7. Creating RAG apps with Semantic Kernel and Kernel Memory

Фильм және анимация

In this video, we will explore how to create RAG apps using Semantic Kernel and Kernel Memory. Kernel Memory makes ingestion, partition, creating chunks of memories, partition and retrieval of information extremely easy.
#artificialintelligence #semantickernel #aiconsultant #chatgpt #chatgptplugins #RAGApps

Пікірлер: 5

  • @krishtheindian
    @krishtheindian3 ай бұрын

    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.

  • @user-fg8dv1fi1z
    @user-fg8dv1fi1z3 ай бұрын

    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 ?

  • @mytube538
    @mytube5383 ай бұрын

    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

  • @mytube538

    @mytube538

    3 ай бұрын

    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.

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