Weaviate 是一个云原生的向量数据库，能够存储对象和向量，实现大规模语义搜索。它将向量相似性搜索与关键词过滤、检索增强生成（RAG）和重新排序结合在单一查询界面中。常见用例包括 RAG 系统、语义和图像搜索、推荐引擎、聊天机器人以及内容分类。

Weaviate 支持两种向量存储方式：导入时使用集成模型（OpenAI、Cohere、HuggingFace 等）自动向量化，或直接导入预计算的向量嵌入。生产部署受益于内置的多租户、复制、RBAC 授权及其他许多功能。

想快速开始，可以看看以下教程之一：

• 快速入门 - Weaviate Cloud
• 快速启动 - 本地 Docker 实例

安装

Weaviate 提供多种安装和部署选项：

• Docker
• Kubernetes
• 织云

查看安装文档了解更多部署选项，比如AWS和GCP。

开始

你可以用 Docker 轻松启动 Weaviate 和本地向量嵌入模型。 创建一个 docker-compose.yml 文件：

Weaviate

Weaviate is a cloud-native vector database that stores both objects and vectors, enabling semantic search at scale. It combines vector similarity search with keyword filtering, retrieval-augmented generation (RAG), and reranking in a single query interface. Common use cases include RAG systems, semantic and image search, recommendation engines, chatbots, and content classification.

Weaviate supports two approaches to store vectors: automatic vectorization at import using integrated models (OpenAI, Cohere, HuggingFace, and others) or direct import of pre-computed vector embeddings. Production deployments benefit from built-in multi-tenancy, replication, RBAC authorization, and many other features.

To get started quickly, have a look at one of these tutorials:

• Quickstart - Weaviate Cloud
• Quickstart - local Docker instance

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:1.36.0
    ports:
      - "8080:8080"
      - "50051:50051"
    environment:
      ENABLE_MODULES: text2vec-model2vec
      MODEL2VEC_INFERENCE_API: http://text2vec-model2vec:8080