rag-implementation
Provides practical guidance for building RAG systems with vector databases and semantic search. Includes code examples for document loading, chunking, embedding, retrieval, and prompt engineering. Covers multiple vector stores (Chroma, Pinecone, Weaviate) and retrieval strategies (hybrid search, multi-query, contextual compression).
Packaged view
This page reorganizes the original catalog entry around fit, installability, and workflow context first. The original raw source lives below.
Install command
npx @skill-hub/cli install microck-ordinary-claude-skills-rag-implementation
Repository
Skill path: skills_categorized/machine-learning/rag-implementation
Provides practical guidance for building RAG systems with vector databases and semantic search. Includes code examples for document loading, chunking, embedding, retrieval, and prompt engineering. Covers multiple vector stores (Chroma, Pinecone, Weaviate) and retrieval strategies (hybrid search, multi-query, contextual compression).
Open repositoryBest for
Primary workflow: Analyze Data & AI.
Technical facets: Data / AI, Backend, Testing, Integration.
Target audience: AI/ML teams looking for install-ready agent workflows..
License: Unknown.
Original source
Catalog source: SkillHub Club.
Repository owner: Microck.
This is still a mirrored public skill entry. Review the repository before installing into production workflows.
What it helps with
- Install rag-implementation into Claude Code, Codex CLI, Gemini CLI, or OpenCode workflows
- Review https://github.com/Microck/ordinary-claude-skills before adding rag-implementation to shared team environments
- Use rag-implementation for ai/ml workflows
Works across
Favorites: 0.
Sub-skills: 0.
Aggregator: No.