Graph-Based Memory
Graph-based memory is an AI memory architecture that stores information as interconnected nodes and relationships, enabling rich contextual understanding and persistent knowledge across interactions.
Understanding Graph-Based Memory
Traditional AI memory is either short-term, limited to a single conversation, or stored as flat key-value pairs. Graph-based memory organizes information as a network of entities and relationships. A person connects to their projects, which connect to tasks, which connect to meetings and documents. This structure allows an AI system to traverse relationships and build rich context. When you mention a project, the AI can instantly access related tasks, relevant emails, team members involved, and upcoming deadlines. Graph-based memory also enables temporal reasoning, understanding how relationships change over time.
How GAIA Uses Graph-Based Memory
GAIA maintains a graph-based memory system that connects all aspects of your digital life. When you discuss a project, GAIA accesses related emails, tasks, calendar events, documents, and team interactions stored as connected nodes.
Related Concepts
Knowledge Graph
A knowledge graph is a structured representation of information that organizes data as entities, their attributes, and the relationships between them, enabling machines to understand and reason about connected information.
Embeddings
Embeddings are dense numerical vector representations of data, such as text, images, or audio, that capture semantic meaning and relationships in a high-dimensional space.
Context Awareness
Context awareness in AI is the ability to understand the full situation surrounding a task or interaction, including who is involved, what has happened before, related projects, deadlines, and the user's preferences and patterns.
Semantic Search
Semantic search is a search technique that understands the meaning and intent behind a query, returning results based on conceptual relevance rather than exact keyword matches.
