**This is a research demo. Support is not provided.

Shopify Help Center Search via GPT

Quickly surface answers from Shopify's help center using GPT.

Technologies used

• ScrapingBee to scrape list of help center urls
• Mongodb to store scraped data
• OpenAI to created embeddings vector points and completion prompt
• Pinecone to store vectors in db

How this works

1. Run tasks/1-sitemap-to-csv.js to convert Shopify's Help Center Sitemap.xml into CSV and drop all columns except urls.
2. Convert CSV into array of links.
3. Run tasks/2-scrape.js to scrape the article text from every link using ScrapingBee and isnert text into Mongodb using url as unique index.
4. Run tasks/3-generate-embeddings.js to generate OpenAI embeddings and upsert into Pinecone.

Why?

Created this as a research experiment in order to learn OpenAI embeddings + Pinecone. Added bonus was to have a way to quickly surface answers for my Shopify platform questions.

How to optimize this further

• Split article text into smaller documents to decrease cost of token usage. Split by H2/section.
• Test different models to see one cost. Curie is 10x cheaper than Davinci.
• Search documents with a normal search engine (Algolia) and pass that document into open AI rather than using embedding’s and Pinecone.
• Cache results for common queries.
• Test a shorter prompt to further save tokens.

Preview