Building a LangChain Custom Medical Agent with Memory

In this video I go through how to build a custom agent with memory and custom search of a particular web domain.

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[toddnedd]

It's always nice when someone takes the time to explain it. This helps a lot than just reading the documentation. Thanks!

[ubergraham]

Outstanding, Sam. As a physician this really helps me understand how you can unlock the web with just LangChain and LLMs. Bravo!

[autonomousreviews]

The more Python I learn, the more awesome your videos become :) Thank you for sharing!

[pankymathur]

This is definitely going in “Gem List” Thank You!

[mathematicus]

Hey Sam! Just wanted to drop a quick note to express my sincere gratitude for your incredibly helpful videos. They’ve been a game changer for me, offering clarity and guidance when I needed it most. A thousand thanks for your hard work and dedication. Keep up the great work! 🌟 #ThankYouSam

[MadhavanSureshRobos]

Great job as usual! Adding a source like bing in the chain might add a little edge to the response. Really useful content keep it coming

[kevon]

This video was SO helpful for troubleshooting!

[mytechnotalent]

Fantastic job Sam! Looking for these LangChain vids just wish we did not use the OpenAPI key and rather an open-source HuggingFace example. I would encourage you to go in that direction.

[helipilot]

Hi Sam, I love your videos! They are so helpful.

[GyroO]

This is mind-blowing Sam
Appreciate your work
Only one observation I have is that in any medical context, sources should always be mentioned
Can you add it to the notebook or show us how to do it please?

[bibutikoley]

Hi Sam,
Great tutorial and learning a lot from you.

Just one question on this, Is it possible to add citations to the answer and list down the links from which the answer is generated?

[clarissamarsfiels]

Please also consider doing these videos with local LLMs from transformers, (Python)-Llama-Cpp, autogptq etc., at least a few. I do not have an OpenAI API key nor am I really interested in paying for one or getting one in general. However, I do have successfully run several local LLM using my Hardware, which are surprisingly good! Like, I'm not talking "gibberish good", I am talking like "ChatGPT good". I think using local LLMs will give you even more freedom, especially in picking the model you actually want to use (Different model, different purpose).

[julesgransden]

Hey Sam, I'm trying to do my own implementation of this, but it seems like when the agent is determining the best answer, it gets caught up in the details of webMD. For example, it will first assess the sprained ankle, but then realize that it could be linked with a blood clot condition and then focus on that and the final answer is an explanation on how to avoid blood clots and other diseases, when in reality all I wanted to know was what I need to do once my ankle is sprained. Let me know if you could help! thank you so much!!

[ugyaltenzing]

Thank you for the video! I have a use case where my agent will query a dataframe/csv and also have the memory buffer. The architecture i went through was make a custom agent ( same as you have done in the video) and use the dataframe agent as a tool for querying the dataframe. What are your thoughts on this?

[rafaeldelrey]

Your videos are some of the most advanced, yet you still made them comprehensible. Thanks a lot. Do you think these could be made better with gpt functions? Or ReAct perform similarly?

[bingolio]

Excellent video. 1 quick question, if one wanted to make a “subject matter experts” could it be done using a similar approach with out fine tuning a model?

[scienceofart]

Hi Sam, thanks for your contributions. Can you create a new custom agent tutorial because it is already little bit outdated.

[odedy]

Fantastic video Sam. Thank you. I built a similar agent using custom tools, I updated the prompt to write a full detailed report incorporating also information from all its observations however the final answer is always shortly summarized without the full details. Any idea why and how to solve?

[mssman]

I can't seem to find the source for this video on the GitHub repo in the description. Can you give a link to the source from this video?

[OfficialChatbotBuilder]

But how is this scalable and where will the actual user interact? I feel like LangChain is a waste of time.