Weaviate is a low-latency vector search engine and vector database that uses machine learning for vectorizing and storing data, and answering natural language queries. The engine facilitates semantic search, classification, question-answer-extraction, customizable models, and more. It supports different media types such as images, texts, etc.

Weaviate has been developed in the Go language, supporting vectors and objects so that engineers can combine vector search and structured filtering. Access is available through REST, GraphQL, and other languages.

Project Background

  •  Database: Weaviate
  • Author: Bob van Luijt & SeMI Technologies
  • Initial Release: March 2016
  • Type: Vector Database
  • License: BSD-3-Clause License
  • Contains: HNSW ANN plugin, modules, data schema, GraphQL references, client libraries and CLI, etc. 
  • Language: Go
  • GitHub: /semi-technologies/weaviate has 1.8k stars, 97 releases, and 19 contributors 
  • Runs On: Linux, Windows, macOS
  • Stackflow: /questions/tagged/weaviate
  • Twitter: /semi_tech 


  • Semantic search
  • Image search
  • Similarity search
  • Anomaly detection
  • Power recommendation engines
  • E-commerce search
  • Data classification in ERP systems
  • Automated data harmonization
  • Cybersecurity threat analysisWeav
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