Vector Database Architecture Diagram Template

What this template is for

A vector database architecture diagram shows how a vector store ingests data and answers similarity searches. This template lays out the two paths every vector database has: the write path, where source data is run through an embedding model and upserted into storage, and the query path, where a query is embedded and matched against the index. At the center is the database itself — an approximate nearest neighbor (ANN) index such as HNSW, a metadata filter for hybrid constraints, and the underlying vector storage. Use it to design a vector search system, explain how ANN search differs from exact search, or document where embeddings are generated versus stored.

When to use this template

• Design a vector search system and decide where embeddings are generated.
• Explain why a vector DB uses approximate (ANN) rather than exact nearest-neighbor search.
• Document the write path and query path separately for a design review.
• Show where metadata filtering applies relative to the ANN index.
• Plan capacity by tracing what gets stored: vectors plus metadata plus the index.
• Compare a managed vector DB against a vector extension on an existing database.

How to use it

1. 1Draw the write path at the top: data source → embedding model → upsert into the database.
2. 2Draw the query path at the bottom: query text → query embedding → ANN search.
3. 3Place the vector database in the center with three parts: ANN index, metadata filter, storage.
4. 4Connect the write path into the database (upsert) and the query path into the index (search).
5. 5Add the results node on the far right and connect the database to it.
6. 6Label the index as HNSW (or your ANN algorithm) to make the search method explicit.

Quick example