YassQueenDB

YassQueenDB ?‍♀️??

!This readme is written by ChatGPT!

YassQueenDB is a graph database library that allows you to store, analyze, and search through your data in a graph format. By using the Universal Sentence Encoder, it provides an efficient and semantic approach to handle text data. ?

Benefits ?

1. Semantic understanding of text data
2. Efficient handling of relationships between data
3. Easy-to-use graph manipulation functions
4. Text summarization capabilities
5. Keyword extraction for easy indexing and searching ?
6. Flexible graph creation and management

Drawbacks ?

1. Requires downloading and loading the Universal Sentence Encoder model ?
2. Limited to text data only
3. Might be slow for very large datasets based on your system resources

Features ?

Graph Management

• Create, select, and delete graphs ?
• Add and delete nodes ?
• Add and delete edges ↔️

Text Analysis

• Semantic search for nodes ?
• Summarize sections and paragraphs
• Split paragraphs into sentences and create nodes from them

Data Manipulation ?

• Generate embeddings for text data ?
• Extract keywords from input data ?️
• Laplacian eigenmaps for dimensionality reduction ?
• Save and load graphs to/from files ?

Usage

from yassqueendb import GraphDatabase

#Create a YassQueenDB instance

db = GraphDatabase()

# Load the sentence encoder

db.load_sentence_encoder()

# Create a graph

db.create_graph('example_graph')

# Add a node

db.add_node('This is a sample sentence.')

# Add an edge between nodes

db.add_edge('node1', 'node2', 'relationship_name')

# Search for a node

db.search_node('node1')

# Show the graph

db.show_graph()

# Save the graph to a file

db.save_graph_to_file('example_graph.pickle')

# Load the graph from a file

db.load_graph_from_file('example_graph.pickle')

# Summarize a section of text

section = "This is a section of text. It has multiple paragraphs."
section_summary = db.summarize_section(section)`

Get started with YassQueenDB and unleash the power of semantic graph databases! ???