asukla

6 ms·

1. 1.▲
   by asukla 3y ago

   To get good RAG performance you will need a good chunking strategy. Simply getting all the text is not good enough and knowing the boundaries of table, list, paragraph, section etc. is helpful. Great work by llamaindex team. Also feel free
2. 2.▲
   by asukla 3y ago

   Feel free to try - https://github.com/nlmatics/llmsherpa . It is fully open source - both client and server and it not ML augmented, so very fast and cheap to run.
3. 3.▲
   by asukla 3y ago

   I wrote about split points and the need for including section hierarchy in this post: https://ambikasukla.substack.com/p/efficient-rag-with-docume... All this is automated in the llmsherpa parser https://git
4. 4.▲
   by asukla 3y ago

   Thanks for the post. Please use this server with the llmsherpa LayoutPDFReader to get optimal chunks for your LLM/RAG project: https://github.com/nlmatics/llmsherpa . See examples and notebook in the repo.
5. 5.▲
   by asukla 3y ago

   You can see examples in llmsherpa project - https://github.com/nlmatics/llmsherpa . This project nlm-ingestor provides you the backend to work with llmsherpa. The llmsherpa library is very convenient to use for extracti
6. 6.▲
   by asukla 3y ago

   No, we are not doing the same thing. Most cloud parsers use a vision model and they are lot slower, expensive and you need to write code on the top of these to extract good chunks. You can use llmsherpa library - https://github.c
7. 7.▲
   by asukla 3y ago

   You can use the library in conjunction with llmsherpa LayoutPDFReader. Some examples are here with notebook: https://github.com/nlmatics/llmsherpa Here's another notebook from the repo with examples: https:/
8. 8.▲
   by asukla 3y ago

   To run the docker image on apple silicon, you can use the following command to pull - it will be slower but works: docker pull --platform linux/x86_64 ghcr.io/nlmatics/nlm-ingestor:latest