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minor reorg of top-level docs (#1098)

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* minor reorg of top-level docs

Signed-off-by: Panos Vagenas <pva@zurich.ibm.com>

* fix typo [no ci]

Signed-off-by: Panos Vagenas <35837085+vagenas@users.noreply.github.com>

---------

Signed-off-by: Panos Vagenas <pva@zurich.ibm.com>
Signed-off-by: Panos Vagenas <35837085+vagenas@users.noreply.github.com>
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2
README.md
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@ -123,6 +123,6 @@ For individual model usage, please refer to the model licenses found in the orig
Docling has been brought to you by IBM.
[supported_formats]: https://ds4sd.github.io/docling/supported_formats/
[supported_formats]: https://ds4sd.github.io/docling/usage/supported_formats/
[docling_document]: https://ds4sd.github.io/docling/concepts/docling_document/
[integrations]: https://ds4sd.github.io/docling/integrations/

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docs/examples/develop_formula_understanding.py
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@ -1,6 +1,6 @@
# WARNING
# This example demonstrates only how to develop a new enrichment model.
# It does not run thr actual formula understanding model.
# It does not run the actual formula understanding model.
import logging
from pathlib import Path

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docs/examples/develop_picture_enrichment.py
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@ -1,6 +1,6 @@
# WARNING
# This example demonstrates only how to develop a new enrichment model.
# It does not run thr actual picture classifier model.
# It does not run the actual picture classifier model.
import logging
from pathlib import Path

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docs/faq.md → docs/faq/index.md
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@ -149,7 +149,7 @@ This is a collection of FAQ collected from the user questions on <https://github
**Details**:
Using the [`HybridChunker`](./concepts/chunking.md#hybrid-chunker) often triggers a warning like this:
Using the [`HybridChunker`](../concepts/chunking.md#hybrid-chunker) often triggers a warning like this:
> Token indices sequence length is longer than the specified maximum sequence length for this model (531 > 512). Running this sequence through the model will result in indexing errors
This is a warning that is emitted by transformers, saying that actually *running this sequence through the model* will result in indexing errors, i.e. the problematic case is only if one indeed passes the particular sequence through the (embedding) model.

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docs/index.md
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@ -47,6 +47,6 @@ Docling simplifies document processing, parsing diverse formats — including ad
Docling has been brought to you by IBM.
[supported_formats]: ./supported_formats.md
[supported_formats]: ./usage/supported_formats.md
[docling_document]: ./concepts/docling_document.md
[integrations]: ./integrations/index.md

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docs/installation.md → docs/installation/index.md
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docs/enrichments.md → docs/usage/enrichments.md
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@ -6,10 +6,10 @@ The following table provides an overview of the default enrichment models availa
| Feature | Parameter | Processed item | Description |
| ------- | --------- | ---------------| ----------- |
| Code understanding | `do_code_enrichment` | `CodeItem` | See [docs below](#code-understanding). |
| Formula understanding | `do_formula_enrichment` | `TextItem` with label `FORMULA` | See [docs below](#formula-understanding). |
| Picrure classification | `do_picture_classification` | `PictureItem` | See [docs below](#picture-classification). |
| Picture description | `do_picture_description` | `PictureItem` | See [docs below](#picture-description). |
| Code understanding | `do_code_enrichment` | `CodeItem` | See [docs below](#code-understanding). |
| Formula understanding | `do_formula_enrichment` | `TextItem` with label `FORMULA` | See [docs below](#formula-understanding). |
| Picrure classification | `do_picture_classification` | `PictureItem` | See [docs below](#picture-classification). |
| Picture description | `do_picture_description` | `PictureItem` | See [docs below](#picture-description). |
## Enrichments details
@ -204,7 +204,7 @@ pipeline_options.picture_description_options = PictureDescriptionApiOptions(
End-to-end code snippets for cloud providers are available in the examples section:
- [IBM watsonx.ai](./examples/pictures_description_api.py)
- [IBM watsonx.ai](../examples/pictures_description_api.py)
## Develop new enrichment models
@ -212,5 +212,5 @@ End-to-end code snippets for cloud providers are available in the examples secti
Beside looking at the implementation of all the models listed above, the Docling documentation has a few examples
dedicated to the implementation of enrichment models.
- [Develop picture enrichment](./examples/develop_picture_enrichment.py)
- [Develop formula enrichment](./examples/develop_formula_understanding.py)
- [Develop picture enrichment](../examples/develop_picture_enrichment.py)
- [Develop formula enrichment](../examples/develop_formula_understanding.py)

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docs/usage.md → docs/usage/index.md
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@ -22,7 +22,7 @@ A simple example would look like this:
docling https://arxiv.org/pdf/2206.01062
```
To see all available options (export formats etc.) run `docling --help`. More details in the [CLI reference page](./reference/cli.md).
To see all available options (export formats etc.) run `docling --help`. More details in the [CLI reference page](../reference/cli.md).
### Advanced options
@ -104,7 +104,7 @@ The options in this list require the explicit `enable_remote_services=True` when
#### Adjust pipeline features
The example file [custom_convert.py](./examples/custom_convert.py) contains multiple ways
The example file [custom_convert.py](../examples/custom_convert.py) contains multiple ways
one can adjust the conversion pipeline and features.
##### Control PDF table extraction options
@ -183,13 +183,13 @@ You can limit the CPU threads used by Docling by setting the environment variabl
!!! note
This section discusses directly invoking a [backend](./concepts/architecture.md),
This section discusses directly invoking a [backend](../concepts/architecture.md),
i.e. using a low-level API. This should only be done when necessary. For most cases,
using a `DocumentConverter` (high-level API) as discussed in the sections above
should suffice — and is the recommended way.
By default, Docling will try to identify the document format to apply the appropriate conversion backend (see the list of [supported formats](./supported_formats.md)).
You can restrict the `DocumentConverter` to a set of allowed document formats, as shown in the [Multi-format conversion](./examples/run_with_formats.py) example.
By default, Docling will try to identify the document format to apply the appropriate conversion backend (see the list of [supported formats](../supported_formats.md)).
You can restrict the `DocumentConverter` to a set of allowed document formats, as shown in the [Multi-format conversion](../examples/run_with_formats.py) example.
Alternatively, you can also use the specific backend that matches your document content. For instance, you can use `HTMLDocumentBackend` for HTML pages:
```python
@ -214,9 +214,9 @@ print(dl_doc.export_to_markdown())
## Chunking
You can chunk a Docling document using a [chunker](concepts/chunking.md), such as a
You can chunk a Docling document using a [chunker](../concepts/chunking.md), such as a
`HybridChunker`, as shown below (for more details check out
[this example](examples/hybrid_chunking.ipynb)):
[this example](../examples/hybrid_chunking.ipynb)):
```python
from docling.document_converter import DocumentConverter

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docs/supported_formats.md → docs/usage/supported_formats.md
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@ -1,6 +1,6 @@
Docling can parse various documents formats into a unified representation (Docling
Document), which it can export to different formats too — check out
[Architecture](./concepts/architecture.md) for more details.
[Architecture](../concepts/architecture.md) for more details.
Below you can find a listing of all supported input and output formats.
@ -22,7 +22,7 @@ Schema-specific support:
|--------|-------------|
| USPTO XML | XML format followed by [USPTO](https://www.uspto.gov/patents) patents |
| JATS XML | XML format followed by [JATS](https://jats.nlm.nih.gov/) articles |
| Docling JSON | JSON-serialized [Docling Document](./concepts/docling_document.md) |
| Docling JSON | JSON-serialized [Docling Document](../concepts/docling_document.md) |
## Supported output formats

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mkdocs.yml
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@ -54,12 +54,14 @@ theme:
nav:
- Home:
- "Docling": index.md
- Installation: installation.md
- Usage: usage.md
- Supported formats: supported_formats.md
- Enrichment features: enrichments.md
- FAQ: faq.md
- Docling v2: v2.md
- Installation:
- Installation: installation/index.md
- Usage:
- Usage: usage/index.md
- Supported formats: usage/supported_formats.md
- Enrichment features: usage/enrichments.md
- FAQ:
- FAQ: faq/index.md
- Concepts:
- Concepts: concepts/index.md
- Architecture: concepts/architecture.md
@ -73,11 +75,8 @@ nav:
- "Batch conversion": examples/batch_convert.py
- "Multi-format conversion": examples/run_with_formats.py
- "Figure export": examples/export_figures.py
- "Figure enrichment": examples/develop_picture_enrichment.py
- "Table export": examples/export_tables.py
- "Multimodal export": examples/export_multimodal.py
- "Annotate picture with local vlm": examples/pictures_description.ipynb
- "Annotate picture with remote vlm": examples/pictures_description_api.py
- "Force full page OCR": examples/full_page_ocr.py
- "Automatic OCR language detection with tesseract": examples/tesseract_lang_detection.py
- "RapidOCR with custom OCR models": examples/rapidocr_with_custom_models.py
@ -91,6 +90,12 @@ nav:
- examples/rag_haystack.ipynb
- examples/rag_langchain.ipynb
- examples/rag_llamaindex.ipynb
- 🖼️ Picture annotation:
- "Annotate picture with local VLM": examples/pictures_description.ipynb
- "Annotate picture with remote VLM": examples/pictures_description_api.py
- ✨ Enrichment development:
- "Figure enrichment": examples/develop_picture_enrichment.py
- "Formula enrichment": examples/develop_formula_understanding.py
- 🗂️ More examples:
- examples/rag_weaviate.ipynb
- RAG with Granite [↗]: https://github.com/ibm-granite-community/granite-snack-cookbook/blob/main/recipes/RAG/Granite_Docling_RAG.ipynb