Python + AI livestream series: Resources #166
Replies: 10 comments 16 replies
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Thanks for sharing with this community @pamelafox - looking forward to the series in October |
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@pamelafox thanks for the cool session today. I have a short question:
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Will this series talk about how to deploy RAG applications? I am enjoying the series but so far there has not been any discussion about how to deploy AI based web apps. For example, if I have a RAG application that uses a ChromaDB vector database, a FastAPI server, and some kind of web app frontend, then how would I deploy such an app? If this series doesn't talk about deployment, are there any references that you can share to help me deploy my RAG app? |
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Structured outputs is probably one of the most useful use of LLMs for me that I recently thought of / discovered for myself, and I call it "creating complexity from chaos". I played with this idea and made a prototype for an app that reads a collection of news and images about say a mountaineering accident, then extracts details from each news source into a single event json document, and then writes a report about the event: https://accident-reports-frontend-5t5l4f4etq-uw.a.run.app/ Caveats for things I needed:
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@pamelafox can we please add this URL https://github.com/Azure-Samples/rag-with-azure-ai-search-notebooks/ in the table above |
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Small thing but in the 7th session I think links are mixed up. |
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So I had a question from today's Agents session. When using short term memory, you can summarise the messages/info and stick into the context as a message but i'm guessing the original messages don't get lost but remain in memory, maybe a cache or somewhere? With long-term memory, both summary and original probably go into some DB |
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Hi Pamela, thanks for the series. I have been watching to learn and also to prepare for upcoming interviews where I need to design and start prototyping an AI powered system in Python with LLMs, with AI tools allowed. When starting from scratch in an interview setting, how would you go about it? Do you have a particular stack you rely on, and how do you frame key trade offs such as cost, latency, and scalability? Any brief tips for live prompting and iteration would be great too: framing the brief, setting constraints, quick checks to verify outputs, deciding what to accept or discard, and when to stop iterating. Thank you. |
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Does the Microsoft Agent Framework support creating agents that are not LLM agents? Can I create an agent that is for controlling a robot, an agent for interacting with a database, an agent that performs some computational task, etc.? I like the idea of an agent framework to orchestrate workflows but not everything needs to use an LLM so I'm curious if such a framework can be used without an LLM. |
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Thanks for shahirng |
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Join us for our 9-part live stream series on using Python with Generative AI models! 🐍 🤖
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