yobix-ai/extractous
Fast and efficient unstructured data extraction. Written in Rust with bindings for many languages.
Build with Backblaze B2
SDKs, agent skills, IDE extensions, and reference pipelines from Backblaze Labs. All open source.
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
The project lists 3 contributors on GitHub.
Gained 21% fewer stars in the last 90 days than in the 90 days before.
Use Cases & Benefits
- Provides fast and efficient extraction of text and metadata from various unstructured document formats using a Rust core with multi-language bindings.
- Delivers significantly faster processing and lower memory usage than alternatives by leveraging Rust's native performance and multi-threading without external services.
- Use for building high-performance ETL pipelines that require local extraction of content from PDFs, Word documents, and other complex file types.
- Use for integrating OCR-based text extraction from scanned images and documents into machine learning or NLP workflows.
- Use for developing multi-language applications needing consistent, efficient unstructured data extraction with support for many file formats including emails and web documents.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
Apache 2.0
Permissive: commercial use, modification and redistribution are allowed with attribution; includes an explicit patent grant from contributors.
Community files
- README: present
Watch-outs
- Warning: No commits in over a year (last push Dec 2024).
- No GitHub releases; pin a commit or tag instead of a release.
General guidance from repository metadata, not legal advice. How we score
From the README
Extractous offers a fast and efficient solution for extracting content and metadata from various documents types such as PDF, Word, HTML, and many other formats. Our goal is to deliver a fast and efficient comprehensive solution in Rust with bindings for many programming languages.
Demo: showing that Extractous 🚀 is 25x faster than the popular unstructured-io library ($65m in funding and 8.5k GitHub stars). For complete benchmarking details please consult our benchmarking repository
Extractous was born out of frustration with the need to rely on external services or APIs for content extraction from unstructured data. Do we really need to call external APIs or run special servers just for content extraction? Couldn't extraction be performed locally and efficiently?
In our search for solutions, unstructured-io stood out as the popular and widely-used library for parsing unstructured content with in-process parsing. However, we identified several significant limitations:
In contrast, Extractous maintains a dedicated focus on text and metadata extraction. It achieves significantly faster processing speeds and lower memory utilization through native code execution.
With Extractous, the need for external services or APIs is eliminated, making data processing pipelines faster and more efficient.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
Forks
98+1
Open issues
34+1
Compared with peers in Data Engineering
Fastest-growing repositories GStars tracks in this cohort for the week of Nov 1, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| apache/airflow | 47.3K | — | 63 | 40 days ago |
| pion/webrtc | 16.9K | — | 60 | 1 day ago |
| flexprice/flexprice | 6.9K | — | 65 | 5 days ago |
| truelockmc/streambert | 6.4K | — | 28 | 120 days ago |
| lakehq/sail | 3.5K | — | 63 | 28 days ago |
| yobix-ai/extractous | 1.8K | — | 7 | — |
| Category median | 6.9K | — | 63 | — |
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