Prototyping: Wine Marketplace & Recommendation Algorithm
For the first, unbuilt prototype, we initially wanted to get some kind of prototype in front of people in the wine industry to gauge reactions and get input on what would be most useful to them to connect buyers and sellers. We also wanted to validate the need on both sides. Ultimately, though, outside of a simple napkin sketch which we both showed and described during our calls, this prototype was never built using code.
Given some initial interest in a recommendation algorithm for wine, we also wanted to play around with creating a simple MVP. This MVP took in a natural language query of a wine people might want (i.e., "give me a wine that goes well with gouda cheese...") and then would return a list of wines with details on where to learn more. Once again, the high-level rationale for this prototype was to present it to folks in the wine space and gauge any initial interest.
The tech stack, which is described more in our document about the book recommendation algorithm, is as follows:
- Index lots of wine reviews + other data. We think using a popular site like CellarTracker will be good to start off with, and we can easily expand to more sources.
- Chunk and encode data into embeddings. There are existing embeddings models (OpenAI), chunking libraries (Langchain), and vector databases (Chroma) that we can use to make this efficient.
- Upon a user's query, find relevant embeddings, and prompt the model with similar embeddings. We will intend to use low cost ChatGPT API.
- Build a simple frontend around everything. We can do this with a package like Streamlit and expand into more sophisticated frontend after validation.
We talked to around 10 people in the wine space, from financiers, to vineyard owners, to folks who were Masters of Wine.