mce-deepeval-adapter 0.1.4

DeepEval integration adapter for Metrics Computation Engine

MCE DeepEval Adapter

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

Installation

Install via MCE extras:

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

Prerequisites

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

Supported DeepEval Metrics

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

Metric Name	Description
AnswerRelevancyMetric	Measures how relevant the model answer is to the user query
RoleAdherenceMetric	Evaluates adherence to specified roles across a conversation
TaskCompletionMetric	Assesses whether the task was completed given tool calls and responses
ConversationCompletenessMetric	Evaluates whether the conversation covered necessary elements
BiasMetric	Detects various forms of bias in responses
CoherenceMetric	Scores coherence and logical flow of the output
GroundednessMetric	Evaluates how well outputs are grounded in the provided input/context
TonalityMetric	Evaluates tone and stylistic appropriateness of the output
ToxicityMetric	Identifies toxic or unsafe content in outputs
AnswerCorrectnessMetric	Measures correctness of the answer versus expected output
GeneralStructureAndStyleMetric	Evaluates structure and style quality of the output

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

Usage

Basic Usage

import asyncio
from mce_deepeval_adapter.adapter import DeepEvalMetricAdapter
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 DeepEval metrics
registry = MetricRegistry()

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

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

Using with MCE REST API

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

{
  "metrics": [
    "deepeval.AnswerRelevancyMetric",
    "deepeval.RoleAdherenceMetric",
  ],
  "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.