Help Center/ Cloud Search Service/ User Guide/ Using OpenSearch for Data Search/ Enhancing Search Capabilities for OpenSearch Clusters/ Configuring Vector Search for OpenSearch Clusters/ Using Nested Fields for Vector Search
Updated on 2025-09-05 GMT+08:00
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Using Nested Fields for Vector Search

Nested fields allow multiple vectorized records to be stored in a single document. For example, in an RAG scenario, documents usually need to be segmented by paragraph or by a fixed length, and then vectorized into multiple semantic vectors. By means of nested fields, these vectors can be written into a same Elasticsearch document. For a document that contains multiple vector records, if the query vector matches any of them, the document is returned.

Constraints

Only OpenSearch 2.19.0 clusters support this feature.

Creating a Vector Index

Create a vector index with nested fields. The index contains an id field whose type is keyword, and an embedding field whose type is nested. The embedding field contains two subfields: chunk and emb. The chunk subfield is of the keyword type, and the emb subfield is of the vector type.

PUT my_index
{
  "settings": {
    "index.vector": true
  },
  "mappings": {
    "properties": {
      "id": {
        "type": "keyword"
      },
      "embedding": {
        "type": "nested",
        "properties": {
          "chunk": {
            "type": "keyword"
          },
          "emb": {
            "type": "vector",
            "dimension": 2,
            "indexing": true,
            "algorithm": "GRAPH",
            "metric": "euclidean"
          }
        }
      }
    }
  }
}

Importing Vector Data

Use the bulk operation to write data in arrays. Each document contains two vector records.

POST my_index/_bulk
{"index":{}}
{"id": 1, "embedding": [{"chunk":1,"emb": [1, 1]}, {"chunk":2,"emb": [2, 2]}]}
{"index":{}}
{"id": 2, "embedding": [{"chunk":1,"emb": [2, 2]}, {"chunk":2,"emb": [3, 3]}]}
{"index":{}}
{"id": 3, "embedding": [{"chunk":1,"emb": [3, 3]}, {"chunk":2,"emb": [4, 4]}]}

Vector Search

The nested query is required for nested fields. To perform such a query, you need to set the path parameter to specify the nested path, and set score_mode to max, indicating the maximum similarity between all vectors in the document and the query vector.

  • Standard query

    Query the top 10 documents that are most similar to vector [1, 1].

    GET my_index/_search
{
  "_source": {"excludes": ["embedding"]},
  "query": {
    "nested": {
      "path": "embedding",
      "score_mode": "max",
      "query": {
        "vector": {
          "embedding.emb": {
            "vector": [1, 1],
            "topk": 10
          }
        }
      }
    }
  }
}

    An example of the query result:

    {
  "took" : 2,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 3,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "my_index",
        "_type" : "_doc",
        "_id" : "Hc4Vc5QBSxCnghau22AE",
        "_score" : 1.0,
        "_source" : {
          "id" : 1
        }
      },
      {
        "_index" : "my_index",
        "_type" : "_doc",
        "_id" : "Hs4Vc5QBSxCnghau22AE",
        "_score" : 0.33333334,
        "_source" : {
          "id" : 2
        }
      },
      {
        "_index" : "my_index",
        "_type" : "_doc",
        "_id" : "H84Vc5QBSxCnghau22AE",
        "_score" : 0.11111111,
        "_source" : {
          "id" : 3
        }
      }
    ]
  }
}
  • Pre-filtering query

    First retrieve documents whose ID is ["2", "3"], and then return the top 10 documents that are most similar to the query vector [1, 1].

    GET my_index/_search
{
  "query": {
    "nested": {
      "path": "embedding",
      "score_mode": "max",
      "query": {
        "vector": {
          "embedding.emb": {
            "vector": [1, 1],
            "topk": 10,
            "filter": {
              "terms": {"id": ["2", "3"]}
            }
          }
        }
      }
    }
  }
}

    An example of the query result:

    {
  "took" : 3,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 2,
      "relation" : "eq"
    },
    "max_score" : 0.33333334,
    "hits" : [
      {
        "_index" : "my_index",
        "_type" : "_doc",
        "_id" : "3t0ZypcB-Tff59gMTZO2",
        "_score" : 0.33333334,
        "_source" : {
          "id" : 2,
          "embedding" : [
            {
              "chunk" : 1,
              "emb" : [
                2,
                2
              ]
            },
            {
              "chunk" : 2,
              "emb" : [
                3,
                3
              ]
            }
          ]
        }
      },
      {
        "_index" : "my_index",
        "_type" : "_doc",
        "_id" : "390ZypcB-Tff59gMTZO2",
        "_score" : 0.11111111,
        "_source" : {
          "id" : 3,
          "embedding" : [
            {
              "chunk" : 1,
              "emb" : [
                3,
                3
              ]
            },
            {
              "chunk" : 2,
              "emb" : [
                4,
                4
              ]
            }
          ]
        }
      }
    ]
  }
}