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

0•

클라우드 인프라

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

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.

주요 기능

010 GitHub stars

02Support for multiple vector stores including OpenSearch, S3 Vectors, and Pinecone

03Automated data ingestion from S3, Web, Confluence, and SharePoint

04GraphRAG implementation using Amazon Neptune for relationship-aware retrieval

05Hybrid search configuration combining vector and keyword retrieval

06Advanced chunking strategies including semantic, hierarchical, and custom Lambda

사용 사례

01Building enterprise-grade document Q&A systems with verifiable source citations

02Creating interconnected knowledge domains with GraphRAG and Neptune Analytics

03Implementing cost-optimized RAG for massive datasets using S3 Vectors

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add adaptationio/skrillz bedrock-knowledge-bases

For use in Claude.ai and ChatGPT

Download Skill