docs: clean get-started

docs/getstarted/evals.md

@@ -171,13 +171,13 @@ Viewing the sample-level results in a CSV file, as shown above, is fine for quic
 
 For this you may sign up and setup [app.ragas.io]() easily. If not, you may use any alternative tools available to you. 
 
-In order to use the [app.ragas.io](http://app.ragas.io) dashboard, you need to have an account on [app.ragas.io](https://app.ragas.io/). If you don't have one, you can sign up for one [here](https://app.ragas.io/login). You will also need to generate a [Ragas API key](https://app.ragas.io/dashboard/settings/app-tokens).
+In order to use the [app.ragas.io](http://app.ragas.io) dashboard, you need to have an account on [app.ragas.io](https://app.ragas.io/). If you don't have one, you can sign up for one [here](https://app.ragas.io/login). You will also need to generate a [Ragas APP token](https://app.ragas.io/dashboard/settings/app-tokens).
 
 Once you have the API key, you can use the `upload()` method to export the results to the dashboard.
 
 ```python
 import os
-os.environ["RAGAS_API_KEY"] = "your_api_key"
+os.environ["RAGAS_APP_TOKEN"] = "your_app_token"
 ```
 
 Now you can view the results in the dashboard by following the link in the output of the `upload()` method.

docs/getstarted/index.md

@@ -12,5 +12,5 @@ If you have any questions about Ragas, feel free to join and ask in the `#questi
 Let's get started!
 
 - [Evaluate your first AI app](./evals.md)
-- [Run ragas metrics for evaluating RAG](rag_evaluation.md)
+- [Run ragas metrics for evaluating RAG](rag_eval.md)
 - [Generate test data for evaluating RAG](rag_testset_generation.md)

docs/getstarted/rag_eval.md

@@ -165,7 +165,7 @@ from ragas.llms import LangchainLLMWrapper
 
 
 evaluator_llm = LangchainLLMWrapper(llm)
-from ragas.metrics import LLMContextRecall, LLMContextPrecisionWithReference, Faithfulness, FactualCorrectness
+from ragas.metrics import LLMContextRecall, Faithfulness, FactualCorrectness
 
 result = evaluate(dataset=evaluation_dataset,metrics=[LLMContextRecall(), Faithfulness(), FactualCorrectness()],llm=evaluator_llm)
 result
@@ -180,19 +180,19 @@ Output
 
 Once you have evaluated, you may want to view, analyse and share results. This is important to interpret the results and understand the performance of your RAG system. For this you may sign up and setup [app.ragas.io]() easily. If not, you may use any alternative tools available to you. 
 
-In order to use the [app.ragas.io](http://app.ragas.io) dashboard, you need to have an account on [app.ragas.io](https://app.ragas.io/). If you don't have one, you can sign up for one [here](https://app.ragas.io/login). You will also need to generate a [Ragas API key](https://app.ragas.io/dashboard/settings/app-tokens).
+In order to use the [app.ragas.io](http://app.ragas.io) dashboard, you need to have an account on [app.ragas.io](https://app.ragas.io/). If you don't have one, you can sign up for one [here](https://app.ragas.io/login). You will also need to generate a [Ragas APP token](https://app.ragas.io/dashboard/settings/app-tokens).
 
 Once you have the API key, you can use the `upload()` method to export the results to the dashboard.
 
 ```python
 import os
-os.environ["RAGAS_API_KEY"] = "your_api_key"
+os.environ["RAGAS_APP_TOKEN"] = "your_app_token"
 ```
 
 Now you can view the results in the dashboard by following the link in the output of the `upload()` method.
 
 ```python
-results.upload()
+result.upload()
 ```
 
 ![](rag_eval.gif)

docs/getstarted/rag_testset_generation.md

@@ -59,13 +59,13 @@ dataset.to_pandas()
 
 You can also use other tools like [app.ragas.io](https://app.ragas.io/) or any other similar tools available for you in the [Integrations](../howtos/integrations/index.md) section.
 
-In order to use the [app.ragas.io](https://app.ragas.io/) dashboard, you need to have an account on [app.ragas.io](https://app.ragas.io/). If you don't have one, you can sign up for one [here](https://app.ragas.io/login). You will also need to have a [Ragas API key](https://app.ragas.io/settings/api-keys).
+In order to use the [app.ragas.io](https://app.ragas.io/) dashboard, you need to have an account on [app.ragas.io](https://app.ragas.io/). If you don't have one, you can sign up for one [here](https://app.ragas.io/login). You will also need to have a [Ragas APP token](https://app.ragas.io/settings/api-keys).
 
 Once you have the API key, you can use the `upload()` method to export the results to the dashboard.
 
 ```python
 import os
-os.environ["RAGAS_API_KEY"] = "your_api_key"
+os.environ["RAGAS_APP_TOKEN"] = "your_app_token"
 dataset.upload()
 ```