---
source_url: "https://zvec.org/?utm_source=openai"
title: "Zvec | A lightweight, lightning-fast, in-process vector database"
mirrored_at: 2026-08-04T01:31:47.941Z
host: zvec.org
cited_in_42a: true
mirror_canonical: "https://index.42a.ai/zvec.org/index__q__utm_source_openai"
---

> **Original source:** https://zvec.org/?utm_source=openai

A lightweight, lightning-fast, in-process vector database

High-Performance semantic search, made simple

## Meet Zvec

The simple way to build AI applications with vectors

With Zvec, build high-performance applications without the complexity holding you back.

## Simple, Intuitive Python API

zvec

```
import zvecschema = zvec.CollectionSchema(    name="example",    vectors=zvec.VectorSchema("embedding", zvec.DataType.VECTOR_FP32, 4),)collection = zvec.create_and_open(path="./zvec_example", schema=schema)
```

```
import zveccollection = zvec.open("./zvec_example")collection.insert(zvec.Doc(id="1", vectors={"embedding": [0.1, 0.2, 0.3, 0.4]}))
```

```
import zveccollection = zvec.open("./zvec_example")results = collection.query(    queries=zvec.Query("embedding", vector=[0.4, 0.3, 0.3, 0.1]),    topk=10,)
```

10MTotal Vectors Indexed

~1 HourIndex Build Time

8500+Queries per Second (QPS)

## Build Powerful AI Applications

📚

### RAG (Retrieval-Augmented Generation)

Enhance LLM responses with information retrieved from your knowledge base

🖼️

### Image Search

Find visually or semantically similar images at scale

💻

### Code Search

Find code snippets by describing what you want in natural language