Release new docs

v2.4.x/site/en/userGuide/manage-indexes/index-with-gpu.md

@@ -191,7 +191,7 @@ collection.search(
 
 When using GPU indexes, be aware of certain constraints:
 
-- For __GPU_IVF_FLAT__, the maximum value for __limit__ is 256.
+- For __GPU_IVF_FLAT__, the maximum value for __limit__ is 1024.
 
 - For __GPU_IVF_PQ__ and __GPU_CAGRA__, the maximum value for __limit__ is 1024.
 
@@ -211,4 +211,4 @@ When using GPU indexes, be aware of certain constraints:
 
 - __In which scenarios are GPU indexes like CAGRA, GPU_IVF_PQ, GPU_IVF_FLAT, and GPU_BRUTE_FORCE most suitable?__
 
-    CAGRA indexes are ideal for scenarios that demand enhanced performance, albeit at the cost of consuming more memory. For environments where memory conservation is a priority, the __GPU_IVF_PQ__ index can help minimize storage requirements, though this comes with a higher loss in precision. The __GPU_IVF_FLAT__ index serves as a balanced option, offering a compromise between performance and memory usage. Lastly, the __GPU_BRUTE_FORCE__ index is designed for exhaustive search operations, guaranteeing a recall rate of 1 by performing traversal searches.
+    CAGRA indexes are ideal for scenarios that demand enhanced performance, albeit at the cost of consuming more memory. For environments where memory conservation is a priority, the __GPU_IVF_PQ__ index can help minimize storage requirements, though this comes with a higher loss in precision. The __GPU_IVF_FLAT__ index serves as a balanced option, offering a compromise between performance and memory usage. Lastly, the __GPU_BRUTE_FORCE__ index is designed for exhaustive search operations, guaranteeing a recall rate of 1 by performing traversal searches.

version.json

@@ -1,4 +1,4 @@
 {
-    "version": "v2.5.x",
+    "version": "v2.4.x",
     "released": "yes"
 }