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List indexes
This operation returns a list of all indexes in a project.
Authorizations
Response
The list of indexes that exist in the project.
The URL address where the index is hosted.
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'.
cosine, euclidean, dotproduct 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 - 45Configuration needed to deploy a pod-based index.
The environment where the index is hosted.
The type of pod to use. One of s1, p1, or p2 appended with . and one of x1, x2, x4, or x8.
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.
The number of pods to be used in the index. This should be equal to shards x replicas.'
x > 1The 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 name of the collection to be used as the source for the index.
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.
Whether deletion protection is enabled/disabled for the index.
disabled, enabled The dimensions of the vectors to be inserted in the index.
1 < x < 20000The embedding model and document fields mapped to embedding inputs.
The name of the embedding model used to create the index.
The dimensions of the vectors to be inserted in the index.
1 < x < 20000Identifies the name of the text field from your document model that is embedded.
The distance metric to be used for similarity search. You can use 'euclidean', 'cosine', or 'dotproduct'. If not specified, the metric will be defaulted according to the model. Cannot be updated once set.
cosine, euclidean, dotproduct The read parameters for the embedding model.
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 write parameters for the embedding model.
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.
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