Update README.md

README.md

@@ -41,6 +41,14 @@ embeddings.search("""
 """)
 ```
 
+## Use Cases
+
+An embeddings index generated by txtai is a fully encapsulated index format. It doesn't require a database server or dependencies outside of the Python install.
+
+The Wikipedia index works well as a fact-based context source for retrieval augmented generation (RAG). In other words, search results from this model can be passed to LLM prompts as the context in which to answer questions.
+
+See this [article](https://neuml.hashnode.dev/embeddings-in-the-cloud) for additional examples on how to use this model.
+
 # Source
 
 https://dumps.wikimedia.org/svwiki/20240220/dumpstatus.json
@@ -64,11 +72,3 @@ https://dumps.wikimedia.org/svwiki/20240220/svwiki-20240220-pages-articles-multi
 https://dumps.wikimedia.org/svwiki/20240220/svwiki-20240220-pages-articles-multistream6.xml-p6319737p7819736.bz2
 
 https://dumps.wikimedia.org/svwiki/20240220/svwiki-20240220-pages-articles-multistream6.xml-p7819737p8827284.bz2
-
-## Use Cases
-
-An embeddings index generated by txtai is a fully encapsulated index format. It doesn't require a database server or dependencies outside of the Python install.
-
-The Wikipedia index works well as a fact-based context source for retrieval augmented generation (RAG). In other words, search results from this model can be passed to LLM prompts as the context in which to answer questions.
-
-See this [article](https://neuml.hashnode.dev/embeddings-in-the-cloud) for additional examples on how to use this model.