---
title: "PineconeDocumentStore"
id: pinecone-document-store
slug: "/pinecone-document-store"
description: "Use a Pinecone vector database with Haystack."
---

# PineconeDocumentStore

Use a Pinecone vector database with Haystack.

|               |                                                                                            |
| :------------ | :----------------------------------------------------------------------------------------- |
| API reference | [Pinecone](/reference/integrations-pinecone)                                                      |
| GitHub link   | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/pinecone |

[Pinecone](https://www.pinecone.io/) is a cloud-based vector database. It is fast and easy to use.
Unlike other solutions (such as Qdrant and Weaviate), it can’t run locally on the user's machine but provides a generous free tier.

### Installation

You can simply install the Pinecone Haystack integration with:

```shell
pip install pinecone-haystack
```

### Initialization

- To use Pinecone as a Document Store in Haystack, sign up for a free Pinecone [account](https://app.pinecone.io/) and get your API key.
  The Pinecone API key can be explicitly provided or automatically read from the environment variable `PINECONE_API_KEY` (recommended).
- In Haystack, each `PineconeDocumentStore` operates in a specific namespace of an index. If not provided, both index and namespace are `default`.
  If the index already exists, the Document Store connects to it. Otherwise, it creates a new index.
- When creating a new index, you can provide a `spec` in the form of a dictionary. This allows choosing between serverless and pod deployment options and setting additional parameters. Refer to the [Pinecone documentation](https://docs.pinecone.io/reference/api/control-plane/create_index) for more details. If not provided, a default spec with serverless deployment in the `us-east-1` region will be used (compatible with the free tier).
- You can provide `dimension` and `metric`, but they are only taken into account if the Pinecone index does not already exist.

Then, you can use the Document Store like this:

```python
from haystack import Document
from haystack_integrations.document_stores.pinecone import PineconeDocumentStore

## Make sure you have the PINECONE_API_KEY environment variable set
document_store = PineconeDocumentStore(
		index="default",
		namespace="default",
		dimension=5,
  	metric="cosine",
  	spec={"serverless": {"region": "us-east-1", "cloud": "aws"}}
)

document_store.write_documents([
    Document(content="This is first", embedding=[0.0]*5),
    Document(content="This is second", embedding=[0.1, 0.2, 0.3, 0.4, 0.5])
    ])
print(document_store.count_documents())

```

### Supported Retrievers

[`PineconeEmbeddingRetriever`](../pipeline-components/retrievers/pineconedenseretriever.mdx): Retrieves documents from the `PineconeDocumentStore` based on their dense embeddings (vectors).
