What's the difference between using a list index with response_mode=tree_summarize and a tree index with mode=summarize

what happened to the multi-step query decomposition tweet?

I used the example notebook @jerryjliu0 created, but a lot of the query responses talk about the "previous context" or "existing answer"

Is there any advice on good ways to compose indices? The documentation shows a list index on top of tree indices, but not sure if that's the best way. In my case, I have two types of documents. I have about 200 documents of each type, about 20 pages each on average. I was thinking of wrapping each document in it's own list index to force the model to use the entire document. Then, I'd probably put all the documents into two vector or tree indices (one for each type), and then a list index on top of the two vector/tree indices to force the model to look at both types of documents. Any thoughts on better ways to compose would be greatly appreciated.

I have pre-chunked text, and pre-computed embedding that I want to insert into an index