverter.detect_format(data)
|
||||
|
||||
logger.info(f"检测到世界书格式: {format_type}")
|
||||
|
||||
if format_type == "sillytavern":
|
||||
# SillyTavern 格式,需要转换
|
||||
logger.info(f"正在转换 SillyTavern 格式为内部格式")
|
||||
return worldbook_service.import_from_sillytavern(name, data)
|
||||
elif format_type == "internal":
|
||||
# 已经是内部格式,直接保存
|
||||
logger.info(f"检测到内部格式,直接保存")
|
||||
return worldbook_service.import_internal_format(name, data)
|
||||
else:
|
||||
raise HTTPException(status_code=400, detail="无法识别的世界书格式")
|
||||
|
||||
except json.JSONDecodeError:
|
||||
raise HTTPException(status_code=400, detail="Invalid JSON format")
|
||||
except ValueError as e:
|
||||
raise HTTPException(status_code=400, detail=str(e))
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to import worldbook '{name}': {str(e)}")
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@router.get("/{name}/export")
|
||||
async def export_worldbook(name: str):
|
||||
async def export_worldbook(name: str, format: str = "internal"):
|
||||
"""
|
||||
导出世界书为 SillyTavern 格式
|
||||
导出世界书(支持 internal 和 sillytavern 两种格式)
|
||||
|
||||
Args:
|
||||
name: 世界书名称
|
||||
format: 导出格式 ('internal' 或 'sillytavern'),默认 internal
|
||||
"""
|
||||
raise HTTPException(status_code=501, detail="Not Implemented")
|
||||
try:
|
||||
if format.lower() == "sillytavern":
|
||||
# 导出为 SillyTavern 格式(可能丢失特殊设置)
|
||||
logger.info(f"导出世界书 '{name}' 为 SillyTavern 格式")
|
||||
st_data = worldbook_service.export_to_sillytavern(name)
|
||||
|
||||
return JSONResponse(
|
||||
content=st_data,
|
||||
headers={
|
||||
"Content-Disposition": f"attachment; filename={name}_sillytavern.json"
|
||||
}
|
||||
)
|
||||
else:
|
||||
# 导出为内部格式(保留所有设置)
|
||||
logger.info(f"导出世界书 '{name}' 为内部格式")
|
||||
internal_data = worldbook_service.get_worldbook(name)
|
||||
|
||||
return JSONResponse(
|
||||
content=internal_data,
|
||||
headers={
|
||||
"Content-Disposition": f"attachment; filename={name}.json"
|
||||
}
|
||||
)
|
||||
except FileNotFoundError as e:
|
||||
raise HTTPException(status_code=404, detail=str(e))
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to export worldbook '{name}': {str(e)}")
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@@ -52,6 +52,9 @@ class Settings:
|
||||
# 临时文件目录
|
||||
TEMP_PATH = DATA_PATH / "temp"
|
||||
|
||||
# ComfyUI 工作流目录
|
||||
COMFYUI_WORKFLOWS_PATH = DATA_PATH / "comfyui_workflows"
|
||||
|
||||
def ensure_directories(self):
|
||||
"""确保所有配置的目录存在，如果不存在则创建"""
|
||||
directories = [
|
||||
@@ -60,6 +63,7 @@ class Settings:
|
||||
self.PRESET_PATH,
|
||||
self.CHAT_PATH,
|
||||
self.TEMP_PATH,
|
||||
self.COMFYUI_WORKFLOWS_PATH,
|
||||
]
|
||||
for directory in directories:
|
||||
directory.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
231
backend/models/README.md
Normal file
231
backend/models/README.md
Normal file
@@ -0,0 +1,231 @@
|
||||
# Backend Models 数据模型说明
|
||||
|
||||
## 目录结构
|
||||
|
||||
```
|
||||
models/
|
||||
├── __init__.py # 包初始化,导出所有模型
|
||||
├── sillytavern.py # SillyTavern 兼容模型 (仅用于导入/导出)
|
||||
├── internal.py # 内部业务模型 (项目核心使用)
|
||||
└── README.md # 本文件
|
||||
```
|
||||
|
||||
## 模型分类
|
||||
|
||||
### 1. SillyTavern 兼容模型 (`sillytavern.py`)
|
||||
|
||||
**用途**: 仅用于与 SillyTavern 格式的数据进行导入/导出兼容
|
||||
|
||||
**特点**:
|
||||
- 严格遵循 SillyTavern 官方规范
|
||||
- 不参与内部业务逻辑
|
||||
- 所有字段名、结构与 SillyTavern 保持一致
|
||||
- 前缀 `ST` 表示 SillyTavern
|
||||
|
||||
**主要模型**:
|
||||
- `STWorldInfo` - SillyTavern 世界书
|
||||
- `STCharacterCard` - SillyTavern 角色卡
|
||||
- `STChatHeader` / `STChatMessage` - SillyTavern 聊天记录
|
||||
- `STGenerationPreset` - SillyTavern 采样预设
|
||||
- `STPromptPreset` - SillyTavern 提示词预设
|
||||
|
||||
**使用场景**:
|
||||
```python
|
||||
# 从 SillyTavern 导入时
|
||||
st_data = json.load(file)
|
||||
st_character = STCharacterCard(**st_data)
|
||||
|
||||
# 转换为内部模型
|
||||
internal_character = converter.st_to_internal(st_character)
|
||||
|
||||
# 导出到 SillyTavern 时
|
||||
st_data = converter.internal_to_st(internal_character)
|
||||
json.dump(st_data.dict(), file)
|
||||
```
|
||||
|
||||
### 2. 内部业务模型 (`internal.py`)
|
||||
|
||||
**用途**: 项目内部真正使用的数据结构,所有业务逻辑都基于这些模型
|
||||
|
||||
**特点**:
|
||||
- 继承并扩展了 SillyTavern 的功能
|
||||
- 添加了项目特色功能 (如 LOGIC 激活、RAG 配置、outputSchema 等)
|
||||
- 所有 API 响应、数据存储、工作流交换都使用这些模型
|
||||
- 无前缀,直接使用语义化名称
|
||||
|
||||
**主要模型**:
|
||||
|
||||
#### 世界书相关
|
||||
- `ActivationType` - 激活方式枚举 (PERMANENT/KEYWORD/RAG/LOGIC)
|
||||
- `LogicExpression` - 逻辑表达式
|
||||
- `RAGConfig` - RAG 检索配置
|
||||
- `WorldInfoEntry` - 世界书条目
|
||||
- `WorldInfo` - 世界书
|
||||
|
||||
#### 角色卡相关
|
||||
- `OutputSchemaField` - 结构化输出 schema
|
||||
- `CharacterCard` - 角色卡
|
||||
|
||||
#### 聊天记录相关
|
||||
- `ChatHeader` - 聊天头
|
||||
- `ChatMessage` - 聊天消息
|
||||
- `ChatLog` - 完整聊天记录
|
||||
|
||||
#### 预设相关
|
||||
- `GenerationPreset` - 采样参数预设
|
||||
- `PromptRole` - Prompt 角色枚举
|
||||
- `PromptEntry` - Prompt 条目
|
||||
- `PromptPresetView` - Prompt 预设视图
|
||||
|
||||
#### RAG 配置
|
||||
- `RAGSearchConfig` - RAG 搜索配置
|
||||
- `CharacterRAGConfig` - 角色卡 RAG 配置
|
||||
- `ChatRAGConfig` - 聊天 RAG 配置
|
||||
|
||||
**使用场景**:
|
||||
```python
|
||||
# 业务逻辑中直接使用
|
||||
from models import CharacterCard, WorldInfo
|
||||
|
||||
character = CharacterCard(
|
||||
id="uuid-123",
|
||||
name="Alice",
|
||||
description="...",
|
||||
...
|
||||
)
|
||||
|
||||
# API 响应
|
||||
@app.get("/characters/{id}")
|
||||
async def get_character(id: str):
|
||||
character = service.get_character(id)
|
||||
return character # 返回 internal 模型
|
||||
```
|
||||
|
||||
## 数据转换流程
|
||||
|
||||
```
|
||||
SillyTavern 文件
|
||||
↓ (导入)
|
||||
STCharacterCard (sillytavern.py)
|
||||
↓ (转换器)
|
||||
CharacterCard (internal.py)
|
||||
↓ (业务处理)
|
||||
CharacterCard (internal.py)
|
||||
↓ (转换器)
|
||||
STCharacterCard (sillytavern.py)
|
||||
↓ (导出)
|
||||
SillyTavern 文件
|
||||
```
|
||||
|
||||
## 开发规范
|
||||
|
||||
### ✅ 正确做法
|
||||
|
||||
1. **业务逻辑使用 internal 模型**
|
||||
```python
|
||||
from models import CharacterCard
|
||||
|
||||
def create_character(data: dict) -> CharacterCard:
|
||||
return CharacterCard(**data)
|
||||
```
|
||||
|
||||
2. **导入时使用转换器**
|
||||
```python
|
||||
from models import STCharacterCard, CharacterCard
|
||||
from models.converters import CharacterConverter
|
||||
|
||||
def import_character(file_path: str) -> CharacterCard:
|
||||
st_data = load_json(file_path)
|
||||
st_char = STCharacterCard(**st_data)
|
||||
return CharacterConverter.st_to_internal(st_char)
|
||||
```
|
||||
|
||||
3. **API 响应使用 internal 模型**
|
||||
```python
|
||||
@app.get("/characters")
|
||||
async def list_characters() -> List[CharacterCard]:
|
||||
return service.list_characters()
|
||||
```
|
||||
|
||||
### ❌ 错误做法
|
||||
|
||||
1. **不要在业务逻辑中直接使用 ST 模型**
|
||||
```python
|
||||
# 错误!
|
||||
from models import STCharacterCard
|
||||
|
||||
def process_character(char: STCharacterCard):
|
||||
...
|
||||
```
|
||||
|
||||
2. **不要混合使用两种模型**
|
||||
```python
|
||||
# 错误!
|
||||
character = CharacterCard(...)
|
||||
character.name = st_character.data.name # 不要混用
|
||||
```
|
||||
|
||||
3. **不要在 API 中暴露 ST 模型**
|
||||
```python
|
||||
# 错误!
|
||||
@app.get("/characters")
|
||||
async def list_characters() -> List[STCharacterCard]:
|
||||
...
|
||||
```
|
||||
|
||||
## 添加新模型
|
||||
|
||||
当需要添加新的数据类型时:
|
||||
|
||||
1. **判断用途**:
|
||||
- 如果是为了 SillyTavern 兼容 → 添加到 `sillytavern.py`
|
||||
- 如果是项目内部使用 → 添加到 `internal.py`
|
||||
|
||||
2. **遵循命名规范**:
|
||||
- SillyTavern 模型: 前缀 `ST`
|
||||
- 内部模型: 无前缀,使用清晰的语义化名称
|
||||
|
||||
3. **添加详细注释**:
|
||||
```python
|
||||
class MyModel(BaseModel):
|
||||
"""
|
||||
模型用途说明
|
||||
|
||||
详细描述该模型的作用、使用场景等
|
||||
"""
|
||||
field1: str = Field(..., description="字段说明")
|
||||
```
|
||||
|
||||
4. **在 `__init__.py` 中导出**:
|
||||
```python
|
||||
from .internal import MyModel
|
||||
|
||||
__all__ = [
|
||||
...,
|
||||
'MyModel',
|
||||
]
|
||||
```
|
||||
|
||||
## 转换器 (待实现)
|
||||
|
||||
`models/converters.py` 将提供双向转换功能:
|
||||
|
||||
```python
|
||||
class CharacterConverter:
|
||||
@staticmethod
|
||||
def st_to_internal(st_char: STCharacterCard) -> CharacterCard:
|
||||
"""SillyTavern → Internal"""
|
||||
...
|
||||
|
||||
@staticmethod
|
||||
def internal_to_st(int_char: CharacterCard) -> STCharacterCard:
|
||||
"""Internal → SillyTavern"""
|
||||
...
|
||||
```
|
||||
|
||||
## 总结
|
||||
|
||||
- **sillytavern.py** = 外部兼容层 (Import/Export Only)
|
||||
- **internal.py** = 内部业务层 (Core Business Logic)
|
||||
- **永远在业务逻辑中使用 internal 模型**
|
||||
- **通过转换器进行格式转换**
|
||||
61
backend/models/__init__.py
Normal file
61
backend/models/__init__.py
Normal file
@@ -0,0 +1,61 @@
|
||||
"""
|
||||
数据模型包
|
||||
|
||||
导出项目内部真正使用的数据结构 (Internal Models)。
|
||||
SillyTavern 兼容模型将在需要导入/导出时单独引用。
|
||||
"""
|
||||
|
||||
# 内部业务模型 (项目核心使用)
|
||||
from .internal import (
|
||||
# 世界书
|
||||
ActivationType,
|
||||
LogicOperator,
|
||||
LogicExpression,
|
||||
RAGConfig,
|
||||
WorldInfoEntry,
|
||||
WorldInfo,
|
||||
|
||||
# 角色卡
|
||||
OutputSchemaField,
|
||||
CharacterCard,
|
||||
|
||||
# 聊天记录
|
||||
ChatHeader,
|
||||
ChatMessage,
|
||||
ChatLog,
|
||||
|
||||
# 预设
|
||||
GenerationPreset,
|
||||
|
||||
# 提示词预设
|
||||
PromptRole,
|
||||
PromptEntry,
|
||||
PromptPresetView,
|
||||
|
||||
# RAG 配置
|
||||
RAGSearchConfig,
|
||||
CharacterRAGConfig,
|
||||
ChatRAGConfig,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
# 内部模型
|
||||
'ActivationType',
|
||||
'LogicOperator',
|
||||
'LogicExpression',
|
||||
'RAGConfig',
|
||||
'WorldInfoEntry',
|
||||
'WorldInfo',
|
||||
'OutputSchemaField',
|
||||
'CharacterCard',
|
||||
'ChatHeader',
|
||||
'ChatMessage',
|
||||
'ChatLog',
|
||||
'GenerationPreset',
|
||||
'PromptRole',
|
||||
'PromptEntry',
|
||||
'PromptPresetView',
|
||||
'RAGSearchConfig',
|
||||
'CharacterRAGConfig',
|
||||
'ChatRAGConfig',
|
||||
]
|
||||
379
backend/models/converters.py
Normal file
379
backend/models/converters.py
Normal file
@@ -0,0 +1,379 @@
|
||||
"""
|
||||
数据模型转换器
|
||||
|
||||
提供 SillyTavern 格式与内部格式之间的双向转换功能。
|
||||
所有导入/导出操作都应该通过转换器进行,确保数据格式的一致性。
|
||||
"""
|
||||
import uuid
|
||||
from typing import Dict, Any, List, Optional
|
||||
from datetime import datetime
|
||||
|
||||
from models.internal import (
|
||||
WorldInfo,
|
||||
WorldInfoEntry,
|
||||
ActivationType,
|
||||
)
|
||||
|
||||
|
||||
class WorldBookConverter:
|
||||
"""世界书数据转换器
|
||||
|
||||
负责 SillyTavern 格式和项目内部格式之间的转换。
|
||||
|
||||
SillyTavern 格式特点:
|
||||
- entries 是 dict (key 为 uid)
|
||||
- 使用 constant 字段表示常驻激活
|
||||
- position 是字符串 (如 "after_char")
|
||||
|
||||
项目内部格式特点:
|
||||
- entries 是 list
|
||||
- 使用 activationType 枚举
|
||||
- position 是数字 (0-5)
|
||||
- 包含 trigger_config 结构(前端需要)
|
||||
"""
|
||||
|
||||
@staticmethod
|
||||
def detect_format(data: Dict[str, Any]) -> str:
|
||||
"""
|
||||
智能检测世界书数据格式
|
||||
|
||||
Args:
|
||||
data: 世界书数据
|
||||
|
||||
Returns:
|
||||
'sillytavern' | 'internal' | 'unknown'
|
||||
"""
|
||||
# 检查 entries 类型
|
||||
entries = data.get("entries")
|
||||
if not entries:
|
||||
return "unknown"
|
||||
|
||||
# SillyTavern 特征: entries 是 dict
|
||||
if isinstance(entries, dict):
|
||||
return "sillytavern"
|
||||
|
||||
# 内部格式特征: entries 是 list
|
||||
if isinstance(entries, list):
|
||||
# 进一步检查是否有 trigger_config
|
||||
if len(entries) > 0 and isinstance(entries[0], dict):
|
||||
first_entry = entries[0]
|
||||
if "trigger_config" in first_entry:
|
||||
return "internal"
|
||||
# 也可能是简化的内部格式
|
||||
if "activationType" in first_entry or "position" in first_entry:
|
||||
return "internal"
|
||||
|
||||
return "unknown"
|
||||
|
||||
# 位置映射: SillyTavern 字符串 -> 内部数字
|
||||
POSITION_MAP_ST_TO_INTERNAL = {
|
||||
"after_char": 0,
|
||||
"before_char": 1,
|
||||
"before_example": 2,
|
||||
"after_example": 3,
|
||||
"author_note": 4,
|
||||
"system_prompt": 5,
|
||||
}
|
||||
|
||||
# 位置映射: 内部数字 -> SillyTavern 字符串
|
||||
POSITION_MAP_INTERNAL_TO_ST = {
|
||||
0: "after_char",
|
||||
1: "before_char",
|
||||
2: "before_example",
|
||||
3: "after_example",
|
||||
4: "author_note",
|
||||
5: "system_prompt",
|
||||
}
|
||||
|
||||
@staticmethod
|
||||
def st_to_internal(st_data: Dict[str, Any], name: str = None) -> Dict[str, Any]:
|
||||
"""
|
||||
将 SillyTavern 格式的世界书转换为内部格式
|
||||
|
||||
Args:
|
||||
st_data: SillyTavern 格式的世界书数据
|
||||
name: 世界书名称(可选,优先使用 st_data 中的 name)
|
||||
|
||||
Returns:
|
||||
内部格式的世界书字典(包含 trigger_config)
|
||||
"""
|
||||
now = int(datetime.now().timestamp())
|
||||
|
||||
# 转换条目
|
||||
entries = []
|
||||
st_entries = st_data.get("entries", {})
|
||||
|
||||
# SillyTavern 的 entries 可能是 dict 或 list
|
||||
if isinstance(st_entries, dict):
|
||||
entries_list = list(st_entries.values())
|
||||
elif isinstance(st_entries, list):
|
||||
entries_list = st_entries
|
||||
else:
|
||||
entries_list = []
|
||||
|
||||
for st_entry in entries_list:
|
||||
if not isinstance(st_entry, dict):
|
||||
continue
|
||||
|
||||
# 判断激活类型
|
||||
is_constant = st_entry.get("constant", False)
|
||||
activation_type = ActivationType.PERMANENT if is_constant else ActivationType.KEYWORD
|
||||
|
||||
# 转换位置
|
||||
st_position = st_entry.get("position", "after_char")
|
||||
internal_position = WorldBookConverter.POSITION_MAP_ST_TO_INTERNAL.get(st_position, 0)
|
||||
|
||||
# 构建 trigger_config (前端期望的格式)
|
||||
trigger_config = WorldBookConverter._build_trigger_config(
|
||||
is_constant=is_constant,
|
||||
key=st_entry.get("key", []),
|
||||
keysecondary=st_entry.get("keysecondary", []),
|
||||
selective=st_entry.get("selective", True)
|
||||
)
|
||||
|
||||
# 创建内部格式的条目
|
||||
entry_dict = {
|
||||
"uid": st_entry.get("uid", str(uuid.uuid4())),
|
||||
"key": st_entry.get("key", []),
|
||||
"keysecondary": st_entry.get("keysecondary", []),
|
||||
"content": st_entry.get("content", ""),
|
||||
"comment": st_entry.get("comment", ""),
|
||||
"activationType": activation_type.value,
|
||||
"trigger_config": trigger_config,
|
||||
"order": st_entry.get("order", 100),
|
||||
"position": internal_position,
|
||||
"depth": st_entry.get("depth", 4),
|
||||
"role": st_entry.get("role", 0),
|
||||
"probability": st_entry.get("probability", 100),
|
||||
"group": st_entry.get("group", []),
|
||||
"disable": st_entry.get("disable", False),
|
||||
"createdAt": now,
|
||||
"updatedAt": now
|
||||
}
|
||||
|
||||
entries.append(entry_dict)
|
||||
|
||||
# 创建内部格式的世界书
|
||||
worldbook_data = {
|
||||
"id": str(uuid.uuid4()),
|
||||
"name": name or st_data.get("name", "Unnamed"),
|
||||
"description": st_data.get("description", ""),
|
||||
"entries": entries,
|
||||
"createdAt": now,
|
||||
"updatedAt": now,
|
||||
"version": 1
|
||||
}
|
||||
|
||||
return worldbook_data
|
||||
|
||||
@staticmethod
|
||||
def internal_to_st(worldbook_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""
|
||||
将内部格式的世界书转换为 SillyTavern 格式
|
||||
|
||||
Args:
|
||||
worldbook_data: 内部格式的世界书字典
|
||||
|
||||
Returns:
|
||||
SillyTavern 格式的世界书数据
|
||||
"""
|
||||
# 转换条目
|
||||
st_entries = {}
|
||||
|
||||
for entry_data in worldbook_data.get("entries", []):
|
||||
if not isinstance(entry_data, dict):
|
||||
continue
|
||||
|
||||
uid = entry_data.get("uid", str(uuid.uuid4()))
|
||||
|
||||
# 从 trigger_config 或 activationType 判断是否常驻
|
||||
is_constant = WorldBookConverter._is_constant_entry(entry_data)
|
||||
|
||||
# 提取关键词
|
||||
key, keysecondary = WorldBookConverter._extract_keywords(entry_data)
|
||||
|
||||
# 转换位置
|
||||
internal_position = entry_data.get("position", 0)
|
||||
st_position = WorldBookConverter.POSITION_MAP_INTERNAL_TO_ST.get(internal_position, "after_char")
|
||||
|
||||
# 创建 SillyTavern 格式的条目
|
||||
st_entry = {
|
||||
"uid": uid,
|
||||
"key": key,
|
||||
"keysecondary": keysecondary,
|
||||
"content": entry_data.get("content", ""),
|
||||
"comment": entry_data.get("comment", ""),
|
||||
"constant": is_constant,
|
||||
"selective": not is_constant,
|
||||
"order": entry_data.get("order", 100),
|
||||
"position": st_position,
|
||||
"depth": entry_data.get("depth", 4),
|
||||
"probability": entry_data.get("probability", 100),
|
||||
"group": entry_data.get("group", []),
|
||||
"disable": entry_data.get("disable", False)
|
||||
}
|
||||
|
||||
st_entries[uid] = st_entry
|
||||
|
||||
# 创建 SillyTavern 格式的世界书
|
||||
st_data = {
|
||||
"name": worldbook_data.get("name", ""),
|
||||
"description": worldbook_data.get("description", ""),
|
||||
"entries": st_entries
|
||||
}
|
||||
|
||||
return st_data
|
||||
|
||||
@staticmethod
|
||||
def normalize_entry(entry_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""
|
||||
规范化条目数据,确保包含所有必需字段和 trigger_config
|
||||
|
||||
Args:
|
||||
entry_data: 条目数据(可能来自不同来源)
|
||||
|
||||
Returns:
|
||||
规范化后的条目数据
|
||||
"""
|
||||
now = int(datetime.now().timestamp())
|
||||
|
||||
# 如果已经有 trigger_config,直接返回
|
||||
if "trigger_config" in entry_data and entry_data["trigger_config"]:
|
||||
return entry_data
|
||||
|
||||
# 否则从其他字段构建 trigger_config
|
||||
is_constant = WorldBookConverter._is_constant_entry(entry_data)
|
||||
key, keysecondary = WorldBookConverter._extract_keywords(entry_data)
|
||||
|
||||
trigger_config = WorldBookConverter._build_trigger_config(
|
||||
is_constant=is_constant,
|
||||
key=key,
|
||||
keysecondary=keysecondary,
|
||||
selective=entry_data.get("selective", True)
|
||||
)
|
||||
|
||||
# 添加缺失的字段
|
||||
normalized = {
|
||||
"uid": entry_data.get("uid", str(uuid.uuid4())),
|
||||
"key": key,
|
||||
"keysecondary": keysecondary,
|
||||
"content": entry_data.get("content", ""),
|
||||
"comment": entry_data.get("comment", ""),
|
||||
"activationType": entry_data.get("activationType",
|
||||
ActivationType.PERMANENT.value if is_constant
|
||||
else ActivationType.KEYWORD.value),
|
||||
"trigger_config": trigger_config,
|
||||
"order": entry_data.get("order", 100),
|
||||
"position": entry_data.get("position", 0),
|
||||
"depth": entry_data.get("depth", 4),
|
||||
"role": entry_data.get("role", 0),
|
||||
"probability": entry_data.get("probability", 100),
|
||||
"group": entry_data.get("group", []),
|
||||
"disable": entry_data.get("disable", False),
|
||||
"createdAt": entry_data.get("createdAt", now),
|
||||
"updatedAt": entry_data.get("updatedAt", now)
|
||||
}
|
||||
|
||||
return normalized
|
||||
|
||||
@staticmethod
|
||||
def _build_trigger_config(
|
||||
is_constant: bool,
|
||||
key: List[str],
|
||||
keysecondary: List[str],
|
||||
selective: bool = True
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
构建 trigger_config 结构
|
||||
|
||||
Args:
|
||||
is_constant: 是否常驻激活
|
||||
key: 主关键词列表
|
||||
keysecondary: 次要关键词列表
|
||||
selective: 是否选择性匹配
|
||||
|
||||
Returns:
|
||||
trigger_config 字典
|
||||
"""
|
||||
return {
|
||||
"triggers": {
|
||||
"constant": [is_constant, None],
|
||||
"keyword": [
|
||||
not is_constant,
|
||||
{
|
||||
"key": key,
|
||||
"keysecondary": keysecondary,
|
||||
"selective": selective,
|
||||
"selectiveLogic": 0,
|
||||
"matchWholeWords": False,
|
||||
"caseSensitive": False
|
||||
}
|
||||
],
|
||||
"rag": [False, {
|
||||
"threshold": 0.75,
|
||||
"top_k": 5,
|
||||
"query_template": None
|
||||
}],
|
||||
"condition": [False, {
|
||||
"variable_a": "",
|
||||
"operator": "=",
|
||||
"variable_b": ""
|
||||
}]
|
||||
}
|
||||
}
|
||||
|
||||
@staticmethod
|
||||
def _is_constant_entry(entry_data: Dict[str, Any]) -> bool:
|
||||
"""
|
||||
判断条目是否为常驻激活
|
||||
|
||||
Args:
|
||||
entry_data: 条目数据
|
||||
|
||||
Returns:
|
||||
是否常驻激活
|
||||
"""
|
||||
# 优先从 trigger_config 判断
|
||||
if "trigger_config" in entry_data and entry_data["trigger_config"]:
|
||||
try:
|
||||
return entry_data["trigger_config"]["triggers"]["constant"][0]
|
||||
except (KeyError, IndexError, TypeError):
|
||||
pass
|
||||
|
||||
# 其次从 activationType 判断
|
||||
if "activationType" in entry_data:
|
||||
return entry_data["activationType"] == ActivationType.PERMANENT.value
|
||||
|
||||
# 最后从 constant 字段判断
|
||||
if "constant" in entry_data:
|
||||
return entry_data["constant"]
|
||||
|
||||
return False
|
||||
|
||||
@staticmethod
|
||||
def _extract_keywords(entry_data: Dict[str, Any]) -> tuple:
|
||||
"""
|
||||
从条目数据中提取关键词
|
||||
|
||||
Args:
|
||||
entry_data: 条目数据
|
||||
|
||||
Returns:
|
||||
(key, keysecondary) 元组
|
||||
"""
|
||||
# 优先从 trigger_config 提取
|
||||
if "trigger_config" in entry_data and entry_data["trigger_config"]:
|
||||
try:
|
||||
keyword_config = entry_data["trigger_config"]["triggers"]["keyword"][1]
|
||||
if keyword_config:
|
||||
key = keyword_config.get("key", [])
|
||||
keysecondary = keyword_config.get("keysecondary", [])
|
||||
return key, keysecondary
|
||||
except (KeyError, IndexError, TypeError):
|
||||
pass
|
||||
|
||||
# 否则从顶层字段提取
|
||||
key = entry_data.get("key", [])
|
||||
keysecondary = entry_data.get("keysecondary", [])
|
||||
|
||||
return key, keysecondary
|
||||
301
backend/models/internal.py
Normal file
301
backend/models/internal.py
Normal file
@@ -0,0 +1,301 @@
|
||||
"""
|
||||
项目内部数据结构定义
|
||||
|
||||
这是本项目真正使用的核心数据模型,所有业务逻辑都基于这些类型。
|
||||
与 sillytavern.py 不同,这里的模型不参与导入导出兼容,而是专注于:
|
||||
- 内部业务逻辑处理
|
||||
- API 响应数据结构
|
||||
- 数据存储格式
|
||||
- 工作流引擎数据交换
|
||||
|
||||
所有从 SillyTavern 导入的数据都会转换为这些内部模型进行处理,
|
||||
导出时再从内部模型转换回 SillyTavern 格式。
|
||||
"""
|
||||
from enum import Enum
|
||||
from typing import List, Optional, Dict, Any
|
||||
from pydantic import BaseModel, Field
|
||||
from datetime import datetime
|
||||
|
||||
|
||||
# ==================== 世界书 (World Info) ====================
|
||||
|
||||
class ActivationType(str, Enum):
|
||||
"""
|
||||
自定义激活方式类型（4种枚举）
|
||||
|
||||
这是项目的核心创新点之一,相比 SillyTavern 的简单 constant/selective 标志,
|
||||
我们提供了更灵活的激活机制。
|
||||
"""
|
||||
PERMANENT = 'permanent' # 永久激活 - 始终包含在上下文中
|
||||
KEYWORD = 'keyword' # 关键词触发 - 匹配关键词时激活
|
||||
RAG = 'rag' # RAG 检索激活 - 基于向量相似度检索
|
||||
LOGIC = 'logic' # 逻辑表达式激活 - 基于变量条件判断
|
||||
|
||||
|
||||
class LogicOperator(str, Enum):
|
||||
"""逻辑运算符（用于 LOGIC 激活类型）"""
|
||||
EQUALS = 'equals' # 等于
|
||||
NOT_EQUALS = 'not_equals' # 不等于
|
||||
CONTAINS = 'contains' # 包含
|
||||
NOT_CONTAINS = 'not_contains' # 不包含
|
||||
GREATER = 'greater' # 大于
|
||||
LESS = 'less' # 小于
|
||||
|
||||
|
||||
class LogicExpression(BaseModel):
|
||||
"""
|
||||
逻辑表达式结构（用于 LOGIC 激活类型）
|
||||
|
||||
示例: variable1="mood", operator="equals", variable2="happy"
|
||||
表示当 mood 变量等于 happy 时激活该条目
|
||||
"""
|
||||
variable1: str = Field(..., description="第一个变量名")
|
||||
operator: LogicOperator = Field(..., description="比较运算符")
|
||||
variable2: str = Field(..., description="第二个变量名或值")
|
||||
|
||||
|
||||
class RAGConfig(BaseModel):
|
||||
"""
|
||||
RAG 配置（用于 RAG 激活类型）
|
||||
|
||||
控制如何从向量数据库中检索相关内容
|
||||
"""
|
||||
libraryId: str = Field(..., description="绑定的 RAG 库 ID")
|
||||
threshold: Optional[float] = Field(0.7, ge=0, le=1, description="相似度阈值 (0-1)")
|
||||
maxEntries: Optional[int] = Field(5, gt=0, description="最大返回条目数")
|
||||
|
||||
|
||||
class WorldInfoEntry(BaseModel):
|
||||
"""
|
||||
项目内部世界书条目结构
|
||||
|
||||
这是世界书的核心单元,每个条目代表一段可以被动态注入到对话上下文中的知识。
|
||||
相比 SillyTavern,我们添加了 activationType、logicExpression、ragConfig 等高级功能。
|
||||
"""
|
||||
uid: str = Field(..., description="条目唯一标识符 (UUID)")
|
||||
key: Optional[List[str]] = Field(None, description="主关键词列表 (用于 KEYWORD 激活)")
|
||||
keysecondary: Optional[List[str]] = Field(None, description="次要关键词列表 (可选过滤)")
|
||||
content: str = Field(..., description="条目内容 - 激活时注入的文本")
|
||||
activationType: ActivationType = Field(..., description="激活方式")
|
||||
logicExpression: Optional[LogicExpression] = Field(None, description="逻辑表达式 (LOGIC 类型使用)")
|
||||
ragConfig: Optional[RAGConfig] = Field(None, description="RAG 配置 (RAG 类型使用)")
|
||||
order: int = Field(0, description="插入顺序 - 数值越大越靠近末尾")
|
||||
position: Optional[str] = Field('after_char', description="插入位置")
|
||||
depth: Optional[int] = Field(None, description="插入深度 (当 position='at_depth' 时使用)")
|
||||
probability: Optional[float] = Field(100, ge=0, le=100, description="激活概率 (0-100)")
|
||||
group: Optional[List[str]] = Field(None, description="所属组标签")
|
||||
disable: bool = Field(False, description="是否禁用")
|
||||
createdAt: int = Field(default_factory=lambda: int(datetime.now().timestamp()), description="创建时间戳")
|
||||
updatedAt: int = Field(default_factory=lambda: int(datetime.now().timestamp()), description="最后更新时间戳")
|
||||
|
||||
|
||||
class WorldInfo(BaseModel):
|
||||
"""
|
||||
项目内部世界书结构
|
||||
|
||||
世界书是角色知识的集合,可以绑定到角色卡上,在对话中动态提供背景信息。
|
||||
"""
|
||||
id: str = Field(..., description="世界书唯一标识符 (UUID)")
|
||||
name: str = Field(..., description="世界书名称")
|
||||
description: Optional[str] = Field(None, description="世界书描述")
|
||||
entries: List[WorldInfoEntry] = Field(default_factory=list, description="条目数组")
|
||||
createdAt: int = Field(default_factory=lambda: int(datetime.now().timestamp()), description="创建时间戳")
|
||||
updatedAt: int = Field(default_factory=lambda: int(datetime.now().timestamp()), description="最后更新时间戳")
|
||||
version: int = Field(1, description="版本号 (用于数据迁移)")
|
||||
|
||||
|
||||
# ==================== 角色卡 (Character Card) ====================
|
||||
|
||||
class OutputSchemaField(BaseModel):
|
||||
"""
|
||||
Vercel AI SDK Output.object() 的表头定义
|
||||
|
||||
用于结构化输出,让 LLM 按照指定格式返回数据。
|
||||
这是项目的特色功能,支持动态表格生成。
|
||||
"""
|
||||
name: str = Field(..., description="字段名称")
|
||||
type: str = Field(..., description="字段类型 (string/number/boolean/array/object)")
|
||||
description: str = Field(..., description="字段描述")
|
||||
required: Optional[bool] = Field(None, description="是否必需")
|
||||
enum: Optional[List[str]] = Field(None, description="枚举值 (字符串固定选项)")
|
||||
fields: Optional[List['OutputSchemaField']] = Field(None, description="嵌套字段 (object 类型)")
|
||||
|
||||
|
||||
class CharacterCard(BaseModel):
|
||||
"""
|
||||
项目内部角色卡结构
|
||||
|
||||
角色卡是对话 AI 的核心定义,包含人设、场景、开场白等。
|
||||
相比 SillyTavern,我们添加了 categories、outputSchema、worldInfoId 等功能。
|
||||
"""
|
||||
id: str = Field(..., description="角色唯一标识符 (UUID)")
|
||||
name: str = Field(..., description="角色名称")
|
||||
description: str = Field(..., description="角色详细描述")
|
||||
personality: str = Field(..., description="角色性格特征")
|
||||
scenario: str = Field(..., description="场景设定")
|
||||
first_mes: str = Field(..., description="首条开场消息")
|
||||
mes_example: str = Field(..., description="对话示例")
|
||||
categories: List[str] = Field(default_factory=list, description="分类标签 (用于前端筛选)")
|
||||
worldInfoId: Optional[str] = Field(None, description="绑定的世界书 ID")
|
||||
outputSchema: Optional[List[OutputSchemaField]] = Field(None, description="输出 schema 定义 (结构化输出)")
|
||||
avatarPath: Optional[str] = Field(None, description="角色头像路径")
|
||||
alternate_greetings: Optional[List[str]] = Field(None, description="替代问候语数组")
|
||||
tags: Optional[List[str]] = Field(None, description="标签数组")
|
||||
createdAt: int = Field(default_factory=lambda: int(datetime.now().timestamp()), description="创建时间戳")
|
||||
updatedAt: int = Field(default_factory=lambda: int(datetime.now().timestamp()), description="最后更新时间戳")
|
||||
lastChatAt: Optional[int] = Field(None, description="最后聊天时间戳")
|
||||
isFavorite: bool = Field(False, description="收藏状态")
|
||||
version: int = Field(1, description="版本号")
|
||||
|
||||
|
||||
# ==================== 聊天记录 (Chat Log) ====================
|
||||
|
||||
class ChatHeader(BaseModel):
|
||||
"""
|
||||
项目内部聊天记录头
|
||||
|
||||
包含聊天的元数据,如参与角色、创建时间等。
|
||||
"""
|
||||
id: str = Field(..., description="聊天唯一标识符 (UUID)")
|
||||
displayName: str = Field(..., description="显示名称 (聊天标题)")
|
||||
characterId: str = Field(..., description="关联的角色卡 ID")
|
||||
userName: str = Field("User", description="用户角色名")
|
||||
characterName: str = Field(..., description="AI 角色名称")
|
||||
tableData: Optional[Dict[str, Any]] = Field(None, description="表格数据 (对应 outputSchema)")
|
||||
createdAt: int = Field(default_factory=lambda: int(datetime.now().timestamp()), description="创建时间戳")
|
||||
updatedAt: int = Field(default_factory=lambda: int(datetime.now().timestamp()), description="最后更新时间戳")
|
||||
messageCount: int = Field(0, description="消息数量")
|
||||
ragLibraryId: Optional[str] = Field(None, description="关联的 RAG 历史消息库 ID")
|
||||
|
||||
|
||||
class ChatMessage(BaseModel):
|
||||
"""
|
||||
项目内部聊天消息
|
||||
|
||||
单条对话消息,支持多版本 (swipes)、token 统计等功能。
|
||||
"""
|
||||
id: str = Field(..., description="消息唯一标识符 (UUID)")
|
||||
name: str = Field(..., description="发送者名称")
|
||||
is_user: bool = Field(..., description="是否为用户消息")
|
||||
is_system: Optional[bool] = Field(None, description="是否为系统消息")
|
||||
sendDate: str = Field(..., description="发送日期 ISO 字符串")
|
||||
mes: str = Field(..., description="消息内容文本")
|
||||
chatId: str = Field(..., description="关联的聊天 ID")
|
||||
swipes: Optional[List[str]] = Field(None, description="替换回答数组 (多版本)")
|
||||
swipe_id: Optional[int] = Field(0, description="当前选择的版本索引")
|
||||
tokenCount: Optional[int] = Field(None, description="Token 数量 (用于统计)")
|
||||
isTemporary: Optional[bool] = Field(None, description="是否为临时消息 (未保存)")
|
||||
|
||||
|
||||
class ChatLog(BaseModel):
|
||||
"""
|
||||
项目内部完整聊天记录
|
||||
|
||||
包含聊天头和所有消息,是完整的对话历史。
|
||||
"""
|
||||
header: ChatHeader = Field(..., description="聊天头")
|
||||
messages: List[ChatMessage] = Field(default_factory=list, description="消息列表")
|
||||
|
||||
|
||||
# ==================== 预设 (Preset) ====================
|
||||
|
||||
class GenerationPreset(BaseModel):
|
||||
"""
|
||||
项目内部采样参数预设
|
||||
|
||||
控制 LLM 生成的参数配置,如温度、top_p 等。
|
||||
"""
|
||||
id: str = Field(..., description="预设唯一标识符 (UUID)")
|
||||
name: str = Field(..., description="预设名称")
|
||||
temperature: float = Field(1.0, ge=0, le=2, description="温度 (控制随机性)")
|
||||
topP: float = Field(1.0, ge=0, le=1, description="Top P (核采样)")
|
||||
topK: int = Field(0, ge=0, description="Top K")
|
||||
repetitionPenalty: float = Field(1.0, ge=0, description="重复惩罚")
|
||||
frequencyPenalty: Optional[float] = Field(None, description="频率惩罚")
|
||||
presencePenalty: Optional[float] = Field(None, description="存在惩罚")
|
||||
maxLength: Optional[int] = Field(None, gt=0, description="最大生成长度")
|
||||
isDefault: bool = Field(False, description="是否为默认预设")
|
||||
createdAt: int = Field(default_factory=lambda: int(datetime.now().timestamp()), description="创建时间戳")
|
||||
updatedAt: int = Field(default_factory=lambda: int(datetime.now().timestamp()), description="最后更新时间戳")
|
||||
|
||||
|
||||
# ==================== 提示词预设 (Prompt Preset) ====================
|
||||
|
||||
class PromptRole(str, Enum):
|
||||
"""
|
||||
Prompt 角色类型
|
||||
|
||||
内部业务层只保留三种角色,简化了 SillyTavern 的复杂角色系统。
|
||||
"""
|
||||
SYSTEM = 'system' # 系统指令
|
||||
AI = 'ai' # AI 助手
|
||||
USER = 'user' # 用户
|
||||
|
||||
|
||||
class PromptEntry(BaseModel):
|
||||
"""
|
||||
内部业务层 - Prompt 条目
|
||||
|
||||
提示词模板的基本单元,可以组合成完整的提示词预设。
|
||||
这是基于某个 character_id 生成的"当前视图"。
|
||||
"""
|
||||
identifier: str = Field(..., description="稳定关联键 (用于回写)")
|
||||
name: str = Field(..., description="条目名 (前端显示)")
|
||||
enabled: bool = Field(True, description="是否启用 (当前作用域下的业务状态)")
|
||||
content: str = Field(..., description="条目内容 (静态内容视图)")
|
||||
order: int = Field(..., description="条目顺序 (前端展示和拖拽排序)")
|
||||
role: PromptRole = Field(..., description="角色类型")
|
||||
tokenCount: int = Field(0, description="总 token 数 (派生显示字段)")
|
||||
isSystemNode: bool = Field(False, description="是否固有节点 (不可删除)")
|
||||
|
||||
|
||||
class PromptPresetView(BaseModel):
|
||||
"""
|
||||
内部业务层 - Prompt 预设视图
|
||||
|
||||
基于某个 character_id 的"当前视图",包含已排序、已过滤的条目列表。
|
||||
"""
|
||||
characterId: str = Field(..., description="关联的角色 ID")
|
||||
entries: List[PromptEntry] = Field(default_factory=list, description="当前视图的条目列表")
|
||||
updatedAt: int = Field(default_factory=lambda: int(datetime.now().timestamp()), description="最后更新时间戳")
|
||||
version: int = Field(1, description="版本号")
|
||||
|
||||
|
||||
# ==================== RAG 配置 ====================
|
||||
|
||||
class RAGSearchConfig(BaseModel):
|
||||
"""RAG 搜索配置"""
|
||||
topK: int = Field(5, gt=0, description="每次检索返回的结果数")
|
||||
threshold: float = Field(0.7, ge=0, le=1, description="相似度阈值 (0-1)")
|
||||
maxContextLength: int = Field(2000, gt=0, description="最大上下文长度 (字符数)")
|
||||
|
||||
|
||||
class CharacterRAGConfig(BaseModel):
|
||||
"""
|
||||
角色卡 RAG 世界书库配置
|
||||
|
||||
记录角色卡关联的 RAG 知识库,用于动态检索相关知识。
|
||||
"""
|
||||
characterId: str = Field(..., description="角色卡ID")
|
||||
ragLibraryIds: List[str] = Field(default_factory=list, description="关联的RAG库ID列表")
|
||||
enabled: bool = Field(True, description="是否启用")
|
||||
searchConfig: Optional[RAGSearchConfig] = Field(None, description="搜索配置")
|
||||
position: str = Field('after_char', description="RAG内容插入位置")
|
||||
createdAt: int = Field(default_factory=lambda: int(datetime.now().timestamp()), description="创建时间戳")
|
||||
updatedAt: int = Field(default_factory=lambda: int(datetime.now().timestamp()), description="最后更新时间戳")
|
||||
|
||||
|
||||
class ChatRAGConfig(BaseModel):
|
||||
"""
|
||||
聊天会话 RAG 历史消息配置
|
||||
|
||||
记录聊天会话关联的 RAG 历史消息库,用于智能检索历史对话。
|
||||
"""
|
||||
chatId: str = Field(..., description="聊天会话ID")
|
||||
ragLibraryId: Optional[str] = Field(None, description="关联的RAG历史消息库ID")
|
||||
enabled: bool = Field(True, description="是否启用")
|
||||
searchConfig: Optional[Dict[str, Any]] = Field(None, description="搜索配置")
|
||||
autoIndex: bool = Field(True, description="是否自动索引新消息")
|
||||
indexConfig: Optional[Dict[str, Any]] = Field(None, description="索引配置")
|
||||
createdAt: int = Field(default_factory=lambda: int(datetime.now().timestamp()), description="创建时间戳")
|
||||
updatedAt: int = Field(default_factory=lambda: int(datetime.now().timestamp()), description="最后更新时间戳")
|
||||
11
backend/services/__init__.py
Normal file
11
backend/services/__init__.py
Normal file
@@ -0,0 +1,11 @@
|
||||
"""
|
||||
业务服务层
|
||||
|
||||
包含项目的核心业务逻辑，协调 Models、Utils 和 LLM 组件。
|
||||
"""
|
||||
from .prompt_assembler import PromptAssembler, PromptConfig
|
||||
|
||||
__all__ = [
|
||||
'PromptAssembler',
|
||||
'PromptConfig',
|
||||
]
|
||||
173
backend/services/comfyui_workflow_manager.py
Normal file
173
backend/services/comfyui_workflow_manager.py
Normal file
@@ -0,0 +1,173 @@
|
||||
"""
|
||||
ComfyUI Workflow Manager
|
||||
管理工作流 JSON 文件的上传、删除和加载
|
||||
"""
|
||||
import json
|
||||
import os
|
||||
from pathlib import Path
|
||||
from typing import List, Dict, Optional
|
||||
from fastapi import UploadFile, HTTPException
|
||||
import shutil
|
||||
from core.config import settings
|
||||
|
||||
# 工作流目录 - 使用统一的数据目录
|
||||
WORKFLOW_DIR = settings.COMFYUI_WORKFLOWS_PATH
|
||||
WORKFLOW_DIR.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
|
||||
class WorkflowManager:
|
||||
"""ComfyUI 工作流管理器"""
|
||||
|
||||
@staticmethod
|
||||
def list_workflows() -> List[Dict[str, str]]:
|
||||
"""列出所有可用的工作流"""
|
||||
workflows = []
|
||||
|
||||
for json_file in WORKFLOW_DIR.glob("*.json"):
|
||||
try:
|
||||
with open(json_file, 'r', encoding='utf-8') as f:
|
||||
workflow_data = json.load(f)
|
||||
|
||||
workflows.append({
|
||||
"filename": json_file.name,
|
||||
"name": json_file.stem,
|
||||
"nodes_count": len(workflow_data),
|
||||
"size": json_file.stat().st_size
|
||||
})
|
||||
except Exception as e:
|
||||
print(f"Error loading workflow {json_file.name}: {e}")
|
||||
continue
|
||||
|
||||
return workflows
|
||||
|
||||
@staticmethod
|
||||
def load_workflow(filename: str) -> Dict:
|
||||
"""加载指定工作流"""
|
||||
filepath = WORKFLOW_DIR / filename
|
||||
|
||||
if not filepath.exists():
|
||||
raise HTTPException(status_code=404, detail=f"Workflow '{filename}' not found")
|
||||
|
||||
if not filepath.suffix == '.json':
|
||||
raise HTTPException(status_code=400, detail="Invalid file type")
|
||||
|
||||
try:
|
||||
with open(filepath, 'r', encoding='utf-8') as f:
|
||||
return json.load(f)
|
||||
except json.JSONDecodeError as e:
|
||||
raise HTTPException(status_code=500, detail=f"Invalid JSON: {str(e)}")
|
||||
|
||||
@staticmethod
|
||||
async def upload_workflow(file: UploadFile) -> Dict[str, str]:
|
||||
"""上传工作流文件"""
|
||||
# 验证文件名
|
||||
if not file.filename or not file.filename.endswith('.json'):
|
||||
raise HTTPException(status_code=400, detail="File must be a JSON file")
|
||||
|
||||
# 安全检查：防止路径遍历攻击
|
||||
safe_filename = os.path.basename(file.filename)
|
||||
if not safe_filename:
|
||||
raise HTTPException(status_code=400, detail="Invalid filename")
|
||||
|
||||
filepath = WORKFLOW_DIR / safe_filename
|
||||
|
||||
# 如果文件已存在，先备份
|
||||
if filepath.exists():
|
||||
backup_path = WORKFLOW_DIR / f"{safe_filename}.bak"
|
||||
shutil.copy2(filepath, backup_path)
|
||||
|
||||
# 保存文件
|
||||
try:
|
||||
content = await file.read()
|
||||
|
||||
# 验证 JSON 格式
|
||||
try:
|
||||
workflow_data = json.loads(content)
|
||||
|
||||
# 基本验证：检查是否是 ComfyUI 工作流
|
||||
if not isinstance(workflow_data, dict):
|
||||
raise ValueError("Workflow must be a JSON object")
|
||||
|
||||
# 检查是否包含必要的节点类型
|
||||
has_sampler = any(
|
||||
node.get("class_type") == "KSampler"
|
||||
for node in workflow_data.values()
|
||||
if isinstance(node, dict)
|
||||
)
|
||||
|
||||
if not has_sampler:
|
||||
raise ValueError("Invalid ComfyUI workflow: missing KSampler node")
|
||||
|
||||
except json.JSONDecodeError:
|
||||
raise HTTPException(status_code=400, detail="Invalid JSON format")
|
||||
except ValueError as e:
|
||||
raise HTTPException(status_code=400, detail=str(e))
|
||||
|
||||
# 写入文件
|
||||
with open(filepath, 'w', encoding='utf-8') as f:
|
||||
f.write(content.decode('utf-8'))
|
||||
|
||||
return {
|
||||
"message": "Workflow uploaded successfully",
|
||||
"filename": safe_filename,
|
||||
"size": len(content)
|
||||
}
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
# 如果出错，恢复备份
|
||||
backup_path = WORKFLOW_DIR / f"{safe_filename}.bak"
|
||||
if backup_path.exists():
|
||||
shutil.move(backup_path, filepath)
|
||||
raise HTTPException(status_code=500, detail=f"Upload failed: {str(e)}")
|
||||
|
||||
@staticmethod
|
||||
def delete_workflow(filename: str) -> Dict[str, str]:
|
||||
"""删除工作流文件"""
|
||||
# 安全检查
|
||||
safe_filename = os.path.basename(filename)
|
||||
if not safe_filename or not safe_filename.endswith('.json'):
|
||||
raise HTTPException(status_code=400, detail="Invalid filename")
|
||||
|
||||
filepath = WORKFLOW_DIR / safe_filename
|
||||
|
||||
if not filepath.exists():
|
||||
raise HTTPException(status_code=404, detail=f"Workflow '{filename}' not found")
|
||||
|
||||
# 不允许删除默认工作流
|
||||
if safe_filename == "default_txt2img.json":
|
||||
raise HTTPException(
|
||||
status_code=403,
|
||||
detail="Cannot delete default workflow"
|
||||
)
|
||||
|
||||
try:
|
||||
filepath.unlink()
|
||||
return {"message": f"Workflow '{safe_filename}' deleted successfully"}
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=f"Delete failed: {str(e)}")
|
||||
|
||||
@staticmethod
|
||||
def replace_prompt_in_workflow(workflow: Dict, prompt: str) -> Dict:
|
||||
"""
|
||||
在工作流中替换提示词
|
||||
找到第一个 CLIPTextEncode 节点，替换其 text 字段
|
||||
"""
|
||||
import copy
|
||||
workflow_copy = copy.deepcopy(workflow)
|
||||
|
||||
# 查找 CLIPTextEncode 节点（通常是正向提示词）
|
||||
for node_id, node in workflow_copy.items():
|
||||
if isinstance(node, dict) and node.get("class_type") == "CLIPTextEncode":
|
||||
if "text" in node.get("inputs", {}):
|
||||
# 替换提示词
|
||||
node["inputs"]["text"] = prompt
|
||||
return workflow_copy
|
||||
|
||||
# 如果没有找到 CLIPTextEncode 节点，抛出错误
|
||||
raise ValueError("No CLIPTextEncode node found in workflow")
|
||||
|
||||
|
||||
# 全局实例
|
||||
workflow_manager = WorkflowManager()
|
||||
172
backend/services/llm_model_service.py
Normal file
172
backend/services/llm_model_service.py
Normal file
@@ -0,0 +1,172 @@
|
||||
"""
|
||||
LLM 模型管理服务
|
||||
|
||||
提供获取不同 LLM 提供商可用模型列表的功能
|
||||
"""
|
||||
from typing import List, Dict, Any, Optional
|
||||
import requests
|
||||
|
||||
|
||||
class LLMModelService:
|
||||
"""LLM 模型管理服务"""
|
||||
|
||||
@staticmethod
|
||||
def get_openai_models(api_key: str, base_url: Optional[str] = None) -> List[str]:
|
||||
"""
|
||||
获取 OpenAI 兼容 API 的模型列表
|
||||
|
||||
Args:
|
||||
api_key: API Key
|
||||
base_url: API 基础 URL，默认为 OpenAI 官方 API
|
||||
|
||||
Returns:
|
||||
模型名称列表
|
||||
"""
|
||||
try:
|
||||
# 默认使用 OpenAI 官方 API
|
||||
if not base_url:
|
||||
base_url = "https://api.openai.com/v1"
|
||||
|
||||
# 确保 base_url 以 /v1 结尾
|
||||
if not base_url.endswith('/v1'):
|
||||
base_url = base_url.rstrip('/') + '/v1'
|
||||
|
||||
response = requests.get(
|
||||
f"{base_url}/models",
|
||||
headers={
|
||||
"Authorization": f"Bearer {api_key}",
|
||||
"Content-Type": "application/json"
|
||||
},
|
||||
timeout=10
|
||||
)
|
||||
|
||||
if response.status_code != 200:
|
||||
raise Exception(f"HTTP {response.status_code}: {response.text}")
|
||||
|
||||
data = response.json()
|
||||
models = [model['id'] for model in data.get('data', [])]
|
||||
|
||||
# 过滤出聊天模型（可选）
|
||||
chat_models = [
|
||||
m for m in models
|
||||
if any(keyword in m.lower() for keyword in ['gpt', 'chat'])
|
||||
]
|
||||
|
||||
# 如果没有找到聊天模型，返回所有模型
|
||||
return chat_models if chat_models else models
|
||||
|
||||
except Exception as e:
|
||||
raise Exception(f"获取 OpenAI 模型列表失败: {str(e)}")
|
||||
|
||||
@staticmethod
|
||||
def get_anthropic_models(api_key: str) -> List[str]:
|
||||
"""
|
||||
获取 Anthropic Claude 模型列表
|
||||
|
||||
Args:
|
||||
api_key: API Key
|
||||
|
||||
Returns:
|
||||
模型名称列表
|
||||
"""
|
||||
try:
|
||||
# Anthropic 没有公开的模型列表 API，返回已知模型
|
||||
return [
|
||||
"claude-3-5-sonnet-20241022",
|
||||
"claude-3-5-haiku-20241022",
|
||||
"claude-3-opus-20240229",
|
||||
"claude-3-sonnet-20240229",
|
||||
"claude-3-haiku-20240307",
|
||||
"claude-2.1",
|
||||
"claude-2.0",
|
||||
"claude-instant-1.2"
|
||||
]
|
||||
|
||||
except Exception as e:
|
||||
raise Exception(f"获取 Anthropic 模型列表失败: {str(e)}")
|
||||
|
||||
@staticmethod
|
||||
def get_ollama_models(base_url: str = "http://localhost:11434") -> List[str]:
|
||||
"""
|
||||
获取 Ollama 本地模型列表
|
||||
|
||||
Args:
|
||||
base_url: Ollama API 地址
|
||||
|
||||
Returns:
|
||||
模型名称列表
|
||||
"""
|
||||
try:
|
||||
response = requests.get(
|
||||
f"{base_url}/api/tags",
|
||||
timeout=10
|
||||
)
|
||||
|
||||
if response.status_code != 200:
|
||||
raise Exception(f"HTTP {response.status_code}: {response.text}")
|
||||
|
||||
data = response.json()
|
||||
models = [model['name'] for model in data.get('models', [])]
|
||||
|
||||
return models
|
||||
|
||||
except Exception as e:
|
||||
raise Exception(f"获取 Ollama 模型列表失败: {str(e)}")
|
||||
|
||||
@staticmethod
|
||||
def detect_provider(api_url: str) -> str:
|
||||
"""
|
||||
根据 API URL 检测提供商类型
|
||||
|
||||
Args:
|
||||
api_url: API 地址
|
||||
|
||||
Returns:
|
||||
提供商类型: 'openai', 'anthropic', 'ollama', 'unknown'
|
||||
"""
|
||||
api_url_lower = api_url.lower()
|
||||
|
||||
if 'openai' in api_url_lower or 'api.openai.com' in api_url_lower:
|
||||
return 'openai'
|
||||
elif 'anthropic' in api_url_lower or 'api.anthropic.com' in api_url_lower:
|
||||
return 'anthropic'
|
||||
elif 'ollama' in api_url_lower or 'localhost:11434' in api_url_lower or '127.0.0.1:11434' in api_url_lower:
|
||||
return 'ollama'
|
||||
elif 'siliconflow' in api_url_lower or 'silicon.cloud' in api_url_lower:
|
||||
# SiliconFlow 等兼容 OpenAI API 的服务
|
||||
return 'openai'
|
||||
elif 'deepseek' in api_url_lower:
|
||||
# DeepSeek 等兼容 OpenAI API 的服务
|
||||
return 'openai'
|
||||
else:
|
||||
# 默认尝试 OpenAI 兼容 API
|
||||
return 'openai'
|
||||
|
||||
@staticmethod
|
||||
def get_models_by_provider(
|
||||
provider: str,
|
||||
api_key: str,
|
||||
api_url: Optional[str] = None
|
||||
) -> List[str]:
|
||||
"""
|
||||
根据提供商类型获取模型列表
|
||||
|
||||
Args:
|
||||
provider: 提供商类型 ('openai', 'anthropic', 'ollama')
|
||||
api_key: API Key
|
||||
api_url: API 地址（可选）
|
||||
|
||||
Returns:
|
||||
模型名称列表
|
||||
"""
|
||||
if provider == 'openai':
|
||||
return LLMModelService.get_openai_models(api_key, api_url)
|
||||
elif provider == 'anthropic':
|
||||
return LLMModelService.get_anthropic_models(api_key)
|
||||
elif provider == 'ollama':
|
||||
base_url = api_url or "http://localhost:11434"
|
||||
# 移除 /v1 后缀（如果有）
|
||||
base_url = base_url.replace('/v1', '').replace('/v1/', '')
|
||||
return LLMModelService.get_ollama_models(base_url)
|
||||
else:
|
||||
raise Exception(f"不支持的提供商: {provider}")
|
||||
221
backend/services/prompt_assembler.py
Normal file
221
backend/services/prompt_assembler.py
Normal file
@@ -0,0 +1,221 @@
|
||||
"""
|
||||
提示词组装器 (Prompt Assembler)
|
||||
|
||||
负责根据 SillyTavern 规范将角色卡、世界书、聊天历史等组件
|
||||
拼装成最终的 LLM 消息列表。
|
||||
"""
|
||||
import re
|
||||
from typing import List, Dict, Optional
|
||||
from langchain_core.messages import SystemMessage, HumanMessage, AIMessage, BaseMessage
|
||||
|
||||
from models.internal import CharacterCard, ChatMessage, WorldInfoEntry
|
||||
|
||||
|
||||
class PromptConfig:
|
||||
"""提示词组装配置"""
|
||||
def __init__(
|
||||
self,
|
||||
an_position: str = "after_history", # "before_history" or "after_history"
|
||||
an_depth: int = 4,
|
||||
post_history_instructions: Optional[str] = None
|
||||
):
|
||||
self.an_position = an_position
|
||||
self.an_depth = an_depth
|
||||
self.post_history_instructions = post_history_instructions
|
||||
|
||||
|
||||
class PromptAssembler:
|
||||
"""
|
||||
轻量级提示词组装核心
|
||||
|
||||
不依赖复杂的框架，只负责纯粹的文本拼接和位置插入。
|
||||
"""
|
||||
|
||||
# SillyTavern 的位置枚举映射
|
||||
POS_WI_BEFORE = 0
|
||||
POS_WI_AFTER = 1
|
||||
POS_EXAMPLES_BEFORE = 2
|
||||
POS_EXAMPLES_AFTER = 3
|
||||
POS_AN_TOP = 4
|
||||
POS_AN_BOTTOM = 5
|
||||
POS_DEPTH = 6
|
||||
POS_OUTLET = 7
|
||||
|
||||
def assemble(
|
||||
self,
|
||||
character: CharacterCard,
|
||||
chat_history: List[ChatMessage],
|
||||
user_input: str,
|
||||
active_entries: List[WorldInfoEntry],
|
||||
config: PromptConfig = PromptConfig()
|
||||
) -> List[BaseMessage]:
|
||||
"""
|
||||
执行完整的提示词组装流程
|
||||
|
||||
Returns:
|
||||
List[BaseMessage]: 准备好发送给 LLM 的消息列表
|
||||
"""
|
||||
# 1. 按位置分组世界书条目
|
||||
grouped_entries = self._group_entries_by_position(active_entries)
|
||||
|
||||
# 2. 组装 Story String (包含 Pos 0-3)
|
||||
story_string = self._build_story_string(character, grouped_entries)
|
||||
|
||||
# 3. 组装 Author's Note (包含 Pos 4-5)
|
||||
authors_note_content = self._build_authors_note(grouped_entries, config.an_depth)
|
||||
|
||||
# 4. 处理 Chat History 并注入 Depth 条目 (Pos 6)
|
||||
processed_history = self._inject_depth_entries(chat_history, grouped_entries.get(self.POS_DEPTH, []))
|
||||
|
||||
# 5. 准备 Outlet 替换字典 (Pos 7)
|
||||
outlet_map = {entry.uid: entry.content for entry in grouped_entries.get(self.POS_OUTLET, [])}
|
||||
|
||||
# 6. 最终封装为 Messages
|
||||
return self._wrap_to_messages(
|
||||
story_string,
|
||||
authors_note_content,
|
||||
processed_history,
|
||||
user_input,
|
||||
outlet_map,
|
||||
config
|
||||
)
|
||||
|
||||
def _group_entries_by_position(self, entries: List[WorldInfoEntry]) -> Dict[int, List[WorldInfoEntry]]:
|
||||
"""将激活的条目按 position 分组"""
|
||||
grouped = {}
|
||||
for entry in entries:
|
||||
# 这里假设 entry.position 存储的是我们定义的 0-7 整数
|
||||
pos = entry.position if isinstance(entry.position, int) else 1 # 默认为 wiAfter
|
||||
if pos not in grouped:
|
||||
grouped[pos] = []
|
||||
grouped[pos].append(entry)
|
||||
|
||||
# 对每个组内的条目按 order 排序
|
||||
for pos in grouped:
|
||||
grouped[pos].sort(key=lambda x: x.order)
|
||||
return grouped
|
||||
|
||||
def _build_story_string(self, character: CharacterCard, grouped: Dict) -> str:
|
||||
"""组装故事字符串 (Story String)"""
|
||||
parts = []
|
||||
|
||||
# Pos 0: wiBefore
|
||||
for entry in grouped.get(self.POS_WI_BEFORE, []):
|
||||
parts.append(entry.content)
|
||||
|
||||
# 角色核心信息
|
||||
parts.append(f"[Character('{character.name}')]\n{character.description}\n")
|
||||
parts.append(f"Personality: {character.personality}\n")
|
||||
parts.append(f"Scenario: {character.scenario}\n")
|
||||
|
||||
# Pos 1: wiAfter
|
||||
for entry in grouped.get(self.POS_WI_AFTER, []):
|
||||
parts.append(entry.content)
|
||||
|
||||
# Pos 2: Examples Before
|
||||
for entry in grouped.get(self.POS_EXAMPLES_BEFORE, []):
|
||||
parts.append(entry.content)
|
||||
|
||||
# 示例对话
|
||||
if character.mes_example:
|
||||
parts.append(f"<START>\n{character.mes_example}")
|
||||
|
||||
# Pos 3: Examples After
|
||||
for entry in grouped.get(self.POS_EXAMPLES_AFTER, []):
|
||||
parts.append(entry.content)
|
||||
|
||||
return "\n".join(parts)
|
||||
|
||||
def _build_authors_note(self, grouped: Dict, depth: int) -> str:
|
||||
"""组装作者笔记 (Author's Note)"""
|
||||
parts = []
|
||||
|
||||
# Pos 4: AN Top
|
||||
for entry in grouped.get(self.POS_AN_TOP, []):
|
||||
parts.append(entry.content)
|
||||
|
||||
# AN 核心内容 (这里简化为一个占位，实际应从角色卡或设置获取)
|
||||
parts.append(f"[Author's note at depth {depth}]")
|
||||
|
||||
# Pos 5: AN Bottom
|
||||
for entry in grouped.get(self.POS_AN_BOTTOM, []):
|
||||
parts.append(entry.content)
|
||||
|
||||
return "\n".join(parts)
|
||||
|
||||
def _inject_depth_entries(self, history: List[ChatMessage], depth_entries: List[WorldInfoEntry]) -> List[Dict]:
|
||||
"""
|
||||
在聊天历史的指定深度插入条目 (Pos 6)
|
||||
返回一个包含 role 和 content 的字典列表，方便后续转换
|
||||
"""
|
||||
# 先将历史转换为中间格式
|
||||
msg_list = []
|
||||
for msg in history:
|
||||
msg_list.append({"role": "user" if msg.is_user else "assistant", "content": msg.mes})
|
||||
|
||||
# 按 depth 分组插入
|
||||
# d0 通常指最新用户输入之前，即列表末尾
|
||||
for entry in depth_entries:
|
||||
depth = entry.depth if entry.depth is not None else 0
|
||||
# 计算插入索引 (从后往前数)
|
||||
insert_index = max(0, len(msg_list) - depth)
|
||||
|
||||
# 确定角色
|
||||
role_map = {"system": "system", "user": "user", "assistant": "assistant"}
|
||||
role = role_map.get(str(entry.position).split('_')[-1] if '_' in str(entry.position) else "system", "system")
|
||||
|
||||
msg_list.insert(insert_index, {"role": "system", "content": entry.content})
|
||||
|
||||
return msg_list
|
||||
|
||||
def _replace_outlets(self, text: str, outlet_map: Dict[str, str]) -> str:
|
||||
"""执行 Outlet 宏替换 (Pos 7)"""
|
||||
def replacer(match):
|
||||
uid = match.group(1)
|
||||
return outlet_map.get(uid, "")
|
||||
|
||||
# 匹配 {{outlet::UID}}
|
||||
return re.sub(r"\{\{outlet::([^}]+)\}\}", replacer, text)
|
||||
|
||||
def _wrap_to_messages(
|
||||
self,
|
||||
story_string: str,
|
||||
an_content: str,
|
||||
history: List[Dict],
|
||||
user_input: str,
|
||||
outlet_map: Dict[str, str],
|
||||
config: PromptConfig
|
||||
) -> List[BaseMessage]:
|
||||
"""将组装好的文本块封装为 LangChain Messages"""
|
||||
messages = []
|
||||
|
||||
# 1. System Message (Story String + Outlet 替换)
|
||||
final_story = self._replace_outlets(story_string, outlet_map)
|
||||
if final_story:
|
||||
messages.append(SystemMessage(content=final_story))
|
||||
|
||||
# 2. Author's Note (根据配置位置插入)
|
||||
if an_content and config.an_position == "before_history":
|
||||
messages.append(SystemMessage(content=self._replace_outlets(an_content, outlet_map)))
|
||||
|
||||
# 3. Chat History
|
||||
for msg_data in history:
|
||||
if msg_data["role"] == "user":
|
||||
messages.append(HumanMessage(content=msg_data["content"]))
|
||||
elif msg_data["role"] == "assistant":
|
||||
messages.append(AIMessage(content=msg_data["content"]))
|
||||
else:
|
||||
messages.append(SystemMessage(content=msg_data["content"]))
|
||||
|
||||
# 4. Author's Note (如果在 History 之后)
|
||||
if an_content and config.an_position == "after_history":
|
||||
messages.append(SystemMessage(content=self._replace_outlets(an_content, outlet_map)))
|
||||
|
||||
# 5. Post-History Instructions & User Input
|
||||
final_input = user_input
|
||||
if config.post_history_instructions:
|
||||
final_input = f"{config.post_history_instructions}\n\n{user_input}"
|
||||
|
||||
messages.append(HumanMessage(content=final_input))
|
||||
|
||||
return messages
|
||||
381
backend/services/worldbook_service.py
Normal file
381
backend/services/worldbook_service.py
Normal file
@@ -0,0 +1,381 @@
|
||||
"""
|
||||
World Book Service
|
||||
世界书服务层 - 处理世界书及条目的 CRUD 操作
|
||||
"""
|
||||
import json
|
||||
import os
|
||||
import uuid
|
||||
from pathlib import Path
|
||||
from typing import List, Dict, Any, Optional
|
||||
from datetime import datetime
|
||||
|
||||
from models.internal import WorldInfo, WorldInfoEntry, ActivationType
|
||||
from models.converters import WorldBookConverter
|
||||
from core.config import settings
|
||||
|
||||
|
||||
class WorldBookService:
|
||||
"""世界书服务类"""
|
||||
|
||||
@staticmethod
|
||||
def _get_worldbook_path(name: str) -> Path:
|
||||
"""获取世界书文件路径"""
|
||||
return settings.WORLDBOOKS_PATH / f"{name}.json"
|
||||
|
||||
@staticmethod
|
||||
def _load_worldbook(name: str) -> Optional[Dict[str, Any]]:
|
||||
"""加载世界书 JSON 文件"""
|
||||
path = WorldBookService._get_worldbook_path(name)
|
||||
if not path.exists():
|
||||
return None
|
||||
|
||||
try:
|
||||
with open(path, 'r', encoding='utf-8') as f:
|
||||
return json.load(f)
|
||||
except Exception as e:
|
||||
raise ValueError(f"Failed to load worldbook '{name}': {str(e)}")
|
||||
|
||||
@staticmethod
|
||||
def _save_worldbook(name: str, data: Dict[str, Any]):
|
||||
"""保存世界书到 JSON 文件"""
|
||||
path = WorldBookService._get_worldbook_path(name)
|
||||
try:
|
||||
with open(path, 'w', encoding='utf-8') as f:
|
||||
json.dump(data, f, ensure_ascii=False, indent=2)
|
||||
except Exception as e:
|
||||
raise ValueError(f"Failed to save worldbook '{name}': {str(e)}")
|
||||
|
||||
@staticmethod
|
||||
def list_worldbooks() -> List[Dict[str, Any]]:
|
||||
"""
|
||||
获取所有世界书的列表（仅基本信息）
|
||||
|
||||
Returns:
|
||||
世界书列表，每个包含 name, description, entries_count 等
|
||||
"""
|
||||
worldbooks = []
|
||||
|
||||
for json_file in settings.WORLDBOOKS_PATH.glob("*.json"):
|
||||
try:
|
||||
with open(json_file, 'r', encoding='utf-8') as f:
|
||||
data = json.load(f)
|
||||
|
||||
worldbooks.append({
|
||||
"name": data.get("name", json_file.stem),
|
||||
"description": data.get("description", ""),
|
||||
"entries_count": len(data.get("entries", [])),
|
||||
"createdAt": data.get("createdAt", 0),
|
||||
"updatedAt": data.get("updatedAt", 0)
|
||||
})
|
||||
except Exception as e:
|
||||
print(f"Error loading worldbook {json_file.name}: {e}")
|
||||
continue
|
||||
|
||||
# 按更新时间排序
|
||||
worldbooks.sort(key=lambda x: x.get("updatedAt", 0), reverse=True)
|
||||
return worldbooks
|
||||
|
||||
@staticmethod
|
||||
def get_worldbook(name: str) -> Dict[str, Any]:
|
||||
"""
|
||||
获取指定世界书的完整数据
|
||||
|
||||
Args:
|
||||
name: 世界书名称
|
||||
|
||||
Returns:
|
||||
世界书完整数据
|
||||
"""
|
||||
data = WorldBookService._load_worldbook(name)
|
||||
if not data:
|
||||
raise FileNotFoundError(f"Worldbook '{name}' not found")
|
||||
|
||||
return data
|
||||
|
||||
@staticmethod
|
||||
def create_worldbook(name: str, description: str = "") -> Dict[str, Any]:
|
||||
"""
|
||||
创建新世界书
|
||||
|
||||
Args:
|
||||
name: 世界书名称
|
||||
description: 世界书描述
|
||||
|
||||
Returns:
|
||||
创建的世界书数据
|
||||
"""
|
||||
# 检查是否已存在
|
||||
if WorldBookService._get_worldbook_path(name).exists():
|
||||
raise ValueError(f"Worldbook '{name}' already exists")
|
||||
|
||||
now = int(datetime.now().timestamp())
|
||||
worldbook_data = {
|
||||
"id": str(uuid.uuid4()),
|
||||
"name": name,
|
||||
"description": description,
|
||||
"entries": [],
|
||||
"createdAt": now,
|
||||
"updatedAt": now,
|
||||
"version": 1
|
||||
}
|
||||
|
||||
WorldBookService._save_worldbook(name, worldbook_data)
|
||||
return worldbook_data
|
||||
|
||||
@staticmethod
|
||||
def update_worldbook(name: str, description: Optional[str] = None) -> Dict[str, Any]:
|
||||
"""
|
||||
更新世界书基本信息
|
||||
|
||||
Args:
|
||||
name: 世界书名称
|
||||
description: 新的描述（可选）
|
||||
|
||||
Returns:
|
||||
更新后的世界书数据
|
||||
"""
|
||||
data = WorldBookService._load_worldbook(name)
|
||||
if not data:
|
||||
raise FileNotFoundError(f"Worldbook '{name}' not found")
|
||||
|
||||
if description is not None:
|
||||
data["description"] = description
|
||||
|
||||
data["updatedAt"] = int(datetime.now().timestamp())
|
||||
WorldBookService._save_worldbook(name, data)
|
||||
|
||||
return data
|
||||
|
||||
@staticmethod
|
||||
def delete_worldbook(name: str) -> bool:
|
||||
"""
|
||||
删除世界书
|
||||
|
||||
Args:
|
||||
name: 世界书名称
|
||||
|
||||
Returns:
|
||||
是否删除成功
|
||||
"""
|
||||
path = WorldBookService._get_worldbook_path(name)
|
||||
if not path.exists():
|
||||
raise FileNotFoundError(f"Worldbook '{name}' not found")
|
||||
|
||||
path.unlink()
|
||||
return True
|
||||
|
||||
@staticmethod
|
||||
def list_entries(name: str) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
获取世界书的所有条目
|
||||
|
||||
Args:
|
||||
name: 世界书名称
|
||||
|
||||
Returns:
|
||||
条目列表
|
||||
"""
|
||||
data = WorldBookService._load_worldbook(name)
|
||||
if not data:
|
||||
raise FileNotFoundError(f"Worldbook '{name}' not found")
|
||||
|
||||
return data.get("entries", [])
|
||||
|
||||
@staticmethod
|
||||
def get_entry(name: str, uid: str) -> Dict[str, Any]:
|
||||
"""
|
||||
获取世界书的指定条目
|
||||
|
||||
Args:
|
||||
name: 世界书名称
|
||||
uid: 条目 UID
|
||||
|
||||
Returns:
|
||||
条目数据
|
||||
"""
|
||||
data = WorldBookService._load_worldbook(name)
|
||||
if not data:
|
||||
raise FileNotFoundError(f"Worldbook '{name}' not found")
|
||||
|
||||
for entry in data.get("entries", []):
|
||||
if entry.get("uid") == uid:
|
||||
return entry
|
||||
|
||||
raise FileNotFoundError(f"Entry '{uid}' not found in worldbook '{name}'")
|
||||
|
||||
@staticmethod
|
||||
def create_entry(name: str, entry_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""
|
||||
在世界书中创建新条目
|
||||
|
||||
Args:
|
||||
name: 世界书名称
|
||||
entry_data: 条目数据（不包含 uid, createdAt, updatedAt）
|
||||
|
||||
Returns:
|
||||
创建的条目数据
|
||||
"""
|
||||
data = WorldBookService._load_worldbook(name)
|
||||
if not data:
|
||||
raise FileNotFoundError(f"Worldbook '{name}' not found")
|
||||
|
||||
# 生成 UID 和时间戳
|
||||
now = int(datetime.now().timestamp())
|
||||
new_entry = {
|
||||
"uid": str(uuid.uuid4()),
|
||||
"key": entry_data.get("key", []),
|
||||
"keysecondary": entry_data.get("keysecondary", []),
|
||||
"content": entry_data.get("content", ""),
|
||||
"activationType": entry_data.get("activationType", ActivationType.KEYWORD.value),
|
||||
"logicExpression": entry_data.get("logicExpression"),
|
||||
"ragConfig": entry_data.get("ragConfig"),
|
||||
"order": entry_data.get("order", 0),
|
||||
"position": entry_data.get("position", "after_char"),
|
||||
"depth": entry_data.get("depth"),
|
||||
"probability": entry_data.get("probability", 100),
|
||||
"group": entry_data.get("group", []),
|
||||
"disable": entry_data.get("disable", False),
|
||||
"createdAt": now,
|
||||
"updatedAt": now
|
||||
}
|
||||
|
||||
data["entries"].append(new_entry)
|
||||
data["updatedAt"] = now
|
||||
WorldBookService._save_worldbook(name, data)
|
||||
|
||||
return new_entry
|
||||
|
||||
@staticmethod
|
||||
def update_entry(name: str, uid: str, entry_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""
|
||||
更新世界书的指定条目
|
||||
|
||||
Args:
|
||||
name: 世界书名称
|
||||
uid: 条目 UID
|
||||
entry_data: 更新的字段
|
||||
|
||||
Returns:
|
||||
更新后的条目数据
|
||||
"""
|
||||
data = WorldBookService._load_worldbook(name)
|
||||
if not data:
|
||||
raise FileNotFoundError(f"Worldbook '{name}' not found")
|
||||
|
||||
for i, entry in enumerate(data.get("entries", [])):
|
||||
if entry.get("uid") == uid:
|
||||
# 更新字段
|
||||
for key, value in entry_data.items():
|
||||
if key not in ["uid", "createdAt"]: # 不修改 UID 和创建时间
|
||||
entry[key] = value
|
||||
|
||||
# 更新时间戳
|
||||
entry["updatedAt"] = int(datetime.now().timestamp())
|
||||
data["entries"][i] = entry
|
||||
data["updatedAt"] = entry["updatedAt"]
|
||||
|
||||
WorldBookService._save_worldbook(name, data)
|
||||
return entry
|
||||
|
||||
raise FileNotFoundError(f"Entry '{uid}' not found in worldbook '{name}'")
|
||||
|
||||
@staticmethod
|
||||
def delete_entry(name: str, uid: str) -> bool:
|
||||
"""
|
||||
删除世界书的指定条目
|
||||
|
||||
Args:
|
||||
name: 世界书名称
|
||||
uid: 条目 UID
|
||||
|
||||
Returns:
|
||||
是否删除成功
|
||||
"""
|
||||
data = WorldBookService._load_worldbook(name)
|
||||
if not data:
|
||||
raise FileNotFoundError(f"Worldbook '{name}' not found")
|
||||
|
||||
original_length = len(data.get("entries", []))
|
||||
data["entries"] = [e for e in data.get("entries", []) if e.get("uid") != uid]
|
||||
|
||||
if len(data["entries"]) == original_length:
|
||||
raise FileNotFoundError(f"Entry '{uid}' not found in worldbook '{name}'")
|
||||
|
||||
data["updatedAt"] = int(datetime.now().timestamp())
|
||||
WorldBookService._save_worldbook(name, data)
|
||||
|
||||
return True
|
||||
|
||||
@staticmethod
|
||||
def import_from_sillytavern(name: str, st_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""
|
||||
从 SillyTavern 格式导入世界书
|
||||
|
||||
Args:
|
||||
name: 世界书名称
|
||||
st_data: SillyTavern 格式的世界书数据
|
||||
|
||||
Returns:
|
||||
转换后的内部格式世界书数据
|
||||
"""
|
||||
# 使用转换器进行转换
|
||||
worldbook_data = WorldBookConverter.st_to_internal(st_data, name)
|
||||
|
||||
# 保存到文件
|
||||
WorldBookService._save_worldbook(name, worldbook_data)
|
||||
|
||||
return worldbook_data
|
||||
|
||||
@staticmethod
|
||||
def import_internal_format(name: str, internal_data: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""
|
||||
直接导入内部格式的世界书(无需转换)
|
||||
|
||||
Args:
|
||||
name: 世界书名称
|
||||
internal_data: 内部格式的世界书数据
|
||||
|
||||
Returns:
|
||||
内部格式世界书数据
|
||||
"""
|
||||
# 确保包含必要的字段
|
||||
if "name" not in internal_data:
|
||||
internal_data["name"] = name
|
||||
|
||||
# 规范化所有条目,确保有 trigger_config
|
||||
if "entries" in internal_data and isinstance(internal_data["entries"], list):
|
||||
normalized_entries = []
|
||||
for entry in internal_data["entries"]:
|
||||
if isinstance(entry, dict):
|
||||
normalized_entry = WorldBookConverter.normalize_entry(entry)
|
||||
normalized_entries.append(normalized_entry)
|
||||
internal_data["entries"] = normalized_entries
|
||||
|
||||
# 保存文件
|
||||
WorldBookService._save_worldbook(name, internal_data)
|
||||
|
||||
return internal_data
|
||||
|
||||
@staticmethod
|
||||
def export_to_sillytavern(name: str) -> Dict[str, Any]:
|
||||
"""
|
||||
导出为 SillyTavern 格式
|
||||
|
||||
Args:
|
||||
name: 世界书名称
|
||||
|
||||
Returns:
|
||||
SillyTavern 格式的世界书数据
|
||||
"""
|
||||
data = WorldBookService._load_worldbook(name)
|
||||
if not data:
|
||||
raise FileNotFoundError(f"Worldbook '{name}' not found")
|
||||
|
||||
# 使用转换器进行转换
|
||||
st_data = WorldBookConverter.internal_to_st(data)
|
||||
|
||||
return st_data
|
||||
|
||||
|
||||
# 全局实例
|
||||
worldbook_service = WorldBookService()
|
||||
17
backend/utils/__init__.py
Normal file
17
backend/utils/__init__.py
Normal file
@@ -0,0 +1,17 @@
|
||||
"""
|
||||
工具类包
|
||||
|
||||
提供通用的工具函数和辅助类，如文件操作、LLM 调用封装等。
|
||||
"""
|
||||
from .file_utils import get_all_roles_and_chats, read_jsonl_file, write_jsonl_file
|
||||
from .llm_client import get_llm, get_fast_llm, get_creative_llm, get_streaming_llm
|
||||
|
||||
__all__ = [
|
||||
'get_all_roles_and_chats',
|
||||
'read_jsonl_file',
|
||||
'write_jsonl_file',
|
||||
'get_llm',
|
||||
'get_fast_llm',
|
||||
'get_creative_llm',
|
||||
'get_streaming_llm',
|
||||
]
|
||||
130
backend/utils/file_utils.py
Normal file
130
backend/utils/file_utils.py
Normal file
@@ -0,0 +1,130 @@
|
||||
"""
|
||||
文件操作工具函数
|
||||
|
||||
提供文件和目录操作的通用工具
|
||||
"""
|
||||
from pathlib import Path
|
||||
from typing import Dict, List
|
||||
import json
|
||||
import logging
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def get_all_roles_and_chats(data_path: Path) -> Dict[str, List[str]]:
|
||||
"""
|
||||
获取所有角色和聊天列表
|
||||
|
||||
Args:
|
||||
data_path: 数据目录路径
|
||||
|
||||
Returns:
|
||||
Dict[str, List[str]]: 字典结构，键是角色名称，值是该角色的聊天列表
|
||||
"""
|
||||
chat_dir = data_path / "chat"
|
||||
result = {}
|
||||
|
||||
if not chat_dir.exists():
|
||||
logger.warning(f"聊天目录不存在: {chat_dir}")
|
||||
return result
|
||||
|
||||
for entry in chat_dir.iterdir():
|
||||
try:
|
||||
if entry.is_dir():
|
||||
jsonl_files = []
|
||||
|
||||
for file in entry.iterdir():
|
||||
if file.is_file() and file.suffix == '.jsonl':
|
||||
jsonl_files.append(file.stem)
|
||||
|
||||
if jsonl_files:
|
||||
result[entry.name] = jsonl_files
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"处理文件夹 {entry.name} 时出错: {str(e)}")
|
||||
continue
|
||||
|
||||
return result
|
||||
|
||||
|
||||
def ensure_directory_exists(path: Path) -> None:
|
||||
"""
|
||||
确保目录存在,如果不存在则创建
|
||||
|
||||
Args:
|
||||
path: 目录路径
|
||||
"""
|
||||
path.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
|
||||
def read_json_file(file_path: Path) -> dict:
|
||||
"""
|
||||
读取 JSON 文件
|
||||
|
||||
Args:
|
||||
file_path: 文件路径
|
||||
|
||||
Returns:
|
||||
dict: JSON 数据
|
||||
"""
|
||||
with open(file_path, 'r', encoding='utf-8') as f:
|
||||
return json.load(f)
|
||||
|
||||
|
||||
def write_json_file(file_path: Path, data: dict) -> None:
|
||||
"""
|
||||
写入 JSON 文件
|
||||
|
||||
Args:
|
||||
file_path: 文件路径
|
||||
data: 要写入的数据
|
||||
"""
|
||||
with open(file_path, 'w', encoding='utf-8') as f:
|
||||
json.dump(data, f, ensure_ascii=False, indent=2)
|
||||
|
||||
|
||||
def read_jsonl_file(file_path: Path) -> List[dict]:
|
||||
"""
|
||||
读取 JSONL 文件
|
||||
|
||||
Args:
|
||||
file_path: 文件路径
|
||||
|
||||
Returns:
|
||||
List[dict]: JSONL 数据列表
|
||||
"""
|
||||
lines = []
|
||||
with open(file_path, 'r', encoding='utf-8') as f:
|
||||
for line in f:
|
||||
line = line.strip()
|
||||
if line:
|
||||
try:
|
||||
lines.append(json.loads(line))
|
||||
except json.JSONDecodeError as e:
|
||||
logger.warning(f"解析 JSONL 行失败: {e}")
|
||||
return lines
|
||||
|
||||
|
||||
def append_to_jsonl_file(file_path: Path, data: dict) -> None:
|
||||
"""
|
||||
追加数据到 JSONL 文件
|
||||
|
||||
Args:
|
||||
file_path: 文件路径
|
||||
data: 要追加的数据
|
||||
"""
|
||||
with open(file_path, 'a', encoding='utf-8') as f:
|
||||
f.write(json.dumps(data, ensure_ascii=False) + '\n')
|
||||
|
||||
|
||||
def write_jsonl_file(file_path: Path, data_list: List[dict]) -> None:
|
||||
"""
|
||||
写入 JSONL 文件 (覆盖模式)
|
||||
|
||||
Args:
|
||||
file_path: 文件路径
|
||||
data_list: 数据列表
|
||||
"""
|
||||
with open(file_path, 'w', encoding='utf-8') as f:
|
||||
for data in data_list:
|
||||
f.write(json.dumps(data, ensure_ascii=False) + '\n')
|
||||
88
backend/utils/llm_client.py
Normal file
88
backend/utils/llm_client.py
Normal file
@@ -0,0 +1,88 @@
|
||||
"""
|
||||
LLM 客户端工具
|
||||
|
||||
提供统一的 LLM 接口,支持多种模型提供商。
|
||||
使用 LangChain 的 ChatModel 抽象,简化不同厂商 API 的调用。
|
||||
"""
|
||||
from typing import Optional
|
||||
from langchain_core.language_models.chat_models import BaseChatModel
|
||||
from core.config import settings
|
||||
|
||||
|
||||
def get_llm(
|
||||
provider: str = "openai",
|
||||
model: Optional[str] = None,
|
||||
temperature: float = 0.7,
|
||||
streaming: bool = False,
|
||||
**kwargs
|
||||
) -> BaseChatModel:
|
||||
"""
|
||||
获取 LLM 实例
|
||||
|
||||
Args:
|
||||
provider: 模型提供商 ("openai", "anthropic", "ollama")
|
||||
model: 模型名称 (如果不指定则使用配置中的默认值)
|
||||
temperature: 温度参数 (0-2)
|
||||
streaming: 是否启用流式输出
|
||||
**kwargs: 其他参数传递给模型
|
||||
|
||||
Returns:
|
||||
BaseChatModel: LangChain 的聊天模型实例
|
||||
"""
|
||||
|
||||
if provider == "openai":
|
||||
from langchain_openai import ChatOpenAI
|
||||
|
||||
return ChatOpenAI(
|
||||
model=model or settings.OPENAI_MODEL or "gpt-4",
|
||||
temperature=temperature,
|
||||
api_key=settings.OPENAI_API_KEY,
|
||||
streaming=streaming,
|
||||
**kwargs
|
||||
)
|
||||
|
||||
elif provider == "anthropic":
|
||||
from langchain_anthropic import ChatAnthropic
|
||||
|
||||
return ChatAnthropic(
|
||||
model=model or settings.ANTHROPIC_MODEL or "claude-3-opus-20240229",
|
||||
temperature=temperature,
|
||||
api_key=settings.ANTHROPIC_API_KEY,
|
||||
max_tokens=kwargs.pop("max_tokens", 4096),
|
||||
streaming=streaming,
|
||||
**kwargs
|
||||
)
|
||||
|
||||
elif provider == "ollama":
|
||||
try:
|
||||
from langchain_ollama import ChatOllama
|
||||
|
||||
return ChatOllama(
|
||||
model=model or settings.OLLAMA_MODEL or "llama3",
|
||||
base_url=settings.OLLAMA_BASE_URL or "http://localhost:11434",
|
||||
temperature=temperature,
|
||||
**kwargs
|
||||
)
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"langchain-ollama not installed. Run: pip install langchain-ollama"
|
||||
)
|
||||
|
||||
else:
|
||||
raise ValueError(f"Unsupported provider: {provider}. Use 'openai', 'anthropic', or 'ollama'")
|
||||
|
||||
|
||||
# 便捷函数 - 常用配置
|
||||
def get_fast_llm(provider: str = "openai") -> BaseChatModel:
|
||||
"""获取快速响应的 LLM (低温度,适合事实性问题)"""
|
||||
return get_llm(provider, temperature=0.3)
|
||||
|
||||
|
||||
def get_creative_llm(provider: str = "openai") -> BaseChatModel:
|
||||
"""获取创造性 LLM (高温度,适合创意写作)"""
|
||||
return get_llm(provider, temperature=0.9)
|
||||
|
||||
|
||||
def get_streaming_llm(provider: str = "openai") -> BaseChatModel:
|
||||
"""获取支持流式输出的 LLM"""
|
||||
return get_llm(provider, streaming=True)
|
||||
Reference in New Issue
Block a user