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_KEY
environment variable.Specify it in the
clientParams
optional parameter:clientParams: { apiKey: ..., }
The value of this parameter is a
PineconeConfiguration
object, 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.