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evals/evaluation/deepeval/README.md

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+DeepEval is a simple-to-use, open-source LLM evaluation framework, for evaluating 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, hallucination, answer relevancy, RAGAS, etc., which uses LLMs and various other NLP models that runs locally on your machine for evaluation.
+
+We customize models to support more local LLMs services for the evaluation of metrics such as , hallucination, answer relevancy, etc.
+
+# 🚀 QuickStart
+
+
+## Installation
+
+```
+pip install ../../../requirements.txt
+```
+
+## Launch Service of LLM-as-a-Judge
+
+To setup a LLM model, we can use [tgi-gaudi](https://github.com/huggingface/tgi-gaudi) to launch a service. For example, the follow command is to setup the [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) model on 2 Gaudi2 cards:
+
+```
+# please set your llm_port and hf_token
+
+docker run -p {your_llm_port}:80 --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e PT_HPU_ENABLE_LAZY_COLLECTIVES=true -e OMPI_MCA_btl_vader_single_copy_mechanism=none -e HF_TOKEN={your_hf_token} -e PREFILL_BATCH_BUCKET_SIZE=1 -e BATCH_BUCKET_SIZE=8 --cap-add=sys_nice --ipc=host ghcr.io/huggingface/tgi-gaudi:2.0.5 --model-id mistralai/Mixtral-8x7B-Instruct-v0.1 --max-input-tokens 2048 --max-total-tokens 4096 --sharded true --num-shard 2 --max-batch-total-tokens 65536 --max-batch-prefill-tokens 2048
+```
+
+## Writing your first test case
+
+```python
+import pytest
+from deepeval import assert_test
+from deepeval.metrics import AnswerRelevancyMetric
+from deepeval.test_case import LLMTestCase
+
+def test_case():
+    from evals.evaluation.deepeval.models.endpoint_models import TGIEndpointModel
+    endpoint = TGIEndpointModel(model="http://localhost:{your_llm_port}/generate")
+    answer_relevancy_metric = AnswerRelevancyMetric(threshold=0.5, model=endpoint)
+    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."]
+    )
+    assert_test(test_case, [answer_relevancy_metric])
+```
+
+## Acknowledgements
+
+The evalution inherits from [deepeval](https://github.com/confident-ai/deepeval) repo. Thank for the founders of Confident AI.