Pinecone

Configure your own Pinecone instance as data sink.

Configure via UI

Configure via API / Client

```python from llama_cloud.types import CloudPineconeVectorStore

ds = { ‘name’: '', ‘sink_type’: ‘PINECONE’, ‘component’: CloudPineconeVectorStore( api_key=‘<api_key>’, index_name=‘<index_name>’, name_space=‘<name_space>’, # optional insert_kwargs=‘<insert_kwargs>’ # optional ) } data_sink = client.data_sinks.create_data_sink(request=ds)

</TabItem>
<TabItem value="typescript" label="TypeScript Client" default>
```Typescript
const ds = {
  'name': 'pinecone',
  'sinkType': 'PINECONE',
  'component': {
      'api_key': '<api_key>',
      'index_name': '<index_name>',
      'name_space': '<name_space>',  // optional
      'insert_kwargs': '<insert_kwargs>'  // optional
  }
}

data_sink = await client.dataSinks.createDataSink({
projectId: projectId,
body: ds
})

Filter Syntax

When using Pinecone as a data sink, you can apply filters using the following syntax:

Filter Operator	Pinecone Equivalent	Description
==	$eq	Equals
!=	$ne	Not equal
>	$gt	Greater than
<	$lt	Less than
>=	$gte	Greater than or equal
<=	$lte	Less than or equal
in	$in	Value is in a list
nin	$nin	Value is not in a list

These filters can be applied to metadata fields when querying your Pinecone index to refine search results based on specific criteria.