Function Calling via ChatGPT API - First Look With LangChain
2024 ж. 13 Мам.
44 886 Рет қаралды
Twitter: / gregkamradt
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Code: github.com/gkamradt/langchain...
Blog Post: openai.com/blog/function-call...
0:00 - Intro
0:57 - Blog Post Overview
3:27 - OpenAI Simple Example
7:10 - LangChain Simple Example
8:59 - LangChain In Depth Example
I appreciate your quick coverage of the latest updates from OpenAI.
Your right man.
Wow, great video. I'm impressed by how fast you are putting out amazing content. This just came out today!
Great work pulling those examples together so quickly. Really useful to get an idea of what's possible. Thanks. 👍
Awesome thanks Mr. Adams!
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.
Wow! What a great comment - thank you Jessa!
Great video and thanks for the update👍
Mind blown. This is great stuff, beautifully explained.
Awesome thanks Pop - what're you building?
That's great news! Most of my code is managing the transformation to/from JSON.
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
Thanks for sharing the code. Great video man
Thank you ! This is the channel to follow right now
Nice! Thank you for the support
Great information - thank you.
That was quick man.
Excellent!!! Well explained,,
Glad you liked it
This is awesome.
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?
This makes me think of the LLM's as Tool Makers/Users paper from a week back or something.
wow.! Really good tutorial
Awesome thank you!
Nice!
That's a beautiful green and that purple suits it so well. I did not understand anything from the presentation.
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.
Curious how you see it as different from the openAPI agent?
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?
Function calling is a tool that langchain agents can use. If you don’t want to use langchain then you’ll need to deal with the raw openai library which has different ergonomics. I would recommend sticking with langchain agents as they provide more support for function calling
@@DataIndependent thank you! Just confirmed my idea!
Parameters can have more complex types? I mean, like arrays or objects.
Here's an idea, Greg: explore/finish updating agents and re-do your podcast with Weaviate!!! And thanks for the class!
Nice! thanks for the tip and an agent video should be on the docket. I'm not 100% happy with the agent ecosystem right now and rather than putting out a complicated and fragile example I wanted to wait till the tech settles a bit more.
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.
Thanks for the comment and yep I’m aligned with you.
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?
I tried implementing it and got few of my doubts clear. Function calling decide it self which function to call based on function list provided. it also retries once or two time i case of issue. I found it faster then SK. but Still SK chain of thought produce better results which matters more. I am struggling to get correct results with Function calling as in case of any issue even if do manual retry it kind of stuck on same result. any suggestions...
Holy. This open endless opportunities.
totally - what're you building?
@DataIndependent I'm in the R&D phase of building microenvironments for classification systems mapping while managing token limitations and output volatility by framing prompting through the leverage of functions
Appreciate this example, the blog post alone didn't do it for me! - One question: with langchain, Can I use my own tools in`format_tool_to_openai_function(t)`? I'm guessing I need to make them a BaseTool type from the docs.
Yes! You can make your own custom tools with langchain
Great Explanation ❤ I have a doubt here, How is this function calling feature different from the Agents that we already have in Langchain? Because agents are also capable of deciding a tool ( a function ) based on description. ( Pls note that I am complete beginner in Langchain)
Functions are a tool that agents take advantage of. Functions make it much easier to get structured data out of a piece of text. We used to have to prompt our way into structured data but this takes care of it for us
@@DataIndependent Ok, Got it
I see what you are doing, but do you have an example where the program knows how to generate the content and to figure out the recursive manner it has to work ? Right now it looks we are invoking it multiple times, but how to automate this in a chatbot? And how to keep sending the data back to the chatbot from a key-value db?
This is 'chain-of-thoughts.' The problem you're discussing is something I've been facing for the last three months. My solution is the AutoGen project, which helps me handle retries when irrelevant content is provided from the vector database, creating tests, and progressing step by step
@@AIonRails Yes, i was using the langchain agents and I did read about the autogen but it is not working as advertised, Now that openAI has come out with their own agent framework but that is still not available on the Azure Open AI - Do you have any study guide or open source implementation that I may refer as guide?
@@prasenjitgiri919 what do you plan to achieve using autogen? I have made few agents, and its power is inspiring. Actually right now trying to implement Obsidian based AutoGen framework. And yes, agent openai agent framework sound very very interesting. As a backend developer, I just need to provide Rest API for agent interaction...
This is so powerful! Is there any chance you could make a video about how to use GPT functions with a RetrievalQA agent?
Oh cool - How come you want to see that combo together? What is the business use case?
@@DataIndependent Well, my understanding has evolved in the last 48 hours since I posted this comment, but it's just a bit more complex now. My specific use case is that I've trained an LLM with custom data using a few documents and langchain. And I've now seen the limitations of doing that. My chatbot is now able to answer many different types of questions on my small data set which consists of 50 or so festival events with speaker name, title, description, date and time. My chatbot is able to answer questions such as "tell me of an event that talks about AI" or "when is xxx speaking". But it fails to answer "list all events on the 28th" or "how many events are there in total". And this is where a functions agent comes in handy. A functions agent can identify certain questions that pertain to a date or to asking an aggregate question about events, and when that happens it can use a custom tool that will query the data, a csv or pandas dataframe, return all the events for the requested date, and pass that back to the llm for processing. But in the event that the user does not ask such a question, then it needs to be able to fall back to a QA agent for answering more general questions about the data. I hope that wasn't too much all at once! Hope it makes sense now. It would be AWESOME if you made a video with such an example in mind!! Thanks! Love the content!
What's the difference between it and Langchain agent? I think LangChain agent is event better, for we don't need continue the interactions manually.
This is *exactly* the issue I've been struggling with, as I'm sure so many others have. Google recommend comes through!
During the demo for the weather in SF, there seems to be no real call to a weather api to acquire the weather details. Is the new version of GPT-4 somehow linked to the internet? Furthermore, something that still confuses me is that although GPT-4 is calling a "function", the get_current_weather(..) function doesn't seem to do much aside from make the outputs legible. I thought functions had to be like the Tools we use in Langchain? Thank you!
This code didn’t actually call an API, it was just a fake function for demo purposes. And yes that’s a big part, properly formatting a response in json so you handle it elsewhere.
Very cool. Is it possible to use these functions in an agent yet? In other words, to get them to execute the function with the prescribed parameters?
Latest version of LangChain supports this: agent_chain = initialize_agent(tools, llm, agent=AgentType.OPENAI_FUNCTIONS, verbose=True, memory=memory)
Yes! To what Bryan said. I made this video before they came out with that update
Would you trust a system which can't tell the difference between celsius and fahrenheit to do decisions for you?
Good question! For certain tasks I would! It's not copy and paste ready yet but it offloads a lot of intelligence for you.
How do you do function calling from a QnA chain? Some answer from the user might require function calling.
You could set up your own custom chain and put it at the tail end. Or else just take the output from the user and manually write code to pass it to a function
@@DataIndependent I guess I am trying to ask can we decide what kind of chain to call dynamically based on user input or chatgpt response.
I am assuming that the function_call in OpenAI returns back the name of the function to be called and if we had multiple function we will use that to call the function. Right now in your example it is just hardcoded to call get_current_weather. Is it correct?
For the top example yes, but check out the bottom example when I have two functions. It returns back w/ which one to pick.
@@DataIndependent I see, Thanks! I stopped after the vanilla example as I don't know much about LangChain, thanks for the video again.
This is truly impressive. However, I notice that you manually executed the request to the language model three times. I wonder how the plugin architecture determines when and how many times to make those requests automatically. Moreover, how do you think it discerns the appropriate stopping point?
Yes! Manually did it because the conventions aren't quite there yet do it it cleanly w/ automation. I imagine that LangChain will support this first class by the end of the week. Thanks for the call out. I think it knows the stopping point by simply looking at the requests and seeing if they were satisfied or not. I would need to play with it more to see how it'll do at scale w/ more requests.
@@DataIndependent I really appreciate your response. Thank you for the awesome video again!
How would you go about setting GPT to try and get the information that you need for the functions to work? thanks :)
Could you explain your question more? What use case are you solving for?
@@DataIndependent thanks for responding - So for example, in the video the function requires year, category and amount props - but is there a way to get the AI to be asking the user for this information?
Interestingly, the assistant seems to just use the function output almost "as is". I'm unable to get the assistant to use the function output as context only and focus on the users actual question... Example: my function returns all the information about a property, and the assistant seems to just output the whole block of information instead of using it as a context/knowledge base to answer the users question. Nothing I put in the system message affects how the assistant uses the information from the function call. Any idea how this could be achieved?
I would do a two step process 1) Have it return the information you want and then 2) Have it use that information in another response If you give me more specifics via contact@dataindepentent.com I can check it out
Based on this video, it seems that the "gpt 0613" model was used. So which model should I use now? Please guide me.
You can still use that one, it should be still live
How can we do this via rest api?
What is the different between OpenAi Plugins vs OpenAi Function Calling?
You don’t need to define a manifest file for open at functions. Plus extraction and tagging are super easy with functions
Why would I use LangChain now that openai takes care of the agent bit for me?
Langchain is an entire framework to build AI apps, they use gpt functions to augment the tools they already had. You could do it manually, but why?
I am getting this error: "NameError: name 'user_location' is not defined" in function_response...What should I want to do?
Are you running the code exactly as it stands on the notebook?
king
Thanks Brogs
@@DataIndependent you should do a video on few shot prompting with the new function calls. One of the benefits of Kor
Is there any reason why I'm getting a traceback error saying that gpt-4-0613 doesn't exist?
Nah you shouldn’t be, unless you don’t have access to gpt4 yet?
Pretty cool!! But I fear that OpenAI is now starting to compete directly with LangChain or AutoGPT, and it looks like function is just missing a longer memory store(pinecone?) to handle more tasks with more steps.
"Uploaded 4 hours ago" I only first heard the news 3 hours ago 😂
I went for the speed run! This announcement felt worthy, not all do. I learned about it with the tweet
Hey @greg, would be very helpful if you can make same series for LlmaIndex as well. This series is very good, keep it up!!
Wonderful! Thank you! What's the business case you want apply with them? I like connecting code to practical examples
@@DataIndependent I know we can use LangChain for building indexes for schematic search search but I think LlmaIndex is more efficient and gives user a lot of flexibility. Thus it can be used where we are adding custom memory to a chatbot/ application.
required is not defining what parameters should be returned, it is just saying what parameters are required
Good call - got this out in a rush and didn’t crisp that part up. What’re you building w/ functions?
Could we use it to call GPT again in a different function? 🤔
With a specific prompt and purpose (no idea, this is falling out of my brain in real time)
I guess that would just make it a LangChain agent wouldn't it.
Ya, this is where the agent work from LangChain will help out
Content is excellent, but please! could you not speak so fast. The amount of times I have to go back & listen to what you say several times is making the ergonomics difficult. BTW English is my natural language (Nottingham UK)
This is the first time I've heard this comment and it's important to get community feedback - thank you!
Somehow I just don‘t see it. For straight forward tasks I don‘t get langchain nor this function call feature. Of course, when it comes to chatbots, it makes total sense, I understand that. But apart from that, I‘d use vanilla python. The examples I see are all like this one here, where it solves a non problem. The closest to make sense was a hr assistant. But even there, a structured input like „dropdown get current vacation days“ would have killed the whole ai part. Not to be misunderstood, only talking about the chaining tools, not the ai itself.
It really helps with reliability when pulling information out. Before we had to pass instructions on how to respond. Now these models have been fine tuned on json output which helps a lot
How sad that one single corporation are setting the standard for all future frameworks. They could've used open standards like LMQL or similar, but as all scumcorp, they just want to control the developer ecosystem, and too many smart developers don't care shit about open standards or our common development ecosystem..
This is how it's always been... one company takes the reigns and leads the way. The others follow suit and introduce iterations that attempt to surpass the original, and sometimes yes, failing. I'm not sure if your comment is directed at OpenAI or not, but what you are frustrated about is not a unique phenomenon
In the Ad Hoc Example Financial Forecast, you are answering (ChatMessage) to the function_call with human language: "Just made the following updates...". However those actions should be automatically done by your scripts and therefore not necessarily have a human language response to it. Could you instead just do a back and forth of: AIMessage(content=str(first_response.additional_kwargs)), ChatMessage(role='function', additional_kwargs = {'name': first_response.additional_kwargs['function_call']['name'],content="OK"}), AIMessage(content=str(second_response.additional_kwargs)), ChatMessage(role='function', additional_kwargs = {'name': second_response.additional_kwargs['function_call']['name'],content="OK"}), AIMessage(content=str(third_response.additional_kwargs)), ChatMessage(role='function', additional_kwargs = {'name': third_response.additional_kwargs['function_call']['name'],content="OK"}), Acknowledging you did what was asked, without having to put it into words? Would gpt4 get it?
It’s a good question, I’ll need to test on that. But i would imagine that a more explicit confirmation of the action taken would help. If you test it out let me know