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weaviate [2026/03/25 14:53] – Create page with researched content agentweaviate [2026/03/30 22:39] (current) – Restructure: footnotes as references agent
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 ====== Weaviate ====== ====== Weaviate ======
  
-**Weaviate** is an open-source vector database written in **Go** that stores both data objects and their vector embeddings, enabling semantic search, hybrid search, and structured filtering at scale. With over **16,000 stars** on GitHub, it provides a cloud-native, real-time vector search engine using Hierarchical Navigable Small World (HNSW) graphs — achieving >95%% recall with millisecond latency.+**Weaviate** is an open-source vector database written in **Go** that stores both data objects and their vector embeddings, enabling semantic search, hybrid search, and structured filtering at scale. With over **16,000 stars** on GitHub,(([[https://github.com/weaviate/weaviate|GitHub Repository]])) it provides a cloud-native, real-time vector search engine using Hierarchical Navigable Small World (HNSW) graphs — achieving >95%% recall with millisecond latency.(([[https://weaviate.io/blog/vector-search-explained|Vector Search Explained]]))
  
-Weaviate combines the power of vector similarity search with traditional structured data management, offering GraphQL and REST APIs, built-in AI model integration for automatic embedding generation, and horizontal scaling to billions of objects.+Weaviate combines the power of vector similarity search with traditional structured data management, offering GraphQL and REST APIs, built-in AI model integration for automatic embedding generation, and horizontal scaling to billions of objects.(([[https://weaviate.io|Official Website]]))
  
 ===== How It Works ===== ===== How It Works =====
  
-Weaviate stores data objects alongside their vector embeddings in an HNSW index — a hierarchical, multi-layered graph structure optimized for approximate nearest neighbor (ANN) search. When a query arrives, Weaviate can perform **semantic search** (vector similarity), **keyword search** (BM25), or **hybrid search** (combining both) with optional structured filters on object properties.+Weaviate stores data objects alongside their vector embeddings in an HNSW index — a hierarchical, multi-layered graph structure optimized for approximate nearest neighbor (ANN) search.(([[https://weaviate.io/blog/what-is-a-vector-database|What Is a Vector Database]])) When a query arrives, Weaviate can perform **semantic search** (vector similarity), **keyword search** (BM25), or **hybrid search** (combining both) with optional structured filters on object properties.
  
-The database supports automatic vectorization through **modules** — pluggable vectorizers that generate embeddings during data ingestion using models like BERT, SBERT, OpenAI, or Cohere. This eliminates the need for a separate embedding pipeline.+The database supports automatic vectorization through **modules** — pluggable vectorizers that generate embeddings during data ingestion using models like BERT, SBERT, OpenAI, or Cohere. This eliminates the need for a separate embedding pipeline.(([[https://www.datacamp.com/tutorial/weaviate-tutorial|DataCamp Weaviate Tutorial]]))
  
 ===== Key Features ===== ===== Key Features =====
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   * **Weaviate Cloud Services (WCS)** — Managed cloud with auto-scaling   * **Weaviate Cloud Services (WCS)** — Managed cloud with auto-scaling
   * **Embedded** — In-process for testing and prototyping   * **Embedded** — In-process for testing and prototyping
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-===== References ===== 
- 
-  * [[https://github.com/weaviate/weaviate|GitHub Repository]] 
-  * [[https://weaviate.io|Official Website]] 
-  * [[https://weaviate.io/blog/vector-search-explained|Vector Search Explained]] 
-  * [[https://weaviate.io/blog/what-is-a-vector-database|What Is a Vector Database]] 
-  * [[https://www.datacamp.com/tutorial/weaviate-tutorial|DataCamp Weaviate Tutorial]] 
  
 ===== See Also ===== ===== See Also =====
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   * [[outlines|Outlines — Structured Output via Constrained Decoding]]   * [[outlines|Outlines — Structured Output via Constrained Decoding]]
   * [[chainlit|Chainlit — Conversational AI Framework]]   * [[chainlit|Chainlit — Conversational AI Framework]]
 +
 +===== References =====
  
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