Databricks launches Lakebase Search with full-text and vector search natively in Postgres for AI agents
vector-search
29 tracked signals on vector-search.
Databricks introduces NEAREST BY Join syntax for scalable vector search in runtime
Systematic evals beat gut-feel releases: 94% pass rates beat 'it looked good'
“I ran 200 different test scenarios, 94% of them passed, so we're releasing this feature.”
Microsoft SQL engine adds agentic RAG and chat completion by calling AI models via REST from stored procedures.
“It's the number one AI functionality we built into the engine for SQL 25 and Azure SQL for Hyperscale.”
SQL Server 2025 adds native vector search enabling semantic, meaning-based queries beyond keyword matching.
“But vector search is completely different uh in the sense uh that it really is a search for meaning.”
Amazon Nova Multimodal Embeddings delivered highest F1 scores for natural-language semantic search over aerial imagery at scale.
Serenity Star runs production RAG for AI agents at scale on Azure Database for PostgreSQL.
“It is a live product with thousands of agents and millions of executions, and we use Azure Database for PostgreSQL for our RAG implementation.”
RAG isn't dead; hybrid tool-rich retrieval is becoming the default for serious agentic search.
“rag is dead, how hybrid tool tool rich retrieval is becoming a default for serious agentic search.”
MongoDB positions itself as the database backbone for AI across labs, AI-natives, and enterprises.
“once they moved to MongoDB, they now have 40 million production agents built on MongoDB”
Microsoft announced public preview of Azure HorizonDB, a cloud-native PostgreSQL with built-in AI features for agents.
“Horizon DB is designed for this new era of AI agents. So it comes with built-in AI models, AI pipelines and AI functions.”
Azure SQL Hyperscale adds native vector search and agentic RAG patterns in T-SQL
“Hyperscale provides all the AI capabilities for today's developer, including things like vector searching or REST APIs built into the SQL Server engine.”
Microsoft's CAF-aligned Azure AI landing zone delivers enterprise RAG at millions-of-documents scale
“The result is a grounded response traceable to enterprise data, not a hallucination.”
AWS offers vector search across existing data stores, requiring no data migration for agentic AI
“Vectors are the language of AI. They bridge frontier models and the scattered organizational knowledge accumulated over decades.”
AWS multi-agent Bedrock system uses Claude Haiku 4.5 to outperform single-model insurance document classification
“In our testing, single-model approaches struggled with edge cases and complex documents that require both textual and visual analysis.”
Microsoft SQL Server adds native DiskANN vector search alongside traditional relational data
Microsoft SQL Server adds updatable vector indexes with faster builds and iterative filtering
“we've made it updatable. The original index was not updatable.”
FOX Sports rebuilt search on Databricks, doubling content discovery with real-time semantic search
“over 25% of all searches happen before a user type a single letter”
Azure SQL Database lets developers build vectors on operational data and ground LLMs like OpenAI and Anthropic via Microsoft Foundry.
“You can you can essentially talk to LLMs with the with the truth that you have in your operational data.”
Azure SQL Database lets developers build vector search on operational data and ground LLMs like OpenAI and Anthropic via Microsoft Foundry.
“you can essentially talk to LLMs with the truth that you have in your operational data”
AWS shows how to build a conversational protein research copilot using Amazon Bedrock AgentCore and Strands Agents SDK.
Azure Cosmos DB enables reliable multi-agent AI apps by storing memories and vector-searchable metadata across the user journey.
“In the beginning, the latency and personalization is the most important... Whereas in the back half, consistency and reliability become more important.”
Azure Cosmos DB adds AI-powered schema design, the Cosmos DB Agent Kit, and MCP integration for building AI-native apps faster.
“MCP integration super important is like a USBC for apps and agents these days.”
TiDB consolidates agent memory into one SQL database, replacing the three-database-plus-ETL stack most teams use.
“you're running millions of agents at the same time and each with their own state”
Databricks combines Lakebase and AI Search to power real-time e-commerce personalization.
Azure SQL Database Hyperscale adds RAG, vector indexing, and semantic search without requiring new SQL skills.
“Microsoft SQL has really been innovating over the past couple years, including modern capabilities that help you build applications, integrate with analytics and AI, take advantage of rag, vector indexing, advantage of rag, vector indexing, semantic search, all of this while continuing to work with the same SQL foundation that you already know.”
AWS presents a RAG-based avatar system for capturing and retrieving institutional knowledge at scale
Azure SQL Hyperscale can consolidate vector, graph, document, and analytics workloads to avoid polyglot database tax.
“When I say poly cloud tax, I mean having having the tax that you pay for choosing multiple databases.”
Progress introduces Agentic RAG, a RAG-as-a-service platform for AI search over unstructured data.
“It's not humanly possible to make use of all that data we collect without using some sort of AI.”
Microsoft demos VS Code agents building SQL Server vector search apps with GitHub Copilot