Pinecone plugin

The Pinecone plugin provides indexer and retriever implementations that use the Pinecone cloud vector database.

Installation

npm i --save genkitx-pinecone

Configuration

To use this plugin, specify it when you initialize Genkit:

import { genkit } from 'genkit';
import { pinecone } from 'genkitx-pinecone';

const ai = genkit({
  plugins: [
    pinecone([
      {
        indexId: 'bob-facts',
        embedder: textEmbedding004,
      },
    ]),
  ],
});

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, use these references with ai.retrieve() and ai.index():

// To use the index you configured when you loaded the plugin:
let docs = await ai.retrieve({ retriever: pineconeRetrieverRef, query });

// To specify an index:
export const bobFactsRetriever = pineconeRetrieverRef({
  indexId: 'bob-facts',
});
docs = await ai.retrieve({ retriever: bobFactsRetriever, query });

// To use the index you configured when you loaded the plugin:
await ai.index({ indexer: pineconeIndexerRef, documents });

// To specify an index:
export const bobFactsIndexer = pineconeIndexerRef({
  indexId: 'bob-facts',
});
await ai.index({ indexer: bobFactsIndexer, documents });

See the Retrieval-augmented generation page for a general discussion on indexers and retrievers.