# Pinecone AI integration on Definable

> Long-term Memory for AI.

## What this connects

Long-term Memory for AI. The Pinecone vector database makes it easy to build high-performance vector search applications. Developer-friendly, fully managed, and easily scalable without infrastructure hassles.

Vendor: https://www.pinecone.io/

## Tools available

**48** tools available. First 12:

- `PINECONE_CANCEL_BULK_IMPORT` — Cancel Bulk Import — Tool to cancel a bulk import operation in Pinecone. Use when you need to stop an ongoing import operation that is not yet finished.
- `PINECONE_CHAT_ASSISTANT` — Chat with Assistant — Tool to chat with a Pinecone assistant and get structured responses with citations. Use when you need to query an assistant that has access to your knowledge base and want to get back answers with document references and citations.
- `PINECONE_CHAT_COMPLETION_ASSISTANT` — Chat with Assistant — Tool to chat with a Pinecone assistant through an OpenAI-compatible interface. Use when you need to interact with a Pinecone assistant that has access to indexed documents and can answer questions based on retrieved context.
- `PINECONE_CONFIGURE_INDEX` — Configure Index — Tool to configure an existing Pinecone index, including pod type, replicas, deletion protection, and tags. Use when you need to scale an index vertically or horizontally, enable/disable deletion protection, or update tags. The change is asynchronous; check index status for completion.
- `PINECONE_CREATE_ASSISTANT` — Create Assistant — Tool to create a new Pinecone assistant for RAG (Retrieval-Augmented Generation) applications. Use when you need to initialize a new assistant that can have files uploaded and support chat interactions.
- `PINECONE_CREATE_BACKUP` — Create Backup — Tool to create a backup of a Pinecone index for disaster recovery and version control. Use when you need to preserve the current state of an index including vectors, metadata, and configuration.
- `PINECONE_CREATE_INDEX` — Create Index — Tool to create a Pinecone index with specified configuration. Use when you need to initialize a new vector database index for storing and querying embeddings.
- `PINECONE_CREATE_INDEX_FOR_MODEL` — Create Index with Embedding Model — Tool to create a Pinecone index with integrated embedding model for automatic vectorization. Use when you need to set up a new index that automatically converts text to vectors using a pre-configured embedding model.
- `PINECONE_CREATE_INDEX_FROM_BACKUP` — Create Index from Backup — Tool to create an index from a backup. Use when you need to restore or duplicate index data from a previously saved backup.
- `PINECONE_CREATE_NAMESPACE` — Create Namespace — Tool to create a namespace within a serverless Pinecone index. Use when you need to organize vectors into isolated partitions.
- `PINECONE_DELETE_ASSISTANT` — Delete Assistant — Tool to permanently delete a Pinecone assistant. Use when you need to remove an assistant from your project.
- `PINECONE_DELETE_BACKUP` — Delete Backup — Tool to delete a backup. Use when you need to permanently remove a backup from your project.

## Auth

Auth schemes: `API_KEY`, `OAUTH2`.

## How agents use Pinecone

Inside a Definable workflow, Pinecone is one of the tools the **Distributor specialist** can call. Example coordination patterns:

- **Researcher → Pinecone** — the Researcher (GPT-5.5) pulls context from Pinecone (records, threads, documents), synthesises findings, and briefs the rest of the team.
- **Writer → Distributor → Pinecone** — the Writer (Claude Opus 4.7) drafts copy in brand voice, the Verifier passes it, then the Distributor writes the result into Pinecone (create record, post message, draft email).
- **Designer / Engineer → Distributor → Pinecone** — the Designer ships an asset or the Engineer ships a code change, the Distributor delivers it via Pinecone (attach file, open PR comment, post status).

The Verifier checks every Pinecone call. On rate limit, schema drift, or auth refresh it self-heals and retries — the workflow completes without manual intervention.

## Categories

- artificial intelligence — https://definable.ai/apps/category/artificial-intelligence/
- databases — https://definable.ai/apps/category/databases/

## Related

- HTML page: https://definable.ai/apps/pinecone/
- Same category (artificial intelligence): https://definable.ai/apps/category/artificial-intelligence/
- All integrations: https://definable.ai/apps/
- Workflow (multi-agent loop): https://definable.ai/workflow/
- Apps llms.txt index: https://definable.ai/llms-apps.txt