newsGoogle News — LLMTrust 62 · AggregatorPublished 1mo agoLive · 1mo ago
A Production RAG Pipeline for PDFs: Relational Parsing, TOC Retrieval, Typed Answers - Towards Data Science
A Production RAG Pipeline for PDFs: Relational Parsing, TOC Retrieval, Typed Answers Towards Data Science
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- PossiblePossibly related (embedding) · 51%Tencent/WeKnora →
- PossiblePossibly related (embedding) · 50%Set up a retrieval pipeline →
- PossiblePossibly related (embedding) · 49%thiswillbeyourgithub/wdoc →
- PossiblePossibly related (embedding) · 48%tuirk/Kompl →
- PossiblePossibly related (embedding) · 48%yfedoseev/pdf_oxide →
- PossiblePossibly related (embedding) · 55%F2-AI-Inc/docray →
- PossiblePossibly related (embedding) · 50%thomas-villani/all2md →
- PossiblePossibly related (embedding) · 56%santushtmatra11/structured-document-retriever →
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repoF2-AI-Inc/docrayrepothiswillbeyourgithub/wdocreposantushtmatra11/structured-document-retrieverrepoyfedoseev/pdf_oxiderepoTencent/WeKnorarepoDocSlicer/DocSlicerrepothomas-villani/all2mdtutorialSet up a retrieval pipelinerepoparsee-ai/parsee-corerepoPSPDFKit/ai-assistant-demorepotuirk/Komplrepokchatterjee4040/full-stack-RAG-Retrieval-Augmented-Generation-Document-Assistant