building-rag-systems
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.
npx skills add majiayu000/claude-skill-registry --skill building-rag-systems-abdullahmalik17-digital-fte-2 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Building RAG Systems Production-grade RAG with semantic chunking, incremental updates, and filtered retrieval. ## Quick Start ```bash # Dependencies pip install qdrant-client openai pydantic python-frontmatter # Core components # 1. Crawler → discovers files, extracts path metadata # 2. Parser → extracts frontmatter, computes file hash # 3. Chunker → semantic split on ## headers, 400 tokens, 15% overlap # 4. Embedder → batched OpenAI embeddings # 5. Uploader → Qdrant upsert with indexed payloads ``` --- ## Ingestion Pipeline ### Architecture ``` ┌──────────┐ ┌────────┐ ┌─────────┐ ┌──────────┐ ┌──────────┐ │ Crawler │ -> │ Parser │ -> │ Chunker │ -> │ Embedder │ -> │ Uploader │ └──────────┘ └────────┘ └─────────┘ └──────────┘ └──────────┘ │ │ │ │ │ Discovers Extracts Splits by Generates Upserts to files frontmatter semantic vectors Qdrant + file hash boundaries (batched) (batched) ``` ### Semantic Chunking (NOT Fixed-Size) ```python class SemanticChunker: """ Production chunking: - Split on ## headers (semantic boundaries) - Target 400 tokens (NVIDIA benchmark optimal) - 15% overlap for context continuity - Track prev/next for context expansion """ SECTION_PATTERN = re.compile(r"
- Quick Start
- Ingestion Pipeline
- Architecture
- Semantic Chunking (NOT Fixed-Size)
- Change Detection (Incremental Updates)
- Batched Embeddings
- Qdrant Collection with Payload Indexes
- Retrieval Patterns
- Comprehensive Filter Builder
- Context Expansion (Walk Chunk Chain)
- Full Document Retrieval
- Payload Schema
- Anti-Patterns
- Verification
Dependencies pip install qdrant-client openai pydantic python-frontmatter Core components
What does the building-rag-systems skill do?
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.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill building-rag-systems-abdullahmalik17-digital-fte-2 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.