Vector queries on INFINI Pizza

Vector query (kNN)

Mon, 01 Jan 0001 00:00:00 +0000

Vector query (kNN) #

Finds the k nearest neighbors of a query vector — similarity search over dense_vector / sparse_vector fields. The query vector can be supplied directly, or as natural-language text that the serving layer embeds with the field’s configured model (schema-driven semantic search).

Examples #

POST /my-collection/_search
{
 "query": {
 "vector": {
 "field": "embedding",
 "query_vector": [0.12, 0.45, 0.1, 0.0, -0.3, 0.2, 0.05, -0.1],
 "k": 10
 }
 }
}

Text in, embedding server-side (requires an embedding endpoint — see below):

Multi-vector query

Mon, 01 Jan 0001 00:00:00 +0000

Multi-vector query #

Executes several vector queries — each targeting a (potentially different) vector field — and fuses their results into one ranking.

Examples #

POST /my-collection/_search
{
 "query": {
 "multi_vector": {
 "queries": [
 { "field": "title_embedding", "query": "search engines", "k": 50 },
 { "field": "body_embedding", "query": "search engines", "k": 50 }
 ],
 "fusion": "weighted_sum",
 "weights": [2.0, 1.0],
 "k": 10
 }
 }
}

Parameters for `multi_vector` #

queries
(Required, array of vector queries) The individual vector queries to execute.
fusion
(Optional) Fusion strategy for combining results:
- "weighted_sum" — weighted sum of normalized scores; each query’s score is multiplied by its weight (from weights) before summing.
- {"rrf": {"k": 60}} — Reciprocal Rank Fusion: combines rankings rather than raw scores, with k as the RRF smoothing constant (default 60).
weights
(Optional, array of numbers) Per-query weights, must match queries.len() if provided. For WeightedSum: score multipliers; for RRF: rank contribution multipliers. Defaults to equal weight (1.0) per query.
k
(Optional, integer, default: 10) Final top-k to return after fusion.

Semantic query

Mon, 01 Jan 0001 00:00:00 +0000

Semantic query #

Semantic search with zero field knowledge: the vector field and the embedding model resolve from the collection schema, and the request is plain text.

POST /my-collection/_search
{"query": {"semantic": "find documents about search engines"}}

The gateway embeds the text server-side through the field’s configured model (the same inference path as text vector queries) and rewrites the clause into a native vector query — so semantic composes inside bool like any other leaf:

Vector queries on INFINI Pizza

Vector query (kNN)

Vector query (kNN) #

Examples #

Multi-vector query

Multi-vector query #

Examples #

Parameters for multi_vector #

Semantic query

Semantic query #

Parameters for `multi_vector` #