building-rag-systems
CommunityBuild production-grade RAG pipelines with context.
System Documentation
What problem does it solve?
Build production RAG systems with semantic chunking, incremental indexing, and filtered retrieval. Use when implementing document ingestion pipelines, vector search with Qdrant, or context-aware retrieval. Covers chunking strategies, change detection, payload indexing, and context expansion. NOT when doing simple similarity search without production requirements.
Core Features & Use Cases
- Semantic chunking with semantic boundaries based on ## headers
- Incremental indexing and change detection to minimize reprocessing
- Filtered retrieval via payload indexes for precise queries
- Use Case: integrate document ingestion, embedding, and retrieval into production-grade workflows
Quick Start
Use the rag systems skill to bootstrap a production-grade RAG pipeline with the provided ingestion, embedding, and retrieval components.
Dependency Matrix
Required Modules
None requiredComponents
💻 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: building-rag-systems Download link: https://github.com/AbdullahMalik17/My_skills/archive/main.zip#building-rag-systems Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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