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How is this different from AgentOps?

This is the best training on this topic so far

Could you do integrating it with langchain please

does relying on events to trigger sufficient to cover all use cases?

How much effort was it to set something up like this? Is this scalable for more complex data?

Hi Sir - Cannot access ur git link, please can you keep the project open.

the number of open tabs made me understand memory at a different level

Does thie studio only works in Machine?

Is there git code for this?

Thanks for the mem0 video

@ajibdeb4059 Thanks for the video. I didn't find any content related to connecting agents to the knowledge base. Could you help with that?

Hello ... nice tutorial video ... What is the datatype of embedding in the code in python ?

Hi Rajib, I am in a dilemma, New to Langchain agent framwork. What i like is the abstractions and length of features it provides to build a true agent. What i fear is that it is too abstract and there are icebergs underneath which i dont have any clue. What if something goes wrong? Difficult to troubleshoot and may be i do not have the flexibilty enough to quickly do a work around. So i am thinking of devloping a very basic functionality which i will have full control. Any thoughts or your experience of taking Langchain to production? Your feedback is appreciated. THanks

Thanks for the video, loved how you went through every single step!

Its easy to see that the physics teacher agent was built after the math agent since the description in the physics teacher agent still references to mathematics and the instructions in the moderator related to the physics teacher agent mention maths not physics. The typical error of a copy & paste.

Nice video. Would like to see the next step for expanded Supervisor Agent capability. I appreciate that you are doing this without abstraction as i want to understand this before looking at a langgraph version, but would like to see that too :)

There is an orchestrator, but it could be exactly an event-driven orchestrator :) Great video!

Do we need to reindex all documents, everytime we add new document. Is there any way to run it programitically

Amazing explanation!! But if we don't change the existing prompts then, there's no point in creating 'Parser Lambda functions', right?

what about the prompt templates? Does this video only cover handling the response in the lambda function? What exactly are the prompt templates? How are those passed to the LLM?

Hi @rajibdeb4059, @EccleezyAvicii, do you know how to implement partial response? like if the Tool takes time to provide response, I will show temporary message to the user? Your prompt reply will be greatly appreciated.

I don't think I could've found a better video. As an intern I am so grateful thank you so much !

Great Explanation! However if we have to build a KB for prod, would it not be better to create a separate lambda function where we can have more control over how we create KB using Langchain and upload it to Vector DB? And then create a seprate RAG Agent with Lambda Function and Open API?

Can't we create a KB using LangChain because that way we have more control over how the chunking for the documents are done?

Good explanation Rajib, May I know how is it different from when we create a KG using Neo4j?

Which Authentication type you set for your tool ?

You can decompose the compound questions into simple questions. The Langchain page has a good example for this. Thanks for this video!

Can u provide the code that goes in the tool schema?

thank you! very clear approach and code!😶

Can you share the colab / github link? Awesome work, been following you for some months now.

Hi Rajib, great work. What should we do if we want to create index and use Vertex AI Matching Engine to index the multimodal embeddings ( including for example text and image embedding for each item)? how should this be done? Does matchingengine index support that?

thanks for the video. I learnt a lot

You cant do Integration 🤣🤣🤣🤣🤣🤣

Thank you sir 💐

Hi Raj, Could you please load some videos for pdf document data extraction including bounding boxes

I tried the one in Langchain, but didn't give good results. Are you seeing good results with gpt3. 5?

Please cover Langgraph sir

great work

Sir, great tutorial as always, thanks. Can you please guide me on getting the API keys.

Thanks Sir. Actually I am working on some project based on it and find difficult for me to find materials to understand the concept practically. After watching this video, I understand and implement it successfully and step ahead. At last Thanks Sir for this video.

what is the benefit of using this vs a straight forward vector search on a query-response pair? i can only think of a sql type search on historical conversational data that uses the neo4j graph nodes.

beautifully explained!!!

You are mixing agents with tools. The idea of orchestration is good, however, the example you present is confusing because it treats agents as functions/tools Thanks for the effort, though.

Sir can u please tell me what you are teaching and what your all tutorials are about to ?

Is there any way that we can access agent apps created outside as an api call

How did you get the value of document_library_id?

Thanks for the video. Short and straightforward. Help me with the following questions? 1. Why did you load the separate csv which has the vector embeddings? I think we can create the vector embedding on the plot property itself. 2. Can you build a chatbot based on this? The example which you have used can be easily retrieved by using the simple cypher query. So can you do some more of text based search and let system perform a Symantec search on it? 3. How are these functions are different from LLM functions?

I couldn't see the data_dir when I ran this example in my linux machine, because of that I couldn't see the saved memory, also I feel there is no context from the prompt response

Hi Rajib! I always like your videos. Thank you for producing them. I'd like to suggest moving to vanilla python, at least for some of the videos. Some orgs are not able to use Langchain/graph/Semantic Kernel/etc. in prod (due to various reasons). Thank you for considering ;)

Is it different from knowledge graph,