chore: typo fix (#1465)

* 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 Signed-off-by: nkh0472 <67589323+nkh0472@users.noreply.github.com> * chore: typo fix Signed-off-by: nkh0472 <67589323+nkh0472@users.noreply.github.com> --------- Signed-off-by: nkh0472 <67589323+nkh0472@users.noreply.github.com>
2025-12-08 20:58:11 +00:00 · 2025-04-28 14:52:09 +08:00
parent 3afbe6c969
commit a097ccd8d5
14 changed files with 19 additions and 19 deletions
--- a/docs/examples/backend_xml_rag.ipynb
+++ b/docs/examples/backend_xml_rag.ipynb
@@ -569,7 +569,7 @@
    "The `DoclingDocument` format of the converted patents has a rich hierarchical structure, inherited from the original XML document and preserved by the Docling custom backend.\n",
    "In this notebook, we will leverage:\n",
    "- The `SimpleDirectoryReader` pattern to iterate over the exported XML files created in section [Fetch the data](#fetch-the-data).\n",
-    "- The LlamaIndex extensions, `DoclingReader` and `DoclingNodeParser`, to ingest the patent chunks into a Milvus vectore store.\n",
+    "- The LlamaIndex extensions, `DoclingReader` and `DoclingNodeParser`, to ingest the patent chunks into a Milvus vector store.\n",
    "- The `HierarchicalChunker` implementation, which applies a document-based hierarchical chunking, to leverage the patent structures like sections and paragraphs within sections.\n",
    "\n",
    "Refer to other possible implementations and usage patterns in the [Chunking](../../concepts/chunking/) documentation and the [RAG with LlamaIndex](../rag_llamaindex/) notebook."
--- a/docs/examples/hybrid_chunking.ipynb
+++ b/docs/examples/hybrid_chunking.ipynb
@@ -206,7 +206,7 @@
   "source": [
    "Points to notice looking at the output chunks below:\n",
    "- Where possible, we fit the limit of 64 tokens for the metadata-enriched serialization form (see chunk 2)\n",
-    "- Where neeeded, we stop before the limit, e.g. see cases of 63 as it would otherwise run into a comma (see chunk 6)\n",
+    "- Where needed, we stop before the limit, e.g. see cases of 63 as it would otherwise run into a comma (see chunk 6)\n",
    "- Where possible, we merge undersized peer chunks (see chunk 0)\n",
    "- \"Tail\" chunks trailing right after merges may still be undersized (see chunk 8)"
   ]
--- a/docs/examples/pictures_description.ipynb
+++ b/docs/examples/pictures_description.ipynb
@@ -279,7 +279,7 @@
    "## Use other vision models\n",
    "\n",
    "The examples above can also be reproduced using other vision model.\n",
-    "The Docling options `PictureDescriptionVlmOptions` allows to speficy your favorite vision model from the Hugging Face Hub."
+    "The Docling options `PictureDescriptionVlmOptions` allows to specify your favorite vision model from the Hugging Face Hub."
   ]
  },
  {
--- a/docs/examples/rag_milvus.ipynb
+++ b/docs/examples/rag_milvus.ipynb
@@ -32,7 +32,7 @@
    "\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.\n"
+    "2. **Run this notebook on Google Colab.** Converting all documents in the notebook takes ~8 minutes on a Google Colab T4 GPU.\n"
   ]
  },
  {
--- a/docs/examples/rag_weaviate.ipynb
+++ b/docs/examples/rag_weaviate.ipynb
@@ -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)."
   ]
  }
 ],
--- a/docs/faq/index.md
+++ b/docs/faq/index.md
@@ -137,7 +137,7 @@ This is a collection of FAQ collected from the user questions on <https://github
    ### Some images are missing from MS Word and Powerpoint

    The image processing library used by Docling is able to handle embedded WMF images only on Windows platform.
-    If you are on other operaring systems, these images will be ignored.
+    If you are on other operating systems, these images will be ignored.


 ??? question "`HybridChunker` triggers warning: 'Token indices sequence length is longer than the specified maximum sequence length for this model'"
--- a/docs/v2.md
+++ b/docs/v2.md
@@ -37,7 +37,7 @@ docling ./input/dir --output ./scratch --abort-on-error

 ### Setting up a `DocumentConverter`

-To accomodate many input formats, we changed the way you need to set up your `DocumentConverter` object.
+To accommodate many input formats, we changed the way you need to set up your `DocumentConverter` object.
 You can now define a list of allowed formats on the `DocumentConverter` initialization, and specify custom options
 per-format if desired. By default, all supported formats are allowed. If you don't provide `format_options`, defaults
 will be used for all `allowed_formats`.
@@ -151,7 +151,7 @@ conv_result: ConversionResult = doc_converter.convert("https://arxiv.org/pdf/240
 ## Inspect the converted document:
 conv_result.document.print_element_tree()

-## Iterate the elements in reading order, including hierachy level:
+## Iterate the elements in reading order, including hierarchy level:
 for item, level in conv_result.document.iterate_items():
    if isinstance(item, TextItem):
        print(item.text)