Welcome to the Query System project, a powerful tool for information retrieval based on a Boolean retrieval model coupled with a Vector Space model. This versatile system allows users to perform phrase queries, retrieve documents ranked by similarity scores, and gain insights from processed text data.
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Boolean Retrieval Model: A robust Boolean retrieval model forms the foundation of the system, enabling users to execute precise and complex queries using operators such as "AND," "OR," and "NOT."
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Vector Space Model: Leveraging a Vector Space model enhances the system's capability to retrieve documents ranked based on similarity scores, providing users with relevant and ordered results.
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Phrase Queries: The system supports phrase queries, allowing users to search for specific phrases within the document collection.
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Internal Workflow: A streamlined internal workflow processes the collection of text data, employs Natural Language Processing (NLP) techniques, and generates basic insights. The workflow also constructs a positional index of the collection for efficient query processing.
The code is organized into two main parts:
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Query-System-notebooks: This section contains Jupyter Notebooks explaining the underlying logic, implementation details, and examples of using the Query System.
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Query-System-CMD: Find the main executable file for running the Query System via the command line. Refer to this file to interact with the system using the command-line interface.
Explore the Query-System-notebooks Jupyter Notebooks file for in-depth explanations and examples of using the Query System.
Example usage:
To run the Query System on your collection of files, follow these steps:
- Clone the repository to your local machine.
- Navigate to the
Query-System-CMDdirectory. - Open the
Query-System-CMDpython file and change the commented line of code to your collection directory that you want to run the query system on - Run the main executable file to start the system in the command-line interface.