Axel Remillat
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LIVE — try it//Architecture

VEGA — RAG assistant

A résumé you can talk to. A full RAG pipeline, running in production on this site.

Pipeline // data flow

1
Documents (CV · Markdown)
ingestion + chunking
2
Embeddings
OpenAI text-embedding-3-large → 1536 d
3
pgvector · Supabase
cosine search · top-k
4
gpt-4o-mini
sourced answer generation
5
Text-to-speech
TTS → voice
Daily token capPer-IP rate limit

01 // Context

A PDF résumé just sits there: a recruiter or a client can't ask a precise question or check a skill in a few seconds. I wanted living proof of what I deliver — a complete RAG system in production, not a throwaway demo.

02 // The challenge

Make a profile you can query in plain language, by voice, with sourced answers, acceptable latency and controlled costs — all hosted in production, not running locally on my laptop.

03 // Result / status

Live on this site. Pipeline deployed on Vercel + Supabase: document ingestion → embeddings → vector search (pgvector) → generation (gpt-4o-mini) → speech synthesis. A daily token cap and per-IP rate limiting keep costs under control. You can try it right now.