Support Sparse values in index API (pinecone-io#133)

Author: acatav

pinecone/core/client/api/index_operations_api.py

@@ -52,7 +52,7 @@ def __configure_index(
         ):
             """configure_index  # noqa: E501
 
-            Configure index to change pod type and number of replicas.  # noqa: E501
+            This operation specifies the pod type and number of replicas for an index.  # noqa: E501
             This method makes a synchronous HTTP request by default. To make an
             asynchronous HTTP request, please pass async_req=True
 
@@ -63,7 +63,7 @@ def __configure_index(
                 index_name (str): The name of the index
 
             Keyword Args:
-                patch_request (PatchRequest): The desired configuration changes for the index.. [optional]
+                patch_request (PatchRequest): The desired pod type and replica configuration for the index.. [optional]
                 _return_http_data_only (bool): response data without head status
                     code and headers. Default is True.
                 _preload_content (bool): if False, the urllib3.HTTPResponse object

pinecone/core/client/api/vector_operations_api.py

@@ -303,7 +303,7 @@ def __describe_index_stats(
         ):
             """DescribeIndexStats  # noqa: E501
 
-            The `DescribeIndexStats` operation returns statistics about the index's contents. For example: The vector count per namespace and the number of dimensions.  # noqa: E501
+            The `DescribeIndexStats` operation returns statistics about the index's contents, including the vector count per namespace, the number of dimensions, and the index fullness. The index fullness result  may be inaccurate during pod resizing; to get the status of a pod resizing process, use [`describe_index`](https://www.pinecone.io/docs/api/operation/describe_index/).  # noqa: E501
             This method makes a synchronous HTTP request by default. To make an
             asynchronous HTTP request, please pass async_req=True
 
@@ -422,7 +422,7 @@ def __describe_index_stats1(
         ):
             """DescribeIndexStats  # noqa: E501
 
-            The `DescribeIndexStats` operation returns statistics about the index's contents. For example: The vector count per namespace and the number of dimensions.  # noqa: E501
+            The `DescribeIndexStats` operation returns statistics about the index's contents, including the vector count per namespace, the number of dimensions, and the index fullness. The index fullness result  may be inaccurate during pod resizing; to get the status of a pod resizing process, use [`describe_index`](https://www.pinecone.io/docs/api/operation/describe_index/).  # noqa: E501
             This method makes a synchronous HTTP request by default. To make an
             asynchronous HTTP request, please pass async_req=True
 

pinecone/core/client/model/create_request.py

@@ -168,7 +168,7 @@ def _from_openapi_data(cls, name, dimension, *args, **kwargs):  # noqa: E501
             pods (int): The number of pods for the index to use,including replicas.. [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.. [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
+            pod_type (str): The type of pod to use. One of `s1`, `p1`, or `p2` appended with `.` and one of `x1`, `x2`, `x4`, or `x8`.. [optional] if omitted the server will use the default value of "p1.x1"  # 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. 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
             source_collection (str): The name of the collection to create an index from. [optional]  # noqa: E501
@@ -264,7 +264,7 @@ def __init__(self, name, dimension, *args, **kwargs):  # noqa: E501
             pods (int): The number of pods for the index to use,including replicas.. [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.. [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
+            pod_type (str): The type of pod to use. One of `s1`, `p1`, or `p2` appended with `.` and one of `x1`, `x2`, `x4`, or `x8`.. [optional] if omitted the server will use the default value of "p1.x1"  # 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. 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
             source_collection (str): The name of the collection to create an index from. [optional]  # noqa: E501

pinecone/core/client/model/index_meta_database.py

@@ -101,6 +101,7 @@ def openapi_types():
             'shards': (int,),  # noqa: E501
             'pod_type': (str,),  # noqa: E501
             'index_config': (dict,),  # noqa: E501
+            'metadata_config': ({str: (bool, date, datetime, dict, float, int, list, str, none_type)},),  # noqa: E501
             'status': (IndexMetaDatabaseStatus,),  # noqa: E501
         }
 
@@ -119,6 +120,7 @@ def discriminator():
         'shards': 'shards',  # noqa: E501
         'pod_type': 'pod_type',  # noqa: E501
         'index_config': 'index_config',  # noqa: E501
+        'metadata_config': 'metadata_config',  # noqa: E501
         'status': 'status',  # noqa: E501
     }
 
@@ -172,6 +174,7 @@ def _from_openapi_data(cls, *args, **kwargs):  # noqa: E501
             shards (int): [optional]  # noqa: E501
             pod_type (str): [optional]  # noqa: E501
             index_config (dict): [optional]  # noqa: E501
+            metadata_config ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}): [optional]  # noqa: E501
             status (IndexMetaDatabaseStatus): [optional]  # noqa: E501
         """
 
@@ -263,6 +266,7 @@ def __init__(self, *args, **kwargs):  # noqa: E501
             shards (int): [optional]  # noqa: E501
             pod_type (str): [optional]  # noqa: E501
             index_config (dict): [optional]  # noqa: E501
+            metadata_config ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}): [optional]  # noqa: E501
             status (IndexMetaDatabaseStatus): [optional]  # noqa: E501
         """
 

pinecone/core/client/model/patch_request.py

@@ -142,7 +142,7 @@ def _from_openapi_data(cls, *args, **kwargs):  # noqa: E501
                                 through its discriminator because we passed in
                                 _visited_composed_classes = (Animal,)
             replicas (int): The desired number of replicas for the index.. [optional]  # noqa: E501
-            pod_type (str): The new pod type for the index.. [optional]  # noqa: E501
+            pod_type (str): The new pod type for the index. One of `s1`, `p1`, or `p2` appended with `.` and one of `x1`, `x2`, `x4`, or `x8`.. [optional]  # noqa: E501
         """
 
         _check_type = kwargs.pop('_check_type', True)
@@ -225,7 +225,7 @@ def __init__(self, *args, **kwargs):  # noqa: E501
                                 through its discriminator because we passed in
                                 _visited_composed_classes = (Animal,)
             replicas (int): The desired number of replicas for the index.. [optional]  # noqa: E501
-            pod_type (str): The new pod type for the index.. [optional]  # noqa: E501
+            pod_type (str): The new pod type for the index. One of `s1`, `p1`, or `p2` appended with `.` and one of `x1`, `x2`, `x4`, or `x8`.. [optional]  # noqa: E501
         """
 
         _check_type = kwargs.pop('_check_type', True)

pinecone/core/client/model/query_request.py

@@ -36,7 +36,9 @@
 
 def lazy_import():
     from pinecone.core.client.model.query_vector import QueryVector
+    from pinecone.core.client.model.sparse_values import SparseValues
     globals()['QueryVector'] = QueryVector
+    globals()['SparseValues'] = SparseValues
 
 
 class QueryRequest(ModelNormal):
@@ -110,6 +112,7 @@ def openapi_types():
             'include_metadata': (bool,),  # noqa: E501
             'queries': ([QueryVector],),  # noqa: E501
             'vector': ([float],),  # noqa: E501
+            'sparse_vector': (SparseValues,),  # noqa: E501
             'id': (str,),  # noqa: E501
         }
 
@@ -126,6 +129,7 @@ def discriminator():
         'include_metadata': 'includeMetadata',  # noqa: E501
         'queries': 'queries',  # noqa: E501
         'vector': 'vector',  # noqa: E501
+        'sparse_vector': 'sparseVector',  # noqa: E501
         'id': 'id',  # noqa: E501
     }
 
@@ -179,6 +183,7 @@ def _from_openapi_data(cls, top_k, *args, **kwargs):  # 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. 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
+            sparse_vector (SparseValues): [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
         """
 
@@ -271,6 +276,7 @@ def __init__(self, top_k, *args, **kwargs):  # 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. 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
+            sparse_vector (SparseValues): The sparse values of the query 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
         """
 
@@ -308,4 +314,4 @@ def __init__(self, top_k, *args, **kwargs):  # noqa: E501
             setattr(self, var_name, var_value)
             if var_name in self.read_only_vars:
                 raise ApiAttributeError(f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate "
-                                        f"class with read only attributes.")
+                                     f"class with read only attributes.")

pinecone/core/client/model/query_vector.py

@@ -34,6 +34,10 @@
 from pinecone.core.client.exceptions import ApiAttributeError
 
 
+def lazy_import():
+    from pinecone.core.client.model.sparse_values import SparseValues
+    globals()['SparseValues'] = SparseValues
+
 
 class QueryVector(ModelNormal):
     """NOTE: This class is auto generated by OpenAPI Generator.
@@ -77,6 +81,7 @@ def additional_properties_type():
         This must be a method because a model may have properties that are
         of type self, this must run after the class is loaded
         """
+        lazy_import()
         return (bool, date, datetime, dict, float, int, list, str, none_type,)  # noqa: E501
 
     _nullable = False
@@ -91,8 +96,10 @@ def openapi_types():
             openapi_types (dict): The key is attribute name
                 and the value is attribute type.
         """
+        lazy_import()
         return {
             'values': ([float],),  # noqa: E501
+            'sparse_values': (SparseValues,),  # noqa: E501
             'top_k': (int,),  # noqa: E501
             'namespace': (str,),  # noqa: E501
             'filter': ({str: (bool, date, datetime, dict, float, int, list, str, none_type)},),  # noqa: E501
@@ -105,6 +112,7 @@ def discriminator():
 
     attribute_map = {
         'values': 'values',  # noqa: E501
+        'sparse_values': 'sparseValues',  # noqa: E501
         'top_k': 'topK',  # noqa: E501
         'namespace': 'namespace',  # noqa: E501
         'filter': 'filter',  # noqa: E501
@@ -154,6 +162,7 @@ def _from_openapi_data(cls, values, *args, **kwargs):  # noqa: E501
                                 Animal class but this time we won't travel
                                 through its discriminator because we passed in
                                 _visited_composed_classes = (Animal,)
+            sparse_values (SparseValues): The sparse data of the query vector [optional]  # noqa: E501
             top_k (int): An override for the number of results to return for this query vector.. [optional]  # noqa: E501
             namespace (str): An override the namespace to search.. [optional]  # noqa: E501
             filter ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}): An override for the metadata filter to apply. This replaces the request-level filter.. [optional]  # noqa: E501
@@ -242,6 +251,7 @@ def __init__(self, values, *args, **kwargs):  # noqa: E501
                                 Animal class but this time we won't travel
                                 through its discriminator because we passed in
                                 _visited_composed_classes = (Animal,)
+            sparse_values (SparseValues): This is the sparse data of the vector [optional]  # noqa: E501
             top_k (int): An override for the number of results to return for this query vector.. [optional]  # noqa: E501
             namespace (str): An override the namespace to search.. [optional]  # noqa: E501
             filter ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}): An override for the metadata filter to apply. This replaces the request-level filter.. [optional]  # noqa: E501

pinecone/core/client/model/scored_vector.py

@@ -34,6 +34,10 @@
 from pinecone.core.client.exceptions import ApiAttributeError
 
 
+def lazy_import():
+    from pinecone.core.client.model.sparse_values import SparseValues
+    globals()['SparseValues'] = SparseValues
+
 
 class ScoredVector(ModelNormal):
     """NOTE: This class is auto generated by OpenAPI Generator.
@@ -75,6 +79,7 @@ def additional_properties_type():
         This must be a method because a model may have properties that are
         of type self, this must run after the class is loaded
         """
+        lazy_import()
         return (bool, date, datetime, dict, float, int, list, str, none_type,)  # noqa: E501
 
     _nullable = False
@@ -89,11 +94,13 @@ def openapi_types():
             openapi_types (dict): The key is attribute name
                 and the value is attribute type.
         """
+        lazy_import()
         return {
             'id': (str,),  # noqa: E501
             'score': (float,),  # noqa: E501
             'values': ([float],),  # noqa: E501
-            'metadata': ({str: (bool, date, dict, float, int, list, str, none_type)},),  # noqa: E501
+            'sparse_values': (SparseValues,),  # noqa: E501
+            'metadata': ({str: (bool, date, datetime, dict, float, int, list, str, none_type)},),  # noqa: E501
         }
 
     @cached_property
@@ -105,6 +112,7 @@ def discriminator():
         'id': 'id',  # noqa: E501
         'score': 'score',  # noqa: E501
         'values': 'values',  # noqa: E501
+        'sparse_values': 'sparseValues',  # noqa: E501
         'metadata': 'metadata',  # noqa: E501
     }
 
@@ -154,6 +162,7 @@ def _from_openapi_data(cls, id, *args, **kwargs):  # noqa: E501
                                 _visited_composed_classes = (Animal,)
             score (float): This is a measure of similarity between this vector and the query vector.  The higher the score, the more they are similar.. [optional]  # noqa: E501
             values ([float]): This is the vector data, if it is requested.. [optional]  # noqa: E501
+            sparse_values (SparseValues): the sparse data of the vector [optional]  # noqa: E501
             metadata ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}): This is the metadata, if it is requested.. [optional]  # noqa: E501
         """
 
@@ -242,6 +251,7 @@ def __init__(self, id, *args, **kwargs):  # noqa: E501
                                 _visited_composed_classes = (Animal,)
             score (float): This is a measure of similarity between this vector and the query vector.  The higher the score, the more they are similar.. [optional]  # noqa: E501
             values ([float]): This is the vector data, if it is requested.. [optional]  # noqa: E501
+            sparse_values (SparseValues): This is the sparse data of the vector [optional]  # noqa: E501
             metadata ({str: (bool, date, datetime, dict, float, int, list, str, none_type)}): This is the metadata, if it is requested.. [optional]  # noqa: E501
         """