RAGeval

Drop-in LLMOps observability. Self-hosted. SQLite-default. Persona-aware. Multi-judge consensus.

Version: v0.1.10 | pip install omnismart-rageval

🔗 Live dashboard: https://rageval.ysiddo-ai-projects.app/ · browser dashboard (score a query + view metrics). Also fully scriptable — API: /health, /eval/* via curl/HTTPie. On-demand backend (first request ~30–60 s to wake). Self-hosting: see SELF_HOSTING.md.

The 60-Second Pitch

from rageval import track

@track(model="anthropic/claude-sonnet-4-6", persona="cfo")
async def answer_question(query: str, context_chunks: list[str]) -> str:
    ...

That's it. Open the dashboard at localhost:8003.

What It Measures

Metric	Definition
Retrieval relevance	Cosine sim between query and retrieved chunks (BGE-large by default)
Groundedness consensus	Multi-judge LLM scoring (Claude Haiku 4.5 + Groq LLaMA 3.3 + GPT-5-mini), flags disagreement
Faithfulness	Per-sentence max-similarity to any chunk (NLI proxy)
Cost	USD per interaction, tracked by model
Latency	End-to-end wall-clock

Comparison vs Alternatives

Feature	RAGeval	Phoenix	Langfuse	TruLens
Self-hosted	✅	✅	✅	✅
SQLite default	✅	❌	❌	❌
Drop-in decorator	✅	partial	❌	partial
Persona-aware	✅	❌	❌	❌
Multi-judge consensus	✅	❌	❌	❌
Cost tracking	✅	✅	✅	partial
Setup time	60 sec	10 min	15 min	10 min

Quick Start

pip install --index-url https://gateway.ysiddo-ai-projects.app/pypi/simple/ omnismart-rageval     # distribution name; CLI + import remain `rageval`
rageval init                 # creates ~/.rageval/rageval.db
rageval serve --port 8003

Integration

FastAPI

from rageval import track

@app.post("/ask")
@track(model="anthropic/claude-sonnet-4-6", persona="cfo")
async def ask(query: str):
    chunks = await retriever.search(query)
    return await llm.generate(query, chunks=chunks)

LangChain

@track(model="groq/llama-3.3-70b-versatile")
def chain_invoke(query: str, context_chunks: list[str]):
    return chain.invoke({"query": query, "context": context_chunks})

Endpoints

Method	Path	Purpose
GET	/health	Liveness
POST	/eval/log	Score + store
POST	/eval/score	Score only (no storage)
GET	/eval/metrics?days=7	Aggregate dashboard data
GET	/eval/queries	Query log (filter by needs_review)
GET	/eval/cost-report?days=30	Cost breakdown by day + model
GET	/eval/alerts	Recent flagged queries
POST	/eval/retrieval-bench	A/B compare retrieval strategies
POST	/eval/embedding-comparison	Compare embedding models

Tests

38 test functions across smoke, API, evaluator, decorator, DSPy integration, store, and e2e:

pytest tests/ -q

License

AGPL-3.0

⚖️ License & Enterprise Use (Dual-License)

This project is open-source under the AGPL-3.0 License. It is completely free for researchers, students, and open-source hobbyists.

Commercial Use: The AGPLv3 license requires that any proprietary network service (SaaS, internal corporate tools) that uses or modifies this code must also open-source its entire backend.

If you wish to use this framework in a closed-source commercial environment, or require Enterprise features (SSO, Active Directory, Custom VPC Deployment, Strict RBAC), you must obtain a Commercial License. Please reach out to discuss commercial licensing and integration consulting.

📡 Anonymous Telemetry

This project collects anonymous, GDPR-compliant startup pings to help the author understand usage volume and prioritize development.

• What is collected: A startup event timestamp and anonymized deployment origin. No API keys, no user prompts, and no sensitive application data is ever collected.
• How to disable: We respect your privacy and development environment. To opt-out, simply set TELEMETRY_OPT_OUT=true in your .env file.

Licensing

This project is licensed under the AGPL-3.0 License.

Commercial Use: If you wish to use this software commercially without releasing your own source code, please see COMMERCIAL.md to obtain a commercial license.

Telemetry: See TELEMETRY.md for our privacy-first data practices.