Vectorize
Build automated RAG Pipelines that keep data in your Milvus/Zilliz Cloud vector database fresh.
Use this integration for FreeWhat is Vectorize?
Vectorize makes it fast and easy to build and maintain automated retrieval-augmented generation (RAG) pipelines, simplifying the process of getting high-quality data into your Milvus/Zilliz Cloud vector database.
Each pipeline connects to one or more data sources and can ingest multiple file types—documents, spreadsheets, presentations, and more. You choose the AI platform, embedding model, and vectorization strategy that best fits your data set. Your embeddings are updated as the source data changes, so your AI applications can always access the freshest, most relevant data.
Why Use Vectorize with Milvus/Zilliz Cloud?
Creating and managing RAG pipelines while ensuring your vector indexes provide optimal relevancy can be both time-consuming and complex. The process requires extracting and preprocessing data to ensure it’s clean, consistent, and properly formatted. Selecting the best AI platform, embedding model, and chunking strategy often involves significant experimentation and guesswork. Even after building the pipeline, maintaining its quality by updating embeddings as your source data evolves adds another layer of complexity.
Vectorize simplifies this entire process, enabling you to build and maintain automated RAG pipelines with ease. It streamlines the journey from raw data to high-quality embeddings, seamlessly integrating with Milvus and Zilliz Cloud vector database to handle the heavy lifting. Milvus/Zilliz Cloud delivers the scalability to store and retrieve billion-scale vector data with ultra-low latency while supporting real-time data updates. This ensures that your LLM always has access to the most up-to-date contextual information, consistently delivering accurate and relevant results.
The integration of Vectorize with Milvus/Zilliz Cloud creates a powerful and efficient RAG framework. Within minutes, you can deploy a robust pipeline capable of managing billion-scale vector data and providing real-time answers. This combination not only simplifies implementation but also ensures your RAG pipelines are ready to scale with your needs, freeing you to focus on delivering insights rather than managing infrastructure.
How Vectorize Works with Milvus/Zilliz Cloud
Vectorize extracts unstructured data from one or more sources.
It splits the data in chunks, using the strategy you select.
Vector indexes are generated using the embedding model you chose.
Vector indexes are written to your Zilliz Cloud database.
Zilliz Cloud stores the vector data, performs vector similarity searches, and provides the retrieved results with the LLM.
As your data evolves, Vectorize automatically updates your vector indexes.
How to Use Vectorize with Zilliz Cloud / Miluvs