Understand the project
Practical overview
When it fits
Use Tinyvector when an application needs vector similarity search and a compact, self-hosted server fits the workload.
Who it is for
- Developers building document retrieval or embedding-based chat applications.
- Developers adding similarity search to a website or store.
- Operators who want to run a compact vector-search server themselves.
Documented uses
- Store embeddings and search for similar vectors.
- Provide a retrieval layer for embedded documents.
- Add embedding-based search to website or store content.
Before you choose
Requirements & limits
The project is open source under the MIT license. It can be run with Docker or installed through Cargo.
- Tinyvector stores indexes in memory for querying.
- Applications need to bring their own embeddings; integrated model generation is described as planned.
- Docker Compose and Kubernetes deployments need a volume bound to
/tinyvector/storagefor persistence.
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
Install and launch Tinyvector with Cargo
Install the latest tagged release and start the Tinyvector server.
Before you start
- Cargo; Rust may need to be installed first.
Steps
Run the command below.
cargo install tinyvectorRun
tinyvectorto start the server.
Expected outcome
The installed Tinyvector server starts.
Evidence and transparency
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 17 October 2026
- Original website title
- m1guelpf/tinyvector · Official homepage title · retained in its source language