Cohere RAG - Keyflow Docs

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.

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.

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.