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AI Makerspace
Registrace 28. 01. 2023
Learn how to build, ship, and share production Large Language Model applications with us!
Video
Real Time RAG with Haystack 2 0 and Bytewax
zhlédnutí 993Před měsícem
Real Time RAG with Haystack 2 0 and Bytewax
Pulse AI: Personalized B2B Content Marketing, by Arthi Kasturirangan
zhlédnutí 223Před měsícem
Pulse AI: Personalized B2B Content Marketing, by Arthi Kasturirangan
RagTime: Your Digital Second Brain, by Phil Mui
zhlédnutí 372Před měsícem
RagTime: Your Digital Second Brain, by Phil Mui
Kevin: Your AI Pair Programmer, by Allan Tan
zhlédnutí 304Před měsícem
Kevin: Your AI Pair Programmer, by Allan Tan
Teach2Learn: LLMs as Virtual Students, by Jerry Chiang and Yohan Mathew
zhlédnutí 106Před měsícem
Teach2Learn: LLMs as Virtual Students, by Jerry Chiang and Yohan Mathew
PharmAssistAI: Easily navigate complex FDA guidelines, by Raj Kumar
zhlédnutí 438Před měsícem
PharmAssistAI: Easily navigate complex FDA guidelines, by Raj Kumar
Anti-Money Laundering Compliance, by Miguel Costa
zhlédnutí 59Před měsícem
Anti-Money Laundering Compliance, by Miguel Costa
StudyBuddy: AI Assisted Exam Training, by Ursula Deriu
zhlédnutí 65Před měsícem
StudyBuddy: AI Assisted Exam Training, by Ursula Deriu
ClearPolicy: Insurance Policy Simplification, by André Fichel
zhlédnutí 142Před měsícem
ClearPolicy: Insurance Policy Simplification, by André Fichel
Jupyter Notebook Tutor: An AI Agent that Teaches AI, by Julien de Lambilly
zhlédnutí 188Před měsícem
Jupyter Notebook Tutor: An AI Agent that Teaches AI, by Julien de Lambilly
CaseCompass: An AI Companion for Support Agents, by Efrain Martinez
zhlédnutí 65Před měsícem
CaseCompass: An AI Companion for Support Agents, by Efrain Martinez
EchoLinkAI: Elevating Slack Communitcations with Agents, by Nithin Kamavaram
zhlédnutí 569Před měsícem
EchoLinkAI: Elevating Slack Communitcations with Agents, by Nithin Kamavaram
ProPrepPal: Virtual assistance for Interviews, Meetings, and Conferences, by Shoshana and Mike
zhlédnutí 101Před měsícem
ProPrepPal: Virtual assistance for Interviews, Meetings, and Conferences, by Shoshana and Mike
Jira Time Coordinator: AI-Assisted Ticket Assignment, by Monalisha Singh
zhlédnutí 205Před měsícem
Jira Time Coordinator: AI-Assisted Ticket Assignment, by Monalisha Singh
PoppyChat: Chatbots for Pediatric Dentistry, by Jay Ozer
zhlédnutí 85Před měsícem
PoppyChat: Chatbots for Pediatric Dentistry, by Jay Ozer
RefineMyResume: Fine-Tuning your resume to get the job!, by Santhosh Maila
zhlédnutí 83Před měsícem
RefineMyResume: Fine-Tuning your resume to get the job!, by Santhosh Maila
RAG, Fine-Tuning, and Agents: The Three Key Patterns of Generative AI
zhlédnutí 2,3KPřed měsícem
RAG, Fine-Tuning, and Agents: The Three Key Patterns of Generative AI
NBA Stats Tracker: Warriors Edition, by Arindam Ganguly
zhlédnutí 111Před 3 měsíci
NBA Stats Tracker: Warriors Edition, by Arindam Ganguly
Biomedical Research Assistant, by Robert Eubanks
zhlédnutí 167Před 3 měsíci
Biomedical Research Assistant, by Robert Eubanks
RN-PrepPro, by Milani McGraw and Lilly Bakalis
zhlédnutí 65Před 3 měsíci
RN-PrepPro, by Milani McGraw and Lilly Bakalis
ResearchPaperSimplify, by Anurag Lahon
zhlédnutí 436Před 3 měsíci
ResearchPaperSimplify, by Anurag Lahon
Omg, didn't know about that. Definitely going to the pipeline. Thanks for sharing! About the newsletters, there was theneurondaily, rundown ai, and what was the third one please? Something Valley when you all nodded so it must be an important one 😄 BTW, The Batch from Andrew NG is also a great one.
Hey Moad! Cerebral Valley - here you go! cerebralvalley.ai/ The Batch is a classic for sure! TL;DR, Ben's Bites, and many others. If you look at just a couple you should be able to pattern match from week to week!
@@AI-Makerspace many thanks!
Always inspiration
Excellent session! Your explanations are so helpful and very much appreciated
How can we track testing and benchmarking agents using AgentOps
At this point it looks like the brunt of these features are in development - we'll comment when there's more of substance!
AIMS-AgentOps-CrewAI-Demo: github.com/chris-alexiuk/AIMS-AgentOps-CrewAI-Demo/tree/main Event Slides: www.canva.com/design/DAGJ55plCbY/PDASCroDOh-Upiu_lICWkw/view?DAGJ55plCbY&
Greg I 'd like to talk about llama3 great lectuce. 😊
How can I access this Colab (llama_index_tracing tutorial.ipynb)? Can you share the link?
We've shared your comment with the presenter! We expect he'll get back with you shortly!
On a scale from 1 to John Wick how much do you love your dog? 😆🤣🤣😆
Watch the full video! czcams.com/video/8tS_84-5Hmo/video.html
Thank you thank you thank you thank you thank you
CZcams quality still 720p. Really hard on the brain to follow the code examples.
Stay tuned! Thanks for the ping and the reminder!
Thank you thank you thank you for this fantastic explanation of DSPy. I didn't understand it before this great webinar.❤
You're welcome! 🫶
Great point about agents, but I have a question. Whats != Agentic between other ?
Thanks for your question Givanildo! We do need to clarify this idea that we're playing with on distinguishing between "multi-agent" and "multiple agent," even internally. There is, admittedly, ambiguity here, and we will seek to clarify it more fully in our next multi-agent event. But we created it because we have found it to be a useful mental model. The point of making this distinction was to recognize that the complexity and dynamism of multi-agent systems varies, sometimes quite widely. For instance, multi-agent systems presented today often feel constrained, linear, and prescriptive. It many cases multi-agent systems could just as well be single agent systems created with one prompt (albeit a quite long one). Conversely, a truly complex "multi-agent" system would, in our estimation, *require* the use of multiple agents, even to the point that each LLM was fine-tuned to a specific task. Something like that - what do you think!?
Thank you very much for providing historical context, to me this is one of the most important aspects of learning about new tech, and understanding where, how, and why it fits in.
💯we couldn't agree more!
good stuff. Thx!
AIMS-CrewAI-Demo: github.com/AI-Maker-Space/AIMS-CrewAI-Demo Event Slides: www.canva.com/design/DAGJPj_tw-c/hSR7mYwEiRqCoK_L4RRcEg/view?DAGJPj_tw-c&
Nice
Good video but one question: Why did you choose to create the testset step-by-step yourself and not use the provided TestSetGenerator from Ragas? Was is not available back then?
That's right! They had just rolled it out it when we had them on for this more recent event: czcams.com/users/liveAnr1br0lLz8?si=_wIYqsL4vcVM5QDq
Wow great video. I just found your channel, already a fan. 🦾🤖
Let's goooooo!
Does ragas work only on openai model, which model i can use for testset genrator as critic and generative model please help me out
You can use any LLM that has OpenAI API compatibility. This means most closed source models, as well as open source options through certain hosting strategies (NIMs, vLLM, etc)
@@AI-Makerspace i dont have openai api key Can i use models from hugging face
Please help me out I want to genarate the test set data using models other than hugging face Like generate model and critic model
best video i found on learning word2vec concepts by building up from simple examples. very helpful for people who learn by doing.
You're speaking our language @sillystuff - we're all about learning, building, shipping, and sharing! Thanks for the comment and we're glad you found it useful!
amazing talk~~
top
Has there been any update yet?
great talk - thank you:)
Very useful 👌
More more more DSPy please!
absolutely
Loud and clear guys! We'll see what we can do 🤓
Nice intro dudes lol music hooked me
This is the most awaited and most useful and trendy thing in the market.
DSPy - Advanced Prompt Engineering?: colab.research.google.com/drive/1Il47YSattSnWV5cfSzSD7b2rWuyv7n6O?usp=sharing Event Slides: www.canva.com/design/DAGIl1M_SYI/1nhzSuhN8YQ0uxN_wqP99g/view?DAGIl1M_SYI&
Stoked that you are diving into DSPy! Would be cool if you could do a RAG demo and show how to generate an evaluation metric from free form text responses.
@@seanbergman8927 we think RAG is definitely the next step here for sure! Stay tuned!
RAGAS starts at time 20:15, before which is just an overview of langchain and the RAG QA pipeline
Thanks for the timestamp here MrTulufan!
this is quite useful,its really commendable how you come up with trendy topics with codes.
Thanks Rakesh! We agree - Allan really crushed it with this one!
Can anyone tell me how ragas actually calculates these numbers. Like manually I get it, but what do the algorithms or functions look like? Like how does it measure faithfulness?
Hey Ravi great question! We go a bit deeper into this in our more recent event with the creators! czcams.com/users/liveAnr1br0lLz8?si=UG6vRnSY9oVtAuAT We'd recommend reading through the docs and digging into the source to go EVEN deeper! e.g., docs.ragas.io/en/stable/concepts/metrics/faithfulness.html
The Loss Function in LLMs - Cross Entropy: colab.research.google.com/drive/1VEk0mdGYKfPezWDJ4ErajNNlr4pq2Duk?usp=sharing Event Slides: www.canva.com/design/DAGH7_m5E48/HlpvFnc2VbCsjNiiFGdYnQ/view?DAGH7_m5E48&
This is really great explanation. I have one query, lets say I want to improve the performance by focusing on Faithfulness or Answer Relevance, so which RAG optimization techniques I should follow to increase Faithfulness or which techniques can improve Relevance or Precision etc.
The answer is, unfortunately, it depends! The whole system needs to work together (from data quality, to retrieval quality, to model performance, to prompting), and it needs to work for your use case. What is the best metric to use for your use case? That also depends. It all comes down to metrics-driven development: docs.ragas.io/en/stable/concepts/metrics_driven.html , but you need to decide which direction to drive! There are some simple things to do after you set up RAG like reranking, but for any given use case the details really matter with regards to what steps you should take.
Can I use vertex model as the policy model?
Multi-Agent Systems - LangGraph Pilot Demo: colab.research.google.com/drive/1YDQs2RySVelF9BjCYgSq2qC_xiIVdYpN?usp=sharing Slides: www.canva.com/design/DAGHLLEXvXQ/7tm3VDBZw3YKZJHsG-C8Vw/edit?DAGHLLEXvXQ&
Needed more content for this and code with more complex architectures .
This was completed in a 30min. Lightning Lesson - so we didn't dive too deep - but we do have a separate video where we dive in a bit deeper! Check it out here: czcams.com/users/liveulTvNAXI_1E?si=-Pvd5A4KS1GKXWnG
this is quite ueful, can you help me with how to assign a name to my rag app?? can you make a video on DSPY with custom data RAG.
DSPy is on the horizon!
Thanks ! Where can I find the colab notebook?
Thanks for the reminder! colab.research.google.com/drive/1YDQs2RySVelF9BjCYgSq2qC_xiIVdYpN?usp=sharing
I just built a multi-agent article researching and writing team using Flowise. They’ve just added a UI for creating chatflows powered by LangGraph. No code needed - just clever prompt engineering.
Where can we download ppt file?
Thanks for the reminder! www.canva.com/design/DAGHLLEXvXQ/7tm3VDBZw3YKZJHsG-C8Vw/edit?DAGHLLEXvXQ&
@@AI-Makerspace put it in the description please, in case in future this comment gets lost, btw amazing video thanks a lot for sharing !
@@jdavidnorena got it going in a comment!
Mistral Fine-Tune: colab.research.google.com/drive/1RLl-n_pIAQKldWpc-UD8MhAhHO4wzYAl?usp=sharing Event Slides: www.canva.com/design/DAGHR69OPB4/Xy3m9rj3eWTfF9uFsEDDgQ/view?DAGHR69OPB4&
I like the way you simplify and explain- starting with the big picture and then breaking down in to the details.❤
This model solved many problems to users who doesn't want to send private documents to cloud just for fine tuning or RAG. Thank's to Gradient for this great milestone.
Great context. I really rllearnd something
Nice!
How LLMs Choose the Next Token: colab.research.google.com/drive/1dGdMVkwlHDIitsMDWOhPTS1jwxM_5qmD?usp=sharing Event Slides: www.canva.com/design/DAGGnrrq5zg/Wij3fvn2jmXVMRjx6LrnvA/view?DAGGnrrq5zg&
Great Work. Please consider using data which is not part of the training data as the haystack for the long context search to create a more real-world example.
There doesn't seem to be evidence to suggest that the data being known makes this task easier - but we can definitely make that modification!
Good job!! I would like to have the code :(
You got it Damian! github.com/bytewax/real-time-rag-workshop/tree/main/workshops/aimakerspace-2024