Vector databases

A vector database stores each chunk's embedding with its text and metadata, and searches them by similarity.

• Examples: pgvector (in Postgres), Qdrant, Pinecone, Chroma.
• Metadata (source, date, team) lets you filter and cite.
• Re-index documents when they change.

dim, chunks, bytes_per_number = 768, 100_000, 4
print(round(dim * chunks * bytes_per_number / 1_000_000, 1), "MB")

307.2 MB

Remember: Store the source with every chunk, so answers can cite it.

Common mistake: Leaving old chunks in the index after a policy changes.