medusa-extender VS MeiliSearch

Medusa’s community plugin, Medusa Extender, provides multivendor support and full customization capabilities to create a custom ecommerce website.

In the previous parts of this series, you learned how to build a marketplace using Medusa and Medusa Extender. You associated users, products, and orders to a store.

Medusa is open-source project which provides many powerful e-commerce functions and extensions as Shopify. I follows the article for multi-vendor marketplaces shared from Shahed Nasser who is very outstanding engineer. But it only shows for backend modification with medusa-extender, not include the change of frontend side.

The Medusa server uses the method [buildQuery_](https://github.com/adrien2p/medusa-extender/releases/tag/v1.7.0) in OrderService to build the query necessary to retrieve the orders from the database. You’ll be overriding the OrderService, and particularly the buildQuery_ method to add a selector condition for the store_id if there is a currently logged-in user that has a store.

Medusa on steroid, take your medusa project to the next level with some badass features 🚀 The extender provides an out-of-the-box application architecture which allows developers and teams to create highly testable, scalable, loosely coupled, and easily maintainable applications. In also increase medusa extensibility and customisation for special use cases. It comes with handy decorators approach to increase the DX and full typings support for easier development. It is a complement to medusa but not a replacement 🚀

We needed to introduce a new service for search. As we settled on using meilisearch, we needed a way to sync updates on our models with the records in meilisearch. We could've continued to use callbacks but we needed something better.

Meilisearch

In this case, a good strategy is to use vector search only when the keyword/prefix search returns none or just a small number of results. A good candidate for this is MeiliSearch. It uses custom ranking rules to provide results as fast as the user can type.

With the VertexAI endpoint set up and tested, our next step is to work with LibreChat. LibreChat is an open-source ChatGPT clone that can integrate with various AI models, including the PaLM 2 models via the VertexAI API. It's built using React, MongoDB, and Meilisearch technologies.

Meilisearch seems like it is the best open source option.

https://www.meilisearch.com/

Starting with v1.3, you can use Meilisearch as a vector store. Meilisearch allows you to store vector embeddings alongside your documents conveniently. You will need to create the vector embeddings using your third-party tool of choice (Hugging Face, OpenAI). As we published the first v1.3 release candidate, you can try out vector search today.

I work at Meilisearch, an open-source search engine built in Rust. 🦀

https://www.meilisearch.com/ https://github.com/meilisearch