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Weaviate Adds PDF Support to Agent Skills for Autonomous Document Ingestion

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Weaviate, an AI database for building applications, added PDF support to its Agent Skills framework. Using a single prompt, agents like Claude Code or Cursor can now autonomously set up collections and ingest PDFs using ColModernVBERT, a multimodal model (AI that understands text and images together) for page-level embedding.

Multi-vector retrieval (a search method using multiple data points for higher accuracy) is often too resource-intensive. This update integrates MUVERA, an algorithm that compresses complex embeddings into an efficient format. This allows you to achieve high-quality retrieval while significantly reducing the computational and memory costs usually required for large-scale search.

You can now point any compatible agent at a document library to build a searchable database without writing manual ingestion logic. The skill is available via GitHub and can be installed using npx skills add or as a Claude Code plugin. CSV and JSON formats are also supported for agent-led imports.

View the full update on github.com
Weaviate AI Database
Weaviate AI Database
@weaviate_io
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PDF import just landed in Weaviate Agent Skills! Point Claude Code (or any agent) at a PDF, and it handles everything from schema setup, collection configuration, to embedding each page using ColModernVBERT multimodal model. MUVERA then makes multi-vector retrieval efficient by reducing memory usage and computational cost while preserving retrieval quality. In short: your entire document library becomes searchable in Weaviate with a single prompt. CSV, JSON, and JSONL are already supported! Try it: https://t.co/CvQ30X2pcU

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