腾讯云DeepSeek API对接微信小程序

1. 开发环境准备
   在开始开发微信小程序与腾讯云DeepSeek API的对接之前，我们需要做好必要的环境准备工作。本章将指导你完成开发环境的搭建，包括安装最新版本的微信开发者工具（IDE），配置小程序的基本信息，如果你习惯于wxml语法的开发，本教程中也会有关于腾讯云AI代码助手插件的安装，需要注意的是本教程对于腾讯云AI代码助手插件 仅停留在安装的的这一步骤，因为我还是习惯于uniapp 的使用。 让我们按照以下步骤，一步步完成开发环境的准备工作。

系统架构

1.1 微信开发者工具安装
注意：第一次使用微信开发者工具时，第一步并不是下载微信开发者工具哦! 而是前往小程序后台, 进行注册并完成小程序开发者认证,这一内容本篇不会详细讲解。

关于：微信开发者工具， 我们需要点链接选择符合自己电脑版本的ide 进行下载

下载完成之后按照提示进行安装即可

当开发工具安装完成之后， 我们首先创建一个空的项目模板 如下图所示

1.2 腾讯云AI代码助手配置
腾讯云AI代码助手现已完全集成到微信开发者工具中，配置过程变得更加简便。具体步骤如下：

如上图所示三步帮你安装腾讯云AI 代码助手

点击图中所示的 1(扩展)。
在2(输入框)中输入腾讯云 即可
点击 3 （安装） ，等待安装完成之后我们就可以体验啦
如下所示，腾讯AI代码助手目前已整合了tencent-deepseek R1模型。 开发者可以尽情的体验tencent-deepseek R1模型带来的便捷开发体验了喽~~

1.3 文案生成流程

2. 小程序界面设计
   温馨提示：由于本人习惯于uniapp 项目的开发 因此本项目使用的是uniapp 来打包运行我们的小程序项目

UI界面如下图所示：

2.1 搭建基础UI界面
我们的UI界面设计简洁，主要包含以下四个核心组件：

关键词输入区：用户通过输入关键词，来觉得接下来文案的主要风格。
类型选择区： 用户通过选择公众号，小红书等类型，来觉得文案生成的类型。
结果展示区：用于展示腾讯云DeepSeek API 返回的结果。
运行效果图如下

3. 腾讯云DeepSeek API集成
   下面是腾讯云DeepSeek API 最新计费标准， 注意自己的token使用哦！

📢 腾讯云DeepSeek API 最新计费标准
🕒 生效时间：2025年2月26日 0时起
DeepSeek 模型价格表

模型类型	输入价格	输出价格
DeepSeek-R1	0.004元/千token	0.016元/千token
DeepSeek-V3	0.002元/千token	0.008元/千token

3.1 API接入配置
关于 DeepSeek API 接口 的相关文档可以点击链接进行查看， 接下来我们将逐步实现如和配置 腾讯云DeepSeek 为我们接下来的开发作准备

创建API KEY
API 使用前提是需要用于一个API KEY ，访问地址 来创建我们的API KEY 如下图所示， 点击按钮 创建API KEY 即可， 点击查看时可以看到已经创建的 API KEY

编写py代码 进行本地请求
我这里用的是Flask 框架，为我们小程序调用DeepSeek API提供接口服务，代码如下：

代码语言：python代码运行次数：1
运行
AI代码解释
import os
import logging
from flask import Flask, request, jsonify
from flask_cors import CORS
from openai import OpenAI
from typing import Dict, Any

初始化Flask应用

app = Flask(name)
CORS(app) # 启用跨域支持

配置日志记录

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(name)

环境变量配置

OPENAI_CONFIG = {
"api_key": os.getenv("OPENAI_API_KEY", "sk-pXLyNnk8bdl8zkmFkBIi7KBRiaLTATlmGsr8QfTwek7Zawfk"),
"base_url": os.getenv("OPENAI_BASE_URL", "https://api.lkeap.cloud.tencent.com/v1")
}

初始化OpenAI客户端

client = OpenAI(**OPENAI_CONFIG)

def validate_request(data: Dict[str, Any]) -> tuple:
"""请求参数验证"""
if not data.get('content'):
logger.warning("Missing content parameter")
return False, "Content parameter is required"

if len(data['content']) > 1000:logger.warning("Content length exceeds limit")return False, "Content exceeds maximum length of 1000 characters"return True, ""

def process_stream_response(stream) -> dict:
"""处理流式响应数据"""
result = {"reasoning_content": "", "answer_content": ""}

for chunk in stream:if not chunk.choices:continuedelta = chunk.choices[0].delta# 使用getattr安全获取属性if reasoning := getattr(delta, 'reasoningcontent', None):result["reasoning_content"] += reasoningif content := getattr(delta, 'content', None):result["answer_content"] += contentreturn result

@app.route('/deepseekapi', methods=['POST'])
def generate_content():
"""
处理生成请求
---
parameters:
- name: body
in: body
required: true
schema:
type: object
properties:
content:
type: string
responses:
200:
description: 生成结果
400:
description: 无效请求
500:
description: 服务器错误
"""
# 请求验证
data = request.get_json()
print(data)
is_valid, message = validate_request(data)
if not is_valid:
return jsonify({"error": message}), 400

try:# 创建流式请求stream = client.chat.completions.create(model="deepseek-r1",messages=[{"role": "user", "content": data['content']}],stream=True,temperature=0.7,  # 添加生成参数max_tokens=1000)# 处理流式响应processed_data = process_stream_response(stream)# 结果后处理processed_data["answer_content"] = processed_data["answer_content"].strip()return jsonify(processed_data)except Exception as e:logger.error(f"API请求失败: {str(e)}", exc_info=True)return jsonify({"error": "服务暂时不可用，请稍后再试"}), 500

if name == "main":
app.run(
host=os.getenv("FLASK_HOST", "0.0.0.0"),
port=int(os.getenv("FLASK_PORT", 5001)),
debug=os.getenv("FLASK_DEBUG", "false").lower() == "true"
)
验证
我们的Flask 框架暴露出来的请求信息如下：

代码语言：bash
AI代码解释
curl --location --request POST 'http://192.168.31.134:5001/deepseekapi'
--header 'User-Agent: Apifox/1.0.0 (https://apifox.com)'
--header 'Content-Type: application/json'
--header 'Accept: /'
--header 'Host: 192.168.31.134:5001'
--header 'Connection: keep-alive'
--data-raw '{
content: "请根据以下内容生成500字小红书种草文案：↵关键词：三亚,海边,游泳,拍照↵"
}'
我么通过postman 来测试其连通性，如下图所示

运行服务

postman 数据返回

postman 接口配置示例

我们输入的content内容是：请根据以下内容生成500字小红书种草文案：↵关键词：三亚,海边,游泳,拍照↵, 发送请求查看结果

此时我们的DeepSeek接口的本地调用就已经完成了。

整体来说整个过程还是蛮丝滑的， 接下来我们分析一下 如和实现小程序的对接。

4. 小程序代码实现
   ui 代码层
   代码语言：js
   AI代码解释
   tip: 关于注释地方会在最下面的附录项进行讲解说明

   接口调用方法
   代码语言：js
   AI代码解释
   startGenerate() {
   // 1. 判断用户是否上传图片或输入关键词
   if (!this.textArea) {
   uni.showToast({
   title: "请输入关键词",
   icon: "none",
   });
   return;
   }

           // 2. 配置prompt提示词let prompt = `请根据以下内容生成500字${this.typeText}文案：\n`;// 添加关键词内容if (this.textArea) {prompt += `关键词：${this.textArea}\n`;}// 添加图片信息// if (this.fileList.length > 0) {//     prompt += `${this.fileList[0].url}\n`;// }// 3. 设置加载状态this.loading = true;// 4. 接口调用request({url: "/deepseekapi",method: "POST",data: {content: prompt,},}).then((res) => {console.log("生成成功:", res);this.loading = false;this.resultText =res.answer_content || "文案生成失败,请稍后重试...";}).catch((err) => {console.error("生成失败:", err);this.loading = false;});},
   

   此处我们主要实现的事对于提示词的拼接，然后进行接口的调用， 需要注意的是 ，我进行的提示词拼接可能并不是最完美的， 具体应用到业务层时还需要微调哦！！

   效果演示
   如下图所示:

   附录
   该小程序其实最初的设计是有个图片识别功能， 通过识别图片来生成对应的文案， 但是目前如果仅使用腾讯云DeepSeek API 是无法实现该功能的，需要结合 大模型知识引擎 中的图片对话或文件对话(实时文档解析+对话) 的接口，通过识别图片内容之后再次调用腾讯云DeepSeek API 来获取对应的文案。

   总结
   通过本教程，开发者可以快速搭建一个基于腾讯云DeepSeek API的文案生成小程序，为内容创作提供AI助手支持。后续还可以扩展更多功能，如图片识别等高级特性，进一步提升应用的实用性。

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