Merge pull request #77 from pinecone-io/4d5bd6

Update Code

Author: rajat08

pinecone/core/client/api/vector_operations_api.py

@@ -409,15 +409,15 @@ def __fetch(
         ):
             """Fetch  # noqa: E501
 
-            The `Fetch` operation looks up and returns vectors, by id, from a single namespace. The returned vectors include the vector data and/or metadata.  # noqa: E501
+            The `Fetch` operation looks up and returns vectors, by ID, from a single namespace. The returned vectors include the vector data and/or metadata.  # noqa: E501
             This method makes a synchronous HTTP request by default. To make an
             asynchronous HTTP request, please pass async_req=True
 
             >>> thread = api.fetch(ids, async_req=True)
             >>> result = thread.get()
 
             Args:
-                ids ([str]): The vector ids to fetch.
+                ids ([str]): The vector IDs to fetch. Does not accept values containing spaces.
 
             Keyword Args:
                 namespace (str): [optional]

pinecone/core/client/model/create_request.py

@@ -168,7 +168,7 @@ def _from_openapi_data(cls, name, dimension, *args, **kwargs):  # 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
-            metadata_config ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}, none_type): Configuration for the behavior of Pinecone's internal metadata index. [optional]  # noqa: E501
+            metadata_config ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}, none_type): Configuration for the behavior of Pinecone's internal metadata index. By default, all metadata is indexed; when `metadata_config` is present, only specified metadata fields are indexed. To specify metadata fields to index, provide a JSON object of the following form:     ```   {\"indexed\": [\"example_metadata_field\"]   ``` . [optional]  # noqa: E501
         """
 
         _check_type = kwargs.pop('_check_type', True)
@@ -263,7 +263,7 @@ def __init__(self, name, dimension, *args, **kwargs):  # 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
-            metadata_config ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}, none_type): Configuration for the behavior of Pinecone's internal metadata index. [optional]  # noqa: E501
+            metadata_config ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}, none_type): Configuration for the behavior of Pinecone's internal metadata index. By default, all metadata is indexed; when `metadata_config` is present, only specified metadata fields are indexed. To specify metadata fields to index, provide a JSON object of the following form:     ```   {\"indexed\": [\"example_metadata_field\"]   ``` . [optional]  # noqa: E501
         """
 
         _check_type = kwargs.pop('_check_type', True)

pinecone/core/client/model/delete_request.py

@@ -150,7 +150,7 @@ def _from_openapi_data(cls, *args, **kwargs):  # noqa: E501
             ids ([str]): Vectors to delete.. [optional]  # noqa: E501
             delete_all (bool): This indicates that all vectors in the index namespace should be deleted.. [optional] if omitted the server will use the default value of False  # noqa: E501
             namespace (str): The namespace to delete vectors from, if applicable.. [optional]  # noqa: E501
-            filter ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}): If specified, the metadata filter here will be used to select the vectors to delete. This is mutually exclusive with specifying ids to delete in the ids param or using delete_all=True. See https://www.pinecone.io/docs/metadata-filtering/`.. [optional]  # noqa: E501
+            filter ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}): If specified, the metadata filter here will be used to select the vectors to delete. This is mutually exclusive with specifying ids to delete in the ids param or using delete_all=True. See https://www.pinecone.io/docs/metadata-filtering/.. [optional]  # noqa: E501
         """
 
         _check_type = kwargs.pop('_check_type', True)
@@ -235,7 +235,7 @@ def __init__(self, *args, **kwargs):  # noqa: E501
             ids ([str]): Vectors to delete.. [optional]  # noqa: E501
             delete_all (bool): This indicates that all vectors in the index namespace should be deleted.. [optional] if omitted the server will use the default value of False  # noqa: E501
             namespace (str): The namespace to delete vectors from, if applicable.. [optional]  # noqa: E501
-            filter ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}): If specified, the metadata filter here will be used to select the vectors to delete. This is mutually exclusive with specifying ids to delete in the ids param or using delete_all=True. See https://www.pinecone.io/docs/metadata-filtering/`.. [optional]  # noqa: E501
+            filter ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}): If specified, the metadata filter here will be used to select the vectors to delete. This is mutually exclusive with specifying ids to delete in the ids param or using delete_all=True. See https://www.pinecone.io/docs/metadata-filtering/.. [optional]  # noqa: E501
         """
 
         _check_type = kwargs.pop('_check_type', True)

pinecone/core/client/model/describe_index_stats_response.py

@@ -151,7 +151,7 @@ def _from_openapi_data(cls, *args, **kwargs):  # noqa: E501
                                 _visited_composed_classes = (Animal,)
             namespaces ({str: (NamespaceSummary,)}): A mapping for each namespace in the index from namespace name to a summary of its contents.. [optional]  # noqa: E501
             dimension (int): The dimension of the indexed vectors.. [optional]  # noqa: E501
-            index_fullness (float): The fullness of the index.. [optional]  # noqa: E501
+            index_fullness (float): The fullness of the index. The granularity of this metric is 10%.. [optional]  # noqa: E501
         """
 
         _check_type = kwargs.pop('_check_type', True)
@@ -235,7 +235,7 @@ def __init__(self, *args, **kwargs):  # noqa: E501
                                 _visited_composed_classes = (Animal,)
             namespaces ({str: (NamespaceSummary,)}): A mapping for each namespace in the index from namespace name to a summary of its contents.. [optional]  # noqa: E501
             dimension (int): The dimension of the indexed vectors.. [optional]  # noqa: E501
-            index_fullness (float): The fullness of the index.. [optional]  # noqa: E501
+            index_fullness (float): The fullness of the index. The granularity of this metric is 10%.. [optional]  # noqa: E501
         """
 
         _check_type = kwargs.pop('_check_type', True)

pinecone/core/client/model/query_request.py

@@ -177,9 +177,9 @@ def _from_openapi_data(cls, top_k, *args, **kwargs):  # noqa: E501
             filter ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}): The filter to apply. You can use vector metadata to limit your search. See https://www.pinecone.io/docs/metadata-filtering/.. [optional]  # noqa: E501
             include_values (bool): Indicates whether vector values are included in the response.. [optional] if omitted the server will use the default value of False  # noqa: E501
             include_metadata (bool): Indicates whether metadata is included in the response as well as the ids.. [optional] if omitted the server will use the default value of False  # noqa: E501
-            queries ([QueryVector]): DEPRECATED. The query vectors.. [optional]  # noqa: E501
-            vector ([float]): The query vector. This should be the same length as the dimension of the index being queried.. [optional]  # noqa: E501
-            id (str): The vector's unique id.. [optional]  # noqa: E501
+            queries ([QueryVector]): DEPRECATED. The query vectors. Each `query()` request can contain only one of the parameters `queries`, `vector`, or  `id`.. [optional]  # noqa: E501
+            vector ([float]): The query vector. This should be the same length as the dimension of the index being queried. Each `query()` request can contain only one of the parameters `id` or `vector`.. [optional]  # noqa: E501
+            id (str): The unique ID of the vector to be used as a query vector. Each `query()` request can contain only one of the parameters `queries`, `vector`, or  `id`.. [optional]  # noqa: E501
         """
 
         _check_type = kwargs.pop('_check_type', True)
@@ -269,9 +269,9 @@ def __init__(self, top_k, *args, **kwargs):  # noqa: E501
             filter ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}): The filter to apply. You can use vector metadata to limit your search. See https://www.pinecone.io/docs/metadata-filtering/.. [optional]  # noqa: E501
             include_values (bool): Indicates whether vector values are included in the response.. [optional] if omitted the server will use the default value of False  # noqa: E501
             include_metadata (bool): Indicates whether metadata is included in the response as well as the ids.. [optional] if omitted the server will use the default value of False  # noqa: E501
-            queries ([QueryVector]): DEPRECATED. The query vectors.. [optional]  # noqa: E501
-            vector ([float]): The query vector. This should be the same length as the dimension of the index being queried.. [optional]  # noqa: E501
-            id (str): The vector's unique id.. [optional]  # noqa: E501
+            queries ([QueryVector]): DEPRECATED. The query vectors. Each `query()` request can contain only one of the parameters `queries`, `vector`, or  `id`.. [optional]  # noqa: E501
+            vector ([float]): The query vector. This should be the same length as the dimension of the index being queried. Each `query()` request can contain only one of the parameters `id` or `vector`.. [optional]  # noqa: E501
+            id (str): The unique ID of the vector to be used as a query vector. Each `query()` request can contain only one of the parameters `queries`, `vector`, or  `id`.. [optional]  # noqa: E501
         """
 
         _check_type = kwargs.pop('_check_type', True)