Milvus
Milvus is an open-source vector database designed for scalable similarity search and AI applications. The Milvus connector enables you to perform vector database operations including querying scalar fields, searching vectors, and upserting vectors for efficient similarity search and scalable data querying. This connector is particularly useful for applications that need to perform semantic search, build recommendation systems, implement RAG (Retrieval-Augmented Generation) pipelines, or manage vector embeddings for AI and machine learning applications.
Power AI-ready data operations with Milvus and Nexla. Our Milvus connector makes it simple to ingest, transform, chunk, and deliver structured or unstructured data to Milvus — all without coding. Nexla automatically organizes raw text and documents into reusable data products that you can easily prepare for vector search and retrieval-augmented generation (RAG) using our built-in transforms like agentic chunking and incremental loading. With real-time validation, schema checks, and comprehensive monitoring, Nexla keeps your Milvus workflows fast, secure, and fully governed for production AI use cases.
Features
Type: Vector Database
- AI-Ready Data Preparation: Automatically chunk, vectorize, and index data from any source into your vector database for fast, contextually relevant search
- Advanced RAG Integration: Query vector databases to power retrieval-augmented generation workflows with query rewriting, re-ranking, and multi-model orchestration
- Enterprise RAG Framework: Build production-ready RAG applications with built-in access controls, evaluation grading, and NVIDIA NIM hardware acceleration