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Describe a model

Get a description of a model hosted by Pinecone.

You can use hosted models as an integrated part of Pinecone operations or for standalone embedding and reranking. For more details, see Vector embedding and Rerank results.

Python
from pinecone import Pinecone

pc = Pinecone(api_key="YOUR_API_KEY")

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

print(model)
JavaScript
const pc = new Pinecone({ apiKey: 'YOUR_API_KEY' });

const model = await pc.inference.getModel('llama-text-embed-v2');

console.log(model);
Java
import io.pinecone.clients.Inference;
import io.pinecone.clients.Pinecone;
import org.openapitools.inference.client.ApiException;
import org.openapitools.inference.client.model.ModelInfo;

public class DescribeModel {
    public static void main(String[] args) throws ApiException {
        Pinecone pinecone = new Pinecone.Builder("YOUR_API_KEY").build();

        Inference inference = pinecone.getInferenceClient();

        ModelInfo modelInfo = inference.describeModel("llama-text-embed-v2");
        System.out.println(modelInfo);
    }
}
Go
package main

import (
    "context"
    "encoding/json"
    "fmt"
    "log"

    "github.com/pinecone-io/go-pinecone/v4/pinecone"
)

func prettifyStruct(obj interface{}) string {
  	bytes, _ := json.MarshalIndent(obj, "", "  ")
    return string(bytes)
}

func main() {
    ctx := context.Background()

    pc, err := pinecone.NewClient(pinecone.NewClientParams{
        ApiKey: "YOUR_API_KEY",
    })
    if err != nil {
        log.Fatalf("Failed to create Client: %v", err)
    }

    model, err := pc.Inference.DescribeModel(ctx, "llama-text-embed-v2")
    if err != nil {
        log.Fatalf("Failed to get model: %v", err)
    }
    fmt.Printf(prettifyStruct(model))
}
C#
using Pinecone;
using Pinecone.Inference;

var pinecone = new PineconeClient("YOUR_API_KEY");

var model = await pinecone.Inference.Models.GetAsync("llama-text-embed-v2");

Console.WriteLine(model);
curl
PINECONE_API_KEY="YOUR_API_KEY"

curl "https://api.pinecone.io/models/llama-text-embed-v2" \
    -H "Api-Key: $PINECONE_API_KEY" \
    -H "X-Pinecone-Api-Version: 2025-04"
Python
{'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'}
JavaScript
{
  "model": "llama-text-embed-v2",
  "shortDescription": "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",
  "vectorType": "dense",
  "defaultDimension": 1024,
  "modality": "text",
  "maxSequenceLength": 2048,
  "maxBatchSize": 96,
  "providerName": "NVIDIA",
  "supportedDimensions": [ 384, 512, 768, 1024, 2048 ],
  "supportedMetrics": [ "Cosine", "DotProduct" ],
  "supportedParameters": [
    {
      parameter: 'input_type',
      type: 'one_of',
      valueType: 'string',
      required: true,
      allowedValues: [Array],
      min: undefined,
      max: undefined,
      _default: undefined
    },
    {
      parameter: 'truncate',
      type: 'one_of',
      valueType: 'string',
      required: false,
      allowedValues: [Array],
      min: undefined,
      max: undefined,
      _default: 'END'
    },
    {
      parameter: 'dimension',
      type: 'one_of',
      valueType: 'integer',
      required: false,
      allowedValues: [Array],
      min: undefined,
      max: undefined,
      _default: 1024
    }
  ]
}
Java
class ModelInfo {
    model: llama-text-embed-v2
    shortDescription: 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
    vectorType: dense
    defaultDimension: 1024
    modality: text
    maxSequenceLength: 2048
    maxBatchSize: 96
    providerName: NVIDIA
    supportedDimensions: [384, 512, 768, 1024, 2048]
    supportedMetrics: [cosine, dotproduct]
    supportedParameters: [class ModelInfoSupportedParameter {
        parameter: input_type
        type: one_of
        valueType: string
        required: true
        allowedValues: [class class org.openapitools.inference.client.model.ModelInfoSupportedParameterAllowedValuesInner {
            instance: query
            isNullable: false
            schemaType: anyOf
        }, class class org.openapitools.inference.client.model.ModelInfoSupportedParameterAllowedValuesInner {
            instance: passage
            isNullable: false
            schemaType: anyOf
        }]
        min: null
        max: null
        _default: null
        additionalProperties: null
    }, class ModelInfoSupportedParameter {
        parameter: truncate
        type: one_of
        valueType: string
        required: false
        allowedValues: [class class org.openapitools.inference.client.model.ModelInfoSupportedParameterAllowedValuesInner {
            instance: END
            isNullable: false
            schemaType: anyOf
        }, class class org.openapitools.inference.client.model.ModelInfoSupportedParameterAllowedValuesInner {
            instance: NONE
            isNullable: false
            schemaType: anyOf
        }, class class org.openapitools.inference.client.model.ModelInfoSupportedParameterAllowedValuesInner {
            instance: START
            isNullable: false
            schemaType: anyOf
        }]
        min: null
        max: null
        _default: class class org.openapitools.inference.client.model.ModelInfoSupportedParameterDefault {
            instance: END
            isNullable: false
            schemaType: anyOf
        }
        additionalProperties: null
    }, class ModelInfoSupportedParameter {
        parameter: dimension
        type: one_of
        valueType: integer
        required: false
        allowedValues: [class class org.openapitools.inference.client.model.ModelInfoSupportedParameterAllowedValuesInner {
            instance: 384
            isNullable: false
            schemaType: anyOf
        }, class class org.openapitools.inference.client.model.ModelInfoSupportedParameterAllowedValuesInner {
            instance: 512
            isNullable: false
            schemaType: anyOf
        }, class class org.openapitools.inference.client.model.ModelInfoSupportedParameterAllowedValuesInner {
            instance: 768
            isNullable: false
            schemaType: anyOf
        }, class class org.openapitools.inference.client.model.ModelInfoSupportedParameterAllowedValuesInner {
            instance: 1024
            isNullable: false
            schemaType: anyOf
        }, class class org.openapitools.inference.client.model.ModelInfoSupportedParameterAllowedValuesInner {
            instance: 2048
            isNullable: false
            schemaType: anyOf
        }]
        min: null
        max: null
        _default: class class org.openapitools.inference.client.model.ModelInfoSupportedParameterDefault {
            instance: 1024
            isNullable: false
            schemaType: anyOf
        }
        additionalProperties: null
    }]
    additionalProperties: null
}
Go
{
  "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": [
        {
          "StringValue": "query",
          "IntValue": null,
          "FloatValue": null,
          "BoolValue": null
        },
        {
          "StringValue": "passage",
          "IntValue": null,
          "FloatValue": null,
          "BoolValue": null
        }
      ],
      "parameter": "input_type",
      "required": true,
      "type": "one_of",
      "value_type": "string"
    },
    {
      "allowed_values": [
        {
          "StringValue": "END",
          "IntValue": null,
          "FloatValue": null,
          "BoolValue": null
        },
        {
          "StringValue": "NONE",
          "IntValue": null,
          "FloatValue": null,
          "BoolValue": null
        },
        {
          "StringValue": "START",
          "IntValue": null,
          "FloatValue": null,
          "BoolValue": null
        }
      ],
      "default": {
        "StringValue": "END",
        "IntValue": null,
        "FloatValue": null,
        "BoolValue": null
      },
      "parameter": "truncate",
      "required": false,
      "type": "one_of",
      "value_type": "string"
    },
    {
      "allowed_values": [
        {
          "StringValue": null,
          "IntValue": 384,
          "FloatValue": null,
          "BoolValue": null
        },
        {
          "StringValue": null,
          "IntValue": 512,
          "FloatValue": null,
          "BoolValue": null
        },
        {
          "StringValue": null,
          "IntValue": 768,
          "FloatValue": null,
          "BoolValue": null
        },
        {
          "StringValue": null,
          "IntValue": 1024,
          "FloatValue": null,
          "BoolValue": null
        },
        {
          "StringValue": null,
          "IntValue": 2048,
          "FloatValue": null,
          "BoolValue": null
        }
      ],
      "default": {
        "StringValue": null,
        "IntValue": 1024,
        "FloatValue": null,
        "BoolValue": null
      },
      "parameter": "dimension",
      "required": false,
      "type": "one_of",
      "value_type": "integer"
    }
  ],
  "type": "embed",
  "vector_type": "dense"
}
C#
{
  "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",
  "vector_type": "dense",
  "default_dimension": 1024,
  "modality": "text",
  "max_sequence_length": 2048,
  "max_batch_size": 96,
  "provider_name": "NVIDIA",
  "supported_dimensions": [
    384,
    512,
    768,
    1024,
    2048
  ],
  "supported_metrics": [
    "cosine",
    "cosine"
  ],
  "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,
      "allowed_values": [
        "END",
        "NONE",
        "START"
      ],
      "default": "END"
    },
    {
      "parameter": "dimension",
      "type": "one_of",
      "value_type": "integer",
      "required": false,
      "allowed_values": [
        384,
        512,
        768,
        1024,
        2048
      ],
      "default": 1024
    }
  ]
}
curl
{
  "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",
  "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
  ],
  "supported_parameters": [
    {
      "parameter": "input_type",
      "required": true,
      "type": "one_of",
      "value_type": "string",
      "allowed_values": [
        "query",
        "passage"
      ]
    },
    {
      "parameter": "truncate",
      "required": false,
      "default": "END",
      "type": "one_of",
      "value_type": "string",
      "allowed_values": [
        "END",
        "NONE",
        "START"
      ]
    },
    {
      "parameter": "dimension",
      "required": false,
      "default": 1024,
      "type": "one_of",
      "value_type": "integer",
      "allowed_values": [
        384,
        512,
        768,
        1024,
        2048
      ]
    }
  ]
}

GET /models/{model_name}

Authorizations

Section titled “Authorizations”
Api-Keystringrequired

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

Path Parameters

Section titled “Path Parameters”
model_namestringrequired

The name of the model to look up.

Typestring

Response

Section titled “Response”

200 — The model details.

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

modelstringrequired

The name of the model.

Example: multilingual-e5-large

Typestring
short_descriptionstringrequired

A summary of the model.

Example: multilingual-e5-large

Typestring
typestringrequired

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

Example: embed

Typestring
vector_type?string

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

Typestring
default_dimension?integer

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

Required range: 1 <= x <= 20000. Example: 1024

Typeinteger
modality?string

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

Example: text

Typestring
max_sequence_length?integer

The maximum tokens per sequence supported by the model.

Required range: 1 <= x. Example: 512

Typeinteger
max_batch_size?integer

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

Required range: 1 <= x. Example: 96

Typeinteger
provider_name?string

The name of the provider of the model.

Example: NVIDIA

Typestring
supported_dimensions?integer[]

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

Typeinteger[]
supported_metrics?enum<string>[]

The distance metrics supported by the model for similarity search.

Typeenum<string>[]
supported_parametersobject[]required
Show child attributes
parameterstringrequired

The name of the parameter.

Example: input_type

Typestring
typestringrequired

The parameter type e.g. 'one_of', 'numeric_range', or 'any'. If the type is 'one_of', then 'allowed_values' will be set, and the value specified must be one of the allowed values. 'one_of' is only compatible with value_type 'string' or 'integer'. If 'numeric_range', then 'min' and 'max' will be set, then the value specified must adhere to the value_type and must fall within the [min, max] range (inclusive). If 'any' then any value is allowed, as long as it adheres to the value_type.

Example: one_of

Typestring
value_typestringrequired

The type of value the parameter accepts, e.g. 'string', 'integer', 'float', or 'boolean'.

Example: string

Typestring
requiredbooleanrequired

Whether the parameter is required (true) or optional (false).

Example: true

Typeboolean
allowed_values?string[]

The allowed parameter values when the type is 'one_of'.

Typestring[]
min?number

The minimum allowed value (inclusive) when the type is 'numeric_range'.

Example: 1

Typenumber
max?number

The maximum allowed value (inclusive) when the type is 'numeric_range'.

Example: 1

Typenumber
default?string
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