rag-expert

name	rag-expert
description	Retrieval-Augmented Generation patterns on Oracle Cloud Infrastructure — embeddings, vector stores, hybrid search, reranking, and production RAG architecture
version	1.0.0
platform	["claude-code","cline","cursor","roocode"]
activation	{"cline":"@skills/rag-expert/SKILL.md","cursor":"@skills/rag-expert/SKILL.md"}

RAG Expert for OCI

You are an expert in Retrieval-Augmented Generation patterns on Oracle Cloud Infrastructure.

When to Use

• Building RAG systems on OCI
• Selecting embedding models and vector stores
• Optimizing retrieval quality
• Enterprise RAG architecture

OCI RAG Architecture

┌─────────────────────── Security & Governance ─────────────────────┐
│                                                                    │
│  ┌──────────┐    ┌──────────────┐    ┌──────────────────────┐     │
│  │Documents │───▶│ Processing   │───▶│ Embedding            │     │
│  │          │    │ (Doc Under-  │    │ (Cohere Embed 4)     │     │
│  └──────────┘    │  standing)   │    └───────────┬──────────┘     │
│                  └──────────────┘                │                 │
│                                        ┌────────▼────────┐        │
│                                        │ Vector Store    │        │
│                                        │ (26ai AI Vector │        │
│                                        │  Search)        │        │
│                                        └────────┬────────┘        │
│                                                 │                  │
│  ┌──────────┐    ┌──────────────┐    ┌─────────▼─────────┐       │
│  │  Query   │───▶│  Retrieval   │───▶│  Reranking         │       │
│  │          │    │  + Hybrid    │    │  (Rerank 3.5)      │       │
│  └──────────┘    └──────────────┘    └─────────┬─────────┘       │
│                                                 │                  │
│                  ┌──────────────┐    ┌─────────▼─────────┐       │
│                  │   Response   │◀───│  Generation        │       │
│                  │              │    │  (Command A)       │       │
│                  └──────────────┘    └───────────────────┘       │
│                                                                    │
└──────────────────── Observability & Evaluation ────────────────────┘

OCI Components for RAG

Component	OCI Service	Alternatives
Embeddings	Cohere Embed 4 (multimodal)	Embed Multilingual 3
Vector Store	Oracle AI Database 26ai	OCI Search, OpenSearch
LLM	Cohere Command A	Llama 4 Maverick, Gemini 2.5
Document Processing	Document Understanding	Custom parsers
Reranking	Cohere Rerank 3.5	-
Orchestration	GenAI Agent Hub	Oracle ADK

Embedding Models on OCI

Model	Dimensions	Best For
Cohere Embed 4	1024	Multimodal (text + images)
Cohere Embed Multilingual 3	1024	100+ languages

Vector Store Options

Oracle AI Database 26ai (Recommended)

• Native AI Vector Search with Unified Hybrid (vector + keyword)
• Select AI Agent for in-database AI
• Combine with relational, JSON, graph data
• Best for: Existing Oracle customers, enterprise

OCI Search (Managed)

• Fully managed, integrated with GenAI Agents
• Good for: Quick start, managed solution

Retrieval Optimization

1. Hybrid Search (26ai)

SELECT id, title,
       (0.7 * (1 - VECTOR_DISTANCE(embedding, :qvec, COSINE))
        + 0.3 * SCORE(1)) AS hybrid_score
FROM documents
WHERE CONTAINS(content, :keyword_query, 1) > 0
ORDER BY hybrid_score DESC
FETCH FIRST 10 ROWS ONLY;

2. Reranking

Always rerank with Cohere Rerank 3.5 for production quality.

3. Chunking Strategy

• Fixed size (512 tokens, 50 overlap) for simple docs
• Semantic chunking for complex documents
• Hierarchical (Document > Section > Paragraph) for enterprise

Quality Metrics

Metric	Target	How to Measure
Retrieval Recall	>90%	Ground truth comparison
Answer Relevance	>4.5/5	LLM-as-judge
Faithfulness	>95%	Citation verification
Latency (P95)	<3s	End-to-end timing

Before Building Custom RAG

Check OCI AI Blueprints first:

• Llama Stack blueprint includes vLLM + ChromaDB + Postgres + Jaeger
• Repository: https://github.com/oracle-quickstart/oci-ai-blueprints

---

Cline Activation

To use this skill in Cline, reference it at the start of your message:

@skills/rag-expert/SKILL.md

Design a production RAG system on OCI using Oracle AI Database 26ai for hybrid search, Cohere Embed 4 for embeddings, and Cohere Rerank 3.5. The use case is enterprise contract analysis.

Or in a .clinerules workflow:

## RAG Architecture
When designing RAG systems on OCI, load @skills/rag-expert/SKILL.md. Use the 3-tier diagram standard, always include reranking, prefer Oracle AI Database 26ai for hybrid search, and check AI Blueprints before building custom.

Triggers: RAG, retrieval-augmented generation, vector search OCI, embeddings OCI, hybrid search, Cohere Embed, Rerank 3.5, OCI RAG architecture