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Bring your own dense vector embeddings to Elasticsearch

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Bring your own dense vector embeddings to Elasticsearch

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This tutorial demonstrates how to index documents that already have dense vector embeddings into Elasticsearch. You’ll also learn the syntax for searching these documents using a knn query.

You’ll find links at the end of this tutorial for more information about deploying a text embedding model in Elasticsearch, so you can generate embeddings for queries on the fly.

This is an advanced use case. Refer to Semantic search for an overview of your options for semantic search with Elasticsearch.

Step 1: Create an index with dense_vector mapping

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Each document in our simple dataset will have:

  • A review: stored in a review_text field

  • An embedding of that review: stored in a review_vector field

    • The review_vector field is defined as a dense_vector data type.

The dense_vector type automatically uses int8_hnsw quantization by default to reduce the memory footprint required when searching float vectors. Learn more about balancing performance and accuracy in Dense vector quantization.

resp = client.indices.create(
    index="amazon-reviews",
    mappings={
        "properties": {
            "review_vector": {
                "type": "dense_vector",
                "dims": 8,
                "index": True,
                "similarity": "cosine"
            },
            "review_text": {
                "type": "text"
            }
        }
    },
)
print(resp)
const response = await client.indices.create({
  index: "amazon-reviews",
  mappings: {
    properties: {
      review_vector: {
        type: "dense_vector",
        dims: 8,
        index: true,
        similarity: "cosine",
      },
      review_text: {
        type: "text",
      },
    },
  },
});
console.log(response);
PUT /amazon-reviews
{
  "mappings": {
    "properties": {
      "review_vector": {
        "type": "dense_vector",
        "dims": 8, 
        "index": true, 
        "similarity": "cosine" 
      },
      "review_text": {
        "type": "text"
      }
    }
  }
}

The dims parameter must match the length of the embedding vector. Here we’re using a simple 8-dimensional embedding for readability. If not specified, dims will be dynamically calculated based on the first indexed document.

The index parameter is set to true to enable the use of the knn query.

The similarity parameter defines the similarity function used to compare the query vector to the document vectors. cosine is the default similarity function for dense_vector fields in Elasticsearch.

Step 2: Index documents with embeddings

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Index a single document

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First, index a single document to understand the document structure.

resp = client.index(
    index="amazon-reviews",
    id="1",
    document={
        "review_text": "This product is lifechanging! I'm telling all my friends about it.",
        "review_vector": [
            0.1,
            0.2,
            0.3,
            0.4,
            0.5,
            0.6,
            0.7,
            0.8
        ]
    },
)
print(resp)
const response = await client.index({
  index: "amazon-reviews",
  id: 1,
  document: {
    review_text:
      "This product is lifechanging! I'm telling all my friends about it.",
    review_vector: [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8],
  },
});
console.log(response);
PUT /amazon-reviews/_doc/1
{
  "review_text": "This product is lifechanging! I'm telling all my friends about it.",
  "review_vector": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8] 
}

The size of the review_vector array is 8, matching the dims count specified in the mapping.

Bulk index multiple documents

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In a production scenario, you’ll want to index many documents at once using the _bulk endpoint.

Here’s an example of indexing multiple documents in a single _bulk request.

resp = client.bulk(
    operations=[
        {
            "index": {
                "_index": "amazon-reviews",
                "_id": "2"
            }
        },
        {
            "review_text": "This product is amazing! I love it.",
            "review_vector": [
                0.1,
                0.2,
                0.3,
                0.4,
                0.5,
                0.6,
                0.7,
                0.8
            ]
        },
        {
            "index": {
                "_index": "amazon-reviews",
                "_id": "3"
            }
        },
        {
            "review_text": "This product is terrible. I hate it.",
            "review_vector": [
                0.8,
                0.7,
                0.6,
                0.5,
                0.4,
                0.3,
                0.2,
                0.1
            ]
        },
        {
            "index": {
                "_index": "amazon-reviews",
                "_id": "4"
            }
        },
        {
            "review_text": "This product is great. I can do anything with it.",
            "review_vector": [
                0.1,
                0.2,
                0.3,
                0.4,
                0.5,
                0.6,
                0.7,
                0.8
            ]
        },
        {
            "index": {
                "_index": "amazon-reviews",
                "_id": "5"
            }
        },
        {
            "review_text": "This product has ruined my life and the lives of my family and friends.",
            "review_vector": [
                0.8,
                0.7,
                0.6,
                0.5,
                0.4,
                0.3,
                0.2,
                0.1
            ]
        }
    ],
)
print(resp)
const response = await client.bulk({
  operations: [
    {
      index: {
        _index: "amazon-reviews",
        _id: "2",
      },
    },
    {
      review_text: "This product is amazing! I love it.",
      review_vector: [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8],
    },
    {
      index: {
        _index: "amazon-reviews",
        _id: "3",
      },
    },
    {
      review_text: "This product is terrible. I hate it.",
      review_vector: [0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1],
    },
    {
      index: {
        _index: "amazon-reviews",
        _id: "4",
      },
    },
    {
      review_text: "This product is great. I can do anything with it.",
      review_vector: [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8],
    },
    {
      index: {
        _index: "amazon-reviews",
        _id: "5",
      },
    },
    {
      review_text:
        "This product has ruined my life and the lives of my family and friends.",
      review_vector: [0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1],
    },
  ],
});
console.log(response);
POST /_bulk
{ "index": { "_index": "amazon-reviews", "_id": "2" } }
{ "review_text": "This product is amazing! I love it.", "review_vector": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8] }
{ "index": { "_index": "amazon-reviews", "_id": "3" } }
{ "review_text": "This product is terrible. I hate it.", "review_vector": [0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1] }
{ "index": { "_index": "amazon-reviews", "_id": "4" } }
{ "review_text": "This product is great. I can do anything with it.", "review_vector": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8] }
{ "index": { "_index": "amazon-reviews", "_id": "5" } }
{ "review_text": "This product has ruined my life and the lives of my family and friends.", "review_vector": [0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2, 0.1] }

Step 3: Search documents with embeddings

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Now you can query these document vectors using a knn retriever. knn is a type of vector search, which finds the k most similar documents to a query vector. Here we’re simply using a raw vector for the query text, for demonstration purposes.

resp = client.search(
    index="amazon-reviews",
    retriever={
        "knn": {
            "field": "review_vector",
            "query_vector": [
                0.1,
                0.2,
                0.3,
                0.4,
                0.5,
                0.6,
                0.7,
                0.8
            ],
            "k": 2,
            "num_candidates": 5
        }
    },
)
print(resp)
const response = await client.search({
  index: "amazon-reviews",
  retriever: {
    knn: {
      field: "review_vector",
      query_vector: [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8],
      k: 2,
      num_candidates: 5,
    },
  },
});
console.log(response);
POST /amazon-reviews/_search
{
  "retriever": {
    "knn": {
      "field": "review_vector",
      "query_vector": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8], 
      "k": 2, 
      "num_candidates": 5 
    }
  }
}

In this simple example, we’re sending a raw vector as the query text. In a real-world scenario, you’ll need to generate vectors for queries using an embedding model.

The k parameter specifies the number of results to return.

The num_candidates parameter is optional. It limits the number of candidates returned by the search node. This can improve performance and reduce costs.

Learn more

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In this simple example, we’re sending a raw vector for the query text. In a real-world scenario you won’t know the query text ahead of time. You’ll need to generate query vectors, on the fly, using the same embedding model that generated the document vectors.

For this you’ll need to deploy a text embedding model in Elasticsearch and use the query_vector_builder parameter. Alternatively, you can generate vectors client-side and send them directly with the search request.

Learn how to use a deployed text embedding model for semantic search.

If you’re just getting started with vector search in Elasticsearch, refer to Semantic search.

« Tutorial: semantic search with a deployed model Search across clusters »