chore: import upstream snapshot with attribution

docs/content/tutorials/tutorial-setup.mdx

@@ -0,0 +1,129 @@
+---
+id: tutorial-setup
+title: Set Up DeepEval
+sidebar_label: Set Up DeepEval
+---
+
+import { ASSETS } from "@site/src/assets";
+
+## Installing DeepEval
+
+**DeepEval** is a powerful LLM evaluation framework. Here's how you can easily get started by installing and running your first evaluation using DeepEval.
+
+Start by installing DeepEval using pip:
+
+```bash
+pip install -U deepeval
+```
+
+### Write your first test
+
+Let's evaluate the correctness of an LLM output using [`GEval`](https://deepeval.com/docs/metrics-llm-evals), a powerful metric based on LLM-as-a-judge evaluation.
+
+:::note
+When you pass a file explicitly (e.g. `deepeval test run evals/my_eval.py`), DeepEval runs it regardless of its name. The `test_` prefix (like `test_app.py`) is only needed for pytest's automatic discovery when you point it at a directory.
+:::
+
+```python title="test_app.py"
+from deepeval import evaluate
+from deepeval.test_case import LLMTestCase, SingleTurnParams
+from deepeval.metrics import GEval
+
+correctness_metric = GEval(
+    name="Correctness",
+    criteria="Determine if the 'actual output' is correct based on the 'expected output'.",
+    evaluation_params=[SingleTurnParams.ACTUAL_OUTPUT, SingleTurnParams.EXPECTED_OUTPUT],
+    threshold=0.5
+)
+
+test_case = LLMTestCase(
+    input="I have a persistent cough and fever. Should I be worried?",
+    # Replace this with the actual output from your LLM application
+    actual_output="A persistent cough and fever could signal various illnesses, from minor infections to more serious conditions like pneumonia or COVID-19. It's advisable to seek medical attention if symptoms worsen, persist beyond a few days, or if you experience difficulty breathing, chest pain, or other concerning signs.",
+    expected_output="A persistent cough and fever could indicate a range of illnesses, from a mild viral infection to more serious conditions like pneumonia or COVID-19. You should seek medical attention if your symptoms worsen, persist for more than a few days, or are accompanied by difficulty breathing, chest pain, or other concerning signs."
+)
+
+evaluate([test_case], [correctness_metric])
+```
+
+To run your first evaluation, enter the following command in your terminal:
+
+```bash
+deepeval test run test_app.py
+```
+
+:::note
+DeepEval's powerful **LLM-as-a-judge** metrics (like `GEval` used in this example) rely on an underlying LLM called the _Evaluation Model_ to perform evaluations. By default, DeepEval uses OpenAI's models for this purpose.
+
+So you'll have to set your `OPENAI_API_KEY` as an environment variable as shown below.
+
+```bash
+export OPENAI_API_KEY="your_api_key"
+
+```
+
+To use ANY custom LLM of your choice, [Check out our docs on custom evaluation models](https://deepeval.com/guides/guides-using-custom-llms).
+:::
+
+Congratulations! You've successfully run your first LLM evaluation with DeepEval.
+
+## Setting Up Confident AI
+
+While DeepEval works great standalone, you can connect it to [Confident AI](https://www.confident-ai.com) — an AI quality platform with observability, evals, and monitoring that DeepEval integrates with natively for dashboards, logging, collaboration, and more. **It’s free to get started.**
+
+You can [sign up here](https://www.confident-ai.com), or run:
+
+```bash
+deepeval login
+```
+
+Navigate to your Settings page and copy your Confident AI API Key from the Project API Key box. If you used the `deepeval login` command to log in, you'll be prompted to paste your Confident AI API Key after creating an account.
+
+<div
+  style={{
+    display: "flex",
+    alignItems: "center",
+    justifyContent: "center",
+  }}
+>
+  <ImageDisplayer src={ASSETS.tutorialSetup01} />
+</div>
+
+Alternatively, if you already have an account, you can log in directly using Python:
+
+```python title="main.py"
+deepeval.login("your-confident-api-key")
+```
+
+Or through the CLI:
+
+```bash
+deepeval login --confident-api-key "your-confident-api-key"
+```
+
+:::note[Login persistence]
+`deepeval login` persists your key to a dotenv file by default (.env.local).
+To change the target, use `--save`, e.g.:
+
+```bash
+# custom path
+deepeval login --confident-api-key "ck_..." --save dotenv:.env.custom
+```
+
+For compatibility, the key is saved under `api_key` and `CONFIDENT_API_KEY`.
+Secrets are never written to the JSON keystore.
+:::
+
+:::tip[Logging out / rotating keys]
+Use deepeval logout to clear the JSON keystore and remove saved keys from your dotenv file:
+
+```bash
+# default removes from .env.local
+deepeval logout
+
+# or specify a custom target
+deepeval logout --save dotenv:.myconf.env
+```
+:::
+
+You're all set! You can now evaluate LLMs locally and monitor them in Confident AI.