!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. 📚🧠🚀
- Semantic understanding of text data 📖
- Efficient handling of relationships between data 💼
- Easy-to-use graph manipulation functions 🛠️
- Text summarization capabilities 📝
- Keyword extraction for easy indexing and searching 🔎
- Flexible graph creation and management 🌐
- Requires downloading and loading the Universal Sentence Encoder model 📥
- Limited to text data only 🚫
- Might be slow for very large datasets based on your system resources 🐌
- Create, select, and delete graphs 📁
- Add and delete nodes 📌
- Add and delete edges
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- Semantic search for nodes 🧐
- Summarize sections and paragraphs 📝
- Split paragraphs into sentences and create nodes from them 📚
- Generate embeddings for text data 🔍
- Extract keywords from input data 🏷️
- Laplacian eigenmaps for dimensionality reduction 📉
- Save and load graphs to/from files 💾
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! 🎉💃🕺