GET
/
models
/
{model_name}
from pinecone import Pinecone

pc = Pinecone(api_key="YOUR_API_KEY")

model = pc.inference.get_model(model_name="llama-text-embed-v2")

print(model)

{'default_dimension': 1024,
 'max_batch_size': 96,
 'max_sequence_length': 2048,
 'modality': 'text',
 'model': 'llama-text-embed-v2',
 'provider_name': 'NVIDIA',
 '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).',
 'supported_dimensions': [384, 512, 768, 1024, 2048],
 'supported_metrics': [cosine, dotproduct],
 'supported_parameters': [{'allowed_values': ['query', 'passage'],
                           'parameter': 'input_type',
                           'required': True,
                           'type': 'one_of',
                           'value_type': 'string'},
                          {'allowed_values': ['END', 'NONE', 'START'],
                           'default': 'END',
                           'parameter': 'truncate',
                           'required': False,
                           'type': 'one_of',
                           'value_type': 'string'},
                          {'allowed_values': [384, 512, 768, 1024, 2048],
                           'default': 1024,
                           'parameter': 'dimension',
                           'required': False,
                           'type': 'one_of',
                           'value_type': 'integer'}],
 'type': 'embed',
 'vector_type': 'dense'}

from pinecone import Pinecone

pc = Pinecone(api_key="YOUR_API_KEY")

model = pc.inference.get_model(model_name="llama-text-embed-v2")

print(model)

{'default_dimension': 1024,
 'max_batch_size': 96,
 'max_sequence_length': 2048,
 'modality': 'text',
 'model': 'llama-text-embed-v2',
 'provider_name': 'NVIDIA',
 '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).',
 'supported_dimensions': [384, 512, 768, 1024, 2048],
 'supported_metrics': [cosine, dotproduct],
 'supported_parameters': [{'allowed_values': ['query', 'passage'],
                           'parameter': 'input_type',
                           'required': True,
                           'type': 'one_of',
                           'value_type': 'string'},
                          {'allowed_values': ['END', 'NONE', 'START'],
                           'default': 'END',
                           'parameter': 'truncate',
                           'required': False,
                           'type': 'one_of',
                           'value_type': 'string'},
                          {'allowed_values': [384, 512, 768, 1024, 2048],
                           'default': 1024,
                           'parameter': 'dimension',
                           'required': False,
                           'type': 'one_of',
                           'value_type': 'integer'}],
 'type': 'embed',
 'vector_type': 'dense'}

Authorizations

​
Api-Key
string
header
required

An API Key is required to call Pinecone APIs. Get yours from the console.

Path Parameters

​
model_name
string
required

The name of the model to look up.

Response

The model details.

Represents the model configuration including model type, supported parameters, and other model details.

​
model
string
required

The name of the model.

Example:

"multilingual-e5-large"

​
short_description
string
required

A summary of the model.

Example:

"multilingual-e5-large"

​
type
string
required

The type of model (e.g. 'embed' or 'rerank').

Example:

"embed"

​
supported_parameters
object[]
required
​
vector_type
string

Whether the embedding model produces 'dense' or 'sparse' embeddings.

​
default_dimension
integer

The default embedding model dimension (applies to dense embedding models only).

Required range: 1 <= x <= 20000
Example:

1024

​
modality
string

The modality of the model (e.g. 'text').

Example:

"text"

​
max_sequence_length
integer

The maximum tokens per sequence supported by the model.

Required range: x >= 1
Example:

512

​
max_batch_size
integer

The maximum batch size (number of sequences) supported by the model.

Required range: x >= 1
Example:

96

​
provider_name
string

The name of the provider of the model.

Example:

"NVIDIA"

​
supported_dimensions
integer[]

The list of supported dimensions for the model (applies to dense embedding models only).

​
supported_metrics
enum<string>[]

The distance metrics supported by the model for similarity search.