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https://github.com/smashwilson/hubot-markov

Hubot watches all, and builds a markov model from everything you say.
https://github.com/smashwilson/hubot-markov

coffeescript hubot markov-model

Last synced: 17 days ago
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Hubot watches all, and builds a markov model from everything you say.

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README 

        
# Hubot Markov Model



[![npm](https://img.shields.io/npm/v/hubot-markov.svg?style=plastic)](https://www.npmjs.com/package/hubot-markov) | ![Continuous Integration](https://github.com/smashwilson/hubot-markov/workflows/Continuous%20Integration/badge.svg)



Generates a markov model based on everything that your Hubot sees in your chat.



## Installing



1. Add `hubot-markov` to your `package.json` with `npm install --save hubot-markov`:



```json

  "dependencies": {

    "hubot-markov": "~2.0.0"

  },

```



2. Require the module in `external-scripts.json`:



```json

["hubot-markov"]

```



3. Run `npm update` and restart your Hubot.



Consult the [upgrading guide](./docs/upgrade.md) for instructions on migrating from older major versions.



## Commands



By default, saying anything at all in chat trains the model. The robot is always watching!



`Hubot: markov` will randomly generate text based on the current contents of its model.



`Hubot: markov your mother is a` will generate a random phrase seeded with the phrase you give it. This command might output "your mother is a classy lady", for example. Remember: Hubot is an innocent soul, and what he says only acts as a mirror for everything in your hearts.



`Hubot: remarkov` and `Hubot: mmarkov` are similar, but traverse node transitions in different directions: `remarkov` chains backwards from a given ending state, and `mmarkov` chains both forward and backward.



## Configuration



The Hubot markov model can optionally be configured by setting environment variables:



* `HUBOT_MARKOV_DEFAULT_MODEL` _(default: true)_ controls the inclusion of the default, forward-chaining model that learns from all text messages. Set this to `false` to omit the default model and disable the `markov` and `mmarkov` commands.



* `HUBOT_MARKOV_REVERSE_MODEL` _(default: true)_ controls the inclusion of the reverse model. Setting this to `false` saves some space in your database, but doesn't let you use `remarkov` or `mmarkov`.



* `HUBOT_MARKOV_PLY` _(default: 1)_ controls the *order* of the default models that are built; effectively, how many previous states (words) are considered to choose the next state. You can bump this up if you'd like, but the default of 1 is both economical with storage and maximally hilarious.



* `HUBOT_MARKOV_LEARN_MIN` _(default: 1)_ controls the minimum length of a phrase that will be used to train the default models. Set this higher to avoid training your model with a bunch of immediate terminal transitions like "lol".



* `HUBOT_MARKOV_GENERATE_MAX` _(default: 50)_ controls the maximum size of a markov chain that will be generated by the `markov`, `remarkov`, and `mmarkov` commands.



* `HUBOT_MARKOV_STORAGE` _(default: memory)_ controls the backing storage used to persist the default models. Choices include:

  * `memory`, the default, which stores transitions entirely in-process (lost on restart);

  * `redis`, which stores data in a Redis cache; or

  * `postgres`, which stores data in a PostgreSQL database.



* `HUBOT_MARKOV_STORAGE_URL` supplies additional configuration required by the `redis` and `postgres` storage backends. The formats are `redis://${USER}:${PASSWORD}@${HOSTNAME}:${PORT}/${DBNUM}` and `postgres://${USER}:${PASSWORD}@${HOSTNAME}:${PORT}/${DATABASE}` with defaults omitted.



* `HUBOT_MARKOV_RESPOND_CHANCE` controls the chance that Hubot will respond un-prompted to a message it sees by using the last word in the message as the seed. Set this to a value between 0 and 1.0 to enable the feature. Leaving this variable unset or setting it to 0 will disable the feature.



* `HUBOT_MARKOV_INCLUDE_URLS` _(default: false)_ will default to ignoring messages that include URLs from the default models.



* `HUBOT_MARKOV_IGNORELIST` _(default: empty)_ is interpreted as a comma-separated list of usernames to ignore for purposes of markov indexing. You can use this to prevent the output of other bots or integrations from clogging up your model.



* `HUBOT_MARKOV_IGNORE_MESSAGE_LIST` _(default: empty)_ is interpreted as a comma-separated list of sub-phrases to ignore from default and reverse models. Is a basic substring match for every message. Use this to ignore terms, like your bot's username



* `HUBOT_MARKOV_LEARNING_LISTEN_MODE` - _(default: 'catch-all')_ change the robot.listen mode for learning, helpful if you have other plugins that interfere with robot.catchAll. Options:

  * 'catch-all', learn message if no other scripts process message. 

  * 'hear-all', learn every message, regardless if another script handled it.

  *  If set to another value, will use it as a robot.listen regex pattern

  

* `HUBOT_MARKOV_RESPOND_LISTEN_MODE` - _(default: 'catch-all')_ change the robot.listen mode for responding, helpful if you have other plugins that interfere with robot.catchAll. Options:

  * 'catch-all', learn message if no other scripts process message. 

  * 'hear-all', learn every message, regardless if another script handled it.

  *  If set to another value, will use it as a robot.listen regex pattern



To re-use a PostgreSQL connection with other parts of your Hubot, define a robot method called `getDatabase` that returns the connection object. This package uses [pg-promise](https://www.npmjs.com/package/pg-promise).



## Custom models



Store and generate text from arbitrary sources and in more complex commands by using the programmatic API available at `robot.markov`. Call `robot.markov.createModel` during script initialization to configure a model, then use `robot.markov.modelNamed` to access the model instance in commands that train it or generate from it.



### Example: Manual Model



```coffee

module.exports = (robot) ->

  MODELNAME = 'manual'



  # Create or connect to a model with all default options

  robot.markov.createModel MODELNAME



  robot.respond /modeladd\s+(.+)/, (msg) ->

    robot.markov.modelNamed MODELNAME, (model) ->

      model.learn msg.match[1], ->

        msg.reply 'Input accepted.'



  robot.respond /modelgen(?:\s+(.+))/, (msg) ->

    robot.markov.modelNamed MODELNAME, (model) ->

      model.generate msg.match[1] or '', 50, (output) ->

        msg.reply output

```



### Example: Letter-Based Model



```coffee

module.exports = (robot) ->

  MODELNAME = 'letters'



  # Create or connect to a model with a custom pre- and post-processor

  robot.markov.createModel MODELNAME, {}, (model) ->

    model.processWith

      pre: (input) -> input.split('')

      post: (output) -> output.join('')



  robot.catchAll (msg) ->

    # Filter out "lol"

    return if /^\s*l(o+)l\s*/.test msg.text



    robot.markov.modelNamed MODELNAME, (model) ->

      model.learn msg.text



  robot.respond /lettergen(?:\s+(.+))/, (msg) ->

    robot.markov.modelNamed MODELNAME, (model) ->

      model.generate msg.match[1] or '', 100, (output) ->

        msg.reply output

```



The full API is available in [the docs/ directory.](./docs/api.md)