| 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:
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
