Automate Your Knowledge Base with Google Drive and Supabase Vector Stores
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Effortlessly ingest documents from Google Drive, transform them into searchable vector embeddings, and enable AI-powered Q&A directly from your Supabase database. This workflow automates the creation and querying of a smart knowledge base.
About This Workflow
This n8n workflow empowers you to create a dynamic and intelligent knowledge base by seamlessly integrating Google Drive, Supabase, and the power of Large Language Models (LLMs). Documents stored in Google Drive, such as EPUB files, are automatically fetched and processed. The content is then prepared by splitting it into manageable chunks and generating vector embeddings using OpenAI. These embeddings, along with the original content and metadata, are stored in a Supabase database, ensuring efficient semantic search. The workflow also includes essential setup instructions for your Supabase instance, including enabling the pgvector extension, creating necessary tables, and configuring security policies. Finally, it sets up an AI-powered chatbot that can retrieve information from your Supabase vector store and answer questions based on the ingested documents, demonstrating a complete RAG (Retrieval Augmented Generation) pipeline.
Key Features
- Automated Document Ingestion: Connect directly to Google Drive to download and process your documents.
- Flexible Data Loading: Supports various document loaders, including EPUB, for versatile content handling.
- Intelligent Text Splitting: Organizes content into optimal chunks for embedding and retrieval.
- OpenAI Embeddings Integration: Leverages powerful OpenAI models for generating accurate vector representations.
- Supabase Vector Store: Utilizes Supabase with the
pgvectorextension for robust and scalable vector database capabilities. - AI-Powered Q&A: Enables a chatbot interface to query your knowledge base and receive contextually relevant answers.
How To Use
- Configure Google Drive Node: Connect your Google Drive account and specify the
fileIdof the document you want to process. - Set up Supabase Database:
- Enable the
pgvectorextension in your Supabase project. - Create a table (e.g., 'Kadampa') with
embedding(VECTOR(1536)),metadata(JSONB), andcontent(TEXT) columns. - Ensure correct row-level security policies are in place.
- Create the
match_documentsfunction in Supabase for vector retrieval (SQL provided in the workflow).
- Enable the
- Configure Data Loader: Use the
Default Data Loadernode, specifyingepubLoaderfor EPUB files and settingdataTypetobinary. - Chunk and Embed Documents:
- Use the
Recursive Character Text Splitterto break down content. - Utilize the
Embeddings OpenAI Insertionnode with your chosen OpenAI model (e.g., 'text-embedding-3-small') to generate embeddings for insertion.
- Use the
- Upsert to Supabase: Configure the
Vector Store Retrievernode to connect to your Supabase table (e.g., 'Kadampa') and upsert the generated embeddings, content, and metadata. - Set up Chatbot Trigger: Use the
When chat message receivednode to act as your chatbot's entry point. - Configure Retrieval:
- Use the
Embeddings OpenAI Retrievalnode to generate embeddings for incoming queries. - Configure the
Vector Store Supabasenode with your Supabase table name and thematch_documentsquery name. - Set up the
retrieverVectorStorenode to define retrieval parameters liketopK.
- Use the
- Generate Answers: Connect the
OpenAI Chat ModelandChain Retrieval Qanodes to process the retrieved information and formulate an answer to the user's query. - Customize Output: Use the
Customize Responsenode to format the AI's answer for the user.
Apps Used
Workflow JSON
{
"id": "993edb3c-1406-4f93-abf5-5c05b9bf3a1f",
"name": "Automate Your Knowledge Base with Google Drive and Supabase Vector Stores",
"nodes": 21,
"category": "DevOps",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
Get This Workflow
ID: 993edb3c-1406...
About the Author
Crypto_Watcher
Web3 Developer
Automated trading bots and blockchain monitoring workflows.
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