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Elastic Docs ›Elasticsearch Guide [8.15] ›Search your data ›Semantic search

Tutorial: semantic search with a deployed model

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Tutorial: semantic search with a deployed model

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  • For the easiest way to perform semantic search in the Elastic Stack, refer to the semantic_text end-to-end tutorial.
  • This tutorial was written before the inference endpoint and semantic_text field type was introduced. Today we have simpler options for performing semantic search.

This guide shows you how to implement semantic search with models deployed in Elasticsearch: from selecting an NLP model, to writing queries.

Select an NLP model

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Elasticsearch offers the usage of a wide range of NLP models, including both dense and sparse vector models. Your choice of the language model is critical for implementing semantic search successfully.

While it is possible to bring your own text embedding model, achieving good search results through model tuning is challenging. Selecting an appropriate model from our third-party model list is the first step. Training the model on your own data is essential to ensure better search results than using only BM25. However, the model training process requires a team of data scientists and ML experts, making it expensive and time-consuming.

To address this issue, Elastic provides a pre-trained representational model called Elastic Learned Sparse EncodeR (ELSER). ELSER, currently available only for English, is an out-of-domain sparse vector model that does not require fine-tuning. This adaptability makes it suitable for various NLP use cases out of the box. Unless you have a team of ML specialists, it is highly recommended to use the ELSER model.

In the case of sparse vector representation, the vectors mostly consist of zero values, with only a small subset containing non-zero values. This representation is commonly used for textual data. In the case of ELSER, each document in an index and the query text itself are represented by high-dimensional sparse vectors. Each non-zero element of the vector corresponds to a term in the model vocabulary. The ELSER vocabulary contains around 30000 terms, so the sparse vectors created by ELSER contain about 30000 values, the majority of which are zero. Effectively the ELSER model is replacing the terms in the original query with other terms that have been learnt to exist in the documents that best match the original search terms in a training dataset, and weights to control how important each is.

Deploy the model

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After you decide which model you want to use for implementing semantic search, you need to deploy the model in Elasticsearch.

To deploy ELSER, refer to Download and deploy ELSER.

Map a field for the text embeddings

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Before you start using the deployed model to generate embeddings based on your input text, you need to prepare your index mapping first. The mapping of the index depends on the type of model.

ELSER produces token-weight pairs as output from the input text and the query. The Elasticsearch sparse_vector field type can store these token-weight pairs as numeric feature vectors. The index must have a field with the sparse_vector field type to index the tokens that ELSER generates.

To create a mapping for your ELSER index, refer to the Create the index mapping section of the tutorial. The example shows how to create an index mapping for my-index that defines the my_embeddings.tokens field - which will contain the ELSER output - as a sparse_vector field.

resp = client.indices.create(
    index="my-index",
    mappings={
        "properties": {
            "my_tokens": {
                "type": "sparse_vector"
            },
            "my_text_field": {
                "type": "text"
            }
        }
    },
)
print(resp)
response = client.indices.create(
  index: 'my-index',
  body: {
    mappings: {
      properties: {
        my_tokens: {
          type: 'sparse_vector'
        },
        my_text_field: {
          type: 'text'
        }
      }
    }
  }
)
puts response
const response = await client.indices.create({
  index: "my-index",
  mappings: {
    properties: {
      my_tokens: {
        type: "sparse_vector",
      },
      my_text_field: {
        type: "text",
      },
    },
  },
});
console.log(response);
PUT my-index
{
  "mappings": {
    "properties": {
      "my_tokens": { 
        "type": "sparse_vector" 
      },
      "my_text_field": { 
        "type": "text" 
      }
    }
  }
}

The name of the field that will contain the tokens generated by ELSER.

The field that contains the tokens must be a sparse_vector field.

The name of the field from which to create the sparse vector representation. In this example, the name of the field is my_text_field.

The field type is text in this example.

Generate text embeddings

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Once you have created the mappings for the index, you can generate text embeddings from your input text. This can be done by using an ingest pipeline with an inference processor. The ingest pipeline processes the input data and indexes it into the destination index. At index time, the inference ingest processor uses the trained model to infer against the data ingested through the pipeline. After you created the ingest pipeline with the inference processor, you can ingest your data through it to generate the model output.

This is how an ingest pipeline that uses the ELSER model is created:

resp = client.ingest.put_pipeline(
    id="my-text-embeddings-pipeline",
    description="Text embedding pipeline",
    processors=[
        {
            "inference": {
                "model_id": ".elser_model_2",
                "input_output": [
                    {
                        "input_field": "my_text_field",
                        "output_field": "my_tokens"
                    }
                ]
            }
        }
    ],
)
print(resp)
response = client.ingest.put_pipeline(
  id: 'my-text-embeddings-pipeline',
  body: {
    description: 'Text embedding pipeline',
    processors: [
      {
        inference: {
          model_id: '.elser_model_2',
          input_output: [
            {
              input_field: 'my_text_field',
              output_field: 'my_tokens'
            }
          ]
        }
      }
    ]
  }
)
puts response
const response = await client.ingest.putPipeline({
  id: "my-text-embeddings-pipeline",
  description: "Text embedding pipeline",
  processors: [
    {
      inference: {
        model_id: ".elser_model_2",
        input_output: [
          {
            input_field: "my_text_field",
            output_field: "my_tokens",
          },
        ],
      },
    },
  ],
});
console.log(response);
PUT _ingest/pipeline/my-text-embeddings-pipeline
{
  "description": "Text embedding pipeline",
  "processors": [
    {
      "inference": {
        "model_id": ".elser_model_2",
        "input_output": [ 
          {
            "input_field": "my_text_field",
            "output_field": "my_tokens"
          }
        ]
      }
    }
  ]
}

Configuration object that defines the input_field for the inference process and the output_field that will contain the inference results.

To ingest data through the pipeline to generate tokens with ELSER, refer to the Ingest the data through the inference ingest pipeline section of the tutorial. After you successfully ingested documents by using the pipeline, your index will contain the tokens generated by ELSER. Tokens are learned associations capturing relevance, they are not synonyms. To learn more about what tokens are, refer to this page.

Now it is time to perform semantic search!

Search the data

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Depending on the type of model you have deployed, you can query rank features with a sparse vector query, or dense vectors with a kNN search.

Beyond semantic search with hybrid search

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In some situations, lexical search may perform better than semantic search. For example, when searching for single words or IDs, like product numbers.

Combining semantic and lexical search into one hybrid search request using reciprocal rank fusion provides the best of both worlds. Not only that, but hybrid search using reciprocal rank fusion has been shown to perform better in general.

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