Moqui Powered by AI? The thoughts of a Spanish entrepreneur.-

[jcigala]

Thank you all the people in the Moqui Open Collaboration Meet, past Friday, March 14, 2025.

There, David and others started to talk about Moqui’s architecture and the possibilities that AI could bring to us for the User-centered interface design, among other excitement ideas!

Javi López is a successful Spanish entrepreneur.

Javi López’s post builds on his 2022 thread predicting the decline of websites, now emphasizing Gemini’s latest release as a step toward a future where AI interfaces handle all tasks via natural language and gestures, rendering traditional apps and websites obsolete.

The post highlights Gemini’s advancements, such as real-time tool creation and multimodal capabilities, positioning it as a leader in the AI race alongside competitors like OpenAI and xAI, amid a broader industry shift toward foundational models requiring massive computational resources.

It references the rapid evolution of AI tools like DALL·E 2 and ChatGPT, suggesting that Gemini’s progress signals a “reset” in how humans work and interact, with profound implications for industries and the concept of work itself.

https://x.com/javilop/status/1901169134685057477

[grozadanut]

This AI topic is very interesting. I’m linking here some similar posts related to AI:

How Rocket.Chat + ChatGPT Integrations Work with Moqui

Hi, @heguangyong I have been exploring how can we integrate, moqui with the AI kinds of stuff. I am trying to try out your component but facing an issue with the build. Are there any specific steps that I have to follow to set up your component in the Moqui, Here are the steps that I have been following Clone the following component in my moqui GitHub - heguangyong/moqui-wechat GitHub - heguangyong/moqui-openai run the ./gradlew build command to build. Here is the error I am getting …

Industry Trends

Throwing this out to the forum for input. I am also planning to write something to post on LinkedIn once I have my thoughts more organized. AI has been all the rage for the past couple of years. I have been skeptical because it had the feel of just being the next tech hype cycle, or as I put it in a LinkedIn post during the 2023 SaaStr conference, “a solution looking for a problem”. I still believe it is a lot of hype, but the fact is that AI is where investors are putting their money. And I th…

From my own point of view, there are 3 cases where AI could help Moqui:

1. Help Moqui developers to write code. I think this is handled pretty well by already existing tools like Github Copilot, Codeium etc…(ask ChatGPT for more examples)
2. For Moqui backend users it could help them to use the system easier with the AI user interface, as presented in this post. I think this is the most innovative idea. On the practical side, I have no idea what implementing an AI interface would take. Considering it took heguangyong(first post) a couple months for research only, I guess it would be pretty complicated.
3. For end users it might help them for example to place orders, find items, respond to questions etc… So I guess customer support. I think this might be the most practical case to be implemented as a Moqui component. There are already multiple solutions for AI customer support(Botpress, tawk.to AI Assist, Live Helper Chat…) that integrate both social platforms/chats and AI, so I believe the easiest approach for Moqui to use AI is to just integrate with their apis.

And I also have a question: how can we participate in the Moqui Open Collaboration Meet and where do we find info about next events?

[jcigala]

Eastern Moqui Call Starting

The invite is just a google calendar event with the entire moqui google group added. If you join the google group (here), you should see it on your calendar. If you want a direct invite, reach out and I can add your email directly.

[grozadanut]

Thank you @jcigala!
Okay, here is my understanding of AI so far:

AI doesn’t actually understand text, only numbers, so when you are chatting with ChatGPT for example, he is actually transforming text to numbers behind the scenes. These numbers are called a vector(eg.: 1, 0.5, -0.9…), hence a vector database is a database that stores these numbers.

So what is RAG(Retrieval Augmented Generation)?
RAG is a way of enhancing the AI to respond from your knowledge base. How does it work?

1. You convert your knowledge base(PDFs, FAQ, product database etc…) to embeddings(this is just a fancy name for vectors, basically you convert text to numbers).
2. The user asks a question. You search the vector database and return the embeddings most similar to the question.
3. You send the question along with the embeddings to an LLM to format a nice looking response.

That sounds like a lot of work, I don’t want to be an AI engineer, I want to work less, not more.
That’s what Langchain is for. Basically, it’s a framework that does all of these low level tasks for you. It’s not an AI itself, but more like an interface to AI. You use it to communicate to AI(any LLM model interchangeably), convert text to vectors, search the vector database and other AI stuff. Langchain4j is the one for Java developers.

Okay, but I don’t need AI to answer questions, I need him to talk to my fiance and schedule all of our date nights and put them in my calendar.
Those are called Tools in Langchain(or Functions for OpenAI). Basically a tool enables the AI to call external APIs, for example to create events in your calendar or get your intimate messages from WhatsApp. The problem is that these tools are not AI native, they are Langchain specific, meaning that if you want to give up the Langchain framework, you will lose the tool functionality. Unless you use OpenAI, which has it’s own tools called Functions, but then you will need to port the code between these 2, so again a pain in the groin.

Ha, gotcha you now, the AI-tool communication was already standardized. Haven’t you heard about MCP?
Indeed, the very new and trendy Model Context Protocol created by Anthropic seems to be the most promising standardization of AI-tool communication so far. There are still a few drawbacks to it though. First the LLM needs to be adapted for it. Currently OpenAI and Anthropic seems to be the ones that officially support it. Second, it’s the job of the AI to call the tools, you just specify what your tools can do, and he decides when to call and use them. Now for enterprise or more sensitive use cases you might need a bit more control over the workflow.

Nevertheless, MCP is still a great addition, as it may simplify your AI-tools interface, so you only need to write it once, and you won’t need to rewrite it in case you need to change the Langchain framework. And you can still use them together, MCP for tool communication, and Langchain for managing the workflow, so you have the best of 2 worlds.

Seems like @heguangyong wrote an MCP server for Moqui, so maybe he can share his experience so far? GitHub - heguangyong/moqui-mcp

Bonus
What about using AI to search products instead of the current Elasticsearch implementation?
Depends on the use case. AI search would be more user friendly, since you can do queries in natural language, but the main drawback is that it is a lot more computationally demanding compared to a traditional Elastic search.

Note: So far, in my opinion, MCP seems to be the most practical, easiest and fastest solution to have a natural language interface for Moqui. Combined with a local LLM(eg. ollama with open webui), you also get data privacy. But not so sure about the reliability part.