Step 1. Prepare data

A folder contains a list of legal articles 
A json file contains all documents

Step 2. Document indexing

1. Build TF-IDF vectors from corpus

    echo "=========================================="
    echo "Indexing TFIDF ..."
    INPUT=data/all_articles.json
    OUTPUT=output/model_TFIDF.pkl
    python document-indexing.py --index_type tfidf --file_type json --input $INPUT --output $OUTPUT

2. Build TF-IDF vectors from corpus then using MDS to reduce the space

    echo "=========================================="
    echo "Indexing MDS ..."
    INPUT=data/all_articles.json
    OUTPUT=output/model_TFIDF_MDS.pkl
    python document-indexing.py --index_type mds --file_type json --input $INPUT --output $OUTPUT

Step 3. Build topic vectors from corpus

1. Build topic model file from corpus

    echo "=========================================="
    echo "Creating topic vectors ..."
    INPUT=data/all_articles.json
    OUTPUT=output/topic.pickle
    python create-topic-model.py --file_type json --input $INPUT --output $OUTPUT

Step 4. Retrieval

Support 3 types of query:

1) query on TF-IDF space
2) query on MDS space (using MDS to reduce dimension)
3) query on TF-IDF space with injection of topic vectors

Step 5. API

1. start search api (default port = 8081)

    $ python search-api.py --port 8081

Step 6. Run API on web browser

Notes: using "_" instead of spaces Query: A demand for payment shall not have the effect URL:

http://0.0.0.0:8081/api/search/A_demand_for_payment_shall_not_have_the_effect

Notes

1. Upload to server

    git checkout master
    git add -A . && git commit -m "Upload"
    git push origin master 

1. Download from server

    git checkout master
    git pull origin master