Weaviate on Ubuntu 24.04

Weaviate is an open source, cloud-native vector database designed for AI-powered search, knowledge graphs, and semantic retrieval. It allows developers and enterprises to store, index, and query data using vector embeddings, enabling modern AI applications such as semantic search, recommendation engines, and retrieval-augmented generation (RAG) pipelines. Weaviate is suitable for businesses and developers seeking a scalable, API-first database for structured and unstructured data.

Features of Weaviate:

• Vector-based storage and retrieval for AI and semantic search applications.
• API-first design with REST and GraphQL endpoints for flexible integration.
• Schema-based data modeling supporting classes, properties, and relationships.
• Supports multiple data types including text, images, and embeddings.
• Modular architecture with optional AI modules for text2vec, classification, and generative AI.
• Containerized deployment with Docker and Docker Compose support.
• Persistent storage with volumes and enterprise-ready scaling capabilities.
• Seamless integration with popular ML/embedding providers such as OpenAI, Cohere, Hugging Face, and Ollama.

Weaviate Usage Instructions:

$ sudo su
$ docker ps
$ docker run -d -p 8080:8080 -p 50051:50051 cr.weaviate.io/semitechnologies/weaviate:latest
$ curl http://localhost:8080/v1/meta
    

Disclaimer: Weaviate is an open-source project maintained by Semi Technologies. This document and any distributed software are not affiliated with, endorsed by, or sponsored by any third-party organizations unless explicitly stated. Users should review and comply with all applicable licensing terms and evaluate suitability for production use before deployment.