infino

SQL, full-text, and vector search over your data on object storage — one engine, no server to run.

Infino keeps your data in Apache Parquet on object storage (local disk, Amazon S3, or any S3-compatible store) and runs SQL, full-text (BM25), and vector search over it from a single system. Each file is a valid Parquet file with BM25 and vector indexes embedded directly inside it; a table composes many such files with snapshot-isolated reads, append-only writes, and atomic commits. It runs in your process — there is no daemon, no cluster, and no managed service to operate.

Use it for RAG, agent memory, hybrid search, and semantic search: an embedded vector database, full-text (BM25) search engine, and SQL query engine in one library.

Install

npm install @infino-ai/infino

A prebuilt native binary is selected automatically at install time — no Rust toolchain required. Supported platforms:

| Platform | Architectures | | --------------------- | ------------- | | macOS | x64, arm64 | | Linux (glibc) | x64, arm64 | | Linux (musl / Alpine) | x64, arm64 |

Requires Node.js >= 18. apache-arrow is installed as a dependency and used at the boundary (passing in Tables, or { arrow: true } results).

Quickstart

import { connect, IndexSpec } from "@infino-ai/infino";

// Connect to a catalog. Use a local path or an S3 URI for durable storage;
// "memory://" is ephemeral and handy for tests.
const db = connect("./data");

// 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.
const embed = (topic) => { const v = Array(16).fill(0.0); v[topic] = 1.0; return v; };

// Declare a schema and which columns to index. An `_id` column is added
// automatically — you don't define it.
const docs = db.createTable(
  "docs",
  { source: "large_utf8", body: "large_utf8", embedding: { vector: 16 } },
  new IndexSpec().fts("body").vector("embedding", 16, 1, "cosine"),
);

// Append rows. One append is one atomic commit.
docs.append([
  { source: "help-center", body: "To cancel a subscription, open Settings then Billing.", embedding: embed(0) },
  { source: "help-center", body: "Refunds return to the original payment method.",         embedding: embed(0) },
  { source: "blog",        body: "Enable dark mode under Settings then Appearance.",        embedding: embed(1) },
]);

// Retrieve context to ground an agent's next answer — keyword, vector,
// hybrid (BM25 + vector fused in one pass), or SQL:
const keyword  = docs.bm25Search("body", "cancel subscription", 5);                          // BM25
const semantic = docs.vectorSearch("embedding", embed(0), 5);                                // vector kNN
const hybrid   = docs.hybridSearch("body", "cancel subscription", "embedding", embed(0), 5); // fused
const billing  = db.querySql("SELECT body FROM docs WHERE source = 'help-center'");          // SQL filter

CommonJS works too — const { connect, IndexSpec } = require("@infino-ai/infino");.

The API is synchronous. In a long-running server, run calls in a worker_thread so a query doesn't block the event loop.

Documentation

Full docs, guides, and the API reference live at infino.ai/docs:

• Quickstart — install to first query
• Core concepts — superfiles, commits, and indexes
• Guides — Tables & indexing · Search: BM25, vector, hybrid · Embeddings · Storage & credentials
• SQL reference — query tables and the search table-valued functions
• API reference — the full Node surface, generated from the package
• Integrations — LangChain, CrewAI, Vercel AI SDK, MCP
• Examples — runnable agent-memory and hybrid-search-service demos

Building from source

The binding is built with napi-rs and requires a Rust toolchain.

cd infino-node
npm install && npm run build && npm test

License

Apache-2.0.