> For the complete documentation index, see [llms.txt](https://docs.n8n.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.chainretrievalqa.md).

# Question and Answer Chain

Learn how to use the Question and Answer Chain node in n8n. Follow technical documentation to integrate Question and Answer Chain node into your workflows.

Use the Question and Answer Chain node to use a [vector store](#user-content-fn-1)[^1] as a retriever.

On this page, you'll find the node parameters for the Question and Answer Chain node, and links to more resources.

## Node parameters <a href="#node-parameters" id="node-parameters"></a>

### Query <a href="#query" id="query"></a>

The question you want to ask.

## Templates and examples <a href="#templates-and-examples" id="templates-and-examples"></a>

[Browse n8n-nodes-langchain.chainretrievalqa integration templates](https://n8n.io/integrations/retrieval-qanda-chain) or [search all templates](https://n8n.io/workflows/)

## Related resources <a href="#related-resources" id="related-resources"></a>

Refer to [LangChain's documentation on retrieval chains](https://js.langchain.com/docs/tutorials/rag/) for examples of how LangChain can use a vector store as a retriever.

View n8n's [Advanced AI](/build/integrate-ai.md) documentation.

## Common issues <a href="#common-issues" id="common-issues"></a>

For common errors or issues and suggested resolution steps, refer to [Common Issues](/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.chainretrievalqa/common-issues.md).

[^1]: A vector store, or vector database, stores mathematical representations of information. Use with embeddings and retrievers to create a database that your AI can access when answering questions.