> ## Documentation Index
> Fetch the complete documentation index at: https://docs.neum.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# PineconeSink

> PineconeSink enables seamless integration with Pinecone’s vector database, supporting vector storage and similarity search for advanced data retrieval.

The `PineconeSink` class is a connector for Pinecone, a vector database that excels in managing and querying high-dimensional data. It allows for efficient storage and similarity searches of vector data.

## Properties

Required properties:

* `api_key`: The API key for Pinecone services.
* `environment`: The environment parameter for Pinecone.
* `index`: The name of the index in Pinecone where data will be stored.
* `namespace`: A namespace within the Pinecone index to organize data. You can define the namespace to any string you want.

<Note>Namespaces are only supported for paid tiers. If you are using a GCP Starter tier, then pass an empty string to the namespace.</Note>

<CodeGroup>
  ```python Local Development
  from neumai.SinkConnectors import PineconeSink

  # Setup the PineconeSink with required credentials and index information
  pinecone_sink = PineconeSink(
      api_key = "your-pinecone-api-key",
      environment = "your-pinecone-environment",
      index = "your-pinecone-index",
      namespace = "namespace-string"
  )
  ```

  ```json Cloud
  {
      "sink": {
          "sink_name":"PineconeSink",
          "sink_information":{
              "api_key": "your-pinecone-api-key",
              "environment": "your-pinecone-environment",
              "index": "your-pinecone-index",
              "namespace": "namespace-string"
          }
      }
  }
  ```
</CodeGroup>