Update README.md

README.md

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 ---
 license: apache-2.0
+language:
+- en
+tags:
+- RAG
+- Embeddings
+- Clustering
+- Graph
+- knowledge graph
+- GraphRAG
+size_categories:
+- 1M<n<10M
 ---
+
+
+# Dataset for Benchmarking RAG, GraphRAG and LLM Embeddings
+
+### Benchmarking LLM embedding is still based on comparing it with another LLM or QA datasets. 
+
+This dataset offers another method to test or compare the LLM embeddings.
+
+This all-against-all comparison of text data [Wikipedia Embeddings dataset](https://huggingface.co/datasets/Supabase/wikipedia-en-embeddings)
+
+As the original dataset contains embeddings from three different LLMs, this can be programmatically checked and manually verified. 
+
+RAG systems can be tested to verify if the setup works as per the requirements. Because this data contains both embeddings and actual clustered data, manual verification is also easier. 
+
+Graph-RAG now uses extraction of entities to make connections between documents. That removes the context from data. This dataset has actual lines/sentences/chunks compared in the dataset. This offers contextual links between documents where similar chunks can be connected based how much those chunks are similar. 
+
+## Method
+1. The actual data is fed to the Graph AI method after being split into sentences. These are linguistic sentence structures. 
+1. This makes each entry in the original dataset as documents. 
+1. The Graph AI compares the sentences method similar to all-against-all comparison. 
+1. Resulting clusters of Sentences, Documents are then normalized for generating document graphs. 
+
+## Uses
+* Can be used for benchmark RAG, GraphRAG and Embeddings. 
+* Can be used for Vector based Clustering methods.