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Seekstorm

SeekStorm is an Apache-2.0-licensed Rust search library and server for vector and lexical retrieval. Developers can embed the library in an application or operate the multi-tenant server, which is accompanied by a REST API and client libraries.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Seekstorm — OpenViST project profile in RAG & Knowledge Systems.
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Understand the project

Practical overview

When it fits

Use SeekStorm when building a search or retrieval component and choosing between an embedded Rust library and a self-hosted multi-tenant server. Its examples are especially relevant when index schemas, vector retrieval or facet filtering are part of the design.

Who it is for

  • Rust developers adding search directly to an application.
  • Teams building a self-hosted search backend for multiple tenants.
  • Developers building retrieval for knowledge repositories that need vector, lexical or faceted search.

Documented uses

  • Add vector or lexical search to an application.
  • Create a backend for hybrid retrieval.
  • Index documents and return facets or apply facet filters in queries.
  • Connect an InstantSearch.js interface to a SeekStorm backend using the listed adapter.
Before you choose

Requirements & limits

The public repository identifies SeekStorm as open source under the Apache License 2.0.

  • Choose the library for in-process use or the server for a separate REST-backed service.
  • The README lists REST clients for Rust, Python, TypeScript, C#, and Java.
  • Vector indexing can use the documented internal-inference path or an external-inference path.
  • Facets involve schema configuration, values supplied during document indexing, and query-time facet parameters.
  • The InstantSearch adapter is a separate frontend integration component, not the search engine itself.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Create a faceted search index in Rust

Set up the documented schema and index metadata for faceted indexing and search.

Before you start

  • A Rust project
  • The seekstorm, tokio and serde_json crates
  • An asynchronous Rust runtime

Steps

  1. Add the seekstorm, tokio and serde_json crates to the project.

  2. Define the schema, including a field marked for facets.

  3. Configure index metadata and create the index.

  4. Set facet values when indexing documents, then specify facet queries or filters at query time.

Expected outcome

A configured index that can return facets in search results and use facet filters.

Documentation 1 ↗
Source-based practical guide

Configure a vector-search index

Create an index configured for the documented internal-inference vector-search path.

Before you start

  • A Rust project using SeekStorm
  • An asynchronous Rust runtime
  • A schema with fields configured for vector indexing

Steps

  1. Define a schema that marks the relevant text fields for vector indexing.

  2. Configure index metadata with internal inference using Model2Vec and PotionBase2M.

  3. Set the documented chunk size and scalar quantization option.

  4. Create the index at the chosen index path.

Expected outcome

An index configured for vector retrieval with the documented internal-inference settings.

Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
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Sources checked
13 October 2026
Original website title
SeekStorm/SeekStorm · Official homepage title · retained in its source language
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