Demo with 500k+ documents should be running at search.lookies.io.

# sudo apt-get install python3.4-dev
virtualenv -p /usr/local/bin/python3 py3env # see: which python3
source py3env/bin/activate
pip install Flask pymongo
pip install nltk    # for the stemmer (todo)

pip install gensim  # for word2vec # cython numpy word2vec
wget https://s3.amazonaws.com/mordecai-geo/GoogleNews-vectors-negative300.bin.gz
# mirror of: https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit?usp=sharing

python app.py
# listenning on localhost:6001

green

I clearly need to transition from build then test/maintain to test then build/maintain...

• The tokenizer is tested.
• The rest still not so much...

• All the logic is in the class term_document_matrix_abstract in term_document.py
• Low-level details are drafted in abstract methods and left to be implemented
• A implementation using a dict-of-dicts is available.
• A sparse matrix could be usefull as well and interesting for a comparaison.

• String transforms and token filters can be used easily to create a tokenizer
• Stemming, lowercasing and some others are shown as an example in tokenizers.py
• Tokenization is available through

tokens = my_tokenizer.tokenize(string)

• Extensible : we just need to provide the term_document_matrix_abstract constructor with an iterable over documents
• Available : a JSON file reader and one fetching docs from MongoDB.
• We could read the JSON in chunks but since we are keeping all the data in memory anyway...

• Maybe more SOLID to have the term-doc-freq data structure as member of the main data structure
• A full-fledged class for documents instead of a dict could help
• Typing is poor (Python..)
• Python3 only.

• Add bigrams transformation (continue work from train_word2vec..)
• See how to improve perf.

• Indexing : Time should grow in O(tokens) ~= O(documents)
• Index size : the dict-of-dicts approach is heavy...
• Search : O(n * ln(n)) where n is the number of documents where the query terms appear.