Data & Research · GitHub ·244 ★

vector-storage

Vector Storage is a vector database that enables semantic similarity searches on text documents in the browser's local storage. It uses OpenAI embeddings to convert documents into vectors and allows searching for similar documents based on cosine similarity.

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Details

Author
nitaiaharoni1
Category
Data & Research
Platform
GitHub
Framework
openai
Language
typescript
Stars
244
First indexed
2026-05-15
Last active
2024-12-11
Directory sync
2026-05-15
@nitaiaharoni1

Overview

Vector Storage is a vector database that enables semantic similarity searches on text documents in the browser's local storage. It uses OpenAI embeddings to convert documents into vectors and allows searching for similar documents based on cosine similarity.

Quick start

git

git clone https://github.com/nitaiaharoni1/vector-storage

Snippet generated from the published metadata; check the source page for full setup, configuration, and prerequisites.

What vector-storage can do

embeddingragdata
  • Embedding — Computes vector embeddings for semantic search.
  • Rag — Retrieves grounded context before answering.
  • Data — Reads, transforms, and analyses structured data.

Frequently asked questions

What is vector-storage?
Vector Storage is a vector database that enables semantic similarity searches on text documents in the browser's local storage. It uses OpenAI embeddings to convert documents into vectors and allows searching for similar documents based on cosine similarity.
How do I install vector-storage?
Use git: `git clone https://github.com/nitaiaharoni1/vector-storage`. Full setup details on the source page linked above.
Is vector-storage open source?
vector-storage is published on GitHub.
What are alternatives to vector-storage?
Comparable agents include ragflow, autoresearch, OpenBB. Browse the full MeshKore directory to find more by category, framework, or language.

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Source & freshness

Profile data for vector-storage is sourced from GitHub, published by nitaiaharoni1.

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