Description
Vald is a cloud-native, highly scalable distributed search engine designed for fast approximate nearest neighbor (ANN) searches. It utilizes the NGT algorithm to efficiently search through dense vector data, making it ideal for applications requiring high-speed vector searches. Vald's architecture is based on Kubernetes, allowing for seamless scalability and management. The platform supports automatic vector indexing and backup, ensuring data integrity and disaster recovery. Vald's distributed indexing system allows vector data to be spread across multiple agents, enhancing search efficiency and reliability.
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Vald offers customizable ingress and egress filtering, enabling users to tailor the search process to their specific needs. The platform supports multiple programming languages, including Go, Java, Node.js, and Python, and provides both gRPC and REST APIs for integration. Vald's microservice-based architecture ensures that components are decoupled, promoting agility and maintainability.
The platform is designed to be easy to use, with features like auto-healing and data persistency, which reduce maintenance costs and prevent data loss. Vald can be deployed on Kubernetes clusters using Helm charts, simplifying the deployment process. Its observability features, such as Prometheus and Jaeger exporters, provide real-time monitoring capabilities.
Vald is suitable for various use cases, including similarity searching, related image search, and speech recognition. Its ability to handle large-scale vector data makes it a valuable tool for industries that require efficient data processing and retrieval. While the platform does not specify pricing details, its open-source nature suggests a flexible and cost-effective solution for businesses looking to implement advanced search capabilities.
- CategoryVector Databases & Retrieval
- Pricing ModelOpen source
- DeploymentKubernetes
- PlatformsCloud
Vald's Core Features
Highly scalable distributed vector search engine
Fast approximate nearest neighbor search
Cloud-native architecture
Automatic vector indexing and backup
Customizable ingress and egress filtering
Supports Go, Java, Node.js, and Python
gRPC and REST API integration
Microservice-based architecture
Auto-healing and data persistency
Real-time monitoring with Prometheus and Jaeger
Kubernetes-based deployment
Helm chart support for easy deployment
Distributed indexing across multiple agents
Index replication and automatic rebalancing
Multi-language support
How to use Vald?
Deploy: Use Helm charts to deploy Vald on Kubernetes
Configure: Set up ingress/egress filters and indexing parameters
Integrate: Connect using gRPC or REST APIs
Monitor: Utilize Prometheus and Jaeger for real-time monitoring
Scale: Adjust Kubernetes configurations for scalability
Vald's Use Cases
- Similarity Searching
- Image Search
- Speech Recognition
- Data Retrieval
- Custom Filtering
