ragzero 0.1.0

Production-ready RAG with near-zero hallucination. Pluggable providers (OpenAI, Anthropic, Gemini, OpenRouter, Azure, Voyage, ...), graph + agentic + multilingual strategies, a FastAPI server with a bundled React UI, and one-click publishing of a configured setup as a public ask-only assistant.

ragzero

Production-ready RAG with near-zero hallucination — install and run in 60 seconds.

A complete RAG system in one pip install: pluggable LLM/embedding providers, four composable retrieval strategies (Simple, Graph, Agentic, Multilingual), a FastAPI server, and a pre-built React UI bundled inside the package. No npm, no Docker, no configuration required for the basic case.

Install and run

pip install "ragzero[all]"
ragzero serve
# → open http://127.0.0.1:8000

That's it. The web UI opens, you paste an API key, upload some documents, and ask questions.

What you get

• 🚫 Near-zero hallucination by design — constrained generation, citation enforcement, refusal-first fallback when evidence is thin
• 🔌 Pluggable providers for both LLMs (Anthropic, OpenAI, Azure, Gemini, OpenRouter) and embeddings (OpenAI, Azure, Gemini, Voyage, OpenRouter, sentence-transformers) — pick any model name, not a hard-coded list
• 🧠 Four RAG strategies, composable: Simple, Graph (vector + entity graph), Agentic (plan → search → reflect), Multilingual (ask in any language, answer in any language)
• 📊 Live progress UI — see chunk-by-chunk embedding progress and graph build progress in real time via SSE
• 👥 Multi-user isolation — each browser session has its own credentials, corpus, and config
• 📣 Publish a public assistant — turn your configured setup into a shareable, ask-only page (/a/<slug>); visitors only ask and read answers, never seeing your data, models, or keys
• 🧪 150 tests covering every pipeline step

Install with the providers you actually use

The base install includes the server and the core library. Provider SDKs are extras:

# Minimal — only mock providers (good for testing)
pip install ragzero

# With one specific provider
pip install "ragzero[openai]"           # OpenAI, Azure OpenAI, and OpenRouter
pip install "ragzero[anthropic]"        # Claude
pip install "ragzero[gemini]"           # Google Gemini
pip install "ragzero[voyage]"           # Voyage AI embeddings
pip install "ragzero[local]"            # sentence-transformers (local embeddings)
pip install "ragzero[faiss]"            # FAISS vector store
pip install "ragzero[graph]"            # NetworkX for Graph RAG

# Everything
pip install "ragzero[all]"

Usage

As a CLI tool

ragzero serve --port 8000              # web UI + API
ragzero serve --admin-token SECRET     # gate the admin console (see "Publish" below)
ragzero info                           # which provider SDKs are installed
ragzero query --docs ./my-docs --query "What is RAG?"   # one-shot CLI query
ragzero version

As a Python library

from ragzero import build_dev_pipeline, Document
from datetime import datetime, timezone

pipeline = build_dev_pipeline()
pipeline.ingest_documents([
    Document(
        doc_id="d1",
        source="my-notes.txt",
        content="The sky is blue because of Rayleigh scattering...",
        doc_type="txt",
        created_at=datetime.now(timezone.utc),
    )
])
answer = pipeline.answer("Why is the sky blue?")
print(answer.text)
print(f"Confidence: {answer.confidence}")
for c in answer.citations:
    print(f"  - {c.source} (chunk {c.chunk_id})")

For real providers, build the pipeline from YAML:

from ragzero.rag.config import load_pipeline

pipeline = load_pipeline("my_config.yaml", overrides={
    "backends": {
        "llm": "anthropic",
        "llm_settings": {"api_key": "sk-ant-...", "model": "claude-opus-4-7"},
        "embedder": "openai",
        "embedder_settings": {"api_key": "sk-...", "model": "text-embedding-3-large"},
    },
})

The four RAG strategies

Strategy	Best for
Simple	Standard Q&A over well-structured documents
Graph	Multi-hop questions ("what companies did X's co-founders work at?")
Agentic	Complex questions requiring decomposition + iterative search
Multilingual	Cross-language Q&A — wraps any other strategy

All four compose: Multilingual( Agentic( pipeline ) ) lets you ask in Hindi and have the agent reason in English and return the answer in Hindi.

Publish a public assistant

Once you've connected a model and indexed your data, you can publish a public, ask-only assistant for other people — they only ask questions and read answers, and never see your data, models, keys, or settings.

1. Start the server with an admin token so the console is protected:

   ragzero serve --admin-token YOUR_SECRET        # or set RAGZERO_ADMIN_TOKEN
   

2. In the console, open the Publish tab. Set the name, accent color, welcome message, suggested questions, allowed modes, and whether citations/confidence are shown. Optionally add an access code and tune the per-visitor rate limit + daily cap.
3. Click Publish. Share the public link: http://<host>/a/<slug>.

The public page talks only to a locked-down API (GET /public/meta, POST /public/query) that returns just the fields you allowed. To share beyond your own machine, bind to all interfaces (--host 0.0.0.0) for your LAN, use a tunnel (cloudflared tunnel --url http://localhost:8000) for the internet, or run it behind a reverse proxy on a server.

⚠️ Always set --admin-token before exposing the server beyond localhost — otherwise the admin console (and your data/keys) would be reachable too. The server warns you if you bind to a non-local address without one.

Configuration

The bundled config/dev.yaml is loaded by default. Override with --config or RAGZERO_CONFIG:

ragzero serve --config /etc/ragzero/prod.yaml
# or
RAGZERO_CONFIG=/etc/ragzero/prod.yaml ragzero serve

Key knobs (all editable from the UI's Config tab too):

pipeline:
  chunk_size: 1000
  chunk_overlap: 150

confidence:
  half_life_days: 180          # how fast freshness decays
  w_freshness: 0.30
  w_source: 0.40
  w_consistency: 0.30

fallback:
  min_aggregate_confidence: 0.65   # below this → refuse
  require_min_citations: 2

Testing

git clone https://github.com/aasu14/ragzero.git
cd ragzero
pip install -e ".[dev,all]"
pytest                                  # 150 tests
python3 run_tests.py                    # alternative stdlib runner (no pytest)

Security notes

• Session API keys are kept in memory only — never written to disk or logs
• HTTP-only SameSite=Lax session cookies
• Admin console + /api/* can be gated with --admin-token / RAGZERO_ADMIN_TOKEN; set it before exposing the server beyond localhost
• The public assistant has per-visitor rate limiting + a daily cap and an optional access code; the /public/* API never returns data, models, or keys
• A published Space is persisted to ~/.ragzero/space.json (mode 0600) and includes the provider credentials from the snapshot — protect that file / the host machine accordingly
• POST /api/ingest/path reads server-side filesystem — guard access in production
• No built-in CSRF — add tokens if exposing the admin API across origins; for heavy public traffic put nginx/Cloudflare in front

License

MIT — see LICENSE.