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Tools for creating chatbots

Project description

chatbot_utils
=============

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.. contents:: Table of Contents

Chatbot utils provides easy-to-use tools for building a chatbot capable of
returning flexible, contextual responses when provided with text input.

Supports Python 2.x and 3.x.

By *Contextual responses*, I mean something like this;

::

human >> hey, what time is it?
bot >> it's 10.32pm
human >> is that past my bedtime?
bot >> no, you're good

The second phrase typed by the human, ``"is that past my bedtime?"``, is
ambiguous, and required the bot to understand that this was an incomplete
question related to the previous question, i.e. the **context**.

Installation
------------

>From PyPi
#########

TODO

>From Github
###########

#. ``git clone github.com/eriknyquist/chatbot_utils``
#. ``cd chatbot_utils``
#. ``python setup.py build``
#. ``python setup.py install``

Example bot with chatbot_utils
------------------------------

.. code-block:: python

import random
import time

from chatbot_utils.responder import Responder, Context

random.seed(time.time())

responder = Responder()

# Add a context for talking about cats
cat_context = Context()
cat_context.add_entry_phrases(
(["(.* )?(talk about|tell( me)? about) cats?.*"], ["Sure, I love cats"])
)

cat_context.add_responses(
(["(.* )?favou?rite thing about (them|cats?).*"], ["They are fuzzy"]),
(["(.* )?(do )?you have (one|(a )?cat).*"], ["No, computer programs can't have cats."])
)

# Add a context for talking about cats
dog_context = Context()
dog_context.add_entry_phrases(
(["(.* )?(talk about|tell( me)? about) dogs?.*"], ["Sure, I think dogs are great"])
)

dog_context.add_responses(
(["(.* )?favou?rite thing about (them|dogs?).*"], ["They are loyal"]),
(["(.* )?(do )?you have (one|(a )?dog).*"], ["No, computer programs can't have dogs."])
)

responder.add_default_response(["Oh, really?", "Mmhmm.", "Indeed.", "How fascinating."])
responder.add_responses(
(["(.* )?hello.*"], ["How do you do?", "Hello!", "Oh, hi."]),
(["(. *)?(good)?bye.*"], ["Alright then, goodbye.", "See ya.", "Bye."])
)

responder.add_contexts(cat_context, dog_context)

while True:
text = raw_input(" > ")
resp, matchgroups = responder.get_response(text)
print("\n\"%s\"\n" % (random.choice(resp)))

Save this file as ``simple_bot.py`` and run it with ``python simple_bot.py``.
Example output:

::

#~$ python simple_bot.py

> hello!

"Hello!"

> hey, can we talk about dogs for a bit?

"Sure, I think dogs are great"

> what's your favourite thing about them?

"They are loyal"

> do you have one?

"No, computer programs can't have dogs."

> OK, let's talk about cats now

"Sure, I love cats"

> do you have one?

"No, computer programs can't have cats."

> and what's your favourite thing about them?

"They are fuzzy"

Performance characterizations
-----------------------------

A core component of ``chatbot_utils`` is a custom dictionary called a ReDict,
which expects values to be set with regular expressions as keys. Values can then
be retrieved from the dict by providing input text as the key, and any values
with a matching associated regular expression will be returned.

ReDicts with a large number of regular expressions (for example, a Responder
with several thousand pattern/response pairs added using the ``add_response``
method) may take a significant amount of time when compiling the regular
expression(s) initially. By default, this is done automatically on first
attempt to access a ReDict, but you can also call ``Responder.compile()``
explicitly to control when the regular expressions associated with a responder
are compiled.

One additional quirk to note is that having more parenthesis groups in your
regular expressions results in a significant increase in compile time for
ReDicts with a large number of items.

Analysis: compile time & fetch time with 100k items, no parenthesis groups
##########################################################################

Each regular expression in the 100k items of test data used for this analysis
was 14-19 characters in length, used several common special characters
and was of the following form:

::

foo? 10|bar* 10

The *Time to compile* was calculated simply by timing the ``ReDict.compile()``
method. The *Time to fetch* is an average calculated by randomly fetching 10% of
the total number of items in the dict (e.g. for a dict with 1000 pattern/value
pairs added, 100 randomly-selected items would be fetched).

.. image:: images/100000_items_no_extra_groups.png

Analysis: compile time & fetch time with 100k items, extra parenthesis groups
#############################################################################

Each regular expression in the 100k items of test data used for this analysis
was at least 25-30 characters in length, used several common special characters
and was of the following form (note the addition parenthesis groups):

::

(f)(o)o? 10|b((a)(r)*) 10

Same as the previous test, the *Time to compile* was calculated by timing the
``ReDict.compile()`` method, and the *Time to fetch* is an average calculated by
randomly fetching 10% of the total number of items in the dict.

.. image:: images/100000_items_extra_groups.png

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