Argus RAG Studio
An open-source, self-hosted platform that covers the full RAG lifecycle — Build, Retrieve & Generate, Evaluate, and Operate & Deploy — in one place. Not a "works-once RAG demo": it ships with an evaluation harness that measures quality in numbers, config sweeps that auto-explore optimal settings, a feedback loop, and agent-based remote deployment, so it runs on-premises and in air-gapped (closed) networks.
Highlights
Measure → optimize → improve loop
Quality is measured in numbers with golden sets, Hit Rate/MRR, and 3-axis LLM-as-Judge; config sweeps auto-explore the best combination of chunking, search mode, and reranker; and 👍/👎 user feedback is promoted back into the golden set — the loop most self-built RAG stacks never get to is a built-in feature.
Hybrid search + cited answers
Vector (pgvector) and lexical (tsvector) results are fused with RRF and reordered by reranking (LLM or cross-encoder), and answers stream over SSE with [n] citations. Federated queries search knowledge bases with heterogeneous embeddings at once.
Built for Korean documents
A dedicated Rust parser for HWP/HWPX (rhwp), kss Korean sentence splitting, a VLM/OCR (PaddleOCR) pipeline for scanned documents, and AI-Hub-compatible annotation — an axis general open-source RAG frameworks do not cover.
Air-gap · agent-based remote deployment
Models are brought in as packs and auto-installed at deploy time, while per-host agents remotely deploy workers, embedding, reranker, and VLM servers. With the zot registry even containers stay fully offline — meeting network-separation requirements in finance, government, and defense.
Platform Architecture
The frontend dashboard, RAG backend, inference servers, and data stores/registry work together, and inference and workers can be deployed separately via agents to scale in stages.
Core Capabilities
From ingestion, parsing, and chunking to hybrid search & generation, evaluation, config sweeps, retrieval fine-tuning, versions & observability, agent deployment & air-gap, and annotation & images — twelve pillars covering the full RAG lifecycle in a single platform.
Ingestion pipeline
Multi-format documents are processed by async workers through upload → parse → chunk → embed → index.
5 parse strategies
Pick the parse strategy per collection to match the document (auto-fallback when uninstalled).
8 chunking strategies
Chunking decides half of retrieval quality — implemented down to table preservation and meaning boundaries.
Knowledge base design — fail-closed isolation
Collections are designed as security isolation boundaries, not just topic buckets.
Hybrid search & generation
Search meaning and keywords in parallel, fuse the results, and generate cited answers.
Model flexibility
Swap embedding, reranker, generation LLM, VLM, and OCR per workload.
Evaluation harness
Measure quality in numbers with golden datasets and an LLM judge.
Config sweeps & improvement loop
Auto-explore combinations of chunking, search mode, top-k, and reranker, compared on a leaderboard.
Retrieval fine-tuning
Tune embeddings and rerankers to your domain terms and acronyms.
Pipeline versions & observability
Treat search, rerank, and generation settings as versionable assets, and instrument every query.
Agent-based deployment & air-gap
Per-host Argus Agents deploy workers and inference servers; closed networks import models as packs.
Annotation & image pipeline
Turn in-document images and scans into knowledge via OCR and VLM.
An open-source RAG platform
Argus RAG Studio is published on GitHub under the Apache License 2.0. The entire RAG engine — backend (FastAPI), frontend (Next.js), and the standalone embedding/reranker servers — is open, so enterprises can verify the code directly, extend it to fit their environment, and operate it without sending data outside.
- Apache 2.0 with no commercial-use restrictions
- Verify and extend the code yourself
- Self-host in air-gapped / on-premises