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chore: typo fix (#1465)

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* typo fix

Signed-off-by: nkh0472 <67589323+nkh0472@users.noreply.github.com>

* chore: typo fix

Signed-off-by: nkh0472 <67589323+nkh0472@users.noreply.github.com>

* chore: typo fix

Signed-off-by: nkh0472 <67589323+nkh0472@users.noreply.github.com>

* chore: typo fix

Signed-off-by: nkh0472 <67589323+nkh0472@users.noreply.github.com>

* chore: typo fix

Signed-off-by: nkh0472 <67589323+nkh0472@users.noreply.github.com>

* chore: typo fix

Signed-off-by: nkh0472 <67589323+nkh0472@users.noreply.github.com>

* chore: typo fix

Signed-off-by: nkh0472 <67589323+nkh0472@users.noreply.github.com>

* chore: typo fix

Signed-off-by: nkh0472 <67589323+nkh0472@users.noreply.github.com>

* chore: typo fix

Signed-off-by: nkh0472 <67589323+nkh0472@users.noreply.github.com>

* chore: typo fix

Signed-off-by: nkh0472 <67589323+nkh0472@users.noreply.github.com>

* chore: typo fix

Signed-off-by: nkh0472 <67589323+nkh0472@users.noreply.github.com>

* chore: typo fix

Signed-off-by: nkh0472 <67589323+nkh0472@users.noreply.github.com>

* chore: typo fix

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* chore: typo fix

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---------

Signed-off-by: nkh0472 <67589323+nkh0472@users.noreply.github.com>
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nkh0472
2025-04-28 14:52:09 +08:00
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commit a097ccd8d5
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docs/examples/rag_weaviate.ipynb
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@@ -43,7 +43,7 @@
"\n",
"Note: For best results, please use **GPU acceleration** to run this notebook. Here are two options for running this notebook:\n",
"1. **Locally on a MacBook with an Apple Silicon chip.** Converting all documents in the notebook takes ~2 minutes on a MacBook M2 due to Docling's usage of MPS accelerators.\n",
"2. **Run this notebook on Google Colab.** Converting all documents in the notebook takes ~8 mintutes on a Google Colab T4 GPU."
"2. **Run this notebook on Google Colab.** Converting all documents in the notebook takes ~8 minutes on a Google Colab T4 GPU."
]
},
{
@@ -716,7 +716,7 @@
"id": "7tGz49nfUegG"
},
"source": [
"We can see that our RAG pipeline performs relatively well for simple queries, especially given the small size of the dataset. Scaling this method for converting a larger sample of PDFs would require more compute (GPUs) and a more advanced deployment of Weaviate (like Docker, Kubernetes, or Weaviate Cloud). For more information on available Weaviate configurations, check out the [documetation](https://weaviate.io/developers/weaviate/starter-guides/which-weaviate)."
"We can see that our RAG pipeline performs relatively well for simple queries, especially given the small size of the dataset. Scaling this method for converting a larger sample of PDFs would require more compute (GPUs) and a more advanced deployment of Weaviate (like Docker, Kubernetes, or Weaviate Cloud). For more information on available Weaviate configurations, check out the [documentation](https://weaviate.io/developers/weaviate/starter-guides/which-weaviate)."
]
}
],