Authorizations
Headers
Required date-based version header
Body
The desired configuration for the index.
The configuration needed to create 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 spec object defines how the index should be deployed.
For serverless indexes, you define only the cloud and region where the index should be hosted. For pod-based indexes, you define the environment where the index should be hosted, the pod type and size to use, and other index characteristics.
- Serverless
- Pod-based
- BYOC
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'. If the 'vector_type' is 'sparse', the metric must be 'dotproduct'. If the vector_type is dense, the metric defaults to 'cosine'.
Possible values: cosine, euclidean, or dotproduct.
Whether deletion protection is enabled/disabled for the index.
Possible values: disabled or enabled.
Custom user tags added to an index. Keys must be 80 characters or less. Values must be 120 characters or less. Keys must be alphanumeric, '', or '-'. Values must be alphanumeric, ';', '@', '', '-', '.', '+', or ' '. To unset a key, set the value to be an empty string.
{ "tag0": "val0", "tag1": "val1" }The index vector type. You can use 'dense' or 'sparse'. If 'dense', the vector dimension must be specified. If 'sparse', the vector dimension should not be specified.
Response
The index has been successfully created.
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 distance metric to be used for similarity search. You can use 'euclidean', 'cosine', or 'dotproduct'. If the 'vector_type' is 'sparse', the metric must be 'dotproduct'. If the vector_type is dense, the metric defaults to 'cosine'.
Possible values: cosine, euclidean, or dotproduct.
The URL address where the index is hosted.
"semantic-search-c01b5b5.svc.us-west1-gcp.pinecone.io"
The spec object defines how the index should be deployed.
- Serverless
- Pod-based
- BYOC
{
"pod": {
"environment": "us-east-1-aws",
"metadata_config": {
"indexed": ["genre", "title", "imdb_rating"]
},
"pod_type": "p1.x1",
"pods": 1,
"replicas": 1,
"shards": 1
}
}The current status of the index
{
"ready": true,
"state": "ScalingUpPodSize"
}The index vector type. You can use 'dense' or 'sparse'. If 'dense', the vector dimension must be specified. If 'sparse', the vector dimension should not be specified.
The dimensions of the vectors to be inserted in the index.
1 <= x <= 200001536
The private endpoint URL of an index.
"semantic-search-c01b5b5.svc.private.us-west1-gcp.pinecone.io"
Whether deletion protection is enabled/disabled for the index.
Possible values: disabled or enabled.
Custom user tags added to an index. Keys must be 80 characters or less. Values must be 120 characters or less. Keys must be alphanumeric, '', or '-'. Values must be alphanumeric, ';', '@', '', '-', '.', '+', or ' '. To unset a key, set the value to be an empty string.
{ "tag0": "val0", "tag1": "val1" }The embedding model and document fields mapped to embedding inputs.
{
"field_map": { "text": "your-text-field" },
"metric": "cosine",
"model": "multilingual-e5-large",
"read_parameters": { "input_type": "query", "truncate": "NONE" },
"write_parameters": { "input_type": "passage" }
}