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大模型应用python+Java后端+Vue前端的整合

news 2025/11/15 11:06:08

后端：使用Java作为主后端语言，处理业务逻辑、用户认证、数据持久化等。

集成Python服务调用
java后端调用Python

大模型服务：使用Python编写，通过Flask或FastAPI等框架提供HTTP API。

提供API接口

前端：使用Vue.js或React等框架，这里我们选择Vue.js作为前端框架。

实现用户界面

通信：前端通过HTTP请求与Java后端交互，Java后端在需要调用大模型时，通过HTTP客户端调用Python服务。

项目架构

用户界面 (Vue.js) || HTTP (RESTful API)|
Java后端 (Spring Boot) || 内部HTTP调用|
Python大模型服务 (Flask/FastAPI)

在这里插入图片描述

AI核心代码

# ai-service/app/main.py
from fastapi import FastAPI,HTTPException
from pydantic import BaseModel
import torch
from transformers import AutoTokenizer,AutoModelForCausalLM
import logging
from typing import List,Optional
import asyncio
import json
import numpy as np
from sentence_transformers import SentenceTransformer#配置日志
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)app = FastAPI(title="AI Model Service",version="1.0.0")class ChatRequest(BaseModel):message:strconversation_id:Optional[str] = Nonemax_length:int = 512temperature:float = 0.7class ChatResponse(BaseModel):response:strconversation_id:strtokens_used:intprocessing_time:floatclass EmbeddingRequest(BaseModel):texts:List[List[float]]model:strclass AIModelManager:def __init__(self):self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")self.tokenizer = Noneself.model = Noneself.embedding_model = Noneself.load_models()def load_models(self):"""加载AI模型"""try:logger.info("Loading language model...")# 使用一个较小的模型示例，实际项目中替换为您的模型model_name = "microsoft/DialoGPT-medium"self.tokenizer = AutoTokenizer.from_pretrained(model_name)self.model = AutoModelForCausalLM.from_pretrained(model_name)self.model.to(self.device)logger.info("Loading embedding model...")self.embedding_model = SentenceTransformer('all-MiniLM-L6-v2')logger.info("All models loaded successfully")except Exception as e:logger.error(f"Error loading models: {e}")raiseasync def generate_response(self, request: ChatRequest)-> ChatResponse:"""生成对话响应"""import timestart_time = time.time()try:#编码输入inputs = self.tokenizer.encode(request.message + self.tokenizer.eos_token,return_tensors='pt')inputs = inputs.to(self.device)#生成响应with torch.no_grad():outputs = self.model.generate(inputs,max_length=request.max_length,temperature=request.temperature,pad_token_id=self.tokenizer.eos_token_id,do_sample=True)#解码响应response = self.tokenizer.decode(outputs[:,inputs.shape[-1]:][0],skip_special_tokens=True)processing_time = time.time() - start_timereturn ChatResponse(response=response,conversation_id=request.conversation_id or "default",tokens_used=len(outputs[0]),processing_time=processing_time)except Exception as e:logger.error(f"Error generating response: {e}")raise HTTPException(status_code=500, detail=str(e))async def get_embeddings(self, request: EmbeddingRequest)->EmbeddingResponse:"""获取文本嵌入向量"""try:embeddings = self.embedding_model.encode(request.texts)return EmbeddingResponse(embeddings=embeddings.tolist(),model="all-MiniLM-L6-v2")except Exception as e:logger.error(f"Error generating embeddings: {e}")raise HTTPException(status_code=500, detail=str(e))#全局模型管理器
model_manager = AIModelManager()@app.on_event("startup")
async def startup_event():"""应用启动时初始化模型"""await asyncio.sleep(1)# 给其他服务启动时间@app.post("/chat",response_model=ChatResponse)
async def chat_endpoint(request:ChatRequest):"""对话接口"""return await model_manager.generate_response(request)@app.post("/embeddings", response_model=EmbeddingResponse)
async def embeddings_endpoint(request:EmbeddingRequest):"""嵌入向量接口"""return await model_manager.get_embeddings(request)@app.get("/health")
async def health_check():"""健康检查"""return {"status": "healthy", "models_loaded": model_manager.model is not None}if __name__ == "__main__":import unicornuvicorn.run(app,host="0.0.0.0", port=8000)

AI服务依赖文件

# ai-service/requirements.txt
fastapi==0.104.1
uvicorn==0.24.0
torch==2.1.0
transformers==4.35.0
sentence-transformers==2.2.2
numpy==1.24.3
pydantic==2.4.2
python-multipart==0.0.6

AI服务Dockerfile

# ai-service/Dockerfile
FROM python:3.9-slimWORKDIR /app# 安装系统依赖
RUN apt-get update && apt-get install -y \gcc \g++ \&& rm -rf /var/lib/apt/lists/*# 复制依赖文件
COPY requirements.txt .# 安装Python依赖
RUN pip install --no-cache-dir -r requirements.txt# 复制应用代码
COPY . .# 暴露端口
EXPOSE 8000# 启动命令
CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]

Java后端服务实现

<!-- backend/pom.xml -->
<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"><modelVersion>4.0.0</modelVersion><parent><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-parent</artifactId><version>3.1.5</version></parent><groupId>com.example</groupId><artifactId>ai-backend</artifactId><version>1.0.0</version><properties><java.version>17</java.version></properties><dependencies><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-web</artifactId></dependency><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-data-jpa</artifactId></dependency><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-validation</artifactId></dependency><dependency><groupId>mysql</groupId><artifactId>mysql-connector-java</artifactId><version>8.0.33</version></dependency><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-data-redis</artifactId></dependency><dependency><groupId>org.springdoc</groupId><artifactId>springdoc-openapi-starter-webmvc-ui</artifactId><version>2.2.0</version></dependency></dependencies>
</project>

# backend/src/main/resources/application.yml
server:port: 8080servlet:context-path: /spring:datasource:url: jdbc:mysql://mysql:3306/ai_db?useSSL=false&serverTimezone=UTCusername: rootpassword: passworddriver-class-name: com.mysql.cj.jdbc.Driverjpa:hibernate:ddl-auto: updateshow-sql: trueproperties:hibernate:dialect: org.hibernate.dialect.MySQLDialectredis:host: redisport: 6379timeout: 2000ai:service:url: http://ai-service:8000logging:level:com.example.ai: DEBUG

AI服务客户端

// backend/src/main/java/com/example/ai/service/AIServiceClient.java
package com.example.ai.service;import com.example.ai.model.ChatRequest;
import com.example.ai.model.ChatResponse;
import com.example.ai.model.EmbeddingRequest;
import com.example.ai.model.EmbeddingResponse;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.http.HttpEntity;
import org.springframework.http.HttpHeaders;
import org.springframework.http.MediaType;
import org.springframework.http.ResponseEntity;
import org.springframework.stereotype.Service;
import org.springframework.web.client.RestTemplate;import java.util.List;@Service
public class AIServiceClient {private final RestTemplate restTemplate;@Value("${ai.service.url:http://ai-service:8000}")private String aiServiceUrl;public AIServiceClient(RestTemplate restTemplate) {this.restTemplate = restTemplate;}public ChatResponse chat(ChatRequest request){String url = aiServiceUrl + "/chat";HttpHeaders headers = new HttpHeaders();headers.setContentType(MediaType.APPLICATION_JSON);HttpEntity<ChatRequest> entity = new HttpEntity<>(request, headers);ResponseEntity<ChatResponse> response = restTemplate.postForEntity(url, entity, ChatResponse.class);return response.getBody();}public EmbeddingResponse getEmbeddings(List<String> texts){String url = aiServiceUrl + "/embeddings";HttpHeaders headers = new HttpHeaders();headers.setContentType(MediaType.APPLICATION_JSON);EmbeddingRequest request = new EmbeddingRequest(texts);HttpEntity<EmbeddingRequest> entity = new HttpEntity<>(request, headers);ResponseEntity<EmbeddingResponse> response = restTemplate.postForEntity(url, entity, EmbeddingResponse.class);return response.getBody();}public boolean healthCheck() {try {String url = aiServiceUrl + "/health";ResponseEntity<String> response = restTemplate.getForEntity(url, String.class);return response.getStatusCode().is2xxSuccessful();} catch (Exception e) {return false;}}
}

数据模型

// backend/src/main/java/com/example/ai/model/ChatRequest.java
package com.example.ai.model;import lombok.Data;
import jakarta.validation.constraints.NotBlank;@Data
public class ChatRequest {@NotBlank(message = "消息不能为空")private String message;private String conversationId;private Integer maxLength = 512;private Double temperature = 0.7;
}// backend/src/main/java/com/example/ai/model/ChatResponse.java
package com.example.ai.model;import lombok.Data;
import java.time.LocalDateTime;@Data
public class ChatResponse {private String response;private String conversationId;private Integer tokensUsed;private Double processingTime;private LocalDateTime timestamp;
}// backend/src/main/java/com/example/ai/model/EmbeddingRequest.java
package com.example.ai.model;import lombok.Data;
import java.util.List;@Data
public class EmbeddingRequest {private List<String> texts;public EmbeddingRequest(List<String> texts) {this.texts = texts;}
}// backend/src/main/java/com/example/ai/model/EmbeddingResponse.java
package com.example.ai.model;import lombok.Data;
import java.util.List;@Data
public class EmbeddingResponse {private List<List<Double>> embeddings;private String model;
}