A guide to building long-term compounding knowledge infrastructure. See GitHub for details.

Weaviate

Weaviate is an open-source, cloud-native vector database for storing objects and vectors, enabling scalable semantic search and structured filtering for AI applications.

Introduction

Weaviate is an open-source, cloud-native vector database that stores both objects and vectors, combining semantic search, keyword filtering, and high availability for large-scale AI applications.

Key Features

  • Automatic vectorization or import of pre-computed embeddings
  • Combines vector search and keyword filtering in a single query interface
  • Enterprise features: multi-tenancy, replication, RBAC authorization
  • Cloud-native architecture with fault tolerance and scalability

Use Cases

  • RAG systems and intelligent Q&A
  • Semantic and image search
  • Recommendation engines and content classification
  • Chatbots and knowledge bases

Technical Highlights

  • Supports multiple embedding models (OpenAI, Cohere, HuggingFace, etc.)
  • Direct import or automatic generation of vectors
  • Multi-language clients and API support
  • Active community and continuous development

Comments

Weaviate
Resource Info
Author Weaviate
Added Date 2025-09-04
Type
Tool
Tags
RAG OSS Utility Deployment