Update README (#25)

* update readme

* update metrics info

* fix type checks

* add citations

* update readme

* add emojis

* update readme

README.md

@@ -32,8 +32,22 @@
     <p>
 </h4>
 
+ragas is a framework that helps you evaluate your Retrieval Augmented Generation (RAG) pipelines. RAG denotes a class of LLM applications that use external data to augment the LLM’s context. There are existing tools and frameworks that help you build these pipelines but evaluating it and quantifying your pipeline performance can be hard.. This is were ragas (RAG Assessment) comes in
 
-## Quickstart 
+ragas provides you with the tools based on the latest research for evaluating LLM generated text  to give you insights about your RAG pipeline. ragas can be integrated with your CI/CD to provide continuous check to ensure performance.
+
+## Installation 🛡
+
+```bash
+pip install ragas
+```
+if you want to install from source 
+```bash
+git clone https://github.com/explodinggradients/ragas && cd ragas
+pip install -e .
+```
+
+## Quickstart 🔥
 
 This is a small example program you can run to see ragas in action!
 ```python
@@ -61,14 +75,14 @@ results = e.eval(ds["ground_truth"], ds["generated_text"])
 print(results)
 ```
 If you want a more in-depth explanation of core components, check out our quick-start notebook
-## Metrics
+## 🧰 Metrics
 
-### Character based
+### ✏️ Character based 
 
 - **Levenshtein distance** the number of single character edits (additional, insertion, deletion) required to change your generated text to ground truth text.
 - **Levenshtein** **ratio** is obtained by dividing the Levenshtein distance by sum of number of characters in generated text and ground truth. This type of metrics is suitable where one works with short and precise texts.
 
-### N-Gram based
+### 🖊 N-Gram based
 
 N-gram based metrics as name indicates uses n-grams for comparing generated answer with ground truth. It is suitable to extractive and abstractive tasks but has its limitations in long free form answers due to the word based comparison.
 
@@ -80,7 +94,7 @@ N-gram based metrics as name indicates uses n-grams for comparing generated answ
 
 It measures precision by comparing  clipped n-grams in generated text to ground truth text. These matches do not consider the ordering of words.
 
-### Model Based
+### 🪄 Model Based
 
 Model based methods uses language models combined with NLP techniques to compare generated text with ground truth.  It is well suited for free form long or short answer types. 
 
@@ -98,5 +112,5 @@ Model based methods uses language models combined with NLP techniques to compare
 
     Best used to measure factual consistencies between ground truth and generated text. Scores can range from 0 to 1. Higher score indicates better factual consistency between ground truth and generated answer. Employs QA-QG paradigm followed by NLI to compare ground truth and generated answer. Q2Score score is highly correlated with human judgement.
 
-Checkout [citations](./citations.md) for related publications.
+📜 Checkout [citations](./citations.md) for related publications.