docs: add CTA for Ragas app (#2023)

Author: shahules786

README.md

@@ -85,15 +85,21 @@ await metric.single_turn_ascore(SingleTurnSample(**test_data))
 
 Find the complete [Quickstart Guide](https://docs.ragas.io/en/latest/getstarted/evals)
 
-### Analyze your Evaluation
+## Want help in improving your AI application using evals?
 
-Sign up for [app.ragas.io](https://app.ragas.io) to review, share and analyze your evaluations</a>
+In the past 2 years, we have seen and helped improve many AI applications using evals. 
+
+We are compressing this knowledge into a product to replace vibe checks with eval loops so that you can focus on building great AI applications. 
+
+If you want help with improving and scaling up your AI application using evals.
+
+
+🔗 Book a [slot](https://bit.ly/3EBYq4J) or drop us a line: [[email protected]](mailto:[email protected]).
+
+
+![](/docs/_static/ragas_app.gif)
 
-<p align="left">
-    <img src="docs/getstarted/ragas_get_started_evals.gif" height="300">
-</p>
 
-See [how to use it](https://docs.ragas.io/en/latest/getstarted/evals/#analyzing-results)
 
 ## 🫂 Community
 
@@ -132,3 +138,13 @@ At Ragas, we believe in transparency. We collect minimal, anonymized usage data
 ✅ Publicly available aggregated [data](https://github.com/explodinggradients/ragas/issues/49)
 
 To opt-out, set the `RAGAS_DO_NOT_TRACK` environment variable to `true`.
+
+### Cite Us
+```
+@misc{ragas2024,
+  author       = {ExplodingGradients},
+  title        = {Ragas: Supercharge Your LLM Application Evaluations},
+  year         = {2024},
+  howpublished = {\url{https://github.com/explodinggradients/ragas}},
+}
+```

docs/_static/ragas_app.gif

@@ -166,58 +166,23 @@ Output
 3	summarise given text\nSupply chain challenges ...	Supply chain challenges in North America, caus...	1
 ```
 
-Viewing the sample-level results in a CSV file, as shown above, is fine for quick checks but not ideal for detailed analysis or comparing results across evaluation runs. For a better experience, use [app.ragas.io](https://app.ragas.io/) to view, analyze, and compare evaluation results interactively.
+Viewing the sample-level results in a CSV file, as shown above, is fine for quick checks but not ideal for detailed analysis or comparing results across evaluation runs. 
 
+### Want help in improving your AI application using evals?
 
-## Analyzing Results
+In the past 2 years, we have seen and helped improve many AI applications using evals. 
 
-For this you may sign up and setup [app.ragas.io](https://app.ragas.io) easily. If not, you may use any alternative tools available to you. 
+We are compressing this knowledge into a product to replace vibe checks with eval loops so that you can focus on building great AI applications.
 
-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_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()
-```
-
-![](ragas_get_started_evals.gif)
+If you want help with improving and scaling up your AI application using evals.
 
 
+🔗 Book a [slot](https://bit.ly/3EBYq4J) or drop us a line: [[email protected]](mailto:[email protected]).
 
-## Aligning Metrics
 
-In the example above, we can see that the LLM-based metric mistakenly marks some summary as accurate, even though it missed critical details like growth numbers and market domain. Such mistakes can occur when the metric does not align with your specific evaluation preferences. For example, 
+![](../_static/ragas_app.gif)
 
-![](eval_mistake1.png)
-
-
-To fix these results, ragas provides a way to align the metric with your preferences, allowing it to learn like a machine learning model. Here's how you can do this in three simple steps:
-
-1. **Annotate**: Accept, reject, or edit evaluation results to create training data (at least 15-20 samples).
-2. **Download**: Save the annotated data using the `Annotated JSON` button in [app.ragas.io](https://app.ragas.io/).
-3. **Train**: Use the annotated data to train your custom metric.
-
-To learn more about this, refer to how to [train your own metric guide](./../howtos/customizations/metrics/train_your_own_metric.md)
-
-[Download sample annotated JSON](../_static/sample_annotated_summary.json)
-
-```python
-from ragas.config import InstructionConfig, DemonstrationConfig
-demo_config = DemonstrationConfig(embedding=evaluator_embeddings)
-inst_config = InstructionConfig(llm=evaluator_llm)
-
-metric.train(path="<your-annotated-json.json>", demonstration_config=demo_config, instruction_config=inst_config)
-```
 
-Once trained, you can re-evaluate the same or different test datasets. You should notice that the metric now aligns with your preferences and makes fewer mistakes, improving its accuracy.
 
 
 ## Up Next

docs/getstarted/evals.md

@@ -176,26 +176,19 @@ Output
 {'context_recall': 1.0000, 'faithfulness': 0.8571, 'factual_correctness': 0.7280}
 ```
 
-## Analyze Results
+### Want help in improving your AI application using evals?
 
-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 the past 2 years, we have seen and helped improve many AI applications using evals. 
 
-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).
+We are compressing this knowledge into a product to replace vibe checks with eval loops so that you can focus on building great AI applications.
 
-Once you have the API key, you can use the `upload()` method to export the results to the dashboard.
+If you want help with improving and scaling up your AI application using evals.
 
-```python
-import os
-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.
+🔗 Book a [slot](https://bit.ly/3EBYq4J) or drop us a line: [[email protected]](mailto:[email protected]).
 
-```python
-result.upload()
-```
+![](../_static/ragas_app.gif)
 
-![](rag_eval.gif)
 
 ## Up Next
 

docs/getstarted/rag_eval.md

@@ -58,21 +58,8 @@ dataset.to_pandas()
 Output
 ![testset](./testset_output.png)
 
-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 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_APP_TOKEN"] = "your_app_token"
-dataset.upload()
-```
-
-Now you can view the results in the dashboard by following the link in the output of the `upload()` method.
-
-![Visualization with Ragas Dashboard](./testset_output_dashboard.png)
+!!! note
+Generating synthetic test data can be confusing and hard, but if you need we are happy to help you with it. We have built pipelines to generate test data for various use cases. If you need help with it, please talk to us by booking a [slot](https://bit.ly/3EBYq4J) or drop us a line: [[email protected]](mailto:[email protected]).
 
 ## A Deeper Look