https://github.com/kacemmathlouthi/kubeflow-demo
full-stack Retrieval-Augmented Generation (RAG) chatbot designed as a PoC for GSoC 2025 (Project 12) with Kubeflow. It combines GitHub-based documentation ingestion, vector search, and LLM-powered responses into an interactive real-time assistant.
https://github.com/kacemmathlouthi/kubeflow-demo
chatbot documentation kubeflow langchain llms rag reactjs retrieval-augmented-generation weaviate
Last synced: about 2 months ago
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full-stack Retrieval-Augmented Generation (RAG) chatbot designed as a PoC for GSoC 2025 (Project 12) with Kubeflow. It combines GitHub-based documentation ingestion, vector search, and LLM-powered responses into an interactive real-time assistant.
- Host: GitHub
- URL: https://github.com/kacemmathlouthi/kubeflow-demo
- Owner: KacemMathlouthi
- Created: 2025-03-20T03:08:53.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-06-09T00:38:48.000Z (5 months ago)
- Last Synced: 2025-06-09T01:27:57.525Z (5 months ago)
- Topics: chatbot, documentation, kubeflow, langchain, llms, rag, reactjs, retrieval-augmented-generation, weaviate
- Language: TypeScript
- Homepage: https://kubeflow.kacem-mathlouthi.tn/
- Size: 1.73 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 🤖 Kubeflow Documentation Assistant (GSoC 2025 PoC)
> 🧪 **Proof of Concept** for [Kubeflow GSoC Project 12 – “Empowering Kubeflow Documentation with LLMs”]
🔗 **Live Demo:** [kubeflow.kacem-mathlouthi.tn](https://kubeflow.kacem-mathlouthi.tn)
---
## 📌 Project Overview
This is a **full-stack Retrieval-Augmented Generation (RAG)** chatbot designed as a **PoC for GSoC 2024 (Project 12)** with Kubeflow. It combines GitHub-based documentation ingestion, vector search, and LLM-powered responses into an interactive real-time assistant.
---
## 🧠 How It Works
```mermaid
graph TD;
A[User Question] --> B[Similarity Search];
B --> C[Prompt + Docs];
C --> D[LLM via GROQ API];
D --> E[Chatbot Response];
```
1. 🧑 User submits a question
2. 🔍 Similar documents are retrieved using vector search
3. 🧾 Retrieved chunks are passed as context to an LLM
4. 🤖 Final response is generated and streamed back to the frontend
---
## 🧰 Tech Stack
### 🔙 Backend (FastAPI)
- FastAPI + WebSockets
- Azure OpenAI & GROQ for LLM & embeddings
- Weaviate (local or remote) for vector storage
- LangChain for text chunking & prompt formatting
- Docker & Poetry
### 🌐 Frontend (React)
- React + TypeScript + Vite
- TailwindCSS + ShadCN UI for design
- WebSocket-based real-time chat
- Docker + NGINX for serving
- Markdown rendering with syntax highlighting
---
## 📦 Repo Structure
```
kacemmathlouthi-kubeflow-demo/
├── backend/ # FastAPI, RAG logic, Weaviate, Gitingest integration
├── frontend/ # Chat UI, WebSocket client, Tailwind
├── docker-compose.yml
└── README.md # ← You're here
```
---
## 🚀 Getting Started
### 🐳 Run with Docker Compose (recommended)
```bash
# From the root directory
docker-compose up --build
```
- Frontend available at: `http://localhost`
- Backend API at: `http://localhost:8000`
### 💻 Run locally
#### Backend
```bash
cd backend
poetry install
poetry run uvicorn app.main:app --reload
```
#### Frontend
```bash
cd frontend
npm install
npm run dev
```
---
## 🌍 Live Demo
The application is deployed via **NGINX on Azure**, and available here:
👉 **[kubeflow.kacem-mathlouthi.tn](https://kubeflow.kacem-mathlouthi.tn/)**
---
## ✅ Implemented Features
- 🔁 Full RAG pipeline with LLM response generation
- 🔗 GitHub repo ingestion via Gitingest
- 🧠 Embedding + vector search with Weaviate
- 🌐 Real-time WebSocket chat
- ⚙️ Configurable LLM model, temperature, max tokens
- 🧾 Markdown support in replies
- 🚀 Dockerized & deployed to Azure
More info in [`frontend/src/components/implemented-features.tsx`](frontend/src/components/implemented-features.tsx)
---
## 🧪 Planned Features
- 🧠 Multi-turn memory and session tracking
- 📚 Ingest all Kubeflow repos, issues, articles, StackOverflow, etc.
- 🧬 RAG pipeline benchmarking & rerankers
- 🧠 Fine-tuning with Kubeflow Trainer
- ⚙️ Modular pipeline with Kubeflow Pipelines
- 📊 User feedback & prompt engineering
See [`frontend/src/components/upcoming-features.tsx`](frontend/src/components/upcoming-features.tsx)
---