What is Vector Database?
A vector database stores embeddings, numeric vectors that capture the meaning of text or other data, and returns the nearest neighbors to a query by similarity using approximate nearest neighbor search.
How It Works
A vector database stores embeddings, which are lists of numbers that capture the meaning of a piece of text, an image or another item. When a query arrives, it is turned into an embedding too, and the database returns the stored items closest to it by similarity. To stay fast at scale, most systems use approximate nearest neighbor search rather than comparing against every vector. For example, a passage about ending a contract early lands near a question about termination, even with no shared words. Common options include pgvector (a Postgres extension), Pinecone, Qdrant, Weaviate and Chroma. It is the retrieval layer in most RAG pipelines.
Go deeper: AI automation services · RAG pipelines explained, stage by stage · ROI calculator
Key Benefits
- Search by meaning, not just keywords
- Fast similarity lookup at scale through approximate nearest neighbor search
- Powers the retrieval step in RAG pipelines
- pgvector adds it to a Postgres database you may already run
Common Use Cases
- Retrieval step for a document chatbot
- Semantic search across a knowledge base
- Finding similar support tickets or past cases
- Recommending related content or products
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Related Terms
Workflow Automation
The use of technology to automate repeatable business processes, reducing manual intervention and ensuring tasks are completed consistently and efficiently.
AI Agents
Autonomous software programs powered by artificial intelligence that can perceive their environment, make decisions, and take actions to accomplish specific goals.
Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) is a technique where a system first retrieves relevant passages from your own documents, then has a large language model answer using only those passages, so responses stay grounded in your data.
Large Language Model (LLM)
A large language model (LLM) is a neural network trained on very large text collections to predict the next token, which lets it understand and generate human language for tasks like answering, summarizing and drafting.
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