This final year project focuses on searching Quranic verses using lexical, semantic, and combined approaches.
The Quran Search API allows you to search for text using various measures such as lexical, semantic, and lexical semantic.
It provides an efficient way to access and retrieve information from the Quran.
In this project, we used two exciting methods: one for measuring lexical similarity and another for gauging semantic similarity. In the application, users have the flexibility to opt for either of these methods independently or harness the power of both in combination.
For measuring Lexical Similarity the methods used are TF-IDF and Cosine similarity.
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TF-IDF: TF-IDF is used to evaluate the importance of words in the translation of the Qur'an. TF-IDF is a method used to evaluate the importance of a word in a document or text. This is done by calculating how often the word appears in the document (Term Frequency) and the extent to which the word is unique across documents (Inverse Document Frequency).
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Cosine similarity is a method used to measure the degree to which two vectors (in this case, vector representations of words) are in lexical relationship to each other. In the context of Ayatnesia, cosine similarity is used to compare the TF-IDF vector between the verses of the Qur'an and the keyword. The higher the cosine similarity value between the verse and the keyword, the more lexically similar the two things are.
While to measure Semantic Similarity in Ayatnesia when searching for verses in the Qur'an, the method used is Word Embedding with FastText. This method involves representing words as high-dimensional vectors in a semantic space using FastText, a powerful word embedding tool.
These vector representations capture the semantic meanings of words, allowing Ayatnesia to compare and measure the similarity between verses in the Qur'an based on their semantic content. By utilizing Word Embedding with FastText, Ayatnesia enables users to find verses with similar semantic meanings, enhancing the effectiveness of verse searches and facilitating a deeper understanding of the Qur'an.
The API empowers you with these features to enhance your search capabilities and facilitate a comprehensive analysis of the Quran. 🚀
💎 Key Features:
- Get all Qur'an Surahs
- Get detailed information about a specific Quran Surah
- Search using lexical measures
- Search using semantic measures
- Search using lexical semantic measures
- Python >= 3
- Clone or download de repository:
$ git clone https://github.com/utrodus/fastapi-quran-finder.git
- Open the console inside the project directory and create a virtual environment (You can skip this step if you have docker installed).
$ python -m venv venv
$ source venv/Scripts/activate
- Install the app
(venv) $ pip install -r requirements.txt
$ uvicorn main:app --reload
$ python src/setup_nltk.py
first off all, run preprocessing quran data for preprocessing ayahs for each surahs
$ python src/preprocessing/quran_preprocessing.py
This command will build a Docker image named "ayatnesia" using the Dockerfile in the current directory. The -t option assigns a tag (name) to the image.
$ docker build -t ayatnesia .
Run the following command to save the Docker image as a tar file:
$ docker save ayatnesia -o ayatnesia.tar
Start a Docker container using the following command:
$ docker run -d --name ayatnesia-container -p 3100:3100 ayatnesia
Once you are running the server open the Swagger UI App to checkout the API documentation.