Which vector stores are supported by this skill?

The skill provides patterns for OpenSearch Serverless, Amazon S3 Vectors, Neptune Analytics (GraphRAG), Pinecone, MongoDB Atlas, and Redis Enterprise.

GraphRAG uses Amazon Neptune Analytics to link related content via knowledge graphs, improving retrieval accuracy for interconnected data like legal precedents or product catalogs.

How does this skill handle document chunking?

It provides implementation patterns for fixed-size, semantic, hierarchical, and custom Lambda-based chunking strategies to optimize search relevance.

What is Amazon Bedrock Knowledge Bases?

It is a fully managed AWS service that automates the RAG workflow, including data ingestion, embedding generation, storage in vector databases, and retrieval with source attribution.

Can I use this skill to crawl websites?

Yes, it includes configurations for web crawlers to automatically scrape and index public website content for your knowledge base.

Amazon Bedrock Knowledge Bases

Name: Amazon Bedrock Knowledge Bases
Author: adaptationio

byadaptationio

数据科学与机器学习

Builds and manages fully managed RAG solutions using Amazon Bedrock for semantic search and document-based AI applications.

关于

This skill empowers developers to architect and implement robust Retrieval-Augmented Generation (RAG) workflows directly within their AWS environment. It provides comprehensive guidance on configuring data sources like S3 and web crawlers, managing diverse vector stores including OpenSearch and Neptune Analytics for GraphRAG, and optimizing chunking strategies to enhance retrieval accuracy. Whether you're building enterprise Q&A systems or integrating knowledge bases with AI agents, this skill streamlines the process of ingesting, indexing, and querying private data with source attribution and session management.

主要功能

Hybrid search capabilities combining semantic vector and keyword-based retrieval
Support for diverse vector stores like OpenSearch Serverless and S3 Vectors
GraphRAG integration using Amazon Neptune Analytics for relationship-aware retrieval
Multi-source data ingestion including S3, Web, Confluence, and SharePoint
Advanced chunking strategies including semantic, hierarchical, and custom patterns
0 GitHub stars

使用场景

Building enterprise-grade Q&A systems over proprietary document collections
Implementing relationship-aware retrieval with GraphRAG for complex data domains
Optimizing RAG performance and storage costs using Amazon S3 Vectors

关于

主要功能

Hybrid search capabilities combining semantic vector and keyword-based retrieval
Support for diverse vector stores like OpenSearch Serverless and S3 Vectors
GraphRAG integration using Amazon Neptune Analytics for relationship-aware retrieval
Multi-source data ingestion including S3, Web, Confluence, and SharePoint
Advanced chunking strategies including semantic, hierarchical, and custom patterns
0 GitHub stars

使用场景

Building enterprise-grade Q&A systems over proprietary document collections
Implementing relationship-aware retrieval with GraphRAG for complex data domains
Optimizing RAG performance and storage costs using Amazon S3 Vectors