Vector database
A vector database stores text as numerical embeddings and retrieves entries by semantic similarity rather than keyword match, forming the retrieval layer of most RAG systems.
Options range from dedicated services such as Pinecone to extensions on databases you already run, such as pgvector on PostgreSQL. For most applications the choice matters far less than the chunking and re-ranking strategy around it, and running one fewer piece of infrastructure is worth a great deal.
Related terms
- RAG (retrieval-augmented generation)
Retrieval-augmented generation is a technique where relevant documents are fetched from an external store and inserted into a language model’s prompt, so the model answers from that specific source material rather than from its training data alone.
- Chunking
Chunking is the process of splitting source documents into smaller passages for embedding and retrieval, and the chunk size and boundary strategy largely determine how well a RAG system can find relevant information.
- Hybrid search
Hybrid search combines semantic vector similarity with traditional keyword search, then merges the two result sets, so that a retrieval system handles both conceptual questions and exact terms like product codes or error strings.
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