Concepts

A short tour of the nouns you will meet everywhere in Quiver.

Collections

A collection is a named set of vectors of a fixed dimension, with a fixed distance metric (cosine, dot, or l2) and an index configuration. You create a collection, upsert points into it, and search it. Each collection has its own data-encryption key (envelope encryption), so dropping a collection crypto-shreds it — its key is destroyed and its data becomes unrecoverable.

Points, vectors, and payloads

A point is an id, a vector, and an optional JSON payload. The vector is what you search by; the payload carries metadata ({"tag": "x", "year": 2024}). Payload fields can be declared filterable so they participate in hybrid search, and they can be client-side-encrypted so the server stores only ciphertext for the sensitive fields while cleartext siblings stay filterable.

Metrics

• cosine — angle between vectors (normalize-and-dot); the usual choice for text embeddings.
• dot — inner product; for models trained with dot-product objectives.
• l2 — Euclidean distance; required by the DCPE encrypted-search mode (its secret scaling preserves L2 ordering, not cosine/dot).

Indexes

The index is how Quiver answers approximate nearest-neighbour queries quickly. Pick per collection (see Indexing & memory frugality):

• HNSW — a fast in-memory graph; the default.
• IVF — inverted-file clustering; pairs well with quantization.
• Vamana — the DiskANN graph, in-memory or disk-resident.
• disk_vamana — the memory-frugality wedge: the graph and full-precision vectors live in the encrypted on-disk index while only compact PQ codes stay in RAM.
• colbert — an opt-in ColBERTv2/PLAID token-pool index for multi-vector collections.

Every index supports incremental updates, so a streaming workload never pays an O(N) rebuild per write.

Quantization

Optional compression of the stored vectors — product, scalar, or binary — trading a little recall for a large drop in memory. The tradeoff table documents the knobs.

Filtering & hybrid search

A search can carry a filter over filterable payload fields. Quiver’s planner either pre-filters to an exact scan (when the filter is selective) or post-filters the ANN results — so metadata constraints compose with vector similarity.

Multi-vector documents

A multivector collection stores each document as a set of token vectors and ranks documents by MaxSim late interaction (ColBERT). See Multi-vector / late interaction.

Tenancy, keys, and access control

Authentication is by API key; authorization is default-deny RBAC with roles (read ⊆ write ⊆ admin) and collection scopes (exact names or a trailing-* prefix for per-namespace isolation). Optional mutual TLS adds a second factor, and an append-only audit log records every mutating or administrative action. See Self-hosting & configuration.

Embeddable vs server

One binary runs three ways: an in-process embeddable database, a server (REST + gRPC), and an MCP server (stdio) so AI agents can drive it. A data directory is portable between them.