Langchain Rag

What it does

The guide links document loading and splitting to vector storage and retrieval. It helps make pipeline stages visible instead of treating RAG as a single opaque model call.

Best for

Developers building question answering over a defined corpus.

Tradeoffs and requirements

Retrieval can expose documents across permission boundaries. Embedding providers may receive content, and similarity does not establish factual support for an answer.

Unknown; inspect source requirements and supporting scripts.

Install from the source

Inspect the pinned source snapshot and its supporting files first. The command selects this skill through the open-source skills CLI. Node.js and agent-specific setup may be required. Installation can download code. Catalog ingestion never executes skill instructions or install scripts.

npx skills add https://github.com/langchain-ai/langchain-skills/tree/126c384d8552dcb476b226c244172186cc3f4328/config/skills/langchain-rag --skill langchain-rag

Review pinned skill folder ↗ · CLI documentation ↗

After source review, use npx skills check and npx skills update to inspect and apply upstream updates. Re-check changed instructions before running them.

Safety & review signals

Static review only. This is not a security audit or a guarantee of safety.

SKILL.md text only; scripts, dependencies and runtime behavior not audited.

• Network Access — review the source context.

Allowed tools declaration: Unknown. Runtime permissions and network behavior: Unknown.

AI Craft Pad Skill Score

86/100 · Metadata coverage 100% · Model 1.0

Scores summarize observable discovery signals. Popularity and repository activity apply to the whole repository. Unknown runtime safety earns no certification.

Documentation

100/100

100

Maintenance

100/100

100

License Clarity

100/100

100

Repository Health

100/100

100

Compatibility Evidence

80/100

80

Popularity

56/100

56

Originality

80/100

80

Static Safety

60/100

60

Suggested workflow

Choose permitted documents, define chunk and metadata rules and build a small retrieval fixture before generating answers.

How to judge the result

Evaluate relevant and irrelevant questions, inspect retrieved passages and access filters and test updates and deletion so stale documents do not silently remain searchable.