> ## Documentation Index
> Fetch the complete documentation index at: https://docs.keyflow.space/llms.txt
> Use this file to discover all available pages before exploring further.

# Cohere RAG

### **Overview:**

The `Cohere RAG` block is a powerful tool that utilizes Cohere's RAG (Retrieval-Augmented Generation) capabilities to generate responses based on user input. This block can be particularly useful in various applications, such as chatbots, virtual assistants, and customer support systems.

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### Inputs & Outputs

| I/O    | Feature             | Type     | Simple Explanation                                                     |
| ------ | ------------------- | -------- | ---------------------------------------------------------------------- |
| input  | `question`          | string   | The question to be asked to the model.                                 |
| input  | `web_search`        | checkbox | This checkbox decides whether to use search the web or not.            |
| input  | `files`             | selector | Select the files you want to provide to the model for further context. |
| input  | `model`             | selector | The Cohere model to be used for the RAG.                               |
| output | `response`          | string   | The response generated by the Cohere model.                            |
| output | `citations`         | object   | Provides citations for the generated response.                         |
| output | `related_documents` | any\[]   | Provides a list of related documents for the generated response.       |

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### Use Cases

Consider how this block can enhance various processes:

* **Chatbots:** Utilize Cohere's RAG capabilities to generate responses to user queries, enhancing the chatbot's ability to understand and respond to user inputs.
* **Virtual Assistants:** Incorporate Cohere's RAG into virtual assistants to provide personalized responses based on user inputs, improving the overall user experience.
* **Customer Support Systems:** Utilize Cohere's RAG to generate responses to customer inquiries, ensuring efficient and accurate support for customers.

In essence, the `Cohere RAG` block is a versatile tool that can be applied in a wide range of scenarios, offering valuable insights and opportunities for data analysis and decision-making.