The Pinecone plugin provides indexer and retriever implementatons that use the Pinecone cloud vector database.
Installation
npm
i
--save
genkitx-pinecone
Configuration
To use this plugin, specify it when you call configureGenkit()
:
import
{
pinecone
}
from
' genkitx
-
pinecone
' ;
export
default
configureGenkit
({
plugins
:
[
pinecone
([
{
indexId
:
' bob
-
facts
' ,
embedder
:
textEmbeddingGecko
,
},
]),
],
// ...
});
You must specify a Pinecone index ID and the embedding model you want to use.
In addition, you must configure Genkit with your Pinecone API key. There are two ways to do this:
-
Set the
PINECONE_API_KEYenvironment variable. -
Specify it in the
clientParamsoptional parameter:clientParams : { apiKey : ..., }The value of this parameter is a
PineconeConfigurationobject, which gets passed to the Pinecone client; you can use it to pass any parameter the client supports.
Usage
Import retriever and indexer references like so:
import
{
pineconeRetrieverRef
}
from
' genkitx
-
pinecone
' ;
import
{
pineconeIndexerRef
}
from
' genkitx
-
pinecone
' ;
Then, pass the references to retrieve()
and index()
:
// To use the index you configured when you loaded the plugin:
let
docs
=
await
retrieve
({
retriever
:
pineconeRetrieverRef
,
query
});
// To specify an index:
export
const
bobFactsRetriever
=
pineconeRetrieverRef
({
indexId
:
' bob
-
facts
' ,
});
docs
=
await
retrieve
({
retriever
:
bobFactsRetriever
,
query
});
// To use the index you configured when you loaded the plugin:
await
index
({
indexer
:
pineconeIndexerRef
,
documents
});
// To specify an index:
export
const
bobFactsIndexer
=
pineconeIndexerRef
({
indexId
:
' bob
-
facts
' ,
});
await
index
({
indexer
:
bobFactsIndexer
,
documents
});
See the Retrieval-augmented generation page for a general discussion on indexers and retrievers.
