Overview
This workflow automates a Retrieval-Augmented Generation (RAG) chatbot using n8n and LangChain nodes. It processes documents, splits text, stores embeddings in an in-memory vector store, and enables interactive Q&A via chat triggers.
Key Features
- Document Ingestion: Loads and processes documents using the Default Data Loader.
- Text Splitting: Utilizes Recursive Character Text Splitter for optimal chunking.
- Vector Embeddings: Generates embeddings (e.g., via Cohere) and stores them in an in-memory vector store for fast retrieval.
- Retrieval QA Chain: Employs a retrieval-based question-answering chain to provide accurate responses based on ingested data.
- Interactive Triggers: Supports both manual and chat-based triggers for flexible user interaction.
- File Operations: Reads and writes files from disk to support persistent data handling.
Benefits
- Enhanced Knowledge Access: Quickly retrieves relevant information from large document sets.
- Automation: Reduces manual effort in document processing and Q&A handling.
- Scalability: In-memory vector storage ensures rapid response times for chat interactions.
- Seamless Integration: Leverages LangChain's advanced NLP capabilities within n8n's automation framework.
Use Cases
- Internal knowledge base chatbots
- Automated customer support
- Document search and retrieval
- FAQ automation for websites or internal portals
