fixes

Author: lgayhardt

articles/machine-learning/prompt-flow/tools-reference/index-lookup-tool.md

@@ -173,20 +173,20 @@ If you have a flow that contains one of these tools, follow the steps below to u
    :::image type="content" source="./media/index-lookup-tool/mlindex-box.png" alt-text="Screenshot of the expanded Index Lookup node with the mlindex_content box outlined in red." lightbox="./media/index-lookup-tool/mlindex-box.png":::
 
 1. In the Generate drawer that appears, follow the instructions below to upgrade from the three legacy tools:
-- If using the legacy **Vector Index Lookup** tool, select “Registered Index" in the “index_type” dropdown. Select your vector index asset from the “mlindex_asset_id” dropdown.
-- If using the legacy **Faiss Index Lookup** tool, select “Faiss” in the “index_type” dropdown and specify the same path as in the legacy tool.
-- If using the legacy **Vector DB Lookup** tool, select AI Search or Pinecone depending on the DB type in the “index_type” dropdown and fill in the information as necessary.
-8. After filling in the necessary information, select save. 
-9. Upon returning to the node, there should be information populated in the “mlindex_content” textbox. Select the “queries” textbox next, and select the search terms you want to query. You’ll want to select the same value as the input to the “embed_the_question” node, typically either “\${inputs.question}” or “${modify_query_with_history.output}” (the former if you’re in a standard flow and the latter if you’re in a chat flow).
+    - If using the legacy **Vector Index Lookup** tool, select “Registered Index" in the “index_type” dropdown. Select your vector index asset from the “mlindex_asset_id” dropdown.
+    - If using the legacy **Faiss Index Lookup** tool, select “Faiss” in the “index_type” dropdown and specify the same path as in the legacy tool.
+    - If using the legacy **Vector DB Lookup** tool, select AI Search or Pinecone depending on the DB type in the “index_type” dropdown and fill in the information as necessary.
+1. After filling in the necessary information, select save. 
+1. Upon returning to the node, there should be information populated in the “mlindex_content” textbox. Select the “queries” textbox next, and select the search terms you want to query. You’ll want to select the same value as the input to the “embed_the_question” node, typically either “\${inputs.question}” or “${modify_query_with_history.output}” (the former if you’re in a standard flow and the latter if you’re in a chat flow).
 
    :::image type="content" source="./media/index-lookup-tool/mlindex-with-content.png" alt-text="Screenshot of the expanded Index Lookup node with index information in the cells." lightbox="./media/index-lookup-tool/mlindex-with-content.png":::
 
-10. Select a query type by clicking on the dropdown next to “query_type.” “Vector” will produce identical results as the legacy flow, but depending on your index configuration, other options including "Hybrid" and "Semantic" might be available.
+1. Select a query type by clicking on the dropdown next to “query_type.” “Vector” will produce identical results as the legacy flow, but depending on your index configuration, other options including "Hybrid" and "Semantic" might be available.
 
     :::image type="content" source="./media/index-lookup-tool/vector-search.png" alt-text="Screenshot of the expanded Index Lookup node with vector search outlined in red." lightbox="./media/index-lookup-tool/vector-search.png":::
 
-11. Edit downstream components to consume the output of your newly added node, instead of the output of the legacy Vector Index Lookup node.
-12. Delete the Vector Index Lookup node and its parent embedding node.
+1. Edit downstream components to consume the output of your newly added node, instead of the output of the legacy Vector Index Lookup node.
+1. Delete the Vector Index Lookup node and its parent embedding node.
 
 ## Next steps