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chatbot-utils-1.1.0
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Tools for creating chatbots
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | chatbot-utils-1.1.0 |
| نام | chatbot-utils |
| نسخه کتابخانه | 1.1.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Erik Nyquist |
| ایمیل نویسنده | eknyquist@gmail.com |
| آدرس صفحه اصلی | http://github.com/eriknyquist/chatbot_utils |
| آدرس اینترنتی | https://pypi.org/project/chatbot-utils/ |
| مجوز | Apache 2.0 |
chatbot_utils
=============
.. |travis_badge| image:: https://travis-ci.org/eriknyquist/chatbot_utils.svg?branch=master
:target: https://travis-ci.org/eriknyquist/chatbot_utils
.. |docs_badge| image:: https://readthedocs.org/projects/chatbot-utils/badge/?version=latest
:target: https://chatbot-utils.readthedocs.io/en/latest/?badge=latest
|travis_badge| |docs_badge|
.. 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
#########
::
pip install chatbot_utils
From Github
###########
#. ``git clone github.com/eriknyquist/chatbot_utils``
#. ``cd chatbot_utils``
#. ``python setup.py build``
#. ``python setup.py install``
API documentation
-----------------
Read the API documentation `here <https://chatbot-utils.readthedocs.io/>`_
Example bot with chatbot_utils, showing how to use contexts
-----------------------------------------------------------
The following example shows how to create a bot that can provide contexual responses
to specific questions:
.. code-block:: python
import random
import time
from chatbot_utils.responder import Responder
from chatbot_utils.utils import ContextCreator, get_input
random.seed(time.time())
responder = Responder()
# Add a context for talking about cats
with ContextCreator(responder) as ctx:
# Phrase to trigger entry into cat context
ctx.add_entry_phrases((["(.* )?(talk about|tell( me)? about) cats?.*"], ["Sure, I love cats"]))
# These phrases will only be recognized after the entry phrase has been seen
ctx.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 nested context inside the cat context (you can do this as deep as you like)
with ContextCreator(ctx) as subctx:
# Phrase to trigger entry into cat food context, will only be recognized when we're already in the cat context
subctx.add_entry_phrases((["(.* )?(talk about|tell( me)? about) food?.*"], ["Sure, let's talk about cat food"]))
# These phrases will only be recognized after BOTH entry phrases have been seen
subctx.add_responses(
(["(.* )?(favou?rite|best) type( of food)?.*"], ["Computer programs do not eat cat food."]),
)
# Add explicit exit phrase for cat food subcontext (if no exit phrase is added,
# then he only way to exit the context is using a phrase that was added to the top-level
# responder object with Responder.add_response())
subctx.add_exit_phrases((["(.* )?(stop talking about ((dog )?food|this)|talk about something else).*"], ["OK, no more dog food talk."]))
# Add a context for talking about dogs
with ContextCreator(responder) as ctx:
# Phrase to trigger entry into dog context
ctx.add_entry_phrases((["(.* )?(talk about|tell( me)? about) dogs?.*"], ["Sure, I think dogs are great"]))
# These phrases will only be recognized after the entry phrase has been seen
ctx.add_responses(
(["(.* )?favou?rite thing about (them|dogs?).*"], ["They are loyal"]),
(["(.* )?(do )?you have (one|(a )?dog).*"], ["No, computer programs can't have dogs."])
)
# Add a nested context inside the dog context (you can do this as deep as you like)
with ContextCreator(ctx) as subctx:
# Phrase to trigger entry into dog food context, will only be recognized when we're already in the dog context
subctx.add_entry_phrases((["(.* )?(talk about|tell( me)? about) food?.*"], ["Sure, let's talk about dog food"]))
# These phrases will only be recognized after BOTH entry phrases have been seen
subctx.add_responses(
(["(.* )?(favou?rite|best) type( of food)?.*"], ["Computer programs do not eat dog food."]),
)
# One of these responses will be randomly chosen whenever an unrecognized phrase is seen
responder.add_default_response(["Oh, really?", "Mmhmm.", "Indeed.", "How fascinating."])
# These phrases will only be recognized when no context is active
responder.add_responses(
(["(.* )?hello.*"], ["How do you do?", "Hello!", "Oh, hi."]),
(["(. *)?(good)?bye.*"], ["Alright then, goodbye.", "See ya.", "Bye."])
)
# Simple prompt to get input from command line and pass to responder
while True:
text = get_input(" > ")
resp, groups = responder.get_response(text)
print("\n\"%s\"\n" % (random.choice(resp)))
Save this file as ``example_bot.py`` and run it with ``python example_bot.py``.
Example output:
::
#~$ python example_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"
Example bot with chatbot_utils, showing how to use format tokens
----------------------------------------------------------------
The following example shows how to create a bot that can remember what you said your
favourite movie was, ad report it back later when asked:
.. code-block:: python
from chatbot_utils.responder import Responder
from chatbot_utils.utils import ContextCreator, get_input
responder = Responder()
responder.add_default_response("Please tell me what your favourite movie is")
responder.add_responses(
# When the bot is told what my favourite film is, it will save whatever film I said (4th
# parenthesis group, or p3) in a new variable named "faveMovie"
(["(.* )?(favou?rite|fave) (movie|film) is (.*)$"],
"Cool, I will remember that your favourite film is {p3}!;;faveMovie={p3}"),
# When the bot is asked to recall what my favourite film is, it will report the value of 'faveMovie'
(["(.*)?(what is|what'?s|(can you )?tell me )?(what('?s)? )?my (fave|favou?rite) (movie|film).*"],
"Your favourite film is {faveMovie}!")
)
# Simple prompt to get input from command line and pass to responder
while True:
text = get_input(" > ")
resp, groups = responder.get_response(text)
print("\n\"%s\"\n" % resp)
Save this file as ``example_bot.py`` and run it with ``python example_bot.py``.
Example output:
::
#~$ python example_bot.py
> howdy!
"Please tell me what your favourite movie is"
> hmm, OK, I guess my favourite film is Gone With The Wind
"Cool, I will remember that your favourite film is Gone With The Wind!"
> hey, can you tell me what my fave movie is?
"Your favourite film is Gone With The Wind!"
> alright, now my favorite movie is spiderman 2
"Cool, I will remember that your favourite film is spiderman 2!"
> what's my favourite film?
"Your favourite film is spiderman 2!"
>
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
نحوه نصب
نصب پکیج whl chatbot-utils-1.1.0:
pip install chatbot-utils-1.1.0.whl
نصب پکیج tar.gz chatbot-utils-1.1.0:
pip install chatbot-utils-1.1.0.tar.gz