Can we use Elastic VectorDB for a RAG-based chatbot in CS-Cart?

Elastic Search
1

Hello,

When using CS-Cart as an eCommerce platform, can Elastic VectorDB support a RAG-enabled chatbot to deliver improved, context-aware customer experiences?

2

Hey @KS_Tomar ,

Absolutely! Elasticsearch works really well as the retrieval layer in any RAG pipeline. Some high level steps you need to do:

  • Generate embeddings for the applicable fields (product name, description, FAQ, policies etc.) and store them alongside your standard keyword fields in each document in your index.
  • Configure your middleware so that it generates an embedding using the same model for each query a user makes.
  • Perform a hybrid search to combine your vector/semantic search results with your standard keyword search allowing your chatbot to pull context that’s both semantically relevant and filtered by whichever categories you prefer.
  • Feed those results back into your RAG pipeline and into the LLM to generate a response that is contextually relevant and grounded in your data.

Some resources that might be helpful:

I’m curious, are you running Elastic Cloud or are you self hosting?

Deployment choice can change how you integrate things, and there are some shortcuts/features in Cloud that make this setup easier and more efficient.

Hope this helps! Let me know if you have any more questions.

1 Like
3

Yes, you can use Elastic VectorDB as the retrieval backend for a RAG-based chatbot in CS-Cart.

  • In a RAG (Retrieval-Augmented Generation) setup, the chatbot retrieves relevant documents from a knowledge store and then generates responses via an LLM (like OpenAI’s GPT).
  • Elastic’s vector search capabilities allow you to store embeddings of product descriptions, FAQs, and other CS-Cart content, and retrieve semantically relevant information.
  • This approach ensures that chatbot responses are accurate, grounded in your store data, and not purely based on the LLM’s internal knowledge.

A practical example of this in action is Webkul’s CS-Cart OpenAI ChatBot. This module demonstrates a RAG approach by storing embeddings of your CS-Cart content in Pinecone, a vector database.

  • When a user asks a question, the query is embedded and Pinecone returns the top-K relevant documents.
  • OpenAI’s LLM uses the retrieved content + user query to generate a precise answer.

If you want to replace Pinecone with Elastic, it’s entirely possible. You would need to configure your Elastic server with an index containing a dense_vector field for embeddings and push your CS-Cart content embeddings into this index.

The chatbot’s retrieval logic would then query Elastic using vector search (or a hybrid vector + text search) to fetch the top relevant documents.

These can then be passed to the OpenAI LLM for answer generation. By leveraging Elastic in this way, you can unify your search infrastructure, eliminate the need for an external vector database, and even combine keyword search with semantic search for hybrid relevance.