rag-research

Name: rag-research
Availability: InStock
Author: docutray

Optimize document retrieval with RAG-based semantic search across indexed content.

2|1|Updated Oct 27, 2025

Search Tags:#embeddings #rag #chunking #semantic-search #vector-database #retrieval-augmented #document-indexing

Ready to run

npx skills add https://github.com/docutray/docutray-claude-code-plugins --skill rag-research

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: rag-research
Source: https://github.com/docutray/docutray-claude-code-plugins/tree/main/plugins/rag-research/skills/rag-research
Command: npx skills add https://github.com/docutray/docutray-claude-code-plugins --skill rag-research

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

The RAG Research Skill helps developers and data professionals optimize retrieval and document indexing workflows using RAG-based semantic search. It covers best practices for embedding, chunking, and vector databases to improve accuracy and efficiency.

Core Features & Use Cases

Document Indexing Pipeline: extract text, chunk content, generate embeddings, and store vectors for retrieval.
Embedding Models: guidance on recommended models and runtimes for CPU inference.
Chunking Strategies: guidance on chunk size and overlap to balance context and precision.
Troubleshooting & Guidance: tips for improving search results, diagnosing poor coverage, and re-indexing.
Use Cases: indexing and searching large document collections, knowledge bases, and code/documentation retrieval.

Quick Start

Install the rag-research plugin, index your documents, and run semantic searches using the plugin commands.

Frequently Asked Questions about rag-research

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

FAQPage Schema

How do I improve retrieval accuracy for semantic search over indexed documents?▼

Improve semantic search accuracy by optimizing chunk size and overlap to balance context and precision, selecting recommended embedding models for CPU inference, and re-indexing content to ensure optimal document retrieval quality.

What is the best way to chunk documents for a RAG pipeline?▼

The best way to chunk documents for a RAG pipeline is to apply balanced chunk sizes with appropriate overlap, ensuring sufficient context is preserved for embedding generation while maintaining retrieval precision across the vector database.

Why does my document indexing pipeline return poor search results?▼

Poor search results in a document indexing pipeline often stem from suboptimal chunking strategies, inadequate embedding models, or outdated vector indexes, requiring troubleshooting and re-indexing to improve coverage and retrieval performance.

Can I use CPU inference for generating embeddings across large knowledge bases?▼

Yes, you can use CPU inference for generating embeddings across large knowledge bases by utilizing recommended embedding models and runtimes, enabling efficient document indexing and semantic search without requiring specialized GPU hardware.

When do I need a vector database for retrieval-augmented generation workflows?▼

You need a vector database for retrieval-augmented generation workflows when storing and searching extracted text embeddings, enabling fast semantic search and knowledge retrieval across large document collections and codebases.

How do I set up a document indexing pipeline for code and documentation retrieval?▼

Set up a document indexing pipeline by extracting text, chunking content, generating embeddings, and storing vectors in a database, enabling accurate semantic search and knowledge retrieval across code and documentation.