Can a workflow be converted to a tool?

Has someone created or has the knowledge of a RetrySQLQueryEngine? - the one which retries to generate proper sql statement if it errors out once?

I have a usecase, in which I need to ingest over 1000+ documents and over which I need to create a VectorIndex and SummaryIndex. I am able to sucessfully create the VectorIndex by adding metadata and retrieving using VectorIndexAutoRetriever. However, I am stuck at creating the SummaryIndex as I need to retrieve only one particular document (identified by the metadata) and create its summary. How can I achieve this?

@Logan M - I am trying to understand how can i use RouterQueryEngine with Chat History. If i just stuff the query with all the chat messages will it work? or do i need to do something else? How can I achieve this?

I would also like to point out that i tried playing with max_colwidth parameter, but it didn't make any difference. Here is how I am setting it -

How can I see the formatted prompt which is sent to the LLM? I can see what kind of prompts are used by using query_engine.get_prompts() but not the formatted ones which are sent to LLM.

Hi, how do I see the embeddings that are generated by the VectorStoreIndex for each of the nodes?
When I iterate over the nodes of the index, I find that the TextNode object has embeddings=None.
Now, I am wondering where the embeddings are stored.

I am trying to extract structure data from the unstructured text using structured_predict, but if my Pydantic model has a field with type as datetime.date, llama-index is not able to parse the string and throws an error saying "Invalid Date Format". How can this be rectified

load_index_from_storage vs VectorStoreIndex.from_vector_store() -> does both of these do the same thing?

Because when I see the print the index.docstore.docs from the index returned by load_index_from_storage , I can see the documents, but same is not the case when I do it for the index returned from VectorStoreIndex.from_vector_store()

How can I store the indices in a VectorStore such that I will be able to build a RecursiveRetriever after loading it from a persistent storage? Currently I am finding it difficult to populate the node_dict parameter while creating the instance of RecursiveRetriever

So here is the scenario that I am trying to accomplish, I have a pdf containing text + images + tables. I need to develop a RAG which is needs to retrieve images along with the text based on the query relevance.

I can achieve this by creating TextNodes, ImageNodes and IndexNode and then using RecursiveRetriever to retrieve the nodes along with the images.
However, this approach has a problem where in, if the there are more TextNodes with the relevant text (more then similarity_top_k) then the ImageNode wont be retrieved.
To avoid this, is it possible to do some workaround (or have a feature in the library) such that the RecursiveRetriever retrieves TextNodes and ImageNodes separately along with the scores, so that as a user I can decide weather to pass just the TextNodes or TextNode + ImageNode to the LLM in its context.

This use-case is an important one IMO, and I feel that this should be built in the library, I would love to hear some discussions on this and more than happy to contribute if the need araises.