As Rocket's MV evangelist, I'm starting a discussion. What are you thinking or doing with AI around your MV app?

As Rocket's MV evangelist, I'm starting a discussion. What are you thinking or doing with AI around your MV app?

Dear Mike

I am delighted to share with you my recent foray into the realm of artificial intelligence (AI), specifically in connection with RocketD3. Over the past few months, I have dedicated myself to exploring and leveraging the potential of this technology, uncovering its impressive ability to analyze data through natural language processing. This advancement has enabled me, among other things, to interact in an almost conversational manner with my APIs, CSV files, PDFs, and to develop chatbots for data reception. From my experience, these capabilities far exceed those of conventional systems and represent a significant leap forward in data management and process automation.

My current project utilizes Python-Flask in conjunction with HTMX, a JavaScript library that, unlike HTML, allows for a direct and highly efficient connection from the frontend to the D3 backend via the MVSToolkit API, reducing the reliance on conventional JavaScript. This is particularly ideal for interacting with Pick databases, focusing more on server-side processing. For those interested in exploring this technology, I recommend visiting https://htmx.org/, where you will find detailed information on its functionality.

In the realm of AI integration, I have chosen to employ LangChain (https://python.langchain.com/docs/get_started/introduction), a framework and library specialized in the development of AI applications that rely on Natural Language Processing (NLP) and advanced language models, such as the GPT-4 APIs. This approach has significantly streamlined the development process, allowing me to create more efficient and effective solutions.

In the coming days, I plan to publish in the cloud and share a practical example of how to modernize Pick/D3 applications through the use of APIs and the implementation of artificial intelligence. This case will demonstrate the considerable potential that can be achieved by integrating AI into D3 systems. I hope this contribution will be of interest to you and serve to inspire and guide those in this forum who seek to innovate using AI.

I appreciate the opportunity to share some of what I have accomplished and am eager to receive your feedback and questions.

Dear Mike

I am delighted to share with you my recent foray into the realm of artificial intelligence (AI), specifically in connection with RocketD3. Over the past few months, I have dedicated myself to exploring and leveraging the potential of this technology, uncovering its impressive ability to analyze data through natural language processing. This advancement has enabled me, among other things, to interact in an almost conversational manner with my APIs, CSV files, PDFs, and to develop chatbots for data reception. From my experience, these capabilities far exceed those of conventional systems and represent a significant leap forward in data management and process automation.

My current project utilizes Python-Flask in conjunction with HTMX, a JavaScript library that, unlike HTML, allows for a direct and highly efficient connection from the frontend to the D3 backend via the MVSToolkit API, reducing the reliance on conventional JavaScript. This is particularly ideal for interacting with Pick databases, focusing more on server-side processing. For those interested in exploring this technology, I recommend visiting https://htmx.org/, where you will find detailed information on its functionality.

In the realm of AI integration, I have chosen to employ LangChain (https://python.langchain.com/docs/get_started/introduction), a framework and library specialized in the development of AI applications that rely on Natural Language Processing (NLP) and advanced language models, such as the GPT-4 APIs. This approach has significantly streamlined the development process, allowing me to create more efficient and effective solutions.

In the coming days, I plan to publish in the cloud and share a practical example of how to modernize Pick/D3 applications through the use of APIs and the implementation of artificial intelligence. This case will demonstrate the considerable potential that can be achieved by integrating AI into D3 systems. I hope this contribution will be of interest to you and serve to inspire and guide those in this forum who seek to innovate using AI.

I appreciate the opportunity to share some of what I have accomplished and am eager to receive your feedback and questions.

Dear Mike

I am delighted to share with you my recent foray into the realm of artificial intelligence (AI), specifically in connection with RocketD3. Over the past few months, I have dedicated myself to exploring and leveraging the potential of this technology, uncovering its impressive ability to analyze data through natural language processing. This advancement has enabled me, among other things, to interact in an almost conversational manner with my APIs, CSV files, PDFs, and to develop chatbots for data reception. From my experience, these capabilities far exceed those of conventional systems and represent a significant leap forward in data management and process automation.

My current project utilizes Python-Flask in conjunction with HTMX, a JavaScript library that, unlike HTML, allows for a direct and highly efficient connection from the frontend to the D3 backend via the MVSToolkit API, reducing the reliance on conventional JavaScript. This is particularly ideal for interacting with Pick databases, focusing more on server-side processing. For those interested in exploring this technology, I recommend visiting https://htmx.org/, where you will find detailed information on its functionality.

In the realm of AI integration, I have chosen to employ LangChain (https://python.langchain.com/docs/get_started/introduction), a framework and library specialized in the development of AI applications that rely on Natural Language Processing (NLP) and advanced language models, such as the GPT-4 APIs. This approach has significantly streamlined the development process, allowing me to create more efficient and effective solutions.

In the coming days, I plan to publish in the cloud and share a practical example of how to modernize Pick/D3 applications through the use of APIs and the implementation of artificial intelligence. This case will demonstrate the considerable potential that can be achieved by integrating AI into D3 systems. I hope this contribution will be of interest to you and serve to inspire and guide those in this forum who seek to innovate using AI.

I appreciate the opportunity to share some of what I have accomplished and am eager to receive your feedback and questions.