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Commits, branches and sync with the but CLI
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Building a RAG pipeline with LangChain v1.0.0
Use this resource to build a RAG system with LangChain, from document loading to retrieval
New
- 466 downloads
- 214 likes
- 157 saves
Free
Install with the CLI
In a terminal, from the folder you want:
npx --yes palatekit@latest install rag-pipeline-langchainWith no option, the skill goes to ~/.agents/skills/; add --agent claude-code (or codex, cursor…) to target an agent.
First time: connect the CLI with
npx --yes palatekit@latest loginThe agent follows the full pipeline: loading documents, splitting them with RecursiveCharacterTextSplitter, creating OpenAI embeddings and storing them in Chroma, FAISS or Pinecone. It then connects a retriever to the LLM. You get working RAG pipeline code, matched to your vector store.
Mostly picked by
Updated November 2, 2026
Previews
Details
- Install guides
- Generates with
- Your agent's model
- Licence
- MIT
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What you get
- SKILL.md: five-step pipeline, from loading to generation
- SKILL.md: comparison of vector stores (Chroma, FAISS, Pinecone)
Good fit for
- You want answers grounded in your own documents
- You are comparing vector stores for a prototype
Not for
Tags
- rag
- langchain
- embeddings
- vector store
About
You say
“Build a RAG over my Markdown files, with OpenAI embeddings and Chroma.”
Skill published by langchain-ai under the MIT license, delivered in its original version.
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How to use it
- Install the skill in your agent's skills folder
- Ask your agent to build a RAG system with LangChain, from document loading to retrieval
- Review and adjust the result
Content preview
Files
- LICENSE1.1 KB
- NOTICE-PALATEKIT.md0.5 KB
- SKILL.md15.6 KB
Origin and licence
Listed unmodified
- Author
- langchain-ai
- Source
- github.com/langchain-ai/langchain-skills/tree/126c384d8552dcb476b226c244172186cc3f4328/config/skills/langchain-rag
- Licence
- MIT
Notice
« langchain-rag » (langchain-ai, MIT) livré dans sa version d'origine : fiche en français et en anglais par PalateKit.
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