如何通过HTTP API检索Doc

本文介绍如何通过HTTP API在Collection中进行相似性检索。

前提条件

已创建Cluster：创建Cluster。
已获得API-KEY：API-KEY管理。

Method与URL

HTTP

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

使用示例

说明

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。
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

Bash

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

Bash

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

Bash

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

Bash

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
#       }
#     }
#   ]
# }

向量检索高级参数

说明

详情可参考向量检索高级参数。

curl -XPOST \
  -H 'dashvector-auth-token: YOUR_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{
    "vector": [0.1, 0.2, 0.3, 0.4], 
    "vector_param":{ "radius": 0.53, "is_linear": false, "ef": 1000 },
    "topk": 10,
    "include_vector": true
}' https://YOUR_CLUSTER_ENDPOINT/v1/collections/quickstart_euclidean/query

#example output:
#{
#    "code": 0,
#    "request_id": "59df860b-7d29-466b-a345-0bfe9e27329e",
#    "message": "Success",
#    "output": [
#        {
#            "id": "2",
#            "vector": [
#                0.20000000298023224,
#                0.30000001192092896,
#                0.4000000059604645,
#                0.5
#            ],
#            "fields": {
#                "anykey1": "str-value",
#                "anykey2": 1,
#                "name": "zhangshan",
#                "weight": null,
#                "anykey3": true,
#                "anykey4": 3.1415925,
#                "age": 70
#            },
#            "score": 0.04
#        },
#        {
#            "id": "3",
#            "vector": [
#                0.30000001192092896,
#                0.4000000059604645,
#                0.5,
#                0.6000000238418579
#            ],
#            "fields": {
#                "name": null,
#                "weight": null,
#                "age": null
#            },
#            "score": 0.16000001
#        },
#        {
#            "id": "4",
#            "vector": [
#                0.4000000059604645,
#                0.5,
#                0.6000000238418579,
#                0.699999988079071
#            ],
#            "fields": {
#                "name": "zhangsan",
#                "weight": null,
#                "age": 20
#            },
#            "score": 0.36
#        }
#    ]
#}

多向量检索

说明

详情可参考多向量检索。

RrfRanker示例

curl -XPOST \
  -H 'dashvector-auth-token: YOUR_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{
    "vectors": {"title": {"vector": [0.1, 0.2, 0.3, 0.4]}, "content": {"vector": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6], "param": {"num_candidates": 10}}},
    "topk": 20,
    "rerank": {"ranker_name": "rrf", "ranker_params": {"rank_constant":"100"} }
}' https://YOUR_CLUSTER_ENDPOINT/v1/collections/multi_vector_demo/query

# example output:
#{
#    "code": 0,
#    "request_id": "db20ba2b-9dc6-4c23-9266-430e6fb1a70d",
#    "message": "Success",
#    "output": [
#        {
#            "id": "1",
#            "fields": {
#                "author": null
#            },
#            "score": 0.019704912
#        },
#        {
#            "id": "2",
#            "fields": {
#                "author": "zhangsan"
#            },
#            "score": 0.00990099
#        },
#        {
#            "id": "3",
#            "fields": {
#                "author": null,
#                "anykey": "anyvalue"
#            },
#            "score": 0.009803922
#        }
#    ]
#}

WeightedRanker示例

curl -XPOST \
  -H 'dashvector-auth-token: YOUR_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{
    "vectors": {"title": {"vector": [0.1, 0.2, 0.3, 0.4]}, "content": {"vector": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6], "param": {"num_candidates": 10}}},
    "topk": 20,
    "rerank": {"ranker_name": "weighted", "ranker_params": {"weights": "{\"title\":0.2, \"content\":0.8}" }}
}' https://YOUR_CLUSTER_ENDPOINT/v1/collections/multi_vector_demo/query

# example output:
#{
#    "code": 0,
#    "request_id": "c7413fa9-92fd-4493-8f21-6c65c83e7b91",
#    "message": "Success",
#    "output": [
#        {
#            "id": "1",
#            "fields": {
#                "author": null
#            },
#            "score": 0.8156271
#        },
#        {
#            "id": "3",
#            "fields": {
#                "author": null,
#                "anykey": "anyvalue"
#            },
#            "score": 0.5880098
#        },
#        {
#            "id": "2",
#            "fields": {
#                "author": "zhangsan"
#            },
#            "score": 0.2
#        }
#    ]
#}

使用多向量的一个向量执行检索

curl -XPOST \
  -H 'dashvector-auth-token: YOUR_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{
    "vectors": {"title": {"vector": [0.1, 0.2, 0.3, 0.4], "param":{ "radius": 0.1, "is_linear": true, "ef": 1000 }}},
    "topk": 20,
    "include_vector": true
}' https://YOUR_CLUSTER_ENDPOINT/v1/collections/multi_vector_demo/query

# example output:
#{
#    "code": 0,
#    "request_id": "b9bc3d5a-8edf-4d5b-916d-0ced6ae570cb",
#    "message": "Success",
#    "output": [
#        {
#            "id": "4",
#            "vectors": {
#                "title": [
#                    0.10000000149011612,
#                    0.20000000298023224,
#                    0.30000001192092896,
#                    0.4000000059604645
#                ]
#            },
#            "fields": {
#                "author": "zhangsan"
#            },
#            "score": 0.0
#        },
#        {
#            "id": "2",
#            "vectors": {
#                "title": [
#                    0.10000000149011612,
#                    0.20000000298023224,
#                    0.30000001192092896,
#                    0.4000000059604645
#                ]
#            },
#            "fields": {
#                "author": "zhangsan"
#            },
#            "score": 0.0
#        }
#    ]
#}

入参描述

说明

vector和id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 | 是 | Cluster的Endpoint，可在控制台Cluster详情中查看 |
| {CollectionName} | path | str | 是 | Collection名称 |
| dashvector-auth-token | header | str | 是 | api-key |
| vector | body | array | 否 | 向量数据 |
| sparse_vector | body | dict | 否 | 稀疏向量 |
| id | body | str | 否 | 主键，表示根据主键对应的向量进行相似性检索 |
| topk | body | int | 否 | 返回topk相似性结果，默认10 |
| filter | body | str | 否 | 过滤条件，需满足SQL where子句规范，详见条件过滤检索 |
| include_vector | body | bool | 否 | 是否返回向量数据，默认false |
| output_fields | body | array | 否 | 返回field的字段名列表，默认返回所有Fields |
| partition | body | str | 否 | Partition名称 |
| vectors | body | dict | 否 | 多个向量检索，类型为Map<String, VectorQuery>，详情参考多向量检索 |
| rerank | body | dict | 否 | 融合排序参数，详情参考多向量检索 |
| vector_param | body | dict | 否 | 高级检索参数，详情参考向量检索高级参数 |

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 | |
| usage | map | 对Serverless实例（按量付费）集合的Doc检索请求，成功后返回实际消耗的读请求单元数 | { Usage: { read_units: 8 } } |