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I'm not getting advanced prompts what to do??

If I understand correctly it's just a system prompt and user prompt run over the entire text contents of the PDF file. I was a bit surprised that the entire PDF text fit in the context window! I'm trying this out now and using 01-preview to create the SKOS and it's doing a good job. This seems like a powerful method! Now to figure out what to do with the SKOS after it's been generated. Any insights into applications for this?

I am glad that there is someone who does all these latest experiments and post videos consistently. It's a great help to us. Keep going.

Interesting topic Rajib! Thank you for introducing. This seems like a great step 0 for creating knowledge graphs.

Only way to extract the kg via cli only?

Hi there, another question, how can I preserve markdown formatting of the text after extraction? My contact details are on my channel, thanks

Informative!

best explanation. did you get the explanation of why short term memory is not getting built?

Hi there, great video. Unfortunately i have had a lot of issues with the pdf extract package. There are lot's of dependencies issues. Which packages / versions did you use? Thanks

thanks, but I didn't see tool_calls in the new code, what happend

Very informative

Thank you a lot for this video! There is quite some content about Mem0 on TH-cam already - but as it is with most hyped or promising frameworks, most creators unfortunately do not take the effort of actually looking at the codebase and explaining how it works, but rather just scrape the surface by showing off the usage examples they found in the docs in the "quickstart" section. Contributions like yours are what open source actually needs, so thanks again!

I’m curious, how much better are the results than if you just issue a prompt to summarize directly to GOT-4/o/o1? The results would need to be objectively better in order to justify the agent complexity.

Thank you Sir for the video, but how is this different from adding few shot examples to the prompt. Do the few shot examples we pass in a prompt are same as the hints here? Please help understand, Thanks again!

Nice, Speed scroll capture the images and then send the images in one parralllel batch inference and restitch for faster speed. Gemini flash 1.5 is multimodal and allows 15 free rpm at <128K token window and can batch process images easily withing this limit.

Hi

Hi can you help to learn?(Not laws i mean your other content) I would like to reach your level and learn from you please, algorithms but also application side like karparthy,fe fei li to be able to opensource and be able to do something (like healthcare, entertainment,finances)etc

What's the difference between doing this and trying to call a db to save the response to it.

Also, will u provide github link to this project?

This is amazing. Is there a way we can let the LLM to also display the graphs or results on the streamlit app because thats an important feature

To deploy, you need can check out the integrations section.

Would love more videos on multion. Do you think it can be used in production/

Hey, is the hub depreciated now ? what handles that now ? for integrating into langchain

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!