Function Calling via ChatGPT API - First Look With LangChain

0:00 - Intro
0:57 - Blog Post Overview
3:27 - OpenAI Simple Example
7:10 - LangChain Simple Example
8:59 - LangChain In Depth Example

[hadijannat]

I appreciate your quick coverage of the latest updates from OpenAI.

[georgesanchez]

Kor punching the air lol. Always a lovely day when OpenAI releases an update, but man it’s an amazing day when you get a context window upgrade + cost cut at the same time

[jessaco.]

Another topical and beautifully executed video. How is your content always so good and so up to date in this ever changing space?! Stay classy, Greg.

[mrwadams]

Great work pulling those examples together so quickly. Really useful to get an idea of what's possible. Thanks. 👍

[grtbigtreehugger]

That's great news! Most of my code is managing the transformation to/from JSON.

[felipejaramillo]

Wow, great video. I'm impressed by how fast you are putting out amazing content. This just came out today!

[cholst]

This makes me think of the LLM's as Tool Makers/Users paper from a week back or something.

[developer_george]

Thank you ! This is the channel to follow right now

[greendsnow]

That's a beautiful green and that purple suits it so well.
I did not understand anything from the presentation.

[lucasamadsen]

Here's an idea, Greg: explore/finish updating agents and re-do your podcast with Weaviate!!!

And thanks for the class!

[sachinreddy]

Thanks for sharing the code. Great video man

[nitinverma]

Great job explaining function calling. The only thing I'm struggling to understand is the use case. Currently, I'm using a semantic kernel with a stepwise planner. When I provide a one-line prompt like "show me a list of all the tables, " the system creates a plan and goes through my functions, often finding the correct function on its own and passing the right parameters to obtain the desired result. However, this process is slow and requires many iterations to achieve the result. So, does this mean we should manually call the functions one by one to achieve the desired result? If so, would I need to select the appropriate function for every prompt or use case, a task that the planner used to handle automatically, or am I completely misunderstanding the process?

[popothebright]

Mind blown. This is great stuff, beautifully explained.

[MrDocFP]

Thank you so much @Greg! Great Videos! Just one question: in which situations would you recommend using Function Calling rather then LangChain Agents? Aren't those different approaches to solve the same problem?

[yubogao]

That's amazing, I'd appreciate your speed. Only read openai's documentation is just boring, your video is quite clear that I can make a small demo using Jupyter notebook to test function calling. Furthermore, if LangChain updates and supports this new version of ChatGPT, I hope to see your video about it.

[codediporpal]

This is *exactly* the issue I've been struggling with, as I'm sure so many others have. Google recommend comes through!

[Rundik]

Would you trust a system which can't tell the difference between celsius and fahrenheit to do decisions for you?

[DylanHumphreys]

Great video, but 4:14 is a bit confusing. enum is (in this example) providing chatgpt with the list of all possible values for "unit" e.g. celsius or Fahrenheit, if you wanted your users to be able to request the temperature in kelvin, and you'd already updated your function to return it, then you would add "kelvin" to that enum list.
Futher for clarification on the "required" parameter. That lists the values that MUST be supplied to your function. There could be additional parameters which arent required ... to keep with our example, the evaluation, which if not supplied assumes sea level, but is not required for the function to actually run successfully, and return something.

[shuntianli]

Hello, I have watched most of your videos. great info. I have question around how to chat with CSV files with large amount of data. is there suggestion?

[henkhbit]

Great video and thanks for the update👍