Galaxia is a graph language model (GLM) and knowledge graph platform that automates the transformation of unstructured data into structured and semantically rich graphs. It combines graph-based data structures with natural language processing to build a transparent and flexible system for organizing, retrieving and reasoning over information.
- Automated Graph Construction
Converts unstructured text into structured, interconnected knowledge by automatically extracting entities and relationships - eliminating the need for manual NLP preprocessing. - Knowledge Augmentation (Data Level)
Enriches input data with additional context such as synonyms, taxonomies and similarities to support more accurate retrieval and understanding. Replaces the need for traditional embeddings. - Knowledge Augmentation (AI Model Level)
Enhances AI models by injecting external domain knowledge, represented as graph structures, at inference time. Improves reasoning, accuracy and context awareness (Galaxia Graph RAG). - Semantic Retrieval
Retrieves information based on semantic relationships in the graph, not just vector similarity - enabling more accurate and explainable results. - Automated Retrieval
Uses built-in, flexible algorithms to automatically locate and extract relevant information from the graph with no manual query engineering required. - In-Memory Processing
Graphs are processed directly in RAM, making retrieval fast and easily scalable. - Retrieval Transparency
Provides clear visibility into which data was used for a result and how data points are connected.
Galaxia integrates with common AI frameworks for easy use in existing pipelines:
Build Your Graph
https://beta.smabbler.com
Documentation
https://smabbler.gitbook.io/smabbler