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TinyVecDB

TinyVecDB is an embedded vector database library with a native C core. Applications use it to store vectors with metadata in a database file and retrieve similar vectors without operating a separate database server.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

TinyVecDB — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use TinyVecDB when an application needs local vector storage and similarity search without a client-server database setup, particularly for self-contained applications, semantic-search prototypes, or resource-constrained environments.

Who it is for

  • Developers adding local vector search to desktop or self-contained applications.
  • Teams prototyping semantic search or retrieval workflows.
  • Python and JavaScript/TypeScript developers who want documented bindings for an embedded vector index.

Documented uses

  • Insert vectors alongside metadata into a local database file.
  • Search for similar vectors using cosine similarity, with optional metadata filtering.
  • Update or delete stored vectors, retrieve paginated results, and inspect index statistics.
Before you choose

Requirements & limits

The repository is public, displays an MIT license, and documents npm and pip installation. It lists Windows, macOS, and Linux support.

  • The application supplies vectors; TinyVecDB provides their storage and similarity search rather than generating embeddings.
  • Database configuration uses a file path; dimensions can be set or derived from the first vector.
  • Metadata filtering is available alongside similarity search.
  • The project describes local, embedded use and identifies distributed cloud architectures and real-time replication as less suitable workloads.

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

Store and search vectors with TinyVecDB in JavaScript or TypeScript

Create a local vector database file, insert sample vectors, and retrieve similar vectors.

Before you start

  • TinyVecDB installed with npm.
  • Vectors and query vectors must use the configured dimensionality; the example uses 512 dimensions.

Steps

  1. Import TinyVecClient and connect to ./vectors.db with { dimensions: 512 }; the connection creates the file if it does not exist.

  2. Create vector insertions using 512-dimensional Float32Array values and metadata, then call await client.insert(insertions).

  3. Create a 512-dimensional query vector and call await client.search(queryVector, 5) to retrieve five similar vectors.

  4. Optionally provide a filter such as { filter: { category: { $eq: "example" } } } as the third argument to search.

Expected outcome

Vectors are stored in the local database file, and a query returns similar vectors, optionally filtered by metadata.

Guide limitations

  • The example uses generated sample vectors rather than embeddings from a model.
  • The README identifies complex multi-join requirements, extensive administration or monitoring, distributed cloud architectures, and real-time replication as less ideal use cases.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
17 October 2026
Original website title
tylerpuig/tinyvec · Official homepage title · retained in its source language
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