MindCraft AI

embedding 多模态向量模型

POST

https://api.mindcraft.com.cn/v1/embeddings

提示

headers 需要添加 "Bearer {{api_key}}" 用来认证
点击获取api_key

请求参数

Header 参数

Authorization

string

认证信息

必需

示例值:

Bearer {{api_key}}

Body 参数application/json

model

enum<string>

调用的向量模型

必需

枚举值:

qwen-multimodal-embedding-v1"

示例值:

qwen-multimodal-embedding-v1

input

array [object {3}]

会话和提问

必需

text

string

字符串数据

可选

示例值:

你好啊

image

string

图片链接数据

可选

示例值:

https://mitalinlp.oss-cn-hangzhou.aliyuncs.com/dingkun/images/1712648554702.jpg

video

string

视频链接数据

可选

示例值:

https://mitalinlp.oss-cn-hangzhou.aliyuncs.com/dingkun/multimodel-test/video/24i4ncHuf6A.mp4

示例

{
    "model": "qwen-multimodal-embedding-v1",
    "input": [
        {
            "text": "你好啊"
        },
        {
            "image": "https://mitalinlp.oss-cn-hangzhou.aliyuncs.com/dingkun/images/1712648554702.jpg"
        }
    ]
}

示例代码

Shell

JavaScript

Java

Swift

PHP

Python

HTTP

Objective-C

Ruby

OCaml

Dart

curl --location --request POST 'https://api.mindcraft.com.cn/v1/embeddings' \
--header 'Authorization: Bearer ' \
--header 'Content-Type: application/json' \
--data-raw '{
    "model": "qwen-multimodal-embedding-v1",
    "input": [
        {
            "text": "你好啊"
        },
        {
            "image": "https://mitalinlp.oss-cn-hangzhou.aliyuncs.com/dingkun/images/1712648554702.jpg"
        }
    ]
}'

返回响应

🟢200成功

application/json

Body

string

必需

model

string

必需

usage

object

必需

duration

integer

必需

image_count

integer

必需

input_tokens

integer

必需

data

array [object {3}]

必需

embedding

array[number]

必需

index

integer

必需

type

string

必需

示例

{
    "id": "bfc7ec0a998148fe889a899e032b91ea",
    "model": "qwen-multimodal-embedding-v1",
    "usage": {
        "duration": 0,
        "image_count": 1,
        "input_tokens": 5
    },
    "data": [
        {
            "embedding": [
                -0.035929545760154724,
                -0.042329493910074234,
                -0.02364613115787506
            ],
            "index": 0,
            "type": "text"
        },
        {
            "embedding": [
                0.0515291653573513,
                0.007719388231635094,
                0.02654351107776165
            ],
            "index": 1,
            "type": "image"
        }
    ]
}

任务（task）数据获取

embedding 文字向量模型