Artificial intelligence is transforming the way we interact with technology, and AI agents are one of the most powerful advancements in this area. However, to make these agents truly effective, it’s essential to train them with data specific to your business.
In this article, we will explore how to create an AI agent using the RAG technique (Retrieval-Augmented Generation) to train models with custom information. You will learn three practical ways to implement this in your own project.
Ready? Happy reading!
What is an AI agent and how does it work with RAG?
Before we get into the practical part, it is important to understand the concept of an AI agent and how it can be improved using RAG.
Basically, an AI agent is a system that can interpret commands, process information, and generate responses autonomously. To do this, it needs three fundamental elements:
- AI model: the agent is based on models such as GPT, Llama or Claude, responsible for interpreting and generating text based on learned patterns;
- Base prompt: these are the instructions that define how the agent should behave and structure its responses;
- memory: essential for AI to remember previous interactions. Some agents have both short-term and long-term memory, allowing the conversation to continue.
In addition to these features, an AI agent can be even more efficient when using the RAG (Retrieval-Augmented Generation) technique, as we mentioned earlier. This means that, instead of relying exclusively on the model's prior knowledge, it can query external databases, such as documents, PDFs, Notion pages, or spreadsheets.
In this way, an agent trained with RAG becomes an expert in specific content, ensuring more precise and contextualized responses.
Method 1: Creating an agent with Dify
Now that you understand the basics, let's get to the practical part: how to create an AI agent trained with your own data!
One of the easiest and most effective ways to create a RAG-trained agent is by using Difyi. This tool allows you to integrate knowledge bases into your assistant quickly and intuitively.
To train your agent at Dify, follow the step by step below:
- access the “Knowledge Base” tab within the Dify platform;
- upload your documents, such as PDFs, HTML files, spreadsheets or web pages;
- Dify processes the files and transforms them into numeric vectors, converting the textual content into a format that AI can interpret efficiently.
This process is known as embedding, in which the tool structures the data on a vector basis, allowing the AI to search and retrieve the most relevant information whenever a question is asked.
Additionally, Dify makes it easy to create virtual databases by organizing knowledge into chunks of information. This way, when a user asks the agent a question, the agent quickly identifies which chunk of text best fits the desired answer.
With Difyi, you can create specialized agents for different purposes, such as:
- customer support assistants, who access FAQs and technical manuals;
- customer service chatbots, which answer questions about products and services;
- sales agents, which use strategic information to personalize approaches.
The best part? Dify automates this entire process behind the scenes, making implementation simple and practical.
Method 2: Creating an agent with OpenAI Assistants and RAG
Another efficient way to train an AI agent with RAG is to use OpenAI Assistants. This solution allows you to create custom assistants, define specific behaviors, and incorporate documents so that the AI can query and respond accurately.
Unlike Dify, which automates much of the process, OpenAI offers greater control over the agent’s settings. To create your assistant using this tool, follow the steps below:
- access the OpenAI platform and go to the “Assistants” tab;
- create a new wizard, defining a name, description and specific instructions;
- choose an AI model, such as GPT-4 Turbo, to ensure more complete and contextual answers;
- Upload files that he will use as reference, such as technical manuals, internal documents, or knowledge bases.
When documents are added to the platform, OpenAI transforms this content into a vector database. This way, the agent can consult the information whenever necessary, without relying solely on the model's pre-trained knowledge.
This allows it to provide more personalized and up-to-date responses without requiring a complete AI re-processing. Additionally, OpenAI manages all the infrastructure needed to store and retrieve this information, making it easy to implement for those who don’t want to set up their own database.
One of the main advantages of this approach is its ease of implementation, as OpenAI takes care of the technical part, making the process simple and intuitive. In addition, the model guarantees high accuracy, combining the power of GPT-4 Turbo with specific information about your business, making the assistant much more effective.
If your goal is to create a specialized AI agent without having to set up an advanced technical environment, OpenAI Assistants can be a great choice.
Method 3: Creating an agent with N8N and Supabase
The third way to create an AI agent trained with RAG is by using the integration between N8N and Supabase. This approach allows greater control over the data and optimizes the search for relevant information within the vector database.
While tools like Dify and OpenAI Assistants simplify the process, using N8N in conjunction with Supabase offers more versatility and reduces operational costs by allowing the framework to be fully configured and managed within your own environment.
To create an AI agent trained with this combination, follow the steps below:
- configure the vector database in supabase to store the reference documents;
- upload the files that the agent will use as a knowledge base, such as manuals, FAQs or technical ebooks;
- integrate Supabase with N8N to enable AI to query data and provide contextualized answers;
- develop automated flows in N8N to structure agent interactions with users;
- optimize agent responses by ensuring that they can access the most relevant blocks of information within the database.
But why use N8N and Supabase? with RAG?
Unlike other solutions, this approach allows for an advanced level of customization and control over the vector database. When a user asks the agent a question, it fetches the most relevant vector of data from Supabase, ensuring that the answer is based on the stored documents.
Additionally, N8N allows you to connect the AI agent to different applications, such as Whatsapp, Slack and Google Drive, expanding the possibilities of use and automation. This flexibility makes the model ideal for companies that need a highly specialized agent.
Among the main advantages of this implementation, the following stand out:
- greater control over datas, allowing adjustments and customizations as needed;
- cost reduction, as Supabase replaces paid solutions for vector storage;
- advanced automation, with intelligent flows and integrations in N8N;
- scalability, allowing the knowledge base to grow according to business needs;
- greater efficiency, as the agent accesses information directly from the vector database, without relying solely on the AI model.
If you are looking for flexibility and cost reduction, N8N + Supabase is a powerful solution for training specialized AI agents with RAG.
Conclusion
Training an AI agent with your own data is an essential strategy for obtaining more accurate responses aligned with the context of your business. With RAG, you can transform internal files and documents into structured knowledge for AI, optimizing processes and improving the user experience.
If you want to dive deeper into the topic and learn how to create your own AI agents, check out the complete N8N course at NoCode Startup and take your automation to the next level!
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