mce-ragas-adapter 0.1.3

RAGAS integration adapter for Metrics Computation Engine

MCE Ragas Adapter

A Python adapter library that integrates Ragas metrics as third-party plugins for the Metric Computation Engine (MCE). This adapter enables seamless use of Ragas's LLM evaluation metrics within the MCE framework for evaluating agentic applications.

Installation

Install via MCE extras:

pip install "metrics-computation-engine[ragas]"

Prerequisites

• Metric Computation Engine (MCE) installed
• Agentic applications instrumented with AGNTCY's observe SDK

Supported Ragas Metrics

The following Ragas metrics are supported by this adapter (use with the ragas. prefix in service payloads):

Metric Name	Description
TopicAdherenceScore	Evaluates whether the model response adheres to the intended topic

For requests to support additional Ragas metrics, please file an issue in our repo: agntcy/telemetry-hub.

Usage

Basic Usage

import asyncio
from mce_ragas_adapter.adapter import RagasMetricAdapter
from metrics_computation_engine.models.requests import LLMJudgeConfig
from metrics_computation_engine.registry import MetricRegistry

# Initialize LLM configuration
llm_config = LLMJudgeConfig(
    LLM_BASE_MODEL_URL="https://api.openai.com/v1",
    LLM_MODEL_NAME="gpt-4o",
    LLM_API_KEY="your-api-key-here"
)

# Create registry and register Ragas metrics
registry = MetricRegistry()

# Method 1: Direct registration with metric name
registry.register_metric(RagasMetricAdapter, "TopicAdherenceScore")

# Method 2: Using get_metric_class helper with prefix
from metrics_computation_engine.util import get_metric_class
metric, metric_name = get_metric_class("ragas.TopicAdherenceScore")
registry.register_metric(metric, metric_name)

Using with MCE REST API

When using the MCE as a service, include Ragas metrics in your API request:

{
  "metrics": [
    "ragas.TopicAdherenceScore"
  ],
  "llm_judge_config": {
    "LLM_API_KEY": "your-api-key",
    "LLM_MODEL_NAME": "gpt-4o",
    "LLM_BASE_MODEL_URL": "https://api.openai.com/v1"
  },
  "data_fetching_infos": {
    "batch_config": {
      "time_range": { "start": "2024-01-01T00:00:00Z", "end": "2024-12-31T23:59:59Z" }
    },
    "session_ids": []
  }
}

Contributing

Contributions are welcome! Please follow these steps to contribute:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Commit your changes (git commit -am 'Add new feature').
4. Push to the branch (git push origin feature-branch).
5. Create a new Pull Request.