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How to estimate Cost and Usage of evaluations and testset generation

When using LLMs for evaluation and test set generation, cost will be an important factor. Ragas provides you some tools to help you with that.

Implement TokenUsageParser

By default Ragas does not calculate the usage of tokens for evaluate(). This is because langchain's LLMs do not always return information about token usage in a uniform way. So in order to get the usage data, we have to implement a TokenUsageParser.

A TokenUsageParser is function that parses the LLMResult or ChatResult from langchain models generate_prompt() function and outputs TokenUsage which Ragas expects.

For an example here is one that will parse OpenAI by using a parser we have defined.

from langchain_openai.chat_models import ChatOpenAI
from langchain_core.prompt_values import StringPromptValue

gpt4o = ChatOpenAI(model="gpt-4o")
p = StringPromptValue(text="hai there")
llm_result = gpt4o.generate_prompt([p])

# lets import a parser for OpenAI
from ragas.cost import get_token_usage_for_openai

get_token_usage_for_openai(llm_result)

TokenUsage(input_tokens=9, output_tokens=9, model='')

You can define your own or import parsers if they are defined. If you would like to suggest parser for LLM providers or contribute your own ones please check out this issue 🙂.

Token Usage for Evaluations

Let's use the get_token_usage_for_openai parser to calculate the token usage for an evaluation.

from ragas import EvaluationDataset
from datasets import load_dataset

dataset = load_dataset("explodinggradients/amnesty_qa", "english_v3")

eval_dataset = EvaluationDataset.from_hf_dataset(dataset["eval"])

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You can pass in the parser to the evaluate() function and the cost will be calculated and returned in the Result object.

from ragas import evaluate
from ragas.metrics import LLMContextRecall

from ragas.cost import get_token_usage_for_openai

result = evaluate(
    eval_dataset,
    metrics=[LLMContextRecall()],
    llm=gpt4o,
    token_usage_parser=get_token_usage_for_openai,
)

Evaluating:   0%|          | 0/20 [00:00<?, ?it/s]

result.total_tokens()

TokenUsage(input_tokens=25097, output_tokens=3757, model='')

You can compute the cost for each run by passing in the cost per token to Result.total_cost() function.

In this case GPT-4o costs $5 for 1M input tokens and $15 for 1M output tokens.

result.total_cost(cost_per_input_token=5 / 1e6, cost_per_output_token=15 / 1e6)

1.1692900000000002

Token Usage for Testset Generation

You can use the same parser for testset generation but you need to pass in the token_usage_parser to the generate() function. For now it only calculates the cost for the generation process and not the cost for the transforms.

For an example let's load an existing KnowledgeGraph and generate a testset. If you want to know more about how to generate a testset please check out the testset generation.

from ragas.testset.graph import KnowledgeGraph

# loading an existing KnowledgeGraph
# make sure to change the path to the location of the KnowledgeGraph file
kg = KnowledgeGraph.load("../../../experiments/scratchpad_kg.json")
kg

KnowledgeGraph(nodes: 47, relationships: 109)

Choose your LLM

Install the langchain-openai package

pip install langchain-openai

then ensure you have your OpenAI key ready and available in your environment

import os
os.environ["OPENAI_API_KEY"] = "your-openai-key"

Wrapp the LLMs in LangchainLLMWrapper so that it can be used with ragas.

from ragas.llms import LangchainLLMWrapper
from langchain_openai import ChatOpenAI
from langchain_openai import OpenAIEmbeddings
generator_llm = LangchainLLMWrapper(ChatOpenAI(model="gpt-4o"))
generator_embeddings = LangchainEmbeddingsWrapper(OpenAIEmbeddings())

Install the langchain-aws package

pip install langchain-aws

then you have to set your AWS credentials and configurations

config = {
    "credentials_profile_name": "your-profile-name",  # E.g "default"
    "region_name": "your-region-name",  # E.g. "us-east-1"
    "llm": "your-llm-model-id",  # E.g "anthropic.claude-3-5-sonnet-20240620-v1:0"
    "embeddings": "your-embedding-model-id",  # E.g "amazon.titan-embed-text-v2:0"
    "temperature": 0.4,
}

define you LLMs and wrap them in LangchainLLMWrapper so that it can be used with ragas.

from langchain_aws import ChatBedrockConverse
from langchain_aws import BedrockEmbeddings
from ragas.llms import LangchainLLMWrapper
from ragas.embeddings import LangchainEmbeddingsWrapper

generator_llm = LangchainLLMWrapper(ChatBedrockConverse(
    credentials_profile_name=config["credentials_profile_name"],
    region_name=config["region_name"],
    base_url=f"https://bedrock-runtime.{config['region_name']}.amazonaws.com",
    model=config["llm"],
    temperature=config["temperature"],
))
generator_embeddings = LangchainEmbeddingsWrapper(BedrockEmbeddings(
    credentials_profile_name=config["credentials_profile_name"],
    region_name=config["region_name"],
    model_id=config["embeddings"],
))

If you want more information on how to use other AWS services, please refer to the langchain-aws documentation.

from ragas.testset import TestsetGenerator
from ragas.llms import llm_factory

tg = TestsetGenerator(llm=llm_factory(), knowledge_graph=kg)
# generating a testset
testset = tg.generate(testset_size=10, token_usage_parser=get_token_usage_for_openai)

# total cost for the generation process
testset.total_cost(cost_per_input_token=5 / 1e6, cost_per_output_token=15 / 1e6)

0.20967000000000002