Vector Database
: A specialized database designed to store, index, and query high-dimensional vector embeddings efficiently. Vector databases enable similarity search, powering RAG (Retrieval Augmented Generation) systems, semantic search, recommendation engines, and AI applications that need to find contextually similar content. Popular options include Pinecone, Weaviate, Qdrant, ChromaDB, and pgvector.
Why It Matters for AI-Coded Apps
Vector databases are a core component of RAG-based AI applications. When vibe coding a chatbot or knowledge base, AI tools generate vector database integrations with common security issues: exposed API keys, missing access controls, injection through metadata fields, and no encryption of sensitive embeddings.
Real-World Example
A vibe-coded support chatbot stores customer conversation embeddings in Pinecone. The AI-generated code hardcodes the API key in the frontend, has no access control on queries (any user can search all conversations), and does not sanitize metadata fields, allowing injection attacks through conversation content.
How to Detect and Prevent It
Never expose vector database API keys in frontend code. Implement access controls so users can only query their own data (use namespace/tenant isolation). Sanitize all metadata before storage. Encrypt sensitive embeddings at rest. Monitor query patterns for data exfiltration attempts. Use server-side API routes to proxy vector database queries.
Frequently Asked Questions
Which vector database should I use for my AI app?
For quick prototyping: ChromaDB (local, easy setup). For production: Pinecone (managed, scalable), Weaviate (feature-rich, hybrid search), or pgvector (if you already use PostgreSQL). Choose based on scale, query patterns, and whether you want managed or self-hosted infrastructure.
Can vector databases leak sensitive data?
Yes. If embeddings are generated from sensitive documents, similarity search can reveal content from documents a user should not access. Implement row-level security, namespace isolation, and access controls. Some vector databases support attribute-based access control to filter results by user permissions.
What is the difference between a vector database and a regular database?
Regular databases index exact values for precise matching (SQL queries). Vector databases index high-dimensional vectors for similarity matching (finding nearest neighbors). You query a regular database with exact criteria; you query a vector database with a vector to find similar items.
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