🔍 Ambar: Document Search Engine
Ambar is an open-source document search engine with automated crawling, OCR, tagging and instant full-text search.
Ambar defines a new way to implement full-text document search into your workflow.
- Easily deploy Ambar with a single
- Perform Google-like search through your documents and contents of your images
- Tag your documents
- Use a simple REST API to integrate Ambar into your workflow
- Fuzzy Search (John~3)
- Phrase Search ("John Smith")
- Search By Author (author:John)
- Search By File Path (filename:*.txt)
- Search By Date (when: yesterday, today, lastweek, etc)
- Search By Size (size>1M)
- Search By Tags (tags:ocr)
- Search As You Type
- Supported language analyzers: English
Ambar 2.0 only supports local fs crawling, if you need to crawl an SMB share of an FTP location - just mount it using standard linux tools. Crawling is automatic, no schedule is needed due to crawlers monitor file system events and automatically process new, changed and removed files.
Ambar supports large files (>30MB)
Supported file types:
- ZIP archives
- Mail archives (PST)
- MS Office documents (Word, Excel, Powerpoint, Visio, Publisher)
- OCR over images
- Email messages with attachments
- Adobe PDF (with OCR)
- OCR languages: Eng, Rus, Ita, Deu, Fra, Spa, Pl, Nld
- OpenOffice documents
- RTF, Plaintext
- HTML / XHTML
- Multithread processing
Notice: Ambar requires Docker to run
You can build Docker images by yourself or buy prebuilt Docker images for $50 here.
- Installation instruction for prebuilt images: here
- Tutorial on how to build images from scratch see below
If you want to see how Ambar works w/o installing it, try our live demo. No signup required.
Building the images yourself
All the images required to run Ambar can be built locally. In general, each image can be built by navigating into the directory of the component in question, performing the compilation steps required and building the image like that:
# From project root $ cd FrontEnd $ docker build . -t <image_name>
The resulting image can be referred to by the name specified, and run by the containerization tooling of your choice.
In order to use a local Dockerfile with
docker-compose, simply change the
image option to
build, setting the value to the relative path of the directory containing the Dockerfile. Then run
docker-compose build to build the relevant images. For example:
# docker-compose.yml from project root, referencing local dockerfiles pipeline0: build: ./Pipeline/ image: chazu/ambar-pipeline localcrawler: image: ./LocalCrawler/
Note that some of the components require compilation or other build steps be performed on the host before the docker images can be built. For example,
# Assuming a suitable version of node.js is installed (docker uses 8.10) $ npm install $ npm run compile
Is it open-source?
Yes, it's fully open-source.
Is it free?
Yes, it is forever free and open-source.
Does it perform OCR?
Yes, it performs OCR on images (jpg, tiff, bmp, etc) and PDF's. OCR is perfomed by well-known open-source library Tesseract. We tuned it to achieve best perfomance and quality on scanned documents. You can easily find all files on which OCR was perfomed with
Which languages are supported for OCR?
Supported languages: Eng, Rus, Ita, Deu, Fra, Spa, Pl, Nld. If you miss your language please contact us on email@example.com.
Does it support tagging?
What about searching in PDF?
Yes, it can search through any PDF, even badly encoded or with scans inside. We did our best to make search over any kind of pdf document smooth.
What is the maximum file size it can handle?
It's limited by amount of RAM on your machine, typically it's 500MB. It's an awesome result, as typical document managment systems offer 30MB maximum file size to be processed.
I have a problem what should I do?
Request a dedicated support session by mailing us on firstname.lastname@example.org