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docs: add Getting Started page (#2113)
* docs: add Getting Started page Signed-off-by: Panos Vagenas <pva@zurich.ibm.com> * refactor usage Signed-off-by: Panos Vagenas <pva@zurich.ibm.com> * minor renaming Signed-off-by: Panos Vagenas <pva@zurich.ibm.com> --------- Signed-off-by: Panos Vagenas <pva@zurich.ibm.com>
This commit is contained in:
Panos Vagenas
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17
docs/getting_started/index.md
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17
docs/getting_started/index.md
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@@ -0,0 +1,17 @@
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🐣 Ready to kick off your Docling journey? Let's dive right into it!
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<div class="grid">
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<a href="../installation/" class="card"><b>⬇️ Installation</b><br />Quickly install Docling in your environment</a>
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<a href="../usage/" class="card"><b>▶️ Usage</b><br />Get a jumpstart on basic Docling usage</a>
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<a href="../concepts/" class="card"><b>🧩 Concepts</b><br />Learn Docling fundamentals and get a glimpse under the hood</a>
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<a href="../examples/" class="card"><b>🧑🏽🍳 Examples</b><br />Try out recipes for various use cases, including conversion, RAG, and more</a>
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<a href="../integrations/" class="card"><b>🤖 Integrations</b><br />Check out integrations with popular AI tools and frameworks</a>
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<a href="../reference/document_converter/" class="card"><b>📖 Reference</b><br />See more API details</a>
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</div>
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## What's next
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🚀 The journey has just begun! Join us and become a part of the growing Docling community!
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- <a href="https://github.com/docling-project/docling">:fontawesome-brands-github: GitHub</a>
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- <a href="https://linkedin.com/company/docling/">:fontawesome-brands-linkedin: LinkedIn</a>
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9
docs/index.md
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9
docs/index.md
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@@ -40,16 +40,11 @@ Docling simplifies document processing, parsing diverse formats — including ad
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## Get started
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<div class="grid">
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<a href="concepts/" class="card"><b>Concepts</b><br />Learn Docling fundamentals</a>
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<a href="examples/" class="card"><b>Examples</b><br />Try out recipes for various use cases, including conversion, RAG, and more</a>
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<a href="integrations/" class="card"><b>Integrations</b><br />Check out integrations with popular frameworks and tools</a>
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<a href="reference/document_converter/" class="card"><b>Reference</b><br />See more API details</a>
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</div>
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Check out our [getting started](./getting_started/index.md) page to get the ball rolling!
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## Live assistant
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Do you want to leverage the power of AI and get a live support on Docling?
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Do you want to leverage the power of AI and get live support on Docling?
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Try out the [Chat with Dosu](https://app.dosu.dev/097760a8-135e-4789-8234-90c8837d7f1c/ask?utm_source=github) functionalities provided by our friends at [Dosu](https://dosu.dev/).
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[](https://app.dosu.dev/097760a8-135e-4789-8234-90c8837d7f1c/ask?utm_source=github)
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192
docs/usage/advanced_options.md
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192
docs/usage/advanced_options.md
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## Model prefetching and offline usage
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By default, models are downloaded automatically upon first usage. If you would prefer
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to explicitly prefetch them for offline use (e.g. in air-gapped environments) you can do
|
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that as follows:
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**Step 1: Prefetch the models**
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Use the `docling-tools models download` utility:
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```sh
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$ docling-tools models download
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Downloading layout model...
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Downloading tableformer model...
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Downloading picture classifier model...
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Downloading code formula model...
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Downloading easyocr models...
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Models downloaded into $HOME/.cache/docling/models.
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```
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Alternatively, models can be programmatically downloaded using `docling.utils.model_downloader.download_models()`.
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**Step 2: Use the prefetched models**
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```python
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from docling.datamodel.base_models import InputFormat
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from docling.datamodel.pipeline_options import EasyOcrOptions, PdfPipelineOptions
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from docling.document_converter import DocumentConverter, PdfFormatOption
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artifacts_path = "/local/path/to/models"
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pipeline_options = PdfPipelineOptions(artifacts_path=artifacts_path)
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doc_converter = DocumentConverter(
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format_options={
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InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
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}
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)
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```
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Or using the CLI:
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```sh
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docling --artifacts-path="/local/path/to/models" FILE
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```
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Or using the `DOCLING_ARTIFACTS_PATH` environment variable:
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```sh
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export DOCLING_ARTIFACTS_PATH="/local/path/to/models"
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python my_docling_script.py
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```
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## Using remote services
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|
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The main purpose of Docling is to run local models which are not sharing any user data with remote services.
|
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Anyhow, there are valid use cases for processing part of the pipeline using remote services, for example invoking OCR engines from cloud vendors or the usage of hosted LLMs.
|
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|
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In Docling we decided to allow such models, but we require the user to explicitly opt-in in communicating with external services.
|
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|
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```py
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from docling.datamodel.base_models import InputFormat
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from docling.datamodel.pipeline_options import PdfPipelineOptions
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from docling.document_converter import DocumentConverter, PdfFormatOption
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pipeline_options = PdfPipelineOptions(enable_remote_services=True)
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doc_converter = DocumentConverter(
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format_options={
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InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
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}
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)
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```
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|
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When the value `enable_remote_services=True` is not set, the system will raise an exception `OperationNotAllowed()`.
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|
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_Note: This option is only related to the system sending user data to remote services. Control of pulling data (e.g. model weights) follows the logic described in [Model prefetching and offline usage](#model-prefetching-and-offline-usage)._
|
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|
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### List of remote model services
|
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|
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The options in this list require the explicit `enable_remote_services=True` when processing the documents.
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|
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- `PictureDescriptionApiOptions`: Using vision models via API calls.
|
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|
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|
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## Adjust pipeline features
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|
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The example file [custom_convert.py](../examples/custom_convert.py) contains multiple ways
|
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one can adjust the conversion pipeline and features.
|
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|
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### Control PDF table extraction options
|
||||
|
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You can control if table structure recognition should map the recognized structure back to PDF cells (default) or use text cells from the structure prediction itself.
|
||||
This can improve output quality if you find that multiple columns in extracted tables are erroneously merged into one.
|
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|
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|
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```python
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from docling.datamodel.base_models import InputFormat
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from docling.document_converter import DocumentConverter, PdfFormatOption
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from docling.datamodel.pipeline_options import PdfPipelineOptions
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pipeline_options = PdfPipelineOptions(do_table_structure=True)
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pipeline_options.table_structure_options.do_cell_matching = False # uses text cells predicted from table structure model
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doc_converter = DocumentConverter(
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format_options={
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InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
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}
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)
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```
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Since docling 1.16.0: You can control which TableFormer mode you want to use. Choose between `TableFormerMode.FAST` (faster but less accurate) and `TableFormerMode.ACCURATE` (default) to receive better quality with difficult table structures.
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|
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```python
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from docling.datamodel.base_models import InputFormat
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from docling.document_converter import DocumentConverter, PdfFormatOption
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from docling.datamodel.pipeline_options import PdfPipelineOptions, TableFormerMode
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pipeline_options = PdfPipelineOptions(do_table_structure=True)
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pipeline_options.table_structure_options.mode = TableFormerMode.ACCURATE # use more accurate TableFormer model
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doc_converter = DocumentConverter(
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format_options={
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InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
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}
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)
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```
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|
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|
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## Impose limits on the document size
|
||||
|
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You can limit the file size and number of pages which should be allowed to process per document:
|
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|
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```python
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from pathlib import Path
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from docling.document_converter import DocumentConverter
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|
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source = "https://arxiv.org/pdf/2408.09869"
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converter = DocumentConverter()
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result = converter.convert(source, max_num_pages=100, max_file_size=20971520)
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```
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|
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## Convert from binary PDF streams
|
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|
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You can convert PDFs from a binary stream instead of from the filesystem as follows:
|
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|
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```python
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from io import BytesIO
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from docling.datamodel.base_models import DocumentStream
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from docling.document_converter import DocumentConverter
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buf = BytesIO(your_binary_stream)
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source = DocumentStream(name="my_doc.pdf", stream=buf)
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converter = DocumentConverter()
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result = converter.convert(source)
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```
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## Limit resource usage
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You can limit the CPU threads used by Docling by setting the environment variable `OMP_NUM_THREADS` accordingly. The default setting is using 4 CPU threads.
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## Use specific backend converters
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!!! note
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This section discusses directly invoking a [backend](../concepts/architecture.md),
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i.e. using a low-level API. This should only be done when necessary. For most cases,
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using a `DocumentConverter` (high-level API) as discussed in the sections above
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should suffice — and is the recommended way.
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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)).
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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.
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Alternatively, you can also use the specific backend that matches your document content. For instance, you can use `HTMLDocumentBackend` for HTML pages:
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```python
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import urllib.request
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from io import BytesIO
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from docling.backend.html_backend import HTMLDocumentBackend
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from docling.datamodel.base_models import InputFormat
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from docling.datamodel.document import InputDocument
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url = "https://en.wikipedia.org/wiki/Duck"
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text = urllib.request.urlopen(url).read()
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in_doc = InputDocument(
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path_or_stream=BytesIO(text),
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format=InputFormat.HTML,
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backend=HTMLDocumentBackend,
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filename="duck.html",
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)
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backend = HTMLDocumentBackend(in_doc=in_doc, path_or_stream=BytesIO(text))
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dl_doc = backend.convert()
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print(dl_doc.export_to_markdown())
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```
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265
docs/usage/index.md
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265
docs/usage/index.md
vendored
@@ -1,262 +1,45 @@
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## Conversion
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## Basic usage
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### Convert a single document
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### Python
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To convert individual PDF documents, use `convert()`, for example:
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In Docling, working with documents is as simple as:
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1. converting your source file to a Docling document
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2. using that Docling document for your workflow
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For example, the snippet below shows conversion with export to Markdown:
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```python
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from docling.document_converter import DocumentConverter
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source = "https://arxiv.org/pdf/2408.09869" # PDF path or URL
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source = "https://arxiv.org/pdf/2408.09869" # file path or URL
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converter = DocumentConverter()
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result = converter.convert(source)
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print(result.document.export_to_markdown()) # output: "### Docling Technical Report[...]"
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doc = converter.convert(source).document
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print(doc.export_to_markdown()) # output: "### Docling Technical Report[...]"
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```
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Docling supports a wide array of [file formats](./supported_formats.md) and, as outlined in the
|
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[architecture](../concepts/architecture.md) guide, provides a versatile document model along with a full suite of
|
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supported operations.
|
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|
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### CLI
|
||||
|
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You can also use Docling directly from your command line to convert individual files —be it local or by URL— or whole directories.
|
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You can additionally use Docling directly from your terminal, for instance:
|
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|
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```console
|
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docling https://arxiv.org/pdf/2206.01062
|
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```
|
||||
You can also use 🥚[SmolDocling](https://huggingface.co/ds4sd/SmolDocling-256M-preview) and other VLMs via Docling CLI:
|
||||
|
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The CLI provides various options, such as 🥚[SmolDocling](https://huggingface.co/ds4sd/SmolDocling-256M-preview) (incl. MLX acceleration) & other VLMs:
|
||||
```bash
|
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docling --pipeline vlm --vlm-model smoldocling https://arxiv.org/pdf/2206.01062
|
||||
```
|
||||
This will use MLX acceleration on supported Apple Silicon hardware.
|
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|
||||
For all available options, run `docling --help` or check the [CLI reference](../reference/cli.md).
|
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|
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To see all available options (export formats etc.) run `docling --help`. More details in the [CLI reference page](../reference/cli.md).
|
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## What's next
|
||||
|
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### Advanced options
|
||||
|
||||
#### Model prefetching and offline usage
|
||||
|
||||
By default, models are downloaded automatically upon first usage. If you would prefer
|
||||
to explicitly prefetch them for offline use (e.g. in air-gapped environments) you can do
|
||||
that as follows:
|
||||
|
||||
**Step 1: Prefetch the models**
|
||||
|
||||
Use the `docling-tools models download` utility:
|
||||
|
||||
```sh
|
||||
$ docling-tools models download
|
||||
Downloading layout model...
|
||||
Downloading tableformer model...
|
||||
Downloading picture classifier model...
|
||||
Downloading code formula model...
|
||||
Downloading easyocr models...
|
||||
Models downloaded into $HOME/.cache/docling/models.
|
||||
```
|
||||
|
||||
Alternatively, models can be programmatically downloaded using `docling.utils.model_downloader.download_models()`.
|
||||
|
||||
**Step 2: Use the prefetched models**
|
||||
|
||||
```python
|
||||
from docling.datamodel.base_models import InputFormat
|
||||
from docling.datamodel.pipeline_options import EasyOcrOptions, PdfPipelineOptions
|
||||
from docling.document_converter import DocumentConverter, PdfFormatOption
|
||||
|
||||
artifacts_path = "/local/path/to/models"
|
||||
|
||||
pipeline_options = PdfPipelineOptions(artifacts_path=artifacts_path)
|
||||
doc_converter = DocumentConverter(
|
||||
format_options={
|
||||
InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
|
||||
}
|
||||
)
|
||||
```
|
||||
|
||||
Or using the CLI:
|
||||
|
||||
```sh
|
||||
docling --artifacts-path="/local/path/to/models" FILE
|
||||
```
|
||||
|
||||
Or using the `DOCLING_ARTIFACTS_PATH` environment variable:
|
||||
|
||||
```sh
|
||||
export DOCLING_ARTIFACTS_PATH="/local/path/to/models"
|
||||
python my_docling_script.py
|
||||
```
|
||||
|
||||
#### Using remote services
|
||||
|
||||
The main purpose of Docling is to run local models which are not sharing any user data with remote services.
|
||||
Anyhow, there are valid use cases for processing part of the pipeline using remote services, for example invoking OCR engines from cloud vendors or the usage of hosted LLMs.
|
||||
|
||||
In Docling we decided to allow such models, but we require the user to explicitly opt-in in communicating with external services.
|
||||
|
||||
```py
|
||||
from docling.datamodel.base_models import InputFormat
|
||||
from docling.datamodel.pipeline_options import PdfPipelineOptions
|
||||
from docling.document_converter import DocumentConverter, PdfFormatOption
|
||||
|
||||
pipeline_options = PdfPipelineOptions(enable_remote_services=True)
|
||||
doc_converter = DocumentConverter(
|
||||
format_options={
|
||||
InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
|
||||
}
|
||||
)
|
||||
```
|
||||
|
||||
When the value `enable_remote_services=True` is not set, the system will raise an exception `OperationNotAllowed()`.
|
||||
|
||||
_Note: This option is only related to the system sending user data to remote services. Control of pulling data (e.g. model weights) follows the logic described in [Model prefetching and offline usage](#model-prefetching-and-offline-usage)._
|
||||
|
||||
##### List of remote model services
|
||||
|
||||
The options in this list require the explicit `enable_remote_services=True` when processing the documents.
|
||||
|
||||
- `PictureDescriptionApiOptions`: Using vision models via API calls.
|
||||
|
||||
|
||||
#### Adjust pipeline features
|
||||
|
||||
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
|
||||
|
||||
You can control if table structure recognition should map the recognized structure back to PDF cells (default) or use text cells from the structure prediction itself.
|
||||
This can improve output quality if you find that multiple columns in extracted tables are erroneously merged into one.
|
||||
|
||||
|
||||
```python
|
||||
from docling.datamodel.base_models import InputFormat
|
||||
from docling.document_converter import DocumentConverter, PdfFormatOption
|
||||
from docling.datamodel.pipeline_options import PdfPipelineOptions
|
||||
|
||||
pipeline_options = PdfPipelineOptions(do_table_structure=True)
|
||||
pipeline_options.table_structure_options.do_cell_matching = False # uses text cells predicted from table structure model
|
||||
|
||||
doc_converter = DocumentConverter(
|
||||
format_options={
|
||||
InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
|
||||
}
|
||||
)
|
||||
```
|
||||
|
||||
Since docling 1.16.0: You can control which TableFormer mode you want to use. Choose between `TableFormerMode.FAST` (faster but less accurate) and `TableFormerMode.ACCURATE` (default) to receive better quality with difficult table structures.
|
||||
|
||||
```python
|
||||
from docling.datamodel.base_models import InputFormat
|
||||
from docling.document_converter import DocumentConverter, PdfFormatOption
|
||||
from docling.datamodel.pipeline_options import PdfPipelineOptions, TableFormerMode
|
||||
|
||||
pipeline_options = PdfPipelineOptions(do_table_structure=True)
|
||||
pipeline_options.table_structure_options.mode = TableFormerMode.ACCURATE # use more accurate TableFormer model
|
||||
|
||||
doc_converter = DocumentConverter(
|
||||
format_options={
|
||||
InputFormat.PDF: PdfFormatOption(pipeline_options=pipeline_options)
|
||||
}
|
||||
)
|
||||
```
|
||||
|
||||
|
||||
#### Impose limits on the document size
|
||||
|
||||
You can limit the file size and number of pages which should be allowed to process per document:
|
||||
|
||||
```python
|
||||
from pathlib import Path
|
||||
from docling.document_converter import DocumentConverter
|
||||
|
||||
source = "https://arxiv.org/pdf/2408.09869"
|
||||
converter = DocumentConverter()
|
||||
result = converter.convert(source, max_num_pages=100, max_file_size=20971520)
|
||||
```
|
||||
|
||||
#### Convert from binary PDF streams
|
||||
|
||||
You can convert PDFs from a binary stream instead of from the filesystem as follows:
|
||||
|
||||
```python
|
||||
from io import BytesIO
|
||||
from docling.datamodel.base_models import DocumentStream
|
||||
from docling.document_converter import DocumentConverter
|
||||
|
||||
buf = BytesIO(your_binary_stream)
|
||||
source = DocumentStream(name="my_doc.pdf", stream=buf)
|
||||
converter = DocumentConverter()
|
||||
result = converter.convert(source)
|
||||
```
|
||||
|
||||
#### Limit resource usage
|
||||
|
||||
You can limit the CPU threads used by Docling by setting the environment variable `OMP_NUM_THREADS` accordingly. The default setting is using 4 CPU threads.
|
||||
|
||||
|
||||
#### Use specific backend converters
|
||||
|
||||
!!! note
|
||||
|
||||
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.
|
||||
Alternatively, you can also use the specific backend that matches your document content. For instance, you can use `HTMLDocumentBackend` for HTML pages:
|
||||
|
||||
```python
|
||||
import urllib.request
|
||||
from io import BytesIO
|
||||
from docling.backend.html_backend import HTMLDocumentBackend
|
||||
from docling.datamodel.base_models import InputFormat
|
||||
from docling.datamodel.document import InputDocument
|
||||
|
||||
url = "https://en.wikipedia.org/wiki/Duck"
|
||||
text = urllib.request.urlopen(url).read()
|
||||
in_doc = InputDocument(
|
||||
path_or_stream=BytesIO(text),
|
||||
format=InputFormat.HTML,
|
||||
backend=HTMLDocumentBackend,
|
||||
filename="duck.html",
|
||||
)
|
||||
backend = HTMLDocumentBackend(in_doc=in_doc, path_or_stream=BytesIO(text))
|
||||
dl_doc = backend.convert()
|
||||
print(dl_doc.export_to_markdown())
|
||||
```
|
||||
|
||||
## Chunking
|
||||
|
||||
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)):
|
||||
|
||||
```python
|
||||
from docling.document_converter import DocumentConverter
|
||||
from docling.chunking import HybridChunker
|
||||
|
||||
conv_res = DocumentConverter().convert("https://arxiv.org/pdf/2206.01062")
|
||||
doc = conv_res.document
|
||||
|
||||
chunker = HybridChunker(tokenizer="BAAI/bge-small-en-v1.5") # set tokenizer as needed
|
||||
chunk_iter = chunker.chunk(doc)
|
||||
```
|
||||
|
||||
An example chunk would look like this:
|
||||
|
||||
```python
|
||||
print(list(chunk_iter)[11])
|
||||
# {
|
||||
# "text": "In this paper, we present the DocLayNet dataset. [...]",
|
||||
# "meta": {
|
||||
# "doc_items": [{
|
||||
# "self_ref": "#/texts/28",
|
||||
# "label": "text",
|
||||
# "prov": [{
|
||||
# "page_no": 2,
|
||||
# "bbox": {"l": 53.29, "t": 287.14, "r": 295.56, "b": 212.37, ...},
|
||||
# }], ...,
|
||||
# }, ...],
|
||||
# "headings": ["1 INTRODUCTION"],
|
||||
# }
|
||||
# }
|
||||
```
|
||||
Check out the Usage subpages (navigation menu on the left) as well as our [featured examples](../examples/index.md) for
|
||||
additional usage workflows, including conversion customization, RAG, framework integrations, chunking, serialization,
|
||||
enrichments, and much more!
|
||||
|
||||
13
mkdocs.yml
13
mkdocs.yml
@@ -54,10 +54,13 @@ theme:
|
||||
nav:
|
||||
- Home:
|
||||
- "Docling": index.md
|
||||
- Getting started:
|
||||
- Getting started: getting_started/index.md
|
||||
- Installation:
|
||||
- Installation: installation/index.md
|
||||
- Usage:
|
||||
- Usage: usage/index.md
|
||||
- Advanced options: usage/advanced_options.md
|
||||
- Supported formats: usage/supported_formats.md
|
||||
- Enrichment features: usage/enrichments.md
|
||||
- Vision models: usage/vision_models.md
|
||||
@@ -156,6 +159,9 @@ markdown_extensions:
|
||||
slugify: !!python/object/apply:pymdownx.slugs.slugify
|
||||
kwds:
|
||||
case: lower
|
||||
- pymdownx.emoji:
|
||||
emoji_index: !!python/name:material.extensions.emoji.twemoji
|
||||
emoji_generator: !!python/name:material.extensions.emoji.to_svg
|
||||
- admonition
|
||||
- pymdownx.details
|
||||
- attr_list
|
||||
@@ -175,3 +181,10 @@ plugins:
|
||||
|
||||
extra_css:
|
||||
- stylesheets/extra.css
|
||||
|
||||
extra:
|
||||
social:
|
||||
- icon: fontawesome/brands/github
|
||||
link: https://github.com/docling-project/docling
|
||||
- icon: fontawesome/brands/linkedin
|
||||
link: https://linkedin.com/company/docling/
|
||||
|
||||
Reference in New Issue
Block a user