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Crate infino

Crate infino 

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§infino

Crates.io docs.rs CI License: Apache-2.0

infino is a fast retrieval engine that runs SQL, full-text (BM25), and vector search over a single copy of your data on object storage. Data stays in Parquet on S3 (or Azure, GCS, or local disk) and you query it at scale — embedded in your process, with no separate search server or vector database to run.

  • Speed per dollar — object-storage economics at search-engine speeds; on a 1-million-document index, warm BM25 queries return in the microsecond range.
  • Multi-modal queries — keyword (BM25), vector, and SQL over the same rows.
  • Object-storage-native — snapshot-isolated reads and atomic commits over S3, Azure, GCS, or local disk.
  • Open format, no lock-in — spec-compliant Parquet, so anything that reads Parquet can read your data.

§Install

cargo add infino

infino installs the mimalloc global allocator by default. If you embed infino in a process that already sets a global allocator, turn it off to avoid a second one: infino = { version = "0.1", default-features = false }.

§Quickstart

use std::sync::Arc;

use infino::arrow_array::{FixedSizeListArray, Float32Array, LargeStringArray, RecordBatch};
use infino::arrow_schema::{DataType, Field, Schema};
use infino::{connect, BoolMode, IndexSpec, Metric, VectorFilter, VectorSearchOptions};

// Tiny stand-in for your embedding model so this runs as-is — a 16-dim
// one-hot by topic. Real embeddings are dense and higher-dimensional.
fn embed(topic: usize) -> Vec<f32> {
    let mut v = vec![0.0_f32; 16];
    v[topic] = 1.0;
    v
}

// A knowledge base your agent retrieves over. "memory://" is in-process;
// use "./data" or "s3://bucket/prefix" to persist.
let db = connect("memory://")?;

let item = Arc::new(Field::new("item", DataType::Float32, true));
let schema = Arc::new(Schema::new(vec![
    Field::new("source", DataType::LargeUtf8, false),
    Field::new("body", DataType::LargeUtf8, false),
    Field::new("embedding", DataType::FixedSizeList(item.clone(), 16), false),
]));
let docs = db.create_table(
    "docs",
    schema.clone(),
    IndexSpec::new().fts("body").vector("embedding", 16, 1, Metric::Cosine),
)?;

let flat: Vec<f32> = [0usize, 0, 1].iter().flat_map(|&t| embed(t)).collect();
docs.append(&RecordBatch::try_new(
    schema,
    vec![
        Arc::new(LargeStringArray::from(vec!["help-center", "help-center", "blog"])),
        Arc::new(LargeStringArray::from(vec![
            "To cancel a subscription, open Settings then Billing.",
            "Refunds return to the original payment method.",
            "Enable dark mode under Settings then Appearance.",
        ])),
        Arc::new(FixedSizeListArray::new(item, 16, Arc::new(Float32Array::from(flat)), None)),
    ],
)?)?;

// Retrieve context to ground the agent's next answer:
let keyword = docs.bm25_search("body", "cancel subscription", 5, BoolMode::Or, None)?;
let semantic = docs.vector_search("embedding", &embed(0), 5, VectorSearchOptions::new(), None, None)?;
// hybrid: BM25 + vector, fused with reciprocal-rank fusion:
let hybrid = docs.hybrid_search(
    "body", "cancel subscription", BoolMode::Or,
    "embedding", &embed(0), VectorSearchOptions::new(), 5, None,
)?;
// vector kNN, restricted to rows whose body matches a keyword (pushdown filter):
let filtered = docs.vector_search(
    "embedding", &embed(0), 5, VectorSearchOptions::new(),
    Some(VectorFilter { column: "body", query: "billing", mode: BoolMode::Or }), None,
)?;
let billing = db.query_sql("SELECT body FROM docs WHERE source = 'help-center'")?;
assert_eq!(keyword.iter().map(|b| b.num_rows()).sum::<usize>(), 1);   // BM25
assert!(semantic.iter().map(|b| b.num_rows()).sum::<usize>() >= 1);   // vector kNN
assert!(hybrid.iter().map(|b| b.num_rows()).sum::<usize>() >= 1);     // hybrid (BM25 + vector)
assert_eq!(filtered.iter().map(|b| b.num_rows()).sum::<usize>(), 1);  // vector + keyword filter
assert_eq!(billing.iter().map(|b| b.num_rows()).sum::<usize>(), 2);   // SQL filter

§Operations

The public surface is a small connection-and-table API. Everything except the two entry-point functions is a method on one of two handles, so the operations live on the Connection and Supertable pages:

Supporting types: IndexSpec, Metric, BoolMode, VectorSearchOptions, VectorFilter, ConnectOptions, MutationStats, GcReport, and the InfinoError, OptimizeError, and GcError error enums.

§Cargo features

  • default — enables the bundled mimalloc global allocator. Disable with default-features = false if your process already installs a global allocator.

§Other languages

infino also ships Python (pip install infino) and Node.js (npm install @infino-ai/infino) bindings. For concepts, guides, and multi-language examples, see the full documentation at infino.ai/docs.

Re-exports§

pub use arrow_array;
pub use arrow_schema;

Structs§

CompactionSettings
Compaction settings: target size, fill floor, and memory budget.
ConnectOptions
Catalog entry points and handle: open a Connection, then create / open / drop / list tables. Storage configuration for connect_with.
Connection
Catalog entry points and handle: open a Connection, then create / open / drop / list tables. A catalog connection. Cheap to clone (one Arc); clones share the same catalog.
GcReport
Outcome of a crate::Supertable::gc sweep: what was reclaimed and what was intentionally kept.
GcSettings
Gc settings used by optimize()’s bundled gc sweep.
IndexSpec
Catalog entry points and handle: open a Connection, then create / open / drop / list tables. Declares the search indexes to build over a table’s columns.
MutationStats
Per-call outcome from one delete / update. Returned by the public Supertable::update / Supertable::delete (which fold writer + commit) and carried, one per buffered mutation, in CommitResult.outcomes.
OptimizeOptions
Options for crate::Supertable::optimize.
Supertable
Single-table handle: append / update / delete / bm25_search / vector_search / schema. The public handle is the catalog wrapper, which serves a local or a hosted table behind one type; the engine’s concrete handle stays internal (reachable as supertable::Supertable only under test-helpers). A single-table handle: append / update / delete, the search surface (bm25_search / vector_search / hybrid_search / token_match / exact_match), count, schema, optimize, and gc. Cheap to clone (one Arc); clones share the same table.
VectorFilter
An optional text-predicate filter for vector kNN search. When supplied, kNN is ranked only among rows matching the predicate (pushdown, not post-filter). Built from an FTS-indexed column, a query string, and a BoolMode.
VectorSearchOptions
Value types named by the public method signatures. Tuning knobs for vector search (Supertable::vector_search).

Enums§

BoolMode
Boolean-mode for multi-term queries. Default operator for a query’s bare (sigil-less) terms. Terms carrying an explicit clause sigil keep their polarity regardless of mode: +term is a must (every hit contains it), -term a must-not (hard exclusion).
ColdFetchMode
Catalog entry points and handle: open a Connection, then create / open / drop / list tables. How a disk-cache miss is serviced when reading cold superfiles from object storage. The cache-servicing modes need a cache (ConnectOptions::with_cache_dir); RangeOnly is the cacheless path and does not currently use a disk cache.
GcError
Errors raised by crate::Supertable::gc.
InfinoError
The single public error type for the curated API. Coarse, stable error type returned by every public infino method.
Metric
Distance metric for a vector index. Stored per-column in inf.vec.columns JSON, applied at query time.
OptimizeError
Errors raised by crate::Supertable::optimize.

Constants§

BUILDER_ID
Compile-time-baked writer identification, written to inf.builder KV. Format: infino/<crate-version>+<git-short-hash>[-dirty], or …+unknown when built outside a git checkout (e.g. crates.io). Captured at build time by build.rs; not user-overridable.

Functions§

connect
Catalog entry points and handle: open a Connection, then create / open / drop / list tables. Open (or create) a catalog rooted at uri.
connect_with
Catalog entry points and handle: open a Connection, then create / open / drop / list tables. Open (or create) a catalog rooted at uri with explicit storage configuration (credentials / region / endpoint the URI can’t carry).