deepeval
The LLM Evaluation Framework
Stars: 13678
DeepEval is a simple-to-use, open-source LLM evaluation framework specialized for unit testing LLM outputs. It incorporates various metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., and runs locally on your machine for evaluation. It provides a wide range of ready-to-use evaluation metrics, allows for creating custom metrics, integrates with any CI/CD environment, and enables benchmarking LLMs on popular benchmarks. DeepEval is designed for evaluating RAG and fine-tuning applications, helping users optimize hyperparameters, prevent prompt drifting, and transition from OpenAI to hosting their own Llama2 with confidence.
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DeepEval is a simple-to-use, open-source LLM evaluation framework, for evaluating and testing large-language model systems. It is similar to Pytest but specialized for unit testing LLM outputs. DeepEval incorporates the latest research to evaluate LLM outputs based on metrics such as G-Eval, task completion, answer relevancy, hallucination, etc., which uses LLM-as-a-judge and other NLP models that run locally on your machine for evaluation.
Whether your LLM applications are AI agents, RAG pipelines, or chatbots, implemented via LangChain or OpenAI, DeepEval has you covered. With it, you can easily determine the optimal models, prompts, and architecture to improve your RAG pipeline, agentic workflows, prevent prompt drifting, or even transition from OpenAI to hosting your own Deepseek R1 with confidence.
[!IMPORTANT] Need a place for your DeepEval testing data to live 🏡❤️? Sign up to the DeepEval platform to compare iterations of your LLM app, generate & share testing reports, and more.
Want to talk LLM evaluation, need help picking metrics, or just to say hi? Come join our discord.
🥳 You can now share DeepEval's test results on the cloud directly on Confident AI
- Supports both end-to-end and component-level LLM evaluation.
- Large variety of ready-to-use LLM evaluation metrics (all with explanations) powered by ANY LLM of your choice, statistical methods, or NLP models that run locally on your machine:
- G-Eval
- DAG (deep acyclic graph)
-
RAG metrics:
- Answer Relevancy
- Faithfulness
- Contextual Recall
- Contextual Precision
- Contextual Relevancy
- RAGAS
-
Agentic metrics:
- Task Completion
- Tool Correctness
-
Others:
- Hallucination
- Summarization
- Bias
- Toxicity
-
Conversational metrics:
- Knowledge Retention
- Conversation Completeness
- Conversation Relevancy
- Role Adherence
- etc.
- Build your own custom metrics that are automatically integrated with DeepEval's ecosystem.
- Generate synthetic datasets for evaluation.
- Integrates seamlessly with ANY CI/CD environment.
-
Red team your LLM application for 40+ safety vulnerabilities in a few lines of code, including:
- Toxicity
- Bias
- SQL Injection
- etc., using advanced 10+ attack enhancement strategies such as prompt injections.
- Easily benchmark ANY LLM on popular LLM benchmarks in under 10 lines of code., which includes:
- MMLU
- HellaSwag
- DROP
- BIG-Bench Hard
- TruthfulQA
- HumanEval
- GSM8K
-
100% integrated with Confident AI for the full evaluation & observability lifecycle:
- Curate/annotate evaluation datasets on the cloud
- Benchmark LLM app using dataset, and compare with previous iterations to experiment which models/prompts works best
- Fine-tune metrics for custom results
- Debug evaluation results via LLM traces
- Monitor & evaluate LLM responses in product to improve datasets with real-world data
- Repeat until perfection
[!NOTE] DeepEval is available on Confident AI, an LLM evals platform for AI observability and quality. Create an account here.
- 🦄 LlamaIndex, to unit test RAG applications in CI/CD
- 🤗 Hugging Face, to enable real-time evaluations during LLM fine-tuning
Let's pretend your LLM application is a RAG based customer support chatbot; here's how DeepEval can help test what you've built.
Deepeval works with Python>=3.9+.
pip install -U deepeval
Using the deepeval platform will allow you to generate sharable testing reports on the cloud. It is free, takes no additional code to setup, and we highly recommend giving it a try.
To login, run:
deepeval login
Follow the instructions in the CLI to create an account, copy your API key, and paste it into the CLI. All test cases will automatically be logged (find more information on data privacy here).
Create a test file:
touch test_chatbot.pyOpen test_chatbot.py and write your first test case to run an end-to-end evaluation using DeepEval, which treats your LLM app as a black-box:
import pytest
from deepeval import assert_test
from deepeval.metrics import GEval
from deepeval.test_case import LLMTestCase, LLMTestCaseParams
def test_case():
correctness_metric = GEval(
name="Correctness",
criteria="Determine if the 'actual output' is correct based on the 'expected output'.",
evaluation_params=[LLMTestCaseParams.ACTUAL_OUTPUT, LLMTestCaseParams.EXPECTED_OUTPUT],
threshold=0.5
)
test_case = LLMTestCase(
input="What if these shoes don't fit?",
# Replace this with the actual output from your LLM application
actual_output="You have 30 days to get a full refund at no extra cost.",
expected_output="We offer a 30-day full refund at no extra costs.",
retrieval_context=["All customers are eligible for a 30 day full refund at no extra costs."]
)
assert_test(test_case, [correctness_metric])Set your OPENAI_API_KEY as an environment variable (you can also evaluate using your own custom model, for more details visit this part of our docs):
export OPENAI_API_KEY="..."
And finally, run test_chatbot.py in the CLI:
deepeval test run test_chatbot.py
Congratulations! Your test case should have passed ✅ Let's breakdown what happened.
- The variable
inputmimics a user input, andactual_outputis a placeholder for what your application's supposed to output based on this input. - The variable
expected_outputrepresents the ideal answer for a giveninput, andGEvalis a research-backed metric provided bydeepevalfor you to evaluate your LLM output's on any custom with human-like accuracy. - In this example, the metric
criteriais correctness of theactual_outputbased on the providedexpected_output. - All metric scores range from 0 - 1, which the
threshold=0.5threshold ultimately determines if your test have passed or not.
Read our documentation for more information on more options to run end-to-end evaluation, how to use additional metrics, create your own custom metrics, and tutorials on how to integrate with other tools like LangChain and LlamaIndex.
If you wish to evaluate individual components within your LLM app, you need to run component-level evals - a powerful way to evaluate any component within an LLM system.
Simply trace "components" such as LLM calls, retrievers, tool calls, and agents within your LLM application using the @observe decorator to apply metrics on a component-level. Tracing with deepeval is non-instrusive (learn more here) and helps you avoid rewriting your codebase just for evals:
from deepeval.tracing import observe, update_current_span
from deepeval.test_case import LLMTestCase
from deepeval.dataset import Golden
from deepeval.metrics import GEval
from deepeval import evaluate
correctness = GEval(name="Correctness", criteria="Determine if the 'actual output' is correct based on the 'expected output'.", evaluation_params=[LLMTestCaseParams.ACTUAL_OUTPUT, LLMTestCaseParams.EXPECTED_OUTPUT])
@observe(metrics=[correctness])
def inner_component():
# Component can be anything from an LLM call, retrieval, agent, tool use, etc.
update_current_span(test_case=LLMTestCase(input="...", actual_output="..."))
return
@observe
def llm_app(input: str):
inner_component()
return
evaluate(observed_callback=llm_app, goldens=[Golden(input="Hi!")])You can learn everything about component-level evaluations here.
Alternatively, you can evaluate without Pytest, which is more suited for a notebook environment.
from deepeval import evaluate
from deepeval.metrics import AnswerRelevancyMetric
from deepeval.test_case import LLMTestCase
answer_relevancy_metric = AnswerRelevancyMetric(threshold=0.7)
test_case = LLMTestCase(
input="What if these shoes don't fit?",
# Replace this with the actual output from your LLM application
actual_output="We offer a 30-day full refund at no extra costs.",
retrieval_context=["All customers are eligible for a 30 day full refund at no extra costs."]
)
evaluate([test_case], [answer_relevancy_metric])DeepEval is extremely modular, making it easy for anyone to use any of our metrics. Continuing from the previous example:
from deepeval.metrics import AnswerRelevancyMetric
from deepeval.test_case import LLMTestCase
answer_relevancy_metric = AnswerRelevancyMetric(threshold=0.7)
test_case = LLMTestCase(
input="What if these shoes don't fit?",
# Replace this with the actual output from your LLM application
actual_output="We offer a 30-day full refund at no extra costs.",
retrieval_context=["All customers are eligible for a 30 day full refund at no extra costs."]
)
answer_relevancy_metric.measure(test_case)
print(answer_relevancy_metric.score)
# All metrics also offer an explanation
print(answer_relevancy_metric.reason)Note that some metrics are for RAG pipelines, while others are for fine-tuning. Make sure to use our docs to pick the right one for your use case.
In DeepEval, a dataset is simply a collection of test cases. Here is how you can evaluate these in bulk:
import pytest
from deepeval import assert_test
from deepeval.dataset import EvaluationDataset, Golden
from deepeval.metrics import AnswerRelevancyMetric
from deepeval.test_case import LLMTestCase
dataset = EvaluationDataset(goldens=[Golden(input="What's the weather like today?")])
for golden in dataset.goldens:
test_case = LLMTestCase(
input=golden.input,
actual_output=your_llm_app(golden.input)
)
dataset.add_test_case(test_case)
@pytest.mark.parametrize(
"test_case",
dataset.test_cases,
)
def test_customer_chatbot(test_case: LLMTestCase):
answer_relevancy_metric = AnswerRelevancyMetric(threshold=0.5)
assert_test(test_case, [answer_relevancy_metric])# Run this in the CLI, you can also add an optional -n flag to run tests in parallel
deepeval test run test_<filename>.py -n 4Alternatively, although we recommend using deepeval test run, you can evaluate a dataset/test cases without using our Pytest integration:
from deepeval import evaluate
...
evaluate(dataset, [answer_relevancy_metric])
# or
dataset.evaluate([answer_relevancy_metric])DeepEval auto-loads .env.local then .env from the current working directory at import time.
Precedence: process env -> .env.local -> .env.
Opt out with DEEPEVAL_DISABLE_DOTENV=1.
cp .env.example .env.local
# then edit .env.local (ignored by git)DeepEval is available on Confident AI, an evals & observability platform that allows you to:
- Curate/annotate evaluation datasets on the cloud
- Benchmark LLM app using dataset, and compare with previous iterations to experiment which models/prompts works best
- Fine-tune metrics for custom results
- Debug evaluation results via LLM traces
- Monitor & evaluate LLM responses in product to improve datasets with real-world data
- Repeat until perfection
Everything on Confident AI, including how to use Confident is available here.
To begin, login from the CLI:
deepeval loginFollow the instructions to log in, create your account, and paste your API key into the CLI.
Now, run your test file again:
deepeval test run test_chatbot.pyYou should see a link displayed in the CLI once the test has finished running. Paste it into your browser to view the results!
Using .env.local or .env is optional. If they are missing, DeepEval uses your existing environment variables. When present, dotenv environment variables are auto-loaded at import time (unless you set DEEPEVAL_DISABLE_DOTENV=1).
Precedence: process env -> .env.local -> .env
cp .env.example .env.local
# then edit .env.local (ignored by git)Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.
Features:
- [x] Integration with Confident AI
- [x] Implement G-Eval
- [x] Implement RAG metrics
- [x] Implement Conversational metrics
- [x] Evaluation Dataset Creation
- [x] Red-Teaming
- [ ] DAG custom metrics
- [ ] Guardrails
Built by the founders of Confident AI. Contact [email protected] for all enquiries.
DeepEval is licensed under Apache 2.0 - see the LICENSE.md file for details.
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TrustLLM is a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. The document explains how to use the trustllm python package to help you assess the performance of your LLM in trustworthiness more quickly. For more details about TrustLLM, please refer to project website.
AI-YinMei
AI-YinMei is an AI virtual anchor Vtuber development tool (N card version). It supports fastgpt knowledge base chat dialogue, a complete set of solutions for LLM large language models: [fastgpt] + [one-api] + [Xinference], supports docking bilibili live broadcast barrage reply and entering live broadcast welcome speech, supports Microsoft edge-tts speech synthesis, supports Bert-VITS2 speech synthesis, supports GPT-SoVITS speech synthesis, supports expression control Vtuber Studio, supports painting stable-diffusion-webui output OBS live broadcast room, supports painting picture pornography public-NSFW-y-distinguish, supports search and image search service duckduckgo (requires magic Internet access), supports image search service Baidu image search (no magic Internet access), supports AI reply chat box [html plug-in], supports AI singing Auto-Convert-Music, supports playlist [html plug-in], supports dancing function, supports expression video playback, supports head touching action, supports gift smashing action, supports singing automatic start dancing function, chat and singing automatic cycle swing action, supports multi scene switching, background music switching, day and night automatic switching scene, supports open singing and painting, let AI automatically judge the content.