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"An open framework and dataset for building a distributed-agent chatbot based on _Natural Language Processing in Action_."

Project description

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qary

Use NLP in Action to build a virtual assistant that actually assists! Most bots manipulate you to make money for their corporate masters. Your bot can help protect you and amplify your abilities and prosocial instincts.

This hybrid chatbot combines 4 techniques explained in NLP in Action:

1. search: [chatterbot](https://github.com/gunthercox/ChatterBot), [will](https://github.com/skoczen/will)
2. pattern matching and response templates: Alexa, [AIML](https://github.com/keiffster/program-y)
3. generative deep learning: [robot-bernie](https://github.com/nlpia/robot-bernie), [movie-bot](https://github.com/totalgood/nlpia/blob/master/src/nlpia/book/examples/ch10_movie_dialog_chatbot.py)
4. grounding: [snips](https://github.com/snipsco/snips-nlu)

The presentations for San Diego Python User Group are in docs/

Install

You’ll want to install and use the conda package manager within Anaconda3, especially if your development environment is not a open standard operating system like Linux.

git clone git@github.com:nlpia/qary
cd qary
conda env create -n nlpia -f environment.yml  # or environment-windoze.yml
conda activate nlpia
pip install --editable .

Usage

$ bot --help
usage: bot [-h] [--version] [--name STR] [-p] [-b STR] [-v] [-vv]
           [words [words ...]]

Command line bot application, e.g. bot how do you work?

positional arguments:
  words                Words to pass to bot as an utterance or conversational
                       statement requiring a bot reply or action.

optional arguments:
  -h, --help           show this help message and exit
  --version            show program's version number and exit
  --name STR           IRC nick or CLI command name for the bot
  -p, --persist        Don't exit. Retain language model in memory and
                       maintain dialog until user says 'exit', 'quit' or 'bye'
                       (this is the default behavior if you do not provide a statement)
  -b STR, --bots STR   comma-separated list of bot personalities to load
                       default: pattern,parul,search_fuzzy,time,eliza
  -v, --verbose        set loglevel to INFO
  -vv, --very-verbose  set loglevel to DEBUG

Examples

You can run bot just like any other command line app, giving it your statement/query as an argument.

$ bot hello
# 2019-11-21 12:42:13,620 WARNING:nlpia.constants:107:            <module> Starting logger in nlpia.constants...
# 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 64350/64350 [00:00<00:00, 540679.58it/s]
BOT: Hello

And if you want quicker turnaround on your bot you can just run it in peristent mode (without any positional arguments for your statement or words).

Travis’s probabilistic reply selector is working nicely to chose a reply from multiple sources (default settings):

  • pattern_bots.py: regex patterns and greeting templates
  • fuzzy_search_bots.py: movie dialog fuzzy matching
  • parul_bots.py: Wikipedia searches using conventional TFIDF like a search engine
  • eliza_bots.py: A python port of the ELIZA therapist bot
  • time_bots.py: A time and productivity tracker that parses your git logs and bash history
$ bot
(nlpia) hobs@Hobsons-MacBook-Air:~/code/chatbot/qary$ bot
# 2019-11-21 12:59:05,854 WARNING:nlpia.constants:107:            <module> Starting logger in nlpia.constants...
# 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 64350/64350 [00:00<00:00, 495935.48it/s]
YOU: Hi
BOT: hey there. tired of breathing?
YOU: Hello
BOT: How do you do. Please state your problem.
YOU: What is an ELIZA chatbot?
BOT: joey never told you we went out, did he?
YOU: ^[[A
BOT: I am sorry! I dont understand you
YOU: What is an ELIZA chatbot?
BOT: Does that question interest you ?
YOU: What is a chatbot?
BOT: hello barbie is an internet-connected version of the doll that uses a chatbot provided by the company toytalk, which previously used the chatbot for a range of smartphone-based characters for children.
YOU: Hello
BOT: hello.
YOU: Hello
BOT: How do you do. Please state your problem.
YOU: bye
$

Work in Progress

  • Travis (@travis-harper): model management, context filtering, and the addition of more conversational agents
  • Nima (@hulkgeek): question answering bot based on his state of the art question classifier
  • Xavier (@spirovanni): employment counselor for workforce.org and the city of San Diego
  • Hobson (@hobson): infrastructure (CI, webapp) and framework features (nltk->spacy, USE vectors)
  • Erturgrul: Turkish wikipedia QA bot (parul bot)
  • You: What big chatbot idea would you like to make a reality?

Ideas

Please submit your feature ideas github issues. Here are a few ideas to get you started.

  1. movie dialog in django database to hold the statement->response pairs
    1. graph schema compatible with MxGraph (draw.io) and other js libraries for editing graphs/flow charts.
    2. ubuntu dialog corpus in db
    3. mindfulness faq corpus in db
    4. famous quotes as responses to the statement “tell me something inspiring”
    5. jokes for “tell me a joke”
    6. data science faq
    7. nlpia faq
    8. psychology/self-help faq
  2. html django template so there is a web interface to the app rather than just the command line command bot
  3. use Django Rest Framework to create a basic API that returns json containing a reply to any request sent to the local host url, like http://localhost:8000/api?statement='Hello world' might return {‘reply’: ‘Hello human!’}
  4. have the command line app use the REST API from #3 rather than the slow reloading of the csv file every time you talk to the bot
  5. use database full text search to find appropriate statements in the database that we have a response for
  6. use semantic search instead of text similarity (full text search or fuzzywyzzy text matches)
    1. add embedding vectors (300D document vectors from spacy) to each statement and response in the db
    2. create a semantic index of the document vectors using annoy so “approximate nearest neighbors” (semantic matches) can be found quickly
    3. load the annoy index of the document vectors every time the server is started and use it to find the best reply in the database.
    4. use universal sentence encodings instead of docvecs from spacy.
  7. create a UX for dialog graph creation/design:
    1. install mxgraph in the django app
    2. create a basic page based on this mxgraph example so the user can build and save dialog to the db as a graph: tutorial, example app
    3. convert the dialog graph into a set of records/rows in the qary db so it acts
  8. tag different dialog graphs in the db so the user can turn them on/off for their bot
    1. allow the user to prioritize some dialogs/models over others
    2. allow the user to create their own weighting function to prioritize individual statements produced by the api
  9. train a character-based generative model
    1. decoder half of autoencoder to generate text based on docvecs from spacy
    2. decoder part of autoencoder to generate text based on universal sentence encodings
    3. train model to generate reply embeddings (doc vecs and/or use vecs) using statement embeddings (dialog engine encoder-decoder using docvecs or use vecs for the encoder half
  10. add a therapy/mindfulness-coach feature to respond with mindfulness ideas to some queries/statements
  11. add the “translate ‘this text’ to spanish” feature
    1. train character-based LSTM models on english-spanish, english-french, english-german, english<->whatever
    2. add module for this to the django app/api
  12. AIML engine fallback

Inspiration

A lot of the patterns and ideas were gleaned from other awesome prosocial chatbots and modular open source frameworks.

Mental Health Coaches

Open Source Frameworks

  • will
    • lang: python
    • web: zeromq
    • db: redis, couchbase, flat file, user-defined
    • integrations: hipchat, rocketchat, shell, slack
  • ai-chatbot-framework
    • lang: python
    • web: flask
    • orm: flask?
    • db: mongodb
    • nice general json syntax for specifying intent/goals for conversation manager (agent)
  • rasa
    • lang: python
    • web: sanic (async)
    • orm: sqlalchemy
    • db: sqlite
    • rich, complex, mature framework
  • botpress
    • javascript (typescript)
    • meta-framework allowing your to write your own modules in javascript
  • Program-Y
    • python
    • web: flask (rest), sanic (async)
    • db: aiml flat files (XML)
    • integrations: facebook messenger, google search, kik, line, alexa, webchat, viber

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