Join the DZone community and get the full member experience.

Join For Free

Milvus is an open-source vector database for building high-performance AI applications such as semantic search engines, AI chatbots, natural language processing (NLP) systems, and retrieval-augmented generation (RAG). It can manage and search massive vector collections, making it ideal for handling large-scale datasets with billions of entries.

In this article, you'll learn the concept of vector databases, how to install Milvus, and a step-by-step guide on how to build RAG with Milvus in 10 minutes. Whether you're an ML engineer or a hobbyist exploring vector databases, this guide will help you better understand Milvus and its practical applications.

Let's start with the concept of vector databases.

Vector databases are custom-designed systems that efficiently store, index, and query high-dimensional vector data. Unlike traditional databases, which are super fast at handling structured data in rows and columns, vector databases are optimized for working with complex, unstructured data (such as text, images, and videos) represented as mathematical vectors.

Their unique architecture shines in scenarios such as:

A vector similarity search workflow is built around four essential components:

Info: Choosing the right embedding model is crucial as it determines how effectively your data's context and meaning are captured in the vector format. This directly impacts the accuracy and relevance of search results and AI-driven insights.

Retrieval-Augmented Generation (RAG) is a technique for reducing LLM hallucinations and improving output accuracy by retrieving relevant contextual information from external sources. Vector databases play an important role in this process, and they serve as knowledge bases by storing, indexing, and retrieving embedded data representations. When a user submits a query, it is converted into a vector, and the database retrieves similar vectors by calculating distances between them in high-dimensional space.

Now that we understand the fundamentals of vector databases and RAG, let's learn how to get Milvus up and running on your system.

Milvus Lite currently supports the following environments:

Now, let's walk through a practical example of implementing a basic RAG pipeline using Milvus Lite and LangChain. We'll create a system to answer questions about Milvus by querying a Milvus vector store populated with the Milvus doc files.

First, we'll install the necessary packages and import the required modules. These imports set up our environment with LangChain for document loading and embedding, Milvus for vector storage, and Rich for enhanced terminal output.

To access GitHub and OpenAI services, we need to set up API keys. Remember to keep your API keys secure, preferably using environment variables.

We'll use LangChain's GithubFileLoader to fetch Markdown files from the Milvus repository. This step retrieves all Markdown files from the Milvus GitHub repository, which will serve as our knowledge base.

We'll use OpenAI's text embedding model to convert our documents into vector representations. This embedding model transforms text into high-dimensional vectors, capturing semantic meaning.

Now, we'll set up our Milvus vector store. This step creates a Milvus collection and populates it with vector representations of our documents.

Finally, we'll perform a similarity search to answer a question about Milvus. This query searches for the most similar document to our question, effectively retrieving the most relevant information from our knowledge base.

At the end of running the script, your terminal should display output similar to the image below:

The retrieved content answers our previous question, demonstrating this RAG system's capability to pull information directly from Milvus' GitHub files.