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update the embeddings
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docs/docs/features/embed.md

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@@ -3,7 +3,7 @@ title: Embedding
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description: Inference engine for embedding, the same as OpenAI's
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---
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Embeddings are lists of numbers (floats). To find how similar two embeddings are, we measure the [distance](https://en.wikipedia.org/wiki/Cosine_similarity) between them. Shorter distances mean they're more similar; longer distances mean less similarity.
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Embeddings are lists of numbers (floats). To find how similar two embeddings are, we measure the [distance](https://en.wikipedia.org/wiki/Cosine_similarity) between them.
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## Activating Embedding Feature
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```js title="Nitro"
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{
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"data": [
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{
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"embedding": [
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-0.9874749,
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0.2965493,
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...
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-0.253227
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],
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"index": 0,
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"object": "embedding"
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}
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]
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"embedding": [
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-0.9874749,
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0.2965493,
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...
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-0.253227
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],
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"index": 0,
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"object": "embedding"
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}
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```
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"embedding": [
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0.0023064255,
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-0.009327292,
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.... (1536 floats total for ada-002)
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....
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-0.0028842222,
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],
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"index": 0,
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"object": "embedding"
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}
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```
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</div>
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The embedding feature in Nitro demonstrates a high level of compatibility with OpenAI, simplifying the transition between using OpenAI and local AI models. For more detailed information and advanced use cases, refer to the comprehensive [API Reference](https://nitro.jan.ai/api-reference).
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The embedding feature in Nitro demonstrates a high level of compatibility with OpenAI. For more detailed information and advanced use cases, refer to the comprehensive [API Reference](https://nitro.jan.ai/api-reference).

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