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Hubot watches all, and builds a markov model from everything you say.

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# 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)

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