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search on object storage — SQL, full-text, and vector search.","maintainers":[{"name":"infinohq","email":"tech@infino.ai"}],"readme":"# infino\n\n[![npm](https://img.shields.io/npm/v/@infino-ai/infino.svg)](https://www.npmjs.com/package/@infino-ai/infino)\n[![Node](https://img.shields.io/node/v/@infino-ai/infino.svg)](https://www.npmjs.com/package/@infino-ai/infino)\n[![Downloads](https://img.shields.io/npm/dm/@infino-ai/infino.svg)](https://www.npmjs.com/package/@infino-ai/infino)\n[![License](https://img.shields.io/npm/l/@infino-ai/infino.svg)](https://www.apache.org/licenses/LICENSE-2.0)\n\n**SQL, full-text, and vector search over your data on object storage — one engine, no server to run.**\n\nInfino keeps your data in Apache Parquet on object storage (local disk, Amazon\nS3, or any S3-compatible store) and runs SQL, full-text (BM25), and vector\nsearch over it from a single system. Each file is a valid Parquet file with BM25\nand vector indexes embedded directly inside it; a table composes many such files\nwith snapshot-isolated reads, append-only writes, and atomic commits. It runs in\nyour process — there is no daemon, no cluster, and no managed service to operate.\n\nUse it for **RAG**, **agent memory**, **hybrid search**, and **semantic\nsearch**: an embedded **vector database**, **full-text (BM25)** search engine,\nand **SQL** query engine in one library.\n\n## Install\n\n```sh\nnpm install @infino-ai/infino\n```\n\nA prebuilt native binary is selected automatically at install time — no Rust\ntoolchain required. Supported platforms:\n\n| Platform              | Architectures |\n| --------------------- | ------------- |\n| macOS                 | x64, arm64    |\n| Linux (glibc)         | x64, arm64    |\n| Linux (musl / Alpine) | x64, arm64    |\n\nRequires Node.js >= 18. `apache-arrow` is installed as a dependency and used at\nthe boundary (passing in `Table`s, or `{ arrow: true }` results).\n\n## Quickstart\n\n```javascript\nimport { connect, IndexSpec } from \"@infino-ai/infino\";\n\n// Connect to a catalog. Use a local path or an S3 URI for durable storage;\n// \"memory://\" is ephemeral and handy for tests.\nconst db = connect(\"./data\");\n\n// Tiny stand-in for your embedding model so this runs as-is — a 16-dim\n// one-hot by topic. Real embeddings are dense and higher-dimensional.\nconst embed = (topic) => { const v = Array(16).fill(0.0); v[topic] = 1.0; return v; };\n\n// Declare a schema and which columns to index. An `_id` column is added\n// automatically — you don't define it.\nconst docs = db.createTable(\n  \"docs\",\n  { source: \"large_utf8\", body: \"large_utf8\", embedding: { vector: 16 } },\n  new IndexSpec().fts(\"body\").vector(\"embedding\", 16, \"cosine\"),\n);\n\n// Append rows. One append is one atomic commit.\ndocs.append([\n  { source: \"help-center\", body: \"To cancel a subscription, open Settings then Billing.\", embedding: embed(0) },\n  { source: \"help-center\", body: \"Refunds return to the original payment method.\",         embedding: embed(0) },\n  { source: \"blog\",        body: \"Enable dark mode under Settings then Appearance.\",        embedding: embed(1) },\n]);\n\n// Retrieve context to ground an agent's next answer — keyword, vector,\n// hybrid (BM25 + vector fused in one pass), or SQL:\nconst keyword  = docs.bm25Search(\"body\", \"cancel subscription\", 5);                          // BM25\nconst semantic = docs.vectorSearch(\"embedding\", embed(0), 5);                                // vector kNN\nconst hybrid   = docs.hybridSearch(\"body\", \"cancel subscription\", \"embedding\", embed(0), 5); // fused\nconst billing  = db.querySql(\"SELECT body FROM docs WHERE source = 'help-center'\");          // SQL filter\n```\n\nCommonJS works too — `const { connect, IndexSpec } = require(\"@infino-ai/infino\");`.\n\n> The API is synchronous. In a long-running server, run calls in a\n> [`worker_thread`](https://nodejs.org/api/worker_threads.html) so a query\n> doesn't block the event loop.\n\n## Documentation\n\nFull docs, guides, and the API reference live at **[infino.ai/docs](https://infino.ai/docs)**:\n\n- [Quickstart](https://infino.ai/docs/quickstart) — install to first query\n- [Core concepts](https://infino.ai/docs/core-concepts) — superfiles, commits, and indexes\n- Guides — [Tables & indexing](https://infino.ai/docs/guides/tables) ·\n  [Search: BM25, vector, hybrid](https://infino.ai/docs/guides/search) ·\n  [Embeddings](https://infino.ai/docs/guides/embeddings) ·\n  [Storage & credentials](https://infino.ai/docs/guides/storage)\n- [SQL reference](https://infino.ai/docs/sql-reference) — query tables and the search table-valued functions\n- [API reference](https://infino.ai/docs/api-reference) — the full Node surface, generated from the package\n- [MCP server](https://infino.ai/docs/integrations/mcp) — expose Infino retrieval to an AI agent (Claude, Cursor, …)\n- [Examples](examples) — runnable agent-memory and hybrid-search-service demos\n\n## Building from source\n\nThe binding is built with [napi-rs](https://napi.rs/) and requires a Rust\ntoolchain.\n\n```sh\ncd infino-node\nnpm install && npm run build && npm test\n```\n\n## License\n\nApache-2.0.\n","readmeFilename":"README.md"}