Library
Drop-in patterns — hybrid search + MMR + HyDE, durable ingest, streaming citations. Production-ready today.
Open-source Nuxt 3 + pgvector reference for teams shipping RAG — and a learning track for devs starting from zero.
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What is hypar?
RAG (Retrieval-Augmented Generation) lets an AI answer questions from your own documents — but production RAG is hard: chunking strategy, hybrid retrieval, durable ingestion, citations, evals. hypar is a fully-working TypeScript reference app. Read the code like a tutorial, run it locally in minutes, adapt the patterns to your stack.
Try these in the demo:
Pre-loaded with the hypar docs — ask anything.
Run locally
git clone https://github.com/albegosu/hypar.git
cd hypar
cp .env.example .env
# Edit .env — set GOOGLE_API_KEY at minimum
docker compose --profile full up -d --build
open http://localhost:3000Architecture at a glance
Read the full architecture →What's inside
Hybrid retrieval
pgvector cosine similarity combined with BM25, then MMR diversification.
Multi-provider embeddings
Gemini, OpenAI or Ollama. Switch at runtime without re-ingesting.
Durable ingestion
Workflow SDK with per-step retries; status polled, never lost on restart.
Vitest suite
Chunking, text helpers, agent commands, and search utilities covered in CI.
Source citations
Inline [1], [2] markers persisted on every assistant message and audited.
Rate limits and admin APIs
30/min chat, 10/min upload. /api/admin/* accepts a signed-in session or ADMIN_API_KEY.
Roadmap — 10 stages
View full roadmap →01 · Now
Traceability
02 · Next
Measurable quality
03
SOTA retrieval
04
Robustness
05
Pluggable
06
Beyond text
07
Real memory
08
Knowledge graph
09
Self-hosted
10
Live product