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RAGeval

CI License: AGPL v3

PyPI License: AGPL v3

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

🔗 Live demo: https://rageval.ysiddo-ai-projects.app/demo/ · 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).

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 + Groq Llama + 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 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

License

MIT

⚖️ 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: Only the project name and a "startup" event timestamp. No PII, no API keys, no user data.
  • How to disable: We respect your privacy. 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.

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