Overview
Overview This workflow implements a complete Retrieval-Augmented Generation (RAG) system for document ingestion and intelligent querying. It allows users to upload documents, convert them into vector embeddings, and query them using natural language. The system retrieves relevant document context and generates accurate AI responses while using caching to improve performance and reduce costs. This workflow is ideal for building AI knowledge bases, document assistants, and internal search systems. How It Works 1. Input & Configuration Receives requests via webhook (rag-system) Supports two actions: upload → process documents query → answer questions Defines: Chunk size & overlap TopK retrieval count Database table names Document Upload Flow Text Extraction Extracts text from uploaded PDF documents Text Chunking Splits text into overlapping chunks for better retrieval accuracy Document Structuring Converts chunks into structured documents Embedding Generation Generates vector embeddings using OpenAI Vector Storage Stores embeddings in PGVector (Postgres) Upload Logging Logs document metadata (user, filename, timestamp) Response Returns success message via webhook Query Flow Cache Check Checks if query result exists in cache (last 1 hour) Cache Routing If cached → return cached response If not → proceed to retrieval Cache Hit Flow Format Cached Response Standardizes cached output format Respond to User Returns cached answer with cached: true Cache Miss Flow Vector Retrieval Retrieves top relevant document chunks from PGVector AI Answer Generation Uses LLM with retrieved context Generates accurate, context-based answer Cache Storage Saves query + response in database for reuse Response Returns generated answer with cached: false Setup Instructions Webhook Setup Configure endpoint (rag-system) Send payload with: action: upload / query user_id document or query OpenAI Setup Add API credentials for: Embeddings Chat model Postgres + PGVector Enable PGVector extension Create tables: documents query_cache upload_log Configure Parameters Adjust: Chunk size (e.g., 1000) Overlap (e.g., 200) TopK (e.g., 5) Optional Enhancements Add authentication layer Add multi-tenant filtering (user_id) Use Cases AI document search systems Internal knowledge base assistants Customer support knowledge retrieval Legal or compliance document analysis SaaS AI chat with custom data Requirements OpenAI API key Postgres database with PGVector n8n instance (cloud or self-hosted) Key Features Full RAG architecture (upload + query) PDF document ingestion pipeline Semantic search with vector embeddings Context-aware AI responses Query caching for performance optimization Multi-user support via metadata filtering Scalable and modular design Summary A complete RAG-based AI system that enables document ingestion, semantic search, and intelligent query answering. It combines vector databases, LLMs, and caching to deliver fast, accurate, and scalable AI-powered knowledge retrieval. An n8n automation workflow template by ResilNext.
ResilNext
web
free
Others in the same category, ranked by how often they are opened.
