RAG Q&A Agent
Search your Qdrant vector store for relevant context, then answer with an LLM — grounded in your own documents.
The full canvas, before you import it
Click any node to see its config.
Click a node to select it — same as the Heym editor; the panel shows its settings.
4 nodes · Free & source-available
RAG Q&A Agent
The retrieval-augmented answer pattern in four nodes: question → search → answer → output. Ground your LLM in your own documents and stop hallucinations at the source.
What this workflow does
- Input receives the user question
- RAG node (search mode) retrieves the top-K most relevant chunks from Qdrant
- LLM node uses the retrieved context to answer accurately
- Output returns the grounded answer
Use cases
- Internal knowledge base chatbot
- Policy and compliance Q&A
- Product documentation assistant
Setup
Configure the RAG node with your Qdrant collection (same one used in the RAG Document Ingest template). The LLM system instruction already references $RagSearch.context — connect your preferred model.
FAQ
Can I combine both RAG templates? Yes — run Ingest once per document, then re-use Q&A for every question.
Which embedding model should I use? Use the same model in both Ingest and Q&A to ensure vector compatibility.
How to import this template
- 1Click Import → Copy JSON on this page.
- 2Open your Heym and navigate to a workflow canvas.
- 3PressCmd+V/Ctrl+V— nodes appear instantly.
- 4Add your API keys in the node config panels and click Run.
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