DeepTextSearch 0.1

Deep Text Search is an AI-powered multilingual text search and recommendation engine with state-of-the-art transformer-based multilingual text embedding (50+ languages).

Deep Text Search - AI-Based Text Search & Recommendation System

Deep Text Search is an AI-powered multilingual text search and recommendation engine with state-of-the-art transformer-based multilingual text embedding (50+ languages).

Creators

Nilesh Verma

Features

• Faster Search.
• High Accurate Text Recommendation and Search Output Result.
• Best for Implementing on python based web application or APIs.
• Best implementation for College students and freshers for project creation.
• Applications are Text based News, Social media post, E-commerce Product recommendation and other text-based platforms that want to implement text recommendation and search.

Installation

This library is compatible with both windows and Linux system you can just use PIP command to install this library on your system:

pip install DeepImageSearch

How To Use?

We have provided the Demo folder under the GitHub repository, you can find the example in both .py and .ipynb file. Following are the ideal flow of the code:

1. Importing the Important Classes

There are three important classes you need to load LoadData - for data loading, TextEmbedder - for embedding the text to data, TextSearch - For searching the text.

# Importing the proper classes
from DeepTextSearch import LoadData,TextEmbedder,TextSearch

2. Loading the Images Data

For loading the images data we need to use the LoadData object, from there we can import text from the CSV file.

# Load data from CSV file
data = LoadData().from_csv("../your_file_name.csv")

3. Embedding and Saving The File in Local Folder

For Embedding we are using state of the art multilangual Sentence Transformer Embedding,We also store the information of the Embedding for fruther use on the local path [embedding-data/] folder.

# For Serching we need to Embed Data first, After Embedding all the data stored on the local path
TextEmbedder().embed(corpus_list=data)

3. Searching

For Searching and Recommendation we are Comparing Cosian Similarity and then corpus are arrenged there similarity score:

# for searching, you need to give the query_text  and the number of the similar text you want
TextSearch().find_similar(query_text="What are the key features of Node.js?",top_n=10)

Complete Code

# Importing the proper classes
from DeepTextSearch import LoadData,TextEmbedder,TextSearch
# Load data from CSV file
data = LoadData().from_csv("../your_file_name.csv")
# For Serching we need to Embed Data first, After Embedding all the data stored on the local path
TextEmbedder().embed(corpus_list=data)
# for searching, you need to give the query_text  and the number of the similar text you want
TextSearch().find_similar(query_text="What are the key features of Node.js?",top_n=10)

License

MIT License

Copyright (c) 2021 Nilesh Verma

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

More cool features will be added in future. Feel free to give suggestions, report bugs and contribute.