Vector Database
A vector database stores data as high-dimensional numerical vectors (embeddings) and enables fast similarity search across them. It is the core infrastructure behind RAG systems, semantic search, and recommendation engines in AI applications.
How It Works
Traditional databases search by exact matches. You query for a specific ID, keyword, or value. Vector databases work differently. They search by meaning. You give them a vector representing a concept, and they find the most similar vectors in the database.
This works because AI embedding models convert text (or images, or audio) into numerical vectors where similar items end up close together in the vector space. The sentence "How do I reset my password?" and "I forgot my login credentials" would have vectors that are near each other, even though they share no keywords.
In a RAG system, your documents get split into chunks, each chunk gets converted to a vector, and those vectors get stored in the database. When a user asks a question, the question gets converted to a vector too, and the database returns the closest matching chunks. Those chunks then go to the LLM as context for generating the answer.
Popular vector databases include Pinecone, Weaviate, Qdrant, Milvus, and pgvector (a PostgreSQL extension). Each has different tradeoffs around scale, speed, hosting options, and cost.
For enterprise deployments, the key decisions are: how many vectors you need to store, what latency you can tolerate, whether you need hybrid search (combining vector and keyword search), and whether the data needs to stay on your own infrastructure.
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