This operation returns a list of all collections in a project.
Serverless indexes do not support collections.


List collections

The CollectionModel describes the configuration and status of a Pinecone collection.

The dimension of the vectors stored in each record held in the collection.

The environment where the collection is hosted.

The number of records stored in the collection.

ApiKeyAuth

This operation returns a list of all the collections in your current project.

Multiple collections with different states

The list of collections that exist in the project.

Unauthorized. Possible causes: Invalid API key.

The response shape used for all error responses.

Pinecone Docs

Overview

Pinecone Documentation

Database quickstart

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Key features

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Understanding indexes

Create an index

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Get started with sparse indexes

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List record IDs

Target an index

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Upsert and search with integrated inference

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Pinecone integrations enable you to build and deploy AI applications faster and more efficiently. Integrate Pinecone with your favorite frameworks, data sources, and infrastructure providers.

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Return all vectors in an index

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Embedding values changed when upserted

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Keep customer data separate in Pinecone

Limitations of querying by ID

Create and manage vectors with metadata

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Metadata string value returned as a datetime object

Remove a metadata field from a record

Index creation error - missing spec parameter

Serverless index creation error - max serverless indexes

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Error: Handshake read failed when connecting

PineconeAttribute errors with LangChain

Error: Cannot import name 'Pinecone' from 'pinecone'

Python AttributeError: module pinecone has no attribute init

Best Practices

Differences between Lexical and Semantic Search regarding relevancy

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Import data

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Introduction

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Known limitations

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List indexes

Create a Pinecone index. This is where you specify the measure of similarity, the dimension of vectors to be stored in the index, which cloud provider you would like to deploy with, and more.
  
For guidance and examples, see [Create an index](https://docs.pinecone.io/guides/indexes/create-an-index#create-a-serverless-index).


Create a serverless integrated inference index for a specific embedding model.

Refer to the [model guide](https://docs.pinecone.io/guides/inference/understanding-inference#embedding-models) for available models and model details.

Create an index for an embedding model

Describe an index

Delete an index

Configure 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](http://docs.pinecone.io/guides/indexes/pods/create-a-pod-based-index#create-a-pod-index-from-a-collection) from the collection. For guidance and examples, see [Configure an index](http://docs.pinecone.io/guides/indexes/pods/manage-pod-based-indexes).

Configure an index

Return statistics about the contents of an index, including the vector count per namespace, the number of dimensions, and the index fullness.

Serverless indexes scale automatically as needed, so index fullness is relevant only for pod-based indexes.

Get index stats

Upsert vectors into a namespace. If a new value is upserted for an existing vector ID, it will overwrite the previous value.

For guidance and examples, see [Upsert data](https://docs.pinecone.io/guides/data/upsert-data#upsert-vectors).

Upsert vectors

Upsert text into a namespace.

Upserting text is supported only for [indexes with integrated embedding](https://docs.pinecone.io/reference/api/2025-01/control-plane/create_for_model).

For guidance and examples, see [Upsert data](https://docs.pinecone.io/guides/data/upsert-data#upsert-text).

Upsert text

Look up and return vectors by ID from a single namespace. The returned vectors include the vector data and/or metadata.

For guidance and examples, see [Fetch data](https://docs.pinecone.io/guides/data/fetch-data).

Fetch vectors

Update a vector in a namespace. If a value is included, it will overwrite the previous value. If a `set_metadata` is included, the values of the fields specified in it will be added or overwrite the previous value.

For guidance and examples, see [Update data](https://docs.pinecone.io/guides/data/update-data).

Update a vector

Delete vectors by id from a single namespace.

For guidance and examples, see [Delete data](https://docs.pinecone.io/guides/data/delete-data).

Delete vectors

List the IDs of vectors in a single namespace of a serverless index. An optional prefix can be passed to limit the results to IDs with a common prefix.

Returns up to 100 IDs at a time by default in sorted order (bitwise "C" collation). If the `limit` parameter is set, `list` returns up to that number of IDs instead. Whenever there are additional IDs to return, the response also includes a `pagination_token` that you can use to get the next batch of IDs. When the response does not include a `pagination_token`, there are no more IDs to return.

For guidance and examples, see [List record IDs](https://docs.pinecone.io/guides/data/list-record-ids).

**Note:** `list` is supported only for serverless indexes.

List vector IDs

Search a namespace with a query vector or record ID and return the IDs of the most similar records, along with their similarity scores.

Search with a vector

APIs

Database

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List collections

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Response