Excellent breakdown! This video really helped me grasp the concepts, and it's one of the first Langchain videos that clicked for me. While Harrison's brilliance is undeniable, Lance, you're truly a great teacher.I love your approach of using diagrams to break down complex code into easily understandable logic. It really enhances the learning experience!
Thank you. This video was informative. Your explanation was clear. For me personally the examples that us local LLMs are interesting. Thanks once again.
Lance - isn't the workflow you outline here a DAG? The LangGraph docs are very explicit about not using LangGraph for DAGs - can you help us understand this nuance? "The main use is for adding cycles to your LLM application. Crucially, this is NOT a DAG framework. If you want to build a DAG, you should just use LangChain Expression Language."
(This is Lance, guy in the video.) Good feedback. I record w/ Loom. It was a UX issue w/ Loom where vids were not defaulted recorded to 1080p. Apologies!
Great video! I have a question about recent RAG papers which talking about RAG paradigms such as advanced RAG and modular RAG. Could we say that using LangGraph have we applying modular rag? I’m not sure in which paradigm fall self rag and crag. Thanks!!
Thank you for breaking this down! Is there a way to do cyclical/iterative agents when one produces an answer and the other checks the answer, and if not correct/satisfied with it, sends it back to the first agent to produce a better answer?
Hi, I have one question here related to the Retrieval Evaluator. Let's suppose we are not allowed to do a web search. Can we again play around with chunking and different retrieval methods?
I'm working on developing RAG Application on CSV File. It is working fine for some queries, but queries like 1. Get total count of persons 2. Get average salary These type of questions that include to search all the rows of the given document Is not working fine. Is there any fix I can do or Implementing RAG is not a correct option.
at around 13 minutes, based on your story i would assume that it wouldnt run web search as there are at least a few documents that are relevant, but as i understand even if 1 of the retrieved documents isnt relevant it will do a web search?
Excellent breakdown! This video really helped me grasp the concepts, and it's one of the first Langchain videos that clicked for me. While Harrison's brilliance is undeniable, Lance, you're truly a great teacher.I love your approach of using diagrams to break down complex code into easily understandable logic. It really enhances the learning experience!
great video Lance! The way you diagrammed the flow made it easy to understand the concepts.
This is sooooo cool 🤯
And I love that you guys are putting out all these educational videos - thank you!
Thank you. This video was informative. Your explanation was clear. For me personally the examples that us local LLMs are interesting. Thanks once again.
Great tutorial. Keep up the great work!
Lance - isn't the workflow you outline here a DAG? The LangGraph docs are very explicit about not using LangGraph for DAGs - can you help us understand this nuance?
"The main use is for adding cycles to your LLM application. Crucially, this is NOT a DAG framework. If you want to build a DAG, you should just use LangChain Expression Language."
wow. Excellent demonstration.
Thanks Lance for more one great tutorial! Really useful and easy to follow.
Please record your videos in 1080p
(This is Lance, guy in the video.) Good feedback. I record w/ Loom. It was a UX issue w/ Loom where vids were not defaulted recorded to 1080p. Apologies!
Lance, you are great! Thank you for your splendid video!
Very Clear and informative . Thank you
Thank you for the great explanation!
Really great video! Keep 'em coming
Great video! I have a question about recent RAG papers which talking about RAG paradigms such as advanced RAG and modular RAG. Could we say that using LangGraph have we applying modular rag? I’m not sure in which paradigm fall self rag and crag.
Thanks!!
very helpful! thank you.
Thank you for breaking this down!
Is there a way to do cyclical/iterative agents when one produces an answer and the other checks the answer, and if not correct/satisfied with it, sends it back to the first agent to produce a better answer?
Hi, I have one question here related to the Retrieval Evaluator. Let's suppose we are not allowed to do a web search. Can we again play around with chunking and different retrieval methods?
Thanks for that ! Do you have a video that helps build an ui associated for prompting ?
Excellent !
Do you have any resources for deploying this type of solution on AWS/GCP?
Very interesting, I assume this would have very long response times?
(This is Lance, guy in the video.) I'm running on a Mac M2, 32GB. Latency is ~5-10 sec for final generations.
@@r.lancemartin7992 lance from langchain has a nice ring to it😅😂
What are other possibly ways to utilize this on a local database? I mean, if there are no relevant docs what it can do?
How to deploy langgraph using langserve, can you please help me do it?
I'm working on developing RAG Application on CSV File. It is working fine for some queries, but queries like
1. Get total count of persons
2. Get average salary
These type of questions that include to search all the rows of the given document Is not working fine. Is there any fix I can do or Implementing RAG is not a correct option.
Are you using csv agent for the solution you built? if not, then you can try either csv agent or pandasAI.
What worked for me was converting my CSV / multi page xlsx into a minimal database
And then query that database, SQL queries worked much better for me
It works for csv's but if it's pdf @tushii
@@sridevigogusetty8371 what would you like to do with your pdf ?
@@Tushii for example if I have some pdf which has financial transactions of i want to query how I will convert that to db
at around 13 minutes, based on your story i would assume that it wouldnt run web search as there are at least a few documents that are relevant, but as i understand even if 1 of the retrieved documents isnt relevant it will do a web search?
(This is Lance, guy in the video.) Yes, with this logic it will do web search if *any* documents are irrelevant.
is thsi still effective with conversational memory?
Wooooo Skynet wooooooo