I wish the code would process the timeline.

Dangggggggg!!!

Shame that the hook with videos is start open source and then get people draw into handling supporting functions via a commercial platform (for $$$)

Yeah, this is cool but not helping. I can open the LiveKit interface but can't find a way to get the agent to connect. API keys all correct

Sir, your AI voice assistant demo's are one of the most valuable and appreciated youtube videos I have come across. Please keep them coming and also would be great to do a demo with Groq for solving latency issue. You are doing great work man and your students are really appriciating it! Thanks a lot brother!

Excellent video very helpful THANK YOU!

Man this is very instructive video! Thank you for filming this!

Maestro! Very well explained Santiago, mis 10+ 😊

in summary: add a missing indicator

DeepGram does not look available to use their website is simply a contact form

My only gripe with Mojo is that I wish it was marketed less towards AI and more towards general usage. It literally fixes the 2 major issues with Python that being lack of static typing and bad performance, if they market it as general purpose this could be a drop in replacement for python

I love how you explain your code!

will this help for automatic1111

Thanks for an amzing content can we use giskard without openai key

great video as always. Does this system keep chat history?

Can you do using gemini pro

Teaching style is too unique and too good 👍

This is a very interesting benchmark. Another example is something like, given 1 datapoint e.g. an image of a creature you haven't see before, be able to classify 100 images for whether that creature is visible in the image or not. You, me or even a young child can easily solve a problem like this. Current neural nets require enormous amounts of data and still make mistakes a human would never make. All this AI is going to be smarter than us hype will only be true once we discover algorithmic ideas that get machines to learn given very little data. We absolutely need new ideas.

Got stuck with the API Keys, i'm not sure how to set them

How do you make those models that interact with data, Like I once saw someone create something really amazing that inteprets data from a a database and makes interpretations and reports from the data wothout hallucinating (It only fetches from the underlying DB)

This was a really cool stuff. Thanks for sharing such a quality stuff. I ran it on vscode and it worked. The main problem is the latency. It took like 10s for the conversation. Not sure if it is because of the free account or their websocket API has the issue.

Hey there, the last book "Generative AI with LangChain" that you mentioned really cleared what I want. Thank you.

Loved watching it... You explain every thing so nicely!!!❤

Hi there! I'm new to AI and RAG systems and have spent the past few days diving into your tutorial to understand each step and debug along the way. I have to say, THIS IS A GREAT TUTORIAL! I do have a question about alternative local LLM approaches. Regarding the accuracy of asking questions and retrieving relevant information from PDF documents like research papers and books using local models, would you recommend this approach over using tools like Ollama UI and LM Studio? Are there specific advantages or disadvantages to consider when choosing between these methods?

Not sure if this is representative of AI in general. DeepMind and other AIs have solved games (eg chess, go) using similar examples and training. Thoughts?

Best Video on KZbin regarding RAG. Must watch....

AI cant solve yet! I don't agree, that todays LLMs are not finding solutions for these problems. They are finding solutions, just wrong ones. Its a bit funny at the first glance, but in reality what you have shown the success is at 35%. Its not like its at 0%. 35 will grow and in time its gonna be 85. They are faking reason, better and better each day.

Great content! Extremely helpful to get hands-on introduction into LLM and RAG. Thank you!

At last some sanity in the web!

Hi, sir I'm in college learning BCA (Bachelor of computer Application). What would you recommend me to learn to for long term goal. I'm confused please share your thoughts 💭 Thankyou love your videos❤

What is love?

Thank you for sharing these with us ❤

There are a lot of contents how to fine-tune LLMs with LoRA or QLoRA. You gave us same food just with ‘apple genius’ keyword.

可以把你视频通话这里运行吗

You are using an LLM to create a question and a LLM to get another answers and than let an LLM eval both answers, but how do you evaluate the output of the Initial Tests? At this point you are trusting the facts of an LLM by tusting the answers of the llm

thanks brother!!!😄😄😄

You sounds like a hero of all Machine Learning realm. Thank you very much for the video sir.

Immediately i saw this video i knew i had to subscribe ASAP. I tried it and its up and running though i had some errors cos i am not a python programmer. But i want to use it to build an Ai that can be installed on a vessel (ship). Thanks for sharing ❤

Thanks a lot for this awesome content❤ It would be super helpful if you could make a video for people starting their IT careers in 2024. Maybe cover what areas they should focus on. Thanks again!

Oriented - great job👍

All open-source AI models are bad. They hallucinate a lot and give wrong answers. Don't use them. Don't waste your time. Ask your open-source model what the longest beach in the world is and see if it answers correctly. If it gets it wrong, ask again. The answer is Cassino Beach.

Great video, keep going. Just wonder if you can add barge-in function?

I think Python also supports it. e.g. grade: str # here if age < 11: grade = "Elementary" ... # The code block remains the same

I’ve becoming more and more disappointed with Mojo . The resulting language is nothing alike python, which is great for its simplicity and readability. And the overall performance is not really better than rust or other languages …

Love the video. Great breakdown. Would like to see more detail in evaluation results (e.g. it is now .73 good. WTH...!?), how tweaking the pipeline gives different eval results, and e.g. Ragas versus Giskard.

Thanks ! Exactly what I was looking for. I’ve been cracking my head on how the hell to test a RAG system. How the hell is business going to give me 1000+ questions to test and how can a human verify the response. Top content.

Santiago, the voices of assistant are only in english? or also in spanish? abrazo crack!

This is great stuff, Santiago! I wish you had posted this video a few weeks ago. We just completed our final class project where we trained five different BertClassifier models on five different tasks. Our fine-tuning and inference code structure is very similar to yours. We definitely could have used this approach to use just the specialized adapters instead of the full BERT models. However, I have one question: I'm not clear whether the full model will ever be used during this process after we get fine-tuned adapters, or just the fine-tuned weight matrix for evaluation and inference?

Thank you for the content very helpful, I have one question, how do I know it is running locally? I mean for every model we used the langchain library as if it were consulted through the API.

for some wierd reason, the bot won't run on windows. Had to run using WSL2. Hope it helps anyone taking this code out for a spin :)