infino

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 arrow_array::{FixedSizeListArray, Float32Array, LargeStringArray, RecordBatch};
use 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
}

# fn main() -> Result<(), Box<dyn std::error::Error>> {
// 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
# Ok(())
# }

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:

[connect] / [connect_with] open a [Connection]. The backend follows the URI scheme (s3://, az://, gs://, file://, bare path, memory://); credentials are passed via [ConnectOptions::with_storage_option] (object_store's aws_* / azure_* / google_* keys), never read from the environment.
[Connection] — the catalog: create_table, open_table, drop_table, list_tables, and query_sql.
[Supertable] — a single table:
- Search — bm25_search, vector_search, hybrid_search, token_match, exact_match, and count. Each search returns Arrow rows as Vec<RecordBatch>.
- Write — append, update, delete.
- Maintain — optimize, gc, and schema.

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.

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