Milvus Lite Helps AI Developers Build Applications Faster

Zilliz is introducing Milvus Lite, a lightweight vector database that runs locally within the Python application, making it a compact and efficient solution ideal for environments with limited computing resources such as laptops, Jupyter Notebooks, and mobile or edge devices.

Based on the open-source Milvus vector database, Milvus Lite reuses the core components for vector indexing and query parsing while removing elements designed for high scalability in distributed systems.

Milvus Lite integrates with various AI development stacks including LangChain and LlamaIndex, enabling its use as a vector store in Retrieval Augmented Generation (RAG) pipelines without the need for server setup.

Milvus Lite shares the Milvus API, ensuring that client-side code works for both small-scale local deployments and Milvus servers deployed on Docker or Kubernetes with billions of vectors.

Despite the availability of numerous vector search solutions, an easy-to-start option that also works for large-scale production deployments was missing, according to the company.

As the creators of Milvus, Zilliz designed Milvus Lite to help AI developers build applications faster while ensuring a consistent experience across various deployment options, including Milvus on Kubernetes, Docker, and managed cloud services.

Milvus Lite is a crucial addition to our suite of offerings within the Milvus ecosystem. It provides developers with a versatile tool that supports every stage of their development journey. From prototyping to production environments and from edge computing to large-scale deployments, Milvus is now the only vector database that covers use cases of any size and all stages of development.

Milvus Lite supports all the basic operations available in Milvus, such as creating collections and inserting, searching, and deleting vectors. It will soon support advanced features like hybrid search, according to the company. Milvus Lite loads data into memory for efficient searches and persists it as an SQLite file.

Milvus Lite is included in the Python SDK of Milvus and can be deployed with a simple pip install pymilvus.

For scalability, an AI application developed with Milvus Lite can easily transition to using Milvus deployed on Docker or Kubernetes by simply specifying the uri with the server endpoint.

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