Build Smart Drupal Chatbots With RAG Integration and Ollama
As a Drupal developer who values open-source solutions, I was excited to explore the potential of combining Drupal and Ollama. Drupal is one of the most popular CMS platforms worldwide. Tesla, Nokia, and Oxford University are just a few examples of high-traffic websites powered by Drupal. Among the many reasons why Drupal is used by so many companies, the core one that resonates with me is its open-source nature.
Ollama is also open-source and is being used extensively to create applications around LLMs.
I have used these two powerful tools to create an application called Drupal RAG Integration.
Let’s understand the use case
Imagine you run a tech blog on Drupal. A visitor asks, ‘What was your latest article about AI?’ Your chatbot can now provide an accurate, up-to-date response based on your actual content.
Integration with OpenAI or any other LLM alone is not sufficient. While they can answer general questions they’ve been trained on, they can’t handle content specific to your website. One option is to train the LLMs, but that is too expensive and requires machine learning expertise. With RAG, we can implement this functionality easily.
In Retrieval Augmented Generation (RAG) architecture, we can retrieve additional context from our custom data source and pass it to general-purpose LLMs to generate personalized and accurate responses.
About the Application
This integration empowers Drupal site owners to create intelligent, content-aware chatbots without the need for expensive AI training or external services.
All Drupal content, when created, updated, or deleted, is stored in the Chroma vector store and will be used later to retrieve additional context for the LLM to generate responses.
I have used FastAPI to provide APIs for interacting with LLMs and storing data in the Chroma vector storage.
On the Drupal side, I’ve built a module called drupal_rag_integration that feeds data to the RAG app backend and provides a form for users to ask questions and receive generated responses.
Architecture
To better understand how Drupal RAG Integration works, let’s take a look at its high level architecture.
The diagram above illustrates the flow of data from Drupal content creation to the Chroma vector store, and how queries are processed through the RAG system.
Demo
In this short video, you’ll see how quickly the chatbot responds with accurate, site-specific information.
Codebase
The code for application is available on my github.
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Drupal module: https://github.com/saxenaakansha30/drupal-rag-integration-module
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Rag FastAPI codebase: https://github.com/saxenaakansha30/drupal-rag-app
Explain please!!
In the next article, we’ll dive deeper into the technical details.
Stay tuned!!
Posts in this series
- Inside the Codebase: A Deep Dive Into Drupal Rag Integration
- Build Smart Drupal Chatbots With RAG Integration and Ollama
- DocuMentor: Build a RAG Chatbot With Ollama, Chroma & Streamlit
- Song Recommender: Building a RAG Application for Beginners From Scratch
- Retrieval Augmented Generation (RAG): A Beginner’s Guide to This Complex Architecture.