fixing more errors

Author: georgewallace

docs/reference/data-analysis/aggregations/search-aggregations-bucket-reverse-nested-aggregation.md

@@ -74,13 +74,12 @@ GET /issues/_search
   }
 }
 ```
-%  TEST[continued]
-%  TEST[s/_search/_search?filter_path=aggregations/]
 
 As you can see above, the `reverse_nested` aggregation is put in to a `nested` aggregation as this is the only place in the dsl where the `reverse_nested` aggregation can be used. Its sole purpose is to join back to a parent doc higher up in the nested structure.
 
 1. A `reverse_nested` aggregation that joins back to the root / main document level, because no `path` has been defined. Via the `path` option the `reverse_nested` aggregation can join back to a different level, if multiple layered nested object types have been defined in the mapping
-
+%  TEST[continued]
+%  TEST[s/_search/_search?filter_path=aggregations/]
 
 Possible response snippet:
 

docs/reference/elasticsearch/rest-apis/reciprocal-rank-fusion.md

@@ -89,14 +89,13 @@ GET example-index/_search
     }
 }
 ```
-%  TEST[skip:example fragment]
 
 In the above example, we execute the `knn` and `standard` retrievers independently of each other. Then we use the `rrf` retriever to combine the results.
 
 1. First, we execute the kNN search specified by the `knn` retriever to get its global top 50 results.
 2. Second, we execute the query specified by the `standard` retriever to get its global top 50 results.
 3. Then, on a coordinating node, we combine the kNN search top documents with the query top documents and rank them based on the RRF formula using parameters from the `rrf` retriever to get the combined top documents using the default `size` of `10`.
-
+%  TEST[skip:example fragment]
 
 Note that if `k` from a knn search is larger than `rank_window_size`, the results are truncated to `rank_window_size`. If `k` is smaller than `rank_window_size`, the results are `k` size.
 
@@ -165,14 +164,13 @@ GET example-index/_search
     }
 }
 ```
-%  TEST[skip:example fragment]
 
 In the above example, we execute each of the two `standard` retrievers independently of each other. Then we use the `rrf` retriever to combine the results.
 
 1. First we run the `standard` retriever specifying a term query for `blue shoes sales` using the standard BM25 scoring algorithm.
 2. Next we run the `standard` retriever specifying a sparse_vector query for `What blue shoes are on sale?` using our [ELSER](docs-content://solutions/search/semantic-search/semantic-search-elser-ingest-pipelines.md) scoring algorithm.
 3. The `rrf` retriever allows us to combine the two top documents sets generated by completely independent scoring algorithms with equal weighting.
-
+%  TEST[skip:example fragment]
 
 Not only does this remove the need to figure out what the appropriate weighting is using linear combination, but RRF is also shown to give improved relevance over either query individually.