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Elastic Docs ›Elasticsearch Guide [8.18] ›Query DSL ›Vector queries

Semantic query

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A newer version is available. Check out the latest documentation.

Semantic query

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The semantic query type enables you to perform semantic search on data stored in a semantic_text field.

Example request

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resp = client.search(
    index="my-index-000001",
    query={
        "semantic": {
            "field": "inference_field",
            "query": "Best surfing places"
        }
    },
)
print(resp)
const response = await client.search({
  index: "my-index-000001",
  query: {
    semantic: {
      field: "inference_field",
      query: "Best surfing places",
    },
  },
});
console.log(response);
GET my-index-000001/_search
{
  "query": {
    "semantic": {
      "field": "inference_field",
      "query": "Best surfing places"
    }
  }
}

Top-level parameters for semantic

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field
(Required, string) The semantic_text field to perform the query on.
query
(Required, string) The query text to be searched for on the field.

Refer to this tutorial to learn more about semantic search using semantic_text and semantic query.

Hybrid search with the semantic query

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The semantic query can be used as a part of a hybrid search where the semantic query is combined with lexical queries. For example, the query below finds documents with the title field matching "mountain lake", and combines them with results from a semantic search on the field title_semantic, that is a semantic_text field. The combined documents are then scored, and the top 3 top scored documents are returned.

resp = client.search(
    index="my-index",
    size=3,
    query={
        "bool": {
            "should": [
                {
                    "match": {
                        "title": {
                            "query": "mountain lake",
                            "boost": 1
                        }
                    }
                },
                {
                    "semantic": {
                        "field": "title_semantic",
                        "query": "mountain lake",
                        "boost": 2
                    }
                }
            ]
        }
    },
)
print(resp)
const response = await client.search({
  index: "my-index",
  size: 3,
  query: {
    bool: {
      should: [
        {
          match: {
            title: {
              query: "mountain lake",
              boost: 1,
            },
          },
        },
        {
          semantic: {
            field: "title_semantic",
            query: "mountain lake",
            boost: 2,
          },
        },
      ],
    },
  },
});
console.log(response);
POST my-index/_search
{
  "size" : 3,
  "query": {
    "bool": {
      "should": [
        {
          "match": {
            "title": {
              "query": "mountain lake",
              "boost": 1
            }
          }
        },
        {
          "semantic": {
            "field": "title_semantic",
            "query": "mountain lake",
            "boost": 2
          }
        }
      ]
    }
  }
}

You can also use semantic_text as part of Reciprocal Rank Fusion to make ranking relevant results easier:

resp = client.search(
    index="my-index",
    retriever={
        "rrf": {
            "retrievers": [
                {
                    "standard": {
                        "query": {
                            "term": {
                                "text": "shoes"
                            }
                        }
                    }
                },
                {
                    "standard": {
                        "query": {
                            "semantic": {
                                "field": "semantic_field",
                                "query": "shoes"
                            }
                        }
                    }
                }
            ],
            "rank_window_size": 50,
            "rank_constant": 20
        }
    },
)
print(resp)
const response = await client.search({
  index: "my-index",
  retriever: {
    rrf: {
      retrievers: [
        {
          standard: {
            query: {
              term: {
                text: "shoes",
              },
            },
          },
        },
        {
          standard: {
            query: {
              semantic: {
                field: "semantic_field",
                query: "shoes",
              },
            },
          },
        },
      ],
      rank_window_size: 50,
      rank_constant: 20,
    },
  },
});
console.log(response);
GET my-index/_search
{
  "retriever": {
    "rrf": {
      "retrievers": [
        {
          "standard": {
            "query": {
              "term": {
                "text": "shoes"
              }
            }
          }
        },
        {
          "standard": {
            "query": {
              "semantic": {
                "field": "semantic_field",
                "query": "shoes"
              }
            }
          }
        }
      ],
      "rank_window_size": 50,
      "rank_constant": 20
    }
  }
}
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