​
Overview

We provide out-of-the-box evaluation metrics for your RAG application. You can use them to evaluate your RAG applications and compare against different settings of your production RAG application.

Currently, we provide support for following evaluation metrics:

Context Relevancy

Answer Relevancy

Groundedness

Custom Metric

​
Quickstart

Here is a basic example of running evaluation:

example.py
from embedchain import App

app = App()

# Add data sources
app.add("https://www.forbes.com/profile/elon-musk")

# Run evaluation
app.evaluate(["What is the net worth of Elon Musk?", "How many companies Elon Musk owns?"])
# {'answer_relevancy': 0.9987286412340826, 'groundedness': 1.0, 'context_relevancy': 0.3571428571428571}

Under the hood, Embedchain does the following:

  1. Runs semantic search in the vector database and fetches context
  2. LLM call with question, context to fetch the answer
  3. Run evaluation on following metrics: context relevancy, groundedness, and answer relevancy and return result

​
Advanced Usage

We use OpenAI’s gpt-4 model as default LLM model for automatic evaluation. Hence, we require you to set OPENAI_API_KEY as an environment variable.

​
Step-1: Create dataset

In order to evaluate your RAG application, you have to setup a dataset. A data point in the dataset consists of questions, contexts, answer. Here is an example of how to create a dataset for evaluation:

from embedchain.utils.eval import EvalData

data = [
    {
        "question": "What is the net worth of Elon Musk?",
        "contexts": [
            "Elon Musk PROFILEElon MuskCEO, ...",
            "a Twitter poll on whether the journalists' ...",
            "2016 and run by Jared Birchall.[335]...",
        ],
        "answer": "As of the information provided, Elon Musk's net worth is $241.6 billion.",
    },
    {
        "question": "which companies does Elon Musk own?",
        "contexts": [
            "of December 2023[update], ...",
            "ThielCofounderView ProfileTeslaHolds ...",
            "Elon Musk PROFILEElon MuskCEO, ...",
        ],
        "answer": "Elon Musk owns several companies, including Tesla, SpaceX, Neuralink, and The Boring Company.",
    },
]

dataset = []

for d in data:
    eval_data = EvalData(question=d["question"], contexts=d["contexts"], answer=d["answer"])
    dataset.append(eval_data)

​
Step-2: Run evaluation

Once you have created your dataset, you can run evaluation on the dataset by picking the metric you want to run evaluation on.

For example, you can run evaluation on context relevancy metric using the following code:

from embedchain.evaluation.metrics import ContextRelevance
metric = ContextRelevance()
score = metric.evaluate(dataset)
print(score)

You can choose a different metric or write your own to run evaluation on. You can check the following links:

​
Metrics

​
Context Relevancy

Context relevancy is a metric to determine “how relevant the context is to the question”. We use OpenAI’s gpt-4 model to determine the relevancy of the context. We achieve this by prompting the model with the question and the context and asking it to return relevant sentences from the context. We then use the following formula to determine the score:

context_relevance_score = num_relevant_sentences_in_context / num_of_sentences_in_context

​
Examples

You can run the context relevancy evaluation with the following simple code:

from embedchain.evaluation.metrics import ContextRelevance

metric = ContextRelevance()
score = metric.evaluate(dataset)  # 'dataset' is definted in the create dataset section
print(score)
# 0.27975528364849833

In the above example, we used sensible defaults for the evaluation. However, you can also configure the evaluation metric as per your needs using the ContextRelevanceConfig class.

Here is a more advanced example of how to pass a custom evaluation config for evaluating on context relevance metric:

from embedchain.config.evaluation.base import ContextRelevanceConfig
from embedchain.evaluation.metrics import ContextRelevance

eval_config = ContextRelevanceConfig(model="gpt-4", api_key="sk-xxx", language="en")
metric = ContextRelevance(config=eval_config)
metric.evaluate(dataset)

​
ContextRelevanceConfig

model
str

The model to use for the evaluation. Defaults to gpt-4. We only support openai’s models for now.

api_key
str

The openai api key to use for the evaluation. Defaults to None. If not provided, we will use the OPENAI_API_KEY environment variable.

language
str

The language of the dataset being evaluated. We need this to determine the understand the context provided in the dataset. Defaults to en.

prompt
str

The prompt to extract the relevant sentences from the context. Defaults to CONTEXT_RELEVANCY_PROMPT, which can be found at embedchain.config.evaluation.base path.

​
Answer Relevancy

Answer relevancy is a metric to determine how relevant the answer is to the question. We prompt the model with the answer and asking it to generate questions from the answer. We then use the cosine similarity between the generated questions and the original question to determine the score.

answer_relevancy_score = mean(cosine_similarity(generated_questions, original_question))

​
Examples

You can run the answer relevancy evaluation with the following simple code:

from embedchain.evaluation.metrics import AnswerRelevance

metric = AnswerRelevance()
score = metric.evaluate(dataset)
print(score)
# 0.9505334177461916

In the above example, we used sensible defaults for the evaluation. However, you can also configure the evaluation metric as per your needs using the AnswerRelevanceConfig class. Here is a more advanced example where you can provide your own evaluation config:

from embedchain.config.evaluation.base import AnswerRelevanceConfig
from embedchain.evaluation.metrics import AnswerRelevance

eval_config = AnswerRelevanceConfig(
    model='gpt-4',
    embedder="text-embedding-ada-002",
    api_key="sk-xxx",
    num_gen_questions=2
)
metric = AnswerRelevance(config=eval_config)
score = metric.evaluate(dataset)

​
AnswerRelevanceConfig

model
str

The model to use for the evaluation. Defaults to gpt-4. We only support openai’s models for now.

embedder
str

The embedder to use for embedding the text. Defaults to text-embedding-ada-002. We only support openai’s embedders for now.

api_key
str

The openai api key to use for the evaluation. Defaults to None. If not provided, we will use the OPENAI_API_KEY environment variable.

num_gen_questions
int

The number of questions to generate for each answer. We use the generated questions to compare the similarity with the original question to determine the score. Defaults to 1.

prompt
str

The prompt to extract the num_gen_questions number of questions from the provided answer. Defaults to ANSWER_RELEVANCY_PROMPT, which can be found at embedchain.config.evaluation.base path.

​
Groundedness

Groundedness is a metric to determine how grounded the answer is to the context. We use OpenAI’s gpt-4 model to determine the groundedness of the answer. We achieve this by prompting the model with the answer and asking it to generate claims from the answer. We then again prompt the model with the context and the generated claims to determine the verdict on the claims. We then use the following formula to determine the score:

groundedness_score = (sum of all verdicts) / (total # of claims)

You can run the groundedness evaluation with the following simple code:

from embedchain.evaluation.metrics import Groundedness
metric = Groundedness()
score = metric.evaluate(dataset)    # dataset from above
print(score)
# 1.0

In the above example, we used sensible defaults for the evaluation. However, you can also configure the evaluation metric as per your needs using the GroundednessConfig class. Here is a more advanced example where you can configure the evaluation config:

from embedchain.config.evaluation.base import GroundednessConfig
from embedchain.evaluation.metrics import Groundedness

eval_config = GroundednessConfig(model='gpt-4', api_key="sk-xxx")
metric = Groundedness(config=eval_config)
score = metric.evaluate(dataset)

​
GroundednessConfig

model
str

The model to use for the evaluation. Defaults to gpt-4. We only support openai’s models for now.

api_key
str

The openai api key to use for the evaluation. Defaults to None. If not provided, we will use the OPENAI_API_KEY environment variable.

answer_claims_prompt
str

The prompt to extract the claims from the provided answer. Defaults to GROUNDEDNESS_ANSWER_CLAIMS_PROMPT, which can be found at embedchain.config.evaluation.base path.

claims_inference_prompt
str

The prompt to get verdicts on the claims from the answer from the given context. Defaults to GROUNDEDNESS_CLAIMS_INFERENCE_PROMPT, which can be found at embedchain.config.evaluation.base path.

​
Custom

You can also create your own evaluation metric by extending the BaseMetric class. You can find the source code for the existing metrics at embedchain.evaluation.metrics path.

You must provide the name of your custom metric in the __init__ method of your class. This name will be used to identify your metric in the evaluation report.

from typing import Optional

from embedchain.config.base_config import BaseConfig
from embedchain.evaluation.metrics import BaseMetric
from embedchain.utils.eval import EvalData

class MyCustomMetric(BaseMetric):
    def __init__(self, config: Optional[BaseConfig] = None):
        super().__init__(name="my_custom_metric")

    def evaluate(self, dataset: list[EvalData]):
        score = 0.0
        # write your evaluation logic here
        return score

Was this page helpful?

🧩 Embedding modelsOverview