> ## Documentation Index
> Fetch the complete documentation index at: https://docs.trieve.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Working with SPLADE v2

> Learn how to use SPLADE with TVI.

## What is splade?

SPLADE is similar to other inverted index approaches like BM25. SPLADE includes neural term expansion, meaning that it is able to match on synonyms much better than traditional BM25.

## Using Splade with Trieve Vector Inference

<Steps>
  <Step title="Update embedding_models.yaml">
    To use SPLADE with Trieve Vector Inference, you will need to adapt both the `doc` and `query` models

    The SPLADE `document` model is the model you use to encode files, where the `query` model is the one to encode the query that you will be searching with

    ```yaml embedding_models.yaml theme={null}
    models:
      # ...
      spladeDoc:
        replicas: 1
        modelName: naver/efficient-splade-VI-BT-large-doc
        isSplade: true
      spladeQuery:
        replicas: 1
        modelName: naver/efficient-splade-VI-BT-large-query
        isSplade: true
      # ...
    ```
  </Step>

  <Step title="Upgrade your TVI cluster">
    Update TVI to include your models

    ```bash theme={null}
    helm upgrade -i vector-inference \
        oci://registry-1.docker.io/trieve/embeddings-helm \
        -f embedding_models.yaml
    ```
  </Step>

  <Step title="Get embeddings endpoint">
    ```sh theme={null}
    kubectl get ing
    ```
  </Step>

  <Step title="Make call to generate sparse vector">
    ```sh theme={null}
    ENDPOINT="k8s-default-vectorin...elb.amazonaws.com"

    curl -X POST \
         -H "Content-Type: application/json"\
         -d '{"inputs": "test input"}' \
         --url http://$ENDPOINT/embed_sparse
    ```

    For more information checkout the [API reference](/vector-inference/embed_sparse) for sparse vectors.
  </Step>
</Steps>