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torvalds-dev / weaviate

Weaviate is an open source vector database that stores both objects and vectors, allowing for combining vector search with structured filtering with the fault-tolerance and scalability of a cloud-native database, all accessible through GraphQL, REST, and various language clients.
https://weaviate.io/developers/weaviate/
BSD 3-Clause "New" or "Revised" License
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what are the features of weaviate? #6

Open torvalds-dev opened 8 months ago

torvalds-dev[bot] commented 8 months ago

torvalds.dev is analyzing the ticket

torvalds-dev[bot] commented 8 months ago

Based on the README.md file, Weaviate is an open-source vector database that is robust, scalable, cloud-native, and fast. It allows you to turn your text, images, and more into a searchable vector database using state-of-the-art ML models.

Here are some of its features:

  1. Speed: Weaviate performs a 10-NN neighbor search out of millions of objects in single-digit milliseconds.

  2. Flexibility: You can use Weaviate to vectorize your data at import time, or you can upload your own vectors. It supports popular services and model hubs such as OpenAI, Cohere, HuggingFace, and more.

  3. Production-readiness: Weaviate is designed for rapid prototyping to production at scale. It is built with scaling, replication, and security in mind.

  4. Beyond search: Weaviate powers lightning-fast vector searches, but it is also capable of recommendation, summarization, and integrations with neural search frameworks.

  5. Interfaces: You can use Weaviate with Python, JavaScript/TypeScript, Go, and Java clients. It also supports GraphQL API to retrieve objects and properties.

As for the actions that should be taken, it depends on the context of the issue. If you're looking to understand more about Weaviate, you might want to explore the documentation, quickstart tutorial, and contributor guide mentioned in the README. If you're facing a specific problem with Weaviate, you might want to raise an issue in the GitHub repository or ask for help in the community forum or Slack channel.

The relevant files to this issue are likely the README.md file and any documentation or tutorial files in the repository. If you're facing a specific problem, the relevant files would be those related to the feature or functionality you're having trouble with.