Building RAG Pipelines: Connect Your Knowledge Base to Any LLM

Diagram showing RAG pipeline architecture with vector database and LLM

You have probably experienced it: you ask ChatGPT about your company policy and it confidently invents an answer that sounds plausible but is completely wrong. This is the hallucination problem, and it is the single biggest barrier to using LLMs in business workflows. RAG pipelines solve this by grounding every LLM response in real, retrieved documents from your own knowledge base.

How RAG Works

  • Ingestion — your documents (PDFs, web pages, Notion, Confluence) are split into chunks and converted to vector embeddings
  • Storage — embeddings are stored in a vector database like Pinecone, Weaviate, or Qdrant
  • Retrieval — when a user asks a question, the query is embedded and the most similar document chunks are retrieved
  • Generation — the retrieved chunks are injected into the LLM prompt as context, and the model generates a grounded response

Choosing Your Vector Database

For small-scale pipelines (under 100,000 documents), Supabase pgvector or ChromaDB are excellent free options. For production at scale, Pinecone offers managed infrastructure with fast query times. Weaviate and Qdrant provide hybrid search that combines vector similarity with keyword matching for better precision. Choose based on your scale, budget, and whether you need self-hosting.

Vector database visualization showing document embeddings

Building a RAG Pipeline with n8n

n8n makes RAG accessible without writing application code. Use the Document Loader node to ingest files, the Embeddings node to generate vectors, and the Vector Store node to persist them. When a query comes in through a webhook or chat trigger, the Vector Store Retriever node fetches relevant chunks, and the AI Agent node generates the final answer with source citations.

The beauty of RAG is that it separates your knowledge from your model. You can upgrade from GPT-4 to GPT-5, or switch to a local LLM, without re-processing your entire knowledge base. Your vector store persists independently, making your pipeline future-proof and model-agnostic.