I'm trying to use LlamaIndex to summarize a collection of 50 research papers simultaneously. The standard VectorStoreIndex often misses the "big picture" and focuses too much on specific chunks. Has anyone had success with the TreeIndex or the SubQuestionQueryEngine for synthesizing information across multiple sources?
3 answers
For multi-document tasks, the TreeIndex in LlamaIndex is specifically designed for this "bottom-up" summarization. It works by summarizing small groups of nodes and then summarizing those summaries into a root node. This hierarchical approach ensures that the high-level themes aren't lost in the noise of individual chunks. Another strategy I've found useful is the SubQuestionQueryEngine. It breaks down a complex summary request into several sub-questions targeted at specific documents, then aggregates the answers. This is much more effective than a simple semantic search because it forces the LLM to look at every source before concluding. If you're dealing with 50 papers, the token cost can be high, so consider using a "Map-Reduce" pattern to save on your context window limits.
Have you experimented with the ListIndex combined with a custom response mode like tree_summarize? I've found that for smaller sets of documents, this provides a much more coherent narrative than the more complex engine types in LlamaIndex.
The DocumentSummaryIndex is another great feature in LlamaIndex. It pre-calculates summaries for each document during indexing, making the final synthesis much faster.
Pamela's suggestion is the gold standard for large-scale research. Pre-summarizing saves so much time during the actual query phase. It’s worth the extra indexing cost.
Kevin, I tried the ListIndex but found it a bit slow for 50 documents because it iterates through everything. However, the tree_summarize response mode is a lifesaver. I ended up combining a VectorStoreIndex for finding relevant papers and then feeding those results into a summarization task. It significantly cut down the processing time while still keeping the quality high. Thanks for the suggestion!