Building AI applications that require reasoning over unstructured text data is a complex and time-consuming process, often requiring manual effort to extract entities, relationships, and context. Traditional methods for knowledge graph construction and retrieval lack the speed, scalability, and transparency needed for rapid AI development.

Smabbler Galaxia is a graph language model (GLM) and knowledge graph platform that automates the transformation of unstructured text into structured, semantically rich graphs. By combining graph-based data structures with natural language processing (NLP), Galaxia enables users to organize, retrieve, and reason over information without manual design or embeddings. The platform enriches raw data with synonyms, similarities, and taxonomies, and provides built-in retrieval algorithms for rapid Graph RAG (Retrieval-Augmented Generation) development. Galaxia's architecture supports low computational requirements, running efficiently on CPUs without the need for GPU processing.

Automated graph construction from unstructured text data, eliminating manual effort for entity and relation extraction.

Knowledge augmentation at the data level, enhancing raw data with additional context for improved information retrieval.

Built-in flexible retrieval algorithms that automatically locate and retrieve relevant data points.

In-memory processing for scalable performance by adding more RAM or servers.

Transparent and explainable retrieval, showing the connection between data points and the data used to provide information.

APIs and SDKs for integration into AI pipelines, such as LangChain and LlamaIndex.

Support for hypergraph structures with multi-way relationships, enabling the modeling of complex data.

Galaxia is designed for AI builders and developers who need to transform data into graph structures and build knowledge-powered AI/LLM applications.