rag-research
OfficialBoost retrieval with RAG-driven semantic search.
Software Engineering#embeddings#rag#chunking#semantic-search#vector-database#retrieval-augmented#document-indexing
Authordocutray
Version1.0.0
Installs0
System Documentation
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.
Dependency Matrix
Required Modules
None requiredComponents
references
💻 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: rag-research Download link: https://github.com/docutray/docutray-claude-code-plugins/archive/main.zip#rag-research Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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