Introduction
Milvus is an open-source, cloud-native vector database built for managing and querying billions of embeddings, making it a core component in Cake’s real-time retrieval infrastructure. Designed for speed, scalability, and integration with machine learning workflows, Milvus allows teams to efficiently store, index, and query high-dimensional vectors generated by models like BERT, OpenAI embeddings, and custom LLM encoders. It supports the backbone of RAG pipelines, personalization engines, multi-modal retrieval, and agent memory systems.
Key benefits of using Milvus include:
High-Performance Vector Indexing: Supports state-of-the-art indexing algorithms like IVF, HNSW, and DiskANN, enabling sub-second similarity search on millions to billions of vectors.
Multi-Modal Support: Stores and queries image, text, audio, and multimodal embeddings—ideal for diverse use cases like semantic search, recommendation, and visual Q&A.
Hybrid Search Capabilities: Allows combining vector search with structured (metadata) filters—useful for RAG pipelines conditioned on user segments, time, content type, and more.
Horizontal Scalability and Persistence: Built to scale out with distributed architecture and persistent storage using Milvus 2.0 and beyond, with compatibility for cloud and Kubernetes deployments.
Ecosystem Integrations: Natively integrates with tools like LangChain, LlamaIndex, Hugging Face, and supports gRPC, RESTful, and Python SDKs for easy adoption in ML and backend pipelines.
Milvus is used to power vector search for RAG document stores, agent memory graphs, embedding evaluation benchmarks, and semantic labeling systems. It sits alongside orchestration frameworks (e.g., PipeCat, Airflow), model services (e.g., vLLM, TGI), and observability stacks (e.g., LangFuse, Phoenix) as a central component of real-time AI infrastructure. By adopting Milvus, you can ensure its AI systems are backed by low-latency, high-scale retrieval infrastructure—enabling fast, relevant, and context-aware responses across all LLM-powered experiences.