# Conversational Retrieval QA Chain

A chain for performing question-answering tasks with a retrieval component.

<figure><img src="/files/oO8BfLtTAINe1yqUD83L" alt=""><figcaption></figcaption></figure>

## Definitions

**A retrieval-based question-answering chain**, which integrates with a retrieval component and allows you to configure input parameters and perform question-answering tasks.\
**Retrieval-Based Chatbots:** Retrieval-based chatbots are chatbots that generate responses by selecting pre-defined responses from a database or a set of possible responses. They "retrieve" the most appropriate response based on the input from the user.\
**QA (Question Answering):** QA systems are designed to answer questions posed in natural language. They typically involve understanding the question and searching for or generating an appropriate answer.

## Inputs

* [Language Model](/integrations/langchain/chat-models.md)
* [Vector Store Retriever](/integrations/langchain/vector-stores.md)
* [Memory (optional)](/integrations/langchain/memory.md)

## Parameters

| Name                    | Description                                                                                                                                               |
| ----------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Return Source Documents | To return citations/sources that were used to build up the response                                                                                       |
| System Message          | An instruction for LLM on how to answer query                                                                                                             |
| Chain Option            | Method on how to summarize, answer questions, and extract information from documents. Read [more](https://js.langchain.com/docs/modules/chains/document/) |

## Outputs

| Name                           | Description                   |
| ------------------------------ | ----------------------------- |
| ConversationalRetrievalQAChain | 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://docs.flowiseai.com/integrations/langchain/chains/conversational-retrieval-qa-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.