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
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( vectors=zvec.VectorQuery("embedding", vector=[0.4, 0.3, 0.3, 0.1]), topk=10,)8500+Queries per Second (QPS)
Build Powerful AI Applications
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RAG (Retrieval-Augmented Generation)
Enhance LLM responses with information retrieved from your knowledge base
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Image Search
Find visually or semantically similar images at scale
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Code Search
Find code snippets by describing what you want in natural language