Dev Local Vector Store

The Dev Local Vector Store plugin provides a local, file-based vector store for development and testing purposes. It is not intended for production use.

Installation

npm install @genkit-ai/dev-local-vectorstore

Configuration

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

import { devLocalVectorstore } from '@genkit-ai/dev-local-vectorstore';
import { googleAI } from '@genkit-ai/google-genai';
import { genkit } from 'genkit';

const ai = genkit({
  plugins: [
    // googleAI provides the embedding models
    googleAI(),

    // Configure the local vector store with an embedder
    devLocalVectorstore([
      {
        indexName: 'my_vectorstore',
        embedder: googleAI.embedder('gemini-embedding-001'),
      },
    ]),
  ],
});

Configuration Options

• indexName (string): A unique name for this vector store instance. This is used as the indexer and retriever reference.
• embedder (EmbedderReference): The embedding model to use. Must be a configured embedder in your Genkit project.

Usage

Indexing Documents

The Dev Local Vector Store automatically creates indexes. To populate with data, use the indexer reference and ai.index:

import { devLocalIndexerRef } from '@genkit-ai/dev-local-vectorstore';
import { Document } from 'genkit/retriever';

// Create the indexer reference
const myIndexer = devLocalIndexerRef('my_vectorstore');

// Create documents from text
const data = [
  'This is the first document.',
  'This is the second document.',
  'This is the third document.',
  'This is the fourth document.',
];

const documents = data.map((text) => Document.fromText(text));

// Index the documents
await ai.index({
  indexer: myIndexer,
  documents,
});

Retrieving Documents

Use ai.retrieve with the retriever reference:

import { devLocalRetrieverRef } from '@genkit-ai/dev-local-vectorstore';

// Create the retriever reference
const myRetriever = devLocalRetrieverRef('my_vectorstore');

// Retrieve documents relevant to a query
const docs = await ai.retrieve({
  retriever: myRetriever,
  query: 'search query',
  options: { k: 3 }, // Return top 3 results
});

// Process the retrieved documents
docs.forEach((doc) => {
  console.log(doc.content);
});