Announcing Lakebase Search: agent-native retrieval built into Lakebase Postgres
Today, we're introducing Lakebase Search: hybrid vector and full-text retrieval built...
The rapid development and adoption of large language models and AI agents necessitate more efficient and robust retrieval augmented generation capabilities directly within data infrastructure.
This development allows for more sophisticated and autonomous AI agents to access and process information directly from structured data sources, enhancing their capabilities and reducing latency in critical enterprise applications.
Data platforms are evolving to natively support agent-native retrieval, shifting the burden of connecting LLMs to data from application layers to the underlying database infrastructure.
- · Databricks
- · AI Agent Developers
- · Enterprises adopting AI
- · Hybrid cloud data platforms
Seamless access to proprietary enterprise data for AI agents enables more accurate and contextually relevant responses.
Increased efficiency and automation in workflows as AI agents can autonomously retrieve and act upon internal enterprise knowledge.
The blurring of lines between traditional database systems and AI inference engines, leading to a new class of 'intelligent' data infrastructure.
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Read at Databricks Blog