What is Amazon Bedrock Knowledge Bases?

It is a fully managed AWS service that automates the RAG workflow, including document ingestion, embedding generation, and retrieval with source attribution.

Which vector databases can I configure with this skill?

This skill supports Amazon OpenSearch Serverless, S3 Vectors, Neptune Analytics, Pinecone, MongoDB Atlas, and Redis Enterprise Cloud.

How does this skill improve RAG accuracy?

It provides implementation patterns for advanced chunking strategies (semantic and hierarchical) and hybrid search configurations to improve relevance.

Does it support data sources other than S3?

Yes, it supports web crawlers, Confluence, SharePoint, and Salesforce, allowing you to index enterprise data from multiple platforms.

What is GraphRAG in the context of this skill?

GraphRAG uses Amazon Neptune Analytics to create relationship-aware retrieval, allowing the AI to understand connections between documents for more comprehensive answers.

Amazon Bedrock Knowledge Bases

Name: Amazon Bedrock Knowledge Bases
Author: adaptationio

byadaptationio

Cloud Infrastructure

Configures and manages enterprise RAG workflows using Amazon Bedrock for automated data ingestion and retrieval.

About

This skill empowers developers to architect, implement, and optimize Retrieval-Augmented Generation (RAG) systems using Amazon Bedrock Knowledge Bases. It provides deep technical guidance on orchestrating data ingestion from sources like S3, web crawlers, and Confluence, while managing high-performance vector stores including OpenSearch Serverless and Neptune GraphRAG. Use this skill to streamline the creation of document Q&A systems, implement semantic search, and configure advanced chunking strategies that ensure high retrieval accuracy and source attribution within Claude-powered applications.

Key Features

0 GitHub stars
Support for multiple vector stores including OpenSearch, S3 Vectors, and Pinecone
Automated data ingestion from S3, Web, Confluence, and SharePoint
GraphRAG implementation using Amazon Neptune for relationship-aware retrieval
Hybrid search configuration combining vector and keyword retrieval
Advanced chunking strategies including semantic, hierarchical, and custom Lambda

Use Cases

Building enterprise-grade document Q&A systems with verifiable source citations
Creating interconnected knowledge domains with GraphRAG and Neptune Analytics
Implementing cost-optimized RAG for massive datasets using S3 Vectors

About

Key Features

0 GitHub stars
Support for multiple vector stores including OpenSearch, S3 Vectors, and Pinecone
Automated data ingestion from S3, Web, Confluence, and SharePoint
GraphRAG implementation using Amazon Neptune for relationship-aware retrieval
Hybrid search configuration combining vector and keyword retrieval
Advanced chunking strategies including semantic, hierarchical, and custom Lambda

Use Cases

Building enterprise-grade document Q&A systems with verifiable source citations
Creating interconnected knowledge domains with GraphRAG and Neptune Analytics
Implementing cost-optimized RAG for massive datasets using S3 Vectors