rag-implementation

# RAG Implementation Patterns

**Purpose:** Provide complete RAG pipeline templates, chunking strategies, vector database schemas, and retrieval patterns for building production-ready RAG systems with Vercel AI SDK.

**Activation Triggers:**
- Building RAG (Retrieval Augmented Generation) systems
- Implementing semantic search functionality
- Creating AI-powered knowledge bases
- Document ingestion and embedding generation
- Vector database integration
- Hybrid search (vector + keyword) implementation

**Key Resources:**
- `templates/rag-pipeline.ts` - Complete RAG pipeline template
- `templates/vector-db-schemas/` - Database schemas for Pinecone, Chroma, pgvector, Weaviate
- `templates/chunking-strategies.ts` - Document chunking implementations
- `templates/retrieval-patterns.ts` - Semantic search and hybrid search patterns
- `scripts/chunk-documents.sh` - Document chunking utility
- `scripts/generate-embeddings.sh` - Batch embedding generation
- `scripts/validate-rag-setup.sh` - Validate RAG configuration
- `examples/` - Complete RAG implementations (chatbot, Q&A, search)

## Core RAG Pipeline

### 1. Document Ingestion → Chunking → Embedding → Storage → Retrieval → Generation

**Template:** `templates/rag-pipeline.ts`

**Workflow:**
```typescript
// 1. Ingest documents
const documents = await loadDocuments()

// 2. Chunk documents
const chunks = await chunkDocuments(documents, {
  chunkSize: 1000
  overlap: 200
  strategy: 'semantic'
})

// 3. Generate embeddings
const embeddings = await embedMany({
  model: openai.embedding('text-embedding-3-small')
  values: chunks.map(c => c.text)
})

// 4. Store in vector DB
await vectorDB.upsert(chunks.map((chunk, i) => ({
  id: chunk.id
  embedding: embeddings.embeddings[i]
  metadata: chunk.metadata
})))

// 5. Retrieve relevant chunks
const query = await embed({
  model: openai.embedding('text-embedding-3-small')
  value: userQuestion
})

const results = await vectorDB.query({
  vector: query.embedding
  topK: 5
})

// 6.