Below is a dialogue between software developers, the first one used ChatGPT in his solution of code task and makes a comment,   the second one gives him reply on his solution. Write a reply that appropriately answers the comment.

### Comment:
some quick notes for self subscribing to github events by pushing to some type of repo agents config, similar to how our bot is configured to listen to certain github events. e.g. i listen to comment events, and then those tagging me. comment.includespavlovcik automatically dispatch the event data to  using repo dispatch i code my logic to automatically answer questions. e.g. i am requested for a review i code my logic to automatically review. benefits. self hosting openai credentials and logic customizable!. can securely commentact as the user using personal access token.. collect cash automatically as### Reply:
i really believe we need to adopt langchain for what is being discussed here, simply relying on the gpt. or  model and some prompt engineering isnt going to be enough. its dataset is good enough to get away with most things but for the specific and complex things like code interpretation if rebuilding review, embeddingvector creation, storage and proper agents especially in terms of the tooling involved wed need code splitter would be superb for embedding repos and codebases output parser to be able to create complex schema from specific user input while working with other complex schemas like permit generation etc retrievers to work with embeddings, vectors, documents etc memory depending on which style of agent i.e plan upfront and execute or plan and execute based on previous output we will need a more complex memory system. callbacks which come with some built in handlers etc toolkit of course we will need to isolate functions of the bot which can be used as modular tools for the agents. permit generation, saving to db, indexing repos, vectorizing them, retrievers for various things. so in my mind, all of the org repos would be embeddedvectored, maybe all of the issues and prs too eventually this allows for search, similarity comparisons, in depth understanding of complex and large codebases, maybe even the db of devs to work with those in the same ways. specific retrievers for specific jobsdata, every public facing function we want the agent to be able to execute such as comments, payout etc etc. agent types agent docsthere are two main types of agents action agents at each timestep, decide on the next action using the outputs of all previous actions planandexecute agents decide on the full sequence of actions up front, then execute them all without updating the plani think it gives us a far more robust and structured way to interact with the black box that is ai.i had suggested superagi and while its great for getting an idea of agents and prototyping, its py based so wed need to write a toolkit in python for the bot for agent actions whereas langchain is just as powerful if not more so and is a ts package.im not clued up at all in github apps and actions if im honest but i know that working with raw prompts and gpt is a bitchin regards to the kernel application idea, have you considered creating a model training dataset? if we were to formulate a plan to train a model on say specific aspects such as writingexecuting custom arbitrary logic if that is still being considered, i see that another option has been suggested whereby partners would just write their own handlers which is good for those with that ability but restricts a lot of others depending on the target demographic we assume at least a bit of knowhow dont we?create your own custom models by finetuning our base models with your training data. once you finetune a model, youll be billed only for the tokens you use in requests to that model.feed it the supa logs and bot logic, docs, and maybe a chronological showcase in diffs of the bot source code along with a lot of manually generated potential user prompts or requests and we can train it on correct routes, error handling, callbackhandlers etc. fewshot learning fsl is a machine learning framework that enables a pretrained model to generalize over new categories of data using only a few labeled samples per class. it falls under the paradigm of metalearning, which means learning to learn.some common use cases where finetuning can improve results particularly fsl setting the style, tone, format, or other qualitative aspects improving reliability at producing a desired output correcting failures to follow complex prompts handling many edge cases in specific ways performing a new skill or task thats hard to articulate in a prompt