Weaviate stands out as a vector search engine due to its hybrid search capabilities, modular architecture, and use of GraphQL for queries. Here’s a breakdown of its key features:
Hybrid Search Combines Vector and Keyword-Based Results
Weaviate’s hybrid search merges vector-based semantic search with keyword-based BM25 scoring, allowing developers to balance relevance from both approaches. For example, a search for “Java” could return results about the programming language (via vector similarity) and documents containing the exact keyword “Java” (via BM25). Users adjust the weighting between vector and keyword results using a parameter like alpha, which determines the influence of each method. This hybrid approach improves accuracy in ambiguous scenarios, such as distinguishing between “Apple the company” and “apple the fruit,” by leveraging both context and exact matches. Developers benefit from this flexibility when building applications like e-commerce search engines, where users might mix product names (keywords) and descriptive intent (semantic meaning).
Modules Extend Functionality for Custom Use Cases
Weaviate’s modular design lets developers plug in pre-built or custom modules to handle tasks like vectorization, NLP, or image processing. For instance, the text2vec-transformers module integrates models like BERT or Sentence-BERT to automatically convert text into vectors during data ingestion. Similarly, the multi2vec-clip module enables image-to-vector embeddings for multimodal searches. Modules can be swapped without altering core infrastructure, making Weaviate adaptable to domain-specific needs. A practical example is a recommendation system that uses a custom module to generate embeddings from user behavior data, ensuring results align with unique business logic. This modularity reduces the need for external preprocessing pipelines, streamlining development.
GraphQL API Enables Flexible, Expressive Queries
Weaviate uses GraphQL for querying, allowing developers to retrieve data with precision. For example, a query can fetch articles nearVector (vector search) while filtering by publication date using a where clause, all in a single request. The syntax supports combining hybrid search parameters, metadata filters, and nested data retrieval. A sample query might search for “sustainable energy” semantically, filter results to papers published after 2020, and return specific fields like title and author. GraphQL’s structure eliminates over-fetching, improving efficiency for applications like real-time recommendation engines. This approach is more developer-friendly than REST APIs for complex searches, as it provides a unified interface for diverse search logic.