quepy | python framework to transform natural language questions | SQL Database library

by machinalis Python Version: 0.2 License: Non-SPDX

X-Ray Key Features Code Snippets(1)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | quepy Summary

quepy is a Python library typically used in Database, SQL Database applications. quepy has no bugs, it has no vulnerabilities, it has build file available and it has medium support. However quepy has a Non-SPDX License. You can install using 'pip install quepy' or download it from GitHub, PyPI.

A python framework to transform natural language questions to queries in a database query language.

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Quality

Security

License

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Support

quepy has a medium active ecosystem.

It has 1241 star(s) with 301 fork(s). There are 96 watchers for this library.

It had no major release in the last 12 months.

There are 22 open issues and 13 have been closed. On average issues are closed in 281 days. There are 6 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of quepy is 0.2

Quality

quepy has 0 bugs and 46 code smells.

Security

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

quepy code analysis shows 0 unresolved vulnerabilities.

There are 18 security hotspots that need review.

License

quepy has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

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

Deployable package is available in PyPI.

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

Top functions reviewed by kandi - BETA

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

Takes a string of text and returns a dictionary of tokens
Run nltkagger
Return morphy tag
Ensures that the given string is valid
Generate code for given expression
Convert characters to unicode
Escape special characters
Convert an expression into a graph representation
Count the probability of a string
Calculate a predicate sum from a string
Count tokens from a string
Computes Lemmas from a string

Get all kandi verified functions for this library.

quepy Key Features

No Key Features are available at this moment for quepy.

quepy Examples and Code Snippets

Query using NLP [PYTHON]

Python

Lines of Code : 15

License : Strong Copyleft (CC BY-SA 4.0)

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training_data.append({"class":"greeting", "sentence":"how are you?"})
training_data.append({"class":"greeting", "sentence":"how is your day?"})
training_data.append({"class":"greeting", "sentence":"good day"})
training_data.append({"class"

Community Discussions

Trending Discussions on quepy

Query using NLP [PYTHON]

QUESTION

Query using NLP [PYTHON]

Asked 2017-Jul-04 at 13:48

I'm working on a project where I need to extract "inputs" and "query intent" from text.

For example "What is the status of asset X26TH?"

In this case the main issue is to extract asset id which is X26TH, but how can I make my code understand that it's an id?

The other thing is to understand the query intent which is asset status. I found a good library for this called quepy, but it's meant for linux and I couldn't set it up on windows.

Please help me with the techniques and libraries.

...

ANSWER

Answered 2017-Jul-04 at 10:32

So you have two problems, ID extraction and intent detection.

ID Extraction

If your IDs follow a regular pattern and definitely don't look like English, you can catch them with a regex - if that's possible, that's great since it's very easy to do. If you have a fixed list of product IDs, just check to see if any of them are in the input. If neither of those work then you'll have to get more sophisticated.

Can you get your users to remember a little syntax? If you can request that they write things with a prefix like id:X26TH or similar that would make your job easier. You may find the way the plumber in Plan9 works informative.

If you need to work with whatever the users throw at you, you should look into using a sequence labeller or Named Entity Recognition (NER) system to get IDs. CRFs are probably a good fit for this task; here's a good technial introduction, and the New York Times also used one with success. Besides being trickier to set up a downside of this is that it will require training data, but there's really no way to avoid that.

Intent Detection

This is usually modelled as a text classification problem. You can find an overview of how to do that here. Here's some training examples from the article:

Source https://stackoverflow.com/questions/44897645

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

Vulnerabilities

No vulnerabilities reported

Install quepy

You can install using 'pip install quepy' or download it from GitHub, PyPI.
You can use quepy like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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