Rerank
curl --request POST \
--url https://api.example.com/v1/rerank \
--header 'Content-Type: application/json' \
--data '
{
"model": "<string>",
"query": "<string>",
"documents": [
"<string>"
],
"top_n": 123,
"return_documents": true
}
'import requests
url = "https://api.example.com/v1/rerank"
payload = {
"model": "<string>",
"query": "<string>",
"documents": ["<string>"],
"top_n": 123,
"return_documents": True
}
headers = {"Content-Type": "application/json"}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {'Content-Type': 'application/json'},
body: JSON.stringify({
model: '<string>',
query: '<string>',
documents: ['<string>'],
top_n: 123,
return_documents: true
})
};
fetch('https://api.example.com/v1/rerank', 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.example.com/v1/rerank",
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' => '<string>',
'query' => '<string>',
'documents' => [
'<string>'
],
'top_n' => 123,
'return_documents' => true
]),
CURLOPT_HTTPHEADER => [
"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.example.com/v1/rerank"
payload := strings.NewReader("{\n \"model\": \"<string>\",\n \"query\": \"<string>\",\n \"documents\": [\n \"<string>\"\n ],\n \"top_n\": 123,\n \"return_documents\": true\n}")
req, _ := http.NewRequest("POST", url, payload)
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.example.com/v1/rerank")
.header("Content-Type", "application/json")
.body("{\n \"model\": \"<string>\",\n \"query\": \"<string>\",\n \"documents\": [\n \"<string>\"\n ],\n \"top_n\": 123,\n \"return_documents\": true\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://api.example.com/v1/rerank")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["Content-Type"] = 'application/json'
request.body = "{\n \"model\": \"<string>\",\n \"query\": \"<string>\",\n \"documents\": [\n \"<string>\"\n ],\n \"top_n\": 123,\n \"return_documents\": true\n}"
response = http.request(request)
puts response.read_bodyEmbeddings и rerank
Rerank
Semantically rerank search results
POST
/
v1
/
rerank
Rerank
curl --request POST \
--url https://api.example.com/v1/rerank \
--header 'Content-Type: application/json' \
--data '
{
"model": "<string>",
"query": "<string>",
"documents": [
"<string>"
],
"top_n": 123,
"return_documents": true
}
'import requests
url = "https://api.example.com/v1/rerank"
payload = {
"model": "<string>",
"query": "<string>",
"documents": ["<string>"],
"top_n": 123,
"return_documents": True
}
headers = {"Content-Type": "application/json"}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {'Content-Type': 'application/json'},
body: JSON.stringify({
model: '<string>',
query: '<string>',
documents: ['<string>'],
top_n: 123,
return_documents: true
})
};
fetch('https://api.example.com/v1/rerank', 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.example.com/v1/rerank",
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' => '<string>',
'query' => '<string>',
'documents' => [
'<string>'
],
'top_n' => 123,
'return_documents' => true
]),
CURLOPT_HTTPHEADER => [
"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.example.com/v1/rerank"
payload := strings.NewReader("{\n \"model\": \"<string>\",\n \"query\": \"<string>\",\n \"documents\": [\n \"<string>\"\n ],\n \"top_n\": 123,\n \"return_documents\": true\n}")
req, _ := http.NewRequest("POST", url, payload)
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.example.com/v1/rerank")
.header("Content-Type", "application/json")
.body("{\n \"model\": \"<string>\",\n \"query\": \"<string>\",\n \"documents\": [\n \"<string>\"\n ],\n \"top_n\": 123,\n \"return_documents\": true\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://api.example.com/v1/rerank")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["Content-Type"] = 'application/json'
request.body = "{\n \"model\": \"<string>\",\n \"query\": \"<string>\",\n \"documents\": [\n \"<string>\"\n ],\n \"top_n\": 123,\n \"return_documents\": true\n}"
response = http.request(request)
puts response.read_bodyДата обновления: 2026-06-06
Overview
Reorder a set of documents by semantic relevance to a query. Commonly used as the second-stage reranking step in RAG (Retrieval-Augmented Generation) pipelines.Implemented following the SiliconFlow Rerank API format.
Supported Models
| Model | Description |
|---|---|
gte-rerank-v2 | Multilingual reranking model, recommended |
gte-rerank-v2 | Primarily English |
Request Parameters
Reranking model name, e.g.
gte-rerank-v2Query text
List of documents to rerank
Return the top N results. Defaults to returning all
Whether to include the original document text in the response
Response Format
{
"model": "gte-rerank-v2",
"results": [
{
"index": 2,
"relevance_score": 0.9875,
"document": { "text": "Most relevant document content" }
},
{
"index": 0,
"relevance_score": 0.7432,
"document": { "text": "Second most relevant document content" }
},
{
"index": 1,
"relevance_score": 0.1205,
"document": { "text": "Less relevant document content" }
}
],
"usage": {
"total_tokens": 128
}
}
Code Examples
import requests
response = requests.post(
"https://api.crazyrouter.com/v1/rerank",
headers={
"Authorization": "Bearer sk-xxx",
"Content-Type": "application/json"
},
json={
"model": "gte-rerank-v2",
"query": "What is a vector database",
"documents": [
"A vector database is a database system specialized for storing and retrieving high-dimensional vectors",
"Relational databases use tables to store structured data",
"Vector databases support approximate nearest neighbor search, suitable for semantic retrieval scenarios",
"Redis is an in-memory key-value store"
],
"top_n": 2,
"return_documents": True
}
)
data = response.json()
for result in data["results"]:
print(f"[{result['relevance_score']:.4f}] {result['document']['text']}")
curl -X POST https://api.crazyrouter.com/v1/rerank \
-H "Authorization: Bearer sk-xxx" \
-H "Content-Type: application/json" \
-d '{
"model": "gte-rerank-v2",
"query": "What is a vector database",
"documents": [
"A vector database is a database system specialized for storing and retrieving high-dimensional vectors",
"Relational databases use tables to store structured data"
],
"top_n": 2
}'
Typical RAG Pipeline
User Query → Embedding Retrieval Top-K → Rerank → LLM Generates Answer
The number of input documents for reranking should not exceed 100. Too many documents will increase latency and cost.
⌘I