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docs/tutorials/rag.md

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+# Evaluate a simple RAG system
+
+In this tutorial, we will write a simple evaluation pipeline to evaluate a RAG (Retrieval-Augmented Generation) system. At the end of this tutorial, you’ll learn how to evaluate and iterate on a RAG system using evaluation-driven development.
+
+```mermaid
+flowchart LR
+    A["Query<br/>'What is Ragas 0.3?'"] --> B[Retrieval System]
+    
+    C[Document Corpus<br/> Ragas 0.3 Docs📄] --> B
+    
+    B --> D[LLM + Prompt]
+    A --> D
+    
+    D --> E[Final Answer]
+```
+
+We will start by writing a simple RAG system that retrieves relevant documents from a corpus and generates an answer using an LLM.
+
+```bash
+python -m ragas_examples.rag_eval.rag
+```
+
+
+Next, we will write down a few sample queries and expected outputs for our RAG system. Then convert them to a CSV file.
+
+```python
+import pandas as pd
+
+samples = [
+    {"query": "What is Ragas 0.3?", "grading_notes": "- Ragas 0.3 is a library for evaluating LLM applications."},
+    {"query": "How to install Ragas?", "grading_notes": "- install from source  - install from pip using ragas[examples]"},
+    {"query": "What are the main features of Ragas?", "grading_notes": "organised around - experiments - datasets - metrics."}
+]
+pd.DataFrame(samples).to_csv("datasets/test_dataset.csv", index=False)
+```
+
+To evaluate the performance of our RAG system, we will define a llm based metric that compares the output of our RAG system with the grading notes and outputs pass/fail based on it.
+
+```python
+from ragas.metrics import DiscreteMetric
+my_metric = DiscreteMetric(
+    name="correctness",
+    prompt = "Check if the response contains points mentioned from the grading notes and return 'pass' or 'fail'.\nResponse: {response} Grading Notes: {grading_notes}",
+    allowed_values=["pass", "fail"],
+)
+```
+
+Next, we will write the experiment loop that will run our RAG system on the test dataset and evaluate it using the metric, and store the results in a CSV file.
+
+```python
+@experiment()
+async def run_experiment(row):
+    response = rag_client.query(row["query"])
+    
+    score = my_metric.score(
+        llm=llm,
+        response=response.get("answer", " "),
+        grading_notes=row["grading_notes"]
+    )
+
+    experiment_view = {
+        **row,
+        "response": response.get("answer", ""),
+        "score": score.value,
+        "log_file": response.get("logs", " "),
+    }
+    return experiment_view
+```
+
+Now whenever you make a change to your RAG pipeline, you can run the experiment and see how it affects the performance of your RAG. 
+
+## Running the example end to end
+
+1. Setup your OpenAI API key
+```bash
+export OPENAI_API_KEY="your_openai_api_key"
+```
+2. Run the evaluation
+```bash
+python -m ragas_examples.rag_eval.evals
+```
+
+Voila! You have successfully run your first evaluation using Ragas. You can now inspect the results by opening the `experiments/experiment_name.csv` file.