GPTfy Glossary
Vector Database
A database optimized for storing and querying high-dimensional vectors (embeddings) — the storage layer that makes semantic search and RAG fast at scale.
Traditional databases excel at exact-match lookups (find this customer ID). Vector databases excel at similarity search (find documents *like* this one). Internally they use approximate-nearest-neighbor algorithms (HNSW, IVF) to find similar vectors among millions or billions of stored embeddings in milliseconds.
Major players: Pinecone, Weaviate, Qdrant, Chroma, Milvus. Cloud providers offer managed options: Azure AI Search, AWS OpenSearch, Google Vertex AI Matching Engine. Salesforce-specific: Data Cloud's vector capabilities, and platform-side options in gptfy and similar tools that handle the vector indexing transparently.
Choosing a vector DB depends on: scale (millions vs. billions of vectors), latency requirements, hybrid-search needs (vectors + structured filters), and ops model (managed vs. self-hosted). For most Salesforce-AI workloads, a managed solution embedded in the AI platform is the right choice — the underlying database is an implementation detail.
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