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Architecture
Models
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.
GET
/
models
/
{model_name}
Copy
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: 2025-10"
Copy
{
"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
]
}
]
}
Copy
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: 2025-10"
Copy
{
"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
]
}
]
}
Authorizations
Path Parameters
The name of the model to look up.
Response
200
application/json
The model details.
Represents the model configuration including model type, supported parameters, and other model details.
Was this page helpful?
Copy
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: 2025-10"
Copy
{
"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
]
}
]
}
Assistant
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