Weaviate - Detailed Review

Frequently Asked Questions about Weaviate

Q: What is Weaviate and what does it do?

Weaviate is an open-source, AI-native vector database that stores and manages data as vectors, enabling semantic search capabilities. It integrates machine learning models to process, store, and search data, making it particularly useful for applications requiring AI-powered insights.

Q: How does Weaviate differ from other database systems like Elasticsearch?

Unlike Elasticsearch, which relies on inverted indexes, Weaviate is a vector-native search database. This means it stores data as vectors, allowing for semantic search in addition to traditional fast and filtered searches. This unique combination of data storage enables both fast and contextually relevant search results.

Q: What are the key features of Weaviate?

Weaviate offers several key features, including vector search, machine learning integration, scalability, graph queries, automated classification, customization, and real-time indexing. It also supports multimodal searches, allowing the import and search of text, audio, and video data.

Q: How does Weaviate handle data iteration and pagination?

Weaviate supports cursor-based iteration and pagination through result sets. You can use the after operator for cursor-based iteration and the offset and limit operators for pagination in GraphQL API calls. This helps in efficiently managing and retrieving large datasets.

Q: What is the best practice for updating data in Weaviate?

For updating data, it is recommended to use the batch API. Start with a small batch size (e.g., 100 per batch) and adjust as needed. For unidirectional relationships, import all related objects first, then import the main objects with references set. For complex relationships, import objects without references and then add the references later.

Q: Does Weaviate offer managed services, and what are the pricing options?

Yes, Weaviate offers managed services through its cloud offerings. The pricing model includes a pay-as-you-go option starting at $0.05 per million dimensions, a serverless cloud option starting at $25 per month for 1 million vector dimensions, and an enterprise cloud option starting at $2.64 per AI Unit (AIU). Different SLA tiers are also available to meet varying support needs.

Q: How can I deploy Weaviate?

Weaviate offers multiple deployment options, including serverless cloud, local inference containers, and self-deployment or marketplace deployment. You can choose from various configurations to suit your needs, from evaluation and development to production environments. Zero-downtime updates and data replication for high availability are also available.

Q: Can I use my own vectors with Weaviate?

Yes, you can add your own vectors to Weaviate and still benefit from its indexing and search capabilities. This flexibility allows you to integrate your existing vector data seamlessly into the Weaviate ecosystem.

Q: What kind of support does Weaviate offer?

Weaviate provides different Service Level Agreement (SLA) tiers, including Standard, Professional, and Business Critical, each with varying response times and support options. Additionally, users have access to a community forum, client libraries, and dedicated customer support on a private Slack channel for cloud customers.

Q: How does Weaviate handle multimodal data?

Weaviate’s multimodal modules can import and perform searches on various types of data, including text, audio, and video. This capability allows you to build generative AI tools using your entire dataset, regardless of the data type.

By addressing these questions, you can gain a comprehensive understanding of Weaviate’s capabilities, deployment options, and how it can be integrated into your projects.