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本文介绍如何通过HTTP API在Collection中进行相似性检索。

前提条件

Method与URL

POST https://{Endpoint}/v1/collections/{CollectionName}/query

使用示例

说明
  1. 需要使用您的api-key替换示例中的YOUR_API_KEY、您的Cluster Endpoint替换示例中的YOUR_CLUSTER_ENDPOINT,代码才能正常运行。

  2. 本示例需要参考新建Collection-使用示例提前创建好名称为quickstart的Collection

根据向量进行相似性检索

curl -XPOST \
  -H 'dashvector-auth-token: YOUR_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{
    "vector": [0.1, 0.2, 0.3, 0.4],
    "topk": 10,
    "include_vector": true
  }' https://YOUR_CLUSTER_ENDPOINT/v1/collections/quickstart/query

# example output:
# {
#   "code": 0,
#   "request_id": "2cd1cac7-f1ee-4d15-82a8-b65e75d8fd13",
#   "message": "Success",
#   "output": [
#     {
#       "id": "1",
#       "vector":[
#         0.10000000149011612,
#         0.20000000298023224,
#         0.30000001192092896,
#         0.4000000059604645
#       ],
#       "fields": {
#         "name": "zhangshan",
#         "weight": null,
#         "age": 20,
#         "anykey": "anyvalue"
#       },
#       "score": 0.3
#     }
#   ]
# }

根据主键(对应的向量)进行相似性检索

curl -XPOST \
  -H 'dashvector-auth-token: YOUR_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{
    "id": "1",
    "topk": 1,
    "include_vector": true
  }' https://YOUR_CLUSTER_ENDPOINT/v1/collections/quickstart/query

# example output:
# {
#   "code":0,
#   "request_id":"fab4e8a2-15e4-4b55-816f-3b66b7a44962",
#   "message":"Success",
#   "output":[
#     {
#       "id":"1",
#       "vector":[
#         0.10000000149011612,
#         0.20000000298023224,
#         0.30000001192092896,
#         0.4000000059604645
#       ],
#        "fields": {
#         "name": "zhangshan",
#         "weight": null,
#         "age": 20,
#         "anykey": "anyvalue"
#       },
#       "score": 0.3
#     }
#   ]
# }

带过滤条件的相似性检索

curl -XPOST \
  -H 'dashvector-auth-token: YOUR_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{
    "filter": "age > 18",
    "topk": 1,
    "include_vector": true
  }' https://YOUR_CLUSTER_ENDPOINT/v1/collections/quickstart/query
  
# example output:
# {
#   "code":0,
#   "request_id":"4c7331d8-fba1-4c3a-8673-124568670de7",
#   "message":"Success",
#   "output":[
#     {
#       "id":"1",
#       "vector":[
#         0.10000000149011612,
#         0.20000000298023224,
#         0.30000001192092896,
#         0.4000000059604645
#       ],
#        "fields": {
#         "name": "zhangshan",
#         "weight": null,
#         "age": 20,
#         "anykey": "anyvalue"
#       },
#       "score": 0.0
#     }
#   ]
# }

带有Sparse Vector的向量检索

curl -XPOST \
  -H 'dashvector-auth-token: YOUR_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{
    "vector": [0.1, 0.2, 0.3, 0.4],
    "sparse_vector":{"1":0.4, "10000":0.6, "222222":0.8},
    "topk": 1,
    "include_vector": true
  }' https://YOUR_CLUSTER_ENDPOINT/v1/collections/quickstart/query

# example output:
# {
#   "code":0,
#   "request_id":"ad84f7a0-b4b2-4023-ae80-b6f092609a53",
#   "message":"Success",
#   "output":[
#     {
#       "id":"2",
#       "vector":[
#         0.10000000149011612,
#         0.20000000298023224,
#         0.30000001192092896,
#         0.4000000059604645
#       ],
#       "fields":{"name":null,"weight":null,"age":null},
#       "score":1.46,
#       "sparse_vector":{
#         "10000":0.6,
#         "1":0.4,
#         "222222":0.8
#       }
#     }
#   ]
# }

入参描述

说明

vectorid两个入参需要二选一使用,如都不传入,则仅完成条件过滤。

参数

Location

类型

必填

说明

{Endpoint}

path

str

Cluster的Endpoint,可在控制台Cluster详情中查看

{CollectionName}

path

str

Collection名称

dashvector-auth-token

header

str

api-key

vector

body

array

向量数据

sparse_vector

body

dict

稀疏向量

id

body

str

主键,表示根据主键对应的向量进行相似性检索

topk

body

int

返回topk相似性结果,默认10

filter

body

str

过滤条件,需满足SQL where子句规范,详见

include_vector

body

bool

是否返回向量数据,默认false

output_fields

body

array

返回field的字段名列表,默认返回所有Fields,[]代表不返回Fields

partition

body

str

Partition名称

出参描述

字段

类型

描述

示例

code

int

返回值,参考返回状态码说明

0

message

str

返回消息

success

request_id

str

请求唯一id

19215409-ea66-4db9-8764-26ce2eb5bb99

output

array

相似性检索结果,Doc列表

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