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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Developer APIs to Accelerate LLM Projects
| Date | Stars |
|---|---|
| 2026-07-31 | 1747 |
| 2026-08-04 | 1747 |
| 2026-08-06 | 1747 |
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# LLM Sherpa LLM Sherpa provides strategic APIs to accelerate large language model (LLM) use cases. ## What's New > [!IMPORTANT] > llmsherpa back end service is now fully open sourced under Apache 2.0 Licence. See [https://github.com/nlmatics/nlm-ingestor](https://github.com/nlmatics/nlm-ingestor) > - You can now run your own servers using a docker image! > - Support for different file formats: DOCX, PPTX, HTML, TXT, XML > - OCR Support is built in > - Blocks now have co-ordinates - use bbox propery of blocks such as sections > - A new indent parser to better align all headings in a document to their corresponding level > - The free server and paid server are not updated with latest code and users are requested to spawn their own servers using instructions in nlm-ingestor ## LayoutPDFReader Most PDF to text parsers do not provide layout information. Often times, even the sentences are split with arbritrary CR/LFs making it very difficult to find paragraph boundaries. This poses various challenges in chunking and adding long running contextual information such as section header to the passages while indexing/vectorizing PDFs for LLM applications such as retrieval augmented generation (RAG). LayoutPDFReader solves this problem by parsing PDFs along with hierarchical layout information such as: 1. Sections and subsections along with their levels. 2. Paragraphs - combines lines. 3. Links between sections and paragraphs. 4. Tables along with the section the tables are found in. 5. Lists and nested lists. 6. Join content spread across pages. 7. Removal of repeating headers and footers. 8. Watermark removal. With LayoutPDFReader, developers can find optimal chunks of text to vectorize, and a solution for limited context window sizes of LLMs. You can experiment with the library directly in **Google Colab** [here](https://colab.research.google.com/drive/1hx5Y2TxWriAuFXcwcjsu3huKyn39Q2id?usp=sharing) Here's a [writeup](https://open.substack.com/pub/ambikasukla/p/efficient-rag-with-document-layout?r=ft8uc&utm_campaign=post&utm_medium=web) explaining the problem and our approach. Here'a LlamaIndex [blog](https://medium.com/@kirankurup/mastering-pdfs-extracting-sections-headings-paragraphs-and-tables-with-cutting-edge-parser-faea18870125) explaining the need for smart chunking. **API Reference**: [https://llmsherpa.readthedocs.io/](https://llmsherpa.readthedocs.io/) [How to use with Google Gemini Pro](https://medium.com/nlmatics/using-google-gemini-pro-with-your-pdfs-7c191a2fcd98) [How to use with Cohere Embed3](https://medium.com/nlmatics/ask-your-pdf-with-cohere-embed-v3-3eb5dab36945) ### Important Notes * The LayoutPDFReader is tested on a wide variety of PDFs. That being said, it is still challenging to get every PDF parsed correctly. * OCR is currently not supported. Only PDFs with a text layer are supported. > [!NOTE] > LLMSherpa uses a free and open api server. The server does not store your PDFs except for temporary storage during parsing. This server will be decommissioned soon. > Self-host your own private server using instructions at [https://github.com/nlmatics/nlm-ingestor](https://github.com/nlmatics/nlm-ingestor) > [!IMPORTANT] > Private available at [Microsoft Azure Marketplace](https://azuremarketplace.microsoft.com/en-us/marketplace/apps/nlmaticscorp1686371242615.layout_pdf_parser?tab=Overview) > will be decommissioned soon. Please move to your self-hosted instance using instructions at [https://github.com/nlmatics/nlm-ingestor](https://github.com/nlmatics/nlm-ingestor). ### Installation ```bash pip install llmsherpa ``` ### Read a PDF file The first step in using the LayoutPDFReader is to provide a url or file path to it and get back a document object. ```python from llmsherpa.readers import LayoutPDFReader llmsherpa_api_url = "https://readers.llmsherpa.com/api/document/developer/parseDocument?renderFormat=all" pdf_url = "https://arxiv.org/pdf/1910.13461.pdf" # also allowed is a file path e
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:401dcd8a53907dbf, llm:description: 'Developer APIs to Accelerate LLM Projects' (nlmatics/llmsherpa)
matched fp:401dcd8a53907dbf, llm:description: 'Developer APIs to Accelerate LLM Projects' (nlmatics/llmsherpa)
matched fp:401dcd8a53907dbf, llm:description: 'Developer APIs to Accelerate LLM Projects' (nlmatics/llmsherpa)