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nemo-retriever

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Index, search, and extract from multimodal docs fast.

Data & Analytics#pdf#embeddings#multimodal#retrieval#lancedb#document-processing#vector-search
Authorsayalinvidia
Version1.0.0
Installs0

System Documentation

What problem does it solve?

AI workflows struggle to efficiently search across large, multimodal document collections (PDFs, Word, images, audio, video) and extract structured information; manual grep or single-file reads are brittle and slow.

Core Features & Use Cases

  • Supports indexing multimodal corpora with the NeMo Retriever; enables fast cross-document search, page-level extraction, and extraction of quotes or clauses; works with PDFs, images, Office docs, HTML, TXT, audio, and video.
  • Use cases include legal contracts, research reports, invoices, and audits where you need to locate passages, extract data, or summarize across many documents.
  • Quick-start oriented for developers to bootstrap a retrieval workflow in agent skills.

Quick Start

Ingest your document folder with the default Nemo Retriever ingest flow and run a sample query to retrieve the top hits.

Dependency Matrix

Required Modules

None required

Components

scriptsreferences

💻 Claude Code Installation

Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.

Please help me install this Skill:
Name: nemo-retriever
Download link: https://github.com/sayalinvidia/sayali-skills-test/archive/main.zip#nemo-retriever

Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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