friendliai/weaviate
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Description: Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database.
License: BSD-3-Clause
Stars: 0
Forks: 0
Open issues: 0
Created: 2024-08-17T08:59:56Z
Pushed: 2024-08-22T08:39:15Z
Default branch: main
Fork: yes
Parent repository: weaviate/weaviate
Archived: no
README: Weaviate
   
Overview
Weaviate is a cloud-native, open source vector database that is robust, fast, and scalable.
To get started quickly, have a look at one of these pages:
- Quickstart tutorial To see Weaviate in action
- Contributor guide To contribute to this project
For more details, read through the summary on this page or see the system documentation.
> [!NOTE] > Help us improve your experience by sharing your feedback, ideas and thoughts: Fill out our Community Experience Survey, preferably by June 14th, 2024.
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Why Weaviate?
Weaviate uses state-of-the-art machine learning (ML) models to turn your data - text, images, and more - into a searchable vector database.
Here are some highlights.
Speed
Weaviate is fast. The core engine can run a 10-NN nearest neighbor search on millions of objects in milliseconds. See benchmarks.
Flexibility
Weaviate can vectorize your data at import time. Or, if you have already vectorized your data, you can upload your own vectors instead.
Modules give you the flexibility to tune Weaviate for your needs. More than two dozen modules connect you to popular services and model hubs such as OpenAI, Cohere, VoyageAI and HuggingFace. Use custom modules to work with your own models or third party services.
Production-readiness
Weaviate is built with scaling, replication, and security in mind so you can go smoothly from rapid prototyping to production at scale.
Beyond search
Weaviate doesn't just power lightning-fast vector searches. Other superpowers include recommendation, summarization, and integration with neural search frameworks.
Who uses Weaviate?
- Software Engineers
- Weaviate is an ML-first database engine
- Out-of-the-box modules for AI-powered searches, automatic classification, and LLM integration
- Full CRUD support
- Cloud-native, distributed system that runs well on Kubernetes
- Scales with your workloads
- Data Engineers
- Weaviate is a fast, flexible vector database
- Use your own ML model or third party models
- Run locally or with an inference service
- Data Scientists
- Seamless handover of Machine Learning models to engineers and MLOps
- Deploy and maintain your ML models in production reliably and efficiently
- Easily package custom trained models
What can you build with Weaviate?
A Weaviate vector database can search text, images, or a combination of both. Fast vector search provides a foundation for chatbots, recommendation systems, summarizers, and classification systems.
Here are some examples that show how Weaviate integrates with other AI and ML tools:
Use Weaviate with third party embeddings
Use Weaviate as a document store
Use Weaviate as a memory backend
Demos
These demos are working applications that highlight some of Weaviate's capabilities. Their source code is available on GitHub.
How can you connect to…
Excerpt shown — open the source for the full document.
Notability
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