To achieve this functionality, you would need to integrate Bagisto with a system capable of generating and querying vector embeddings of your product data. Here's a simplified breakdown of the process:
Data Preparation: Ensure your product data is structured in a way that facilitates the generation of vector embeddings. Each product should be associated with relevant features and attributes.
Embedding Generation: Utilize machine learning or deep learning techniques to generate vector embeddings for your products. This involves representing each product as a numerical vector based on its characteristics.
Integration with Bagisto: Develop custom modules or plugins to integrate the embedding generation and querying process with Bagisto. This involves accessing the database, querying the vector embeddings based on customer queries or recommendations, and retrieving the relevant products.
Testing and Optimization: Thoroughly test the integration to ensure it functions as expected. You may need to optimize the system for performance and accuracy.
Note: We already working on the modules like chatbots, symentic search, etc. You can contact webkul for qoutes as well.
