How to Train ChatGPT With Your Data: Step-by-step guide

In the dynamic world of online interaction, a chatbot is your brand's virtual ambassador. With our step-by-step guide, you'll learn what it takes to train ChatGPT like AI Assistant on your own data.

Step 1: Prepare Data For Training AI Assistant

Data Collection

• Gather all relevant documents, articles, databases, and text data.

• Ensure you have the necessary rights to use this data for training.

• Consider using web scraping tools like Beautiful Soup or Scrapy for collecting data from websites.

Data Cleaning

• Remove irrelevant information, formatting issues, or sensitive data.

• Make sure that the data you use for training purposes contains up to date information and doesn't contradict each other.

• Format the data in a way that's easy for GPT models to process.

  
  

Step 2: Chunk the Data

Splitting data into manageable chunks is crucial for efficient processing and retrieval. You can use libraries like LangChain for this purpose:

from langchain.docstore.document import Document
from langchain.text_splitter import RecursiveCharacterTextSplitter

docs = ['Text from Doc 1', 'Text from Doc 2', 'Text from Doc 3', ...]
for doc in docs:
      docs.append(Document(page_content=doc, metadata={'doc_meta': {'type': 'raw text'}}))

text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=650,
    chunk_overlap=150,
    length_function=len,
    is_separator_regex=False,
)
documents = text_splitter.split_documents(docs)

Step 3: Convert to Embeddings & Store Chunks in Vector Database

Here is how you can store embedded chunks in LanceDb. For more vector databases, check out Milvus, Faiss, or Qdrant.

import lancedb
from langchain.vectorstores import LanceDB
from langchain.embeddings.openai import OpenAIEmbeddings
  
db = lancedb.connect(f'lancedb_database')
db.drop_table(f'lancedb_table', ignore_missing=True)

embeddings = OpenAIEmbeddings() 
table = db.create_table(
  f'lancedb_table', 
  data=[
    {
      'id': '1',
      'vector': embeddings.embed_query('Hello World'), 
      'text': 'Hello World', 
      'doc_meta': {'type': ''} # it's important that the schema is consistent 
    } 
  ], 
  mode='overwrite') 

LanceDB.from_documents(documents, embeddings, connection=table)

Step 4: Set up RAG Architecture

Given user query, search for relevant chunks of information from your vector database:

user_query = "What can you do?"

# Load LancaDB database
db = lancedb.connect(f'lancedb_database') 
table = db.open_table(f'lancedb_table') 

# Initiate OpenAI Embeddings
embeddings = OpenAIEmbeddings(model="text-embedding-ada-002") 

# Convert user query to embeddings and search most relevant chunks of information in your LanceDB database:
df = table.search(embeddings.embed_query(user_query)).metric("cosine").limit(5).to_df()
results = [row for i, row in df.iterrows() if row._distance < 0.5]

Now, paste those results into your prompt:

Here's an example of a prompt that works well for RAG:

# OBJECTIVES:

You are a knowledgeable AI assistant with access to a database of information.
Your goal is answer user questions in maximum 3 sentences, based on the DATA provided below, surrounded by ``. 
If the information in the context is insufficient to answer the question accurately, respond with "I'm sorry, but I don't have enough information to answer that question confidently."


===
  
# DATA:
{context}

Step 5: Integrate Your AI Assistant

Integrating your chatbot with various platforms can expand its reach and utility. Here are some options:

1. Website Widget

You can create a simple chatbot widget for your website using HTML, CSS, and JavaScript.

2. WhatsApp Integration

To integrate your AI Assistant with WhatsApp, you can use the WhatsApp Business API. You'll need to set up a WhatsApp Business account and use a service like Twilio to facilitate the integration.

3. Telegram Bot

For Telegram integration, you can use the python-telegram-bot library.

Step 6: Set Up Analytics and Log Conversations

Logging conversations and setting up analytics is crucial for improving your chatbot over time. Set up a database to log all the conversations that are happening between your users and chatbot, for further analysis.

Simplifying the Process with Chatbotly

If the technical complexity seems daunting, there's good news. Chatbotly has already taken care of everything, offering a super simple solution. With Chatbotly's no-code tool, you can create a custom GPT model on your data without writing a single line of code. Simply upload your documents, and Chatbotly will handle the rest.

Step 1: Upload Your Documents

Begin by uploading relevant documents—FAQs, product information, or any data that will empower your chatbot. This sets the foundation for a chatbot that's not just responsive but also well-informed.

Step 2: Define Personality of the Chatbot

Give your chatbot a personality that aligns with your brand. Is it friendly, professional, or perhaps a bit quirky? Defining the personality adds a human touch to the interactions and enhances user engagement.

Step 3: Update Branding

Ensure your chatbot seamlessly integrates with your brand. Update branding elements such as colors, logo, and messaging style. Consistency is key to creating a cohesive and recognizable brand identity.

Step 4: Train and Test

Now, it's time to train your chatbot. Utilize user-friendly platforms like Chatbotly for a hassle-free training process. Test its responses to various queries to ensure accuracy and coherence. Shakurova's blog on chatbot development best practices emphasizes the importance of rigorous testing to refine your chatbot's capabilities.

Step 5: Analyse Conversation Logs

Once your chatbot is live, dive into the conversation logs. Analyze user interactions to understand their needs, pain points, and common queries. This valuable data allows you to fine-tune your chatbot for optimal performance.

Step 6: Improve the Performance

Continuous improvement is the key to a successful chatbot. Based on the insights from conversation logs, make necessary adjustments to enhance the chatbot's performance. This iterative process ensures your chatbot evolves with your audience's expectations.

Final thoughts:

In just five minutes, you've transformed your website's interaction with a smart and engaging chatbot. By following these simple steps and incorporating best practices, you've not only saved time but also laid the foundation for a powerful virtual assistant. Elevate your user experience—try it out now and watch your website come to life with a GPT-powered chatbot! Explore more about Chatbotly at chatbotly.co.