Concepts

Core data object concepts, schema design, and data organization principles in Weaviate.

Theoretical explanation of client-side and server-side batch imports.

Modular architecture overview for extending Weaviate functionality with specialized add-on components.

Persistent, fault-tolerant storage architecture for objects, vectors, and inverted index management, including HNSW snapshots and commit log compaction.

Vector compression techniques reducing memory footprint and costs while improving search speed performance.

Filtered vector search capabilities combining semantic similarity with structured scalar filtering.

Search result reordering techniques using alternative models to improve search relevance and accuracy.

Multi-node cluster architecture and horizontal scaling strategies for high-availability Weaviate deployments.

RESTful, GraphQL and gRPC API interfaces with client library support for Weaviate integration.

CPU, memory, and GPU resource planning guidelines for optimal Weaviate performance at scale.