# finic-ai/rag-stack

🤖 Deploy a private ChatGPT alternative hosted within your VPC. 🔮 Connect it to your organization's knowledge base and use it as a corporate oracle. Supports open-source LLMs like Llama 2, Falcon, and GPT4All.

Repository: https://github.com/finic-ai/rag-stack
Canonical: https://ross.abutalabs.com/products/rag-stack
Homepage: https://www.chatmyfiles.com/
Language: TypeScript
License: MIT
License Family: permissive
Last push: 2023-09-11T16:38:23+00:00

## Health v2 (maintenance only)
Score: 29/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 82
- inputs: {"age_days": 1148, "days_push": 1087, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1586, forks 138 (observed 2026-08-28T04:05:07.678634+00:00)

## What it is
RAGstack is a self-hostable private ChatGPT alternative that deploys open-source LLMs (Llama 2, Falcon, GPT4All) alongside the Qdrant vector database inside your VPC. It includes a server and UI for uploading PDFs and chatting over your organization's knowledge base using retrieval-augmented generation.

## Use cases
- deploy a private chatgpt alternative inside my vpc
- chat with my company's internal documents using an llm
- build a corporate knowledge base chatbot with open-source models
- run retrieval augmented generation on-premise for security compliance
- query pdfs and confluence docs with a self-hosted llm
- set up qdrant and llama 2 for enterprise rag

## When to choose
- you need data privacy and want LLM inference and embeddings kept inside your own VPC
- you want to avoid OpenAI API costs by using open-source models like Llama 2 or Falcon
- you need a quick, batteries-included RAG stack with vector database, server, and chat UI
- your organization requires on-premise or self-hosted knowledge base search

## When to avoid
- you want the highest answer quality from frontier commercial models like GPT-4
- you need a fully managed service without operating Kubernetes/GKE infrastructure
- your project needs active development and frequent updates - the last release was in 2023
- you only need a lightweight embedding or vector search library rather than a full chat application

## Facets
- artifact type: application
- maturity: maintenance
- function: rag, llm-inference, vector-database, chatbot, chat-interface, self-hosted
- domain: large-language-models, artificial-intelligence, self-hosted, chatbots
- platform: self-hosted, cloud
- tags: private-chatgpt, vpc-deployment, qdrant, llama-2, falcon, gpt4all, knowledge-base, pdf-chat, corporate-search, retrieval-augmented-generation, docker, web-server, typescript

## Member repositories
- finic-ai/rag-stack (main) score 29

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:07.678634+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:55:28.369741+00:00, confidence not recorded.
  - readme: https://github.com/finic-ai/rag-stack (fetched 2026-08-28T04:05:07.678634+00:00, sha 49af1446a96d)
  - homepage: https://www.chatmyfiles.com/ (fetched 2026-08-29T11:26:12.059455+00:00, sha 44136fa355b3)
- Data as of 2026-08-30T08:39:29.467469+00:00.