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4 changes: 2 additions & 2 deletions modus/api-generation.mdx
Original file line number Diff line number Diff line change
Expand Up @@ -105,11 +105,11 @@ Since the `classify` function isn't exported from the module, Modus doesn't
include it in the generated GraphQL API.

```ts
import { models } from "@hypermode/functions-as"
import { models } from "@hypermode/modus-sdk-as"
import {
ClassificationModel,
ClassifierResult,
} from "@hypermode/models-as/models/experimental/classification"
} from "@hypermode/modus-sdk-as/models/experimental/classification"

const modelName: string = "my-classifier"

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4 changes: 2 additions & 2 deletions modus/sdk/models.mdx
Original file line number Diff line number Diff line change
Expand Up @@ -51,7 +51,7 @@ To begin, import the `models` namespace from the SDK:
You'll also need to import one or more classes for the model you are working with. For example:

```ts
import { ClassificationModel } from "@hypermode/models-as/models/experimental/classification"
import { ClassificationModel } from "@hypermode/modus-sdk-as/models/experimental/classification"
```

</Tab>
Expand Down Expand Up @@ -110,7 +110,7 @@ The base class for all models that Hypermode functions can invoke.
<Tip>
If you are implementing a custom model, you should extend this class. You'll
also need classes to represent the input and output types for your model. See
the implementations of the pre-defined models in the `models-as` repository
the implementations of the pre-defined models in the Modus GitHub repository
for examples.
</Tip>

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4 changes: 2 additions & 2 deletions modus/search.mdx
Original file line number Diff line number Diff line change
Expand Up @@ -161,8 +161,8 @@ func Embed(text []string) ([][]float32, error) {
```

```ts AssemblyScript
import { models } from "@hypermode/functions-as"
import { EmbeddingsModel } from "@hypermode/models-as/models/experimental/embeddings"
import { models } from "@hypermode/modus-sdk-as"
import { EmbeddingsModel } from "@hypermode/modus-sdk-as/models/experimental/embeddings"

export function embed(texts: string[]): f32[][] {
// "minilm" is the model name declared in the application manifest
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