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, 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, 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)?;
// 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_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(())
# }

API overview

The public surface is a small connection-and-table API:

• connect / connect_with open a Connection. The backend follows the URI scheme (s3://, az://, file://, bare path, memory://); credentials are passed via ConnectOptions::with_storage_option (object_store's aws_* / azure_* keys), never read from the environment.
• Connection — create_table, open_table, drop_table, list_tables, query_sql.
• Supertable (the table handle) — append, update, delete, schema, and the search methods bm25_search, vector_search, hybrid_search, token_match, and exact_match (each returns Arrow rows as Vec<RecordBatch>).
• Supporting types — IndexSpec, Metric, BoolMode, VectorSearchOptions, ConnectOptions, MutationStats, and the InfinoError enum.

Other languages

infino also ships Python (pip install infino) and Node.js (npm install @infino-ai/infino) bindings. For multi-language guides and examples, see the project repository.