📖 Answer document questions with Ollama and Qdrant retrieval chat bot
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Description
Quick overview
This workflow provides a local RAG chatbot in n8n that answers chat questions using context retrieved from a Qdrant collection, generating responses with a local Ollama chat model and Ollama embeddings.
How it works
- Receives a user question from the n8n chat trigger.
- Retrieves the most relevant document chunks from a Qdrant vector collection using an Ollama-powered embedding model.
- Injects the retrieved context into a retrieval QA prompt that instructs the assistant to answer only from the provided passages and to decline when information is missing.
- Uses the Ollama chat model (qwen2.5:7b) to generate a concise answer grounded in the retrieved context.
Setup
- Run the companion indexing workflow first (Index local documents for RAG using Ollama embeddings and Qdrant, https://creators.n8n.io/workflows/17764) to embed your document content into Qdrant.
- Start Ollama locally and pull the required models (qwen2.5:7b and nomic-embed-text:latest).
- Create n8n credentials for your local Ollama service and your Qdrant instance.
- Set the Qdrant collection name to match the one used during indexing (for example, “handbook”).
- If recall is too narrow for multi-fact questions, increase the retriever Top K setting to fetch more chunks.
Requirements
- Ollama installed and running locally with qwen2.5:7b and nomic-embed-text:latest pulled
- A Qdrant instance, self-hosted or cloud, with a collection already populated by the companion Index workflow
- n8n credentials set up for your local Ollama service and your Qdrant instance
Customization
- Swap qwen2.5:7b for a different Ollama chat model if you want to trade speed for stronger reasoning
- Raise the Vector Store Retriever’s Top K setting to pull in more chunks for multi-fact questions
- Edit the QA chain prompt to change tone, add source citations, or make refusals stricter on out of scope questions
Additional info
Companion workflow, run this one first: Index local documents for RAG using Ollama embeddings and Qdrant, https://creators.n8n.io/workflows/17764
Runs 100 percent locally with no OpenAI API keys, so documents never leave your machine. Good fit for clients who care about data residency or privacy.
🔗 Nodes Used
Question and Answer Chain, Ollama Chat Model, Vector Store Retriever, Chat Trigger, Qdrant Vector Store, Embeddings Ollama
📥 Import
Download workflow.json and import into n8n:
Workflow menu → Import from File