Weaviate向量数据库：信息检索与推荐系统利器

项目概述

Weaviate 是一个开源的向量数据库，能够存储对象和向量，支持将向量搜索与结构化过滤相结合，具备云原生数据库的容错性和可扩展性，适用于信息检索和推荐系统等场景。

项目地址

https://github.com/weaviate/weaviate

项目页面预览

关键指标

• Stars：15407
• 主要语言：Go
• License：BSD 3-Clause “New” or “Revised” License
• 最近更新：2026-01-15T18:05:13Z
• 默认分支：main

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安装部署要点（README 精选）

Installation

Weaviate offers multiple installation and deployment options:

• Docker
• Kubernetes
• Weaviate Cloud

See the installation docs for more deployment options, such as AWS and GCP.

Getting started

You can easily start Weaviate and a local vector embedding model with Docker.
Create a docker-compose.yml file:

services:
  weaviate:
    image: cr.weaviate.io/semitechnologies/weaviate:1.32.2
    ports:
      - "8080:8080"
      - "50051:50051"
    environment:
      ENABLE_MODULES: text2vec-model2vec
      MODEL2VEC_INFERENCE_API: http://text2vec-model2vec:8080

  # A lightweight embedding model that will generate vectors from objects during import
  text2vec-model2vec:
    image: cr.weaviate.io/semitechnologies/model2vec-inference:minishlab-potion-base-32M

Start Weaviate and the embedding service with:

docker compose up -d

Install the Python client (or use another client library):

pip install -U weaviate-client

The following Python example shows how easy it is to populate a Weaviate database with data, create vector embeddings and perform semantic search:

import weaviate
from weaviate.classes.config import Configure, DataType, Property

# Connect to Weaviate
client = weaviate.connect_to_local()

# Create a collection
client.collections.create(
    name="Article",
    properties=[Property(name="content", data_type=DataType.TEXT)],
    vector_config=Configure.Vectors.text2vec_model2vec(),  # Use a vectorizer to generate embeddings during import
    # vector_config=Configure.Vectors.self_provided()  # If you want to import your own pre-generated embeddings
)

# Insert objects and generate embeddings
articles = client.collections.get("Article")
articles.data.insert_many(
    [
        {"content": "Vector databases enable semantic search"},
        {"content": "Machine learning models generate embeddings"},
        {"content": "Weaviate supports hybrid search capabilities"},
    ]
)

# Perform semantic search
results = articles.query.near_text(query="Search objects by meaning", limit=1)
print(results.objects[0])

client.close()

This example uses the Model2Vec vectorizer, but you can choose any other embedding model provider or bring your own pre-generated vectors.

常用命令（从 README 提取）

docker compose up -d

pip install -U weaviate-client

通用部署说明

1. 下载源码并阅读 README
2. 安装依赖（pip/npm/yarn 等）
3. 配置环境变量（API Key、模型路径、数据库等）
4. 启动服务并测试访问
5. 上线建议：Nginx 反代 + HTTPS + 进程守护（systemd / pm2）

免责声明与版权说明

本文仅做开源项目整理与教程索引，源码版权归原作者所有，请遵循对应 License 合规使用。