#Vector Databases
8 articles with this tag
Databricks Puts AI Search Inside Postgres
Databricks integrates agent-native retrieval into Lakebase Postgres with Lakebase Search, offering cost-effective hybrid search.
Claude Code Benchmarking: Semantic Search vs. Grep
Turbopuffer's Kuba Rogut benchmarks semantic code retrieval on Claude Code, revealing how semantic search enhances AI agent precision and efficiency compared to grep.
Neo4j CEO Emil Eifrem on Graph Databases and AI
Neo4j CEO Emil Eifrem discusses the symbiotic relationship between graph databases and AI, highlighting how relational context is crucial for modern AI applications.
pgvector: Postgres's AI Vector Power-Up
pgvector brings vector embeddings and similarity search directly into PostgreSQL, simplifying AI apps like RAG and semantic search.
Cloudflare AI Search Simplifies Agent Development
Cloudflare AI Search offers a simplified, plug-and-play primitive for developers to integrate robust search capabilities into AI agents.
Chroma's Context-1: Faster, Cheaper AI Search
Chroma Context-1, a 20B parameter AI model, offers frontier-level search performance at a fraction of the cost and latency, using self-editing to manage context efficiently.
AI Search & The Future of Databases
Simon Eskildsen of TurboPuffer discusses the critical role of databases in AI search, the rise of vector databases, and the challenges of scaling data infrastructure.
IBM Master Inventor Martin Keen on Agentic Storage
IBM Master Inventor Martin Keen explains 'Agentic Storage,' detailing how AI agents interact with diverse storage systems and the critical safety layers needed for responsible operation.