Pinecone

Configure your own Pinecone instance as data sink.

Configure via UI

Configure via API / Client

Python Client

from llama_cloud.types import CloudPineconeVectorStore

ds = {
  'name': '<your-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)

TypeScript Client

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.