Data Connectivity
Data Connectivity refers to the mechanisms, protocols, and architectures that enable seamless exchange of information between disparate systems, applications, and data sources. In the context of modern AI systems, it specifically addresses how large-language-models interact with external tools, real-time data, and user contexts.
Core Principles
- Interoperability: Standardized interfaces allowing heterogeneous systems to communicate.
- Real-time Access: Low-latency retrieval of external data during inference.
- Security & Governance: Controlled access patterns ensuring data privacy and integrity.
Key Protocols and Standards
Model Context Protocol (MCP)
The model-context-protocol is an emerging open standard designed to simplify the integration of AI models with external resources.
- Purpose: Solves inherent complexities in connecting llms to external tools, data sources, and control interfaces.
- Mechanism: Provides a unified interface for AI models to request context, execute tools, and retrieve data without custom integrations for each source.
- Source Integration: See Model Context Protocol: Standardizing AI Model Interaction with External Resources for detailed technical breakdown.