diff --git a/content/develop/interact/search-and-query/advanced-concepts/geo.md b/content/develop/interact/search-and-query/advanced-concepts/geo.md
index 5f28a13d2..96e8fe5f8 100644
--- a/content/develop/interact/search-and-query/advanced-concepts/geo.md
+++ b/content/develop/interact/search-and-query/advanced-concepts/geo.md
@@ -166,7 +166,7 @@ example, the query below returns one primitive that lies within the boundary
 of the green square (from the example above) but omits the square itself:
 
 ```bash
-> FT.SEARCH geomidx "(-@name:(Green Square) @geom:[WITHIN $qshape])" PARAMS 2 qshape "POLYGON ((1 1, 1 3, 3 3, 3 1, 1 1))" RETURN 1 name DIALECT 4
+> FT.SEARCH geomidx "(-@name:(Green Square) @geom:[WITHIN $qshape])" PARAMS 2 qshape "POLYGON ((1 1, 1 3, 3 3, 3 1, 1 1))" RETURN 1 name DIALECT 2
 
 1) (integer) 1
 2) "shape:4"
diff --git a/content/develop/interact/search-and-query/advanced-concepts/vectors.md b/content/develop/interact/search-and-query/advanced-concepts/vectors.md
index b55c9ca4f..50352f078 100644
--- a/content/develop/interact/search-and-query/advanced-concepts/vectors.md
+++ b/content/develop/interact/search-and-query/advanced-concepts/vectors.md
@@ -223,7 +223,7 @@ FT.SEARCH <index_name>
   <primary_filter_query>=>[KNN <top_k> @<vector_field> $<vector_blob_param> $<vector_query_params> AS <distance_field>]
   PARAMS <query_params_count> [$<vector_blob_param> <vector_blob> <query_param_name> <query_param_value> ...]
   SORTBY <distance_field>
-  DIALECT 4
+  DIALECT 2
 ```
 **Parameters**
 
@@ -243,7 +243,7 @@ FT.SEARCH <index_name>
 **Example**
 
 ```
-FT.SEARCH documents "*=>[KNN 10 @doc_embedding $BLOB]" PARAMS 2 BLOB "\x12\xa9\xf5\x6c" DIALECT 4
+FT.SEARCH documents "*=>[KNN 10 @doc_embedding $BLOB]" PARAMS 2 BLOB "\x12\xa9\xf5\x6c" DIALECT 2
 ```
 
 **Use query attributes**
@@ -272,7 +272,7 @@ FT.SEARCH <index_name>
   @<vector_field>:[VECTOR_RANGE (<radius> | $<radius_param>) $<vector_blob_param> $<vector_query_params>]
   PARAMS <vector_query_params_count> [<vector_query_param_name> <vector_query_param_value> ...]
   SORTBY <distance_field>
-  DIALECT 4
+  DIALECT 2
 ```
 
 | Parameter         | Description                                                                                       |
@@ -324,7 +324,7 @@ where `<primary_filter_query>` defines document selection and filtering.
 **Example**
 
 ```
-FT.SEARCH documents "(@title:Sports @year:[2020 2022])=>[KNN 10 @doc_embedding $BLOB]" PARAMS 2 BLOB "\x12\xa9\xf5\x6c" DIALECT 4
+FT.SEARCH documents "(@title:Sports @year:[2020 2022])=>[KNN 10 @doc_embedding $BLOB]" PARAMS 2 BLOB "\x12\xa9\xf5\x6c" DIALECT 2
 ```
 
 ### How filtering works
@@ -399,25 +399,25 @@ Below are a number of examples to help you get started. For more comprehensive w
 Return the 10 nearest neighbor documents for which the `doc_embedding` vector field is the closest to the query vector represented by the following 4-byte blob:
 
 ```
-FT.SEARCH documents "*=>[KNN 10 @doc_embedding $BLOB]" PARAMS 2 BLOB "\x12\xa9\xf5\x6c" SORTBY __vector_score DIALECT 4
+FT.SEARCH documents "*=>[KNN 10 @doc_embedding $BLOB]" PARAMS 2 BLOB "\x12\xa9\xf5\x6c" SORTBY __vector_score DIALECT 2
 ```
 
 Return the top 10 nearest neighbors and customize the `K` and `EF_RUNTIME` parameters using query parameters. See the "Optional arguments" section in [FT.SEARCH command]({{< baseurl >}}commands/ft.search). Set the `EF_RUNTIME` value to 150, assuming `doc_embedding` is an `HNSW` index:
 
 ```
-FT.SEARCH documents "*=>[KNN $K @doc_embedding $BLOB EF_RUNTIME $EF]" PARAMS 6 BLOB "\x12\xa9\xf5\x6c" K 10 EF 150 DIALECT 4
+FT.SEARCH documents "*=>[KNN $K @doc_embedding $BLOB EF_RUNTIME $EF]" PARAMS 6 BLOB "\x12\xa9\xf5\x6c" K 10 EF 150 DIALECT 2
 ```
 
 Assign a custom name to the distance field (`vector_distance`) and then sort using that name:
 
 ```
-FT.SEARCH documents "*=>[KNN 10 @doc_embedding $BLOB AS vector_distance]" PARAMS 2 BLOB "\x12\xa9\xf5\x6c" SORTBY vector_distance DIALECT 4
+FT.SEARCH documents "*=>[KNN 10 @doc_embedding $BLOB AS vector_distance]" PARAMS 2 BLOB "\x12\xa9\xf5\x6c" SORTBY vector_distance DIALECT 2
 ```
 
 Use [query attributes]({{< baseurl >}}develop/interact/search-and-query/advanced-concepts/query_syntax#query-attributes) syntax to specify optional parameters and the distance field name:
 
 ```
-FT.SEARCH documents "*=>[KNN 10 @doc_embedding $BLOB]=>{$EF_RUNTIME: $EF; $YIELD_DISTANCE_AS: vector_distance}" PARAMS 4 EF 150 BLOB "\x12\xa9\xf5\x6c" SORTBY vector_distance DIALECT 4
+FT.SEARCH documents "*=>[KNN 10 @doc_embedding $BLOB]=>{$EF_RUNTIME: $EF; $YIELD_DISTANCE_AS: vector_distance}" PARAMS 4 EF 150 BLOB "\x12\xa9\xf5\x6c" SORTBY vector_distance DIALECT 2
 ```
 
 To explore additional Python vector search examples, review recipes for the [`Redis Python`](https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/vector-search/00_redispy.ipynb) client library and the [`Redis Vector Library`](https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/vector-search/01_redisvl.ipynb).
@@ -429,31 +429,31 @@ For these examples, assume you created an index named `movies` with records of d
 Among the movies that have `'Dune'` in the `title` field and `year` between `[2020, 2022]`, return the top 10 nearest neighbors, sorted by `movie_distance`:
 
 ```
-FT.SEARCH movies "(@title:Dune @year:[2020 2022])=>[KNN 10 @movie_embedding $BLOB AS movie_distance]" PARAMS 2 BLOB "\x12\xa9\xf5\x6c" SORTBY movie_distance DIALECT 4
+FT.SEARCH movies "(@title:Dune @year:[2020 2022])=>[KNN 10 @movie_embedding $BLOB AS movie_distance]" PARAMS 2 BLOB "\x12\xa9\xf5\x6c" SORTBY movie_distance DIALECT 2
 ```
 
 Among the movies that have `action` as a category tag, but not `drama`, return the top 10 nearest neighbors, sorted by `movie_distance`:
 
 ```
-FT.SEARCH movies "(@category:{action} ~@category:{drama})=>[KNN 10 @doc_embedding $BLOB AS movie_distance]" PARAMS 2 BLOB "\x12\xa9\xf5\x6c" SORTBY movie_distance DIALECT 4
+FT.SEARCH movies "(@category:{action} ~@category:{drama})=>[KNN 10 @doc_embedding $BLOB AS movie_distance]" PARAMS 2 BLOB "\x12\xa9\xf5\x6c" SORTBY movie_distance DIALECT 2
 ```
 
 Among the movies that have `drama` or `action` as a category tag, return the top 10 nearest neighbors and explicitly set the filter mode (hybrid policy) to "ad-hoc brute force" rather than it being auto-selected:
 
 ```
-FT.SEARCH movies "(@category:{drama | action})=>[KNN 10 @doc_embedding $BLOB HYBRID_POLICY ADHOC_BF]" PARAMS 2 BLOB "\x12\xa9\xf5\x6c" SORTBY __vec_score DIALECT 4
+FT.SEARCH movies "(@category:{drama | action})=>[KNN 10 @doc_embedding $BLOB HYBRID_POLICY ADHOC_BF]" PARAMS 2 BLOB "\x12\xa9\xf5\x6c" SORTBY __vec_score DIALECT 2
 ```
 
 Among the movies that have `action` as a category tag, return the top 10 nearest neighbors and explicitly set the filter mode (hybrid policy) to "batches" and batch size 50 using a query parameter:
 
 ```
-FT.SEARCH movies "(@category:{action})=>[KNN 10 @doc_embedding $BLOB HYBRID_POLICY BATCHES BATCH_SIZE $BATCH_SIZE]" PARAMS 4 BLOB "\x12\xa9\xf5\x6c" BATCH_SIZE 50 DIALECT 4
+FT.SEARCH movies "(@category:{action})=>[KNN 10 @doc_embedding $BLOB HYBRID_POLICY BATCHES BATCH_SIZE $BATCH_SIZE]" PARAMS 4 BLOB "\x12\xa9\xf5\x6c" BATCH_SIZE 50 DIALECT 2
 ```
 
 Run the same query as above and use the query attributes syntax to specify optional parameters:
 
 ```
-FT.SEARCH movies "(@category:{action})=>[KNN 10 @doc_embedding $BLOB]=>{$HYBRID_POLICY: BATCHES; $BATCH_SIZE: 50}" PARAMS 2 BLOB "\x12\xa9\xf5\x6c" DIALECT 4
+FT.SEARCH movies "(@category:{action})=>[KNN 10 @doc_embedding $BLOB]=>{$HYBRID_POLICY: BATCHES; $BATCH_SIZE: 50}" PARAMS 2 BLOB "\x12\xa9\xf5\x6c" DIALECT 2
 ```
 
 To explore additional Python vector search examples, review recipes for the [`Redis Python`](https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/vector-search/00_redispy.ipynb) client library and the [`Redis Vector Library`](https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/vector-search/01_redisvl.ipynb).
@@ -466,19 +466,19 @@ For these examples, assume you created an index named `products` with records of
 Return 100 products for which the distance between the `description_vector` field and the specified query vector blob is at most 5:
 
 ```
-FT.SEARCH products "@description_vector:[VECTOR_RANGE 5 $BLOB]" PARAMS 2 BLOB "\x12\xa9\xf5\x6c" LIMIT 0 100 DIALECT 4
+FT.SEARCH products "@description_vector:[VECTOR_RANGE 5 $BLOB]" PARAMS 2 BLOB "\x12\xa9\xf5\x6c" LIMIT 0 100 DIALECT 2
 ```
 
 Run the same query as above and set the `EPSILON` parameter to `0.5`, assuming `description_vector` is HNSW index, yield the vector distance between `description_vector` and the query result in a field named `vector_distance`, and sort the results by that distance.
 
 ```
-FT.SEARCH products "@description_vector:[VECTOR_RANGE 5 $BLOB]=>{$EPSILON:0.5; $YIELD_DISTANCE_AS: vector_distance}" PARAMS 2 BLOB "\x12\xa9\xf5\x6c" SORTBY vector_distance LIMIT 0 100 DIALECT 4
+FT.SEARCH products "@description_vector:[VECTOR_RANGE 5 $BLOB]=>{$EPSILON:0.5; $YIELD_DISTANCE_AS: vector_distance}" PARAMS 2 BLOB "\x12\xa9\xf5\x6c" SORTBY vector_distance LIMIT 0 100 DIALECT 2
 ```
 
 Use the vector range query as a filter: return all the documents that contain either `'shirt'` in their `type` tag with their `year` value in the range `[2020, 2022]` or a vector stored in `description_vector` whose distance from the query vector is no more than `0.8`, then sort the results by their vector distance, if it is in the range:
 
 ```
-FT.SEARCH products "(@type:{shirt} @year:[2020 2022]) | @description_vector:[VECTOR_RANGE 0.8 $BLOB]=>{$YIELD_DISTANCE_AS: vector_distance}" PARAMS 2 BLOB "\x12\xa9\xf5\x6c" SORTBY vector_distance DIALECT 4
+FT.SEARCH products "(@type:{shirt} @year:[2020 2022]) | @description_vector:[VECTOR_RANGE 0.8 $BLOB]=>{$YIELD_DISTANCE_AS: vector_distance}" PARAMS 2 BLOB "\x12\xa9\xf5\x6c" SORTBY vector_distance DIALECT 2
 ```
 
 To explore additional Python vector search examples, review recipes for the [`Redis Python`](https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/vector-search/00_redispy.ipynb) client library and the [`Redis Vector Library`](https://github.com/redis-developer/redis-ai-resources/blob/main/python-recipes/vector-search/01_redisvl.ipynb).
diff --git a/content/develop/interact/search-and-query/basic-constructs/field-and-type-options.md b/content/develop/interact/search-and-query/basic-constructs/field-and-type-options.md
index 783f1bab1..502f6c760 100644
--- a/content/develop/interact/search-and-query/basic-constructs/field-and-type-options.md
+++ b/content/develop/interact/search-and-query/basic-constructs/field-and-type-options.md
@@ -131,7 +131,7 @@ and `<shape>` is the shape of interest, specified in the
 format. For example, the query below finds shapes that contain the point (2, 2):
 
 ```
-FT.SEARCH idx "(@geom:[CONTAINS $qshape])" PARAMS 2 qshape "POINT (2 2)" RETURN 1 name DIALECT 4
+FT.SEARCH idx "(@geom:[CONTAINS $qshape])" PARAMS 2 qshape "POINT (2 2)" RETURN 1 name DIALECT 2
 ```
 
 See
diff --git a/content/develop/interact/search-and-query/indexing/geoindex.md b/content/develop/interact/search-and-query/indexing/geoindex.md
index ce31baa40..e9429f137 100644
--- a/content/develop/interact/search-and-query/indexing/geoindex.md
+++ b/content/develop/interact/search-and-query/indexing/geoindex.md
@@ -96,7 +96,7 @@ example, the query below returns any shapes within the boundary
 of the green square but omits the green square itself:
 
 {{< clients-example geoindex gshape_query >}}
-> FT.SEARCH geomidx "(-@name:(Green Square) @geom:[WITHIN $qshape])" PARAMS 2 qshape "POLYGON ((1 1, 1 3, 3 3, 3 1, 1 1))" RETURN 1 name DIALECT 4
+> FT.SEARCH geomidx "(-@name:(Green Square) @geom:[WITHIN $qshape])" PARAMS 2 qshape "POLYGON ((1 1, 1 3, 3 3, 3 1, 1 1))" RETURN 1 name DIALECT 2
 
 1) (integer) 1
 2) "shape:4"