A vector database is a database specifically designed to store, index, and query vector data. Ordinary databases excel at precise field-based queries, such as how much the order number equals; Vector databases excel at querying by similarity, such as which document segments are closest to this problem.
Why AI applications need it
Large model applications often need to handle unstructured content: documents, web pages, customer service records, images, and audio. The Embedding model converts this content into high-dimensional vectors, which the vector database stores and quickly finds the most similar content when users ask questions.
It is not a replacement for ordinary databases
Vector databases address semantic similarity searches, not replace MySQL, PostgreSQL, or business systems. In real projects, the two often work together: the business database stores users, permissions, orders, and original text; Vector database saves vector and document fragment indexes; When searching, you also need to filter metadata such as permissions, time, and category.
What to look for when choosing a model
- Data volume and query latency: just tens of thousands of document segments are not the same as hundreds of millions of vectors.
- Filtering capability: The enterprise knowledge base must be filtered by department, permission, and time.
- Mixed search: Relying solely on vectors may miss precise words like numbers, product names, and codes.
- Operation and maintenance methods: The cost differences between self-hosted, cloud services, and embedded solutions vary greatly.
Common misconceptions
Many RAG projects fail not because the vector database is not advanced enough, but because document slicing, embedding, permissions, reordering, and prompts are not properly adjusted. The vector database is an important foundation, but it only handles "finding similar content faster" and cannot automatically guarantee correct answers.
