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Ask coding questions directly from the terminal

Project description

codequestion: Ask coding questions directly from the terminal

codequestion is a Python terminal program that allows an user to ask coding questions directly from the terminal. Many developers will have a web browser window open while they develop and run web searches as questions arise. codequestion attempts to make that process faster so you can focus on development.

The default model for codequestion is built off the Stack Exchange Dumps on codequestion runs locally against a pre-trained model using data from Stack Exchange. No network connection is required once installed. The model executes similarity queries to find similar questions to the input query.

An example of how codequestion works is shown below:



The easiest way to install is via pip and PyPI

pip install codequestion

You can also use Git to clone the repository from GitHub and install it manually:

git clone
cd codequestion
pip install .

Python 3.5+ is supported

Downloading a model

Once codequestion is installed, a model needs to be downloaded.

python -m

The model will be stored in ~/.codequestion/

The model can also be manually installed if the machine doesn't have direct internet access. Pre-trained models are pulled from the github release page

unzip ~/.codequestion

It is possible for codequestion to be customized to run against a custom question-answer repository and more will come on that in the future. At this time, only the Stack Exchange model is supported.

Running queries

The fastest way to run queries is to start a codequestion shell


A prompt will come up. Queries can be typed directly into the console.

Technical overview

The full source code for codequestion is available on github. Code is licensed under the MIT license.

git clone
cd codequestion

The following is an overview of how this project works.

Processing the raw data dumps

The raw 7z XML dumps from Stack Exchange are processed through a series of steps. Only highly scored questions with answers are retrieved for storage in the model. Questions and answers are consolidated into a single SQLite file called questions.db. The schema for questions.db is below.

questions.db schema

Source TEXT
Question TEXT
QuestionUser TEXT
Answer TEXT
AnswerUser TEXT
Reference TEXT


codequestion builds a sentence embeddings index for questions.db. Each question in the questions.db schema is tokenized and resolved to a word embedding. The word embedding model is a custom fastText model built on questions.db. Once each token is converted to word embeddings, a weighted sentence embedding is created. Word embeddings are weighed using a BM25 index over all the tokens in the repository, with one modification. Tags are used to boost the weights of tag tokens.

Once questions.db is converted to a collection of sentence embeddings, they are normalized and stored in faiss, which allows for fast similarity searches.


codequestion tokenizes each query using the same method as during indexing. Those tokens are used to build a sentence embedding. That embedding is queried against the faiss index to find the most similar questions.

Model accuracy

The following sections show test results for various word vector/scoring combinations. SE 300d word vectors with BM25 scoring does the best against this dataset. Even with the reduced vocabulary of < 1M Stack Exchange questions, SE 300d - BM25 does reasonably well against the STS Benchmark.

StackExchange Query

Models scored using Mean Reciprocal Rank (MRR)

Model MRR
SE 300d - BM25 76.3
ParaNMT - BM25 67.4
FastText - BM25 66.1
BM25 49.5
TF-IDF 45.9

STS Benchmark

Models scored using Pearson Correlation

Model Supervision Dev Test
ParaNMT - BM25 Train 82.6 78.1
FastText - BM25 Train 79.8 72.7
SE 300d - BM25 Train 77.0 69.1


To reproduce the tests above, you need to download the test data into ~/.codequestion/test

mkdir -p ~/.codequestion/test/stackexchange
wget -P ~/.codequestion/test/stackexchange
tar -C ~/.codequestion/test -xvzf Stsbenchmark.tar.gz
python -m codequestion.evaluate

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