# Vectara QA Chain

A chain for performing question-answering tasks with Vectara.

<figure><img src="/files/Qf58Oxzg4leRKpN0e3dB" alt=""><figcaption></figcaption></figure>

## Definitions

**A retrieval-based question-answering chain**, which integrates with a Vectara retrieval component and allows you to configure input parameters and perform question-answering tasks.

## Inputs

* [Vectara Store](/readme/chatflows/langchain/vector-stores/vectara.md)

## Parameters

| Name                   | Description                                                   |
| ---------------------- | ------------------------------------------------------------- |
| Summarizer Prompt Name | model to be used in generating the summary                    |
| Response Language      | desired language for the response                             |
| Max Summarized Results | number of top results to use in summarization (defaults to 7) |

## Outputs

| Name           | Description                   |
| -------------- | ----------------------------- |
| VectaraQAChain | Final node to return response |


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://tailwindsdocs.innovativesol.com/readme/chatflows/langchain/chains/vectara-chain.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.