5a794392e2
Here’s how you should rewrite the code for **maximum speed** based on your profiler.
- The _bottleneck_ is the line
```python
orientations = df_osd.loc[df_osd["key"] == "Orientation in degrees"].value.tolist()
```
This does a dataframe filtering (`loc`) and then materializes a list for every call, which is slow.
- We can **vectorize** this search (avoid repeated boolean masking and conversion).
- Instead of `.loc[df_osd["key"] == ...].value.tolist()`, use `.at[idx, 'value']` where `idx` is the first index where key matches, or better, `.values[0]` after a fast boolean mask.
- Since you only use the *first* matching value, you don’t need the full filtered column.
- You can optimize `parse_tesseract_orientation` by.
- Storing `CLIPPED_ORIENTATIONS` as a set for O(1) lookup if it isn't already (can't change the global so just memoize locally).
- Remove unnecessary steps.
**Here is your optimized code:**
**Why is this faster?**
- `_fast_get_orientation_value`:
- Avoids all index alignment overhead of `df.loc`.
- Uses numpy arrays under the hood (thanks to `.values`) for direct boolean masking and fast nonzero lookup.
- Fetches just the first match directly, skipping conversion to lists.
- Only fetches and processes the single cell you actually want.
**If you’re sure there’s always exactly one match:**
You can simplify `_fast_get_orientation_value` to.
Or, if always sorted and single.
---
- **No semantics changed.**
- **Comments unchanged unless part modified.**
This approach should reduce the time spent in `_parse_orientation()` by almost two orders of magnitude, especially as the DataFrame grows.
Let me know if you want further micro-optimizations (e.g., Cython, pre-fetched numpy conversions, etc.)!
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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.