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相似性检索

最近更新时间:2024-09-09 15:13:01

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相似度检索是基于多维向量进行 相似度计算 的检索方式。相似度计算通过计算查询向量与数据库存储的向量之间的相似度,找到与查询的多维向量最相似的文档。
说明:
在创建 Collection 时,需指定向量的 索引类型(如 HNSW 等)与 相似度计算 方法。数据库存储的向量将会按照指定的索引类型进行索引。那么,在向量检索时,便会依据索引并使用已选择的相似性计算方法进行匹配,快速高效地获取目标向量。具体信息,请参见 /collection/create

主要功能

腾讯云向量数据库(Tencent Cloud VectorDB)较全面覆盖各种相似度检索场景。您可以根据实际存储数据的特点与应用场景,选择不同的检索方式。
数据库类别
精确查询支持方式
Base Database
支持指定多维向量数值,检索与指定的多维向量数值最相似的 Top K 条文档。
支持指定 id(Document ID),检索与该 id 的向量值最相似的 Top K 条文档。
应用 Embedding 功能,支持输入原始文本,检索与该文本信息最相似的 Top K 条文档。
支持指定 id、多维向量或原始文本,搭配标量字段的 Filter 表达式一并检索与 id、多维向量、原始文本相似的文档。
支持批量进行相似度检索,即支持输入多条向量数据值、多个 id 分别检索与每一个向量数值、每一个 id 相似的 Top K 条数据。
AI Database
支持输入文本信息,在指定的文件 id(DocumentSet ID)或文件名,检索相似的文本内容。
支持指定多个文件 id(DocumentSet ID)或文件名,单次批量检索多个文件的信息,最大数量为 20 个。
支持指定标量字段的 Filter 表达式 过滤文件信息。
支持指定多个文件 id(DocumentSet ID)或文件名,并搭配标量字段的 Filter 表达式一起过滤多个文件信息。

应用示例

本文简单给出相似度检索的请求示例,方便您直观理解相似度检索的功能。Base 类数据库,相似性检索,请参见接口文档 /document/search。AI 类数据库,请参见 /ai/documentSet/search

根据 ID 进行相似度检索

如下示例使用 /document/search 接口,分别查询与 id(Document ID)为0001 、0002、0003相似度最高的 Top3 的数据。
curl -i -X POST \\
-H 'Content-Type: application/json' \\
-H 'Authorization: Bearer account=root&api_key=A5VOgsMpGWJhUI0WmUbY********************' \\
http://10.0.X.X:80/document/search \\
-d '{
"database": "db-test",
"collection": "book-vector",
"search": {
"documentIds": [
"0001",
"0002",
"0003"
],
"params": {
"ef": 200
},
"retrieveVector": true,
"limit": 3
}
}'
执行成功,返回如下信息:

{
    "code": 0,
    "msg": "operation success",
    "documents": [
        [
            {
                "id": "0001",
                "vector": [
                    0.21230000257492066,
                    0.23000000417232514,
                    0.21299999952316285
                ],
                "score": 1.0000001192092896,
                "bookName": "三国演义",
                "author": "罗贯中",
                "page": 21
            },
            {
                "id": "0002",
                "vector": [
                    0.21230000257492066,
                    0.2199999988079071,
                    0.21299999952316285
                ],
                "score": 0.9997729063034058,
                "bookName": "西游记",
                "page": 22,
                "author": "吴承恩"
            },
            {
                "id": "0003",
                "vector": [
                    0.21230000257492066,
                    0.20999999344348908,
                    0.21299999952316285
                ],
                "score": 0.9990617036819458,
                "author": "曹雪芹",
                "page": 23,
                "bookName": "红楼梦"
            }
        ],
        [
            {
                "id": "0002",
                "vector": [
                    0.21230000257492066,
                    0.2199999988079071,
                    0.21299999952316285
                ],
                "score": 1.000000238418579,
                "bookName": "西游记",
                "page": 22,
                "author": "吴承恩"
            },
            {
                "id": "0001",
                "vector": [
                    0.21230000257492066,
                    0.23000000417232514,
                    0.21299999952316285
                ],
                "score": 0.9997729063034058,
                "author": "罗贯中",
                "bookName": "三国演义",
                "page": 21
            },
            {
                "id": "0003",
                "vector": [
                    0.21230000257492066,
                    0.20999999344348908,
                    0.21299999952316285
                ],
                "score": 0.9997580051422119,
                "author": "曹雪芹",
                "bookName": "红楼梦",
                "page": 23
            }
        ],
        [
            {
                "id": "0003",
                "vector": [
                    0.21230000257492066,
                    0.20999999344348908,
                    0.21299999952316285
                ],
                "score": 1.0,
                "bookName": "红楼梦",
                "page": 23,
                "author": "曹雪芹"
            },
            {
                "id": "0002",
                "vector": [
                    0.21230000257492066,
                    0.2199999988079071,
                    0.21299999952316285
                ],
                "score": 0.9997580051422119,
                "author": "吴承恩",
                "bookName": "西游记",
                "page": 22
            },
            {
                "id": "0001",
                "vector": [
                    0.21230000257492066,
                    0.23000000417232514,
                    0.21299999952316285
                ],
                "score": 0.9990617036819458,
                "bookName": "三国演义",
                "author": "罗贯中",
                "page": 21
            }
        ]
    ]
}


根据指定的多维向量进行相似度检索

使用 /document/search 接口查询与指定向量("vectors": [[0.3123,0.43,0.213]])最相似的 Top3 数据。
注意:
如下示例不可直接复制运行,与 创建数据库 相同,api_key=4jpv6gzQTpq1Ev6iz2DUgAbv**************** 与 10.0.X.X 还需要依据实际情况进行替换之后,才能在 CVM 运行。
curl -i -X POST \\
-H 'Content-Type: application/json' \\
-H 'Authorization: Bearer account=root&api_key=A5VOgsMpGWJhUI0WmUbY********************' \\
http://10.0.X.X:80/document/search \\
-d '{
"database": "db-test",
"collection": "book-vector",
"search": {
"vectors": [
[
0.3123,
0.43,
0.213
]
],
"params": {
"ef": 200
},
"filter": "bookName in (\\"三国演义\\",\\"西游记\\")",
"retrieveVector": true,
"limit": 3
}
}'
检索信息如下所示。

{
"code": 0,
"msg": "operation success",
"documents": [
[
{
"id": "0001",
"vector": [
0.21230000257492066,
0.23000000417232514,
0.21299999952316285
],
"score": 0.9714228510856628,
"page": 21,
"author": "罗贯中",
"bookName": "三国演义"
},
{
"id": "0002",
"vector": [
0.21230000257492066,
0.2199999988079071,
0.21299999952316285
],
"score": 0.9668837785720825,
"bookName": "西游记",
"author": "吴承恩",
"page": 22
}
]
]
}


根据文本进行向量检索

如下示例,在集合 book-emb 中,使用 /document/search 接口查询与输入文本'天下大势,分久必合,合久必分'最相似的 Top3 数据。
curl -i -X POST \\
-H 'Content-Type: application/json' \\
-H 'Authorization: Bearer account=root&api_key=A5VOgsMpGWJhUI0WmUbY********************' \\
http://10.0.X.X:80/document/search \\
-d '{
"database": "db-test",
"collection": "book-emb",
"search": {
"embeddingItems": [
"天下大势,分久必合,合久必分"
],
"limit": 3,
"params": {
"ef": 200
},
"retrieveVector": false,
"outputFields": [
"id",
"author",
"text",
"bookName"
]
}
}'
检索信息如下所示。
{
"code": 0,
"msg": "operation success",
"documents": [
[
{
"id": "0001",
"score": 0.9792740345001221,
"author": "罗贯中",
"bookName": "三国演义",
"text": "话说天下大势,分久必合,合久必分。"
},
{
"id": "0002",
"score": 0.7909859418869019,
"text": "混沌未分天地乱,茫茫渺渺无人间。",
"bookName": "西游记",
"author": "吴承恩"
},
{
"id": "0003",
"score": 0.6858994364738464,
"author": "曹雪芹",
"bookName": "红楼梦",
"text": "甄士隐梦幻识通灵,贾雨村风尘怀闺秀。"
}
]
]
}