Build an OpenAI RAG system with document upload, semantic search and caching
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 q...
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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.
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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
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Document Upload Flow