智能模型路由
curl --request POST \
--url https://direct.evolink.ai/v1/chat/completions \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data '
{
"model": "evolink/auto",
"messages": [
{
"role": "user",
"content": "介绍一下人工智能的发展历史"
}
],
"temperature": 0.7,
"top_p": 0.9,
"top_k": 40,
"stream": false
}
'import requests
url = "https://direct.evolink.ai/v1/chat/completions"
payload = {
"model": "evolink/auto",
"messages": [
{
"role": "user",
"content": "介绍一下人工智能的发展历史"
}
],
"temperature": 0.7,
"top_p": 0.9,
"top_k": 40,
"stream": False
}
headers = {
"Authorization": "Bearer <token>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
body: JSON.stringify({
model: 'evolink/auto',
messages: [{role: 'user', content: '介绍一下人工智能的发展历史'}],
temperature: 0.7,
top_p: 0.9,
top_k: 40,
stream: false
})
};
fetch('https://direct.evolink.ai/v1/chat/completions', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));<?php
$curl = curl_init();
curl_setopt_array($curl, [
CURLOPT_URL => "https://direct.evolink.ai/v1/chat/completions",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 30,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
CURLOPT_POSTFIELDS => json_encode([
'model' => 'evolink/auto',
'messages' => [
[
'role' => 'user',
'content' => '介绍一下人工智能的发展历史'
]
],
'temperature' => 0.7,
'top_p' => 0.9,
'top_k' => 40,
'stream' => false
]),
CURLOPT_HTTPHEADER => [
"Authorization: Bearer <token>",
"Content-Type: application/json"
],
]);
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
}package main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "https://direct.evolink.ai/v1/chat/completions"
payload := strings.NewReader("{\n \"model\": \"evolink/auto\",\n \"messages\": [\n {\n \"role\": \"user\",\n \"content\": \"介绍一下人工智能的发展历史\"\n }\n ],\n \"temperature\": 0.7,\n \"top_p\": 0.9,\n \"top_k\": 40,\n \"stream\": false\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("Authorization", "Bearer <token>")
req.Header.Add("Content-Type", "application/json")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}HttpResponse<String> response = Unirest.post("https://direct.evolink.ai/v1/chat/completions")
.header("Authorization", "Bearer <token>")
.header("Content-Type", "application/json")
.body("{\n \"model\": \"evolink/auto\",\n \"messages\": [\n {\n \"role\": \"user\",\n \"content\": \"介绍一下人工智能的发展历史\"\n }\n ],\n \"temperature\": 0.7,\n \"top_p\": 0.9,\n \"top_k\": 40,\n \"stream\": false\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://direct.evolink.ai/v1/chat/completions")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"model\": \"evolink/auto\",\n \"messages\": [\n {\n \"role\": \"user\",\n \"content\": \"介绍一下人工智能的发展历史\"\n }\n ],\n \"temperature\": 0.7,\n \"top_p\": 0.9,\n \"top_k\": 40,\n \"stream\": false\n}"
response = http.request(request)
puts response.read_body{
"id": "chatcmpl-20260308112637503180122ABCD1234",
"model": "gpt-5.4",
"object": "chat.completion",
"created": 1741428397,
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "人工智能的发展历史可以追溯到20世纪50年代..."
},
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 15,
"completion_tokens": 120,
"total_tokens": 135
}
}{
"error": {
"code": 400,
"message": "Invalid request parameters",
"type": "invalid_request_error"
}
}{
"error": {
"code": 401,
"message": "Invalid or expired token",
"type": "authentication_error"
}
}{
"error": {
"code": 402,
"message": "Insufficient quota",
"type": "insufficient_quota_error",
"fallback_suggestion": "https://evolink.ai/dashboard/billing"
}
}{
"error": {
"code": 403,
"message": "Access denied for this feature",
"type": "permission_error"
}
}{
"error": {
"code": 429,
"message": "Rate limit exceeded",
"type": "rate_limit_error",
"fallback_suggestion": "retry after 60 seconds"
}
}{
"error": {
"code": 500,
"message": "Internal server error",
"type": "internal_server_error",
"fallback_suggestion": "try again later"
}
}{
"error": {
"code": 502,
"message": "Upstream AI service unavailable",
"type": "upstream_error",
"fallback_suggestion": "try again later"
}
}{
"error": {
"code": 503,
"message": "Service temporarily unavailable",
"type": "service_unavailable_error",
"fallback_suggestion": "retry after 30 seconds"
}
}EvoLink Auto
EvoLink Auto - 智能模型路由
系统自动选择最合适的模型处理请求
POST
/
v1
/
chat
/
completions
智能模型路由
curl --request POST \
--url https://direct.evolink.ai/v1/chat/completions \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data '
{
"model": "evolink/auto",
"messages": [
{
"role": "user",
"content": "介绍一下人工智能的发展历史"
}
],
"temperature": 0.7,
"top_p": 0.9,
"top_k": 40,
"stream": false
}
'import requests
url = "https://direct.evolink.ai/v1/chat/completions"
payload = {
"model": "evolink/auto",
"messages": [
{
"role": "user",
"content": "介绍一下人工智能的发展历史"
}
],
"temperature": 0.7,
"top_p": 0.9,
"top_k": 40,
"stream": False
}
headers = {
"Authorization": "Bearer <token>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
body: JSON.stringify({
model: 'evolink/auto',
messages: [{role: 'user', content: '介绍一下人工智能的发展历史'}],
temperature: 0.7,
top_p: 0.9,
top_k: 40,
stream: false
})
};
fetch('https://direct.evolink.ai/v1/chat/completions', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));<?php
$curl = curl_init();
curl_setopt_array($curl, [
CURLOPT_URL => "https://direct.evolink.ai/v1/chat/completions",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 30,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
CURLOPT_POSTFIELDS => json_encode([
'model' => 'evolink/auto',
'messages' => [
[
'role' => 'user',
'content' => '介绍一下人工智能的发展历史'
]
],
'temperature' => 0.7,
'top_p' => 0.9,
'top_k' => 40,
'stream' => false
]),
CURLOPT_HTTPHEADER => [
"Authorization: Bearer <token>",
"Content-Type: application/json"
],
]);
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
}package main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "https://direct.evolink.ai/v1/chat/completions"
payload := strings.NewReader("{\n \"model\": \"evolink/auto\",\n \"messages\": [\n {\n \"role\": \"user\",\n \"content\": \"介绍一下人工智能的发展历史\"\n }\n ],\n \"temperature\": 0.7,\n \"top_p\": 0.9,\n \"top_k\": 40,\n \"stream\": false\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("Authorization", "Bearer <token>")
req.Header.Add("Content-Type", "application/json")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}HttpResponse<String> response = Unirest.post("https://direct.evolink.ai/v1/chat/completions")
.header("Authorization", "Bearer <token>")
.header("Content-Type", "application/json")
.body("{\n \"model\": \"evolink/auto\",\n \"messages\": [\n {\n \"role\": \"user\",\n \"content\": \"介绍一下人工智能的发展历史\"\n }\n ],\n \"temperature\": 0.7,\n \"top_p\": 0.9,\n \"top_k\": 40,\n \"stream\": false\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://direct.evolink.ai/v1/chat/completions")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"model\": \"evolink/auto\",\n \"messages\": [\n {\n \"role\": \"user\",\n \"content\": \"介绍一下人工智能的发展历史\"\n }\n ],\n \"temperature\": 0.7,\n \"top_p\": 0.9,\n \"top_k\": 40,\n \"stream\": false\n}"
response = http.request(request)
puts response.read_body{
"id": "chatcmpl-20260308112637503180122ABCD1234",
"model": "gpt-5.4",
"object": "chat.completion",
"created": 1741428397,
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "人工智能的发展历史可以追溯到20世纪50年代..."
},
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 15,
"completion_tokens": 120,
"total_tokens": 135
}
}{
"error": {
"code": 400,
"message": "Invalid request parameters",
"type": "invalid_request_error"
}
}{
"error": {
"code": 401,
"message": "Invalid or expired token",
"type": "authentication_error"
}
}{
"error": {
"code": 402,
"message": "Insufficient quota",
"type": "insufficient_quota_error",
"fallback_suggestion": "https://evolink.ai/dashboard/billing"
}
}{
"error": {
"code": 403,
"message": "Access denied for this feature",
"type": "permission_error"
}
}{
"error": {
"code": 429,
"message": "Rate limit exceeded",
"type": "rate_limit_error",
"fallback_suggestion": "retry after 60 seconds"
}
}{
"error": {
"code": 500,
"message": "Internal server error",
"type": "internal_server_error",
"fallback_suggestion": "try again later"
}
}{
"error": {
"code": 502,
"message": "Upstream AI service unavailable",
"type": "upstream_error",
"fallback_suggestion": "try again later"
}
}{
"error": {
"code": 503,
"message": "Service temporarily unavailable",
"type": "service_unavailable_error",
"fallback_suggestion": "retry after 30 seconds"
}
}智能模型路由
EvoLink Auto 是智能模型路由功能,系统会根据您的请求内容自动选择合适的 AI 模型,无需手动指定具体模型。核心优势
- 智能匹配:自动分析请求内容,选择合适的模型处理
- 成本优化:在保证质量的前提下,优先选择性价比高的模型
- 负载均衡:自动在多个模型间分配请求,提高系统稳定性
- 透明可见:响应中返回实际使用的模型名称,方便追踪和优化
工作原理
系统根据请求的复杂度、长度和类型,在模型池中选择最适配的模型进行处理。支持的模型
EvoLink Auto 会在以下模型之间智能路由:GPT-4、GPT-3.5、Claude、Gemini 等主流 AI 模型。使用限制
- 不适用于需要指定特定模型能力的场景(如必须使用 GPT-4 的视觉功能)
- 不保证每次请求使用相同的模型
使用场景
适用于不确定使用哪个模型,或希望系统自动优化模型选择的场景。只需将
model 参数设置为 evolink/auto,系统将自动为您选择合适的模型。BaseURL 说明:默认 BaseURL 为
https://direct.evolink.ai,对文本模型支持更好,支持长连接;https://api.evolink.ai 是多模态主力地址,对文本模型作为备用地址使用。授权
##所有接口均需要使用Bearer Token进行认证##
获取 API Key:
访问 API Key 管理页面 获取您的 API Key
使用时在请求头中添加:
Authorization: Bearer YOUR_API_KEY
请求体
application/json
使用智能路由
可用选项:
evolink/auto 示例:
"evolink/auto"
对话消息列表
Minimum array length:
1Show child attributes
Show child attributes
示例:
[ { "role": "user", "content": "介绍一下人工智能的发展历史" } ]
采样温度,控制输出的随机性
说明:
- 较低值(如 0.2): 更确定、更聚焦的输出
- 较高值(如 1.5): 更随机、更有创意的输出
必填范围:
0 <= x <= 2示例:
0.7
核采样(Nucleus Sampling)参数
说明:
- 控制从累积概率前多少的token中采样
- 例如 0.9 表示从累积概率达到90%的token中选择
- 默认值: 1.0(考虑所有token)
建议: 不要同时调整 temperature 和 top_p
必填范围:
0 <= x <= 1示例:
0.9
Top-K 采样参数
说明:
- 例如 10 表示限制每次采样时只考虑概率最高的 10 个 token
- 较小的值会使输出更加聚焦
- 默认不限制
必填范围:
x >= 1示例:
40
是否以流式方式返回响应
true: 流式返回,逐块实时返回内容false: 等待完整响应后一次性返回
示例:
false
⌘I