add Docling converter (#292)

* add Docling converter

Signed-off-by: Panos Vagenas <[email protected]>

* Update and rename docling-converter.md to docling.md

---------

Signed-off-by: Panos Vagenas <[email protected]>
Co-authored-by: Bilge Yücel <[email protected]>

integrations/docling.md

@@ -0,0 +1,94 @@
+---
+layout: integration
+name: Docling
+description: Use Docling to locally parse and chunk PDF, DOCX, and other document types in Haystack
+authors:
+    - name: DS4SD
+      socials:
+        github: DS4SD
+pypi: https://pypi.org/project/docling-haystack
+repo: https://github.com/DS4SD/docling-haystack
+type: Data Ingestion
+report_issue: https://github.com/DS4SD/docling/issues
+logo: /logos/docling.png
+version: Haystack 2.0
+toc: true
+---
+### **Table of Contents**
+- [Overview](#overview)
+- [Installation](#installation)
+- [Usage](#usage)
+- [License](#license)
+
+## Overview
+
+[Docling](https://github.com/DS4SD/docling) locally parses PDF, DOCX, HTML, and other
+document formats into a rich standardized representation (incl. layout, tables etc.),
+which it can then export to Markdown, JSON, and others.
+
+Check out the [Docling docs](https://ds4sd.github.io/docling/) for more details.
+
+This integration introduces Docling support, enabling Haystack users to:
+- use various document types in LLM applications with ease and speed, and
+- leverage Docling's rich format for advanced, document-native grounding.
+
+## Installation
+
+```bash
+pip install docling-haystack
+```
+
+## Usage
+
+### Components
+
+This integration introduces `DoclingConverter`, a component which reads document
+file paths (local or URL) and outputs Haystack `Document` objects.
+
+`DoclingConverter` supports two different export modes, see `export_type` initialization
+argument further below.
+
+### Use Docling Converter
+
+#### Docling Converter Initialization
+
+`DoclingConverter` creation can be parametrized via the following `__init__()`
+arguments, most of which refer to the initialization and usage of the underlying Docling
+[`DocumentConverter`](https://ds4sd.github.io/docling/usage/) and
+[chunker](https://ds4sd.github.io/docling/concepts/chunking/) instances:
+
+- `converter`: The Docling `DocumentConverter` to use; if not set, a system default is
+  used.
+- `convert_kwargs`: Any parameters to pass to Docling conversion; if not set, a system
+  default is used.
+- `export_type`: The export mode to use: `ExportType.DOC_CHUNKS` (default) chunks each
+  input document (see `chunker`) and captures each individual chunk as a separate
+  Haystack `Document`, while `ExportType.MARKDOWN` captures each input document as a
+  separate Haystack `Document` (in which case splitting is likely required downstream).
+- `md_export_kwargs`: Any parameters to pass to Markdown export (in case of
+  `ExportType.MARKDOWN`).
+- `chunker`: The Docling chunker instance to use; if not set, a system default is used
+  (in case of `ExportType.DOC_CHUNKS`).
+- `meta_extractor`: The extractor instance to use for populating the output document
+  metadata; if not set, a system default is used.
+
+#### Standalone
+
+```python
+from docling_haystack.converter import DoclingConverter
+
+converter = DoclingConverter()
+documents = converter.run(paths=["https://arxiv.org/pdf/2408.09869"])["documents"]
+
+print(repr(documents[2].content))
+# -> Abstract\nThis technical report introduces Docling [...]
+```
+
+#### In a Pipeline
+
+Check out [this notebook](https://ds4sd.github.io/docling/examples/rag_haystack/)
+illustrating usage in a complete example with indexing and RAG pipelines.
+
+### License
+
+MIT License.