All news with #vector database tag

Amazon OpenSearch Service Adds Batch AI Inference Support

🧠 You can now run asynchronous batch AI inference inside Amazon OpenSearch Ingestion pipelines to enrich and ingest very large datasets for Amazon OpenSearch Service domains. The same AI connectors previously used for real-time calls to Amazon Bedrock, Amazon SageMaker, and third parties now support high-throughput, offline jobs. Batch inference is intended for offline enrichment scenarios—generating up to billions of vector embeddings—with improved performance and cost efficiency versus streaming inference. The feature is available in regions that support OpenSearch Ingestion on domains running 2.17+.

Amazon Neptune Analytics Launches in Mumbai Region

📍 Amazon Neptune Analytics is now available in the Asia Pacific (Mumbai) Region, enabling customers to create and manage analytics graphs locally. Neptune Analytics is a memory-optimized graph engine designed for fast, in-memory processing of large graph datasets, supporting optimized analytic algorithms, low-latency graph queries, and vector search within traversals. It complements Amazon Neptune Database, and you can load data from a Neptune Database, snapshots, or Amazon S3. To get started, create a new Neptune Analytics graph via the AWS Management Console or AWS CLI; see the Neptune pricing page for region and cost details.

Source-of-Truth Authorization for RAG Knowledge Bases

🔒 This post presents an architecture to enforce strong, source-of-truth authorization for Retrieval-Augmented Generation (RAG) knowledge bases using Amazon S3 Access Grants with Amazon Bedrock. It explains why vector DB metadata filtering is insufficient—permission changes can be delayed and complex identity memberships are hard to represent—and recommends validating permissions at the data source before returning chunks to an LLM. The blog includes a practical Python walkthrough for exchanging identity tokens, retrieving caller grant scopes, filtering returned chunks, and logging withheld items to reduce the risk of sensitive data leaking into LLM prompts.

Amazon OpenSearch Serverless Adds Disk-Optimized Vectors

🔍 Amazon has added disk-optimized vector storage to OpenSearch Serverless, offering a lower-cost alternative to memory-optimized vectors while maintaining equivalent accuracy and recall. The disk-optimized option may introduce slightly higher latency, so it is best suited for semantic search, recommendation systems, and other AI search scenarios that do not require sub-millisecond responses. As a fully managed service, OpenSearch Serverless continues to automatically scale compute capacity (measured in OCUs) to match workload demands.

Amazon OpenSearch Service adds OpenSearch 3.1 for vectors

🚀 Amazon OpenSearch Service now supports OpenSearch 3.1, bringing targeted improvements for vector-driven and traditional search workloads. The release bundles Lucene 10 for optimized vector field indexing, faster indexing times, reduced index sizes, sparse indexing, and vector quantization to lower memory usage. It also improves range query and high-cardinality aggregation latency and introduces a new Search Relevance Workbench for iterative quality testing. Additional vector search enhancements include Z-score normalization for more reliable hybrid search and memory-optimized Faiss support; OpenSearch 3.1 is available in all AWS Regions.

Target modernizes search with hybrid AlloyDB AI platform

🔍 Target rebuilt its on-site search to combine lexical keyword matching with semantic vector retrieval, using AlloyDB AI to power filtered vector queries at scale. The engineering team implemented a multi-index architecture and a multi-channel relevance framework so hybrid queries can apply native SQL filters alongside vector similarity. The overhaul produced measurable gains — ~20% improvement in product discovery relevance, halved "no results" occurrences, and large latency reductions — while consolidating the stack and accelerating development.

Amazon Neptune Integrates with Zep for Long-Term Memory

🧠 Amazon Web Services announced integration of Amazon Neptune with Zep, an open-source memory server for LLM applications, enabling persistent long-term memory and contextual history. Developers can use Neptune Database or Neptune Analytics as the graph store and Amazon OpenSearch as the text-search layer within Zep’s memory system. The integration enables graph-powered retrieval, multi-hop reasoning, and hybrid search across graph, vector, and keyword modalities, simplifying the creation of personalized, context-aware LLM agents.

What's New in Google Data Cloud: August Product Roundup

🔔 This Google Cloud roundup summarizes recent product milestones, GA launches, previews, and integrations across the data analytics, BI, and database portfolio. It highlights updates to BigQuery, Firestore, Cloud SQL, AlloyDB, and adjacent services aimed at easing ingestion, migration, and AI-driven operations. Notable items include MongoDB-compatible Firestore GA, PSC networking improvements for Database Migration Service, and a redesigned BigQuery data ingestion experience. The post also emphasizes resilience and DR enhancements such as immutable backups and Near Zero Downtime maintenance.

Make Websites Conversational with NLWeb and AutoRAG

🤖 Cloudflare offers a one-click path to conversational search by combining Microsoft’s NLWeb open standard with Cloudflare’s managed retrieval engine, AutoRAG. The integration crawls and indexes site content into R2 and a managed vector store, serves embeddings and inference via Workers AI, and exposes both a user-facing /ask endpoint and an agent-focused /mcp endpoint. Publishers get continuous re-indexing, controlled agent access, and observability through an AI Gateway, removing much of the infrastructure burden for conversational experiences.

Amazon RDS Supports MariaDB 11.8 with Vector Engine

🚀 Amazon RDS for MariaDB now supports MariaDB 11.8 (minor 11.8.3), the community's latest long-term maintenance release. The update introduces MariaDB Vector, enabling storage of vector embeddings and use of retrieval-augmented generation (RAG) directly in the managed database. It also adds controls to limit maximum temporary file and table sizes to better manage storage. You can upgrade manually, via snapshot restore, or with Amazon RDS Managed Blue/Green deployments; 11.8 is available in all regions where RDS MariaDB is offered.

Amazon Neptune Adds BYOKG RAG Support via GraphRAG

🔍 Amazon Web Services announced general availability of Bring Your Own Knowledge Graph (BYOKG) support for Retrieval-Augmented Generation (RAG) using the open-source GraphRAG Toolkit. Developers can now connect domain-specific graphs stored in Amazon Neptune (Database or Analytics) directly to LLM workflows, combining graph queries with vector search. This reduces hallucinations and improves multi-hop and temporal reasoning, easing operationalization of graph-aware generative AI.

Amazon Neptune integrates with Cognee for GenAI memory

🧠 Amazon Neptune now integrates with Cognee to provide graph-native memory for agentic generative AI applications. The integration enables developers to use Amazon Neptune Analytics as the persistent graph and vector store behind Cognee’s memory layer, supporting large-scale memory graphs, long-term memory, and multi-hop reasoning. Hybrid retrieval across graph, vector, and keyword modalities helps agents deliver more personalized, cost-efficient, and context-aware experiences; documentation and a sample notebook are available to accelerate adoption.