About

The project aims to implement a sophisticated search algorithm using a plain tsv file with input entries.

Starting REST API server

Start rest api server:

python rest-api.py --verbose --w2v /home/dima/models/ArModel100w2v.txt --port 8080 --debug

Parameters:

• verbose - controls quantity of active prints primarily during processing the query (i.e. output of tokens and lemmas)
• w2v - path to the w2v model for looking up similar words (if not set, then w2v model will have not been created and as a consequence server will start much faster) it is possible that in future other embeddings will be available as well (suche as bert, elmo, fasttext and so on)
• port - port on which web server will be running on
• debug - passes over directly to the app.run method of the flask module

Sending queries

curl --request POST \
  --url http://localhost:8080/search \
  --header 'content-type: application/json' \
  --data '{
	"query": "грачи улятели",
	"fix-misspellings": true,
	"use-embeddings": false,
	"similar-tokens-score-weight": 1.5,
	"similar-tokens-quantity": 2,
	"products-quantity": 10,
	"similar-products-quantity": 5,
	"min-word-difference-ratio": 90
}' | python -m json.tool | ascii2uni -a U -q

Parameters:

• query - search query to use
• fix-misspellings - wether or not use module for fixing spelling mistakes by applying Levenstein distance
• max-word-difference-ratio - gives minimal score of similarity between word to fix and found fixed word for exchange to make sense
• use-embeddings - wether or not apply embeddings to search for similar tokens
• similar-tokens-score-weight - how much score brings one match of lemma or token when searching for similar words
• similar-tokens-quantity - how many similar tokens to consider
• products-quantity - how many products return as a result of direct search
• similar-products-quantity - how many products return as a result of search by similar words found using embeddings Response example:

{
  "http://tender.hack.spb/1159309": {
    "name": "Кonos фильтр бумажный для кофеварок №4, 80 шт (Folie)",
    "score": 1,
    "keywords": [
      "бумажный"
    ]
  },
  "http://tender.hack.spb/1100321": {
    "name": "Полотенца бумажные 110 шт., KIMBERLY-CLARK Scott, комплект 16 шт., Slimfold, белые, 29,5х19 см, М-fold, диспенсер 601535, АРТ.5856",
    "score": 1,
    "keywords": [
      "бумажный"
    ]
  },
  "http://tender.hack.spb/1159308": {
    "name": "Кonos фильтр бумажный для кофеварок №4, 100 шт (Folie)",
    "score": 1,
    "keywords": [
      "бумажный"
    ]
  },
  "http://tender.hack.spb/1159304": {
    "name": "Кonos фильтр бумажный для кофеварки №4, 100 шт",
    "score": 1,
    "keywords": [
      "бумажный"
    ]
  },
  "http://tender.hack.spb/1161231": {
    "name": "Бумажные фильтр-мешки Karcher 5 шт. 6.904-322.0",
    "score": 1,
    "keywords": [
      "бумажный"
    ]
  }
}

Additional scripts

• make-graph.py - to convert data from tsv format to ttl
• good_inqury.py - for fixing spelling mistakes
• data_parsers.py - for parsing tsv rows and converting them to dicts
• calculate-name-embeddings.py - for calculating embedding vectors of product names with ELMo as a base model (requires a lot of computing power)
• load_elmo_embeddings.py - to test that ELMo embeddings were saved properly
• preprocess-user-input.py - the main model which processes user's query - splits it into tokens, lemmatizes and connects to some other modules
• rest-api.py - code for starting flask http server
• run-remote-sparql-query.py - basic example of running a simple sparqle query referring to a remote blazegraph instance
• run-sparql-query.py - basic example of running a simple sparqle query referring to a local blazegraph instance