Authorizations
Response
This operation returns a list of all the indexes that you have previously created, and which are associated with the given project
The list of indexes that exist in the project.
from pinecone import Pinecone
pc = Pinecone(api_key='YOUR_API_KEY')
index_list = pc.list_indexes()
print(index_list)
[
{
"name": "example-index",
"metric": "cosine",
"host": "example-index-fa77d8e.svc.aped-4627-b74a.pinecone.io",
"spec": {
"serverless": {
"cloud": "aws",
"region": "us-east-1"
}
},
"status": {
"ready": true,
"state": "Ready"
},
"vector_type": "dense",
"dimension": 1024,
"deletion_protection": "disabled",
"tags": null,
"embed": {
"model": "llama-text-embed-v2",
"field_map": {
"text": "text"
},
"dimension": 1024,
"metric": "cosine",
"write_parameters": {
"dimension": 1024.0,
"input_type": "passage",
"truncate": "END"
},
"read_parameters": {
"dimension": 1024.0,
"input_type": "query",
"truncate": "END"
},
"vector_type": "dense"
}
},
{
"name": "example-index-2",
"metric": "cosine",
"host": "example-index-2-ea1c34b.svc.aped-4627-b74a.pinecone.io",
"spec": {
"serverless": {
"cloud": "aws",
"region": "us-east-1"
}
},
"status": {
"ready": true,
"state": "Ready"
},
"vector_type": "dense",
"dimension": 1024,
"deletion_protection": "disabled",
"tags": null,
"embed": {
"model": "llama-text-embed-v2",
"field_map": {
"text": "text"
},
"dimension": 1024,
"metric": "cosine",
"write_parameters": {
"dimension": 1024.0,
"input_type": "passage",
"truncate": "END"
},
"read_parameters": {
"dimension": 1024.0,
"input_type": "query",
"truncate": "END"
},
"vector_type": "dense"
}
}
]
List all indexes in a project.
from pinecone import Pinecone
pc = Pinecone(api_key='YOUR_API_KEY')
index_list = pc.list_indexes()
print(index_list)
[
{
"name": "example-index",
"metric": "cosine",
"host": "example-index-fa77d8e.svc.aped-4627-b74a.pinecone.io",
"spec": {
"serverless": {
"cloud": "aws",
"region": "us-east-1"
}
},
"status": {
"ready": true,
"state": "Ready"
},
"vector_type": "dense",
"dimension": 1024,
"deletion_protection": "disabled",
"tags": null,
"embed": {
"model": "llama-text-embed-v2",
"field_map": {
"text": "text"
},
"dimension": 1024,
"metric": "cosine",
"write_parameters": {
"dimension": 1024.0,
"input_type": "passage",
"truncate": "END"
},
"read_parameters": {
"dimension": 1024.0,
"input_type": "query",
"truncate": "END"
},
"vector_type": "dense"
}
},
{
"name": "example-index-2",
"metric": "cosine",
"host": "example-index-2-ea1c34b.svc.aped-4627-b74a.pinecone.io",
"spec": {
"serverless": {
"cloud": "aws",
"region": "us-east-1"
}
},
"status": {
"ready": true,
"state": "Ready"
},
"vector_type": "dense",
"dimension": 1024,
"deletion_protection": "disabled",
"tags": null,
"embed": {
"model": "llama-text-embed-v2",
"field_map": {
"text": "text"
},
"dimension": 1024,
"metric": "cosine",
"write_parameters": {
"dimension": 1024.0,
"input_type": "passage",
"truncate": "END"
},
"read_parameters": {
"dimension": 1024.0,
"input_type": "query",
"truncate": "END"
},
"vector_type": "dense"
}
}
]
from pinecone import Pinecone
pc = Pinecone(api_key='YOUR_API_KEY')
index_list = pc.list_indexes()
print(index_list)
[
{
"name": "example-index",
"metric": "cosine",
"host": "example-index-fa77d8e.svc.aped-4627-b74a.pinecone.io",
"spec": {
"serverless": {
"cloud": "aws",
"region": "us-east-1"
}
},
"status": {
"ready": true,
"state": "Ready"
},
"vector_type": "dense",
"dimension": 1024,
"deletion_protection": "disabled",
"tags": null,
"embed": {
"model": "llama-text-embed-v2",
"field_map": {
"text": "text"
},
"dimension": 1024,
"metric": "cosine",
"write_parameters": {
"dimension": 1024.0,
"input_type": "passage",
"truncate": "END"
},
"read_parameters": {
"dimension": 1024.0,
"input_type": "query",
"truncate": "END"
},
"vector_type": "dense"
}
},
{
"name": "example-index-2",
"metric": "cosine",
"host": "example-index-2-ea1c34b.svc.aped-4627-b74a.pinecone.io",
"spec": {
"serverless": {
"cloud": "aws",
"region": "us-east-1"
}
},
"status": {
"ready": true,
"state": "Ready"
},
"vector_type": "dense",
"dimension": 1024,
"deletion_protection": "disabled",
"tags": null,
"embed": {
"model": "llama-text-embed-v2",
"field_map": {
"text": "text"
},
"dimension": 1024,
"metric": "cosine",
"write_parameters": {
"dimension": 1024.0,
"input_type": "passage",
"truncate": "END"
},
"read_parameters": {
"dimension": 1024.0,
"input_type": "query",
"truncate": "END"
},
"vector_type": "dense"
}
}
]
This operation returns a list of all the indexes that you have previously created, and which are associated with the given project
The list of indexes that exist in the project.
Show child attributes
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