README.md•2.11 kB
# Dev Local Vector Store for Genkit
This is a simple implementation of a vector store that can be used to local development and testing.
This plugin is not meant to be used in production.
## Installing the plugin
```bash
npm i --save @genkit-ai/dev-local-vectorstore
```
## Using the plugin
```ts
import { Document, genkit } from 'genkit';
import {
googleAI,
gemini20Flash, // Replaced gemini15Flash with gemini20Flash
textEmbeddingGecko001,
} from '@genkit-ai/googleai';
import {
devLocalVectorstore,
devLocalIndexerRef,
devLocalRetrieverRef,
} from '@genkit-ai/dev-local-vectorstore';
const ai = genkit({
plugins: [
googleAI(),
devLocalVectorstore([
{
indexName: 'BobFacts',
embedder: textEmbeddingGecko001,
},
]),
],
model: gemini20Flash, // Use gemini20Flash
});
// Reference to a local vector database storing Genkit documentation
const indexer = devLocalIndexerRef('BobFacts');
const retriever = devLocalRetrieverRef('BobFacts');
async function main() {
// Add documents to the index. Only do it once.
await ai.index({
indexer: indexer,
documents: [
Document.fromText('Bob lives on the moon.'),
Document.fromText('Bob is 42 years old.'),
Document.fromText('Bob likes bananas.'),
Document.fromText('Bob has 11 cats.'),
],
});
const question = 'How old is Bob?';
// Consistent API to retrieve most relevant documents based on semantic similarity to query
const docs = await ai.retrieve({
retriever: retriever,
query: question,
});
const result = await ai.generate({
prompt: `Use the provided context from the Genkit documentation to answer this query: ${question}`,
docs, // Pass retrieved documents to the model
});
console.log(result.text);
}
main();
```
The sources for this package are in the main [Genkit](https://github.com/firebase/genkit) repo. Please file issues and pull requests against that repo.
Usage information and reference details can be found in [official Genkit documentation](https://genkit.dev/docs/get-started/).
License: Apache 2.0