Database
Architecture
List indexes
This operation returns a list of all indexes in a project.
# pip install pinecone[grpc]
from pinecone.grpc import PineconeGRPC as Pinecone
pc = Pinecone(api_key="YOUR_API_KEY")
pc.list_indexes()
{
"indexes": [
{
"dimension": 384,
"host": "semantic-search-c01b5b5.svc.us-west1-gcp.pinecone.io",
"metric": "cosine",
"name": "semantic-search",
"spec": {
"pod": {
"environment": "us-west1-gcp",
"pod_type": "p1.x1",
"pods": 4,
"replicas": 2,
"shards": 2
}
},
"status": {
"ready": true,
"state": "Ready"
}
},
{
"dimension": 200,
"host": "image-search-a31f9c1.svc.us-east1-gcp.pinecone.io",
"metric": "dotproduct",
"name": "image-search",
"spec": {
"serverless": {
"cloud": "aws",
"region": "us-east-1"
}
},
"status": {
"ready": false,
"state": "Initializing"
}
}
]
}# pip install pinecone[grpc]
from pinecone.grpc import PineconeGRPC as Pinecone
pc = Pinecone(api_key="YOUR_API_KEY")
pc.list_indexes()
Authorizations
Response
The list of indexes that exist in the project.
The IndexModel describes the configuration and status of a Pinecone index.
The name of the index. Resource name must be 1-45 characters long, start and end with an alphanumeric character, and consist only of lower case alphanumeric characters or '-'.
1 - 45"example-index"
The dimensions of the vectors to be inserted in the index.
1 <= x <= 200001536
The distance metric to be used for similarity search. You can use 'euclidean', 'cosine', or 'dotproduct'.
cosine, euclidean, dotproduct The URL address where the index is hosted.
"semantic-search-c01b5b5.svc.us-west1-gcp.pinecone.io"
Configuration needed to deploy a pod-based index.
The environment where the index is hosted.
"us-east1-gcp"
The type of pod to use. One of s1, p1, or p2 appended with . and one of x1, x2, x4, or x8.
The number of replicas. Replicas duplicate your index. They provide higher availability and throughput. Replicas can be scaled up or down as your needs change.
x >= 1The number of shards. Shards split your data across multiple pods so you can fit more data into an index.
x >= 1The number of pods to be used in the index. This should be equal to shards x replicas.'
x >= 11
Configuration for the behavior of Pinecone's internal metadata index. By default, all metadata is indexed; when metadata_config is present, only specified metadata fields are indexed. These configurations are only valid for use with pod-based indexes.
By default, all metadata is indexed; to change this behavior, use this property to specify an array of metadata fields that should be indexed.
{
"indexed": ["genre", "title", "imdb_rating"]
}
The name of the collection to be used as the source for the index.
"movie-embeddings"
{
"environment": "us-east1-gcp",
"metadata_config": {
"indexed": ["genre", "title", "imdb_rating"]
},
"pod_type": "p1.x1",
"pods": 1,
"replicas": 1,
"shards": 1,
"source_collection": "movie-embeddings"
}
Configuration needed to deploy a serverless index.
{
"pod": {
"environment": "us-east-1-aws",
"metadata_config": {
"indexed": ["genre", "title", "imdb_rating"]
},
"pod_type": "p1.x1",
"pods": 1,
"replicas": 1,
"shards": 1
}
}
Whether deletion protection is enabled/disabled for the index.
disabled, enabled Was this page helpful?
# pip install pinecone[grpc]
from pinecone.grpc import PineconeGRPC as Pinecone
pc = Pinecone(api_key="YOUR_API_KEY")
pc.list_indexes()
{
"indexes": [
{
"dimension": 384,
"host": "semantic-search-c01b5b5.svc.us-west1-gcp.pinecone.io",
"metric": "cosine",
"name": "semantic-search",
"spec": {
"pod": {
"environment": "us-west1-gcp",
"pod_type": "p1.x1",
"pods": 4,
"replicas": 2,
"shards": 2
}
},
"status": {
"ready": true,
"state": "Ready"
}
},
{
"dimension": 200,
"host": "image-search-a31f9c1.svc.us-east1-gcp.pinecone.io",
"metric": "dotproduct",
"name": "image-search",
"spec": {
"serverless": {
"cloud": "aws",
"region": "us-east-1"
}
},
"status": {
"ready": false,
"state": "Initializing"
}
}
]
}