changing pods definition

Author: rajat08

pinecone/core/client/model/create_request.py

@@ -161,9 +161,9 @@ def _from_openapi_data(cls, name, dimension, *args, **kwargs):  # noqa: E501
                                 _visited_composed_classes = (Animal,)
             index_type (str): The type of vector index. Pinecone supports 'approximated'.. [optional] if omitted the server will use the default value of "approximated"  # noqa: E501
             metric (str): The distance metric to be used for similarity search. You can use 'euclidean', 'cosine', or 'dotproduct'.. [optional] if omitted the server will use the default value of "cosine"  # noqa: E501
-            pods (int): The total number of pods the index uses. pods=shards*replicas.. [optional] if omitted the server will use the default value of 1  # noqa: E501
+            pods (int): The number of pods for the index to use, per replica.. [optional] if omitted the server will use the default value of 1  # noqa: E501
             replicas (int): The number of replicas. Replicas duplicate your index. They provide higher availability and throughput.. [optional] if omitted the server will use the default value of 1  # noqa: E501
-            shards (int): The number of shards to be used in the index. We recommend you use 1 shard per 1GB of data.. [optional] if omitted the server will use the default value of 1  # noqa: E501
+            shards (int): The number of shards to be used in the index.. [optional] if omitted the server will use the default value of 1  # noqa: E501
             pod_type (str): The type of pod to use. One of 's1' or 'p1'.. [optional] if omitted the server will use the default value of "p1"  # noqa: E501
             index_config (dict): [optional]  # noqa: E501
         """
@@ -255,9 +255,9 @@ def __init__(self, name, dimension, *args, **kwargs):  # noqa: E501
                                 _visited_composed_classes = (Animal,)
             index_type (str): The type of vector index. Pinecone supports 'approximated'.. [optional] if omitted the server will use the default value of "approximated"  # noqa: E501
             metric (str): The distance metric to be used for similarity search. You can use 'euclidean', 'cosine', or 'dotproduct'.. [optional] if omitted the server will use the default value of "cosine"  # noqa: E501
-            pods (int): The total number of pods the index uses. pods=shards*replicas.. [optional] if omitted the server will use the default value of 1  # noqa: E501
+            pods (int): The number of pods for the index to use, per replica.. [optional] if omitted the server will use the default value of 1  # noqa: E501
             replicas (int): The number of replicas. Replicas duplicate your index. They provide higher availability and throughput.. [optional] if omitted the server will use the default value of 1  # noqa: E501
-            shards (int): The number of shards to be used in the index. We recommend you use 1 shard per 1GB of data.. [optional] if omitted the server will use the default value of 1  # noqa: E501
+            shards (int): The number of shards to be used in the index.. [optional] if omitted the server will use the default value of 1  # noqa: E501
             pod_type (str): The type of pod to use. One of 's1' or 'p1'.. [optional] if omitted the server will use the default value of "p1"  # noqa: E501
             index_config (dict): [optional]  # noqa: E501
         """

pinecone/manage.py

@@ -183,7 +183,8 @@ def describe_index(name: str):
     db = response['database']
     ready = response['status']['ready']
     return IndexDescription(name=db['name'], index_type=db['index_type'], metric=db['metric'],
-                            replicas=db['replicas'], dimension=db['dimension'], shards=db['shards'], pods=db['pods'], pod_type=db['pod_type'],
+                            replicas=db['replicas'], dimension=db['dimension'], shards=db['shards'],
+                            pods=db.get('pods', db['shards']), pod_type=db.get('pod_type', 'p1'),
                             index_config=db['index_config'], status={'ready': ready})
 
 

specs/index_service.json

@@ -232,7 +232,7 @@
           },
           "pods": {
             "type": "integer",
-            "description": "The total number of pods the index uses, including replicas",
+            "description": "The number of pods for the index to use, per replica.",
             "default": 1
           },
           "replicas": {
@@ -242,7 +242,7 @@
           },
           "shards": {
             "type": "integer",
-            "description": "The number of shards to be used in the index. We recommend you use 1 shard per 1GB of data.",
+            "description": "The number of shards to be used in the index.",
             "default": 1
           },
           "pod_type": {

specs/pinecone_api.json

@@ -893,7 +893,7 @@
           },
           "pods": {
             "type": "integer",
-            "description": "The total number of pods the index uses, including replicas",
+            "description": "The number of pods for the index to use, per replica.",
             "default": 1
           },
           "replicas": {
@@ -903,7 +903,7 @@
           },
           "shards": {
             "type": "integer",
-            "description": "The number of shards to be used in the index. We recommend you use 1 shard per 1GB of data.",
+            "description": "The number of shards to be used in the index.",
             "default": 1
           },
           "pod_type": {