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"
}
}
]
Documentation Index
Fetch the complete documentation index at: https://docs.pinecone.io/llms.txt
Use this file to discover all available pages before exploring further.
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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