-
MilVus Terminology.
MilVus Terminology Table Of Contents: AutoID Auto Index Attu Birdwatcher Bulk Writer Bulk Insert Cardinal Channel Collection Dependency Dynamic Schema Embeddings Entity Field Filter Filtered Search Hybrid Search Index Kafka Milvus Connector Knowhere Log Broker Log Snapshot Log Subscriber Message Storage Metric Type Mmap Milvus Backup Milvus CDC Milvus CLI Milvus Migration Milvus Cluster Milvus Standalone Milvus Vector Partition Partition Key PChannel PyMilvus Query Range Search Schema Search Segment Spark Milvus Connector Shard Sparse Vector Unstructured Data VChannel Vector Zilliz Cloud (1) AutoID auto_id (bool) Whether allows the primary field to automatically increment. Setting this to True makes the primary field automatically increment.
-
MilVus Versions.
MilVus Versions Table Of Contents: Different MilVus Versions. (1) Different MilVus Versions.
-
Supported Indexes & Matrices In MilVus.
Supported Indexes & Metrices In MilVus. Table Of Contents: Supported Indexes. Supported Metrices. (1) Supported Indexes Indexes are an organization unit of data. You must declare the index type and similarity metric before you can search or query inserted entities. If you do not specify an index type, Milvus will operate brute-force search by default. Index Types: Indexes are an organization unit of data. You must declare the index type and similarity metric before you can search or query inserted entities. If you do not specify an index type, Milvus will operate brute-force search by default. HNSW: HNSW is a
-
What Is MilVus?
MillVus DB Introduction Table Of Contents: What Is A Vector Data Base? What Is MilVus DB? Why We Need Vector DataBase? Why MilVus? What Indexes And Metrics Are Supported? Example Applications Of MilVus. (1) What Is A Vector Data Base? A vector database stores, manages and indexes high-dimensional vector data. Data points are stored as arrays of numbers called “vectors,” which are clustered based on similarity. This design enables low-latency queries, making it ideal for AI applications. In this simple vector database, the documents in the upper right are likely similar to each other. Vector numbers can represent complex objects
