Build your own no-code virtual chat assistant, fully local and open source!

Introduction

In this article, we will explore how to build your own AI chat assistant using a low-code solution called n8n, in combination with tools like MinIO for file storage and Quadrant for vector storage. This method allows you to create a chat assistant that can efficiently answer questions based on documents, effectively handling customer inquiries without them having to sift through lengthy files.

Understanding the RAG Model

At the forefront of this solution is the Retrieval-Augmented Generation (RAG) model, which enhances the capabilities of traditional chat models. RAG focuses on improving conversational AI's response accuracy by vectorizing documents, breaking them down into manageable chunks, and storing them in a special vector database. Instead of searching through entire documents, the model retrieves relevant chunks based on the statistical similarities between user queries and vectorized content.

Why Use Vectorization?

Vectorization is key for managing large documents efficiently. By transforming document text into numerical vectors, you avoid loading the entire document's context window for each user question. This approach not only saves computing resources but also speeds up response times. For a use case example, consider accessing a 25-page document on feral cats; a RAG system would pull out only the relevant parts, streamlining user queries significantly.

Benefits in Real-World Applications

Creating a chat assistant can revolutionize customer interactions, whether it's addressing inquiries about terms and conditions, restaurant allergen information, or any business-related questions. Instead of clients wading through extensive documents, they can simply ask their questions and receive accurate, context-driven responses.

Setting Up Your Chat Assistant

To set up your chat assistant, you will need:

n8n to manage workflows.
MinIO as an S3 compatible storage solution for uploading documents.
Quadrant/Currant as your vector database.

These components can be run locally using Docker, which you can configure easily with the appropriate docker-compose file.

Uploading Documents

For testing and local operations, you can build a MinIO instance to handle file uploads. By creating a bucket, you can manage the files used for your chat assistant. Instead of using Google Drive or Dropbox to upload files, which can involve complicated authentication settings, MinIO simplifies the process with straightforward S3 compatibility.

Processing Files and Storing Vectors

Once files are uploaded, they need to be extracted and processed using n8n. Depending on the file type (PDF, XLSX, DOCX), the system will extract the text and convert it into vectors. You can further refine this step by implementing various configurations to ensure that the extracted chunks are manageable and facilitate accurate retrieval.

Building the Chat Workflow

The final stage involves constructing the chat interface. Using n8n's built-in chat capabilities, you can set up a conversation flow where user inputs are routed to the vector database for querying. The assistant will then generate contextually relevant responses based on the retrieved information.

Testing Your Chat Assistant

It is essential to continually test and refine your assistant. You can assess its effectiveness by asking specific questions to validate if it can accurately retrieve the right chunks of information from the documents you've uploaded. Iteration will help optimize responses and improve the overall user experience.

Conclusion

By leveraging n8n, MinIO, and Quadrant, you can create an efficient, no-code virtual chat assistant that runs fully on local infrastructure. This core setup allows you to customize your implementation easily while providing significant opportunities for enhancing business operations and improving customer engagement.

Keywords

no-code
virtual chat assistant
open-source
n8n
vectorization
RAG model
MinIO
Quadrant
document retrieval

FAQ

1. What do I need to build my own virtual chat assistant?
You need n8n for workflow management, MinIO for file storage, and Quadrant for vector storage.

2. Why is vectorization important?
It allows for efficient document processing by breaking down texts into numerical vectors, enabling the RAG model to retrieve relevant information without loading entire documents.

3. Can I use other types of files with this setup?
Yes, the system supports various file types, including PDFs, XLSX, DOCX, and more.

4. Is this solution scalable?
Yes, while this setup is intended for local testing, it can be scaled up to accommodate more extensive deployments and user engagements.

5. Do I need any coding skills to implement this?
No, the entire process can be conducted using low-code or no-code tools, making it accessible for individuals with minimal technical skills.