A vector database stores data as mathematical vectors instead of rows and columns. These vectors represent the semantic meaning of text images or audio. When you search you convert your query into a vector and find the closest matches in the database. This allows you to find concepts rather than just keyword matches. For example searching for “feline” might return documents about “cats” even if the word cat never appears.
Here is a simple Python example using ChromaDB which can run locally:
import chromadb
client = chromadb.Client()
collection = client.create_collection("my_docs")
collection.add(
documents=["The dog played in the park", "Cats are independent pets"],
ids=["doc1", "doc2"]
)
results = collection.query(
query_texts=["pets that like independence"],
n_results=1
)
print(results['documents'])
In-browser? Why Not!
In-browser vector databases move this storage and computation into the user’s web browser. This eliminates the need for a backend server or cloud API. You use libraries like LanceDB or ChromaDB compiled to WebAssembly. The data lives in IndexedDB or OPFS on the user’s device. Queries run locally using JavaScript or WebAssembly bindings.
We need this for privacy sensitive applications where data should never leave the device. It also reduces latency since there is no network round trip.
Use cases include personal note taking apps with semantic search offline document analysis tools private chatbots that process local files and browser extensions that summarize pages without sending content to third parties.
Browser-based Vector Database?
Modern browsers like Google Chrome Opera Edge and Firefox now support WebGPU. This allows in-browser vector databases to use the GPU for accelerated similarity search.
The GPU handles the heavy matrix math required for comparing thousands of vectors. This makes local search nearly as fast as cloud solutions while keeping all data on your machine.
Privacy is guaranteed because the vectors and raw data never touch a remote server.
The following is a list of the best top open-source vector database that runs directly using your browser.
1- EntityDB
EntityDB is a free and open-source in-browser database that is designed for storing and querying vectors. It works on top of IndexedDB for persistent storage and Transformers.js. With it, you can generate embeddings from text on fly, and build search systems or recommendation engine.
It is fairly easy to use, and comes with detailed documentation and examples.
EntityDB Features
- In-browser: Runs entirely in the browser using IndexedDB for local storage.
- Seamless Integration with Transformers: Easily generate text embeddings with Hugging Face models via Transformers.js.
- Cosine Similarity Search: Efficient querying based on cosine similarity between vectors.
- Flexible: Supports both automatic embedding generation and manual insertion of pre-computed embeddings.
- Lightweight: No need for a server-side component or complex setup.
2- VectorSearch.js
VectorSearch.js is a client-side library that performs semantic vector search over millions of vectors in milliseconds directly in the browser, leveraging IndexDB for storage and WebGPU for acceleration.
It prioritizes privacy and zero cost by running entirely on local hardware, supporting models like Google’s EmbeddingGemma and all-Mini-L6-v2 through a stack built on LiteRT.js, Transformers.js, and TensorFlow.js.
3- Vector IDB
Vector IDB transforms IndexedDB into a local browser-based vector database, enabling offline semantic search without API keys. It stores and queries vectors efficiently using models like MiniLM, offering a privacy-focused, zero-cost alternative to cloud services like Pinecone for in-browser AI applications.
4- Client Vector Search
Client-vector-search is a high-performance library for embedding, searching, and caching vectors directly in the browser or Node.js. It outperforms cloud services like Pinecone and OpenAI’s ADA-002 by leveraging local transformer models (such as gte-small) and WebGPU acceleration. By calculating cosine similarity on-device, it ensures zero-latency responses, complete data privacy, and no API costs.
The tool supports browser caching for efficiency and is production-ready, maintained by the team behind searchbase.app. It is ideal for developers building offline-capable semantic search, recommendation engines, or privacy-focused applications that require instant results without relying on external servers or complex infrastructure.
5- Vectra
Vectra is a local-first, file-backed vector database that runs in-memory for sub-millisecond latency. It supports MongoDB-style filtering and cosine similarity, with each index stored as a simple folder on disk.
Now featuring browser/Electron support via IndexedDB, local HuggingFace embeddings without API keys, and an optional gRPC server for cross-language access, it’s lightweight, MIT-licensed, and agent-ready.
It is the most idea solution if you wanna build an Electron-based app.
6- sqlite-vec
sqlite-vec is a lightweight, dependency-free C extension for SQLite that enables vector search anywhere SQLite runs. It supports float, int8, and binary vectors with metadata filtering, offering a portable, “fast enough” solution for local AI applications across Python, Node.js, Rust, and more.
7- in-browser-vector-db
in-browser-vector-db is a promise-based, Web Worker-compatible library for client-side vector search. It supports float32 and binary vectors with metadata filtering, offering efficient local storage and querying directly in the browser.
These libraries let you run semantic search directly in the browser, keeping data private and offline. Use them for personal note apps, local document analysis, or browser extensions where sending data to a cloud server isn’t an option.
They leverage WebGPU for speed, offering a zero-cost, low-latency alternative to services like Pinecone for developers building local-first AI tools.
