Releases·Tool Release
AWS benchmarks OpenSearch Serverless configs for Bedrock Knowledge Bases RAG vector store selection
An AWS Machine Learning blog post guides builders choosing a customer-managed vector store for Amazon Bedrock Knowledge Bases, comparing Amazon OpenSearch Service, Amazon Aurora PostgreSQL with pgvector, and Amazon S3 Vectors across three RAG use cases. It includes original benchmarks on OpenSearch Serverless Classic collections using Amazon's ESCI shopping dataset, measuring NDCG@10, latency, and index size across embedding dimension and data type configurations. The guidance helps practitioners trade off latency, cost, and search quality when building RAG on AWS.
0 engagement1 sourceThu, Sep 17, 2026, 03:53 PM
AI Overview
- AWS compares Bedrock Knowledge Bases vector backends — OpenSearch Service, Aurora PostgreSQL with pgvector, and S3 Vectors — across three RAG use cases
- Aurora PostgreSQL pgvector supports IVFFlat and HNSW indexing, L2/cosine/inner-product metrics, and vectors up to 2,000 dimensions in single precision
- S3 Vectors delivers sub-second similarity queries while cutting vector storage costs up to 90 percent versus traditional vector databases
- OpenSearch Serverless benchmarks used Amazon's ESCI dataset: 1,215,851 products, 5,000 sampled queries, seven FAISS HNSW configurations measured on NDCG@10, latency, and index size
- OpenSearch Serverless NextGen collections (GA May 2026) aren't yet compatible with the Bedrock Knowledge Bases Retrieve API; benchmarks used Classic collections
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Social·Community
Builder writes guide to production-grade RAG on Amazon Bedrock, covering Knowledge Bases, chunking, and evaluation
A developer posted a practical guide on building reliable RAG applications on AWS using Amazon Bedrock, shared on Reddit for community feedback. The guide emphasizes engineering considerations for production—retrieval quality, chunking, evaluation—over introductory explanations, making it a practitioner-focused resource for teams building on AWS.
A developer posted a practical guide on building reliable RAG applications on AWS using Amazon Bedrock, shared on Reddit for community feedback. The guide emphasizes engineering considerations for production—retrieval quality, chunking, evaluation—over introductory explanations, making it a practitioner-focused resource for teams building on AWS.
7 engagement1 sourceredditThu, Oct 8, 2026, 07:00 AM
Releases·Tool Release
Amazon Bedrock Knowledge Bases builds a cited claims Q&A assistant using agentic retrieval and metadata filters
AWS published a technical how-to for building an insurance claims assistant on Amazon Bedrock Knowledge Bases, using agentic retrieval to answer natural-language questions over claim documents with citations. The guide combines S3-indexed claim files, metadata filtering, Guardrails grounding checks, and multi-turn conversation. A 40-question evaluation on synthetic data showed 90.5% retrieval recall and 81.2% citation recall.
AWS published a technical how-to for building an insurance claims assistant on Amazon Bedrock Knowledge Bases, using agentic retrieval to answer natural-language questions over claim documents with citations. The guide combines S3-indexed claim files, metadata filtering, Guardrails grounding checks, and multi-turn conversation. A 40-question evaluation on synthetic data showed 90.5% retrieval recall and 81.2% citation recall.
0 engagement1 sourcerssWed, Sep 30, 2026, 03:37 PM
Releases·Tool Release
Amazon Bedrock Knowledge Bases adds TwelveLabs Marengo Embed 3.0 for video, audio, and image search
AWS announced general availability of TwelveLabs Marengo Embed 3.0 as an embedding model in Amazon Bedrock Knowledge Bases, enabling natural language search over video, audio, and image assets without custom pipelines. The managed service handles segmentation, frame sampling, transcription, and embedding automatically, removing the need to stitch together transcription services, frame extraction, vector databases, and sync logic.
AWS announced general availability of TwelveLabs Marengo Embed 3.0 as an embedding model in Amazon Bedrock Knowledge Bases, enabling natural language search over video, audio, and image assets without custom pipelines. The managed service handles segmentation, frame sampling, transcription, and embedding automatically, removing the need to stitch together transcription services, frame extraction, vector databases, and sync logic.
0 engagement1 sourcerssThu, Sep 10, 2026, 09:15 PM
Releases·Tool Release
NVIDIA NeMo Agent Toolkit adds Amazon S3 Vectors as custom memory provider on Amazon EKS
An AWS Storage Blog post walks through implementing Amazon S3 Vectors as a custom persistent memory provider for NVIDIA's NeMo Agent Toolkit (NAT), deployed on Amazon EKS using a multi-agent investment research use case. It covers NAT's memory plugin interface, the auto_memory_agent workflow, multi-agent memory sharing via metadata filters, and operational deployment patterns. Practitioners get a concrete recipe for production agent memory with elastic vector storage, strong write consistency, and IAM-based isolation.
An AWS Storage Blog post walks through implementing Amazon S3 Vectors as a custom persistent memory provider for NVIDIA's NeMo Agent Toolkit (NAT), deployed on Amazon EKS using a multi-agent investment research use case. It covers NAT's memory plugin interface, the auto_memory_agent workflow, multi-agent memory sharing via metadata filters, and operational deployment patterns. Practitioners get a concrete recipe for production agent memory with elastic vector storage, strong write consistency, and IAM-based isolation.
0 engagement1 sourcerssThu, Oct 1, 2026, 05:34 PM
Releases·Tool Release
AWS publishes patterns for observable and multi-tenant agentic retrieval on Amazon Bedrock Knowledge Bases
AWS published two posts on building agentic retrieval with Managed Amazon Bedrock Knowledge Bases: one on observability for enterprise agentic retrieval using AWS CloudFormation, and one on multi-tenant agentic chat over enterprise data. Both target the step beyond single-source RAG, where an agent routes questions across knowledge bases and retrieves iteratively. The multi-tenant post emphasizes per-tenant document isolation enforced from verified identity rather than client-supplied values.
AWS published two posts on building agentic retrieval with Managed Amazon Bedrock Knowledge Bases: one on observability for enterprise agentic retrieval using AWS CloudFormation, and one on multi-tenant agentic chat over enterprise data. Both target the step beyond single-source RAG, where an agent routes questions across knowledge bases and retrieves iteratively. The multi-tenant post emphasizes per-tenant document isolation enforced from verified identity rather than client-supplied values.
0 engagement2 sourcesrssMon, Aug 31, 2026, 06:56 PM