RAG Pipeline & AI Chatbot

Built an end-to-end RAG Pipeline & AI Chatbot in n8n designed to transform business documents and knowledge sources into an intelligent conversational system capable of retrieving relevant information and generating context-aware answers.

The workflow combines knowledge ingestion and conversational retrieval in one automated architecture. Documents are collected from Google Drive, downloaded, processed into manageable chunks, converted into vector embeddings, and stored in a Pinecone vector database, creating a searchable knowledge base for AI-powered conversations.

When a user submits a question, the chatbot analyzes the request and retrieves semantically relevant information from the vector database instead of relying only on the language model's existing knowledge. The retrieved context is supplied to the AI model to generate a more relevant and grounded response based on the stored documents.

The system integrates document automation, OpenAI embeddings, Pinecone vector search, conversational AI, memory, and RAG orchestration through n8n. It can be adapted for company knowledge bases, documentation assistants, customer support, internal search, employee help desks, research repositories, and other knowledge-intensive applications.

The result is a RAG system that turns static documents into a practical AI knowledge system.