Пікірлер

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  @user-se9qv5pi1q11 сағат бұрын

  If we can estimate that type of llm usage, why we cant use this interface to train or fine tune llms to not just solving tasks but for making right controlling decisions in some type of workflow? Is there are any researches about this idea?

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  @palashjyotiborah988811 сағат бұрын

  Please improve the microphone quality. Why wont you do this? We have been requesting for ages.

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  @brandonwinston9 сағат бұрын

  Also, could just run the audio through the Adobe audio optimizer.

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  @darkmatter958312 сағат бұрын

  keep doing you are doing great 🎉🎉🎉🎉❤❤❤

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  @ibrahimsaidi723919 сағат бұрын

  Keep up the good work Brace. Much appreciated 🙏🏾

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  @maxlgemeinderat920223 сағат бұрын

  Can you go more into Detail about the Memory checkpoint? I have difficulties to understand how i can use the chat history e.g. In memory history

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  @AmanKumar-qx2wlКүн бұрын

  Nice Explaination, Examples are great

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  @tolorunlekedanieljesutoni4628Күн бұрын

  Hi please has anyone here ever worked on building a chat bot that respond to people like a particular person?, i.e chat bot that respond or generate replies like trump or barack

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  @jellz77Күн бұрын

  Hi Lance - great video again!Question for you. Recently I’ve been omitting LLM function calling just as a precautionary measure. I’m basically separating out the LLM from my functions (like api calls) and just asking the LLM to return a jsonOutput compliant with the parms in the api function. Am I doing myself a disservice by keeping these separate?

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  @journeymanaipodКүн бұрын

  Great video! I've been loving this new framework

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  @user-wr4yl7tx3wКүн бұрын

  but it doesn't seem like firework is free.

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  @GuriLudhianaКүн бұрын

  Knowladgeable

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  @sravan9253Күн бұрын

  For everything if you say "you can look into the notebook" why put up a video? The video is running as if being chased by someone.

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  @sabre_code2 күн бұрын

  Few days back tried a lot.. finally went with gemini model. Worked fantastically.

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  @rossanovinicius73732 күн бұрын

  For anyone looking to save time: Not even cloning the repository makes this work. It only functions in a development environment. Any attempt to run the build fails with the error [EmptyChannelError]. Langchain seems more focused on releasing videos and new features than on ensuring functionality, and doesn't even have the courtesy to respond to those trying to resolve the issue.

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  @AdvogaIA2 күн бұрын

  exactly!

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  @MalcolmJones-bossjones2 күн бұрын

  6:10 I had an "ah-ha" 💡💡💡 moment from what you said about grabbing different info from a trace instead of having to go directly to the run, thank you so much for that. This helps me with a problem I am currently stuck on.

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  @jennievo1002 күн бұрын

  Excellent video! Thank you. Would you know how to handle the potential case that the agent goes into infinite loop, e.g. it gets stuck at the hallucinating check. I can only think of keeping track of the threshold for number of checks, and am wondering if there's a more elegant way to do that in Langchain.

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  @pragyantiwari38852 күн бұрын

  Literally, I was dealing with llama3 and integrating tools within it...got many errors... And now I just got this video

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  @Slimshady683562 күн бұрын

  First

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  @GuriLudhiana2 күн бұрын

  Knowledgeable

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  @user-kj5ci9ro1p2 күн бұрын

  thx

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  @user-kj5ci9ro1p2 күн бұрын

  Thx

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  @husnainyousaf91414 күн бұрын

  first time in life i had to watch it at 0.75x speed. Worth to watch.

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  @mahoanghai33645 күн бұрын

  Great tutorial <3

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  @deanchanter2175 күн бұрын

  Would to a see full end to end python example with something like reflex

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  @balusubhanuprakash80455 күн бұрын

  What's this witchcraft 😵

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  @stanTrX6 күн бұрын

  Thanks. How to take two inputs for a function (tool)?

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  @hectorcastro24676 күн бұрын

  Gold

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  @wshobson6 күн бұрын

  Awesome Brace! Absolutely love this!

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  @AdvogaIA6 күн бұрын

  Good video. But how could I save the messages and access them again? Since the messages are displayed in {elements} without any map, how could I access them again?

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  @amazingsly2 күн бұрын

  Same question I have. I want to store the messages in the database. Maybe someone will help

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  @hxxzxtf6 күн бұрын

  🎯 Key points for quick navigation: 00:15 *📊 The retrieval process in RAG involves indexing documents, splitting them into smaller chunks, and storing their embeddings in an index.* 00:41 *🔍 Documents are embedded into a high-dimensional space where similar documents are located near each other.* 01:36 *💡 The location of a document in this space is determined by its semantic meaning or content.* 02:03 *🔎 Retrieval involves searching for nearby documents to a given question in this high-dimensional space.* 02:56 *📈 LangChain provides many different embedding models, indexes, document loaders, and splitters that can be combined to test different ways of doing indexing or retrieval.* Made with HARPA AI

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  @hxxzxtf6 күн бұрын

  🎯 Key points for quick navigation: 00:02 *📹 The second video in the RAG from Scratch series focuses on indexing, a crucial component of RAG pipelines.* 00:28 *🔍 The goal of indexing is to retrieve documents related to a given question using numerical representations of documents.* 00:53 *📊 Numerical representations of documents are used for easy comparison and search, with approaches including sparse vectors and machine learning-based embedding methods.* 01:08 *💡 Embedding methods compress documents into fixed-length vectors that capture their meaning, allowing for efficient search and retrieval.* 02:03 *📈 Documents are split into smaller chunks to accommodate embedding models' limited context windows, and each chunk is compressed into a vector representation.* Made with HARPA AI

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  @hxxzxtf6 күн бұрын

  🎯 Key points for quick navigation: 00:03 *📹 The "RAG from Scratch" series will cover basic principles and advanced topics for building LLM applications with LangChain.* 00:15 *🔒 LLMs haven't seen all data, including private or recent data, due to limited pre-training runs.* 00:44 *📊 LLMs have context windows that are increasing in size, representing dozens to hundreds of pages of information.* 01:10 *💻 Retrieval-Augmented Generation (RAG) is a popular paradigm for connecting LLMs to external data, involving three stages: indexing, retrieval, and generation.* 02:06 *📝 Future videos will explore methods and tricks for RAG's three basic components in detail.* Made with HARPA AI

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  @meditatvio59586 күн бұрын

  Where are we heading to ?!?😮😮😮

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  @tourtlelaser6 күн бұрын

  Your audio is so quiet

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  @hrudaykumar33446 күн бұрын

  crisp and informative series

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  @mahoanghai33647 күн бұрын

  Very cool <3

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  @drlordbasil7 күн бұрын

  I really love the work bud! :D Always listen to you in background tabs while coding <3

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  @riot1212127 күн бұрын

  This is the future of UI and UX I've been uinable to stop thinking about this and now I find that there are incredibly bbright minds already implementing it. Pleasure to watch and learn :)

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  @mrmetaverse_2 күн бұрын

  totally agree. Like imagine an older user has trouble finding the "share document" button. They ask their on screen agent, and the UI changes, removing all irrelevant buttons, and making the share button much larger. This really changes a lot. Once you know how to ask the question, you can learn faster, and do so much more. LLM chatbots and generative UI are a wonderful step towards making it easier to ask "the question".

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  @akimodeli7 күн бұрын

  It's beautiful people like you that make the world a better place. Kudos brother! 👏

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  @janwillemaltink22167 күн бұрын

  awesome content, i noticed that in de readme.md with the gitclone command is a small typo. (bracesprou instead of bracesproul)

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  @feossandon7 күн бұрын

  Thankyou for this content :D!

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  @cyAbhishek7 күн бұрын

  Wowwwwwww, literally what I wanted

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  @gitmaxd7 күн бұрын

  Massive Applause! This was a fantastic video series with a lot of value for any skill level. This is the ‘last mile’ of the LangGraph learning path. The LangChain LangGraph Series is a great place to start with LangGraph, then the Deep Learning “AI Agents in LangGraph” series. This one ties everything together with GenUI and Vercel .

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  @cyAbhishek7 күн бұрын

  Completely agree

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  @fkxfkx7 күн бұрын

  Very nice 👌

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  @darkmatter95837 күн бұрын

  ❤ huge fan continue great cotent have a question, i want to get as aws do your documentation in q pdf for reading, to be easier to handle the api documentatioon langchain and langgraph, would be helpful or if not how can i approach that problem because i feel bad and limited, your github repo dpnt see the same inforrmation as your api documentation

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  @varunmehra57 күн бұрын

  WHERE IS THE PYTHON VIDEO!!!

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  @user-bw6qh4zz4q4 күн бұрын

  kzread.info/dash/bejne/lmep0a6blqW2m9o.html

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  @lw25197 күн бұрын

  I'm still confuse about Chain and Agent, when I can use Chain, when should I use Agent, is anyone can help me to answer my query, please? Thanks !

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  @captainkirk89997 күн бұрын

  Just installed llama3 , it can not remember my name!

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  @samueljabes82678 күн бұрын

  How i can acess this jupyter notebook?

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  @nguyenanhnguyen76588 күн бұрын

  Rerank model that is trained on relevant dataset will help, and that is the most important