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intelligent-ingestion

Ingest and chunk documents for retrieval-augmented generation workflows.

2|1|Updated Jan 31, 2026
Search Tags:#embeddings#rag#pipeline#chunking#document#ingest#vertex-ai
One-click install
npx skills add https://github.com/Agentient/vibekit --skill intelligent-ingestion
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: intelligent-ingestion
Source: https://github.com/Agentient/vibekit/tree/main/plugins/rag-tools/skills/intelligent-ingestion
Command: npx skills add https://github.com/Agentient/vibekit --skill intelligent-ingestion

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This STUB provides a placeholder for intelligent document ingestion to support Retrieval-Augmented Generation (RAG) workflows. The current SKILL.md indicates this is a STUB and not yet implemented, serving as a placeholder to preserve the planned plugin structure.

Core Features & Use Cases

  • Planned Intelligent Chunking Strategies (fixed-size, semantic, sentence-boundary, markdown-aware) for diverse documents.
  • Concurrent online embeddings with explicit RETRIEVAL_DOCUMENT task_type for retrieval accuracy.
  • Bypass batch API limitations to ensure correct task_type in retrieval workflows.
  • Document preprocessing and normalization for consistent ingestion results.

Quick Start

Activate the intelligent-ingestion skill when the core implementation is available and review the parent plugin README for planned capabilities.

Frequently Asked Questions about intelligent-ingestion

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I chunk documents for a retrieval-augmented generation workflow?▼

Document chunking for RAG workflows involves segmenting text using fixed-size, semantic, or sentence-boundary strategies. This approach prepares diverse documents for concurrent online embeddings and retrieval tasks.

What is the best way to handle document ingestion for RAG pipelines?▼

Document ingestion for RAG pipelines requires preprocessing and normalizing files before applying intelligent chunking. This ensures consistent ingestion results and satisfies modular preprocessing contracts for extensible workflows.

Does this document ingestion approach support concurrent online embeddings?▼

Yes, concurrent online embeddings are supported with an explicit RETRIEVAL_DOCUMENT task type. This bypasses batch API limitations to ensure correct task types during retrieval workflows.

Can I use semantic chunking for diverse document types?▼

Semantic chunking can be applied to diverse document types alongside fixed-size and sentence-boundary strategies. These modular preprocessing patterns support consistent ingestion across various document formats.

Why does my batch API limit task types during document ingestion?▼

Batch API limitations can restrict task types during document ingestion. Bypassing these limits by using concurrent online embeddings ensures the correct RETRIEVAL_DOCUMENT task type is applied for retrieval accuracy.

Do I need to normalize documents before chunking them for RAG?▼

Document preprocessing and normalization are required before chunking to achieve consistent ingestion results. This step ensures diverse documents are properly prepared for concurrent online embeddings.

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