faqbot | : question : An email bot to answer FAQ | Bot library

by techx Python Version: Current License: AGPL-3.0

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | faqbot Summary

faqbot is a Python library typically used in Automation, Bot, Deep Learning applications. faqbot has no bugs, it has no vulnerabilities, it has build file available, it has a Strong Copyleft License and it has low support. You can download it from GitHub.

A bot to manage emails that ask frequenty asked questions.

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Quality

Security

License

Reuse

Support

faqbot has a low active ecosystem.

It has 18 star(s) with 5 fork(s). There are 50 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 3 have been closed. On average issues are closed in 52 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of faqbot is current.

Quality

faqbot has 0 bugs and 0 code smells.

Security

faqbot has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

faqbot code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

faqbot is licensed under the AGPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

faqbot releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

faqbot saves you 44748 person hours of effort in developing the same functionality from scratch.

It has 52669 lines of code, 106 functions and 115 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed faqbot and discovered the below as its top functions. This is intended to give you an instant insight into faqbot implemented functionality, and help decide if they suit your requirements.

Authenticate the user
Encode a JWT token
Generate a new UUID
Return True if the JWT token is valid
Generate config from defaults
Load a configuration file
Save configuration to file
Returns the path to store
Show the statistics panel
Get the menu
Redirect to the menu
Save the commands to a file
Render Quill
Decorator to require authentication
Render the template
Dump menu to config file
Load commands

Get all kandi verified functions for this library.

faqbot Key Features

No Key Features are available at this moment for faqbot.

faqbot Examples and Code Snippets

No Code Snippets are available at this moment for faqbot.

Community Discussions

Trending Discussions on faqbot

How to get the integration of QnA maker and the Luis BOT to work accurately?

What types of input should I use in this neural network?

QUESTION

How to get the integration of QnA maker and the Luis BOT to work accurately?

Asked 2019-May-23 at 08:39

The issue I was facing was that I am following this documentation for integrating both LUIS and QnA maker

https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/tutorials/integrate-qnamaker-luis

And I have modified the code for a FAQbot. I have two intents, one the FAQ intent which has the QnA maker and then another Intent. When I ask a question in the chatbot from the FAQ intent it gives an accurate response and when I ask a completely different question also it goes to the other Intent. However, when I ask another new question which is not in the knowledge base but has a few words which are similar to the existing questions it gives me an answer predicting it to be from the FAQ intent. instead of the other intent. How do I improve the accuracy of the model?

...

ANSWER

Answered 2019-May-23 at 08:39

When you plan to build a LUIS model, choose a good naming convention. Else It would be hard for you when you referring the particular intent from your code.

Don’t use too lengthy words as intent names. Just use short descriptive wordings. Using Camel case or dot separated phrases is a good practice

Must Remember About LUIS:

Do define distinct intents

Make sure the vocabulary for each intent is just for that intent and not overlapping with a different intent

Do find sweet spot for intents

Use prediction data from LUIS to determine if your intents are overlapping. Overlapping intents confuse LUIS. The result is that the top scoring intent is too close to another intent. Because LUIS does not use the exact same path through the data for training each time, an overlapping intent has a chance of being first or second in training

Balance your utterances across all intents

In order for LUIS predictions to be accurate, the quantity of example utterances in each intent (except for the None intent), must be relatively equal.

If you have an intent with 100 example utterances and an intent with 20 example utterances, the 100-utterance intent will have a higher rate of prediction

Here are some Do and Don't for LUIS:

Note: For more details you could refer this docs

Update:

If you still have any query feel free to share in comment. Thanks and happy coding!

Source https://stackoverflow.com/questions/56249624

QUESTION

What types of input should I use in this neural network?

Asked 2017-Feb-11 at 08:35

I have a system in which users can send many chat messages, and many of them tend to be questions, like How do I register an account? How do I do this? Where is X?

To help with this, I'm interested in creating a FAQbot that takes a chat message then decides whether to send a helpful message explaining how to do things. So a user types in How do I register an account? and the bot should reply something along the lines of You can register by ... The bot should reply like that if the user types How do I register?, I can't register an account!, Can somebody please tell me how to register?, or anything else like that. There would be a fixed set of answers this bot would give to various questions we'd expect users to have, so I plan on training the neural network to output 0 for a message that doesn't need a reply, or some other ID number that corresponds to a given reply message.

I was thinking that I would log all chat messages, go through and classify them all with which message or no message would be an appropriate reply, and then feed that into a neural network. I would also create tools to correct inappropriate replies or lack thereof.

I'm using C# and will probably use a library such as Accord.NET, although I'm not asking for the specific implementation. My one question is how I would take the chat message string and turn it into valid input for the neural network. I imagine it would tell the network which words and punctuation were used, which order the words were in, and which words follow which other words. However, the specific implementation of that eludes me.

...

ANSWER

Answered 2017-Feb-11 at 06:52

Firstly - seems like your task is some kind of natural text classification(each FAQ question presents one class).

Also - maybe TF-IDFvector? Another way that I see - present input as matrix of word vector (from word2vec or something like it), but seems like in this case ANN will have many params - so it not seems like you'll can train it properly.

p.s. also - maybe you'll prefer api.ai, or, maybe - Watson nlc-based bot?

Source https://stackoverflow.com/questions/42170799

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install faqbot

You need to place the config.py file inside the faqbot directory. We have provided an example config in the faqbot directory, config.example.py.

Support

Try to use black and follow that style for everything outside of legacy/. Contribute by opening pull requests and report bugs by creating issues. Email parsing in general is nasty, so opening bugs for parsing errors will be really helpful.

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