如何使用文本切片及向量化模型服务_智能开放搜索 OpenSearch(Open Search)-阿里云帮助中心

描述：进行文本切分和切块向量化

请求语法

POST /v3/openapi/apps/{app_group_identity}/actions/knowledge-split

注：app_group_identity表示应用名称。

请求参数

SplitDoc
参数名	参数类型	描述	备注
title	String	数据标题	选填
content	String	处理数据内容	必填
use_embedding	Boolean	是否需要向量化： true：是 false：否	不填则为false
model	String	需要使用的向量化模型	无

请求体示例

{
  "title":"测试标题",
  "content":"测试文本",
  "use_embedding":true,
}

返回参数

响应名	响应类型	描述
chunks	List<ChunkContext>	切片后的文本数据对象

ChunkContext
响应名	响应类型	描述
chunk_id	String	切片id
chunk	String	切片后的文本数据
embedding	String	向量化后的向量
type	String	文本类型：文本类型：text，图片类型：image
img_url	String	若是图片类型数据，需要有图片的url

响应体示例

{
  "request_id":"111111111",
  "status":"OK";
  "errors":[],
  "result":[
  {
    "chunk_id":"1",
    "chunk":"测试切片文本1",
    "embedding":"-0.010441,-0.002826,-0.022911,0.000847,0.025610,0.019213,-0.019912,0.008210,0.011974,-0.010120,-0.003866,-0.008091,-0.006889,-0.034774,...-0.012572,0.009668,0.010963,-0.005273,-0.005072,-0.002190,-0.001554,-0.000058",
    "type":"text"
  },
  {
    "chunk_id":"2",
    "chunk":"测试切片文本2",
    "embedding":"-0.010441,-0.002826,-0.022911,0.000847,0.025610,0.019213,-0.019912,0.008210,0.011974,-0.010120,-0.003866,-0.008091,-0.006889,-0.034774,...-0.012572,0.009668,0.010963,-0.005273,-0.005072,-0.002190,-0.001554,-0.000058",
    "type":"image",
    "img_url":"http://127.0.0.1"
  },
  {
    "chunk_id":"3",
    "chunk":"测试切片文本3",
    "type":"text"
  }
]
}

说明

文本切片向量化后的向量维度为1536维。