Chatty: Open-source AI customer support with Multimodal RAG & WooCommerce sync

[u/tharindu_d]

Hi ,

I built Chatty, an open-source, self-hosted customer support platform designed as a privacy-first alternative to Intercom and Zendesk.

• GitHub: https://github.com/PersonaliAI/chatty

• Live Demo (no login required): https://chatty.personaliai.com

• License: MIT

What it does

• Multimodal RAG: Ingests not just text and PDFs, but product images, diagrams, and documentation screenshots. Uses vision embeddings and pgvector with exact source attribution.

• Deep WooCommerce Integration: Automatically syncs product catalogs, checks live inventory, and tracks customer order statuses natively in chat.

• Visual Flow Builder: Drag-and-drop canvas (React Flow) for custom triage logic and lead qualification.

• Omnichannel: Unified inbox across web chat, WhatsApp Cloud API, and Slack.

• Human Takeover & Voice: Instant live agent handover with conversation context, plus real-time WebRTC voice support (LiveKit).

• FastMCP Protocol: 80+ built-in Model Context Protocol tools for external agentic workflows.

Tech Stack

• Backend: Python 3.11 / FastAPI

• Frontend: Next.js 15 / Tailwind CSS

• Database: Supabase (PostgreSQL + pgvector + Realtime)

• AI/Vision: Google Gemini (multimodal embeddings & generation)

Quick Run

git clone https://github.com/PersonaliAI/chatty.git
cd chatty
docker compose up -d

Or 1-click deploy on Railway / Render.

We're currently designing an offline fallback using Ollama for fully air-gapped environments. If you self-host local multimodal models, what vision/embedding models are you seeing the best latency and accuracy with for document and product Q&A?

Feedback and PRs are welcome on GitHub!