A Vector Database is a specialized storage system designed to handle high-dimensional vector embeddings—numerical representations of unstructured data like text, images, or audio. Unlike traditional relational databases that rely on exact keyword matching, vector databases utilize mathematical algorithms to measure the distance between data points, identifying semantic similarities. This architecture is the backbone of modern Generative AI, powering recommendation engines, semantic search, and Retrieval-Augmented Generation (RAG) workflows by providing Large Language Models (LLMs) with scalable, long-term memory.

What is the difference between a vector database and a traditional database?

Traditional databases (SQL or NoSQL) generally rely on exact keyword matches or fixed values to retrieve rows. Vector databases, conversely, use "embeddings" to perform similarity searches (nearest neighbor search). This allows them to find data that is conceptually similar to a query, even if the specific keywords don't match exactly.

Why do I need a vector database for Generative AI?

They are essential for Retrieval-Augmented Generation (RAG). LLMs have a knowledge cutoff; a vector database stores your up-to-date, proprietary data as vectors. When a user asks a question, the system retrieves the most relevant context from the database and feeds it to the LLM, ensuring accurate, factual answers with fewer hallucinations.

How does vector search actually work?

It follows a mathematical process to determine relevance:

1. Embedding: Data is converted into a list of floating-point numbers (vectors).
2. Indexing: Vectors are mapped using techniques like HNSW (Hierarchical Navigable Small World) for speed.
3. Distance Calculation: The database calculates the distance (using Cosine Similarity or Euclidean Distance) between the query vector and stored vectors to find the closest matches.

Is it better to use a dedicated vector database or a plugin?

It depends on your scale. Dedicated tools (like Weaviate or Pinecone) are optimized purely for high-scale vector performance. However, traditional databases with vector plugins (like PostgreSQL with pgvector) are excellent for simplifying your stack if you don't require massive throughput.

How does Shakudo support vector database implementation?

Shakudo is tool-agnostic, meaning we orchestrate whichever vector solution fits your needs best—whether that’s open-source options like Milvus and Qdrant, or extensions like pgvector. Crucially, Shakudo deploys these databases entirely within your own secure VPC or on-prem infrastructure, ensuring your sensitive embeddings and proprietary context data never leave your governance boundary.