Update README.md

README.md

@@ -13,4 +13,13 @@ tags:
 # How does RAG works?
     1. Ready/ Preprocess your input data i.e. tokenization & vectorization
     2. Feed the processed data to the Language Model.
-    3. Indexing the stored data that matches the context of the query.
+    3. Indexing the stored data that matches the context of the query.
+# Implementing RAG with llama-index
+### 1. Load relevant data and build an index
+    from llama_index import VectorStoreIndex, SimpleDirectoryReader
+    documents = SimpleDirectoryReader("data").load_data()
+    index = VectorStoreIndex.from_documents(documents)
+### 2. Query your data
+    query_engine = index.as_query_engine()
+    response = query_engine.query("What did the author do growing up?")
+    print(response)