milvus-io/milvus

Quickstart

This installs pymilvus, the Python SDK for Milvus. Use MilvusClient to create a client:

• You can also try Milvus Lite for quickstart by installing pymilvus[milvus-lite]. To create a local vector database, simply instantiate a client with a local file name for persisting data:
• You can also specify the credentials to connect to your deployed Milvus server or Zilliz Cloud:

$ pip install -U pymilvus

Demos and Tutorials

Here is a selection of demos and tutorials to show how to build various types of AI applications made with Milvus:

You can explore a comprehensive Tutorials Overview covering topics such as Retrieval-Augmented Generation (RAG), Semantic Search, Hybrid Search, Question Answering, Recommendation Systems, and various quick-start guides. These resources are designed to help you get started quickly and efficiently.

• Build RAG with Milvus — RAG — vector search
• Advanced RAG Optimizations — RAG — vector search, full text search
• Full Text Search with Milvus — Text Search — full text search
• Hybrid Search with Milvus — Hybrid Search — hybrid search, multi vector, dense embedding, sparse embedding
• Image Search with Milvus — Semantic Search — vector search, dynamic field
• Multimodal Search using Multi Vectors — Semantic Search — multi vector, hybrid search
• Movie Recommendation with Milvus — Recommendation System — vector search
• Graph RAG with Milvus — RAG — graph search
• Contextual Retrieval with Milvus — Quickstart — vector search
• Vector Visualization — Quickstart — vector search
• HDBSCAN Clustering with Milvus — Quickstart — vector search
• Use ColPali for Multi-Modal Retrieval with Milvus — Quickstart — vector search

Build Milvus from Source Code

• Linux systems (Ubuntu 20.04 or later recommended):
• MacOS systems with x8664 (Big Sur 11.5 or later recommended):
• MacOS systems with Apple Silicon (Monterey 12.0.1 or later recommended):

Ecosystem and Integration

Milvus integrates with a comprehensive suite of AI development tools, such as LangChain, LlamaIndex, OpenAI and HuggingFace, making it an ideal vector store for GenAI applications such as Retrieval-Augmented Generation (RAG). Milvus works with both open-source embedding models and embedding services, in text, image and video modalities. Milvus also provides a convenient utility [pymilvus[model]](https://milvus.io/docs/embeddings.md), users can use the simple wrapper code to transform unstructured data into vector embeddings and leverage reranking models for optimized search results. The Milvus

Check out https://milvus.io/docs/integrationsoverview.md for more details.