Article Recommender: Build article recommendations with Pinecone

Understand the project

Practical overview

Use it when you want to study or adapt a concrete indexing-and-querying pattern for content-based article recommendations using Pinecone.

TypeScript developers learning how to implement content-based article recommendations.
Teams prototyping similarity-based discovery for a publication or content catalog.

Create embeddings from article text and store them in a Pinecone index.
Query the index for articles similar to previously read content.
Use article metadata such as section, URL, title, publication and author alongside indexed content.

Before you choose

Running the workflow requires Pinecone credentials and an index configuration; it is not presented as a hosted service.
The example's indexing workflow uses the first 100,000 records of the cited dataset.
Embeddings are produced with a local Transformers.js model, while similarity search uses Pinecone.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Follow the documented indexing workflow to create embeddings from the sample article CSV and upsert them into Pinecone.

Install the project dependencies with npm install.
Copy .env.example to .env and set PINECONE_API_KEY, PINECONE_INDEX, PINECONE_CLOUD and PINECONE_REGION.
Download and extract the dataset, then place all-the-news-2-1.csv in the data directory.
Run npm run index to create embeddings and load records into Pinecone in batches.

Article records are embedded and upserted into the configured Pinecone index for the example's recommendation workflow.

Evidence and transparency

Source record details

Source availability: Active
Sources checked: 15 October 2026
Original website title: pinecone-io/recommender-example-typescript · Official homepage title · retained in its source language