Build Your Own AI Chatbot with Your Documents
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Empower your applications by building custom AI chatbots that can answer questions based on your own documents. This workflow leverages LangChain and Supabase to enable Retrieval Augmented Generation (RAG) for intelligent document interaction.
About This Workflow
This n8n workflow provides a robust framework for creating AI-powered chatbots capable of understanding and responding to queries based on specific documents. By integrating with Google Drive for data ingestion, LangChain for natural language processing, and Supabase for vector storage, it enables seamless Retrieval Augmented Generation (RAG). The workflow meticulously prepares your data by splitting it into manageable chunks, embedding it for semantic search, and storing it in a Supabase vector database. When a chat message is received, it retrieves relevant information using a custom Supabase function and leverages an OpenAI chat model to generate contextualized answers. This is an ideal solution for building knowledge-base assistants, customer support bots, or any application requiring AI-driven insights from your proprietary content.
Key Features
- Document Ingestion: Easily load data from sources like Google Drive.
- Intelligent Text Splitting: Breaks down large documents into manageable pieces for efficient processing.
- Vector Database Integration: Utilizes Supabase with
pgvectorfor powerful semantic search and storage. - Custom Retrieval Logic: Implements a custom Supabase function for precise document matching.
- AI-Powered Question Answering: Employs LangChain and OpenAI to generate human-like responses based on your data.
How To Use
- Connect to Google Drive: Configure the 'Google Drive' node to access your document (e.g., an EPUB file) by providing its URL.
- Load and Split Data: The 'Default Data Loader' node will ingest the binary data, and 'Recursive Character Text Splitter' will segment it into smaller pieces.
- Configure Supabase: Ensure your Supabase database has the
pgvectorextension enabled, a table withembedding,metadata, andcontentcolumns, and thematch_documentscustom function set up (SQL provided in the sticky notes). - Embed and Insert Data: Use the 'Embeddings OpenAI Insertion' node to create embeddings for your text chunks and then use a Supabase vector store node (not explicitly shown but implied by
Vector Store RetrieverandRetrieve by Query) to upsert this data into your Supabase table. - Set Up Chat Trigger: Configure the 'When chat message received' node to act as your chatbot's entry point, defining initial messages and response mode.
- Configure Retrieval: The 'Embeddings OpenAI Retrieval' node creates embeddings for incoming queries, and the 'Vector Store Supabase' node (or similar) uses your custom
match_documentsfunction to find relevant information in Supabase. - AI Question Answering: The 'Question and Answer Chain' node, combined with 'OpenAI Chat Model', processes the retrieved information and generates a natural language answer.
- Customize Output: Use the 'Customize Response' node to format the final output before it's sent back to the user.
Apps Used
Workflow JSON
{
"id": "aa6ac484-25c2-45ba-95f7-7b93a8a20fe7",
"name": "Build Your Own AI Chatbot with Your Documents",
"nodes": 25,
"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: aa6ac484-25c2...
About the Author
Crypto_Watcher
Web3 Developer
Automated trading bots and blockchain monitoring workflows.
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