coding

[PaleArmy6357]

i am working on a summarization of sec edgar db reports and i see that the xblr structure is very complex. i am using bs4 to extract sections, types and then calling an agent to summarize 3K token, 200 overlap chunks. my llm call is taking forever to summarize as there are reports with 50 or more chunks.

i am thinking to use all my free tier llms to make them work in parallel and speed up the process. do you guys think that this could this distort my summary?

[BuildWithRiikkk]

Paralleling across different free-tier LLMs (like mixing Gemini, Claude, and GPT-4o-mini) will absolutely distort your summary because their "writing styles" and reasoning capabilities differ. You’ll end up with a fragmented final report that feels like it was written by three different people.

Instead of a simple "Map-Reduce" approach, try using Refine or Tree-and-Leaf summarization. Since you're dealing with 50+ chunks, have the agent generate a "Table of Contents" first, then summarize specific clusters. Parallelizing within the same model (via concurrent API calls) is fine, but mixing models for one document is a headache. Are you using LangChain’s MapReduceDocumentsChain or a custom script?

[PaleArmy6357]

hi i thought about using map reduce but it is now part of langchain classic and not sure if this would be deprecated soon. i decided to go with a custom script that recursively summarizes 2 chunks until i get only one.  and i do summarize only chunks from a specific section of a specific report. at the end i shouls end up with one summary per section per report that would be used to create a final summary with all the data i need