Pinecone Integration

Pinecone is the vector database powering the RAG (Retrieval-Augmented Generation) system for AI email generation.

Setup

Two Pinecone indexes are required:

Index	Dimensions	Embedding Model
Primary	1536	text-embedding-3-small (OpenAI)
Large	3072	text-embedding-3-large (OpenAI)

Both indexes are queried and updated simultaneously.

Configuration

PINECONE_API_KEY=your-api-key
PINECONE_INDEX=your-index-name           # 1536-dim
PINECONE_INDEX_LARGE=your-large-index    # 3072-dim

Vector Metadata

Each vector in Pinecone stores metadata used for filtering:

{
  "title": "Product Showcase with Tabs",
  "type": "html",
  "technique": "tabs",
  "complexity": "intermediate",
  "emailPurpose": "ecommerce",
  "bestPracticeTags": ["responsive", "accessible"],
  "contentHash": "abc123..."
}

Query Patterns

Technique-Filtered Query

# Pinecone query with metadata filter
index.query(
  vector=embedding,
  top_k=10,
  filter={"technique": {"$eq": "tabs"}},
  include_metadata=True
)

Broad Unfiltered Query

index.query(
  vector=embedding,
  top_k=15,
  include_metadata=True
)

Content Management

Admin endpoints manage Pinecone content:

Endpoint	Action
POST /api/admin/submit-content	Upload new content (embeds + stores in both indexes)
POST /api/admin/update-content	Re-embed and update existing content
POST /api/admin/delete-content	Remove from both indexes
GET /api/admin/list-content	Browse content with filters

See RAG Overview for the full retrieval pipeline.