Automated Graph Construction
Automated Graph Construction enables converting unstructured data into interconnected knowledge. Some of the key benefits include speed and scalability by eliminating manual effort to apply NLP techniques for entity and relations extraction, and graph construction.
Knowledge Augmentation - at Data Level
Enhancing and expanding raw data with additional information and context to help retrieve relevant information, which is then used for knowledge augmentation in generative AI systems. It replaces the need for traditional embeddings.
Knowledge Augmentation - at AI Model Level
Enhancing AI model by integrating external knowledge (e.g., domain-specific information or information retrieved from knowledge base) represented as a graph structure and providing it at inference time to improve AI model reasoning, accuracy, and contextual understanding(Galaxia Graph RAG)
Transparency and Explainability
In Galaxia, its retrieval transparency and explainability are an inherent feature. It clearly shows the connection between data points and what data was used to provide information.
Semantic Retrieval
Galaxia enables retrieval-augmented generation (RAG) workflows where information is retrieved based on semantic relationships, not just vector similarity, improving accuracy and explainability in AI applications.
Automated retrieval
Galaxia has built-in flexible retrieval algorithms that automatically locate and retrieve relevant data points.
In-memory processing
Galaxia Graphis processed directly in RAM. It makes it easily scalable by adding more RAM or servers.
