No Code RAG Agents? You HAVE to Check out n8n + LangChain

Summary

This video demonstrates how to create a full-fledged RAG (Retrieval-Augmented Generation) AI agent without writing any code, using n8n, a workflow automation tool, and LangChain, an AI development library. By integrating Google Drive as the knowledge base, the AI agent can answer questions from stored documents. The creator walks through building this no-code agent step-by-step, leveraging n8n's workflow features, including triggers, actions, and LangChain’s tools for chat memory and vector databases. Viewers can download the workflows and easily implement the solution themselves for powerful AI-driven automation.

Takeaways

• 🤖 Building AI agents using RAG (retrieval-augmented generation) can be done without coding by using n8n and LangChain.
• 🔧 n8n is a no-code workflow automation tool that allows users to create advanced AI agents with tool calling and RAG.
• 📂 The AI agent can utilize Google Drive as its knowledge base by uploading documents for the agent to access and answer questions.
• 💡 n8n workflows are simple to set up, consisting of triggers and actions that connect to AI models, databases, and external tools like Google Drive.
• 🚀 The LangChain AI agent node in n8n simplifies complex tasks like tool calling, memory management, and interaction with vector databases.
• 💬 The n8n interface provides a built-in chat window to easily test and iterate AI agents directly within the UI.
• 📄 Users can customize their vector databases and memory settings, with options to store data locally or use services like Supabase and Pinecone.
• 🔄 AI agents can retrieve documents from the vector database, which are indexed based on user queries and stored as vector embeddings using OpenAI models.
• 📊 Workflow nodes allow integration with Google Drive, enabling the AI agent to find, download, and add files to its knowledge base.
• 🌐 The workflow can be embedded into websites, allowing users to deploy the AI chatbot with RAG capabilities via a simple HTML embed code.

Q & A

What does the speaker mean by 'RAG AI agent'?

RAG stands for Retrieval-Augmented Generation, a technique used in AI where external data, like documents, are retrieved and incorporated into a language model's responses. In this case, the speaker builds a RAG AI agent using Google Drive as a knowledge base, allowing the AI to answer questions based on uploaded documents.

How does n8n help in building AI workflows without code?

n8n is a no-code workflow automation tool that allows users to build complex AI workflows by connecting different nodes, such as triggers and actions. It integrates with tools like LangChain, enabling the creation of RAG AI agents without writing code. Users can import or create workflows, including those for AI agents, using simple drag-and-drop functionality.

What role does LangChain play in the workflow?

LangChain is a library for AI development that simplifies the integration of advanced AI capabilities like tool calling and memory. In the workflow described, LangChain powers the AI agent by enabling it to interact with tools, handle memory, and retrieve information from a vector database without requiring manual coding.

What is the benefit of self-hosting n8n over using services like Zapier?

Self-hosting n8n offers significant cost advantages, as users can avoid expensive monthly fees associated with services like Zapier. Additionally, it provides greater control over the workflows and scaling capabilities, allowing users to customize and expand their setup without restrictions.

What is the significance of using a vector store in the AI agent?

A vector store is crucial in RAG workflows because it allows the AI agent to retrieve and store document embeddings as vectors. This enables the AI to efficiently search for and retrieve relevant documents based on user queries. In the speaker's setup, the vector store holds documents from Google Drive, which the AI uses to answer questions.

How does the AI agent retrieve documents from Google Drive?

The AI agent is programmed to search for files in Google Drive using a query provided by the user. It downloads the file, extracts the text, and stores it in the vector database for future use. The AI agent can later retrieve this information to answer questions based on the content of those documents.

Why is Superbase recommended for production use?

Superbase is recommended for production environments because it provides a more scalable and robust solution for managing both chat memory and vector storage. Unlike the in-memory storage option, which is more suitable for local or demo purposes, Superbase can handle larger datasets and ensures better data persistence and synchronization across workflows.

What options does the AI agent offer for different language models?

The AI agent supports multiple language models, including OpenAI's GPT models, Anthropic's Claude, Meta's Llama, and Grok. Users can select the model based on their needs and easily configure the credentials for accessing these models.

What is the purpose of chunking documents when adding them to the vector database?

Chunking documents into smaller parts (in this case, 1,000 characters) helps optimize the retrieval process. When a document is queried, the AI searches the vector database for the most relevant chunks, making the process more efficient and accurate than trying to retrieve an entire large document at once.

How can the AI agent be embedded into a website?

The AI agent can be easily embedded into a website using a code snippet generated by n8n. The speaker demonstrates how to copy the code and insert it into an HTML editor or website, creating a chat widget that allows users to interact with the AI agent directly on the site.

Outlines

00:00

🤖 Building an AI Agent without Code Using n8n and LangChain

This paragraph introduces the concept of creating advanced AI agents using RAG (Retrieval-Augmented Generation) without writing any code. The speaker plans to demonstrate how to use the no-code workflow automation tool n8n in combination with LangChain to build an AI agent that answers questions using documents stored in Google Drive. They emphasize that even though they enjoy coding, using no-code tools like n8n can be more efficient for many projects.

05:00

⚙️ Workflow Automation with n8n: Triggers and Actions

The paragraph explains how workflows in n8n are structured around triggers and actions. Triggers start the workflow, such as receiving a chat message, while actions execute tasks, like interacting with AI models or Google Drive. The user can easily add a chat window in the UI for testing purposes. The LangChain integration in n8n simplifies setting up AI agents by automatically providing tools, memory, and chat models. It supports multiple AI models like OpenAI and others, making it versatile for various applications.

10:01

🧠 Integrating RAG: Tools and Vector Stores

This section covers the integration of custom tools and vector stores into the RAG agent. The speaker explains how n8n offers built-in tools like a vector store, which stores and retrieves documents based on user queries. They recommend using Superbase for production environments to manage both chat memory and vector stores. For quick demonstrations, everything is stored locally in memory, with embedding models using OpenAI for document processing. The speaker highlights the flexibility of n8n, which allows for customizing workflows and tool usage without writing code.

15:02

📂 Managing Documents with Google Drive and Vector Databases

This paragraph dives into how documents from Google Drive can be added to the RAG agent's knowledge base. A specific workflow is set up to search, download, and add documents to the vector database for future queries. The speaker demonstrates how users can instruct the agent to retrieve and store relevant files from Google Drive. They also explain the importance of controlling which documents get added to the knowledge base to avoid bloating the vector database. The process involves setting up parameters for querying Google Drive and handling the text extraction and embedding tasks.

🔄 File Querying and Knowledge Base Testing

Here, the speaker tests the AI agent’s functionality by asking it a question that it cannot answer initially because the required document hasn’t been added yet. After adding the document from Google Drive to the knowledge base, they repeat the question, and the agent retrieves the correct information. This test showcases how the AI agent can dynamically add files to its knowledge base and answer questions based on the newly added data. They also mention the option of scheduling automatic updates to the vector database.

🔧 Workflow Troubleshooting: Handling Workflow IDs and Memory Keys

The speaker discusses a technical issue in n8n where the workflow ID used to call tools must differ from the ID of the main workflow, resulting in errors if not handled correctly. The workaround involves setting up a separate workflow to reference the correct memory key when interacting with the vector database. This ensures that data retrieval and insertion use the same workflow, avoiding conflicts. They explain how using Superbase as a vector store eliminates this issue by allowing dynamic memory key management.

🌐 Embedding Chatbots in Websites and Future Plans

This final section demonstrates how to embed the AI chatbot into a website using a simple code snippet. The speaker highlights how the chat widget can interact with users and answer questions based on the RAG integration. They conclude by teasing future content, focusing on deploying the system into production environments and integrating with Superbase. They encourage viewers to provide feedback and express interest in more advanced tutorials on RAG and no-code AI development.

Keywords

💡RAG (Retrieval-Augmented Generation)

RAG is a technique in AI where a model retrieves relevant documents from a knowledge base (like Google Drive) to answer questions or generate content. In the video, RAG is used to empower an AI agent to access specific documents from a vector store, ensuring that the AI can provide contextually accurate answers based on the user’s uploaded files.

💡n8n

n8n is a no-code workflow automation tool that integrates with various platforms like Google Drive and LangChain. In the video, n8n is highlighted for its ability to build AI agents with RAG capabilities without writing any code. The speaker praises its flexibility, scalability, and the ease with which users can self-host it, unlike expensive alternatives such as Zapier.

💡LangChain

LangChain is a framework designed for building applications powered by language models. It allows developers to integrate tools like memory, embeddings, and vector stores into AI workflows. In the video, LangChain is seamlessly integrated into n8n, enabling the creation of a RAG AI agent without coding. The speaker demonstrates its role in handling chat models, tool calling, and memory management.

💡Vector Database

A vector database stores and retrieves data in vector form, which is critical for tasks like semantic search and information retrieval in AI. In the video, the vector database is used to store and retrieve document embeddings (vectorized versions of documents) that the AI agent uses to answer queries. The speaker demonstrates adding Google Drive documents to this database for future use by the AI agent.

💡Google Drive Integration

Google Drive integration allows the AI agent to access documents stored in the user’s Google Drive. In the video, the workflow enables the AI agent to search, retrieve, and extract text from Google Drive files to update its knowledge base, demonstrating how external documents can be seamlessly incorporated into the RAG system.

💡Chat Memory

Chat memory allows an AI agent to remember past interactions and conversations. In the video, the speaker uses local chat memory for storing previous chats but also mentions other options, such as using databases like Postgres or Redis. This memory enables the AI agent to provide continuity and context in conversations by recalling earlier parts of the interaction.

💡Embeddings

Embeddings are vector representations of data (text, images, etc.), which capture the semantic meaning of that data. In the video, the speaker uses OpenAI’s text-embedding models to convert Google Drive documents into vectors that the AI can retrieve and use in answering user queries. This process is essential for the RAG functionality of the AI agent.

💡No-code AI Development

No-code AI development refers to building AI systems without writing any programming code, often by using drag-and-drop platforms or workflow automation tools. The video focuses heavily on how n8n allows users to create a RAG-powered AI agent without coding, showing how powerful AI models and workflows can be constructed through visual tools and pre-built integrations.

💡API Integration

API integration refers to the process of connecting applications to external services via their APIs (Application Programming Interfaces). In the video, the AI agent connects to services like OpenAI for language models, Google Drive for document storage, and even LangChain’s AI agent node through APIs. The integration is shown to be seamless within the n8n workflow environment.

💡Superbase

Superbase is a platform that offers hosted databases and backend services. In the video, Superbase is recommended for production-level RAG setups, particularly because it can manage both chat memory and vector databases. The speaker highlights it as a superior choice for long-term, scalable solutions over using local memory or in-memory databases.

Highlights

Using n8n and LangChain, you can build a full-blown RAG AI agent using Google Drive as a knowledge base with zero coding required.

n8n is a workflow automation tool that is self-hosted, scalable, and cheaper than alternatives like Zapier.

RAG (Retrieval-Augmented Generation) AI agents can be built simply with n8n, integrating directly with LangChain for advanced AI development.

The agent can retrieve documents from Google Drive and answer user queries by utilizing a vector database and LangChain’s capabilities.

The no-code approach allows for rapid AI agent development, using pre-built workflows that handle chat models, memory, and document retrieval.

Testing the AI agent is easy with a built-in chat window in n8n, allowing real-time feedback and adjustments.

You can use multiple chat models like OpenAI, Claude, Grok, or LLaMA for flexible integration in the AI agent setup.

Custom tools can be added to the AI agent workflows in n8n, including any n8n workflows packaged as tools for multi-step processes.

Google Drive documents can be added dynamically to the AI agent's knowledge base via vector store retrieval, ensuring precise control over what information is accessed.

The integration allows vector databases to store chunks of documents for later retrieval, ensuring efficient RAG-based answers.

Using embeddings from OpenAI’s text models, documents are processed into vectors for effective knowledge retrieval.

The workflow includes an option to search for Google Drive documents by query, download them, and add them to the vector database for RAG integration.

A workaround is required in n8n to call a secondary workflow for vector database operations, ensuring seamless data insertion and retrieval.

The embedded chat interface can be added to any website, offering a smooth user experience while interacting with the AI agent.

This no-code workflow setup can be scaled to production using tools like Supabase for both chat memory and vector storage, further enhancing the AI’s capabilities.

Transcripts

building Advanced AI agents with rag is

definitely not something that's limited

to complex and custom coded applications

today I'm going to show you how to use

my favorite workflow automation tool n8n

along with my favorite library for AI

development Lang chain to build a

full-blown rag AI agent using my Google

Drive as my knowledge base so the agent

can answer questions using the documents

that I upload and when I say no code I

mean that today we are literally going

to write zero lines of code to build our

AI agent and don't get me wrong I love

to code my own agents but I would be

dumb to code them myself every single

time when a lot of the times I can make

them very simply without code using n8n

n8n is a workflow automation tool

similar to make.com or zapier but it is

way better because you can self-host it

so you don't have to pay hundreds and

hundreds of dollars a month like I did

at one point for zapier and you can

scale it infinitely so definitely more

on that in a future video but on top on

top of that which already makes n8n

amazing it integrates directly with Lang

chain so it is so so easy to build AI

agents that are really powerful with

tool calling and rag just like what I'm

about to show you now so without further

Ado let's get right into the meat of it

and see how easy it is to build a rag

agent with no code all right so I have

already fully built out these NN

workflows for my rag AI agent because I

just want to walk through it with you

very very smoothly to give you a lot of

value very quickly you can also steal

these workflows from me if you want I've

got a link to a GitHub repository in the

description of this video so you can

download these workflows as Json files

and then you go into your own n8n

instance in the top right here you click

on the three dots and then import from

file and in seconds you can bring in

this entire workflow into your own n8n

instance so you can take this further

and build something amazing with Rag and

so with that we can actually dive into

what this workflow looks like so just

like with make.com and zapier every

workflow in N an is made up of two four

parts you have your triggers which is

what starts these workflows and then you

have your actions the nodes within the

workflow that actually do things like

interacting with AI or your Google drive

or your vector database whatever it

might be so the trigger for this and and

workflow is a chat input so when chat

message received if you click into this

the options are quite simple you just

give authentication optionally then also

the initial message that's going to be

there in the chat widget for the user

when they first click on it to interact

with the agent in a chat bot and then

when you add this as a trigger to an NA

end workflow you're immediately given

this option in the bottom middle here to

open up a chat window and this is one of

my favorite Parts about n it makes it so

easy to quickly test your AI agents

because you have a chat window directly

in the UI to test things out as you're

iterating on the prompt or the tools or

the vector database whatever it might be

so this just makes it so easy to test

things and iterate on things and so

we'll even be looking at this later when

I give a demonstration of this AI agent

with Rag and so we have our trigger here

that I just covered and when there's a

chat message that comes through it flows

immediately into our rag AI agent which

is a tools agent that uses Lang chain

under the hood and so the way that I

know that this uses Lang chain aside

from just the documentation telling me

if I click on the plus icon here to add

a new node to the workflow and I search

for Lang chain sure enough AI agent

which that's the node that I chose here

is the second option and this just does

everything for me I don't have to code

anything to have my tools my memory and

my chat model if I want to get more

custom with it I can use the Lang chain

code node to actually code things if I

want something more robust that maybe no

code can't do for me because no code

can't do everything but it can do most

things and so with this use case I can

just use the AI agent from laying chain

here and it's got everything for me it's

got the chat model so I'm using open AI

in this case and I can use any model

that I want it's really easy to set up

my credentials as well I just need an

API key

and then also if I wanted a different

chat model I can click on this connector

here I could use claw if I want to

select anthropic I could use grock for

llama 3.1 I could use ol llama um open

AI like I already have selected pretty

much everything that I actually care

about is here already and so even if

there's something else like if I wanted

to use fireworks for example maybe I

could set up custom code here but for

most cases I'm happy with everything

here so it is just amazing no code

needed for all these Integrations and

then also for the chat memory there's a

lot of options for that so I'm just

using chat memory which is basically

going to be stored locally on my n

instance um but there are other options

as well so if I click on the memory

connector here we could use something

like postgress chat memory so having a

SQL table that manages all the

conversations or using reddis for

example a lot of options here as well

and then for the tool calling the best

part of the AI agent where we actually

have the rag integration there are a lot

of options for Tool as well so if I

click on this connector there are a lot

of custom tools that are already

provided for you like a calculator or a

vector store which is how I actually

retrieve the documents to answer

questions with rag um and then my

favorite part is you can literally call

any n8n workflow as a tool and so you

set up a workflow to do something like

interacting with Google Drive and a

multi-step workflow which I'll show a

little bit and then you just tell the AI

agent how to use this workflow like what

parameters to give and then also you

tell it when to use that workflow just

like you would with any tool that you

might Define in custom code and so you

can literally have all the power of any

na workflow you want even if there's a

ton of steps there just packaged as a

neat little tool that you can just shove

onto your rag AI agent and so yeah just

really cool stuff um and so there's two

tools that I have here for this agent

the first one is that built-in Vector

store tool where I retrieve the

documents based on a user query um to

answer a question or something like like

that and so I add this on as a tool

that's connected here and then it asks

me to supply a vector store and so I'm

just using an in-memory Vector store so

similar to my chat memory it's going to

just be stored locally on my n8n

instance so there'll be like files that

are there on the server that I am

hosting my n8n there are other options

as well though so you could use um for

example a sua base Vector store or a

pine cone Vector store uh if you want my

recommendation I would actually highly

recommend to go with super base here and

the reason that is if I go back to the

chat memory here this is just a nice

little gold nugget for you here uh you

can use a super based postgress table

for your chat memory and then you can

also set up a superbase vector store so

you can have everything managed for your

Rag and your chat memories all within

one place in super base so that's my

recommendation if you want to take

something like this to production but

just for a quick demonstration purpose

here I'm running everything locally for

my memory and my Vector store uh and

then for your embedding models there's a

lot of options for that as well I'm just

using open AI for my embeddings as well

so same credentials and then I'm just

using the text embedding three large

model uh from open AI for my embedding

so that's what actually takes the

different chunks of documents that I put

into my Vector database and turns them

into vectors for retrieval

later um and then on top of that I have

a model that's associated with this tool

to retrieve information so I'm just

using the same uh GPT 40 mini here as

well um because you're going to get the

documents from the vector database and

you need a large language model to

process that and pick out the right

information and so I'm using GPT for

that again as well so that is everything

for the rag retrieval the actual

retrieval part of rag now to put

documents in the vector database

knowledge base I have another tool here

and this is a NN workflow as a tool like

I was mentioning earlier and so the way

that my chatot works here and you could

take this in a milon different ways is

it's going to start out with nothing in

the vector database but I have infinite

access to my Google Drive so I can talk

to my AI chat bot and say I have this

file in Google Drive I want you to add

it to your knowledge base for future

reference and so what it's going to do

for this tool here is I have a little

description that says use this to search

for a file in Google Drive so the user

might say I want my meeting notes from

last week to be added to the knowledge

base so it'll search for those meeting

notes in Google Drive download it and

add it to the vector database knowledge

base for future querying so that's the

description to tell my AI agent when to

use this tool and then I have parameters

so in a Json example here I tell the AI

agent what parameters to give this

workflow so that it can execute properly

and so in this case I'm just giving one

parameter here which is a query how are

you going to search Google drive to find

that file to download and then add to

the vector database very very simple

couple of other options here like the

workflow ID so this tells it what

workflow and na end to actually execute

for this tool and then also the field

that's going to be outputed from that

workflow that you want to use as the

response that would then tell the large

language model what happened when it

invoked that tool very very simple stuff

and so the workflow that I have for this

is actually this one right here so it

starts out with a web hook and so this

gets the query and then it passes it

into the Google Drive and so let me

click into this and show you it's going

to search for a file or a folder based

on the query here and so if you search

for um 822 meeting notes for example it

would find that word doc file that has

those meeting notes in your Google Drive

and then on the next action it's going

to download that file then after it

downloads it it's going to extract the

text from that file and so now at this

point in the workflow I have all the

text from that file that I've downloaded

from Google Drive and I can now put it

into my Vector database so it's added as

the knowledge base so the AI agent can

use that to answer questions later and

so for this I'm just using the same user

documents memory key so the way that I

retrieve my documents is using this

memory key and the way that I insert

documents is also using this key

otherwise those two things are not going

to sync up um and then it's kind of a

similar setup here where I just have to

choose my embeddings which I'm going to

use gbt 40 or not gbt 40 that's for the

llm I'm going to use the text 3 large

and beddings from open Ai and then I

have a default data loader so this is

going to basically Define how I take my

text and put it as vectors in my Vector

database and so I'm going to split into

chunks basically just using a 1,000

chunk size text splitter so all the same

kind of stuff that you would see if you

were to code a rag agent yourself um but

you have all the power and customization

still within n with absolutely no code

and so I'm not really losing out on much

here doing it in a workflow with no code

which is the best part of it um and so

with all this together I can now test

this out a little bit so let me show one

quick thing here I'm going to go to a

couple of files that I have here for rag

I've got two meeting notes Here one from

822 and one from 823 and these this is

just fake data that I made up just to

have something here for rag um and so

what I'm going to do is I'm going to

tell my AI agent to add these to its

knowledge base and then I'm going to ask

a question that could only be answered

when it has this in its knowledge base

so I'm going to go back to my rag agent

here I'm going to save it and then I'll

go to the chat window in the bottom

middle and first I'm going to ask it a

question that it shouldn't know at this

point because it doesn't have these

meeting notes in its knowledge base I'm

going to say what are the action items

from the meeting on

8:22 and it's going to take a little bit

to get a response try to find it in the

vector database and sure enough I don't

have access to that specific um folder

or that specific file right here and so

I'll say okay cool uh find the

822 meeting notes in the drive and add

them to your knowledge base I don't have

to get really specific in my quest like

this but I'm just doing it right now to

absolutely make sure that it works for a

good demo here and there we go and that

was like super quick too which is also a

really huge plus with na it is as fast

so I have successfully added the meeting

notes to my knowledge base so now I'm

going to ask the exact same question

here and this time it's going to have an

answer and it's going to be based on

this file that we have here so the

action items from the meeting on 8:22 R

plan a budget make a bunch of money

everything that we have exactly right

here in our Google drive file so I

proved that it didn't have this

information before I showed in real time

how I was able to add it and then get an

answer from this document and the reason

you might want to do it this way the

other way you could do it is you could

just feed in every single file in your

Google drive into the vector database

automatically like every morning or

every hour something like that and then

that would be there automatically in the

knowledge base um but that when you do

it that way sometimes your vector

database can get bloated by a ton of

files that you don't necessarily want to

have available for Rag and so this way I

have control over exactly what I give to

my agent to have knowledge of later on

when I want to reference something again

in a conversation like maybe to remember

something like the action items here so

that's why I have it set it up this way

um but yeah there are a lot of ways that

you could do this the best part about n

is however you want to do it you can do

it very easily in these workflows like

it was it just took minutes to create

this workflow to find a file in Google

Drive download it and put it in the

vector database but you could set up

something similar that you'd run on a

schedule basis to uh maybe pull files

from a specific folder and constantly

hydrate your vector database with those

files there's a ton of things you could

do U so it's very very powerful stuff

the one thing that I wanted to mention

is that I have a second workflow

involved here and that is simply a

workflow to call this web hook so

there's kind of a little issue with naan

that's unfortunate but I found a really

solid work around here when you have a

tool that references a workflow ID you

can't reference the workflow ID of the

workflow that has the agent so I can't

just take what I have right here is my

workflow ID and dump that in here

because it gives some weird errors

saying that there's like not credentials

or something like that so you have to

reference a separate workflow

so I just have this workflow that then

hooks right back into um this web hook

within my original workflow um and the

reason you need to do that is because in

my in-memory Vector store here my memory

key it says here that it's prefixed by

the workflow ID to avoid Collision so if

I am inserting into a vector database in

a different workflow it's going to have

a different memory key because it's

prefixed by a separate workflow ID so I

have to have my retrieval be the exact

same workflow as

I have the insertions the data

insertions into the vector database

otherwise they'll be prefixed by

different workflow IDs and so that data

won't actually be available for the

agent so it's like a really really

technical thing that I just had to solve

but I have it solved for you now and so

that's why there'll be two workflows in

the GitHub repository um but yeah it's

just like a tiny little thing that you

had to do unfortunately um but however

if you were to use um a production

Vector database like super base then you

would't have to worry about this because

when you set up a a supera base Vector

store here as the tool for the regag AI

agent you get to define the memory key

in a more Dynamic way where it's not

going to be prefixed by the workflow ID

that's something that's just done for

the inmemory vector store so you

wouldn't have this issue and you could

actually have this whole tool to add a

Google drive file to a vector database

as that separate workflow instead of

having this weird workaround where you

need to invoke this separate workflow

that just hooks then right back into

this part of uh this whole workflow

setup that I have here so I hope that

makes s it's all Built For You at this

point so now you can just take these

workflows that I have steal them for

yourselves and expand upon this to make

the vector database production or to add

more tools or to improve upon the rag or

the prompts whatever you want to do this

is yours to play around with no code at

all and you can even embed this in a

website so I'll show that really quick

as well just to give you a lot here so I

can take um the embed so I go to more

info right here uh when I open my chat

window and I know that this is code we

didn't have have to write any code

though and you don't have to know code

to be able to embed this you can just

take this right here copy it you and you

can go over to your website I'm just

going to go to an online HTML editor

here you can paste it in here and then

when I click run I have this chat widget

in the bottom right like you see on a

lot of websites where I can click into

this and there we go we've got our chat

bot now and I can say something like Hi

how are you and then after it gives me

an answer super super fast um I can say

what are the action items from 820 too

and so that's going to again reference

the document with rag giv me the answers

because I've already inserted that into

the vector database super cool um so

yeah that's pretty much everything for

this

workflow um I'm definitely going to be

making more videos on rag with NN in the

future because this is super powerful

stuff and one thing that I'm probably

going to do is also focus on super base

and getting things deployed in

production so if you're interested in

that let me know I would really

appreciate knowing just so I can know

that that's something that you want to

see and then uh I'll probably make a

video on it so yeah more on rag to come

I hope that you found this useful and

that you're able to build really cool

things with Rag and N using this as your

foundation if you found this helpful I

would really really appreciate a like

and a subscribe and with that I will see

you in the next video