RAGstack - Private ChatGPT in Your VPC

Deploy a private ChatGPT alternative powered by open-source LLMs in your VPC

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Updated Jul 2026 · Added Jul 2026

github.com

Vector DatabasesFreevector-databases

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What is Ragstack?

RAGstack is an open-source platform for deploying a private ChatGPT alternative within your VPC, connecting it to your organization's knowledge base. It implements Retrieval Augmented Generation (RAG) to augment large language models with external data from documents, SaaS apps, and web pages. The platform supports open-source LLMs like Llama 2, Falcon, and GPT4All, along with a vector database (Qdrant) and a simple server/UI for PDF upload and chatting. It's designed for enterprises that need a private, reliable corporate oracle without the cost and latency of fine-tuning.

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Key Features of Ragstack

Retrieval Augmented Generation (RAG) architecture
Multiple open-source LLM support (Llama 2, Falcon, GPT4All)
Qdrant vector database integration
PDF upload and processing
Private VPC deployment
Simple server and UI interface
Local and cloud deployment options
GPU-enabled GKE cluster support

Who Should Use Ragstack?

Create a corporate oracle for internal knowledge base queries

Chat over PDFs and organizational documents

Private enterprise AI assistants

Integrate with SaaS apps like Confluence and Salesforce

Build AI systems with current data beyond training data

Ragstack: Pros & Cons

✓Pros

Open-source and self-hostable
Cheaper than fine-tuning alternatives
Faster and more reliable than fine-tuning
Source of information provided with each response
Supports multiple open-source LLMs
Highly performant vector database (Qdrant in Rust)
Private deployment within your VPC

Frequently Asked Questions about Ragstack

What is Retrieval Augmented Generation (RAG)?

RAG is a technique that augments large language model capabilities by retrieving information from other systems and inserting it into the LLM's context window via a prompt. This allows LLMs to access information beyond their training data, which is necessary for enterprise use cases.

What open-source LLMs does RAGstack support?

RAGstack supports GPT4All (for local deployment), Falcon-7b (for cloud deployment), and Llama 2 7B parameter version (for cloud deployment on GPU-enabled GKE clusters).

How does RAGstack compare to fine-tuning?

RAGstack works better than fine-tuning because it's cheaper, faster, and more reliable. The source of information is provided with each response, improving transparency and accuracy.

What vector database does RAGstack use?

RAGstack uses Qdrant, an open-source vector database written in Rust that is highly performant and self-hostable.