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Vector Search Meets GraphQL
- Bob van Luijt built Weaviate to let users find graph nodes by semantic descriptions rather than exact keywords.
- He chose GraphQL for developer-friendly traversal while keeping a vector-first data model.
Embeddings Bridge Schema Gaps
- Embeddings act as a bridge to target data objects without knowing their internal schema or keywords.
- Weaviate stores many embedding types (text, image, domain-specific) to support varied use cases.
Mix Neural Search With Filters
- Combine semantic vector queries with scalar or named-entity filters to get precise answers when needed.
- Use distance cutoffs or returned certainty scores to tune relevance and control serendipity.