AI
Cohere RAG
AI
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.
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.
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