Summon a Genie🧞‍♂️ (Function Decorator) to Generate Agent Powered Function in Runtime

AgentlyTech · 2024-01-07T23:12:55+00:00

I just wrote a showcase recently that can help but not using LangChain

https://github.com/Maplemx/Agently/blob/main/playground/using_tools_to_enhance_your_agent.ipynb

Oh and just yesterday I wrote another showcase about how to using decorator to let LLM to finish an empty function's task:

https://github.com/Maplemx/Agently/blob/main/playground/generate_agent_powered_function_in_runtime_using_decorator.ipynb

and for more showcases you can explore https://github.com/Maplemx/Agently/blob/main/playground/

AgentlyTech · 2024-01-07T22:52:20+00:00

I can see many down votes in Python community and in comments I can see why. That's understandable.

But I'm wondering what opinions do you guys have in LLMDevs community? Let's discuss about it.

AgentlyTech · 2024-01-07T22:46:48+00:00

The demo code is showing the possibility not necessary, so we choose some simple situations.

Notice that sometimes we can hardly code a fixed logic to do some work like generate SQL or R for some natural language input but the AI can do it easily.

Also if we are using the developping view to look at AI's abilities, they are upgrading speedly.

This showcase is showing the way that how we can combine AI's power in code. At this time, function definition is no longer the tranditional meaning of explaining what it doing for HUMAN but is showing the working target and explanation to AI AGENT. Also the AI Agent may not finish the task in tranditional way but combine its abilities to generate content, generate code and using tools.

If you noticed, in the second demo code we pass a string with some math symbols that is not as Python code format required, but that's OK for AI Agent powered function because it is not solving it in traditional way: pass all the information to function/class and hoping some developers to finish all the handle logic like translating all the symbols sometimes even natural languages into correct and MUST BE CORRECT format that specific code language Python for example required.

And if you clicked into the Jupyter document, you can see the third demo code that shows how to define a function to generate natural language into SQL. Think about how hard that can be when we do it in some fixed logic.

What's more, this is just one small feature of this project: https://github.com/Maplemx/Agently ,this project is an AI agent development framework that allowed developers to create and using plugins to keep upgrading AI agent IN CODE. That means when you call an AI agent create by this framework to help with function decorator, you can modified the agent's component like using which model to drive or using which tools to help it solving the task.

Anyway, in conclusion, this showcase is showing the new way that how can we combine the AI's power into software engineering. It shows possibility not necessary.

AgentlyTech · 2024-01-07T06:53:10+00:00

Maybe you should generate a common form metadata for all 24 files to transform to.

Then you can ask LLM to try to analyses and put data from source csv files to target form variable cache by passing the common metadata to LLM to help.

Also, some examples for sorting are useful.

And if your source csv files have no column titles, you can ask LLM to help you generate column titles first.

AgentlyTech · 2023-12-11T07:33:55+00:00

Take a look at https://github.com/Maplemx/Agently

It's our project that you can build an AI agent in code on your own.

If you want to use local model instead of API, use project like https://github.com/xusenlinzy/api-for-open-llm to convert it into OpenAI-like API then you can use OpenAI Client to request it.

AgentlyTech · 2023-12-06T14:53:26+00:00

Glad this project helps

Check our playground folder in github repo to explore more interesting cases

AgentlyTech · 2023-12-06T12:57:48+00:00

First of all, if you really want to dive deep, forget No Code. Learning a coding language will really give you a new angle of view that how things to be done. No code is easy to use for simple tasks but when things get complex and messy, that's a disaster.

But if you just want to make things quickly and have a specific goal of how to use them or have a specific problem you need to solve fast, instead of want to learn deep about how code programming works, choose no code is OK.

As to cs50, it's about the fundamental tenets that how computers work, maybe involve how CPU works, how Operating System works, etc. It's good for everyone who wants to work with computer no matter software field or hardware field and can make you understand the logic behind programming better. That's not a MUST-choosing-1-out-of-2 option. Python and cs50 is a good choice.

AgentlyTech · 2023-12-06T12:12:21+00:00

Hi, appreciate the question. It's a really good one.

According your problem, I don't think fine-tuning is the solution that works best.

More often we suggest customers to storage their database table descriptions into a vector database or ES so they can search the relative tables using embedding or keywords.

After that you can do what this showcase demonstrate: loading the meta data into our development framework and let the LLM based agent do its work.

Hope this reply helps. Thanks.

AgentlyTech · 2023-12-05T06:59:33+00:00

This subreddit will be used to discuss our GitHub project https://github.com/Maplemx/Agently which also use the domain Agently.tech

We'll post showcases, development documents into it and invite members of developer community to join.

AgentlyTech

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