Sam Nasr

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Yesterday I delivered a presentation on "Data Cleansing using Databricks".  Listed below are the questions that came up as well as the answers related to Databricks.   Does state transfer from one cell to another in the Databricks notebook? Yes, state does transfer from one cell to another, as long as you're running in the same notebook session and on the same cluster.   Can Databricks Jobs (aka "pipelines") be accessed through a notebook? Yes, you can fully interact with Jobs from a notebook using the Databricks REST API or the Databricks CLI. Some of these interactions from a notebook include: Trigger a job run Check job run status Cancel a run Retrieve job metadata   However, you can't directly "open" the Jobs UI from a notebook, and you can't modify job definitions without using the API   Listed below is an example for accessing a Job from a notebook import requests import json   token = dbutils.secre...

Retrieval ‑ Augmented Generation (RAG) has become one of the most important design patterns in modern AI because it gives language models direct access to external knowledge. Instead of relying solely on what a model has memorized during training, RAG systems retrieve relevant information from documents, databases, or other sources and feed it into the model at generation time. This idea dramatically improves accuracy, reduces hallucinations, and allows AI systems to stay current without constant retraining.   RAG has evolved into a rich ecosystem of architectural layers, addressing different challenges: Core Retrieval layer focuses on improving how information is found, from basic vector search to more advanced techniques like query expansion and hierarchical retrieval. Structure ‑ Aware layer organizes and interprets data based on relationships, formats, or time, enabling retrieval from graphs, tables, or multimodal sources. Reasoning ‑ Enhanced layer st...

I recently had a .NET solution where I wanted to use the same App.config in 2 different projects.  I wanted to always ensure that changes made in the App.config of the original project would be reflected in other project(s) automatically.  Listed below are the steps I used to facilitate that process. 1.      Right-click your project in Solution Explorer 2.      Select "Add" -> "Existing Item..." 3.      Navigate to the file that you want to add to the solution 4.      [Important]  Instead of hitting Enter or clicking the Add button, you want to click the down-arrow icon at the right edge of the Add button, and select "Add As Link".    

Q: Can we upload documents like pdf as input and configure the agents to retrieve the required content that is expected   A: The short answer is yes, but with some configuration.  Working in the Agent Playground, you have the option to “Add” Knowledge.  This knowledge can be from a variety of different data sources as seen below. As stated below “Currently only a single instance per each type of data source is supported.”  In my scenario, I had a single text file setup when I configured my agent, before being published.   Another option would be to utilize the Azure AI Search to index multiple documents from a data store, where documents can be uploaded after the agent is published.  As of today, there are 6 other options available for accessing documents    

AI agents and agentic AI are related but not the same. AI agents are task oriented systems built around LLMs, while agentic AI refers to a broader paradigm where AI systems exhibit autonomy, goal directed behavior, and self improving capabilities.   AI Agents vs. Agentic AI AI Agents: perform tasks but do not necessarily set their own goals. •             Modular systems built around LLMs or LIMs. •             Designed for narrow, task specific automation. •             Operate through tool integration, prompt engineering, and workflow orchestration.   Examples: ·                   A customer service chatbot ·              ...

Deploying a model in Azure AI Foundry can be done in 9 different ways.  Depending on the type of deployment chosen, it may impact one of more factors, such as cost, latency, efficiency for processing large datasets, compliance.  Listed below is a description of each deployment type, along with advantages and disadvantages.  For more details, please visit https://learn.microsoft.com/en-us/azure/ai-foundry/foundry-models/concepts/deployment-types Deployment Type Description Advantage Disadvantage Global Standard Shared global infrastructure for general-purpose model inference. Cost-effective and easy to scale. Performance may vary under high demand. Global Provisioned Dedicated global infrastructure for consistent performance. Reliable throughput and latency. Higher cost due to dedicated resources. Global Batch Asynchronous globa...

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