Chat Completions 格式

向指定模型发送对话消息并获取生成结果。本接口遵循 OpenAI Chat Completions（聊天补全）格式，支持非流式和 SSE 流式响应。

请求

POST /v1/chat/completions
Content-Type: application/json
Authorization: Bearer sk-your-api-key

调用示例

cURL

curl -X POST "https://www.walmind.cn/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $API_KEY" \
  -d '{
    "model": "your-model-id",
    "messages": [
      {"role": "system", "content": "你是一个有帮助的助手。"},
      {"role": "user", "content": "解释一下什么是向量数据库。"}
    ],
    "temperature": 0.7,
    "stream": false
  }'

JavaScript

const response = await fetch('https://www.walmind.cn/v1/chat/completions', {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
    Authorization: `Bearer ${process.env.API_KEY}`,
  },
  body: JSON.stringify({
    model: 'your-model-id',
    messages: [
      {role: 'system', content: '你是一个有帮助的助手。'},
      {role: 'user', content: '解释一下什么是向量数据库。'},
    ],
    temperature: 0.7,
    stream: false,
  }),
});

const data = await response.json();
console.log(data.choices[0].message.content);

Python

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["API_KEY"],
    base_url="https://www.walmind.cn/v1",
)

response = client.chat.completions.create(
    model="your-model-id",
    messages=[
        {"role": "system", "content": "你是一个有帮助的助手。"},
        {"role": "user", "content": "解释一下什么是向量数据库。"},
    ],
    temperature=0.7,
)
print(response.choices[0].message.content)

Go

package main

import (
    "bytes"
    "fmt"
    "io"
    "net/http"
    "os"
)

func main() {
    body := []byte(`{
      "model": "your-model-id",
      "messages": [{"role": "user", "content": "解释一下什么是向量数据库。"}],
      "temperature": 0.7,
      "stream": false
    }`)

    req, _ := http.NewRequest("POST", "https://www.walmind.cn/v1/chat/completions", bytes.NewReader(body))
    req.Header.Set("Content-Type", "application/json")
    req.Header.Set("Authorization", "Bearer "+os.Getenv("API_KEY"))

    resp, err := http.DefaultClient.Do(req)
    if err != nil {
        panic(err)
    }
    defer resp.Body.Close()

    result, _ := io.ReadAll(resp.Body)
    fmt.Println(string(result))
}

Java

import java.net.URI;
import java.net.http.HttpClient;
import java.net.http.HttpRequest;
import java.net.http.HttpResponse;

public class ChatCompletion {
    public static void main(String[] args) throws Exception {
        String body = "{\"model\":\"your-model-id\",\"messages\":[{\"role\":\"user\",\"content\":\"解释一下什么是向量数据库。\"}],\"temperature\":0.7,\"stream\":false}";

        HttpRequest request = HttpRequest.newBuilder()
            .uri(URI.create("https://www.walmind.cn/v1/chat/completions"))
            .header("Content-Type", "application/json")
            .header("Authorization", "Bearer " + System.getenv("API_KEY"))
            .POST(HttpRequest.BodyPublishers.ofString(body))
            .build();

        HttpResponse<String> response = HttpClient.newHttpClient()
            .send(request, HttpResponse.BodyHandlers.ofString());
        System.out.println(response.body());
    }
}

常用参数

参数	类型	说明
model	string	模型列表中的模型 ID，必填
messages	array	按顺序排列的消息，必填
stream	boolean	是否使用 SSE 流式响应，默认 false
temperature	number	输出随机性，范围和默认值以模型支持情况为准
max_tokens	integer	限制生成 Token 数量
top_p	number	核采样参数
tools	array	工具调用定义（模型支持时使用）

非流式响应

{
  "id": "chatcmpl-example",
  "object": "chat.completion",
  "choices": [
    {
      "index": 0,
      "message": {"role": "assistant", "content": "向量数据库用于存储和检索向量表示。"},
      "finish_reason": "stop"
    }
  ],
  "usage": {"prompt_tokens": 20, "completion_tokens": 18, "total_tokens": 38}
}

流式响应

设置 stream: true 后，响应使用 text/event-stream 返回多个数据块。客户端应持续读取，直到收到 data: [DONE]。

curl -X POST "https://www.walmind.cn/v1/chat/completions" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $API_KEY" \
  -d '{"model":"your-model-id","messages":[{"role":"user","content":"你好"}],"stream":true}'