RAG (Retrieval-Augmented Generation)
🍃 MongoDB RAG Architecture
Native Vector Search: MongoDB's built-in vector search capabilities enable semantic similarity matching without requiring separate vector databases. Store embeddings, metadata, and application data together in a unified document model.
Implementation: Use Atlas Search with vector indexing to create embeddings directly in your MongoDB collections, enabling rich filtering and hybrid search patterns.
Implementation: Use Atlas Search with vector indexing to create embeddings directly in your MongoDB collections, enabling rich filtering and hybrid search patterns.
- Native vector indexing with automatic scaling
- Document model stores vectors with rich metadata
- Real-time updates without retraining models
- Hybrid search combining vector and text queries
- Single platform for all RAG components
- Rich filtering with traditional MongoDB queries
Setup Complexity:
Low
🐘 PostgreSQL RAG Architecture
pgvector Extension: PostgreSQL with pgvector provides vector search capabilities through an extension. Requires careful schema design to handle vectors alongside relational data, often needing complex joins for metadata filtering.
Implementation: Install pgvector extension, create vector columns, and design normalized schemas. Performance optimization requires careful index tuning and query planning for vector operations.
Implementation: Install pgvector extension, create vector columns, and design normalized schemas. Performance optimization requires careful index tuning and query planning for vector operations.
- pgvector extension for vector operations
- Relational model requires normalized schemas
- Complex joins for metadata filtering
- Separate vector and text search systems
- Manual index optimization for performance
- Additional infrastructure for full-text search
Setup Complexity:
High
