PrivateGPT and Zilliz Cloud Integration
PrivateGPT and Zilliz Cloud integrate to build secure, private RAG applications, combining PrivateGPT's offline AI framework for document Q&A with 100% privacy alongside Zilliz Cloud's high-performance vector database for scalable embedding storage and similarity search.
Use this integration for FreeWhat is PrivateGPT
PrivateGPT is a production-ready AI project that enables users to ask questions about their documents using Large Language Models without an internet connection while ensuring 100% privacy. It offers an API divided into high-level and low-level blocks, a Gradio UI client, and useful tools like bulk model download scripts and ingestion scripts. Conceptually, PrivateGPT wraps a RAG pipeline and exposes its primitives, being ready to use and providing a full implementation of the API and RAG pipeline.
By integrating with Zilliz Cloud (fully managed Milvus), PrivateGPT gains access to a fully managed, high-performance vector database designed for billion-scale vector storage and rapid similarity searches, providing organizations a secure and scalable framework for storing and retrieving embeddings within private environments for use cases like private chatbots, document analysis, and recommendation systems.
Benefits of the PrivateGPT + Zilliz Cloud Integration
- 100% data privacy with scalable storage: PrivateGPT ensures complete data privacy by operating without internet connection, while Zilliz Cloud provides enterprise-grade vector storage that can be deployed in private, controlled environments.
- Production-ready RAG pipeline: PrivateGPT provides a complete RAG pipeline implementation out of the box, with Zilliz Cloud handling the high-performance vector storage and retrieval layer for scalable similarity search.
- Flexible model configuration: The integration supports customizable LLM and embedding models through Ollama, while Zilliz Cloud efficiently stores and indexes the generated embeddings regardless of model choice.
- Simple deployment: The setup requires minimal configuration — just install the Milvus module, update the vector store settings, and PrivateGPT is ready to use with Zilliz Cloud as the backend.
How the Integration Works
PrivateGPT serves as the application framework, providing the RAG pipeline, API, and Gradio UI for document ingestion and question-answering. It handles document processing, embedding generation through Ollama, query orchestration, and LLM-based response generation — all while maintaining complete data privacy.
Zilliz Cloud serves as the vector database backend, storing and indexing the document embeddings generated by PrivateGPT for fast similarity search. It enables efficient retrieval of relevant document context when users ask questions, supporting billion-scale vector storage with low-latency search.
Together, PrivateGPT and Zilliz Cloud create a secure, production-ready document QA solution: documents are ingested and embedded through PrivateGPT's pipeline and stored in Zilliz Cloud. When users ask questions, PrivateGPT retrieves relevant context from Zilliz Cloud's vector store and generates private, contextually informed responses using the local LLM — all without any data leaving the organization's environment.
Step-by-Step Guide
1. Clone the PrivateGPT Repository
Clone the repository and navigate to it:
$ git clone https://github.com/zylon-ai/private-gpt $ cd private-gpt2. Install Poetry
Install Poetry for dependency management. Follow the instructions on the official Poetry website to install it.
3. Install Available Modules
Run the following command to use Poetry to install the required module dependencies:
$ poetry install --extras "llms-ollama embeddings-ollama vector-stores-milvus ui"4. Start Ollama Service
Go to ollama.ai and follow the instructions to install Ollama on your machine. After the installation, make sure the Ollama desktop app is closed.
Start Ollama service (it will start a local inference server, serving both the LLM and the Embeddings):
$ ollama serveInstall the models to be used. The default
settings-ollama.yamlis configured to usellama3.18b LLM (~4GB) andnomic-embed-textEmbeddings (~275MB):$ ollama pull llama3.1 $ ollama pull nomic-embed-text5. Change Milvus Settings
In the file
settings-ollama.yaml, set the vectorstore to Milvus:vectorstore: database: milvusYou can also add custom Milvus configuration to specify your settings:
milvus: uri: http://localhost:19530 collection_name: my_collectionThe available configuration options are:
uri— default is set to a local file; you can also set up a more performant Milvus server on Docker or Kubernetes, or use Zilliz Cloud by adjusting theuriandtokento the Public Endpoint and API Key in Zilliz Cloud.token— pair with Milvus server on Docker/Kubernetes or Zilliz Cloud API key.collection_name— the name of the collection, default is "milvus_db".overwrite— overwrite the data in collection if it existed, default is True.6. Start PrivateGPT
Once all settings are done, you can run PrivateGPT with a Gradio UI:
PGPT_PROFILES=ollama make runThe UI will be available at
http://0.0.0.0:8001. You can play around with the UI and ask questions about your documents.Learn More
- Use Milvus in PrivateGPT — Official Milvus tutorial for using Milvus in PrivateGPT
- Securing AI: Advanced Privacy Strategies with PrivateGPT and Milvus — Zilliz blog on privacy strategies with PrivateGPT
- What are Private LLMs? Running Large Language Models Privately — Zilliz tutorial on private LLMs and PrivateGPT
- PrivateGPT GitHub Repository — PrivateGPT source code and community resources
- PrivateGPT Documentation — Official PrivateGPT documentation