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Live Agent

Coordinates the execution of processes which interact with Intelie Live and simplifies their deployment.

Processes are implemented inside modules, segmented by its goal. The existing processes can be found at live_agent/modules

Each module can have:

  • datasources: Process which generates and send events to live
  • monitors: Processes which respond to events generated by queries
  • logic_adapters: Classes which handle messages received by the chatbot

A live_agent module should expose a PROCESSES dictionary, listing all processes it provides. A process is started by a function (usually named start) which accepts two parameters:

  • settings: a dictionary of the settings for this process;
  • kwargs: a dictionary of extra parameters provided by live-agent's runtime to this process.

The set of active modules (among other things) is defined using a settings file. The module chatbot includes an example settings file.

live-agent requires python 3.7 or newer.

Usage

# 1- Create a virtualenv using your preferred tool

# 2- Activate the virtualenv

# 3- Install live-agent (you should use a requirements.txt file to manage your dependencies)
(virtualenv)$ pip install live-agent[chatbot]

# 4- Bootstrap a new agent
(virtualenv)$ create-agent
Creating the agent files:
- Creating "README.md"
- Creating "settings.json"
- Creating folder "tools"
- Creating folder "modules"
- Creating "modules/__init__.py"
Adding project settings:
- Creating "dev-requirements.txt"
- Creating "pyproject.toml"
- Creating ".pre-commit-config.yaml"
done

# 5- Create the initial structure for each of your agent's modules
(virtualenv)$ add-agent-module example --empty
Creating the module "example"
- Creating folder "modules"
- Creating folder "modules/example"
- Creating folder "modules/example/logic_adapters"
- Creating folder "modules/example/monitors"
- Creating folder "modules/example/datasources"
done

# 5.1- Or, use a sample module as reference
(virtualenv)$ add-agent-module example
Creating the module "example"
- Creating folder "modules"
- Removing old folder "modules/example"
- Creating folder "modules/example" with example code

The module "example" contains a "requirements.txt" file
Make sure that these dependencies are added to the main requirements

In order to run the agent with this module, execute:
agent-control console --settings=modules/example/settings_template.json
done

# 6- Implement the features you need on your modules and add them to settings.json
# Use the command `validate-settings` to validate the settings
$ validate-settings --settings=settings.json

# 7- Execute the agent
$ agent-control console --settings=settings.json

Development

This project uses black and pre-commit

Project setup:

# 1- Create a virtualenv

# 2- Activate the virtualenv

# 3- Install project requirements
$ pip install -r requirements.txt -r live_agent/modules/chatbot/requirements.txt

# 4- Check is your settings file seems to be correct
$ validate-settings --settings=modules/chatbot/settings_template.json

# 5- Execute the agent
$ ./live_agent/scripts/agent-control console --settings=modules/chatbot/settings_template.json

Reading logs

This project uses eliot for logging. Eliot generates log messages as json objects, which can be parsed by tools like eliot-tree and eliot-prettyprint or sent to Intelie Live.

The log file is stored at /var/log/live-agent.log by default. Make sure the user which will start the agent can write to this file. The log messages are also sent to live, using the event_type dda_log by default.

# Reading the log with eliot-prettyprint
$ tail -f /var/log/live-agent.log | eliot-prettyprint

# Reading the log with eliot-tree (extra dependency, already on requirements.txt)
$ eliot-tree -l 0 /var/log/live-agent.log

Building releases

In order to generate an installable package you will need to use docker.

  • Install docker (check the documentation for your system: https://docs.docker.com/install/)
  • Add your user to the group docker: $ usermod -aG docker <username>.
  • Log off and log on again for the group to be recognized. (or you can simply $ su - <username> on your terminal)

The packager requires packages fabric and virtualenv. It will generate a rpm file for installation on the target system.

$ tools/package.sh c7
  • c7: Build for centos7 and derivates (redhat 7, amazon linux 2, etc)

Testing the built packages

(As of now the testing is entirely manual)

In a container:
$ tools/test-envs/run_centos_container.sh 7

# Build dir will be available at /packages, so you can install and test
In a VM:

This allows to a more complete test, including running the app as a service

# cd to `tools/test-envs/RedHat7`
$ cd tools/test-envs/RedHat7

# Starting VM:
$ vagrant up

# Connecting to the machine:
$ `vagrant ssh`

# To transfer files, copy to/from the `transf` subdirectory,
# it is automatically mapped as `/transf` at the test VM

# Stopping:

$ vagrant halt    # Stop
$ vagrant destroy # Completely erase the machine

Installing on the target system

$ rpm -Uvh <rpmfile>

Publishing to pypi

# Build the packages
$ python setup.py egg_info sdist

# Validate the package
$ twine check dist/*

# Upload the package
$ twine upload dist/*