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        "author": "Erik Nyquist",
        "author_email": "eknyquist@gmail.com",
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        "description": "chatbot_utils\n=============\n\n.. |travis_badge| image:: https://travis-ci.org/eriknyquist/chatbot_utils.svg?branch=master\n    :target: https://travis-ci.org/eriknyquist/chatbot_utils\n\n.. |docs_badge| image:: https://readthedocs.org/projects/chatbot-utils/badge/?version=latest\n    :target: https://text-game-maker.readthedocs.io\n\n|travis_badge| |docs_badge|\n\n.. contents:: Table of Contents\n\nChatbot utils provides easy-to-use tools for building a chatbot capable of\nreturning flexible, contextual responses when provided with text input.\n\nSupports Python 2.x and 3.x.\n\nBy *Contextual responses*, I mean something like this;\n\n::\n\n    human >> hey, what time is it?\n      bot >> it's 10.32pm\n    human >> is that past my bedtime?\n      bot >> no, you're good\n\nThe second phrase typed by the human, ``\"is that past my bedtime?\"``, is\nambiguous, and required the bot to understand that this was an incomplete\nquestion related to the previous question, i.e. the **context**.\n\nInstallation\n------------\n\n>From PyPi\n#########\n\nTODO\n\n>From Github\n###########\n\n#. ``git clone github.com/eriknyquist/chatbot_utils``\n#. ``cd chatbot_utils``\n#. ``python setup.py build``\n#. ``python setup.py install``\n\nExample bot with chatbot_utils\n------------------------------\n\n.. code-block:: python\n\n    import random\n    import time\n\n    from chatbot_utils.responder import Responder, Context\n\n    random.seed(time.time())\n\n    responder = Responder()\n\n    # Add a context for talking about cats\n    cat_context = Context()\n    cat_context.add_entry_phrases(\n        ([\"(.* )?(talk about|tell( me)? about) cats?.*\"], [\"Sure, I love cats\"])\n    )\n\n    cat_context.add_responses(\n        ([\"(.* )?favou?rite thing about (them|cats?).*\"], [\"They are fuzzy\"]),\n        ([\"(.* )?(do )?you have (one|(a )?cat).*\"], [\"No, computer programs can't have cats.\"])\n    )\n\n    # Add a context for talking about cats\n    dog_context = Context()\n    dog_context.add_entry_phrases(\n        ([\"(.* )?(talk about|tell( me)? about) dogs?.*\"], [\"Sure, I think dogs are great\"])\n    )\n\n    dog_context.add_responses(\n        ([\"(.* )?favou?rite thing about (them|dogs?).*\"], [\"They are loyal\"]),\n        ([\"(.* )?(do )?you have (one|(a )?dog).*\"], [\"No, computer programs can't have dogs.\"])\n    )\n\n    responder.add_default_response([\"Oh, really?\", \"Mmhmm.\", \"Indeed.\", \"How fascinating.\"])\n    responder.add_responses(\n        ([\"(.* )?hello.*\"], [\"How do you do?\", \"Hello!\", \"Oh, hi.\"]),\n        ([\"(. *)?(good)?bye.*\"], [\"Alright then, goodbye.\", \"See ya.\", \"Bye.\"])\n    )\n\n    responder.add_contexts(cat_context, dog_context)\n\n    while True:\n        text = raw_input(\" > \")\n        resp, matchgroups = responder.get_response(text)\n        print(\"\\n\\\"%s\\\"\\n\" % (random.choice(resp)))\n\nSave this file as ``simple_bot.py`` and run it with ``python simple_bot.py``.\nExample output:\n\n::\n\n     #~$ python simple_bot.py\n\n     > hello!\n\n     \"Hello!\"\n\n     > hey, can we talk about dogs for a bit?\n\n     \"Sure, I think dogs are great\"\n\n     > what's your favourite thing about them?\n\n     \"They are loyal\"\n\n     > do you have one?\n\n     \"No, computer programs can't have dogs.\"\n\n     > OK, let's talk about cats now\n\n     \"Sure, I love cats\"\n\n     > do you have one?\n\n     \"No, computer programs can't have cats.\"\n\n     > and what's your favourite thing about them?\n\n     \"They are fuzzy\"\n\nPerformance characterizations\n-----------------------------\n\nA core component of ``chatbot_utils`` is a custom dictionary called a ReDict,\nwhich expects values to be set with regular expressions as keys. Values can then\nbe retrieved from the dict by providing input text as the key, and any values\nwith a matching associated regular expression will be returned.\n\nReDicts with a large number of regular expressions (for example, a Responder\nwith several thousand pattern/response pairs added using the ``add_response``\nmethod) may take a significant amount of time when compiling the regular\nexpression(s) initially. By default, this is done automatically on first\nattempt to access a ReDict, but you can also call ``Responder.compile()``\nexplicitly to control when the regular expressions associated with a responder\nare compiled.\n\nOne additional quirk to note is that having more parenthesis groups in your\nregular expressions results in a significant increase in compile time for\nReDicts with a large number of items.\n\nAnalysis: compile time & fetch time with 100k items, no parenthesis groups\n##########################################################################\n\nEach regular expression in the 100k items of test data used for this analysis\nwas 14-19 characters in length, used several common special characters\nand was of the following form:\n\n::\n\n    foo? 10|bar* 10\n\nThe *Time to compile* was calculated simply by timing the ``ReDict.compile()``\nmethod. The *Time to fetch* is an average calculated by randomly fetching 10% of\nthe total number of items in the dict (e.g. for a dict with 1000 pattern/value\npairs added, 100 randomly-selected items would be fetched).\n\n.. image:: images/100000_items_no_extra_groups.png\n\nAnalysis: compile time & fetch time with 100k items, extra parenthesis groups\n#############################################################################\n\nEach regular expression in the 100k items of test data used for this analysis\nwas at least 25-30 characters in length, used several common special characters\nand was of the following form (note the addition parenthesis groups):\n\n::\n\n    (f)(o)o? 10|b((a)(r)*) 10\n\nSame as the previous test, the *Time to compile* was calculated by timing the\n``ReDict.compile()`` method, and the *Time to fetch* is an average calculated by\nrandomly fetching 10% of the total number of items in the dict.\n\n.. image:: images/100000_items_extra_groups.png\n\n",
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