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keras-search-engine

A simple document and image search engine implemented in keras

Features

  • Document level encoding using GloVe Word Embedding
  • Support Full Text Search using GloVe Embedding
  • Support Document Indexing via web api
  • Support Image Search
  • Support Image Indexing via web api
  • Document auto-encoding using LSTM encoder-decoder with GloVe Embedding

Usage

Running Web Api Server

Goto keras_search_engine_web directory and run the following command:

python flaskr.py

Now navigate your browser to http://localhost:5000 and you can try out

  • the story book search (by which user enters search query on the story books that are loaded from keras_search_engine_train/data/texts)
  • the image search (by which user uploads an image to search among images stored in the keras_search_engine_train/data/images)

Invoke web api to index and search text document

With the web api server running, you can index a new document by calling the following web api:

http://localhost:5000/index_text

For example the following is the curl command to call the web api to index some documents:

curl -H 'Content-Type application/json' -X POST -d '{"doc":"Whether you think that you can, or that you can't, you are usually right."}' http://localhost:5000/index_text
curl -H 'Content-Type application/json' -X POST -d '{"doc":"Try to learn something about everything and everything about something."}' http://localhost:5000/index_text
curl -H 'Content-Type application/json' -X POST -d '{"doc":"You can avoid reality, but you cannot avoid the consequences of avoiding reality."}' http://localhost:5000/index_text
curl -H 'Content-Type application/json' -X POST -d '{"doc":"A mathematician is a device for turning coffee into theorems."}' http://localhost:5000/index_text
curl -H 'Content-Type application/json' -X POST -d '{"doc":"In theory, there is no difference between theory and practice. But in practice, there is."}' http://localhost:5000/index_text
curl -H 'Content-Type application/json' -X POST -d '{"doc":"I find that the harder I work, the more luck I seem to have."}' http://localhost:5000/index_text

To query using the web api, you can call the following web api:

curl -H 'Content-Type application/json' -X POST -d '{"query":"mathematician and coffee", "limit": 3, "model": "glove"}' http://localhost:5000/search_text

Invoke web api to index and search image

With the web api server running, you can index a new image by calling the following web api via POST request:

http://localhost:5000/index_image

You can query similar images by calling the following web api POST request:

http://localhost:5000/search_image/10

where 10 is the limit on the number of images returned

Use SearchEngineClient

There is also a SearchEngineClient class in the keras_search_engine_client, the sample codes looks like:

from keras_search_engine_client.search_engine_client import SearchEngineClient
client = SearchEngineClient()

# text indexing and search
doc_count = client.doc_count()
if doc_count < 4:
    client.index_text('Whether you think that you can, or that you can.')
    client.index_text('Try to learn something about everything and everything about something.')
    client.index_text('You can avoid reality, but you cannot avoid the consequences of avoiding reality.')
    client.index_text('A mathematician is a device for turning coffee into theorems.')
    client.index_text('In theory, there is no difference between theory and practice. But in practice, there is.')
    client.index_text('I find that the harder I work, the more luck I seem to have.')
client.search_text(query='mathematician and coffee', limit=3, model='glove')
client.search_text(query='mathematician and coffee', limit=3, model='doc-encoder')

# image indexing and search
client.index_image('./images/Pokemon7.png')
client.search_image('./images/Pokemon1.jpg', 6)

Configure to run on GPU on Windows

  • Step 1: Change tensorflow to tensorflow-gpu in requirements.txt and install tensorflow-gpu
  • Step 2: Download and install the CUDA® Toolkit 9.0 (Please note that currently CUDA® Toolkit 9.1 is not yet supported by tensorflow, therefore you should download CUDA® Toolkit 9.0)
  • Step 3: Download and unzip the cuDNN 7.4 for CUDA@ Toolkit 9.0 and add the bin folder of the unzipped directory to the $PATH of your Windows environment