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Merge pull request #26 from pinecone-io/spec-changes
changes to spec for create_index changes
2 parents bc43a56 + 5b0dbda commit f82e3c1

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pinecone/core/client/model/create_request.py

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@@ -90,8 +90,10 @@ def openapi_types():
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'dimension': (int,), # noqa: E501
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'index_type': (str,), # noqa: E501
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'metric': (str,), # noqa: E501
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'pods': (int,), # noqa: E501
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'replicas': (int,), # noqa: E501
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'shards': (int,), # noqa: E501
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'pod_type': (str,), # noqa: E501
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'index_config': (dict,), # noqa: E501
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}
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@@ -105,8 +107,10 @@ def discriminator():
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'dimension': 'dimension', # noqa: E501
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'index_type': 'index_type', # noqa: E501
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'metric': 'metric', # noqa: E501
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'pods': 'pods', # noqa: E501
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'replicas': 'replicas', # noqa: E501
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'shards': 'shards', # noqa: E501
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'pod_type': 'pod_type', # noqa: E501
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'index_config': 'index_config', # noqa: E501
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}
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@@ -157,8 +161,10 @@ def _from_openapi_data(cls, name, dimension, *args, **kwargs): # noqa: E501
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_visited_composed_classes = (Animal,)
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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
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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
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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
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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
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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
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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
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index_config (dict): [optional] # noqa: E501
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"""
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@@ -249,8 +255,10 @@ def __init__(self, name, dimension, *args, **kwargs): # noqa: E501
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_visited_composed_classes = (Animal,)
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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
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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
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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
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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
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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
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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
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index_config (dict): [optional] # noqa: E501
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"""
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pinecone/core/client/model/index_meta_database.py

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@@ -96,8 +96,10 @@ def openapi_types():
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'dimensions': (str,), # noqa: E501
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'index_type': (str,), # noqa: E501
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'metric': (str,), # noqa: E501
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'pods': (int,), # noqa: E501
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'replicas': (int,), # noqa: E501
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'shards': (int,), # noqa: E501
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'pod_type': (str,), # noqa: E501
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'index_config': (dict,), # noqa: E501
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'status': (IndexMetaDatabaseStatus,), # noqa: E501
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}
@@ -112,8 +114,10 @@ def discriminator():
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'dimensions': 'dimensions', # noqa: E501
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'index_type': 'index_type', # noqa: E501
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'metric': 'metric', # noqa: E501
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'pods': 'pods', # noqa: E501
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'replicas': 'replicas', # noqa: E501
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'shards': 'shards', # noqa: E501
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'pod_type': 'pod_type', # noqa: E501
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'index_config': 'index_config', # noqa: E501
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'status': 'status', # noqa: E501
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}
@@ -163,8 +167,10 @@ def _from_openapi_data(cls, *args, **kwargs): # noqa: E501
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dimensions (str): [optional] # noqa: E501
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index_type (str): [optional] # noqa: E501
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metric (str): [optional] # noqa: E501
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pods (int): [optional] # noqa: E501
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replicas (int): [optional] # noqa: E501
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shards (int): [optional] # noqa: E501
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pod_type (str): [optional] # noqa: E501
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index_config (dict): [optional] # noqa: E501
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status (IndexMetaDatabaseStatus): [optional] # noqa: E501
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"""
@@ -252,8 +258,10 @@ def __init__(self, *args, **kwargs): # noqa: E501
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dimensions (str): [optional] # noqa: E501
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index_type (str): [optional] # noqa: E501
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metric (str): [optional] # noqa: E501
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pods (int): [optional] # noqa: E501
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replicas (int): [optional] # noqa: E501
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shards (int): [optional] # noqa: E501
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pod_type (str): [optional] # noqa: E501
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index_config (dict): [optional] # noqa: E501
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status (IndexMetaDatabaseStatus): [optional] # noqa: E501
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"""

specs/index_service.json

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@@ -230,6 +230,11 @@
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"description": "The distance metric to be used for similarity search. You can use 'euclidean', 'cosine', or 'dotproduct'.",
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"default": "cosine"
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},
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"pods": {
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"type": "integer",
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"description": "The total number of pods the index uses. pods=shards*replicas.",
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"default": 1
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},
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"replicas": {
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"type": "integer",
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"description": "The number of replicas. Replicas duplicate your index. They provide higher availability and throughput.",
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"description": "The number of shards to be used in the index. We recommend you use 1 shard per 1GB of data.",
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"default": 1
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},
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"pod_type": {
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"type" : "string",
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"description": "The type of pod to use. One of 's1' or 'p1'.",
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"default": "p1"
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},
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"index_config": {
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"oneOf": [
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{
@@ -267,12 +277,18 @@
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"metric": {
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"type": "string"
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},
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"pods": {
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"type": "integer"
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},
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"replicas": {
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"type": "integer"
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},
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"shards": {
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"type": "integer"
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},
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"pod_type": {
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"type": "string"
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},
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"index_config": {
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"oneOf": [
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{

specs/pinecone_api.json

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@@ -891,6 +891,11 @@
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"description": "The distance metric to be used for similarity search. You can use 'euclidean', 'cosine', or 'dotproduct'.",
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"default": "cosine"
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},
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"pods": {
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"type": "integer",
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"description": "The total number of pods the index uses. pods=shards*replicas.",
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"default": 1
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},
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"replicas": {
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"type": "integer",
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"description": "The number of replicas. Replicas duplicate your index. They provide higher availability and throughput.",
@@ -901,6 +906,11 @@
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"description": "The number of shards to be used in the index. We recommend you use 1 shard per 1GB of data.",
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"default": 1
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},
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"pod_type": {
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"type": "string",
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"description": "The type of pod to use. One of 's1' or 'p1'.",
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"default": "p1"
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},
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"index_config": {
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"oneOf": [
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{
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"metric": {
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"type": "string"
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},
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"pods": {
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"type": "integer"
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},
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"replicas": {
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"type": "integer"
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},
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"shards": {
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"type": "integer"
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},
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"pod_type": {
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"type": "string"
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},
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"index_config": {
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"oneOf": [
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{

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