From e801bfd3d164e1d1a5ccc3af59c5453acb8b4448 Mon Sep 17 00:00:00 2001 From: Stijn Goossens <22433228+StijnGoossens@users.noreply.github.com> Date: Sat, 4 Nov 2023 19:33:21 +0000 Subject: [PATCH] docs: add diagram to readme --- README.md | 28 +++++++++++----------------- images/evaluation_feedback_loop.png | Bin 0 -> 202736 bytes src/llm_app_eval/app.py | 3 ++- src/llm_app_eval/eval_properties.py | 28 +++++++++++++++++++--------- src/llm_app_eval/evaluator.py | 3 ++- 5 files changed, 34 insertions(+), 28 deletions(-) create mode 100644 images/evaluation_feedback_loop.png diff --git a/README.md b/README.md index e3d3f61..95cab95 100644 --- a/README.md +++ b/README.md @@ -1,22 +1,16 @@ [![Open in Dev Containers](https://img.shields.io/static/v1?label=Dev%20Containers&message=Open&color=blue&logo=visualstudiocode)](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/StijnGoossens/llm-app-eval) -# llm-app-eval - -Evaluates LLM-based applications. - -**Check the `example.ipynb` notebook for an example of how to use this package.** - -## To-do's -- [x] Convert EHBO notes into question-answer pairs, using OpenAI Function Calling. -- [x] Turn the question-answer pairs into a test set. -- [x] Build LLM component to evaluate the given answers by comparing it with the reference answer. -- [x] Build LLM 'app' that can answer the questions. -- [x] Evaluate the LLM app with the LLM evaluator. -- [x] Streamlit page to view the evaluation results. -- [x] Combine the evaluation results into a single metric. -- [x] Evaluate and compare different LLM apps (GPT-3.5, GPT-4, with RAG) -- [x] Integrate with MLflow for experiment tracking. -- [ ] Streamlit page to view, edit and add test cases. +# Evaluation of LLM-based applications + +An implementation of the principles of evaluating LLM-based applications. This repository accompanies the blog post ['Steady the Course: Navigating the Evaluation of LLM-based Applications'](https://medium.com/@stijn.sg.goossens/steady-the-course-navigating-the-evaluation-of-llm-based-applications-8b7a22734fc9). + +💡 Check out the [example notebook](src/llm_app_eval/example.ipynb) for an end-to-end illustration of the most important concepts (LLM app, test case, test properties and Evaluator), including the integration with MLflow. + +🔑 Add your OpenAI API key to a file named `openai_key` in the root directory before running the notebook. + +The image below shows an architectural overview of the evaluation framework and illustrates an important feedback loop. See the aforementioned blog post for more information. The scope of this repository is indicated by the green box. + +![Evaluation feedback loop](images/evaluation_feedback_loop.png) ## Using diff --git a/images/evaluation_feedback_loop.png b/images/evaluation_feedback_loop.png new file mode 100644 index 0000000000000000000000000000000000000000..1c2ef48b2ed95fed4e69b2624555449c8041eb29 GIT binary patch literal 202736 zcmce;by$>J*EmcnAtE9mNT*0i%g};!ccT)+FoeR;4F=L3Lkoh$fV4D-(j9{|N;5D> z4btCmo)A1f=l9QheQ?3-xp%F-_Nu+s+V>j`HF+WeDgq1)3?fAZSuG5VD?=C<*gx>D zpq~_Fu4SP=aDdXP(ij+}k%Y%jaM9oCEflm=F)%z?F);jJU|<}gANj9fV7T1Jz*u{X zfdNRuz@T`RTB|9Bu6S!@plGeCig6EJ#>2qHyorI0E@7g7F)*nx&PPL+FqAOw{I{%y zdH3IKurM%QgD`OZ-Np$0dHxfH{yXpUuTPwqR~T2(-)^2i$;A9MdM4I?dR`gI#Qski z`vG;%&+ui=z`8XJ2?f`Vr-U{-B-re5L!5QE#&iGdg0J?nsn1_-6uO<+% zIHQ582EB}p*mev3*S-F3AM}HG%w1GgL0eE=a+}ya`__-aO zfIPe+A|gEZ`FQyFxX>-QoIM;MPu#g2oSFVL$bZI>wQ{y_0zHF(93AM-$9?kD(FG#T z$ap@{e}Dc3ri~jC@QLyK z>GAKq41aZ#;1v+~cc+U_fA3-N#~v4-{@z2~34}((lXK9{k@pu^7oYvRy%^6qsaznP 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zb&MxIqOwn@m~9K8H=am4y;x}oa>9f-k4sSiq#1JNY7$^>%c(A^?^PHQ7M)|&+kAj0 zX8If04T!WE>)!yi9QXnGU&P?VnGY`!eS_-C;jBMa(Jg1|x&B{)g&zis&%o{LdvT!d zu%8vpe;eV;F}|31Km_cULI3jDsws^tX!NS3+#gQJuTQIqBl@9pcX;<*5?*Mlx#TY$ z^|y8L4IrqN}}mg_qX|BApCbs8BwTE@952#i(ce%T_-4(TuT z692(l4g|q1lRGChwtk4uzyfhlwc5Di!rWh9`xER!CWwBW)wfmRe?i}OG5GrQ!=tol z;A0-h-_Y=L2%WF9);bClXa4oKKkt9LdU=W#DS9{6^V<&JKcBioV`$uLA>r*Gw| PropertyResult: +def output_similarity( + test_case: TestCase, llm_app_result: OutputFormat +) -> Optional[PropertyResult]: if test_case.reference_output and llm_app_result.answer: app_output_emb = get_embedding(llm_app_result.answer) reference_emb = get_embedding(test_case.reference_output.answer) @@ -31,7 +35,7 @@ def output_similarity(test_case: TestCase, llm_app_result: OutputFormat) -> Prop return result -def output_verbosity(test_case: TestCase, llm_app_result: OutputFormat) -> PropertyResult: +def output_verbosity(test_case: TestCase, llm_app_result: OutputFormat) -> Optional[PropertyResult]: if test_case.reference_output and llm_app_result.answer: result = PropertyResult( feedback="", score=len(llm_app_result.answer) / len(test_case.reference_output.answer) @@ -59,7 +63,9 @@ def evaluate_property_with_llm( ) -def factually_consistent(test_case: TestCase, llm_app_result: OutputFormat) -> PropertyResult: +def factually_consistent( + test_case: TestCase, llm_app_result: OutputFormat +) -> Optional[PropertyResult]: if test_case.reference_output and llm_app_result.answer: result = evaluate_property_with_llm( model=PROPERTY_LLM, @@ -71,7 +77,9 @@ def factually_consistent(test_case: TestCase, llm_app_result: OutputFormat) -> P return result -def improves_historical_answer(test_case: TestCase, llm_app_result: OutputFormat) -> PropertyResult: +def improves_historical_answer( + test_case: TestCase, llm_app_result: OutputFormat +) -> Optional[PropertyResult]: if test_case.test_input and test_case.historical_output and llm_app_result.answer: result = evaluate_property_with_llm( model=PROPERTY_LLM, @@ -85,7 +93,7 @@ def improves_historical_answer(test_case: TestCase, llm_app_result: OutputFormat def takes_feedback_into_account( test_case: TestCase, llm_app_result: OutputFormat -) -> PropertyResult: +) -> Optional[PropertyResult]: if ( test_case.test_input and test_case.historical_output @@ -102,12 +110,14 @@ def takes_feedback_into_account( return result -def length_within_bounds(test_case: TestCase, llm_app_result: OutputFormat) -> PropertyResult: +def length_within_bounds( + test_case: TestCase, llm_app_result: OutputFormat +) -> Optional[PropertyResult]: if test_case.reference_output and llm_app_result.answer: if len(llm_app_result.answer) <= 1.2 * len(test_case.reference_output.answer): - result = PropertyResult(feedback="The answer is not too long.", pass_fail=True) + result = PropertyResult(feedback="The answer is not too long.", score=1) else: - result = PropertyResult(feedback="The answer is too long.", pass_fail=False) + result = PropertyResult(feedback="The answer is too long.", score=0) else: result = None return result diff --git a/src/llm_app_eval/evaluator.py b/src/llm_app_eval/evaluator.py index c26b981..6141cd3 100644 --- a/src/llm_app_eval/evaluator.py +++ b/src/llm_app_eval/evaluator.py @@ -6,10 +6,11 @@ import mlflow import pandas as pd -from llm_app import BaseApp, InputFormat, OutputFormat from pydantic import BaseModel from tqdm import tqdm +from llm_app_eval.llm_app import BaseApp, InputFormat, OutputFormat + class TestCase(BaseModel): test_id: str