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Expose new pc.inference.list_models() and pc.inference.get_model() (#488)
## Problem We need to expose a new endpoint for discovering available inference models ## Solution - Regenerate code off the latest spec - Wire the new method up in the sync and async implementations of Inference - `pc.inference.get_model` - `pc.inference.list_models` - Make some adjustments in model_utils to be less fragile if unexpected values appear in enum fields - Implement new tests for these list_models endpoints. ## Usage ```python from pinecone import Pinecone pc = Pinecone() models = pc.inference.list_models() models[0] # { # "model": "llama-text-embed-v2", # "short_description": "A high performance dense embedding model optimized for multilingual and cross-lingual text question-answering retrieval with support for long documents (up to 2048 tokens) and dynamic embedding size (Matryoshka Embeddings).", # "type": "embed", # "supported_parameters": [ # { # "parameter": "input_type", # "type": "one_of", # "value_type": "string", # "required": true, # "allowed_values": [ # "query", # "passage" # ] # }, # { # "parameter": "truncate", # "type": "one_of", # "value_type": "string", # "required": false, # "default": "END", # "allowed_values": [ # "END", # "NONE", # "START" # ] # }, # { # "parameter": "dimension", # "type": "one_of", # "value_type": "integer", # "required": false, # "default": 1024, # "allowed_values": [ # 384, # 512, # 768, # 1024, # 2048 # ] # } # ], # "vector_type": "dense", # "default_dimension": 1024, # "modality": "text", # "max_sequence_length": 2048, # "max_batch_size": 96, # "provider_name": "NVIDIA", # "supported_metrics": [ # "Cosine", # "DotProduct" # ], # "supported_dimensions": [ # 384, # 512, # 768, # 1024, # 2048 # ] # } ``` And async ```python import asyncio from pinecone import PineconeAsyncio async def main(): with PineconeAsyncio() as pc: await pc.inference.list_models() asyncio.run(main()) ``` ## Type of Change - [x] New feature (non-breaking change which adds functionality)
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‎pinecone/__init__.py

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_inference_lazy_imports = {
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"RerankModel": ("pinecone.inference", "RerankModel"),
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"EmbedModel": ("pinecone.inference", "EmbedModel"),
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"ModelInfo": ("pinecone.inference.models", "ModelInfo"),
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"ModelInfoList": ("pinecone.inference.models", "ModelInfoList"),
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"EmbeddingsList": ("pinecone.inference.models", "EmbeddingsList"),
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"RerankResult": ("pinecone.inference.models", "RerankResult"),
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}
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_db_data_lazy_imports = {

‎pinecone/__init__.pyi

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from pinecone.config import Config
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from pinecone.config import ConfigBuilder
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from pinecone.config import PineconeConfig
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from pinecone.inference import RerankModel
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from pinecone.inference import EmbedModel
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from pinecone.inference import (
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RerankModel,
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EmbedModel,
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ModelInfo,
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ModelInfoList,
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EmbeddingsList,
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RerankResult,
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)
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from pinecone.db_data.dataclasses import (
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Vector,
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SparseValues,
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# Inference classes
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"RerankModel",
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"EmbedModel",
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"ModelInfo",
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"ModelInfoList",
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"EmbeddingsList",
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"RerankResult",
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# Data classes
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"Vector",
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"SparseValues",

‎pinecone/core/openapi/db_control/model/backup_list.py

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pagination (PaginationResponse): [optional] # noqa: E501
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"""
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_enforce_allowed_values = kwargs.pop("_enforce_allowed_values", False)
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_check_type = kwargs.pop("_check_type", True)
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_spec_property_naming = kwargs.pop("_spec_property_naming", False)
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_path_to_item = kwargs.pop("_path_to_item", ())
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)
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self._data_store = {}
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self._enforce_allowed_values = _enforce_allowed_values
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self._check_type = _check_type
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self._spec_property_naming = _spec_property_naming
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self._path_to_item = _path_to_item
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required_properties = set(
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[
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"_enforce_allowed_values",
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"_data_store",
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"_check_type",
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"_spec_property_naming",
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pagination (PaginationResponse): [optional] # noqa: E501
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"""
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_enforce_allowed_values = kwargs.pop("_enforce_allowed_values", True)
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_check_type = kwargs.pop("_check_type", True)
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_spec_property_naming = kwargs.pop("_spec_property_naming", False)
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_path_to_item = kwargs.pop("_path_to_item", ())
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)
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self._data_store = {}
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self._enforce_allowed_values = _enforce_allowed_values
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self._check_type = _check_type
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self._spec_property_naming = _spec_property_naming
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self._path_to_item = _path_to_item

‎pinecone/core/openapi/db_control/model/backup_model.py

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created_at (str): Timestamp when the backup was created. [optional] # noqa: E501
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"""
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_enforce_allowed_values = kwargs.pop("_enforce_allowed_values", False)
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_check_type = kwargs.pop("_check_type", True)
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_spec_property_naming = kwargs.pop("_spec_property_naming", False)
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_path_to_item = kwargs.pop("_path_to_item", ())
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self._enforce_allowed_values = _enforce_allowed_values
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self._check_type = _check_type
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self._spec_property_naming = _spec_property_naming
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self._path_to_item = _path_to_item
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required_properties = set(
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"_enforce_allowed_values",
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"_data_store",
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"""
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_enforce_allowed_values = kwargs.pop("_enforce_allowed_values", True)
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_check_type = kwargs.pop("_check_type", True)
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_path_to_item = kwargs.pop("_path_to_item", ())
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self._path_to_item = _path_to_item

‎pinecone/core/openapi/db_control/model/byoc_spec.py

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"""
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_enforce_allowed_values = kwargs.pop("_enforce_allowed_values", False)
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_check_type = kwargs.pop("_check_type", True)
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_spec_property_naming = kwargs.pop("_spec_property_naming", False)
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_path_to_item = kwargs.pop("_path_to_item", ())
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self._enforce_allowed_values = _enforce_allowed_values
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_enforce_allowed_values = kwargs.pop("_enforce_allowed_values", True)
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_check_type = kwargs.pop("_check_type", True)
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‎pinecone/core/openapi/db_control/model/collection_list.py

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collections ([CollectionModel]): [optional] # noqa: E501
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"""
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_enforce_allowed_values = kwargs.pop("_enforce_allowed_values", False)
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‎pinecone/core/openapi/db_control/model/collection_model.py

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‎pinecone/core/openapi/db_control/model/configure_index_request.py

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‎pinecone/core/openapi/db_control/model/configure_index_request_embed.py

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‎pinecone/core/openapi/db_control/model/configure_index_request_spec.py

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‎pinecone/core/openapi/db_control/model/configure_index_request_spec_pod.py

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self._spec_property_naming = _spec_property_naming
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self._path_to_item = _path_to_item
@@ -180,6 +182,7 @@ def _from_openapi_data(cls: Type[T], *args, **kwargs) -> T: # noqa: E501
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required_properties = set(
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[
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"_enforce_allowed_values",
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"_data_store",
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"_check_type",
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"_spec_property_naming",
@@ -228,6 +231,7 @@ def __init__(self, *args, **kwargs) -> None: # noqa: E501
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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
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"""
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_enforce_allowed_values = kwargs.pop("_enforce_allowed_values", True)
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_check_type = kwargs.pop("_check_type", True)
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_spec_property_naming = kwargs.pop("_spec_property_naming", False)
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_path_to_item = kwargs.pop("_path_to_item", ())
@@ -243,6 +247,7 @@ def __init__(self, *args, **kwargs) -> None: # noqa: E501
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)
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self._data_store = {}
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self._enforce_allowed_values = _enforce_allowed_values
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self._check_type = _check_type
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self._spec_property_naming = _spec_property_naming
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self._path_to_item = _path_to_item

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