Run ALL Your AI Locally in Minutes (LLMs, RAG, and more)

Summary

The video introduces a comprehensive package for setting up local AI infrastructure, developed by n8n. It includes tools like llama for LLMs, Quadrant for vector databases, and PostgreSQL for SQL databases. The presenter guides viewers through installation using Docker Compose and customizing the setup for workflow automations in n8n. The script also covers extending the package for a full Rag AI agent, showcasing its capabilities and future expansion plans.

Takeaways

• 🎉 The video introduces an exciting package for local AI developed by the n8n team.
• 🛠️ The package includes components like llama for LLMs, Quadrant for vector databases, PostgreSQL for SQL databases, and n8n for workflow automation.
• 🌐 The presenter is enthusiastic about the potential of running your own AI infrastructure, especially with powerful open-source models like llama.
• 📚 The GitHub repository for the self-hosted AI starter kit is basic, with key files being the environment variable file and the Docker Compose file.
• 🔑 Before starting, ensure you have dependencies like git and Docker installed.
• 💻 Clone the repository and set up your environment variables for services like PostgreSQL.
• 📝 The Docker Compose file needs modifications for exposing necessary ports and pulling specific models for tools like llama.
• 🖥️ Detailed instructions are provided for different system architectures, including those with Nvidia GPUs and Mac users.
• 🔍 The video demonstrates how to check the running containers in Docker and interact with them.
• 🔗 The presenter shares a workflow in n8n that uses PostgreSQL for chat memory, Quadrant for the vector database, and llama for the LLM and embedding model.
• 📈 The video also covers setting up a pipeline to ingest files from Google Drive into the local vector database.
• 📝 Custom code is provided to avoid duplicate vectors in the vector database when updating documents.
• 📱 The presenter plans to expand the setup in the future with additional features like caching and possibly a self-hosted frontend.

Q & A

What is the main topic of the video?

The main topic of the video is about setting up a local AI infrastructure using a package developed by the n8n team, which includes components like llama for the LLM, Quadrant for the vector database, Postgres for the SQL database, and n8n for workflow automations.

Why is the presenter excited about this package?

The presenter is excited because the package is a comprehensive solution for local AI needs, making it easy to set up and extend, and it includes powerful open-source models that can compete with closed-source models.

What are the key components included in the package?

The package includes llama for the LLM, Quadrant for the vector database, Postgres for the SQL database, and n8n for workflow automations.

What is the purpose of using Postgres in this setup?

Postgres is used in the setup for the SQL database needs and to serve as the chat memory for AI agents.

How does the presenter plan to extend the package?

The presenter plans to extend the package by adding components like Redis for caching, a self-hosted Superbase for authentication, and possibly a frontend. They also consider incorporating best practices for LLMs and n8n workflows.

What are the prerequisites for setting up this local AI infrastructure?

The prerequisites include having Git and Docker installed, with Docker Desktop also recommended for its Docker Compose feature.

What is the role of n8n in this local AI setup?

n8n is used to tie together the different components of the local AI infrastructure with workflow automations.

How does the presenter suggest customizing the Docker Compose file?

The presenter suggests adding a line to expose the Postgres port and pulling an additional olama embedding model to customize the Docker Compose file.

What is the significance of the 'env' file in this setup?

The 'env' file is significant as it contains credentials for services like Postgres and n8n Secrets, which are crucial for customizing the setup to the user's needs.

How does the presenter demonstrate the functionality of the local AI setup?

The presenter demonstrates the functionality by creating a fully local Rag AI agent within n8n, using Postgres for chat memory, Quadrant for the vector database, and olama for the LLM and embedding model.

What is the importance of the custom node in the n8n workflow?

The custom node is important for managing the ingestion of documents into the vector database by ensuring there are no duplicate vectors, which could confuse the LLM.

Outlines

00:00

🚀 Introduction to Local AI Package

The speaker is excited to introduce a comprehensive package for local AI developed by the n8n team. This package includes components like llama for the LLMs quadrant, vector database, PostgreSQL for SQL database needs, and n8n for workflow automations. The video aims to guide viewers on setting up this package quickly and extending its capabilities to create a fully functional local AI agent. The speaker emphasizes the growing accessibility and power of open-source AI models, suggesting that now is the time to invest in local AI infrastructure.

05:00

🛠️ Setting Up the Local AI Environment

The speaker provides a step-by-step guide to setting up the local AI environment. The process begins with downloading the code from the GitHub repository using a git clone command. Afterward, the speaker recommends using Visual Studio Code to edit the necessary files. The video covers the installation of dependencies like git and Docker, and the use of Docker Compose to bring together services like PostgreSQL, quadrant, and llama. The speaker also points out the need to edit the environment variable file to set up credentials and secrets for services like PostgreSQL and n8n.

10:01

🔧 Customizing Docker Compose for Local AI

The speaker discusses the need to customize the Docker Compose file to ensure proper functionality. This includes exposing the PostgreSQL port to enable its use within n8n workflows and pulling an additional llama embedding model to facilitate the use of llama for vector databases. The speaker provides a detailed walkthrough of the necessary code changes and mentions providing a customized version of the Docker Compose file for viewers' convenience.

15:03

🌐 Accessing and Managing Local AI Services

The speaker demonstrates how to access and manage the various local AI services using Docker. They show how to view running containers, check the output, and even execute Linux commands within each container. The video also covers setting up a fully local rag AI agent within n8n, using PostgreSQL for chat memory, quadrant for the vector database, and llama for the LLM and embedding model. The speaker provides a detailed explanation of the workflow and how to test the agent using a chat widget.

20:03

📈 Future Expansions and Conclusion

In the final part, the speaker talks about future plans to expand the local AI setup. They mention potential additions like Redis for caching, a self-hosted super base instead of vanilla PostgreSQL, and possibly incorporating frontend elements. The speaker also expresses excitement about the potential of the local AI stack and encourages viewers to like and subscribe for more content on the topic.

Keywords

💡Local AI

Local AI refers to the practice of running artificial intelligence models and applications directly on local machines or personal devices, as opposed to relying on cloud-based services. In the video, the presenter is excited about a package that enables local AI development, emphasizing the growing accessibility and power of open-source AI models that can now compete with closed-source models. This concept is central to the video's theme of setting up a personal AI infrastructure.

💡n8n

n8n is an open-source workflow automation tool that facilitates the connection and automation of various services and databases without the need for complex coding. In the script, n8n is highlighted as a crucial component in tying together different elements of the local AI setup, including the workflow automations that create a full Rag AI agent.

💡Docker

Docker is a platform that uses containerization technology to simplify the deployment of applications. In the video script, Docker is used to package the local AI setup, making it easy to install and run the various services needed for the AI infrastructure. Docker Compose is specifically mentioned as part of the setup process.

💡Llama

Llama, in the context of the video, refers to an open-source AI model similar to large language models like GPT. The script mentions 'llama' as one of the components of the local AI package, indicating its role in providing language processing capabilities to the AI agent being set up.

💡Vector Database

A vector database is a type of database designed to store and retrieve vector representations of data, which is useful for AI applications that rely on machine learning models. In the script, 'Quadrant' is mentioned as the vector database used for the Rag AI agent, highlighting its importance in managing the knowledge base for the AI.

💡Postgress

Postgress, or PostgreSQL, is an open-source relational database management system. In the video, it is part of the local AI package and is used for handling SQL database needs within the AI infrastructure. The script provides instructions on how to set up PostgreSQL within the Docker environment.

💡Rag AI Agent

Rag stands for 'Retrieval-Augmented Generation' and refers to a type of AI model that retrieves information to assist in generating responses. In the video, the presenter demonstrates how to create a full Rag AI agent using the local infrastructure, which involves using tools like n8n, PostgreSQL, and Quadrant to manage the agent's memory and knowledge base.

💡Workflow Automation

Workflow automation involves the use of software to automate procedural tasks. In the script, workflow automation is central to setting up the local AI, as it involves creating automated processes that allow the AI agent to function effectively, such as managing chat interactions and retrieving data from a knowledge base.

💡Environment Variable File

An environment variable file contains settings that configure the runtime environment for an application. In the video script, the presenter mentions editing this file to set up credentials for services like PostgreSQL, which is a necessary step in customizing the local AI package to the user's needs.

💡Embeddings

Embeddings in AI refer to the representation of data points (like words or documents) in a vector space, which allows for complex patterns to be identified. The script discusses using an 'olama embedding model', indicating that embeddings are used to transform data into a form that can be effectively utilized by the AI agent for tasks like Rag.

💡Google Drive

Google Drive is a file storage and synchronization service. In the context of the video, it is used as a source for documents that are ingested into the AI's knowledge base. The script describes a workflow that monitors Google Drive for new or updated files and incorporates them into the local AI system.

Highlights

Introduction of a comprehensive package for local AI developed by the n8n team.

Excitement about the ease of setup and the potential of the package for local AI.

The package includes components like llama, quadrant for vector database, postgress for SQL, and n8n for workflow automation.

Emphasis on the accessibility and power of open-source AI models like llama.

Instructions on setting up the package in minutes.

The GitHub repository for the self-hosted AI starter kit by n8n contains essential files for setup.

Clarification that the official instructions are lacking and will be improved upon in the video.

Prerequisites for setup include having git and Docker installed.

Downloading the repository is the first step in the setup process.

Editing the environment variable file is necessary to set up credentials for services like postgress.

Customizations to the Docker compose file are required to expose postgress port and pull an olama embedding model.

Different Docker compose commands are provided based on the user's system architecture.

A live demonstration of the Docker containers running the local AI services.

Introduction to creating a fully local rag AI agent within n8n.

Explanation of the workflow for using postgress as chat memory and quadrant as a vector database.

Details on how to use olama for embeddings and parsing responses from rag.

Demonstration of the custom code used to prevent duplicate vectors in the vector database.

A test of the AI agent showing its ability to retrieve information from the knowledge base.

Plans for future enhancements to the local AI stack, including caching and authentication.

Transcripts

have you ever wished for a single

package that you could easily install

that has everything you need for local

AI well I have good news for you today

because I have exactly what you are

looking for I have actually never been

so excited to make a video on something

before today I'm going to show you an

incredible package for local AI

developed by the n8n team and this thing

has it all it's got old llama for the

llms quadrant for the vector database

postgress for the SQL database and then

n8n to tie it Al together with workflow

automations this thing is absolutely

incredible and I'm going to show you how

to set it up in just minutes then I'll

even show you how to extend it to make

it better and use it to create a full

rag AI agent in n8n so stick around

because I have a lot of value for you

today running your own AI infrastructure

is the way of the future especially

because of how accessible is becoming

and because open-source models like

llama are getting to the point where

they're so powerful that they're

actually able to compete with close

Source models like GPT and clad so now

is the time to jump on this and what I'm

about to show you is an excellent start

to doing so and at the end of this video

I'll even talk about how I'm going to

extend this package in the near future

just for you to make it even better all

right so here we are in the GitHub

repository for the self-hosted AI

starter kit by n8n now this repo is

really really basic and I love it there

are basically just two files that we

have to care about here we have our

environment variable file where we'll

set credentials for things like

postgress and then we have Docker

compose the caml file here where we'll

basically be bringing in everything

together like postgress quadrant and

olama to have a single package for our

local AI now the first thing that I want

to mention here is that this read me has

instructions for how to install

everything yourself but honestly it's

quite lacking and there's a couple of

holes that I want to fill in here with

ways to extend it to really make it what

I think that you need and so I'll go

through that a little bit and we'll

actually get this installed on our

computer now there are a couple of

dependencies before you start basically

you just need git and Docker so I'd

recommend installing GitHub desktop and

then Docker desktop as well because this

also has Docker compose with it which is

what we need to bring everything

together for one package so with that we

can go ahead and get started downloading

this on our computer so the first thing

you want to do to download this code is

copy the get clone command here with the

URL of the repository you'll go into a

terminal then and then paste in this

command for me I've already cloned this

that's why I get this error message but

you're going to get this code downloaded

on your computer and then you can change

your directory into this new repository

that you've pulled and so with this we

can now go and edit the files in any

editor of our choice I like using VSS

code and so if you have VSS code as well

you can just type in code Dot and this

is going to pull up everything in Visual

Studio code now the official

instructions in the readme that we just

saw would tell you at this point to run

everything with the docker compos post

command now that is not actually the

right Next Step I'm not really sure why

they say that cuz we have to actually go

and edit a couple of things in the code

to make it customized for us and that

starts with the EnV file so you're going

to want to go into your EnV file I've

just made a env. example file in this

case because I already have my

credentials set up so you'll go into

your EnV and then set up your postgress

username and password the database name

and then also a couple of n8n Secrets

these can be whatever you want just make

sure that they are very very here and

basically just a long alpha numeric

string and then with that we can go into

our Docker compose file and here's where

I want to make a couple of extensions to

really fill in the gaps so the couple of

things that were missing in the original

Docker compose file first of all for

some reason the postgress container

doesn't have the port exposed by default

so you can't actually go and use

postgress as your database in an NN

workflow I think n uses postgress

internally which is why it's set up like

that initially but we want to actually

be a to use postgress for our chat

memory for our agents and so I'm going

to show you how to do that basically all

you have to do is go down to the

postgress service here and then just add

these two lines of code right here ports

and then just a single item where we

have 5432 map to the port 5432 inside

the container and that way we can go

Local Host 5432 and access postgress so

that is super super important otherwise

we won't actually be able to access it

within an NA end workflow we're going to

be doing that later when we build the

rag AI agent now the other thing that we

want to do is we want to use olama for

our embeddings for our Vector database

as well now the base command when we

initialize olama is just this part right

here so we sleep for 3 seconds and then

we pull llama 3.1 with oama so that's

why we have llama 3.1 Available To Us by

default but what I've added here is

another line to pull one of the olama

embedding models and we need this if we

want to be able to use AMA for our

Rag and so I've added this line as well

that is very very key so that is

literally everything that you have to

change in the code to get this to work

and I'll even have a link in the

description of this video to my version

of this you can pull that directly if

you want to have all the customizations

that we just went over here and with

that we can go ahead and actually start

it with Docker compose and so the

installation instructions in the readme

are actually kind of useful here because

there's a slightly different Docker

compose command that you want to run

based on your architecture so if you

have a Nvidia G

you can follow these instructions which

are a bit more complicated but if you

want to you can and then you can run

with a GPU Nvidia profile and then if

you are a Mac User you follow this

Command right here and then for everyone

else like what I'm going to use in this

case even though I have a Nvidia GPU

I'll just keep it simple with Docker

compose d-profile CPU up and so we'll

copy this command go into our terminal

here and paste it in and in my case I

have already created all these

containers and so it's going to run

really really fast for me but in your

case it's going to have to pull each of

the images for olama postgress n8n and

quadrant and then start them all up and

it'll take a little bit because I also

have to do things like pulling llama 3.1

for the old llama container and so in my

case it's going to blast through this

pretty quick because it's already done a

lot of this I did that on purpose so it

can be a quicker walkthrough for you

here um but you can see all the

different containers the different

colors here that are running everything

to set me up for each of the different

services and so like right here for

example it pulled llama 3.1 and then

right here it pulled the embedding model

that I chose from AMA as well um and so

at this point it's basically done so I'm

going to pause here and come back when

everything is ready all right so

everything is good to go and now I'm

going to actually take you in a Docker

so we can see all of this running live

so you're going to want to open up your

Docker desktop and then you'll see one

record here for the self-hosted AI

starter kit you can click on this button

on the left hand side to expand it and

then we can see every container that is

currently running or ran for the setup

so they're going to four containers each

running for one of our different local

AI services and we can actually click

into each one of them which is super

cool because we can see the output of

each container and even go to the exec

tab to run Linux commands within each of

these containers and so you can actually

do things in real time as well without

having to restart the containers you can

go into the postgress container and run

commands to query your tables and stuff

you can go into actually I'll show you

this really quick you can go into the

olama container and you can pull in real

time like if I want to go to exec here I

can do AMA pull llama

3.1 if I can spell it right 70b so I can

pull models in real time and have those

updated and available to me in n8n

without having to actually restart

anything which is super super cool all

right so now is the really really fun

part because we get to use all the local

infrastructure that we spun up just now

to create a fully local rag AI agent

within n8n and so to access your new

self-hosted n8n you can just go to Local

Host Port 5678 and the way that you know

that this is the URL is either through

the docker logs for your n container or

in the readme that we went over um that

was in the GitHub repository we cloned

and with that we can dive into this

workflow that I created to use postgress

for the chat memory quadrant for Rag and

olama for the llm and the embedding

model and so this is a full rag AI agent

that I've already built out I don't want

to build it from scratch just because I

want this to be a quicker smooth walk

through for you but I'll still go step

by step through everything that I set up

here and so that you can understand it

for yourself and also just steal this

from me CU I'm going to have this in the

description link as well so you can pull

this workflow and bring it into your own

n8n instance and so with that we can go

ahead and get started so there are two

parts to this workflow first of all we

have the agent itself with the chat

interaction here so this chat widget is

how we can interact with our agent and

then we also have the workflow that is

going to bring files from Google Drive

into our knowledge base with quadrant

and so I'll show the agent first and

then I'll dive very quickly into how I

have this pipeline set up to pull files

in from a Google drive folder into my

knowledge base so we have the trigger

that I just mentioned there where we

have our chat input and that is fed

directly into this AI agent where we

hook up all the different local stuff

and so first of all we have our olama

chat model and so I'm referencing llama

3.1 colon latest which is the 8 billion

parameter model but if you want to do an

AMA PLL Within the container like I

showed you how to do you can use

literally any olama llm right here it is

just so so simple to set up and then for

the credentials here it is very easy you

just have to put in this base URL right

here it is so important that for the URL

you use

HTTP and instead of Local Host you

reference host. doer. internal otherwise

it will not work and then the port for

Alama is if you don't change it

11434 and you can get this port either

in the Docker compost file or in the

logs for the AMA container you'll see

this in a lot of places and so with that

we've got our llm set up for this agent

and then for the memory of course we're

going to use postgress and so I'll click

into this and we're just going to have

any kind of table name you have here and

N will create this automatically in your

postgress database and it'll get the

session ID from the previous node and

then for the credentials here this is

going to be based on what you set in

yourb file so we have our host which is

host. doer. internal again just like

with AMA and then the database name user

and password all three of those you

defined in your EnV file that we went

over earlier and the port for postgress

is

5432 and so with that we've got our

local chat memory set up it is that

simple and so we can move on to the last

part of this agent which is the tool for

rag so we have the vector store tool

that we attach to our agent and then we

hook in our quadrant Vector store for

this and so we're just going to retrieve

any documents based on the query that

comes into our agent and then for the

credentials for Quadrant we just have an

API key which this was filled in for me

by default so I hope it is for you as

well I think it's just the password for

the NN instance and then for the

quadrant URL this should look very very

familiar HTTP host. doer. internal and

then the port for Quadrant is 6333 again

you can get this from the docker compose

file because we have to expose that Port

make it available or you can get it from

the quadrant logs as well

and so one other thing that I want to

show that is so so cool with hosting

quadrant locally is if you go to local

hostport

6333 like I have right here you can see

in the top left slash dashboard it's

going to take you to your very own

self-hosted quadrant dashboard where you

can see all your collections your

knowledge base basically and you can see

all the different vectors that you have

in there you can click into visualize

and I can actually go and see all my

different vectors which this is a

document that I already have inserted as

I was testing things um so you can see

all the metadata the contents of each

chunk it is so so cool so we'll go back

to this in a little bit here but just

know that like you have so much

visibility into your own quadrant

instance and you can even go and like

run your own queries to uh get

collections or delete vectors or do a

search uh it's just really awesome so

yeah hosting quadrant is a beautiful

thing um and so with that we have our

quadrant Vector store and then we're

using olama for embeddings using that

embedding model that I pulled that I

added to the docker compost file and

then we're just going to use llama 3.1

again to parse the responses that we get

from rag when we do our lookups so that

is everything for our agent and so we'll

test this in a little bit but first I

want to actually show you the workflow

for ingesting files into our knowledge

base and so the way that works is we

have two triggers here basically

whenever a file is created in a specific

folder in Google Drive or if a file is

updated in that same folder we want to

run this pipeline to download the file

and put it into our quadrant Vector

database running locally and so that

folder that I have right here is this

meeting notes folder in my Google Drive

and specifically the document that I'm

going to use for testing purposes here

is these fake meeting notes that I made

I just generated something really really

silly here about a company that is

selling robotic pets and AI startup um

and so we're going to use this document

for our rag I'm not going to do a bunch

bunch of different documents um because

I want to keep this really simple right

now but you can definitely do that and

the quadrant Vector database can handle

that but for now I'm just using this

single document and so I'll walk through

step by step what this flow actually

looks like to ingest this into the

vector database and so first of all I'm

going to fetch a test event which is

going to be the creation of this meeting

Note file that I just showed you and

then we're going to feed that into this

node here which is going to extrapolate

a couple of key pieces of information

including the file ID and the folder ID

and so once we have that I'm going to go

on to this next step right here and this

is a very very important step okay let

me just stop here for a second there are

a lot of rag tutorials with n8n on

YouTube that miss this when you have

this Step at the end here I'm just going

to skip to the end really quick whether

this is super base quadrant pine cone it

doesn't matter when you have this

inserter it is not an upsert it is just

an insert and so what that means means

is if you reinsert a document you're

actually going to have duplicate vectors

for that document so if I update a

document in Google Drive and it

reinserts the vectors into my quadrant

Vector database I'm going to have the

old vectors for the first time I

ingested my document and then new

vectors for when I updated the file it

does not get rid of the old files or

update the vectors in place that is so

important to keep in mind and so I'm

giving a lot of value to you right here

by including this node and it's actually

custom code because there's not a way to

do it without code in n8n but it is all

good because you can just copy this from

me I'm going to have a link to this

workflow in the description like I said

so you can just download this and bring

it into your own n8n take my code here

which basically just uses Lang chain to

connect to my quadrant Vector store get

all of the vector IDs where the metadata

file ID is equal to the ID of the file

I'm currently ingesting and then it just

deletes those vectors so basically we

clear everything that's currently in the

vector database for this file so that we

can reinsert it and make sure that we

have zero duplicates that is so so

important because you don't want

different versions of your file existing

at the same time in your knowledge base

that will confuse the heck out of your

llm and so this is a very very important

step and so I'll run this as well and

that's going to delete everything so I

can even go back to quadrant here go to

my Collections and you can see now that

this number was nine when I first showed

this quadrant dashboard and now it is

zero but it's going to go back up to 9

when I finish this workflow so next up

we're going to download this Google

drive

file nice and simple uh then we're going

to extract the text from it and so this

it doesn't matter if it's a PDF a CSP a

Google doc it'll take the file and get

the raw text from it and then we're

going to insert it into our quadrant

Vector store and so now I'm going to run

test step here and we're going to go

back to the UI after it's done doing

these insertions you can see here nine

items because it chunked up my document

so we go back here and I'll refresh it

right now it's zero I'll refresh and

there we go boom we're back up to nine

chunks and the reason there's so many

chunks for such a small document is

because if we go to my chunk size here

in my recursive character text splitter

I have a chunk size of 100 so every

single time I put in a document it's

going to get split up into 100 character

chunks so I want to keep it small just

because I'm running llama 3 .1 locally I

don't have the most powerful computer

and so I want my prompts to be small so

I'm keeping my context lower by having

smaller chunk sizes and not returning a

lot of documents when I perform Rag and

so the other thing that I wanted to show

really quickly here is my document data

loader and so or my default data loader

I'm adding two pieces of metadata here

the file ID and the folder ID the more

important one right here is the file ID

because that is how I know that a vector

is tied to a specific document so I use

that in that other step right here to

delete the old document vectors before I

insert the new one so that's how I make

that connection there so that's kind of

the most in-depth part of this

walkthrough is how that all works and

having this custom code here but just

know that this is so so important so

just take this from me I hope that it

makes sense to an extent I spent a lot

of time making this work for you um so

yeah with that that is everything we've

got our agent fully set up everything

ingested in uh we have the document

currently in the knowledge base CU I ran

through that step by step and so now we

can go ahead and test this thing so I'm

going to go to the chat widget here

actually I'm going to save it first and

then go to the chat widget and then I'll

ask it a question that it can only

answer if it actually has the document

in the knowledge base and can retrieve

it so I'll say what is the ad campaign

focusing on and because this is llama

3.1 running locally it's going to

actually take a little bit to get a

response because I don't have the BPS

computer so I'm going to pause and come

back when it has an answer for me all

right so we got an answer from llama 3.1

and this is looking pretty good it's a

little bit awkward at the start of the

response here uh but this is just the

raw output without any instructions from

me to the model on how to format a

response and so you can very very easily

fix this by just adding to the system

prompt for the llm and telling it how to

respond with the information it's given

from rag but overall it does have the

right answer and it's talking about

robotic pets which obviously it is only

going to get that if it's using regag on

the meaning notes document that I have

uploaded through my Google Drive so this

is working absolutely beautifully now I

would probably want to do a lot more

testing with this whole setup U but just

to keep things simple right now I'm

going to leave it at this as a simple

example um but yeah I would encourage

you to just take this forward keep

working on this agent and um yeah it's

fully fully local it is just a beautiful

thing so I hope that this whole local AI

setup is just as cool for you as it is

for me because I have been having a

blast with this and I will continue to

as I keep expanding on it so just just

as I promised in the start of the video

I want to talk a little bit about how

I'm planning on expanding this in the

future to make it even better cuz here's

the thing this whole stack that I showed

here is a really good starting point but

there's some things I want to add on to

it as well to make it even more robust

things like redis for caching or a

self-hosted super base instead of the

vanilla postgress CU then it can handle

things like authentication as well maybe

even turning this into a whole local AI

Tech stack that would even include

things like the front end as well or

maybe baking in best practices for red

and llms or na end workflows for that to

make this more of like a template as

well to actually make it really really

easy to get started with local AI so I

hope that you're excited about that if

you are or if you found this video just

helpful in general getting you set up

with your local AI Tech stack I would

really appreciate a like and a subscribe

and with that I will see you in the next

video