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Describe a model

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}

Parameters

Section titled “Parameters”
  • model_name (path, string, required) — The name of the model to look up.

Responses

Section titled “Responses”
  • 200 — The model details.
  • 401 — Unauthorized. Possible causes: Invalid API key.
  • 404 — Model not found.
  • 500 — Internal server error.
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