--- layout: article title: Integrating Pinecone description: Learn how to integrate Pinecone into your Appwrite project. difficulty: intermediate readtime: 15 --- Pinecone is a vector database that allows you to store and query high-dimensional vectors. It is a great tool for building recommendation systems, search engines, and more. In this tutorial, we'll show you how to integrate Pinecone into your Appwrite project. Inside an Appwrite Function, we'll create a method to that indexes an Appwrite table into Pinecone. We'll also create a method to query the Pinecone index and return the results. # Prerequisites {% #prerequisites %} - An Appwrite project - An Appwrite table - An [OpenAI API key](https://platform.openai.com/account/api-keys) - A [Pinecone API key](https://docs.pinecone.io/guides/getting-started/quickstart#2-get-your-api-key) - A Pinecone index {% section #step-1 step=1 title="Create new function" %} Head to the [Appwrite Console](https://cloud.appwrite.io/console) then click on **Functions** in the left sidebar and then click on the **Create Function** button. {% only_dark %} ![Create function screen](/images/docs/functions/dark/template.avif) {% /only_dark %} {% only_light %} ![Create function screen](/images/docs/functions/template.avif) {% /only_light %} 1. In the Appwrite Console's sidebar, click **Functions**. 1. Click **Create function**. 1. Under **Connect Git repository**, select your provider. 1. After connecting to GitHub, under **Quick start**, select the **Node.js** starter template. 1. In the **Variables** step, add the `PINECONE_API_KEY`, generate it [here](https://docs.pinecone.io/guides/getting-started/quickstart#2-get-your-api-key). Add the `OPENAI_API_KEY`, generate it [here](https://platform.openai.com/account/api-keys).For the `APPWRITE_API_KEY`, tick the box to **Generate API key on completion**. 1. Follow the step-by-step wizard and create the function. {% /section %} {% section #step-2 step=2 title="Add dependencies" %} Once the function is created, navigate to the freshly created repository and clone it to your local machine. Install the `@pinecone-database/pinecone` package to simplify the process of interacting with the Pinecone API. We'll also install the `openai` package to interact with the OpenAI API. ```bash npm install @pinecone-database/pinecone openai ``` {% /section %} {% section #step-3 step=3 title="Create utility function" %} For this example, the function will be able to take both `GET` and `POST` requests. Create a new `src/utils.js` file with the following code: ```js import path from 'path'; import { fileURLToPath } from 'url'; import fs from 'fs'; const __filename = fileURLToPath(import.meta.url); const __dirname = path.dirname(__filename); const staticFolder = path.join(__dirname, '../static'); export function getStaticFile(fileName) { return fs.readFileSync(path.join(staticFolder, fileName)).toString(); } ``` {% /section %} {% section #step-4 step=4 title="Handle GET request" %} Write the `GET` request handler in the `src/main.js` file. ```js import { getStaticFile } from './utils.js'; export default async ({ req, res, error }) => { if (req.method === 'GET') { const html = getStaticFile('index.html'); return res.text(html, 200, { 'Content-Type': 'text/html; charset=utf-8' }); } }; ``` The code checks if all required environment variables are present and then returns the static HTML page when a `GET` request is made. {% /section %} {% section #step-5 step=5 title="Create web page" %} Create a HTML web page that the function will serve. Create a new file at `static/index.html` with some HTML boilerplate: ```html ``` Within the `` tag, Add a `` tag that will define the style and scripts. ```html Pinecone Demo ``` And after the `` tag add this `` which will contain the actual form: ```html

Pinecone Demo

Use this demo to verify that the sync between Appwrite Databases and Pinecone was successful. Search your Pinecone vector database using the input below.

``` This will render a form that will submit your search query to the function and display the results. {% /section %} {% section #step-6 step=6 title="Setup SDKs" %} Add methods necessary to integrate with the OpenAI and Pinecone APIs Import `openai` and `@pinecone-database/pinecone` at the top of the `main.js` file: ```js import { Pinecone } from '@pinecone-database/pinecone'; import { OpenAI } from 'openai'; ``` Add the following code at the end of request handler in the `main.js` file: ```js const openai = new OpenAI(); const pinecone = new Pinecone(); const pineconeIndex = pinecone.index(process.env.PINECONE_INDEX_ID); ``` The functions checks the request method, and then initializes the OpenAI and Pinecone SDKs. {% /section %} {% section #step-7 step=7 title="Handle search requests" %} To handle the search requests, add the following code to the end of the request handler in the `main.js` file: ```js if (req.path === '/search') { const queryEmbedding = await openai.embeddings.create({ model: 'text-embedding-ada-002', input: req.body.prompt, }); const searchResults = await pineconeIndex.query({ vector: queryEmbedding.data[0].embedding, topK: 5, }); return res.json(searchResults); } ``` For all requests with the path `/search`, the function sends the search query to the OpenAI API to get the embedding. The function then queries the Pinecone index with the embedding and returns the results. {% /section %} {% section #step-8 step=8 title="Handle indexing requests" %} The Appwrite table needs to be indexed into the Pinecone index. Create a new file at `src/appwrite.js` with the following code: ```js import { Client, TablesDB, Query } from 'node-appwrite'; export default class AppwriteService { constructor() { const client = new Client(); client .setEndpoint( process.env.APPWRITE_ENDPOINT ?? 'https://.cloud.appwrite.io/v1' ) .setProject(process.env.APPWRITE_FUNCTION_PROJECT_ID) .setKey(process.env.APPWRITE_API_KEY); this.tablesDB = new TablesDB(client); } async getAllRows(databaseId, tableId) { const cumulative = []; let cursor = null; do { const queries = [Query.limit(100)]; if (cursor) { queries.push(Query.cursorAfter(cursor)); } const { rows } = await this.tablesDB.listRows({ databaseId, tableId, queries }); if (rows.length === 0) { break; } cursor = rows[rows.length - 1].$id; cumulative.push(...rows); } while (cursor); return cumulative; } } ``` The service provides a method to iterate the rows contained within an entire table, fetching the limit of 100 rows per request. ```js const appwrite = new AppwriteService(); const rows = await appwrite.getAllRows( process.env.APPWRITE_DATABASE_ID, process.env.APPWRITE_TABLE_ID ); const embeddings = await Promise.all( rows.map(async (row) => { const record = await openai.embeddings.create({ model: 'text-embedding-ada-002', input: JSON.stringify(row), }); return { id: row.$id, values: record.data[0].embedding, metadata: row, }; }) ); await pineconeIndex.upsert(embeddings); ``` The code fetches all rows from the Appwrite table, then sends each row to the OpenAI API to get the embedding. The embeddings are then uploaded to the Pinecone index. {% /section %} {% section #step-9 step=9 title="Test the function" %} Now that the function is deployed, test it by visiting the function URL in your browser. This should show the UI created earlier and to test it, write a search query and click the submit button. After a brief moment you should see the matched results. {% /section %}