# Pinecone Docs

> Official Pinecone documentation for the vector database, Assistant, inference APIs, SDKs, and building production search and AI applications.

- [Pinecone Database (494 pages)](https://docs.pinecone.io/_llms/pinecone-database.md): Documentation for Pinecone Database.

## Get started

- [Pinecone Assistant](https://docs.pinecone.io/guides/assistant/overview.md): Overview of Pinecone Assistant, a managed service for building production-grade RAG chat and agent applications grounded in your data.

## Quickstart

- [Pinecone Assistant: SDK quickstart](https://docs.pinecone.io/guides/assistant/quickstart/sdk-quickstart.md): Quickstart for Pinecone Assistant using the Python or Node.js SDK to create an assistant, upload documents, and chat with your data via RAG.
- [Pinecone Assistant: n8n quickstart](https://docs.pinecone.io/guides/assistant/quickstart/n8n-quickstart.md): Build an n8n workflow with Pinecone Assistant and OpenAI to download files via HTTP, upload documents, and chat with them from an automation.

## Get started

- [Pricing and limits](https://docs.pinecone.io/guides/assistant/pricing-and-limits.md): Understand Pinecone Assistant pricing for ingestion units, chat tokens, and storage, plus plan-based service limits for file uploads and knowledge size.

## Build an assistant

- [Create an assistant](https://docs.pinecone.io/guides/assistant/create-assistant.md): Create a Pinecone Assistant with custom instructions, metadata, and region settings using the API, Python SDK, Node.js SDK, or console.
- [Manage assistants](https://docs.pinecone.io/guides/assistant/manage-assistants.md): List, describe, update, and delete Pinecone assistants and check assistant status using the API, Python SDK, Node.js SDK, or Pinecone console.

## Upload your data

- [Files in Pinecone Assistant](https://docs.pinecone.io/guides/assistant/files-overview.md): Overview of Pinecone Assistant files: supported file types (PDF, DOCX, JSON, MD, TXT), metadata filters, storage, and signed URL access.
- [Upload files](https://docs.pinecone.io/guides/assistant/upload-files.md): Upload local files to a Pinecone assistant with Python, JavaScript, or curl, track ingestion operations, and see how uploads are billed as ingestion units.
- [Multimodal context for assistants](https://docs.pinecone.io/guides/assistant/multimodal.md): Enable multimodal PDF context in Pinecone Assistant to analyze charts, images, and diagrams using OCR and visual understanding for RAG.
- [Manage files](https://docs.pinecone.io/guides/assistant/manage-files.md): List files in your Pinecone assistant, check individual file ingestion status by ID, view metadata, and delete files using the API, SDKs, or console.

## Chat with an assistant

- [Chat through the standard interface](https://docs.pinecone.io/guides/assistant/chat-with-assistant.md): Chat with Pinecone Assistant through the standard interface with default, streaming, or JSON responses, plus citations and chat history support.
- [Chat through the OpenAI-compatible interface](https://docs.pinecone.io/guides/assistant/chat-through-the-openai-compatible-interface.md): Chat with Pinecone Assistant using the OpenAI-compatible Chat Completion API for inline citations, streaming responses, and easy integration.

## Evaluate answers

- [Evaluation overview](https://docs.pinecone.io/guides/assistant/evaluation-overview.md): Overview of Pinecone Assistant response evaluation: measure correctness, completeness, and alignment scores to benchmark RAG system quality.
- [Evaluate answers](https://docs.pinecone.io/guides/assistant/evaluate-answers.md): Evaluate RAG system answers with Pinecone Assistant using correctness, completeness, and alignment metrics against a ground truth answer.

## Retrieve context snippets

- [Context snippets overview](https://docs.pinecone.io/guides/assistant/context-snippets-overview.md): Learn how Pinecone Assistant retrieves context snippets with relevancy scores and references for RAG applications and agentic workflows.
- [Retrieve context snippets](https://docs.pinecone.io/guides/assistant/retrieve-context-snippets.md): Retrieve context snippets and citations from a Pinecone Assistant to power your own LLM, RAG application, or agentic workflow with signed URLs.

## Integrate with AI agents

- [Use an Assistant MCP server](https://docs.pinecone.io/guides/assistant/mcp-server.md): Connect AI agents to a Pinecone Assistant MCP server via remote or local Model Context Protocol endpoints, including Cursor and Claude Desktop.

## Manage billing

- [Upgrade your plan](https://docs.pinecone.io/guides/assistant/admin/upgrade-billing-plan.md): Learn how Pinecone Assistant admins upgrade to a paid plan to unlock higher limits, more files, evaluations, and advanced access controls.
- [Change your payment method](https://docs.pinecone.io/guides/assistant/admin/change-payment-method.md): Update the credit card or payment method on file for your Pinecone organization to keep billing details current and avoid failed charges.
- [Downgrade your plan](https://docs.pinecone.io/guides/assistant/admin/downgrade-billing-plan.md): Downgrade your Pinecone subscription from a paid tier back to the free Starter plan, including steps to review usage limits before switching.
- [Download a usage report](https://docs.pinecone.io/guides/assistant/admin/download-usage-report.md): Export a detailed Pinecone usage and cost report for your organization to analyze index consumption, credits, and monthly billing charges.
- [Access your invoices](https://docs.pinecone.io/guides/assistant/admin/access-your-invoices.md): View, download, and manage your Pinecone billing invoices from the console, including past invoice history, payment status, and PDF exports.

## Manage security

- [Security overview](https://docs.pinecone.io/guides/assistant/admin/security-overview.md): Overview of Pinecone Assistant admin security features including API keys, single sign-on, service accounts, and audit logs across your organization.
- [Manage roles and access](https://docs.pinecone.io/guides/assistant/admin/manage-rbac.md): Assign and manage roles for users, service accounts, and API keys using the Pinecone console or the Admin API.
- [Configure SSO with Okta](https://docs.pinecone.io/guides/assistant/admin/configure-sso-with-okta.md): Integrate Okta with Pinecone to enable single sign-on, configure SAML settings, and manage secure user authentication for your organization.
- [Configure audit logs](https://docs.pinecone.io/guides/assistant/admin/configure-audit-logs.md): Set up Pinecone audit logs streamed to an Amazon S3 bucket to track user, service account, and API actions across the control plane.

## Manage organizations

- [Organizations overview](https://docs.pinecone.io/guides/assistant/admin/organizations-overview.md): Learn how Pinecone organizations group projects under shared billing, and how organization owners, members, and roles control access and permissions.
- [Monitor usage and cost](https://docs.pinecone.io/guides/assistant/admin/monitor-spend-and-usage.md): Monitor Pinecone Assistant usage and cost, set monthly spend alerts, and track token usage across chat, context retrieval, and evaluation.
- [Manage organization members](https://docs.pinecone.io/guides/assistant/admin/manage-organization-members.md): Invite new members to your Pinecone organization, change their organization roles, and remove users to control access across all projects.
- [Manage service accounts at the organization-level](https://docs.pinecone.io/guides/assistant/admin/manage-organization-service-accounts.md): Create and manage organization-level Pinecone service accounts, retrieve access tokens, and grant programmatic access to the Admin API.

## Manage projects

- [Projects overview](https://docs.pinecone.io/guides/assistant/admin/projects-overview.md): Understand how Pinecone projects contain assistants, indexes, and users, and review project owner and project user roles and their permissions.
- [Create a project](https://docs.pinecone.io/guides/assistant/admin/create-a-project.md): Create a new Pinecone project in the console or through the Admin API, add tags, and optionally enable a customer-managed encryption key (CMEK).
- [Manage projects](https://docs.pinecone.io/guides/assistant/admin/manage-projects.md): View project details, rename projects, add project tags, and delete Pinecone projects using the console or the Admin API with an access token.
- [Manage project members](https://docs.pinecone.io/guides/assistant/admin/manage-project-members.md): Add users to a Pinecone project, assign project owner or user roles, edit permissions, and remove members to control access to indexes and API keys.
- [Manage API keys](https://docs.pinecone.io/guides/assistant/admin/manage-api-keys.md): Create, view, update, and delete Pinecone API keys for a project, plus assign custom permission roles to control data plane and control plane access.
- [Manage service accounts at the project-level](https://docs.pinecone.io/guides/assistant/admin/manage-project-service-accounts.md): Add and manage project-level service accounts in Pinecone Assistant to enable programmatic Admin API access, roles, and permissions.

## APIs

- [Assistant API reference](https://docs.pinecone.io/reference/api/assistant/introduction.md): Assistant API reference overview covering document upload, chat, and RAG endpoints supported by the Pinecone Python and Node.js SDKs.
- [Authentication](https://docs.pinecone.io/reference/api/assistant/authentication.md): Assistant API authentication with API keys, HTTP headers, and SDK client initialization for Pinecone Assistant requests and RAG apps.
- [Pinecone Assistant limits](https://docs.pinecone.io/reference/api/assistant/assistant-limits.md): Reference for Pinecone Assistant limits, including file size, storage, chat token, and rate limits across the Starter, Builder, Standard, and Enterprise plans.

## Assistants

- [List assistants](https://docs.pinecone.io/reference/api/2026-04/assistant/list_assistants.md): List of all assistants in a project.
- [Create an assistant](https://docs.pinecone.io/reference/api/2026-04/assistant/create_assistant.md): Create an assistant. This is where you specify the underlying training model, which cloud provider you would like to deploy with, and more.
- [Check assistant status](https://docs.pinecone.io/reference/api/2026-04/assistant/describe_assistant.md): Get the status of an assistant.
- [Update an assistant](https://docs.pinecone.io/reference/api/2026-04/assistant/update_assistant.md): Update an existing assistant. You can modify the assistant's instructions.
- [Delete an assistant](https://docs.pinecone.io/reference/api/2026-04/assistant/delete_assistant.md): Delete an existing assistant.

## Files

- [List Files](https://docs.pinecone.io/reference/api/2026-04/assistant/list_files.md): List all files in an assistant, with an option to filter files with metadata.
- [Upload a file](https://docs.pinecone.io/reference/api/2026-04/assistant/upload_file.md): Upload a file to the specified assistant.
- [Upsert a file](https://docs.pinecone.io/reference/api/2026-04/assistant/upsert_file.md): Create or replace a file in the specified assistant. If a file with the given `assistant_file_id` already exists, it will be replaced with the new file. If it doesn't exist, a new file will be created with that identifier.
- [Describe a file](https://docs.pinecone.io/reference/api/2026-04/assistant/describe_file.md): [Get the current status and metadata of a file](https://docs.pinecone.io/guides/assistant/manage-files#get-the-status-of-a-file) uploaded to an assistant.
- [Delete a file](https://docs.pinecone.io/reference/api/2026-04/assistant/delete_file.md): [Delete an uploaded file](https://docs.pinecone.io/guides/assistant/manage-files#delete-a-file) from an assistant.

## Operations

- [List operations](https://docs.pinecone.io/reference/api/2026-04/assistant/list_operations.md): List all operations for an assistant. Returns operations that are in progress, as well as recently completed or failed operations. Both successful and failed operations are retained for 30 days after completion. Use the `operation_type` and `status` query parameters to filter results.
- [Describe an operation](https://docs.pinecone.io/reference/api/2026-04/assistant/describe_operation.md): Get the status of an operation.

## Chat

- [Chat with an assistant](https://docs.pinecone.io/reference/api/2026-04/assistant/chat_assistant.md): Chat with an assistant and get back citations in structured form.
- [Chat through an OpenAI-compatible interface](https://docs.pinecone.io/reference/api/2026-04/assistant/chat_completion_assistant.md): Chat with an assistant. This endpoint is based on the OpenAI Chat Completion API, a commonly used and adopted API.

## Evaluation

- [Evaluate an answer](https://docs.pinecone.io/reference/api/2026-04/assistant/metrics_alignment.md): Evaluate the correctness and completeness of a response from an assistant or a RAG system. The correctness and completeness are evaluated based on the precision and recall of the generated answer with respect to the ground truth answer facts. Alignment is the harmonic mean of correctness and complet…

## Context snippets

- [Retrieve context from an assistant](https://docs.pinecone.io/reference/api/2026-04/assistant/context_assistant.md): Retrieve context snippets from an assistant to use as part of RAG or any agentic flow.

## API keys

- [Create an API key](https://docs.pinecone.io/reference/api/2026-04/admin-assistant/create_api_key.md): Create an API key for a project to authenticate Data Plane and Control Plane requests.
- [List API keys](https://docs.pinecone.io/reference/api/2026-04/admin-assistant/list_api_keys.md): List all API keys in a project.
- [Get API key details](https://docs.pinecone.io/reference/api/2026-04/admin-assistant/fetch_api_key.md): Get an API key's details, excluding its secret.
- [Update an API key](https://docs.pinecone.io/reference/api/2026-04/admin-assistant/update_api_key.md): Update an API key's name and roles.
- [Delete an API key](https://docs.pinecone.io/reference/api/2026-04/admin-assistant/delete_api_key.md): Delete an API key from a project.

## Projects

- [Create a new project](https://docs.pinecone.io/reference/api/2026-04/admin-assistant/create_project.md): Create a new project.
- [List projects](https://docs.pinecone.io/reference/api/2026-04/admin-assistant/list_projects.md): List all projects in an organization.
- [Get project details](https://docs.pinecone.io/reference/api/2026-04/admin-assistant/fetch_project.md): Get a project's details.
- [Update a project](https://docs.pinecone.io/reference/api/2026-04/admin-assistant/update_project.md): Update a project's name, maximum number of Pods, or customer-managed encryption key (CMEK).
- [Delete a project](https://docs.pinecone.io/reference/api/2026-04/admin-assistant/delete_project.md): Delete a project and all its configuration; delete its indexes, assistants, backups, and collections first.

## Service accounts

- [Create an access token](https://docs.pinecone.io/reference/api/2026-04/admin-assistant/get_token.md): Obtain an access token for a service account using the OAuth2 client credentials flow. An access token is needed to authorize requests to the Pinecone Admin API. The host domain for OAuth endpoints is `login.pinecone.io`.

## Architecture

- [Pinecone Assistant architecture](https://docs.pinecone.io/reference/architecture/assistant-architecture.md): Pinecone Assistant architecture overview covering document ingestion, chunking, vector retrieval, and LLM response generation for RAG.

## APIs

- [Assistant API reference](https://docs.pinecone.io/reference/api/assistant/introduction.md): Assistant API reference overview covering document upload, chat, and RAG endpoints supported by the Pinecone Python and Node.js SDKs.
- [Authentication](https://docs.pinecone.io/reference/api/assistant/authentication.md): Assistant API authentication with API keys, HTTP headers, and SDK client initialization for Pinecone Assistant requests and RAG apps.
- [Pinecone Assistant limits](https://docs.pinecone.io/reference/api/assistant/assistant-limits.md): Reference for Pinecone Assistant limits, including file size, storage, chat token, and rate limits across the Starter, Builder, Standard, and Enterprise plans.

## Assistants

- [List assistants](https://docs.pinecone.io/reference/api/2025-10/assistant/list_assistants.md): List of all assistants in a project.
- [Create an assistant](https://docs.pinecone.io/reference/api/2025-10/assistant/create_assistant.md): Create an assistant. This is where you specify the underlying training model, which cloud provider you would like to deploy with, and more.
- [Check assistant status](https://docs.pinecone.io/reference/api/2025-10/assistant/describe_assistant.md): Get the status of an assistant.
- [Update an assistant](https://docs.pinecone.io/reference/api/2025-10/assistant/update_assistant.md): Update an existing assistant. You can modify the assistant's instructions.
- [Delete an assistant](https://docs.pinecone.io/reference/api/2025-10/assistant/delete_assistant.md): Delete an existing assistant.

## Files

- [List Files](https://docs.pinecone.io/reference/api/2025-10/assistant/list_files.md): List all files in an assistant, with an option to filter files with metadata.
- [Upload file to assistant](https://docs.pinecone.io/reference/api/2025-10/assistant/upload_file.md): Upload a file to the specified assistant.
- [Describe a file upload](https://docs.pinecone.io/reference/api/2025-10/assistant/describe_file.md): Get the status and metadata of a file uploaded to an assistant.
- [Delete an uploaded file](https://docs.pinecone.io/reference/api/2025-10/assistant/delete_file.md): Delete an uploaded file from an assistant.

## Chat

- [Chat with an assistant](https://docs.pinecone.io/reference/api/2025-10/assistant/chat_assistant.md): Chat with an assistant and get back citations in structured form.
- [Chat through an OpenAI-compatible interface](https://docs.pinecone.io/reference/api/2025-10/assistant/chat_completion_assistant.md): Chat with an assistant. This endpoint is based on the OpenAI Chat Completion API, a commonly used and adopted API.

## Evaluation

- [Evaluate an answer](https://docs.pinecone.io/reference/api/2025-10/assistant/metrics_alignment.md): Evaluate the correctness and completeness of a response from an assistant or a RAG system. The correctness and completeness are evaluated based on the precision and recall of the generated answer with respect to the ground truth answer facts. Alignment is the harmonic mean of correctness and complet…

## Context snippets

- [Retrieve context from an assistant](https://docs.pinecone.io/reference/api/2025-10/assistant/context_assistant.md): Retrieve context snippets from an assistant to use as part of RAG or any agentic flow.

## API keys

- [Create an API key](https://docs.pinecone.io/reference/api/2025-10/admin-assistant/create_api_key.md): Create a new API key for a project. Developers can use the API key to authenticate requests to Pinecone's Data Plane and Control Plane APIs.
- [List API keys](https://docs.pinecone.io/reference/api/2025-10/admin-assistant/list_api_keys.md): List all API keys in a project.
- [Get API key details](https://docs.pinecone.io/reference/api/2025-10/admin-assistant/fetch_api_key.md): Get the details of an API key, excluding the API key secret.
- [Update an API key](https://docs.pinecone.io/reference/api/2025-10/admin-assistant/update_api_key.md): Update the name and roles of an API key.
- [Delete an API key](https://docs.pinecone.io/reference/api/2025-10/admin-assistant/delete_api_key.md): Delete an API key from a project.

## Projects

- [Create a new project](https://docs.pinecone.io/reference/api/2025-10/admin-assistant/create_project.md): Creates a new project.
- [List projects](https://docs.pinecone.io/reference/api/2025-10/admin-assistant/list_projects.md): List all projects in an organization.
- [Get project details](https://docs.pinecone.io/reference/api/2025-10/admin-assistant/fetch_project.md): Get details about a project.
- [Update a project](https://docs.pinecone.io/reference/api/2025-10/admin-assistant/update_project.md): Update a project's configuration details. You can update the project's name, maximum number of Pods, or enable encryption with a customer-managed encryption key (CMEK).
- [Delete a project](https://docs.pinecone.io/reference/api/2025-10/admin-assistant/delete_project.md): Delete a project and all its associated configuration. Before deleting a project, you must delete all indexes, assistants, backups, and collections associated with the project. Other project resources, such as API keys, are automatically deleted when the project is deleted.

## Service accounts

- [Create an access token](https://docs.pinecone.io/reference/api/2025-10/admin-assistant/get_token.md): Obtain an access token for a service account using the OAuth2 client credentials flow. An access token is needed to authorize requests to the Pinecone Admin API. The host domain for OAuth endpoints is `login.pinecone.io`.

## Architecture

- [Pinecone Assistant architecture](https://docs.pinecone.io/reference/architecture/assistant-architecture.md): Pinecone Assistant architecture overview covering document ingestion, chunking, vector retrieval, and LLM response generation for RAG.

## APIs

- [Assistant API reference](https://docs.pinecone.io/reference/api/assistant/introduction.md): Assistant API reference overview covering document upload, chat, and RAG endpoints supported by the Pinecone Python and Node.js SDKs.
- [Authentication](https://docs.pinecone.io/reference/api/assistant/authentication.md): Assistant API authentication with API keys, HTTP headers, and SDK client initialization for Pinecone Assistant requests and RAG apps.
- [Pinecone Assistant limits](https://docs.pinecone.io/reference/api/assistant/assistant-limits.md): Reference for Pinecone Assistant limits, including file size, storage, chat token, and rate limits across the Starter, Builder, Standard, and Enterprise plans.

## Assistants

- [List assistants](https://docs.pinecone.io/reference/api/2025-04/assistant/list_assistants.md): List of all assistants in a project.
- [Create an assistant](https://docs.pinecone.io/reference/api/2025-04/assistant/create_assistant.md): Create an assistant. This is where you specify the underlying training model, which cloud provider you would like to deploy with, and more.
- [Check assistant status](https://docs.pinecone.io/reference/api/2025-04/assistant/describe_assistant.md): Get the status of an assistant.
- [Update an assistant](https://docs.pinecone.io/reference/api/2025-04/assistant/update_assistant.md): Update an existing assistant. You can modify the assistant's instructions.
- [Delete an assistant](https://docs.pinecone.io/reference/api/2025-04/assistant/delete_assistant.md): Delete an existing assistant.

## Files

- [List Files](https://docs.pinecone.io/reference/api/2025-04/assistant/list_files.md): List all files in an assistant, with an option to filter files with metadata.
- [Upload file to assistant](https://docs.pinecone.io/reference/api/2025-04/assistant/upload_file.md): Upload a file to the specified assistant.
- [Describe a file upload](https://docs.pinecone.io/reference/api/2025-04/assistant/describe_file.md): Get the status and metadata of a file uploaded to an assistant.
- [Delete an uploaded file](https://docs.pinecone.io/reference/api/2025-04/assistant/delete_file.md): Delete an uploaded file from an assistant.

## Chat

- [Chat with an assistant](https://docs.pinecone.io/reference/api/2025-04/assistant/chat_assistant.md): Chat with an assistant and get back citations in structured form.
- [Chat through an OpenAI-compatible interface](https://docs.pinecone.io/reference/api/2025-04/assistant/chat_completion_assistant.md): Chat with an assistant. This endpoint is based on the OpenAI Chat Completion API, a commonly used and adopted API.

## Evaluation

- [Evaluate an answer](https://docs.pinecone.io/reference/api/2025-04/assistant/metrics_alignment.md): Evaluate the correctness and completeness of a response from an assistant or a RAG system. The correctness and completeness are evaluated based on the precision and recall of the generated answer with respect to the ground truth answer facts. Alignment is the harmonic mean of correctness and complet…

## Context snippets

- [Retrieve context from an assistant](https://docs.pinecone.io/reference/api/2025-04/assistant/context_assistant.md): Retrieve context snippets from an assistant to use as part of RAG or any agentic flow.

## API keys

- [Create an API key](https://docs.pinecone.io/reference/api/2025-04/admin-assistant/create_api_key.md): Create a new API key for a project. Developers can use the API key to authenticate requests to Pinecone's Data Plane and Control Plane APIs.
- [List API keys](https://docs.pinecone.io/reference/api/2025-04/admin-assistant/list_api_keys.md): List all API keys in a project.
- [Get API key details](https://docs.pinecone.io/reference/api/2025-04/admin-assistant/fetch_api_key.md): Get the details of an API key, excluding the API key secret.
- [Update an API key](https://docs.pinecone.io/reference/api/2025-04/admin-assistant/update_api_key.md): Update the name and roles of an API key.
- [Delete an API key](https://docs.pinecone.io/reference/api/2025-04/admin-assistant/delete_api_key.md): Delete an API key from a project.

## Projects

- [Create a new project](https://docs.pinecone.io/reference/api/2025-04/admin-assistant/create_project.md): Creates a new project.
- [List projects](https://docs.pinecone.io/reference/api/2025-04/admin-assistant/list_projects.md): List all projects in an organization.
- [Get project details](https://docs.pinecone.io/reference/api/2025-04/admin-assistant/fetch_project.md): Get details about a project.
- [Update a project](https://docs.pinecone.io/reference/api/2025-04/admin-assistant/update_project.md): Update a project's configuration details. You can update the project's name, maximum number of Pods, or enable encryption with a customer-managed encryption key (CMEK).
- [Delete a project](https://docs.pinecone.io/reference/api/2025-04/admin-assistant/delete_project.md): Delete a project and all its associated configuration. Before deleting a project, you must delete all indexes, assistants, backups, and collections associated with the project. Other project resources, such as API keys, are automatically deleted when the project is deleted.

## Service accounts

- [Get an access token](https://docs.pinecone.io/reference/api/2025-04/admin-assistant/get_token.md): Obtain an access token for a service account using the OAuth2 client credentials flow. An access token is needed to authorize requests to the Pinecone Admin API. The host domain for OAuth endpoints is `login.pinecone.io`.

## Architecture

- [Pinecone Assistant architecture](https://docs.pinecone.io/reference/architecture/assistant-architecture.md): Pinecone Assistant architecture overview covering document ingestion, chunking, vector retrieval, and LLM response generation for RAG.

## APIs

- [Assistant API reference](https://docs.pinecone.io/reference/api/assistant/introduction.md): Assistant API reference overview covering document upload, chat, and RAG endpoints supported by the Pinecone Python and Node.js SDKs.
- [Authentication](https://docs.pinecone.io/reference/api/assistant/authentication.md): Assistant API authentication with API keys, HTTP headers, and SDK client initialization for Pinecone Assistant requests and RAG apps.
- [Pinecone Assistant limits](https://docs.pinecone.io/reference/api/assistant/assistant-limits.md): Reference for Pinecone Assistant limits, including file size, storage, chat token, and rate limits across the Starter, Builder, Standard, and Enterprise plans.

## Assistants

- [List assistants](https://docs.pinecone.io/reference/api/2025-01/assistant/list_assistants.md): List of all assistants in a project.
- [Create an assistant](https://docs.pinecone.io/reference/api/2025-01/assistant/create_assistant.md): Create an assistant. This is where you specify the underlying training model, which cloud provider you would like to deploy with, and more.
- [Check assistant status](https://docs.pinecone.io/reference/api/2025-01/assistant/describe_assistant.md): Get the status of an assistant.
- [Update an assistant](https://docs.pinecone.io/reference/api/2025-01/assistant/update_assistant.md): Update an existing assistant. You can modify the assistant's instructions.
- [Delete an assistant](https://docs.pinecone.io/reference/api/2025-01/assistant/delete_assistant.md): Delete an existing assistant.

## Files

- [List Files](https://docs.pinecone.io/reference/api/2025-01/assistant/list_files.md): List all files in an assistant, with an option to filter files with metadata.
- [Upload file to assistant](https://docs.pinecone.io/reference/api/2025-01/assistant/upload_file.md): Upload a file to the specified assistant.
- [Describe a file upload](https://docs.pinecone.io/reference/api/2025-01/assistant/describe_file.md): Get the status and metadata of a file uploaded to an assistant.
- [Delete an uploaded file](https://docs.pinecone.io/reference/api/2025-01/assistant/delete_file.md): Delete an uploaded file from an assistant.

## Chat

- [Chat with an assistant](https://docs.pinecone.io/reference/api/2025-01/assistant/chat_assistant.md): Chat with an assistant and get back citations in structured form.
- [Chat through an OpenAI-compatible interface](https://docs.pinecone.io/reference/api/2025-01/assistant/chat_completion_assistant.md): Chat with an assistant. This endpoint is based on the OpenAI Chat Completion API, a commonly used and adopted API.

## Evaluation

- [Evaluate an answer](https://docs.pinecone.io/reference/api/2025-01/assistant/metrics_alignment.md): Evaluate the correctness and completeness of a response from an assistant or a RAG system. The correctness and completeness are evaluated based on the precision and recall of the generated answer with respect to the ground truth answer facts. Alignment is the harmonic mean of correctness and complet…

## Context snippets

- [Retrieve context from an assistant](https://docs.pinecone.io/reference/api/2025-01/assistant/context_assistant.md): Retrieve context snippets from an assistant to use as part of RAG or any agentic flow.

## Architecture

- [Pinecone Assistant architecture](https://docs.pinecone.io/reference/architecture/assistant-architecture.md): Pinecone Assistant architecture overview covering document ingestion, chunking, vector retrieval, and LLM response generation for RAG.

## Examples

- [Assistant examples](https://docs.pinecone.io/examples/assistant.md): Notebooks and Next.js sample apps for Pinecone Assistant: quickstart, context snippet retrieval, and a full-stack chat UI with citations.

## Release notes

- [2026 releases](https://docs.pinecone.io/assistant-release-notes/2026.md): Pinecone release notes for 2026: new features, improvements, and API changes.
- [2025 releases](https://docs.pinecone.io/assistant-release-notes/2025.md): Pinecone release notes for 2025: new features, improvements, and API changes.
- [2024 releases](https://docs.pinecone.io/assistant-release-notes/2024.md): Pinecone release notes for 2024: new features, improvements, and API changes.

## Policies

- [Feature availability](https://docs.pinecone.io/assistant-release-notes/feature-availability.md): Pinecone feature availability across public preview, general availability, and limited availability releases, with links to release notes.

## Get started

- [Pinecone Nexus](https://docs.pinecone.io/guides/nexus/overview.md): Pinecone Nexus is the knowledge engine for agents. It compiles your data into queryable knowledge once, then serves grounded, cited answers to agents on every call.
- [Nexus quickstart](https://docs.pinecone.io/guides/nexus/quickstart.md): Deploy Pinecone Nexus with BYOC, curate a context from your own documents, and run KnowQL queries that return grounded, cited multi-document answers.
- [Nexus key concepts](https://docs.pinecone.io/guides/nexus/concepts.md): Learn the core Pinecone Nexus concepts: sources, workspaces, contexts, manifests, artifacts, tasks, sessions, and the Query API, and how they connect.

## The knowledge engine

- [How Nexus works](https://docs.pinecone.io/guides/nexus/how-it-works.md): How Nexus distills curated artifacts for retrieval, then answers with code over a retrieval SDK.
- [Context design](https://docs.pinecone.io/guides/nexus/context-design.md): The manifest, the information stack (chunks, artifacts, and edges), and the artifact-kind reference.
- [Curation](https://docs.pinecone.io/guides/nexus/how-curation-works.md): The write path that turns sources into chunks and artifacts, incrementally, driven by the manifest.

## Queries and sessions

- [Queries](https://docs.pinecone.io/guides/nexus/how-queries-work.md): The query runtime that gathers evidence and composes a grounded, cited answer.
- [Query tracing](https://docs.pinecone.io/guides/nexus/query-tracing.md): Inspect a Pinecone Nexus query trace to see reasoning steps, retrieval tool calls, token usage, latency, cost breakdown, and cache savings.

## Tools

- [MCP server](https://docs.pinecone.io/guides/nexus/mcp-server.md): Query your Pinecone Nexus contexts from any Model Context Protocol client, such as Claude Desktop, over Streamable HTTP.

## Bring your own cloud

- [Nexus BYOC overview](https://docs.pinecone.io/guides/nexus/byoc/overview.md): What Nexus bring your own cloud is, how it relates to Database BYOC, and its architecture.
- [Data residency and limits](https://docs.pinecone.io/guides/nexus/byoc/reference.md): Where your data lives and travels in Nexus BYOC, plus authentication, encryption, cluster footprint, and limitations.
- [Deploy Nexus BYOC](https://docs.pinecone.io/guides/nexus/byoc/deploy.md): Install and operate Pinecone Nexus in your own cloud account.

## APIs

- [Pinecone Nexus API](https://docs.pinecone.io/reference/api/nexus/introduction.md): Programmatic access to Nexus: manage contexts and sources, curate, and query with KnowQL.
- [Authentication](https://docs.pinecone.io/reference/api/nexus/authentication.md): Authenticate to the Nexus API with your Pinecone API key, sent directly or exchanged for a short-lived session token.

## Auth

- [Exchange a Pinecone API key for a session token](https://docs.pinecone.io/api-reference/auth/exchange-a-pinecone-api-key-for-a-session-token.md)
- [Current identity (whoami)](https://docs.pinecone.io/api-reference/auth/current-identity-whoami.md)

## Project

- [Get the active project](https://docs.pinecone.io/api-reference/project/get-the-active-project.md)
- [Project-wide task stats](https://docs.pinecone.io/api-reference/project/project-wide-task-stats.md): Aggregate task counters across the whole project (all contexts), broken down by state and workflow. The per-context equivalent is `GET /contexts/{slug}/tasks/stats`.

## Connectors

- [List the project's linked connectors](https://docs.pinecone.io/api-reference/connectors/list-the-projects-linked-connectors.md)
- [Get a connector](https://docs.pinecone.io/api-reference/connectors/get-a-connector.md)
- [Unlink a connector](https://docs.pinecone.io/api-reference/connectors/unlink-a-connector.md)
- [Start the OAuth link flow for a provider](https://docs.pinecone.io/api-reference/connectors/start-the-oauth-link-flow-for-a-provider.md): Returns an `authorize_url` for the user to open in a browser. The connector is persisted by the provider's OAuth redirect callback once the user approves access.
- [Link a connector with an API key](https://docs.pinecone.io/api-reference/connectors/link-a-connector-with-an-api-key.md)
- [Browse a connector's folders/files](https://docs.pinecone.io/api-reference/connectors/browse-a-connectors-foldersfiles.md)
- [List a context's connectors (with per-context enabled flag)](https://docs.pinecone.io/api-reference/connectors/list-a-contexts-connectors-with-per-context-enabled-flag.md)
- [Enable or disable a connector for a context](https://docs.pinecone.io/api-reference/connectors/enable-or-disable-a-connector-for-a-context.md)

## Contexts

- [List contexts (newest first, with stats)](https://docs.pinecone.io/api-reference/contexts/list-contexts-newest-first-with-stats.md)
- [Create a context](https://docs.pinecone.io/api-reference/contexts/create-a-context.md): The context is created empty and is not queryable until you import sources and curate them (curate is explicit — there is no auto-curate). Optionally seed a `manifest`. A `work` context is the exception: it is queryable from day zero and is built from traces of work rather than source documents.
- [Get a context](https://docs.pinecone.io/api-reference/contexts/get-a-context.md)
- [Update a context](https://docs.pinecone.io/api-reference/contexts/update-a-context.md): All fields optional; absent leaves untouched. Empty string clears description/guide; `{}` clears the manifest back to defaults. A supplied manifest is validated against the manifest schema.
- [Delete a context (and its Pinecone indexes)](https://docs.pinecone.io/api-reference/contexts/delete-a-context-and-its-pinecone-indexes.md)
- [Fork a context (copy manifest into a fresh target)](https://docs.pinecone.io/api-reference/contexts/fork-a-context-copy-manifest-into-a-fresh-target.md): Copies the source context's manifest and lifecycle metadata onto an **already-created** target context in the same project, so the target's query and curate gates pass. It does not create the target — create it first with `POST /contexts`. The target's index stays empty until curated. The source mus…
- [Per-context task statistics](https://docs.pinecone.io/api-reference/contexts/per-context-task-statistics.md)
- [Trigger an on-demand self-tuning optimize](https://docs.pinecone.io/api-reference/contexts/trigger-an-on-demand-self-tuning-optimize.md): Tunes the manifest from real query traffic and chains a forced re-curate. A body is required but every field in it is optional, so `{}` is valid — and a no-op when no `candidate_queries` are supplied. The context must have been curated, and only one optimize runs at a time.
- [Run the Design-flow explore agent (propose a manifest)](https://docs.pinecone.io/api-reference/contexts/run-the-design-flow-explore-agent-propose-a-manifest.md): Inspects the uploaded source and proposes manifest-template matches, returned in the task's `output` (see `ExploreOutput`). Persists nothing on the index — review the proposal, then apply it via the manifest update + a forced curate. Body is optional.
- [Estimate the token + time cost of curating the in-progress manifest](https://docs.pinecone.io/api-reference/contexts/estimate-the-token-+-time-cost-of-curating-the-in-progress-manifest.md): Spins up the `profile` runtime to estimate the token + time cost of curating the sources under the in-progress manifest (sent in the request body), returned as the task's `output` (see `ProfileEstimateOutput`). Persists nothing; the console polls it for the Design-page cost box. Body is optional.

## Manifest

- [Get the context's manifest](https://docs.pinecone.io/api-reference/manifest/get-the-contexts-manifest.md): The context's pinned manifest document. A context with no pinned manifest returns `{}` — the runtime fills defaults. Writes go through the `manifest` field on `PUT /contexts/{slug}`.
- [List the built-in manifest templates](https://docs.pinecone.io/api-reference/manifest/list-the-built-in-manifest-templates.md): The catalog the console seeds new contexts from.

## Curation

- [Get the curation ledger](https://docs.pinecone.io/api-reference/curation/get-the-curation-ledger.md): The full ledger: per-source hashes, edges, corpus groups, chunk-id lineage, version pin, reclaim intents, pointers, and glossary. This pays an O(corpus) chunk-id-lineage read — use `GET /contexts/{slug}/curate/version/pin` on the per-turn path.
- [(Re)build the context's index from its sources](https://docs.pinecone.io/api-reference/curation/rebuild-the-contexts-index-from-its-sources.md): Curate the staged sources under the active manifest. **Required before querying** — there is no auto-curate. Body is optional. Set `force: true` to do a full rebuild (use after editing the manifest); the default is an incremental curate. Search contexts only — a work context builds via `work`/`groom…
- [Drop named sources from the index + ledger](https://docs.pinecone.io/api-reference/curation/drop-named-sources-from-the-index-+-ledger.md)

## Source Files

- [Import source documents from a project connector](https://docs.pinecone.io/api-reference/source-files/import-source-documents-from-a-project-connector.md): Pull source documents from a linked project connector (Box, ...) into the context. Supply either `folder` or `files`. Stages the source only — curate explicitly before querying.
- [Upload a source file or archive](https://docs.pinecone.io/api-reference/source-files/upload-a-source-file-or-archive.md): One file per request, max 2 GiB. Archives (`.zip`, `.tar`, `.tar.gz`, `.tgz`) are expanded by the import runtime. Stages the source only — it does not index anything. Curate explicitly with `POST /contexts/{slug}/curate` before querying.
- [List the source file tree (root)](https://docs.pinecone.io/api-reference/source-files/list-the-source-file-tree-root.md)
- [Source file/dir/size counts](https://docs.pinecone.io/api-reference/source-files/source-filedirsize-counts.md): Aggregate counts. Under preview mode the response also carries the per-context quota (`max_files_per_context`, `max_bytes_per_context`, `used_files`, `used_bytes`); those fields are absent otherwise.
- [Flat manifest of source files (path, size, last_modified)](https://docs.pinecone.io/api-reference/source-files/flat-manifest-of-source-files-path-size-last_modified.md): Flat source-object inventory used to render the source page without walking the tree. Excludes the `_inbox/` staging directory.
- [List the source file tree under a path](https://docs.pinecone.io/api-reference/source-files/list-the-source-file-tree-under-a-path.md)
- [Read a source file (raw bytes)](https://docs.pinecone.io/api-reference/source-files/read-a-source-file-raw-bytes.md)
- [Delete a source file](https://docs.pinecone.io/api-reference/source-files/delete-a-source-file.md)

## Knowledge

- [List curated knowledge (root)](https://docs.pinecone.io/api-reference/knowledge/list-curated-knowledge-root.md)
- [Knowledge file/dir/size counts](https://docs.pinecone.io/api-reference/knowledge/knowledge-filedirsize-counts.md)
- [List curated knowledge under a path](https://docs.pinecone.io/api-reference/knowledge/list-curated-knowledge-under-a-path.md)
- [Read a curated knowledge file (raw bytes)](https://docs.pinecone.io/api-reference/knowledge/read-a-curated-knowledge-file-raw-bytes.md)

## Query

- [Run one KnowQL query turn](https://docs.pinecone.io/api-reference/query/run-one-knowql-query-turn.md): Send `ask` (the natural-language question) and, when starting a new session, a `scope` of 1–10 contexts. Continue an existing session with `session_id` or `previous_query_id`. Scoped search contexts must be curated (work contexts are queryable immediately), and a scope may not mix work and search co…
- [Fetch one query (turn)](https://docs.pinecone.io/api-reference/query/fetch-one-query-turn.md)
- [Cancel an in-progress query turn](https://docs.pinecone.io/api-reference/query/cancel-an-in-progress-query-turn.md): Idempotent — an already-terminal query is returned unchanged. On success the turn's `status` becomes `cancelled`.
- [Fetch the recorded trace for a query turn](https://docs.pinecone.io/api-reference/query/fetch-the-recorded-trace-for-a-query-turn.md): The per-turn debug trace (steps, tool calls, strategy, cost, rollup). Trace persistence is unconditional — every turn lands a trace blob — so this is available once the turn is terminal.
- [Stream a query turn's events (SSE)](https://docs.pinecone.io/api-reference/query/stream-a-query-turns-events-sse.md): Server-sent event stream of the turn's events as they land — the same `type`-named events POST /query emits with `stream: true`, but as a standalone, resumable subscription. Reconnect with `Last-Event-ID` to replay from where the stream dropped.
- [Record thumbs-up/down feedback on a query turn](https://docs.pinecone.io/api-reference/query/record-thumbs-updown-feedback-on-a-query-turn.md)
- [List the selectable query models](https://docs.pinecone.io/api-reference/query/list-the-selectable-query-models.md): The model catalog the `model` field on `POST /query` draws from. Returns the default model, every catalog entry, the tier → model-id map, per-phase defaults, and the curate-capable model ids. Only entries with `available: true` are selectable (others are coming soon and rejected with `400`).
- [List project-wide query sessions (newest first)](https://docs.pinecone.io/api-reference/query/list-project-wide-query-sessions-newest-first.md)
- [Get a session with its queries (conversation order)](https://docs.pinecone.io/api-reference/query/get-a-session-with-its-queries-conversation-order.md)
- [Delete a session and its queries](https://docs.pinecone.io/api-reference/query/delete-a-session-and-its-queries.md)

## Tasks

- [List tasks (project-wide, paginated)](https://docs.pinecone.io/api-reference/tasks/list-tasks-project-wide-paginated.md)
- [Get a task (with steps)](https://docs.pinecone.io/api-reference/tasks/get-a-task-with-steps.md)
- [Cancel/terminate a task](https://docs.pinecone.io/api-reference/tasks/cancelterminate-a-task.md): Body is optional. Max body 1 MiB.
- [Live token + runtime stats for a task](https://docs.pinecone.io/api-reference/tasks/live-token-+-runtime-stats-for-a-task.md)

## Task Files

- [Read a task file (raw bytes)](https://docs.pinecone.io/api-reference/task-files/read-a-task-file-raw-bytes.md): Serves from the live container while the task runs, and from the archived blob store afterwards. This is how a finished pack's `<slug>.context.zip` is downloaded.
- [Stat a task file](https://docs.pinecone.io/api-reference/task-files/stat-a-task-file.md)
- [List a task's files under a path](https://docs.pinecone.io/api-reference/task-files/list-a-tasks-files-under-a-path.md)
- [List a task's working-directory files](https://docs.pinecone.io/api-reference/task-files/list-a-tasks-working-directory-files.md)
- [Delete a task file](https://docs.pinecone.io/api-reference/task-files/delete-a-task-file.md)

## Release notes

- [2026 releases](https://docs.pinecone.io/nexus-release-notes/2026.md): Pinecone release notes for 2026: new features, improvements, and API changes.

## Policies

- [Feature availability](https://docs.pinecone.io/nexus-release-notes/feature-availability.md): Pinecone feature availability across public preview, general availability, and limited availability releases, with links to release notes.

## Get started

- [Pinecone Marketplace](https://docs.pinecone.io/guides/marketplace/overview.md): Pinecone Marketplace is a no-code platform for building, publishing, and operating grounded knowledge applications from vertical templates and connectors.
- [Marketplace quickstart](https://docs.pinecone.io/guides/marketplace/quickstart.md): Pinecone Marketplace quickstart: pick a template app, connect a Google Drive folder, configure the deployment, and publish your first knowledge app.
- [Marketplace concepts](https://docs.pinecone.io/guides/marketplace/concepts.md): Learn Pinecone Marketplace concepts including knowledge applications, deployments, templates, manifests, KAT routing, layouts, and visual components.
- [Marketplace pricing and limits](https://docs.pinecone.io/guides/marketplace/pricing-and-limits.md): How Pinecone Marketplace usage is billed through Assistant and index consumption, plus deployment limits on connectors, files, and consumer auth.

## Templates

- [Templates overview](https://docs.pinecone.io/guides/marketplace/templates-overview.md): How Pinecone Marketplace templates work, what they define, and how operators customize a deployment after creating it from a template.
- [Template catalog](https://docs.pinecone.io/guides/marketplace/template-catalog.md): Browse the vertical apps in the Pinecone Marketplace template catalog, including Customer Support, HR Benefits, Deal Desk, and Financial Filings Analyzer.

## Build a knowledge application

- [Create a deployment](https://docs.pinecone.io/guides/marketplace/create-a-deployment.md): Create a new Pinecone Marketplace deployment from a vertical template, name it, choose a layout, and configure operating parameters for your use case.
- [Configure operating parameters](https://docs.pinecone.io/guides/marketplace/configure-operating-parameters.md): Tune the system prompt, starter prompts, and response style of a Pinecone Marketplace knowledge application through operating parameter settings.
- [Configure layouts](https://docs.pinecone.io/guides/marketplace/configure-layouts.md): Choose a chat, search, structured, or hybrid consumer layout for a Pinecone Marketplace knowledge application to match your end-user experience.
- [Configure components](https://docs.pinecone.io/guides/marketplace/configure-components.md): Enable visual components like comparison tables, timelines, and coverage matrices in a Pinecone Marketplace knowledge application interface.

## Connect data sources

- [Connectors overview](https://docs.pinecone.io/guides/marketplace/connectors-overview.md): How Pinecone Marketplace connectors ingest documents, keep them in sync with source systems, and let operators add manually uploaded files.

## Multi-domain orchestration

- [Knowledge Agent Toolkit (KAT) overview](https://docs.pinecone.io/guides/marketplace/kat-overview.md): How the Knowledge Agent Toolkit (KAT) orchestrates multi-domain Pinecone Marketplace apps with manifests, disambiguation, slot filling, and guardrails.
- [Multi-domain routing](https://docs.pinecone.io/guides/marketplace/multi-domain-routing.md): How the Knowledge Agent Toolkit (KAT) routes queries across multiple knowledge bases in a Pinecone Marketplace deployment for multi-domain answers.

## Publish and operate

- [Publish a deployment](https://docs.pinecone.io/guides/marketplace/publish-a-deployment.md): Publish a Pinecone Marketplace deployment to promote a building version to active, run introspection, generate starter prompts, and evaluate quality.
- [Manage versions and rollback](https://docs.pinecone.io/guides/marketplace/manage-versions-and-rollback.md): Use versioned publishing in Pinecone Marketplace to stage edits, compare active and building versions, and roll back a deployment when needed.
- [Evaluations](https://docs.pinecone.io/guides/marketplace/evaluations.md): How Pinecone Marketplace runs automatic evaluations on every publish, scoring faithfulness and relevance to catch regressions in a new version.
- [Analytics and event logs](https://docs.pinecone.io/guides/marketplace/analytics-and-event-logs.md): Track end-user activity, refusal rates, feedback, and event logs in a Pinecone Marketplace deployment for analytics, auditing, and debugging.

## Access control

- [Deployer authentication](https://docs.pinecone.io/guides/marketplace/deployer-auth.md): Sign in to Pinecone Marketplace as an operator to create deployments, manage sessions across devices, and audit deployer actions in the event log.
- [Consumer authentication](https://docs.pinecone.io/guides/marketplace/consumer-auth-overview.md): Configure consumer sign-in for a Pinecone Marketplace deployment using link access or Google sign-in to control who can use your knowledge application.

## Use a knowledge application

- [End user guide](https://docs.pinecone.io/guides/marketplace/end-user/overview.md): End-user overview of Pinecone Marketplace knowledge applications, including how to sign in with link access or Google and read grounded citations.
- [Ask questions](https://docs.pinecone.io/guides/marketplace/end-user/ask-questions.md): Tips for end users on asking clear, specific questions in a Pinecone Marketplace knowledge application, using follow-ups, and handling clarifications.
- [Understand answers](https://docs.pinecone.io/guides/marketplace/end-user/citations.md): Read citations, work with visual components, export and share answers, and give feedback in a Pinecone Marketplace knowledge application.

## APIs

- [Pinecone Marketplace API](https://docs.pinecone.io/reference/api/marketplace/introduction.md): Pinecone Marketplace API reference overview covering deployments, templates, connectors, versions, evaluations, and analytics endpoints.
- [Authentication](https://docs.pinecone.io/reference/api/marketplace/authentication.md): Authenticate to the Pinecone Marketplace API using project-scoped API keys, including the Api-Key header, key scoping, and deployment permissions.

## Deployments

- [Deployments](https://docs.pinecone.io/reference/api/marketplace/deployments.md): Marketplace Deployments API reference for listing, creating, updating, and deleting knowledge application deployments programmatically.

## Templates

- [Templates](https://docs.pinecone.io/reference/api/marketplace/templates.md): Marketplace Templates API reference for listing vertical template catalogs and retrieving default operating parameters for deployments.

## Connectors

- [Connectors](https://docs.pinecone.io/reference/api/marketplace/connectors.md): Marketplace Connectors API reference for attaching source connectors, updating configuration, and triggering syncs across deployments.

## Versions and publishing

- [Versions and publishing](https://docs.pinecone.io/reference/api/marketplace/versions.md): Marketplace Versions API reference for publishing building versions, listing version history, and rolling back deployments programmatically.

## Evaluations

- [Evaluations](https://docs.pinecone.io/reference/api/marketplace/evaluations.md): Marketplace Evaluations API reference for triggering evaluation runs, retrieving scores, and inspecting per-question detail per version.

## Events and analytics

- [Events and analytics](https://docs.pinecone.io/reference/api/marketplace/events.md): Marketplace Events and Analytics API reference for streaming deployment event logs and reading conversation and feedback counters.

## Release notes

- [2026 releases](https://docs.pinecone.io/marketplace-release-notes/2026.md): Pinecone release notes for 2026: new features, improvements, and API changes.

## Policies

- [Feature availability](https://docs.pinecone.io/marketplace-release-notes/feature-availability.md): Pinecone feature availability across public preview, general availability, and limited availability releases, with links to release notes.

## Other

- [AGENTS JAVASCRIPT](https://docs.pinecone.io/AGENTS-JAVASCRIPT.md)
- [AGENTS PYTHON](https://docs.pinecone.io/AGENTS-PYTHON.md)
- [Semantic search](https://docs.pinecone.io/examples/sample-apps/legal-semantic-search.md): Next.js sample app for semantic search over PDF legal documents using Pinecone serverless indexes and Voyage AI voyage-law-2 embeddings.
- [Multi-tenant RAG](https://docs.pinecone.io/examples/sample-apps/namespace-notes.md): Next.js multi-tenant RAG sample app that uses Pinecone namespaces to isolate document context and chatbot memory per workspace tenant.
- [Pinecone Assistant](https://docs.pinecone.io/examples/sample-apps/pinecone-assistant.md): Next.js sample chat app that connects to a Pinecone Assistant for grounded answers over uploaded PDFs with citations and file references.
- [Multimodal search](https://docs.pinecone.io/examples/sample-apps/shop-the-look.md): Sample Next.js + FastAPI app for multimodal search across text, images, and videos using Pinecone and Google Vertex AI multimodal embeddings.
- [Monitor assistants](https://docs.pinecone.io/guides/assistant/monitor-assistants.md): Track Pinecone Assistant performance in the console with time-series metrics for requests, error counts, chat and context latency, and file processing.
- [Create and load private datasets](https://docs.pinecone.io/guides/data/create-and-load-private-datasets.md): Create custom Pinecone datasets with the pinecone-datasets library, define metadata schema, and upload to your own S3, GCS, or local storage bucket.
- [Use public Pinecone datasets](https://docs.pinecone.io/guides/data/use-public-pinecone-datasets.md): Browse Pinecone's catalog of public benchmark datasets like ANN, MSMARCO, and Quora, then load and upsert them with the pinecone-datasets Python library.
- [Use sample datasets](https://docs.pinecone.io/guides/data/use-sample-datasets.md): Load a pre-built movies sample dataset from the Pinecone console to quickly test indexing, similarity search, and quickstart workflows without uploading your own data.
- [Build a RAG chatbot](https://docs.pinecone.io/guides/get-started/build-a-rag-chatbot.md): Build a Python RAG chatbot with Pinecone, OpenAI, and LangChain that retrieves context from private data to reduce LLM hallucinations.
- [Export a backup](https://docs.pinecone.io/guides/manage-data/export-backup.md): Export a Pinecone index backup or selected namespaces as Parquet files to your Amazon S3 or Google Cloud Storage bucket for reimport or revisions.
- [HoneyHive](https://docs.pinecone.io/integrations/honeyhive.md): Use HoneyHive with Pinecone to capture OpenTelemetry traces of SDK calls and visualize spans for observability and evaluation of RAG pipelines.
- [all-MiniLM-L12-v2](https://docs.pinecone.io/models/all-MiniLM-L12-v2.md): Use the all-MiniLM-L12-v2 embedding or reranking model with Pinecone: specs and index setup. all-MiniLM-L12-v2 is a sentence and short paragraph encoder.
- [all-mpnet-base-v2](https://docs.pinecone.io/models/all-mpnet-base-v2.md): Use the all-mpnet-base-v2 embedding or reranking model with Pinecone: specs and index setup. all-mpnet-base-v2 is a sentence and short paragraph encoder.
- [bge-reranker-v2-m3](https://docs.pinecone.io/models/bge-reranker-v2-m3.md): Use the bge-reranker-v2-m3 embedding or reranking model with Pinecone: specs and index setup. This is an open source, high performance, multilingual model.
- [CLIP](https://docs.pinecone.io/models/clip.md): CLIP (Contrastive Language–Image Pre-training) builds on a large body of work on zero-shot transfer, natural language supervision, and multimodal learning.
- [CLIP-ViT-B-32-laion2B-s34B-b79K](https://docs.pinecone.io/models/CLIP-ViT-B-32-laion2B-s34B-b79K.md): It's particularly well-suited for tasks like: - **Zero-Shot Image Classification:** Classify images based on text descriptions without further training.
- [embed-english-light-v3.0](https://docs.pinecone.io/models/cohere-embed-english-light-v3.0.md): Cohere embed-english-light-v3.0 on Pinecone: 384-dim text embeddings, 512-token context, low-dimensional storage for fast semantic search.
- [embed-english-v3.0](https://docs.pinecone.io/models/cohere-embed-english-v3.0.md): Use Cohere embed-english-v3.0 with Pinecone for English embeddings: 1024 dimensions, cosine or dot product, and query vs document input types.
- [embed-multilingual-v3.0](https://docs.pinecone.io/models/cohere-embed-multilingual-v3.0.md): Use the embed-multilingual-v3.0 embedding or reranking model with Pinecone: specs and index setup. Multilingual embedding model ideal for easy to use text.
- [cohere-rerank-3.5](https://docs.pinecone.io/models/cohere-rerank-3.5.md): Rerank has improved dramatically in cases where the user is expressing explicitly or implicitly constraints on what they would like returned.
- [cohere-rerank-4-fast](https://docs.pinecone.io/models/cohere-rerank-4-fast.md): Cohere Rerank 4.0 Fast is Cohere's latest reranking model, providing high-quality relevance scoring across enterprise search workloads.
- [e5-base-v2](https://docs.pinecone.io/models/e5-base-v2.md): Use the e5-base-v2 embedding or reranking model with Pinecone: specs and index setup. Ideal model for good performance while keeping with open source and.
- [e5-large-v2](https://docs.pinecone.io/models/e5-large-v2.md): Use the e5-large-v2 embedding or reranking model with Pinecone: specs and index setup. Ideal model for high performance while keeping with open source. Works.
- [gte-base](https://docs.pinecone.io/models/gte-base.md): Use the gte-base embedding or reranking model with Pinecone: specs and index setup. Ideal model for good performance while keeping with open source and.
- [gte-large](https://docs.pinecone.io/models/gte-large.md): Use the gte-large embedding or reranking model with Pinecone: specs and index setup. Larger GTE variant for more high quality embeddings. Ideal model for.
- [instructor-large](https://docs.pinecone.io/models/instructor-large.md): Use the instructor-large embedding or reranking model with Pinecone: specs and index setup. An instruction-finetuned text embedding model that can generate.
- [instructor-xl](https://docs.pinecone.io/models/instructor-xl.md): Use the instructor-xl embedding or reranking model with Pinecone: specs and index setup. An instruction-finetuned text embedding model that can generate text.
- [jina-clip-v2](https://docs.pinecone.io/models/jina-clip-v2.md): Use the jina-clip-v2 embedding or reranking model with Pinecone: specs and index setup. Jina CLIP v2 is a state-of-the-art **multilingual and multimodal.
- [jina-embeddings-v2-base-en](https://docs.pinecone.io/models/jina-embeddings-v2-base-en.md): Use the jina-embeddings-v2-base-en embedding or reranking model with Pinecone: specs and index setup. Ideal for text embeddings where short queries are.
- [jina-embeddings-v3](https://docs.pinecone.io/models/jina-embeddings-v3.md): Jina embeddings v3 on Pinecone: multilingual text embeddings with 1024/512 dims, 8192-token context, task adapters, and Matryoshka support.
- [jina-embeddings-v4](https://docs.pinecone.io/models/jina-embeddings-v4.md): Jina embeddings v4 on Pinecone: multimodal text and image embeddings with 32k tokens, flexible 128-2048 dims, and multi-vector retrieval.
- [llama-text-embed-v2](https://docs.pinecone.io/models/llama-text-embed-v2.md): Developed by NVIDIA Research, it is built on the Llama 3.2 1B architecture and optimized for high retrieval quality with low-latency inference.
- [Marengo-retrieval-2.6](https://docs.pinecone.io/models/marengo-retrieval-2-6.md): Use the Marengo-retrieval-2.6 embedding or reranking model with Pinecone: specs and index setup. The video understanding engine generates embeddings for all.
- [mistral-embed](https://docs.pinecone.io/models/mistral-embed.md): Use the mistral-embed embedding or reranking model with Pinecone: specs and index setup. High performance embedding model from Mistral AI, with a context.
- [multilingual-e5-large](https://docs.pinecone.io/models/multilingual-e5-large.md): Use the multilingual-e5-large embedding or reranking model with Pinecone: specs and index setup. Ideal multilingual model for high performance while keeping.
- [pinecone-rerank-v0](https://docs.pinecone.io/models/pinecone-rerank-v0.md): Pinecone rerank v0 on Pinecone Inference: reranking model for RAG relevance scoring with 512-token context and query-document pair scores.
- [pinecone-sparse-english-v0](https://docs.pinecone.io/models/pinecone-sparse-english-v0.md): Use the pinecone-sparse-english-v0 embedding or reranking model with Pinecone: specs and index setup. Built on the innovations of the DeepImpact.
- [rerank-2](https://docs.pinecone.io/models/rerank-2.md): Voyage AI rerank-2 on Pinecone: quality-focused multilingual reranker with 16000-token context for RAG relevance scoring and search reorder.
- [rerank-2-lite](https://docs.pinecone.io/models/rerank-2-lite.md): Voyage AI rerank-2-lite on Pinecone: multilingual reranker balancing latency and quality with 8000-token context for RAG search results.
- [rerank-english-v2](https://docs.pinecone.io/models/rerank-english-v2.md): Use the rerank-english-v2 embedding or reranking model with Pinecone: specs and index setup. Good reranking model, consumes both a query and a list of.
- [text-embedding-3-large](https://docs.pinecone.io/models/text-embedding-3-large.md): Use the text-embedding-3-large embedding or reranking model with Pinecone: specs and index setup. Most powerful OpenAI embedding model, with a larger.
- [text-embedding-3-small](https://docs.pinecone.io/models/text-embedding-3-small.md): Use the text-embedding-3-small embedding or reranking model with Pinecone: specs and index setup. Most cost effective OpenAI embedding model, great for.
- [text-embedding-ada-002](https://docs.pinecone.io/models/text-embedding-ada-002.md): Use the text-embedding-ada-002 embedding or reranking model with Pinecone: specs and index setup. Legacy embedding model from OpenAI, great for general.
- [voyage-01](https://docs.pinecone.io/models/voyage-01.md): Use the voyage-01 embedding or reranking model with Pinecone: specs and index setup. The highest-quality text embedding model from the first generation of.
- [voyage-2](https://docs.pinecone.io/models/voyage-02.md): Use the voyage-2 embedding or reranking model with Pinecone: specs and index setup. The base-size text embedding model from the second generation of Voyage.
- [voyage-3](https://docs.pinecone.io/models/voyage-3.md): Voyage AI voyage-3 on Pinecone: general-purpose multilingual text embeddings with 1024 dimensions and 32k-token context for retrieval RAG.
- [voyage-3-large](https://docs.pinecone.io/models/voyage-3-large.md): Voyage AI voyage-3-large on Pinecone: top-quality multilingual text embeddings with 32k context and flexible 256/512/1024/2048 dimensions.
- [voyage-3-lite](https://docs.pinecone.io/models/voyage-3-lite.md): Voyage AI voyage-3-lite on Pinecone: cost- and latency-optimized 512-dim text embeddings with 32k-token context for high-throughput search.
- [voyage-code-2](https://docs.pinecone.io/models/voyage-code-2.md): Voyage AI voyage-code-2 on Pinecone: 1536-dim code embeddings with 16k-token context, optimized for source code search and code retrieval.
- [voyage-code-3](https://docs.pinecone.io/models/voyage-code-3.md): Voyage AI voyage-code-3 on Pinecone: code embeddings with 32k context and flexible 256/512/1024/2048 dims for source code search and RAG.
- [voyage-finance-2](https://docs.pinecone.io/models/voyage-finance-2.md): Voyage AI voyage-finance-2 on Pinecone: 1024-dim finance-domain embeddings with 32k-token context for financial RAG and document retrieval.
- [voyage-large-2](https://docs.pinecone.io/models/voyage-large-2.md): Use the voyage-large-2 embedding or reranking model with Pinecone: specs and index setup. The highest-quality text embedding model from the second generation.
- [voyage-law-2](https://docs.pinecone.io/models/voyage-law-2.md): Voyage AI voyage-law-2 on Pinecone: 1024-dim legal-domain embeddings with 16k-token context for legal document retrieval and contract RAG.
- [voyage-lite-02-instruct](https://docs.pinecone.io/models/voyage-lite-02-instruct.md): Use the voyage-lite-02-instruct embedding or reranking model with Pinecone: specs and index setup. The base-size text embedding model from the second.
- [voyage-multimodal-3](https://docs.pinecone.io/models/voyage-multimodal-3.md): Voyage AI voyage-multimodal-3 on Pinecone: 1024-dim embeddings for interleaved text and images like PDFs, slides, tables, and screenshots.
- [Pinecone datasets](https://docs.pinecone.io/reference/tools/pinecone-datasets.md): Use the pinecone-datasets Python library to load public Pinecone example datasets and iterate over vectors to automate benchmark queries.

## OpenAPI Specs

- [db_control_2026-01.alpha.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2026-01.alpha/db_control_2026-01.alpha.oas.yaml)
- [db_data_2026-01.alpha.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2026-01.alpha/db_data_2026-01.alpha.oas.yaml)
- [inference_2026-04.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2026-04/inference_2026-04.oas.yaml)
- [admin_2026-04.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2026-04/admin_2026-04.oas.yaml)
- [oauth_2026-04.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2026-04/oauth_2026-04.oas.yaml)
- [db_control_2026-04.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2026-04/db_control_2026-04.oas.yaml)
- [db_data_2026-04.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2026-04/db_data_2026-04.oas.yaml)
- [db_control_2025-10.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2025-10/db_control_2025-10.oas.yaml)
- [db_data_2025-10.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2025-10/db_data_2025-10.oas.yaml)
- [inference_2025-10.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2025-10/inference_2025-10.oas.yaml)
- [admin_2025-10.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2025-10/admin_2025-10.oas.yaml)
- [oauth_2025-10.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2025-10/oauth_2025-10.oas.yaml)
- [db_control_2025-04.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2025-04/db_control_2025-04.oas.yaml)
- [db_data_2025-04.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2025-04/db_data_2025-04.oas.yaml)
- [inference_2025-04.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2025-04/inference_2025-04.oas.yaml)
- [admin_2025-04.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2025-04/admin_2025-04.oas.yaml)
- [oauth_2025-04.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2025-04/oauth_2025-04.oas.yaml)
- [db_control_2025-01.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2025-01/db_control_2025-01.oas.yaml)
- [db_data_2025-01.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2025-01/db_data_2025-01.oas.yaml)
- [inference_2025-01.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2025-01/inference_2025-01.oas.yaml)
- [db_control_2024-10.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2024-10/db_control_2024-10.oas.yaml)
- [db_data_2024-10.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2024-10/db_data_2024-10.oas.yaml)
- [inference_2024-10.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2024-10/inference_2024-10.oas.yaml)
- [control_2024-07.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2024-07/control_2024-07.oas.yaml)
- [data_2024-07.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2024-07/data_2024-07.oas.yaml)
- [control_2024-04.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2024-04/control_2024-04.oas.yaml)
- [data_2024-04.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2024-04/data_2024-04.oas.yaml)
- [assistant_control_2026-04.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2026-04/assistant_control_2026-04.oas.yaml)
- [assistant_data_2026-04.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2026-04/assistant_data_2026-04.oas.yaml)
- [assistant_evaluation_2026-04.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2026-04/assistant_evaluation_2026-04.oas.yaml)
- [assistant_control_2025-10.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2025-10/assistant_control_2025-10.oas.yaml)
- [assistant_data_2025-10.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2025-10/assistant_data_2025-10.oas.yaml)
- [assistant_evaluation_2025-10.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2025-10/assistant_evaluation_2025-10.oas.yaml)
- [assistant_control_2025-04.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2025-04/assistant_control_2025-04.oas.yaml)
- [assistant_data_2025-04.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2025-04/assistant_data_2025-04.oas.yaml)
- [assistant_evaluation_2025-04.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2025-04/assistant_evaluation_2025-04.oas.yaml)
- [assistant_control_2025-01.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2025-01/assistant_control_2025-01.oas.yaml)
- [assistant_data_2025-01.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2025-01/assistant_data_2025-01.oas.yaml)
- [assistant_evaluation_2025-01.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2025-01/assistant_evaluation_2025-01.oas.yaml)
- [nexus_2026-07.oas](https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2026-07/nexus_2026-07.oas.yaml)
- [control_plane_api](/control_plane_api.json)
- [data_plane_api](/data_plane_api.json)
- [pinecone](/pinecone.json)

> The links below point to documentation indexes. Follow each `/_llms/` index recursively until you reach documentation pages.

## Indexes

- [Pinecone Database (494 pages)](https://docs.pinecone.io/_llms/pinecone-database.md): Documentation for Pinecone Database.
- [Pinecone Database / Reference (315 pages)](https://docs.pinecone.io/_llms/pinecone-database/reference.md): Documentation for Pinecone Database / Reference.
- [Pinecone Database / Reference / 2026-04 (latest) (96 pages)](https://docs.pinecone.io/_llms/pinecone-database/reference/2026-04-latest.md): Documentation for Pinecone Database / Reference / 2026-04 (latest).