Chapterwise nodes

MMitchMcD

hi all, if i am embedding a book, is there a way to make each chapter a node, so that when i ask "summarize chapter x" it works through this node only? would appreciate guidance. thank you!

11 comments

WWhiteFang_Jr

I wouldn't recommend single node for each chapter as it can be very long and high chance llm token limit will get crossed.

You can create set of nodes and define relation for each chapter in. Still all the nodes will be used to create summary may not get possible.

If the chapters are small in size then you can try following this link customise the nodes or document object as per your requirement.
https://gpt-index.readthedocs.io/en/latest/end_to_end_tutorials/usage_pattern.html#basic-usage-pattern

MMitchMcD

thank you , the chapters are not small , 3-4 pages of text, will the basic-usage-pattern be enough to address queries like "summarize chapter 3" as an example?

WWhiteFang_Jr

Not entirely though. You can try adding metadata to each chapter or try DocumentsummaryIndex along with metadata. This may be able to help you in query like these.

bbmax

Are you using a vector db @MitchMcD ?

WWhiteFang_Jr

Will it help @bmax to tackle this scenario?

bbmax

I was just wondering, I was thinking if he was using a vector db, he could do what you said with a chapter in each node's metadata, use a retriever to get all of the nodes there and then pass into documentSummaryIndex

bbmax

trying to figure out how to do it myself lol

bbmax

like can you just do

Plain Text

filters = MetadataFilters(filters=[ExactMatchFilter(key="name", value="Chapter 1")])
retriever = index.as_retriever(filters=filters)
retriever.retrieve()

to get all nodes

bbmax

but you can't do empty retrieve() so, wondering how

WWhiteFang_Jr

Yeah but while response generation all the nodes may not get used if the node length crosses token limit, that's what I'm thinking. So it may look half cooked response

MMitchMcD

that's the plan

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Chapterwise nodes