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Hi guys, I ma having issues with my code

Hi guys, I ma having issues with my code. So the response from the LLM gets the correct answer (in the log) but it does understand the context and it fails to return the correct answer. PS: This setup is just for testing.
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Oh no, I see the LlamaToolKit in there πŸ˜… Quite deprecated/unmaintained, I'm surprised it works still

So you are making an index per document using the sentence window stuff, then putting that into a sub question query engine

How many index/documents do you have?
No, the LlamaToolKit doesnt work, I was just testing the memory module. It actually sucks. πŸ™‚
So its about 14 files
I think the main issue is that every tool description is the exact same, minus a tiny piece (the doc name)

Maybe generating a summary on the fly (and caching that somewhere) and using that as the description would be better?

Plain Text
from llama_index import SummaryIndex

index = SummaryIndex(nodes)

response = index.as_query_engine(response_mode='tree_summarize', use_async=True).query("Summarize the provided text.")
Hi logan I implemented this but I am still having the same issue. It find the answer but doesnt notice it
Just a look at the output
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out of curiosity, which LLM are you using?
You'll notice it queried nearly every document it looks like :PSadge: Thats a lot of returned context for an LLM to sift through
ideally it would only query 1-5 at most πŸ€”
I wonder if using a smarter LLM for the question generator would help
like gpt-4 or gpt-4-turbo
if openai is not an option, then you'll need to tackle this another way -- likely something that relies on embeddings, like a retriever router? https://docs.llamaindex.ai/en/stable/examples/query_engine/RetrieverRouterQueryEngine.html
I am using 3.5 turbo
I will have a look at that
Hi @Logan M logan, I am still struggling to get specific information out of the LLM. I am really not sure how to improve upon this,
I have implements retriever routers with no success
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