PrivateGPT
Build secure, scalable RAG applications with PrivateGPT and Milvus / Zilliz Cloud
Use this integration for FreeWhat is PrivateGPT?
PrivateGPT is an AI framework that enables secure, private interaction with large language models (LLMs) without external servers or cloud reliance. It offers a suite of APIs and tools to easily set up a context-aware LLM environment. At its core, PrivateGPT wraps a Retrieval-Augmented Generation (RAG) pipeline, providing ready-to-use components for vector embedding creation, similarity search with vector databases like Milvus and Zilliz Cloud, and LLM inference—empowering users to handle tasks privately and efficiently.
Why Integrating PrivateGPT and Milvus / Zilliz Cloud?
Integrating PrivateGPT with Milvus or its managed service, Zilliz Cloud, provides organizations a secure and scalable framework for working with unstructured data in advanced AI applications. Milvus and Zilliz Cloud are both high-performance vector databases designed for billion-scale vector storage and fast similarity search, making them ideal for building scalable, enterprise-level RAG applications. When combined with PrivateGPT, they ensure vector embeddings are stored and retrieved within a private, offline environment that prioritizes data privacy.
Together, PrivateGPT and Milvus/Zilliz Cloud create a robust RAG pipeline that enables seamless, secure data flow—from data ingestion and real-time vector search to content generation. This integration is especially valuable for privacy-focused AI applications, such as private chatbots and personalized recommendation systems, empowering organizations to retain full control over their data while unlocking the potential of unstructured information.
How PrivateGPT and Milvus / Zilliz Cloud Work Together
Working with PrivateGPT and Milvus or Zilliz Cloud is straightforward. In a typical RAG setup within the PrivateGPT framework, users just need to select their preferred AI tools based on their specific needs.
For instance, users might choose Hugging Face to encode unstructured data into vector embeddings, Milvus to store these embeddings and perform vector similarity searches, and Llama or Mistral as the large language model (LLM) to generate responses based on retrieved information.
The architecture of the PrivateGPT RAG framework is illustrated below.
privategpt architecture
How to Use PrivateGPT with Milvus/Zilliz Cloud
Tutorial: Build a RAG with Milvus and Unstructured | Milvus Documentation
Milvus GitHub: https://github.com/milvus-io/milvus
PrivateGPT GitHub: https://github.com/zylon-ai/private-gpt
Blog: Securing AI: Advanced Privacy Strategies with PrivateGPT and Milvus