docs-search-example

Search through the Tigris documentation and blog with the power of AI!

Setup

Clone the docs and blog repos.

cd var
git clone https://github.com/tigrisdata/tigris-blog
git clone https://github.com/tigrisdata/tigris-os-docs

Ingesting data to lancedb

node ingest.ts

Searching docs in lancedb

import * as lancedb from "@lancedb/lancedb";
import "@lancedb/lancedb/embedding/openai";
import { LanceSchema, getRegistry } from "@lancedb/lancedb/embedding";
import { Utf8 } from "apache-arrow";

const func = getRegistry()
  .get("openai")
  ?.create({ model: "text-embedding-3-small" });

const contentSchema = LanceSchema({
  text: func.sourceField(new Utf8()),
  vector: func.vectorField(),
  title: new Utf8(),
  url: new Utf8(),
});

const tbl = await db.openTable("content", contentSchema);

const query = magic_get_query_somehow(); // fill this in

const actual = await tbl.search(query).limit(25).toArray();
console.log(`found ${actual.length} results:`);

for (const result of actual) {
  console.log(`* ${result.title}: ${result.url}\n${result.text}\n`);
}

Running the app

Envvars you need:

Name	Description
AWS_ACCESS_KEY_ID	Tigris access key ID
AWS_SECRET_ACCESS_KEY	Tigris secret access key
BUCKET_NAME	xe-stream-lancedb
OPENAI_API_KEY	API key for the OpenAI API (needed for generating embeddings with text-embedding-3-small)

Put them in .env.

Install the dependencies:

npm install

Run the server:

node index.js

You'll see an index page like this on port 3000:

Enter in some words and you'll see results!

And that's how easy it is to have your own data in a vector database on Tigris! You can use the basis of this for a RAG chatbot pipeline.