hubot-markov
v2.2.2
Published
Build a markov model of everything Hubot hears.
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Hubot Markov Model
Generates a markov model based on everything that your Hubot sees in your chat.
Installing
- Add
hubot-markov
to yourpackage.json
withnpm install --save hubot-markov
:
"dependencies": {
"hubot-markov": "~2.0.0"
},
- Require the module in
external-scripts.json
:
["hubot-markov"]
- Run
npm update
and restart your Hubot.
Consult the upgrading guide 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 tofalse
to omit the default model and disable themarkov
andmmarkov
commands.HUBOT_MARKOV_REVERSE_MODEL
(default: true) controls the inclusion of the reverse model. Setting this tofalse
saves some space in your database, but doesn't let you useremarkov
ormmarkov
.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 themarkov
,remarkov
, andmmarkov
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; orpostgres
, which stores data in a PostgreSQL database.
HUBOT_MARKOV_STORAGE_URL
supplies additional configuration required by theredis
andpostgres
storage backends. The formats areredis://${USER}:${PASSWORD}@${HOSTNAME}:${PORT}/${DBNUM}
andpostgres://${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 usernameHUBOT_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.
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
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
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.