650 lines
29 KiB
Python
650 lines
29 KiB
Python
"""
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API 路由定义
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"""
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import os
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import json
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import uuid
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from datetime import datetime
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from typing import Dict, List
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from pathlib import Path
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from fastapi import HTTPException, File, UploadFile
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from fastapi.responses import JSONResponse, StreamingResponse
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import dashscope
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from dashscope import Generation, MultiModalConversation
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# 导入模型和工具函数（使用绝对路径）
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import sys
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from pathlib import Path
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sys.path.append(str(Path(__file__).parent.parent))
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from models.chat_models import ChatRequest, ModelInfo
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from utils.helpers import (
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get_current_timestamp,
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generate_unique_id,
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format_api_response,
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extract_delta_content
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)
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# 模拟数据库 - 实际应用中应使用持久化存储
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conversations_db: Dict[str, dict] = {}
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# 配置上传目录
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upload_dir = Path("uploads")
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upload_dir.mkdir(exist_ok=True)
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async def chat_endpoint_handler(body: dict):
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"""
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聊天接口处理器 - 与阿里云百炼API兼容的接口
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这个端点会接收前端的聊天请求并转发到阿里云百炼API
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"""
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try:
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# 确保 body 是字典类型
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if not isinstance(body, dict):
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print(f"[ERROR] Request body is not a dictionary: {type(body)}")
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raise HTTPException(
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status_code=400,
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detail=f"Request body must be a JSON object, got {type(body).__name__}: {body}"
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)
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# 检查请求格式并适配
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# 如果是OpenAI兼容格式 (来自streamChat)
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if 'messages' in body:
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messages = body.get('messages', [])
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model = body.get('model', 'qwen-plus')
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stream = body.get('stream', True)
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temperature = body.get('temperature', 0.7)
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max_tokens = body.get('max_tokens', 2000)
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else:
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# 否则是前端简化格式 (来自chat函数)
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# 需要将其转换为OpenAI兼容格式
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message_text = body.get('message', '')
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# 检查message是否已经是格式化的列表（带图片的情况）
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if isinstance(message_text, list):
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user_content = message_text
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else:
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# 如果是字符串，转换为标准格式
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user_content = [{"type": "text", "text": message_text}]
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messages = [
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{"role": "system", "content": body.get('systemPrompt', '你是一个支持视觉理解的助手。')},
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{"role": "user", "content": user_content}
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]
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model = body.get('model', 'qwen-plus')
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stream = body.get('stream', False) # 默认为非流式
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temperature = body.get('temperature', 0.7)
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max_tokens = body.get('maxTokens', 2000)
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# 检查是否包含图像内容，如果是多模态请求，使用MultiModalConversation
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has_images = any(
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isinstance(msg, dict) and
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isinstance(msg.get('content'), list) and
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any(isinstance(item, dict) and item.get('type') == 'image_url' for item in msg.get('content', []))
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for msg in messages if isinstance(msg, dict)
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)
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if has_images:
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# 使用多模态API处理图像
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return await multimodal_chat_handler(messages, model, stream, temperature, max_tokens)
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else:
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# 使用常规聊天API
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if stream:
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# 流式响应
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async def event_generator():
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try:
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responses = Generation.call(
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model=model,
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messages=messages,
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stream=True,
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max_tokens=max_tokens,
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temperature=temperature
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)
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full_content = "" # 用于累计完整内容
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for idx, response in enumerate(responses):
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if response.status_code == 200:
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# 检查响应是否包含预期的内容
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# DashScope API的响应结构可能是 output.choices 或 output.text
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content = None
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# 尝试从 output.choices 获取内容
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if (hasattr(response, 'output') and
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response.output and
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hasattr(response.output, 'choices') and
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response.output.choices is not None and
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len(response.output.choices) > 0 and
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'message' in response.output.choices[0] and
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'content' in response.output.choices[0]['message']):
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content = response.output.choices[0]['message']['content']
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# 只有当内容发生变化时才发送增量
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if len(content) > len(full_content):
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delta_content = extract_delta_content(content, full_content)
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full_content = content
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if delta_content.strip(): # 只有当有非空白新内容时才发送
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# 构建 SSE 数据块
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data = {
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"id": f"chatcmpl-{generate_unique_id()}",
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"object": "chat.completion.chunk",
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"created": get_current_timestamp(),
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"model": model,
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"choices": [
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{
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"index": 0,
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"delta": {"content": delta_content},
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"finish_reason": None
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}
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]
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}
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yield f"data: {json.dumps(data, ensure_ascii=False)}\n\n"
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# 否则尝试从 output.text 获取内容（DashScope特定格式）
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elif (hasattr(response, 'output') and
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response.output and
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'text' in response.output):
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content = response.output.get('text')
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# 只有当内容发生变化时才发送增量
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if len(content) > len(full_content):
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delta_content = extract_delta_content(content, full_content)
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full_content = content
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if delta_content.strip(): # 只有当有非空白新内容时才发送
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# 构建 SSE 数据块
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data = {
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"id": f"chatcmpl-{generate_unique_id()}",
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"object": "chat.completion.chunk",
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"created": get_current_timestamp(),
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"model": model,
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"choices": [
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{
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"index": 0,
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"delta": {"content": delta_content},
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"finish_reason": None
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}
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]
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}
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yield f"data: {json.dumps(data, ensure_ascii=False)}\n\n"
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else:
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# 错误处理
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error_data = {
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"error": {
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"message": f"API Error: {response.code} - {response.message}",
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"type": "api_error",
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"param": None,
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"code": response.code
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}
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}
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yield f"data: {json.dumps(error_data, ensure_ascii=False)}\n\n"
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break
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# 发送结束信号
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finish_data = {
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"id": f"chatcmpl-{generate_unique_id()}",
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"object": "chat.completion.chunk",
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"created": get_current_timestamp(),
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"model": model,
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"choices": [
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{
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"index": 0,
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"delta": {},
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"finish_reason": "stop"
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}
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]
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}
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yield f"data: {json.dumps(finish_data, ensure_ascii=False)}\n\n"
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yield "data: [DONE]\n\n"
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except Exception as e:
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error_data = {
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"error": {
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"message": str(e),
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"type": "server_error"
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}
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}
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yield f"data: {json.dumps(error_data, ensure_ascii=False)}\n\n"
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return StreamingResponse(event_generator(), media_type="text/event-stream")
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else:
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# 非流式响应
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response = Generation.call(
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model=model,
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messages=messages,
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stream=False,
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max_tokens=max_tokens,
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temperature=temperature
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)
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if response.status_code == 200:
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# 检查响应是否包含预期的内容
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# DashScope API的响应结构可能是 output.choices 或 output.text
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content = None
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# 尝试从 output.choices 获取内容
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if (hasattr(response, 'output') and
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response.output and
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hasattr(response.output, 'choices') and
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response.output.choices is not None and
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len(response.output.choices) > 0 and
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'message' in response.output.choices[0] and
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'content' in response.output.choices[0]['message']):
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content = response.output.choices[0]['message']['content']
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# 否则尝试从 output.text 获取内容（DashScope特定格式）
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elif (hasattr(response, 'output') and
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response.output and
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'text' in response.output):
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content = response.output.get('text')
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if content:
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# 构建前端期望的响应格式
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chat_response = format_api_response(
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content=content,
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conversation_id=body.get('conversationId'),
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model=model
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)
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if hasattr(response, 'usage') and response.usage:
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chat_response["usage"] = {
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"promptTokens": response.usage.input_tokens,
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"completionTokens": response.usage.output_tokens,
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"totalTokens": response.usage.total_tokens
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}
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return JSONResponse(content=chat_response, ensure_ascii=False)
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else:
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raise HTTPException(
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status_code=500,
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detail="API Response does not contain expected content"
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)
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else:
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raise HTTPException(
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status_code=500,
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detail=f"API Error: {response.code} - {response.message}"
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)
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except Exception as e:
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print(f"[ERROR] Error in chat endpoint: {str(e)}")
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import traceback
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print(f"[ERROR] Traceback: {traceback.format_exc()}")
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raise HTTPException(status_code=500, detail=str(e))
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async def multimodal_chat_handler(messages, model, stream, temperature, max_tokens):
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"""
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多模态聊天处理器 - 处理包含图像的消息
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"""
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try:
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# 将OpenAI格式的消息转换为DashScope MultiModalConversation格式
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dashscope_messages = []
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for i, msg in enumerate(messages):
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# 验证 msg 是否为字典类型，如果不是则跳过或处理为字符串
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if not isinstance(msg, dict):
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# 如果消息不是字典，将其作为纯文本处理
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dashscope_content = [
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{'text': str(msg)}
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]
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dashscope_messages.append({
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'role': 'user',
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'content': dashscope_content
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})
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continue
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role = msg.get('role', 'user')
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content = msg.get('content', '')
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if isinstance(content, str):
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# 纯文本内容
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dashscope_content = [
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{'text': content}
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]
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elif isinstance(content, list):
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# 包含图像和文本的内容
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dashscope_content = []
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for j, item in enumerate(content):
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if isinstance(item, dict):
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if item.get('type') == 'text':
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dashscope_content.append({'text': item.get('text', '')})
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elif item.get('type') == 'image_url':
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# 处理 image_url 可能是字符串或字典两种情况
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image_url_value = item.get('image_url', '')
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if isinstance(image_url_value, str):
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# 如果 image_url 是字符串，直接使用
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img_url = image_url_value
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elif isinstance(image_url_value, dict) and 'url' in image_url_value:
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# 如果 image_url 是字典，从中获取 url
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img_url = image_url_value.get('url', '')
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else:
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# 其他情况视为错误或空值
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img_url = ''
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# 如果URL是http格式，提取文件名并转换为file://格式
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if img_url.startswith('http://') or img_url.startswith('https://'):
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# 提取URL中的文件名部分 (例如从 http://localhost:8000/uploads/filename.jpg 提取 uploads/filename.jpg)
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from urllib.parse import urlparse
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parsed_url = urlparse(img_url)
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path_parts = parsed_url.path.split('/')
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# 从路径中找到uploads部分及后面的文件名
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try:
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uploads_index = path_parts.index('uploads')
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filename = '/'.join(path_parts[uploads_index:]) # 例如: uploads/filename.jpg
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img_url = f"file://{filename}"
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except ValueError:
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# 如果路径中没有uploads部分，使用原始路径
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img_url = f"file://{parsed_url.path.lstrip('/')}"
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elif not img_url.startswith('file://'):
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# 如果既不是网络URL也不是file://协议，假设是相对路径
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img_url = f"file://{img_url}"
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if img_url.startswith('file://'):
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# 确保本地文件存在
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import os
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local_path = img_url[7:] # 移除 "file://" 前缀
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if not os.path.exists(local_path):
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print(f"[WARNING] Image file does not exist: {local_path}")
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dashscope_content.append({'image': img_url})
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else:
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# 将非字典内容转换为文本
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dashscope_content.append({'text': str(item)})
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else:
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# 其他情况转换为文本
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dashscope_content = [
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{'text': str(content)}
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]
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dashscope_messages.append({
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'role': role,
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'content': dashscope_content
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})
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if stream:
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# 多模态流式响应
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async def multimodal_event_generator():
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try:
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responses = MultiModalConversation.call(
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model=model.replace('qwen-', 'qwen-vl-') if 'qwen-' in model else 'qwen-vl-max',
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messages=dashscope_messages,
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stream=True,
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max_tokens=max_tokens,
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temperature=temperature
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)
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full_content = ""
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for response in responses:
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if response.status_code == 200:
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content = None
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# 从多模态响应中提取内容
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if (hasattr(response, 'output') and
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response.output and
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hasattr(response.output, 'choices') and
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response.output.choices is not None and
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len(response.output.choices) > 0 and
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'message' in response.output.choices[0]):
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message = response.output.choices[0]['message']
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if 'content' in message:
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content_items = message['content']
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# 从内容项中提取文本
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extracted_text = ""
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for item in content_items:
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if isinstance(item, dict) and 'text' in item:
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extracted_text += item['text']
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content = extracted_text
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# 只有当内容发生变化时才发送增量
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if len(content) > len(full_content):
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delta_content = extract_delta_content(content, full_content)
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full_content = content
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if delta_content.strip():
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data = {
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"id": f"chatcmpl-{generate_unique_id()}",
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"object": "chat.completion.chunk",
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"created": get_current_timestamp(),
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"model": model,
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"choices": [
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{
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"index": 0,
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"delta": {"content": delta_content},
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"finish_reason": None
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}
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]
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}
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yield f"data: {json.dumps(data, ensure_ascii=False)}\n\n"
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else:
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error_data = {
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"error": {
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"message": f"Multimodal API Error: {response.code} - {response.message}",
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"type": "api_error",
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"param": None,
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"code": response.code
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}
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}
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yield f"data: {json.dumps(error_data, ensure_ascii=False)}\n\n"
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break
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finish_data = {
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"id": f"chatcmpl-{generate_unique_id()}",
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"object": "chat.completion.chunk",
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"created": get_current_timestamp(),
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"model": model,
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"choices": [
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{
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"index": 0,
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"delta": {},
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"finish_reason": "stop"
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}
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]
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}
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yield f"data: {json.dumps(finish_data, ensure_ascii=False)}\n\n"
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yield "data: [DONE]\n\n"
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except Exception as e:
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error_data = {
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"error": {
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"message": str(e),
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"type": "server_error"
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}
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}
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yield f"data: {json.dumps(error_data, ensure_ascii=False)}\n\n"
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return StreamingResponse(multimodal_event_generator(), media_type="text/event-stream")
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else:
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# 多模态非流式响应
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response = MultiModalConversation.call(
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model=model.replace('qwen-', 'qwen-vl-') if 'qwen-' in model else 'qwen-vl-max',
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messages=dashscope_messages,
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stream=False,
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max_tokens=max_tokens,
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temperature=temperature
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)
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if response.status_code == 200:
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content = None
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if (hasattr(response, 'output') and
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response.output and
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hasattr(response.output, 'choices') and
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response.output.choices is not None and
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len(response.output.choices) > 0 and
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'message' in response.output.choices[0]):
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message = response.output.choices[0]['message']
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if 'content' in message:
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content_items = message['content']
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# 从内容项中提取文本
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extracted_text = ""
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for item in content_items:
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if isinstance(item, dict) and 'text' in item:
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extracted_text += item['text']
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content = extracted_text
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if content:
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return JSONResponse(content={"result": content}, ensure_ascii=False)
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else:
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raise HTTPException(
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status_code=500,
|
||
detail="Multimodal API Response does not contain expected content"
|
||
)
|
||
else:
|
||
raise HTTPException(
|
||
status_code=500,
|
||
detail=f"Multimodal API Error: {response.code} - {response.message}"
|
||
)
|
||
|
||
except Exception as e:
|
||
print(f"[ERROR] Error in multimodal chat handler: {str(e)}")
|
||
import traceback
|
||
print(f"[ERROR] Traceback: {traceback.format_exc()}")
|
||
raise HTTPException(status_code=500, detail=str(e))
|
||
|
||
|
||
async def get_models_handler():
|
||
"""获取模型列表处理器"""
|
||
models = [
|
||
ModelInfo(
|
||
id="qwen-max",
|
||
name="通义千问 Max",
|
||
description="最强大的模型",
|
||
maxTokens=8192,
|
||
provider="Aliyun"
|
||
),
|
||
ModelInfo(
|
||
id="qwen-plus",
|
||
name="通义千问 Plus",
|
||
description="能力均衡",
|
||
maxTokens=8192,
|
||
provider="Aliyun"
|
||
),
|
||
ModelInfo(
|
||
id="qwen-turbo",
|
||
name="通义千问 Turbo",
|
||
description="速度更快、成本更低",
|
||
maxTokens=8192,
|
||
provider="Aliyun"
|
||
),
|
||
ModelInfo(
|
||
id="qwen-vl-max",
|
||
name="通义万相 VL-Max",
|
||
description="支持视觉理解的多模态模型",
|
||
maxTokens=8192,
|
||
provider="Aliyun"
|
||
),
|
||
ModelInfo(
|
||
id="qwen-vl-plus",
|
||
name="通义万相 VL-Plus",
|
||
description="支持视觉理解的多模态模型",
|
||
maxTokens=8192,
|
||
provider="Aliyun"
|
||
)
|
||
]
|
||
return [model.dict() for model in models]
|
||
|
||
|
||
async def get_conversations_handler():
|
||
"""获取所有对话处理器"""
|
||
return list(conversations_db.values())
|
||
|
||
|
||
async def get_conversation_handler(conversation_id: str):
|
||
"""获取特定对话处理器"""
|
||
conversation = conversations_db.get(conversation_id)
|
||
if not conversation:
|
||
raise HTTPException(status_code=404, detail="对话不存在")
|
||
return conversation
|
||
|
||
|
||
async def save_conversation_handler(data: dict):
|
||
"""保存或更新对话处理器"""
|
||
try:
|
||
conversation_id = data.get('id') or generate_unique_id()
|
||
|
||
conversation = {
|
||
"id": conversation_id,
|
||
"title": data.get('title', '新对话'),
|
||
"messages": data.get('messages', []),
|
||
"updatedAt": datetime.utcnow().isoformat(),
|
||
"createdAt": data.get('createdAt', datetime.utcnow().isoformat())
|
||
}
|
||
|
||
conversations_db[conversation_id] = conversation
|
||
return conversation
|
||
|
||
except Exception as e:
|
||
print(f"[ERROR] Error saving conversation: {str(e)}")
|
||
raise HTTPException(status_code=500, detail=str(e))
|
||
|
||
|
||
async def delete_conversation_handler(conversation_id: str):
|
||
"""删除对话处理器"""
|
||
if conversation_id in conversations_db:
|
||
del conversations_db[conversation_id]
|
||
return {"success": True, "message": "删除成功"}
|
||
else:
|
||
raise HTTPException(status_code=404, detail="对话不存在")
|
||
|
||
|
||
async def upload_file_handler(file: UploadFile = File(...)):
|
||
"""文件上传处理器"""
|
||
try:
|
||
# 检查文件类型
|
||
allowed_types = ['image/jpeg', 'image/png', 'image/gif', 'image/webp', 'text/plain', 'application/pdf']
|
||
if file.content_type not in allowed_types:
|
||
raise HTTPException(status_code=400, detail=f"不支持的文件类型: {file.content_type}")
|
||
|
||
# 生成唯一文件名
|
||
file_extension = Path(file.filename).suffix.lower()
|
||
unique_filename = f"{int(datetime.utcnow().timestamp())}_{generate_unique_id()}{file_extension}"
|
||
file_path = upload_dir / unique_filename
|
||
|
||
# 保存文件
|
||
with open(file_path, "wb") as f:
|
||
content = await file.read()
|
||
f.write(content)
|
||
|
||
# 返回文件信息
|
||
file_url = f"http://localhost:8000/uploads/{unique_filename}"
|
||
result = {
|
||
"url": file_url,
|
||
"name": file.filename,
|
||
"size": len(content),
|
||
"mimeType": file.content_type
|
||
}
|
||
|
||
print(f"[INFO] File uploaded: {result}")
|
||
return result
|
||
|
||
except Exception as e:
|
||
print(f"[ERROR] Upload error: {str(e)}")
|
||
raise HTTPException(status_code=500, detail=f"上传失败: {str(e)}")
|
||
|
||
|
||
def serve_upload_handler(filename: str):
|
||
"""提供上传文件访问处理器"""
|
||
file_path = upload_dir / filename
|
||
if not file_path.exists():
|
||
raise HTTPException(status_code=404, detail="文件不存在")
|
||
|
||
from fastapi.responses import FileResponse
|
||
return FileResponse(str(file_path))
|
||
|
||
|
||
async def stop_generation_handler(message_id: str = None):
|
||
"""停止生成处理器"""
|
||
message = f"已发出停止指令，消息ID: {message_id}" if message_id else "已发出停止指令"
|
||
return {"success": True, "message": message} |