llmsherpa
Parsr
| llmsherpa | Parsr | |
|---|---|---|
| 6 | 7 | |
| 970 | 5,660 | |
| 16.2% | 0.7% | |
| 6.6 | 4.6 | |
| 7 days ago | 5 months ago | |
| Jupyter Notebook | JavaScript | |
| MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
llmsherpa
-
LlamaCloud and LlamaParse
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 to try https://github.com/nlmatics/llmsherpa which takes into account some of the things I mentioned.
-
Show HN: Open-source Rule-based PDF parser for RAG
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://github.com/nlmatics/llmsherpa which you can use as an API over this library.
Parsr
-
LlamaCloud and LlamaParse
I'm part of the team that build LlamaParse. It's net improvement compare to other PDF->Structured Text extractors (I build several in the past, includig https://github.com/axa-group/Parsr).
For character extraction, LlamaParse use a mixture of OCR / character extraction from the PDF (it's the only parser I'm aware of that address some of the buggy PDF font issues, check the 'text' mode to see raw document before reconstruction), use a mixture of heuristic and Machine learning models to reconstruct the document.
Once plug with a Recursive retrieval strategy, allow you to get Sota result on question answering over complexe text (see notebook: https://github.com/run-llama/llama_parse/blob/main/examples/...).
AMA
-
Issue getting Parsr GUI up and running
Link to the Github Repository and pre defined Docker Compose Build file for Parsr
-
PDF GPT allows you to chat with the contents of your PDF file
I would check out https://github.com/Unstructured-IO/unstructured (what lang chain uses) or https://github.com/axa-group/Parsr (probably what unstructured copied to get their startup off the ground lol)
-
Converting PDF into HTML: is it possble?
Things I still want to try: - Parsr
-
Does anyone know where I can get access to a prebuilt general document understanding model?
I personally haven't used it yet, but I heard some good things about Parsr: https://github.com/axa-group/Parsr
- Turn your (PDF,Image) documents into structured data
-
[D] What pdf parser do you use for paragraph parsing for huggingface models
Parsing PDFs is very non-trivial process. Google and Amazon parses are largely based on OCRing. There are some advanced state-of-the-art NN-based OCR approaches but they are not very stable, but a stable industry standard is Tesseract, and nice all-in-one open source tools that brings a ton of tools together is https://github.com/axa-group/Parsr . hope this helps
What are some alternatives?
unstructured - Open source libraries and APIs to build custom preprocessing pipelines for labeling, training, or production machine learning pipelines.
grobid - A machine learning software for extracting information from scholarly documents
txtai - 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
Teedy - Lightweight document management system packed with all the features you can expect from big expensive solutions
llama_parse - Parse files for optimal RAG
marker - Convert PDF to markdown quickly with high accuracy
Ambar - :mag: Ambar: Document Search Engine
paperetl - 📄 ⚙️ ETL processes for medical and scientific papers
deriveODM - DeriveODM is a reactive ODM - Object Document Mapper - framework, a "wrapper" around MongoDB, that removes all the hassle of data-persistence by handling it transparently in the background, in a DRY manner.
nlm-ingestor - This repo provides the server side code for llmsherpa API to connect. It includes parsers for various file formats.
gpt4-pdf-chatbot-langchain - GPT4 & LangChain Chatbot for large PDF docs