Too many data lakes become abyssal: dark, cold, and intimidating places where data goes in and is rarely trusted or used again. A well-designed data lake should feel more like an oasis. A place of clarity, structure, and reassurance. Clean shorelines (schemas), clear water (data quality), and visible paths beneath the surface (lineage, metadata, and … Continue reading →

The more I work in Langchain, the more I discover its strength for creating powerful, custom Large Language Model interactive AI powered chatbots. For example, right now I am using it to create a custom chatbot that is trained on our proprietary internal documentation, reports, and log files. This uses something called RAG (Retrieval Augmented … Continue reading →

Over the next serveral weeks I will be diving into my work in creating a personal assistant, much like a private chatGPT. The main reason will be so I can explore the different capabilities of LangChain and how I can use it to do exploratory data analysis on my library of PDF files, emails, and … Continue reading →