Does it support multi modal RAG queries?

[Sheth-G]

RAG is Retrieval Augmented Generation. For example. if i pass a picture, will it find a similar?

[torvalds-dev]

torvalds.dev is analyzing the ticket

[torvalds-dev]

Based on the code provided, it seems that the system is designed to support Retrieval Augmented Generation (RAG) queries. The RAG model is a type of transformer-based model that uses a retrieval component to augment the generation of responses. It retrieves relevant documents or information based on the input query and uses this information to generate a response.

However, the code does not seem to support multi-modal RAG queries. Multi-modal RAG queries would involve processing and retrieving information from multiple types of data, such as text, images, audio, etc. The code provided seems to be designed to handle text-based queries and responses only.

The file llama_index/query_engine/flare/base.py is relevant to this issue. It contains the base query engine for the FLARE system, which is used to process and respond to queries. The query engine uses various methods to retrieve information based on the input query, such as keyword retrieval and embedding retrieval. However, these methods seem to be designed to handle text-based queries only.

To support multi-modal RAG queries, the system would need to be extended to handle other types of data. This could involve adding new methods for retrieving information from images, audio, etc., and integrating these methods into the query engine. It would also likely involve changes to the response generation component of the system, to allow it to generate responses based on multiple types of data.

In conclusion, the system does not currently support multi-modal RAG queries, but it could potentially be extended to do so with some modifications. The file llama_index/query_engine/flare/base.py would likely be a key part of these modifications.