Meilisearch vs SeMI's Weaviate
Meilisearch and SeMI's Weaviate are both open source search engines, but they have different design goals and use cases. Here's a comparison between the two: Search Functionality: Meilisearch is a full-text search engine that can perform search queries across structured and unstructured data. It supports features like typo tolerance, synonyms, and pagination. SeMI's Weaviate, on the other hand, is a knowledge graph search engine that focuses on searching for structured data in a graph format. It uses natural language processing (NLP) to understand queries and return related entities. Data Sources and Indexing: Meilisearch supports indexing from a variety of sources including databases, JSON files, and APIs. It also has a built-in scheduler to perform periodic indexing. SeMI's Weaviate primarily indexes data from knowledge graphs like DBpedia, Wikidata, or company-specific knowledge graphs that are defined by users. Deployment and Scalability: Meilisearch can be deployed as a standalone server or as a Docker container. It is designed to be highly scalable and can be clustered for improved performance. SeMI's Weaviate can also be deployed as a standalone server or as a Docker container. It is designed to be highly scalable and can be deployed in a distributed setup. Community and Support: Meilisearch has a large and active community with documentation, tutorials, and community-contributed plugins. It also provides commercial support and enterprise-grade features for its paid version. SeMI's Weaviate has a smaller community compared to Meilisearch, but it provides enterprise-grade support and consulting services for its users. Overall, Meilisearch and SeMI's Weaviate serve different use cases and have different strengths. Meilisearch is a general-purpose search engine that can index a variety of data sources, while SeMI's Weaviate is a knowledge graph search engine that focuses on structured data in a graph format.