How we leveraged distilabel to create an Argilla 2.0 Chatbot

TL;DR

Discover how to build a Chatbot for a tool of your choice (Argilla 2.0 in this case) that can understand technical documentation and chat with users about it.

In this article, we'll show you how to leverage distilabel and fine-tune a domain-specific embedding model to create a conversational model that's both accurate and engaging.

This article outlines the process of creating a Chatbot for Argilla 2.0. We will:

• create a synthetic dataset from the technical documentation to fine-tune a domain-specific embedding model,
• create a vector database to store and retrieve the documentation and
• deploy the final Chatbot to a Hugging Face Space allowing users to interact with it, storing the interactions in Argilla for continuous evaluation and improvement.

Table of Contents

• Generating Synthetic Data for Fine-Tuning a domain-specific Embedding Models
• Downloading and chunking data
• Generating synthetic data for our embedding model using distilabel
• Explore the datasets in Argilla
  • An Argilla dataset with chunks of technical documentation
  • An Argilla dataset with triplets to fine tune an embedding model
  • An Argilla dataset to track the chatbot conversations
• Fine-Tune the embedding model
  • Prepare the embedding dataset
  • Load the baseline model
  • Define the loss function
  • Define the training strategy
  • Train and save the final model
• The vector database
• Connect to the database
  • Instantiate the fine-tuned model
• Create the table with the documentation chunks
  • Populate the table
  • Store the database in the Hugging Face Hub
• Creating our ChatBot
• The Gradio App
• Deploy the ChatBot app on Hugging Face Spaces
• Playing around with our ChatBot
• Next steps

Generating Synthetic Data for Fine-Tuning Custom Embedding Models

Need a quick recap on RAG? Brush up on the basics with this handy intro notebook. We'll wait for you to get up to speed!

Downloading and chunking data

Chunking data means dividing your text data into manageable chunks of approximately 256 tokens each (chunk size used in RAG later).

Click to see docs_dataset.py help message

1. load_data:

MultipleQueries definition
Distilabel Pipeline
Click to see the result Click to see Database class
Click to see Settings class
Click to see the system prompt and the bot template