# pykosinus
pykosinus is an open-source Python library for text similarity search scoring. It provides a fast and memory-efficient way to calculate cosine similarity scores, making it suitable for various text similarity applications. The library is designed to be user-friendly and encourages contributions from the community.
## Installation
To install pykosinus, make sure you have Python 3.8.17 or higher installed. Then, you can install the library using pip:
```shell
pip install pykosinus
```
## Additional Library for Mac Users
If you are using pykosinus on a Mac, you may need to install the GCC compiler to enable certain features. GCC is a widely used compiler for various programming languages.
To install GCC on macOS, you can use Homebrew, a popular package manager for macOS. Follow these steps to install GCC using Homebrew:
- Open a terminal window.
- Install Homebrew by running the following command:
```sh
[/bin/bash](VALID_FILE) -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
```
- Install GCC by running the following command:
```sh
brew install gcc
```
- Verify the installation by running the following command:
```sh
gcc --version
```
- Set gfortran
```sh
export FC=gfortran
```
- Verify gfortran installation
```sh
gfortran --version
```
- Install openblas and set pkg config openblas
```sh
brew install openblas
```
```sh
export PKG_CONFIG_PATH="/opt/homebrew/opt/openblas/lib/pkgconfig"
```
## Usage
To use pykosinus in your Python project, you can follow these steps:
- Import the necessary modules and classes:
```python
from pykosinus import Content
from pykosinus.lib.scoring import TextScoring
```
- Create an instance of the **TextScoring** class, providing the collection name as a parameter:
```python
similarity = TextScoring(collection_name)
```
- Set the contents to be searched using the **push_contents** method, passing a list of **Content** objects:
```python
contents = [
Content(
content="Lorem ipsum dolor sit amet, consectetur adipiscing elit.",
identifier="blog-1",
section="blog_title",
),
Content(
content="Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.",
identifier="blog-2",
section="blog_title",
),
Content(
content="Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris.",
identifier="blog-3",
section="blog_title",
),
# Add more contents as needed
]
similarity.push_contents(contents)
```
- Initialize the similarity search by calling the **initialize** method:
```python
similarity.initialize()
```
- Perform a similarity search by calling the **search** method, providing a keyword and an optional threshold:
```python
results = similarity.search(keyword="search keyword", threshold=0.2)
```
- The **search** method returns a list of **ScoringResult** objects, which contain the relevant information about the search results. You can access the properties of each result, such as **identifier**, **content**, **section**, **similar**, and **score**.
```python
for result in results:
print(
result.identifier, result.content, result.section, result.similar, result.score
)
```
## Contributing
pykosinus welcomes contributions from the community. If you would like to contribute to the library, please follow these steps:
- Fork the pykosinus repository on [**GitHub**](https://github.com/ruriazz/pykosinus).
- Create a new branch for your feature or bug fix.
- Make your changes and commit them with descriptive commit messages.
- Push your changes to your forked repository.
- Submit a pull request to the master pykosinus repository, explaining the changes you have made.
## Versioning
pykosinus is currently in version 0.2.0. We encourage continuous development and contributions from other contributors to improve and expand the library.
## License
pykosinus is released under the [MIT License](https://opensource.org/licenses/MIT).
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"description": "# pykosinus\n\npykosinus is an open-source Python library for text similarity search scoring. It provides a fast and memory-efficient way to calculate cosine similarity scores, making it suitable for various text similarity applications. The library is designed to be user-friendly and encourages contributions from the community.\n\n## Installation\n\nTo install pykosinus, make sure you have Python 3.8.17 or higher installed. Then, you can install the library using pip:\n\n```shell\npip install pykosinus\n```\n\n## Additional Library for Mac Users\nIf you are using pykosinus on a Mac, you may need to install the GCC compiler to enable certain features. GCC is a widely used compiler for various programming languages.\n\nTo install GCC on macOS, you can use Homebrew, a popular package manager for macOS. Follow these steps to install GCC using Homebrew:\n\n- Open a terminal window.\n- Install Homebrew by running the following command:\n```sh\n[/bin/bash](VALID_FILE) -c \"$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)\"\n```\n- Install GCC by running the following command:\n```sh\nbrew install gcc\n```\n- Verify the installation by running the following command:\n```sh\ngcc --version\n```\n- Set gfortran\n```sh\nexport FC=gfortran\n```\n\n- Verify gfortran installation\n```sh\ngfortran --version\n```\n\n- Install openblas and set pkg config openblas\n```sh\nbrew install openblas\n```\n```sh\nexport PKG_CONFIG_PATH=\"/opt/homebrew/opt/openblas/lib/pkgconfig\"\n```\n\n\n## Usage\nTo use pykosinus in your Python project, you can follow these steps:\n\n- Import the necessary modules and classes:\n```python\nfrom pykosinus import Content\nfrom pykosinus.lib.scoring import TextScoring\n```\n\n- Create an instance of the **TextScoring** class, providing the collection name as a parameter:\n```python\nsimilarity = TextScoring(collection_name)\n```\n\n- Set the contents to be searched using the **push_contents** method, passing a list of **Content** objects:\n```python\ncontents = [\n Content(\n content=\"Lorem ipsum dolor sit amet, consectetur adipiscing elit.\",\n identifier=\"blog-1\",\n section=\"blog_title\",\n ),\n Content(\n content=\"Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.\",\n identifier=\"blog-2\",\n section=\"blog_title\",\n ),\n Content(\n content=\"Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris.\",\n identifier=\"blog-3\",\n section=\"blog_title\",\n ),\n # Add more contents as needed\n]\nsimilarity.push_contents(contents)\n```\n\n- Initialize the similarity search by calling the **initialize** method:\n```python\nsimilarity.initialize()\n```\n\n- Perform a similarity search by calling the **search** method, providing a keyword and an optional threshold:\n```python\nresults = similarity.search(keyword=\"search keyword\", threshold=0.2)\n```\n\n- The **search** method returns a list of **ScoringResult** objects, which contain the relevant information about the search results. You can access the properties of each result, such as **identifier**, **content**, **section**, **similar**, and **score**.\n```python\nfor result in results:\n print(\n result.identifier, result.content, result.section, result.similar, result.score\n )\n```\n\n\n## Contributing\npykosinus welcomes contributions from the community. If you would like to contribute to the library, please follow these steps:\n- Fork the pykosinus repository on [**GitHub**](https://github.com/ruriazz/pykosinus).\n- Create a new branch for your feature or bug fix.\n- Make your changes and commit them with descriptive commit messages.\n- Push your changes to your forked repository.\n- Submit a pull request to the master pykosinus repository, explaining the changes you have made.\n\n## Versioning\npykosinus is currently in version 0.2.0. We encourage continuous development and contributions from other contributors to improve and expand the library.\n\n## License\npykosinus is released under the [MIT License](https://opensource.org/licenses/MIT).\n",
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