Configure an index
This operation configures an existing index.
For serverless indexes, you can configure index deletion protection, tags, and integrated inference embedding settings for the index. For pod-based indexes, you can configure the pod size, number of replicas, tags, and index deletion protection.
It is not possible to change the pod type of a pod-based index. However, you can create a collection from a pod-based index and then create a new pod-based index with a different pod type from the collection. For guidance and examples, see Configure an index.
Path Parameters
The name of the index to configure.
Body
Configuration used to scale an index.
Whether deletion protection is enabled/disabled for the index.
disabled, 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.
Configure the integrated inference embedding settings for this index.
You can convert an existing index to an integrated index by specifying the embedding model and field_map. The index vector type and dimension must match the model vector type and dimension, and the index similarity metric must be supported by the model. Refer to the model guide for available models and model details.
You can later change the embedding configuration to update the field map, read parameters, or write parameters. Once set, the model cannot be changed.
Response
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 - 45The 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 URL address where the index is hosted.
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 < 20000Whether deletion protection is enabled/disabled for the index.
disabled, 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.
The embedding model and document fields mapped to embedding inputs.
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