Overview
This workflow implements a two-stage document retrieval chatbot that leverages OpenAI's language models and Supabase's vector search capabilities. It enables users to interact with a chat interface, retrieve relevant documents, and receive AI-generated responses based on document content.
Key Features
- Chat Trigger: Listens for incoming chat messages to initiate the workflow.
- Document Retrieval: Uses Supabase vector search to find and filter documents based on semantic similarity to the user's query.
- AI Integration: Employs OpenAI for both embedding creation and generating conversational responses.
- Automated Embedding Updates: Keeps document embeddings up-to-date for accurate search results.
- Aggregation & Filtering: Aggregates and filters files to ensure only the most relevant documents are used in responses.
Benefits
- Enhanced Productivity: Automates the process of searching and summarizing documents, saving significant time.
- Accurate Responses: Delivers contextually relevant answers by combining vector search with AI-powered language understanding.
- Seamless Integration: Connects Supabase and OpenAI within n8n for a unified, low-code automation solution.
Use Cases
- Customer support chatbots that reference internal documentation.
- Knowledge management systems for quick information retrieval.
- Automated assistants for HR, sales, or project management teams.
