> maybe do the llms and shit on cloud if the current setup at home is not powerful enough

- make a docker container hosting a tiny llm and connect it to a terminal or something and interact it by attaching yourself to that container

- now make another container that runs a really complex calculation(calculate pi or a really long loop) and outputs its metrics to a log in realtime
	- these 2 containers will now be a docker compose cluster
	- this 2nd container will now be monitored by the first container with an llm
	- the llm should be able detect a few things from the said log file
	- enable communication across these two containers using docker network bridge

- hook up Prometheus to the second container and make the llm detect the same thing but through the prometheus db
	- these 3 containers should now be a docker compose cluster
	- achieve this by trying to make the first container(llm one) be able to look at the logs that prometheus pulled through whatever means necessary


There will be a product(an app, service, another llm) that will be running in a container(container H(CH), standing for the heart).

Another container(container R(CR) standing for red blood cells) that has Prometheus that generates log data of the container(H) containing the product. The purpose of these logs are for system admins or supervisors to diagnose or do some hads on review of the performance and health.

Another container (cont W for white blood cells) Will be another logging server that will have logs that are ONLY relevent to the llm, which will be concise and minimal inorder to save llm computational costs(again tweaked per requirements)

Container B(CB) standing for the brain which will be an actually full blown llm(local or api) that will do all the decision making for us, it will look at container W's logs and take actions.
This llm will be a GraphRAG powerd llm and the RAG will sort of be a modular part. The RAG will contain data specific to the product being monitored, all the context that will ever be required regarding it's working, behaviour, details, just all the context basically, and also specially will have history of fixes for problems encountered for the llm to use In the near future.Once a fix is applied and the changes in the logs indicate a good condition, the problems nd fix will be stored in the rag. The W container will forcefully be altered to indicate anytime the solution form history is used(it will have the key and value(the soluton)  edited in the logs to show what was used) so that the llm can check if the solution from the history worked or it has to use another fix from the history or it has to deploy and entirely new fix.
The results from the prev fix will also give it more info regarding the previous fixes and exact conditions they will apply so the past fix will again be modified to be more specific.

We will have a fault prediction + detection model that will be running on another container(container S(CS) standing for the spine) monitoring the product(container H), it's job is to future proof the resources available for the app and it's  Health. Also for DDos protection all kinds of network lrotection too
Upon predicting any hazards, it will trigger the llm with the required information to take actions. Be it scaling, checking and changing network/firewall rules, etc.

Now container N(CN) Standing for the Nervous system will contain a MCP, this MCP is responsible for taking and running the actions tailored by the llm on CH to make changes.
The MCP will have its own data which has the *Code* req to take action of CH as they are app specific that is hosted on CH, this will again be native to the app and will be modular to be swapped in for a diff app.
It will recieve a seq of methods to be executed which will decided from the rag and the function calls itself from the data of the MCP.
The parameters of the FC will be decided using the data of MCP and be filled in by looking at the logs.
The CN data will also contain network commands and healchk commands to query very specific info about CH which will used to fill in the parameters.
CN will also have a secondary database connected to it which will have kubernetes Specific code which will also be modular in code it has to be updated or swapped for a diff kubernetes solution.

Container E standing  for E(CE) will be hosting graphana for us to look at or the user to look at

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There will be a product(an app, service, another llm) that will be running in a container(container H(CH), standing for the heart). It can also be a suite of containers if req by the customer and in that case also use docker compose.

Another container(container R(CR) standing for red blood cells) that has Prometheus that generates log data of the container(H) containing the product. The purpose of these logs are for system admins or supervisors to diagnose or do some hads on review of the performance and health.

Another container (cont W for white blood cells) Will be another logging server that will have logs that are ONLY relevent to the llm, which will be concise and minimal inorder to save llm computational costs(again tweaked per requirements)

Container B(CB) standing for the brain which will be an actually full blown llm(local or api) that will do all the decision making for us, it will look at container W's logs and take actions.
This llm will be a GraphRAG powerd llm and the RAG will sort of be a modular part. The RAG will contain data specific to the product being monitored, all the context that will ever be required regarding it's working, behaviour, details, just all the context basically, and also specially will have history of fixes for problems encountered for the llm to use In the near future.Once a fix is applied and the changes in the logs indicate a good condition, the problems nd fix will be stored in the rag. The W container will forcefully be altered to indicate anytime the solution form history is used(it will have the key and value(the soluton)  edited in the logs to show what was used) so that the llm can check if the solution from the history worked or it has to use another fix from the history or it has to deploy and entirely new fix.
The results from the prev fix will also give it more info regarding the previous fixes and exact conditions they will apply so the past fix will again be modified to be more specific.this contsiner id mostly on standby unless its triggered by CW or CS.

We will have a fault prediction + detection model that will be running on another container(container S(CS) standing for the spine) monitoring the product(container H), it's job is to future proof the resources available for the app and it's  Health. Also for DDos protection all kinds of network lrotection too
Upon predicting any hazards, it will trigger the llm with the required information to take actions. Be it scaling, checking and changing network/firewall rules, etc.

Now container N(CN) Standing for the Nervous system will contain a MCP, this MCP is responsible for taking and running the actions tailored by the llm on CH to make changes.
The MCP will have its own data which has the *Code* req to take action of CH as they are app specific that is hosted on CH, this will again be native to the app and will be modular to be swapped in for a diff app.
It will recieve a seq of methods to be executed which will decided from the rag and the function calls itself from the data of the MCP.
The parameters of the FC will be decided using the data of MCP and be filled in by looking at the logs.
The CN data will also contain network commands and healchk commands to query very specific info about CH which will used to fill in the parameters.
CN will also have a secondary database connected to it which will have kubernetes Specific code which will also be modular in code it has to be updated or swapped for a diff kubernetes solution.

Container E standing  for E(CE) will be hosting graphana for us to look at or the user to look at

This is the project i am currently working on
I want you to understand it, make some improvements, add a few bells whistles to it.
Notice how ive named the contauner for easy reference in the future
I want you to rewrite it with better description and verbosity and explaination and logical grouping of the  desc of the containers

.

Functions that accept other functions as parameters so that llm can create a single chain of commands and doesnt require Multiple triggers to the MCP.