Building a RAG Chatbot with n8n and Pinecone
Step-by-step tutorial on creating a retrieval-augmented generation chatbot using n8n as the orchestrator.
What Is RAG?
Retrieval-Augmented Generation (RAG) is a pattern where you combine a large language model with a knowledge base. Instead of relying solely on the model's training data, you retrieve relevant documents from a vector database and include them in the prompt. This makes responses accurate, up-to-date, and grounded in your own data.
Setting Up the Vector Store
First, I ingested the company's knowledge base — FAQs, pricing documents, policies, and fleet data — into Pinecone. Each document was split into chunks, embedded using OpenAI's embedding model, and stored with metadata. This creates a searchable index that the chatbot can query in real time.
The n8n Workflow
The workflow starts with a WhatsApp webhook trigger. When a customer sends a message, n8n extracts the text, generates an embedding, queries Pinecone for the most relevant chunks, and constructs a prompt that includes the retrieved context. OpenAI then generates a natural, accurate response that gets sent back via the WhatsApp API.
Handling Edge Cases
Not every question can be answered by the bot. I added confidence scoring — if the retrieval similarity is below a threshold, the bot gracefully escalates to a human agent. It also supports multi-language responses (English, French, Arabic) by detecting the input language and responding accordingly.
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