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- Specify `"hypermode"` for models that [Hypermode hosts](/hosted-models).
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- Otherwise, specify a name that matches a host defined in the [`connections`](#connections) section of the manifest.
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<ResponseFieldname="dedicated"type="boolean">
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Set to `true` to use a dedicated instance of the model for your project. Defaults to `false`.
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</ResponseField>
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### Auto-deployed models
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<Tip>
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When using `hugging-face` as the `provider` and `hypermode` as the `connection`, Hypermode automatically facilitates the
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connection to an instance of a shared or dedicated instance of the model. Your project's functions securely access the
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hosted model, with no further configuration required. For more details, see [hosted models](/hosted-models).
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When using `hugging-face` as the `provider` and `hypermode` as the `host`, Hypermode automatically deploys a dedicated
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instance of the defined `sourceModel` when deploying your project. Your project's functions securely connect to the
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hosted model, with no further configuration required.
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</Tip>
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## Collections
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Collections are a key integration aspect with [models](#models) to create smart, searchable text. The `collections`
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object in the app manifest allows you to define groupings of text and functions to embed inserted text with.
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Collections simplify the usage of vector embeddings to build natural language search features. The `collections` object
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allows you to define indexed data types that are automatically embedded and searchable based on the search method you
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define.
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Each collection requires a unique name, specified as a key, containing only alphanumeric characters and hyphens.
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```json hypermode.json
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For more detail on implementing collections, see [Search](/modus/search).
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```json modus.json
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{
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"collections": {
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// This defines a collection of products, having two search methods.
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"myProducts": {
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"searchMethods": {
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"searchMethod1": {
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// The embedder is the name of the function that will be used to generate vector embeddings.
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// By default, it uses a sequential index.
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"embedder": "embed"
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},
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// This is an example of a second search method.
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// It could use a different embedder or index type, if desired.
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"searchMethod2": {
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"embedder": "embed",
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"searchMethod": {
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"embedder": "myEmbedder",
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"index": {
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"type": "sequential"
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}
@@ -321,46 +369,15 @@ object in the app manifest allows you to define groupings of text and functions
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}
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```
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Each collection requires a unique name, specified as a key, containing only alphanumeric characters and hyphens.
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### Collections properties
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<ResponseFieldname="searchMethods"type="object">
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If provided, adds an index on top of the collection. Each key-value pair is a
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search method and values.
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<ResponseFieldname="embedder"type="string">
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An exported function name used by the search method to embed text in the collection. Function must have a call signature with input `string[]` and return type `f32[][]`.
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<Accordiontitle="Examples">
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This example specifies a `searchMethod` with an embedder named `embed`
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```json
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"searchMethod1": {
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"embedder": "embed"
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}
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```
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</Accordion>
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</ResponseField>
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<ResponseFieldname="index"type="string">
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If provided, describes the index mechanism used by the search method. `type`: specifies the type of the index. For example, `sequential` (default).
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<Accordiontitle="Examples">
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This example specifies a `searchMethod` with an embedder named `embed` and an index with type `sequential`
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