docs: Move tutorials out of experimental (#2235)

Author: rhlbhatnagar

docs/experimental/tutorials/agent.md renamed to docs/tutorials/agent.md

@@ -60,18 +60,18 @@ from ragas import experiment
 
 @experiment()
 async def run_experiment(row):
-    question = row["question"]
-    expected_answer = row["answer"]
+    expression = row["expression"]
+    expected_result = row["expected"]
 
     # Get the model's prediction
-    prediction = math_agent.solve(question)
+    prediction = math_agent.solve(expression)
 
     # Calculate the correctness metric
-    correctness = correctness_metric.score(prediction=prediction.get("result"), actual=expected_answer)
+    correctness = correctness_metric.score(prediction=prediction.get("result"), actual=expected_result)
 
     return {
-        "question": question,
-        "expected_answer": expected_answer,
+        "expression": expression,
+        "expected_result": expected_result,
         "prediction": prediction.get("result"),
         "log_file": prediction.get("log_file"),
         "correctness": correctness.value

docs/experimental/tutorials/index.md renamed to docs/tutorials/index.md

@@ -5,7 +5,7 @@
 1. Install ragas_examples
 
 ```bash
-pip install ragas_experimental[examples]
+pip install ragas[examples]
 ```
 2. Setup your OpenAI API key
 

docs/experimental/tutorials/prompt.md renamed to docs/tutorials/prompt.md

@@ -14,7 +14,7 @@ We will start by testing a simple prompt that classifies movie reviews as positi
 First, make sure you have installed ragas examples and setup your OpenAI API key:
 
 ```bash
-pip install ragas_experimental[examples]
+pip install ragas[examples]
 export OPENAI_API_KEY = "your_openai_api_key"
 ```
 
@@ -69,7 +69,7 @@ async def run_experiment(row):
     experiment_view = {
         **row,
         "response":response,
-        "score":score.result,
+        "score":score.value,
     }
     return experiment_view
 ```
@@ -92,7 +92,7 @@ async def run_experiment(row, model):
     experiment_view = {
         **row,
         "response": response,
-        "score": score.result,
+        "score": score.value,
     }
     return experiment_view
 

docs/experimental/tutorials/rag.md renamed to docs/tutorials/rag.md

@@ -28,7 +28,7 @@ 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_experimental"},
+    {"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)
@@ -50,7 +50,7 @@ Next, we will write the experiment loop that will run our RAG system on the test
 ```python
 @experiment()
 async def run_experiment(row):
-    response = rag_client.query(row["question"])
+    response = rag_client.query(row["query"])
 
     score = my_metric.score(
         llm=llm,