Automated Knowledge Base & AI Chatbot Solution
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This n8n workflow automates the creation of an AI-powered knowledge base and chatbot. It ingests documents, generates embeddings, stores them in a database, and enables real-time question answering through a chat interface.
About This Workflow
This sophisticated n8n workflow empowers businesses to build intelligent and responsive AI assistants by transforming static documents into dynamic, searchable knowledge bases. The process begins by fetching content from a Google Document, which is then intelligently segmented into manageable chunks. Each chunk is converted into a numerical representation (embedding) using advanced AI models via Together AI and securely stored in a Supabase database. This forms the foundation of your custom knowledge base. When a user interacts with the chatbot, their query is also embedded and used to retrieve the most relevant information from the knowledge base. This context is then fed to a powerful Large Language Model (LLM) via OpenRouter, enabling it to provide accurate and context-aware answers.
Key Features
- Automated Document Ingestion: Seamlessly import and process content from Google Docs.
- Intelligent Content Chunking: Breaks down large documents into digestible segments for efficient processing.
- Powerful Embeddings: Utilizes state-of-the-art AI models for accurate text representation.
- Scalable Database Storage: Leverages Supabase for robust and secure storage of embeddings.
- Real-time AI Chat: Enables interactive question answering with context-aware responses.
- Flexible LLM Integration: Supports various LLM providers for customized AI capabilities.
How To Use
- Initial Data Ingestion (Run Once):
- Ensure your Google Doc URL is correctly configured in the 'Content for the Training' node.
- Execute the workflow to fetch, chunk, embed, and store your initial document content in Supabase.
- This step creates the foundational knowledge base.
- Chat Interface Configuration:
- The 'When chat message received' node acts as your chat trigger. Configure any desired initial messages.
- The 'Embend User Message' node will process incoming user queries.
- Embedding Search and Retrieval:
- The 'Search Embeddings' node queries your Supabase database to find the most relevant content chunks based on the user's embedded message.
- The 'Aggregate' node prepares the retrieved chunks for the LLM.
- AI Response Generation:
- The 'Basic LLM Chain' node constructs the prompt for the LLM, including the retrieved context and the user's question.
- The 'OpenRouter Chat Model' node processes the prompt and generates a natural language response.
- The chatbot will then present this response to the user.
Apps Used
Workflow JSON
{
"id": "7212285a-a56b-45f7-b2fa-1434a6db7b2c",
"name": "Automated Knowledge Base & AI Chatbot Solution",
"nodes": 11,
"category": "Operations",
"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: 7212285a-a56b...
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