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Upgrade to a paid plan and connect to GCP

Create a new organization connected to GCP

Next steps

Set up billing through GCP Marketplace

Pinecone Docs

Assistant examples

Overview

Pinecone Documentation

Database quickstart

Pinecone Database quickstart

Assistant quickstart

Pinecone Assistant quickstart

Key features

Build a RAG chatbot

Semantic search

Image search

Multimodal search

Recommender

Threat detection

More examples

Glossary

Understanding indexes

Create an index

Create a serverless index

Implement multitenancy

Implement multitenancy using namespaces

Manage indexes

Manage serverless indexes

Understanding backups

Back up an index

Restore an index

Understanding pod-based indexes

Migrate a pod-based index to serverless

Choose a pod type

Choose a pod type and size

Create a pod-based index

Manage pod-based indexes

Scale pod-based indexes

Sparse indexes

Get started with sparse indexes

Upsert data

Query data

Fetch data

Update data

Delete data

Understanding imports

Import data

List record IDs

Target an index

Understanding metadata

Manage RAG documents

Understanding data freshness

Check data freshness

Understanding hybrid search

Encode sparse vectors

Upsert sparse-dense vectors

Query sparse-dense vectors

Understanding Pinecone Inference

Embed data

Rerank documents

Upsert and search with integrated inference

Understanding Pinecone Assistant

Create an assistant

Manage files

Chat with an assistant

Manage assistants

Understanding evaluation

Evaluate answers

Understanding context snippets

Retrieve context snippets

Local development

Local development with Pinecone Local

Automated testing

CI/CD with Pinecone Local and GitHub Actions

Move to production

Performance tuning

Understanding security

Configure SSO with Okta

Configure CMEK

Configure customer-managed encryption keys

Connect to AWS PrivateLink

Manage storage integrations

Integrate with Amazon S3

Monitoring

Understanding organizations

Change your billing plan

Set up billing through AWS Marketplace

Set up billing through Azure Marketplace

Understanding cost

Monitor your usage

Manage cost

Manage organization members

Understanding projects

Create a project

Manage API keys

Manage project members

Rename a project

Introduction

API reference

Authentication

Versioning

API versioning

Database limits

Pinecone Database limits

Inference limits

Pinecone Inference limits

Assistant limits

Pinecone Assistant limits

Known limitations

Errors

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

Search a namespace with a query text, query vector, or record ID and return the most similar records, along with their similarity scores. Optionally, rerank the initial results based on their relevance to the query. 

Searching with text is supported only for [indexes with integrated embedding](https://docs.pinecone.io/guides/indexes/create-an-index#integrated-embedding). Searching with a query vector or record ID is supported for all indexes. 

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

Search with text

Start an asynchronous import of vectors from object storage into an index.

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

Start import

List all recent and ongoing import operations.

By default, `list_imports` returns up to 100 imports per page. If the `limit` parameter is set, `list` returns up to that number of imports 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 imports. When the response does not include a `pagination_token`, there are no more imports to return.

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

List imports

Return details of a specific import operation.

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

Describe an import

Cancel an import operation if it is not yet finished. It has no effect if the operation is already finished.

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

Cancel an import

List all collections in a project.
Serverless indexes do not support collections.


List collections

Create a Pinecone collection.
  
Serverless indexes do not support collections.


Create a collection

Get a description of a collection.
Serverless indexes do not support collections.


Describe a collection

Delete an existing collection.
Serverless indexes do not support collections.


Delete a collection

This operation returns a list of all indexes in a project.

Create an index for vectors created with an external embedding model.

For guidance and examples, see [Create an index](https://docs.pinecone.io/guides/indexes/create-an-index#external-embedding).

Create an index with integrated embedding.

With this type of index, you provide source text, and Pinecone uses a [hosted embedding model](https://docs.pinecone.io/guides/inference/understanding-inference#embedding-models) to convert the text automatically during [upsert](https://docs.pinecone.io/reference/api/2025-01/data-plane/upsert_records) and [search](https://docs.pinecone.io/reference/api/2025-01/data-plane/search_records).

For guidance and examples, see [Create an index](https://docs.pinecone.io/guides/indexes/create-an-index#integrated-embedding).

Create an index with integrated embedding

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).

Upsert source text into a namespace. Pinecone converts the text to vectors automatically using the hosted embedding model associated with the index.

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).

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).

Delete vectors, by id, from a single namespace.

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

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.

This 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.

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

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

List all recent and ongoing import operations.

By default, this returns up to 100 imports per page. If the `limit` parameter is set, `list` returns up to that number of imports 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 imports. When the response does not include a `pagination_token`, there are no more imports to return.

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

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


This operation creates a Pinecone collection.
  
Serverless indexes do not support collections.


This operation gets a description of a collection.
Serverless indexes do not support collections.


This operation deletes an existing collection.
Serverless indexes do not support collections.


This operation deploys 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).


This operation deletes an existing index.

This operation configures an existing index. 

For serverless indexes, you can configure only index deletion protection and tags. 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).

The `describe_index_stats` operation returns 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.

The `upsert` operation writes 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).

The `query` operation searches a namespace, using a query vector. It retrieves the ids of the most similar items in a namespace, along with their similarity scores.

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

Query vectors

The `fetch` operation looks up and returns 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).

The `update` operation updates 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).

The `delete` operation deletes vectors, by id, from a single namespace.

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

The `list` operation lists 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.

By default, `list` returns up to 100 IDs per page 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_vectors` is supported only for serverless indexes.

The `start_import` operation starts an asynchronous import of vectors from object storage into an index. 

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

The `list_imports` operation lists all recent and ongoing import operations. 

By default, `list_imports` returns up to 100 imports per page. If the `limit` parameter is set, `list` returns up to that number of imports 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 imports. When the response does not include a `pagination_token`, there are no more imports to return.

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

The `describe_import` operation returns details of a specific import operation. 

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

The `cancel_import` operation cancels an import operation if it is not yet finished. It has no effect if the operation is already finished. 

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

This operation deploys 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).


This operation configures the pod size and number of replicas for a pod-based index.

It is not possible to change the pod type of an index. However, you can create a collection from an index and then [create a new 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.

This operation returns a list of all collections in a project.

Serverless indexes do not support collections.


This operation creates a Pinecone collection.

Serverless indexes do not support collections.


This operation gets a description of a collection.

Serverless indexes do not support collections.


This operation deletes an existing collection.

Serverless indexes do not support collections.


Generate embeddings for input data.

For guidance and examples, see [Generate embeddings](https://docs.pinecone.io/guides/inference/generate-embeddings).

Rerank documents according to their relevance to a query.

For guidance and examples, see [Rerank documents](https://docs.pinecone.io/guides/inference/rerank).

[Generate vector embeddings](https://docs.pinecone.io/guides/inference/generate-embeddings) for input data. This endpoint uses [Pinecone Inference](https://docs.pinecone.io/guides/inference/understanding-inference).

Generate vectors

[Rerank query results](https://docs.pinecone.io/guides/inference/rerank) according to their relevance to a query. This endpoint uses [Pinecone Inference](https://docs.pinecone.io/guides/inference/understanding-inference).

Rerank results

List of all assistants in a project.

For guidance and examples, see [Manage assistants](https://docs.pinecone.io/guides/assistant/manage-assistants#list-assistants-for-a-project).

List assistants

Create an assistant. This is where you specify the underlying training model, which cloud provider you would like to deploy with, and more.

For guidance and examples, see [Create an assistant](https://docs.pinecone.io/guides/assistant/create-assistant)

Get the status of an assistant.

For guidance and examples, see [Manage assistants](https://docs.pinecone.io/guides/assistant/manage-assistants#get-the-status-of-an-assistant)

Check assistant status

Update an existing assistant. You can modify the assistant's instructions.

For guidance and examples, see [Manage assistants](https://docs.pinecone.io/guides/assistant/manage-assistants#add-instructions-to-an-assistant).

Get started

Indexes

Data

Inference

Operations

Admin

Set up billing through GCP Marketplace

Upgrade to a paid plan and connect to GCP

Create a new organization connected to GCP

Next steps

Get started

Indexes

Data

Inference

Operations

Admin

​Upgrade to a paid plan and connect to GCP

​Create a new organization connected to GCP

​Next steps

Upgrade to a paid plan and connect to GCP

Create a new organization connected to GCP

Next steps