rag-forge-evaluator 0.1.1

Evaluation engine: RAGAS, DeepEval, LLM-as-Judge, and audit report generation

rag-forge-evaluator

RAG pipeline evaluation engine for the RAG-Forge toolkit: RAGAS, DeepEval, LLM-as-Judge, and the RAG Maturity Model.

Installation

pip install rag-forge-evaluator

Usage

from rag_forge_evaluator.assess import RMMAssessor

assessor = RMMAssessor()
result = assessor.assess(config={
    "retrieval_strategy": "hybrid",
    "input_guard_configured": True,
    "output_guard_configured": True,
})
print(result.badge)  # e.g., "RMM-3 Better Trust"

Features

• RMM (RAG Maturity Model) scoring (levels 0-5)
• RAGAS, DeepEval, and LLM-as-Judge evaluators
• Golden set management with traffic sampling
• Cost estimation
• HTML and PDF report generation

Bring your own judge provider

rag-forge-evaluator ships with Claude and OpenAI judges out of the box, but the JudgeProvider protocol is intentionally minimal so you can plug in any LLM — Gemini, Cohere, Bedrock, Ollama, vLLM, or a private model behind your own gateway. Implementing one is ~20 lines:

# my_gemini_judge.py
import os
import google.generativeai as genai


class GeminiJudge:
    """Minimal judge implementation backed by Google Gemini."""

    def __init__(self, model: str = "gemini-2.5-pro", api_key: str | None = None) -> None:
        key = api_key or os.environ.get("GOOGLE_API_KEY")
        if not key:
            raise ValueError("GOOGLE_API_KEY not set")
        genai.configure(api_key=key)
        self._model_name = model
        self._client = genai.GenerativeModel(model)

    def judge(self, system_prompt: str, user_prompt: str) -> str:
        response = self._client.generate_content(
            [system_prompt, user_prompt],
            generation_config={"max_output_tokens": 4096},
        )
        return response.text or ""

    def model_name(self) -> str:
        return self._model_name

Wire it into an audit by passing the instance directly to LLMJudgeEvaluator:

from my_gemini_judge import GeminiJudge
from rag_forge_evaluator.metrics.llm_judge import LLMJudgeEvaluator

judge = GeminiJudge(model="gemini-2.5-pro")
evaluator = LLMJudgeEvaluator(judge=judge)
result = evaluator.evaluate(samples)

The protocol contract:

class JudgeProvider(Protocol):
    def judge(self, system_prompt: str, user_prompt: str) -> str: ...
    def model_name(self) -> str: ...

That's it. Anything that responds to those two methods works. Implementation hints:

• Always set max_tokens >= 4096 for faithfulness/hallucination metrics. Long responses produce 30-50 enumerated claims; smaller budgets truncate the JSON mid-array and the metric ends up skipped.
• Wrap your client with retry logic for transient 429/5xx. The Anthropic and OpenAI SDKs honor a max_retries constructor arg with built-in exponential backoff — most provider SDKs offer something similar.
• Return the raw response text, including any prose around the JSON. The shared response parser handles code fences, leading prose, trailing prose, and truncated output, so you don't need to clean anything up.

First-party Gemini, Bedrock, and Ollama judges are tracked for v0.1.2.

License

MIT