3b8deae9ce
Here are targeted optimizations based on the profiling output and the code.
### Major bottlenecks & optimization strategies
#### 1. `_process_special_clusters`:
- **Main bottleneck:**
- The nested loop: for each special cluster, loop through all regular clusters and compute `.bbox.intersection_over_self(special.bbox)`.
- This is `O(N*M)` for N special and M regular clusters and is by far the slowest part.
- **Optimization:**
- **Pre-index regular clusters by bounding box for fast containment:**
- Build a simple R-tree-like spatial grid (using bins, or just a fast bbox filtering pass) to filter out regular clusters that are definitely non-overlapping before running the expensive geometric calculation.
- **If spatial index unavailable:** Pre-filter regulars to those whose bbox intersects the special’s bbox (quick min/max bbox checks), greatly reducing pairwise calculations.
#### 2. `_handle_cross_type_overlaps`:
- **Similar bottleneck:** Again, checking every regular cluster for every wrapper.
- We can apply the same bbox quick-check.
#### 3. Miscellaneous.
- **`_deduplicate_cells`/`_sort_cells` optimizations:** Minor, but batch sort/unique patterns can help.
- **Avoid recomputation:** Avoid recomputing thresholds/constants in hot loops.
Below is the optimized code addressing the biggest O(N*M) loop, using fast bbox intersection check for quick rejection before expensive calculation.
We achieve this purely with local logic in the function (no external indices needed), and respect your constraint not to introduce module-level classes.
Comments in the code indicate all changes.
**Summary of changes:**
- For both `_process_special_clusters` and `_handle_cross_type_overlaps`, we avoid unnecessary `.intersection_over_self` calculations by pre-filtering clusters based on simple bbox intersection conditions (`l < rx and r > lx and t < by and b > ty`).
- This turns expensive O(N*M) geometric checks into a two-stage filter, which is extremely fast for typical bbox distributions.
- All hot-spot loops now use local variables rather than repeated attribute lookups.
- No changes are made to APIs, outputs, or major logic branches; only faster candidate filtering is introduced.
This should reduce total runtime of `_process_special_clusters` and `_handle_cross_type_overlaps` by an order of magnitude on large documents.
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Docling
Docling simplifies document processing, parsing diverse formats — including advanced PDF understanding — and providing seamless integrations with the gen AI ecosystem.
Features
- 🗂️ Parsing of multiple document formats incl. PDF, DOCX, PPTX, XLSX, HTML, WAV, MP3, images (PNG, TIFF, JPEG, ...), and more
- 📑 Advanced PDF understanding incl. page layout, reading order, table structure, code, formulas, image classification, and more
- 🧬 Unified, expressive DoclingDocument representation format
- ↪️ Various export formats and options, including Markdown, HTML, DocTags and lossless JSON
- 🔒 Local execution capabilities for sensitive data and air-gapped environments
- 🤖 Plug-and-play integrations incl. LangChain, LlamaIndex, Crew AI & Haystack for agentic AI
- 🔍 Extensive OCR support for scanned PDFs and images
- 👓 Support of several Visual Language Models (SmolDocling)
- 🎙️ Support for Audio with Automatic Speech Recognition (ASR) models
- 💻 Simple and convenient CLI
Coming soon
- 📝 Metadata extraction, including title, authors, references & language
- 📝 Chart understanding (Barchart, Piechart, LinePlot, etc)
- 📝 Complex chemistry understanding (Molecular structures)
Installation
To use Docling, simply install docling from your package manager, e.g. pip:
pip install docling
Works on macOS, Linux and Windows environments. Both x86_64 and arm64 architectures.
More detailed installation instructions are available in the docs.
Getting started
To convert individual documents with python, use convert(), for example:
from docling.document_converter import DocumentConverter
source = "https://arxiv.org/pdf/2408.09869" # document per local path or URL
converter = DocumentConverter()
result = converter.convert(source)
print(result.document.export_to_markdown()) # output: "## Docling Technical Report[...]"
More advanced usage options are available in the docs.
CLI
Docling has a built-in CLI to run conversions.
docling https://arxiv.org/pdf/2206.01062
You can also use 🥚SmolDocling and other VLMs via Docling CLI:
docling --pipeline vlm --vlm-model smoldocling https://arxiv.org/pdf/2206.01062
This will use MLX acceleration on supported Apple Silicon hardware.
Read more here
Documentation
Check out Docling's documentation, for details on installation, usage, concepts, recipes, extensions, and more.
Examples
Go hands-on with our examples, demonstrating how to address different application use cases with Docling.
Integrations
To further accelerate your AI application development, check out Docling's native integrations with popular frameworks and tools.
Get help and support
Please feel free to connect with us using the discussion section.
Technical report
For more details on Docling's inner workings, check out the Docling Technical Report.
Contributing
Please read Contributing to Docling for details.
References
If you use Docling in your projects, please consider citing the following:
@techreport{Docling,
author = {Deep Search Team},
month = {8},
title = {Docling Technical Report},
url = {https://arxiv.org/abs/2408.09869},
eprint = {2408.09869},
doi = {10.48550/arXiv.2408.09869},
version = {1.0.0},
year = {2024}
}
License
The Docling codebase is under MIT license. For individual model usage, please refer to the model licenses found in the original packages.
LF AI & Data
Docling is hosted as a project in the LF AI & Data Foundation.
IBM ❤️ Open Source AI
The project was started by the AI for knowledge team at IBM Research Zurich.