rag-frameworks

v2026.09.24

Use when "RAG", "retrieval augmented generation", "LangChain", "LlamaIndex", "sentence transformers", "embeddings", "document QA", "chatbot with documents", "semantic search"

GitHub
安装命令
npx skhub add eyadsibai/rag-frameworks
Markdown
SKILL.md

RAG Frameworks

Frameworks for building retrieval-augmented generation applications.

Comparison

FrameworkBest ForLearning CurveFlexibility
LangChainAgents, chains, toolsSteeperHighest
LlamaIndexData indexing, simple RAGGentleMedium
Sentence TransformersCustom embeddingsLowHigh

LangChain

Orchestration framework for building complex LLM applications.

Core concepts:

  • Chains: Sequential operations (retrieve → prompt → generate)
  • Agents: LLM decides which tools to use
  • LCEL: Declarative pipeline syntax with | operator
  • Retrievers: Abstract interface to vector stores

Strengths: Rich ecosystem, many integrations, agent capabilities Limitations: Abstractions can be confusing, rapid API changes

Key concept: LCEL (LangChain Expression Language) for composable pipelines.


LlamaIndex

Data framework focused on connecting LLMs to external data.

Core concepts:

  • Documents → Nodes: Automatic chunking and indexing
  • Index types: Vector, keyword, tree, knowledge graph
  • Query engines: Retrieve and synthesize answers
  • Chat engines: Stateful conversation over data

Strengths: Simple API, great for document QA, data connectors Limitations: Less flexible for complex agent workflows

Key concept: "Load data, index it, query it" - simpler mental model than LangChain.


Sentence Transformers

Generate high-quality embeddings for semantic similarity.

Popular models:

ModelDimensionsQualitySpeed
all-MiniLM-L6-v2384GoodFast
all-mpnet-base-v2768BetterMedium
e5-large-v21024BestSlow

Key concept: Bi-encoder architecture - encode query and documents separately, compare with cosine similarity.


RAG Architecture Patterns

PatternDescriptionWhen to Use
Naive RAGRetrieve top-k, stuff in promptSimple QA
Parent-ChildRetrieve chunks, return parent docsContext preservation
Hybrid SearchVector + keyword searchBetter recall
Re-rankingRetrieve many, re-rank with cross-encoderHigher precision
Query ExpansionGenerate variations of queryAmbiguous queries

Decision Guide

ScenarioRecommendation
Simple document QALlamaIndex
Complex agents/toolsLangChain
Custom embedding pipelineSentence Transformers
Production RAGLangChain or custom
Quick prototypeLlamaIndex
Maximum controlBuild custom with Sentence Transformers

Resources

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v2026.09.24

发布时间

Sep 24, 2026

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plugins/ltk-data/skills/rag-frameworks

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master

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