Indexing: BM25 full-text and IVF vector indexes

You declare indexes once, at table creation, with an IndexSpec. Two index types, chained fluently.

Index	Declare with	Powers
Full-text (BM25)	`fts(column)`	keyword search, hybrid, the pushdown filter
Vector (IVF)	`vector(column, dim, metric)`	semantic kNN, hybrid

Declare both on a table to enable hybrid search. Scalar columns need no index; filter them with SQL.

spec = (
    infino.IndexSpec()
    .fts("body")                               # BM25 over `body`
    .vector("embedding", 384, "cosine")    # vector index over `embedding`
)

const spec = new IndexSpec()
  .fts("body")                                 // BM25 over `body`
  .vector("embedding", 384, "cosine");     // vector index over `embedding`

use infino::{IndexSpec, Metric};

let spec = IndexSpec::new()
    .fts("body")                                  // BM25 over `body`
    .vector("embedding", 384, Metric::Cosine); // vector index over `embedding`

Full-text index: `fts(column)`

Builds a BM25 inverted index over a text column, powering bm25_search, token_match, and the hybrid / pushdown-filter paths. Call fts once per text column you want to search.

Vector index: `vector(column, dim, metric)`

Builds an IVF (inverted-file) vector index for kNN.

Parameter	Type	Description
`column`	string	the fixed-width vector column to index
`dim`	int	embedding dimension; must match your model’s output and the values you append
`metric`	`cosine` / `l2sq` / `negdot` (`Metric::Cosine` …)	distance metric (see Embeddings)

Partitioning is automatic. The engine derives the IVF partition (centroid) count from the data when the index is built, and it calibrates probe width and rerank budget per table and per k at query time. There are no partition or probe knobs to set, and the same declaration works from thousands of rows to hundreds of millions.

You can index multiple text columns (call fts per column) and multiple vector columns. Scalar columns need no index declaration. Filter them with SQL (query_sql) or as a pushdown pre-filter on vector search (see Search).

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Indexing: BM25 full-text and IVF vector indexes

Full-text index: `fts(column)`

Vector index: `vector(column, dim, metric)`

See also

​Full-text index: fts(column)

​Vector index: vector(column, dim, metric)

​See also

Full-text index: `fts(column)`

Vector index: `vector(column, dim, metric)`

See also