15.02.2026

Zvec: Alibaba's Lightning-Fast In-Process Vector Database

Name: Akmatori
Author: Akmatori

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Vector databases have become essential infrastructure for AI-powered applications. From semantic search to RAG pipelines, teams need fast and reliable vector similarity search. However, running a separate vector database service adds operational complexity. Zvec solves this by embedding directly into your application as a library.

What is Zvec?

Zvec is an in-process vector database built on Proxima, Alibaba's battle-tested vector search engine. Instead of deploying a separate service, you import Zvec as a Python or Node.js package. It runs wherever your code runs, whether that's a notebook, server, CLI tool, or edge device.

The project handles both dense and sparse embeddings natively, with support for multi-vector queries in a single call. Combined with hybrid search capabilities, you can filter results by metadata while searching by vector similarity.

Key Features

Blazing Performance: Searches billions of vectors in milliseconds
Zero Configuration: Install via pip or npm and start searching immediately
Dense + Sparse Vectors: Native support for both embedding types
Hybrid Search: Combine semantic similarity with structured filters
Cross-Platform: Runs on Linux (x86_64, ARM64) and macOS (ARM64)

Installation

For Python (3.10-3.12):

pip install zvec

For Node.js:

npm install @zvec/zvec

Quick Usage Example

import zvec

# Define collection schema
schema = zvec.CollectionSchema(
    name="embeddings",
    vectors=zvec.VectorSchema("vec", zvec.DataType.VECTOR_FP32, 768),
)

# Create and open collection
collection = zvec.create_and_open(path="./my_vectors", schema=schema)

# Insert documents
collection.insert([
    zvec.Doc(id="doc_1", vectors={"vec": embedding_1}),
    zvec.Doc(id="doc_2", vectors={"vec": embedding_2}),
])

# Query by similarity
results = collection.query(
    zvec.VectorQuery("vec", vector=query_embedding),
    topk=10
)

When to Use Zvec

Zvec excels in scenarios where operational simplicity matters. Use it for embedding search in microservices, local development environments, edge deployments, or anywhere you want to avoid managing a separate database cluster. For SRE teams building AI-powered monitoring or log analysis tools, Zvec provides low-latency search without the overhead of external dependencies.

Conclusion

Zvec brings production-grade vector search to your application without infrastructure complexity. By running in-process, it eliminates network latency and simplifies deployment. Check out the GitHub repository to get started.

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Akmatori team