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Local Chatbot with Retrieval Augmented Generation (RAG)

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Build a fully local RAG chatbot that answers questions from PDF documents.

n8nAI & LLMIntermediate👁 29K views
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Updated 2026-06-15

What is the Local Chatbot with Retrieval Augmented Generation (RAG) workflow?

This n8n workflow creates a completely local Retrieval Augmented Generation system that ingests PDF files into a Qdrant vector database and enables an AI Agent to answer questions using retrieved document chunks.

It is intended for users who want to run private, self-hosted document chatbots without relying on external APIs or cloud services.

What it automates

Internal document Q&A

Employees upload company PDFs like policies or reports to Qdrant and query the chatbot for precise answers without leaving the local environment.

Research paper assistant

Researchers load academic PDFs into the vector store and ask targeted questions to retrieve and synthesize information from the documents.

Product manual support

Support teams ingest product manuals as PDFs and use the RAG chatbot to generate accurate responses based on the uploaded content.

Apps & integrations used

AI AgentOllama Chat ModelSimple MemoryRecursive Character Text SplitterDefault Data LoaderQdrant Vector StoreEmbeddings Ollama

How to set up Local Chatbot with Retrieval Augmented Generation (RAG)

  1. 1Install n8n + Ollama + Qdrant using the Self-hosted AI starter kit
  2. 2Install Llama 3.2 and mxbai-embed-large models in Ollama
  3. 3Import the workflow template into n8n
  4. 4Run the Data Ingestion part of the workflow
  5. 5Upload PDF files to populate the Qdrant vector store
  6. 6Test the AI Agent chatbot with sample questions

Local Chatbot with Retrieval Augmented Generation (RAG): pros & cons

Pros

  • +Fully local and private with no external API calls
  • +Answers grounded in your own PDF documents via RAG
  • +Supports unlimited PDF uploads to Qdrant
  • +Uses open-source Ollama models and Qdrant store

Cons

  • –Requires self-hosted setup of n8n, Ollama and Qdrant
  • –Needs specific models pre-installed
  • –Data ingestion step must be run manually before chatting
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Frequently asked questions

It builds a local RAG chatbot that answers questions about uploaded PDF files using Qdrant for retrieval and Ollama for generation.

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