├── .python-version ├── app ├── __init__.py ├── api │ ├── v1 │ │ ├── __init__.py │ │ ├── models.py │ │ └── chat.py │ └── dependencies.py ├── utils │ ├── __init__.py │ ├── stream_parser.py │ ├── http_client.py │ ├── logger.py │ └── file_utils.py ├── models │ └── schemas.py ├── main.py ├── config.py └── services │ ├── chat_service.py │ └── kiira_client.py ├── .gitignore ├── main.py ├── pyproject.toml ├── .env.example ├── .dockerignore ├── docker-compose.yml ├── Dockerfile ├── .github └── workflows │ └── docker-publish.yml ├── README.md └── uv.lock /.python-version: -------------------------------------------------------------------------------- 1 | 3.11 2 | -------------------------------------------------------------------------------- /app/__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | Kiira2API 应用包 3 | """ 4 | __version__ = "0.1.0" 5 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Python-generated files 2 | __pycache__/ 3 | *.py[oc] 4 | build/ 5 | dist/ 6 | data/ 7 | wheels/ 8 | *.egg-info 9 | 10 | # Virtual environments 11 | .venv 12 | .env 13 | memory_bank.md -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | """ 2 | 兼容入口：从 app.main 导入应用 3 | 保留此文件以便使用 uvicorn main:app 启动 4 | """ 5 | 6 | from app.main import app 7 | 8 | if __name__ == "__main__": 9 | import uvicorn 10 | uvicorn.run(app, host="0.0.0.0", port=8999) 11 | -------------------------------------------------------------------------------- /app/api/v1/__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | API v1 路由模块 3 | """ 4 | from fastapi import APIRouter 5 | from . import chat, models 6 | router = APIRouter(prefix="/v1") 7 | 8 | router.include_router(chat.router) 9 | router.include_router(models.router) 10 | -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "kiira2api" 3 | version = "0.1.0" 4 | description = "Add your description here" 5 | requires-python = ">=3.11" 6 | dependencies = [ 7 | "fastapi>=0.121.2", 8 | "requests>=2.32.5", 9 | "uvicorn[standard]>=0.38.0", 10 | "pydantic-settings>=2.0.0", 11 | ] 12 | -------------------------------------------------------------------------------- /.env.example: -------------------------------------------------------------------------------- 1 | # API Key 配置（用于接口鉴权） 2 | # 如果使用默认值 sk-123456，将跳过鉴权验证 3 | API_KEY=sk-123456 4 | 5 | # Kiira AI API 基础 URL 6 | BASE_URL_KIIRA=https://www.kiira.ai 7 | 8 | # SeaArt API 基础 URL 9 | BASE_URL_SEAART_API=https://app-matrix-api.api.seaart.ai 10 | BASE_URL_SEAART_UPLOADER=https://aiart-uploader.api.seaart.dev 11 | 12 | # 默认配置 13 | DEFAULT_AGENT_NAME=Nano Banana Pro 🔥👉 Try Free 14 | DEFAULT_CATEGORY=74 15 | 16 | # Agent 列表（支持 JSON 或逗号分隔格式） 17 | # JSON 格式（推荐）: 18 | AGENT_LIST=["Nano Banana Pro 🔥👉 Try Free","Nano Banana Video Pro🔥","Sora 2: AI Video Remixer", "Veo 3: AI Video Weaver", "Midjourney Art Studio"] 19 | -------------------------------------------------------------------------------- /.dockerignore: -------------------------------------------------------------------------------- 1 | # Python 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | *.so 6 | .Python 7 | *.egg-info/ 8 | dist/ 9 | build/ 10 | .venv/ 11 | venv/ 12 | env/ 13 | ENV/ 14 | 15 | # IDE 16 | .vscode/ 17 | .idea/ 18 | *.swp 19 | *.swo 20 | *~ 21 | 22 | # Git 23 | .git/ 24 | .gitignore 25 | .gitattributes 26 | 27 | # Docker 28 | Dockerfile 29 | .dockerignore 30 | docker-compose.yml 31 | 32 | # 文档 33 | *.md 34 | !README.md 35 | docs/ 36 | 37 | # 测试 38 | .pytest_cache/ 39 | .coverage 40 | htmlcov/ 41 | .tox/ 42 | 43 | # 日志 44 | *.log 45 | logs/ 46 | 47 | # 环境变量文件（可选，如果需要可以在运行时挂载） 48 | .env 49 | .env.local 50 | .env.*.local 51 | 52 | # 其他 53 | .DS_Store 54 | *.bak 55 | *.tmp 56 | 57 | data/ -------------------------------------------------------------------------------- /docker-compose.yml: -------------------------------------------------------------------------------- 1 | version: '3.8' 2 | 3 | services: 4 | kiira2api: 5 | build: 6 | context: . 7 | dockerfile: Dockerfile 8 | container_name: kiira2api 9 | ports: 10 | - "8999:8999" 11 | volumes: 12 | - ./data:/app/data 13 | env_file: 14 | - .env 15 | environment: 16 | - PYTHONUNBUFFERED=1 17 | - PYTHONDONTWRITEBYTECODE=1 18 | restart: unless-stopped 19 | healthcheck: 20 | test: ["CMD", "python", "-c", "import requests; requests.get('http://localhost:8999/health')"] 21 | interval: 30s 22 | timeout: 10s 23 | retries: 3 24 | start_period: 5s 25 | networks: 26 | - kiira2api-network 27 | 28 | networks: 29 | kiira2api-network: 30 | driver: bridge 31 | 32 | -------------------------------------------------------------------------------- /app/utils/__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | 工具函数模块 3 | """ 4 | from .http_client import build_headers, make_request 5 | from .file_utils import ( 6 | guess_content_type, 7 | get_file_extension_from_content_type, 8 | decode_base64_img, 9 | get_image_data_and_type 10 | ) 11 | from .stream_parser import parse_stream_response 12 | from .logger import get_logger, setup_logger, configure_root_logger 13 | 14 | __all__ = [ 15 | 'build_headers', 16 | 'make_request', 17 | 'guess_content_type', 18 | 'get_file_extension_from_content_type', 19 | 'decode_base64_img', 20 | 'get_image_data_and_type', 21 | 'parse_stream_response', 22 | 'get_logger', 23 | 'setup_logger', 24 | 'configure_root_logger', 25 | ] 26 | 27 | # 注意：config_loader 不在 __init__.py 中导入，避免循环导入 28 | # 如需使用，请直接从 app.utils.config_loader 导入 -------------------------------------------------------------------------------- /app/api/v1/models.py: -------------------------------------------------------------------------------- 1 | """ 2 | 模型相关 API 路由 3 | """ 4 | from fastapi import APIRouter, Depends 5 | from typing import Optional 6 | from app.models.schemas import ModelInfo 7 | from app.config import AGENT_LIST 8 | from app.api.dependencies import verify_api_key 9 | 10 | router = APIRouter(tags=["models"]) 11 | 12 | @router.get("/models") 13 | async def get_models(api_key: Optional[str] = Depends(verify_api_key)): 14 | """ 15 | Get available models endpoint compatible with OpenAI API format. 16 | """ 17 | models = [] 18 | for agent in AGENT_LIST: 19 | models.append( 20 | ModelInfo( 21 | id=agent, 22 | object="model", 23 | created=1677610602, 24 | owned_by="move132" 25 | ) 26 | ) 27 | return { 28 | "object": "list", 29 | "data": models 30 | } 31 | -------------------------------------------------------------------------------- /Dockerfile: -------------------------------------------------------------------------------- 1 | # 使用 Python 3.11 作为基础镜像 2 | FROM python:3.11-slim as builder 3 | 4 | # 设置工作目录 5 | WORKDIR /app 6 | 7 | # 安装 uv 8 | COPY --from=ghcr.io/astral-sh/uv:latest /uv /usr/local/bin/uv 9 | 10 | # 复制依赖文件 11 | COPY pyproject.toml uv.lock ./ 12 | 13 | # 安装依赖到虚拟环境 14 | RUN uv sync --frozen --no-dev 15 | 16 | # 运行阶段 17 | FROM python:3.11-slim 18 | 19 | # 设置工作目录 20 | WORKDIR /app 21 | 22 | # 安装 uv（用于运行时） 23 | COPY --from=ghcr.io/astral-sh/uv:latest /uv /usr/local/bin/uv 24 | 25 | # 从构建阶段复制虚拟环境 26 | COPY --from=builder /app/.venv /app/.venv 27 | 28 | # 复制应用代码 29 | COPY . . 30 | 31 | # 设置环境变量 32 | ENV PATH="/app/.venv/bin:$PATH" \ 33 | PYTHONUNBUFFERED=1 \ 34 | PYTHONDONTWRITEBYTECODE=1 35 | 36 | # 暴露端口 37 | EXPOSE 8999 38 | 39 | VOLUME ["/app/data"] 40 | # 健康检查 41 | HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \ 42 | CMD python -c "import requests; requests.get('http://localhost:8999/health')" || exit 1 43 | 44 | # 启动命令 45 | CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8999"] 46 | 47 | -------------------------------------------------------------------------------- /app/models/schemas.py: -------------------------------------------------------------------------------- 1 | """ 2 | Pydantic 数据模型定义 3 | """ 4 | from pydantic import BaseModel 5 | from typing import List, Optional, Dict, Any, Union 6 | 7 | class ChatMessage(BaseModel): 8 | """聊天消息模型""" 9 | role: str 10 | content: Union[str, List[Dict[str, Any]]] # 支持文本或多模态内容（文本+图片） 11 | group_id: Optional[str] = None 12 | 13 | 14 | class ChatCompletionRequest(BaseModel): 15 | """聊天完成请求模型""" 16 | model: str 17 | messages: List[ChatMessage] 18 | temperature: Optional[float] = 1.0 19 | max_tokens: Optional[int] = None 20 | stream: Optional[bool] = False 21 | 22 | 23 | class ChatCompletionResponse(BaseModel): 24 | """聊天完成响应模型""" 25 | id: str 26 | object: str = "chat.completion" 27 | created: int 28 | model: str 29 | choices: List[Dict[str, Any]] 30 | usage: Optional[Dict[str, int]] = None 31 | 32 | 33 | class ModelInfo(BaseModel): 34 | """模型信息模型""" 35 | id: str 36 | object: str = "model" 37 | created: int 38 | owned_by: str 39 | 40 | 41 | class ModelsResponse(BaseModel): 42 | """模型列表响应模型""" 43 | object: str = "list" 44 | data: List[ModelInfo] 45 | -------------------------------------------------------------------------------- /.github/workflows/docker-publish.yml: -------------------------------------------------------------------------------- 1 | name: Build and Push Docker Image 2 | 3 | on: 4 | push: 5 | branches: 6 | - main 7 | - master 8 | tags: 9 | - 'v*' 10 | workflow_dispatch: 11 | 12 | env: 13 | REGISTRY: ghcr.io 14 | IMAGE_NAME: ${{ github.repository }} 15 | 16 | jobs: 17 | build-and-push: 18 | runs-on: ubuntu-latest 19 | permissions: 20 | contents: read 21 | packages: write 22 | 23 | steps: 24 | - name: Checkout repository 25 | uses: actions/checkout@v4 26 | 27 | - name: Set up Docker Buildx 28 | uses: docker/setup-buildx-action@v3 29 | 30 | - name: Log in to Container Registry 31 | uses: docker/login-action@v3 32 | with: 33 | registry: ${{ env.REGISTRY }} 34 | username: ${{ github.actor }} 35 | password: ${{ secrets.GITHUB_TOKEN }} 36 | 37 | - name: Extract metadata (tags, labels) for Docker 38 | id: meta 39 | uses: docker/metadata-action@v5 40 | with: 41 | images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }} 42 | tags: | 43 | type=ref,event=branch 44 | type=semver,pattern={{version}} 45 | type=semver,pattern={{major}}.{{minor}} 46 | type=semver,pattern={{major}} 47 | type=raw,value=latest,enable={{is_default_branch}} 48 | type=sha,prefix={{branch}}- 49 | 50 | - name: Build and push Docker image 51 | uses: docker/build-push-action@v5 52 | with: 53 | context: . 54 | file: ./Dockerfile 55 | push: true 56 | tags: ${{ steps.meta.outputs.tags }} 57 | labels: ${{ steps.meta.outputs.labels }} 58 | cache-from: type=gha 59 | cache-to: type=gha,mode=max 60 | platforms: linux/amd64,linux/arm64 61 | 62 | -------------------------------------------------------------------------------- /app/utils/stream_parser.py: -------------------------------------------------------------------------------- 1 | """ 2 | 流式响应解析工具 3 | 优化JSON解析,避免重复解析同一数据 4 | """ 5 | import json 6 | from typing import Optional, Dict, Any 7 | 8 | from app.utils.logger import get_logger 9 | 10 | logger = get_logger(__name__) 11 | 12 | 13 | def extract_media_from_data(data: Dict[str, Any]) -> Optional[tuple[str, str]]: 14 | """ 15 | 从已解析的数据中提取媒体URL 16 | 避免重复JSON解析,提升性能 17 | 18 | Args: 19 | data: 已解析的JSON数据字典 20 | 21 | Returns: 22 | (url, type) tuple 如果找到媒体,否则返回None 23 | """ 24 | try: 25 | # 在choices数组->每个choice下的sa_resources数组中查找type为"video"或"image"且有"url" 26 | if "choices" in data and isinstance(data["choices"], list): 27 | for choice in data["choices"]: 28 | if isinstance(choice, dict) and "sa_resources" in choice and isinstance(choice["sa_resources"], list): 29 | for resource in choice["sa_resources"]: 30 | if ( 31 | isinstance(resource, dict) 32 | and resource.get("type") in ("video", "image") 33 | and "url" in resource 34 | and resource["url"] 35 | ): 36 | return resource["url"], resource["type"] 37 | except Exception as e: 38 | logger.debug(f"提取媒体URL时出错: {e}") 39 | 40 | return None 41 | 42 | 43 | def parse_stream_response(line: str) -> Optional[tuple[str, str]]: 44 | """ 45 | 解析流式响应中的媒体URL(兼容旧接口) 46 | 建议使用extract_media_from_data以避免重复JSON解析 47 | 48 | Args: 49 | line: SSE格式的响应行 50 | 51 | Returns: 52 | (url, type) tuple 如果找到媒体,否则返回None 53 | """ 54 | if not line.startswith("data: "): 55 | return None 56 | 57 | try: 58 | json_str = line[6:] # 移除 "data: " 前缀 59 | data = json.loads(json_str) 60 | return extract_media_from_data(data) 61 | except json.JSONDecodeError: 62 | pass 63 | except Exception as e: 64 | logger.debug(f"解析响应时出错: {e}") 65 | 66 | return None 67 | 68 | -------------------------------------------------------------------------------- /app/main.py: -------------------------------------------------------------------------------- 1 | """ 2 | FastAPI 应用主入口 3 | """ 4 | import uvicorn 5 | import json 6 | import logging 7 | from contextlib import asynccontextmanager 8 | from fastapi import FastAPI 9 | from app.api.v1 import router as v1_router 10 | from app.utils.logger import configure_root_logger, get_logger 11 | from app.config import API_KEY, AGENT_LIST, DEFAULT_AGENT_NAME 12 | # 配置根日志记录器（彩色输出） 13 | configure_root_logger(level=logging.INFO, use_color=True) 14 | 15 | logger = get_logger(__name__) 16 | # 定义一些颜色代码 17 | CYAN = "\033[36m" 18 | GREEN = "\033[32m" 19 | YELLOW = "\033[33m" 20 | MAGENTA = "\033[35m" 21 | RESET = "\033[0m" 22 | # 紫色 23 | RED = "\033[31m" 24 | ORANGE = "\033[38;5;208m" 25 | 26 | settings = { 27 | "API_KEY": API_KEY, 28 | "AGENT_LIST": AGENT_LIST, 29 | "DEFAULT_AGENT_NAME": DEFAULT_AGENT_NAME 30 | } 31 | project_logo_str = fr"""{GREEN} 32 | ██ ▄█▀ ▄▄ ▄▄ ▄▄▄▄ ▄▄▄ ████▄ ▄████▄ █████▄ ██ 33 | ████ ██ ██ ██▄█▄ ██▀██ ▄██▀ ██▄▄██ ██▄▄█▀ ██ 34 | ██ ▀█▄ ██ ██ ██ ██ ██▀██ ███▄▄ ██ ██ ██ ██ 35 | -------------------------------------------------- 36 | 37 | Kiira2API - 基于Kiira AI的逆向API服务 38 | 项目地址 : https://github.com/move132/kiira2api 39 | 作者 : move132 40 | 版本 : 1.0.0 41 | 当前环境变量信息 42 | {json.dumps(settings, ensure_ascii=False, indent=2)} 43 | """ 44 | @asynccontextmanager 45 | async def lifespan(app: FastAPI): 46 | """应用生命周期管理""" 47 | # 启动时执行 48 | print(f"{GREEN}{'=' * 50}{RESET}") 49 | print(f"{GREEN}🚀Kiira2API 启动成功{RESET}") 50 | print(project_logo_str) 51 | print(f"{GREEN}{'=' * 50}{RESET}") 52 | yield 53 | 54 | app = FastAPI(title="Kiira2API", version="1.0.0", lifespan=lifespan) 55 | 56 | # 注册 API 路由 57 | app.include_router(v1_router) 58 | 59 | 60 | @app.get("/") 61 | async def root(): 62 | """根路径，返回 API 基本信息""" 63 | return {"message": "Kiira2API is running", "version": "1.0.0", "author": "move132", "project_url": "https://github.com/move132/kiira2api"} 64 | 65 | 66 | @app.get("/health") 67 | async def health(): 68 | """健康检查端点""" 69 | return {"status": "healthy"} 70 | 71 | 72 | if __name__ == "__main__": 73 | uvicorn.run(app, host="0.0.0.0", port=8999, log_level="info") -------------------------------------------------------------------------------- /app/api/dependencies.py: -------------------------------------------------------------------------------- 1 | """ 2 | API 依赖项 3 | """ 4 | from typing import Optional 5 | from fastapi import Header, HTTPException, status 6 | from app.config import API_KEY 7 | from app.utils.logger import get_logger 8 | 9 | logger = get_logger(__name__) 10 | 11 | 12 | async def verify_api_key( 13 | authorization: Optional[str] = Header(None, alias="Authorization"), 14 | x_api_key: Optional[str] = Header(None, alias="X-API-Key") 15 | ) -> Optional[str]: 16 | """ 17 | 验证 API Key 18 | 19 | 支持两种方式： 20 | 1. Authorization: Bearer (OpenAI 兼容格式) 21 | 2. X-API-Key: (备用方式) 22 | 23 | Args: 24 | authorization: Authorization header 25 | x_api_key: X-API-Key header 26 | 27 | Returns: 28 | Optional[str]: 验证通过的 API Key，如果使用默认值则返回 None 29 | 30 | Raises: 31 | HTTPException: API Key 无效或缺失 32 | """ 33 | # 如果未配置 API_KEY，跳过验证 34 | if not API_KEY: 35 | logger.warning("API_KEY 未配置或使用默认值，跳过鉴权验证") 36 | return None 37 | 38 | api_key = None 39 | # 方式 1: 从 Authorization header 获取 (Bearer token) 40 | if authorization: 41 | try: 42 | scheme, token = authorization.split(" ", 1) 43 | if scheme.lower() == "bearer": 44 | api_key = token 45 | except ValueError: 46 | pass 47 | 48 | # 方式 2: 从 X-API-Key header 获取 49 | if not api_key and x_api_key: 50 | api_key = x_api_key 51 | 52 | # 验证 API Key 53 | if not api_key: 54 | logger.warning("请求缺少 API Key") 55 | raise HTTPException( 56 | status_code=status.HTTP_401_UNAUTHORIZED, 57 | detail="Missing API Key. Please provide API Key via Authorization header (Bearer token) or X-API-Key header.", 58 | headers={"WWW-Authenticate": "Bearer"}, 59 | ) 60 | 61 | if api_key != API_KEY: 62 | logger.warning(f"API Key 验证失败: {api_key[:10]}...") 63 | raise HTTPException( 64 | status_code=status.HTTP_401_UNAUTHORIZED, 65 | detail="Invalid API Key", 66 | headers={"WWW-Authenticate": "Bearer"}, 67 | ) 68 | 69 | logger.debug("API Key 验证通过") 70 | return api_key 71 | 72 | -------------------------------------------------------------------------------- /app/config.py: -------------------------------------------------------------------------------- 1 | """ 2 | 应用配置 3 | 使用 pydantic-settings 从环境变量或 .env 文件中读取配置 4 | """ 5 | import json 6 | from typing import List 7 | from pydantic import Field, field_validator 8 | from pydantic_settings import BaseSettings, SettingsConfigDict 9 | 10 | class Settings(BaseSettings): 11 | """应用配置类""" 12 | 13 | model_config = SettingsConfigDict( 14 | env_file='.env', 15 | env_file_encoding='utf-8', 16 | case_sensitive=False, 17 | extra='ignore', # 忽略未定义的字段 18 | ) 19 | 20 | # Kiira AI API 基础 URL 21 | base_url_kiira: str = Field( 22 | default='https://www.kiira.ai', 23 | alias='BASE_URL_KIIRA', 24 | description='Kiira AI API 基础 URL' 25 | ) 26 | 27 | # SeaArt API 基础 URL 28 | base_url_seaart_api: str = Field( 29 | default='https://app-matrix-api.api.seaart.ai', 30 | alias='BASE_URL_SEAART_API', 31 | description='SeaArt API 基础 URL' 32 | ) 33 | 34 | base_url_seaart_uploader: str = Field( 35 | default='https://aiart-uploader.api.seaart.dev', 36 | alias='BASE_URL_SEAART_UPLOADER', 37 | description='SeaArt Uploader API 基础 URL' 38 | ) 39 | # api key 40 | api_key: str = Field( 41 | default='sk-123456', 42 | alias='API_KEY', 43 | description='API Key' 44 | ) 45 | # 默认配置 46 | default_agent_name: str = Field( 47 | default='Nano Banana Pro🔥', 48 | alias='DEFAULT_AGENT_NAME', 49 | description='默认代理名称' 50 | ) 51 | 52 | # Agent 列表配置 53 | # 支持两种格式： 54 | # 1. JSON 格式：AGENT_LIST=["Dress up Game", "NanoBanana PlayLab"] 55 | # 2. 逗号分隔：AGENT_LIST=Dress up Game,NanoBanana PlayLab 56 | agent_list: List[str] = Field( 57 | default_factory=list, 58 | alias='AGENT_LIST', 59 | description='Agent 列表配置' 60 | ) 61 | 62 | @field_validator('agent_list', mode='before') 63 | @classmethod 64 | def parse_agent_list(cls, v): 65 | """解析 Agent 列表，支持 JSON 和逗号分隔格式""" 66 | if v is None: 67 | return [] 68 | 69 | # 如果是列表，直接返回 70 | if isinstance(v, list): 71 | return v 72 | 73 | # 如果是字符串 74 | if isinstance(v, str): 75 | v = v.strip() 76 | # 尝试解析为 JSON 77 | if v.startswith('[') and v.endswith(']'): 78 | try: 79 | return json.loads(v) 80 | except json.JSONDecodeError: 81 | pass 82 | 83 | # 否则按逗号分隔 84 | if v: 85 | return [item.strip() for item in v.split(',') if item.strip()] 86 | 87 | return [] 88 | 89 | 90 | # 创建全局配置实例 91 | settings = Settings() 92 | 93 | # 为了向后兼容，导出原有的变量名（大写） 94 | BASE_URL_KIIRA = settings.base_url_kiira 95 | BASE_URL_SEAART_API = settings.base_url_seaart_api 96 | BASE_URL_SEAART_UPLOADER = settings.base_url_seaart_uploader 97 | DEFAULT_AGENT_NAME = settings.default_agent_name 98 | AGENT_LIST = settings.agent_list 99 | API_KEY = settings.api_key 100 | 101 | # 导出配置类和实例，方便高级用法 102 | __all__ = [ 103 | 'Settings', 104 | 'settings', 105 | 'BASE_URL_KIIRA', 106 | 'BASE_URL_SEAART_API', 107 | 'BASE_URL_SEAART_UPLOADER', 108 | 'DEFAULT_AGENT_NAME', 109 | 'AGENT_LIST', 110 | 'API_KEY', 111 | ] 112 | -------------------------------------------------------------------------------- /app/utils/http_client.py: -------------------------------------------------------------------------------- 1 | """ 2 | HTTP 客户端工具函数 3 | 提供复用连接的HTTP Session和统一超时管理 4 | """ 5 | import json 6 | import requests 7 | from typing import Optional, Dict, Any, Tuple 8 | 9 | from app.config import BASE_URL_KIIRA 10 | from app.utils.logger import get_logger 11 | 12 | logger = get_logger(__name__) 13 | 14 | # 全局Session单例,复用HTTP连接以提升性能 15 | _session: Optional[requests.Session] = None 16 | 17 | # 默认超时配置: (连接超时, 读取超时) 秒 18 | DEFAULT_TIMEOUT: Tuple[int, int] = (3, 15) 19 | 20 | 21 | def build_headers( 22 | device_id: str, 23 | token: Optional[str] = None, 24 | referer: Optional[str] = None, 25 | content_type: str = 'application/json', 26 | accept: str = '*/*', 27 | accept_language: str = 'zh,zh-CN;q=0.9,en;q=0.8,ja;q=0.7', 28 | sec_fetch_site: str = 'same-origin' 29 | ) -> Dict[str, str]: 30 | """ 31 | 构建通用请求头 32 | 33 | Args: 34 | device_id: 设备ID 35 | token: 认证token 36 | referer: Referer头 37 | content_type: Content-Type 38 | accept: Accept头 39 | accept_language: Accept-Language头 40 | sec_fetch_site: Sec-Fetch-Site头 41 | 42 | Returns: 43 | 请求头字典 44 | """ 45 | 46 | headers = { 47 | 'accept': accept, 48 | 'accept-language': accept_language, 49 | 'cache-control': 'no-cache', 50 | 'content-type': content_type, 51 | 'origin': BASE_URL_KIIRA, 52 | 'pragma': 'no-cache', 53 | 'priority': 'u=1, i', 54 | 'referer': referer or BASE_URL_KIIRA, 55 | 'sec-fetch-dest': 'empty', 56 | 'sec-fetch-mode': 'cors', 57 | 'sec-fetch-site': sec_fetch_site, 58 | 'user-agent': 'Mozilla/5.0 (iPhone; CPU iPhone OS 13_2_3 like Mac OS X) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/13.0.3 Mobile/15E148 Safari/604.1', 59 | 'x-app-id': 'gen.seagen.app', 60 | 'x-device-id': device_id, 61 | 'x-language': 'en', 62 | 'x-platform': 'web' 63 | } 64 | 65 | if token: 66 | headers['token'] = token 67 | 68 | return headers 69 | 70 | 71 | def get_session() -> requests.Session: 72 | """ 73 | 获取全局HTTP Session单例 74 | Session会复用TCP连接,减少握手开销 75 | 76 | Returns: 77 | requests.Session实例 78 | """ 79 | global _session 80 | if _session is None: 81 | _session = requests.Session() 82 | # 设置连接池大小 83 | adapter = requests.adapters.HTTPAdapter( 84 | pool_connections=10, 85 | pool_maxsize=20, 86 | pool_block=False 87 | ) 88 | _session.mount('http://', adapter) 89 | _session.mount('https://', adapter) 90 | return _session 91 | 92 | 93 | def make_request( 94 | method: str, 95 | url: str, 96 | device_id: str, 97 | token: Optional[str] = None, 98 | headers: Optional[Dict[str, str]] = None, 99 | json_data: Optional[Dict[str, Any]] = None, 100 | timeout: Optional[Tuple[int, int]] = None, 101 | **kwargs 102 | ) -> Optional[Dict[str, Any]]: 103 | """ 104 | 发送HTTP请求的通用方法 105 | 使用全局Session复用连接,提升性能 106 | 107 | Args: 108 | method: HTTP方法 109 | url: 请求URL 110 | device_id: 设备ID 111 | token: 认证token 112 | headers: 自定义请求头（如果提供，将覆盖默认头） 113 | json_data: JSON数据 114 | timeout: 超时配置(连接超时,读取超时),默认(3,15)秒 115 | **kwargs: 其他requests参数 116 | 117 | Returns: 118 | 响应JSON数据，失败返回None 119 | """ 120 | if headers is None: 121 | headers = build_headers(device_id=device_id, token=token) 122 | 123 | # 使用默认超时 124 | if timeout is None: 125 | timeout = DEFAULT_TIMEOUT 126 | 127 | try: 128 | session = get_session() 129 | response = session.request( 130 | method=method, 131 | url=url, 132 | headers=headers, 133 | json=json_data, 134 | timeout=timeout, 135 | **kwargs 136 | ) 137 | response.raise_for_status() 138 | return response.json() 139 | except requests.exceptions.Timeout as e: 140 | logger.error(f"请求超时 {method} {url}: {e}") 141 | return None 142 | except requests.exceptions.RequestException as e: 143 | logger.error(f"请求失败 {method} {url}: {e}") 144 | return None 145 | except json.JSONDecodeError as e: 146 | logger.error(f"JSON解析失败: {e}") 147 | return None 148 | except Exception as e: 149 | logger.error(f"未知错误: {e}", exc_info=True) 150 | return None 151 | -------------------------------------------------------------------------------- /app/utils/logger.py: -------------------------------------------------------------------------------- 1 | """ 2 | 统一日志处理模块，支持彩色输出 3 | """ 4 | import logging 5 | import sys 6 | from typing import Optional 7 | 8 | 9 | class ColoredFormatter(logging.Formatter): 10 | """彩色日志格式化器""" 11 | 12 | # ANSI 颜色代码 13 | COLORS = { 14 | 'DEBUG': '\033[36m', # 青色 15 | 'INFO': '\033[32m', # 绿色 16 | 'WARNING': '\033[33m', # 黄色 17 | 'ERROR': '\033[31m', # 红色 18 | 'CRITICAL': '\033[35m', # 紫色 19 | 'RESET': '\033[0m', # 重置 20 | } 21 | 22 | def __init__(self, use_color: bool = True, *args, **kwargs): 23 | super().__init__(*args, **kwargs) 24 | self.use_color = use_color and sys.stdout.isatty() 25 | 26 | def format(self, record: logging.LogRecord) -> str: 27 | """格式化日志记录""" 28 | if self.use_color: 29 | # 保存原始值 30 | original_levelname = record.levelname 31 | 32 | # 获取颜色 33 | color = self.COLORS.get(record.levelname, self.COLORS['RESET']) 34 | reset = self.COLORS['RESET'] 35 | 36 | # 给级别名称添加颜色 37 | record.levelname = f"{color}{record.levelname}{reset}" 38 | 39 | # 先获取格式化后的消息内容（这样能正确处理所有格式化情况） 40 | formatted_message = record.getMessage() 41 | 42 | # 给消息内容添加颜色 43 | # 临时替换消息，以便在格式化时包含颜色 44 | original_msg = record.msg 45 | original_args = record.args 46 | record.msg = f"{color}{formatted_message}{reset}" 47 | record.args = () # 清空参数，因为消息已经格式化 48 | 49 | # 格式化日志记录 50 | formatted = super().format(record) 51 | 52 | # 恢复原始值（避免影响其他处理器） 53 | record.levelname = original_levelname 54 | record.msg = original_msg 55 | record.args = original_args 56 | 57 | return formatted 58 | else: 59 | return super().format(record) 60 | 61 | 62 | def setup_logger( 63 | name: Optional[str] = None, 64 | level: int = logging.INFO, 65 | use_color: bool = True, 66 | format_string: Optional[str] = None 67 | ) -> logging.Logger: 68 | """ 69 | 设置并返回配置好的日志记录器 70 | 71 | Args: 72 | name: 日志记录器名称，如果为 None 则使用调用模块的名称 73 | level: 日志级别，默认为 INFO 74 | use_color: 是否使用彩色输出，默认为 True 75 | format_string: 自定义格式字符串，如果为 None 则使用默认格式 76 | 77 | Returns: 78 | 配置好的日志记录器 79 | """ 80 | # 创建日志记录器 81 | logger = logging.getLogger(name) 82 | logger.setLevel(level) 83 | 84 | # 不添加处理器，让日志传播到根日志记录器统一处理 85 | # 这样可以避免重复输出 86 | # 如果根日志记录器已配置，子日志记录器会自动使用根日志记录器的配置 87 | 88 | return logger 89 | 90 | 91 | def get_logger(name: Optional[str] = None) -> logging.Logger: 92 | """ 93 | 获取日志记录器（便捷函数） 94 | 95 | Args: 96 | name: 日志记录器名称，如果为 None 则使用调用模块的名称 97 | 98 | Returns: 99 | 日志记录器实例 100 | """ 101 | return setup_logger(name=name) 102 | 103 | 104 | # 配置根日志记录器 105 | def configure_root_logger( 106 | level: int = logging.INFO, 107 | use_color: bool = True, 108 | format_string: Optional[str] = None 109 | ) -> None: 110 | """ 111 | 配置根日志记录器 112 | 113 | Args: 114 | level: 日志级别，默认为 INFO 115 | use_color: 是否使用彩色输出，默认为 True 116 | format_string: 自定义格式字符串，如果为 None 则使用默认格式 117 | """ 118 | if format_string is None: 119 | format_string = '%(asctime)s | %(levelname)-8s | %(name)s | %(message)s' 120 | 121 | root_logger = logging.getLogger() 122 | root_logger.setLevel(level) 123 | 124 | # 清除现有的处理器和基本配置 125 | root_logger.handlers.clear() 126 | 127 | # 禁用默认的 basicConfig，避免重复配置 128 | # 通过设置 root_logger 的 propagate 为 True（默认），让所有子日志记录器传播到根日志记录器 129 | 130 | # 创建控制台处理器 131 | console_handler = logging.StreamHandler(sys.stdout) 132 | console_handler.setLevel(level) 133 | 134 | # 创建彩色格式化器 135 | formatter = ColoredFormatter( 136 | use_color=use_color, 137 | fmt=format_string, 138 | datefmt='%Y-%m-%d %H:%M:%S' 139 | ) 140 | console_handler.setFormatter(formatter) 141 | 142 | # 添加处理器到根日志记录器 143 | root_logger.addHandler(console_handler) 144 | 145 | # 设置第三方库的日志级别 146 | logging.getLogger("uvicorn").setLevel(logging.INFO) 147 | logging.getLogger("uvicorn.access").setLevel(logging.INFO) 148 | logging.getLogger("httpx").setLevel(logging.WARNING) 149 | logging.getLogger("httpcore").setLevel(logging.WARNING) 150 | 151 | -------------------------------------------------------------------------------- /app/utils/file_utils.py: -------------------------------------------------------------------------------- 1 | """ 2 | 文件处理工具函数 3 | """ 4 | import base64 5 | import requests 6 | from typing import Optional, Tuple 7 | 8 | from app.utils.logger import get_logger 9 | 10 | logger = get_logger(__name__) 11 | 12 | 13 | def guess_content_type(file_name: str, default: str = "image/jpeg") -> str: 14 | """ 15 | 根据文件名猜测 Content-Type 16 | 17 | Args: 18 | file_name: 文件名 19 | default: 默认Content-Type 20 | 21 | Returns: 22 | Content-Type字符串 23 | """ 24 | suffix = file_name.lower() 25 | if suffix.endswith((".png", ".jpeg", ".jpg", ".webp", ".gif")): 26 | if suffix.endswith(".png"): 27 | return "image/png" 28 | if suffix.endswith(".webp"): 29 | return "image/webp" 30 | if suffix.endswith(".gif"): 31 | return "image/gif" 32 | return "image/jpeg" 33 | return default 34 | 35 | 36 | 37 | def get_file_extension_from_content_type(content_type: str) -> str: 38 | """ 39 | 根据 content_type 返回对应的文件扩展名 40 | 41 | Args: 42 | content_type: MIME类型 43 | 44 | Returns: 45 | 文件扩展名（包含点号） 46 | """ 47 | content_type_map = { 48 | "image/jpeg": ".jpg", 49 | "image/jpg": ".jpg", 50 | "image/png": ".png", 51 | "image/webp": ".webp", 52 | "image/gif": ".gif", 53 | } 54 | return content_type_map.get(content_type.lower(), ".jpg") 55 | 56 | 57 | 58 | def decode_base64_img(b64_string: str, content_type: str) -> Tuple[Optional[bytes], Optional[str]]: 59 | """ 60 | 解码 base64 字符串到图片数据 61 | 62 | Args: 63 | b64_string: base64编码的字符串 64 | content_type: 图片的Content-Type 65 | 66 | Returns: 67 | (图片数据, Content-Type) 元组，失败返回 (None, None) 68 | """ 69 | try: 70 | return base64.b64decode(b64_string), content_type 71 | except Exception as e: 72 | logger.error(f"解析 base64 图片失败：{e}") 73 | return None, None 74 | 75 | 76 | 77 | def get_image_data_and_type(image_path: str, file_name: str) -> Tuple[Optional[bytes], Optional[str]]: 78 | """ 79 | 获取图片数据和 Content-Type，支持本地路径、URL 和 base64 80 | 81 | Args: 82 | image_path: 图片路径（本地路径、URL或base64字符串） 83 | file_name: 文件名（用于猜测类型） 84 | 85 | Returns: 86 | (图片数据, Content-Type) 元组，失败返回 (None, None) 87 | """ 88 | # URL 图片 89 | if image_path.startswith(("http://", "https://")): 90 | logger.info("检测到 image_path 是图片URL，正在下载...") 91 | try: 92 | img_resp = requests.get(image_path, timeout=30) 93 | img_resp.raise_for_status() 94 | content_type = img_resp.headers.get("Content-Type", guess_content_type(file_name)) 95 | if not content_type.startswith("image/"): 96 | content_type = guess_content_type(file_name) 97 | return img_resp.content, content_type 98 | except requests.exceptions.RequestException as e: 99 | logger.error(f"图片URL下载失败: {e}") 100 | return None, None 101 | 102 | # data:image/xxx;base64 格式 103 | data_url_prefix = "data:image/" 104 | if image_path.strip().startswith(data_url_prefix): 105 | logger.info("检测到 image_path 是 data:image/xxx;base64 格式，正在解码...") 106 | try: 107 | head, b64data = image_path.split(",", 1) 108 | content_type = guess_content_type(file_name) 109 | if ";base64" in head: 110 | if "png" in head: 111 | content_type = "image/png" 112 | elif "webp" in head: 113 | content_type = "image/webp" 114 | elif "gif" in head: 115 | content_type = "image/gif" 116 | 117 | return decode_base64_img(b64data, content_type) 118 | logger.error("不是标准的 data:image/xxx;base64 编码") 119 | except Exception as e: 120 | logger.error(f"解析 data URL 图片失败：{e}") 121 | return None, None 122 | 123 | # 纯 base64 字符串 124 | _img_str = image_path.strip() 125 | # 简单判断是否为 base64 串（长度>128，且只包含 base64 字符） 126 | if len(_img_str) > 128 and all(c.isalnum() or c in "+/=" for c in _img_str): 127 | logger.info("检测到 image_path 可能是 base64 图片串，正在解码...") 128 | content_type = guess_content_type(file_name) 129 | return decode_base64_img(_img_str, content_type) 130 | 131 | # 本地文件路径 132 | logger.info("检测到 image_path 是本地文件，正在读取内容...") 133 | try: 134 | with open(image_path, "rb") as f: 135 | data = f.read() 136 | content_type = guess_content_type(file_name) 137 | return data, content_type 138 | except FileNotFoundError: 139 | logger.error(f"文件不存在: {image_path}") 140 | return None, None 141 | except Exception as e: 142 | logger.error(f"读取本地文件失败：{e}") 143 | return None, None 144 | 145 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Kiira2API 2 | 3 | [![Python](https://img.shields.io/badge/Python-3.11+-blue.svg)](https://www.python.org/) 4 | [![FastAPI](https://img.shields.io/badge/FastAPI-0.121.2-green.svg)](https://fastapi.tiangolo.com/) 5 | [![License](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE) 6 | 7 | 基于 FastAPI 的 API 服务，提供兼容 OpenAI API 格式的聊天完成和模型查询接口。项目采用模块化架构，整合了 Kiira AI 客户端功能。 8 | 9 | ## ✨ 特性 10 | 11 | - 🚀 **OpenAI API 兼容**: 完全兼容 OpenAI API 格式，便于现有客户端集成 12 | - 💬 **聊天完成接口**: 支持 `/v1/chat/completions` 接口，提供流式和非流式响应 13 | - 🤖 **模型查询**: 支持 `/v1/models` 接口，查询可用模型列表 14 | - 🔄 **流式响应**: 支持 Server-Sent Events (SSE) 流式响应 15 | - 📁 **文件上传**: 支持图片、文件上传功能 16 | - ⚙️ **灵活配置**: 支持环境变量和 `.env` 文件配置 17 | - 🏗️ **模块化架构**: 清晰的分层结构，易于维护和扩展 18 | 19 | ## 📋 目录 20 | 21 | - [快速开始](#快速开始) 22 | - [配置说明](#配置说明) 23 | - [API 文档](#api-文档) 24 | - [项目结构](#项目结构) 25 | - [开发指南](#开发指南) 26 | - [许可证](#许可证) 27 | 28 | ## 🚀 快速开始 29 | 30 | ### 环境要求 31 | 32 | - Python >= 3.11 33 | - [uv](https://github.com/astral-sh/uv) 包管理器（推荐） 34 | 35 | ### 安装步骤 36 | 37 | 1. **克隆项目** 38 | 39 | ```bash 40 | git clone https://github.com/move132/kiira2api.git 41 | cd kiira2api 42 | ``` 43 | 44 | 2. **安装依赖** 45 | 46 | ```bash 47 | # 使用 uv 安装依赖（推荐） 48 | uv sync 49 | ``` 50 | 51 | 3. **配置环境变量** 52 | 53 | 首先复制 `.env.example` 到 `.env`： 54 | 55 | ```bash 56 | # Linux/macOS 57 | cp .env.example .env 58 | 59 | # Windows 60 | copy .env.example .env 61 | ``` 62 | 63 | 然后根据需要编辑 `.env` 文件（可选，也可使用环境变量）： 64 | .env 默认配置可以不修改，需要添加额外的agent时添加 65 | 66 | ```env 67 | # Agent 列表 68 | AGENT_LIST=["Nano Banana Pro🔥","Nano Banana Pro 🔥👉 Try Free","Sora 2: AI Video Remixer", "Veo 3: AI Video Weaver", "Midjourney Art Studio"] 69 | ``` 70 | 71 | 4. **运行服务** 72 | 73 | ```bash 74 | # 方式 1: 使用 uv 运行（推荐） 75 | uv run python main.py 76 | 77 | # 方式 2: 使用 uvicorn 直接运行 78 | uvicorn app.main:app --reload --port 8999 79 | 80 | # 方式 3: 直接运行模块 81 | uv run python -m app.main 82 | ``` 83 | 84 | 服务启动后，访问： 85 | - API 文档: http://localhost:8999/docs 86 | - 健康检查: http://localhost:8999/health 87 | 88 | ### 使用 Docker 运行 89 | 90 | 1. **构建镜像** 91 | 92 | ```bash 93 | docker build -t kiira2api:latest . 94 | ``` 95 | 96 | 2. **运行容器** 97 | 98 | ```bash 99 | # 基本运行 100 | docker run -d -p 8999:8999 -v $(pwd)/data:/app/data --name kiira2api kiira2api:latest 101 | 102 | # 使用环境变量 103 | docker run -d -p 8999:8999 \ 104 | -e API_KEY='sk-123456' \ 105 | -e AGENT_LIST='["Dress up Game", "NanoBanana PlayLab"]' \ 106 | --name kiira2api kiira2api:latest 107 | 108 | # 挂载 .env 文件 109 | docker run -d -p 8999:8999 \ 110 | --env-file .env \ 111 | --name kiira2api kiira2api:latest 112 | ``` 113 | 114 | 3. **查看日志** 115 | 116 | ```bash 117 | docker logs -f kiira2api 118 | ``` 119 | 120 | 4. **停止和删除容器** 121 | 122 | ```bash 123 | docker stop kiira2api 124 | docker rm kiira2api 125 | ``` 126 | 127 | ### 使用 Docker Compose 运行（推荐） 128 | 129 | 1. **启动服务** 130 | 131 | ```bash 132 | # 构建并启动服务 133 | docker-compose up -d 134 | 135 | # 或者使用 docker compose（新版本） 136 | docker compose up -d 137 | ``` 138 | 139 | 2. **查看日志** 140 | 141 | ```bash 142 | # 查看日志 143 | docker-compose logs -f 144 | 145 | # 或者 146 | docker compose logs -f 147 | ``` 148 | 149 | 3. **停止服务** 150 | 151 | ```bash 152 | # 停止服务 153 | docker-compose down 154 | 155 | # 停止并删除数据卷 156 | docker-compose down -v 157 | ``` 158 | 159 | 4. **重启服务** 160 | 161 | ```bash 162 | # 重启服务 163 | docker-compose restart 164 | 165 | # 重新构建并启动 166 | docker-compose up -d --build 167 | ``` 168 | 169 | **Docker Compose 优势**: 170 | - 自动挂载 `./data` 目录到容器 171 | - 自动加载 `.env` 文件配置 172 | - 自动配置网络和健康检查 173 | - 支持服务重启策略 174 | - 更简单的管理命令 175 | 176 | ### 使用 GitHub Container 镜像 177 | 178 | 1. **拉取镜像** 179 | 180 | ```bash 181 | # 拉取最新版本 182 | docker pull ghcr.io/move132/kiira2api:latest 183 | 184 | # 拉取特定版本 185 | docker pull ghcr.io/move132/kiira2api:v1.0.0 186 | ``` 187 | 188 | 2. **运行容器** 189 | 190 | ```bash 191 | # 使用 GHCR 镜像运行 192 | docker run -d -p 8999:8999 \ 193 | -v $(pwd)/data:/app/data \ 194 | --name kiira2api \ 195 | ghcr.io/move132/kiira2api:latest 196 | ``` 197 | ## ⚙️ 配置说明 198 | 199 | ### 环境变量 200 | 201 | 所有配置项都支持通过环境变量或 `.env` 文件设置，优先级：**环境变量 > .env 文件 > 默认值** 202 | 203 | | 变量名 | 说明 | 默认值 | 204 | |--------|------|--------| 205 | | `API_KEY` | API 密钥（用于接口鉴权） | `sk-123456` | 206 | | `DEFAULT_AGENT_NAME` | 默认代理名称 | `Nano Banana Pro🔥` | 207 | | `AGENT_LIST` | Agent 列表（JSON 或逗号分隔） | `[]` | 208 | 209 | **注意**: 如果 `API_KEY` 使用默认值 `sk-123456`，系统将跳过鉴权验证。生产环境请务必修改为安全的密钥。 210 | 211 | ## 📚 API 文档 212 | 213 | ### 鉴权说明 214 | 215 | 所有 `/v1/*` 接口都需要进行 API Key 鉴权（除非使用默认值 `sk-123456`）。 216 | 217 | **鉴权方式**（两种方式任选其一）： 218 | 219 | 1. **Authorization Header**（推荐，兼容 OpenAI API 格式）: 220 | ```bash 221 | Authorization: Bearer sk-your-api-key-here 222 | ``` 223 | 224 | 2. **X-API-Key Header**: 225 | ```bash 226 | X-API-Key: sk-your-api-key-here 227 | ``` 228 | 229 | **示例请求**: 230 | 231 | ```bash 232 | curl -X POST "http://localhost:8999/v1/chat/completions" \ 233 | -H "Authorization: Bearer sk-your-api-key-here" \ 234 | -H "Content-Type: application/json" \ 235 | -d '{ 236 | "model": "Nano Banana Pro🔥", 237 | "messages": [{"role": "user", "content": "你好"}] 238 | }' 239 | ``` 240 | 241 | 如果 API Key 无效或缺失，将返回 `401 Unauthorized` 错误。 242 | 243 | ### POST /v1/chat/completions 244 | 245 | 聊天完成接口，兼容 OpenAI API 格式。 246 | 247 | **请求示例**: 248 | 249 | ```bash 250 | curl -X POST "http://localhost:8999/v1/chat/completions" \ 251 | -H "Authorization: Bearer sk-your-api-key-here" \ 252 | -H "Content-Type: application/json" \ 253 | -d '{ 254 | "model": "Nano Banana Pro🔥", 255 | "messages": [ 256 | {"role": "user", "content": "你好"} 257 | ], 258 | "stream": false 259 | }' 260 | ``` 261 | 262 | **流式响应示例**: 263 | 264 | ```bash 265 | curl -X POST "http://localhost:8999/v1/chat/completions" \ 266 | -H "Authorization: Bearer sk-your-api-key-here" \ 267 | -H "Content-Type: application/json" \ 268 | -d '{ 269 | "model": "Nano Banana Pro🔥", 270 | "messages": [ 271 | {"role": "user", "content": "你好"} 272 | ], 273 | "stream": true 274 | }' 275 | ``` 276 | 277 | **响应格式**: 278 | 279 | ```json 280 | { 281 | "id": "chatcmpl-xxx", 282 | "object": "chat.completion", 283 | "created": 1677652288, 284 | "choices": [ 285 | { 286 | "index": 0, 287 | "message": { 288 | "role": "assistant", 289 | "content": "你好！有什么可以帮助你的吗？" 290 | }, 291 | "finish_reason": "stop" 292 | } 293 | ], 294 | "usage": { 295 | "prompt_tokens": 9, 296 | "completion_tokens": 12, 297 | "total_tokens": 21 298 | } 299 | } 300 | ``` 301 | 302 | ### GET /v1/models 303 | 304 | 获取可用模型列表。 305 | 306 | **请求示例**: 307 | 308 | ```bash 309 | curl -X GET "http://localhost:8999/v1/models" \ 310 | -H "Authorization: Bearer sk-your-api-key-here" 311 | ``` 312 | 313 | **响应格式**: 314 | 315 | ```json 316 | { 317 | "object": "list", 318 | "data": [ 319 | { 320 | "id": "Nano Banana Pro🔥", 321 | "object": "model", 322 | "created": 1677610602, 323 | "owned_by": "move132" 324 | } 325 | ] 326 | } 327 | ``` 328 | 329 | ### GET /health 330 | 331 | 健康检查端点。 332 | 333 | **响应格式**: 334 | 335 | ```json 336 | { 337 | "status": "healthy" 338 | } 339 | ``` 340 | 341 | ## 📁 项目结构 342 | 343 | ``` 344 | kiira2api/ 345 | ├── app/ # 应用主目录 346 | │ ├── __init__.py 347 | │ ├── main.py # FastAPI 应用入口 348 | │ ├── config.py # 配置管理 349 | │ ├── models/ # 数据模型 350 | │ │ ├── __init__.py 351 | │ │ └── schemas.py # Pydantic 数据模型定义 352 | │ ├── api/ # API 路由 353 | │ │ ├── __init__.py 354 | │ │ └── v1/ # v1 版本 API 355 | │ │ ├── __init__.py 356 | │ │ ├── chat.py # 聊天相关路由 357 | │ │ └── models.py # 模型相关路由 358 | │ ├── services/ # 业务逻辑服务 359 | │ │ ├── __init__.py 360 | │ │ ├── chat_service.py # 聊天服务 361 | │ │ └── kiira_client.py # Kiira AI 客户端服务 362 | │ └── utils/ # 工具函数 363 | │ ├── __init__.py 364 | │ ├── http_client.py # HTTP 请求工具 365 | │ ├── file_utils.py # 文件处理工具 366 | │ ├── stream_parser.py # 流式响应解析工具 367 | │ └── logger.py # 日志工具 368 | ├── data/ # 数据目录 369 | │ └── account.json # 账户配置（示例） 370 | ├── main.py # 兼容入口 371 | ├── pyproject.toml # 项目配置和依赖管理 372 | ├── uv.lock # 依赖锁定文件 373 | ├── memory_bank.md # 项目记忆库 374 | └── README.md # 项目说明文档 375 | ``` 376 | 377 | ## 🛠️ 开发指南 378 | 379 | ### 技术栈 380 | 381 | - **Python**: >=3.11 382 | - **FastAPI**: 0.121.2 - 现代、快速的 Web 框架 383 | - **Uvicorn**: 0.38.0 - ASGI 服务器 384 | - **Pydantic**: 2.12.4 - 数据验证和序列化 385 | - **Requests**: 2.32.5 - HTTP 客户端库 386 | - **pydantic-settings**: 2.0.0 - 配置管理 387 | 388 | ### 代码规范 389 | 390 | - 使用类型提示（Type Hints） 391 | - 遵循 PEP 8 代码风格 392 | - 使用 Pydantic BaseModel 定义数据模型 393 | - API 路由使用 APIRouter 组织 394 | - 使用 `{{CHENGQI:...}}` 注释标记代码变更 395 | 396 | ### 架构设计 397 | 398 | - **分层架构**: API 层、服务层、工具层清晰分离 399 | - **模块化设计**: 功能按模块拆分，职责清晰 400 | - **依赖注入**: 使用 FastAPI 的依赖注入系统 401 | - **数据验证**: 使用 Pydantic 进行请求/响应数据验证 402 | 403 | ### 开发流程 404 | 405 | 1. **理解需求**: 阅读 `memory_bank.md` 了解项目上下文 406 | 2. **设计方案**: 确定技术实现方案 407 | 3. **编写代码**: 按照项目规范编写代码 408 | 4. **测试验证**: 确保功能正常 409 | 5. **更新文档**: 更新相关文档和记忆库 410 | 411 | ## 📝 更新日志 412 | 413 | ### v1.0.0 (2025-11-20) 414 | 415 | - ✨ 初始版本发布 416 | - 🎉 实现 OpenAI API 兼容接口 417 | - 🔄 支持流式响应 418 | - ⚙️ 支持环境变量配置 419 | - 📦 模块化架构重构 420 | 421 | ## 🤝 贡献 422 | 423 | 欢迎提交 Issue 和 Pull Request！ 424 | 425 | ## 📄 许可证 426 | 427 | 本项目采用 MIT 许可证。详情请参阅 [LICENSE](LICENSE) 文件。 428 | 429 | ## 👤 作者 430 | 431 | **move132** 432 | 433 | - GitHub: [@move132](https://github.com/move132) 434 | - 项目地址: https://github.com/move132/kiira2api 435 | 436 | ## 🙏 致谢 437 | 438 | 感谢所有为本项目做出贡献的开发者！ 439 | 440 | --- 441 | 442 | **注意**: 本项目仅供学习和研究使用。使用本服务时，请遵守相关服务的使用条款。 443 | 444 | -------------------------------------------------------------------------------- /app/services/chat_service.py: -------------------------------------------------------------------------------- 1 | """ 2 | 聊天服务：处理聊天完成业务逻辑 3 | """ 4 | import uuid 5 | import json 6 | import time 7 | import os 8 | from typing import Optional, Dict, Any, List, Iterator 9 | from fastapi import HTTPException 10 | 11 | from app.services.kiira_client import KiiraAIClient 12 | from app.utils.stream_parser import extract_media_from_data 13 | from app.config import DEFAULT_AGENT_NAME, AGENT_LIST 14 | from app.utils.logger import get_logger 15 | 16 | logger = get_logger(__name__) 17 | 18 | 19 | class ChatService: 20 | """聊天服务类""" 21 | 22 | def __init__(self, device_id: Optional[str] = None, token: Optional[str] = None, group_id: Optional[str] = None): 23 | """ 24 | 初始化聊天服务 25 | 26 | Args: 27 | device_id: 设备ID，如果未提供则自动生成 28 | token: 认证token，如果未提供则自动登录获取 29 | group_id: 群组ID，如果未提供则自动查找 30 | """ 31 | self.device_id = device_id or str(uuid.uuid4()) 32 | self.client = KiiraAIClient(device_id=self.device_id, token=token, group_id=group_id) 33 | self._initialized = False 34 | def save_account_info(self): 35 | """保存账号信息到文件（数组格式）""" 36 | try: 37 | account_info = { 38 | "user_name": self.client.user_name, 39 | "group_id": self.client.group_id, 40 | "token": self.client.token 41 | } 42 | os.makedirs("data", exist_ok=True) 43 | account_file = "data/account.json" 44 | accounts = [] 45 | if os.path.exists(account_file): 46 | with open(account_file, "r", encoding="utf-8") as f: 47 | try: 48 | accounts = json.load(f) 49 | if not isinstance(accounts, list): 50 | accounts = [] 51 | except Exception: 52 | accounts = [] 53 | # 追加当前账号信息 54 | accounts.append(account_info) 55 | with open(account_file, "w", encoding="utf-8") as f: 56 | json.dump(accounts, f, ensure_ascii=False, indent=2) 57 | 58 | except Exception as e: 59 | logger.error(f"写入账号信息失败: {e}") 60 | pass 61 | async def _ensure_initialized(self, agent_name: str = DEFAULT_AGENT_NAME): 62 | """确保客户端已初始化（登录并获取群组）""" 63 | if self._initialized: 64 | return 65 | # 如果没有token，先登录 66 | if not self.client.token: 67 | logger.info("正在获取游客Token...") 68 | if not self.client.login_guest(): 69 | raise HTTPException(status_code=500, detail="无法获取认证token") 70 | logger.info("✅ Token获取成功") 71 | # 获取当前用户信息 72 | user_info, name = self.client.get_my_info() 73 | if user_info: 74 | self.client.user_name = name 75 | # 如果没有群组ID，获取群组列表 76 | if not self.client.group_id: 77 | logger.info(f"正在获取聊天群组ID ({agent_name})...") 78 | result = self.client.get_my_chat_group_list(agent_name=agent_name) 79 | if not result: 80 | raise HTTPException( 81 | status_code=404, 82 | detail=f"无法找到指定的Agent群组: {agent_name}" 83 | ) 84 | self.save_account_info() 85 | self._initialized = True 86 | 87 | def _extract_images_from_messages(self, messages: List[Any]) -> List[Dict[str, Any]]: 88 | """ 89 | 从消息中提取图片资源 90 | 91 | Args: 92 | messages: 消息列表（可以是字典或 Pydantic 模型对象） 93 | 94 | Returns: 95 | 资源列表 96 | """ 97 | resources = [] 98 | for message in messages: 99 | # 处理 Pydantic 模型对象或字典 100 | if hasattr(message, 'content'): 101 | content = message.content 102 | elif isinstance(message, dict): 103 | content = message.get("content", "") 104 | else: 105 | continue 106 | 107 | if isinstance(content, str): 108 | # 检查是否包含图片URL 109 | if content.startswith(("http://", "https://")): 110 | # 尝试上传图片 111 | uploaded = self.client.upload_resource(content) 112 | if uploaded: 113 | resources.append({ 114 | "name": uploaded.get('name'), 115 | "size": uploaded.get('size'), 116 | "url": uploaded.get('url'), 117 | "type": "image" 118 | }) 119 | elif isinstance(content, list): 120 | # 处理多模态内容（文本+图片） 121 | for item in content: 122 | if isinstance(item, dict) and item.get("type") == "image_url": 123 | image_url = item.get("image_url", {}).get("url", "") 124 | if image_url: 125 | uploaded = self.client.upload_resource(image_url) 126 | if uploaded: 127 | resources.append({ 128 | "name": uploaded.get('name'), 129 | "size": uploaded.get('size'), 130 | "url": uploaded.get('url'), 131 | "type": "image" 132 | }) 133 | return resources 134 | 135 | def _build_prompt_from_messages(self, messages: List[Any]) -> str: 136 | """ 137 | 从消息列表构建提示词 138 | 139 | Args: 140 | messages: 消息列表（可以是字典或 Pydantic 模型对象） 141 | 142 | Returns: 143 | 组合后的提示词 144 | """ 145 | prompt_parts = [] 146 | for msg in messages: 147 | # 处理 Pydantic 模型对象或字典 148 | if hasattr(msg, 'role') and hasattr(msg, 'content'): 149 | role = msg.role 150 | content = msg.content 151 | elif isinstance(msg, dict): 152 | role = msg.get("role", "") 153 | content = msg.get("content", "") 154 | else: 155 | continue 156 | 157 | if isinstance(content, str): 158 | # 如果是字符串，直接使用（但排除纯URL，因为URL会被提取为图片） 159 | if role == "user" and not content.startswith(("http://", "https://")): 160 | prompt_parts.append(content) 161 | elif role == "assistant": 162 | # 可以添加助手回复的上下文 163 | pass 164 | elif isinstance(content, list): 165 | # 处理多模态内容（文本+图片） 166 | text_parts = [] 167 | for item in content: 168 | if isinstance(item, dict): 169 | if item.get("type") == "text": 170 | text_parts.append(item.get("text", "")) 171 | if text_parts: 172 | prompt_parts.append(" ".join(text_parts)) 173 | 174 | return "\n".join(prompt_parts) if prompt_parts else "" 175 | 176 | async def chat_completion( 177 | self, 178 | messages: List[Any], 179 | model: str = "", 180 | agent_name: str = DEFAULT_AGENT_NAME, 181 | stream: bool = False 182 | ) -> Dict[str, Any]: 183 | """ 184 | 执行聊天完成 185 | 186 | Args: 187 | messages: 消息列表 188 | model: 模型名称（暂时未使用） 189 | model_name: 模型名称 190 | stream: 是否流式返回 191 | 192 | Returns: 193 | 响应数据 194 | """ 195 | # 确保已初始化 196 | await self._ensure_initialized(model) 197 | # 构建提示词 198 | prompt = self._build_prompt_from_messages(messages) 199 | if not prompt: 200 | raise HTTPException(status_code=400, detail="消息内容不能为空") 201 | 202 | # 提取图片资源 203 | resources = self._extract_images_from_messages(messages) 204 | agent_list = self.client.get_agent_list() 205 | # 遍历 agent_list 中的每一个 agent，调用 create_chat_group 206 | if agent_list and isinstance(agent_list, list): 207 | for agent in agent_list: 208 | label = agent.get("label", "") 209 | # 只处理 label 为 AGENT_LIST 的 agent 210 | if label in AGENT_LIST: 211 | account_no = agent.get("account_no") 212 | if account_no: 213 | self.client.create_chat_group(account_no, label) 214 | # 发送消息 215 | task_id = self.client.send_message( 216 | message=prompt, 217 | at_account_no=self.client.at_account_no, 218 | resources=resources if resources else None 219 | ) 220 | 221 | if not task_id: 222 | raise HTTPException(status_code=500, detail="发送消息失败") 223 | 224 | if stream: 225 | # 流式响应在路由中处理 226 | return {"task_id": task_id, "stream": True, "group_id": self.client.group_id, "token": self.client.token} 227 | else: 228 | # 非流式响应：收集所有流式数据后返回 229 | return await self._collect_stream_response(task_id) 230 | 231 | async def _collect_stream_response(self, task_id: str) -> Dict[str, Any]: 232 | """ 233 | 收集流式响应并转换为完整响应 234 | 235 | Args: 236 | task_id: 任务ID 237 | 238 | Returns: 239 | 完整的响应数据 240 | """ 241 | full_content = "" 242 | video_url = None 243 | 244 | for line in self.client.stream_chat_completions(task_id): 245 | if line.startswith("data: "): 246 | if line[6:].strip() == "[DONE]": 247 | break 248 | 249 | try: 250 | json_data = json.loads(line[6:]) 251 | 252 | # 优化: 一次解析同时提取媒体URL和content 253 | parsed_result = extract_media_from_data(json_data) 254 | if parsed_result: 255 | video_url, _ = parsed_result 256 | break 257 | 258 | choices = json_data.get('choices', []) 259 | if choices and isinstance(choices, list) and len(choices) > 0: 260 | choice = choices[0] 261 | if isinstance(choice, dict): 262 | # 优先从 delta 中获取 content 263 | delta = choice.get('delta', {}) 264 | content = delta.get('content', '') if isinstance(delta, dict) else '' 265 | 266 | # 如果没有 delta 内容，尝试从 message 中获取 267 | if not content: 268 | message = choice.get('message', {}) 269 | content = message.get('content', '') if isinstance(message, dict) else '' 270 | 271 | if content: 272 | full_content += content 273 | except json.JSONDecodeError: 274 | pass 275 | except Exception as e: 276 | logger.debug(f"解析响应数据时出错: {e}") 277 | 278 | # 构建 OpenAI 格式的响应 279 | response_id = f"chatcmpl-{task_id}" 280 | created = int(time.time()) 281 | 282 | choices = [{ 283 | "index": 0, 284 | "message": { 285 | "role": "assistant", 286 | "content": full_content 287 | }, 288 | "finish_reason": "stop" 289 | }] 290 | 291 | # 如果有视频URL，添加到响应中 292 | if video_url: 293 | choices[0]["message"]["video_url"] = video_url 294 | 295 | return { 296 | "id": response_id, 297 | "object": "chat.completion", 298 | "created": created, 299 | "model": "sora-2", 300 | "choices": choices, 301 | "usage": { 302 | "prompt_tokens": len(full_content.split()) if full_content else 0, 303 | "completion_tokens": len(full_content.split()) if full_content else 0, 304 | "total_tokens": len(full_content.split()) * 2 if full_content else 0 305 | } 306 | } 307 | 308 | def stream_chat_completion(self, task_id: str) -> Iterator[str]: 309 | """ 310 | 流式返回聊天响应 311 | 312 | Args: 313 | task_id: 任务ID 314 | 315 | Yields: 316 | SSE格式的响应行 317 | """ 318 | for line in self.client.stream_chat_completions(task_id): 319 | yield line 320 | -------------------------------------------------------------------------------- /app/api/v1/chat.py: -------------------------------------------------------------------------------- 1 | """ 2 | 聊天相关 API 路由 3 | """ 4 | import re 5 | import json 6 | from uuid import uuid4 7 | import time 8 | from fastapi import APIRouter, HTTPException, Depends 9 | from typing import Optional 10 | from fastapi.responses import StreamingResponse 11 | from app.models.schemas import ChatCompletionRequest, ChatCompletionResponse 12 | from app.services.chat_service import ChatService 13 | from app.utils.stream_parser import extract_media_from_data 14 | from app.utils.logger import get_logger 15 | from app.api.dependencies import verify_api_key 16 | 17 | logger = get_logger(__name__) 18 | router = APIRouter(tags=["chat"]) 19 | 20 | @router.post("/chat/completions") 21 | async def chat_completions( 22 | request: ChatCompletionRequest, 23 | api_key: Optional[str] = Depends(verify_api_key) 24 | ): 25 | """ 26 | Chat completions endpoint compatible with OpenAI API format. 27 | 参考 temp.py 中的运行示例实现实际业务逻辑。 28 | """ 29 | try: 30 | # 将 Pydantic 模型转换为字典 31 | messages = [{"role": msg.role, "content": msg.content} for msg in request.messages] 32 | 33 | # Group ID: 【{group_id}】 34 | msg_group_id = None 35 | msg_token = None 36 | for msg in request.messages: 37 | # 这里改成从 assistant 内容中获取 group_id 和 token 38 | if getattr(msg, "role", None) == "assistant": 39 | content = getattr(msg, "content", "") 40 | if isinstance(content, str): 41 | match = re.search(r'\{\s*"group_id"\s*:\s*"([^"]+)"\s*,\s*"token"\s*:\s*"([^"]+)"\s*\}', content) 42 | if match: 43 | msg_group_id = match.group(1) 44 | msg_token = match.group(2) 45 | logger.info(f"获取到历史记录中的 Group ID: {msg_group_id}, Token: {msg_token[-20:]}") 46 | break 47 | # 创建聊天服务实例 48 | if not msg_group_id: 49 | logger.warning(f"未获取到历史记录中的 Group ID, 创建新的聊天服务实例") 50 | chat_service = ChatService() 51 | else: 52 | logger.info(f"获取到历史记录中的 Group ID， 直接继续原来的聊天") 53 | chat_service = ChatService(group_id=msg_group_id, token=msg_token) 54 | 55 | last_message = request.messages[-1] if request.messages else None 56 | prompt = ( 57 | last_message.content 58 | if last_message and hasattr(last_message, "content") and isinstance(last_message.content, str) 59 | else "" 60 | ) 61 | if prompt == "hi": 62 | logger.info(f"验证接口是否可用，{request.model}，直接返回正常响应") 63 | return { 64 | "id": str(uuid4()), 65 | "model": request.model, 66 | "object":"chat.completion.chunk", 67 | "choices": [{ 68 | "index":0, 69 | "message":{"role": "assistant", "content": "hi"}, 70 | "finish_reason":"stop" 71 | }], 72 | "created":int(time.time()) 73 | } 74 | # 如果请求流式响应 75 | if request.stream: 76 | if msg_group_id and msg_token: 77 | resources = chat_service._extract_images_from_messages([last_message]) if last_message else [] 78 | logger.info(f"复用对话状态 group_id={msg_group_id}, 提取图片资源数量: {len(resources)}") 79 | 80 | # 直接使用已有的group_id和token 81 | chat_service.client.group_id = msg_group_id 82 | chat_service.client.token = msg_token 83 | 84 | # 如果有at_account_no缓存则使用,否则查询一次 85 | if not chat_service.client.at_account_no: 86 | _, at_account_no = chat_service.client.get_my_chat_group_list(request.model) 87 | else: 88 | at_account_no = chat_service.client.at_account_no 89 | 90 | task_id = chat_service.client.send_message( 91 | message=prompt, 92 | at_account_no=at_account_no, 93 | resources=resources if resources else None 94 | ) 95 | result = { 96 | "task_id": task_id, 97 | "group_id": msg_group_id, 98 | "token": msg_token 99 | } 100 | else: 101 | # 执行聊天完成并获取 task_id 102 | result = await chat_service.chat_completion( 103 | messages=[last_message], 104 | model=request.model, 105 | stream=True 106 | ) 107 | task_id = result.get("task_id") 108 | group_id = result.get("group_id") 109 | token = result.get("token") 110 | if not task_id: 111 | raise HTTPException(status_code=500, detail="无法获取任务ID") 112 | 113 | # 返回流式响应 114 | async def generate_stream(): 115 | """生成 OpenAI 格式的流式响应""" 116 | response_id = f"chatcmpl-{task_id}" 117 | created = int(time.time()) 118 | model = request.model 119 | media_url = None 120 | media_type = None 121 | done_sent = False 122 | if len(request.messages) == 1: 123 | # 发送group_id 124 | # content = f"""
Group ID:【{group_id}】
125 | #
Token:【{token}】
126 | # """ 127 | content = f""" 128 | ```json 129 | {{"group_id": "{group_id}", "token": "{token}"}} 130 | ``` 131 | """ 132 | group_id_chunk = { 133 | "id": response_id, 134 | "object": "chat.completion.chunk", 135 | "created": created, 136 | "model": model, 137 | "choices": [{ 138 | "index": 0, 139 | "delta": {"content": content}, 140 | "finish_reason": "stop" 141 | }] 142 | } 143 | yield f"data: {json.dumps(group_id_chunk)}\n\n" 144 | try: 145 | for line in chat_service.stream_chat_completion(task_id): 146 | if not line or not line.strip(): 147 | continue 148 | # print(line) 149 | # 处理 [DONE] 标记 150 | if line.startswith("data: ") and line[6:].strip() == "[DONE]": 151 | # 如果有视频URL，发送一个包含视频URL的最终块 152 | if media_url: 153 | final_chunk = { 154 | "id": response_id, 155 | "object": "chat.completion.chunk", 156 | "created": created, 157 | "model": model, 158 | "choices": [{ 159 | "index": 0, 160 | "delta": { 161 | "content": 162 | f"\n\n![Generated Image]({media_url})\n\n" if media_type == "image" 163 | else ( 164 | f"生成视频完成.\n[点击下载视频]({media_url})" if media_type == "video" 165 | else f"\n\n{media_url}\n\n" 166 | ) 167 | }, 168 | "finish_reason": "stop" 169 | }] 170 | } 171 | yield f"data: {json.dumps(final_chunk)}\n\n" 172 | else: 173 | # 发送一个带有 finish_reason 的最终块 174 | final_chunk = { 175 | "id": response_id, 176 | "object": "chat.completion.chunk", 177 | "created": created, 178 | "model": model, 179 | "choices": [{ 180 | "index": 0, 181 | "delta": {}, 182 | "finish_reason": "stop" 183 | }] 184 | } 185 | yield f"data: {json.dumps(final_chunk)}\n\n" 186 | 187 | # 发送结束标记 188 | yield "data: [DONE]\n\n" 189 | done_sent = True 190 | break 191 | 192 | # 解析 SSE 格式的数据 193 | if line.startswith("data: "): 194 | json_str = line[6:].strip() 195 | if not json_str or json_str == "[DONE]": 196 | continue 197 | 198 | try: 199 | data = json.loads(json_str) 200 | # logger.info(f"解析数据: {data.get('choices', [])}") 201 | # 优化: 一次解析同时提取媒体URL和content,避免重复JSON解析 202 | parse_result = extract_media_from_data(data) 203 | if parse_result: 204 | parsed_media_url, parsed_media_type = parse_result 205 | media_type = parsed_media_type 206 | media_url = parsed_media_url 207 | logger.debug(f"检测到媒体资源: {media_type} - {media_url[:50]}...") 208 | 209 | # 提取 content 210 | content = "" 211 | choices_data = data.get('choices', []) 212 | if choices_data and isinstance(choices_data, list) and len(choices_data) > 0: 213 | choice = choices_data[0] 214 | if isinstance(choice, dict): 215 | # 优先从 delta 中获取 content 216 | delta = choice.get('delta', {}) 217 | if isinstance(delta, dict): 218 | content = delta.get('content', '') 219 | 220 | # 如果没有 delta 内容，尝试从 message 中获取 221 | if not content: 222 | message = choice.get('message', {}) 223 | if isinstance(message, dict): 224 | content = message.get('content', '') 225 | # 构建 OpenAI 格式的流式响应块 226 | if content: 227 | chunk = { 228 | "id": response_id, 229 | "object": "chat.completion.chunk", 230 | "created": created, 231 | "model": model, 232 | "choices": [{ 233 | "index": 0, 234 | "delta": {"content": content}, 235 | "finish_reason": None 236 | }] 237 | } 238 | yield f"data: {json.dumps(chunk)}\n\n" 239 | 240 | except json.JSONDecodeError: 241 | # 如果不是有效的 JSON，跳过 242 | logger.debug(f"跳过无效的 JSON 行: {line[:100]}") 243 | continue 244 | except Exception as e: 245 | logger.debug(f"解析流式数据时出错: {e}") 246 | continue 247 | 248 | # 如果循环正常结束（没有遇到 [DONE]），发送结束标记 249 | if not done_sent: 250 | # 发送一个带有 finish_reason 的最终块 251 | final_chunk = { 252 | "id": response_id, 253 | "object": "chat.completion.chunk", 254 | "created": created, 255 | "model": model, 256 | "choices": [{ 257 | "index": 0, 258 | "delta": {}, 259 | "finish_reason": "stop" 260 | }] 261 | } 262 | yield f"data: {json.dumps(final_chunk)}\n\n" 263 | yield "data: [DONE]\n\n" 264 | 265 | except Exception as e: 266 | # 发送错误信息 267 | logger.error(f"流式响应错误: {e}", exc_info=True) 268 | error_data = json.dumps({ 269 | "error": { 270 | "message": str(e), 271 | "type": "server_error" 272 | } 273 | }) 274 | yield f"data: {error_data}\n\n" 275 | 276 | return StreamingResponse( 277 | generate_stream(), 278 | media_type="text/event-stream", 279 | headers={ 280 | "Cache-Control": "no-cache", 281 | "Connection": "keep-alive", 282 | } 283 | ) 284 | else: 285 | # 非流式响应 286 | response_data = await chat_service.chat_completion( 287 | messages=messages, 288 | model=request.model, 289 | stream=False 290 | ) 291 | 292 | # 转换为 Pydantic 模型 293 | return ChatCompletionResponse(**response_data) 294 | 295 | except HTTPException: 296 | raise 297 | except Exception as e: 298 | logger.error(f"聊天完成失败: {e}", exc_info=True) 299 | raise HTTPException(status_code=500, detail=f"内部服务器错误: {str(e)}") -------------------------------------------------------------------------------- /app/services/kiira_client.py: -------------------------------------------------------------------------------- 1 | """ 2 | Kiira AI 客户端服务 3 | """ 4 | import uuid 5 | import time 6 | import requests 7 | from pathlib import Path 8 | from typing import Optional, Dict, Any, Iterator, List 9 | from dataclasses import dataclass 10 | 11 | from app.config import ( 12 | BASE_URL_KIIRA, 13 | BASE_URL_SEAART_API, 14 | BASE_URL_SEAART_UPLOADER, 15 | DEFAULT_AGENT_NAME 16 | ) 17 | from app.utils.http_client import build_headers, make_request, get_session 18 | from app.utils.file_utils import ( 19 | get_image_data_and_type, 20 | get_file_extension_from_content_type 21 | ) 22 | from app.utils.logger import get_logger 23 | 24 | logger = get_logger(__name__) 25 | 26 | @dataclass 27 | class KiiraAIClient: 28 | """Kiira AI API 客户端类""" 29 | 30 | device_id: str 31 | token: Optional[str] = None 32 | group_id: Optional[str] = None 33 | at_account_no: Optional[str] = None 34 | user_name: Optional[str] = None 35 | 36 | def __post_init__(self): 37 | """初始化后自动生成设备ID（如果未提供）""" 38 | if not self.device_id: 39 | self.device_id = str(uuid.uuid4()) 40 | 41 | def login_guest(self) -> Optional[str]: 42 | """游客登录，获取 token""" 43 | url = f'{BASE_URL_SEAART_API}/api/v1/login-guest' 44 | headers = build_headers( 45 | device_id=self.device_id, 46 | token='', 47 | referer=f'{BASE_URL_KIIRA}/', 48 | sec_fetch_site='cross-site' 49 | ) 50 | 51 | response_data = make_request('POST', url, device_id=self.device_id, headers=headers, json_data={}) 52 | if response_data and 'data' in response_data and 'token' in response_data['data']: 53 | self.token = response_data['data']['token'] 54 | logger.info(f"获取到游客Token: {self.token[:20]}...") 55 | return self.token 56 | else: 57 | logger.error("登录失败：未获取到token") 58 | return None 59 | 60 | def get_my_info(self) -> Optional[tuple[Dict[str, Any], str]]: 61 | """获取当前用户信息""" 62 | url = f'{BASE_URL_KIIRA}/api/v1/my' 63 | headers = build_headers(device_id=self.device_id, token=self.token, referer=f'{BASE_URL_KIIRA}/chat') 64 | 65 | response_data = make_request('POST', url, device_id=self.device_id, token=self.token, headers=headers, json_data={}) 66 | if response_data and 'data' in response_data: 67 | name = response_data.get('data', {}).get('name', '') 68 | logger.info(f"当前用户信息：{name}") 69 | return response_data, name 70 | return None, None 71 | 72 | def get_my_chat_group_list(self, agent_name: str = DEFAULT_AGENT_NAME) -> Optional[tuple[str, str]]: 73 | """获取当前账户的聊天群组列表，查找指定昵称的群组""" 74 | url = f'{BASE_URL_KIIRA}/api/v1/my-chat-group-list' 75 | headers = build_headers(device_id=self.device_id, token=self.token, accept_language='eh') 76 | data = {"page": 1, "page_size": 999} 77 | response_data = make_request('POST', url, device_id=self.device_id, token=self.token, headers=headers, json_data=data) 78 | 79 | # logger.info(f"获取当前账户的聊天群组列表，查找指定昵称的群组，响应数据: {response_data}") 80 | if not response_data or 'data' not in response_data: 81 | logger.warning("未在响应中找到群组数据") 82 | return None 83 | 84 | items = response_data.get('data', {}).get('items', []) 85 | for item in items: 86 | user_list = item.get('user_list', []) 87 | for user in user_list: 88 | if user.get('nickname') == agent_name: 89 | group_id = item.get('id') 90 | at_account_no = user.get('account_no') 91 | logger.info(f"✅ 找到群组ID: {group_id}, at_account_no: {at_account_no}") 92 | self.group_id = group_id 93 | self.at_account_no = at_account_no 94 | return group_id, at_account_no 95 | # 如果未找到，则获取 agent 列表，并创建聊天群组 96 | logger.warning(f"未在 user_list 中找到 '{agent_name}'，正在尝试在agent 列表中获取 '{agent_name}'，并创建聊天群组") 97 | agent_list = self.get_agent_list() 98 | # 遍历 agent_list 中的每一个 agent，调用 create_chat_group 99 | if agent_list and isinstance(agent_list, list): 100 | for agent in agent_list: 101 | label = agent.get("label", "") 102 | if label in [agent_name]: 103 | account_no_base = agent.get("account_no") 104 | if account_no_base: 105 | group_info = self.create_chat_group([account_no_base], label) 106 | group_id = group_info.get("id") 107 | at_account_no = group_info.get("user_list", [])[0].get("account_no") 108 | self.group_id = group_id 109 | self.at_account_no = at_account_no 110 | return group_id, at_account_no 111 | # 发送消息 112 | logger.warning(f"未找到 '{agent_name}'") 113 | return None, None 114 | 115 | def get_agent_list(self, category_ids: list[str] = [], keyword: str = "") -> Optional[Dict[str, Any]]: 116 | """ 117 | 获取所有 agent(代理) 列表 118 | 119 | Args: 120 | category_ids (list[str], optional): 分类ID列表，默认 [] 121 | keyword (str, optional): 搜索关键词, 默认空字符串 122 | 123 | Returns: 124 | Optional[Dict[str, Any]]: 响应数据 125 | """ 126 | url = f"{BASE_URL_KIIRA}/api/v1/agent-list" 127 | headers = build_headers( 128 | device_id=self.device_id, 129 | token=self.token, 130 | accept_language='zh,zh-CN;q=0.9,en;q=0.8,ja;q=0.7', 131 | referer=f'{BASE_URL_KIIRA}/search' 132 | ) 133 | # curl 的 body 是 {"category_ids":[],"keyword":""} 134 | data = { 135 | "category_ids": category_ids, 136 | "keyword": keyword 137 | } 138 | response_data = make_request( 139 | 'POST', 140 | url, 141 | device_id=self.device_id, 142 | token=self.token, 143 | headers=headers, 144 | json_data=data 145 | ) 146 | if response_data and 'data' in response_data: 147 | # 只返回指定字段 148 | items = response_data['data']['items'] 149 | filtered_items = [] 150 | for item in items: 151 | filtered_items.append({ 152 | "id": item.get("id"), 153 | "label": item.get("label"), 154 | "account_no": item.get("account_no"), 155 | "description": item.get("description"), 156 | }) 157 | # logger.info(f"获取 agent 列表成功，响应: {filtered_items}") 158 | return filtered_items 159 | logger.error(f"获取 agent 列表失败，响应: {response_data}") 160 | return None 161 | 162 | def create_chat_group(self, agent_account_nos: list[str], label: str) -> Optional[Dict[str, Any]]: 163 | """ 164 | 创建新的聊天群组 165 | 166 | Args: 167 | agent_account_nos (list[str]): 代理账户编号列表 168 | 169 | Returns: 170 | Optional[Dict[str, Any]]: 响应数据 171 | """ 172 | url = f"{BASE_URL_KIIRA}/api/v1/create-chat-group" 173 | headers = build_headers( 174 | device_id=self.device_id, 175 | token=self.token, 176 | accept_language='zh,zh-CN;q=0.9,en;q=0.8,ja;q=0.7', 177 | referer=f'{BASE_URL_KIIRA}/search' 178 | ) 179 | 180 | data = { 181 | "agent_account_nos": agent_account_nos 182 | } 183 | 184 | response_data = make_request( 185 | 'POST', 186 | url, 187 | device_id=self.device_id, 188 | token=self.token, 189 | headers=headers, 190 | json_data=data 191 | ) 192 | if response_data and 'data' in response_data: 193 | logger.info(f"✅添加聊天群组{label}成功") 194 | return response_data['data'] 195 | logger.error(f"添加聊天群组{label}失败，响应: {response_data}") 196 | return None 197 | 198 | def _get_upload_presign( 199 | self, 200 | resource_id: str, 201 | file_name: str, 202 | file_size: int, 203 | category: int = 74, 204 | content_type: str = 'image/jpeg' 205 | ) -> Optional[Dict[str, Any]]: 206 | """获取图片上传的 presign 信息（内部方法）""" 207 | url = f"{BASE_URL_SEAART_UPLOADER}/api/upload/pre-sign" 208 | headers = build_headers( 209 | device_id=self.device_id, 210 | token=self.token, 211 | referer=f'{BASE_URL_KIIRA}/', 212 | accept_language='zh', 213 | sec_fetch_site='cross-site' 214 | ) 215 | 216 | data = { 217 | "id": resource_id, 218 | "category": category, 219 | "content_type": content_type, 220 | "file_name": file_name, 221 | "file_size": file_size, 222 | "name": file_name, 223 | "size": file_size 224 | } 225 | 226 | return make_request('POST', url, device_id=self.device_id, token=self.token, headers=headers, json_data=data) 227 | 228 | def _upload_complete(self, resource_id: str) -> Optional[Dict[str, Any]]: 229 | """通知 seaart.dev 上传已完成（内部方法）""" 230 | url = f"{BASE_URL_SEAART_UPLOADER}/api/upload/complete" 231 | headers = build_headers( 232 | device_id=self.device_id, 233 | token=self.token, 234 | referer=f'{BASE_URL_KIIRA}/', 235 | accept_language='zh', 236 | sec_fetch_site='cross-site' 237 | ) 238 | 239 | data = {"id": resource_id} 240 | return make_request('POST', url, device_id=self.device_id, token=self.token, headers=headers, json_data=data) 241 | 242 | def upload_resource(self, image_path: str) -> Optional[Dict[str, Any]]: 243 | """ 244 | 上传文件资源，自动处理预签名、上传和完成步骤。 245 | 支持本地文件、URL 及 base64 字符串。 246 | 247 | Args: 248 | image_path: 本地文件路径、图片 URL 或 base64 字符串。 249 | 250 | Returns: 251 | 包含 'name', 'size', 'url', 'path' 的字典，失败返回 None。 252 | """ 253 | # 1. 解析文件数据和类型 254 | initial_file_name = Path(image_path).name if not (image_path.startswith("http") or image_path.startswith("data:")) else "upload.jpg" 255 | put_data, content_type = get_image_data_and_type(image_path, initial_file_name) 256 | 257 | if not put_data or not content_type: 258 | logger.error("无法获取图片数据和类型，上传失败") 259 | return None 260 | 261 | file_size = len(put_data) 262 | # 根据实际 content_type 调整 file_name 扩展名 263 | base_name = Path(initial_file_name).stem 264 | file_name = base_name + get_file_extension_from_content_type(content_type) 265 | resource_id = str(int(time.time() * 1000)) # 毫秒时间戳作为 resource_id 266 | 267 | # 2. 请求预签名 URL 268 | logger.debug(f"Step 1: 正在请求预签名 URL (Size: {file_size} bytes, Type: {content_type})...") 269 | presign_response = self._get_upload_presign( 270 | resource_id=resource_id, 271 | file_name=file_name, 272 | file_size=file_size, 273 | content_type=content_type 274 | ) 275 | 276 | if not presign_response or 'data' not in presign_response: 277 | logger.error("预签名请求失败或响应格式错误") 278 | return None 279 | 280 | presign_data = presign_response.get("data", {}) 281 | pre_signs = presign_data.get("pre_signs", []) 282 | resource_ret_id = presign_data.get("id") 283 | upload_url = (pre_signs[0].get("url") if pre_signs and isinstance(pre_signs, list) else None) 284 | 285 | if not upload_url: 286 | logger.error(f"没有拿到预签名 URL，响应: {presign_response}") 287 | return None 288 | 289 | logger.debug(f"✅ 预签名响应成功, 资源ID {resource_ret_id}") 290 | 291 | # 3. 直传图片到 GCS 292 | logger.debug("Step 2: 正在直传图片到 GCS...") 293 | 294 | # 检查预签名响应中是否有指定的 headers 295 | presign_headers = {} 296 | if pre_signs and isinstance(pre_signs, list) and len(pre_signs) > 0 and "headers" in pre_signs[0]: 297 | presign_headers.update(pre_signs[0]["headers"]) 298 | 299 | # 实际上传 headers - 使用最少的必要 headers，避免干扰 GCS 签名验证 300 | upload_headers = { 301 | "Content-Type": content_type, 302 | "Content-Length": str(file_size), 303 | } 304 | upload_headers.update(presign_headers) # 合并预签名指定的 headers 305 | 306 | try: 307 | session = get_session() 308 | put_resp = session.put( 309 | upload_url, 310 | headers=upload_headers, 311 | data=put_data, 312 | timeout=60 313 | ) 314 | if put_resp.status_code == 200: 315 | logger.info("✅ 上传成功！") 316 | else: 317 | logger.error(f"上传失败，状态码：{put_resp.status_code}，响应：{put_resp.text[:200]}") 318 | return None 319 | except Exception as e: 320 | logger.error(f"上传失败：{e}") 321 | return None 322 | 323 | # 4. 调用 complete 接口获取最终图片地址 324 | logger.debug(f"Step 3: 正在调用 complete 接口获取最终图片地址...") 325 | complete_data = self._upload_complete(resource_ret_id) 326 | 327 | if complete_data and complete_data.get("status", {}).get("code") == 10000: 328 | image_data = complete_data.get("data", {}) 329 | image_path_ret = image_data.get("path") 330 | image_url = image_data.get("url") 331 | if image_url: 332 | logger.info(f"✅ 资源上传成功: {file_name} ({file_size} bytes)") 333 | return {"name": file_name, "size": file_size, "url": image_url, "path": image_path_ret, "id": resource_id} 334 | else: 335 | logger.warning("⚠️ 未在响应中找到图片URL") 336 | else: 337 | logger.error(f"⚠️ Complete 接口返回错误: {complete_data}") 338 | 339 | return None 340 | 341 | def send_message( 342 | self, 343 | message: str, 344 | at_account_no: str = 'seagen_sora2_agent', 345 | agent_type: str = "agent", 346 | at_account_no_type: str = "bot", 347 | resources: Optional[List[Dict[str, Any]]] = None, 348 | message_id: Optional[str] = None 349 | ) -> Optional[str]: 350 | """向群组发送消息""" 351 | if not self.group_id: 352 | logger.error("未设置群组ID，请先调用 get_my_chat_group_list()") 353 | return None 354 | 355 | url = f'{BASE_URL_KIIRA}/api/v1/send-message' 356 | headers = build_headers(device_id=self.device_id, token=self.token, accept_language='zh') 357 | 358 | if resources is None: 359 | resources = [] 360 | 361 | if message_id is None: 362 | # 使用 uuid1().int 的前17位作为消息ID 363 | message_id = str(uuid.uuid1().int)[:17] 364 | 365 | data = { 366 | "id": message_id, 367 | "at_account_no": at_account_no, 368 | "at_account_no_type": at_account_no_type, 369 | "resources": resources, 370 | "group_id": self.group_id, 371 | "message": message, 372 | "agent_type": agent_type 373 | } 374 | logger.info(f"发送消息: {data}") 375 | response_data = make_request('POST', url, device_id=self.device_id, token=self.token, headers=headers, json_data=data) 376 | if response_data and 'data' in response_data: 377 | task_id = response_data['data'].get('task_id') 378 | if task_id: 379 | logger.info(f"消息发送成功，task_id: {task_id}") 380 | return task_id 381 | 382 | logger.error("发送消息失败：未获取到task_id") 383 | return None 384 | 385 | def stream_chat_completions( 386 | self, 387 | task_id: str, 388 | timeout: int = 180 389 | ) -> Iterator[str]: 390 | """实时流式获取AI聊天响应""" 391 | url = f'{BASE_URL_KIIRA}/api/v1/stream/chat/completions' 392 | headers = build_headers( 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