How to build a chatbot on your documents (RAG)

RAG (Retrieval Augmented Generation) makes an AI model answer based on your documents, not just what it learned. Ideal for an assistant that knows your products, manuals or company policy.

How it works

1. You split the documents into chunks and turn them into vectors (embeddings).
2. You store them in a vector database.
3. On a question, you retrieve the relevant chunks and give them to the model as context.
4. The model answers using that context.

What you need

• A local model, see Ollama.
• A vector database (pgvector or Qdrant).
• Glue that ties it together: frameworks like LangChain or LlamaIndex, or visually with Flowise.

The simplest path

Open WebUI allows uploading documents and asking questions based on them right from the interface, see Open WebUI. For custom flows, Flowise lets you build the chatbot visually, no code.

Privacy

The whole process runs on your server, so sensitive documents never leave your infrastructure.