PeARS

##What is PeARS?

PeARS (Peer-to-peer Agent for Reciprocated Search) is a lightweight, distributed search engine. It relies on people going about their normal business and browsing the web. While they do so, the pages they visit are indexed in the background, and assigned a ‘meaning’ (is this page about cats, fashion, ancient history, Python programming?, etc). From time to time, they can choose to share some or all of these meanings with others, providing the building stones of a giant search engine network, distributed across people.

By linking page meanings with real people doing real browsing, PeARS ensures that the nodes in the network are topically coherent. An individual interested in architecture will probably have indexed a lot of webpages on art, construction and engineering topics. A dog trainer may have spent time buying equipment from online companies she trusts. By sharing the relevant part of their history, they make other people on the PeARS network able to use their specialised knowledge.

Think of PeARS as a layer of virtual agents underlying a community of real people. Your virtual agent is responsible for sharing your Web knowledge in the way you choose, and for contacting other people’s agents to help you answer your queries. This behaviour is very similar to the way people behave offline, both in terms of advertising particular specialisations and of looking for relevant sources when seeking information.

To know more head over to: http://aurelieherbelot.net/pears/

##Set up

This is mostly a development setup since we are not really production-ready yet!! We are working on a demo of PeARS which will soon run at pearsearch.org.

###Clone this repo

git clone -b development https://github.com/PeARSearch/PeARS.git

cd PeARS

###Set up the development environment

You can use the install script

$ python install.py

or the installation instructions below:

1. Set up virtualenv

   We recommend using virtualenv for the development. If you are just here for test it out, skip to the next section.

   Install pip using easy_install

   sudo easy_install pip

   or some other way your distribution supports like:

   sudo yum install python-pip

   Install virtualenv using pip

   sudo pip install virtualenv

   Create a new virtualenv for PeARS and activate it

   virtualenv pears_env && source pears_env/bin/activate

2. Install the build dependencies

   We recommend using pip for installation. In case you don't have this, look inside requirements.txt and install dependencies manually.

   pip install -r requirements.txt

3. Get the semantic space

   In the root directory of the repo, run

   wget http://clic.cimec.unitn.it/~aurelie.herbelot/openvectors.dump.bz2

   then

   python uncompress_db.py openvectors.dump.bz2

4. Run the indexer

   In the root directory of the repo, run

   python indexer.py -h

   It will give instructions on how to index you browsing history. The script automatically extracts your browsing history from Firefox. If you use another browser, please fill a urls.txt text file with urls you want to index an run:

   python indexer.py --file=urls.txt

###Running the PeARS search engine

In the root directory of the repo, run

python run.py

Go to the browser and type localhost:5000. You should find PeARS running there. We have provided a couple of demo pears for you to try. They of course only cover a tiny amount of pages and information. Try some queries related to food (e.g. chocolate cake') or the enviromnent (e.g. endangered species').

####That's it, folks!

Please report to us any issues that you face.