rag-expert

Name: rag-expert
Availability: InStock
Author: oci-ai-architects

Design RAG systems on OCI with hybrid search, embeddings, and reranking.

1|Updated Feb 6, 2026

Search Tags:#embeddings #rag #reranking #oci #vector-store #hybrid-search

Ready to run

npx skills add https://github.com/oci-ai-architects/cline-oci-ai-architect-skills --skill rag-expert-oci-ai-architects

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: rag-expert
Source: https://github.com/oci-ai-architects/cline-oci-ai-architect-skills/tree/main/skills/rag-expert
Command: npx skills add https://github.com/oci-ai-architects/cline-oci-ai-architect-skills --skill rag-expert-oci-ai-architects

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Enables building robust Retrieval-Augmented Generation workflows on Oracle Cloud Infrastructure by providing a structured blueprint, tooling guidance, and architecture patterns that integrate embeddings, vector stores, and reranking into production-ready pipelines.

Core Features & Use Cases

Embedding and vector-store selection for OCI-based RAG
Hybrid search, reranking, and end-to-end generation orchestration
Enterprise knowledge-base retrieval and document-intensive workflows

Quick Start

Load rag-expert in Cline and configure OCI sources, embeddings, vector store, and reranking to bootstrap a production RAG pipeline.

Frequently Asked Questions about rag-expert

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build a RAG pipeline on Oracle Cloud Infrastructure?▼

To build a RAG pipeline on Oracle Cloud Infrastructure, you integrate embeddings, vector stores, and reranking into a production-ready workflow. The rag-expert provides structured blueprints and orchestration patterns for enterprise knowledge bases.

What is hybrid search and reranking in enterprise retrieval workflows?▼

Hybrid search and reranking in enterprise retrieval workflows combine keyword and vector-based document matching, then reorder results by relevance. This mechanism improves generation accuracy for governance-heavy environments and document-intensive tasks.

Can I use Oracle AI Database 26ai as a vector store with Cohere embeddings?▼

Yes, Oracle AI Database 26ai functions as a vector store alongside Cohere Embed 4 for generating embeddings. This combination supports enterprise retrieval systems by storing and querying vector representations of document-intensive workflows.

How do I configure OCI sources and embeddings to bootstrap a production RAG pipeline?▼

Configure OCI sources, embeddings, vector stores, and reranking components to bootstrap a production RAG pipeline. The system orchestrates document processing, embedding generation, and hybrid search for end-to-end generation.

Does this approach support enterprise knowledge bases requiring governance and hybrid search?▼

Yes, this approach supports enterprise knowledge bases requiring governance and hybrid search. It provides architecture patterns equipped for governance-heavy environments, integrating document processing, reranking, and vector-store retrieval.

What are the limitations of building RAG systems without a dedicated vector store and reranking?▼

Building RAG systems without a dedicated vector store and reranking limits retrieval accuracy and relevance scoring. Implementing hybrid search and structured orchestration is necessary to handle document-intensive workflows and enterprise governance requirements effectively.