Developer APIs for LLM project acceleration
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LLM Sherpa provides developer APIs to accelerate LLM projects by offering advanced PDF parsing capabilities. It addresses the challenge of extracting structured data and contextual information from PDFs, enabling developers to create more effective Retrieval Augmented Generation (RAG) systems. The library is designed for developers working with LLMs who need to process and understand PDF documents.
How It Works
LLM Sherpa's core component, LayoutPDFReader
, parses PDFs to extract hierarchical layout information, including sections, paragraphs, tables, and lists, along with their relationships. This approach preserves document structure, unlike basic text extractors, allowing for smarter chunking of text that maintains context (e.g., associating table data with its surrounding section). This detailed parsing facilitates more accurate LLM interactions, especially for tasks requiring understanding of document flow and specific data elements.
Quick Start & Requirements
pip install llmsherpa
llama-index
and an LLM API key (e.g., OpenAI) are recommended.Highlighted Details
Maintenance & Community
The backend service is open-sourced under Apache 2.0 and can be self-hosted via Docker. The project links to a GitHub repository for the backend service (nlm-ingestor
).
Licensing & Compatibility
The library itself is not explicitly licensed in the README, but the backend service is Apache 2.0. The README mentions using OpenAI for LLM integration, implying compatibility with commercial LLM providers.
Limitations & Caveats
The LayoutPDFReader
is still challenging to get perfect for all PDFs, and currently only supports PDFs with a text layer (OCR is not supported). The free public API server mentioned will be decommissioned, requiring users to self-host.
9 months ago
1 day