Build Your Own AI Chatbot for 'How To Transform Your Life'
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Effortlessly create a custom AI chatbot powered by n8n and Langchain, capable of answering questions from Venerable Geshe Kelsang Gyatso's book, 'How To Transform Your Life'. This solution leverages Google Drive for content, Supabase for vector storage, and OpenAI for embeddings and generation.
About This Workflow
This workflow empowers you to build an intelligent AI assistant trained on the profound teachings of 'How To Transform Your Life'. By integrating Google Drive for content ingestion, Langchain for natural language processing, and Supabase for efficient vector database management, you can provide users with instant, accurate answers to their questions about the book.
The process begins with downloading your book content from Google Drive and preparing it using Langchain's data loaders. Crucially, the data is then embedded using OpenAI's powerful models and stored in a Supabase vector database, ensuring efficient retrieval. A pre-configured Supabase setup with the pgvector extension and a custom match_documents function is essential for optimal performance. Finally, an n8n chat trigger initiates the AI conversation, utilizing a retrieval-augmented generation approach with an OpenAI language model to deliver insightful responses.
Key Features
- AI-Powered Q&A: Develop an intelligent chatbot that can answer questions directly from the content of 'How To Transform Your Life'.
- Seamless Content Integration: Easily ingest your book content from Google Drive for AI processing.
- Robust Vector Database: Utilize Supabase with
pgvectorfor efficient storage and retrieval of text embeddings. - OpenAI Integration: Leverage OpenAI's state-of-the-art embedding and language models for advanced AI capabilities.
- No-Code Workflow Automation: Build and manage your AI chatbot with n8n's intuitive visual interface.
How To Use
- Prepare your Supabase Database:
- Ensure the
pgvectorextension is enabled. - Create or modify a table (e.g., 'Kadampa') with
embedding(VECTOR(1536)),metadata(JSONB), andcontent(TEXT) columns. - Implement the
public.match_documentsfunction in Supabase, ensuring the vector dimension matches your embedding model. - Configure appropriate Row Level Security (RLS) policies.
- Ensure the
- Configure Google Drive Node: Replace the placeholder
fileIdwith the actual Google Drive link of your book content (e.g., an EPUB file). - Configure Data Loader and Text Splitter: Ensure the
epubLoaderis selected and configureRecursive Character Text Splitteroptions as needed. - Configure OpenAI Embeddings Nodes:
- For insertion (
Embeddings OpenAI Insertion), specify the model (e.g.,text-embedding-3-small). - For retrieval (
Embeddings OpenAI Retrieval), use the default settings or adjust as necessary.
- For insertion (
- Configure Vector Store Nodes:
Vector Store Retriever: SettopKto control the number of relevant documents retrieved.Retrieve by Query: Specify your Supabase table name (e.g., 'Kadampa') and ensure thequeryNamematches your custom Supabase function (match_documents).
- Configure Language Model and Chat Chain:
OpenAI Chat Model: Select your desired OpenAI model.Question and Answer Chain: Connect this to your retriever and language model.
- Configure Chat Trigger and Response:
When chat message received: Set up your webhook and customize theinitialMessagesto welcome users and explain the chatbot's purpose.Customize Response: Ensure this node correctly maps the AI's output to the desired response format.
Apps Used
Workflow JSON
{
"id": "56d7aaeb-f896-4616-9f0c-a081afd37d2c",
"name": "Build Your Own AI Chatbot for 'How To Transform Your Life'",
"nodes": 28,
"category": "Marketing",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
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ID: 56d7aaeb-f896...
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