Weaviate is an open-source vector search engine. It allows you to bring AI-native applications to life with less hallucination, data leakage, and vendor lock-in. It solves the problem of efficiently storing and searching vector embeddings generated by AI models, enabling developers to build powerful applications that leverage semantic search and similarity matching.
Weaviate Key Features
GraphQL Endpoints
Weaviate offers GraphQL endpoints, providing a flexible and efficient way to query and retrieve data. This allows developers to specify exactly the data they need, reducing the amount of data transferred and improving performance.
Flexible Schema
Weaviate's flexible schema allows you to define the structure of your data in a way that makes sense for your application. You can easily add or modify properties and relationships, without having to worry about rigid database constraints.
High Dimensional Vector Search
Weaviate excels at high-dimensional vector search, enabling you to find similar data points based on their vector embeddings. This is crucial for tasks like semantic search, image retrieval, and recommendation systems.
Multi-Modal Capabilities
Weaviate supports multi-modal data, allowing you to store and search data from various sources, such as text, images, and audio. This enables you to build applications that can understand and process data from multiple modalities.
Open Source
Weaviate is open source, giving you full control over your data and infrastructure. You can deploy it on your own servers or in the cloud, without being locked into a proprietary platform.
How Weaviate Works
Weaviate ingests data and creates vector embeddings using configurable modules. These embeddings are stored in a graph database, which allows for efficient similarity searches and complex queries. The GraphQL API provides a flexible way to interact with the data and retrieve results.
Weaviate Benefits
Reduced Hallucination
By leveraging vector search, Weaviate helps to reduce hallucination in AI applications by providing more accurate and relevant results.
Data Leakage Prevention
Weaviate's open-source nature and flexible deployment options help to prevent data leakage by giving you full control over your data.
Vendor Lock-in Avoidance
By using Weaviate, you avoid vendor lock-in and can easily migrate your data to other systems if needed.
Scalability
Weaviate is designed to scale to handle large datasets and high query volumes.
Cost Efficiency
Weaviate's open-source nature and flexible deployment options can help to reduce costs compared to proprietary vector search engines.
Weaviate Use Cases
Semantic Search
Weaviate can be used to build semantic search engines that understand the meaning of queries and return more relevant results.
Image Retrieval
Weaviate can be used to build image retrieval systems that find similar images based on their visual content.
Recommendation Systems
Weaviate can be used to build recommendation systems that suggest products or content based on user preferences.
Knowledge Graphs
Weaviate can be used to build knowledge graphs that represent relationships between entities.
Weaviate FAQs
What types of data can I store in Weaviate?
You can store any type of data that can be represented as a vector embedding, including text, images, audio, and video.
How do I deploy Weaviate?
You can deploy Weaviate on your own servers, in the cloud, or using a managed service.
How do I query data in Weaviate?
You can query data in Weaviate using the GraphQL API.
Who Should Use Weaviate
AI developers, data scientists, and engineers who need to build applications that leverage vector search and similarity matching. It is perfect for both startups and enterprises who are looking for a flexible, scalable, and cost-effective solution.
