OpenAI format
POST
/v1/embeddings
package main
import ( "fmt" "strings" "net/http" "io")
func main() {
url := "https://api.mirapi.ai/v1/embeddings"
payload := strings.NewReader("{ \"model\": \"text-embedding-ada-002\", \"input\": [ \"example\" ], \"encoding_format\": \"float\", \"dimensions\": 1 }")
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(res) fmt.Println(string(body))
}const url = 'https://api.mirapi.ai/v1/embeddings';const options = { method: 'POST', headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'}, body: '{"model":"text-embedding-ada-002","input":["example"],"encoding_format":"float","dimensions":1}'};
try { const response = await fetch(url, options); const data = await response.json(); console.log(data);} catch (error) { console.error(error);}curl --request POST \ --url https://api.mirapi.ai/v1/embeddings \ --header 'Authorization: Bearer <token>' \ --header 'Content-Type: application/json' \ --data '{ "model": "text-embedding-ada-002", "input": [ "example" ], "encoding_format": "float", "dimensions": 1 }'Converts text into vector embeddings
Authorizations
Section titled “Authorizations”Request Bodyrequired
Section titled “Request Bodyrequired”Media typeapplication/json
object
model
required
string
Example
text-embedding-ada-002input
required
One of:
string
Array<string>
encoding_format
string
dimensions
Output vector dimensions
integer
Responses
Section titled “Responses”Embeddings created
Media typeapplication/json
object
object
string
data
Array<object>
object
object
string
index
integer
embedding
Array<number>
model
string
usage
object
prompt_tokens
integer
total_tokens
integer
Example
{ "object": "list", "data": [ { "object": "embedding" } ]}