# Deliver RAG Applications 10x Faster with Zilliz and Vectorize

Source: https://zilliz.com/blog/deliver-rag-apps-10x-faster-with-zilliz-and-vectorize
Author: Jamie Ferguson
Published: 2024-12-06T12:00:00.000Z

We’re excited to share that [Vectorize](https://vectorize.io/) now integrates with [Milvus](https://zilliz.com/what-is-milvus) and [Zilliz Cloud](https://zilliz.com/cloud)! 

This integration makes it fast and easy to build and maintain [retrieval-augmented generation (RAG) pipelines](https://zilliz.com/learn/Retrieval-Augmented-Generation) that connect to various data sources and AI platforms. Vectorize simplifies the process of getting high-quality data into your Zilliz [vector database](https://zilliz.com/learn/what-is-vector-database) and keeps your [embeddings](https://zilliz.com/glossary/vector-embeddings) up-to-date so your AI applications always have access to the freshest, most relevant data.

You can use Vectorize with fully managed Milvus on Zilliz Cloud or a self-hosted Milvus instance accessible through a public network.


## Powering Smarter AI Applications

[Milvus](https://milvus.io/) is an open-source, high-performance vector database that powers AI applications such as RAG, [multimodal search](https://zilliz.com/blog/a-review-of-hybrid-search-in-milvus), and recommendation engines. [Zilliz Cloud](https://zilliz.com/cloud) is the fully managed service of Milvus, but it is 10x faster than Milvus. In RAG, a [vector database](https://zilliz.com/learn/what-is-vector-database) provides contextual data about specific focus areas to large language models ([LLMs](https://zilliz.com/glossary/large-language-models-(llms))). The higher the quality and relevance of the data, the better your RAG application will perform.

For example, suppose you wanted to build an [AI agent](https://vectorize.io/how-i-finally-got-agentic-rag-to-work-right/) that can answer a user’s questions about how to use a web application in real time. You might provide your LLM with documentation and tutorials from your website, internal company guides, and customer conversations. By giving the LLM contextual information, it can provide reliable responses to questions it couldn’t address otherwise. A tool like this is handy when you keep the data provided to your LLM up-to-date—like if a new section is added to your documentation that explains a feature in more detail.

Zilliz Cloud makes it easy to search and manage your data as vectors, but building a RAG pipeline—extracting data, creating embeddings, and keeping them fresh—can be complex and time-consuming.


## Challenges With Building RAG Pipelines

Turning [unstructured data](https://zilliz.com/learn/introduction-to-unstructured-data) into vector indexes can involve a lot of time and effort. Data is extracted from one or more sources, processed into vector search indexes, and then loaded into your vector database. Sometimes, it’s easy to connect to a data source; sometimes, you need to write custom code. The more data sources you need to draw from, the more complex this process becomes and the more time it takes.

After you’ve extracted the data, you need to clean and format it, which is often a non-trivial amount of work. Then, you need to figure out which [embedding model](https://zilliz.com/ai-models) and [chunking strategy](https://zilliz.com/learn/guide-to-chunking-strategies-for-rag) works best for your dataset. Just because one model and vectorization strategy performs well for one dataset doesn’t mean it’ll work as well for another. So you have to test and figure out the right approach through trial and error—and sometimes toss a coin and hope you guessed it right.

Once you’ve finally figured all of that out, you need to make sure any updates to your dataset are processed and your vector search indexes are regularly updated so your LLM is always working with the latest data and, therefore, provides accurate and reliable results.


## How Vectorize Simplifies RAG Pipelines 

[Vectorize](https://vectorize.io/) significantly reduces the time needed to extract and preprocess data, evaluate embedding models and chunking strategies, and build and maintain pipelines. 

[Creating your RAG pipeline](https://vectorize.io/how-to-build-a-rag-pipeline/) is fast and easy. Specify one or more data sources, select the AI platform, [embedding model](https://zilliz.com/ai-models), and chunking strategy, and then deploy your pipeline. Your pipeline runs on the schedule you set, whether that’s daily, hourly, or in real-time. Vectorize automatically updates your vector database, ensuring your data stays fresh and relevant. 


## Zilliz and Vectorize: Built for AI Engineers

Zilliz Cloud delivers reliable vector storage and high-performance [vector search](https://zilliz.com/learn/vector-similarity-search), while Vectorize automates your RAG pipelines and keeps your embeddings up-to-date. The combination frees you from building and managing pipelines and allows you to focus instead on creating accurate, reliable AI applications. 

![](https://assets.zilliz.com/Final_Vectorize_Zilliz_diagram_7405b3f3a9.png)

Figure: How Zilliz and Vectorize works for AI engineers 


## Getting Started with Vectorize and Zilliz Cloud 

Ready to try it out? Sign up for the [Vectorize platform](https://platform.vectorize.io/), connect your [Milvus](https://github.com/milvus-io/milvus) or [Zilliz Cloud](https://zilliz.com/cloud) instance, and deploy your first RAG pipeline in minutes.