Love your work! Would you consider creating a detailed video on deploying RAGs using FastAPI at a production level? Specifically, I’d love to see how to maintain conversation IDs across multiple sessions, manage state for multiple chains, and set up a scalable infrastructure for concurrent user interactions.
That´s more something for my capstone project of my Udemy course. For YT these very advanced and complex topics are most of the time not worth the effort (unfortunately)
@@codingcrashcourses8533 Thanks for the reply, totally understandable. Looking forward to the Udemy course and appreciate the great content you share on TH-cam!
First of all I would like to thank you personally for this video! Such an amazing and quality content again!! It gave me the basis for many ideas to implement!! 😊 I would like your opinion, if having both vector tables and simple ones in the same db could be a good practice for a chatbot project based on the one in the Azure LangChain course. Thank you again for your help!! 😊
appreciate your work here , I am working on a similar requirement for my LLM app , however my SQL generator node does not follow the schema case sometimes and also some other issues like column names criteria not followed etc this leads to erroneous sql code , do we have some sort of standard rules that can be fed to prompt or any other way to fix this .
how difficult is your schema? You could try to use a validator agent for the schema and might try to fix it, but unfortunately SQL Agents are no 100% workings solutions :/
love your work, really amazing, but i have a question hope you can reply or make video, so with langgraph, can we make a graph have 2 nodes father and son , both or 2 different chatbots, the entry is the father i can keep talking to him when ask him about talking to his son i will transition to the son chatbot (i made till here) but i want to keep talking to the son until he says goodbye so it moves to _end_ is that even possible (i just simplified it by father and son but the real use case are 2 agents and i can keep talking to 1 of them until he transition me to the next agent so i keep talking to him,...)
Ok, so what you want is some determinism. That´s indeed not so easy. I would try use a classifier in the parent, which decides to route to the child. You save that output in a state variable and use that value to bypass the parent and directly talk to the child agent. But the child agent needs a classifier too, where it decides weather to continue the conversation or not. I would do create a TypedDict like this class SharedState(TypedDict): current_agent: str You start with "parent" as value, then you overwrite it in the parent class with a classifier. You then bypass it based on the new value. And the child agent resets current_agent to "parent". Does that help? :)
yes that's so helpful , i tried it out it kinda worked but always pass by the father, what i want is like "time travel" this a powerful hard architecture especially if there's a lot of agents, kindly if you make a video on it would be so helpful for the community ❤️🤘
I was referring to the idea of directly transitioning between different chatbot agents without reverting back to the parent each time. Essentially, after switching to the 'child' agent, I want to remain in that context until the conversation with the child agent naturally concludes, without the need to route back through the parent repeatedly. This behavior mimics a more seamless, 'time-travel-like' jump where once we move to the child agent, we stay there until an explicit handover or exit point is reached. This kind of flow ensures a more dynamic and contextually persistent experience, avoiding the overhead of constant re-routing and maintaining a clear, continuous dialogue state.
@@immortalx678 I think I understand what you want, but I think you need to still use the router. But only the first time it´s LLM based, the second time you use the variable to route. I see no way around that tbh. You need the information of what subagent you talk to
Love your work! Would you consider creating a detailed video on deploying RAGs using FastAPI at a production level? Specifically, I’d love to see how to maintain conversation IDs across multiple sessions, manage state for multiple chains, and set up a scalable infrastructure for concurrent user interactions.
That´s more something for my capstone project of my Udemy course. For YT these very advanced and complex topics are most of the time not worth the effort (unfortunately)
@@codingcrashcourses8533 Thanks for the reply, totally understandable. Looking forward to the Udemy course and appreciate the great content you share on TH-cam!
You’re the best for so many reasons, a great creator and teacher, have learned a lot from you.
Nice timing of the video. Lots of usecases are trying to implement SQL agent
First of all I would like to thank you personally for this video! Such an amazing and quality content again!! It gave me the basis for many ideas to implement!! 😊 I would like your opinion, if having both vector tables and simple ones in the same db could be a good practice for a chatbot project based on the one in the Azure LangChain course. Thank you again for your help!! 😊
sorry for the late response - I think this is totally fine and even the preferred way of doing it.
appreciate your work here , I am working on a similar requirement for my LLM app , however my SQL generator node does not follow the schema case sometimes and also some other issues like column names criteria not followed etc this leads to erroneous sql code , do we have some sort of standard rules that can be fed to prompt or any other way to fix this .
how difficult is your schema? You could try to use a validator agent for the schema and might try to fix it, but unfortunately SQL Agents are no 100% workings solutions :/
im loving your videos, it is realy helping me. When you are going to release the langgraph course?
To be honest, I am behind schedule. Got so much other stuff to do currently :(
Hi Markus, Is your Udemy LangGraph course still online? I cannot seem to find it.
@@DiegoMolinaingmecanico Hello Diego! I still work on it, sorry but it takes more time than I expected
@@codingcrashcourses8533 Thank you fo the prompt response, I understand. Looking forward to getting on to it when is available!
@DiegoMolinaingmecanico i will make a Promotion Video ok it. Just activate the bell :)
love your work, really amazing, but i have a question hope you can reply or make video, so with langgraph, can we make a graph have 2 nodes father and son , both or 2 different chatbots, the entry is the father i can keep talking to him when ask him about talking to his son i will transition to the son chatbot (i made till here) but i want to keep talking to the son until he says goodbye so it moves to _end_ is that even possible (i just simplified it by father and son but the real use case are 2 agents and i can keep talking to 1 of them until he transition me to the next agent so i keep talking to him,...)
Ok, so what you want is some determinism. That´s indeed not so easy. I would try use a classifier in the parent, which decides to route to the child. You save that output in a state variable and use that value to bypass the parent and directly talk to the child agent. But the child agent needs a classifier too, where it decides weather to continue the conversation or not. I would do create a TypedDict like this
class SharedState(TypedDict):
current_agent: str
You start with "parent" as value, then you overwrite it in the parent class with a classifier. You then bypass it based on the new value. And the child agent resets current_agent to "parent".
Does that help? :)
yes that's so helpful , i tried it out it kinda worked but always pass by the father, what i want is like "time travel" this a powerful hard architecture especially if there's a lot of agents, kindly if you make a video on it would be so helpful for the community ❤️🤘
@@immortalx678 What you want the time travel for?
I was referring to the idea of directly transitioning between different chatbot agents without reverting back to the parent each time. Essentially, after switching to the 'child' agent, I want to remain in that context until the conversation with the child agent naturally concludes, without the need to route back through the parent repeatedly. This behavior mimics a more seamless, 'time-travel-like' jump where once we move to the child agent, we stay there until an explicit handover or exit point is reached. This kind of flow ensures a more dynamic and contextually persistent experience, avoiding the overhead of constant re-routing and maintaining a clear, continuous dialogue state.
@@immortalx678 I think I understand what you want, but I think you need to still use the router. But only the first time it´s LLM based, the second time you use the variable to route. I see no way around that tbh. You need the information of what subagent you talk to