GenKit - LanceDB

genkitx-lancedb

Genkit is an open-source framework for building end-to-end AI and RAG pipelines with a clean, TypeScript-first developer experience. The genkitx-lancedb plugin lets you use LanceDB as a high-performance vector store inside your Genkit flows, so you can index, search, and retrieve data efficiently as part of your AI applications.

Installation

pnpm install genkitx-lancedb

Usage

Adding LanceDB plugin to your genkit instance.

TypeScript

import { lancedbIndexerRef, lancedb, lancedbRetrieverRef, WriteMode } from "genkitx-lancedb";
import { textEmbedding004, vertexAI } from "@genkit-ai/vertexai";
import { gemini } from "@genkit-ai/vertexai";
import { z, genkit } from "genkit";
import { Document } from "genkit/retriever";
import { chunk } from "llm-chunk";
import { readFile } from "fs/promises";
import path from "path";
import pdf from "pdf-parse/lib/pdf-parse";

const ai = genkit({
plugins: [
// vertexAI provides the textEmbedding004 embedder
vertexAI(),

// the local vector store requires an embedder to translate from text to vector
lancedb([
{
dbUri: ".db", // optional lancedb uri, default to .db
tableName: "table", // optional table name, default to table
embedder: textEmbedding004,
},
]),
],
});

You can run this app with the following command:

genkit start -- tsx --watch src/index.ts

This’ll add LanceDB as a retriever and indexer to the genkit instance. You can see it in the GUI view Screenshot 2025-05-11 at 7 21 05 PM

Testing retrieval on a sample table Let’s see the raw retrieval results Screenshot 2025-05-11 at 7 21 05 PM

On running this query, you’ll get 5 results fetched from the lancedb table, where each result looks something like this: Screenshot 2025-05-11 at 7 21 18 PM

Creating a custom RAG flow

Now that we’ve seen how you can use LanceDB in a Genkit pipeline, let’s refine the flow and create a RAG. A RAG flow will consist of an index and a retriever with its outputs postprocessed and fed into an LLM for final response

Creating custom indexer flows

You can also create custom indexer flows, utilizing more options and features provided by LanceDB.

TypeScript

export const menuPdfIndexer = lancedbIndexerRef({
// Using all defaults, for dbUri, tableName, and embedder, etc
});

const chunkingConfig = {
minLength: 1000,
maxLength: 2000,
splitter: "sentence",
overlap: 100,
delimiters: "",
} as any;

async function extractTextFromPdf(filePath: string) {
const pdfFile = path.resolve(filePath);
const dataBuffer = await readFile(pdfFile);
const data = await pdf(dataBuffer);
return data.text;
}

export const indexMenu = ai.defineFlow(
{
name: "indexMenu",
inputSchema: z.string().describe("PDF file path"),
outputSchema: z.void(),
},
async (filePath: string) => {
filePath = path.resolve(filePath);

// Read the pdf.
const pdfTxt = await ai.run("extract-text", () => extractTextFromPdf(filePath));

// Divide the pdf text into segments.
const chunks = await ai.run("chunk-it", async () => chunk(pdfTxt, chunkingConfig));

// Convert chunks of text into documents to store in the index.
const documents = chunks.map((text) => {
return Document.fromText(text, { filePath });
});

// Add documents to the index.
await ai.index({
indexer: menuPdfIndexer,
documents,
options: {
writeMode: WriteMode.Overwrite,
} as any,
});
},
);

In your console, you can see the logs Screenshot 2025-05-11 at 7 19 14 PM

Creating custom retriever flows

You can also create custom retriever flows, utilizing more options and features provided by LanceDB.

TypeScript

export const menuRetriever = lancedbRetrieverRef({
tableName: "table", // Use the same table name as the indexer.
displayName: "Menu", // Use a custom display name.
});

export const menuQAFlow = ai.defineFlow(
{ name: "Menu", inputSchema: z.string(), outputSchema: z.string() },
async (input: string) => {
// retrieve relevant documents
const docs = await ai.retrieve({
retriever: menuRetriever,
query: input,
options: {
k: 3,
},
});

const extractedContent = docs.map((doc) => {
if (doc.content && Array.isArray(doc.content) && doc.content.length > 0) {
if (doc.content[0].media && doc.content[0].media.url) {
return doc.content[0].media.url;
}
}
return "No content found";
});

console.log("Extracted content:", extractedContent);

const { text } = await ai.generate({
model: gemini("gemini-2.0-flash"),
prompt: `
You are acting as a helpful AI assistant that can answer
questions about the food available on the menu at Genkit Grub Pub.

Use only the context provided to answer the question.
If you don't know, do not make up an answer.
Do not add or change items on the menu.

Context:
${extractedContent.join("\n\n")}

Question: ${input}`,
docs,
});

return text;
},
);

Now using our retrieval flow, we can ask a question about the ingested PDF Screenshot 2025-05-11 at 7 18 45 PM

​genkitx-lancedb

​Installation

​Usage

​Creating a custom RAG flow

​Creating custom indexer flows

​Creating custom retriever flows

genkitx-lancedb

Installation

Usage

Creating a custom RAG flow

Creating custom indexer flows

Creating custom retriever flows