omnismart-rageval 0.1.1

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

RAGeval

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

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 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