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Tutorial: Dense and sparse workflows using ingest pipelines

Serverless Stack

Important
  • For the easiest way to perform semantic search in the Elastic Stack, refer to the semantic_text end-to-end tutorial.
  • This tutorial predates the inference endpoint and the semantic_text field type. Today there are simpler, higher-level options for semantic search than the ones outlined in this tutorial. The semantic text workflow is the recommended way to perform semantic search for most use cases.

This guide shows how to implement semantic search in Elasticsearch with deployed NLP models: from selecting a model, to configuring ingest pipelines, to running queries.

Elasticsearch supports a wide range of NLP models, including dense and sparse vector models. Choosing the right model is critical for good relevance.

While bringing your own text embedding model is possible, achieving strong results typically requires tuning and evaluation. Selecting a well-performing third-party model from our list is a good start. 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 lower the barrier, Elastic provides Elastic Learned Sparse EncodeR (ELSER), a pre-trained sparse vector model (English-only) that delivers strong out-of-the-box relevance without fine-tuning. This adaptability makes it suitable for various NLP use cases out of the box. Unless you have an ML team, we recommend starting with ELSER.

Sparse vectors are mostly zeros with a small number of non-zero values. This representation is commonly used for textual data. With ELSER, both documents and queries are represented as 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. The ELSER model replaces the terms in the original query with other terms learned from the training dataset that appear in documents matching the original search terms, and assigns weights to control their importance.

After you choose a model, deploy it in Elasticsearch.

To deploy ELSER, refer to Download and deploy ELSER.

To deploy a third-party text embedding model, refer to Deploy a text embedding model.

Before generating embeddings, prepare your index mapping. The mapping depends on whether you use a sparse (ELSER) or dense model.

ELSER outputs token-weight pairs. Use the sparse_vector field to store them.

To create a mapping for your ELSER index, refer to the Create the index mapping section of the tutorial. Create a mapping with a sparse_vector field for the tokens and a text field for the source content. For example:

				PUT my-index
					{
  "mappings": {
    "properties": {
      "my_tokens": {
        "type": "sparse_vector"
      },
      "my_text_field": {
        "type": "text"
      }
    }
  }
}
  1. The field that will contain ELSER-generated tokens.
  2. The field that contains the tokens must be a sparse_vector field.
  3. The source field used to create the sparse vector representation. In this example, the name of the field is my_text_field.
  4. The field type is text in this example.

Dense vector models output numeric embeddings. Use the dense_vector field type for storing dense vectors that the supported third-party model you selected generates. Keep in mind that the model produces embeddings with a certain number of dimensions. The dense_vector field must be configured with the same number of dimensions using the dims option. Refer to the respective model documentation to get information about the number of dimensions of the embeddings.

To review a mapping of an index for an NLP model, refer to the mapping code snippet in the Add the text embedding model to an ingest inference pipeline section of the related tutorial. The example shows how to create an index mapping that defines the my_embeddings.predicted_value field - which will contain the model output - as a dense_vector field.

				PUT my-index
					{
  "mappings": {
    "properties": {
      "my_embeddings.predicted_value": {
        "type": "dense_vector",
        "dims": 384
      },
      "my_text_field": {
        "type": "text"
      }
    }
  }
}
  1. The name of the field that will contain the embeddings generated by the model.
  2. The field that contains the embeddings must be a dense_vector field.
  3. The model produces embeddings with a certain number of dimensions. The dense_vector field must be configured with the same number of dimensions by the dims option. Refer to the respective model documentation to get information about the number of dimensions of the embeddings.
  4. The source field used to create the dense vector representation. In this example, the name of the field is my_text_field.
  5. The field type is text in this example.

Use an ingest pipeline with an inference processor to generate embeddings at ingest time. 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:

				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"
          }
        ]
      }
    }
  ]
}
  1. 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.

Example ingest pipeline using a text embedding model:

				PUT _ingest/pipeline/my-text-embeddings-pipeline
					{
  "description": "Text embedding pipeline",
  "processors": [
    {
      "inference": {
        "model_id": "sentence-transformers__msmarco-minilm-l-12-v3",
        "target_field": "my_embeddings",
        "field_map": {
          "my_text_field": "text_field"
        }
      }
    }
  ]
}
  1. The model ID of the text embedding model you want to use.
  2. The field_map object maps the input document field name (which is my_text_field in this example) to the name of the field that the model expects (which is always text_field).

To ingest data through the pipeline to generate text embeddings with your chosen model, refer to the Add the text embedding model to an inference ingest pipeline section. The example shows how to create the pipeline with the inference processor and reindex your data through the pipeline. After you successfully ingested documents by using the pipeline, your index will contain the text embeddings generated by the model.

Now it is time to perform semantic search!

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.

ELSER text embeddings can be queried using a sparse vector query. The sparse vector query enables you to query a sparse vector field, by providing the inference ID associated with the NLP model you want to use, and the query text:

				GET my-index/_search
					{
   "query":{
    "sparse_vector": {
        "field": "my_tokens",
        "inference_id": "my-elser-endpoint",
        "query": "the query string"
      }
   }
}

Text embeddings produced by dense vector models can be queried using a kNN search. In the knn clause, provide the name of the dense vector field, and a query_vector_builder clause with the model ID and the query text.

				GET my-index/_search
					{
  "knn": {
    "field": "my_embeddings.predicted_value",
    "k": 10,
    "num_candidates": 100,
    "query_vector_builder": {
      "text_embedding": {
        "model_id": "sentence-transformers__msmarco-minilm-l-12-v3",
        "model_text": "the query string"
      }
    }
  }
}

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.

Hybrid search between a semantic and lexical query can be achieved by using an rrf retriever as part of your search request. Provide a sparse_vector query and a full-text query as standard retrievers for the rrf retriever. The rrf retriever uses reciprocal rank fusion to rank the top documents.

				GET my-index/_search
					{
  "retriever": {
    "rrf": {
      "retrievers": [
        {
          "standard": {
            "query": {
              "match": {
                "my_text_field": "the query string"
              }
            }
          }
        },
        {
          "standard": {
            "query": {
             "sparse_vector": {
                "field": "my_tokens",
                "inference_id": "my-elser-endpoint",
                "query": "the query string"
              }
            }
          }
        }
      ]
    }
  }
}

Hybrid search between a semantic and lexical query can be achieved by providing:

  • an rrf retriever to rank top documents using reciprocal rank fusion
  • a standard retriever as a child retriever with query clause for the full-text query
  • a knn retriever as a child retriever with the kNN search that queries the dense vector field
				GET my-index/_search
					{
  "retriever": {
    "rrf": {
      "retrievers": [
        {
          "standard": {
            "query": {
              "match": {
                "my_text_field": "the query string"
              }
            }
          }
        },
        {
          "knn": {
            "field": "text_embedding.predicted_value",
            "k": 10,
            "num_candidates": 100,
            "query_vector_builder": {
              "text_embedding": {
                "model_id": "sentence-transformers__msmarco-minilm-l-12-v3",
                "model_text": "the query string"
              }
            }
          }
        }
      ]
    }
  }
}