PDF RAG System: OpenAI, Pinecone & Cohere Reranking

This n8n workflow implements a full Retrieval-Augmented Generation (RAG) system tailored for PDF documents. It operates in two phases: data ingestion and conversational querying. In the ingestion phase, a PDF is uploaded via Form Trigger, processed by Default Data Loader, split into chunks with Recursive Character Text Splitter, embedded using OpenAI, and upserted into a Pinecone vector index for semantic search.

The conversational phase uses a Chat Trigger to receive user queries. An AI Agent leverages a VectorDB tool to retrieve relevant chunks from Pinecone, which are then refined by Cohere Reranker for better relevance. The agent generates precise, context-aware responses powered by OpenAI, creating a powerful document-based chatbot.

Benefits include reduced hallucination in AI responses, easy scalability with Pinecone, and enhanced accuracy via reranking. Ideal use cases: knowledge bases for enterprises, legal document Q&A, research assistants, customer support from manuals, and academic paper analysis. Note: Requires self-hosted n8n for community nodes and incurs costs from paid APIs.