skills.rest
jillesca
jillescaCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

langchain-rag

Build end-to-end RAG pipelines with LangChain document loaders, embeddings, and vector stores.

3|1|Updated Jun 4, 2025
Search Tags:#langchain#rag#pinecone#chroma#embedding#vector-store#faiss
One-click install
npx skills add https://github.com/jillesca/sp_oncall --skill langchain-rag-jillesca
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langchain-rag
Source: https://github.com/jillesca/sp_oncall/tree/main/.agents/skills/langchain-rag
Command: npx skills add https://github.com/jillesca/sp_oncall --skill langchain-rag-jillesca

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Retrieval Augmented Generation (RAG) systems built with LangChain require coordinating data ingestion, chunking, embedding, and vector-store retrieval to produce grounded answers; this skill provides a cohesive blueprint and practical guidance for assembling reliable RAG pipelines.

Core Features & Use Cases

  • Document Loaders, Text Splitters, Embeddings, and Vector Stores: support for InMemory, FAISS, Chroma, and Pinecone to enable scalable, retrievable context.
  • End-to-end pipelines: load data, chunk into pieces, embed, store, retrieve, and generate answers.
  • RAG-enabled agents: build tools around retrieval to answer questions with sourced context.

Quick Start

Instruct the AI to assemble a LangChain-based RAG workflow using document loaders, chunkers, embeddings, and a vector store to answer questions with retrieved documents.

Frequently Asked Questions about langchain-rag

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a retrieval-augmented generation pipeline with LangChain?▼

To build a LangChain retrieval-augmented generation pipeline, you load data, chunk text into pieces, generate embeddings, store them in a vector database, and retrieve relevant context to generate grounded answers.

What vector stores can I use with LangChain for RAG applications?▼

LangChain RAG pipelines support InMemory, FAISS, Chroma, and Pinecone vector stores. This ensures embedding-store compatibility and enables scalable, retrievable context for generating grounded answers from documents.

Does LangChain support agent-based RAG workflows?▼

Yes, LangChain supports agent-based RAG workflows. You can build tools around document retrieval to answer questions with sourced context, allowing agents to dynamically retrieve and use grounded information during generation.

How do I chunk documents for embeddings in a LangChain RAG system?▼

To chunk documents for embeddings in a LangChain RAG system, you use text splitters after loading data via document loaders. This breaks text into pieces before generating embeddings and storing them in a vector store for retrieval.

Why ensure embedding and vector store compatibility in LangChain?▼

Ensuring embedding and vector store compatibility in LangChain prevents retrieval mismatches. The RAG pipeline requires that generated embedding dimensions match the vector store schema for successful storage and accurate context retrieval.

Related Skills

Explore More Software Engineering Skills→
patornpatorn

langchain-rag

Build end-to-end RAG pipelines with LangChain document loaders, embeddings, and vector stores.

Community
Advanced
llama-farmllama-farm
835

rag-skills

Audit and optimize RAG pipelines with LlamaIndex, ChromaDB, and Celery best practices.

Official
Basic
langchain-ailangchain-ai
113

langchain-rag

Orchestrate end-to-end RAG pipelines with LangChain loaders, splitters, embeddings, and vector stores.

Official
Advanced
BetterStacksBetterStacks
14

unrag

Install and configure RAG primitives in a TypeScript codebase.

Official
Advanced