4lt7ab-grimoire

Your task is to develop a simple yet powerful semantic search engine using the Python package '4lt7ab-grimoire'. This package integrates SQLite with vector similarity search capabilities, making it ideal for applications requiring fast and efficient text search based on meaning rather than exact matches. Your goal is to create a command-line tool that allows users to store documents and then query them using natural language. Here’s a detailed breakdown of your project requirements:

1. **Project Setup**: Begin by installing the '4lt7ab-grimoire' package and setting up a basic Python environment.
2. **Document Storage**: Implement functionality to ingest various types of documents (e.g., PDFs, Word Docs, plain text files). Use the package to convert these documents into vectors that capture their semantic meaning and store them in a SQLite database.
3. **Query Interface**: Develop a user-friendly CLI where users can input queries in natural language. The system should return relevant document snippets or links based on the semantic similarity of the query to the stored documents.
4. **Enhanced Features**:
   - **Batch Processing**: Allow users to upload multiple documents at once.
   - **Search Filters**: Provide options to filter searches by date, file type, or specific keywords.
   - **User Authentication**: Basic authentication to protect document privacy.
5. **Testing and Validation**: Ensure the application works as expected by testing it with a variety of documents and queries.
6. **Documentation**: Write clear documentation explaining how to install and use your application.

In this project, you will leverage the '4lt7ab-grimoire' package's ability to handle large volumes of data efficiently and its capacity for semantic search, making your application both versatile and scalable.