RAG Starter Workflow: Form-Triggered Vector Store & OpenAI Integration

Overview

This workflow enables automated document ingestion, vector storage, and AI-driven retrieval using OpenAI and LangChain nodes in n8n. Users can upload files via a form, which are then processed and embedded for semantic search and chat-based querying.

Key Features

• Form-Based Upload: Users submit documents through a web form trigger.
• Automated Embedding: Documents are loaded and converted into vector embeddings using OpenAI.
• In-Memory Vector Store: Embeddings are stored for fast, context-aware retrieval.
• AI Chat Integration: Users interact with the stored knowledge via a chat interface powered by OpenAI's language model.
• Agent Orchestration: An AI agent coordinates data retrieval and response generation.

Benefits

• Streamlined Knowledge Management: Automates the process of turning documents into searchable knowledge bases.
• Enhanced User Experience: Provides instant, AI-powered answers to user queries.
• Time Savings: Reduces manual data processing and response times.
• Scalable Integration: Easily adapts to various business needs and document types.

Use Cases

• Internal knowledge bases for support teams
• Automated FAQ chatbots
• Document search and retrieval for HR or legal departments

Integrations

• OpenAI for embeddings and chat
• LangChain for vector storage and agent orchestration
• n8n form and chat triggers for user interaction