> 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

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.