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. This means GAIA understands context deeply. It knows that the email from your client relates to the project deadline next week and the task you created yesterday. Over time, GAIA learns your patterns and preferences through this interconnected memory.
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.
Vector Embeddings
Vector embeddings are numerical representations of text, images, or other data that capture semantic meaning, enabling machines to understand similarity and relationships between pieces of information.
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.
Frequently Asked Questions
How does graph-based memory differ from vector memory?
Vector memory stores information as numerical embeddings for similarity search. Graph-based memory stores information as connected entities and relationships. GAIA uses both: vector embeddings in ChromaDB for semantic search and graph structures for understanding relationships between your tasks, emails, meetings, and projects.
Does GAIA's memory persist across sessions?
Yes. GAIA's graph-based memory is persistent. It remembers your projects, preferences, communication patterns, and work context across all interactions, building a deeper understanding of your workflow over time.
