Gemini - 向量
curl --request POST \
--url https://api.tikway.ai/v1beta/models/google/gemini-embedding-2-preview:embedContent \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data '
{
"content": {
"parts": [
{
"text": "什么是向量检索？"
}
]
},
"taskType": "RETRIEVAL_QUERY",
"outputDimensionality": 768
}
'import requests
url = "https://api.tikway.ai/v1beta/models/google/gemini-embedding-2-preview:embedContent"
payload = {
"content": { "parts": [{ "text": "什么是向量检索？" }] },
"taskType": "RETRIEVAL_QUERY",
"outputDimensionality": 768
}
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({
content: {parts: [{text: '什么是向量检索？'}]},
taskType: 'RETRIEVAL_QUERY',
outputDimensionality: 768
})
};
fetch('https://api.tikway.ai/v1beta/models/google/gemini-embedding-2-preview:embedContent', 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://api.tikway.ai/v1beta/models/google/gemini-embedding-2-preview:embedContent",
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([
'content' => [
'parts' => [
[
'text' => '什么是向量检索？'
]
]
],
'taskType' => 'RETRIEVAL_QUERY',
'outputDimensionality' => 768
]),
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://api.tikway.ai/v1beta/models/google/gemini-embedding-2-preview:embedContent"
payload := strings.NewReader("{\n \"content\": {\n \"parts\": [\n {\n \"text\": \"什么是向量检索？\"\n }\n ]\n },\n \"taskType\": \"RETRIEVAL_QUERY\",\n \"outputDimensionality\": 768\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://api.tikway.ai/v1beta/models/google/gemini-embedding-2-preview:embedContent")
.header("Authorization", "Bearer <token>")
.header("Content-Type", "application/json")
.body("{\n \"content\": {\n \"parts\": [\n {\n \"text\": \"什么是向量检索？\"\n }\n ]\n },\n \"taskType\": \"RETRIEVAL_QUERY\",\n \"outputDimensionality\": 768\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://api.tikway.ai/v1beta/models/google/gemini-embedding-2-preview:embedContent")
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 \"content\": {\n \"parts\": [\n {\n \"text\": \"什么是向量检索？\"\n }\n ]\n },\n \"taskType\": \"RETRIEVAL_QUERY\",\n \"outputDimensionality\": 768\n}"
response = http.request(request)
puts response.read_body{
"embedding": {
"values": [
0.012,
-0.031,
0.008
]
}
}向量嵌入
Gemini - 向量
Gemini 单条 embedContent 向量接口。请求字段为 content，不是对话生成接口的 contents；模型由 URL 选择。结果为 embedding.values 数值数组。
POST
https://api.tikway.ai
/
v1beta
/
models
/
google
/
gemini-embedding-2-preview:embedContent
Gemini - 向量
curl --request POST \
--url https://api.tikway.ai/v1beta/models/google/gemini-embedding-2-preview:embedContent \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data '
{
"content": {
"parts": [
{
"text": "什么是向量检索？"
}
]
},
"taskType": "RETRIEVAL_QUERY",
"outputDimensionality": 768
}
'import requests
url = "https://api.tikway.ai/v1beta/models/google/gemini-embedding-2-preview:embedContent"
payload = {
"content": { "parts": [{ "text": "什么是向量检索？" }] },
"taskType": "RETRIEVAL_QUERY",
"outputDimensionality": 768
}
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({
content: {parts: [{text: '什么是向量检索？'}]},
taskType: 'RETRIEVAL_QUERY',
outputDimensionality: 768
})
};
fetch('https://api.tikway.ai/v1beta/models/google/gemini-embedding-2-preview:embedContent', 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://api.tikway.ai/v1beta/models/google/gemini-embedding-2-preview:embedContent",
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([
'content' => [
'parts' => [
[
'text' => '什么是向量检索？'
]
]
],
'taskType' => 'RETRIEVAL_QUERY',
'outputDimensionality' => 768
]),
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://api.tikway.ai/v1beta/models/google/gemini-embedding-2-preview:embedContent"
payload := strings.NewReader("{\n \"content\": {\n \"parts\": [\n {\n \"text\": \"什么是向量检索？\"\n }\n ]\n },\n \"taskType\": \"RETRIEVAL_QUERY\",\n \"outputDimensionality\": 768\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://api.tikway.ai/v1beta/models/google/gemini-embedding-2-preview:embedContent")
.header("Authorization", "Bearer <token>")
.header("Content-Type", "application/json")
.body("{\n \"content\": {\n \"parts\": [\n {\n \"text\": \"什么是向量检索？\"\n }\n ]\n },\n \"taskType\": \"RETRIEVAL_QUERY\",\n \"outputDimensionality\": 768\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://api.tikway.ai/v1beta/models/google/gemini-embedding-2-preview:embedContent")
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 \"content\": {\n \"parts\": [\n {\n \"text\": \"什么是向量检索？\"\n }\n ]\n },\n \"taskType\": \"RETRIEVAL_QUERY\",\n \"outputDimensionality\": 768\n}"
response = http.request(request)
puts response.read_body{
"embedding": {
"values": [
0.012,
-0.031,
0.008
]
}
}授权
Bearer authentication header of the form Bearer <token>, where <token> is your auth token.
请求体
application/json
gemini/embed-content 请求。扩展字段保留，在选定供应商后继续校验。
必填范围:
x >= 1