feat: move experiment from experimental to ragas main (#2175)

Done
---
- [x] This is a cascading PR i.e. you're expected to review and merge
#2174 before looking at
this.

Changes
---
1. Moved and renamed the experiment files, relevant imports in examples
and docs
2. Expanded ragas current utils.py implementation to support behaviour
from experimental utils
3. Added some tests which I deemed useful

Author: NirantK

.github/workflows/ci.yaml

@@ -118,7 +118,7 @@ jobs:
             # Use pytest-xdist to improve test run-time on Linux/macOS
             OPTS=(--dist loadfile -n auto)
           fi
-          
+
           # Run different test suites based on test type
           if [ "${{ matrix.test-type }}" = "full" ]; then
             # Full test suite with notebook tests

CLAUDE.md

@@ -161,7 +161,8 @@ The experimental features are now integrated into the main ragas package:
 
 To use experimental features:
 ```python
-from ragas.experimental import Dataset, experiment
+from ragas.experimental import Dataset
+from ragas import experiment
 from ragas.backends import get_registry
 ```
 

docs/experimental/core_concepts/experimentation.md

@@ -39,7 +39,7 @@ Running an experiment involves:
 The `@experiment` decorator in Ragas simplifies the orchestration, scaling, and storage of experiments. Here's an example:
 
 ```python
-from ragas_experimental import experiment
+from ragas import experiment
 
 # Define your metric and dataset
 my_metric = ...
@@ -115,7 +115,8 @@ Here's a complete example showing how to pass different LLM models to your exper
 
 ```python
 from pydantic import BaseModel
-from ragas.experimental import experiment, Dataset
+from ragas.experimental import Dataset
+from ragas import experiment
 
 class ExperimentResult(BaseModel):
     query: str

docs/experimental/index.md

@@ -39,7 +39,8 @@ pip install ragas-experimental && pip install "ragas-experimental[local]"
 
 ```python
 import numpy as np
-from ragas_experimental import experiment, Dataset
+from ragas_experimental import Dataset
+from ragas import experiment
 from ragas_experimental.metrics import MetricResult, discrete_metric  
 
 # Define a custom metric for accuracy

docs/experimental/tutorials/agent.md

@@ -56,7 +56,7 @@ def correctness_metric(prediction: float, actual: float):
 Next, we will write the experiment loop that will run our agent on the test dataset and evaluate it using the metric, and store the results in a CSV file.
 
 ```python
-from ragas_experimental import experiment
+from ragas import experiment
 
 @experiment()
 async def run_experiment(row):

docs/experimental/tutorials/prompt.md

@@ -55,7 +55,7 @@ def my_metric(prediction: str, actual: str):
 Next, we will write the experiment loop that will run our prompt on the test dataset and evaluate it using the metric, and store the results in a csv file. 
 
 ```python
-from ragas_experimental import experiment
+from ragas import experiment
 
 @experiment()
 async def run_experiment(row):

docs/experimental/tutorials/workflow.md

@@ -49,7 +49,7 @@ my_metric = DiscreteMetric(
 Next, we will write the evaluation experiment loop that will run our workflow on the test dataset and evaluate it using the metric, and store the results in a CSV file.
 
 ```python
-from ragas_experimental import experiment
+from ragas import experiment
 
 @experiment()
 async def run_experiment(row):

examples/agent_evals/evals.py

@@ -1,4 +1,5 @@
-from ragas.experimental import Dataset, experiment
+from ragas.experimental import Dataset
+from ragas import experiment
 from ragas.experimental.metrics.numeric import numeric_metric
 from ragas.experimental.metrics.result import MetricResult
 from agent import get_default_agent

examples/prompt_evals/evals.py

@@ -1,4 +1,5 @@
-from ragas.experimental import Dataset, experiment
+from ragas.experimental import Dataset
+from ragas import experiment
 from ragas.experimental.metrics.result import MetricResult
 from ragas.experimental.metrics.discrete import discrete_metric
 

examples/rag_eval/evals.py

@@ -1,4 +1,5 @@
-from ragas.experimental import Dataset, experiment
+from ragas.experimental import Dataset
+from ragas import experiment
 from ragas.experimental.metrics import DiscreteMetric
 from openai import OpenAI
 from ragas.experimental.llms import llm_factory