Ambar alternatives and similar software solutions
Based on the "Search Engines" category.
Alternatively, view Ambar alternatives based on common mentions on social networks and blogs.
MeiliSearch9.8 9.9 Ambar VS MeiliSearchA lightning-fast search engine that fits effortlessly into your apps, websites, and workflow.
Searx9.3 8.0 L2 Ambar VS SearxPrivacy-respecting metasearch engine
Typesense9.1 9.4 Ambar VS TypesenseOpen Source alternative to Algolia + Pinecone and an Easier-to-Use alternative to ElasticSearch ⚡ 🔍 ✨ Fast, typo tolerant, in-memory fuzzy Search Engine for building delightful search experiences
Yacy7.4 9.1 L1 Ambar VS YacyDistributed Peer-to-Peer Web Search Engine and Intranet Search Appliance
Gigablast6.7 0.0 L1 Ambar VS GigablastNov 20 2017 -- A distributed open source search engine and spider/crawler written in C/C++ for Linux on Intel/AMD. From gigablast dot com, which has binaries for download. See the README.md file at the very bottom of this page for instructions.
sist24.3 0.0 Ambar VS sist2Lightning-fast file system indexer and search tool
Seeks3.6 0.0 L1 Ambar VS SeeksSeeks is a decentralized p2p websearch and collaborative tool.
multiSearchHome1.0 0.0 Ambar VS multiSearchHome:mag_right: Local standalone html homepage to search in 175 search engine (duckduckgo, youtube, twitter, wikipedia, etc..) // FR___: Page d'accueil html autonome, pour chercher dans 175 moteurs de recherche.
Access the most powerful time series database as a service
* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest.
Do you think we are missing an alternative of Ambar or a related project?
:mag: Ambar: Document Search Engine
⚠️ PROJECT ARCHIVED ⚠️
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
Tutorial: Mastering Ambar Search Queries
- 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
- Tutorial on how to build images from scratch see below
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
Then follow this instructions -> https://ambar.cloud/docs/installation
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.
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.
*Note that all licence references and agreements mentioned in the Ambar README section above are relevant to that project's source code only.