Enterprise RAG Architecture

Replace {{PLACEHOLDERS}} with your environment values, then deploy to your stack.

# Enterprise RAG Architecture

> AI Architecture · {{ORGANIZATION_NAME}}

## Purpose

Production RAG (Retrieval-Augmented Generation) architecture for enterprise knowledge bases with governance, evaluation, and observability.

## High-Level Architecture

```
┌─────────────┐    chunk/embed    ┌─────────────┐    retrieve    ┌─────────────┐
│  Documents  │ ────────────────▶ │ Vector Store│ ◀───────────── │   Query     │
│  Wiki, PDF  │                   │ {{VECTOR_DB}}│               │  Gateway    │
└─────────────┘                   └──────┬──────┘               └──────┬──────┘
                                         │                              │
                                         └──────────┬───────────────────┘
                                                    ▼
                                            ┌─────────────┐
                                            │  LLM + Prompt│
                                            │  {{LLM_MODEL}}│
                                            └──────┬──────┘
                                                   ▼
                                            ┌─────────────┐
                                            │  Response +  │
                                            │  Citations   │
                                            └─────────────┘
```

## Ingestion Workflow

1. **Source sync** - Pull from {{CONFLUENCE}}/SharePoint/S3 on schedule
2. **Parse** - Extract text, tables, metadata; preserve doc hierarchy
3. **Chunk** - {{CHUNK_SIZE}} tokens, {{OVERLAP}} overlap, semantic boundaries
4. **Embed** - Model: {{EMBEDDING_MODEL}}; store vector + metadata
5. **Index** - Namespace per domain: `{{DOMAIN}}_kb_v{{VERSION}}`

## Query Workflow

1. User query → intent classification (optional)
2. Hybrid retrieval: vector search + keyword (BM25) rerank top-{{TOP_K}}
3. Inject retrieved chunks into system prompt with citation IDs
4. LLM generates answer; post-filter for PII/policy violations
5. Log query, chunks used, latency, user feedback thumbs up/down

## Governance Controls

- **Access:** RBAC on document sources and vector namespaces
- **PII:** Scan at ingest; block or redact before embedding
- **Prompt injection:** Sanitize retrieved content; system prompt hardening
- **Evaluation:** Golden Q&A set; weekly regression on faithfulness/relevance

## Evaluation Metrics

| Metric | Target |
|--------|--------|
| Retrieval recall@5 | ≥ {{RECALL_TARGET}} |
| Answer faithfulness | ≥ {{FAITHFULNESS_TARGET}} |
| P95 latency | ≤ {{LATENCY_MS}} ms |
| User satisfaction | ≥ {{CSAT_TARGET}} |

## Production Checklist

- [ ] Version embedding index with rollback plan
- [ ] Cache frequent queries
- [ ] Rate limit per user/API key
- [ ] Audit log all LLM calls with chunk citations