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AI with Supreet
เข้าร่วมเมื่อ 30 ต.ค. 2009
Welcome to AI with Supreet where we see AI from a developer's view. Join me as I explore the latest in AI for the dev community and build modern AI first apps.
13. Updated Aura Demo using open AI Voice Models
Technical Details:
- .Net Core MAUI App with ChatGPTNet Nuget from Marco Minerva
- Azure Speech for Speech to Text & real-time conversation transcription
- Open AI Natural Voices with Azure Speech for TTS
- A prompt where I tried giving Aura the personality of Donna from Suits :)
- .Net Core MAUI App with ChatGPTNet Nuget from Marco Minerva
- Azure Speech for Speech to Text & real-time conversation transcription
- Open AI Natural Voices with Azure Speech for TTS
- A prompt where I tried giving Aura the personality of Donna from Suits :)
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11. CRUD with Natural Language Processing
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In this video, we will see how you can query Databases using Natural Language using Semantic Kernel Planners. We use APIs with custom plugins to achieve this. You will also see how you can create custom objects from a user text & use that to call POST methods. Hope you enjoy this. Here's the link to the GIT Repo: github.com/supreettare/CrudWithNLP #artificialintelligence #semantickernel #azurea...
Voice to Voice chat demo in Hindi, developed with Semantic Kernal and Azure AI/ Open AI.
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Voice to Voice chat demo in Hindi, developed with Semantic Kernal and Azure AI/ Open AI.
10. Introducing Aura a multilingual Voice to Voice assistant created with Azure AI & Semantic Kernel
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10. Introducing Aura a multilingual Voice to Voice assistant created with Azure AI & Semantic Kernel
9. Using Semantic and Native Functions together with Planner in Semantic Kernel
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9. Using Semantic and Native Functions together with Planner in Semantic Kernel
8. Troubleshooting issues when writing a good prompt.
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8. Troubleshooting issues when writing a good prompt.
7. Creating RAG apps with Semantic Kernel and Kernel Memory
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7. Creating RAG apps with Semantic Kernel and Kernel Memory
6. Using planner with Semantic Kernel
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6. Using planner with Semantic Kernel
5. Using Embeddings and Semantic Memory in Semantic Kernel Apps
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5. Using Embeddings and Semantic Memory in Semantic Kernel Apps
4. Controlling model’s output with execution settings
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4. Controlling model’s output with execution settings
3 . Creating your own Chat GPT in less than 9 steps
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3 . Creating your own Chat GPT in less than 9 steps
2 . Creating plugins and reading prompts from file in Semantic Kernel
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2 . Creating plugins and reading prompts from file in Semantic Kernel
Getting started with Semantic Kernel in less than 7 steps
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Getting started with Semantic Kernel in less than 7 steps
Hi please make a video..
Sir please make a video
Hi...can you send the code files used in each of these videos?
@@truvishe6948 I didn’t upload all the code to GIT. Will do that next time. Planning to redo this entire playlist with latest version of SK.
Is this same as integrated vectorization available in Azure AI studio?
There’s a difference between the two. Azure’s integrated vectorization focuses on embedding generation for improved search and retrieval within its AI Search platform. In contrast, MCP servers facilitate LLMs’ interaction with external tools and data sources, extending their operational capabilities.
Ur English is so good Hope you get Success Soon
I waiting for next video sir.
New short videos like this coming every day.
Hi sir In new version of semantic kernel plugin planner do we need config.json,skprompt.txt ?
Hello, I’ve not yet played with the latest version of SK. Planning a new YT series on the latest stable version soon.
@@AIWithSupreet Thank you Waiting for the new series
Hi sir I have followed your video everything is working fine except the create operation. Can you share or update the final source codein your github.
Getting error message "Microsoft.SemanticKernel.KernelException: 'The function identifier is empty'" on var summarize = kernel.CreateFunctionFromPrompt(prompt, executionSettings: new OpenAIPromptExecutionSettings { MaxTokens = 100 }, functionName: "summarize");
thanks
The better way to access those files is to setup build action as *Copy to Output Directory - If Newer* in properties of file. Then you can access them like "Plugins/FunPlugIn"
why so slow ?
the using statement is not correct it wont work. you need a different library using Microsoft.SemanticKernel.Connectors.AzureOpenAI; this ruined my whole day
hi i learn semantic kernel from your videos. i like as you explain in your videos. you did a great job. Thank you for explaining in a such a way.
Are you taking this code from the github notebooks?
Where's the code demo?
Only "Microsoft.KernelMemory.Core" package is fine to implement this. May I know the reason of installing "Microsoft.SemanticKernel" package?
Thanks for the Interesting Playlist ..I wanted to Learn exact same thing but was finding it difficult in the Documentations.
i just started with Semantic kernel and found these videos really helpful. Thanks for creating simple and to the point videos.
Great and to the point!
Your videos have been a lifesaver. I'm a total beginner to C# trying to learn semantic kernel, and these have been incredibly helpful. Thanks Supreet!
Don't know if you've rotated your API keys already, but if not you should or you should delete the resource because you showed the API key briefly at the 5 minute mark
Nice
Totally enjoying your series of AI videos.
Awesome. Where can we get a copy of the code?
Great series, appreciated!
Glad you enjoy it.
cool, definitely she hates Donna :-)
Ha ha :) She is actually created based on her persona.
Thank you so much for the video. Although, i have a problem with the answer being cut off mid sentence. Is that a token issue, or is it the fact that pipeline to OpenAi is not SignalR?. Hope you can help. Thanks
Finally found the answer: You can set the maximum number of tokens for the answer when configuring Kernel Memory: var kernelMemory = new KernelMemoryBuilder(builder.Services) //... .WithSearchClientConfig(new() { AnswerTokens = 800 }); The default value for this property is 300.
What is the correct/difference between an config.json like { "schema": 1, "type": "completion", "description": "Find the city name from a given prompt.", "completion": { "max_tokens": 512, "temperature": 0.8, "top_p": 0.0, "presence_penalty": 0.0, "frequency_penalty": 0.0 }, "input": { "parameters": [ { "name": "input", "description": "Text from the user to get the city name from.", "defaultValue": "" } ] } } And the one you have, why do I sometimes see execution_settings and other times completions?
Running it twice give always same joke. Why? Temperature is 0.9 but stil....
Is Kernel memory internally using LLM or LLM is nowhere in the picture here. trying to understand if after retrieving the data from PDF, does it automatically pass it to LLM ?
THis bs doesn't work. System.NotSupportedException: Serialization and deserialization of 'System.Type' instances is not supported. Path: $.Function.Metadata.Parameters.ParameterType.
At which step? Part of the code?
Thanks a ton for this easy to understand tutorial Supreet! I was struggling with env part and it was helpful. However, the code fails and ImportDocumentAsync saying "pipeline start failed". Below is the error message: Microsoft.KernelMemory.Pipeline.BaseOrchestrator[0] Pipeline start failed Azure.RequestFailedException: Service request failed. Status: 303 (See Other) Hope I am missing something. Is the component trying to reach Azure Servers for any validation? I understand serverless meaning "everything happens in local". Pls correct me if I am wrong.
Awesome 🎉, waiting for source code walk-through and I will try to explore the same using Malayalam language. Thanks for the demo
Thank you Rajesh. Source code walk through coming soon.
Impressive
Hi. Thanks for the awesome videos. is there a github repo for this code ? thank you.
The SK team on discord is extremely proactive and while I was recording the video and uploading it here, they also logged this bug/ feature request to be solved in their GitHub. If you are seeing this video after a few weeks of posting it you can track the status of the issue here: github.com/microsoft/semantic-kernel/issues/4797 p.s. Forgot mentioning that I was using semantic kernel 1.2.0 stable release when I encountered these issues.
Keep up the good work Supreet :)
Thank you. Appreciate it. :)
Thanks for the (p)review ! This is very interesting. Just thought about, running that on a server you can load into memory on start up and dont need a database.