This part of the documentation is exclusive to starters that use
meilisearch
How it works
Vector Search utilizes AI-powered embeddings to represent data points (e.g., product descriptions, customer reviews) as vectors in a high-dimensional space. Similar data points have vectors that are close together, enabling the search engine to identify and retrieve conceptually relevant results even for complex or nuanced queries.Benefits
- Improved Search Relevancy: Vector Search significantly improves search result quality by understanding the meaning behind queries, not just the specific keywords.
- Customizable: Users can fine-tune the search experience by adjusting the “semantic ratio” to prioritize keyword-based or semantic search depending on their needs.
- Future-proof: The underlying technology is constantly evolving and improving, ensuring that Enterprise Commerce remains at the forefront of search capabilities.
Setup
Enabling Meilisearch’s Hybrid Search in your Enterprise Commerce project is a straightforward process:- Navigate to your Meilisearch Cloud dashboard.
- Select your project.
- Enable the “AI-powered search” feature.

- Go to the “Indexes” tab and access your default index settings.
- Open the “Embedders” tab.

- Click “Create new embedder”.
- Name your embedder “default”.
- Provide your OpenAI API key.
- Set the “Document template”. This template determines what information from each document is sent to the AI model for embedding generation.