Project Title

weaviate — Open-source vector database for combining vector search with structured filtering

Overview

Weaviate is a cloud-native, open-source vector database designed to store both objects and vectors, enabling the combination of vector search with structured filtering. It leverages state-of-the-art machine learning models to turn data into a searchable vector database, offering speed, flexibility, and production-readiness.

Key Features

Fast 10-NN nearest neighbor search on millions of objects in milliseconds
Flexibility to vectorize data at import time or upload pre-vectorized data
Over two dozen modules for connecting to popular services and model hubs like OpenAI, Cohere, and HuggingFace
Built-in support for scaling, replication, and security

Use Cases

Software Engineers: Utilize ML-first database engine for AI-powered searches and full CRUD support
Data Engineers: Employ fast, flexible vector database with the option to use custom or third-party ML models
Data Scientists: Seamlessly handover ML models to engineers and maintain models in production

Advantages

Speed: Performs fast vector searches and can handle large datasets efficiently
Flexibility: Supports vectorization at import or allows the use of custom vectors
Production-readiness: Designed with scaling, replication, and security for smooth transition from prototyping to production

Limitations / Considerations

Custom module development may require specific expertise in machine learning and the Weaviate framework
The learning curve for new users unfamiliar with vector databases or cloud-native technologies might be steep

Elasticsearch: A widely-used search and analytics engine that offers full-text search but lacks vector search capabilities.
Milvus: An open-source vector database focused on vector similarity search and spatial search, differing in its focus on search rather than combined vector and structured filtering.
Pinecone: A cloud-native vector database designed for easy scalability and performance, differing in its managed service approach compared to Weaviate's self-hosted model.

Basic Information

GitHub: https://github.com/weaviate/weaviate
Stars: 13,928
License: Unknown
Last Commit: 2025-07-16

📊 Project Information

Project Name: weaviate
GitHub URL: https://github.com/weaviate/weaviate
Programming Language: Go
⭐ Stars: 13,928
🍴 Forks: 1,001
📅 Created: 2016-03-30
🔄 Last Updated: 2025-07-16

🏷️ Project Topics

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